arXiv Papers with Code in Systems and Control (January 2026 - June 2026)

PaperId: 1, https://arxiv.org/pdf/2606.27409.pdf   GitHub
Authors:Igor Itkin
Title: Delayed Verification Destabilizes Multi-Agent LLM Belief: Instability Thresholds and Optimal Corrector Placement
Abstract:
Multi‑agent large language model (LLM) systems often rely on verifier and critic agents to suppress hallucinations, but verification is delayed. During this delay, false claims can propagate through the agent network. We model this process as delayed consensus on a graph with grounded corrector nodes. Spectral decomposition by the grounded Laplacian yields a closed‑form stability threshold for the verification dose: correction that is too strong or too delayed can turn consensus into oscillation. The most unstable regime occurs when the communication and verification delays coincide; for delay two, the threshold is the inverse golden ratio. The same framework gives a supermodular placement objective and a greedy (1‑1/e)‑approximation rule for assigning a limited corrector budget to influential nodes. Experiments across five open models confirm the predicted dose‑delay oscillations. By contrast, grounded factual answering makes truth an absorbing boundary and eliminates the effect, suggesting that the instability is specific to signed‑belief tasks while grounded verification remains stabilizing

Authors:Marla Eisman, Brian Lam, Samuel Sonnino, Francesco Borrelli
Title: Racing a Wheeled Quadruped: Active Load Transfer Mitigation via Model Predictive Control
Abstract:
This paper presents a hierarchical control framework using model predictive control (MPC) and reinforcement learning (RL) for active roll control to manage lateral load transfer during autonomous racing of a wheeled quadruped. The framework integrates offline time‑optimal raceline generation, an online MPC planner that actively minimizes the lateral Load Transfer Ratio (LTR), and a low‑level, whole‑body RL policy deployed directly onto the robot's 16 actuators. The MPC is based on a vehicle dynamics bicycle model of the Unitree Go2‑W platform. The robot's leg actuators act as active suspension where knee joints generate anti‑roll torque to bank into turns. Physical track experiments demonstrate that active roll control reduces mean LTR by up to 44%, improves the fastest lap time by 8.7%, and boosts peak lateral acceleration capability by 21.3% to 1.98 m/s^2, maintaining robust high‑speed stability beyond the range of a non‑tilting baseline controller. Supplementary code and video can be found at https://github.com/meisman‑ucb/go2w‑roll‑control‑mpc

Authors:Gabriel Bravo-Palacios, Jianghan Zhang, Zachary Pestrikov, Brian Plancher, Thomas Lew
Title: TurboMPC: Fast, Scalable, and Differentiable Model Predictive Control on the GPU
Abstract:
Robotics increasingly relies on GPUs for parallel simulation, large‑scale learning, and neural‑network inference. For model predictive control (MPC) to scale with this paradigm, solvers must run efficiently on this hardware while remaining fast, differentiable, and compatible with expressive MPC formulations used in robotics. We present TurboMPC, a differentiable MPC solver that runs entirely on the GPU and supports state and control inequality constraints, implicit integrators, cross‑time‑coupled costs, and slack variables. TurboMPC combines sequential quadratic programming (SQP), an alternating direction method of multipliers (ADMM) inner solver, implicit differentiation, and a co‑designed JAX‑CUDA implementation for efficiency and ease of use. In simulation, we validate TurboMPC on constrained planning, humanoid imitation learning, and reinforcement learning with neural‑network cost function tasks, achieving up to 15× and 58× speedups over state‑of‑the‑art CPU and GPU differentiable solvers, respectively. We deploy TurboMPC on a full‑scale car for minimum‑time racing and find that batched, GPU‑accelerated tuning of MPC parameters via Bayesian optimization yields significantly faster driving than a hand‑tuned baseline. TurboMPC also scales to planning horizons of over 8000 knot points while maintaining control of the vehicle. We open‑source TurboMPC at: https://github.com/ToyotaResearchInstitute/turbompc

Authors:Mohammed Nagdi, Evangelos-Marios Nikolados, Alexey Yermakov, Mars Gao, Nathan Kutz, Filippo Menolascina
Title: Learning the Koopman Operator using Attention Free Transformers
Abstract:
Learning Koopman operators with autoencoders enables linear prediction in a latent space, but long‑horizon rollouts often drift off the learned manifold, leading to phase and amplitude errors on systems with switching, continuous spectra, or strong transients. We introduce two complementary components that make Koopman predictors more robust. First, we add an attention‑free latent memory (AFT) block that aggregates a short window of past latents to produce a corrected latent before each Koopman update. Unlike multi‑head attention, AFT operates in linear time and adds only \approx30k parameters (3d^2 + T^2, fewer than matched multi‑head attention), yet captures the local temporal context needed to suppress error divergence. Second, we propose dynamic re‑encoding: lightweight, online change‑point triggers (EWMA, CUSUM, and sequential two‑sample tests) that detect latent drift and project predictions back onto the autoencoder manifold. Across three benchmark systems ‑‑ Duffing oscillator, Repressilator, IRMA ‑‑ our model consistently reduces error accumulation compared to a Koopman autoencoder and matched‑capacity multi‑head attention. We also compare against GRU and Transformer autoencoders, evaluated both from initial conditions and with a 50‑step context, and find that Koopman+AFT (with optional re‑encoding) attains markedly lower long‑horizon error while maintaining lower inference latency. We report improvements over horizons up to 1000 steps, together with ablations over trigger policies. The result is a fast, compact predictor that stays on the learned manifold over long horizons.

Authors:Yiming Wang, Arthur N. Montanari, Adilson E. Motter
Title: Rethinking the green power grid for stability, not just for climate
Abstract:
The 2025 Iberian blackout has renewed concerns about the resilience of power grids with high shares of renewable generation. This commentary argues that renewable generation can not only advance decarbonization but also strengthen grid stability through synthetic inertia, advanced inverter‑based control, and coordinated transmission planning. Rapid advances in energy storage and power electronics make this transition increasingly viable.

Authors:Yuxuan Chen, Haipeng Xie, Yichi Zhang, Shuo Dai, Zhaohong Bie
Title: Mind the Intention: Task-Aware Backdoor Attacks for Forecast-Driven Distribution Network Operations
Abstract:
Accurate distributed energy resources (DERs) forecasting is critical for downstream optimal operations. However, such forecast‑based operation can be highly vulnerable to cyberattacks. While existing research mainly focuses on adversarial attacks, we pivot to a more controllable and persistent threat: backdoor attacks. In time series forecasting, a backdoored model generates an attacker‑specified target pattern whenever a trigger is embedded in historical inputs. This paradigm naturally fits the entire DER forecast‑optimization‑operation chain. In this paper, we investigate whether and how backdoor attacks can compromise distribution network operations and propose GridTroj, a unified backdoor framework tailored for this scenario. Unlike standard time series backdoor approaches that train a poisoned model to match a predefined target only in terms of forecasting error, GridTroj explicitly incorporates the attacker's intention and optimizes the attack toward operational disruption. Specifically, GridTroj coordinates two key modules. The Intention Planner designs operation‑damaging targets and poisoning strategies, while the Backdoor Realizer constructs the corresponding network architecture and training strategy to learn the trigger‑target association. Experiments on three downstream optimization tasks demonstrate that GridTroj can effectively compromise grid operations and outperforms existing baselines. Our code is available at https://github.com/YuxuanCEE/GridTroj.

Authors:Sergei Trashchenkov
Title: Power Systems Agent Benchmark: Executable Evaluation of AI Agents in Electric Power Engineering
Abstract:
Executable evaluation ‑‑ checking the consequences of an agent's actions with a program rather than grading its prose ‑‑ has become a prominent way to assess tool‑using AI agents in software settings. Electric power engineering has not yet had an analogous benchmark: language‑model use is still dominated by retrieval and text question answering, while agents acting on power‑system artifacts remain mostly academic prototypes. We introduce the Power Systems Agent Benchmark, an executable benchmark for power‑engineering agents. An agent receives a structured task and returns a structured solution; a deterministic evaluator recomputes the engineering quantities, checks operational constraints, and returns a feasibility flag, a normalized score, and explicit violations. The benchmark contains 41 task families across eight areas of power engineering, from power flow and protection to stability, microgrids, reliability, power quality, and forecasting. Each task is grounded in a citable source, standard, or documented engineering formulation. To resist contamination, held‑out cases are synthesized on demand by per‑family generators from private seeds: the construction is inspectable, but the instances remain private. In a reference evaluation with three command‑line agents, the strongest score near the compact tier's ceiling, a smaller open model trails, and public and held‑out performance are broadly consistent; a separate public‑split grid with OpenCode and Aider probes harness effects. The reference evaluation doubles as quality control: unanimous failures flag candidate task or evaluator defects, and it exposed a latent evaluator bug missed by self‑consistency checks. The evaluators are compact deterministic surrogates, but the task contract allows their internals to be upgraded to simulator‑backed checks without changing how tasks are posed or solved.

Authors:Linda Mümken, Diyar Altinses, Michael Schwung, Stefan Lier, Andreas Schwung
Title: Distributed Model Predictive Control with Adaptive Safety Zones for Multi-Fleet Drone Operations
Abstract:
Autonomous drone swarms in space‑constrained environments such as warehouses, inspection corridors, and urban delivery routes must share limited airspace safely at high vehicle density. Existing approaches rely on fixed safety zones sized for worst‑case velocity, which wastes airspace in congested scenarios. We replace the fixed radius with an adaptive, speed‑dependent safety sphere whose size scales with braking distance: tight at low speeds, expanded at high speeds. We develop both a centralized model predictive control (MPC) formulation and a distributed MPC (DMPC) in which each drone optimizes locally from detected neighbors, accommodating mixed fleets with non‑cooperative agents. We prove feasibility up to the geometric packing limit evaluated at the minimum radius, establish Lyapunov stability under sufficient conditions on the adaptation parameter, drone density, and prediction horizon, and extend these guarantees to the distributed setting via a contraction condition that preserves the centralized stability margins. We further derive modified sphere‑packing capacity bounds and a throughput‑optimal crossing speed for narrow passages. Simulations confirm that the adaptive framework remains feasible where fixed‑radius methods fail: it roughly doubles the admissible drone count, reduces traversal time through constrained passages by about 25 percent, and enables passage through openings impassable to static safety zones. The centralized variant realizes a larger fraction of the theoretical capacity, while the distributed variant offers a more realistic deployment model for mixed‑fleet operations under the same safety guarantees.

Authors:Nichula Wasalathilaka, Parakrama Ekanayake, Roshan Godaliyadda
Title: RespGeomLib: A Reproducible Parametric Engine for Generating Analysis-Ready Human Airway Lumen Geometry
Abstract:
CT‑derived airway models support pulmonary morphometry and airflow simulation, but are often limited by distal scan resolution and the need for substantial cleanup near bifurcations. Procedural alternatives are reproducible, yet many rely on stitched tubular primitives that introduce non‑smooth junctions and poorly defined open boundaries. We present RespGeomLib, a reproducible parametric engine for generating analysis‑ready human airway lumen surfaces from compact YAML specifications. The framework combines port‑based assembly with implicit smooth‑min junction blending to produce seamless junctions, while avoiding full‑tree voxelization through analytic segments and local implicit extraction around bifurcations. Quantitatively, RespGeomLib yields cleaner junctions than a Boolean/stitch baseline and is substantially faster and more memory‑efficient than whole‑tree global implicit extraction. We further demonstrate morphometry‑guided tree generation, controlled synthetic airway variants, and CFD‑ready export with stable airflow simulation. RespGeomLib targets biomedical workflows requiring reproducible morphometry, controlled synthetic variants, and simulation‑ready lumen geometry. The code is publicly available at https://nichula01.github.io/Respgeomlib/

Authors:Hatim Chergui, Claudia Carballo González, Farhad Rezazadeh, Merouane Debbah
Title: Mitigating Anchoring Bias in LLM-Based Agents for Energy-Efficient 6G Autonomous Networks
Abstract:
This paper presents an autonomous agentic resource negotiation framework designed to enable zero‑touch network slicing in 6G architectures using Large Language Model (LLM) agents. While LLMs offer powerful reasoning capabilities, we demonstrate that such agents inherently suffer from anchoring bias, rigidly adhering to initial heuristic proposals and causing severe network over‑provisioning. To systematically mitigate this cognitive bias, we propose a novel randomized anchoring strategy modeled via a Truncated 3‑Parameter Weibull distribution. This mathematically bounded approach seamlessly integrates with burst‑aware Digital Twins (DTs) employing Conditional Value at Risk (CVaR) to rigorously guarantee strict Service Level Agreement (SLA) tail‑latencies. To validate our methodology, we introduce and prove the \emphBimodal Constraint‑Avoidance Utility Theorem, demonstrating that while feasible negotiations follow classical convex bounds, highly constrained scenarios undergo a phase transition governed by an inverse rational decay envelope. Empirical results generated using a locally hosted 1B‑parameter model otel‑llm‑1b‑it confirm these dual‑regime bounds. Our cognitive de‑biasing successfully dismantles rigid negotiation patterns, forcing agents into active exploration to safely ride SLA boundaries and boost system energy savings up to 25%. Crucially, the lightweight 1B LLM achieves sub‑second inference latencies (0.95s mean), ensuring our multi‑agent framework is compatible with the operational timescales of the O‑RAN non‑Real‑Time RAN Intelligent Controller (non‑RT RIC)\footnoteOur source code is available for non‑commercial use at https://github.com/HatimChergui.

Authors:Jingyi Yan, Ke Sun, Zhenglin Li, Hongying Jia
Title: BT-MTD: Bus Traversal-based Moving Target Defense for Smart Grid
Abstract:
Moving Target Defense (MTD) is a proactive security strategy designed to enhance cyber‑resilience by dynamically altering system parameters, thereby preventing adversaries from acquiring the critical information needed to execute stealth attacks. In this paper, we consider the case in which the operator modifies the admittance of branches to enable MTD, and focus on the problem of effectively protecting the system with fewer number of branch admittance modifications and shorter computational time. Specifically, we identify the ineffectual branches whose admittance modification do not contribute to the improvement of MTD effectiveness via theoretical analysis. Building on these insights, we propose the Bus Traversal‑based MTD (BT‑MTD), which is a bus‑oriented algorithm that traverses over the buses of the network according to analytically derived guidelines. The performance of the BT‑MTD is evaluated and compared with four existing strategies on standard IEEE test systems, demonstrating its robustness and superior performance in effectiveness, efficiency, and computational cost. The code of BT‑MTD is available at: https://github.com/YJY101/BT‑MTD.

Authors:Xilong Wang, Xiaoxing Chen, Patrick Li, Dawn Song, Neil Gong
Title: Same-Origin Policy for Agentic Browsers
Abstract:
Agentic browsers integrate autonomous AI agents into web browsers, enabling users to accomplish web tasks through natural‑language instructions. The same‑origin policy (SOP) is a fundamental browser security mechanism that prevents unauthorized automated cross‑origin data flows induced by scripts. However, whether SOP remains effective in agentic browsers is an open question that has not been systematically studied. In this work, we bridge this gap. We first observe that an agentic browser can itself serve as an automated channel for cross‑origin data flows, potentially leading to SOP violations. To investigate this phenomenon, we construct SOPBench, a benchmark for evaluating SOP violations in agentic browsers. Our evaluation shows that existing agentic browsers frequently violate SOP, both in benign settings and under attacks. To address this problem, we propose SOPGuard, an SOP enforcement mechanism tailored to agentic browsers. We implement SOPGuard in BrowserOS, an open‑source agentic browser. Extensive evaluations demonstrate that SOPGuard effectively enforces SOP while preserving utility and incurring only a small runtime overhead. Our code and data are available at https://github.com/wxl‑lxw/BrowserOS‑SOPGuard.

Authors:Allison Andreyev, Landon Eum, Nestor Tiglao, Romel Gomez
Title: Bounding Boxes as Goals: Language-Conditioned Grasping via Neuro-Symbolic Planning
Abstract:
For robotics to be effectively integrated into household or industrial environments, machines must adapt to natural‑language prompts in real time. Although Vision‑Language Models (VLMs) have enabled zero‑shot generalization in robot task and motion planning (TAMP), current state‑of‑the‑art approaches often remain computationally "heavyweight" or require extensive training on thousands of demonstrations. We present GRASP (Grounded Reasoning and Symbolic Planning), a framework designed as a step toward open‑vocabulary tabletop manipulation. Our approach leverages a pretrained VLM to translate natural‑language queries into neuro‑symbolic goal states, grounded in the physical world via a bounding‑box detection pipeline. Unlike methods that rely on fixed color lists or hard‑coded coordinates, GRASP enables robots to interpret abstract spatial concepts such as "top shelf" and execute tasks without additional fine‑tuning. We achieve 73.3% overall success across 90 real‑robot trials at three difficulty levels, requiring no task‑specific training.

Authors:Khanh D. Nguyen, Hung T. Ho, Chinh T. Nguyen, Thanh Q. Duong, Linh D. Le, Duy M. H. Nguyen, Vien A. Ngo, An T. Le
Title: vla.cpp: A Unified Inference Runtime for Vision-Language-Action Models
Abstract:
Vision‑Language‑Action (VLA) policies are typically shipped as Python/PyTorch stacks that assume a workstation‑class GPU, a mismatch for the hardware on which robots actually run. We present vla.cpp, a portable C++ inference runtime built on llama.cpp. To our knowledge, it is the first ggml‑class engine to natively serve the flow‑matching and diffusion VLA inference pattern, in which a cached vision‑language prefix is consumed by a cross‑attending action expert integrated over several solver steps. A single runtime serves seven architectures spanning five backbone and four action‑head families behind one request/response protocol, with each model packaged as a self‑contained bundle. On LIBERO‑Object, the engine matches a state‑of‑the‑art checkpoint to within one episode out of 200, and runs BitVLA at 100% success in 1.3 GiB of memory. The same bundle runs unchanged across three hardware tiers, from a consumer GPU down to an 8 GB embedded module. A cross‑hardware roofline analysis shows that batch‑1 VLA inference is compute‑bound, so utilization rather than bandwidth is the deployment lever; an IMMA ladder GEMM derived from this analysis cuts BitVLA per‑step latency by 4.5x. We then frame an on‑robot stress test on an ALOHA arm that isolates the latency constraint under which a learned VLA must replan against a moving target on the hardware it was trained for. Code, demo videos, and the reproducible benchmark scaffold are available at https://fai‑modelopt‑tech.github.io/vla‑cpp.github.io/.

Authors:J. S. van Hulst, W. P. M. H. Heemels, D. J. Antunes
Title: Estimating Evolving Functions with Dynamic Gaussian Processes
Abstract:
This paper develops the Dynamic Gaussian Process (DGP), a framework for estimating functions governed by integro‑difference equations (IDEs). IDEs model continuous functions that evolve with discrete‑time dynamics and arise naturally from time‑discretization of linear partial differential equations (PDEs). The DGP extends Gaussian process regression to time‑varying functions and extends Kalman filtering to infinite‑dimensional states. The DGP posterior remains a Gaussian process with closed‑form mean and covariance updates, and separable kernel structure reduces the problem to a finite‑dimensional Kalman filter on basis function coefficients. This paper extends the DGP to vector‑valued states, enabling the treatment of higher‑order PDEs, and provides a stability and approximation error analysis for the basis function approximation. The functional L2 estimation error decomposes exactly into in‑subspace and out‑of‑subspace contributions, and all approximation errors vanish as the number of basis functions grows. The framework is demonstrated on the heat equation and on the wave equation, the latter with a vector‑valued state. Code is available at https://github.com/JvHulst/Dynamic_Gaussian_Processes.

Authors:Prashant Lokur, Nikolce Murgovski
Title: Energy-Optimal Thermal Management of Heat-Pump Battery Electric Vehicles
Abstract:
This paper presents an energy‑optimal hybrid control framework for thermal management of heat‑pump battery electric vehicles (BEVs). The controller coordinates the compressor, coolant pumps, and cabin blower across the coupled refrigerant, coolant, and air loops, while enforcing cabin comfort and component temperature constraints. The framework combines a rule‑based supervisory layer, which handles discrete system configuration, with a continuous nonlinear model predictive control (NMPC) optimizer that minimizes thermal energy consumption over a finite prediction horizon. A control‑oriented model is developed to capture the dominant dynamics of the cabin, refrigerant loop, reconfigurable coolant circuits, and key thermal masses including the battery, motor, and inverter. The model is validated against a high‑fidelity reference, achieving a mean absolute temperature prediction error below \SI1.8\celsius for key thermal states including the battery, motor, and cabin air temperature, while reducing simulation time by approximately \SI85\percent. The terminal cost is computed by linearizing the system about a quasi‑steady operating point and solving the discrete‑time algebraic Riccati equation, ensuring well‑conditioned optimization across varying operating conditions. The proposed framework is evaluated against the built‑in rule‑based controller of MathWorks Simscape \emphElectric Vehicle Thermal Management with Heat Pump model under cold‑climate extended driving conditions, demonstrating consistent reductions of \SI20‑\SI28\percent in thermal energy consumption across all tested scenarios. The complete implementation, developed using the open‑source CasADi framework, is made openly available at \hrefhttps://github.com/PrashantLokur/ThermalEnergyManagementWithHybridControlFrameworkGitHub repository to support reproducibility and further development.

Authors:Dhruv S. Kushwaha, Zoleikha A. Biron
Title: Robust Koopman Control Barrier Filters for Safe Actor-Critic Reinforcement Learning
Abstract:
Safe reinforcement learning (RL) for robotic systems requires policies that improve task performance while satisfying state and input constraints during both training and deployment. Control barrier functions (CBFs) provide a principled mechanism for enforcing forward invariance through minimally invasive safety filters, but their use in model‑free RL is limited by the need for accurate dynamics and hand‑designed barrier certificates. We propose Robust Koopman‑CBF SAC, a safety‑filtered actor‑‑critic framework that learns a finite‑dimensional Koopman predictor from data, constructs affine CBF constraints in the lifted space, and enforces them through a quadratic‑program safety layer. To account for finite‑dimensional Koopman approximation error, the CBF condition is tightened using a projected residual margin estimated from held‑out rollout data. The critic is trained on the executed safe action, while the actor is regularized toward the Koopman‑CBF feasible set, reducing dependence on the filter over training. Across safe‑control benchmarks, the method achieves zero constraint violations on CartPole stabilization and tracking while matching or exceeding unconstrained SAC returns. On high‑dimensional Safety Gymnasium locomotion tasks, the method reduces violations in some settings but also exposes important limitations of first‑order velocity barriers and linear EDMD models, motivating high‑order and multi‑step Koopman‑CBF extensions. These results suggest that robust Koopman‑CBF filters are a promising bridge between model‑free RL and certifiable safety, while clarifying the structural conditions under which such filters remain effective. All code is available at \hrefhttps://github.com/DhruvKushwaha/Koopman‑CBF‑Soft‑Actor‑CriticGithub Repository.

Authors:Shumon Koga, Miroslav Krstic
Title: Safe Trajectory Tracking of the Stefan Problem with Second-Order Moving Boundary Dynamics
Abstract:
This paper considers a safe trajectory tracking of the Stefan problem with a second‑order moving boundary dynamics. The model is given by a parabolic Partial Differential Equation (PDE) defined on a time‑varying domain of moving boundary governed by a second‑order Ordinary Differential Equation (ODE) associated with the Neumann boundary condition. A feedforward control is designed by a series expansion approach to solve the inverse Stefan problem under given reference trajectory of the moving boundary, and the convergence of infinite series is proven. A trajectory tracking controller is derived based on an energy‑shaping, which ensures the safety of the model constraint in the closed‑loop system. The closed‑loop system is also shown to be globally exponentially stable with respect to the tracking error by performing PDE backstepping transformation and Lyapunov analysis. Numerical simulation illustrates an effective tracking performance of the proposed method under a sinusoidal reference trajectory. Code is released at https://github.com/shumon0423/StefanTracking_ACC2026.git.

Authors:Zeyu Shen, Zhuoyuan Wang, Laixi Shi
Title: T2S-MPC: Time-Embedded Online Adaptive Model Predictive Control for Time-Varying Dynamics
Abstract:
Recent advances in learning‑based model predictive control (MPC) have leveraged neural networks for online model learning, achieving strong performance when nonstationary system dynamics deviate from nominal models. However, existing approaches primarily address specific or relatively structured forms of dynamical variation, leaving more general, unknown, and unpredictable time‑varying dynamics insufficiently handled. To tackle this challenge, we propose T2S‑MPC, a framework that adaptively learns a residual dynamics model online and integrates it with the nominal model within the MPC framework to enable fast‑evolving online planning. To make the model time‑aware, we explicitly encode temporal information through a structured time embedding and employ a two‑timescale update scheme, allowing the controller to capture nonstationary dynamics while balancing rapid adaptation with stable learning. We evaluate the proposed method on a 2D quadrotor across stabilization and trajectory tracking tasks under diverse time‑varying disturbances, including linear drifting and periodic perturbations. Experimental results show that T2S‑MPC consistently outperforms classical MPC, neural MPC, and ablated variants in control performance, while also demonstrating strong robustness across a wide range of disturbance conditions without additional tuning. The source code is publicly available at https://github.com/Zeyuu0920/T2S_MPC

Authors:Seulchan Lee, Sanghyun Kim
Title: Manifold-Constrained MPPI: Real-Time Sampling-Based Control Under Hard Constraints
Abstract:
Sampling‑based model predictive control methods, such as Model Predictive Path Integral (MPPI), offer derivative‑free optimization and robustness in complex robotic systems. However, standard MPPI relies on cost‑based soft penalties that cannot guarantee hard‑constraint satisfaction, severely limiting its applicability to highly constrained tasks such as closed‑chain manipulation. To address this, we propose Manifold‑Constrained MPPI (MC‑MPPI), a real‑time sampling‑based control framework that enforces manifold‑based equality constraints while preserving the computational advantages of MPPI. The key idea is to decouple the constrained optimal control problem into latent‑space planning and execution‑level correction. At the planning stage, a Variational Autoencoder (VAE) learns a low‑dimensional latent representation of the constraint manifold, enabling MPPI to efficiently generate near‑feasible candidate trajectories without per‑sample modification. Since this reference enables accurate linearization of the equality constraints, an execution‑level Quadratic Programming (QP) controller resolves the residual manifold mismatch in a single solve rather than through iterative projection. Experiments on a 14‑DoF closed‑chain dual‑arm system in both simulation and real‑world settings demonstrate that MC‑MPPI operates stably at 100 Hz, reliably navigates dynamic environments while effectively maintaining hard equality constraints, and significantly outperforms baseline methods in tracking accuracy. Supplementary videos and implementation details are available at https://rcilab.github.io/mcmppi.

Authors:Monica Chan, Shreyaa Raghavan, Cathy Wu
Title: Open-Source METANET Calibration for Reproducible Freeway Traffic Macroscopic Simulation
Abstract:
METANET is a widely used second‑order macroscopic traffic flow model for freeway networks, supporting applications across traffic simulation, ramp metering, and variable speed limit control. The predictive accuracy of any traffic model, however, hinges on careful calibration to real‑world conditions. Despite its widespread use, there have not been open‑source tools for calibrating METANET's parameters. Without open‑source calibration, results cannot be easily reproduced or extended to other networks. This work provides an open‑source METANET calibration, simulation, and data visualization tool. The calibration is formulated as a nonlinear program (NLP) solved via the interior‑point method (IPOPT), with joint ramp flow estimation. We validate our calibration on real‑world freeway data from two widely used traffic monitoring systems: Interstate‑24 MObility Technology Interstate Observation Network (I‑24 MOTION), one of the largest open‑road trajectory instruments in the country, and loop detector data from the Caltrans Performance Measurement System (PeMS), which spans nearly 40,000 detectors across California freeways and serves as a standard benchmark in traffic research. Models calibrated using our method are able to reproduce these datasets' observed traffic patterns across diverse network geometries and traffic conditions including complex stop‑and‑go congestion waves. As large‑scale traffic monitoring infrastructure continues to expand, open‑source calibration tools are essential for translating growing volumes of sensor data into validated models that can support real‑world traffic control. The complete code is publicly available at https://github.com/woxsao/metanet‑calibration to support reproducible research in freeway traffic modeling and control.

Authors:Matthijs Reyers, Niels Hokke, R. R. Venkatesha Prasad
Title: LoRa and LoRaWAN simulator-cum-emulator with CAD and capture effect in Python
Abstract:
Existing LoRaWAN/LoRa simulators consist of large, complicated C++ codebases and often do not support all device classes. This paper presents the design of a simple to use, Python‑based discrete‑event simulator that addresses these gaps while also introducing a novel method for evaluating real device firmware in the simulator. The simulator is built on a custom asyncio‑based simulation kernel, a three‑phase packet delivery model that reproduces the capture effect, a full LoRaWAN 1.0.4 stack, and a containerized firmware system that cross‑compiles real STM32 C firmware and redirects HAL calls into the simulator via CFFI. The simulator is distributed as a Python package via Github (https://github.com/MatthijsReyers/lora‑simulator) and requires no external simulation framework or dependencies.

Authors:Georg Jank, Mattia Piccinini, Sebastian Wenk, Phillip Pitschi, Johannes Betz, Boris Lohmann
Title: Benchmarking Empirical and Learning-Based Approaches for Feedforward Steering Control in Autonomous Racing
Abstract:
Feedforward steering control is a key component of hierarchical control architectures for autonomous racing. The goal is to reduce steering corrections from the feedback controllers by predicting the vehicle's inverse lateral dynamics. This paper presents a systematic benchmark of two learning‑based and two empirical (analytical) feedforward steering controllers. We introduce a new \acfehd formulation based on a polynomial surface fit that captures velocity‑dependent nonlinear steering behavior with minimal parametrization. We test the feedforward controllers in a high‑fidelity simulation framework based on the real‑world Abu Dhabi Autonomous Racing League competition, using a high‑fidelity double‑track vehicle dynamics simulator. Open‑loop evaluation shows that the learning‑based controllers achieve the lowest prediction errors; however, closed‑loop testing reveals that this improved accuracy does not translate into superior path tracking performance or lap times, even after iterative fine‑tuning. In contrast, the proposed EHD approach achieves the best overall closed‑loop robustness and lap time, highlighting the necessity of evaluating feedforward strategies within the complete trajectory planning and control software stack. Our code is available at https://github.com/TUMRT/steering_ff_control.

Authors:Saurav Ghosh, Abdou Sow, Luke Zhang
Title: Adversarial Stress Testing of SPARK Humanoid Safety Filters
Abstract:
Humanoid robots are difficult to deploy safely because they have high‑dimensional bodies, many collision constraints, and must operate near people and obstacles. Safety filters help by modifying a nominal control action when it may violate collision‑avoidance constraints. Still, nominal benchmark scores do not fully show how these filters behave in harder environments. In this work, we study the robustness of SPARK humanoid safety filters through replication and stress testing. We replicate the SPARK benchmark case G1SportMode_D1_WG_SO_v1 in MuJoCo and evaluate RSSA, RSSS, SSA, CBF, PFM, and SMA under controlled random seeds. We also built a post‑processing pipeline that converts raw SPARK logs into goal‑tracking, minimum‑distance, and collision‑step metrics. Our results show that some methods track the goal more closely, while others reduce collision steps more effectively. The stress tests further indicate that safety behavior can change under obstacle crowding, noisy distance estimates, and delayed obstacle information. These findings suggest that humanoid autonomy should be evaluated beyond nominal performance, using metrics that expose failure modes before deployment.

Authors:Jianghong Dong, Yifeng Zhang, Jiawei Wang, Mengchi Cai, Keqiang Li, Guillaume Sartoretti
Title: REACT: Environment-Adaptive Architecture for Continuous Formation Navigation of Wheeled Mobile Robots
Abstract:
Formation control of wheeled mobile robots (WMRs) has been extensively studied due to its broad applications in fields such as logistics transportation, environmental monitoring, and search and rescue. However, most existing works mainly focus on tracking predefined formations, which limits their adaptability to complex real‑world environments. To address this, we propose REACT (Real‑time Environment‑Adaptive architecture for Continuous formation navigaTion), a hierarchical architecture integrating centralized formation generation and distributed formation maintenance. Specifically, our upper layer generates new environment‑adaptive formations when necessary and uses our proposed TCF‑R2T (Trajectory‑Conflict‑Free Robot‑to‑Target assignment) algorithm to compute conflict‑free WMR‑to‑target assignments in polynomial time, enabling timely formation transitions without trajectory conflicts. At the lower layer, each WMR executes our developed JSTP (Joint Spatio‑Temporal trajectory Planning) method to maintain the generated formation by simultaneously optimizing spatial positions and temporal durations, thereby enhancing coordination among WMRs and enabling continuous navigation in obstacle‑rich environments and dynamic‑obstacle scenarios. Both simulation and real‑world experiments validate the effectiveness and practical applicability of REACT. Experimental videos are available on our project website: https://dongjh20.github.io/REACT‑website.

Authors:Jianye Xu, Bassam Alrifaee
Title: Beyond Safety Filtering: Control Barrier Function-Informed Reinforcement Learning for Connected and Automated Vehicles
Abstract:
Reinforcement Learning (RL) uses rewards to guide learning, yet reward design is typically hand‑crafted using heuristics that can be difficult to tune. We propose a Control Barrier Function (CBF)‑informed reward design for Multi‑Agent RL (MARL) that converts CBF constraint values under joint MARL actions into a reward signal that explicitly guides safe learning. We compare against two heuristic reward baselines in a four‑way multi‑lane intersection with connected and automated vehicles. Results show that our method achieves the highest task performance and is less sensitive to reward hyperparameters, yielding consistently strong performance across the tested hyperparameter range. Code for reproducing the experimental results and a video demonstration are available at https://github.com/bassamlab/SigmaRL.

Authors:Yimian Ding, Jingzehua Xu, Yiyuan Yang, Guanwen Xie, Xinqi Wang, Shuai Zhang
Title: AoI-MDP: An AoI Optimized Markov Decision Process (Student Abstract)
Abstract:
Ocean exploration places high demands on autonomous underwater vehicles, especially when there's observation delay. We propose age of information optimized Markov decision process (AoI‑MDP) to enhance underwater tasks by modeling observation delay as signal delay and including it in the state space. AoI‑MDP also introduces wait time in the action space and integrates AoI with reward functions, optimizing information freshness and decision‑making using reinforcement learning. Simulations show AoI‑MDP outperforms the standard MDP, demonstrating superior performance, feasibility, and generalization in underwater tasks. To accelerate relevant research, we have made the codes available as open‑source at https://github.com/Xiboxtg/AoI‑MDP.

Authors:Harsh Modi, Xiao Liang, Minghui Zheng
Title: Constrained MPC-Based Motion Planning for Morphing Quadrotors in Ultra-Narrow Passages under Limited Perception
Abstract:
This paper introduces a motion planning framework to plan morphology and trajectory for morphing quadrotors under extremely constrained environments. We develop a novel obstacle avoidance cost function for nonlinear model predictive control (MPC) that enables navigation through extremely narrow gaps under limited perception from a 2D LiDAR. Classical artificial potential field‑based costs typically have a high cost in narrow passages, artificially blocking the navigable path. In contrast, we propose a smooth exponential obstacle cost that preserves low traversal cost within narrow gaps while maintaining strong collision avoidance behavior. The formulation avoids hard activation thresholds and introduces a cost reduction factor to reduce the cost within narrow passages. Direct use of 2D LiDAR measurements in MPC allows navigation around arbitrarily shaped obstacles. The method is embedded within an acados‑based nonlinear MPC framework. Simulation and experimental results demonstrate successful traversal of narrow corridors where typical repulsive cost functions would fail. The approach provides a computationally efficient and practical solution for navigating through tight spaces while maintaining safety from the obstacles. While we are implementing the framework on the morphing quadrotors, the cost function formulation is general‑purpose for any mobile robot application, and is not limited to the morphing quadrotors. The implementation code is available at \hrefhttps://github.com/harshjmodi1996/morphocopter_mpcGithub Repo and a short video is available at \hrefhttps://zh.engr.tamu.edu/wp‑content/uploads/sites/310/2026/03/MPC_MorphoCopter_video.mp4Video Link.

Authors:Daniel M. Cherenson, Haejoon Lee, Taekyung Kim, Dimitra Panagou
Title: Distributionally Robust Safety Under Arbitrary Uncertainties: A Safety Filtering Approach
Abstract:
In this work, we study how to ensure probabilistic safety for nonlinear systems under distributional ambiguity. Our approach builds on a backup‑based safety filtering framework that switches between a high‑performance nominal policy and a certified backup policy to ensure safety. To handle arbitrary uncertainties from ambiguous distributions, i.e., where the distribution is not of specific structure and the true distribution is unknown, we adopt a distributionally robust (DR) formulation using Wasserstein ambiguity sets. Rather than solving a high‑dimensional DR trajectory optimization problem online, we exploit the structure of backup‑based safety filtering to reduce safety certification to a one‑dimensional search over the switching time between nominal and backup policies. We then develop a sampling‑based certification procedure with finite‑sample guarantees, where empirical failure probabilities are compared against a Wasserstein‑inflated threshold. We validate our method through simulations across three systems, from a Dubins vehicle to a high‑speed racing car and a fighter jet, demonstrating the broad applicability and computational efficiency.

Authors:Alejandro Murillo-Gonzalez, Mahmoud Ali, Lantao Liu
Title: Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization
Abstract:
Multi‑objective reinforcement learning in robotic domains requires balancing complex, non‑convex trade‑offs between conflicting objectives. While linear scalarization methods provide stability, they are theoretically incapable of recovering solutions within non‑convex regions of the Pareto front. Conversely, static non‑linear scalarizations (e.g., Tchebycheff) can theoretically access these regions but often suffer from severe gradient variance and optimization instability in deep RL. In this work, we propose an Adaptive Smooth Tchebycheff framework that resolves this tension by dynamically modulating the curvature of the optimization landscape. We introduce a novel conflict‑driven controller that regulates the optimization smoothness based on real‑time gradient interference. This allows the agent to anneal toward precise, non‑convex scalarization when objectives align, while elastically reverting to stable, smooth approximations when destructive gradient conflicts emerge. We validate our approach on a challenging robotic stealth visual search task ‑‑ a proxy for monitoring of protected/fragile ecosystems ‑‑ where an agent must balance search, exposure/interference minimization and exploration speed. Extensive ablations confirm that our conflict‑aware adaptation enables the robust discovery of Pareto‑optimal policies in non‑convex regions inaccessible to linear baselines and unstable for static non‑linear methods. Website: https://alejandromllo.github.io/research/pasta/

Authors:Charles Lewis, Amal Elsokary, Siyuan Ji
Title: SHIA: A Direct SysML-Hardware Interface Architecture for Model-Centric Verification
Abstract:
Model‑Based Systems Engineering (MBSE) is widely treated as the backbone of digital engineering, with languages such as the Systems Modeling Language (SysML) providing the means to capture system structure, behaviour, and verification intent. Yet once verification moves to hardware, the system model is routinely left behind. Domain‑specific simulation environments, model transformations, and bespoke tool integrations take over, and the model that began as the authoritative reference drifts out of sync with the implementation it was meant to govern. This paper introduces the SysML Hardware Interface Architecture (SHIA), which keeps an executable SysML model directly inside the verification loop, exchanging messages with physical hardware without intermediate transformation chains, co‑simulation platforms, or broker‑mediated plugins. SHIA is realised through a SysML side server, written in embedded C++ within IBM Rhapsody, and a hardware side server running on a Raspberry Pi, together establishing a bidirectional link between the digital model and the physical system. A logic gate case study demonstrates the approach end‑to‑end, from hardware model construction and prototype assembly to test harness design, behavioural statechart control, and staged verification of each component before integration. The integrated system exchanged messages correctly in both directions, and Karnaugh map comparison between the SysML‑generated and hardware‑generated outputs showed zero discrepancy. The result shows that, when paired with a suitable interface, SysML need not remain a static description that informs downstream tools; it can serve as the executable layer through which hardware behaviour is stimulated, observed, and verified. The work demonstrates a route to model‑governed verification and a shorter digital thread between system architecture and the hardware that realises it.

Authors:Nengneng Yu, Sixian Xiong, Yibo Zhao, Wei Wang, Zaoxing Liu
Title: Enabling Performant and Flexible Model-Internal Observability for LLM Inference
Abstract:
Today's inference‑time workloads increasingly depend on timely access to a model's internal states. We present DMI‑Lib, a high‑speed deep model inspector that treats internal observability as a first‑class systems primitive, decoupling it from the inference hot path via an asynchronous observability substrate built from Ring^2, a GPU‑CPU memory abstraction for capturing and staging tensors, and a policy‑controlled host backend that exports them. DMI‑Lib enables the placement of observation points across a rich space of internal signals and diverse inference backends while preserving serving optimizations and adhering to tight GPU memory budgets. Our experiments demonstrate that DMI‑Lib incurs only 0.4%‑‑6.8% overhead in offline batch inference and an average of 6% in moderate online serving, reducing latency overhead by 2x‑15x compared to existing baselines with similar observability features. DMI‑Lib is open‑sourced at https://github.com/ProjectDMX/DMI.

Authors:Jie Lu, Peihao Yan, Pang-Ning Tan, Y. Thomas Hou, Huacheng Zeng
Title: Demystifying Deep Reinforcement Learning: A Neuro-Symbolic Framework for Interpretable Open RAN Automation
Abstract:
Open Radio Access Networks (O‑RAN) are increasingly adopting data‑driven control through Deep Reinforcement Learning (DRL) to optimize complex tasks such as network slicing and mobility management. However, the deployment of DRL in carrier‑grade networks is hindered by its inherent opacity and stochastic execution, which limit operator trust, auditability, and safe deployment. Existing explainable AI (XAI) approaches primarily provide post‑hoc insights and fail to produce executable, interpretable policies suitable for operational environments. In this paper, we present DeRAN, a neuro‑symbolic framework that bridges the gap between DRL performance and operational transparency by distilling black‑box DRL policies into human‑readable symbolic representations. DeRAN introduces a concept‑driven abstraction layer that transforms high‑dimensional network telemetry into a compact set of semantically meaningful features, enabling interpretable policy learning. Building on the semantically grounded concepts, DeRAN synthesizes symbolic policies using deep symbolic regression (DSR) for continuous control and neurally guided differentiable logic (NUDGE) for discrete decision‑making. We implement DeRAN on a live 5G O‑RAN testbed and evaluate it on two representative use cases. Experimental results demonstrate that DeRAN achieves 78% and 87% of DRL's cumulative rewards in the two use cases, while offering interpretability and auditability by design. Source code is available at https://github.com/Jadejavu/DeRAN

Authors:Gokul Puthumanaillam, Hao Jiang, Ruben Hernandez, Jose Fuentes, Paulo Padrao, Leonardo Bobadilla, Melkior Ornik
Title: Muninn: Your Trajectory Diffusion Model But Faster
Abstract:
Diffusion‑based trajectory planners can synthesize rich, multimodal robot motions, but their iterative denoising makes online planning and control prohibitively slow. Existing accelerations either modify the sampler or compress the network‑‑sacrificing plan quality or requiring retraining without accounting for downstream control risk. We address the problem of making diffusion‑based trajectory planners fast enough for real‑time robot use without retraining the model or sacrificing trajectory quality, and in a way that works across diverse state‑space diffusion architectures. Our key insight is that diffusion trajectory planners expose two signals we can exploit: a cheap probe of how their internal trajectory representation changes across steps, and analytic coefficients that describe how denoiser errors affect the sampler's state update. By calibrating the first signal against the second on offline runs, we obtain a per‑step score that upper‑bounds how far the final trajectory can deviate when we reuse a cached denoiser output, and we treat this bound as an uncertainty budget that we can spend over the denoising process. Building on this insight, we present Muninn, a training‑free caching wrapper that tracks this uncertainty budget during sampling and, at each diffusion step, chooses between reusing a cached denoiser output when the predicted deviation is small and recomputing the denoiser when it is not. Across standard benchmarks Muninn delivers up to 4.6x wall‑clock speedups across several trajectory diffusion models by reducing denoiser evaluations, while preserving task performance and safety metrics. Muninn further certifies that cached rollouts remain within a specified distance of their full‑compute counterparts, and we validate these gains in real‑time closed‑loop navigation and manipulation hardware deployments. Project page: https://github.com/gokulp01/Muninn.

Authors:Yuan Zhong, Jiaxin Cheng, Hefu Ye, Yicong Zhou
Title: PolarNet: Single-Minima Neural Network for Modeling Lyapunov Functions
Abstract:
Learning control strategies with provable stability guarantees continues to be a challenging problem. In this work, we examine a family of training‑time behaviors exhibited by existing neural Lyapunov control methods under specific conditions, which can hinder the synthesis of a provably stable controller. We identify the root cause as the lack of neural network architectural guarantees on the learned Lyapunov function, and propose PolarNet, a network architecture that provably addresses these issues by structurally guarantee to have a single critical point. We provide theoretical guarantee regarding the properness and universality of PolarNet for modeling Lyapunov functions, and show that using it as a drop‑in replacement in existing neural Lyapunov control methods can effectively circumvent particular difficulties in training. We conduct a set of numerical experiments to verify that PolarNet consistently maintains a single critical point and, when used as a drop‑in replacement in existing neural Lyapunov control methods, successfully avoids training failures caused by the lack of architectural guarantees. The code of this paper is available at https://github.com/23‑zy/PolarNet.

Authors:Ángel F. García-Fernández, Yuxuan Xia
Title: Variational PMB filter via coordinate descent Kullback-Leibler divergence minimisation
Abstract:
This paper presents a new derivation of the variational Poisson multi‑Bernoulli (V‑PMB) filter for multi‑target estimation proposed in [#Williams15]. The proposed derivation is based on considering an augmented space that includes the set of target states with their track indices and the global hypothesis variable. Then, we show that the V‑PMB projection performs a coordinate descent Kullback‑Leibler divergence (KLD) minimisation on this augmented space to fit the best possible PMB density to the Poisson multi‑Bernoulli mixture (PMBM) posterior. We also show that this V‑PMB projection keeps the probability hypothesis density of the posterior. The paper also includes a comparison with the PMBM filter and other PMB filter variants, including a track‑oriented Murty‑based implementation, a track‑oriented loopy belief propagation implementation and a global nearest neighbour implementation, showing the benefits of the V‑PMB filter compared to the other PMB filters when targets get in close proximity and then separate.

Authors:Avhishek Biswas, Apala Pramanik, Eylem Ekici, Mehmet C. Vuran
Title: Look Once, Beam Twice: Camera-Primed Real-Time Double-Directional mmWave Beam Management for Vehicular Connectivity
Abstract:
Millimeter‑wave (mmWave) frequencies promise multi‑gigabit connectivity for vehicle‑to‑everything (V2X) networks, but face challenges in terms of severe path loss and mobility‑related beam misalignment. Reliable V2X connectivity requires fast, double‑directional beam alignment. However, existing methods suffer from high training overhead and limited generalization to unseen scenarios. This paper presents VIsion‑based BEamforming(VIBE), a hybrid model‑based, closed‑loop, learning architecture for real‑time double‑directional mmWave beam management primed by camera sensing. VIBE fuses machine learning, model‑based reasoning, and closed‑loop RF feedback to balance beam‑pair establishment latency with link quality. VIBE bypasses exhaustive training overhead and accelerates link establishment by leveraging camera observations to reduce the beam‑search space. Lightweight beam refinement and offset tracking mechanisms adaptively refine beams in response to dynamic application requirements. VIBE is implemented and evaluated across online indoor/outdoor testbeds, public datasets, and real‑time vehicular experiments, demonstrating strong generalization capabilities, making it suitable for real‑time V2X communication. Comparisons with 5G NR hierarchical beamforming show that VIBE consistently maintains lower outage rates. Furthermore, VIBE outperforms state‑of‑the‑art end‑to‑end ML models for beam selection when evaluated on public datasets and achieves outage rates as low as 1.1‑1.4 %. The results show that a hybrid model‑based, closed‑loop learning architecture is better suited for real‑world mmWave vehicular connectivity than end‑to‑end trained ML models. For reproducibility, we publish our code to https://github.com/UNL‑CPN‑Lab/Look‑Once‑Beam‑Twice.

Authors:Milad Hasanzadeh, Amin Kargarian, Mehdi Farasat
Title: Dynamic Quantum-Assisted Co-Design of Control Tuning and Lyapunov Stability Synthesis for Nonlinear Systems
Abstract:
This paper proposes a dynamic quantum‑assisted co‑design framework for nonlinear closed‑loop systems in which controller parameters and Lyapunov‑certificate parameters are redesigned jointly at successive decision epochs. Unlike conventional nonlinear control designs that typically tune controller gains offline and verify stability separately, the proposed method embeds performance improvement and Lyapunov‑based stability synthesis within a unified online optimization loop. The main novelty is a two‑step computational structure that first contracts the continuous admissible search region around the current operating condition using a Black‑Hole‑based calibration procedure and then constructs a finite binary representation only over this calibrated region. The encoded objective is obtained from sampled nonlinear closed‑loop evaluations and approximated by a local quadratic pseudo‑Boolean surrogate, enabling an Ising‑type Hamiltonian representation suitable for quantum‑assisted optimization. Quantum imaginary time evolution is then used to explore the encoded Hamiltonian, and the resulting candidate bitstrings are decoded into continuous controller and Lyapunov parameters. To reduce dependence on the surrogate model, the decoded candidates are re‑evaluated using the original nonlinear closed‑loop cost and Lyapunov penalties before the final update is applied. The framework can accommodate different Lyapunov decay specifications by modifying the stability penalty and is validated on first‑order nonlinear consensus, second‑order nonlinear consensus, and induction‑motor drive control examples. The implementation code used to generate the reported results is available at \hrefhttps://github.com/LSU‑RAISE‑LAB/DQCLS‑NSGitHub.

Authors:Andrea Britto, Thiago Spina, Weiwei Yang, Spencer Fowers, Baosen Zhang, Chris White
Title: Building Power Grid Models from Open Data: A Complete Pipeline from OpenStreetMap to Optimal Power Flow
Abstract:
Access to realistic transmission grid models is essential for power systems research, yet detailed network data in the United States remains restricted under critical‑infrastructure regulations. We present a pipeline that constructs complete, OPF‑solvable transmission network models entirely from publicly available data. The five‑stage pipeline (1) extracts power infrastructure from OpenStreetMap via a local Overpass API instance, (2) reconstructs bus‑branch topology through voltage inference, line merging, and transformer detection, (3) estimates electrical parameters using voltage‑class lookup tables calibrated with U.S. Energy Information Administration (EIA) plant‑level data, (4) allocates hourly demand from EIA‑930 to individual buses using US Census population as a spatial proxy, and (5) solves both DC and AC optimal power flow using PowerModels.jl with a progressive relaxation strategy that automatically loosens constraints on imprecise models. We validate the pipeline on all 48 contiguous US states and six multi‑state regions, including the full Western (5,076 buses) and Eastern (21,697 buses) Interconnections. Of the 48 single‑state models, 42 (88%) converge at the strictest relaxation level for AC‑OPF at peak hour and 44 (92%) off‑peak. Dispatch costs (median 22/MWh) and system losses (median 1.0%) are consistent with real wholesale‑market outcomes. The pipeline relies exclusively on open data sources, enabling reproducible grid analysis without proprietary data. All 54 models (48 single‑state and 6 multi‑state) are publicly released at https://github.com/microsoft/GridSFM.

Authors:Shikhar Shukla
Title: SpecKV: Adaptive Speculative Decoding with Compression-Aware Gamma Selection
Abstract:
Speculative decoding accelerates large language model (LLM) inference by using a small draft model to propose candidate tokens that a larger target model verifies. A critical hyperparameter in this process is the speculation length γ, which determines how many tokens the draft model proposes per step. Nearly all existing systems use a fixed γ (typically 4), yet empirical evidence suggests that the optimal value varies across task types and, crucially, depends on the compression level applied to the target model. In this paper, we present SpecKV, a lightweight adaptive controller that selects γ per speculation step using signals extracted from the draft model itself. We profile speculative decoding across 4 task categories, 4 speculation lengths, and 3 compression levels (FP16, INT8, NF4), collecting 5,112 step‑level records with per‑step acceptance rates, draft entropy, and draft confidence. We demonstrate that the optimal γ shifts across compression regimes and that draft model confidence and entropy are strong predictors of acceptance rate (correlation \approx 0.56). SpecKV uses a small MLP trained on these signals to maximize expected tokens per speculation step, achieving a 56.0% improvement over the fixed‑γ=4 baseline with only 0.34 ms overhead per decision (<0.5% of step time). The improvement is statistically significant (p < 0.001, paired bootstrap test). We release all profiling data, trained models, and notebooks as open‑source artifacts.

Authors:Antoine P. Leeman, Shuyu Zhan, Melanie N. Zeilinger, Glen Chou
Title: VISION-SLS: Safe Perception-Based Control from Learned Visual Representations via System Level Synthesis
Abstract:
We propose VISION‑SLS, a method for nonlinear output‑feedback control from high‑resolution RGB images which provides robust constraint satisfaction guarantees under calibrated uncertainty bounds despite partial observability, sensor noise, and nonlinear dynamics. To enable scalability while retaining guarantees, we propose: (i) a learned low‑dimensional observation map from pretrained visual features with state‑dependent error bounds, and (ii) a causal affine time‑varying output‑feedback policy optimized via System Level Synthesis (SLS). We develop a scalable, novel solver for the resulting nonconvex program that leverages sequential convex programming coupled with efficient Riccati recursions. On two simulated visuomotor tasks (a 4D car and a 10D quadrotor) with >= 512 x 512 pixels and a 59D humanoid task with partial observability, our method enables safe, information‑gathering behavior that reduces uncertainty while guaranteeing constraint satisfaction with empirically‑calibrated error bounds. We also validate our method on hardware, safely controlling a ground vehicle from onboard images, outperforming baselines in safety rate and solve times. Together, these results show that learned visual abstractions coupled with an efficient solver make SLS‑based safe visuomotor output‑feedback practical at scale. The code implementation of our method is available at https://github.com/trustworthyrobotics/VISION‑SLS.

Authors:Subramanyam Natarajan
Title: VEHRON: A Configuration-Driven BEV Simulation Framework for Subsystem-Level Studies
Abstract:
In practical early‑stage battery‑electric vehicle studies, analysis workflows may become fragmented across spreadsheets, notebooks, and project‑specific scripts, making reuse, audit, and extension harder. VEHRON is an open‑source Python framework for a deterministic, traceable workflow built around prescribed‑speed longitudinal simulation of battery‑electric vehicles using validated YAML configuration, packaged drive‑cycle resources, interchangeable subsystem models, and auditable case outputs. VEHRON currently runs as a command‑line workflow in which a vehicle definition and a testcase definition are combined to execute a simulation, emit a flat time series, and write a case package containing copied inputs, resolved configuration, summary metadata, and standard plots. Architecturally, VEHRON is organized around a small simulation engine, a shared state bus, a registry of model selections, schema‑based configuration loading, and extension points for custom battery and HVAC models loaded from external Python files. VEHRON currently focuses on battery‑electric longitudinal simulation with low‑order battery, thermal, auxiliary‑load, and HVAC models. This paper explains how VEHRON is structured, how it is used, which models it implements, and where its present limits lie. Source code is available at https://github.com/vehron‑dev/vehron, with archived release metadata recorded under DOI https://doi.org/10.5281/zenodo.19820111.

Authors:Peihao Yan, Yun Chen, Jie Lu, Qijun Wang, Huacheng Zeng
Title: TARMM: Scaling Delay-Critical Edge AI Offloading in 5G O-RAN via Temporal Graph Mobility Management
Abstract:
Emerging delay‑critical edge AI applications, such as VR perception and real‑time video analytics, impose stringent latency and reliability requirements on 5G networks. However, existing mobility management mechanisms are largely reactive and fail to adapt to dynamic network conditions, resulting in suboptimal handover decisions and degraded performance. In this paper, we present TARMM, a 5G Open Radio Access Network (O‑RAN) system that optimizes user mobility management for delay‑critical edge AI offloading. The core of TARMM is a temporal graph model that captures the spatiotemporal dynamics of the RAN across users and cells, enabling near real‑time handover decisions. Building on this representation, we design a multi‑agent reinforcement learning (MARL) framework with rule‑based action masking and proactive resource preparation to ensure safe, stable, and efficient handovers. We implement TARMM on a multi‑cell indoor 5G O‑RAN testbed and evaluate it using diverse VR workloads. Extensive experiments show that TARMM reduces tail latency by up to 44% and packet loss by up to 56% compared to state‑of‑the‑art approaches. Source code and demo videos are available at: https://margo‑source.github.io/Margo/

Authors:Kevin Riehl, Anastasios Kouvelas, Michail A. Makridis
Title: sumoITScontrol: Traffic Controller Collection for SUMO Traffic Simulations
Abstract:
Reliable benchmarking is essential for progress in intelligent traffic control research. While microscopic traffic simulators such as SUMO enable detailed modelling of individual vehicle interactions, many published control studies still rely on single‑run evaluations and project‑specific baseline implementations, limiting reproducibility and comparability. This paper presents sumoITScontrol, an open‑source and extensible Python framework providing a curated collection of widely used traffic controllers implemented for SUMO via the TraCI interface. The framework includes established methods for both urban and freeway traffic management, such as Max Pressure signal control, SCOOT/SCATS‑inspired adaptive strategies, and ramp metering algorithms including ALINEA, HERO‑inspired, and METALINE. Beyond providing implementations, the paper emphasises methodological best‑practices for controller evaluation in stochastic microscopic environments. Through systematic calibration and replicated simulation experiments, we demonstrate the substantial impact of stochastic variability on performance metrics and highlight the necessity of variance‑aware reporting and statistical hypothesis testing. By combining standardised controller implementations with reproducibility‑oriented evaluation guidelines, sumoITScontrol aims to improve methodological transparency, enable fair benchmarking of novel approaches, and strengthen experimental standards within the SUMO and intelligent transportation systems research communities. Source Code on project's GitHub: https://github.com/DerKevinRiehl/sumoITScontrol/.

Authors:Jon Goikoetxea, Jesús F. Palacián
Title: GCImOpt: Learning efficient goal-conditioned policies by imitating optimal trajectories
Abstract:
Imitation learning is a well‑established approach for machine‑learning‑based control. However, its applicability depends on having access to demonstrations, which are often expensive to collect and/or suboptimal for solving the task. In this work, we present GCImOpt, an approach to learn efficient goal‑conditioned policies by training on datasets generated by trajectory optimization. Our approach for dataset generation is computationally efficient, can generate thousands of optimal trajectories in minutes on a laptop computer, and produces high‑quality demonstrations. Further, by means of a data augmentation scheme that treats intermediate states as goals, we are able to increase the training dataset size by an order of magnitude. Using our generated datasets, we train goal‑conditioned neural network policies that can control the system towards arbitrary goals. To demonstrate the generality of our approach, we generate datasets and then train policies for various control tasks, namely cart‑pole stabilization, planar and three‑dimensional quadcopter stabilization, and point reaching using a 6‑DoF robot arm. We show that our trained policies can achieve high success rates and near‑optimal control profiles, all while being small (less than 80,000 neural network parameters) and fast enough (up to more than 6,000 times faster than a trajectory optimization solver) that they could be deployed onboard resource‑constrained controllers. We provide videos, code, datasets and pre‑trained policies under a free software license; see our project website https://jongoiko.github.io/gcimopt/.

Authors:Julian Skifstad, Xinyue Annie Yang, Glen Chou
Title: Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
Abstract:
Inference‑time LLM alignment methods, particularly activation steering, offer an alternative to fine‑tuning by directly modifying activations during generation. Existing methods, however, often rely on non‑anticipative interventions that ignore how perturbations propagate through transformer layers and lack online error feedback, resulting in suboptimal, open‑loop control. To address this, we show empirically that, despite the nonlinear structure of transformer blocks, layer‑wise dynamics across multiple LLM architectures and scales are well‑approximated by locally‑linear models. Exploiting this property, we model LLM inference as a linear time‑varying dynamical system and adapt the classical linear quadratic regulator to compute feedback controllers using layer‑wise Jacobians, steering activations toward desired semantic setpoints in closed‑loop with minimal computational overhead and no offline training. We also derive theoretical bounds on setpoint tracking error, enabling formal guarantees on steering performance. Using a novel adaptive semantic feature setpoint signal, our method yields robust, fine‑grained behavior control across models, scales, and tasks, including state‑of‑the‑art modulation of toxicity, truthfulness, refusal, and arbitrary concepts, surpassing baseline steering methods. Our code is available at: https://github.com/trustworthyrobotics/lqr‑activation‑steering

Authors:Shida Jiang, Shengyu Tao, Zihe Liu, Scott Moura
Title: CAR-EnKF: A Covariance-Adaptive and Recalibrated Ensemble Kalman Filter Framework
Abstract:
The ensemble Kalman filter (EnKF) is widely used for nonlinear and high‑dimensional state estimation because it replaces complex covariance propagation with simple ensemble statistics. However, conventional EnKF implementations can become overconfident in the presence of measurement nonlinearity. The commonly used covariance inflation technique only partially alleviates this issue. This paper proposes a covariance‑adaptive and recalibrated ensemble Kalman filter (CAR‑EnKF) framework for nonlinear state estimation. The framework introduces two improvements that are only active for nonlinear measurements and reduce to the conventional EnKF framework without covariance inflation in the linear case: (i) a recalibration mechanism that reassesses the effect of the chosen Kalman gain after updating the ensemble mean, and (ii) a positive semidefinite covariance compensation term that accounts for measurement nonlinearity. An adaptive update law based on the normalized innovation squared further tunes the compensation magnitude online. The framework is algorithmically general and is specialized here to the stochastic EnKF and the ensemble transform Kalman filter (ETKF). Experiments on feature‑based SLAM and the Lorenz‑‑96 system show that CAR‑EnKF consistently reduces RMSE relative to conventional EnKF baselines, with especially large improvements at low measurement‑noise levels. The related codes are available at \hrefhttps://github.com/Shida‑Jiang/CAR‑EnKF‑A‑Covariance‑Adaptive‑and‑Recalibrated‑Ensemble‑Kalman‑Filter‑Framework

Authors:António Nunes, Sérgio Brás, Pedro Batista
Title: Nonlinear backstepping with saturation for low-thrust station-keeping of libration point orbits
Abstract:
This paper presents a novel nonlinear backstepping control law for continuous, low‑thrust station‑keeping in the Earth‑Moon system. Quasi‑periodic libration point orbits are targeted under a high‑fidelity model of the dynamics. Almost global uniform exponential stability guarantees are attained, as shown through Lyapunov's stability theory. Saturation of the actuators is formally included in the controller design, such that these guarantees hold even in the event of saturation. The relationship between saturation threshold, control gains, and deviation is studied and an optimal procedure for gain selection is discussed. The control solution is tested numerically through a Monte Carlo analysis over representative application cases, subject to operational errors, constraints, and external perturbations. Station‑keeping under actuation saturation is validated considering a conservative threshold for typical electric propulsion systems.

Authors:Johannes Kübel, Henrik Krauss, Jinjie Li, Moju Zhao
Title: Energy-based Regularization for Learning Residual Dynamics in Neural MPC for Omnidirectional Aerial Robots
Abstract:
Data‑driven Model Predictive Control (MPC) has lately been the core research subject in the field of control theory. The combination of an optimal control framework with deep learning paradigms opens up the possibility to accurately track control tasks without the need for complex analytical models. However, the system dynamics are often nuanced and the neural model lacks the potential to understand physical properties such as inertia and conservation of energy. In this work, we propose a novel energy‑based regularization loss function which is applied to the training of a neural model that learns the residual dynamics of an omnidirectional aerial robot. Our energy‑based regularization encourages the neural network to cause control corrections that stabilize the energy of the system. The residual dynamics are integrated into the MPC framework and improve the positional mean absolute error (MAE) over three real‑world experiments by 23% compared to an analytical MPC. We also compare our method to a standard neural MPC implementation without regularization and primarily achieve a significantly increased flight stability implicitly due to the energy regularization and up to 15% lower MAE. Our code is available under: https://github.com/johanneskbl/jsk_aerial_robot/tree/develop/neural_MPC.

Authors:Aodi Wu, Haodong Han, Xubo Luo, Ruisuo Wang, Shan He, Xue Wan
Title: SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing
Abstract:
Autonomous on‑orbit servicing demands embodied agents that perceive through visual sensors, reason about 3D spatial situations, and execute multi‑phase tasks over extended horizons. We present SpaceMind, a modular and self‑evolving vision‑language model (VLM) agent framework that decomposes knowledge, tools, and reasoning into three independently extensible dimensions: skill modules with dynamic routing, Model Context Protocol (MCP) tools with configurable profiles, and injectable reasoning‑mode skills. An MCP‑Redis interface layer enables the same codebase to operate across simulation and physical hardware without modification, and a Skill Self‑Evolution mechanism distills operational experience into persistent skill files without model fine‑tuning. We validate SpaceMind through 192 closed‑loop runs across five satellites, three task types, and two environments, a UE5 simulation and a physical laboratory, deliberately including degraded conditions to stress‑test robustness. Under nominal conditions all modes achieve 90‑‑100% navigation success; under degradation, the Prospective mode uniquely succeeds in search‑and‑approach tasks where other modes fail. A self‑evolution study shows that the agent recovers from failure in four of six groups from a single failed episode, including complete failure to 100% success and inspection scores improving from 12 to 59 out of 100. Real‑world validation confirms zero‑code‑modification transfer to a physical robot with 100% rendezvous success. Code: https://github.com/wuaodi/SpaceMind

Authors:Mohammed Ezzaldin Babiker Abdullah
Title: Asymmetric-Loss-Guided Hybrid CNN-BiLSTM-Attention Model for Industrial RUL Prediction with Interpretable Failure Heatmaps
Abstract:
Turbofan engine degradation under sustained operational stress necessitates robust prognostic systems capable of accurately estimating the Remaining Useful Life (RUL) of critical components. Existing deep learning approaches frequently fail to simultaneously capture multi‑sensor spatial correlations and long‑range temporal dependencies, while standard symmetric loss functions inadequately penalize the safety‑critical error of over‑estimating residual life. This study proposes a hybrid architecture integrating Twin‑Stage One‑Dimensional Convolutional Neural Networks (1D‑CNN), a Bidirectional Long Short‑Term Memory (BiLSTM) network, and a custom Bahdanau Additive Attention mechanism. The model was trained and evaluated on the NASA Commercial Modular Aero‑Propulsion System Simulation (C‑MAPSS) FD001 sub‑dataset employing a zero‑leakage preprocessing pipeline, piecewise‑linear RUL labeling capped at 130 cycles, and the NASA‑specified asymmetric exponential loss function that disproportionately penalizes over‑estimation to enforce industrial safety constraints. Experiments on 100 test engines achieved a Root Mean Squared Error (RMSE) of 17.52 cycles and a NASA S‑Score of 922.06. Furthermore, extracted attention weight heatmaps provide interpretable, per‑engine insights into the temporal progression of degradation, supporting informed maintenance decision‑making. The proposed framework demonstrates competitive performance against established baselines and offers a principled approach to safe, interpretable prognostics in industrial settings.

Authors:Mohammed Ezzaldin Babiker Abdullah, Rufaidah Abdallah Ibrahim Mohammed
Title: Outperforming Self-Attention Mechanisms in Solar Irradiance Forecasting via Physics-Guided Neural Networks
Abstract:
Accurate Global Horizontal Irradiance (GHI) forecasting is critical for grid stability, particularly in arid regions characterized by rapid aerosol fluctuations. While recent trends favor computationally expensive Transformer‑based architectures, this paper challenges the prevailing "complexity‑first" paradigm. We propose a lightweight, Physics‑Informed Hybrid CNN‑BiLSTM framework that prioritizes domain knowledge over architectural depth. The model integrates a Convolutional Neural Network (CNN) for spatial feature extraction with a Bi‑Directional LSTM for capturing temporal dependencies. Unlike standard data‑driven approaches, our model is explicitly guided by a vector of 15 engineered features including Clear‑Sky indices and Solar Zenith Angle ‑ rather than relying solely on raw historical data. Hyperparameters are rigorously tuned using Bayesian Optimization to ensure global optimality. Experimental validation using NASA POWER data in Sudan demonstrates that our physics‑guided approach achieves a Root Mean Square Error (RMSE) of 19.53 W/m^2, significantly outperforming complex attention‑based baselines (RMSE 30.64 W/m^2). These results confirm a "Complexity Paradox": in high‑noise meteorological tasks, explicit physical constraints offer a more efficient and accurate alternative to self‑attention mechanisms. The findings advocate for a shift towards hybrid, physics‑aware AI for real‑time renewable energy management.

Authors:Mohammed Ezzaldin Babiker Abdullah
Title: Thermodynamic Liquid Manifold Networks: Physics-Bounded Deep Learning for Solar Forecasting in Autonomous Off-Grid Microgrids
Abstract:
The stable operation of autonomous off‑grid photovoltaic systems requires solar forecasting algorithms that respect atmospheric thermodynamics. Contemporary deep learning models consistently exhibit critical anomalies, primarily severe temporal phase lags during cloud transients and physically impossible nocturnal power generation. To resolve this divergence between data‑driven modeling and deterministic celestial mechanics, this research introduces the Thermodynamic Liquid Manifold Network. The methodology projects 22 meteorological and geometric variables into a Koopman‑linearized Riemannian manifold to systematically map complex climatic dynamics. The architecture integrates a Spectral Calibration unit and a multiplicative Thermodynamic Alpha‑Gate. This system synthesizes real‑time atmospheric opacity with theoretical clear‑sky boundary models, structurally enforcing strict celestial geometry compliance. This completely neutralizes phantom nocturnal generation while maintaining zero‑lag synchronization during rapid weather shifts. Validated against a rigorous five‑year testing horizon in a severe semi‑arid climate, the framework achieves an RMSE of 18.31 Wh/m2 and a Pearson correlation of 0.988. The model strictly maintains a zero‑magnitude nocturnal error across all 1826 testing days and exhibits a sub‑30‑minute phase response during high‑frequency optical transients. Comprising exactly 63,458 trainable parameters, this ultra‑lightweight design establishes a robust, thermodynamically consistent standard for edge‑deployable microgrid controllers.

Authors:Mohammed Ezzaldin Babiker Abdullah
Title: Physics-Informed State Space Models for Reliable Solar Irradiance Forecasting in Off-Grid Systems
Abstract:
The stable operation of off‑grid photovoltaic systems requires accurate, computationally efficient solar forecasting. Contemporary deep learning models often suffer from massive computational overhead and physical blindness, generating impossible predictions. This paper introduces the Physics‑Informed State Space Model (PISSM) to bridge the gap between efficiency and physical accuracy for edge‑deployed microcontrollers. PISSM utilizes a dynamic Hankel matrix embedding to filter stochastic sensor noise by transforming raw meteorological sequences into a robust state space. A Linear State Space Model replaces heavy attention mechanisms, efficiently modeling temporal dependencies for parallel processing. Crucially, a novel Physics‑Informed Gating mechanism leverages the Solar Zenith Angle and Clearness Index to structurally bound outputs, ensuring predictions strictly obey diurnal cycles and preventing nocturnal errors. Validated on a multi‑year dataset for Omdurman, Sudan, PISSM achieves superior accuracy with fewer than 40,000 parameters, establishing an ultra‑lightweight benchmark for real‑time off‑grid control.

Authors:Toru Seo
Title: End-to-end differentiable network traffic simulation with dynamic route choice
Abstract:
Optimization using network traffic models requires computing gradients of objective functions with respect to model parameters. However, derivation of such gradients has often been considered difficult or impractical due to their complexity and size. Conventional approaches rely on numerical differentiation or derivative‑free methods that do not scale well with the parameter dimension, or on adjoint methods that require manual derivation for each specific model. This study proposes a novel end‑to‑end differentiable network traffic flow simulator based on automatic differentiation (AD), employing the Link Transmission Model (LTM) and a Dynamic User Optimum (DUO) route choice model. The LTM operates on continuous aggregate state variables through piecewise‑linear min/max operations, which admit subgradients almost everywhere and thus require no smooth relaxation for AD. The DUO is also suitable for AD: although the shortest path search is itself discrete, the resulting diverge ratios at each node are continuous functions of per‑destination vehicle counts and are thus differentiable. In order to demonstrate the capability of the proposed model, we solved a dynamic congestion toll optimization problem on the Chicago‑Sketch dataset with approximately 2500 links, 1 million vehicles, a 3‑hour duration, and 15000 decision variables. The proposed model successfully derived a high‑quality solution in 3000 iterations, taking about 40 minutes. The simulator, implemented in Python and JAX, is released as open‑source software named UNsim (https://github.com/toruseo/UNsim).

Authors:Seyed Amir Ahmad Safavi-Naini, Elahe Meftah, Josh Mohess, Pooya Mohammadi Kazaj, Georgios Siontis, Zahra Atf, Peter R. Lewis, Mauricio Reyes, Girish Nadkarni, Roland Wiest, Stephan Windecker, Christoph Grani, Ali Soroush, Isaac Shiri
Title: Grounding Clinical AI Competency in Human Cognition Through the Clinical World Model and Skill-Mix Framework
Abstract:
The competency of any intelligent agent is bounded by its formal account of the world in which it operates. Clinical AI lacks such an account. Existing frameworks address evaluation, regulation, or system design in isolation, without a shared model of the clinical world to connect them. We introduce the Clinical World Model, a framework that formalizes care as a tripartite interaction among Patient, Provider, and Ecosystem. To formalize how any agent, whether human or artificial, transforms information into clinical action, we develop parallel decision‑making architectures for providers, patients, and AI agents, grounded in validated principles of clinical cognition. The Clinical AI Skill‑Mix operationalizes competency through eight dimensions. Five define the clinical competency space (condition, phase, care setting, provider role, and task) and three specify how AI engages human reasoning (assigned authority, agent facing, and anchoring layer). The combinatorial product of these dimensions yields a space of billions of distinct competency coordinates. A central structural implication is that validation within one coordinate provides minimal evidence for performance in another, rendering the competency space irreducible. The framework supplies a common grammar through which clinical AI can be specified, evaluated, and bounded across stakeholders. By making this structure explicit, the Clinical World Model reframes the field's central question from whether AI works to in which competency coordinates reliability has been demonstrated, and for whom.

Authors:Kevin Riehl, Julius Schlapbach, Anastasios Kouvelas, Michail A. Makridis
Title: Karma Mechanisms for Decentralised, Cooperative Multi Agent Path Finding
Abstract:
Multi‑Agent Path Finding (MAPF) is a fundamental coordination problem in large‑scale robotic and cyber‑physical systems, where multiple agents must compute conflict‑free trajectories with limited computational and communication resources. While centralised optimal solvers provide guarantees on solution optimality, their exponential computational complexity limits scalability to large‑scale systems and real‑time applicability. Existing decentralised heuristics are faster, but result in suboptimal outcomes and high cost disparities. This paper proposes a decentralised coordination framework for cooperative MAPF based on Karma mechanisms ‑ artificial, non‑tradeable credits that account for agents' past cooperative behaviour and regulate future conflict resolution decisions. The approach formulates conflict resolution as a bilateral negotiation process that enables agents to resolve conflicts through pairwise replanning while promoting long‑term fairness under limited communication and without global priority structures. The mechanism is evaluated in a lifelong robotic warehouse multi‑agent pickup‑and‑delivery scenario with kinematic orientation constraints. The results highlight that the Karma mechanism balances replanning effort across agents, reducing disparity in service times without sacrificing overall efficiency. Code: https://github.com/DerKevinRiehl/karma_dmapf

Authors:Jeffrey Fang, Glen Chou
Title: Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU
Abstract:
We present GPU‑SLS, a GPU‑parallelized framework for safe, robust nonlinear model predictive control (MPC) that scales to high‑dimensional uncertain robotic systems and long planning horizons. Our method jointly optimizes an inequality‑constrained, dynamically‑feasible nominal trajectory, a tracking controller, and a closed‑loop reachable set under disturbance, all in real‑time. To efficiently compute nominal trajectories, we develop a sequential quadratic programming procedure with a novel GPU‑accelerated quadratic program (QP) solver that uses parallel associative scans and adaptive caching within an alternating direction method of multipliers (ADMM) framework. The same GPU QP backend is used to optimize robust tracking controllers and closed‑loop reachable sets via system level synthesis (SLS), enabling reachability‑constrained control in both fixed‑ and receding‑horizon settings. We achieve substantial performance gains, reducing nominal trajectory solve times by 97.7% relative to state‑of‑the‑art CPU solvers and 71.8% compared to GPU solvers, while accelerating SLS‑based control and reachability by 237x. Despite large problem scales, our method achieves 100% empirical safety, unlike high‑dimensional learning‑based reachability baselines. We validate our approach on complex nonlinear systems, including whole‑body quadrupeds (61D) and humanoids (75D), synthesizing robust control policies online on the GPU in 20 milliseconds on average and scaling to problems with 2 x 10^5 decision variables and 8 x 10^4 constraints. The implementation of our method is available at https://github.com/Jeff300fang/gpu_sls.

Authors:Indar Kumar, Akanksha Tiwari
Title: Regime-Calibrated Fleet Repositioning with a Spatial Queue-Regret Decomposition
Abstract:
Ride‑hailing and autonomous mobility‑on‑demand operators reposition idle supply before future demand is fully observed. We study a retrieval‑calibrated predict‑then‑optimize approach for this problem: historical demand regimes are matched to the current query block, combined into a calibrated demand prior, and passed to a fleet‑balancing controller. The paper makes three contributions. First, we train a leakage‑safe similarity gate whose objective penalizes demand error, pickup spatial mismatch, and queue shortage risk rather than retrieval rank alone. Second, we develop a spatial queue‑regret decomposition for a stable queueing surrogate, linking demand‑field error to wait through queueing sensitivity, allocator sensitivity, and Wasserstein pickup mismatch. Third, we evaluate learned retrieval and external‑style rebalancing baselines in a common simulator. In the calibrated‑demand gate experiment, across eight New York City scenarios and ten seeds, the spatial gate reduces mean wait to 82.3s, compared with 85.3s for hand‑tuned similarity and 85.8s for a distributional‑only baseline. In a separate replay‑demand controller comparison, a scenario chance‑MPC analog and a share‑target transportation LP improve on Wen‑style rebalancing (92.2s/92.2s vs. 100.1s), a reduced GPR chance‑MPC comparator is intermediate at 94.4s, and an oracle MPC diagnostic is 91.3s.

Authors:Santosh Mohan Rajkumar, Dibyasri Barman, Kumar Vikram Singh, Debdipta Goswami
Title: On Data-Driven Koopman Representations of Nonlinear Delay Differential Equations
Abstract:
This work establishes a rigorous bridge between infinite‑dimensional delay dynamics and finite‑dimensional Koopman learning, with explicit and interpretable error guarantees. While Koopman analysis is well‑developed for ordinary differential equations (ODEs) and partially for partial differential equations (PDEs), its extension to delay differential equations (DDEs) remains limited due to the infinite‑dimensional phase space of DDEs. We propose a finite‑dimensional Koopman approximation framework based on history discretization and a suitable reconstruction operator, enabling a tractable representation of the Koopman operator via kernel‑based extended dynamic mode decomposition (kEDMD). Deterministic error bounds are derived for the learned predictor, decomposing the total error into contributions from history discretization, kernel interpolation, and data‑driven regression. Additionally, we develop a kernel‑based reconstruction method to recover discretized states from lifted Koopman coordinates, with provable guarantees. Numerical results demonstrate convergence of the learned predictor with respect to both discretization resolution and training data, supporting reliable prediction and control of delay systems.

Authors:Daniel Arnström, Emilio Benenati, Giuseppe Belgioioso
Title: \texttt{DR-DAQP}: An Hybrid Operator Splitting and Active-Set Solver for Affine Variational Inequalities
Abstract:
We present \textttDR‑DAQP, an open‑source solver for strongly monotone affine variational inequaliries that combines Douglas‑Rachford operator splitting with an active‑set acceleration strategy. The key idea is to estimate the active set along the iterations to attempt a Newton‑type correction. This step yields the exact AVI solution when the active set is correctly estimated, thus overcoming the asymptotic convergence limitation inherent in first‑order methods. Moreover, we exploit warm‑starting and pre‑factorization of relevant matrices to further accelerate evaluation of the algorithm iterations. We prove convergence and establish conditions under which the algorithm terminates in finite time with the exact solution. Numerical experiments on randomly generated AVIs show that \textttDR‑DAQP is up to two orders of magnitude faster than the state‑of‑the‑art solver \textttPATH. On a game‑theoretic MPC benchmark, \textttDR‑DAQP achieves solve times several orders of magnitude below those of the mixed‑integer solver \textttNashOpt. A high‑performing C implementation is available at \textthttps://github.com/darnstrom/daqp, with easily‑accessible interfaces to Julia, MATLAB, and Python.

Authors:Ravish Gupta, Saket Kumar, Maulik Dang
Title: Agentic AI for Clinical Urgency Mapping and Queue Optimization in High-Volume Outpatient Departments: A Simulation-Based Evaluation
Abstract:
Outpatient departments (OPDs) in Indian public hospitals face severe overcrowding, with daily volumes reaching 200‑‑8,000 patients~\citeaiims2020annual. The prevailing First‑Come‑First‑Served (FCFS) token system treats all patients equally regardless of clinical urgency, leading to dangerous delays for critical cases. We present an agentic AI framework integrating six components: voice‑based multilingual symptom capture (modeled), LLM‑powered severity prediction, load‑aware physician assignment, adaptive queue optimization with urgency drift detection, a multi‑objective orchestrator, and a Patient Memory System for longitudinal context‑aware triage. Evaluated through discrete‑event simulation of a District Hospital in Jabalpur (Madhya Pradesh) with 368 synthetic patients over 30 runs, the framework achieves 94.2% critical patients seen within 10 minutes (vs.~30.8% under FCFS), detects ～236 simulated urgency drift events per session (modeled via stochastic deterioration probabilities), identifies ～11.9 additional hidden‑critical cases via patient memory, and recomposes queue urgency distribution from 13/36/158/161 (Critical/High/Medium/Low) to ～25/178/115/50 through continuous reassessment, while maintaining comparable throughput (～40.4 patients/hour).

Authors:Pietro Zanotta, Panos Stinis, Ján Drgoňa
Title: SCORE: Statistical Certification of Regions of Attraction via Extreme Value Theory
Abstract:
Certifying the Region of Attraction (ROA) for high‑dimensional nonlinear dynamical systems remains a severe computational bottleneck. Traditional deterministic verification methods, such as Sum‑of‑Squares (SOS) programming and Satisfiability Modulo Theories (SMT), provide hard guarantees but suffer from the curse of dimensionality, typically failing to scale beyond 20 dimensions. To overcome these limitations, we propose SCORE, a statistical certification framework that shifts from seeking deterministic guarantees to bounding the worst‑case safety violation with high statistical confidence. By integrating Projected Stochastic Gradient Langevin Dynamics (PSGLD) with Extreme Value Theory (EVT), we frame ROA certification as a constrained extreme‑value estimation problem on the sublevel set boundary. We theoretically demonstrate that modeling the optimization process as a stochastic diffusion on a compact manifold places the local maxima of the Lyapunov derivative into the Weibull maximum domain of attraction. Since the Weibull domain features a finite right endpoint, we can compute a rigorous statistical upper bound on the global maximum of the Lyapunov derivative. Numerical experiments validate that our EVT‑based approach achieves certification tightness competitive to exact SOS programming on a 2D Van der Pol benchmark. Furthermore, we demonstrate unprecedented scalability by successfully certifying a dense, unstructured 500‑dimensional ODE system up to a confidence level of 99.99%, effectively bypassing the severe combinatorial constraints that limit existing formal verification pipelines.

Authors:Ryuta Moriyasu, Carmen Amo Alonso, Marco Pavone
Title: Bilevel MPC for Linear Systems: A Tractable Reduction and Continuous Connection to Hierarchical MPC
Abstract:
Model predictive control (MPC) has been widely used in many fields, often in hierarchical architectures that combine controllers and decision‑making layers at different levels. However, when such architectures are cast as bilevel optimization problems, standard KKT‑based reformulations often introduce nonconvex and potentially nonsmooth structures that are undesirable for real‑time verifiable control. In this paper, we study a bilevel MPC architecture composed of (i) an upper layer that selects the reference sequence and (ii) a lower‑level linear MPC that tracks such reference sequence. We propose a smooth single‑level reduction that does not degrade performance under a verifiable block‑matrix nonsingularity condition. In addition, when the problem is convex, its solution is unique and equivalent to a corresponding centralized MPC, enabling the inheritance of closed‑loop properties. We further show that bilevel MPC is a natural extension of standard hierarchical MPC, and introduce an interpolation framework that continuously connects the two via move‑blocking. This framework reveals optimal‑value ordering among the resulting formulations and provides inexpensive a posteriori degradation certificates, thereby enabling a principled performance‑computational efficiency trade‑off.

Authors:Shuyi Gao, Stavros Orfanoudakis, Shengren Hou, Peter Palensky, Pedro P. Vergara
Title: Topology-Aware Graph Reinforcement Learning for Energy Storage Systems Optimal Dispatch in Distribution Networks
Abstract:
Optimal dispatch of energy storage systems (ESSs) in distribution networks involves jointly improving operating economy and voltage security under time‑varying conditions and possible topology changes. To support fast online decision making, we develop a topology‑aware Reinforcement Learning architecture based on Twin Delayed Deep Deterministic Policy Gradient (TD3), which integrates graph neural networks (GNNs) as graph feature encoders for ESS dispatch. We conduct a systematic investigation of three GNN variants: graph convolutional networks (GCNs), topology adaptive graph convolutional networks (TAGConv), and graph attention networks (GATs) on the 34‑bus and 69‑bus systems, and evaluate robustness under multiple topology reconfiguration cases as well as cross‑system transfer between networks with different system sizes. Results show that GNN‑based controllers consistently reduce the number and magnitude of voltage violations, with clearer benefits on the 69‑bus system and under reconfiguration; on the 69‑bus system, TD3‑GCN and TD3‑TAGConv also achieve lower saved cost relative to the NLP benchmark than the NN baseline. We also highlight that transfer gains are case‑dependent, and zero‑shot transfer between fundamentally different systems results in notable performance degradation and increased voltage magnitude violations. This work is available at: https://github.com/ShuyiGao/GNNs_RL_ESSs and https://github.com/distributionnetworksTUDelft/GNNs_RL_ESSs.

Authors:Quang Manh Hoang, Guilherme Vieira Hollweg, Bang Nguyen, Akhtar Hussain, Wencong Su, Van-Hai Bui
Title: Scalable Impedance Identification of Diverse IBRs via Cluster-Specialized Neural Networks
Abstract:
Modern machine learning approaches typically identify the impedance of a single inverter‑based resource (IBR) and assume similar impedance characteristics across devices. In modern power systems, however, IBRs will employ diverse control topologies and algorithms, leading to highly heterogeneous impedance behaviors. Training one model per IBR is inefficient and does not scale. This paper proposes a scalable impedance identification framework for diverse IBRs via cluster‑specialized neural networks. First, the dataset is partitioned into multiple clusters with similar feature profiles using the K‑means clustering method. Then, each cluster is assigned a specialized feed‑forward neural network (FNN) tailored to its characteristics, improving both accuracy and computational efficiency. In deployment, only a small number of measurements are required to predict impedance over a wide range of operating points. The framework is validated on six IBRs with varying control bandwidths, control structures, and operating conditions, and further tested on a previously unseen IBR using only ten measurement points. The results demonstrate high accuracy in both the clustering and prediction stages, confirming the effectiveness and scalability of the proposed method.

Authors:Shida Jiang, Jaewoong Lee, Shengyu Tao, Scott Moura
Title: Design Guidelines for Nonlinear Kalman Filters via Covariance Compensation
Abstract:
Nonlinear extensions of the Kalman filter (KF), such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), are indispensable for state estimation in complex dynamical systems, yet the conditions for a nonlinear KF to provide robust and accurate estimations remain poorly understood. This work proposes a theoretical framework that identifies the causes of failure and success in certain nonlinear KFs and establishes guidelines for their improvement. Central to our framework is the concept of covariance compensation: the deviation between the covariance predicted by a nonlinear KF and that of the EKF. With this definition and detailed theoretical analysis, we derive three design guidelines for nonlinear KFs: (i) invariance under orthogonal transformations, (ii) sufficient covariance compensation beyond the EKF baseline, and (iii) selection of compensation magnitude that favors underconfidence. Both theoretical analysis and empirical validation confirm that adherence to these principles significantly improves estimation accuracy, whereas fixed parameter choices commonly adopted in the literature are often suboptimal. The codes and the proofs for all the theorems in this paper are available at https://github.com/Shida‑Jiang/Guidelines‑for‑Nonlinear‑Kalman‑Filters.

Authors:Sibasish Mishra, Aritra Sarkar, Sebastian Feld
Title: EQISA: Energy-efficient Quantum Instruction Set Architecture using Sparse Dictionary Learning
Abstract:
The scalability of quantum computing in supporting sophisticated algorithms critically depends not only on qubit quality and error handling, but also on the efficiency of classical control, constrained by the cryogenic control bandwidth and energy budget. In this work, we address this challenge by investigating the algorithmic complexity of quantum circuits at the instruction set architecture (ISA) level. We introduce an energy‑efficient quantum instruction set architecture (EQISA) that synthesizes quantum circuits in a discrete Solovay‑Kitaev basis of fixed depth and encodes instruction streams using a sparse dictionary learned from decomposing a set of Haar‑random unitaries, followed by entropy‑optimal Huffman coding and an additional lossless bzip2 compression stage. This approach is evaluated on benchmark quantum circuits demonstrating over 60% compression of quantum instruction streams across system sizes, enabling proportional reductions in classical control energy and communication overhead without loss of computational fidelity. Beyond compression, EQISA facilitates the discovery of higher‑level composable abstractions in quantum circuits and provides estimates of quantum algorithmic complexity. These findings position EQISA as an impactful direction for improving the energy efficiency and scalability of quantum control architectures.

Authors:Xiucheng Wang, Zixuan Guo, Nan Cheng
Title: RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Construction
Abstract:
Radio maps (RMs) provide spatially continuous propagation characterizations essential for 6G network planning, but high‑fidelity RM construction remains challenging. Rigorous electromagnetic solvers incur prohibitive computational latency, while data‑driven models demand massive labeled datasets and generalize poorly from simplified simulations to complex multipath environments. This paper proposes RadioDiff‑FS, a few‑shot diffusion framework that adapts a pretrained main‑path generator to multipath‑rich target domains with only a small number of high‑fidelity samples. The adaptation is grounded in a theoretical decomposition of the multipath RM into a dominant main‑path component and a directionally sparse residual. This decomposition shows that the cross‑domain shift corresponds to a bounded and geometrically structured feature translation rather than an arbitrary distribution change. A direction‑consistency loss (DCL) is then introduced to constrain diffusion score updates along physically plausible propagation directions, thereby suppressing phase‑inconsistent artifacts that arise in the low‑data regime. Experiments show that RadioDiff‑FS reduces NMSE by 59.5% on static RMs and by 74.0% on dynamic RMs relative to the vanilla diffusion baseline, achieving an SSIM of 0.9752 and a PSNR of 36.37 dB under severely limited supervision. Even in a one‑shot setting with a single target‑domain sample per scene, RadioDiff‑FS outperforms all fully supervised baselines, confirming that the directional constraint provides an effective inductive bias under extreme data scarcity. Code is available at https://github.com/UNIC‑Lab/RadioDiff‑FS.

Authors:Johannes Köhler
Title: Certainty-equivalent adaptive MPC for uncertain nonlinear systems
Abstract:
We provide a method to design adaptive controllers for nonlinear systems using model predictive control (MPC). By combining a certainty‑equivalent MPC formulation with least‑mean‑square parameter adaptation, we obtain an adaptive controller with strong robust performance guarantees: The cumulative tracking error and violation of state constraints scale linearly with noise energy, disturbance energy, and path length of parameter variation. A key technical contribution is developing the underlying certainty‑equivalent MPC that tracks output references, accounts for actuator limitations and desired state constraints, requires no system‑specific offline design, and provides strong inherent robustness properties. This is achieved by leveraging finite‑horizon rollouts, artificial references, recent analysis techniques for optimization‑based controllers, and soft state constraints. For open‑loop stable systems, we derive a semi‑global result that applies to arbitrarily large measurement noise, disturbances, and parametric uncertainty. For stabilizable systems, we derive a regional result that is valid within a given region of attraction and for sufficiently small uncertainty. Applicability and benefits are demonstrated with numerical simulations involving systems with large parametric uncertainty: a linear stable chain of mass‑spring‑dampers and a nonlinear unstable quadrotor navigating obstacles.

Authors:Jianghong Dong, Chunying Yang, Mengchi Cai, Chaoyi Chen, Qing Xu, Jianqiang Wang, Jiawei Wang, Keqiang Li
Title: Multi-Source Human-in-the-Loop Digital Twin Testbed for Connected and Autonomous Vehicles in Mixed Traffic Flow
Abstract:
In the emerging mixed traffic environments, Connected and Autonomous Vehicles (CAVs) have to interact with surrounding human‑driven vehicles (HDVs). This paper introduces MSH‑MCCT (Multi‑Source Human‑in‑the‑Loop Mixed Cloud Control Testbed), a novel CAV testbed that captures complex interactions between various CAVs and HDVs. Utilizing the Mixed Digital Twin concept, which combines Mixed Reality with Digital Twin, MSH‑MCCT integrates physical, virtual, and mixed platforms, along with multi‑source control inputs. Bridged by the mixed platform, MSH‑MCCT allows human drivers and CAV algorithms to operate both physical and virtual vehicles within multiple fields of view. Particularly, this testbed facilitates the coexistence and real‑time interaction of physical and virtual CAVs \& HDVs, significantly enhancing the experimental flexibility and scalability. Experiments on vehicle platooning in mixed traffic showcase the potential of MSH‑MCCT to conduct CAV testing with multi‑source real human drivers in the loop through driving simulators of diverse fidelity. The videos for the experiments are available at our project website: https://dongjh20.github.io/MSH‑MCCT.

Authors:Jianghong Dong, Chunying Yang, Mengchi Cai, Chaoyi Chen, Qing Xu, Jianqiang Wang, Keqiang Li
Title: From Optimizable to Interactable: Mixed Digital Twin-Empowered Testing of Vehicle-Infrastructure Cooperation Systems
Abstract:
Sufficient testing under corner cases is critical for the long‑term operation of vehicle‑infrastructure cooperation systems (VICS). However, existing corner‑case generation methods are primarily AI‑driven, and VICS testing under corner cases is typically limited to simulation. In this paper, we introduce an L5 ''Interactable'' level to the VICS digital twin (VICS‑DT) taxonomy, extending beyond the conventional L4 ''Optimizable'' level. We further propose an L5‑level VICS testing framework, IMPACT (Interactive Mixed‑digital‑twin Paradigm for Advanced Cooperative vehicle‑infrastructure Testing). By enabling direct human interactions with VICS entities, IMPACT incorporates highly uncertain and unpredictable human behaviors into the testing loop, naturally generating high‑quality corner cases that complement AI‑based methods. Furthermore, the mixedDT‑enabled ''Physical‑Virtual Action Interaction'' facilitates safe VICS testing under corner cases, incorporating real‑world environments and entities rather than purely in simulation. Finally, we implement IMPACT on the I‑VIT (Interactive Vehicle‑Infrastructure Testbed), and experiments demonstrate its effectiveness. The experimental videos are available at our project website: https://dongjh20.github.io/IMPACT.

Authors:Hamza Mahmood, Abhishek Halder, Adeel Akhtar
Title: Schrödinger Bridge Over A Compact Connected Lie Group
Abstract:
This work studies the Schrödinger bridge problem for the kinematic equation on a compact connected Lie group. The objective is to steer a controlled diffusion between given initial and terminal densities supported over the Lie group while minimizing the control effort. We develop a coordinate‑free formulation of this stochastic optimal control problem that respects the underlying geometric structure of the Lie group, thereby avoiding limitations associated with local parameterizations or embeddings in Euclidean spaces. We establish the existence and uniqueness of solution to the corresponding Schrödinger system. Our results are constructive in that they derive a geometric controller that optimally interpolates probability densities supported over the Lie group. To illustrate the results, we provide numerical examples on \mathsfSO(2) and \mathsfSO(3). The codes and animations are publicly available at https://github.com/gradslab/SbpLieGroups.git .

Authors:Adrien Corenflos
Title: Robust Automatic Differentiation of Square-Root Kalman Filters via Gramian Differentials
Abstract:
Square‑root Kalman filters propagate state covariances in Cholesky‑factor form for numerical stability, and are a natural target for gradient‑based parameter learning in state‑space models. Their core operation, triangularization of a matrix M \in \mathbbR^n × m, is computed via a QR decomposition in practice, but naively differentiating through it causes two problems: the semi‑orthogonal factor is non‑unique when m > n, yielding undefined gradients; and the standard Jacobian formula involves inverses, which diverges when M is rank‑deficient. Both are resolved by the observation that all filter outputs relevant to learning depend on the input matrix only through the Gramian MM^\top, so the composite loss is smooth in M even where the triangularization is not. We derive a closed‑form chain‑rule directly from the differential of this Gramian identity, prove it exact for the Kalman log‑marginal likelihood and filtered moments, and extend it to rank‑deficient inputs via a two‑component decomposition: a column‑space term based on the Moore‑‑Penrose pseudoinverse, and a null‑space correction for perturbations outside the column space of M.

Authors:Richie R. Suganda, Bin Hu
Title: Formation-Aware Adaptive Conformalized Perception for Safe Leader-Follower Multi-Robot Systems
Abstract:
This paper considers the perception safety problem in distributed vision‑based leader‑follower formations, where each robot uses onboard perception to estimate relative states, track desired setpoints, and keep the leader within its camera field of view (FOV). Safety is challenging due to heteroscedastic perception errors and the coupling between formation maneuvers and visibility constraints. We propose a distributed, formation‑aware adaptive conformal prediction method based on Risk‑Aware Mondrian CP to produce formation‑conditioned uncertainty quantiles. The resulting bounds tighten in high‑risk configurations (near FOV limits) and relax in safer regions. We integrate these bounds into a Formation‑Aware Conformal CBF‑QP with a smooth margin to enforce visibility while maintaining feasibility and tracking performance. Gazebo simulations show improved formation success rates and tracking accuracy over non‑adaptive (global) CP baselines that ignore formation‑dependent visibility risk, while preserving finite‑sample probabilistic safety guarantees. The experimental videos are available on the \hrefhttps://nail‑uh.github.io/iros2026.github.io/project website\footnoteProject Website: https://nail‑uh.github.io/iros2026.github.io/.

Authors:Xiaoxia Xu, Xidong Mu, Yuanwei Liu, Arumugam Nallanathan
Title: Multi-Mode Pinching-Antenna Systems: Mode Selection or Mode Combining?
Abstract:
This letter investigates multi‑mode pinching antenna systems (PASS), where signals of multiple orthogonal modes can be transmitted within a dielectric waveguide and radiated by pinching antennas (PAs). This enables mode‑domain multiplexing for efficient multi‑user communications using a single waveguide. In particular, two operating protocols are proposed, namely mode selection and mode combining. Mode selection enforces each PA to predominantly radiate signal power of one single mode, while mode combining allows each PA to flexibly radiate power of multiple modes. Based on the two protocols, a sum rate maximization problem is formulated for multi‑mode PASS‑enabled multi‑user downlink communications, where the transmit beamforming, PA positions, and PA propagation constants are jointly optimized. To address this rapidly oscillating and highly nonconvex problem, a particle swarm optimization (PSO) based Karush‑Kuhn‑Tucker (KKT)‑parameterized beamforming (PSO‑ KPBF) algorithm is proposed. KKT‑conditioned solutions are exploited to guide the swarm search, thus reducing the search space and achieving fast convergence. Numerical results demonstrate that: 1) Even using a simple uniform mode‑combining design, the multi‑mode PASS significantly outperform conventional single‑mode PASS and hybrid beamforming systems; and 2) Mode combining achieves high spectral efficiency, while mode selection approximates its performance with a lower hardware complexity. Code is released at https://github.com/xiaoxiaxusummer/multi_mode_pinching_antenna

Authors:Yilin Wen, Yi Guo, Bo Zhao, Wei Qi, Zechun Hu, Colin Jones, Jian Sun
Title: PolyFormer: learning efficient reformulations for scalable optimization under complex physical constraints
Abstract:
Real‑world optimization problems are often constrained by complex physical laws that limit computational scalability. These constraints are inherently tied to complex regions, and thus learning models that incorporate physical and geometric knowledge, i.e., physics‑informed machine learning (PIML), offer a promising pathway for efficient solution. Here, we introduce PolyFormer, which opens a new direction for PIML in prescriptive optimization tasks, where physical and geometric knowledge is not merely used to regularize learning models, but to simplify the problems themselves. PolyFormer captures geometric structures behind constraints and transforms them into efficient polytopic reformulations, thereby decoupling problem complexity from solution difficulty and enabling off‑the‑shelf optimization solvers to efficiently produce feasible solutions with acceptable optimality loss. Through evaluations across three important problems (large‑scale resource aggregation, network‑constrained optimization, and optimization under uncertainty), PolyFormer achieves computational speedups up to 6,400‑fold and memory reductions up to 99.87%, while maintaining solution quality competitive with or superior to state‑of‑the‑art methods. These results demonstrate that PolyFormer provides an efficient and reliable solution for scalable constrained optimization, expanding the scope of PIML to prescriptive tasks in scientific discovery and engineering applications.

Authors:Reda El Makroum, Sebastian Zwickl-Bernhard, Lukas Kranzl, Hans Auer
Title: Conversational Demand Response: Bidirectional Aggregator-Prosumer Coordination through Agentic AI
Abstract:
Residential demand response depends on sustained prosumer participation, yet existing coordination is either fully automated, or limited to one‑way dispatch signals and price alerts that offer little possibility for informed decision‑making. This paper introduces Conversational Demand Response (CDR), a coordination mechanism where aggregators and prosumers interact through bidirectional natural language, enabled through agentic AI. A two‑tier multi‑agent architecture is developed in which an aggregator agent dispatches flexibility requests and a prosumer Home Energy Management System (HEMS) assesses deliverability and cost‑benefit by calling an optimization‑based tool. CDR also enables prosumer‑initiated upstream communication, where changes in preferences can reach the aggregator directly. Proof‑of‑concept evaluation shows that interactions complete in under 12 seconds. The architecture illustrates how agentic AI can bridge the aggregator‑prosumer coordination gap, providing the scalability of automated DR while preserving the transparency, explainability, and user agency necessary for sustained prosumer participation. All system components, including agent prompts, orchestration logic, and simulation interfaces, are released as open source to enable reproducibility and further development.

Authors:Olga Krestinskaya, Mohammed E. Fouda, Ahmed Eltawil, Khaled N. Salama
Title: Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators
Abstract:
Software‑hardware co‑design is essential for optimizing in‑memory computing (IMC) hardware accelerators for neural networks. However, most existing optimization frameworks target a single workload, leading to highly specialized hardware designs that do not generalize well across models and applications. In contrast, practical deployment scenarios require a single IMC platform that can efficiently support multiple neural network workloads. This work presents a joint hardware‑workload co‑optimization framework based on an optimized evolutionary algorithm for designing generalized IMC accelerator architectures. By explicitly capturing cross‑workload trade‑offs rather than optimizing for a single model, the proposed approach significantly reduces the performance gap between workload‑specific and generalized IMC designs. The framework is evaluated on both RRAM‑ and SRAM‑based IMC architectures, demonstrating strong robustness and adaptability across diverse design scenarios. Compared to baseline methods, the optimized designs achieve energy‑delay‑area product (EDAP) reductions of up to 76.2% and 95.5% when optimizing across a small set (4 workloads) and a large set (9 workloads), respectively. The source code of the framework is available at https://github.com/OlgaKrestinskaya/JointHardwareWorkloadOptimizationIMC.

Authors:Toru Lin, Shuying Deng, Zhao-Heng Yin, Pieter Abbeel, Jitendra Malik
Title: How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference
Abstract:
Many essential manipulation tasks ‑ such as food preparation, surgery, and craftsmanship ‑ remain intractable for autonomous robots. These tasks are characterized not only by contact‑rich, force‑sensitive dynamics, but also by their "implicit" success criteria: unlike pick‑and‑place, task quality in these domains is continuous and subjective (e.g. how well a potato is peeled), making quantitative evaluation and reward engineering difficult. We present a learning framework for such tasks, using peeling with a knife as a representative example. Our approach follows a two‑stage pipeline: first, we learn a robust initial policy via force‑aware data collection and imitation learning, enabling generalization across object variations; second, we refine the policy through preference‑based finetuning using a learned reward model that combines quantitative task metrics with qualitative human feedback, aligning policy behavior with human notions of task quality. Using only 50‑200 peeling trajectories, our system achieves over 90% average success rates on challenging produce including cucumbers, apples, and potatoes, with performance improving by up to 40% through preference‑based finetuning. Remarkably, policies trained on a single produce category exhibit strong zero‑shot generalization to unseen in‑category instances and to out‑of‑distribution produce from different categories while maintaining over 90% success rates.

Authors:Stefano De Carli, Davide Previtali, Mirko Mazzoleni, Fabio Previdi
Title: Stability properties of Minimal Gated Unit neural networks
Abstract:
In this work, we address the need for efficient and formally stable Recurrent Neural Networks (RNNs) in environments with limited computational resources by analyzing the stability of the Minimal Gated Unit (MGU) network, a lightweight alternative to common gated RNNs used in system identification. We derive sufficient parametric conditions for the MGU network's input‑to‑state stability and incremental input‑to‑state stability properties. These conditions enable a‑posteriori validation of model stability and form the basis for novel stability‑promoting training methodologies, including a warm‑start of the network's parameters and a projected gradient‑based optimization scheme, both of which are presented in this work. Comparative evaluation, including robustness analysis and validation on synthetic and real‑world data (i.e., the Silverbox benchmark), demonstrates that the minimal gated unit network successfully combines formal stability guarantees with superior parameter efficiency and faster inference times compared to other state‑of‑the‑art recurrent neural networks, while maintaining comparable and satisfactory accuracy. Notably, the results attained on the Silverbox benchmark illustrate that the stable MGU network effectively captures the system dynamics, whereas other stable RNNs fail to converge to a reliable model.

Authors:Shivanshu Tripathi, Reza Akbarian Bafghi, Maziar Raissi
Title: Test-Driven Agentic Framework for Reliable Robot Controller
Abstract:
In this work, we present a test‑driven, agentic framework for synthesizing a deployable low‑level robot controller for navigation tasks. Given a 2D map with an image of an ultrasonic sensor‑based robot, or a 3D robotic simulation environment, our framework iteratively refines the generated controller code using diagnostic feedback from structured test suites to achieve task success. We propose a dual‑tier repair strategy to refine the generated code that alternates between prompt‑level refinement and direct code editing. We evaluate the approach across 2D navigation tasks and 3D navigation in the Webots simulator. Experimental results show that test‑driven synthesis substantially improves controller reliability and robustness over one‑shot controller generation, especially when the initial prompt is underspecified. The source code and demonstration videos are available at: https://shivanshutripath.github.io/robotic_controller.github.io.

Authors:Yue Yang, Shuo Cheng, Yu Fang, Homanga Bharadhwaj, Mingyu Ding, Gedas Bertasius, Daniel Szafir
Title: LiLo-VLA: Compositional Long-Horizon Manipulation via Linked Object-Centric Policies
Abstract:
General‑purpose robots must master long‑horizon manipulation, defined as tasks involving multiple kinematic structure changes (e.g., attaching or detaching objects) in unstructured environments. While Vision‑Language‑Action (VLA) models offer the potential to master diverse atomic skills, they struggle with the combinatorial complexity of sequencing them and are prone to cascading failures due to environmental sensitivity. To address these challenges, we propose LiLo‑VLA (Linked Local VLA), a modular framework capable of zero‑shot generalization to novel long‑horizon tasks without ever being trained on them. Our approach decouples transport from interaction: a Reaching Module handles global motion, while an Interaction Module employs an object‑centric VLA to process isolated objects of interest, ensuring robustness against irrelevant visual features and invariance to spatial configurations. Crucially, this modularity facilitates robust failure recovery through dynamic replanning and skill reuse, effectively mitigating the cascading errors common in end‑to‑end approaches. We introduce a 21‑task simulation benchmark consisting of two challenging suites: LIBERO‑Long++ and Ultra‑Long. In these simulations, LiLo‑VLA achieves a 69% average success rate, outperforming Pi0.5 by 41% and OpenVLA‑OFT by 67%. Furthermore, real‑world evaluations across 8 long‑horizon tasks demonstrate an average success rate of 85%. Project page: https://yy‑gx.github.io/LiLo‑VLA/.

Authors:Junghoon Seo, Hakjin Lee, Jaehoon Sim
Title: Distributional Stability of Tangent-Linearized Gaussian Inference on Smooth Manifolds
Abstract:
Gaussian inference on smooth manifolds is central to robotics, but exact marginalization and conditioning are generally non‑Gaussian and geometry‑dependent. We study tangent‑linearized Gaussian inference and derive explicit non‑asymptotic W_2 stability bounds for projection marginalization and surface‑measure conditioning. The bounds separate local second‑order geometric distortion from nonlocal tail leakage and, for Gaussian inputs, yield closed‑form diagnostics from (μ,Σ) and curvature/reach surrogates. Circle and planar‑pushing experiments validate the predicted calibration transition near \sqrt\|Σ\|_\mathrmop/R\approx 1/6 and indicate that normal‑direction uncertainty is the dominant failure mode when locality breaks. These diagnostics provide practical triggers for switching from single‑chart linearization to multi‑chart or sample‑based manifold inference. Code and Jupyter notebooks are available at https://github.com/mikigom/StabilityTLGaussian.

Authors:Kevin Kai-Chun Chang, Ekin Beyazit, Alberto Sangiovanni-Vincentelli, Tichakorn Wongpiromsarn, Sanjit A. Seshia
Title: ScenicRules: An Autonomous Driving Benchmark with Multi-Objective Specifications and Abstract Scenarios
Abstract:
Developing autonomous driving systems for complex traffic environments requires balancing multiple objectives, such as avoiding collisions, obeying traffic rules, and making efficient progress. In many situations, these objectives cannot be satisfied simultaneously, and explicit priority relations naturally arise. Also, driving rules require context, so it is important to formally model the environment scenarios within which such rules apply. Existing benchmarks for evaluating autonomous vehicles lack such combinations of multi‑objective prioritized rules and formal environment models. In this work, we introduce ScenicRules, a benchmark for evaluating autonomous driving systems in stochastic environments under prioritized multi‑objective specifications. We first formalize a diverse set of objectives to serve as quantitative evaluation metrics. Next, we design a Hierarchical Rulebook framework that encodes multiple objectives and their priority relations in an interpretable and adaptable manner. We then construct a compact yet representative collection of scenarios spanning diverse driving contexts and near‑accident situations, formally modeled in the Scenic language. Experimental results show that our formalized objectives and Hierarchical Rulebooks align well with human driving judgments and that our benchmark effectively exposes agent failures with respect to the prioritized objectives. Our benchmark can be accessed at https://github.com/BerkeleyLearnVerify/ScenicRules/.

Authors:Nelson Salazar-Pena, Alejandra Tabares, Andres Gonzalez-Mancera
Title: MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Markets
Abstract:
This paper introduces a novel, open‑source MARL simulation framework for studying implicit cooperation in LEMs, modeled as a decentralized partially observable Markov decision process and implemented as a Gymnasium environment for MARL. Our framework features a modular market platform with plug‑and‑play clearing mechanisms, physically constrained agent models (including battery storage), a realistic grid network, and a comprehensive analytics suite to evaluate emergent coordination. The main contribution is a novel method to foster implicit cooperation, where agents' observations and rewards are enhanced with system‑level key performance indicators to enable them to independently learn strategies that benefit the entire system and aim for collectively beneficial outcomes without explicit communication. Through representative case studies (available in a dedicated GitHub repository in https://github.com/salazarna/marlem, we show the framework's ability to analyze how different market configurations (such as varying storage deployment) impact system performance. This illustrates its potential to facilitate emergent coordination, improve market efficiency, and strengthen grid stability. The proposed simulation framework is a flexible, extensible, and reproducible tool for researchers and practitioners to design, test, and validate strategies for future intelligent, decentralized energy systems.

Authors:Bharathkumar Hegde, Melanie Bouroche
Title: A Collaborative Safety Shield for Safe and Efficient CAV Lane Changes in Congested On-Ramp Merging
Abstract:
Lane changing in dense traffic is a significant challenge for Connected and Autonomous Vehicles (CAVs). Existing lane change controllers primarily either ensure safety or collaboratively improve traffic efficiency, but do not consider these conflicting objectives together. To address this, we propose the Multi‑Agent Safety Shield (MASS), designed using Control Barrier Functions (CBFs) to enable safe and collaborative lane changes. The MASS enables collaboration by capturing multi‑agent interactions among CAVs through interaction topologies constructed as a graph using a simple algorithm. Further, a state‑of‑the‑art Multi‑Agent Reinforcement Learning (MARL) lane change controller is extended by integrating MASS to ensure safety and defining a customised reward function to prioritise efficiency improvements. As a result, we propose a lane change controller, known as MARL‑MASS, and evaluate it in a congested on‑ramp merging simulation. The results demonstrate that MASS enables collaborative lane changes with safety guarantees by strictly respecting the safety constraints. Moreover, the proposed custom reward function improves the stability of MARL policies trained with a safety shield. Overall, by encouraging the exploration of a collaborative lane change policy while respecting safety constraints, MARL‑MASS effectively balances the trade‑off between ensuring safety and improving traffic efficiency in congested traffic. The code for MARL‑MASS is available with an open‑source licence at https://github.com/hkbharath/MARL‑MASS

Authors:Jie Lu, Peihao Yan, Huacheng Zeng
Title: EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN
Abstract:
With over 3.5 million 5G base stations deployed globally, their collective energy consumption (projected to exceed 131 TWh annually) raises significant concerns over both operational costs and environmental impacts. In this paper, we present EExAPP, a deep reinforcement learning (DRL)‑based xApp for 5G Open Radio Access Network (O‑RAN) that jointly optimizes radio unit (RU) sleep scheduling and distributed unit (DU) resource slicing. EExAPP uses a dual‑actor‑dual‑critic Proximal Policy Optimization (PPO) architecture, with dedicated actor‑critic pairs targeting energy efficiency and quality‑of‑service (QoS) compliance. A transformer‑based encoder enables scalable handling of variable user equipment (UE) populations by encoding all‑UE observations into fixed‑dimensional representations. To coordinate the two optimization objectives, a bipartite Graph Attention Network (GAT) is used to modulate actor updates based on both critic outputs, enabling adaptive trade‑offs between power savings and QoS. We have implemented EExAPP and deployed it on a real‑world 5G O‑RAN testbed with live traffic, commercial RU and smartphones. Extensive over‑the‑air experiments and ablation studies confirm that EExAPP significantly outperforms existing methods in reducing the energy consumption of RU while maintaining QoS. The source code is available at https://github.com/EExApp/EExApp.

Authors:Shashank Shekhar, Abhinav Karn, Kris Keshav, Shivam Bansal, Parikshit Pareek
Title: Fast Diffusion with Physics-Correction for ACOPF
Abstract:
Generating large‑scale, physically consistent AC Optimal Power Flow (ACOPF) datasets is essential for modern data‑driven power system applications. The central challenge lies in balancing solution accuracy with computational efficiency. Recent diffusion‑based generative models produce high‑quality samples; however, their slow sampling procedures limit practical scalability. In this work, we argue that exact physical feasibility is ultimately enforced by power flow solvers or projection steps, and therefore the generative model only needs to produce good initializations rather than perfectly feasible solutions. Based on this insight, we propose a fast diffusion framework using Denoising Diffusion Implicit Models (DDIM) combined with physics‑guided corrections during sampling. The proposed method replaces slow stochastic refinement with a small number of deterministic steps and explicit constraint guidance. Experiments on IEEE 6‑, 24‑, and 118‑bus systems show that our approach achieves up to 20 times faster sampling than standard diffusion models while maintaining comparable statistical accuracy and physical consistency. This makes the method well suited for scalable OPF dataset generation and practical power system learning tasks. We release the implementation code at https://github.com/PSquare‑Lab/DDIM_OPF.

Authors:Apurba Prasad Padhy, Fernando Camacho, Saibal Mukhopadhyay
Title: AIRE-Prune: Asymptotic Impulse-Response Energy for State Pruning in State Space Models
Abstract:
State space models (SSMs) often sacrifice capacity, search space, or stability to offset the memory and compute costs of large state dimensions. We introduce a structured post‑training pruning method for SSMs ‑‑ AIRE‑Prune (Asymptotic Impulse‑Response Energy for State PRUN(E)) ‑‑ that reduces each layer's state dimension by directly minimizing long‑run output‑energy distortion. AIRE‑Prune assigns every state a closed‑form asymptotic impulse‑response energy‑based score, i.e., the total impulse‑response energy it contributes over an infinite horizon (time), and normalizes these scores layer‑wise to enable global cross‑layer comparison and selection. This extends modal truncation from single systems to deep stacks and aligns pruning with asymptotic response energy rather than worst‑case gain. Across diverse sequence benchmarks, AIRE‑Prune reveals substantial redundancy in SISO and MIMO SSMs with average pruning of 60.8%, with average accuracy drop of 0.29% without retraining, while significantly lowering compute. Code: https://github.com/falcon‑arrow/AIRE‑Prune.

Authors:Daniel Stein, Shaoyi Huang, Rolf Drechsler, Bing Li, Grace Li Zhang
Title: Late Breaking Results: Conversion of Neural Networks into Logic Flows for Edge Computing
Abstract:
Neural networks have been successfully applied in various resource‑constrained edge devices, where usually central processing units (CPUs) instead of graphics processing units exist due to limited power availability. State‑of‑the‑art research still focuses on efficiently executing enormous numbers of multiply‑accumulate (MAC) operations. However, CPUs themselves are not good at executing such mathematical operations on a large scale, since they are more suited to execute control flow logic, i.e., computer algorithms. To enhance the computation efficiency of neural networks on CPUs, in this paper, we propose to convert them into logic flows for execution. Specifically, neural networks are first converted into equivalent decision trees, from which decision paths with constant leaves are then selected and compressed into logic flows. Such logic flows consist of if and else structures and a reduced number of MAC operations. Experimental results demonstrate that the latency can be reduced by up to 14.9 % on a simulated RISC‑V CPU without any accuracy degradation. The code is open source at https://github.com/TUDa‑HWAI/NN2Logic

Authors:Minjae Kwon, Josephine Lamp, Lu Feng
Title: Safety Generalization Under Distribution Shift in Safe Reinforcement Learning: A Diabetes Testbed
Abstract:
Safe Reinforcement Learning (RL) algorithms are typically evaluated under fixed training conditions. We investigate whether training‑time safety guarantees transfer to deployment under distribution shift, using diabetes management as a safety‑critical testbed. We benchmark safe RL algorithms on a unified clinical simulator and reveal a safety generalization gap: policies satisfying constraints during training frequently violate safety requirements on unseen patients. We demonstrate that test‑time shielding, which filters unsafe actions using learned dynamics models, effectively restores safety across algorithms and patient populations. Across eight safe RL algorithms, three diabetes types, and three age groups, shielding achieves Time‑in‑Range gains of 13‑‑14% for strong baselines such as PPO‑Lag and CPO while reducing clinical risk index and glucose variability. Our simulator and benchmark provide a platform for studying safety under distribution shift in safety‑critical control domains. Code is available at https://github.com/safe‑autonomy‑lab/GlucoSim and https://github.com/safe‑autonomy‑lab/GlucoAlg.

Authors:Ziqian Wang, Chenxi Fang, Zhen Zhang
Title: Self-Supervised Path Planning in Unstructured Environments via Global-Guided Differentiable Hard Constraint Projection
Abstract:
Deploying deep learning agents for autonomous navigation in unstructured environments faces critical challenges regarding safety, data scarcity, and limited computational resources. Traditional solvers often suffer from high latency, while emerging learning‑based approaches struggle to ensure deterministic feasibility. To bridge the gap from embodied to embedded intelligence, we propose a self‑supervised framework incorporating a differentiable hard constraint projection layer for runtime assurance. To mitigate data scarcity, we construct a Global‑Guided Artificial Potential Field (G‑APF), which provides dense supervision signals without manual labeling. To enforce actuator limitations and geometric constraints efficiently, we employ an adaptive neural projection layer, which iteratively rectifies the coarse network output onto the feasible manifold. Extensive benchmarks on a test set of 20,000 scenarios demonstrate an 88.75% success rate, substantiating the enhanced operational safety. Closed‑loop experiments in CARLA further validate the physical realizability of the planned paths under dynamic constraints. Furthermore, deployment verification on an NVIDIA Jetson Orin NX confirms an inference latency of 94 ms, showing real‑time feasibility on resource‑constrained embedded hardware. This framework offers a generalized paradigm for embedding physical laws into neural architectures, providing a viable direction for solving constrained optimization in mechatronics. Source code is available at: https://github.com/wzq‑13/SSHC.git.

Authors:Alexandre Alouadi, Pierre Henry-Labordère, Grégoire Loeper, Othmane Mazhar, Huyên Pham, Nizar Touzi
Title: LightSBB-M: Bridging Schrödinger and Bass for Generative Diffusion Modeling
Abstract:
The Schrodinger Bridge and Bass (SBB) formulation, which jointly controls drift and volatility, is an established extension of the classical Schrodinger Bridge (SB). Building on this framework, we introduce LightSBB‑M, an algorithm that computes the optimal SBB transport plan in only a few iterations. The method exploits a dual representation of the SBB objective to obtain analytic expressions for the optimal drift and volatility, and it incorporates a tunable parameter beta greater than zero that interpolates between pure drift (the Schrodinger Bridge) and pure volatility (Bass martingale transport). We show that LightSBB‑M achieves the lowest 2‑Wasserstein distance on synthetic datasets against state‑of‑the‑art SB and diffusion baselines with up to 32 percent improvement. We also illustrate the generative capability of the framework on an unpaired image‑to‑image translation task (adult to child faces in FFHQ). These findings demonstrate that LightSBB‑M provides a scalable, high‑fidelity SBB solver that outperforms existing SB and diffusion baselines across both synthetic and real‑world generative tasks. The code is available at https://github.com/alexouadi/LightSBB‑M.

Authors:Yogesh Darmwal, Ketan Rajawat
Title: Rank-one Riemannian Subspace Descent for Nonlinear Matrix Equations
Abstract:
We propose a rank‑one Riemannian subspace descent algorithm for computing symmetric positive definite (SPD) solutions to nonlinear matrix equations arising in control theory, dynamic programming, and stochastic filtering. For solution matrices of size n× n, standard approaches for dense matrix equations typically incur \mathcalO(n^3) cost per‑iteration, while the efficient \mathcalO(n^2) methods either rely on sparsity or low‑rank solutions, or have iteration counts that scale poorly. The proposed method entails updating along the dominant eigen‑component of a transformed Riemannian gradient, identified using at most \mathcalO(\log(n)) power iterations. The update structure also enables exact step‑size selection in many cases at minimal additional cost. For objectives defined as compositions of standard matrix operations, each iteration can be implemented using only matrix‑‑vector products, yielding \mathcalO(n^2) arithmetic cost. We prove an \mathcalO(n) iteration bound under standard smoothness assumptions, with improved bounds under geodesic strong convexity. Numerical experiments on large‑scale CARE, DARE, and other nonlinear matrix equations show that the proposed algorithm solves instances (up to n=10,000 in our tests) for which the compared solvers, including MATLAB's \texttticare, structure‑preserving doubling, and subspace‑descent baselines fail to return a solution. These results demonstrate that rank‑one manifold updates provide a practical approach for high‑dimensional and dense SPD‑constrained matrix equations. MATLAB code implementation is publicly available on GitHub : \hrefhttps://github.com/yogeshd‑iitk/nonlinear_matrix_equation_R1RSD\textcolorbluehttps://github.com/yogeshd‑iitk/nonlinear\_matrix \_equation\_R1RSD

Authors:Milad Hasanzadeh, Amin Kargarian, Javad Lavaei
Title: All-Pass Fractional OPF: A Solver-Friendly, Physics-Preserving Approximation of AC OPF
Abstract:
This paper presents a fractional approximation of the AC optimal power flow (AC OPF) problem based on an all‑pass approximation of the exponential power flow kernel. The classical AC OPF relies on trigonometric coupling between bus voltage phasors, which yields a nonconvex program with oscillatory derivatives that can slow, or in some cases destabilize, interior‑point methods. We replace the trigonometric terms with an all‑pass fractional (APF) approximation whose real and imaginary components act as smooth surrogates for the cosine and sine functions, and we introduce a pre‑rotation to shift the argument of the approximation toward its most accurate region, ensuring that the reformulated power flow model preserves physical loss behavior, maintains the symmetry of the classical kernels, and improves the conditioning of the Jacobian and Hessian matrices. The proposed APF OPF formulation remains nonconvex, as in the classical model, but it eliminates trigonometric evaluations and empirically produces larger and more stable Newton steps under standard interior‑point solvers. Numerical results on more than 25 IEEE and PGLib test systems ranging from 9 to 10,000 buses demonstrate that the APF OPF model achieves solutions with accuracy comparable to that of the classical formulation while reducing solver times, indicating a more solver‑friendly nonconvex representation of AC OPF. All code, functions, verification scripts, and generated results are publicly available on \hrefhttps://github.com/LSU‑RAISE‑LAB/APF‑OPFGitHub, along with a README describing how to run and reproduce the experiments.

Authors:Eike Osmers, Dorothea Kolossa
Title: Optimal Calibration of the endpoint-corrected Hilbert Transform
Abstract:
Accurate, low‑latency estimates of the instantaneous phase of oscillations are essential for closed‑loop sensing and actuation, including (but not limited to) phase‑locked neurostimulation and other real‑time applications. The endpoint‑corrected Hilbert transform (ecHT) reduces boundary artefacts of the Hilbert transform by applying a causal narrow‑band filter to the analytic spectrum. This improves the phase estimate at the most recent sample. Despite its widespread empirical use, the systematic endpoint distortions of ecHT have lacked a principled, closed‑form analysis. In this study, we derive the ecHT endpoint operator analytically and demonstrate that its output can be decomposed into a desired positive‑frequency term (a deterministic complex gain that induces a calibratable amplitude/phase bias) and a residual leakage term setting an irreducible variance floor. This yields (i) an explicit characterisation and bounds for endpoint phase/amplitude error, (ii) a mean‑squared‑error‑optimal scalar calibration (c‑ecHT), and (iii) practical design rules relating window length, bandwidth/order, and centre‑frequency mismatch to residual bias via an endpoint group delay. The resulting calibrated ecHT achieves near‑zero mean phase error and remains computationally compatible with real‑time pipelines. Code and analyses are provided at https://github.com/eosmers/cecHT.

Authors:Shibo Shao, Dong Zhou, Guanghui Sun, Liwen Zhang, Mingxuan Jiang
Title: Imitation learning-based spacecraft rendezvous and docking method with Expert Demonstration
Abstract:
Existing spacecraft rendezvous and docking control methods largely rely on predefined dynamic models and often exhibit limited robustness in realistic on‑orbit environments. To address this issue, this paper proposes an Imitation Learning‑based spacecraft rendezvous and docking control framework (IL‑SRD) that directly learns control policies from expert demonstrations, thereby reducing dependence on accurate modeling. We propose an anchored decoder target mechanism, which conditions the decoder queries on state‑related anchors to explicitly constrain the control generation process. This mechanism enforces physically consistent control evolution and effectively suppresses implausible action deviations in sequential prediction, enabling reliable six‑degree‑of‑freedom (6‑DOF) rendezvous and docking control. To further enhance stability, a temporal aggregation mechanism is incorporated to mitigate error accumulation caused by the sequential prediction nature of Transformer‑based models, where small inaccuracies at each time step can propagate and amplify over long horizons. Extensive simulation results demonstrate that the proposed IL‑SRD framework achieves accurate and energy‑efficient model‑free rendezvous and docking control. Robustness evaluations further confirm its capability to maintain competitive performance under significant unknown disturbances. The source code is available at https://github.com/Dongzhou‑1996/IL‑SRD.

Authors:Lu Yue, Yue Fan, Shiwei Lian, Yu Zhao, Jiaxin Yu, Liang Xie, Feitian Zhang
Title: Spatial-VLN: Zero-Shot Vision-and-Language Navigation With Explicit Spatial Perception and Exploration
Abstract:
Zero‑shot Vision‑and‑Language Navigation (VLN) agents leveraging Large Language Models (LLMs) excel in generalization but suffer from insufficient spatial perception. Focusing on complex continuous environments, we categorize key perceptual bottlenecks into three spatial challenges: door interaction,multi‑room navigation, and ambiguous instruction execution, where existing methods consistently suffer high failure rates. We present Spatial‑VLN, a perception‑guided exploration framework designed to overcome these challenges. The framework consists of two main modules. The Spatial Perception Enhancement (SPE) module integrates panoramic filtering with specialized door and region experts to produce spatially coherent, cross‑view consistent perceptual representations. Building on this foundation, our Explored Multi‑expert Reasoning (EMR) module uses parallel LLM experts to address waypoint‑level semantics and region‑level spatial transitions. When discrepancies arise between expert predictions, a query‑and‑explore mechanism is activated, prompting the agent to actively probe critical areas and resolve perceptual ambiguities. Experiments on VLN‑CE demonstrate that Spatial VLN achieves state‑of‑the‑art performance using only low‑cost LLMs. Furthermore, to validate real‑world applicability, we introduce a value‑based waypoint sampling strategy that effectively bridges the Sim2Real gap. Extensive real‑world evaluations confirm that our framework delivers superior generalization and robustness in complex environments. Our codes and videos are available at https://yueluhhxx.github.io/Spatial‑VLN‑web/.

Authors:Alberto Bemporad
Title: Worst-case Nonlinear Regression with Error Bounds
Abstract:
We propose an active‑learning method for nonlinear minimax regression. Given a nonlinear function that can be arbitrarily evaluated over a compact set, we fit a surrogate model, such as a feedforward neural network, by minimizing the maximum absolute approximation error. To handle the nonsmoothness of this worst‑case loss, we introduce a smooth L_\infty approximation that enables efficient gradient‑based training. The training set is iteratively enriched by querying points of largest error via global optimization. We also derive constant and input‑dependent worst‑case error bounds over the entire input domain. The approach is validated on approximations of nonlinear functions and nonconvex sets, uncertain models of nonlinear dynamics, and explicit model predictive control laws. A Python library is available at https://github.com/bemporad/maxfit.

Authors:J. Matoušek, J. Krejčí, J. Duník, R. Zanetti
Title: Tensor Decompositions for Online Grid-Based Terrain-Aided Navigation
Abstract:
This paper presents a practical and scalable grid‑based state estimation method for high‑dimensional models with invertible linear dynamics and with highly non‑linear measurements, such as the nearly constant velocity model with measurements of e.g. altitude, bearing, and/or range. Unlike previous tensor decomposition‑based approaches, which have largely remained at the proof‑of‑concept stage, the proposed method delivers an efficient and practical solution by exploiting decomposable model structure‑specifically, block‑diagonal dynamics and sparsely coupled measurement dimensions. The algorithm integrates a Lagrangian formulation for the time update and leverages low‑rank tensor decompositions to compactly represent and effectively propagate state densities. This enables real‑time estimation for models with large state dimension, significantly extending the practical reach of grid‑based filters beyond their traditional low‑dimensional use. Although demonstrated in the context of terrain‑aided navigation, the method is applicable to a wide range of models with decomposable structure. The computational complexity and estimation accuracy depend on the specific structure of the model. All experiments are fully reproducible, with source code provided alongside this paper (GitHub link: https://github.com/pesslovany/Matlab‑LagrangianPMF).

Authors:Jindřich Duník, Jan Krejčí, Jakub Matoušek, Marek Brandner, Yeongkwon Choe
Title: Lagrangian Grid-based Estimation of Nonlinear Systems with Invertible Dynamics
Abstract:
This paper deals with the state estimation of non‑linear and non‑Gaussian systems with an emphasis on the numerical solution to the Bayesian recursive relations. In particular, this paper builds upon the Lagrangian grid‑based filter (GbF) recently‑developed for linear systems and extends it for systems with nonlinear dynamics that are invertible. The proposed nonlinear Lagrangian GbF reduces the computational complexity of the standard GbFs from quadratic to log‑linear, while preserving all the strengths of the original GbF such as robustness, accuracy, and deterministic behaviour. The proposed filter is compared with the particle filter in several numerical studies using the publicly available MATLAB\textregistered\ implementation\footnotehttps://github.com/pesslovany/Matlab‑LagrangianPMF.

Authors:Elia Cereda, Alessandro Giusti, Daniele Palossi
Title: NanoCockpit: Performance-optimized Application Framework for AI-based Autonomous Nanorobotics
Abstract:
Autonomous nano‑drones, powered by vision‑based tiny machine learning (TinyML) models, are a novel technology gaining momentum thanks to their broad applicability and pushing scientific advancement on resource‑limited embedded systems. Their small form factor, i.e., a few tens of grams, severely limits their onboard computational resources to sub‑100mW microcontroller units (MCUs). The Bitcraze Crazyflie nano‑drone is the de facto standard, offering a rich set of programmable MCUs for low‑level control, multi‑core processing, and radio transmission. However, roboticists very often underutilize these onboard precious resources due to the absence of a simple yet efficient software layer capable of time‑optimal pipelining of multi‑buffer image acquisition, multi‑core computation, intra‑MCUs data exchange, and Wi‑Fi streaming, leading to sub‑optimal control performances. Our NanoCockpit framework aims to fill this gap, increasing the throughput and minimizing the system's latency, while simplifying the developer experience through coroutine‑based multi‑tasking. In‑field experiments on three real‑world TinyML nanorobotics applications show our framework achieves ideal end‑to‑end latency, i.e. zero overhead due to serialized tasks, delivering quantifiable improvements in closed‑loop control performance (‑30% mean position error, mission success rate increased from 40% to 100%).

Authors:Khandakar Shakib Al Hasan, Syed Rifat Raiyan, Hasin Mahtab Alvee, Wahid Sadik
Title: CircuitLM: A Multi-Agent LLM-Aided Design Framework for Generating Circuit Schematics from Natural Language Prompts
Abstract:
Generating accurate circuit schematics from high‑level natural language descriptions remains a persistent challenge in electronic design automation (EDA), as large language models (LLMs) frequently hallucinate components, violate strict physical constraints, and produce non‑machine‑readable outputs. To address this, we present CircuitLM, a multi‑agent pipeline that translates user prompts into structured, visually interpretable \textttCircuitJSON schematics. The framework mitigates hallucination and ensures physical viability by grounding generation in a curated, embedding‑powered component knowledge base through five sequential stages: (i) component identification, (ii) canonical pinout retrieval, (iii) chain‑of‑thought reasoning, (iv) JSON schematic synthesis, and (v) interactive force‑directed visualization. We evaluate the system on a dataset of 100 unique circuit‑design prompts using five state‑of‑the‑art LLMs. To systematically assess performance, we deploy a rigorous dual‑layered evaluation methodology: a deterministic Electrical Rule Checking (ERC) engine categorizes topological faults by strict severity (Critical, Major, Minor, Warning), while an LLM‑as‑a‑judge meta‑evaluator identifies complex, context‑aware design flaws that bypass standard rule‑based checkers. Ultimately, this work demonstrates how targeted retrieval combined with deterministic and semantic verification can bridge natural language to structurally viable, schematic‑ready hardware and safe circuit prototyping. Our code and data are publicly available at https://github.com/Khandakar227/CircuitLM.

Authors:Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah
Title: $α^3$-Bench: A Unified Benchmark of Safety, Robustness, and Efficiency for LLM-Based UAV Agents over 6G Networks
Abstract:
Large Language Models (LLMs) are increasingly used as high level controllers for autonomous Unmanned Aerial Vehicle (UAV) missions. However, existing evaluations rarely assess whether such agents remain safe, protocol compliant, and effective under realistic next generation networking constraints. This paper introduces α^3‑Bench, a benchmark for evaluating LLM driven UAV autonomy as a multi turn conversational reasoning and control problem operating under dynamic 6G conditions. Each mission is formulated as a language mediated control loop between an LLM based UAV agent and a human operator, where decisions must satisfy strict schema validity, mission policies, speaker alternation, and safety constraints while adapting to fluctuating network slices, latency, jitter, packet loss, throughput, and edge load variations. To reflect modern agentic workflows, α^3‑Bench integrates a dual action layer supporting both tool calls and agent to agent coordination, enabling evaluation of tool use consistency and multi agent interactions. We construct a large scale corpus of 113k conversational UAV episodes grounded in UAVBench scenarios and evaluate 17 state of the art LLMs using a fixed subset of 50 episodes per scenario under deterministic decoding. We propose a composite α^3 metric that unifies six pillars: Task Outcome, Safety Policy, Tool Consistency, Interaction Quality, Network Robustness, and Communication Cost, with efficiency normalized scores per second and per thousand tokens. Results show that while several models achieve high mission success and safety compliance, robustness and efficiency vary significantly under degraded 6G conditions, highlighting the need for network aware and resource efficient LLM based UAV agents. The dataset is publicly available on GitHub : https://github.com/maferrag/AlphaBench

Authors:Md. Sadman Haque, Zobaer Ibn Razzaque, Robiul Awoul Robin, Fahim Hafiz, Riasat Azim
Title: An Energy-Efficient Smart Bus Transport Management System with Blind-Spot Collision Detection Ability
Abstract:
Public bus transport systems in developing countries often suffer from a lack of real‑time location updates and for users, making commuting inconvenient and unreliable for passengers. Furthermore, stopping at undesired locations rather than designated bus stops creates safety risks and contributes to roadblocks, often causing traffic congestion. Additionally, issues such as blind spots, along with a lack of following traffic laws, increase the chances of accidents. In this work, we address these challenges by proposing a smart public bus system along with intelligent bus stops that enhance safety, efficiency, and sustainability. Our approach includes a deep learning‑based blind‑spot warning system to help drivers avoid accidents with automated bus‑stop detection to accurately identify bus stops, improving transit efficiency. We also introduce IoT‑based solar‑powered smart bus stops that show real‑time passenger counts, along with an RFID‑based card system to track where passengers board and exit. A smart door system ensures safer and more organised boarding, while real‑time bus tracking keeps passengers informed. To connect all these features, we use an HTTP‑based server for seamless communication between the interconnected network systems. Our proposed system demonstrated approximately 99% efficiency in real‑time blind spot detection while stopping precisely at the bus stops. Furthermore, the server showed real‑time location updates both to the users and at the bus stops, enhancing commuting efficiency. The proposed energy‑efficient bus stop demonstrated 12.71kWh energy saving, promoting sustainable architecture. Full implementation and source code are available at: https://github.com/sadman‑adib/MoveMe‑IoT

Authors:Ismail Cosandal, Sennur Ulukus, Nail Akar
Title: Dual-Regime Absorbing Markov Chain Theory in Remote Estimation: Age-Minimizing Push Policies
Abstract:
For a remote estimation system, we study the optimization of age of incorrect information (AoII), which is a recently proposed semantic‑aware information freshness metric. In particular, we assume an information source that observes a discrete‑time finite‑state Markov chain (DTMC), and occasionally transmits status update packets to a remote monitor which is tasked with remote estimation of the source. For the forward channel from the source to the monitor, we assume the channel delay to be modeled by a general discrete‑time phase‑type (DPH) distribution, whereas the reverse channel from the monitor to the source is assumed to be perfect, ensuring that the source has perfect information on the AoII and the remote estimate at the monitor, at all times. Push‑based transmissions are initiated when AoII exceeds a threshold depending on the current estimation value, i.e., multi‑threshold policy. In this very general setting, our goal is to minimize a weighted sum of the time average of a polynomial function of AoII, depending on the remote estimate, and energy consumption from transmissions. We formulate the problem as a semi‑Markov decision process (SMDP) with the same state‑space of the original DTMC to obtain the optimal multi‑threshold policy, whereas the parameters of the SMDP are obtained by using a novel stochastic tool called dual‑regime absorbing Markov chain (DR‑AMC), and its corresponding absorption time distribution named as dual‑regime DPH (DR‑DPH). The proposed method is validated with numerical examples using comparisons against other policies obtained by exhaustive search, and also various benchmark policies.

Authors:Moh Kamalul Wafi
Title: On the Comparison of Reinforcement Learning and Adaptive Control for Linear Systems under Packet Loss and Uncertainty
Abstract:
This paper presents a comparative study between Adaptive Quantized Control (AQC) and Deep Deterministic Policy Gradient (DDPG) reinforcement learning for uncertain linear systems with input quantization over communication channels subject to packet loss. The considered setting also includes dynamic switching from a nominal unstable system to a more unstable one during operation. The AQC is designed for unknown system dynamics using acknowledgment messages to compensate for packet losses, whereas the DDPG controller is trained using the nominal system model without acknowledgment messages. Numerical results show that the DDPG controller achieves faster transient responses and improved damping within its training environment. However, under model uncertainty, packet loss, and dynamic switching, the AQC consistently demonstrates superior robustness owing to its rigorous Lyapunov stability guarantees. These results highlight the trade‑off between data‑driven performance and model‑based robustness, and provide insight into the applicability of reinforcement learning and adaptive control for networked uncertain systems.

Authors:Aizuo Chen, Xiangyu Meng, Yue Zhu
Title: Stability and Droop Characteristics Analysis of Observer-Synchronized Grid-Forming Control
Abstract:
This paper analyzes the stability and droop characteristics of Observer‑Synchronized grid‑forming control. First, a second‑order nonlinear autonomous model is derived under the quasi‑steady‑state assumption. Based on the derived model, the equilibrium points and nonlinear stability properties are investigated using the qualitative theory of differential equations. Explicit parameter conditions are obtained to guarantee almost global asymptotic stability of the desired equilibrium. Furthermore, an analytical expression of the nonlinear droop characteristic is derived to reveal the relationship between active power and frequency. The theoretical analysis is validated through electromagnetic transient simulations and experiments.

Authors:Costas Mylonas, Titos Georgoulakis, Magda Foti
Title: A Conversational Agentic Interface to Physics-Based Household Digital Twins for Residential Energy Decision Support
Abstract:
Multiple actors around residential energy systems require accessible decision‑support tools: homeowners and tenants for dwelling‑level retrofit choices, consultants and municipal planners for building and district‑level intervention assessment, and retailers and aggregators for estimating residential flexibility and coordinating distributed energy resources. However, existing pathways remain limited, since professional audits are costly and static, rule‑of‑thumb estimates lack household specificity, and high‑fidelity simulation tools require specialized expertise. This paper presents a conversational agentic framework that makes physics‑based household energy simulation accessible through natural language interaction. The proposed system integrates a Household Digital Twin (HDT), built on GridLAB‑D and exposed through a REST‑based microservices architecture, with a two‑tier large language model (LLM) agentic layer that translates user requests into structured, schema‑compliant simulation payloads. To improve reliability, the architecture combines intent routing, a domain‑specific knowledge base, deterministic post‑processing of simulation outputs, and tool‑governed execution policies. The system is evaluated on a curated dataset of 45 prompts with increasing complexity, covering multiple households, seasons, and override scenarios. Results show 100% schema conformance, 96.1% field‑level F1, 90.4% value accuracy, and a 95.6% end‑to‑end simulation success rate. The findings indicate that conversational agentic interfaces can substantially lower the usability barrier of physics‑based household digital twins while preserving the reliability required for residential energy decision support.

Authors:Sam Boeckx, Pieterjan Polfliet, Lieven De Strycker, Liesbet Van der Perre
Title: Electric Field Attenuation Techniques for Inductive Wireless Charging of Medical Implants
Abstract:
Inductive wireless charging of implantable medical devices necessitates careful control of magnetic and electric field emissions to meet strict safety regulations while delivering sufficient power. When designing a comfortable wireless charger that can operate over distances ranging to 10cm or more, it is difficult not to exceed the most stringent E‑field limit of 83~V/m. This paper investigates electric field attenuation techniques for mid‑range wireless power transfer at 6.78~MHz. Using \newacronymfeaFEAfinite element analysis\acrfullfea like Ansys \textregistered HFSS \texttrademark, three mitigation strategies are evaluated; (1) a high‑permittivity dielectric shielding layer to absorb and redistribute electric fields, (2) multiple resonant tuning capacitors distributed along the transmitter coil to lower the voltage swing and confine high E‑field regions, and (3) alternative coil‑array transmitter topologies to spatially localize more confined E‑fields. The results show that each technique significantly reduces the E‑field magnitude without substantially affecting the H‑field. Shielding the transmit coil attenuates the peak E‑field from its initial 1416~V/m to 496~V/m, approximately a 65% reduction. Distributing the tuning capacitance into sixteen smaller capacitors yields a drop from the 1416~V/m to 231~V/m, approximately a 84% reduction. Both techniques preserve the required 8~A/m magnetic field. The third technique, a two‑by‑two coil array transmitter reduced the E‑field from its 1416~V/m to 990~V/m (around 30% reduction), though with a slight magnetic field redistribution. All three methods combined, the E‑field was successfully attenuated to 82~V/m, just below the strictest limit, without compromising power transfer efficiency. This research demonstrates a feasible approach and framework to safely extend the application of wireless charging for medical implants.

Authors:Zhou He, Ning Li, Ruotian Liu, Liang Li, Carla Seatzu
Title: Dynamic Scheduling for Flexible Manufacturing Systems Based on Multi-Agent Deep Reinforcement Learning and Petri Nets
Abstract:
This paper investigates dynamic scheduling for flexible manufacturing systems (FMSs) subject to dynamic events, such as new order arrivals, temporary order cancellations, and machine failures. Traditional methods often face significant challenges in achieving real‑time responsiveness under such conditions. To address this issue, the scheduling problem is formulated as a Markov decision process (MDP) with timed Petri nets, where the future evolution of the system depends exclusively on the current marking and the subsequently executed transitions, independent of historical trajectories. The state space and action space of the MDP are constructed using the notion of basis reachability graph (a compact state space representation) of Petri nets to alleviate the state explosion problem, thereby accelerating model training convergence. Meanwhile, a hierarchical dense reward function is constructed by integrating stepwise guidance with terminal evaluation. Then, a multi‑agent proximal policy optimization algorithm is employed for model training under the centralized training and decentralized execution paradigm to improve scheduling efficiency. Numerical experiments are conducted involving typical dynamic events, and the results demonstrate that the proposed method can effectively handle dynamic events and achieve superior scheduling performance compared with conventional approaches.

Authors:Carl von Holly-Ponientzietz, Saverio Bolognani, Florian Dörfler, Verena Häberle
Title: A Coalitional Stable and Fair Reward Allocation for Dynamic Virtual Power Plants
Abstract:
This paper establishes crucial cooperation criteria for the operation of Dynamic Virtual Power Plants (DVPPs). We propose a control design and reward allocation mechanism to enable and incentivize Distributed Energy Resources (DERs) to provide dynamic ancillary services (DAS). Our results illustrate how the cooperative aggregation of heterogeneous DERs leverages technical complementarities to outperform standalone DAS provision. The proposed reward allocation fulfills critical game‑theoretic criteria, including individual rationality, coalitional stability, incentive compatibility, optimality, fairness and ex‑post consistency. The control design and reward allocation are validated using a case study based on the Finnish power grid.

Authors:Javal Vyas, Milapji Singh Gill, Artan Markaj, Felix Gehlhoff, Mehmet Mercangöz
Title: A Tutorial on Autonomous Fault-Tolerant Control Using Knowledge-Grounded LLM Agents
Abstract:
Fault recovery in process plants still relies heavily on plant operators, especially when faults fall outside predefined supervisory logic. Operators interpret alarms, procedures, P\&IDs, interlocks, and process trends, then decide how to move the plant to a safe operating mode without triggering a shutdown. This paper examines how Large Language Model (LLM) agents can support such recovery decisions. The proposed framework treats the LLM as a constrained supervisory planner. It uses plant‑specific knowledge to propose recovery actions, and every proposal is checked by an external validator (symbolic or simulation‑based) before actuation. The paper develops three design dimensions for applying the framework: the recovery patterns for which LLM agents are useful, the validation strategies that separate admissible from inadmissible proposals, and the deployment constraints imposed by latency, knowledge engineering, safety integration, and model lifecycle management. To make the framework directly usable, two openly available executable Python environments are provided. Both re‑implement established case studies, a modular mixing module and a continuous stirred‑tank reactor, extended with configurable faults and defined interfaces for custom recovery and validation methods.

Authors:Javal Vyas, Milapji Singh Gill, Mehmet Mercangöz
Title: Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models
Abstract:
Engineering specifications such as interlocks, alarm rationalization tables, and cause‑and‑effect (C&E) matrices remain central to process control and safety, yet their creation is still predominantly manual, document‑driven, and prone to inconsistency. This paper presents a semantic‑AI framework that automates the generation of C&E logic by combining a knowledge graph (KG) with a constrained large language model (LLM) layer. The KG builds on an established modular alignment ontology to represent process structure, operating modes, faults, symptoms, causes, and mitigation actions in a machine‑interpretable form. The LLM then transforms this information into operator‑ready safety narratives and Semantic Web Rule Language (SWRL) rules under strict ontology and vocabulary constraints, grounding the generated artifacts in the underlying semantic model. The workflow is demonstrated on a modular process plant, showing how engineering semantics, diagnostic relations, and machine‑verifiable specifications can be generated from a unified knowledge representation with reduced manual effort.

Authors:Mohammadhossein Bakhtiaridoust, Dominik Baumann, Shankar Deka
Title: Uncertainty Quantification via Invariant-Measure Conformal Prediction
Abstract:
Uncertainty quantification for learned stochastic dynamical systems is essential in safety‑critical tasks such as control and monitoring. Standard conformal prediction provides finite‑sample coverage guarantees under exchangeability, but this assumption is typically violated in dynamical systems because trajectory data are temporally dependent, state distributions evolve, and recursive prediction errors accumulate. This paper proposes an invariant‑measure conformal prediction (imCP) framework that calibrates uncertainty using independent samples from an invariant measure of the Markov process induced by the dynamics. This aligns calibration with the stationary operating regime and restores the statistical symmetry needed for rolling one‑step split conformal guarantees. For recursive multi‑step prediction, imCP combines conformal calibration with Lipschitz error propagation through the learned predictor to obtain explicit horizon‑dependent bounds.These pre‑deployment uncertainty tubes are suitable for rolling and receding‑horizon applications, such as self‑triggered control and fault detection, where uncertainty bounds must be computed before future residuals are observed. Numerical experiments show that imCP yields reliable bounds, while non‑invariant calibration can become misaligned during deployment.

Authors:Srimanta Santra, Oleksii Molodchyk, Matti Noack, Timm Faulwasser
Title: Fast Risk Certification of Candidate Trajectories under Uncertain Time-Varying Constraints
Abstract:
This paper studies the certification of a fixed candidate trajectory on a finite certification grid under parametric uncertainty. For each constraint‑time pair, we define a scalar measure of constraint violation and aggregate the resulting pointwise chance constraints into a worst‑case Value‑at‑Risk (VaR) margin. The goal is not to generate a new trajectory, but to assess online whether a trajectory produced by a planner or predictive controller is sufficiently safe on the certification grid. Direct evaluation requires repeated uncertainty propagation and is often too expensive for computationally demanding models. We therefore adopt an offline‑online scheme: offline, a surrogate of the constraint violation map along the candidate trajectory is constructed using polynomial chaos expansion (PCE) when the uncertainty law is known, or kernel regression when only sampled input‑output data are available; online, the surrogate is sampled to evaluate conservative VaR bounds at low computational cost. On the theoretical side, we derive a finite‑sample upper bound for the grid‑based VaR margin using empirical quantiles, the Dvoretzky‑Kiefer‑Wolfowitz (DKW) inequality, and a union bound over all constraint‑time pairs, without assuming a parametric family for the underlying violation distribution. We also show how a uniform surrogate error bound transfers to the certified VaR margin. The approach is illustrated on a crystallization population balance model, where the surrogate‑based risk estimates track direct Monte Carlo results while substantially reducing online evaluation time.

Authors:Tejeswini Jayaramareddy, Hoeseok Yang, Hokeun Kim
Title: Ensuring Deterministic Timing in a Federated GNSS Correction Pipeline with Lingua Franca
Abstract:
Embedded systems that combine hardware interrupts, buffering, and distributed communication are often perceived as inherently asynchronous and difficult to analyze. However, such systems can exhibit a deterministic timing structure when modeled using explicit logical‑time semantics. This paper presents a Global Navigation Satellite System (GNSS) correction‑data pipeline implemented as a federated Lingua Franca (LF) application. The federated LF program decomposes the end‑to‑end pipeline into reactors with explicit time semantics, including a time‑triggered GNSS receiver, a UART interrupt stream derived from baud rate and First‑In First‑Out (FIFO) buffer characteristics, a periodic forwarding task, and downstream processing with jitter monitoring. Federated execution and runtime logs validate the analytically derived deterministic timing structure‑including interrupt cadence, ring‑buffer evolution, packetization behavior, and physical‑‑logical jitter‑yielding a reproducible and predictable timing profile.

Authors:Ozan Alp Topal, Özlem Tuğfe Demir, Cicek Cavdar
Title: Rethinking Energy Efficiency in Cell-Free Massive MIMO: The Role of Processing and Optical Fronthaul
Abstract:
Cell‑free massive MIMO promises uniformly high performance by combining densely distributed radio units, coherent transmission, and centralized processing. Unlike earlier radio generations, it depends on dense fronthaul connectivity and a virtualized cloud‑RAN architecture. In this setting, energy use is no longer driven primarily by active radio components; instead, fronthaul and processing play a dominant role, calling for a fresh perspective on what defines energy efficiency. This work introduces a modular power model that captures the interplay between radios, fronthaul, and cloud processing. The analysis highlights how design choices, such as functional splits and precoding strategies, shape both fronthaul data load and total power consumption. Centralized precoding provides stronger performance with less resource utilization, while flexible activation of radios and processing elements avoids unnecessary overhead. Overall, the energy efficiency of cell‑free massive MIMO grows as antennas are more densely distributed across the coverage area, particularly when combined with end‑to‑end resource allocation.

Authors:Ming Li, Fan Liu, Yifeng Xiong, Jie Xu, Tao Liu
Title: Sensing-Limited Control Under Non-Designable Observation Mechanisms
Abstract:
We study the information‑theoretic limits of controlling unstable linear systems through non‑designable observation mechanisms. Unlike classical communication‑constrained control, the information bottleneck lies in the observation mechanism rather than in a designable encoder‑channel interface. For noiseless linear dynamics, we derive necessary conditions for mean‑square observability and stabilizability, showing that the directed information rate from the unstable state process to the observation process must dominate the open‑loop expansion rate of the unstable modes. We further show that this lower bound persists under additive process disturbances. In the Linear‑Gaussian setting, although the unstable‑state directed information rate remains intractable in closed form, we obtain an exact characterization of the full‑state directed information rate, which upper‑bounds the unstable‑state quantity and yields computable necessary conditions. Under suitable posterior regularity conditions, we also establish sufficient conditions for asymptotic mean‑square observability and, via certainty‑equivalence control, asymptotic mean‑square stabilizability. The key step is an entropy‑to‑error bridge: a strict surplus in directed information over the expansion rate forces posterior uncertainty to collapse and thereby drives the estimation error covariance to zero. These results identify a fundamental feasibility boundary for sensing‑limited control and clarify how classical communication‑based limits must be reinterpreted when the sensing interface is non‑designable.

Authors:Xiaozheng Fu, Yan Chen, Tao Li
Title: Continuous-Time Decentralized Online Estimation With Additive Noises
Abstract:
We study a decentralized online estimation problem with additive communication noises over the fixed digraph. Each node has a linear measurement of an unknown parameter with random measurement matrices and runs a continuous‑time online estimation algorithm. We transform the convergence analysis of the algorithm into the stability analysis of the non‑autonomous linear stochastic differential equation (SDE) with random time‑varying coefficients, and develop the asymptotic stability by numerical approximation theory. Based on the stability results, we show that the algorithm gains can be properly designed to ensure mean square convergence if the measurement matrices and the communication graph satisfy the stochastic spatial‑temporal persistence of excitation condition. Furthermore, a special case where the measurement matrices contain a Markov chain is investigated, and the theoretical results are demonstrated by a numerical example.

Authors:Mathieu Vallee, Eva Schischke, Edmund Widl, Gabriela Zabik, Mohamed-Tahar Mabrouk, Kai Derzsi, Dirk Müller, Sergio Rech, Costanza Saletti, Martina Capone, Sacha Hodencq, Ildar Daminov, Gabriele Leoncini, Marc Clausse
Title: Standardizing case study descriptions for multi-energy systems and networks modeling
Abstract:
Research on Multi‑Energy Systems (MES) often relies on case studies with divergent hypotheses and terminologies, limiting comparability and slowing progress. Discussions at the ECOS 2025 conference highlighted the need for standardized reference case studies to facilitate reuse and comparison. While frameworks like the IEC 62559 standard and the Open Energy Platform (OEP) exist, their adoption for MES remains fragmented. This heterogeneity hinders collaboration and replicability, motivating efforts towards a unified description framework tailored to MES. This paper aims to address this gap by evaluating existing approaches in order to promote a standardized description framework for MES case studies. The goal is to enhance comparability, streamline research, and make a first step towards defining reference case studies and benchmarks in the domain. The study adopts a collaborative approach: after analysing existing description frameworks and selecting the most suitable one, the co‑authors describe their own case studies, followed by cross‑reviews to assess completeness, clarity, and openness of data/models. The description framework is adapted to emphasizeMES‑specific elements, such as system configuration and use case details. A checklist is developed to guide reviews. Preliminary results include a set of standardized case study descriptions and insights from cross‑reviews on framework strengths/limitations. The diversity of case studies underscores the framework's flexibility, while feedback reveals opportunities for improvement and broader adoption. This work provides a foundation for standardized MES case study descriptions, fostering collaboration, comparability, and replicability. By reducing ambiguity and ensuring the availability of relevant information in a consistent format, it accelerates research and benchmarking in the field.

Authors:H. I. Nurdin
Title: Projection Operator Stochastic Equations for Non-Markovian Quantum Systems Under Continuous Measurement-Based Feedback
Abstract:
Quantum Markov models have been successfully used to accurately model various physical quantum systems in fields such as quantum optics, optomechanics and superconducting circuits and they provide the basis for (measurement‑based) quantum feedback control. However, the quantum Markov assumption is a strong one and it is not expected to hold for general quantum systems of interest. The projection operator approach is one approach that has been developed to model non‑Markovian quantum systems by considering its embedding in a larger Markovian quantum system, but mainly in the context of quantum master equations for the dynamics of the unmonitored reduced quantum state of a quantum system. This approach was recently adapted for continuously measured non‑Markovian quantum systems, which enables open‑loop control but did not yet consider the presence of feedback of the stochastic measurement record, deriving non‑Markovian SDEs for the evolution of the projected state of the Markovian embedding. This paper generalizes these stochastic equations to the setting of stochastic feedback based on the continuous‑measurement record and shows that the equations take the same form but that previously deterministic terms become stochastic ones which depend on the measurement record, as would be intuitively expected. The stochastic equations are obtained for a generalized class of measurements that includes continuous (possibly adaptive) homodyne and photon counting measurements.

Authors:Xinming Wang, Fan Tang, Yingli Wei, Yakun He, Zhe Liu, Ping Jiang, Haoyu Wu, Zihan Guo, Chao Shen
Title: A Novel Method for Differential-Algebraic Dynamic Model Discovery in Power Systems: An LLM-Based Multi-Agent Collaborative Framework
Abstract:
With large‑scale integration of emerging power electronic devices represented by grid‑forming inverters, power system dynamics increasingly exhibit strong nonlinearity, multi‑timescale coupling, and black‑box control logic. These features hinder conventional parameter identification requiring known model structures and structure identification based on predefined function libraries, making complete differential‑algebraic dynamic model recovery difficult under weak prior information. To address this challenge, this paper proposes an LLM‑based multi‑agent collaborative framework for differential‑algebraic dynamic model discovery in power systems. It integrates heterogeneous exploratory agents, individual candidate model memories, parameter fitting and evaluation, and a coordinator agent. Under unified measurement‑data constraints, agents generate candidate equation structures in parallel, while candidates are optimized, evaluated, retained, and summarized to provide closed‑loop search guidance. The task is decomposed into differential equation structure discovery and algebraic closure discovery, enabling joint recovery of state dynamics, algebraic constraints, and key intermediate variables with incomplete prior information. Case studies on synchronous generators and grid‑forming inverters show that the proposed method outperforms single‑agent LLM‑based discovery and conventional symbolic regression in reconstruction accuracy, generalization, search efficiency, and noise robustness. In the generator case, OOD MAPE reaches 0.19%; in the inverter case, discovery time is reduced by 25.7% compared with the single‑agent LLM baseline.

Authors:Vojtěch Mlynář, Johannes Berndorfer, Andreas Kugi, Andreas Deutschmann-Olek
Title: FPGA-based LQG controller and hardware-in-the-loop simulator implementation for nanomechanical systems
Abstract:
We present an open‑source framework for real‑time Linear Quadratic Gaussian (LQG) control and hardware‑in‑the‑loop (HIL) simulation on the affordable Red Pitaya STEMlab FPGA platform. The controller implements a discrete‑time Kalman filter and Linear Quadratic Regulator (LQR) for systems with up to three coupled oscillatory degrees of freedom, targeting applications in levitated optomechanics, MEMS/NEMS, and related experimental platforms. Complementing the controller, the HIL simulator provides a~configurable second‑order stochastic plant with nonlinear input and output mappings, enabling realistic closed‑loop testing under real‑time and fixed‑point constraints. A MATLAB‑based workflow automates model configuration, controller synthesis, numerical scaling, and FPGA deployment without requiring specialized hardware expertise. As an end‑to‑end demonstration, we present the stabilization of a levitated nanoparticle in a two‑dimensional double‑well potential, illustrating the complete workflow from model definition and simulation to real‑time feedback control.

Authors:Andrea Mazza, Haoke Wu
Title: Due-to-Heatwaves Faults in Urban Distribution System: An Identification Approach
Abstract:
Distribution system faults occurring during heatwaves (HWs) are not all caused by the HW itself: concurrent factors such as asset ageing, mechanical defects, soil contamination, and operational constraints contribute independently. Hence, indiscriminately attributing all HW‑period faults to thermal stress overestimates system vulnerability and misleads asset‑management decisions. This paper proposes a systematic framework to identify and quantify the subset of summer faults directly attributable to HW occurrences (denoted Due‑to‑HW faults), by distinguishing them from Due‑to‑Others faults. HW events are first characterised through the Excess Heat Factor index. A covariance‑based attribution criterion is then developed to distinguish faults whose occurrence is statistically consistent with HW‑driven thermal mechanisms from those attributable to independent causes. Complementing the attribution framework, a time‑delay model is introduced to estimate the lag between the beginning of a HW and fault occurrence by maximising the normalised covariance between hourly temperature series and shifted fault‑duration series. Applied to six years of operational data from a real MV distribution network, the simulation results show that Due‑to‑HW faults constitute a significant yet variable proportion of total summer faults, underscoring the non‑negligible impact of HW occurrences on summer fault statistics. Beyond documenting the deterioration of fault rate and Mean Time Between Failures across all seasons, the analysis confirms that Time‑Between‑Failures distributions depart significantly from the exponential assumption, with direct implications for the applicability of Poisson‑based reliability models to distribution systems subject to recurrent HW stress.

Authors:Amos Alwala, Gabriel da Silva Lima, Wallace Moreira Bessa
Title: Machine Learning-based Feedback Linearization Control of Quadrotor Subject to Unmodeled Dynamics
Abstract:
The control of agile quadrotors in dynamic and uncertain environments remains an open area of investigation to this day, particularly when the complete system dynamics are partially known or highly nonlinear. This work introduces a novel machine learning‑based feedback‑linearization control framework that employs a Gaussian Radial Basis Function (RBF) neural network (NN) to model and compensate for unmodeled dynamics in real time. The proposed controller leverages the universal approximation capability of RBF networks to model nonlinearities and uncertainties. An online adaptation of the RBF NN updates the network's weights without prior training. The control law is derived using the Lyapunov stability theory, herein guaranteeing closed‑loop stability and providing theoretical guarantee of asymptotic convergence of a trajectory tracking task. Gazebo simulation and real flight experiments are conducted using the Bitcraze's Crazyflie 2.1 quadrotor subject to unmodeled air drag, actuator dynamics, and external disturbance. Despite incomplete knowledge of prior dynamics and presence of external disturbance such as air drag and drift in state estimation, the proposed controller improves trajectory tracking with rapid convergence and reduction of position‑norm and yaw orientation RMSE by more than 7.13% and 49.27% respectively compared to baseline feedback linearization controller.

Authors:Tamim Ahmed, Dacheng Shen, Mengyu Liu, Monowar Hasan
Title: A Simplex-Inspired Architecture for Integrating Quantum Capabilities into Cyber-Physical Systems
Abstract:
Cyber‑physical systems require accurate and reliable system models to ensure safe and efficient operation. Classical Gaussian Process Regression (GPR) provides uncertainty‑aware predictions but suffers from high computational complexity, which limits its scalability in real‑time applications. Quantum‑assisted Gaussian process models reduce complexity in inference, but their practical use is constrained by noise and stability concerns in safety‑critical environments. In this paper, we propose a hybrid classical‑quantum system identification framework based on a Simplex architecture. The framework combines Quantum‑Assisted Hilbert‑Space Gaussian Process Regression (QA‑HSGPR) as a high‑performance module and classical GPR as a high‑assurance module. A runtime monitor evaluates system safety and dynamically switches between the two models. Experiments on a Continuous Stirred‑Tank Reactor benchmark demonstrate that the proposed framework enables a controllable trade‑off between performance and safety for real‑time cyber‑physical systems.

Authors:Yihuai Zhang, Jean Auriol, Nicolas Espitia, Huan Yu
Title: Event-Triggered Gain Scheduling of 2 x 2 Linear Hyperbolic PDEs via Neural Operators (Full Version)
Abstract:
This paper introduces a new framework for event‑triggered gain scheduling applied to linear hyperbolic Partial Differential Equations (PDEs) with time‑ and space‑varying coefficients. The approach leverages neural operators to address the challenges of real‑time control in such systems. At each triggering time, the control input is designed using the classical static backstepping control law, while the gains of the boundary controller are updated according to the triggering mechanism and the spatial variation of the coefficients. Neural operators are employed to learn the mapping between the system parameters in the PDEs and the corresponding backstepping kernels. By integrating neural operators into the event‑triggered framework, we eliminate the need to repeatedly solve complex kernel equations at every triggering instant, thereby reducing computational overhead while ensuring closed‑loop stability. The proposed method is validated through theoretical analysis and numerical simulations, demonstrating its effectiveness and strong potential for real‑time control of time‑varying hyperbolic PDE systems.

Authors:Long Kiu Chung, Shreyas Kousik
Title: ShardNet: Training Neural Controllers with Hard, Non-Convex Constraints
Abstract:
While neural network control policies are powerful, their deployment on safety critical systems depends on ensuring that they obey strict constraints. Existing work often treats safety as a metric to optimize for, which competes with other performance objectives, if training converges at all. Instead, we introduce ShardNet, a neural network architecture that strictly enforces unions of polyhedral constraints by construction, using a differentiable projection layer parameterized by a classification network. The key insight is to embed safety into the neural network's structure, allowing performance to be optimized independently because formal safety guarantees are always given. In contrast with existing neural architectures that can only enforce simple convex constraints, ShardNet enables the first safe‑by‑construction synthesis of forward‑invariant neural network controllers on closed‑loop systems where safety constraints are expressed as nonconvex unions of polyhedras or learned value function level sets. To support this, we also introduce a technique to verify and train such value functions correctly as rectified linear unit (ReLU) networks, which has not previously been possible. On double integrator benchmarks drawn from the literature, ShardNet policies maintain 100% safety on verified sets and achieves significantly lower objective loss compared to existing formal methods. Furthermore, our value function training technique also produces safe sets more than 3 times larger than existing verification approaches.

Authors:Idelfonso B. R. Nogueira, Sigurd Skogestad
Title: A Systematic Approach to Multi-Agent AI from Advanced Regulatory Control Theory: Safe and Auditable LLM Operator Agents for Process Control
Abstract:
Recent literature shows that large language models (LLMs) are useful for general‑purpose tasks yet perform poorly on specific domain ones. One reason is the difficulty of supplying narrow context to a general‑purpose model and of bounding the task it is asked to perform. It is possible to hypothesise that a multi‑agent reformulation under process‑control principles offers a route to address those points, since control theory provides a discipline of decomposing a system into elements of contained scope, each defending one controlled variable, with conflicts resolved by structural priority: MIN/MAX selector networks for CV‑CV switching and split‑range (split‑parallel) logic for MV‑MV switching. The present work proposes such a reformulation, derived from Advanced Regulatory Control (ARC) theory. Each feedback loop in the ARC chain is mapped to one specialised LLM operator agent carrying the loop's control‑theoretic context (controlled variable, setpoint, chain priority, selector kind). The chain's interaction logic (MIN/MAX selectors, override paths) is encapsulated as a single orchestrator agent. Two orchestrator variants are tested: a deterministic rule chain, and a Claude‑based LLM orchestrator at a slower tier. The control principles limit each agent's task and inform how its limitations are handled. The multi‑agent system inherits the safety property of the ARC chain: every constraint conflict is resolved deterministically by the orchestrator, regardless of the LLM output. Evaluated on a dairy‑barn ventilation case over a 4‑day mixed‑season scenario, Qwen 2.5 7B Instruct operator agents running offline on a 24 GB consumer GPU at a 5‑minute cadence produce auditable trajectories, each paired with an operator‑voice rationale that supports a control campaign logbook.

Authors:Prasoon Raghuwanshi, Vimal Bhatia, Sridhar Iyer, Matti Latva-aho, Onel Luis Alcaraz López
Title: TinyML for On-Device and Edge Analytics in Wireless Networks: A Survey of Deployments, Opportunities, and Concept-Drift Mitigation
Abstract:
Ubiquitous intelligence is essential for enabling real‑time, adaptive, autonomous, and scalable operations in the next generation of wireless networks. However, this poses significant challenges in data management and energy consumption on the end‑device/edge side, specially under dynamic environmental conditions. This has driven the adoption of tiny machine learning (tinyML), which offers data‑driven optimization at the end‑device/edge side. In this work, we survey and thoroughly discuss various tapped/untapped deployment possibilities of tinyML in wireless networks. We identify existing frameworks, accustomed to design tinyML algorithms, that could be utilized to solve a range of wireless network problems. We present a federated learning‑based tinyML model update procedure, for both battery‑powered and batteryless end‑devices, to resolve the concept drift problem faced by tinyML models. Furthermore, we discuss the update‑aware checkpointing, fault‑tolerant bootloader, and intermittent‑aware modify operation, which could support federated learning‑based tinyML model update in the case of batteryless end‑devices. Overall, this paper spells out several areas where end‑device/edge intelligence can be utilized in the next generation of wireless systems, as well as ways to mitigate the concept drift problem faced in the case of end‑device intelligence.

Authors:Niklas Schmid, Jared Miller, Tristan Zeller, Marta Fochesato, Tobias Sutter, John Lygeros
Title: Joint Chance Constrained Safe-Optimal Control
Abstract:
We consider the finite‑time optimal control of stochastic systems subject to a probabilistic constraint on the trajectories' safety. Such formulations are known as joint chance constrained optimal control problems. The common practice is to jointly minimise the expected cost of all trajectories, safe and unsafe. This leads to policies which invite constraint violations to exploit low‑cost unsafe trajectories. When constraints represent states of critical failure, such behaviour is undesirable. We demonstrate that this behaviour can be overcome by only minimizing the expected cost of safe trajectories. The underlying rationale follows a practical intuition: In many applications, the cost incurred by unsafe trajectories is irrelevant (e.g., the battery usage of a crashed quadcopter), and one is usually interested in minimizing the cost of trajectories that are safe. We show that this problem can be cast as a constrained Markov Decision Process over an augmented state space. This allows solving it via dynamic programming. We derive bounds on the policies' safety under errors resulting from gridding approximations when the system's state space is continuous. Finally, we empirically compare dynamic programming as well as reinforcement learning solutions on a simulated 2D unicycle system in cluttered reach‑avoid environments.

Authors:Ryohei Oura, Georgios Fainekos, Hideki Okamoto, Bardh Hoxha
Title: Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees
Abstract:
Probabilistic model checking for Markov decision processes (MDPs) provides quantitative guarantees, but often offers limited insight into why undesired outcomes occur. Probability‑raising (PR) causality addresses this by identifying states whose visitation increases the probability of reaching designated states. Existing PR‑cause identification methods, however, use MDP modifications not well‑suited for learning: the gap between conditional and unconditional reachability probabilities can be hard to detect from transition samples, and construction requires reachability probabilities of the MDP, which are unavailable when transition probabilities are unknown. We study unknown MDPs and propose a learning approach with probabilistic guarantees for PR‑cause identification. Our key ingredient is a restart‑based MDP modification that reduces PR‑cause checking to two conditional reachability queries without using reachability values of the original MDP. We prove correctness, establish sample‑complexity bounds, and develop an anytime learning‑and‑checking algorithm based on two‑sided value iteration that progressively classifies states as causal, non‑causal, or undecided. Experiments on two benchmarks demonstrate reliable and fast identification of PR causes.

Authors:Sixu Li, Swaroop Darbha, Yang Zhou
Title: Lateral String Stability for Vehicle Platoons
Abstract:
Connected and automated vehicle (CAV) platooning promises gains in energy efficiency and traffic throughput and, most critically, in safety. These safety benefits hinge on string stability, which determines how disturbances propagate along a platoon. While longitudinal string stability is well studied, lateral string stability, which governs the propagation of path‑tracking errors that can lead to unsafe deviations from the intended path, remains underexplored. Its importance is increasing as autonomous vehicles rely more heavily on onboard sensing and map‑free navigation, where sensor occlusion and dense formations amplify safety risks. This paper presents a new framework for lateral string stability that directly addresses safety‑critical path‑relative tracking errors and enables consistent comparison across vehicles following the same road geometry. Central to this framework is an arc‑length (Eulerian) viewpoint, a departure from traditional analyses, that clarifies how tracking errors at a given point on the path propagate from one vehicle to the next. A formal definition of lateral string stability is introduced along with two control strategies: an onboard‑sensing‑only controller and a novel learn‑from‑predecessor approach utilizing vehicle‑to‑vehicle (V2V) communication. We show that onboard sensing alone cannot guarantee attenuation of path‑tracking errors, imposing a fundamental safety limitation, whereas V2V communication enables true error attenuation.

Authors:Nitesh Kumar, Reyshwanth Ganeshan, Sixu Li, Sivakumar Rathinam, Swaroop Darbha
Title: Privacy-Preserving Decentralized Cooperative Localization with Range-Only Measurements: A Convex Optimization Based Approach
Abstract:
Cooperative localization using range‑based measurements is critical for multi‑robot systems operating in GPS‑denied and unstructured environments. However, traditional cooperative approaches require sharing explicit spatial coordinates across the network, presenting a severe security vulnerability in privacy‑sensitive missions. While recent literature has explored privacy‑preserving alternatives, these methods typically rely on accuracy‑degrading noise injection or computationally prohibitive cryptographic protocols. To overcome these limitations, we propose a novel, natively privacy‑preserving Decentralized Cooperative Localization (DCL) framework based on convex optimization. Discarding probabilistic noise models, we assume strictly bounded measurement noise and formulate the localization problem via Semi‑Definite Programming (SDP) to compute a Maximum‑Volume Inscribed Ellipsoid (MVE). Our approach introduces novel intersection‑plane constraints derived from landmark measurements to significantly tighten individual spatial bounds. To incorporate inter‑robot range measurements securely, we uniquely decompose coupling constraints into localized Linear Matrix Inequalities (LMIs). Agents achieve fleet‑wide spatial consensus by iteratively exchanging only abstract dual variables, completely avoiding the transmission of explicit primal position estimates. Extensive 3D Monte Carlo simulations demonstrate that our DCL framework outperforms existing SDP‑based localization method in accuracy, while guaranteeing operational privacy and maintaining highly scalable, parallelizable computation.

Authors:Ruimin Hu, Mehmet Mercangöz
Title: A Two-Stage Reflection and Reprompting Framework for LLM-Based Solution of Petri Net Reachability Problems in Industrial Applications
Abstract:
Manufacturing systems exhibit strong concurrency, synchronization, and contention for shared reusable resources, which makes fast and reliable scheduling and verification challenging. Petri nets provide a rigorous formalism for modeling such discrete‑event manufacturing systems, but reachability analysis and solving remain difficult for conventional graph search or optimization‑based solvers, particularly under state‑space explosion and evolving production requirements. Recently, Large language models (LLMs) have shown promise as flexible planners that can generate candidate action sequences from textual specifications. However, direct use of LLMs for Petri net reachability remains unreliable. This paper proposes an LLM‑based solving framework augmented with a two‑stage reflection and reprompting mechanism. The combined effects of reflection and re‑clarification improve the accuracy of feasible sequence generation. The proposed method is evaluated on an industrial case modeled as a Petri net. Under a fixed Petri net structure, the proposed strategy is assessed on six solvable reachability configurations. The results demonstrate improved reliability and stability in solving Petri net reachability problems. The proposed framework is further evaluated across multiple LLMs, which indicates that the framework is not tied to any specific model.

Authors:Yousef AlSaqabi
Title: Cooperative RSU Sleep Scheduling for Green V2I Corridors
Abstract:
As vehicle‑to‑infrastructure (V2I) deployments scale, roadside units (RSUs) that consume 10‑25W continuously yet serve negligible traffic during off‑peak hours represent a growing source of energy waste. Sleep scheduling can exploit the pronounced diurnal variation in urban traffic, but the WAVE service restoration overhead of up to 100ms nearly exhausts the 3GPPTS~22.185 latency budget, making independent sleep decisions risky. This paper proposes a cooperative framework in which upstream RSUs share traffic detection signals with downstream neighbors via infrastructure‑to‑infrastructure links, enabling predictive wake‑up that exploits spatial correlation between adjacent intersections. The framework is formulated as a constrained Markov decision process and decomposed into per‑RSU subproblems solvable by value iteration. Four algorithms of increasing sophistication are evaluated on real hourly traffic data from four consecutive signalized intersections in Kuwait City, comprising a total of 762,050 vehicles over five days. The cooperative algorithm reduces corridor energy consumption by 59.5% relative to always‑on operation while maintaining 99% latency compliance, and provides 7.7 percentage points of additional savings over independent per‑RSU optimization at downstream RSUs with spatial correlation \rho >= 0.97. Extrapolated to a 200‑RSU urban deployment, the cooperative approach yields an estimated 5.25 tonnes of CO2 reduction per year.

Authors:Jerzy Baranowski
Title: High-Probability ISS Tubes for Continuous-Time State Estimation
Abstract:
This paper studies a probabilistic interpretation of input‑to‑state stability (ISS) bounds for estimation‑error dynamics in continuous‑time systems. We show that, if the aggregated disturbance satisfies a probabilistic envelope in an essential‑supremum sense, then deterministic ISS bounds immediately induce high‑probability error tubes. To make this interpretation constructive, we also provide explicit sufficient conditions based on quadratic Lyapunov inequalities and specialize them to positive and cooperative systems. The approach is illustrated on a positive compartment model with aggregated measurements, where ISS tubes are compared with nominal uncertainty bands produced by a Kalman‑‑Bucy filter and by Gaussian and robust moving‑horizon estimators. The examples show that ISS tubes provide a conservative but computationally light uncertainty baseline, while robust MHE is less sensitive to outlier contamination than Gaussian‑based

Authors:Zhongyao Hu, Jason J. R. Liu, Jun Shang, Zhan Shu
Title: Privacy-Aware State Estimation: From Coarse to Precise Privacy Protection
Abstract:
This paper addresses the problem of achieving both coarse and precise privacy in state estimation. Coarse privacy forces the eavesdropper's total mean‑square error (MSE) to infinity, but errors along certain confidential directions may remain bounded. This motivates precise privacy, which additionally drives the MSE along prescribed directions to infinity. For coarse privacy, an analytical transformation is established, preserving the user's optimality and driving the eavesdropper's total MSE to infinity at a polynomial‑exponential rate. A stochastic intermittent encryption scheme is further developed, and an explicit lower bound on the encryption probability is derived to guarantee divergence. For precise privacy, by analyzing the behavior of the Riccati equation on the unobservable subspace, we prove that the eavesdropper's directional MSE becomes unbounded if and only if the direction's unstable component lies outside the observable subspace. Finally, a systematic method is proposed to exclude target vectors from the observable subspace, forcing the directional MSE to infinity.

Authors:Alireza Zabihi, Maurizio Vassallo, Damien Ernst, Luis Badesa, Araceli Hernandez
Title: Fair Allocation of Operating Envelopes for Distribution Networks Considering Voltage Unbalance
Abstract:
Operating envelopes (OEs) are increasingly used to allocate limits to distributed energy resources (DERs) while maintaining secure distribution network operation. In unbalanced low‑voltage feeders, OE calculation based only on voltage magnitude and thermal constraints can yield overly optimistic limits because power quality constraints such as voltage unbalance are neglected. This paper proposes a three‑phase unbalanced AC optimal power flow framework for computing coupled P‑‑Q OEs with explicit voltage unbalance factor (VUF) constraints. In addition, two fairness mechanisms for allocating the available P‑‑Q flexibility across multiple PV units are embedded and compared: (i) network‑weighted proportional fairness and (ii) lexicographic max‑‑min fairness. Case studies on unbalanced test feeders illustrate how VUF constraints reshape the P‑‑Q feasible region and the impact of power quality‑constrained operation. The comparison highlights the trade‑off between the efficiency, equity, and practicality of fairness allocation methods.

Authors:Omer Hanani, Alon Kipnis
Title: Robust Extended Kalman Filter for Land Navigation Using Massive Array of MEMS IMUs
Abstract:
We propose a robust Extended Kalman Filter (EKF) architecture for land navigation using an array of hundreds of low‑cost micro‑electromechanical systems (MEMS) inertial sensors. The main challenges in this setting are bursty sensor‑specific bias errors, bias drift, and the need to aggregate many inertial measurements without increasing the computational burden of the navigation filter. To address these challenges, we introduce Robust Inertial Sensor Array Fusion (RISAF), a pre‑filtering framework that combines dynamic percentile gating with real‑time bias tracking before the EKF prediction step. The proposed aggregation suppresses anomalous sensor readings and compensates for individual sensor drift while preserving the vehicle‑level kinematic signal. Because the resulting fused inertial measurements are passed to a standard EKF, the navigation filter retains a minimal state vector and supports real‑time execution. We evaluate RISAF through extensive simulations and real‑world field tests in GNSS‑denied environments, with the data provided as supplementary material. Compared with a baseline that averages the sensor readings, RISAF achieves substantially improved azimuth accuracy and reduced drift accumulation. These results demonstrate that robust fusion of large MEMS inertial arrays can bridge a substantial part of the gap between cost‑effective hardware and tactical‑grade inertial navigation performance.

Authors:Hooman Torkaman, Ellis Oti Boateng, Jignesh Solanki, Anurag Srivastava
Title: An Integrated Two-Stage Deep-Learning Tool for Rapid Post-Hurricane Damage Identification and Repair Scheduling
Abstract:
Post‑hurricane damage assessment and repair scheduling can require computationally intensive simulation and optimization. This paper presents an integrated two‑stage deep‑learning tool for rapid damaged‑line identification and repair‑schedule computation. An available offline synthetic dataset for the IEEE 9500‑node test feeder contains 1,700 hurricane scenarios with exposure features, grid metadata, fragility parameters, OpenDSS outputs, damaged‑line labels, and Adaptive Large Neighborhood Search reference schedules. Stage 1 benchmarks MLP, ResMLP, and GraphSAGE, while Stage 2 compares MLP, DeepSets, and Set Transformer. The selected ResMLP‑Set Transformer pipeline propagates Stage 1 errors into Stage 2 and achieves a damaged‑job F1‑score of 0.920, pairwise order agreement of 0.854, and start‑ and end‑time mean absolute errors of 4.349 min and 4.486 min, respectively. The tool provides rapid initial repair‑log decision support for new hurricane cases.

Authors:Anh Tung Nguyen, Quanyan Zhu
Title: Divergence-based Safety Measure for Large Language Models via Rational Inattention
Abstract:
This paper proposes a divergence‑based safety measure for large language models (LLMs) under embedding‑input attacks. The proposed measure quantifies the worst‑case Kullback‑‑Leibler divergence between the clean and attacked LLMs' output distributions, subject to a stealthiness constraint. This constraint is constructed by leveraging the equivalence between transformer attention used in LLMs and rational inattention modeling human decision‑making. We analyze the proposed divergence‑based safety measure by investigating perfectly undetectable attacks and deriving its upper bound through a Bregman‑divergence argument. The proposed safety measure is applied to two pretrained causal language models, GPT‑2 and GPT‑Neo‑125M, to show nontrivial output‑distribution shifts, illustrating that the measure can distinguish model‑level safety profiles.

Authors:Anh Tung Nguyen, Quanyan Zhu
Title: Detection-Control Games under Hidden Modes: Resilience-Induced Blindness Phenomenon
Abstract:
This paper studies resilient control for cyber‑physical systems operating under hidden degraded or compromised modes. We formulate hidden‑mode detection and belief‑dependent control as a game between two decision makers with different objectives: the detector seeks informative belief updates, while the controller seeks regulation performance. This objective mismatch shows why the usual separation intuition between detector design and controller design may fail, leading to a performance‑reversal phenomenon induced by the resilience of the controller. For a two‑mode linear Gaussian system, we theoretically characterize this phenomenon by linking the resilience margin to the log‑likelihood evidence. The analysis shows that a well‑performing controller with a large resilience margin can suppress mode‑dependent information and slow belief adaptation, which in turn degrades the control performance. The resilience‑induced blindness phenomenon and its mitigation are illustrated in numerical simulations.

Authors:H. E. Tseng
Title: A Sliding Mode Lateral Velocity Observer
Abstract:
A lateral velocity estimation scheme whose stability can be analytically derived (rather than empirically demonstrated through cut‑and‑try) is attempted. The designed adaptive sliding mode observer shows robust performance under a wide variety of maneuvers/ environments, including the more challenging slow J‑turn on low mu.

Authors:Michael A. Boateng, Russell Bent, Sidhant Misra, Parikshit Pareek, Pascal Van Hentenryck, Daniel Molzahn
Title: PACR: Parameter-Optimized AC Power Flow Restoration for AC Feasible DCOPF Dispatch
Abstract:
The DC optimal power flow is widely used in power system operations because of its computational efficiency and scalability. However, DC dispatches are not guaranteed to satisfy the nonlinear AC power‑flow equations or associated operational limits. This paper develops a parameterized, differentiable AC power‑flow restoration method for mapping DC dispatches to AC‑consistent operating points. The method incorporates distributed slack for active‑power balancing and PV/PQ switching for reactive‑power regulation, both implemented using smooth differentiable surrogates with tunable parameters, including slack participation factors, voltage setpoints, and regulation steepness. These parameters are trained offline by differentiating through the AC restoration equations using the implicit function theorem. Once trained, the optimized parameters are fixed and used directly during AC power‑flow recovery from DC dispatches. The approach is evaluated on IEEE, ACTIVSg, and PEGASE test systems using setpoints computed by standard DC optimal power flow. Results show that the optimized restoration method improves AC feasibility recovery across various systems relative to conventional single‑slack AC power‑flow recovery. On the 9,241‑bus case, the optimized method improves cost difference by 80% relative to the conventional recovery baseline and improves solving time relative to ACOPF by 75%.

Authors:Marton Laszlo Ambrus, Xiao Liu, Stuart Hillmansen, Zhongbei Tian
Title: Estimating Available Traction Power in Multi-Train AC Railway Networks from a Distance-Dependent Power Envelope
Abstract:
Decarbonisation is raising the electrical load on mainline alternating‑current railway feeders that were not designed for sustained, simultaneous high‑power demand. When several trains accelerate together on a shared feeder, the contact‑line voltage can fall far enough to trigger rolling‑stock current limitation or feeder protection, eroding capacity and reliability. Preventing this in real time requires a quantity conventional operation does not expose: a localised, continuously updated estimate of the traction power available to each train given the live network state. A railway power‑flow model, with trains represented under a voltage‑dependent automatic current‑limitation characteristic, shows that the minimum network voltage is governed by the product of power and distance rather than by power alone, yielding a distance‑dependent single‑train power envelope. This envelope does not add up when several trains share a feeder, so a conservative pairwise screen is generalised to a solver‑free multi‑train estimate: a calibrated shared‑path voltage model returning the minimum section voltage and the per‑train available power for any number of trains. Calibration uses two short offline solver runs, one fixing the self‑impedance and one the inter‑train coupling through a separation‑dependent factor. Its current‑limitation behaviour follows EN 50388‑1, and on matched multi‑train cases the estimate tracks the full power flow to within about nine per cent on average across two‑, three‑, and four‑train cases, improving as more trains share the feeder, while its online cost scales with the number of trains rather than the network size.

Authors:Xin Zheng, Yifei Jin, Lei Guo
Title: Analysis of Adam Algorithms for Stochastic Dynamic Systems
Abstract:
The adaptive moment estimation algorithm, known as Adam, is widely used in modern machine learning, owing to its low per‑iteration complexity and strong empirical performance. Despite its prevalent use, the theoretical foundation of Adam remains largely unexplored for time‑varying and nonstationary systems. In fact, the existing theoretical analyses of Adam‑type algorithms are primarily concerned with time‑invariant model parameters and explicitly or implicitly rely on independent and identically distributed (i.i.d.) data assumptions, under which the learning taskcan be formulated as minimizing a fixed expected objective with a static minimizer. However, such assumptions are often violated in time‑varying and nonstationary systems, thereby calling for a theoretical investigation beyond the conventional yet idealized i.i.d. setting. The main objective of this paper is to solve this challenging problem by establishing a general theory of Adam for time‑varying and nonstationary stochastic systems. We will introduce some new techniques for analyzing the products of nonstationary and dependent random matrices induced by Adam's coupled first‑ and second‑moment recursions, and will construct a new stochastic Lyapunov function that blends these two moment dynamics. Under a stochastic excitation condition that allows nonstationary and dependent data, we will derive both parameter tracking and output prediction error bounds explicitly, quantifying the effects of stepsize, first‑ and second‑momentum parameters, gradient noise and parameter drift. These bounds not only provide guarantees for Adam performance, but also provide guidelines for hyperparameter selection. Experiments on both synthetic and real‑world data validate our theory and design guidelines.

Authors:Sriharini Krishnakumar, Yaroslav Popryho, Mingeun Choi, Ramin Rahimzadeh Khorasani, Madhavan Swaminathan, Satish Kumar, Inna Partin-Vaisband
Title: A Comprehensive Design Framework for Vertical Power Delivery in High-Performance Computing
Abstract:
Power delivery ‑‑ including high‑to‑low voltage conversion, complex power distribution across heterogeneously integrated chiplets, and efficient interconnect allocation ‑‑ remains a critical bottleneck in high‑performance computing (HPC) systems. Existing vertical power delivery (VPD) solutions are estimated to achieve less than 70% system‑wide end‑to‑end power delivery efficiency, defined from platform input power to delivered on‑chip load power, with substantial energy lost as heat before reaching on‑chip point‑of‑loads (POLs). In the absence of systematic design methodologies, evaluating power quality, exploring architectural alternatives, and optimizing performance rely on computationally prohibitive simulations, resulting in suboptimal designs. This paper introduces an end‑to‑end scalable power delivery framework for HPC systems, including distributed VPD (DVPD) architecture, DVPD design optimization methodology, and analytical models. The framework leverages substrate‑embedded GaN power switches together with arrays of unit inductors and capacitors tailored for HPC applications. Multi‑stage power conversion schemes (48V‑to‑1V, 48V‑to‑24V‑to‑1V, and 48V‑to‑12V‑to‑1V) are explored, with system‑wide voltage drops and power losses evaluated under steady‑state conditions. Design specifications for passive and active devices are formulated to meet next‑generation efficiency targets. For the 48V‑to‑1V case, the proposed DVPD approach achieves 84% system‑wide efficiency while occupying 54% of the area beneath the load system, with efficiency increasing to 87.6% at 75% area utilization across a 1‑‑50~kW load range. Furthermore, steady‑state voltage drops peak at 2.7% and transient drops at 9% (without decoupling capacitors), demonstrating the viability of DVPD for future wafer‑scale HPC platforms.

Authors:Yifan Wang
Title: Q-DASC: State-of-the-Art Safe Quantum Control for HVAC under Local Model Misspecification
Abstract:
Variational quantum reinforcement learning offers a compact policy class for building‑energy control, but it inherits a deployment weakness shared by learned controllers: when the thermal model is locally wrong, a policy that appears safe on the model can violate occupant comfort in the real building. Guarantees that depend on noisy quantum read‑out are also insufficient for safety‑critical control. We address this gap with Q‑DASC, Discrepancy‑Attributed Safe Quantum Control. Q‑DASC wraps a variational‑quantum‑circuit (VQC) policy with a certified classical safety layer that discovers misspecified operating regimes with false‑discovery‑rate control, repairs their local thermal gains with shrinkage, projects the proposed quantum schedule onto the repaired comfort‑feasible set, and attributes residual violations to policy error, model error, or physical limits. Because the final certificate is produced by classical projection, comfort feasibility is invariant to finite‑shot and depolarizing read‑out noise. On real BOPTEST building emulators across three buildings, two localized misspecifications, and three seeds, Q‑DASC reduces average comfort violation from 26.0% for the raw VQC controller and 55.3% for a model‑trusting scheduler to 0.02%, matching a clairvoyant oracle, and remains at 0.24% under NISQ read‑out noise. A repair‑aware VQC variant reaches 0.00% violation and reduces projection intervention, while the default Q‑DASC keeps lower energy and stronger observational‑data behavior. The same wrapper transfers to EnergyPlus heating and cooling benchmarks and to real hospital air‑handling‑unit data. These results establish a safety‑efficiency frontier for deploying quantum policies in physics‑constrained control.

Authors:Quanyan Zhu
Title: Exit-and-Join Dynamics and Equilibrium in Continuum Cooperative Games
Abstract:
This paper develops a continuum theory of exit‑and‑join coalition dynamics in nonatomic cooperative games. We extend the Aumann‑Shapley value and the Aumann‑Drèze value to coalition structures in which each coalition is treated as a restricted nonatomic game, yielding a marginal‑contribution‑based payoff density that governs incentives for agents to remain in, exit, or join coalitions. We derive deterministic mean‑field dynamics from decentralized switching rules and show that payoff‑difference switching recovers replicator dynamics as a special case. We characterize exit‑and‑join equilibrium by the absence of profitable positive‑mass deviations and prove its equivalence with stationarity of the induced mass dynamics under incentive‑compatible and strictly payoff‑responsive switching rates. For mass‑based cooperative games, we construct a Lyapunov function and establish global convergence under strict concavity. We further show that the equilibrium is equivalent to a Wardrop equilibrium of an induced nonatomic population game and admits a variational inequality formulation. The framework is extended to incorporate switching costs and endogenous coalition acceptance rules, leading to constrained equilibria characterized by quasi‑variational inequalities. The proposed theory unifies cooperative value allocation, noncooperative coalition mobility, mean‑field dynamics, evolutionary game theory, and population games within a common framework for analyzing coalition formation and adaptation in large‑scale multi‑agent systems.

Authors:Yifan Wang
Title: Trust-Calibrated Certified Repair for Physics-Constrained Decisions under Localized Model Misspecification
Abstract:
Feasibility‑restoration layers turn learned, market‑based, or optimizer‑generated decisions into actions satisfying hard constraints in systems such as power grids. Yet a repair is only as trustworthy as its constraint model: line parameters, sensitivities, ratings, and topology can be locally wrong, so a decision certified feasible under the nominal model may violate the deployed system. We identify this false safety as a dominant failure mode of model‑trusting repair and propose Trust‑Calibrated Certified Repair (TCR). TCR treats repair as trust calibration and answers four questions in one pipeline: where the physical model is wrong, discovered from measurements with false‑discovery control; how much each constraint should be trusted, set by test‑gated shrinkage and uncertainty‑proportional security margins; what least‑cost intervention restores feasibility, computed by a certified repair program; and why the cost was paid, attributed to genuine congestion versus avoidable model error through dual prices. On a physically grounded dynamic‑line‑rating benchmark whose true ratings follow IEEE 738 under real weather, TCR reaches 98% true‑network feasibility, within two points of a clairvoyant oracle, at lower‑than‑naive cost and with perfect localization. Model‑trusting repair, robust margins, and chance‑constrained tightening leave substantial feasibility or cost gaps. The same method transfers unchanged to transmission redispatch over PGLib‑OPF networks and distribution voltage regulation on the IEEE 33‑bus feeder. Across all three task families, TCR gives the strongest deployable feasibility‑cost frontier under localized physical‑model misspecification. Calibrating trust in the constraint model is the missing ingredient for reliable AI‑assisted engineering decisions.

Authors:Jaekeun Lee, Minwoo Jeong, Jae-Jung Jung, Xiongfei Wang, Shenghui Cui
Title: Negative Resistance Caused by Intra-Loop Coupling in Virtual-Admittance-Based Grid-Forming Control
Abstract:
This paper addresses the harmonic instability problem of the virtual‑admittance (VA)‑based grid‑forming control. It is revealed that the intra‑loop coupling among the VA control, the inner‑loop current control, and the voltage feedforward control results in an \(s^2\)‑term in the equivalent output impedance of the inverter, which induces a negative‑resistance property in the harmonic range. It is worth highlighting that this negative resistance is independent of the control delay. Consequently, this harmonic instability mechanism is fundamentally different from the extensively investigated cases in the literature, which are induced by the digital control delay of inverters. Then, a simple passivity‑oriented damping control is proposed to mitigate the negative resistance arising from the intra‑loop coupling. The method fully retains the well‑established current controller and voltage feedforward, and does not require grid impedance information. Finally, experimental tests verify the theoretical findings and the effectiveness of the damping method.

Authors:Qazi Mairaj ud din, Sidra Ghayour Bhatti, Qadeer Ahmed
Title: Model-Free Budgeted Attack Scheduling for Cyber-Physical Systems
Abstract:
This letter studies the budgeted scheduling of stealthy false data‑injection (FDI) attacks against state estimators in cyber‑physical systems. Existing event‑based attack schedulers require full knowledge of the plant model and assume the residual distribution is exactly Gaussian ‑‑ assumptions that fail for real‑world CPS sensor streams whose residuals are heavy‑tailed and whose dynamics are unknown to the adversary. We propose a model‑free attack‑scheduler that replaces the parametric Gaussian threshold with the empirical quantile of a learned sequence autoencoder residual, calibrated from measurements alone without any plant matrices. We prove that the realized attack rate converges almost surely to the target budget under stationary ergodic residuals. Experiments on two synthetic systems and a real heavy‑duty truck dataset show that the proposed scheduler tracks the budget to within 1‑2% while also preserving the residual magnitude, guaranteeing stealthiness against any residual‑based detector. Comparing with the model‑based baseline ‑‑ granted the true plant and innovation covariance ‑‑ mis‑realizes the budget by up to 8.96% under heavy‑tailed residual distribution, causing the attacker to achieve only 1.37x system degradation when 1.84x is intended.

Authors:Mohamed Khalid M Jaffar
Title: Reachability Guarantees for Cart-Pole Swing-Up and Stabilization
Abstract:
The cart‑pole swing‑up is a canonical benchmark for nonlinear control of underactuated systems, yet an end‑to‑end guarantee linking the global swing‑up maneuver to the local stabilizer is seldom formalized. We present a reachability analysis of a switched energy‑based/LQR controller that certifies convergence to the upright equilibrium from a compact set of initial conditions. The swing‑up law is derived from an energy‑error Lyapunov function; canceling the autonomous conservative term yields a strictly sign‑definite Lyapunov derivative, and convergence follows from LaSalle's invariance principle. We also propose an augmented Lyapunov function to regulate the steady‑state cart velocity to zero, for which we establish almost‑global convergence. For the controller handoff, a switching region is designed to lie strictly within the LQR region of attraction, formally certifying the swing‑up‑to‑stabilization transition. Numerical simulations corroborate the theoretical analysis.

Authors:Binh Nguyen, Colleen Josephson, Mircea Teodorescu, Gert Cauwenberghs, Jason Eshraghian
Title: Neuromorphic Energy-Aware Learning for Adaptive Deep Brain Stimulation
Abstract:
Neuromorphic and edge computing research has focused on reducing the inference cost of neural network controllers, yet in physical closed‑loop systems the actuator can rival or exceed an efficient controller in energy. An efficient controller is therefore necessary but not sufficient, because the actuator becomes the cost worth reducing once inference no longer dominates it. Here, we introduce energy‑aware learning, an approach that incorporates actuator energy directly into the reinforcement learning reward, and demonstrate it in closed‑loop deep brain stimulation (DBS) for Parkinson's disease. A deep spiking Q‑network, trained in a biophysical cortico‑basal ganglia‑thalamic circuit model, learns to suppress pathological alpha‑beta oscillations by 45.2% while reducing stimulation charge by 80.0% relative to continuous DBS. Sparsity‑constrained knowledge distillation compresses the policy onto the SynSense XyloAudio 3 neuromorphic processor at 0.52 mW inference power, yielding 28.1x lower energy per inference than an equivalent artificial neural network on conventional edge hardware. By co‑optimizing stimulation energy and inference efficiency, the framework addresses both major power demands in implantable neuromodulation.

Authors:Yichao Wang, Sameeha Tasneem, Mohamadamin Rajabinezhad, Jinfeng Chen, Qin Lin, Kai Wang, Shan Zuo
Title: Resilient Control Lyapunov Function-based Quadratic Program for Quadrotors Under Cyberattacks
Abstract:
Ensuring the operational safety of quadrotors under partial actuator failures, lumped external disturbances, and malicious cyberattacks is a critical challenge due to the system's underactuated and highly nonlinear nature. Building on the existing result of a fault‑tolerant control approach for a quadrotor experiencing a complete loss of two opposing rotors \citechen2024quadrotor, this letter further addresses the additional challenge of malicious cyberattacks, which could be unknown and unbounded. While the baseline control law, rooted in proportional‑derivative (PD) feedback and observer‑based decoupling, effectively handles mismatched disturbances, it remains vulnerable to maliciously injected cyberattacks on the pseudo‑control channels. To address this, a Resilient Control Lyapunov Function‑based Quadratic Program (RCLF‑QP) is developed, where a resilient compensational term with real‑time online adaptation is designed in the conventional CLF to compensate for the maliciously injected unknown and unbounded attacks. Compared with the PD feedback control, the proposed QP‑based constrained optimization control framework provides a systematic and extensible framework that allows new control objectives and constraints to be seamlessly integrated without altering the underlying stability guarantees. The overall proposed controller integrates a model‑based extended state observer with the proposed RCLF‑QP mechanism to mitigate both lumped disturbances caused by aerodynamics and strong wind, and adversarial cyberattacks injected by malicious adversaries. Simulations in a high‑fidelity environment demonstrate that the proposed RCLF‑QP control architecture prevents trajectory divergence and system instability in scenarios where the baseline controller fails in maintaining the stability of Quadrotors under malicious attacks.

Authors:Feng Guo, Luis D. Couto, Keivan Haghverdi, Khiem Trad, Grietus Mulder
Title: Rapid and robust parameter estimation for electrochemical battery models via BOLT: A batch-optimized local-to-global technique
Abstract:
Accurate and efficient parameter estimation is essential for applying electrochemical battery models in simulation, state estimation, control, and repeated model updating. However, conventional optimization methods, such as particle swarm optimization (PSO) and genetic algorithms (GA), often require many model evaluations and show considerable run‑to‑run variability, limiting their use in time‑sensitive calibration scenarios. This study proposes a Batch‑Optimized Local‑to‑Global Technique (BOLT) for rapid and robust parameter estimation of electrochemical battery models. BOLT combines diversified candidate initialization, batch‑parallel trust‑region reflective (TRF) local refinement, JIT‑accelerated model evaluation, and multi‑condition consistency screening within a unified calibration workflow. Comparative experiments based on a grouped single‑particle model and measured data from a commercial 18650 NMC lithium‑ion cell show that BOLT achieves a favorable trade‑off among voltage‑response accuracy, computational efficiency, and repeated‑run stability. BOLT(32) achieves an average mean absolute error of \(12.4 \pm 0.1\) mV over five operating conditions, requiring only \(20636 \pm 3081\) model calls and \(8.97 \pm 1.20\) s per run. Synthetic‑data validation with a known parameter vector in the grouped SPM formulation further shows that BOLT recovers the reference parameter vector under model‑consistent conditions and remains robust under 1‑‑3 mV voltage‑noise perturbations, with the mean parameter absolute relative error below \(0.6%\). These results indicate that BOLT provides a practical calibration framework for BMS parameter updating, control‑oriented battery digital twins, and second‑life battery screening.

Authors:George Stamatelis, Kyriakos Stylianopoulos, George C. Alexandropoulos
Title: Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter
Abstract:
Online latent state estimation constitutes a fundamental challenge within the artificial intelligence field, serving as a foundational tool for diverse applications, including sequential decision making, anomaly and change‑point detection. In this paper, a novel online distributed sensing framework, where agents collaborate and exchange information to perform latent state estimation, is presented. The proposed estimator combines available partial domain knowledge with the representation capabilities of deep neural networks. In particular, the designed sensing framework incorporates prior estimates, optimized consensus weights, and Kalman‑like recursive updates to perform decentralized inference, without relying on knowledge of noise statistics. Extensive experiments on linear, chaotic (Lorenz), and practical wireless tracking environments reveal that the proposed Covariance‑Agnostic Neural Kalman Consensus Filter (CA‑NKCF) outperforms traditional distributed Kalman and particle filters as well as purely model‑free deep neural networks, exhibiting robustness even when the underlying motion and observation models are misspecified. It is also demonstrated that CA‑NKCF's performance advantage remains stable across varying noise levels, random communication topologies, latent state dimensions, and observation clutter densities induced by scattering objects in wireless systems.

Authors:Domagoj Herceg
Title: PAC-Bayesian Certificates for Quadratic Closed-Loop Control
Abstract:
PAC‑Bayesian bounds provide finite‑sample guarantees for data‑dependent randomized predictors, but applying them to learning‑based control is difficult because the natural objective is a quadratic trajectory cost. Such losses are unbounded, non‑Lipschitz , and lead to response‑dependent Chernoff terms. We employ System Level Synthesis parameterization, which exposes the closed‑loop trajectory map of a linear system directly and makes the quadratic control loss amenable to explicit certification. Moreover, we provide a set of PAC‑Bayes‑Chernoff certificates for posterior distributions over feasible closed‑loop responses. For Gaussian disturbance trajectories with arbitrary covariance, we derive an exact one‑sided Gaussian transform and a tractable quadratic upper bound expressed through closed‑loop sensitivity quantities. We also derive a posterior‑localized surrogate for settings where pointwise closed‑loop response certificates are unavailable or have support related admissibility issues. Although PAC‑Bayes certifies a non‑degenerate posterior, the convex quadratic form of the SLS loss transfers the certificate to the posterior mean response. We present a deterministic mean response deployment result that is particularly suitable for control while retaining the stochastic posterior in the bound. Additionally, we provide a data‑driven bound for this deployment, transitioning away from an oracle bound. Minimizing this bound naturally results in a learning algorithm for control selection from data. Numerical experiments on a double integrator show that the algorithm acts as a sensitivity‑aware finite‑sample regularizer, improving held‑out cost and reducing closed‑loop sensitivity in the low‑data regime

Authors:Aniq Ur Rahman
Title: Estimation--Prediction Tradeoff in Causal Probabilistic Temporal Graphs
Abstract:
Temporal link prediction is usually evaluated by predictive performance on unseen edges, but in probabilistic temporal graphs this criterion can conflate model error with irreducible uncertainty. We study this issue by characterising an inherent estimation‑‑prediction tradeoff in binary logistic models where regimes that maximise Fisher information and improve parameter recoverability are also those with the highest entropy, making individual predictions intrinsically harder even under perfect parameter recovery. We propose a probabilistic causal framework for generating temporal graphs with transient edges and known ground‑truth causal structure, allowing temporal link prediction to be evaluated jointly with causal parameter recovery. For the proposed binary logistic parametrisation, we derive the Cramér‑‑Rao bound and validate the tradeoff between parameter estimation error and irreducible predictive loss. Our results show that predictive accuracy alone may not reflect whether a model has learned the underlying causal mechanism, motivating benchmarks that distinguish reducible model error from intrinsic process uncertainty.

Authors:Youssef Ait Si, Antoine Girard, Adnane Saoud
Title: Specification-aware Robustness Margins for Symbolic Controllers
Abstract:
We address the problem of robust controller synthesis for a class of linear temporal logic (LTL) specifications over families of perturbed systems using symbolic control techniques. Given a dynamical system, a specification, and a symbolic controller synthesized using the fixed‑point algorithm of the specification, the objective is to find the maximal perturbation we can apply to the system while the system continues to satisfy the same specification under the same controller. We first provide general results, by demonstrating that controllers synthesized based on the symbolic model can be refined back to a perturbed version of the concrete system while preserving their correctness. Focusing on four fundamental temporal logic specifications, namely safety, reachability, persistence, and recurrence, we introduce a general measure of the maximal robustness margin. Then, for each class of specifications, we derive a customized version of the measure and establish the corresponding theoretical guarantees. Importantly, the robustness margin depends explicitly on the sequence of sets generated during the fixed‑point computation, allowing for specification‑dependent and less conservative bounds compared to generic abstraction‑based approaches. The theoretical developments are illustrated on two examples, demonstrating the practical applicability and effectiveness of the proposed approach.

Authors:Erik Gustaf Lilljebjörn, Sogol Kharrazi, Jan Åslund, Martin Singull
Title: Effects of motion cueing on longitudinal acceleration perception in a driving simulator
Abstract:
The driveability of a new heavy‑truck driveline is traditionally assessed using physical prototypes. Enabling early evaluation of the driving experience in a human‑in‑the‑loop driving simulator using a virtual prototype has the potential to significantly improve development efficiency. To enable driveability assessment using a moving‑base simulator, participants must be able to perceive small differences in longitudinal acceleration. The just‑noticeable difference (JND) was therefore evaluated for two variants of the classical motion‑cueing algorithm (MCA) tuned specifically for tip‑in/launch tests and compared to a more general variant in a driving simulator with a long linear track. Psychometric functions were fitted to responses obtained using a weighted staircase procedure and analysed using a generalized linear model. No significant differences in JND were found between the motion cueing variants. The mean JND across all participants and MCA variants was 5.4%. The mean point of subjective equality in the JND experiment was ‑1.9%, suggesting that participants perceived the acceleration as higher in the second stimulus of a pair. In a subjective comparison, most participants preferred the motion cueing variants that were tuned for launch manoeuvres over the general variant.

Authors:Youhong Chen, Xiaoyu Tan, Muhammad Sharjeel Javaid, David Angeli, Balarko Chaudhuri
Title: Decentralized Stability of IBR-dominated Power Grids Using Block Diagonal Dominance
Abstract:
The growing penetration of inverter‑based resources (IBRs) necessitates stability assessment methods that are scalable, decentralized, and model‑agnostic. This paper develops a block diagonal dominance (BDD) criterion for decentralized small‑signal stability of IBR‑dominated power grids. The proposed approach forms the basis for an enhanced IBR connection compliance condition from a small‑signal stability perspective that can be evaluated locally for IBRs to be connected to the grid. The proposed approach is shown to be much less conservative than strict diagonal dominance (SDD). Beyond mere stability, we ensure a minimum decay rate or maximum settling time for IBR‑induced oscillation. Crucially, these are achieved without imposing restrictive assumptions on network or IBR models. The framework therefore, offers a practical and theoretically grounded basis for decentralized stability certificate of IBR‑dominated power grids.

Authors:Javal Vyas, Milapji Singh Gill, Artan Markaj, Felix Gehlhoff, Mehmet Mercangöz
Title: From Detection to Action: Using LLM Agents for Fault-Tolerant Control
Abstract:
We propose an agentic Large Language Model (LLM) framework for active Fault‑Tolerant Control (FTC) that transforms fault detection outputs into constraint‑aware recovery actions grounded in plant‑specific knowledge. The approach couples (i) a multi‑agent workflow that decomposes operator duties into monitoring, planning, action synthesis, simulation, validation, and reprompting; (ii) a Digital Process Plant Twin (DPPT) that exposes plant data, models, and a simulation service for pre‑execution testing; and (iii) a Graph Retrieval‑Augmented Generation (Graph RAG) layer built on the CPSMod ontology, which organizes plant knowledge (structure, function, hybrid dynamics, control context, and fault semantics) into a graph that supports relation‑aware, multi‑hop retrieval for the agents. Corrective actions are generated as minimal‑risk state‑machine recovery paths and corresponding discrete commands or continuous setpoint adaptations, then validated deterministically against interlocks, envelopes, and dynamic feasibility before any actuation. If no acceptable plan is found within a bounded time window, control is handed to a safety fallback. The framework is evaluated in simulation on two representative benchmarks: a discrete batch Mixing Module and a Continuous Stirred‑Tank Reactor (CSTR) under closed‑loop PID regulation. Results with lightweight LLMs (GPT‑4o‑mini and GPT‑4.1‑mini) show that semantically grounded agents can derive valid recovery decisions within latency budgets compatible with the respective process dynamics, demonstrating a practical pathway from detection to validated corrective action across both discrete and continuous FTC tasks.

Authors:Frederik Werner, Till Heintzenberg, Markus Lienkamp, Johannes Betz
Title: Drifting in the Future: Stabilizing Path Following Drifting on High-Latency Vehicle Systems
Abstract:
Autonomously controlling and handling a vehicle at and beyond its stability limit is a mathematically and computationally demanding task. Prior demonstrations of automated drifting have been limited to research platforms with instantaneous torque delivery and independently actuated wheels, leaving their applicability to production vehicles with actuator latencies and mechanically coupled axles uncertain. To overcome these issues, we design a predictor to compensate for powertrain delays, develop a revised control formulation to accommodate higher actuation latencies as well as a differential coupling on the driven axle, and introduce brake‑based velocity stabilization. This paper presents the controller framework, the model extensions, and real‑world experimental results. We observe that our controller enables a production sports car with a combustion engine to robustly sustain circular and figure‑eight drifts, limiting lateral error to 1.1 m and sideslip overshoot to 0.06 rad despite actuator delays exceeding 250 ms, while mitigating oscillations and maintaining stable path and sideslip tracking. In conclusion, our results establish that autonomous drifting is feasible on production‑ready vehicles, opening pathways to advanced safety systems capable of stabilizing cars in scenarios where traditional control fails.

Authors:Yifan Wang
Title: Repair-before-veto control for safe lithium-ion fast charging under unknown ambient and cooling-fault conditions
Abstract:
Fast charging is decisive for electric‑vehicle adoption, but field chargers are deployed as one setting while the cell's true thermal state, ambient temperature, and cooling‑system health are uncertain. A current that is safe for a healthy cell at room temperature can overheat the same cell when it is hot or its cooling is degraded. We formulate this as a single‑setting, unknown‑state safe‑fast‑charging problem and solve it with a margin‑aware repair‑before‑veto controller (RACL‑B). RACL‑B requests an aggressive current and repairs it online to the tightest measured margin among terminal voltage, cell temperature, and negative‑electrode lithium‑plating overpotential, rather than committing to a fixed schedule or shutting charging down. We evaluate one deployed setting across nine conditions, spanning 10/25/40 ^\circC ambient temperature and 100/60/40% cooling health, in a high‑fidelity Doyle‑‑Fuller‑‑Newman model with partially reversible lithium plating and lumped thermal coupling. Under a strict 45.0 ^\circC peak‑temperature audit, fixed and ambient‑scheduled protocols overheat in five of nine conditions because neither observes hidden cooling degradation, and rigid protective shutdown fails to deliver the charge in every condition. RACL‑B safely completes all nine conditions, is 37.9% faster than the fastest fixed current safe across the whole envelope, produces the least plated lithium, and remains safe across thermal guard bands. The same margin‑aware principle drives a transient‑credit fault readout (CREST‑B) that, on a real introduced‑fault battery‑pack dataset, gives the strongest learned sequence‑to‑global monitor for localizing cooling‑fault onset under operating‑condition shift. The framework provides a deployable thermal‑safety guarantee for fast charging together with a margin‑aware monitor for the same physical fault class.

Authors:Kushal Pratap Singh, Twinkle Tripathy, Anoop Jain
Title: Bearing-based Circumnavigation with Collision Avoidance in Time-varying Graphs under Limited Target Information
Abstract:
In this paper, we study distributed circumnavigation of a stationary target by a heterogeneous team of agents. Each agent is modelled as a disk rather than a point mass to account for its physical dimensions. The target location is assumed to be accessible only to a small subset of agents, called leaders. The rest, called followers, therefore use only local information available from their designated out‑neighbour in the interaction graph characterised by the selection of nearest neighbours. By controlling only angular speeds, we develop a distributed guidance law to circumnavigate a stationary target. The proposed guidance law works for both static and time‑varying interaction graphs. Inter‑agent collision avoidance is enforced through a logarithmic Barrier Lyapunov (BLF) Function, which guarantees forward invariance of the collision‑free set. We show that every follower converges to circumnavigation about the same target as the leader at the end of its directed path in the interaction graph, provided the initial conditions are admissible. Numerical simulations illustrate the effectiveness of the proposed method for both static and time‑varying topologies.

Authors:Wei Wang, Minwu Chen, Yinyu Chen, Jingyan He
Title: A Bi-Layer TSN Formulation for Separable Scheduling of Mobile Emergency Resources
Abstract:
Separable scheduling unleashes the deployment flexibility of mobile emergency resources by dispatching carriers and functional modules separately yet in a coordinated manner, offering a promising avenue to enhance power system resilience. However, this flexibility induces a distinct carrier‑supported module routing structure, where non‑self‑mobile modules must be routed through compatible carrier movements. The resulting carrier‑module spatio‑temporal coupling makes exact and tractable optimization challenging. This letter identifies this structure and develops a novel exact bi‑layer time‑space network formulation as a mixed‑integer linear program. The proposed formulation represents carrier and module trajectories as interacting network flows and enforces their support relations through explicit arc‑level coupling. Compared with the prior logic‑based model, the proposed formulation preserves exactness while improving modeling flexibility by eliminating mandatory post‑arrival dwelling. Numerical studies validate its correctness and demonstrate substantial computational advantages.

Authors:Dave Broaddus, Rachel DiPirro, Chishang, Yang, Dan Calderone, Wendy Ju, Meeko Oishi
Title: Characterizing Driver Interactions with Autonomous Vehicles via Response Maps
Abstract:
Understanding human responses to autonomous vehicle (AV) behaviors is essential for socially aware interaction, which is crucial for socially compatible navigation in shared traffic environments. We characterize human driving responses in interactions with AVs as feedback laws over the coupled state space of the human driven vehicle and the AV. We model the human driver's actions using a response map, a concept based in game theory, and employ a linear representation to capture driver behaviors as a function of AV behaviors, based on empirical data from a driving simulator study. Our results show that 1) human driver acceleration behavior can be captured using response maps, and 2) human driver responses differ significantly with respect to AV behaviors of yielding, non‑yielding, and responsive to the human driver.

Authors:Biswajit Kumar Dash, Luis Herrera, Filippo Malandra
Title: Real-Time State Estimation in Smart Grids over 5G Networks: Experimental Validation Using Raspberry Pis and Typhoon HIL
Abstract:
Reliable, low‑latency communication is critical for real‑time monitoring and control in modern Smart Grids (SGs). The emergence of 5G networks, with enhanced reliability, significantly lower latency, and native support for massive machine‑type communication, offers strong potential to enable advanced grid applications such as state estimation (SE) and fault detection. While existing studies investigate 5G for SG use cases, most rely on simulations or analytical models; experimental validation using real hardware and SG data remains limited. This paper fills this gap by presenting a fully experimental validation of real‑time SE over a commercial 5G network using a 5G‑based multi‑node testbed built with Raspberry Pi (RPi)‑based SG nodes and a Typhoon Hardware‑in‑the‑Loop (HIL) real‑time simulator. We first characterize 5G communication performance using simulated SG data under varying reporting rates and deployment environments by evaluating Key Performance Indicators (KPIs) such as end‑to‑end delay, jitter, and frame loss. Experimental results show that the worst‑case mean delay observed for the 5G is approximately 6.5x lower than that of our previous LTE cat‑M study at the corresponding reporting rate. We then stream real‑time voltage, current, and phase‑angle measurements‑generated by an IEEE 4‑node feeder model in Typhoon HIL simulator‑to a remote Phasor Data Concentrator (PDC) for SE and fault detection. Results demonstrate that 5G‑enabled measurements support accurate SE under both steady‑state and dynamic load variations. Furthermore, fault‑detection experiments confirm reliable and prompt fault detection, with detection delays as low as 0.80 s.

Authors:Ruixing Ren, Junhui Zhao, Fangfang Wang
Title: Cross-Platform Chinese Offensive Comment Detection via Dual-Threshold Hard Example Mining
Abstract:
Cross‑platform deployment of offensive comment detection for Chinese social media suffers performance degradation. The paper proposes a dual‑threshold hard mining method to address this. First, the clean‑Chinese‑base RoBERTa is finetuned on COLD to establish a binary baseline for fair comparison. Second, a three‑class fine‑labeled test set covering Weibo, Xiaohongshu, Tieba, and Zhihu is constructed, domain distances from the source are quantified using Jaccard and Proxy‑A Distance, as well as the degradation bottleneck of the baseline under domain shift is systematically revealed. Herein, a dual threshold hard example mining strategy is proposed. High‑ and low‑confidence error‑prone samples are filtered from unlabeled corpora by prediction confidence. The model is secondarily finetuned under implicit contexts with merely a small set of manually labeled hard examples, realizing low‑cost cross‑platform domain adaptation. Experiments reveal significant performance gains of the optimized model across four platforms.

Authors:Andres Enriquez Fernandez, John J. Bird
Title: Training Observable Control Policies to Expose Agent State Through Actions
Abstract:
Physical or operational constraints often impose communications limitations on autonomous agents. Such limitations complicate monitoring or multiagent coordination. Even when strong communications are absent, some information may still be available. The remainder of the relevant agent state may be reconstructed via estimation. The actions taken by an agent are a potential source of information ‑‑ as the agent interacts with the environment, these actions may be observed even in the absence of explicit communication. We investigate using actions to estimate the state of an agent, using reinforcement learning to develop policies which make the estimation problem more tractable. Policy observability is encouraged through the training reward and is analyzed using simulation of the trained agent. In an aircraft tracking problem a policy with enhanced observability is found that has minimal impact on nominal task performance.

Authors:Biswajit Kumar Dash, Garrett Thomas, Adedoyin Inaolaji, Filippo Malandra
Title: Threshold Optimization and Dynamic Adaptation of Distributed Optimal Power Flow in 5G Networks
Abstract:
In this paper, we present an experimental evaluation study of the Alternating Direction Method of Multipliers (ADMM), which is a widely used technique in the distributed optimization of power distribution networks. The focus of this study is on how real 5G communication performance affects ADMM in a fully experimental platform that features commercial 5G connectivity and real‑time control. The ADMM‑based Distributed Optimal Power Flow (DOPF) problem is solved using the IEEE 123‑bus unbalanced distribution feeder subdivided into five areas, each managed by a local controller implemented on a Raspberry Pi. To mitigate the impact of the communication network variability, we propose a delay threshold‑based mechanism that yields a 7.75% reduction in convergence time compared to a no‑threshold baseline. We also devised a policy to dynamically update the threshold value based on communication and computation conditions, achieving a 26.42% reduction in the convergence time compared with the static optimal threshold. These results demonstrate the potential of adaptive, communication‑aware control strategies for real‑world Smart Grid (SG) deployments.

Authors:Shivam Bajaj, Varun S. Madabushi, Maegan Tucker, Vijay Gupta
Title: Comparison of Non-Deterministic Nonlinear Systems
Abstract:
We characterize a notion of system comparison, termed as (T_e,γ,δ)‑similarity, for non‑deterministic nonlinear systems. Building on a similar notion recently proposed for stable linear systems, the proposed notion characterizes the dissimilarity between the outputs, measured using the L_2 norm, of two nonlinear dynamical systems in terms of their inputs and disturbances. By establishing a relationship between (T_e,γ,δ)‑similarity and differential dissipativity, we establish equivalence between (T_e,γ,δ)‑similarity of nonlinear systems and the (T_e,γ,δ)‑similarity of their differential dynamics. We characterize the (T_e,γ,δ)‑similarity for nonlinear systems as a Linear Matrix Inequality feasibility problem and also provide necessary and sufficient conditions for solving this feasibility problem. We demonstrate the utility of the proposed notion through its use in two applications: (i) robust hierarchical control applied to a planar aircraft and (ii) the improvement (or design) of abstract models applied to the Moore‑Greitzer model and an electronic circuit.

Authors:Quinna Nguyen, C. Luke Nelson, Daniel S. Drew
Title: Jet impingement cooling with multi-stage ducted electroaerodynamic actuators
Abstract:
Modern high‑performance mobile electronics impose extreme constraints on thermal management, and traditional cooling methods often fail to meet requirements for power density, form factor, and durability. Jet impingement cooling offers a compelling solution but is typically hindered by the need for bulky ancillary hardware. Here, we demonstrate that compact arrays of reduced‑scale electroaerodynamic (EAD) plasma actuators, which are silent, solid‑state devices with no moving parts, can be used for direct jet impingement cooling of electronics. The main contribution is the first rigorous experimental demonstration and system‑level validation of multi‑stage, ducted electroaerodynamic jet arrays as a compact, fan‑replacement impingement cooling solution for mobile electronics. We characterize the performance of both single‑ and multi‑stage ducted actuators, including thermographic analysis of heat transfer coefficients and spatial cooling profiles. We also quantify the relationship between actuator stage count and cooling efficiency, showing that increasing the number of ion acceleration stages enhances jet velocity and heat transfer performance at a reduced efficiency. The actuators are then assembled into an array and directly compared to a conventional fan with similar coverage area, showing competitive performance at a fraction of the volume, weight, and power. Finally, we integrate the array onto a commercial edge AI system and show that thermal regulation during extended inference workloads matches that of a stock fan, without any moving mechanical components or noise. These results confirm that multi‑stage EAD jet arrays are not only viable but advantageous for thermal management in mobile and high‑performance systems, paving the way toward silent and miniaturized solid‑state cooling solutions.

Authors:Mohammadreza Nematollahi, Khashayar Khorasani, Nader Meskin
Title: Resilient Output Containment under Undisclosed Leader Dynamics and Actuator Attacks
Abstract:
This work studies resilient output containment for heterogeneous linear multi‑agent systems with actuator cyber‑attacks over directed network topologies. The leaders generate bounded locally absolutely continuous trajectories; however, their dynamics, velocity bounds, and motion envelopes are undisclosed to the followers. The cyber‑attack model includes state‑ and input‑correlated, as well as bounded exogenous actuator false‑data terms. A continuous two‑layer adaptive control architecture is proposed. The first layer is a virtual‑actuator reconfiguration layer that uses partial state measurements to compensate for actuator attacks in the local tracking‑error dynamics. The second layer is a network interface that generates task‑space commands via an adaptive interaction protocol. This protocol uses only neighbor‑exchanged network‑interface states whose dimensions match those of the plant output, and it does not require global graph knowledge for parameter tuning. For directed graphs, under a leader‑rooted united spanning‑tree condition, a nonsmooth Lyapunov analysis yields asymptotic containment at the command level. The physical outputs then converge to the leader convex hull up to a residual determined by the command‑tracking local controllers. Simulation results using a network of quadrotors with damped suspended loads illustrate the performance of attack recovery and containment tracking.

Authors:Amir Arslan Haghrah, Amir Aslan Haghrah
Title: Scalability of Morality: A Particle-Based Numerical Study on the Decoupling of Law and Ethics in Large-Scale Populations
Abstract:
This study introduces a particle‑based computational framework to investigate the scalability of morality and the systemic decoupling of formal law from decentralized social ethics in expanding populations. While micro‑societies reinforce ethical conduct through local reciprocity, macroscale systems introduce anonymity that strains cognitive memory limitations. We model individual agents as discrete particles with finite memory capacities (L) and dynamically evolving, stochastic choice profiles (μ) regulated by non‑linear social pressure switches. Monte Carlo ensemble simulations demonstrate a distinct, non‑linear phase transition as the population scales (N \to \infty). When the population metric outpaces memory capacity (N \gg L), the local re‑encounter probability drops as \mathcalO(L/N). This structural dilution neutralizes decentralized peer‑to‑peer accountability, causing global behavioral norms to decouple from moral baselines and drift toward a minimalist legal floor. Furthermore, cyclic scale experiments expose a prominent, path‑dependent hysteresis loop, mathematically formalizing the non‑Markovian inertia and irreversible nature of moral decay in self‑organizing social systems.

Authors:Han Zhou, Haojie Chang, David Widen, Christian Fager
Title: Inverse Design of Compact and Wideband Inverted Doherty Power Amplifiers Using Deep Learning
Abstract:
This paper presents a deep learning‑assisted methodology for the inverse synthesis of a compact, wideband inverted Doherty power amplifier (PA). Convolutional neural networks (CNNs) and genetic algorithms (GAs) are jointly employed to generate pixelated Doherty combiner networks that integrate load modulation, impedance matching, power combining, and phase compensation into a single structure. As a proof of concept, we design and fabricate a GaN HEMT Doherty PA with a pixelated output combiner. The prototype achieves a measured peak drain efficiency of 51%‑63% and a 6‑dB back‑off efficiency of 48%‑54% over 1.9‑2.5 GHz. Within the same frequency range, the measured output power is 44+/‑0.3 dBm. Furthermore, with digital predistortion (DPD) applied, the prototype circuit demonstrates an adjacent channel leakage ratio (ACLR) better than ‑53.2 dBc.

Authors:Minghao Chen, Jiale Zheng
Title: XMSE-Aware Adaptive Empirical Bayes Estimation
Abstract:
Empirical Bayes (EB) estimators can match the first‑order asymptotic risk of maximum likelihood (ML) while behaving very differently at second order: recent excess mean squared error (XMSE) analysis shows that kernel‑based EB estimation may be worse than ML when the kernel is poorly aligned with the true parameter. This paper turns that diagnostic into a design principle. We propose an XMSE‑aware mixed estimator that interpolates between ML and EB shrinkage. Its fixed‑weight XMSE is a scalar quadratic, yielding a closed‑form oracle mixing weight that is no worse than both ML and the base EB estimator at the XMSE scale. A plug‑in implementation based on finite‑sample XMSE approximations is proved consistent, with a second‑order oracle regret rate for an interior oracle weight. We further establish a transfer of the regret bound to the fixed‑weight risk curve evaluated at the selected weight, a thresholded boundary rule, and extensions to compact kernel families and to finite and growing kernel dictionaries with high‑probability oracle bounds. Finite impulse response simulations with SURE‑tuned, hard‑selection, and trace‑corrected baselines, together with the public Silverbox and Cascaded Tanks benchmarks, show that the proposed estimator retains most of the benefit of regularization when it is helpful and retreats toward ML under kernel misspecification, with an identified finite‑de analyzed on the benchmarks.

Authors:Yifan Wang
Title: Battery thermal-safety reserve erosion by mandatory cabin ventilation in shared-cooling electric vehicles
Abstract:
Hot‑weather electric‑vehicle thermal management is no longer a separate cabin and battery problem. A single climate system must cool the traction battery, maintain passenger comfort, and admit outdoor air for cabin air quality, while high ambient temperature and solar load derate the compressor serving all three demands. We identify fresh‑air ventilation as a hidden battery‑safety load: on a derated shared cooling loop, the compliant fresh‑air floor consumes finite cabin‑side cooling capacity and removes residual cooling reserve from the battery. In a 40 ^\circC, 800 W m^‑2, 150 kW event, raising the fresh‑air floor from 0.30 to 0.43 lowers peak cabin CO_2 from 1219 to 978 ppm, but raises peak battery temperature from 39.96 to 40.02 ^\circC and reduces the battery cooling bus from 575 to 529 W. We develop a reserve‑aware predictive controller combining a physics‑guided scientific‑machine‑learning surrogate, grid‑connected departure thermal reserve, air‑quality‑priced ventilation allocation, and dual control‑barrier‑function projections for battery temperature and operative comfort. The controller holds the pack at 39.73 ^\circC, caps peak CO_2 at 895 ppm, keeps operative‑temperature RMSE at 0.82 ^\circC, and uses 20.0% less drive cooling energy than fixed maximum‑compressor operation; ablations show that removing either barrier, under‑ventilating, or removing departure reserve breaks joint feasibility. Evidence comes from NASA POWER records, KU Leuven BEV BMS data merged with NASA POWER weather, 45 ^\circC GOTION aging data, 40 ^\circC high‑power NMC thermal identification, EnergyPlus cabin cross‑checks, and OpenModelica/FMI replay. Treating fresh air as a battery thermal‑reserve variable creates an actionable path toward EV thermal management that protects battery life, occupant health, comfort, and efficiency in one shared loop.

Authors:Yifan Wang
Title: When the Timetable Breaks: Physics-Anchored Scientific Machine Learning for Cold-Wave-Robust Battery-Electric Bus Operations
Abstract:
Cold‑climate transit agencies are electrifying fixed‑timetable fleets, but winter exposes a block‑level failure mode hidden by seasonal energy margins: cabin heating can deplete batteries faster than layovers recharge them, causing later trips to start undercharged and making one cold day cascade into timetable infeasibility. We present WeatherRobustBus, an open‑data framework that converts this risk into block‑level failure probability by injecting real hourly weather into real transit duties and propagating cold‑weather energy uncertainty. The framework couples a transparent traction and cabin‑thermal backbone with a bounded monotone residual ensemble, and validates cabin heating against an independent EnergyPlus bus‑cabin simulation driven by the same Toronto weather record. Against this first‑principles reference, it achieves the lowest all‑year error (0.213 kWh RMSE over 8760 hours) and remains reliable in the out‑of‑support cold tail (T \le ‑12^\circC), where pure machine‑learning baselines degrade by 1.5‑‑4x and the best competitor reaches only 1.055 kWh. Embedded in a Monte Carlo block‑feasibility simulator over 60 real Toronto TTC vehicle blocks, the model reveals a sharp weather‑induced failure envelope. A forecast‑triggered robust policy combining opportunity charging, a fuel‑fired cabin‑heating bridge, and modest buffering reduces mean cold‑wave failure probability from 0.759 to 0.112 across eight cold‑wave days; a deconfounded ablation shows opportunity charging is the dominant lever and the heater is a low‑cost complement. WeatherRobustBus provides a reproducible pathway from weather data to winter‑resilience decisions for electric‑bus fleets.

Authors:Swapnil Tripathi, Özkan Karabacak
Title: Construction of Lyapunov density for nonautonomous dynamical systems on hypertorus
Abstract:
We present a semidefinite programming framework for constructing time‑varying Lyapunov densities for nonautonomous dynamical systems on a hypertorus. The formulation leverages Gram matrix representations of hybrid (real‑trigonometric) polynomials. In addition, we introduce a novel block decomposition of these Gram representations to confine the blow‑up of the resulting density to a prescribed set. The results are then applied to establish the almost global synchronization of a time‑varying Kuramoto model and the robust almost‑global stability of a parameter‑varying nonautonomous system. These examples demonstrate the applicability of the proposed method and validate the theoretical results. All computational results are obtained using an open‑source MATLAB implementation, as referenced in the text, thereby facilitating reproducibility of the reported examples.

Authors:Ioanna Kalospyrou, Solomon Brown
Title: Distribution Network Congestion Management via Strategic Aggregator Intervention in Local Energy Markets
Abstract:
High penetration of distributed energy resources increasingly creates congestion in low‑voltage distribution networks, while local energy markets (LEMs) optimise community welfare without explicitly internalising network constraints. This paper investigates whether a profit‑seeking aggregator embedded within a welfare‑oriented LEM can partially internalise distribution‑level congestion through market participation. We develop a post‑clearing, price‑protected intervention in which the aggregator injects additional supply and triggers re‑clearing, with network feasibility validated using nonlinear AC power flow subject to a non‑deterioration constraint on maximum line loading. The mechanism is benchmarked against Distribution System Operator (DSO)‑only corrective control and a hybrid regime with residual DSO action following aggregator intervention. Results on a UK LV feeder show that aggregator participation reduces thermal loading and preserves community welfare relative to DSO‑only control, though it does not fully restore compliance under severe stress. The hybrid regime achieves the strongest technical performance while maintaining lower welfare loss. Overall, aggregator intervention remains privately profitable, indicating partial incentive alignment.

Authors:Rui Zhang, Fuwang Dong, Wei Wang, Zhen Du
Title: ISAC for Sea-Air Networks: Predictive Beam Tracking under Sea Induced Disturbances
Abstract:
In sea‑air communication networks composed of an uncrewed aerial vehicle (UAV) and an uncrewed surface vehicle (USV), the extended target characteristics and three degree of freedom motion of the USV under sea induced disturbances cause beam misalignment in the UAV's tracking of the USV. To address these issues, this paper proposes a predictive beam tracking scheme based on integrated sensing and communication (ISAC) for sea‑air networks. We develop a wide and narrow beam switching scheme based on sub‑array selection, where a time allocation factor is optimized to balance robust state sensing in the wide beam mode and high‑rate communication in the narrow beam mode. Specifically, a wide beam mode provides full USV coverage and state sensing, while a narrow beam mode exploits the estimated state for high‑gain communication with the communication receiver (CR) mounted on the USV. To characterize the CR motion, a sea‑air state evolution model is derived by jointly considering the surge, sway, yaw, and sea induced disturbances of the USV. For the extended target USV, the measurement equation is constructed from multiple scatterer observations, with the measurement noise caused by sea clutter modeled, and an extended Kalman filter (EKF) based CR state prediction and estimation method is developed. In addition, the effect of sea clutter on sensing accuracy is incorporated into the time allocation optimization problem to adjust the time of the wide beam mode. Simulation results demonstrate that the proposed scheme achieves higher tracking accuracy than the state‑of‑the‑art benchmark schemes.

Authors:Wangkun Xu, Hongyang Jia, Yi Wang, Ning Zhang, Fei Teng
Title: Input Convex Neural Network as a Surrogate in Stability-Constrained Optimization for IBR-dominated Power Systems
Abstract:
Input convex neural networks (ICNNs) are increasingly used as surrogates for stability indices and embedded as constraints in power‑system optimization. This letter clarifies two recurring formulation limitations that can negate ICNN convexity benefits: (i) applying generic Big‑M mixed‑integer reformulations introduces auxiliary binaries that are unnecessary for enforcing ICNN sublevel constraints; and (ii) reversing the stability inequality transforms a convex sublevel set into a generally nonconvex superlevel set, invalidating global‑convergence guarantees of cut‑based methods. After clarifying the limitations, we provide (i) an exact LP‑based epigraph reformulation for ReLU‑ICNNs, (ii) an outer‑approximation scheme with global guarantees under the sublevel convention, and (iii) a feasibility‑preserving inner‑approximation scheme for the superlevel convention, with simulations on IEEE 14‑ and 118‑bus unit commitment instances.

Authors:Alex Rose, Christopher Jewison, Jonathan P. How
Title: PRISM: Efficient and Locally Optimal Probabilistic Planning with Reachability Guarantees
Abstract:
Belief‑space planning under motion uncertainty and state and control constraints remains a fundamental challenge, largely due to the difficulty of establishing reachability guarantees in constrained belief spaces. Existing constrained belief‑space planners rely on sampling to construct multi‑query belief roadmaps and explicitly find feasible trajectories between sampled nodes to establish reachability. These methods often struggle to cover the belief space or use robust control techniques that improve coverage at the cost of indirect, high‑cost trajectories; they also lack finite‑time or finite‑memory completeness guarantees. We propose PRISM, a multi‑query motion planning algorithm for belief spaces with state and control constraints that targets both high coverage and low cost. We present a new result on controllability of the state covariance under constraints, which is used by PRISM to decompose belief‑space planning into deterministic mean planning and covariance shrinking. PRISM further includes an online local optimization method that reduces the cost of feasible belief‑space trajectories. Under mild assumptions on the start and goal distributions, we prove that PRISM guarantees full coverage (i.e. completeness) despite actuator and obstacle constraints. In challenging simulated scenarios, PRISM achieves substantially higher roadmap coverage than state‑of‑the‑art belief‑space planning methods while producing trajectories with lower mean cost and cost variance. For example, PRISM achieves 100% coverage in easy and medium‑difficulty scenarios, and, in the hardest scenario, which violates PRISM's coverage assumptions, it still achieves 97‑100% coverage, while all other methods achieve less than 45%.

Authors:Jônatas Augusto Manzolli, Ali Eslami, Luis Miranda-Moreno, Jiangbo Yu
Title: When Agents Meet Electric Bus Fleet Operations: Pricing Behavior, Trade-offs, and Policy Implications in an Aggregator Framework
Abstract:
Agentic systems are changing how complex operational tasks are coordinated, introducing a new paradigm for connecting heterogeneous data sources and automating processes. Electric bus fleets provide a relevant test case. Their operation requires continuous coordination between service reliability, battery state‑of‑charge, charger availability, electricity prices, route‑energy uncertainty, and vehicle‑to‑grid (V2G) opportunities. This paper proposes an agentic aggregator framework that streamlines this decision environment by coupling an optimization‑based electric bus scheduling model with supervisory agents for disturbance detection, tariff adaptation, and schedule evaluation. The optimization core enforces physical feasibility across routes, chargers, batteries, and V2G exchanges, while the agentic layer interprets changing operating conditions, triggers real‑time re‑optimization when needed, and defines how flexibility value is allocated between the aggregator and the public transport operator (PTO). A realistic depot case study evaluates day‑ahead and real‑time operations under profit‑based and operation‑based coordination modes, considering service delays, route‑energy deviations, electricity price shocks, and combined disturbances. The results show that agentic aggregation can support adaptive fleet‑grid coordination by maintaining feasible schedules, activating re‑optimization selectively, and improving the use of charging and V2G flexibility. However, they also reveal a critical trade‑off: the same agentic capability that reduces operational complexity can extract value from the PTO when configured around profit‑oriented pricing. These findings suggest that agentic aggregators can become useful for managing electric bus V2G operations, but their deployment in public‑fleet contexts requires transparent coordination modes, auditable tariff‑setting, and explicit value‑sharing rules.

Authors:Diego Eckhard
Title: pysib: An Open-Source Python Toolbox for Linear System Identification
Abstract:
Discrete‑time polynomial input‑‑output models (ARX, ARMAX, OE, and Box‑‑Jenkins) are usually estimated by prediction‑error methods, but for OE, ARMAX, and BJ the finite‑sample criterion is nonconvex: the estimate a user actually obtains is set by the initialization and the optimization procedure, not only by the asymptotic theory. This article documents the dedicated optimization strategy behind pysib, an open‑source Python toolbox for SISO polynomial system identification. The strategy consists of an ARX‑based initialization, a smoothed‑gradient phase, an incremental Gauss‑‑Newton refinement, and filtered continuation interpreted as cost‑function shaping. This strategy produced the filtered‑continuation results reported by the author in earlier work but had not previously been described or released; it is given here in full and as open Python software, with a common five‑polynomial representation, shared prediction and simulation routines, and the scripts and archived release needed to reproduce the experiments. On a moderate‑noise OE benchmark the strategy returns estimates far more concentrated around the true parameters than a general‑purpose nonlinear‑programming solver, and on a harder nonconvex benchmark filtered continuation raises the success rate from 60% to 100%.

Authors:Waldemar Bauer, Anna Jarosz-Kozyro, Jerzy Baranowski
Title: Bayesian Changepoint Detection for Smart Sensing of Battery Degradation: Cycle-Level Health Indicators and PyMC Implementation
Abstract:
Reliable detection of the onset of accelerated degradation is central to safe and cost‑efficient operation of lithium‑ion batteries. This paper presents a Bayesian single‑changepoint model applied to a simple but physically meaningful cycle‑level health indicator (HI), defined as the ratio of charge time to discharge time. The indicator is computed directly from voltage‑current telemetry typically available in battery management systems (BMS), without access to raw waveforms. The changepoint model is implemented in PyMC using Hamiltonian Monte Carlo and produces posterior distributions for onset time and pre/post‑degradation slopes, together with posterior predictive checks. Experiments on an open 18650‑cell remaining useful life (RUL) dataset show consistent midlife changepoints with narrow highest‑density intervals. The formulation is lightweight, interpretable, and amenable to smart‑sensing deployment on embedded BMS platforms.

Authors:Mikkel Bjørn, Daniel H. Hansen, Grace Melchiors, Kim Guldstrand Larsen, Christian Schilling
Title: Scalable Reachability Analysis of Linear Continuous Systems with Property-Driven Time-Step Adaptation
Abstract:
We study safety verification for linear time‑invariant systems with bounded inputs in continuous time. The standard approach reduces to a reachability analysis in two steps: first discretize time and then apply a forward analysis in the discretized system. Existing algorithms use either a fixed time step or an adaptive time step that changes based on the approximation error compared to the underlying continuous system. In this paper, we present an efficient reachability algorithm that adapts the time step based on a given safety property. Essentially, our algorithm makes the largest possible time step such that it can still prove safety. For this approach to be scalable in practice, we discuss several optimizations such as avoiding the repeated expensive calculation of the matrix exponential during discretization and a careful balance how we tame the approximation error stemming from the states and the inputs. This allows our algorithm to yield a moderate approximation error even when using a large time step, thus requiring much fewer steps than prior algorithms. We demonstrate the effectiveness and scalability on the large‑scale SLICOT benchmark suite, where our algorithm consistently outperforms other state‑of‑the‑art approaches.

Authors:Clement Wong, Andrew Weng, Xin Hui Ooi, Zhiwen Wan, Jeesoon Choi, Seung Yoon Yang, Heejun Jin, Jason Siegel, Anna Stefanopoulou
Title: Health feature extraction from battery energy storage system field fault data
Abstract:
Health monitoring methods are critical for lithium‑ion battery modules connected to the grid to prevent faults that can lead to catastrophic events. However, assessing the health of cells in modules from their operational data presents challenges including variable operating conditions, which directly confound health features, and sparse sensing in the modules, particularly within cells in parallel, which prevents observing critical states of individual cells. Here, we present a framework for extracting and calibrating health features for battery modules from their operational data to identify discriminative features for separating faulty parallel‑connected cell groups within the modules. We applied this framework to operational data from 25 commercial grid‑connected lithium‑ion Battery Energy Storage System (BESS) modules. Each module consisted of 14 series‑connected parallel groups, one of which was confirmed as faulty via post‑mortem investigation; in total, the dataset included 25 faulty and 325 non‑faulty cell groups. A statistical evaluation of these calibrated features demonstrated that group‑level capacity, capacity degradation rate, and dV/dQ peak heights separate faulty parallel‑connected cell groups within the modules with statistical significance (p<0.05). Conversely, group internal resistance did not (p>0.05), indicating that increased resistance was not a primary characteristic of the faults in this dataset. These findings challenge the exclusive reliance on resistance features for fault detection. The observed feature signatures suggest potential failure mechanisms, furthering the understanding of fault behavior in lithium‑ion battery modules during field operation. More importantly, this work demonstrates a framework for robustly monitoring the health of cells in lithium‑ion battery modules under real‑world operations.

Authors:George Dimas, Amin Masoumi, Mert Korkali
Title: Feasibility-Aware Security-Constrained Unit Commitment via Hybrid Soft Actor-Critic with Quantum-Sampled Features
Abstract:
Security‑constrained unit commitment (SCUC) couples binary commitment, economic dispatch, reserves, and network security over a multiperiod horizon, which makes an exact solution expensive at realistic system sizes. This paper proposes a three‑layer hybrid framework in which a Bernoulli hybrid soft actor‑critic (HSAC) policy proposes hourly commitments, a quantum‑sampled auxiliary channel augments the state, and a native SCUC mixed‑integer linear program recovers dispatch and security variables after only a limited subset of commitment binaries is enforced. The method is therefore solver‑compatible rather than an end‑to‑end replacement for exact optimization. We formalize the SCUC‑to‑reinforcement‑learning interface, derive the temporal coverage induced by the fixed cap, and conduct representative experiments on the 14‑, 57‑, and 118‑bus cases. The results show stable, low‑cost recovery in the 14‑bus case; a very low screen‑rejection rate in the 57‑bus case, consistent with learned feasibility generalization under fixed intertemporal SCUC constraints; and a clear coverage bottleneck in the 118‑bus case once the enforcement cap no longer spans a complete commitment period. The 118‑bus case runtime traces nevertheless remain tightly clustered for accepted episodes, indicating that the policy still captures a repeatable recovery pattern across most episodes. The study, therefore, identifies the dominant limitation of the current implementation as the amount of useful commitment information that reaches the recovery model under an exploratory Bernoulli actor and a small enforcement cap, and shows how that limitation governs scalability.

Authors:Arash Baharvandi, Duong Tung Nguyen
Title: A Bilevel Framework for Data Center-Grid Coordination with DLMPs in Unbalanced Three-Phase Distribution Systems
Abstract:
This paper proposes a grid‑aware coordination framework between data centers and distribution grids using a DLMP‑based bilevel optimization model. The data center aggregator (DCA) determines active power demand in response to distribution locational marginal prices (DLMPs), while the distribution system operator (DSO) solves a network‑constrained optimal power flow problem to determine DLMPs in an unbalanced three‑phase system. The model incorporates both active and reactive power consumption of data centers to evaluate their impacts on voltage regulation and phase imbalance. To mitigate adverse network effects, two operating cases are analyzed: without reactive power compensation and with static var generator (SVG)‑based compensation. The proposed approach is validated on the IEEE 37‑bus unbalanced distribution test system. Simulation results show that DLMP‑based coordination captures economically efficient data center operation, and phase‑ and location‑dependent network conditions, while SVG‑based compensation improves voltage profiles and reduces phase unbalance.

Authors:Prabhav Gaur, Premanand Chandramani, Mohammed Alshamari, Yufei Chu, Abu Mitul, John Simons, Ibrahim G. Yayla, Ming Han, Ashok V. Krishnamoorthy
Title: Fiber Bragg grating-based acoustic sensing system enabled by ML-trained, sub-picometer-tunable hybrid III-V/SiN lasers
Abstract:
Distributed acoustic emission (AE) sensing is critical for early detection of structural degradation, yet conventional electrical sensors are difficult to scale and fiber‑based approaches are limited by interrogation complexity and resolution. Here, we report an intelligent fiber Bragg grating (FBG) sensing system enabled by machine learning (ML)‑trained hybrid III‑V/SiN tunable lasers that achieve uniform, mode‑hop‑free, sub‑picometer wavelength control. A supervised gradient‑descent algorithm is used to learn the nonlinear electro‑thermal tuning space of Vernier‑based external‑cavity lasers, enabling continuous tuning with <0.1 pm resolution and <0.5 dB power variation. This capability allows precise alignment to FBG reflection slopes for high‑sensitivity acoustic detection. We demonstrate a four‑laser interrogation system monitoring 16 FBG sensors distributed across multiple metallic structures, operating over a 35 nm wavelength span. The system autonomously identifies sensor resonances, dynamically tracks spectral shifts, and reconfigures interrogation wavelengths in response to localized acoustic events. Using pencil‑lead break tests as calibrated AE sources, we show simultaneous multi‑channel detection and adaptive spatial localization. The combination of narrow linewidth (<10 kHz), wide tunability, and ML‑driven calibration enables robust, scalable, and high‑resolution sensing. This approach establishes a pathway toward fully autonomous, distributed photonic sensing networks for real‑time structural health monitoring.

Authors:Cheng Ouyang, Moeen Ul Islam, Kaixiang Zhang, Zhaojian Li, Xiaobo Tan, Dong Chen
Title: Deep Reinforcement Learning-Enhanced Event-Triggered Data-Driven Predictive Control for a 3D Cable-Driven Soft Robotic Arm
Abstract:
Soft robots are challenging to control due to their nonlinear and time‑varying dynamics. Data‑enabled predictive control (DeePC) offers a model‑free alternative by directly leveraging measured input‑output trajectories to construct a predictive controller. However, its receding‑horizon formulation requires solving a constrained optimization problem at every sampling instant, which can be computationally demanding for real‑time deployment on resource‑limited robotic platforms. To address this limitation, we propose an adaptive reinforcement‑learning‑based event‑triggered DeePC (RL‑ET‑DeePC) framework for soft robotic control. A model‑free RL policy is trained to determine when to invoke the DeePC optimizer based on the current system state representation, thereby reducing unnecessary optimization calls while preserving closed‑loop performance. Simulation results show that RL‑ET‑DeePC reduces optimization frequency by up to 66% compared to periodic DeePC, while maintaining comparable tracking accuracy. Hardware experiments on a three‑dimensional cable‑driven soft robotic arm demonstrate zero‑shot transfer, achieving a 34% reduction in optimization frequency with tracking accuracy comparable to periodic DeePC and more consistent performance than a static threshold‑based event‑triggered baseline.

Authors:Faliang Yin, Hak-Keung Lam, David Watson
Title: Explainable Control Framework (XCF) based on Fuzzy Model-Agnostic Explanation and LLM Agent-Supported Interface
Abstract:
Increasing demand for precise and reliable control in complex scenarios has led to the development of increasingly sophisticated controllers, including data‑driven approaches employing closed box models and mathematically rigorous yet complex designs. This complexity highlights the needs for explainable control that can provide human‑understandable insights into controller behavior. In this paper, an explainable control framework (XCF) along with supporting algorithms and user interface are proposed to explain how controllers determine their control actions and their underlying working mechanism. The novel contributions of this work are threefold: First, the XCF is designed to provide model‑agnostic explanations for controllers in closed‑loop systems and can optionally refine local explanations by system response dynamics. Second, a novel explanation method, hierarchical fuzzy model‑agnostic explanation for control systems (HFMAE‑C), is proposed based on the designed framework. The HFMAE‑C employs a fuzzy logic system to approximate the controller's behavior and system dynamics, providing sample, local, domain and universe level explanations via IF‑THEN rules revealing the controller's decision logic and salience values quantifying the contribution of system states to control actions. Third, a large language model agent‑supported user interface is developed to automatically analyze user requirements, select appropriate algorithms, interpret the generated explanations to a natural language report, and provide interactive consultation. Case studies on inverted pendulum system and Turtlebot obstacle avoidance demonstrate the effectiveness of the proposed method through simulated user experiments and quantitative comparisons with mainstream explainable control approaches.

Authors:Sarah H. Cen, Vaibhav Srivastava, Naomi Ehrich Leonard
Title: Robustness and Leadership in Markov-switching Consensus Networks
Abstract:
We investigate how time‑varying interactions, modeled via a Markov switching graph (MSG), impact the robustness of noisy multi‑agent dynamics in both continuous‑ and discrete‑time settings. Our focus is on the steady‑state performance of consensus and leader‑follower tracking dynamics subject to stochastic noise. Using the framework of Markov jump linear systems (MJLS), we derive expressions for the steady‑state covariance of each agent's deviation from consensus and tracking error, respectively, and use them to quantify individual and group performance as a function of the interaction graphs and the switching dynamics. We extend established notions of robustness, certainty indices, and joint centrality from static graphs to the MSG setting. To gain analytical insight, we specialize our results to systems switching between two topologies and characterize how switching influences performance. Numerical simulations further illustrate how switching topologies affects system robustness in both coordination tasks.

Authors:Yinuo Wang, Gavin Tao, Yuze Liu
Title: Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learning
Abstract:
Underwater vehicles operate from a fixed onboard energy budget that propulsion rapidly depletes, so a controller that completes its task while drawing less thruster power directly extends mission range and endurance. Reinforcement learning yields capable model‑free controllers for station‑keeping and trajectory tracking, but optimizing task accuracy alone drives the policy toward oscillatory, energy‑wasting actuation. The established remedy subtracts an energy penalty from the reward, yet this sets the task‑power trade‑off through a single weight with no physical units: a target power level cannot be specified, the weight must be re‑tuned for every vehicle and task, and a mismatched weight can even raise power. This paper instead formulates energy‑efficient underwater control as a constrained Markov decision process in which average thruster power is subject to an explicit budget, solved with a PPO‑Lagrangian algorithm. The power level is set by declaring a budget in physical units, and a single dual variable is updated online to meet it for each vehicle and task, without manual weight search. Across three vehicles and four tasks in the MarineGym simulator, the energy‑constrained policy draws the least power in all twelve settings, reducing it by 14‑‑65% (up to 64.9%) over a task‑only baseline and below an energy‑reward baseline everywhere, while remaining the smoothest in ten settings and preserving task accuracy except in one deliberately power‑limited regime. Imposing energy as an explicit constraint thus offers a tuning‑free route to energy‑efficient underwater control that needs no per‑vehicle, per‑task weight search.

Authors:Daniyaer Paizulamua, Lin Cheng, Fashun Shi, Haoyu Zheng, Pengfei He, Haiwang Zhong
Title: Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids
Abstract:
Tie‑line power (TLP) shaping is a key requirement for the grid‑friendly operation of industrial microgrids (IMGs). This paper studies the coordination of multi‑timescale heterogeneous adjustable resources in a steel IMG to shape a grid‑friendly TLP trajectory considering multiple objectives. A sequential heterogeneous‑agent coordination (SHAC) framework is proposed, where process loads, hydrogen storage, and battery storage are modeled as functionally heterogeneous agents with cross‑role observations, asynchronous decision intervals, role‑specific rewards and critics. This design captures the heterogeneous temporal effects of different resources on the TLP trajectory and alleviates ambiguous credit assignment and weak inter‑agent coordination. To ensure feasible real‑time execution, process‑knowledge‑based action masking and feasibility projection are embedded into policy execution, and a role‑aware multi‑timescale actor‑‑critic training scheme is developed for agents with different action structures and decision intervals. Numerical studies using real renewable generation and electricity market data show that SHAC effectively eliminates the dependence on predefined reference trajectories and enables adaptive 1‑min online decision‑making, achieving zero production failures with an average computational time of only 0.4 ms per step. Compared with the original operation, SHAC reduces the total grid purchase cost, contract‑demand exceedance time, and cumulative ramp excess by 91.27%, 98.64%, and 96.91%, respectively. These results demonstrate that the proposed framework improves the economic efficiency and grid friendliness of industrial microgrid operation while satisfying strict process‑safety constraints and real‑time computational requirements.

Authors:S. Saharsh, Pushpak Jagtap
Title: Control Barrier Function only Formation Tracking in Multi-Agent Systems
Abstract:
This paper presents a real‑time control framework for formation tracking of heterogeneous multi‑agent systems with non‑linear dynamics. The proposed method formulates a single Control Barrier Function‑like constraint within a quadratic optimization setting that addresses formation tracking. Relying on the relative information of neighboring agents, the controller is designed to operate without the need for manual parameter tuning or a separate nominal formation controller. The leader‑follower framework is validated through simulations of moving formations.

Authors:Alireza Shojaei
Title: Conformal Recovery-Deadline Certificates for Runtime Assurance of Adapting Controllers
Abstract:
Runtime assurance (RTA) protects a safety‑critical system by switching from an advanced controller to a verified safe controller when a monitored condition is violated. The standard latching rule, which trips on the first breach of the safe set and then coasts, is correct for a diverging controller but pathological for a capable online‑adapting one. Such a controller is unsafe by design during a bounded recovery transient. It must excite the plant to identify the fault before it can correct it, so a latching shield trips on that transient and suppresses a controller that would have recovered. We introduce the conformal recovery‑deadline certificate, a split‑conformal, distribution‑free, finite‑sample upper bound on the adapting controller's recovery time that licenses delayed fallback with a coverage guarantee, backstopped by a verified monitor at a hard critical limit. The certified deadline discriminates capable from incapable controllers, keeping the recoverer autonomous while catching the diverger. The construction separates autonomy, governed by statistical coverage, from safety, governed by the verified backstop, as an instance of reliability‑asymmetric design. We prove marginal coverage, a weighted extension that restores coverage under a known fault‑distribution shift, and group‑conditional Mondrian coverage. We demonstrate all three on two unrelated Simplex testbeds: a 6‑DOF spacecraft attitude controller and a torque‑controlled inverted pendulum. Both show the same suppression pathology and the same cure, making the certificate a domain‑general mechanism rather than a single‑system trick.

Authors:Mohammadreza Kamaldar
Title: Deterministic Non-Smooth Safety via Dual-Algebraic Control Barrier Functions
Abstract:
This paper presents a dual‑algebraic framework for control barrier functions (CBFs) that guarantees deterministic execution using exclusively elementary arithmetic. We develop this deterministic approach to solve a fundamental bottleneck in safety‑critical control: pointwise minima naturally compose intersecting safe sets, but generate non‑smooth boundaries where standard Lie derivatives fail. Existing mathematical workarounds inject approximation bias, probabilistic non‑determinism, or combinatorial execution delays that strictly impede hard real‑time hardware certification. By embedding the system state and vector field into the dual‑number ring, our method extracts both the composite barrier value and its exact directional derivative in a single evaluation. The standard floating‑point minimum deterministically isolates a single vertex of the Clarke generalized gradient for the quadratic‑program solver. We prove this selected vertex constitutes a valid Clarke subgradient and the resulting simultaneous‑enforcement safety filter guarantees forward invariance. The arithmetic overhead remains a fixed constant factor, strictly independent of state dimension or constraint count. We extend this framework to arbitrary \min/\max Boolean compositions and systems of higher relative degree, validating the computational scaling on three physical examples.

Authors:Nam T. Nguyen, Truong X. Nghiem
Title: Active Learning for Optimal Experimental Design in Machine Learning-Based Building Energy System Identification
Abstract:
Machine learning (ML) techniques have been commonly adopted to identify the dynamics of building energy systems (BESs), owing to their flexibility relative to first‑principles, physics‑based modeling approaches. Beyond the choice of ML architecture, the quality of the training data plays an essential role in the resulting model performance. Optimal experimental design (OED), realized in this work through active learning (AL), determines which experiments to conduct in order to collect informative data, rather than relying on standard approaches such as uniformly random sampling. This paper proposes a systematic comparison of OED via AL for building energy system identification, with a particular focus on HVAC thermal dynamics. We investigate fourteen AL techniques across two ML model classes, namely a deterministic feedforward neural network and a stochastic Gaussian process, and classify these techniques into four categories: data space, uncertainty, information gain, and model change. To examine the AL algorithms under realistic conditions, we implement and evaluate them on the high‑fidelity building simulator BOPTEST. The results, reported as the root mean square error across multiple test scenarios with varying initial dataset sizes and control input constraints, show that AL‑based models generally outperform models trained via passive learning (PL) with uniformly random control inputs, achieving error reductions of up to 54%, although the magnitude and consistency of this improvement vary across acquisition functions and operating regimes.

Authors:Donald Intal, Abasifreke Ebong
Title: GaN Power Devices and Converter Architectures for AI Data Centers: Efficiency, Reliability, and Deployment Pathways
Abstract:
The growth of artificial‑intelligence workloads is increasing the electrical and thermal demands on data‑center power‑delivery systems, making conversion efficiency, power density, and reliability critical design priorities. This review examines how gallium‑nitride (GaN) power devices can be matched to specific stages of the grid‑to‑load conversion chain, including power‑factor correction, isolated DC/DC conversion, 48‑V intermediate‑bus conversion, and point‑of‑load regulation. Si, SiC, and GaN are compared using converter‑relevant metrics, and lateral, vertical, and specialized GaN architectures are evaluated in terms of voltage scalability, switching behavior, reverse conduction, thermal pathways, gate control, and technology maturity. The analysis shows that GaN provides a stage‑dependent rather than universal advantage. Commercial lateral GaN HEMTs are particularly effective in high‑frequency, low‑to‑mid‑voltage stages, while specialized and hybrid devices support bidirectional operation, normally‑off control, extreme conversion ratios, and integration. Vertical GaN remains an emerging option for higher‑voltage and higher‑power conversion. A quantitative framework links cascaded converter efficiency to electrical‑loss reduction, cooling demand, annual facility energy use, and operational carbon emissions. Broad deployment further requires low‑parasitic packaging, disciplined gate‑drive and EMI co‑design, mission‑profile reliability qualification, scalable manufacturing, and supply‑chain resilience. GaN is therefore best treated as a stage‑specific system lever whose value depends on coordinated device, topology, package, and thermal co‑design.

Authors:Zixin Jiang, Bing Dong
Title: Buildrix: An Open Platform for Sharing and Benchmarking Agentic AI Skills in Building Engineering
Abstract:
Agentic AI offers significant potential to automate complex building‑engineering workflows. However, most existing applications remain isolated proof‑of‑concept demonstrations and lack reusable domain capabilities, human‑verified evaluation cases, and standardized benchmarking infrastructure. This study presents Buildrix, an open, community‑driven platform for developing, sharing, executing, and evaluating agentic AI skills for building engineering. Buildrix integrates three components: a Python command‑line package for developing, validating, publishing, installing, and managing skills and test cases; a web‑based Hub for organizing open challenges, reusable skills, test cases, reviews, and benchmark results; and a local agent harness that supports skill discovery, external toolchain provisioning, progressive context loading, and multi‑step workflow execution. Buildrix skills are organized as standardized, self‑contained packages containing task instructions, executable scripts, dependencies, and supporting resources. Quantitative test cases can be verified by domain experts and promoted to golden test cases for reproducible benchmark evaluation. Buildrix provides an open foundation for reusable capability development, transparent evaluation, and community‑driven advancement of agentic AI in building engineering.

Authors:Chris Williams, Philip Colangelo, Ayse Coskun, Ethan Levine, Andy Neale, Ciaran Roberts, Shayan Sengupta, Nikhil Shirolkar, Varun Sivaram, Sarah Soares, Ethan Tiao, Scott Underwood, Daniel Wilson, Frank Sharp, Luke Wainwright, Harry Petty, Scott Wallace, Brandon Records
Title: Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute
Abstract:
The rapid expansion of artificial intelligence (AI) infrastructure is driving unprecedented growth in electricity demand from data centers. Traditional power‑system planning treats large computing facilities as inflexible peak loads, leading to costly infrastructure upgrades and long delays in grid interconnection. Recent work has shown that AI clusters can reduce electricity consumption during peak demand through software‑based workload orchestration. This article explores how modern GPU‑based AI data centers can operate as grid‑interactive assets that respond dynamically to power system conditions. We describe an architecture integrating grid signals, workload scheduling, and power telemetry for fine‑grained cluster power control. Experimental results from a real‑world deployment on a 130 kW GPU cluster demonstrate multiple forms of flexibility, including rapid load reduction, sustained curtailment, and carbon‑aware operation while preserving service levels for priority jobs. We further demonstrate performance‑aware load shifting across geographically distributed clusters, enabling workloads to migrate toward regions with lower grid stress. Together, these capabilities transform AI infrastructure from static electricity consumers into flexible resources that support grid reliability, accelerate interconnection, and improve computing sustainability.

Authors:Sangwhee Lee, Rafal P. Wojda, Cheol-Hee Jo, Shuntaro Inoue, Pedro Ribeiro, Gui-Jia Su, Mostak Mohammad, Himel Barua, Nishanth Gadiyar, Praveen Kumar, Spencer Cochran, Subho Mukherjee, Whit Vinson, Vandana Rallabandi, Shajjad Chowdhury, Burak Ozpineci
Title: Toward Next-Generation AI Data Centers: Power Delivery Architecture Shifts, Emerging Technologies, and Challenges
Abstract:
The rapid growth of AI workloads is driving unprecedented increases in data center power demand, current transients, and thermal stress, exposing fundamental limitations in traditional 48 V rack architectures, low‑voltage AC distribution, and line‑frequency transformer interfaces. This paper reviews the three stages of architectural shifts required to support next‑generation AI data centers and identifies three enabling technological building blocks: high‑voltage conversion‑ratio DC/DC converters, facility‑level low‑voltage DC distribution, and medium‑voltage solid‑state transformers. The advantages, technical challenges, and potential solutions associated with each building block are reviewed. Finally, future research directions and open challenges are discussed.

Authors:Shambhuraj Sawant, Akhil S Anand, Dirk Reinhardt, Sebastien Gros
Title: Solving Markov Decision Processes with Future Information via MPC
Abstract:
Model Predictive Control (MPC) is widely used in industrial and robotic systems for enforcing constraints and embedding domain knowledge through finite‑horizon optimization‑based planning. However, despite these strengths, an MPC scheme typically does not yield optimal policies for sequential decision‑making problems formulated as Markov Decision Processes (MDPs). Recent combinations of MPC with Reinforcement Learning (RL) alleviate this issue by treating MPC as a parameterized model of the optimal policy of an MDP and adjusting its parameters using data. While these approaches typically consider classical MDPs, many real‑world problems include future information‑‑such as forecasts, prices, or reference trajectories‑‑at decision time, which must be included in the MDP state for optimal decision‑making. Current MPC‑RL approaches do not directly account for this augmented‑state structure, raising the question of how to incorporate future information into MPC to obtain an optimal policy. This work establishes the structural requirements under which a parameterized MPC can exactly represent the optimal value functions and policy of an MDP with future information. We further demonstrate that such a parameterized MPC can serve as a structured function approximator, with its parameters learned using RL. The approach is illustrated on a point‑mass racing task with future reference information.

Authors:Haoyuan Deng, Yihong Zhou, Thomas Morstyn, Yi Wang
Title: Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources
Abstract:
The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by their inherent uncertainties and modelling complexity. As traditional optimization methods struggle with such uncertainty and complexity of DERs, reinforcement learning (RL) has emerged as a promising alternative for DER management. However, standard RL methods suffer from sample inefficiency and sub‑optimality when trained from scratch. Inspired by the training paradigms in large language models, this paper proposes a Supervised Reinforcement Learning (SRL) framework for learning DER coordination policies. This framework first pre‑trains a policy on demonstration data in a supervised‑learning fashion, which is then further fine‑tuned using RL. Furthermore, we propose a two‑step fine‑tuning process: offline fine‑tuning for enhancing policy performance and online fine‑tuning for adapting it to the real‑world dynamics. Experiments demonstrate that RL implementations based on the proposed framework significantly outperform all benchmarks, achieving high cost efficiency even under low‑quality demonstration data.

Authors:Evgeny Nikulchev
Title: Reconstruction of chaotic systems in invariant jet space
Abstract:
Takens' theorem is the gold standard for attractor reconstruction from time series, but it guarantees only topological equivalence and does not preserve metric or group properties such as symmetries. We show that switching from delay‑coordinate space to jet space (signal and its derivatives) allows one to exactly preserve the symmetry group of the original system. This statement is rigorously justified by a theorem on the isomorphism of Lie algebras under jet prolongation. Numerical experiments on the Lorenz and Rössler systems confirm that jet‑space reconstruction preserves geometry and symmetries, whereas Takens embedding distorts them. As quantitative metrics we use a variational elastic energy functional and the correlation dimension. It is shown that jet‑space reconstruction not only outperforms Takens embedding but in some cases yields more accurate estimates of invariants than projections of the original system. The proposed approach provides a coordinate‑invariant criterion for the classification of strange attractors and can serve as a basis for detecting hidden attractors.

Authors:Martina Vinetti, Sabino Roselli, Martin Fabian
Title: Multi-Worker Assembly Line Rebalancing with Relevance-Guided Configuration Preservation
Abstract:
In assembly line balancing, tasks are assigned to stations in order to satisfy a required cycle time. When production conditions change, the line must be rebalanced by modifying the current task allocation, typically aiming to move as few tasks as possible between stations. Similarity measures are commonly used to control such changes, but they generally evaluate configuration preservation by treating all tasks equally, which may not reflect their different practical importance. In this work, a \emphpruned Mean Similarity Factor is proposed for assembly line rebalancing, evaluating similarity only over a subset of structurally relevant tasks identified through a relevance score. The proposed measure is integrated into a compact mixed‑integer linear programming (MILP) formulation that considers practical aspects of manual assembly, specifically workload balance, ergonomic exposure, multi‑worker stations, and positional constraints. Computational experiments on extended benchmark instances derived from the literature show that the proposed approach can obtain optimal rebalancing solutions within reasonable computational times, while maintaining high task colocation and balanced workload and ergonomic distributions. In particular, focusing the similarity evaluation on relevant tasks helps reduce the computational effort.

Authors:Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
Title: Suboptimal and Reduced-Order MPC via Timescale Separation
Abstract:
In this paper, we propose a generalized framework for the design and analysis of suboptimal and reduced‑order nonlinear Model Predictive Control (MPC) architectures. The proposed framework manages real‑time operation of MPC schemes by (i) computing the control action suboptimally, i.e., by running a generic optimal control algorithm for a finite number of iterations, and (ii) relying on a reduced‑order model that neglects part of the plant dynamics (accounting for, e.g., unmodeled dynamics or a low‑level compensator). To rigorously handle the interplay between optimization error and model mismatch, we treat the sampling time as a tunable design parameter. We analyze the resulting closed‑loop system, comprising the full‑order physical plant interconnected with the iterative optimization algorithm (treated as a dynamical system), by leveraging tools from timescale separation. We prove that operating at a sufficiently fast sampling rate ensures that the closed‑loop system maintains recursive feasibility and achieves an exponentially stable equilibrium point. The effectiveness of the proposed framework is validated on an underactuated two‑link robotic arm through virtual experiments in the high‑fidelity MuJoCo physics engine.

Authors:Luyao Zhang, Gabriel Bravo-Palacios, Brian Plancher, Sergio Grammatico
Title: Parallel Dynamic Programming for Conic Linear Quadratic Control
Abstract:
Linear Quadratic (LQ) control problems are at the heart of linear control theory and Model Predictive Control (MPC). While performant, standard approaches to solving such problems are inherently serial, limiting real‑time scalability despite the parallel computing power available on modern multi‑core CPUs. Contributing to addressing this challenge and motivated by ``divide and conquer'' strategies, we present a parallel‑in‑time approach that solves computationally demanding conic optimal control problems through the use of the alternating direction method of multipliers (ADMM). In particular, we formulate the inner primal update of ADMM as an LQ problem and split the reformulated problem along the time horizon. This enables us to derive a variant of the Riccati recursion using dynamic programming to solve each subproblem in parallel. Numerical benchmarks on two real‑world applications demonstrate as much as a 5x speedup compared to existing related approaches on multi‑core CPU hardware.

Authors:Batool Ibrahim, Imad H. Elhajj, Daniel Asmar, Rawan El Hakim
Title: Human-Robot Shared Control for Humanized End-Effector Teleoperation
Abstract:
Recent advances in robotics have enabled robots to operate in shared human environments, emphasizing the importance of effective human robot interaction HRI. Prior studies indicate that anthropomorphism, defined as the incorporation of human like features into robotic systems, facilitates more natural interaction and enhances both task performance and user experience. In robotic arm teleoperation, however, user controlled motions often deviate from human like kinematic characteristics due to intrinsic limitations of teleoperation systems. In this work, we propose a real time framework that generates human like end effector trajectories based on the two thirds power law of voluntary human hand movements, while preserving the operators intended control inputs. The proposed approach is validated through real world experiments conducted on a 6 degree of freedom Dobot CR10 robotic arm. Quantitative analysis demonstrates that the generated trajectories exhibit significantly stronger adherence to human like kinematic profiles compared to conventional teleoperation, with the estimated beta coefficient moving 39.7% closer on average to the theoretical value of 1/3. Furthermore, the method achieves an approximate 34% improvement in motion smoothness, measured by RMS torque rate reduction, with 80% of evaluated motion patterns showing statistically significant improvements while maintaining comparable task completion times.

Authors:Antonio Alcántara, Aysegül Kahraman, Anosh Arshad Sundhu, Spyros Chatzivasileiadis
Title: CONDUCTOR: An LLM-Orchestrated Digital Twin for Uncertainty-Aware Distribution Grid Operations
Abstract:
Large language models (LLMs) are proposed as natural‑language interfaces to power system analysis, yet existing frameworks are validated almost exclusively on synthetic benchmarks and support only deterministic studies. We present CONDUCTOR, an LLM‑orchestrated digital twin for distribution grid operations. An open‑weights LLM orchestrates power system analysis and optimization solvers and, unlike prior systems, also performs uncertainty‑aware studies: probabilistic security assessment, robust corrective dispatch, and flexibility‑envelope and hosting‑capacity characterization. We test it on the Bornholm 60 kV distribution network ‑ a real Danish island power system ‑ using one year of smart‑meter measurements. An operator case study spans deterministic assessment, probabilistic risk quantification, and robust dispatch. Across a 68‑prompt behavioral catalog scoring tool use, evidence consistency, state‑mutation discipline, and refusal calibration, the orchestrator answers 98.5% of tasks correctly on the first attempt ‑ the lone failure being a missing answer, not a wrong one. The full pipeline is released open source.

Authors:Zehua Zhao, Rui Yan, Jianping He, Xiaoming Duan
Title: Multiplayer Reach-Avoid Differential Games with Defender-Side Information Delay
Abstract:
We consider a class of pursuit‑evasion games in which multiple defenders and attackers move in the plane with bounded speeds, while each defender observes the states of other agents with a constant time delay. For the one‑attacker‑one‑defender case, we derive an explicit analytical characterization of the attacker's delayed attack region and prove its convexity under mild assumptions. When the defender can guarantee capture, we formulate a convex optimization problem to compute the capture point and derive optimal strategies for both players. These strategies are shown to constitute a subgame‑perfect Nash equilibrium by exploiting the sequential structure induced by the information delay. The analysis is further extended to the one‑attacker‑multiple‑defender scenario and to the general multiplayer setting. In the latter case, delay‑aware pairwise winning relations are incorporated into a maximum matching formulation to address the defender‑attacker assignment. Numerical simulations for one‑on‑one, one‑vs‑multiple, and multi‑agent cases validate the theoretical results and illustrate the impact of information delay on game outcomes and optimal strategies.

Authors:Jona Klemenc, Alessandro Scagliotti
Title: Trade-off invariance for weighted scalarizations in multi-objective optimization
Abstract:
We consider weighted‑sum scalarizations for an abstract multi‑objective minimization problem defined by the vector‑valued map U\ni u\mapsto ( f_1(u),\ldots, f_N(u)), where U is an arbitrary nonempty set and no topology, convexity, compactness, or lower semicontinuity assumption is imposed. Using the open simplex as parameter space for positive weights, we show that the Trade‑off Invariance Principle for scalarizations yields a generic uniqueness property in the objective space. Namely, for almost every weight vector, all minimizers of the corresponding weighted‑sum scalarization have the same objective vector. Moreover, excluding again a null‑measure subset, all minimizing sequences determine the same limiting objective vector, independently of the chosen sequence. We also give a geometric interpretation of these results in the attainable objective set: for almost every positive weight vector, the scalarization exposes at most one nondominated point. Moreover, minimizing sequences determine at most one asymptotically exposed objective vector in the closure of the attainable set.

Authors:Ushnik Chakrabarti, Binoy Kumar Karmakar
Title: An Integer Linear Programming Approach for Maximum Power Extraction from Solar PV Plants under Partial Shading
Abstract:
Partial shading in solar photovoltaic (SPV) plants, particularly in urban environments, is a common challenge caused by nearby trees, buildings, or other fixed obstructions, leading to a significant reduction in overall system efficiency. Dynamic and static PV array reconfiguration strategies are widely regarded as effective approaches for mitigating the adverse effects of partial shading. However, Dynamic Array Reconfiguration (DAR) is rarely adopted in practical systems due to high switching complexity and substantial computational requirements. In contrast, Static Array Reconfiguration (SAR) does not require complex switching arrangements or additional computational resources, making it more suitable for real‑world implementation. However, SAR is a one‑time configuration and cannot adapt to dynamically changing shading conditions. Existing SAR techniques rearrange PV modules based on assumed shading regions rather than the actual shading pattern, which limits their effectiveness under practical, time‑varying conditions. In this work, an SAR technique is proposed that explicitly considers the actual shading pattern on the PV array. The proposed approach accounts for shading caused by nearby fixed obstructions that varies throughout the day as well as across different seasons. The performance of the proposed technique was evaluated by comparing it with existing methods considering a PV array with a square matrix, and a small‑scale laboratory prototype of non‑square matrix was developed to demonstrate its practical applicability in real‑world scenarios. It has been observed that the method consistently delivers an optimal power output for both software simulation and practical experiment compared to other available techniques.

Authors:Yuzhou Wei, Wenjie Liu, Yifan Xie, Frank Allgöwer, Jian Sun, Gang Wang
Title: Data-Driven Robust MPC for Unknown Nonlinear Systems via Set-Membership Learning
Abstract:
Data‑driven model predictive control (MPC) has become an attractive approach for controlling unknown systems, especially when data are corrupted by noise. However, most existing data‑driven MPC methods focus on linear systems, and little attention has been given to nonlinear dynamics under disturbances. To fill this gap, we propose a robust data‑driven min‑max MPC scheme for unknown nonlinear systems with process disturbances. We represent the unknown nonlinear dynamics using vector fields built from a dictionary of basis functions, yielding an equivalent linear form with unknown matrices. These unknown matrices are characterized by a set‑membership representation derived from noisy input‑state data. Using this uncertainty description, we formulate a min‑max MPC problem. Two online scenarios are studied: i) when state measurements are noise‑free, and, ii) when they are corrupted by process disturbance. For each case, we derive a Lyapunov‑based semidefinite program (SDP) to compute a stabilizing state‑feedback controller. The resulting schemes are shown to guarantee recursive feasibility and either exponential or robust stability of the closed‑loop system depending on whether there is process disturbance. Simulation studies on benchmark examples illustrate the effectiveness and competitive performance of the proposed approach compared to existing data‑driven and model‑based controllers.

Authors:Bideaux Eric, Mohamed Smaoui, Tardy Grégory, Aurore Lanoix
Title: Control Based Enhanced Regenerative Modes for Hydraulic Multi-Actuator Systems
Abstract:
This paper focuses on a control‑based approach for enhancing the regenerative capabilities of hydraulic multi‑actuator systems using individual metering valves. Thanks to this architecture, pressure and displacement of each actuator can be controlled nearly independently. By determining online, the right pressure to be driven, it enables the optimization of regenerative control strategies for resistive or driving forces. Globally, this control strategy behaves such as a load sensing approach but each metering valve is piloted in order to activate regenerative mode when it is allowable. The main contribution relies on optimizing the pressure to be controlled in each actuator and the main pump in order to maximize the regenerative capacity of a hydraulic machine while following a displacement. The effectiveness of the proposed approach is proved in simulation. Only a single pump line regeneration is explored here but extensions to multi‑pump or direct regeneration are also possible.

Authors:Senol Gulgonul
Title: From Stabilizing Regions to Certified Controllers: Closing the Selection Gap in Unified PID/PI Analysis for Time-Delay Plants
Abstract:
A recent unified treatment of PID tuning for time‑delay plants (An, Tang, Sun, Zhang and Chen, Automatica, 2026) combines the D‑partition method with a boundary gradient vector (BGV) to orient the boundaries of stabilizing, relative‑stability and stability‑margin regions. That method answers a feasibility question, namely where admissible gains lie, and it leaves a manual interior‑point test to fix the unstable‑pole count in each cell, with the choice of a single controller left to the user. This note makes three contributions. First, the one operation the BGV leaves manual, the absolute unstable‑pole count, is available analytically: exactly for delay‑free designs through a companion‑matrix or Routh count, and through an argument‑principle (Mikhailov) evaluation for retarded‑type delay loops. Labelling every cell with its analytic count removes the interior‑point test and decides the whole partition. Second, we add the step the BGV framework cannot reach, a time‑domain selection rule that returns one certified controller: among monotone step responses we choose the minimum‑settling‑time PI gains, characterized by a tangency condition, with monotonicity guaranteed by external positivity (a nonnegative closed‑loop impulse response). Third, we flag a neutral‑type pitfall that the unified analysis never delimits: an ideal PID with derivative action on a first‑order‑plus‑dead‑time (FOPTD) plant is of neutral type, with a root chain on the imaginary axis when k Kd = T. We reproduce the authors' delay‑free benchmark exactly, recovering both admissible Kp intervals, and demonstrate the full pipeline on a FOPTD plant, delivering a certified monotone, fast‑settling PI controller that the region‑only method can neither locate nor justify; the selected gains match an independent closed‑form tangency rule to within one percent. All claims are validated numerically.

Authors:Omid Akbarzadeh, MohammadHossein Ashoori, Mohammad H. Mamduhi, Abolfazl Lavaei
Title: Safe Packetized Control for Stochastic Constrained Networked Systems
Abstract:
This work develops a formal framework for the synthesis of packetized safety controllers for discrete‑time polynomial stochastic networked control systems (dt‑PSNCS) operating under communication constraints, including uplink delays (plant‑to‑controller) and downlink packet losses (controller‑to‑actuator). In this setting, the controller is deployed remotely and exchanges information with the plant over an imperfect wireless communication network. Our proposed approach treats the downlink channel as an erasure channel, with packet losses characterized by an independent Bernoulli process. To systematically manage both uplink delays and downlink packet loss, we first introduce a buffer collocated with the plant that accommodates the packetized safety control (PSC) mechanism. We augment the plant and buffer states into a unified augmented‑state representation that accurately captures the system evolution in the presence of communication imperfections. Our proposed framework synthesizes safety controllers based on control barrier certificates (CBCs), providing probabilistic safety guarantees that remain robust in the presence of both communication delays and packet losses. To achieve this, we reformulate the safety constraints as a sum‑of‑squares (SOS) optimization program, thereby facilitating the systematic construction of CBCs and their corresponding safety controllers. We validate the proposed framework through three (physical) case studies, demonstrating its effectiveness and practical applicability.

Authors:Chinmay Shetty, Soham Ghosh
Title: Resilient Substation Design for 500 Year Storm Events Current State of the Art and Challenges for Floodplain Management and Infrastructure Hardening
Abstract:
Electrical substations are increasingly exposed to non‑stationary flood hazards and extreme 500‑year storm events intensified by climate change. Approximately 15 to 20 percent of U.S. transmission and distribution assets, representing about 12,000 to 16,000 facilities, are located within FEMA 100‑year floodplains, with an additional 8,000 situated in 500‑year zones. These vulnerabilities have led to catastrophic outages, as demonstrated by Hurricanes Harvey in 2017 and Ida in 2021, resulting in billions of dollars in economic losses and significant grid instability. This paper presents a comprehensive engineering framework for the Resilient Substation of the Future, integrating site elevation, lime and cement‑based soil stabilization, articulating concrete blocks (ACBs) for erosion control, and green infrastructure strategies that align with northern American standards such as ASCE 24 Class IV, NERC CIP014, and FEMA Risk MAP standards. The framework extends traditional gray‑infrastructure approaches by incorporating flexible ACB revetments that reduce scour, improve stormwater quality, and support LEED sustainability objectives in heat island reduction and habitat restoration. When combined with pozzolanic soil stabilization methods capable of yielding resilient modulus gains of 10 to 20 times and UCS values approaching 600 psi under favorable conditions, the system enhances flood resilience against 0.2 percent annual exceedance probability events. Phased strategies are discussed and include deployable flood barriers, rapid dewatering systems, GIS‑enabled microgrids, and HEC‑RAS software‑based hydroclimatic modeling, collectively reducing lifecycle costs by up to 25 percent while maintaining operational continuity. Through reduced excavation, enhanced aquifer recharge, and self‑healing materials, this framework operationalizes resilience as adaptive, sustainable, and cost‑effective.

Authors:Furkan Sezer
Title: Distributionally Robust Joint Information and Mechanism Design for Multi-Area Power System Coordination
Abstract:
We study a continuous‑time stochastic Stackelberg control problem in which a leader steers a system of strategic followers through two non‑standard channels ‑ the information structure and a transfer mechanism ‑ rather than through the dynamics directly. The latent environment is a jump‑diffusion; the leader commits to a Gaussian public‑signaling channel whose belief consequences are tracked by a finite‑dimensional projection filter (the exact filter being infinite‑dimensional), together with a Groves transfer that aligns the followers' incentives. Under truthful disclosure, efficient behavior is a dominant‑strategy best response, and the induced differential game admits saturated and bang‑bang Nash feedback. We cast the leader's distributionally robust problem, over a relative‑entropy ambiguity neighborhood, as a two‑controller Isaacs equation; prove that incentive alignment collapses the bilevel Stackelberg problem to a single robust control problem with an exact first‑order condition; and characterize the value function as the unique viscosity solution, with a verification theorem valid for the non‑smooth bang‑bang feedback and a semiconcavity result that renders the switching set Lebesgue‑null. We instantiate the framework on resilient multi‑area power‑system coordination under extreme weather. Calibrated to the 2021 Winter Storm Uri, an Isaacs solve over ERCOT's near‑islanded interconnection (a 0.82 GW tie, under 2% of peak) shows mutual aid removes about 8% of social cost, rising to roughly 30% under the FERC/DOE‑recommended interregional transfer capability; a reserve‑scheduling experiment shows that public disclosure lowers welfare cost by 37% under autarky and 48% under market coupling, and that information design and market coupling are complements under common (systemic) risk.

Authors:Tina Dongxu Li, Mouhacine Benosman, Ken Meszaros, Trevor Dardik
Title: A Comparative Study of Bayesian Contextual Bandits for Real-Time Warehouse Sorter Optimization
Abstract:
Efficient sorter diversion control of automated material handling systems (MHS) is critical for optimizing operational efficiency in large‑scale warehouse environments. In this study, we use an inbound receiving sorter at a high‑volume e‑commerce warehouse as our primary use case, where the sorter diversion system relies on cost functions with static weight configurations that fail to adapt to highly dynamic system contexts, such as volume mode, congestion level, equipment physical status, and upstream/downstream dependencies. To address this real‑time sorter diversion optimization challenge, we conducted a comparative study of three candidate hybrid machine learning frameworks: Linear Regression with Gradient Descent Optimization (LR+GDO), XGBoost with Bayesian Optimization (XGB+BO), and Bayesian Contextual Bandits (BCB). Model training and evaluation were enabled by leveraging a high‑fidelity physics‑aware emulator to overcome the cold‑start problem and allow a safe transition from offline to online learning. We performed comprehensive evaluations including reward model predictive accuracy, contextual sensitivity, action distribution, and projected reward uplift. Our results demonstrate that while tree‑based reward models offer slightly better predictive power, the BCB framework achieved overall higher performance with 2.03% reward uplift over the heuristic baseline. Furthermore, BCB exhibits several superior characteristics, such as its decisive time‑optimal policy backed by Bang‑Bang control theory, continuous online learning capability, strategic balance between exploration and exploitation, and significantly shorter inference latency. These results demonstrate the potential of the BCB framework for real‑time control optimization in large‑scale warehouse environments, motivating further investigation toward operational deployment.

Authors:AmirHossein Naghdi, Ali Baheri
Title: Flow-Corrected Thompson Sampling for Non-Stationary Contextual Bandits
Abstract:
We study non‑stationary linear contextual bandits where the reward model drifts over time, rendering classical contextual bandit algorithms brittle because historical data becomes systematically biased. We propose Flow‑Corrected Thompson Sampling (fcTS), a Bayesian method that reuses experience by transporting past rewards to the present using an explicit drift model and incorporating each transported observation with a confidence weight that reflects transport reliability. This yields a unified template that specializes in (i) linear parameter drift via online slope estimation and reward correction, (ii) periodic variation via phase‑aware reuse across cycles, and (iii) recurring regime switches via changepoint detection and regime‑specific posterior memory. The resulting posterior updates remain closed‑form under a linear Gaussian model and can be implemented efficiently with truncated, incrementally updated sufficient statistics. Across five controlled case studies and a semi‑synthetic portfolio‑selection benchmark with multiple overlapping non‑stationarities, fcTS outperforms standard forgetting‑based baselines (discounting, sliding windows, and periodic restarts), with the largest gains in settings exhibiting recurring temporal structure. These results demonstrate that when non‑stationarity is structured, correcting and reweighting historical observations can be substantially more sample‑efficient than uniformly discarding them.

Authors:Sophie Hall, Kai Zhang, Ilia Shilov, Heinrich H. Nax, Saverio Bolognani
Title: Welfarist Control Design -- How to fulfill the societal mandate in multi-agent control?
Abstract:
At the core of most socio‑technical systems lies a scarce resource that is allocated among agents: highway lanes, public transit, road space, water rights, energy access, grid capacity, user attention, pollution rights, etc. With further automation of the underlying allocation processes, control engineers are increasingly tasked to make decisive assumptions regarding what society wants. In practice to date, design choices are largely driven by industry norms and conventions rather than a result of conscientiously responsible and ethical design. In this paper, we look at tools available to control engineers to design systems in a more principled manner in order to match the societal mandate. We consider three control design paradigms: online feedback optimization, control of Markov decision processes, and model predictive control. Beginning with aggregating individual agents' preferences into control design objectives, subsequently ensuring and certifying the fulfillment of those specifications, we argue that the feedback nature of control systems enables appropriate allocation of the shared resources in ways hitherto unparalleled.

Authors:J. Fermín Llorente, Javier A. Smidt, Pedro A. Roncagliolo, Ramón López La Valle
Title: A Kalman Filter-Based Tracking Loop Design for Real-Time Aerospace GNSS Applications with Minimum Pull-Out Probability
Abstract:
Kalman filter‑based (KF‑based) tracking loops are a powerful alternative to traditional phase‑locked loops (PLLs) for Global Navigation Satellite Systems (GNSS) signal tracking. The primary advantage of the KF is its ability to incorporate high‑fidelity models for receiver dynamics and clock errors, allowing the loop to adapt optimally to signal conditions. However, this theoretical optimality is often compromised in practice by the processing delays inherent in real‑time systems with hardware correlators, which existing KF formulations typically neglect. This paper introduces a Modified Kalman filter (mKF) that overcomes this limitation specifically for hardware‑based architectures. By reformulating the measurement update to be consistent with the processing delays, the proposed mKF maintains optimality in a practical implementation. We further present a systematic method for tuning both the process noise covariance matrix and the correlation time, based on an analytical expression for the pull‑out probability (POP), which is validated through Monte Carlo simulation. The mKF is then validated with a GNSS signal simulator, both by post‑processing baseband samples and on a real‑time GPS receiver with hardware correlators. A direct equivalence between the mKF and a one‑delay Digital PLL (DPLL) is established entirely in the digital domain. At equal noise bandwidth, the mKF matches the DPLL's phase error variance while achieving lower error in the higher‑order states. Moreover, the mKF sustains lock at bandwidths inaccessible to the optimal one‑delay DPLL under the same dynamic stress, positioning the proposed architecture as a robust and noise‑efficient solution for high‑dynamic aerospace GNSS applications.

Authors:Saksham Sharma, Shubhankar Gupta, Sumant A Gunagi, Suresh Sundaram
Title: Topological Online Learning for Displacement-based Formation Control
Abstract:
This paper addresses the problem of robust formation control by introducing Topological Online Learning for Displacement‑based (TOLD) formation control, a real‑time edge‑level adaptation framework. Unlike conventional node‑level robust controllers that regulate individual robot inputs without modifying the interaction topology, TOLD updates the interaction topology weights online to directly minimize formation distortion. Two strategies are proposed under the TOLD formation control framework: Online Gradient Flow (OGF) with unconstrained weights and Online Exponential Gradient Flow (OExpGF) with non‑negative convex weights. Theoretical analysis establishes that, for single‑integrator agents over directed graphs, OExpGF guarantees asymptotic consensus, while OGF ensures bounded formation distortion. Simulations with twelve robots under intermittent disturbances show 1.2%‑33.14% median cumulative Root Mean Distortion Error reduction when augmenting TOLD with node‑level controllers. Hardware experiments with Crazyflie 2.0 quadrotors demonstrate over 62% (OGF) and 31.4% (OExpGF) reduction in median formation distortion compared to fixed‑weight consensus.

Authors:Deepanjhan Das, Vishwesh Ramanathan, Shankar Narasimhan
Title: DISPCA : A hybrid iterative-sequential approach for the identification of errors-in-variables model of linear DAE systems
Abstract:
The dynamic behavior of numerous engineering processes is effectively characterized through differential‑algebraic equations (DAEs), commonly referred to as descriptor systems. While substantial progress has been achieved in identifying dynamic models governed by ordinary differential equations (ODEs), limited research has addressed the identification of descriptor systems from measured data. This work presents a systematic methodology for identifying the DAE model of a linear descriptor system in discrete difference equation form under errors‑in‑variables (EIV) setting, where both input and output measurements are corrupted by random noise. The proposed methodology generalizes the identification framework to handle scenarios where the system contains multiple algebraic and different ordered differential relations. The key innovation involves a partial stacking procedure of lagged data matrix with a sequentially increasing lag window that identifies all the differential relations individually. This is preceded by an iterative estimation of the measurement error covariance matrix that is diagonal and heteroskedastic, under large sample conditions. The algorithm simultaneously estimates the number of differential and algebraic relations, observability indices and delay parameters of the differential equations, and all the model coefficients directly from measured data without requiring prior specification from the user. The framework addresses the increased complexity arising from multiple dynamic coupled interactions while maintaining computational tractability through systematic decomposition of the identification problem. Effectiveness of the proposed methodology is demonstrated through several simulation studies.

Authors:Jasmine Jerry Aloor, Hamsa Balakrishnan
Title: Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridors
Abstract:
The use of dedicated corridors for Advanced Air Mobility (AAM) traffic is one of the most commonly proposed pathways to integrating them into existing airspace operations. Most prior research has focused on the design of networks of AAM corridors and conflict resolution for aircraft within corridors. It is also generally believed that while attractive from an implementation perspective, corridor‑based operations may be inefficient, especially in the absence of centralized traffic management. In this paper, we show that contrary to this belief, it is possible for autonomous aircraft to learn to self‑organize into corridor flows in decentralized settings. We illustrate our approach using scenarios in which fixed‑wing aircraft need to safely and efficiently traverse (1) a single corridor with metering after the exit, (2) a sequence of two consecutive corridors, and (3) a corridor that splits into two. We find that in decentralized settings with only local information, the aircraft are able to conform to the corridor boundaries more than 94% of the time and reach their goal in a relatively efficient manner. Furthermore, tactical interventions to handle violations of the separation minimum are needed only infrequently in low‑ and medium‑density settings. However, such tactical interventions become more frequently necessary only when traffic density is high.

Authors:Jiyong Lee, Erhan Kutanoglu, Michael Baldea, Ilias Mitrai
Title: Industrial electrification in the era of data centers: A Bayesian Optimization approach for grid-aware large load allocation
Abstract:
Large loads from industrial electrification and data centers are reshaping the planning and operation of the power grid. Identifying optimal large load siting decisions while accounting for transmission congestion is key to reducing expansion cost and operational risks. In this paper, we propose a leader‑follower bilevel optimization framework to identify optimal large load allocation strategies. The leader determines the allocation of large loads, while the followers determine grid expansion cost and transmission utilization. This modeling approach explicitly integrates strategic planning with detailed short‑term operational decisions. Moreover, we develop a Bayesian Optimization approach to efficiently solve the bilevel optimization problem by treating the followers as a black box. We use the framework to study large‑scale load allocation from electrified oil refineries and data centers on a synthetic power grid that resembles key characteristics of the Texas (ERCOT) system. The results show that these large loads compete for electricity, and under high‑load scenarios, data center demand is distributed across the entire grid, avoiding regions with high demand from industrial electrification.

Authors:Jafar Abbaszadeh Chekan, S. Rasoul Etesami, Cedric Langbort
Title: Regret-Guaranteed Safe Switching: LQR Setting with Unknown Dynamics
Abstract:
We consider learning‑based control in LQR setting, where the parameters associated with each mode are a priori unknown. The next mode to be activated is revealed online only at the time of switching. The objective is to determine both the switching times and the control gains for each mode such that (1) the norm of the system state remains bounded according to a prescribed criterion, and (2) the accumulated cost is minimized. To formalize the state‑norm requirement, we introduce the notion of (α,β)‑controllability for given parameters α and β. We first study the problem in a known model setting and show that, under the switching mechanism described above and under the assumption that each mode is visited infinitely often, the strategy that minimizes the average expected cost consists of applying, in each mode, the feedback gain obtained from the solution of the discrete algebraic Riccati equation, while selecting dwell times that sufficiently satisfy the controllability condition. We refer to this strategy as the benchmark policy. Next, we propose an algorithm for the unknown‑model setting that minimizes the regret, defined as the difference between the cumulative cost incurred by the online algorithm and that of the offline benchmark. By accurately estimating dwell‑time errors, our method achieves an expected regret of \mathcalO(|\mathcalM|^1/4 n_s^3/4 + n_m), where n_s denotes the number of switches, |\mathcalM| is the number of modes, and n_m is the number of malignant switches.

Authors:Ashutosh Balakrishnan, Junse Lee, Francois Baccelli
Title: Joint Visibility Analysis of RIS in Non-Terrestrial Networks through Stochastic Geometry
Abstract:
Non‑Terrestrial networks (NTNs) are a key theme in upcoming 6G communications, especially for ubiquitous coverage. Urban environments, comprising of high rise buildings often result in blocking the line of sight (LoS) path between the user equipment (UE) and the NTN base station (NTN‑BS). In this paper we investigate the situation where reconfigurable intelligent surfaces (RIS) are deployed on the building roof‑tops to ensure multi‑hop connectivity between the UE and the NTN‑BS. In such a scenario, it becomes crucial to statistically study the LoS visibility of the RIS from the UE as well as from the NTN‑BS, hence termed as joint visibility. In this work, accounting for the dual stochasticity arising from the locations of the RIS deployed buildings and the respective random building heights, we statistically study the probability of joint RIS visibility in a two‑dimensional (2D) scenario considering a deterministic location of the NTN‑BS. Further, we study the joint RIS visibility statistics conditional on the UE‑NTN link being LoS or non‑LoS. For the RISs deployed as a point point process (PPP) having exponentially distributed heights, the expected RISs jointly visible under the unconditional and conditional geometric settings are derived in closed form. Interestingly, in the 2D setting, the maximum expected RISs jointly visible, unconditionally, is twice the Basel number (π^2/ 6). The simulated results are analyzed over building density, average building height, the altitude and position of the NTN‑BS. We also illustrate probability heatmaps, demonstrating the strongest chance to have a RIS used conditioned on the system geometry. This study is expected to be useful in planning the deployment of RIS in urban areas, improving the signal and for assessing economic aspects.

Authors:Matthew Deakin, Xu Deng, Shafiq Odhano, Rahmat Heidari
Title: DC Link Capacitor Ripple Constraints Limit the Benefits of Utility-Owned Four-Wire Power Converters
Abstract:
Utilities are increasingly interested in power converters to increase the headroom of their assets by actively controlling power flows on their network. In this work we demonstrate that thermal limits of dc link capacitors can result in substantially diminished benefits of these converters under unbalanced operation, due to constraints on neutral current and double‑line frequency power ripple. Considering nine voltage source converter topologies with varying ripple capabilities, the upper bound (in terms of additional headroom released) increases by more than 80% compared to a no‑ripple case for the application of phase current unbalance mitigation.

Authors:Zhirui Liang
Title: Inference as Flexibility: Ramp Management for Transmission-Connected AI Data Centres
Abstract:
The rapid growth of large AI data centres introduces new operational challenges for power systems, including rapid ramping, oscillatory load behavior, voltage fluctuations, and supply‑demand balancing impacts. For example, the Alberta Electric System Operator (AESO) has identified transmission‑connected data centres (TCDCs) as large non‑conforming loads that may need to limit their point‑of‑connection ramp rates. Existing mitigation approaches mainly rely on exogenous electrical resources, such as battery energy storage systems (BESS). This paper presents a proof‑of‑concept demonstration of a complementary software‑defined mitigation layer: using flexible large language model (LLM) inference serving as endogenous TCDC flexibility to partially offset AI training power ramps. We consider a 150 MW TCDC with training, inference, and base‑load components. A measured LLaMA‑2‑70B fine‑tuning power profile is scaled to represent an aggregate training block, while measured LLaMA‑3.1‑70B inference power traces are used to model batch‑size‑dependent inference flexibility. Three strategies are compared: BESS‑only mitigation, batch‑size‑only control, and coordinated batch‑size plus BESS control. Simulation results show that the hybrid strategy reduces BESS discharge energy by 71% and peak discharge power by 51%, while maintaining near‑complete compliance with a 10 MW/min ramp limit.

Authors:Bo Wang, Miroslav Krstic
Title: Nonholonomic directional pursuit and evasion: Global feedbacks
Abstract:
In a recent paper by the second coauthor, directional pursuit‑evasion for strictly forward‑moving nonholonomic vehicles was solved "half‑globally", namely under favorable initial line‑of‑sight conditions. In this paper, we develop feedback designs that achieve the directional pursuit and evasion objectives from arbitrary initial relative configurations. We achieve globality with completely different approaches to both the design and the analysis. Our designs are less aggressive in both the forward‑speed and steering laws, allowing transient overshoot of the pursuer‑evader range during global reorientation. The only price that we pay for globality is that our feedback laws require a priori knowledge of the opponent's maximal turning rate, whereas in the half‑global work, no known bound of the opponent's turning rate was assumed. For the pursuit problem, the feedback law guarantees finite‑time capture with prescribed directional alignment. For the evasion problem, the feedback law guarantees capture avoidance with a prescribed safety margin and achieves spinaway under a decay condition on the pursuer's turning rate. We illustrate the global capture and spinaway with simulations. The analysis is based on an integral input‑to‑state stability type mechanism induced by an endogenous time dilation, together with finite‑time coextinction and safety‑margin persistence lemmas for singularly coupled scalar inequalities.

Authors:Cole Dickerson, Wahab Khawaja, Ismail Guvenc
Title: Adaptive 5G Resource Allocation for Multistatic ISAC-Based UAV Detection and Tracking
Abstract:
Unmanned aerial vehicles (UAVs) enable numerous commercial and public‑safety applications, yet they also create security risks near critical infrastructure, transportation hubs, and restricted airspace. While integrated sensing and communications (ISAC) can leverage existing wireless networks for UAV surveillance, practical deployment must address competition between sensing and communication demands, as well as the challenges associated with tracking highly maneuverable UAVs with low radar cross section (RCS). This paper investigates adaptive multistatic ISAC for load‑aware UAV detection and tracking in 5G wireless networks. A shared‑resource framework is developed to quantify how sensing waveform length, sensing transmission rate, and beam allocation affect communication throughput in a 5G new radio (NR) system. Detection performance is analyzed using Zadoff‑Chu (ZC) sensing waveforms, while tracking continuity is evaluated through an M‑of‑N detection model. To improve robustness under congestion, software‑defined sensor (SDS) nodes exploit external signals of opportunity (SoO) to provide supplemental passive sensing opportunities when network resources become limited. Results show that adaptive sensing policies outperform fixed sensing reservations by preserving throughput under dynamic load while maintaining useful sensing capability. Under heavy congestion, SDS assistance substantially reduces tracking outage in the simulated scenarios. Cramer‑Rao lower bound (CRLB) analysis demonstrates that multistatic sensing geometries improve localization accuracy and provide more uniform spatial coverage than monostatic sensing alone. These results highlight coordinated adaptive sensing and distributed multistatic support as a practical path toward resilient UAV surveillance in future wireless networks.

Authors:Jesus D. Vasquez Plaza, Yonghao Gui, Jin Dong, Jamie Lian
Title: Stability Enhancement of Centralized UPS Data Center Systems Under Weak-Grid Conditions
Abstract:
Data center power systems are increasingly exposed to weak‑grid conditions due to the evolution of modern power systems and the integration of large and dynamic loads. In centralized uninterruptible power supply (UPS) architectures, the front‑end rectifier plays a critical role in maintaining stable operation and ensuring reliable power delivery to information technology (IT) equipment. However, conventional phase‑locked loop (PLL)‑based proportional‑integral (PI) control strategies may exhibit degraded performance or instability under low short‑circuit ratio (SCR) conditions. This paper investigates the behavior of centralized UPS systems under weak‑grid conditions and demonstrates, through electromagnetic transient simulations, that PI‑controlled rectifiers can become unstable at SCR=2. To address this issue, a power‑based control approach is applied to the three‑phase rectifier, enabling direct regulation of active and reactive power without relying on PLL synchronization. Simulation results show that the proposed control strategy improves system damping and restores stable operation under weak‑grid conditions. The findings highlight the importance of control design for maintaining reliable operation of data center power systems in emerging low‑strength grid environments.

Authors:Prayag Sharma, Jonathan Y. M. Goh, Franck Djeumou
Title: Reference-Free, Long-Horizon Trajectory Optimization for Aggressive Autonomous Driving in Milliseconds
Abstract:
Autonomous vehicles must generate long‑horizon and dynamically feasible trajectories in real time‑even when operating at the limits of vehicle handling‑to ensure safe operation in adverse conditions. However, existing work rarely quantifies the computational demands of generating such trajectories without prior references, warm starts and often defaults to low‑fidelity models, compromising accuracy and control authority. We investigate the modeling and solver design choices that enable real‑time solution of long‑horizon, reference‑free optimal control problems (OCPs) using full vehicle dynamics. To this end, we analyze vehicle stiffness properties to justify the OCP's integration scheme and show that lower‑order A‑stable methods consistently outperform alternatives, with solve time differences reaching two orders of magnitude. We show that robust nonlinear solver performance hinges on understanding barrier parameter update strategies and safeguarding techniques for Hessian indefiniteness, inherent in some interior point methods. Lastly, we propose a computationally efficient method for generating initial guesses using dynamic equilibrium, unlocking real‑time performance and reducing initial infeasibility by up to four orders of magnitude. Extensive benchmarking and high‑fidelity BeamNG simulation demonstrate compute times as low as 55 ms over a 260 m horizon, including high‑speed obstacle avoidance scenarios where drifting emerges as a necessary component of feasible trajectory generation.

Authors:Ali Hosseini, Dragan Kostić, Hassan HosseinNia
Title: Revisiting the generalized first-order reset element with shaping filters
Abstract:
Reset control provides a nonlinear approach for improving closed‑loop performance beyond the limitations of linear time‑invariant controllers. However, the reset action inevitably introduces higher‑order harmonics, which may degrade tracking performance, distort the reset signal, and reduce the reliability of frequency‑domain predictions obtained via describing‑function analysis. This paper revisits the generalized first‑order reset element with shaping filters and develops a systematic framework for suppressing undesired reset‑induced nonlinearities. Analytical conditions are derived for shaping filter coefficients to increase the low‑frequency attenuation slope of the magnitude of the higher‑order sinusoidal input describing functions (HOSIDFs). By modifying the asymptotic attenuation behavior of these higher‑order harmonics, the proposed design provides stronger harmonic suppression in frequency regions where reset action is undesired, while preserving the beneficial first‑order harmonic phase advantage near the desired cross‑over frequency. The reduction in nonlinear behavior is verified through HOSIDF analysis and a superposition‑law test, demonstrating that higher‑order shaping filters make the reset element behave more closely to a linear system at a certain range of frequencies. Experimental validation on an industrial motion stage demonstrates improved tracking performance, reduced higher‑order harmonic content, and selective activation of the reset action in the intended frequency region.

Authors:Ali Hosseini, Dragan Kostić, Hassan HosseinNia
Title: Reliability Assessment and Performance Enhancement of Reset Control Systems
Abstract:
This paper develops a frequency‑domain reliability assessment framework for reset control systems. The closed‑loop higher‑order sinusoidal‑input describing function formulation is extended to explicitly include the reset‑triggering signal generated through a shaping filter. Based on this signal, two metrics are introduced: \(σ_t\), which quantifies reset‑time deviation, and \(σ_d\), which evaluates the tendency toward additional zero crossings. These metrics provide design‑oriented indicators for identifying potentially unreliable reset behavior. To improve reset‑triggering reliability, a first‑order shaping filter is proposed for a generalized first‑order reset element, increasing the low‑frequency attenuation slope of the nonzero higher‑order harmonics. The proposed analysis is evaluated on an industrial motion stage. The results show that the proposed metrics capture reliability issues that are not evident from the first‑order closed‑loop response alone and can therefore support the design of reset controllers with more reliable reset‑triggering behavior.

Authors:Ruixing Ren, Junhui Zhao, He Fang
Title: Local Conformity-Based Evolutionary Game Modeling of UAV Swarm Under Byzantine Attack
Abstract:
Leveraging their flexible and efficient deployment capabilities, unmanned aerial vehicle (UAV) swarms have been widely applied in various mission scenarios. However, the open communication environment also exposes them to the threat of Byzantine attacks. Most existing studies assume independent decision‑making by each UAV, neglecting that local conformity amplifies false information propagation. This paper constructs an evolutionary game model for UAV swarm under malicious attacks based on graph evolutionary game theory, revealing how local conformity rules govern the spread of deceptive strategies. Using death‑birth updating rules, we derive the macroscopic dynamic equation for the fraction of deceptive strategies and the analytical solutions to its evolutionary stable states. Sim ulations reveal observation errors weaken malicious induction, while higher proportions of malicious nodes and greater attack intensity drastically amplify attack impacts. Moreover, the model exhibits strong topological robustness across regular, scale‑free and random networks.

Authors:Maximilian Herrmann, Leander Pfeiffer, Paul Kotyczka
Title: Discrete Geometric Modeling and Extended State Estimation of Continuum Robots
Abstract:
In this paper, we present a fully discrete approach for the accurate and numerically efficient dynamical modeling and state estimation of continuum robots. The model is based on geometrically exact beams in a minimal, strain‑based formulation and derived in the framework of Lie group variational integrators, allowing to preserve important geometric properties that we exploit to achieve high accuracy and numerical efficiency. We then propose a disturbance observer based on an extended Kalman filter formulation that reliably estimates system states as well as model uncertainties and external disturbances. Experiments on a real system validate the accuracy and efficiency of the proposed model and observer.

Authors:Jason Cusati, Cheng-Shun Chuang
Title: Virginia Tech Transportation Safety Index (VTTSI)
Abstract:
The Virginia Tech Transportation Safety Index (VTTSI) is a real‑time, cloud‑native framework for quantifying intersection safety using multimodal connected‑vehicle telemetry and multi‑year VDOT crash history. Traditional crash‑based methods rely on lagged, aggregated data and cannot reflect rapidly changing operational conditions. VTTSI addresses this gap through a hybrid modeling approach that fuses Empirical Bayes (EB) crash stabilization, uplift factors derived from speed and conflict behavior, and a CRITIC‑weighted multi‑criteria decision‑making (MCDM) module combining SAW, EDAS, and CODAS. The system produces interpretable, exposure‑adjusted safety scores on a 0‑‑100 scale every 15 minutes. A cloud‑deployed architecture built on FastAPI, PostgreSQL, PostGIS, and Streamlit supports interactive visualization of traffic volumes, VRU exposure, speed variance, and real‑time incident activity. Validation across intersections demonstrates coherent diurnal patterns, consistency among MCDM methods, and sensitivity to observable operational turbulence. Sensitivity analysis further shows that the RT‑‑SI is robust to parameter perturbations, with deviations typically remaining below one point on the 0‑‑100 scale. By integrating long‑term crash risk with short‑term behavioral dynamics, VTTSI provides a transparent, adaptive, and proactive safety‑monitoring framework suitable for transportation agencies, traffic management centers, fleet operators, and autonomous vehicle systems.%~\citepersaud2007, montella2020systemic, schultz2025_surrogate, Amraji2025CombinedSafetyIndex.

Authors:Yuhao Huang, Novarun Deb, Hamidreza Zareipour
Title: AI Data Centers and Power System Sustainability: Understanding the Sustainability Implications of AI-Driven Data Centers on Power Systems
Abstract:
The rapid expansion of artificial intelligence (AI) has driven unprecedented growth in data center electricity demand. The scale and pace of this load growth carry significant implications for the sustainability of electric power systems. On the one hand, rapid, spatially concentrated data center load growth is outpacing clean energy deployment in several major regions, raising emissions and challenging both grid flexibility and reliability. On the other hand, this fast‑developing and capital‑intensive sector offers abundant opportunities to advance sustainability through clean energy integration and operational innovations. This article provides an overview of the mechanisms through which data center affect power system sustainability, underscoring both risks and the potential. Specifically, this article (i) characterizes AI data center load behavior and categorizes electricity supply configurations by function and sustainability profile, as well as situates these loads within global and regional electricity demand trends; (ii) analyzes sustainability impacts across short‑run operational and long‑run planning mechanisms, evaluates effects on grid carbon emissions and renewable energy utilization, and feasibility of offering system flexibility and participating in ancillary service; and (iii) evaluates real‑world corporate sustainability pathways and highlighting both the system benefits and feasibility limits of current carbon accounting practices. The goal of this work is to synthesize existing knowledge and technological developments and to guide research and development toward a more sustainable integration of AI data centers and electric power systems.

Authors:Lida Lamakani, Efstratios N. Pistikopoulos
Title: Towards Fewer Control Laws via Continuous-Time Multiparametric Programming
Abstract:
Multiparametric programming offers a powerful solution to the computational burden of solving optimal control problems repeatedly online. By solving the problem once offline, it yields the optimal control laws as explicit closed‑form functions of the initial system state, reducing online execution to a direct evaluation with no iterations required. Most existing works build this idea on a discrete‑time foundation, slicing the time horizon into intervals and applying KKT conditions to the resulting algebraic system. This discretization forces a tradeoff: too few intervals and the model fails to capture the true system dynamics, while too many cause the problem size, the number of decision variables, and the number of critical regions to grow rapidly, making both offline preparation and online lookup increasingly expensive. This work develops a multiparametric framework that works directly with the continuous‑time problem. Pontryagin's Maximum Principle (PMP) is applied without any model discretization, and the optimal control is recovered as an explicit function of the initial state. Compared to the discrete‑time formulation, the continuous‑time approach produces substantially fewer critical regions, and this number remains fixed regardless of accuracy requirements, since it reflects the structure of the problem itself rather than a discretization grid. The framework also yields the switching times as explicit functions of the initial state, directly exposing when and how the optimal control structure changes over the horizon. Knowing these switching times in advance allows the real‑time controller to skip unnecessary computations between them, further reducing the online execution cost. Results from a PAROC framework case study demonstrate that the continuous‑time multiparametric approach is a rigorous alternative to the conventional discrete‑time formulations.

Authors:Jung-Hoon Cho, Heling Zhang, Siqi Du, Roy Dong, Cathy Wu
Title: Formalizing Task-Space Complexity for Zero-Shot Generalization
Abstract:
Policies must operate across diverse conditions, yet a single policy is often conservative while fully adaptive schemes can be complex. We study zero‑shot generalization in contextual dynamical systems and introduce a performance‑centric, directional task dissimilarity‑‑the signed divergence‑‑that upper bounds the generalization gap from a source context to a target context. The signed divergence induces \varepsilon‑tolerance sets that certify when a source policy class generalizes, and it yields a concrete notion of task‑space complexity: the minimum number of source contexts needed so that every target context incurs at most \varepsilon generalization gap. Under a mild local smoothness assumption on performance, the induced tolerance sets admit certified inner/outer balls and instance‑dependent volume bounds on task‑space complexity. In the finite‑oracle setting, source selection reduces to set cover; a greedy strategy inherits the standard H(n) approximation guarantee. Using a Mass‑Spring‑Damper system with linear‑quadratic regulator (LQR) controllers and a nonlinear CartPole system with deep reinforcement learning controllers, we show that greedy selection achieves the same \varepsilon‑coverage with fewer policies than uniform or random baselines. Our approach delivers a performance‑based task similarity measure and practical certificates for building generalizable control with simple policies.

Authors:Reza Ghanavati, Behrooz Mosallaei
Title: Short-Term Electricity Demand Forecasting for New England Using a Hybrid Transformer-XGBoost Framework with Weather, Calendar, and COVID-19 Indicators
Abstract:
Accurate short‑term electricity demand forecasting is critical for reliable power system operation, energy market planning, and infrastructure optimization. This paper presents a hybrid framework combining a Transformer encoder for temporal feature extraction with gradient‑boosted decision trees (XGBoost) for daily electricity demand forecasting across New England. The framework integrates meteorological observations from six cities spanning all six New England states, calendar and holiday effects, autoregressive demand lags, and COVID‑19 epidemiological variables. Hyperparameter optimization uses Optuna with a multivariate Tree‑structured Parzen Estimator over 500 trials, with a leakage‑free 70/15/15 chronological train‑validation‑test split. The hybrid model achieves a test RMSE of 8,876 MWh, MAPE of 2.05%, and R‑squared of 0.906. A tabular‑only XGBoost baseline achieves RMSE of 9,304 MWh, MAPE of 2.21%, and R‑squared of 0.896. A Diebold‑Mariano test (Harvey‑Leybourne‑Newbold correction) confirms the 427.7 MWh difference is statistically indistinguishable from noise (DM = ‑1.126, p = 0.262). An ablation study reveals COVID‑19 features improved training accuracy but had asymmetric test effects: removal degraded hybrid RMSE by 3.2% while marginally improving XGBoost‑only by 1.2%. A SHAP temporal analysis shows 5 of 8 COVID features rank higher on the post‑acute test set than during pandemic‑active training, indicating the model over‑applies learned pandemic patterns. These findings establish temporal validity decay as a central mechanism: behavioral disruptions drove a strong COVID‑demand signal during 2020‑2021, but adaptation was complete by mid‑2022, leaving epidemiological features as noise amplifying overfitting to stale pandemic patterns.

Authors:Hongzhao Zheng, Mohamed Atia, Halim Yanikomeroglu
Title: A Metaheuristic Framework for Optimized HAPS-Aided Localization in Urban Areas
Abstract:
High‑altitude platform stations (HAPS), originally designed for communication services, can also provide structured signals of opportunity (SoOP) to augment the global navigation satellite system (GNSS). However, dense urban environments introduce severe blockage and non‑line‑of‑sight (NLOS) conditions that undermine GNSS accuracy and render geometric placement metrics insufficient. To address this, we propose a metaheuristic framework for jointly optimizing the number and placement of HAPS under practical constraints by integrating high‑fidelity 3D city models, ray‑tracing, and multi‑objective optimization to handle the discrete and highly non‑convex design space. Three metaheuristic solutions based on distinct search principles are developed to efficiently explore the solution space, all demonstrating rapid convergence and consistently outperforming a greedy baseline, particularly in the low‑to‑moderate HAPS regime. For representative dense urban scenarios, we show that four HAPS are sufficient to satisfy an 18‑m average 3D positioning error bound (PEB) threshold, while configurations with two to five HAPS achieve over 50% reduction in mean and root mean square (RMS) PEB and up to 94% and 87% reduction in standard deviation and coefficient of variation (CV), respectively, compared to the satellite‑only case. Diminishing returns are observed beyond six HAPS due to geometric redundancy, emphasizing the importance of optimized placement. The framework further demonstrates strong robustness and generalizability across diverse urban environments with varying building morphology and propagation conditions, establishing it as an effective and scalable solution for HAPS‑assisted localization in realistic urban settings.

Authors:Abdulmoneam Ali, Dipankar Maity, Ahmed Arafa
Title: Eigenspace-Based Clustering for Personalized System Identification
Abstract:
We study the problem of system identification in heterogeneous settings, where different systems may follow distinct underlying dynamics. Existing clustered system identification approaches often rely on iterative training‑based cluster assignment, which can be sensitive to learning uncertainty and model initialization. In contrast, we propose a one‑shot, training‑free clustering method that identifies similar systems using the structure of their locally observed data. Specifically, each system estimates a local state covariance matrix, and cluster identities are inferred by measuring the alignment between the leading covariance eigenspaces of different systems. We provide a mathematical interpretation of the proposed similarity score and develop a finite‑sample analysis that characterizes how covariance estimation error induces eigenspace perturbations in terms of the underlying system dynamics. We then derive a probability bound for pairwise false merges and a global clustering success guarantee. Numerical experiments demonstrate that the proposed eigenspace‑based clustering method effectively identifies systems with shared dynamics, leading to lower personalized model‑estimation error compared with training‑based clustering and non‑clustered baselines.

Authors:Yuchen Xia
Title: Integrating Large Language Model Agents with Digital Twins for Industrial Autonomous Systems
Abstract:
Industrial automation is being transformed by digitalization and the increasing use of cyber‑physical systems. Modern production environments require greater adaptability, faster reconfiguration, and more intuitive human‑machine interaction. However, traditional rule‑based systems rely on fixed logic and cannot autonomously adapt to changing conditions. Consequently, current automation systems lack a systematic approach for integrating adaptive and generalizable reasoning capabilities for interpreting, planning, and executing user tasks across dynamic environments and heterogeneous components. This dissertation proposes a three‑layer framework that integrates large language models (LLMs), digital twins, and automation systems into an autonomous system. Autonomy is defined as a design property assigned to system components and enabled through LLM‑based reasoning to achieve adaptive, goal‑oriented behavior. The Task‑Process‑Service‑Resource (TPSR) model is introduced to transform user tasks into executable processes. Four LLM roles are identified: process orchestration, service matching, digital resource generation, and agent‑as‑a‑service. Five peer‑reviewed studies develop and refine these concepts using the design science research methodology. Case studies and prototypes demonstrate adaptive task planning, event‑driven control, simulation‑based parameterization, and digital model generation. Results show high task executability, command correctness, and content‑generation accuracy while reducing manual effort. The framework enables the integration of LLM‑based reasoning into industrial automation systems and improves adaptability and usability. Limitations include dependence on accurate digital representations, the computational demands of LLMs, and the need for human intervention in safety‑critical situations.

Authors:Kristina Kehrer, Tim O. F. Conrad
Title: Mobility-Informed Coupling of ABM, PDE, and ODE Models for Pandemic Simulation in Germany
Abstract:
We present a hybrid modeling framework for simulating the spread of COVID‑19 across Germany. Our approach couples high‑resolution agent‑based models (ABMs) incorporating mobility data from mobile phones with faster, less detailed partial differential equation (PDE) and ordinary differential equation (ODE) models. Mobility between regions is incorporated through data‑driven jump processes that transfer individuals, enabling a balance between accuracy and computational efficiency. Building on earlier studies on pairwise ABM‑ODE, ABM‑PDE, and PDE‑ODE coupling strategies, we develop a hybrid model to unify all three model classes (ABM, PDE, and ODE) within a single framework. To demonstrate the framework's utility, we systematically compare ABM, PDE, and ODE representations of Berlin embedded in a nationwide simulation of Germany, investigating complete travel restrictions to and from selected federal states, and evaluating the Zero‑COVID and No‑COVID strategies. These experiments demonstrate how the framework can be used to analyze the interplay between mobility, regional coupling, and containment measures at the scale of an entire country. Computational performance is analyzed by measuring runtime savings while quantifying error using real‑world infection data. The presented framework enables efficient and accurate simulation of infection dynamics across densely connected regions and provides a tool for evidence‑based evaluation of public health interventions.

Authors:Zhen Qina, Dong-Fan Xie, Heng Ma, Xiaomei Zhao, Zhengbing He
Title: Platooning Connected, Autonomous, and Human-Driven Vehicles: A Deep Reinforcement Learning-based Approach
Abstract:
Conventionally, existing vehicle platooning approaches are designed for connected vehicles, typically including connected autonomous vehicles and connected human‑driven vehicles. Non‑connected vehicles, such as non‑connected autonomous or human‑driven vehicles, are not incorporated. As a result, these platooning approaches may not properly reflect real‑world mixed traffic conditions at the current stage. To address this limitation, this study proposes a hybrid platooning pattern that conditionally permits non‑connected vehicles to join platoons, thereby enhancing platooning diversity and flexibility. However, it was found that the unregulated integration of non‑connected vehicles can trigger rapid platoon expansion, significantly amplifying the risk of disturbance propagation in traffic flow. This, in turn, exacerbates the inherent conflict between traffic throughput and stability. To mitigate these challenges, this paper further develops a hybrid platooning control strategy based on deep reinforcement learning (DRL). This strategy integrates vehicle dynamics, platoon topology, and traffic flow states through a multi‑level state representation network, enabling a dynamic trade‑off between traffic capacity and stability. Numerical simulations demonstrate that the proposed strategy effectively suppresses velocity disturbance propagation by dynamically optimizing platoon structures, thereby significantly enhancing the stability and safety of mixed traffic while reducing fuel consumption and emissions.

Authors:Angan Mukherjee, Tyler A. Soderstrom, Michael J. Kurtz, Victor M. Zavala
Title: Topological Data Analysis for High-Dimensional Dynamic Process Monitoring
Abstract:
Real‑time process monitoring requires methods that extract actionable information from high‑dimensional time‑series data. In this work, we present a new approach for process monitoring that combines tools of topological data analysis (TDA) and machine learning. In the proposed approach, we represent multivariate time‑series data as manifolds and use topological descriptors to summarize the structure of such data; we then use a neural ordinary differential equation to learn the dynamic evolution of the topological structure of the system. Using real data from an industrial process, we show that this trajectory‑based event detection approach is effective at detecting diverse types of events. We contrast this approach against reconstruction‑based approaches such as principal component analysis and autoencoders and against a trajectory‑based approach that uses Koopman autoencoders.

Authors:Qian Zhang, Andrea Pomarico, Costas Mylonas, Magda Foti, Alberto Berizzi, Le Xie
Title: PowerAgentBench-Dyn: A Benchmark for Agentic AI in Power System Dynamic Studies
Abstract:
Large Language Model (LLM)‑based agents are increasingly being used to automate multi‑step engineering work flows by interacting with software tools, interpreting intermediate results, and autonomously planning subsequent actions. Power system dynamic studies represent a particularly promising yet largely unexplored application domain for these agents. Unlike static computational tasks, dynamic studies often require more time on model parameter calibration, engineering judgment, and decision making under constrained action spaces. This paper introduces PowerAgentBench‑Dyn, a benchmark designed to evaluate Agentic AI systems on power system dynamic‑analysis tasks. The benchmark targets problems that cannot be reduced to a single optimization or coding task, but instead require a type of reasoning, tool usage, and iterative experimentation routinely performed by experienced power system engineers. The proposed framework includes two initial benchmark tasks. The first, the Dynamic Model Quality Review Benchmark, evaluates agents' ability to validate and diagnose dynamic models based on model‑quality compliance criteria specified by system operators. The second, the Dynamic Security Risk Screening Benchmark, assesses agents' capability to leverage semantic memory and a limited simulation budget to identify, rank, and analyze the most critical short‑circuit contingencies from an unseen fault dataset, as well as propose and evaluate possible mitigation measures. For each task, we define the simulation environment, observation and action spaces, and evaluation metrics. The benchmark is reproducible in a metric‑based sense: released cases and simulator settings define a deterministic evaluator, while stochastic agent behavior is assessed over repeated runs using success rates and other metrics. The benchmark supports the development of future Agentic AI for power system operation and planning.

Authors:M. van der Hulst, N. Dirkx, R. A. González, K. Tiels, J. van de Wijdeven, T. oomen
Title: Sparse add-on controller design: A Youla approach to system-level performance
Abstract:
The performance of high‑tech systems is often dictated by a few performance objectives shared among the many closed‑loop controlled subsystems operating in the machine, such as synchronization, coordination, and alignment, which necessitates control methods that explicitly address them to achieve optimal performance. The aim of this paper is to introduce a framework that improves system performance through system‑level controllers designed to be implemented as add‑ons to the existing subsystem control structure. The developed method parametrizes all stabilizing system‑level add‑on controllers using the Youla framework, enabling a convex formulation of the sparse \mathcalH_2 synthesis problem. The result is a sparse add‑on controller that achieves the optimal trade‑off between combined performance and interconnection complexity, as demonstrated through numerical simulations.

Authors:Takumi Shinohara, Henrik Sandberg, Karl Henrik Johansson
Title: Data-Driven Control from Poisoned Data: Fundamental Limitations and Secure DeePC
Abstract:
We study a data‑driven control problem in the presence of arbitrary data poisoning attacks. We assume that a subset of offline output data is stored in unprotected locations and may be poisoned by an adversary. We first establish fundamental limitations for data‑driven control arising from such poisoned data: poisoning attacks are not detected/identified from the dataset alone; unprotected data are non‑informative for controller design with worst‑case guarantees; and hard constraints on unprotected outputs are not certifiable. Motivated by these limitations and the data‑enabled predictive control (DeePC) technique, we propose Secure DeePC, a data‑driven control algorithm that is resilient against poisoning attacks. It first runs output‑truncated DeePC using only the protected dataset until the online input becomes persistently exciting. It then uses online measurements to reconstruct the partial offline dataset, and finally returns to full‑output DeePC. Secure DeePC achieves MPC‑equivalent performance in finite time almost surely under certain conditions. Simulation results illustrate the efficacy of the proposed framework against poisoning attacks.

Authors:B Hari Kiran Reddy, Ge Chen, Junjie Qin
Title: Techno-Economic Analysis of Shared Mobile Storage for Demand Charge Reduction
Abstract:
This paper investigates the techno‑economic viability of shared electric vehicle (EV) fleets for demand charge reduction under practical logistical and operational constraints. Unlike idealized models that overlook transit overheads, we propose a high‑fidelity fleet management framework that explicitly accounts for the spatio‑temporal coupling of energy consumption, labor costs for EV drivers, and battery degradation. We formulate the dispatch problem as a mixed‑integer linear program (MILP) that jointly minimizes demand charges and total cost of ownership. To address the computational complexity arising from path‑dependent constraints, we develop a marginal‑value‑based heuristic algorithm that achieves near‑optimal performance with high computational efficiency. Using real‑world data from San Francisco, our analysis reveals that a modest number of EVs can achieve significant demand charge savings, sufficient to recover the ownership and operational expenses. Our results also show how tariff structures, fleet size, and cost components influence overall profitability.

Authors:Yi Lok Lo, Longhao Qian, Hugh H. T. Liu
Title: Contraction-based Neural Control for Cooperative Aerial Payload Transportation with Variable-length Cables
Abstract:
This paper presents a novel neural nonlinear control framework for a multi‑drone slung payload system with variable‑length cables and a rigid‑body payload. The equations of motion are formulated into a decoupled structure, where the payload and cable length dynamics are governed by independent control channels, facilitating modularized controller design on reduced‑order subsystems. A neural control contraction metric (CCM) controller and a neural feedback controller are jointly trained to enforce contraction conditions for the payload subsystem. Separately, a cable length control law is derived that exploits the variable‑length degree of freedom for obstacle avoidance. Numerical simulations demonstrate trajectory tracking of a rigid‑body payload and gate traversal capabilities of the overall system under the proposed control framework.

Authors:Nubius Brandner, Frank Hellmann, Hans Würfel, Jürgen Kurths, Anton Plietzsch, Anna Büttner
Title: Nodal Braess's Paradox and Inertia Destabilization with Dynamic Node and Line Failures in Power Grids
Abstract:
Large‑scale power outages are typically caused by cascading failures. These unfold dynamically through complex interactions between network dynamics and individual component failures. In contrast, the study of cascading failures in physics has focused on analyzing line overloads in the quasi‑static regime. We introduce a new model that integrates the dynamics of node and line failures with a paradigmatic oscillator model for power grid synchronization. This enables us to investigate the collective cascading behavior of coupled failures for the first time. We study the impact of nodal robustness, the ability of nodes to tolerate transient disturbances, and inertia, the ability of nodes to resist frequency deviations, on cascade sizes. We discover two novel mechanisms driving system fragility: i) While low inertia is widely considered a major challenge for power grids, we find that high inertia can amplify cascade sizes unless accompanied by appropriate adjustments of other dynamical properties. ii) Further, we find that an increase in the robustness of individual nodes can paradoxically lead to larger cascades. This latter phenomenon constitutes a novel type of Braess's paradox. Understanding such counterintuitive collective effects may become central for achieving resilient future power grids.

Authors:Jing-Zhe Xu, Zhi-Wei Liu, Ming-Feng Ge, Yan-Wu Wang, Dinxin He
Title: Semiglobal Input-Delay Tolerance Algorithm for Distributed Nonconvex Optimization of Networked Nonlinear Systems
Abstract:
This paper studies a class of distributed optimization problems in networked nonlinear systems (NNSs) subject to input delays and consensus constraints. It introduces input‑delay tolerant semiglobal convergence (IDTSC), meaning that for any prescribed compact initial set there exists an admissible delay bound under which the optimal solution is computed within consensus constraints and all node states converge to the solution. Building on a hierarchical design and input‑to‑state stability analysis, a new semiglobal input‑delay tolerant (SIDT) algorithm is developed that practically achieves IDTSC for distributed optimization under the coupling between input delays and nonlinear dynamics. Further, by relaxing strict convexity requirements through the Polyak‑Łojasiewicz condition, the SIDT algorithm broadens its applicability to nonconvex optimization. Finally, numerical experiments corroborate the theory on NNSs with input delays.

Authors:Aobo Wang, Aifei Xia, Zihao Wang, Lizhu Hao
Title: A Differentiable Composite Approximation Framework for Autonomous Underwater Vehicle Maneuvering Modeling from Sea-Trial Data
Abstract:
Field‑based modeling from onboard measurements can produce autonomous underwater vehicle (AUV) maneuvering models that reflect real operating characteristics. From an approximation perspective, conventional maneuvering models use predefined constraint polynomial bases, whereas data‑driven models use data‑adaptive bases. Motivated by this basis‑function view, this paper presents a differentiable composite‑approximation formulation, in which the polynomial‑basis component and the data‑adaptive basis component are treated as differentiable parts of a single predictor and calibrated jointly. A gradient‑based co‑calibration method is developed for full‑scale AUV maneuvering prediction, where a sensitivity‑aware mechanism regulates bounded polynomial updates while the neural residual captures remaining nonlinear discrepancies under a shared prediction objective. To account for ocean‑current effects in field data, a turning‑motion‑based current estimation and compensation procedure is incorporated to construct current‑compensated learning targets for training and rollout. The framework is evaluated using sea‑trial data collected from a 7‑meter AUV under multiple maneuvering conditions. Results show that the proposed method improves recursive trajectory and velocity prediction compared with polynomial‑only, neural‑only, and frozen‑prior hybrid baselines, demonstrating its applicability to field‑data‑based AUV maneuvering modeling.

Authors:Joong-Gil Kim, Wontae Ye, Geunwoo Cho, Seong-Ho Yun, Se-Hyoung Cho, Yong-Jae Kim
Title: Comparative Study on Agility, Efficiency, and Impact Absorption of Bipedal Robots with Active Toes
Abstract:
Human legs exhibit high efficiency, agility, and impact absorption, with toes playing a crucial role in these capabilities. While many attempts have been made to implement human‑like toes in robots, they have not fully replicated human characteristics nor rigorously validated their benefits. We propose a 14‑DOF biped robot emulating human toes' lightweight, high‑torque, robust nature. To quantitatively analyze the effectiveness of the active toes in terms of agility, efficiency, and impact absorption, we developed a high‑fidelity simulation training environment that reflects actual actuators with coupled transmissions and accurate power consumption. To ensure a fair comparison between configurations with and without active toes, we designed a minimal RL reward function and applied an identical training procedure to both. The simulation results indicate that, at 1.33 m/s walking, the toe‑equipped robot reduced CoT by 17.5% and heel‑strike GRF by 5.0% compared with the toe‑ablation configuration. On the agility test, average and maximum path deviation decreased by 25.0% and 34.0%, respectively.

Authors:Jayanth Bhargav, Mahsa Ghasemi, Shreyas Sundaram
Title: A Unified Framework for Joint Sensor Placement and Scheduling for Intrusion Detection
Abstract:
We consider an intrusion detection task in which a defender must jointly optimize sensor placement locations and orientations to minimize the probability of missed detection of an intruder traversing a protected environment. We decompose this problem into a meta problem, termed SensorPlacement, and an embedded subproblem, termed OrientationScheduling. The OrientationScheduling subproblem, for a fixed sensor placement, is modeled as a 2‑player zero‑sum game between the defender and the intruder, where the defender seeks an orientation strategy for the deployed sensors to minimize the probability of missed detection, while the intruder seeks a path selection strategy to maximize it. Since the defender's strategy space grows combinatorially with the number of sensors and orientations, solving the game via standard linear programming becomes prohibitive. To this end, we develop an iterative and efficient equilibrium‑seeking algorithm that exploits the structure of the game's payoff function and establishes theoretical guarantees for convergence to the Nash equilibrium (NE) of the game. This NE value is then used as a utility measure in the SensorPlacement meta problem. We show that this game‑value‑based utility function is weakly submodular over the set of sensor placements and propose a greedy placement algorithm with near‑optimality guarantees. To our knowledge, this is the first unified framework to integrate game‑theoretic utility design with (weak) submodular optimization, enabling principled joint optimization of sensor placement and orientation scheduling. Through extensive simulations, we demonstrate that the proposed approach achieves near‑optimal detection performance while significantly reducing computation time compared to baselines.

Authors:Quanyan Zhu
Title: Exit-and-Join Dynamics for Decentralized Coalition Formation
Abstract:
This paper studies coalition formation as a decentralized dynamical process driven by unilateral exit‑and‑join decisions. Agents evaluate local moves using the Aumann‑Dreze value, so payoffs are computed within the agent's current coalition rather than through a globally negotiated coalition structure. The resulting model links cooperative payoff allocation with noncooperative best‑response behavior: a terminal partition is precisely a coalition structure with no admissible, individually profitable exit‑and‑join deviation. We establish equilibrium characterizations, identify conditions under which the dynamics admit scalar Lyapunov or exact‑potential representations, and analyze how switching and acceptance costs shape local stability. Numerical experiments test finite‑time stabilization, cost sensitivity, and a special convex‑game benchmark.

Authors:Adeel Akhtar, Matthieu Barreau
Title: Learning Neural Maximal Lyapunov Functions on $\mathsf{SO}(n)$
Abstract:
Establishing stability guarantees for dynamical systems on Lie groups is a fundamental challenge, as classical Lyapunov methods developed for Euclidean spaces do not directly transfer to curved geometries. In this paper, we propose a framework for learning maximal Lyapunov functions for systems evolving on the special orthogonal group \mathsfSO(n). Theoretically, we introduce a neural Lyapunov architecture based on the logarithmic map with proven approximation capabilities, and we formulate the learning problem via a Zubov‑type characterization of the maximal region of attraction. A key technical contribution is the derivation of explicit, numerically tractable formulas for the derivative of the logarithmic map, enabling training through a two‑phase algorithm that balances computational efficiency and accuracy. Empirically, we validate the approach on a low‑dimensional nonlinear system.

Authors:H. Sinan Bank, Daniel R. Herber, Thomas Bradley
Title: AI4SE and SE4AI Exploration: A Decade Looking Back and Forward
Abstract:
The March 2020 INCOSE INSIGHT special issue on AI and Systems Engineering (SE) became the most downloaded issue in the publication's history and launched a research community that now draws over 250 registrants to its annual workshop. In this article, we trace the progress in AI and SE across three phases (labeled here foundational, applied, and LLM inflection) based on the authors' reading of the field's core papers, and describe our opinions of where the community has converged and where critical gaps remain. Separately, a human‑AI agreement literature review leveraging both human expertise and six AI models was performed to assess the relevance of 1,712 INCOSE INSIGHT articles and 889 SERC publications. The results identify five critical research gaps and offer guidance for practitioners navigating AI adoption, assurance, and workforce transformation in SE. We share the agreement data and the AI4SE/SE4AI Explorer web application so readers can compare their own relevance judgments with the human and AI raters.

Authors:Cong Chen, Valentina Norambuena, Lang Tong
Title: Ramping Procurement and Bid-Cost Recovery in Real-Time Market
Abstract:
We study ramping procurement co‑optimized with economic dispatch under net‑demand uncertainty. We examine two flexible ramp product designs implemented by grid operators: single‑interval and multi‑interval co‑optimization. Both rely on rolling‑window stochastic optimization with binding and advisory interval decisions. We develop analytical frameworks to evaluate generator profits, consumer payments, bid cost recovery (BCR), and operational efficiency. In particular, net‑demand uncertainty may lead to generator under‑compensation, requiring discriminatory BCR. While operational efficiency is invariant to energy and ramp prices, producer profits and consumer payments depend critically on pricing. We examine locational marginal pricing (LMP) and two uniform pricing: maximum dispatch cost pricing (MDCP) and maximum temporal locational marginal pricing (MTLMP). With out‑of‑market BCR, LMP yields discriminatory energy prices, whereas MDCP eliminates BCR and MTLMP does so in most cases. This property enables us to establish truthful bidding incentives for price‑taking generators under MDCP. Our analysis highlights trade‑offs between single‑ and multi‑interval co‑optimization and pricing designs: single‑interval energy‑ramp co‑optimization is advantageous under high forecast uncertainty and moderate ramping requirements, whereas multi‑interval co‑optimization is superior when net‑demand forecasts are relatively accurate and ramp needs are challenging. Empirical results on CAISO and ERCOT data show that MDCP and MTLMP increase producer profits with negligible BCR, albeit at the expense of higher consumer payments relative to LMP.

Authors:Xinpei Ni, Melkior Ornik, Glen Chou, Samuel Coogan
Title: Safe, Real-Time Active Model Discrimination and Fault Diagnosis for Nonlinear Systems via Differentiable Reachability
Abstract:
We present a safe, real‑time algorithm for active fault diagnosis and model discrimination for uncertain continuous‑time nonlinear systems with process and measurement disturbances. Given a finite set of candidate models representing nominal and faulty modes, including actuator and sensor faults, we formulate an output‑feedback, time‑varying policy optimization problem that (i) robustly enforces state‑input safety constraints over a finite horizon and (ii) drives the system to produce sampled measurements consistent with at most one model, enabling deterministic diagnosis. To solve this problem in real time, we develop a tractable approximation using interval over‑approximations of reachable state and output sets, and encode diagnosability via a differentiable objective that penalizes overlap between the reachable output sets of possible models. The resulting optimization is solved efficiently online with gradient‑based methods using JAX and differentiable reachability primitives. We evaluate our method on sensor and actuator fault diagnosis (up to 11 fault modes) in several high‑dimensional nonlinear robotic systems, including a simulated quadrotor and fighter‑jet model, a hardware differential‑drive robot, and quadrupedal navigation. Across these case studies, our approach achieves reliable model discrimination in under 50 ms, outperforming baselines in discrimination success rate and speed while providing formal safety guarantees.

Authors:Nanhong Liu, Mucun Sun, Jie Zhang
Title: GDGU: A Gradient Difference-based Graph Unlearning Method for Cyberattack Localization in Electric Vehicle Charging Networks
Abstract:
Electric vehicle charging stations (EVCSs) can expose distribution feeders to cyberattacks. While machine learning methods, including graph neural networks, can localize which bus is compromised, significant challenges remain in data sharing and model training. For example, privacy regulations grant EVCS owners the right to delete their training data from a deployed model, yet retraining from scratch on every request is computationally prohibitive. To address this, we study graph unlearning (GU) for EVCS cyberattack localization, formulated as a feature‑level unlearning problem on a graph‑level multi‑label classification task. Specifically, we propose gradient difference‑based graph unlearning (GDGU), which removes the influence of the requested deletion data through a first‑order parameter correction. The correction is computed from the gradient difference between the original training data and a modified dataset in which only the charging power features at the requested EVCS buses are unlearned. Then, a batch‑normalization recalibration and a brief recovery fine‑tuning step are applied to restore localization utility. We benchmark GDGU against two second‑order GU baselines on the IEEE 34‑bus, 123‑bus, and 8500‑node distribution networks across three graph neural network backbones and cumulative unlearning scenarios. GDGU matches the strongest baseline on localization utility and reaches forgetting fidelity close to full‑retraining, while unlearning 10 to 12 times faster than retraining from scratch and using far less memory than the second‑order GU baselines.

Authors:Bennett Dogbey, Hemanth Manjunatha
Title: pdSTL: Probabilistic Differentiable Signal Temporal Logic for Stochastic Systems
Abstract:
Autonomous robots operating in uncertain environments must satisfy complex temporal and safety specifications despite stochastic dynamics and sensing noise. While Signal Temporal Logic (STL) offers robustness measures for gradient‑based optimization, existing extensions either lack differentiability or ignore belief‑space uncertainty. We introduce pdSTL (probabilistic differentiable Signal Temporal Logic), a framework that unifies probabilistic semantics with differentiable robustness over belief trajectories. pdSTL employs interval‑valued probabilistic semantics to compute conservative satisfaction bounds, propagated compositionally through the STL syntax tree. We formulate the temporal robustness evaluation as a recurrent, LSTM‑style unfolding of STL operators, enabling linear‑time, differentiable monitoring suitable for end‑to‑end trajectory optimization. We validate pdSTL on simulated obstacle avoidance, lane‑change maneuvers, and real‑world Crazyflie quadcopter flight experiments under aerodynamic disturbances. Results demonstrate that pdSTL achieves efficient optimization with formal probabilistic guarantees, significantly outperforming deterministic differentiable STL in maintaining safety margins under real‑world uncertainty.

Authors:Bertrand Travacca
Title: ev-flow: A Reproducible, NHTS-Grounded Generator of Synthetic Plug-in Electric Vehicle Charging Behavior for Eight U.S. Regions
Abstract:
Electric‑vehicle grid‑integration studies need large, behaviorally realistic populations of individual charging profiles, but real charging telemetry is scarce and privacy‑restricted, and the existing open generators are calibrated to non‑U.S. mobility surveys or flatten the regional, seasonal, and equipment heterogeneity that drives aggregate demand. We present \textttev‑flow (import name \textttpev\_synth), an open‑source, MIT‑licensed Python package that generates synthetic plug‑in electric vehicle charging behavior for eight U.S. regions, grounded in 2017 National Household Travel Survey (NHTS) microdata and regional sales‑mix models. A deterministic nine‑stage pipeline (M1‑‑M9) carries each vehicle from survey records to a time‑stamped charging profile: it stitches survey person‑days into donor‑matched 365‑day travel calendars with a temperature‑dependent winter energy uplift, samples behavioral plug‑in start times from the published SPEECh K=16 Gaussian‑mixture parameterization, evaluates a three‑layer Bernoulli plug‑in model, propagates a continuous‑time state‑of‑charge ledger with an explicit PHEV gasoline range‑extension term, and rasterizes plug status to 15‑minute and hourly grids. The package generates residential and workplace profile types with descriptive EVSE brand and connector enrichment; every output is UTC‑stored, timezone‑aware, and bit‑reproducible from a single master seed. A validation runner compares the generated distributions against published bounds and classifies every divergence with literature provenance: the reference \textttbay\_area residential profile rolls up to 11 PASS, 0 unexplained FAIL, 6 explained failures, and 4 explained skips across 21 applicable checks. \textttev‑flow fills a U.S.‑focused, NHTS‑grounded niche complementary to European generators such as emobpy and VencoPy and to charging simulators such as datafev and ACN‑Sim.

Authors:Falak Mandali, Zijian He, Yan Gu
Title: Proprioceptive Invariant State Estimation for Humanoid Robots on Non-Inertial Ground
Abstract:
This paper presents an invariant extended Kalman filtering (InEKF) approach for real‑time state estimation of humanoid robots operating on non‑inertial ground using only onboard proprioceptive sensing. The proposed approach estimates the robot's base position and velocity relative to the moving ground frame without requiring direct measurements of ground motion or externally mounted sensors. By exploiting kinematic constraints at the stance foot through foot‑mounted IMUs, the filter accounts for ground‑induced nonlinearities in the process and measurement models while remaining fully proprioceptive. The estimator is formulated to admit a right‑invariant measurement model, enabling favorable error dynamics under large initial uncertainties. Observability analysis establishes conditions under which the robot's relative base position and velocity are observable with respect to the non‑inertial ground frame. Experiments with the Digit humanoid robot standing and squatting atop a swaying and pitching ground showcase a 96% speedup in convergence rate and an 80% reduction in position estimate errors over existing InEKFs. Walking experiments on a uni‑axially rotating ground achieve an average estimation error of less than 9 cm for an initial error of up to 1 m.

Authors:Ryan Walker Brown, Laura K. Treers, Kathryn A. Daltorio
Title: Simulating Robotic Locomotion in Sand: Resistive Force Theory in an Open-Source Physics Engine
Abstract:
Recent advancements in Resistive Force Theory (RFT) enable approximation of ground reaction forces for locomotion in sand without the computational expense of modeling interactions with individual grains. However, these tools have been absent in 3D physics engines commonly used for robot simulation. We explore if resistive force approximations are sufficient, when integrated with standard dynamics calculations, to provide a stable substrate for a freely walking robot. To determine this, we implement 3D Granular Resistive Force Theory (3D RFT) in a physics simulation engine, MuJoCo. We verify simulations in multiple scenarios to demonstrate that key trends due to end effector shape, speed, and loading are preserved. Our implementation predicts walking distance and foot sinkage of a 12‑Degree of Freedom hexapod robot within 20% of experiments in sand. While RFT has inherent approximations, the open source tool described here has potential to help develop new and improved robot designs to traverse granular media substrates.

Authors:Tessina H. Scholl
Title: A Lyapunov-Based Perspective on Absolute Stability
Abstract:
This article presents a unifying perspective on absolute stability concepts. In particular, it develops a Lyapunov‑like explanatory framework for a nonscalar circle criterion with its small‑gain and strict‑passivity special cases. To this end, a general defining inequality for a Lyapunov‑like function is proposed that avoids strict definiteness conditions, enabled by a strengthening of the sector constraint. We discuss different ways to derive a quadratic solution: via a linear matrix inequality (LMI), an algebraic Riccati equation, and a matrix equation. By exploiting the Kalman‑Yakubovich‑Popov (KYP) lemma, classical frequency‑domain results are recovered. A passivity‑index‑based result is derived that simplifies the evaluation. Overall, the presented interrelations may be useful for both analysis and teaching.

Authors:Ali Mahboub Rad, Roshni Anna Jacob, Bikash Poudel, Mayir Mamtimin, Jie Zhang
Title: Integrated physics-based modeling reveals a thermodynamic gap in small modular reactor load following
Abstract:
Small modular reactors (SMRs) are increasingly considered for flexible power generation; however, many dynamic studies still neglect the thermodynamic coupling between the primary and secondary loops that is essential for accurate assessment of load‑following capability. In this study, we develop a hybrid dynamic framework that couples an equation‑based model of the NuScale integral pressurized water reactor, including the reactor, primary loop, and moving‑boundary helical‑coil once‑through steam generator, with a physics‑based secondary steam cycle comprising the valve, turbine, condenser, and feedwater pump. This approach enforces mass and energy conservation across the coupled system while preserving physically consistent flow interactions across the domain boundary. The integrated model reproduces nominal design‑point conditions and is used to analyze a 5% step load rejection under five control strategies, including a decentralized three‑loop control architecture for the valve, feedwater pump, and control rods. The results show that partial control strategies are insufficient for efficient and safe operation, whereas simultaneous action of all three actuators stabilizes steam pressure, limits adverse thermal excursions in the primary loop and maintains acceptable steam generator operating margins during load‑following maneuvers. Compared with a conventional linear steam‑cycle representation, the coupled framework captures dynamic back‑pressure and variable turbine enthalpy drop that are otherwise neglected, leading to different predictions of transient behavior and required steam flow. These findings show that thermodynamically coupled, physics‑based steam‑cycle models are needed for more accurate assessment of the operational flexibility, efficiency and safety margins of SMRs under realistic load‑following conditions.

Authors:H. M. Sabbir Ahmad, Ehsan Sabouni, Emrullah Celik, Zean Wan, Damola Ajeyemi, Christos G. Cassandras, Wenchao Li
Title: A Mixed-Reality Testbed for Autonomous Vehicles
Abstract:
We propose a mixed‑reality, hardware‑in‑the‑loop (HIL) testbed for autonomous vehicles that seamlessly integrates a physical testbed of mobile robots with a high‑fidelity simulation environment. The virtual simulation enables the creation of diverse, safety‑critical driving scenarios to validate state‑of‑the‑art perception, planning, and control algorithms, while augmenting simulations with physical robots equipped with multimodal sensors in photorealistic virtual environments further facilitating rigorous validation. Our testbed also features vehicular connectivity using wireless communication and can accommodate a large number of agents through the combination of physical robots and virtual simulated agents, supporting research on multi‑agent systems including Connected and Autonomous Vehicles (CAVs). Finally, we present a safety‑guaranteed framework combining perception, planning and a novel online learning‑based controller using Control Barrier Functions (CBFs) for CAVs. Experiments using the proposed framework are used to validate and demonstrate the key functionalities and the overall utility of the testbed to bridge the gap between simulation and real‑world hardware deployment.

Authors:Thomas M. Kwok, Nicholas Koenig, Yue Hu
Title: Seeing Through Occlusion: Deterministic Arm Kinematic Correction for Robot Teleoperation
Abstract:
Markerless, single‑RGB‑D‑camera motion capture provides a low‑cost and non‑invasive alternative to conventional marker‑based systems for robot teleoperation; however, depth estimation often degrades in the presence of self‑occlusion, particularly during upper‑limb motion. This paper presents an Arm Kinematic Correction (AKC) method that improves depth estimation by enforcing geometric constraints based on constant arm lengths. The proposed approach reconstructs occluded joint depths by leveraging wrist positions and predefined arm lengths via a deterministic formulation based on the Pythagorean theorem, thereby avoiding the need for complex probabilistic modeling or parameter tuning. Experimental validation against a Vicon reference system demonstrates reliable performance for both static and dynamic joint motions, evaluated using root‑mean‑square error (RMSE) and Pearson correlation. Furthermore, motion‑mapping teleoperation is successfully demonstrated in both simulated and physical robot environments. The results show that AKC enhances robustness and preserves anatomical consistency under long‑duration, severe self‑occlusion, even when paired with less reliable temporal filters, highlighting its practicality for real‑time applications such as robot teleoperation and human‑robot interaction.

Authors:Maneesha Wickramasuriya, Beomyeol Yu, Jaden Shin, Mason Huslig, Taeyoung Lee, Murray Snyder
Title: Hardware- and Vision-in-the-Loop Validation of Deep Monocular Pose Estimation for Autonomous Maritime UAV Flight
Abstract:
Autonomous UAV operations on ships require reliable vision‑based relative pose estimation, yet at‑sea validation is costly, weather‑dependent, and risky. This paper presents a hardware‑validated vision‑in‑the‑loop framework that enables fully autonomous indoor flight while emulating photorealistic maritime environments. Rendered maritime views are processed onboard by a deep transformer‑based monocular pose estimator. Delayed vision measurements are fused with high‑rate IMU data using a delayed Kalman filter to provide consistent state estimates for geometric control. The system captures critical embedded effects, including perception latency, asynchronous updates, and computational constraints, that are absent in pure simulation. Autonomous takeoff, trajectory tracking, and landing experiments demonstrate stable closed‑loop flight. The results establish a safe and hardware‑realistic intermediate stage for developing maritime UAV autonomy prior to shipboard deployment.

Authors:Till Richter, Niki Kilbertus
Title: OrthoReg: Orthogonal Regularization for Hybrid Symbolic-Neural Dynamical Systems
Abstract:
Dynamical systems are fundamental to modeling the natural world, yet modeling them involves a persistent trade‑off: manually prescribed mechanistic models are interpretable by design but often overly simplistic and misspecified; in contrast, flexible data‑driven neural methods lack physical insight. Hybrid modeling aims for the best of both worlds by combining a prescribed or symbolic, physics‑based component with a flexible neural network. A critical challenge, however, is that the neural component may relearn mechanistic parts, yielding redundant and uninterpretable models, especially when the symbolic structure itself is discovered from data. Existing methods based on standard L^2 regularization rely on a projection argument that breaks when the symbolic component is learned through sparse discovery, allowing the neural augmentation to overlap with symbolic structure. We introduce OrthoReg (Orthogonal Regularization), which directly penalizes overlap between the symbolic and neural components, preventing symbolic structure from being absorbed by the neural residual. This yields a complementary decomposition: the symbolic part captures what the library can express, and the neural part captures what remains. On benchmark dynamical systems with partial library mismatch, OrthoReg improves symbolic recovery and out‑of‑distribution behavior.

Authors:Ali Peivand, Seyyed Mostafa Nosratabadi
Title: Byzantine-Resilient Federated Multi-Agent Optimization Framework for Cyber-Secure Interconnected Microgrids
Abstract:
The escalating digitalization of distribution networks has exposed interconnected Microgrid (MG) clusters to Stealthy False Data Injection Attacks that bypass Bad Data Detectors and propagate through tie‑line couplings and shared learning channels. This paper proposes BR‑FedMAPPO, a Byzantine‑Resilient Federated Multi‑Agent Proximal Policy Optimization framework that learns a triple‑surface Moving Target Defense and an adaptive isolation strategy for cyber‑secure operation. Each MG hosts a local Actor‑Critic Agent whose policy is partitioned into a globally federated shared encoder and a privately retained action head, so no MG exposes the configurations, cardinality, or locations of its D‑FACTS lines, Battery Energy Storage (BES) units, or tie‑line capacities. The action vector perturbs D‑FACTS reactances, redirects BES injections, reshapes inter‑MG exchanges, and includes a continuous islanding signal. A two‑stage Byzantine‑resilient aggregation rule combines trimmed‑mean filtering with reward‑weighted updates. This scheme incorporates a detection‑quality score based on the F1‑score and False Positive Rate to penalize clients causing false alarms. Simulation results on four interconnected MGs based on the IEEE 30‑ and 118‑bus test systems demonstrate effective mitigation of coordinated S‑FDI attacks, containment of cascading disruptions through adaptive isolation, and protection of distributed learning channels against malicious model manipulations while maintaining cost‑aware dispatch performance.

Authors:Hugo O. Garcés, Alejandro J. Rojas, Bernardo A. Hernández, Andrés Escalona, Jonathan M. Palma, Md. Rezwan Parvez, Bhushan Gopaluni, Sirish L. Shah
Title: Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems
Abstract:
This paper compares the performance of model‑free controllers on a nonlinear system under cyberattacks, including false data injection and denial‑of‑service attacks. Four RL reward types are analyzed for accuracy, cost, and resilience. Results show that the Lyapunov reward offers the best resilience with low tracking error. Exponential mode also provides good trade‑offs with acceptable resilience under moderate training conditions. Progressive and linear rewards converge faster but are less robust. RL‑MPCs show strong steady‑state resilience but require longer training times; RL‑PID controllers are faster with significantly less training time. Proximal Policy Optimization outperforms Deep Deterministic Policy Gradient with a significant reduction in KPI variance. This study serves to highlight how well‑designed RL rewards can improve performance and resilience against cyber threats.

Authors:Lorenzo Sani, Zeyu Cao, Meghdad Kurmanji, Alex Iacob, Andrej Jovanovic, Yan Gao, Wanru Zhao, Nicholas D. Lane
Title: FoMoE: Breaking the Full-Replica Barrier with a Federation of MoEs
Abstract:
Pre‑training Large Language Models (LLMs) typically demands large‑scale infrastructure with tightly coupled hardware accelerators. While increasing model and dataset scale remains the dominant driver of performance, Mixture‑of‑Experts (MoEs) architectures have recently achieved state‑of‑the‑art results by decoupling parameter count from computational cost. This efficiency enables training massive models on constrained compute budgets, yet it typically requires the high‑speed interconnects of a single datacenter. To overcome these physical limits, recent approaches such as DiLoCo and Photon use low‑communication data‑parallel methods to enable scaling across geographically distributed, weakly connected data centers. However, these methods suffer from a fundamental inefficiency: they require full model replicas at every site, which imposes prohibitive memory constraints and communication overheads. In this work, we introduce FoMoE, a system that breaks the full‑replica paradigm by partitioning expert layers across workers. We demonstrate that FoMoE: (I) reduces communication costs by up to 1.42x over efficient baselines and 45.44x over DDP via partial expert replication in the studied regimes; (II) achieves empirical throughput speedups of up to 1.4x through a novel skip‑token mechanism; and (III) shows stable routing in the trained proxy regimes and projects the communication/memory benefits to 100B‑scale configurations through system modelling.

Authors:Jacob R. Goodman, Hajg Jasa
Title: Riemannian Metric Preconditioning for Trajectory Tracking
Abstract:
We introduce a rank‑one Riemannian cometric update inducing a modification of the Riemannian metric that makes specific directions of motion cheaper to travel along. We establish basic completeness properties of this reward metric, and give an explicit characterization of its Levi‑‑Civita connection. We propose a preconditioned trajectory‑tracking strategy by adding the connection‑difference term to a standard intrinsic PD control, and illustrate the construction on a connection control‑affine system on the Special Euclidean group with a maze navigation experiment. When the nominal trajectory is an integral curve of the vector field used to define the reward metric, our methodology improves the overall tracking, which is demonstrated through simulation results.

Authors:Margarida Camarinha, Jacob R. Goodman
Title: A Generalized Sasaki Metric on the Second-Order Tangent Bundle
Abstract:
This paper constructs a connection map on the second‑order tangent bundle induced by a linear connection on the base manifold and uses it to define a generalized Sasaki metric. The associated geodesic equations are derived, and jet‑constrained variational problems are shown to yield Riemannian quintics in tension. The construction is then specialized to rigid body attitude dynamics with first‑order actuator dynamics, producing an intrinsic higher‑order trajectory model on the rotation group. Numerical simulations compare quintics in tension with Riemannian cubics as nominal trajectories and show modest reductions in actuator‑relevant cost with comparable tracking performance.

Authors:Carlo Novara, Mattia Boggio, Lorenzo Calogero, Michele Pagone
Title: A Finite-Gain Stability Approach to NMPC Design: the Extended Version
Abstract:
This paper proposes a novel approach to design of Nonlinear Model Predictive Control (NMPC) schemes based on Finite‑Gain Stability (FGS) concepts. The proposed formulation considers the case where the plant is affected by unknown but bounded disturbances, which renders difficult the classical Lyapunov‑based analysis/design. Based on FGS conditions for a closed‑loop system, we develop a systematic NMPC design methodology, allowing us to choose the relevant NMPC parameters that lead to closed‑loop FGS and provide a satisfactory tracking performance, also for the case of time‑varying reference signals. A simulated example is presented to demonstrate the effectiveness of our framework, concerned with lateral/longitudinal control of an automated vehicle.

Authors:Bojun Du, Xiaoyi Fan, Ershun Du, Long Chen, Jianpei Han, Qingchun Hou, Ning Zhang, Chongqing Kang
Title: From Tokens to Energy Flexibility: Quantization-Enabled Demand Response for Data Centers with LLM Inference Workloads
Abstract:
The rapid growth of large language model (LLM) inference is creating significant data‑center loads that face increasing energy‑management challenges under tightening grid conditions and demand response (DR) requirements. Conventional data‑center energy management mainly relies on temporal and spatial workload shifting and campus‑level energy asset scheduling, but it usually treats LLM inference demand as an aggregate load. As a result, these approaches fail to exploit the internal characteristics of LLM serving and therefore overlook the flexibility offered by LLM‑specific techniques such as model quantization. To unlock this flexibility, this paper proposes a quantization‑enabled energy management framework for grid‑responsive LLM inference data centers. First, a quantization‑to‑power model is established to map each model‑‑quantization configuration to a compact set of dispatchable parameters. Second, a two‑stage quantization‑enabled DR model is developed to account for model instance switching, request routing, and precision selection. Third, a multi‑campus co‑optimization method is introduced for DR participation by integrating grid‑side electricity and carbon signals with the quantization‑enabled DR model. Case studies show that the proposed framework reduces total data‑center operating cost by 34.3% without curtailing served token volume, validating model quantization as an effective flexibility lever for grid‑responsive LLM data‑center energy management.

Authors:Yibo Ding, Zhao Xu, Yuhong Zhao, Jian Zhao, Jiaqi Ruan, Zhaoyang Dong
Title: Bridging Data-Driven and Model-Based Methods: A Learn-to-Optimize Architecture for Distributed Optimal Power Flow
Abstract:
This letter proposes a learn‑to‑optimize (LTO) architecture for distributed optimal power flow (D‑OPF) as the nexus between data‑driven and model‑based methods. By unfolding alternating direction method of multipliers (ADMM) into a deep neural network (NN) and embedding differentiable optimization layers, our architecture realizes near‑instantaneous interpretable distributed decision‑making. For mainstream relaxed formulations of D‑OPF, the decisions from our architecture achieve comparable optimality with that of state‑of‑the‑art solvers and excelled feasibility compared with existing data‑driven approaches. Comparative case studies underpin the effectiveness of our architecture regarding the optimality and feasibility.

Authors:Chenchen Zhou, Jose Matias
Title: A Theory-Guided Advanced Regulatory Control Synthesis for Cooling-Limited Exothermic Semi-Batch Reactors
Abstract:
This paper studies theory‑guided advanced regulatory control (ARC) synthesis for cooling‑limited exothermic semi‑batch reactors, whose productivity and thermal safety are governed by changing active constraints. Industrial ARC uses feedback loops, cascades, selectors, feedforward/override logic, and valve‑position elements, but signal selection, pairing, interconnection, and tuning remain heuristic. Nonlinear model predictive control (NMPC) gives a systematic constrained‑operation workflow, but requires a maintained nonlinear model, state estimator, and online optimizer. We combine finite‑horizon minimum‑time optimality with local safety analysis to develop a systematic analysis‑to‑architecture ARC synthesis workflow for cooling‑limited semi‑batch reactors. Under stated assumptions, the workflow translates boundary‑seeking optimality into a cooling‑demand valve‑position‑control (VPC) architecture and translates local safety requirements into near‑boundary tuning rules. On a reduced benchmark and an industrial‑scale polymerization, ARC is nominally competitive with an implemented nominal‑model output‑feedback nonlinear model predictive control (OF‑NMPC) benchmark using extended Kalman filter (EKF) state estimation. In the studied adverse parameter mismatch and unmodeled fault scenarios, ARC keeps temperature‑limit violation at 0%, whereas OF‑NMPC either violates the limit or fails to complete the batch.

Authors:Costas Mylonas, Magda Foti, Andrea Pomarico, Matheus Duarte, Qian Zhang, Emmanouel Varvarigos
Title: PowerAgentBench-SS: A Benchmark for Agentic AI in Power System Steady-State Studies
Abstract:
Power system benchmarks usually evaluate numerical solvers, prediction models, or sequential controllers. These benchmarks are necessary, but they do not directly test whether a Large Language Model (LLM) agent can execute an engineering workflow: inspect a grid case, select tools, call simulators, screen contingencies, propose admissible mitigations, validate results, and produce an auditable evidence trail. This paper introduces PowerAgentBench‑SS, a steady‑state benchmark framework for evaluating tool‑using agents in power system operation and planning studies. The benchmark exposes public case data, action constraints, a tool API, and a validation budget to an agent, while a hidden evaluator recomputes physical validity and scores the submitted report. We define the agent interface, tool contract, evidence log, and risk‑sensitive metrics, including submitted recall, evidence‑backed recall, found recall, false‑safe penalties, severity regret, residual violation score, action cost, tool‑use efficiency, and workflow diagnostics. To make the framework concrete, we instantiate the protocol in a reproducible DC thermal N‑2 contingency‑search pilot on deterministic IEEE 39‑bus operating‑point variants, with scripted baselines, an LLM JSON‑command adapter, three locally hosted Ollama LLM agents, and one OpenAI API agent. The results show why solver‑only or answer‑only evaluation is insufficient: agents are distinguished not only by top‑contingency discovery, but also by validation‑budget use, explicit submission, type coercions, duplicate validations, evidence‑backed reporting, and mitigation behavior.

Authors:Bhargav Pavan Kumar Sistla, Vivek Natarajan
Title: LQR based stabilization of an 1D heat equation with advection and memory effects
Abstract:
We derive a one‑dimensional model for heat transfer in a moving fluid incorporating Fourier conduction, an exponentially decaying memory term, and advection under thermally insulated boundary conditions. We numerically construct a bounded state feedback law driving the closed‑loop solution to zero exponentially with decay rate at least ω>0 for every initial state, i.e., we solve the ω‑stabilization problem. We explicitly describe the eigenvalues of the state operator A, a subset of which converges to a finite negative accumulation point that sets the upper bound on the achievable decay rate. Since A lacks compact resolvent, we show that the spectrum is the closure of its eigenvalues, each of finite algebraic multiplicity, and use this to verify stabilizability. For ω below the accumulation bound, the problem is solvable provided the control operator B satisfies a non‑orthogonality condition. To compute gains, we formulate an LQR problem and solve finite‑dimensional approximations: for each n we construct A_n, B_n approximating A, B and solve the associated algebraic Riccati equation for a gain K_n. We show that, for all sufficiently large n, K_n can be chosen so every eigenvalue of A_n+B_nK_n satisfies \operatornameReλ<‑ω, and we establish stabilizability of (A_n+ωI,B_n) uniformly in n. Hence, for large n, these gains solve the ω‑stabilization problem for the original system. We validate the results numerically with an example.

Authors:Azim Akhtarshenas German Svistunov, Kuangyu Zheng, David Lopez-Perez
Title: Wind-Resilient Trajectory Optimization for UAV-BS Networks: TD3 for Continuous Service Availability
Abstract:
Unmanned aerial vehicle (UAV)‑mounted base stations are highly susceptible to wind disturbances such as gusts and turbulence, which induce positional drift and degrade communication link quality, particularly in emergency scenarios. To address this challenge, we propose a DRL‑based framework for wind‑resilient trajectory adjustment and positioning based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The method models wind as a stochastic kinematic perturbation, avoiding complex aerodynamic modeling, thereby enabling the TD3 agent to learn adaptive control policies that maintain optimal coverage footprints. By prioritizing user‑centric performance metrics under turbulent conditions, the proposed architecture ensures continuous service availability despite external disruptions. Simulation results demonstrate that the TD3‑based approach effectively compensates for wind‑induced displacements and outperforms benchmark methods, including Proximal Policy Optimization (PPO), in terms of throughput stability and robustness in windy environments.

Authors:G. Q. Bao Tran, Daniel Liberzon, Hyungbo Shim
Title: Stability of Slow-Fast Nonlinear Dynamics: Non-Periodic Case
Abstract:
We present sufficient conditions for the semi‑global exponential stability of nonlinear systems whose dynamics have both slow and fast time variations. Unlike most existing results, the fast variation is non‑periodic, thereby allowing a wider class of systems, especially switched systems with fast (non‑periodic) switching and those with quasi‑periodic variations; we therefore rely on general averaging to construct an average system. It is assumed that the average system admits a time‑invariant equilibrium that is globally exponentially stable when the slow variation is frozen, i.e., remaining at a fixed value. This slow variation is allowed to be discontinuous in time, provided its total variation (flows and jumps) is bounded. The main result is illustrated using a nonlinear switched system with slow‑fast non‑periodic switching.

Authors:Mustafa Alhassan, Amjad Iqbal, Peng Hu
Title: Constellation-Level Power Allocation for LEO Space-Based Solar Power
Abstract:
Space‑based solar power (SBSP) has recently gained renewed attention as an appealing technological advancement for providing continuous clean energy using space‑based infrastructure. However, the potential of low‑Earth orbit (LEO) satellite constellations for SBSP remains largely unexplored and lacks detailed simulation‑based studies. In this paper, we introduce a novel LEO SBSP system model and conduct a 24‑hour system‑level simulation of a Walker 4× 5 LEO SBSP constellation at an altitude of 450\,km, beaming 2.45\,GHz microwave power to eight ground stations (GSs) under a greedy allocation policy. The model includes orbital propagation, eclipse cycles, the satellite power chain, Goubau‑‑Brown beam coupling, ITU‑R P.618 atmospheric attenuation, and onboard battery dynamics. The results confirm that the peak DC power delivered reaches 1.986\,MW, while the mean per‑site delivery at the served GS ranged from 40 to 75\,kW. Two of the eight GSs received no service during the run, as their passes were consistently ranked lower under the greedy policy than competing links at the same step. The incident peak power density (PD) at the rectenna remained within the 3.35‑‑5.72\,W/m\textsuperscript2 range, below the International Commission on Non‑Ionizing Radiation Protection (ICNIRP) general‑public exposure limit. For a 20‑satellite Walker LEO at this altitude, realistic per‑site delivery is 50‑‑100 kW, and the rectenna should be sized to the operational incident PD of order 5,W/m\textsuperscript2 rather than to a Geostationary Earth Orbit (GEO)‑era 100,W/m\textsuperscript2 rating.

Authors:Han Zhou, Richard Bannister, Caspar Pierce, Haojie Chang, David Widen, Ludvig Fornstedt, Gabriel Melin, Alexander Bohlin, Pontus Lindeberg Fredriksson, Dilbagh Singh, Christian Fager, Koen Buisman
Title: Deep-Learning-Based Pixelated Microwave Filter Design and Characterization using Electro-Optical Electric-Field Measurements
Abstract:
Traditional microwave filter design typically relies on iterative parameter tuning and predefined topologies, which limits design space and increases development time. This study uses a deep learning approach combining convolutional neural networks with genetic algorithms to automate pixelated microwave filter synthesis. To validate the approach experimentally, both S‑parameter and spatial electric‑field measurements were analyzed. The synthesized low‑pass filter demonstrated excellent agreement between simulated and measured performance, achieving a 7 GHz passband with over 20 dB suppression beyond 9.5 GHz. Electro‑optical measurements, for the first time, revealed electric field patterns that resemble coupled transmission‑lines or stub structures, providing insight into the emergent characteristics of AI‑generated designs.

Authors:Han Zhou, Haojie Chang, David Widen, Christian Fager
Title: Deep Learning-Driven Inverse Design of Doherty Power Amplifiers Using Pixelated Combiners and Dual-State Impedance Synthesis
Abstract:
The output combiner of a Doherty power amplifier (PA) integrates load modulation, impedance matching, and phase compensation within a single network, making its design and synthesis highly challenging. In this paper, we propose a three‑port Doherty combiner design methodology that combines deep convolutional neural networks (CNNs), pixelated layout representations, and genetic algorithms (GA) with dual‑state impedance synthesis to address both peak and back‑off power conditions. As a proof of concept, two GaN HEMT Doherty PA prototypes incorporating three‑port pixelated combiners are designed and fabricated. Both prototypes achieve a measured saturated output power exceeding 44.2 dBm with peak drain efficiency above 71.2% within 2.6‑2.8 GHz. Furthermore, a drain efficiency as high as 64% is measured at the 6‑dB back‑off level. After applying digital predistortion, each prototype achieves an adjacent channel leakage ratio (ACLR) better than ‑51.3 dBc.

Authors:Rajasree Sarkar, Aditya Satish Patil, Arunava Banerjee, Ihsan Berk Altiner, Zongxuan Sun, Kenneth Kim, Chol-Bum Mike Keown
Title: Learning-Based Decision Making for Combustion Phasing Control in Multi-Fuel CI Engines with Latent Fuel Reactivity Estimation
Abstract:
Multi‑fuel compression‑ignition engines offer fuel flexibility but introduce uncertain, time‑varying fuel reactivity, represented by cetane number (CN), which complicates cycle‑to‑cycle combustion‑phasing control. This work formulates CA50 regulation under latent CN variation as a partially observable sequential decision problem and systematically evaluates controllers with increasing temporal and representational capacity, including LinUCB, history‑augmented contextual bandits, observation‑only DDPG, recurrent DDPG, and a proposed GRU‑guided RL framework. A Gaussian‑process surrogate trained on experimental multi‑fuel engine data provides a controlled and reproducible evaluation environment. Results show that myopic and fixed‑history bandit methods degrade under CN variation, observation‑only RL suffers from latent‑state aliasing, and generic recurrence is insufficient when CN evolves rapidly. The proposed framework learns a compact GRU‑based representation of fuel reactivity from combustion history and conditions both actor and critic on this estimated signal rather than oracle CN. By training the policy on the same imperfect fuel‑reactivity information available at deployment, the controller avoids train‑deploy inconsistency in conventional online estimate‑then‑control pipelines. Across unseen CN trajectories, the policy achieves stable CA50 regulation with mean absolute tracking error below 0.25° CA at the training setpoint, while producing smooth, physically consistent SOI and glow‑plug‑power actuation. These results show that combustion control under latent, continuously evolving fuel dynamics requires more than standalone estimation or generic recurrence. By aligning fuel‑reactivity inference with control policy learning, the proposed framework enables reactivity‑aware decision‑making using the same estimated state available during deployment.

Authors:Taha El Hajji, Lars Sjöberg
Title: Electromagnetic Characterization of Magnetic Ring: Case of Circular Cross-Section Shape
Abstract:
This paper introduces a comprehensive two‑dimensional analytical model of a toroidal magnetic ring with circular cross‑section under sinusoidal excitation. Applying Maxwell's equations in local polar coordinates within a complex permeability, the model derives analytical expressions for the internal magnetic field, magnetic flux, complex impedance, and total losses. It rigorously separates the contributions of eddy current losses, hysteresis losses, and winding losses, while explicitly incorporating the skin effect in the conductive core via Bessel functions. An expression for the apparent permeability is also provided, enabling the nonlinear core behavior to be mapped onto simplified linear material models. The resulting analytical model offers a computationally efficient and accurate foundation for standardized magnetic material characterization, such as Brockhaus and Iwatsu ring measurements, as a powerful alternative to 2D and 3D finite element analysis.

Authors:Ankita Samaddar, Sandeep Neema, Daniel Balasubramanian, Xenofon Koutsoukos
Title: Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents
Abstract:
With sophisticated cyber‑attacks becoming increasingly prevalent, modern networks require intelligent autonomous cyber‑defense agents trained via Reinforcement Learning (RL). These agents employ neurosymbolic approaches such as behavior trees with learning‑enabled components (LECs) to learn, reason, adapt, and implement security rules while maintaining critical operations. However, these autonomous networks are partially observable systems, i.e., the cyber‑attacker's (red agent's) actions are not observable, making it difficult for the defender to predict red actions, learn red policies, or assess the attacker's intrusion levels. To address this, we propose a Policy Learning Technique using imitation learning to learn policies for partially observable RL agents with discrete states and discrete actions. We apply this technique in an autonomous cyber environment to predict red agent's actions from network observations and defender actions. Integrated with a neurosymbolic cyber‑defense agent, our method effectively handles different red policies and achieves high prediction accuracy across diverse simulated scenarios.

Authors:Hao Liang, Cheng Tang, Yunzong Xu
Title: Finite-Time Queue Peak Laws in Stochastic Networks: Logarithmic Scaling After Geometric Thresholds
Abstract:
We study finite‑horizon queue peaks in generalized switches, a standard stochastic‑network model in which many queues share constrained service resources. Arrivals may be dependent, time‑varying, and adapted to the past; the standing load condition is uniform interior slack, meaning the conditional mean arrival vector stays in a fixed contraction of the capacity region. We show that this slack reshapes the finite‑time peak law for drift‑minimizing scheduling policies such as MaxWeight. The square‑root envelope that is sharp without slack persists only up to a geometry‑dependent threshold; beyond that threshold, the running maximum grows only logarithmically with the horizon, both with high probability and in expectation. The mechanism is self‑normalization: in the current queue direction, the projected fluctuation scale is normalized by the stabilizing drift scale. This removes capacity geometry from the logarithmic coefficient, while geometry remains in the threshold. Matching lower bounds show that both the logarithmic term and a geometric threshold are unavoidable. When finite‑time state‑space collapse is available, the threshold can be sharpened using local bottleneck geometry. For generalized input‑queued switches, we obtain finite‑time peak bounds with tight logarithmic coefficients. Simulations illustrate the two‑phase envelope, local geometric refinements, and variance‑sensitive improvements predicted by the theory.

Authors:Xiang Yin
Title: Decentralized Decision-Making for Finite-State Systems over Finite Alphabets is Undecidable
Abstract:
This paper investigates decentralized decision‑making for finite‑state transition systems, i.e., discrete‑event systems, under finite communication alphabet constraints. We consider a general decentralized observation framework in which a plant is observed by multiple local agents that transmit symbolic messages over a finite alphabet to a memoryless fusion center. The fusion center then produces a binary decision according to a prescribed fusion rule. We study the fundamental question of whether there exist local decision maps that enable exact reconstruction of a given regular specification language from decentralized observations. Contrary to classical results that rely on specific monotone fusion rules such as conjunction and disjunction, we show that the problem becomes undecidable even under a severely restricted information architecture: binary local decision alphabets and a fixed exclusive‑or (XOR) fusion rule. The proof is based on a reduction from the Thue word problem, a classical undecidable problem in rewriting systems. We further show that decentralized supervisory control, decentralized fault diagnosis, and decentralized fault prognosis are also undecidable under finite communication alphabets. Our results reveal that existing decidability results fundamentally rely on structural properties of fusion rules, in particular their monotone order‑preserving nature. In contrast, non‑monotone fusion rules such as XOR break this structure, leading to undecidability even in highly restricted settings.

Authors:Sebastian Schlor, Frank Allgöwer
Title: Verifiable computations for dynamic encrypted control
Abstract:
Encrypted control can preserve the privacy of data and parameters while the necessary computations can be outsourced to a cloud server. To ensure the integrity of the received values from the cloud, i.e., that they have not been changed, however, strong assumptions or verification algorithms are needed. Previous methods require computationally expensive cryptographic protocols or are only applicable to static computations. In this paper, we present a novel type of verification algorithm for linear dynamic encrypted control. We utilize system‑theoretic input‑output properties of the controller for artificial challenge signals, which are processed in the cloud in parallel with the requested control input, to check the correctness of the results at the plant. This results in almost no additional computational load, wrong computations are revealed with high probability, and no replay attacks are possible.

Authors:S. Perna, C. Lillo, A. R. Di Fazio, M. Russo, G. M. Casolino, P. Varilone, P. Verde
Title: Three-phase model of unbalanced distribution networks with DERs
Abstract:
Classical DistFlow equations for steady‑state distribution network analysis fail to capture the inherent imbalances of three‑phase systems arising from asymmetrical lines, loads, and distributed energy resources (DERs). This paper extends the classical power flow (PF) equations into a rigorous, non‑approximated three‑phase formulation, termed Dist3Flow. The proposed branch flow model (BFM) utilizes the real and imaginary components of nodal voltages and the active and reactive power flows as state variables. Lines are modelled by nonlinear forward and backward equations, while loads and DERs are represented via ZIP models and P‑Q control, respectively. By incorporating specific boundary conditions at the terminal nodes, the formulation generalizes PF analysis to both radial and closed‑ring topologies. The solution is obtained by using a backward/borward sweep (BFS) algorithm. The approach is validated against OpenDSS across various configurations, considering open‑ring and closed‑ring topologies with and without DERs.

Authors:Ueli Schilt, Curtis Meister, Philipp Schuetz
Title: Reducing Building Heat Demand Through Intelligent Control: A Comparative Simulation Study
Abstract:
Space heating remains the dominant energy consumer in buildings. While structural retrofitting can substantially reduce demand, it is often costly and time‑intensive. As an alternative, this study investigates the potential of intelligent heating control strategies to reduce heat consumption with lower investment and faster implementation. Previous studies have shown that replacing conventional heating‑curve‑based controllers with model predictive controllers (MPCs) can reduce heating energy demand. Whereas most studies compare MPC to conventional control, this work evaluates two MPC strategies with different control objectives and quantifies their impact on indoor temperature tracking and heating demand. A virtual residential building model was developed in Python based on ISO 52016‑1 to generate synthetic measurement data. A simplified resistance‑capacitance (RC) model was parametrised using this dataset and used as the internal model for two MPC strategies implemented in MATLAB. The strategies differ only in their optimisation objective: one minimises quadratic heating power, while the other prioritises indoor temperature tracking for thermal comfort. Simulations over six days show that both strategies satisfy comfort and system constraints, but differ in energy use and temperature variation. The comfort‑oriented controller achieves lower total heat consumption than the controller minimising heating power, which is attributed to the penalisation of high heating rates in the quadratic objective function. The results demonstrate the importance of objective function formulation in MPC design and show that high comfort levels can be maintained while achieving lower heating demand without structural modifications to the building envelope.

Authors:Dewan Mahnaaz Mahmud, Vinu Thomas, Bogdan Marinescu
Title: Model-Free Control for Multi-Time Scale Dynamics of Grid-Connected Power Converters
Abstract:
Controller synthesis in power electronics‑based systems depends predominantly on the mathematical model of the system, which is a limitation when the actual system is complex and the mathematical model cannot capture all its dynamics. Model‑free control addresses this limitation by using an ad‑hoc simple model which is compensated by high‑rate evaluation of dynamics in terms of their derivatives. However, application of the model‑free control strategy to power electronics‑based multi‑time scale dynamical systems is challenging because of the derivative action needed to implement such control. Grid‑connected power converters are examples of such systems, yet experimental validation has not been adequately addressed in the literature. This letter presents the validation of such control including the hardware implementation level. An intelligent proportional‑integral (iPI) controller is synthesized and validated on a 16 kW experimental test bench. This proves the benefits of the approach in control of grid‑connected power converters, among which their participation in the secondary voltage control.

Authors:Charalambos D. Charalambous, Stelios Louka, Photios A. Stavrou
Title: Information-Theoretic Meta Dynamic Programming for Signalling and Control of POMDPs
Abstract:
In this paper, we study the information‑theoretic characterization of simultaneous signalling and control over channels modeled by partially observable Markov decision processes (POMDPs). The problem is formulated as an optimization over randomized control strategies that maximize the directed information from actions to observations, subject to an average‑cost constraint. We derive a novel dynamic programming framework in which the state is defined on the space of conditional probability distributions, leading to a high‑level ``meta'' dynamic program. Specifically, we show that two coupled information states, namely, the posterior distribution of the system state and a distribution over such posteriors, satisfy Markov recursions and provide sufficient statistics for optimal control. This structure enables the decomposition of optimal strategies into separated randomized policies that depend only on these information states. Our results establish necessary and sufficient conditions for optimality and unify classical stochastic control and information‑theoretic formulations. In particular, we show that in the absence of signalling, the proposed framework reduces to the standard dynamic programming equations for POMDPs. The developed approach provides a principled foundation for analyzing and designing control systems with intrinsic information constraints.

Authors:Giusy Spacone, Sebastian Frey, Enzo Baraldi, Mattia Orlandi, Luca Benini, Andrea Cossettini
Title: A Wearable Multimodal Ultrasound+Inertial System for Real-Time Virtual Reality Interaction
Abstract:
A‑mode ultrasound (US) is a promising sensing modality for Virtual Reality (VR) interaction, as it enables the mapping of muscular activity into control commands while retaining the benefits of wearable sensing. However, existing approaches still face limitations in terms of wearability and interaction complexity, often relying on external hardware such as cameras. In this work, we propose a fully wearable multimodal interface for real‑time VR‑interaction, based on concurrent US and inertial (accelerometry) sensing from the forearm and upper arm. The system is built on the WULPUS platform and integrates an end‑to‑end software framework for real‑time acquisition, visualization, and communication with a Unity‑based VR environment. A multimodal learning pipeline is introduced for concurrent hand pose and forearm position estimation in 2D space. The interface is evaluated through offline and online experiments with five subjects, during the execution of three functional tasks: cylinder grasping (gross motor) and relocation, marble pinching (fine motor) and relocation, and liquid pouring. For offline experiments, we collect 5 acquisition sessions across multiple days, achieving an average inter‑session accuracy across subjects of 80\pm6% for hand pose estimation and 77\pm7% for forearm position estimation. Online validation with minimal fine‑tuning (5 min) demonstrates success rates of 92.0\pm16.0%, 88.0\pm9.8%, and 96.0\pm8.0% for the three tasks, respectively. With a power consumption of only 19.9~mW, our system enables more than 2.5 days of continuous use on a small 350 mAh LiPo battery without the need for recharge, enabling truly wearable, multimodal, and functionally meaningful VR interaction.

Authors:Yahli Drucker, Vitaly Shaferman
Title: Low-Thrust Orbital Differential Games with Speed Constraint Enforcement Using CostWeighting
Abstract:
This paper considers the problem of a low‑thrust spacecraft pursuit‑evasion differential game with an arbitrary terminal relative speed constraint. It addresses the terminal phase of the engagement for two relatively close spacecraft near a circular orbit. The problem is formulated as a linear‑quadratic zero‑sum differential game, with soft constraints on the terminal relative position and velocity, and running costs on the players' control efforts. An analytical, closed‑loop, minimum‑fuel‑consumption optimal guidance law is derived for each player, forming a saddle‑point solution. It is proven that any terminal speed can be achieved by properly choosing the weighting parameters of the cost function. To verify the optimality of the solution, a conjugate point analysis is performed when the cost function velocity weighting matrix is either positive or negative definite. The negative‑definite case arises at high terminal speeds and is seldom seen in the literature. The performance of the derived guidance law is evaluated in simulations for different target maneuvers and compared to a state‑of‑the‑art optimal‑control‑based guidance law. The simulations show that the derived guidance law satisfies the constraints and offers a substantial advantage over the optimal‑control‑based guidance law when the target is optimally evading.

Authors:Qiang Fu, Siqi Bu, Yang Wang, Mingyu Yan
Title: Dynamic Analysis of Centralized Energy Storage Systems -- A Comparison between Grid-following and Grid-forming Controls
Abstract:
This study investigates the small‑signal stability of centralized energy storage systems (CESSs) using grid‑following (GFL) and grid‑forming (GFM) controls, particularly focusing on bidirectional power flow and multiple energy storage systems (ESSs). To address the issue of complex dynamics in CESSs when comprehensive GFL and GFM control loops are considered, high‑order dynamics are simplified using the virtual damping method by focusing on the dominant oscillation mode. Damping analysis verifies that CESSs using a single‑type control (either GFL or GFM) have dynamic superimposition characteristics. Specifically, as ESS number increases, the damping of GFM‑CESSs improves but that of GFL‑CESSs decreases. The damping sensitivity shows that the damping of GFM‑CESSs is more sensitive to bidirectional power flow and all control loops, whereas that of GFL‑CESSs is more sensitive to d‑axis control loop. Consequently, GFM‑CESSs are preferred for large‑scale integration but are limited in scenarios with significant power reversal. If GFL and GFM controls are hybridized in CESSs, the ratio of GFM‑CESSs should be constrained to avoid instability from modal resonance between GFL‑CESSs and GFM‑CESSs. This highlights that implementing GFM‑CESSs necessitates considering scenario limitations rather than pursuing maximal integration under hybrid integration conditions. The conclusions are validated through modal analysis and time‑domain simulations.

Authors:Yifan Wang
Title: When Dynamics Models Read the Wrong Time Steps: Label-Free Event Credit Re-Anchoring for Robust Global Readouts
Abstract:
Learned dynamics models often answer global physical questions, such as fault severity or impact stiffness, by pooling a per‑step feature sequence into one readout vector. This sequence‑to‑global interface creates an under‑studied temporal credit problem: with only trajectory‑level supervision, a model can predict accurately in training conditions while reading from abundant smooth correlates rather than the brief physical events that determine the target. We call this failure temporal credit dilution. It is not exposed by the training loss and is not removed by standard physics‑informed residuals, because the error lies in where the global readout assigns functional credit. We introduce Credit‑in‑Event, an interface‑level probe for measuring how much pooled credit lands on event steps, and prove in closed form that a pooled linear reader routes credit to a spurious background channel as the event fraction shrinks. We then propose CREST, a training‑free and label‑free readout that estimates a transient event core from learned features and re‑anchors the pooled representation through event‑versus‑rest contrast. Across simulated gear and impact systems, recurrent and attention encoders, and public bearing vibration data, CREST reduces out‑of‑distribution error while restoring event credit. Ablations show that stable‑step selection and receptive‑field shrinking fail, confirming that the gain comes from event‑core credit re‑anchoring rather than a generic locality or stability prior.

Authors:Qiang Fu, Siqi Bu, Zijun Bin, Peng Li, Tong Wang
Title: Instability Caused by Integration of IBRs under Strong Grid Connections -- A Practical Case Study on Large-scale Energy Storage Systems
Abstract:
It has been well known that inverter‑based resources (IBRs) can lead to converter‑driven stability issues under weak grid connections. However, as the number of IBRs increases, instabilities can also occur even under strong grid connections. A practical case is presented to demonstrate this conclusion, using large‑scale energy storage systems (ESSs) as an example. In this study, the ESSs induce oscillations with a frequency of 150 Hz in the d‑q coordinates while providing both capacitive and inductive reactive power support (achieved by ESS functional control loops) to the connected power system. Theoretical analysis reveals that under strong grid connections, the dynamic interactions among power conversion systems (PCSs) of ESSs can be superimposed and intensified as the ESS scale extends, which reduces oscillation damping and leads to system instability. This indicates that ESS functional control loops also have potential instability risks when providing supports to power systems, which should be carefully examined. Finally, major impact factors are identified to mitigate the oscillations, and the conclusions are validated based on the SIMULINK platform. This paper provides valuable practical insights into system instabilities even under strong grid conditions, emphasizing the importance of functional control design and careful planning of the scale for IBR‑dominated systems.

Authors:Qiang Fu, Wenjuan Du, Siqi Bu, Haifeng Wang
Title: Stability Analysis in Large-scale Centralized Bidirectional Inverter-based Stations Connected to Bulk Power Systems through AC and DC Connections
Abstract:
Massive controlled DC resources (CDCRs), such as battery energy storage systems, are connected to AC power systems through bidirectional inverters for power balance requirements. This study investigates converter‑driven stability (CDS) issues in the sub‑synchronous frequency range caused by large‑scale bidirectional inverter‑based stations (IBSs). The impacts of the AC and DC connections of IBSs on subsynchronous oscillations (SSOs) are compared by examining three factors: the number of CDCRs, power flow direction, and control parameters of the inverters. For AC connections, IBSs may induce instability as the number of CDCRs increases, regardless of the power flow direction. To maintain stability, the maximum power amplitude of the IBS is calculated. It is found that switching to DC connections can reduce these instability risks if the DC line resistance is much less than the AC line reactance. Moreover, the method of tuning control parameters is demonstrated to be more effective in improving power‑related critical stability under DC connections. Therefore, The DC‑IBS is preferred for high‑voltage transmission. Finally, the conclusions are validated in power systems connected with both AC‑ and DC‑IBSs under various network topologies and system scales.

Authors:R. Spencer Hallyburton, Miroslav Pajic
Title: Anywhere, Any-Stymie: Remote Activation of Trojan Malware on LiDAR with Modulated Signals
Abstract:
LiDAR sensors are widely deployed in autonomous systems for 3D perception and safety‑critical decision‑making. We identify a previously unexplored attack surface in which dormant malware embedded in the LiDAR sensing pipeline remains inactive during normal operation and can be externally triggered after deployment, without requiring access to sensor hardware or networking at attack time. To operationalize this threat, we design malware capable of low‑level point‑cloud manipulation and embed it into LiDAR firmware. This malware was developed in a closed research test environment with vendor technical support, rather than by exploiting an inherent production supply‑chain vulnerability. To selectively trigger attack activation, we design and implement an optical trigger that remotely activates the malware by delivering a modulated signal into the sensing environment. Once triggered, the malware performs real‑time point cloud manipulation, and we demonstrate false object injection and real object suppression on static and mobile victim platforms. Our evaluation first establishes attack feasibility, including static operation at 300~ft and recorded drive‑by runs reaching 35~mph. We then illustrate quantitatively that injected person‑like artifacts can remain semantically detectable by a state‑of‑the‑art 3D object detector. Finally, we demonstrate multiple modes of safety‑critical impact on a deployed tactical autonomous vehicle. Together, these results highlight the need for stronger integrity guarantees throughout the LiDAR sensor development and deployment pipeline.

Authors:I-Ling Yen, Akeem Mohammed, Farokh Bastani, San-Yih Hwang
Title: OmniDroneX: An LLM-Assisted Holistic Drone-as-a-Service Ecosystem
Abstract:
Despite rapid advances in UAV technologies, current deployments remain limited due to several gaps in UAV systems research. To address these challenges, we propose OmniDroneX, a unified Drone‑as‑a‑Service ecosystem, in which drones are transitioned from fixed function platforms into dynamically composable entities that can be integrated with external infrastructures to offer omni‑capabilities. OmniDroneX bridges low‑level physical primitives with high‑level mission intent through a unified vendor‑agnostic interface (libUAV) and a formal physical‑service abstraction model (PT‑SOA). A core innovation is the diverse application of large language models (LLMs) across multiple layers of the OmniDroneX architecture. LLMs are used to assist in identifying and formalizing primitive device functions and abstract service definitions, supporting automated service composition and workflow generation, and enabling interactive, natural‑language mission specification and refinement. OmniDroneX also incorporates important categories of composition techniques that are essential in dynamic UAV systems, including physical layer composition for drone capability augmentation, as well as spatiotemporal, functional, collaborative, exception‑aware, and QoS‑based service compositions. Collectively, these features allow OmniDroneX to serve as a foundation for scalable, resilient, and self‑evolving UAV ecosystems operating in complex and dynamic environments.

Authors:Mahdieh Zaker, Andrii Mironchenko, Amy Nejati, Abolfazl Lavaei
Title: Data-Driven Stabilizing Controller Design for Linear Infinite Networks
Abstract:
We propose a direct data‑driven method for controller synthesis of infinite networks composed of unknown linear time‑invariant subsystems. Using a single set of noise‑corrupted input‑state trajectories collected from each subsystem, and provided that certain linear matrix inequalities hold, each subsystem is rendered exponentially input‑to‑state stable (eISS) by locally constructing an eISS control Lyapunov function together with an exponentially input‑to‑state stabilizing feedback controller. We then compose these local components under a compositional small‑gain condition in infinite‑dimensional spaces to obtain a global control Lyapunov function and an associated stabilizing controller, ensuring uniform global exponential stability of the infinite network. The approach is validated on a physical case study with unknown dynamics.

Authors:Ching-Yi Lin, Shamik Kundu, Arnab Raha, Sahil Shah
Title: ReRAM-aware Model Finetuning addressing I-V Non-linearity and Retention Errors
Abstract:
Traditional CPU, GPU, and NPU architectures are increasingly limited by the von Neumann bottleneck. While In‑Memory Computing (IMC) using ReRAM crossbar arrays offers a high‑density, energy‑efficient alternative, its practical deployment is constrained through their non‑idealities. Existing hardware‑aware training frameworks often require training from scratch, which is computationally prohibitive for modern large‑scale models. In this work, we propose a finetuning‑based hardware‑aware training algorithm that enables robust DNN deployment on ReRAM with minimal training overhead. Our approach mitigates I‑V non‑linearity by applying a range‑shrunk sinh transformation and incorporates retention errors directly into a regularization loss during the finetuning process. We evaluate our framework across models and tasks such as image classification and question‑answering (QA). Experimental results demonstrate that our method achieves similar accuracy on large‑scale models like ResNet18 and DeiT‑Tiny as the base model. In‑case of ImageNet for MobileNetV3 families the technique has only less than 2% accuracy degradation. Further, applying the technique on the SQuAD v2 dataset results in only 1 point degradation of F‑1 score.

Authors:Shiqi Zhang, Wuwei Wu, Jaemin Oh, Jie Chen, Xiaoning Qian
Title: Perron--Frobenius Operator Matching for Generative Modeling
Abstract:
We introduce Perron‑‑Frobenius Operator Matching (PFOM), a generative framework that matches density evolution via the integral PF operator, subsuming flow, diffusion, and jump models. We prove that among Bregman divergences, only Kullback‑‑Leibler divergence preserves equality between density‑level and sample‑conditioned objectives, yielding a practical loss equivalent to Koopman path matching. We further develop Nesterov‑accelerated training and sampling that stabilize discretization and accelerate convergence. %On Gaussian mixtures and two‑moons, PFOM achieves faster KL/W_2/MMD decrease and improved wall‑clock efficiency with empirical validation. PFOM unifies operator‑theoretic identification with modern generative modeling and opens paths to adaptive dictionaries and high‑dimensional applications.

Authors:Andre N. Costa, Petter Ögren, Carlos H. C. Ribeiro
Title: Agent Utilities over Generalized Voronoi Regions and their Gradients
Abstract:
In this paper, we generalize the concept of Voronoi regions, define agent utility as the integral of a utility density over the corresponding Voronoi region, derive gradients of the utility, and illustrate the approach in a two‑team example from soccer. The generalization of Voronoi regions is in the form of so‑called Cost‑Induced Voronoi (CIV) regions, where the agent state space may differ from the space being partitioned. One example of such regions is when the cost is given by the optimal solution of an LQR control problem. Then the agent states include position as well as velocity, while the partitioned space only includes positions. The agent utility is defined by integrating some utility density over the CIV region of the agent. This utility density might be the probability density of some beneficial event, such as receiving a pass in soccer. The utility is then the overall probability of receiving a pass and the gradient represents a way to improve that probability. We show how this utility gradient can be computed using the Reynolds Transport Theorem from fluid mechanics, and that this approach achieves similar accuracy while reducing computation time by about an order of magnitude compared to a baseline finite‑difference approximation.

Authors:Yue Guan, Dipankar Maity, Panagiotis Tsiotras
Title: Performance-Driven Environment Abstraction with Multi-Timescale Learning
Abstract:
We study performance‑driven environment abstraction for decision‑making in large Markov decision processes. Rather than preserving geometric or topological structure, we seek abstractions that directly optimize decision quality. We model abstraction as a controlled approximation obtained by aggregating the state space and enforcing a shared action distribution within each aggregated state. For a fixed partition, we establish a performance guarantee that separates value‑function approximation error from the loss introduced by action sharing. Guided by this analysis, we develop a multi‑timescale reinforcement learning framework that jointly adapts the policy and a tree‑structured environment abstraction. The resulting algorithm refines and coarsens regions of the state space based on Q‑value discrepancies, balancing performance against abstraction size and complexity. Empirical results demonstrate substantial state compression, improved sample efficiency, and faster replanning compared to actor‑critic baselines.

Authors:Cristian Puerto-Santana, Javier Diaz-Rozo, Carlos Puerto-Santana, Carlos Ocampo-Martinez
Title: Classifying Transient Regimes in Dynamic Systems through Properties of Spatial Curves and Stochastic Processes: A Data-Driven Approach
Abstract:
This article proposes a novel methodology for the classification of transient and stationary regimes in dynamic systems. Several sensor‑based solutions for regime classification in the literature require the setting of several parameters, or are not suitable for scenarios involving multivariate systems that may contain periodic signals. The proposed method introduces a spatial curve representation of the considered system based on its sample mathematical moments. Then, by connecting concepts of stability theory, geometrical properties of spatial curves and stationary stochastic processes, two regime classifiers are designed using the arc length and the curvatures of the proposed curve. Both classifiers are capable of describing and detecting transient regimes, considering behaviors such as: multivariate asymptotically, marginally stability, and cyclostationarity. Furthermore, a quantitative comparison in performance and computation resources of the proposed classifiers against existing classifiers in the literature illustrates that the proposed regime classifier based on the arc length outperforms other techniques in classifying transient regimes for simulated linear, non‑linear, and discontinuous multivariate systems under the specified studied conditions.

Authors:Daniel Flores, Yuanrui Sang, Michael P. McGarry
Title: Line Outage Impact Factor (LOIF): A New Sensitivity Factor for Enhanced Transmission Observability
Abstract:
Transmission failures can lead to cascading failures and system blackout affecting millions of customers if not handled in time, and choosing the best locations to monitor the condition of the transmission system is crucial for power system reliability. In this paper, we propose a new sensitivity factor, the line outage impact factor (LOIF), which is especially useful for power system monitoring and can reveal the impacts of a transmission outage on the power flow of other lines more effectively than existing sensitivity factors, such as the line outage distribution factors (LODF). In this study, we apply the LOIF in transmission line outage detection in three test systems and compare it with LODF using a number of observed transmission line (OTL) selection methods based on these two sensitivity factors. Then we apply a machine learning algorithm to detect the outages of other lines by monitoring the selected OTLs, and the detection accuracy is evaluated using the F1‑score. The results show that, in general, with the same number of OTLs, detection using the OTLs selected using LOIF achieved higher F1‑scores. The pattern was especially consistent in large‑scale systems, showing its potential in real‑world applications.

Authors:Mohammad Bajelani, Christopher A. Strong, Claire J. Tomlin, Jason J. Choi, Klaske van Heusden
Title: Robust Direct Data-Driven Hamiltonian for Safe Set Computation under Measurement Noise and Disturbances
Abstract:
Safe set computation is a fundamental challenge in safety‑critical control systems, especially in direct data‑driven settings where safety analysis is performed directly from noise‑affected measurements, without explicit modeling. A recently proposed method, Data‑Driven Hamiltonian (DDH), enables reachability analysis directly from measurements, without relying on prior knowledge of the underlying system dynamics. This paper extends the DDH framework to a robust setting that accounts for measurement noise, exogenous disturbances, and sampling‑induced state‑velocity estimation error. A Robust Data‑Driven Hamiltonian (R‑DDH) is derived from noisy measurements and shown to yield a certified lower bound on the exact Hamiltonian. This results in a provable under‑approximation of the value function and an inner approximation of the associated safe set. The gap between the data‑driven and exact Hamiltonians is quantified, and it is shown to converge to zero with more data in a noise‑free setting with additive disturbances. The effectiveness of the approach is shown through two case studies: a constrained double integrator and an aircraft taxiing system with a nonlinear closed‑loop controller operating under perceptual uncertainty.

Authors:Revital Frenkel, Vitaly Shaferman
Title: Optimal Powered Descent Guidance with Pyramid-Shaped Approach-Angle Constraints
Abstract:
In this paper, a novel optimal soft‑landing guidance law with inequality approach‑angle path constraints is analytically derived. The proposed guidance law prevents ground collision and enables approach‑angle control by constraining the optimal trajectory to remain within a convex inverted pyramid originating at the landing point. A 3D point‑mass linear kinematic model in a constant gravitational field is employed, together with a quadratic control‑effort cost and terminal constraints on position and velocity. Analytical open‑loop and closed‑loop solutions, together with the optimal final time, are derived using Pontryagin's Minimum Principle and the optimality conditions at the transitions between unconstrained and constrained arcs. It is additionally shown that the optimal final time decreases when the path constraints become active. The resulting guidance law is continuous, piecewise linear in time, and nonlinear in the states in closed‑loop. When a constraint becomes active, the controller cancels the gravitational component normal to the constraint, causing the trajectory to evolve along the constraint surface. The proposed guidance law is evaluated in simulations under various initial conditions, demonstrating accurate landing performance and consistent satisfaction of the path constraints.

Authors:Carlos Eduardo Sanoja
Title: Skill-Constrained Model Predictive Control for Resilient Manufacturing Supply Chains
Abstract:
In skill‑constrained production‑inventory systems, the qualified human capacity available tomorrow depends on training decisions made today: production requires certified workers, certifications decay unless maintained, and training consumes the same scarce worker hours that production needs now. We study a closed‑loop skill‑constrained model predictive controller that, at every shift, solves a finite‑horizon mixed‑integer program over production, inventory, backlog, and training, with binary predicted certification, hard production eligibility, and an interpretable terminal value that prices certified‑capacity gaps at the horizon boundary; only the first‑period action is applied before replanning. On synthetic, seed‑controlled SkillChain‑Gym scenarios ‑ announced and surprise new‑skill shocks, demand shocks, absenteeism, forecast‑ and availability‑quality modes, capacity‑boundary and training‑rate sweeps, and negative controls ‑ we evaluate the controller against production‑only and maintenance‑only ablations, static cross‑training insurance plans, and a strong reactive heuristic, under an ex‑ante locked configuration and paired statistics. The result is regime dependence, not superiority: no policy class dominates. Predictive control helps when skill or labor bottlenecks are forecastable early enough for training to complete; lean static insurance remains hard to beat under surprise shocks, near the demand‑capacity boundary, and wherever pre‑shock slack makes insurance cheap. Attribution ablations separate certification maintenance, re‑acquisition of lapsed certifications, and greenfield skill acquisition. Forecastability, not adaptivity per se, decides when predictive control pays.

Authors:Carlos Eduardo Sanoja
Title: SkillChain-Gym: A Benchmark for Reskilling-Aware Production-Inventory Control under Disruptions
Abstract:
Production planning increasingly has to treat workforce capability as a decision variable: certifications lapse when skills are not maintained, new products require skills the current workforce does not hold, and reskilling competes for the same worker hours needed for production. Existing operations benchmarks usually treat labor as exogenous, while workforce‑planning models with skills and learning are rarely released as reusable testbeds. We introduce SkillChain‑Gym, a benchmark specification for reskilling‑aware production‑inventory control: a single‑site environment with stylized worker skill‑state dynamics, hard threshold certification, forgetting, and capacity‑consuming training actions constrained by the same per‑worker time budget as production. The benchmark includes seed‑controlled disruption scenarios, three feasibility modes with projection diagnostics, deterministic replay, and metrics covering operations, resilience, capability growth, and training‑access distribution. We evaluate production‑only, reactive adaptive, water‑filling adaptive, and static‑insurance policies with budget variants over 60‑shift horizons with paired statistical tests. The results are regime‑dependent rather than a ranking. Training‑capable policies dominate the production‑only baseline, and maintenance training is necessary under forgetting even without disruptions. Among training‑capable classes, adaptive training helps when bottlenecks are visible in the forecast, while a lean static cross‑training plan, a deliberately favorable comparator whose structure encodes relevant skill contingencies, acts as strong insurance under surprise shocks and absenteeism. Capacity slack and the forgetting rate govern the boundary between these regimes. No policy class dominates across regimes, motivating forecast‑driven controllers that decide when to buy skill insurance and when to react.

Authors:Aditya Mishra, Haroon Lone
Title: Beyond Benchmarks: Continuous Edge Inference for Fine-Grained Roadside Perception
Abstract:
Continuous AI inference on resource‑constrained edge hardware introduces deployment effects that are largely invisible to conventional benchmark evaluation, including temporal instability in streaming video, thermal throttling under sustained load, and workload‑dependent performance variability. We present Edge‑TSR, a deployment‑oriented continuous edge inference system for sustained roadside perception on the NVIDIA Jetson Orin Nano. Edge‑TSR integrates detection, tracking, fine‑grained classification, and a lightweight track‑aware temporal stabilization mechanism that improves streaming inference consistency with negligible computational overhead. Our central finding is that benchmark‑centric evaluation systematically overstates deployed edge inference performance. Across three state‑of‑the‑art baselines, we observe consistent 20‑30% relative degradation when transitioning from static‑image evaluation to real‑world streaming deployment. Edge‑TSR addresses this gap through temporal inference stabilization, recovering up to 10.16% classification accuracy over per‑frame inference baselines while maintaining sustained real‑time performance under continuous operation. We evaluate the complete system under diverse real‑world deployment conditions, jointly characterizing inference quality, latency, throughput, and thermal behavior during long‑duration operation. A 55‑minute vehicular deployment over a 26 km route demonstrates sustained operation at 16.18 FPS within safe thermal limits on a single embedded device without cloud offload. Our findings show that deployment‑aware evaluation and temporal inference stabilization are necessary components of continuously operating edge AI systems intended for real‑world sensing deployments. We release a sample annotated streaming video evaluation dataset and full system implementation to support reproducible deployment‑centric evaluation.

Authors:Shaun SWeeney
Title: A Stateful Stochastic Allocation Mechanism with Fairness Guarantees for Networked Electricity Systems
Abstract:
This paper develops and analyses the Fair Play Automatic Market Maker (FP‑AMM), a programmable electricity allocation mechanism in which scarcity allocation is treated as a controlled, stateful, and auditable cyber‑physical process. Existing mechanisms such as locational marginal pricing are memoryless and cannot account for historical service outcomes, preventing guarantees of equitable treatment across market intervals. The FP‑AMM employs a two‑stage stochastic clearing rule comprising service‑priority sampling and inverse‑fairness weighting, coupled with a DC‑OPF feasibility set and bounded shortage memory updated through a saturated integrator. Four main results are established. First, the shortage‑memory state is invariant in [0,1]^N and the update map is a contraction with rate 1‑β. Second, the intra‑interval clearing operator converges linearly to a unique fixed point with contraction factor q\in(0,1). Third, under the Fair Play priority rule, the per‑node delivery ratio converges almost surely to the contracted target F^\star, with a finite‑time O(1/\sqrtT) bound obtained via Lyapunov analysis of the deficit recursion. Fourth, event‑triggered execution guarantees practical ultimate boundedness of the allocation tracking error and quantifies the computation‑fidelity trade‑off. The mechanism is validated on the IEEE 14‑, 57‑, and 118‑bus systems over T=5000 market intervals. Fairness convergence to F^\star is achieved on all benchmarks, peak weak‑bus fairness error is reduced by 54% on the IEEE‑57 network and by up to 55% relative to an equal‑weight baseline during scarcity periods, and DC feasibility is maintained throughout.

Authors:Yuto Watanabe, Yang Zheng
Title: On the Strong Duality in Continuous-time and Discrete-time Linear Quadratic Regulators
Abstract:
This paper revisits the strong duality in the linear quadratic regulator (LQR) for continuous‑time and discrete‑time systems, and explores its interconnection with typical assumptions and the uniqueness of primal‑dual solutions. Using a linear operator Ψ, we formulate a common nonconvex LQR problem that captures both time domains. We then derive its Lagrange dual problem and establish the strong duality via a rank‑constrained tight semidefinite program (SDP) relaxation. Further, we show that the primal‑dual optimal solutions to the SDP relaxation, after dropping the rank constraint, recover the classical algebraic Riccati equations and optimal feedback gains in a constructive manner. The dual derivation and strong duality analysis rely on mild standard assumptions and exploit the properties of the linear operator and its adjoint, revealing a structural symmetry between the two time domains.

Authors:Meet Udeshi, Md Raz, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami
Title: Sandbox-Enabled Digital Twin for Cyber-Physical Systems
Abstract:
Firmware/software in cyber‑physical system (CPS) embedded devices/controllers can have vulnerabilities stemming from multiple sources such as weak security practices, outdated libraries, or supply chain attacks that induce adversarial effects under plant state‑based triggers. However, pre‑deployment validation of CPS controllers typically relies on digital twins that model controller logic as a black box. On the other hand, side channel monitoring and anomaly detection of CPS controller firmware/software is complementary, but is typically exercised with synthetic inputs or under specific CPS operational profiles and does not simultaneously track software execution and CPS plant evolution. To bridge this gap, we present a closed‑loop digital twin framework that hosts unmodified controller binaries in a Linux sandbox (SaMOSA) with its I/O rerouted to an external plant simulator. The framework captures four time‑synchronized side channels (hardware performance counters, system calls, disk activity, network activity) alongside plant state and provides orchestration hooks for automated, repeatable, parameterized runs. We demonstrate the framework on an OpenPLC runtime controlling a Modbus‑connected IEEE 14‑bus power system, and also briefly discuss application to robotics systems. The synchronized traces correlate internal controller execution with plant events, providing an observability foundation for online testing, coverage analysis, and vulnerability detection.

Authors:Willa Gutowski, Ulyana Shyrokaya, Nicholson Koukpaizan, Joshua Hambrick, Slaven Peles, Eve Tsybina
Title: Geospatial sensitivity of transmission-constrained ACOPF to generator retirement
Abstract:
The US faces a growing resource adequacy challenge: new loads are being added at unprecedented scale while aging generating assets are being retired. In transmission‑constrained grids, it is difficult to determine which units can be safely retired and which cannot be retired and instead require lifetime extensions until new generation can be built. Historically, this analysis was prohibitively time consuming. Transmission‑constrained AC optimal power flow (ACOPF) is computationally intensive, and a thorough comparison and prioritization of generators could require hundreds or thousands of scenarios. We present an HPC‑enabled framework that enables computation and geospatial mapping of the effects of generator retirement in terms of voltage magnitude and angle effects in the steady state. Specifically, our framework detects the effects of generator retirement using a simple k‑nearest‑neighbors model and a voltage‑class‑adjusted neighbor model. We demonstrate the results on over 8,000 generator retirement scenarios for a 70,000‑bus transmission‑constrained synthetic grid.

Authors:Abhinav Sinha, Shashi Ranjan Kumar
Title: Distributed Safe Consensus Under Asymmetric Input and Time-Varying Output Constraints
Abstract:
This paper studies safe distributed consensus for single‑integrator multi‑agent systems over connected undirected graphs under simultaneous asymmetric actuator constraints and output safety constraints. Each agent is equipped with a continuously differentiable asymmetric actuator dynamics that maps a commanded control signal to the realized plant input while keeping the latter strictly inside a prescribed admissible interval. To address output safety, a barrier‑coordinate transformation is introduced over a common time‑varying safe interval, and a distributed synchronization law is designed in the transformed coordinates. The resulting controller integrates a graph‑based coordination layer with an actuator‑side tracking layer, thereby enabling simultaneous enforcement of input admissibility, forward invariance of the safe output set, and asymptotic synchronization. For compact admissible sets of initial conditions, it is shown that the closed‑loop solution is complete, all signals remain bounded, the actuator inputs remain strictly within their asymmetric bounds, and the agent outputs remain inside the prescribed safe interval for all time. Moreover, the transformed synchronization errors converge exponentially to zero, and the original agent outputs asymptotically synchronize to a designer‑selected admissible trajectory embedded in the common safe interval. Numerical simulations validate the proposed framework and demonstrate safe consensus under both asymmetric actuation bounds and time‑varying output constraints.

Authors:Abdelrahman Ramadan, Melissa Greeff, Sidney Givigi
Title: Anisotropic Template Ansätze for Robust Positive Invariance under State-Dependent Uncertainty
Abstract:
We establish sufficient conditions for robust positive invariance under state‑ and input‑dependent disturbances with anisotropic covariance structure. The proposed ansatz maps a fixed ellipsoidal template through a GP‑derived positive‑definite matrix field, subsuming scalar homothetic scaling while retaining finite graph‑based verification. The resulting LMI conditions couple the learned field to Schur‑stable dynamics; an isotropic fallback with inflation factor r=1/(1‑γ_\mathrmcl) proves admissibility. During each learning epoch the field is frozen, so online tube evaluation is one GP covariance query and a small matrix square root, with no online set iteration or LMI solve. Quadrotor simulations show a 195× reduction in 3D velocity‑tube volume and a 2.1×10^5 reduction in the joint 7D velocity‑control subspace relative to a non‑adaptive homothetic baseline. This extended version adds full proofs, a separated offline/online complexity analysis, and controller‑sweep, contraction, and projection‑area studies.

Authors:Eric Levy, Soosan Beheshti
Title: A Smart-Scheduled Hybrid (SSH) EKF-FGO State Estimation
Abstract:
Reliable state estimation in robotics and control re quires balancing estimation accuracy against computational cost. While filtering‑based methods such as the Extended Kalman Filter (EKF) provide efficient real‑time updates, and optimisation based formulations using factor graphs improve global consistency, the role of optimisation scheduling is often treated implicitly rather than examined as an explicit design variable. This paper presents an experimental study that explicitly isolates optimisation scheduling using a Smart Scheduled Hybrid (SSH) EKF‑FGO framework as a controlled testbed. By combining EKF‑based state propagation with periodically invoked batch optimisation and holding solver structure and effort fixed, the main contribution of this work is the experimental characterisation of optimisation scheduling as an independent design variable governing the trade‑off between intermediate estimation accuracy and computational cost. Simulation results in a planar SLAM environment show that scheduling strongly influences pre optimisation drift, transient error behaviour, and runtime. In particular, the results identify operating regimes in which most of the benefit of global optimisation can be retained at a fraction of the computational cost, highlighting optimisation scheduling as an under‑explored yet critical consideration in hybrid state estimation systems.

Authors:Man Shi, Vikram Jain, Weijie Jiang, Chao Fang, Antony Joseph, Wim Dehaene, Marian Verhelst
Title: SparseCol: A 1320 BTOPS/W Precision-scalable NPU Exploiting Training-free Structured Bit-level Sparsity and Dynamic Dataflow
Abstract:
Bit‑serial computation enables sequential processing of data at the bit level, providing several advantages, such as scalable computational precision. This approach has gained significant attention, especially for exploiting bit‑level sparsity in AI workloads. While current bit‑serial processors leverage bit‑level sparsity to eliminate the computation associated with zero bits, they face a fundamental trade‑off: either they suffer from low memory‑access and computation efficiency caused by irregular patterns of non‑zero bits, or they incur substantial area overhead from complex online scheduling mechanisms required to reorganize bit‑level data and preserve memory access and computation regularity. Therefore, we present the SparseCol processor, designed to harness extensive bit sparsity while maintaining high hardware utilization across various AI applications, including CNNs, RNNs, and transformers. In contrast to traditional methods, SparseCol exploits structured bit‑level sparsity, denoted by bit‑column sparsity, without requiring any re‑training. Furthermore, SparseCol implements a dynamic dataflow architecture that tackles hardware under‑utilization issues commonly found in existing bit‑serial solutions. Fabricated in 16nm CMOS node, SparseCol delivers 1320 BTOPS/W (BTOPS represents Binary Tera‑Operations Per Second, calculated as #W bits x #A bits TOPS) peak efficiency while maintaining accuracy, outperforming SotA sparse processors in terms of efficiency by 6.8x. Comprehensive evaluations on CNN classification tasks and transformer architectures demonstrate system‑level efficiencies of 745.02 BTOPS/W and 850.5 BTOPS/W, respectively.

Authors:Alberto Maria Nobili, Fabio Salsedo, Alessandro Filippeschi
Title: Friction Characterization of a Cable-Driven Differential Actuation System for Lower-Limb Exoskeletons
Abstract:
Lower‑limb exoskeletons require actuation systems that can provide accurate joint torque control while preserving low mass and encumbrance. Conventional architectures often rely on independently actuated joints and joint‑level torque sensors, increasing system complexity and weight. This paper presents a novel differential actuation architecture for hip‑knee flexion/extension, enabling cooperative torque sharing between two motors via a linear differential mapping between motor and joint. To compensate for transmission losses, a model‑based friction estimation strategy is developed and experimentally implemented, allowing accurate joint torque estimation without the need for torque sensors. The proposed solution is validated on a physical prototype, demonstrating the feasibility of sensorless torque estimation in a differentially actuated hip‑knee module of a lower‑limb exoskeleton.

Authors:Sachin Kadam
Title: SINR-Aware Base Station Deployment in Wide Area IoT Sensor Networks
Abstract:
The rapid expansion of Internet of Things (IoT) applications necessitates the effective deployment of base stations (BSs) to enable consistent connectivity across large geographic areas under interference‑limited conditions. Existing techniques typically use distance‑based or binary coverage models; however, these abstractions fail to account for the influence of co‑channel interference on the quality of communication in dense deployments. In this paper, we investigate the Signal‑to‑Interference‑plus‑Noise Ratio (SINR)‑aware Base Station Deployment (BSD) problem in wide‑area IoT sensor networks. The objective is to determine a minimum‑cost subset of BSs from a predefined set of candidate BSs such that every IoT sensor is covered by at least one BS and a target SINR threshold is satisfied. The problem is formulated as a combinatorial optimization problem, which is NP‑hard. Theoretical analysis establishes that the proposed coverage function is monotone and submodular, enabling the SINR‑aware greedy algorithm to achieve a (1‑1/e)‑approximation to the optimal solution while maintaining a polynomial‑time computational complexity. Numerical evaluations on a real water distribution network dataset demonstrate that the proposed SINR‑aware greedy algorithm achieves near‑optimal base station deployment while significantly reducing computational effort. Compared with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms, the proposed approach attains complete sensor coverage with deployment costs within 12.3% of the best‑performing metaheuristic solution while requiring up to 190 times lower execution time.

Authors:Pengfeng Lin, Yuan Gao, Yuxi Tang, Muhammad Waqas Qaisar, Peifeng Hui, Chuanlin Zhang, Miao Zhu, Peng Wang
Title: Artificial Intelligence for Power-Converter-Rich Electrical Systems: A Review
Abstract:
Power‑converter‑rich electrical systems, formed by renewable generation, electrified transportation, and inverter‑based resources, exhibit strongly nonlinear dynamics, multi‑physics design tradeoffs, fast control requirements, and growing reliability and cybersecurity constraints. These characteristics strain workflows that rely only on physics‑based modeling, sequential optimization, and rule‑based operation. This paper reviews artificial intelligence (AI) for power‑converter‑rich electrical systems through a life‑cycle and deployment‑readiness perspective. The literature is organized across converter design, real‑time control, system‑level operation, and compliance‑oriented governance. For design, we examine surrogate modeling, topology and parameter synthesis, EMI/EMC‑aware optimization, reliability‑oriented design, and knowledge‑assisted workflows. For control, we compare supervised learning, reinforcement learning, learning‑augmented predictive control, and safety‑constrained learning according to their role in closed‑loop implementation. For operations, we focus on microgrid coordination, forecasting, distribution‑system observability, privacy‑preserving coordination, and cyber‑resilient operation where converter‑interfaced resources shape the operating problem. Across these stages, the review emphasizes deployment‑critical gaps, including stability certification, constraint satisfaction, interpretability, extrapolation, data efficiency, sim‑to‑real transfer, embedded latency, cybersecurity, privacy, and standards alignment. The resulting taxonomy is intended to clarify where AI is already useful as an engineering support tool and where further validation is needed before autonomous or safety‑critical deployment.

Authors:Shima Sadat Mousavi, Pol Mestres, Aaron D. Ames
Title: Stability Analysis in Multi-Constraint Safety Filters for Linear Systems
Abstract:
Multi‑constraint safety filters based on control barrier functions for linear systems with affine state constraints yield continuous piecewise‑affine closed‑loop dynamics and may introduce boundary equilibria and unstable active‑set modes. Although they guarantee forward invariance, they can change nominal stability, and it remains unclear when unstable modes cause divergence versus bounded, convergent behavior. This paper develops a geometric framework to separate these cases: leveraging explicit active‑set realizations, we show that equilibria associated with nonempty active sets lie on the corresponding constraint faces and that any unstable directions are tangent to those faces due to exponential enforcement of the active constraints. We characterize mode stability via a minimum‑phase test, certify divergence under fixed active sets using recession cones, and derive tractable linear‑matrix‑inequality conditions for global exponential stability or boundedness using Lyapunov and LaSalle arguments.

Authors:Satoshi Tsuruhara, Kazuhisa Ito
Title: Fast Convergence and Robustness for Two-Layered Forgetting Recursive Least Square under Finite Excitation
Abstract:
Under nonpersistent excitation (non‑PE) conditions, conventional methods such as exponential forgetting (EF) or directional forgetting (DF) recursive least squares (RLS) that rely on direct regressor vectors exhibit inherent limitations in terms of stability guarantees for parameter errors, robustness to system changes, and convergence rates. To address these limitations, this study introduces a novel two‑layer forgetting RLS (TLF‑RLS) identification method based on an augmented regressor matrix constructed using DF, which ensures global exponential stability and enhances robustness under non‑PE condition. However, the convergence rate of the parameter is strongly dependent on the forgetting factor because of the introduction of EF in the outer layer, which causes an estimation windup under non‑PE condition. To address this issue, a novel reconfiguration‑based EF (ReEF) algorithm is proposed, which is achieved through variable‑ and matrix‑based forgetting related to the magnitude of the eigenvalues of the current covariance matrix. Theoretical analysis indicates that TLF‑RLS with ReEF algorithm guarantees uniform ultimate boundedness of the condition number under mild assumptions. Consequently, the proposed method resolves the trade‑off between fast parameter convergence and robustness in both transient and steady‑state responses under changes in system characteristics. Numerical simulations of three aforementioned cases demonstrate the effectiveness of the proposed method.

Authors:Faezeh Pak, Uto Perra, Frank Willems, Theo Hofman, Mauro Salazar
Title: An Optimal Power Management Policy for Hydrogen-based Hybrid Aero Engines
Abstract:
This paper presents a power management policy for a hydrogen‑based hybrid aero engine combining a gas turbine and a solid oxide fuel cell (SOFC). Specifically, we first identify a quadratic quasi‑steady‑state model of the propulsion system and formulate the minimum‑fuel optimal control problem as a function of the power split between gas turbine and SOFC that captures the interconnections between the components and accounts for their operational limits. Second, leveraging the Karush‑Kuhn‑Tucker optimality conditions and partial convexity and monotonicity model properties, we compute the globally optimal steady‑state power split for the different phases of the flight in closed form. Finally, we verify this power management policy with a high‑fidelity integrated static model %simulator across different flight phases, revealing in less than 1.5 % normalized root mean square error in power allocation and less than 0.7 % in predicted fuel consumption. Our results show that the optimal power management policy can be translated into a heuristic control law requesting the highest SOFC power that does not exceed its maximum operating temperature, ultimately paving the way for minimal‑effort on‑board implementations.

Authors:Yajie Zhou, Ao Li, Ashwin Silla, Zaoxing Liu, Vyas Sekar
Title: AIChilles: Automatically Uncovering Hidden Weaknesses in AI-Evolved Systems
Abstract:
The computer systems community has recently seen growing interest in AI‑driven system evolution, where AI agents iteratively rewrite systems. Frameworks such as AdaEvolve and Engram report 12‑60% score improvements over human‑designed algorithms. While these results are promising, there are practical concerns if these AI‑evolved programs can perform worse on unseen workloads and exhibit scalability regressions. Given the speed and scale of AI‑generated code, we need automated mechanisms to uncover such identify hidden weaknesses in AI‑evolved systems programs. To this end, we develop AIChilles that takes as input a baseline program P and an AI‑evolved program P', AIChilles searches for valid workloads where P' regresses relative to P in correctness, runtime, memory usage, or output quality. To tackle the diversity in system applications, weakness types and potential bugs, AIChilles combines deterministic workload‑parameter extraction, agent‑based constraint inference, differential oracles, and code‑frequency coverage to discover diverse failures. Across five system applications and 30 AI‑evolved programs, AIChilles finds 49 distinct hidden weaknesses. We also show that explicitly including AIChilles in the AI‑driven development lifecycle can mitigate several of these weaknesses.

Authors:Sakir Hossain Faruque, Md. Jubair Hossain, Sharun Akter Khushbu
Title: An Integrated System for Real-Time Student Assessment and Career Guidance Using Neural Networks in Computing Disciplines
Abstract:
Many undergraduate students in Computer Science (CS) and Software Engineering (SWE) struggle to identify suitable career paths, particularly when their academic performance, abilities, and interests do not fully align. To address this issue, this study proposes an AI‑driven Student Assessment and Career Prediction System that integrates a Career Guidance Expert (CGE) system with a Web‑Based Student Assessment (WBSA) platform. Within the integrated framework, CGE enhances personalized career recommendations using AI while also assisting students after graduation in identifying suitable jobs, research domains, and higher study opportunities aligned with their skills and interests. The WBSA platform further strengthens interaction between students and faculty through assessments, personalized tasks, mentorship activities, and a secure real‑time chat application. The CGE system employs a Multilayer Perceptron (MLP) model trained on real‑world academic and extracurricular data collected using the snowball sampling method from the students of universities, achieving a validation accuracy of 94.71% in predicting personalized career paths. A pre‑survey was conducted across universities to evaluate the proposed model before deployment. The WBSA system was developed as a modern web application using technologies such as Node.js, Next.js, and PostgreSQL to ensure scalability, responsiveness, and secure data management. The overall system is supported by a secure cloud‑based infrastructure, the platform provides reliable performance while assisting graduates to select suitable career path in IT sector. In addition, a post‑survey involving both students and faculty was conducted to gather feedback and further improve the overall effectiveness and usability of the system.

Authors:Maonan Wang, Zhengyan Huang, Kemou Jiang, Yuhang Fu, Jiayue Zhu, Yuxin Cai, Xingchen Zou, Qiaosheng Zhang, Yi Yu, Ding Wang, Xi Chen, Ben M. Chen, Yuxuan Liang, Zhiyong Cui, Man On Pun, Yirong Chen
Title: OmniTraffic: A Controllable Generation Pipeline and Benchmark for Spatio-Temporal Traffic Reasoning
Abstract:
Traffic scene understanding requires models to reason beyond object recognition, including lane topology, multi‑view geometry, temporal evolution, and signal‑phase semantics. However, existing traffic‑oriented multimodal benchmarks largely emphasize passive visual recognition or isolated video understanding, offering limited support for evaluating structure‑aware traffic reasoning under controlled conditions. We introduce OmniTraffic, a controllable generation pipeline and benchmark for spatio‑temporal traffic reasoning. Built around 12 real‑world intersections reconstructed into editable 3D traffic environments and complemented by surveillance footage from two countries, OmniTraffic supports both controlled and natural‑condition evaluation. It defines a three‑level task hierarchy spanning scene perception, multi‑view and temporal reasoning, and decision support. Using structured traffic metadata, OmniTraffic generates synchronized multi‑view VQA samples covering vehicle states, lane functions, view‑‑BEV correspondence, temporal dynamics, and signal‑phase analysis, resulting in 8M VQA samples and a 3K human‑verified test set. Evaluation of eleven frontier MLLMs reveals a large human‑‑model gap, with the most pronounced failures in topology‑grounded and spatio‑temporal reasoning tasks. Fine‑tuning a lightweight MLLM on simulated OmniTraffic data further improves performance on real‑world traffic scenes, demonstrating the value of simulation‑generated supervision for traffic‑specific multimodal reasoning. Beyond a fixed dataset, OmniTraffic provides an extensible pipeline with configurable intersections, camera views, traffic demands, signal phases, visual conditions, and rare events.

Authors:Nitya Sathyavageeswaran, Anand D. Sarwate, Roy D. Yates, Narayan Mandayam
Title: Age and Stability Trade-offs in Remote Monitoring Systems
Abstract:
Timely information is important in a wide variety of Internet of Things (IoT) services in which a shared server must manage two competing tasks: (i) processing a queue of jobs, and (ii) generating status updates to a remote monitor. This creates a fundamental trade‑off between queue stability and data freshness. In this work, we model this scheduling decision as a Markov Decision Process (MDP) with the objective of minimizing a weighted sum of the average Age of Information (AoI) and the average queue length. We show that the optimal scheduling strategy is a queue‑dependent age threshold which is monotonic. The shape of the switching curve differs according to different priority regimes. Finally, we compare the optimal MDP policy against heuristic policies.

Authors:Marena Trujillo, David Cole, Bri-Mathias Hodge
Title: Nodal Frequency Stability-Constrained UC & ED for Renewable-Dominated Power Systems
Abstract:
In modern power systems with high shares of renewable, inverter‑based resources (IBRs), frequency stability becomes more complex due to the fast dynamics of IBRs and frequency trajectories that vary significantly from bus to bus. In this paper, we present an optimization framework for unit commitment and economic dispatch with endogenous frequency stability constraints at each bus. Two approaches for mitigating excessively low instantaneous frequency values in the event of the largest generator contingency are proposed: 1) by introducing a constraint requiring more thermal generation, and 2) by constraining the maximum power output of the generator that had the largest power output in the incumbent solution. Both approaches proved effective in eliminating dispatch scenarios that resulted in instantaneous frequencies below 58 Hz, while the second approach minimized the difference in production cost values from the non‑stability‑constrained case. Overall, the results indicate that the proposed optimization framework is a more effective alternative to frequency stability‑constrained unit commitment and economic dispatch (UC & ED) than those based on the center‑of‑inertia (COI) principle.

Authors:Devesh Nath, Anutam Srinivasan, Haoran Yin, Ruitong Jiang, Jeffrey Fang, Glen Chou
Title: Pixels to Proofs: Probabilistically-Safe Latent World Model Control via Parallel Conformal Robust MPC
Abstract:
We present SLS^2, a framework for safe feedback motion planning from pixels using robust model predictive control (MPC) in learned latent world models. Our approach trains an action‑conditioned joint‑embedding world model with compact Markovian latent states, enabling efficient gradient‑based trajectory optimization through learned latent dynamics. To enforce safety for the true system despite imperfect latent predictions, we inform a GPU‑accelerated system level synthesis (SLS) robust MPC scheme with conformal prediction to obtain calibrated latent error bounds and robust latent‑space constraint sets. We further learn and conformalize a latent constraint checker, allowing the SLS planner to impose probabilistic safety constraints during closed‑loop execution. We evaluate our method on vision‑based control tasks, where it improves both goal‑reaching performance and safety over latent world‑model and safe‑planning baselines.

Authors:Mohammad A. Ayoubi
Title: Positive-Real Identification of Sparse Mori-Hamiltonians from Partial Observations
Abstract:
Discovering the governing equations of a physical system from data is a central goal across the sciences, yet in most experiments only a few states are accessible while the rest stay hidden. Existing approaches treat this partial observability as an obstacle to be removed by first reconstructing the hidden state ‑‑ a step that is ill‑posed under noise and that discards the physical constraints, such as energy conservation, that the true dynamics obey. We show that for conservative (Hamiltonian) systems no reconstruction is needed: projecting the dynamics onto the measured coordinates yields a memory kernel that we prove to be a lossless positive‑real rational matrix, whose poles are the hidden natural frequencies and whose positive‑semidefinite residues encode the couplings. The governing equation ‑‑ and the underlying Hamiltonian ‑‑ can therefore be read directly from the autocorrelation of the measured signal, with guarantees of uniqueness and physical passivity, and without neural networks. We validate the approach on linear, nonlinear, and chaotic systems under realistic noise. By recovering interpretable equations of motion that conserve energy by construction from partial measurements, the method offers a common tool for problems spanning mechanics, fluid and plasma physics, and beyond.

Authors:Jing Jin, Robert Chu, Ning Yan, Masood S. Mortazavi
Title: LLM4RTL: Tool-Assisted LLM for RTL Generation
Abstract:
Large language models (LLMs) have facilitated impressive progress in software engineering, code generation, tooling, and systems. Concurrently, a significant body of research has developed which explores a growing variety of methods and systems for applying LLMs to hardware and chip design (e.g., systems for RTL code generation based on functional description). However, when it comes to open Verilog/RTL code‑generation, we need high‑quality training samples to build specialized and more effective LLM systems through fine‑tuning or low‑rank adaptation. Here, we propose a ``judge‑renew‑check‑renew‑check'' (JRCRC) pipeline which updates a current public dataset using a hierarchy of state‑of‑the‑art commercial LLM models differing in their costs and capabilities in RTL code generation. This approach achieves a cost‑effective mechanism for filtering and refining code‑generation samples into a higher‑quality training dataset. Our experiments also identify some common weaknesses of LLMs in rule‑based reasoning and logic, and consequently, in RTL code‑generation. Having identified these weaknesses, we develop an architecture for incorporating pre‑processing tools to dynamically assist the LLMs in inferring logical relationships from tabular data formats. With our tools‑assisted architecture for RTL code generation, we achieve significant overall performance gains in the VerilogEval benchmark and outperform many state‑of‑the‑art methods. Our LLM4RTL system achieves performance comparable to that of GPT‑4O using a significantly much smaller LLM.

Authors:Jesse Milzman, Dipankar Maity
Title: On Type Deception in Linear-Quadratic Differential Games
Abstract:
We consider two‑player linear‑quadratic differential games of incomplete information, in which one player has a private type initially unknown to the other. The typed player has incentive to conceal their type, while the uninformed player has the potential to infer it during play. Any ex‑ante equilibrium in this setting will decompose into a deceptive, pooling phase, and a complete‑information, revelatory phase. We demonstrate how to solve both phases via nested Riccati equations. Candidate equilibria are then found by maximizing the game value over a scalar revelation time, for which we provide a gradient in the case of time‑homogeneous system matrices. We conclude by demonstrating our framework in a pursuit‑evasion game with time‑varying control advantages, finding interior optimal revelation times that confirm deception has quantifiable ex‑ante value.

Authors:Stefano Dalla Gasperina, Dong Ho Kang, Haiyun Zhang, Aldo Galvan, Job D. Ramirez, Aaron Kim, Mark Helwig, Kazuto Yokoyama, Takahisa Ueno, Tetsuya Narita, Ann Majewicz-Fey, Ashish D. Deshpande, Luis Sentis
Title: A Bilateral Teleoperation Framework for Dexterous Manipulation
Abstract:
Dexterous teleoperation requires precise arm‑hand coordination, low‑latency feedback, and robust interaction in real‑world contact‑rich environments. This paper presents a modular bilateral teleoperation framework that integrates operator‑side input interfaces with a robot‑side dexterous hand and compliant robotic arm in a unified control architecture. The system supports position‑based hand retargeting, differential arm control, multi‑scale haptic feedback, and shared control for stable manipulation. We validate the framework through a real‑world dexterous manipulation task, highlighting coordinated arm‑hand control and contact‑aware interaction. Beyond feasibility, we identify key design insights related to cross‑embodiment mismatch, haptic feedback granularity, and shared control. The proposed platform provides a practical teleoperation system and a foundation for collecting high‑quality demonstrations for future learning‑from‑demonstration research.

Authors:Senol Gulgonul
Title: Minimum settling-time PI control of pure delay processes under a hard non-overshoot constraint: exact boundary-contact characterization and the role of the MID point
Abstract:
We solve, exactly, the problem of minimum settling‑time PI control of a pure delay process K e^‑Ls under the hard time‑domain constraint of zero overshoot, y(t) <= 1 for all t. The closed loop is a neutral delay system whose step response is piecewise polynomial on the delay segments, with geometrically decaying jump discontinuities at the segment boundaries t = kL. The constrained optimum is characterized by an equioscillation‑type contact structure whose active contacts sit at echo boundaries: kink maxima grazing the setpoint, jumps landing on the settling‑band edge, and boundary troughs anchored to it. The number of contact equations equals the number of gains, so the optimum is exactly computable for every band delta. In a closed‑form regime, delta in [(3‑2 sqrt2)/4, (3‑2 sqrt2)/2] approx [4.29%, 8.58%], the optimal gains are independent of delta: K Kp = 1 ‑ sqrt2/2, K Ki L = sqrt2/2, and the optimal settling time is Ts(delta) = (4 ‑ sqrt2 ‑ 2 sqrt(delta)) L. Outside this window the optimum solves an explicit two‑equation polynomial system per regime, and Ts(delta) is a staircase with exact flats at integer multiples of L from jump‑landing pinning. As delta ‑> 0 the optimal gains converge to K Kp = e^‑2, K Ki L = 4 e^‑2, the generic multiplicity‑induced‑dominancy (GMID) point of the neutral quasipolynomial. The GMID response satisfies the hard constraint and uniquely maximizes the decay rate; yet at every finite delta the delta‑adapted optimum strictly beats the fixed GMID tuning, by about 40% at delta = 2%. The MID point is thus the limit of the optimal gains without ever being the optimal tuning. A numerical extension to first‑order‑plus‑time‑delay plants quantifies the speed/robustness trade across Ms in [1.39, 1.76].

Authors:Shrenik Jadhav, Vidhyashree Nagaraju, Zheng Liu
Title: Data Center Life Cycle Co-Design Optimization
Abstract:
Liquid cooled supercomputers dissipate tens of megawatts of waste heat through cooling plants organized as parallel subloops that serve coolant distribution units. The number of subloops and the assignment of units to them are design decisions fixed at construction, yet they have not been systematically optimized for facilities at this scale. As electricity grids decarbonize, embodied carbon becomes a larger share of facility life cycle emissions and the cost of an unnecessary subloop becomes harder to justify. We present a framework that integrates operational energy from a validated control optimizer based on sequential least squares programming, embodied carbon from a bill of materials, and expected unplanned downtime from a per subloop reliability model. The framework is applied to the Frontier supercomputer, evaluating all 611 ways of partitioning its 25 coolant distribution units into two through six subloops. The life cycle cost and carbon optimum is found at two subloops holding 14 and 11 units, achieving 3,320.7 tonnes of carbon dioxide equivalent and 3.99 million over a seven year horizon, a saving of 50.2 tonnes and 100,000 compared to built four subloop configuration. The optimum remains on the Pareto front in all 15 scenarios of a one at a time sensitivity sweep. A semi‑analytical decision rule generalizes the result, predicting four subloops for Aurora, two for El Capitan, and one for LUMI. When reliability is treated as a hard constraint set by operations policy, the four subloop Frontier deployment is consistent with the constrained optimum.

Authors:Omid Mirzaeedodangeh, Eliot Shekhtman, Nikolai Matni, Lars Lindemann
Title: Robust Conformal CBF and CLF Controllers via Iterative Policy Updates
Abstract:
Conformal prediction (CP) has been used to obtain probabilistic bounds on the error between a learned dynamics model and the true but unknown system. Such CP bounds can then be embedded into robust control Lyapunov function (CLF) and control barrier function (CBF) frameworks. However, such an approach does not retain stability/safety guarantees because of the distribution shift between the closed‑loop trajectory distribution under the deployed CLF/CBF policy and the trajectory distribution from which the CP bound and its guarantees were derived. To address this issue, we propose an episodic framework that iteratively updates the robust conformal CLF/CBF policy while maintaining stability/safety guarantees across episodes. We achieve this by (1) using adversarially robust conformal prediction, and (2) quantifying a distribution shift budget that allows us to control how much the model error can increase across policy updates. This distribution shift budget is derived via a closed‑loop trajectory sensitivity analysis, yielding an implicit and an explicit update rule for the CP bound. We analyze convergence of our algorithm, which we demonstrate on three case studies. To the best of our knowledge, these are the first results that provide stability/safety guarantees for robust conformal CBF/CLF policies.

Authors:Yuhong Jiang, Shiyue Zhao, Junzhi Zhang, Junfeng Zhang, Xinhan Li, Shijie Zhao, Chengkun He
Title: Hamilton-Jacobi Reachability-Based Safe Reinforcement Learning for Emergency Collision Avoidance
Abstract:
Emergency collision avoidance under extreme driving conditions demands safety‑critical control that accounts for both obstacle proximity and vehicle dynamic stability over a future time horizon, yet existing methods often rely on instantaneous or local safety evaluations. This paper proposes a safe reinforcement learning framework guided by a Hamilton‑Jacobi (HJ) reachability based motion safety set that provides forward‑looking safety supervision for constrained policy optimization. Specifically, a unified signed safety function is formulated by combining geometric collision margins and chassis stability limits, and is then extended through reachability analysis into a finite‑horizon motion safety set that characterizes whether safety can be maintained under future vehicle state evolution. To enable practical computation, the motion safety set is approximated from offline extreme driving data, mitigating the computational burden of grid‑based HJ solvers. The learned motion safety set is then embedded as a continuous safety cost into a constrained Markov decision process, and a PID‑Lagrangian policy optimization scheme is employed to adaptively regulate the Lagrange multiplier for safety constraint enforcement. Simulation and real‑vehicle experiments on low‑adhesion obstacle‑avoidance scenarios demonstrate that the proposed method achieves higher goal‑reaching rates, produces smoother avoidance maneuvers, and maintains larger unified safety margins than baseline methods.

Authors:Ahmad F. Taha, Mohamad H. Kazma, Abdallah A. Albustami
Title: Revisiting The PBH Test: Fast Uncontrollability Certificates via Krylov Methods
Abstract:
This letter revisits the classical PBH test through the lens of finite‑horizon reachability. By casting state transfer as a minimum energy, primal optimization problem, we show that unreachable state‑space maneuvers admit dual infeasibility certificates. These certificates are computable without forming the controllability matrix meaning that uncontrollability can be efficiently certified. We prove that any such certificate is a linear combination of uncontrollable generalized eigenvectors, thereby providing a spectral interpretation without a global eigendecomposition. We also devise algorithms based on Krylov sub‑space methods that extract some of the uncontrollable PBH modes from a certificate and demonstrate favorable scaling on large dynamic networks with thousands of nodes.

Authors:Mingyu Wang, Xiaofeng Zong, Jimin Wang, Ji-Feng Zhang
Title: Differentially Private Consensus for Time-Delay Multi-agent Systems
Abstract:
This paper is concerned with the differentially private consensus problem for discrete‑time multi‑agent systems with communication delays. The purpose of the paper is to achieve differentially private consensus for such systems while protecting the entire delayed initial histories of all agents. A novel adjacency relation for delayed histories is introduced, and a Laplace‑noise‑based privacy mechanism is developed, where the noise variance is allowed to vary with time and even increase. By using the difference resolvent function method, decay estimates for the fundamental solutions of the delayed difference equations are derived. Based on these estimates and a backstepping technique, mean square weak consensus, mean square strong consensus, and almost sure strong consensus are established. The estimates for the fundamental solutions are also used to derive an explicit sensitivity bound. Furthermore, a constructive parameter design is provided to achieve a prescribed infinite‑horizon ε^\star‑differential privacy level. Numerical simulations illustrate the theoretical results.

Authors:Or Nahum, Vitaly Shaferman
Title: Optimal Ground-to-Air Interception with Time-Varying Acceleration Bounds
Abstract:
This paper proposes novel optimal‑control‑based guidance laws for ground‑to‑air missiles with time‑varying acceleration bounds. In such engagements, as the missile climbs in altitude, its acceleration bound decreases, which may lead to acceleration saturation and significant miss distances if not explicitly accounted for. The proposed guidance laws incorporate hard acceleration command constraints directly into a linear‑quadratic optimal‑control framework, in contrast to conventional unbounded or softly constrained approaches. Analytically based guidance laws are developed for linear zero‑order and first‑order strictly proper missile dynamics with arbitrary‑order linear target dynamics. Unlike the constant hard‑bound case with minimum‑phase missile dynamics, time‑varying acceleration command bounds permit an initial unsaturated interval in which the proposed guidance laws can anticipate future saturation and reshape the acceleration profile accordingly. This enables earlier maneuvers when the missile possesses greater low‑altitude maneuverability, fundamentally altering the structure of the optimal solution. The proposed approach is evaluated in nonlinear simulations and compared with equivalent unbounded and softly constrained optimal guidance laws. The results demonstrate substantially improved interception performance under saturation, reduced tuning requirements compared to softly constrained guidance laws, and enhanced capability in challenging engagement scenarios.

Authors:Wenjie Wang, Hao Chen, Ran Shu, Solyeon Kwon, Kyoungseok Han, Hongyu Shu
Title: Adaptive Deep Koopman Operator for Vehicle Dynamics Modeling: A Physics-Informed and Tire-Force-Driven Approach
Abstract:
Accurate and adaptive modeling of vehicle dynamics is paramount for the safety of autonomous driving systems, particularly under extreme maneuvers and time‑varying parameters. While Deep Koopman operator theory offers a promising global linearization framework, its online application faces a theoretical bottleneck: the high‑dimensional lifted state space inherently induces a rank‑deficient problem, rendering traditional recursive least squares based updates numerically unstable. To address this, we propose a novel tire‑force‑driven modeling framework with guaranteed online stability. First, an offline Deep Koopman model is constructed by embedding 7DOF dynamic equilibrium constraints into the learning objective, ensuring the structural fidelity and physical interpretability of the lifted manifold. Second, we theoretically reformulate the operator update in the rank‑deficient space as a minimum‑norm solution problem. A Physics‑Informed Variable Step‑Size Normalized Least Mean Squares (PI‑VSS‑NLMS) algorithm is proposed, which leverages the projection property of NLMS to act as a stable pseudo‑inverse solver while incorporating an anchoring mechanism to suppress parameter drift. Extensive simulations on CarSim and Hardware‑in‑the‑Loop validation on dSPACE MicroAutobox III confirm the superiority of the proposed algorithm. It achieves robust prediction accuracy under unseen excitations while guaranteeing real‑time feasibility with an average execution time of 0.421 ms, thus bridging the gap between theoretical models and practical deployment.

Authors:Yuqi Jiang, Yuqi Zhang, Zhiding Liang, Qiang Guan, Yan Li, Ganesh Kumar Venayagamoorthy
Title: REGRID-QAOA: A Resource-Efficient Graph-Reduced Hybrid QAOA Framework for Physics-Constrained Power System Islanding
Abstract:
Quantum computing has rapidly emerged as a powerful paradigm for tackling computationally demanding problems. In particular, quantum optimization shows strong promise for hard combinatorial problems in power systems, where increasing distributed energy penetration heightens the need for intentional islanding to maintain grid reliability and resilience. However, power system islanding is an NP‑hard combinatorial optimization problem that becomes computationally prohibitive for classical solvers as network size grows, motivating the use of quantum computing as a promising alternative pipeline. This study develops a resource‑efficient hybrid QAOA islanding framework that brings physics‑constrained power‑system partitioning into the quantum optimization workflow. The framework combines coherency‑informed graph reduction, physics‑aware constraint modeling, and structured post‑processing to efficiently convert shallow‑circuit QAOA samples into high‑quality feasible islanding decisions without deep circuits or large shot budgets. The proposed framework is validated on the standard IEEE benchmark systems (9‑, 14‑, 24‑, 30‑, 39‑, and 57‑bus), demonstrating that the hybrid workflow achieves Gurobi‑optimal solution quality with a clear quantum resource advantage over vanilla QAOA, while the resulting islanding solutions satisfy all physical feasibility requirements after network separation. This study establishes QAOA‑based islanding as a viable quantum approach for critical infrastructure, with structured post‑processing as the key enabler of quantum resource efficiency.

Authors:Sribalaji C. Anand, George J. Pappas
Title: Resilient Consensus in Agentic AI
Abstract:
Large language model (LLM) agents are increasingly deployed in multi‑agent systems where they must coordinate and agree on shared decisions. We ask whether classical resilient consensus theory, developed for deterministic agents, transfers to LLM agents that may behave adversarially. Framing LLM agreement as a Byzantine consensus game, we run controlled experiments on complete and general communication graphs. We find that prompted LLM agents fail to reach agreement that is achievable in principle: consensus can fail even in settings where classical theory guarantees that a convergent algorithm exists, and this failure persists across temperatures and horizons. At the same time, wrapping the agents with classical resilient consensus filters improves agreement. The benefit of filtering depends on how much robustness the underlying topology already provides. Our results suggest that classical resilient consensus theory is a useful lens for the safety of agentic AI.

Authors:Joseph Q. Zales, Pragya Sharma, Mani Srivastava
Title: CREST: Deployment-Realistic Hardware-in-the-Loop NAS for Embedded Sensing Systems
Abstract:
Deploying neural networks on low‑power microcontrollers (MCUs) requires selecting model architectures under tight memory, latency, and energy constraints. Existing workflows often simplify this process along one or more axes: static proxy costs such as FLOPs or parameters, treating one MCU as representative, and continuous‑inference tests instead of deployed sensing schedules. These assumptions can mis‑rank Pareto‑front candidates, miss infeasible deployments, and obscure schedule‑dependent energy. We present CREST (Cross‑platform Runtime Evaluation and Search Tool), a deployment‑realistic hardware‑in‑the‑loop (HIL) neural architecture search (NAS) framework for MCU sensing systems. CREST keeps the optimizer, HIL measurement boundary, logging, and replay workflow fixed while exposing workload, model family, target backend, schedule, quantization, and scoring policy as configurable axes. This makes deployment effects experimentally separable within one reusable workflow. We evaluate CREST on inertial odometry and audio classification across three Arm Cortex‑M targets. For inertial odometry, measured‑energy HIL search reduces median per‑inference energy by 41.7% versus FLOPs‑based selection and 40.8% versus memory‑traffic‑based selection at similar error. FLOPs‑based selection also chooses infeasible deployments on memory‑constrained targets. On the STM32 N657 target, continuous‑inference and duty‑cycled searches produce different Pareto frontiers. For audio classification, the same application‑level policy selects different DS‑CNN architectures on different boards, and cross‑board replay changes deployment cost substantially. Overall, CREST shows that deployment‑realistic MCU NAS must jointly optimize model architecture, target platform, runtime schedule, and deployment policy rather than relying only on static proxy costs or continuous‑inference measurements.

Authors:Ross Baldick
Title: A state-of-charge based formulation for storage participation in electricity markets: Technical Reference
Abstract:
We consider a storage device and develop a basic market design that represents the salient characteristics of storage such as state‑of‑charge (SOC) and round‑trip efficiency. The contribution of this paper is a market design that reflects these technical characteristics, does not require bids and offers by the storage owner within the market horizon, but does require an end‑of‑horizon bid/offer for deviating the end‑of‑horizon SOC from the start‑of‑horizon SOC\@. Small examples are used to illustrate the market design and large‑scale implementation is considered. Several extensions are sketched in Appendices.

Authors:Alex Rose, Christopher Jewison, Jonathan P. How
Title: Robust Sampling-Based Covariance Steering for Aerocapture Guidance
Abstract:
Aerocapture is a maneuver where a spacecraft dives through the atmosphere of a planet or moon to reduce its velocity and prepare for orbital insertion. Aerocapture allows for higher cruise velocities and reduces fuel consumption, decreasing transit time and increasing payload mass. However, uncertainties in the atmospheric entry state and atmospheric density increase the risk of aerocapture. Dynamic nonlinearities and nonlinearities caused by the state‑dependence of the atmospheric density pose additional challenges. This work develops a robust sampling‑based covariance steering algorithm designed for aerocapture guidance. Our proposed algorithm leverages sampled nonlinear system trajectories to improve evaluation of the delta‑V required for aerocapture and address nonlinearities caused by the aerocapture dynamics and atmospheric disturbances. We perform Monte Carlo simulations with dispersed entry and atmospheric conditions on aerocapture scenarios at Mars and Uranus and demonstrate a 5‑15% reduction in the 99th‑percentile, 99.7th‑percentile, and worst‑case delta‑V required for aerocapture when compared against a state‑of‑the‑art covariance steering algorithm.

Authors:Chenchen Zhou, Zuzhen Ji, Jose Matias
Title: Real-time nonlinear model predictive control framework for event-triggered switching in industrial batch polymerization process
Abstract:
Controlling batch polymerization is challenging because the absence of a steady operating point prevents standard linearization; the dynamics are intrinsically nonlinear; and multi‑phase operation induces state‑triggered switching. This study systematically combines four established real‑time NMPC ingredients, smooth mode blending, advanced‑step warm starts, variable scaling, and a capped iteration budget, to attain real‑time feasibility without ad hoc switching heuristics. We provide practice‑oriented guidance for selecting smoothing gains and locating switching surfaces, and we make explicit the approximations introduced by smoothing such that, with appropriate tuning, the smoothed and original switching logic are numerically indistinguishable at solver‑tolerance levels. All results are obtained in closed‑loop simulation using an industrial gas‑liquid polymerization benchmark with estimator‑in‑the‑loop, compared against PID and conventional NMPC baselines. Results show improved constraint satisfaction and shorter batch duration under bounded computation, while an ablation study quantifies the specific contributions of each component individually

Authors:Chenchen Zhou, Jose Matias
Title: Controller and Control Architecture Co-Design via Mixed-Integer System-Level Synthesis
Abstract:
We study controller and control‑architecture co‑design for dynamic output‑feedback systems. The architecture selects active sensors and actuators, sensor‑to‑actuator links, and link delays, with costs for hardware activation and communication latency. Direct optimization over controller transfer matrices and discrete links is mixed‑integer nonconvex; common alternatives fix the architecture, use regularization, or restrict the controller information pattern to a quadratically invariant (QI) class. We instead optimize finite‑horizon output‑feedback system‑level synthesis (OF‑SLS) responses. Binary variables select sensors, actuators, links, and delays, and indicator constraints zero unavailable FIR response blocks before the selected delays. For implementation‑local OF‑SLS architectures, this gives an exact mixed‑integer convex program over a prescribed finite delay menu. A global solve certifies the best architecture‑response pair for the chosen delay menu, FIR horizon, admissible architecture set, and scalarization weight. The same encoding gives a QI controller‑support reference problem. In a vehicle‑platoon benchmark, 99 of 8748 architectures are QI‑compatible. At equal architecture cost, the selected non‑QI OF‑SLS architecture reduces performance loss by a factor of 3.8 relative to the best QI architecture and outperforms regularization‑based and canonical information‑flow baselines.

Authors:Andrew P. Kendall, John-Paul Clarke
Title: An Analytical Methodology for Quantifying Airspace Conflict Rate and Complexity
Abstract:
Air traffic growth, advanced air mobility, and increasingly autonomous operations are driving the need for scalable and adaptive airspace design methodologies. Central to this challenge is quantifying how traffic flow structure and demand, governed in part by airspace geometry, influence conflict generation and operational complexity. This paper presents an analytical framework for computing conflict rate and conflict probability in structured airspace using stochastic flow models. Traffic streams are modeled as renewal processes with prescribed inter‑arrival time distributions, while interactions between flows are captured through geometry‑dependent minimum spacing constraints at merges and crossings. Within this formulation, closed‑form upper bounds on the expected conflict rate and conflict probability per aircraft are derived as functions of flow configuration and demand. These metrics are interpreted as complementary measures of airspace complexity, reflecting controller workload and per‑aircraft operational risk. The methodology is applied to representative hexagonal cell geometries with varying routing structures and flow distributions. Results reveal non‑monotonic tradeoffs between routing flexibility, capacity, and conflict generation, with intermediate flow configurations outperforming both highly constrained and highly distributed cases. The proposed framework provides a tractable tool for evaluating airspace design alternatives and complexity‑informed traffic management strategies.

Authors:Anton Ponomarev
Title: Compositional small-gain and small-phase stability analysis
Abstract:
We adapt the small‑gain and small‑phase stability analysis to systems composed of MIMO LTI subsystems via an iteration of the series, parallel, and feedback interconnections. Based on a certain set of parameters (including the phase sector and the Crawford number) of the subsystems, we bound the same set of parameters of each consecutive interconnection. This is called "composition of bounds." Composing the gains of subsystems in the same manner enables the small‑gain‑or‑phase analysis of fairly complex systems. The method is illustrated with two examples that are not tractable by the classical small‑phase analysis, and one other example that nevertheless benefits from the compositional approach.

Authors:Georgios Papandroulidakis, Shady Agwa, Themis Prodromakis
Title: An RRAM-based Hardware Implementation of a Radial Basis Function Neuron for Edge Classifiers
Abstract:
The deployment of modern machine learning (ML) solutions on resource‑constrained edge devices highlights implementation challenges. This is especially true for extreme edge applications that include safety‑critical components, such as autonomous navigation tasks. This paper demonstrates an artificial neural network (ANN) design leveraging Metal‑Oxide Resistive RAM (RRAM) based Analogue Content Addressable Memory (ACAM) as an efficient hardware substrate for performing metric‑based classification and online adaptation on the edge. The proposed design is based on a custom Template piXeL (TXL) cell used for building the ACAM module, where each TXL cell acts as a configurable receptive field neuron. These cells employ a Radial Basis activation function to calculate the distance of an input from the programmed receptive field. The TXL can be organised into dense arrays for calculating the distance of a high‑dimensional input against all stored prototypes, effectively performing fast and energy efficient similarity search. This hardware engine enables on‑the‑fly learning, where the receptive field parameters can be tuned to track domain shift. Through simulation of the proposed TXL‑RBF classifier we can achieve 89.1% accuracy on the MNIST dataset while consuming 185fJ per cell per operation when operating at 100MHz.

Authors:Evgeny Makarov, Georgy Taubkin
Title: Recurring Public Transit Schedules: Stable Identification from GTFS and Similarity Analysis
Abstract:
Public transit schedules contain recurring service structures: many dates share the same passenger‑facing timetable, while others differ because of weekends, holidays, seasons, or special events. GTFS does not encode these recurring schedules directly, so downstream scheduling, assignment, and mismatch models lack an explicit recurrent supply object. This paper formalizes recurring schedules as DayTypes, defined by the complete set of Route Pattern trips operating on a date, and develops a stable GTFS‑based extraction method using H3 route‑pattern keys. We then define a schedule‑comparison framework with exact, time‑tolerant, and structural‑comparability metrics that distinguish strict timetable differences from small timing shifts and larger service changes. Validation on Japanese and Canadian GTFS feeds shows substantial schedule compression, a median of four nonempty DayTypes per agency in the pairwise‑analysis sample, hierarchical nesting between service classes, and country‑level differences in the persistence of exact disjointness. The resulting DayTypes provide compact Route‑Pattern‑time schedule sets for timetable synchronization, vehicle scheduling, demand assignment, and schedule‑consolidation analysis.

Authors:Anthony Pineci, Yunzong Xu
Title: Optimal Hidden-Target Learning for Online Inventory Optimization on General Convex Sets
Abstract:
Online inventory optimization (OIO) is online convex optimization with physical memory: inventory carryover makes the feasible action set depend on the past. A natural principle, used in stochastic inventory learning and recently in OIO under a single linear capacity constraint, is to maintain a hidden target chosen by an online learner and implement its projection onto the currently feasible order‑up‑to set. We prove that this simple principle is optimal for OIO on arbitrary bounded convex capacity sets. With online gradient descent as the base learner, the method improves the best known regret guarantee for OIO on general convex sets from inverse to inverse‑square‑root dependence on the common‑demand probability, and we prove a matching lower bound. The same principle gives the first polylogarithmic regret guarantee for strongly convex losses and the first dynamic regret guarantee adapting to Euclidean path variation on general convex capacity sets. The analysis introduces a norm alignment principle: the right state variable is the distance from the hidden target to the feasible set, measured in the same norm as the projection. Under norm alignment, this distance evolves pathwise as a scalar queue, with target movement as arrival and common demand as service. This reduction to one‑dimensional queue control resolves the state dependence and extends the guarantees to general convex capacity sets, beyond the reach of prior productwise approaches. Experiments on synthetic and real‑world inventory data corroborate the theory.

Authors:Regupathi Angappan, Kimberly Moore, Sriharsha Thoram
Title: Towards unified Geophysical Data Requirements for Magnetic Navigation (MagNav)
Abstract:
Magnetic Navigation (MagNav) has emerged as a vital alternative Positioning, Navigation, and Timing (PNT) solution, leveraging Earth's magnetic field for robust navigation in GPS/GNSS‑degraded or denied environments. Despite its potential, the successful deployment of MagNav is currently hindered by the lack of standardized, high‑fidelity geomagnetic reference maps. Existing datasets, primarily designed for geological exploration or academic research, do not meet the distinct operational requirements of navigation systems regarding spatial resolution, error quantification, and global accessibility. This paper initiates a community‑focused dialogue on future geophysical data requirements for MagNav, grounded in extensive real‑world flight trials. We distinguish between two primary use cases with divergent data needs: Operational MagNav, which requires globally consistent, queryable, and uncertainty‑aware datasets for field deployment, and MagNav R&D, which demands comprehensive access to raw survey data to foster innovation. We provide a prioritized set of recommendations for future data requirements, including the development of cohesive, merged datasets, the inclusion of localized 3D uncertainty estimates, and the expansion of the World Magnetic Model (WMM) core field model to spherical harmonic degree 13 to improve consistency. Finally, we emphasize the strategic necessity of designated test ranges to validate these requirements and ensure the operational robustness of MagNav infrastructure.

Authors:Yongyan Cao
Title: Whole-Body Impedance Model Predictive Control for Safe Physical Human--Robot Interaction on Floating-Base Platforms
Abstract:
Floating‑base robots must balance under rigid contact constraints while interacting safely with humans. Existing whole‑body control~(WBC) frameworks allocate the full joint space to locomotion or rely on fixed‑gain impedance feedback that accumulates steady‑state error under sustained physical human‑‑robot interaction~(pHRI) forces. This paper extends the authors' fixed‑base two‑layer Impedance MPC to floating‑base platforms through a three‑level architecture: a centroidal MPC plans contact forces over a 500\,ms horizon; a priority‑driven WBC layer resolves balance into joint torques through contact‑consistent null‑space projection; and the residual null space is governed by a receding‑horizon quadratic program~(QP) that predicts and rejects pHRI disturbances using a Kalman‑augmented state. A contact‑consistent feedback linearization reduces the arm end‑effector plant to a double integrator with a \emphconstant state matrix within each contact mode, enabling offline precomputation of the QP cost and \geq1\,kHz operation. A covariance‑inflation protocol preserves the disturbance estimate across contact‑mode switches, guaranteeing zero steady‑state error under bounded constant pHRI loads, and an Impedance Equivalence Theorem shows the infinite‑horizon limit recovers a classical task‑space impedance law whose effective mass, damping, and stiffness adapt to posture and contact configuration. Simulations on a 17‑DOF biped and the Unitree G1 humanoid validate the design.

Authors:Yongyan Cao
Title: Impedance MPC with Disturbance Estimation for Dexterous Hand Control
Abstract:
Dexterous hands must simultaneously track precise finger trajectories and maintain safe, compliant contact ‑‑ objectives in tension for any fixed‑gain controller. We present an actuator‑agnostic Impedance Model Predictive Control (Impedance MPC) framework for dexterous fingers, instantiating the constant‑A_d offset‑free architecture established for physical human‑robot interaction (pHRI); its stability, recursive‑feasibility, and input‑to‑state‑stability guarantees are inherited by preserving the architectural assumptions. An algebraic feedforward reduces the tendon transmission ‑‑ hydraulic, cable, pneumatic, twisted‑string, or series‑elastic ‑‑ to a constant‑coefficient double integrator, so the QP cost inverse is precomputed offline and a 10‑step receding‑horizon quadratic program runs at 500\,Hz while enforcing hard constraints on contact force (ISO/TS 15066), actuation limits, and jerk. An encoder‑only augmented‑Kalman disturbance state drives steady‑state error to zero under any constant contact load. On a hydraulically actuated finger ‑‑ the worked example platform, adding pressure and cavitation constraints ‑‑ the 500\,Hz Kalman MPC attains 0.5\,mrad RMS, 0.1\,mrad steady‑state, and 6.6\,mrad peak deflection under 1.5\,Nm contact: 183×, 1500×, and 23× better than classical impedance. The realized first‑move stiffness (18\to323\,Nm/rad with update rate) is independently verified. The architecture scales to a 16‑DOF LEAP Hand MuJoCo simulation, recovering from 2.5\,N grasp‑load disturbances within 0.7\,s.

Authors:Shadi Heenatigala, Hasanika Samarasinghe
Title: A Statistical and Machine Learning Framework for Operational Threshold Detection and Deployable Dispatch Controller Development in Hydrogen Multi-Energy Systems
Abstract:
This study presents a statistical and machine learning framework for characterizing a hydrogen‑based multi‑energy system (H‑MES) using one year of high‑resolution operational data. Statistical analysis revealed a binary operation driven by renewable surplus, with solar irradiance explaining 45.7% of rank‑based variance in hydrogen production, a large effect by conventional standards. Only high‑irradiance periods triggered meaningful electrolyzer engagement, while electricity demand exerted a weaker inverse suppression effect (ε^2 = 0.126). Multiple regression confirmed electrolyzer power as the dominant linear predictor, with a synergistic solar‑wind interaction. Notably, Random Forest analysis ranked wind output first in predictive importance despite its weak bivariate correlation (r = 0.167), revealing non‑linear dynamics invisible to parametric methods. A sequence model exploited strong 24‑hour autocorrelation (r = 0.845) for operational forecasting, while a reinforcement learning agent optimized hydrogen revenue dispatch. The core contribution is demonstrating that statistical and machine learning approaches are complementary for H‑MES modeling and control.

Authors:Kai S. Yun, Zeyang Li, Navid Azizan
Title: Provably Safe, Yet Scalable Reinforcement Learning
Abstract:
Safe reinforcement learning (RL) aims to learn policies that optimize rewards while satisfying constraints. Predominant approaches rely on soft‑constrained policy optimization, which has achieved empirical success but does not provide formal safety guarantees for the learned policy. In contrast, methods with strict guarantees typically rely on explicit certificate functions, whose construction requires the direct synthesis and verification of control‑invariant sets, a process that scales poorly with state dimension and often yields overly conservative behavior. In this paper, we present the Provably Safe, yet Scalable RL (PS2‑RL) framework, a novel two‑phase architecture for learning provably safe policies in a scalable manner, designed to overcome the key bottlenecks of prior methods. Rather than explicitly computing invariant sets, PS2‑RL leverages a learned backup policy to forward‑integrate the system dynamics, generating an implicit control‑invariant set online. In the first phase, the backup policy is trained with our proposed safe‑arrival value function, which characterizes the optimal backup policy for invariant‑set construction. In the second phase, an RL policy is trained end‑to‑end through a differentiable projection layer that strictly enforces the safety guarantees induced by the learned backup policy. By maximizing the volume of the implicit control‑invariant set in the first phase, the resulting PS2 policy from the second phase is performant and scalable, while maintaining provable safety. Crucially, PS2‑RL imposes no restrictions on the underlying RL algorithm and can be plugged into any existing training pipeline. We establish theoretical guarantees for the proposed framework and evaluate it on robotic control tasks with state dimensions up to 10, a regime in which prior provably safe RL methods struggle or become impractical.

Authors:Uto Perra, Faezeh Pak, Evangelia Pontika, Sahil Bhapkar, Daniel Ewald, Dario Buzzola, Theo Hofman, Frank Willems, Mauro Salazar
Title: Optimization Models and Steady-State Minimum-Fuel Operating Strategies for Hydrogen-based Hybrid Electric Aerospace Propulsion Systems
Abstract:
This paper presents an optimization framework for the operation of hydrogen‑based hybrid electric aerospace propulsion systems consisting of a hydrogen gas turbine and an electric motor powered by a solid oxide fuel cell, connected to the gas turbine via multiple gas channels and heat exchangers. Our framework computes the minimum‑fuel optimal operating strategies over a flight mission accounting for the complex propulsion system with strong thermodynamic and mechanical coupling between components. First, we identify surrogate optimization models of the components employing high‑fidelity model simulations. Second, we frame the minimum‑fuel optimal control problem over a given flight mission and parse it into a static nonlinear optimization problem that can be efficiently solved with off‑the‑shelf nonlinear programming algorithms. Finally, we apply our optimization framework to a typical flight mission of an advanced, commuter aircraft (Beechcraft 1900D market segment), considering a parallel propulsion system architecture with four different configurations that share a common baseline but differ in the inclusion of an additional battery and by‑pass valves around the two heat exchangers. The resulting optimal trajectories are validated against high‑fidelity simulation results, demonstrating the accuracy of our framework. Results show that adding by‑pass valves around the air and hydrogen heat exchangers can reduce fuel consumption by 19.11 % without the battery, and by 19.56% with the battery. We show that adding a battery yields a slight increase in fuel consumption (below 1%) for future projected energy densities under steady‑state conditions. Conversely, when considering state‑of‑the art energy densities, the additional battery weight outweighs the benefits, limiting its potential applicability to only assisting transients, which are not considered in the present work.

Authors:Fabian Jakob, Andrea Iannelli
Title: A Generalized Plant Perspective on Linear-Convex Feedback Optimization
Abstract:
Feedback optimization is a control approach for driving a dynamical system to the solution of an optimization problem by interconnecting the plant with an algorithm. Existing stability guarantees typically rely on timescale separation, enforced by conservative gain bounds that limit transient performance and require a pre‑stabilized plant. This paper revisits the robust control perspective on feedback optimization. We formulate the plant‑optimizer interconnection as a generalized plant, where the cost gradients are characterized by Zames‑‑Falb Integral Quadratic Constraints. Classical timescale‑separation bounds are recovered as a special case of static multipliers, with dynamic multipliers yielding substantially tighter stability margins. The formulation also enables IQC based synthesis of dynamic output feedback controllers that jointly stabilize the plant and optimize transient performance, with possible model uncertainty absorbed into an uncertainty channel. For constrained problems, the framework extends to dynamic controllers that generalize projected gradient flows. Numerical examples illustrate the benefits and flexibility of the proposed approach.

Authors:António Nunes, Pedro Batista, Sérgio Brás
Title: Orbital Station-Keeping in the Earth-Moon System via Nonlinear Backstepping
Abstract:
A nonlinear orbital station‑keeping solution for the circular and elliptic versions of the Earth‑Moon Restricted Three‑Body Problem (R3BP) is developed via a backstepping technique. Formal guarantees for global asymptotic stability (GAS) are attained, as shown through Lyapunov's stability theory. The adequacy of the proposed control law is evaluated through the means of numerical trials over closed periodic solutions of the circular and elliptic R3BPs. The ramifications of the control gain choice are carefully studied and simulated.

Authors:António Nunes, Sérgio Brás, Pedro Batista
Title: A Floquet Mode LQR for Orbital Station-Keeping in Cislunar Space
Abstract:
A linear optimal control law for orbital station‑keeping in the Earth‑Moon Restricted Three Body Problem (R3BP) is developed via Linear Quadratic Regulator (LQR) theory. First, the cost function is established considering a periodic state‑weight matrix, leveraging stability information of the target orbits retrieved through Floquet theory. Then, the resulting periodic Riccati differential equation is solved and local asymptotic stability guarantees are shown. Finally, the performance of the proposed LQR when tracking periodic orbits in the circular and elliptic R3BPs is analyzed numerically.

Authors:Pol Jane-Soneira, Gösta Stomberg, Ognjen Stanojev, Orcun Karaca, Lennart Harnefors
Title: A Feedback Stability Theorem for Frequency-dependent Compensation of Excess and Lack of Passivity
Abstract:
This article studies the stability of feedback interconnections of linear time‑invariant systems based on frequency‑dependent passivity indices. Using these frequency‑dependent passivity indices, we show that the feedback interconnection of two systems can be certified to be stable even if both systems have a lack of passivity in terms of their scalar passivity indices. The main contribution of this paper is a new stability theorem based on frequency‑dependent passivity indices. Moreover, we discuss the connection of the proposed feedback stability theorem to prior results based on scalar passivity indices. A numerical case study showcases the advantages of frequency‑dependent passivity indices over scalar indices for feedback interconnections of linear systems.

Authors:Yuan Yang, Lipeng Zhu, Chao Deng, Jiayong Li, Quan Zhou, Cong Zhang
Title: Intelligent Domain Adaptation for Power System Transient Stability Assessment Under Varying Operating Scenarios
Abstract:
While deep learning‑based transient stability assessment (TSA) approaches have exhibited great potential in power system stability monitoring, they are prone to undergo performance degradation in practical contexts with frequent variations of operating conditions. To address this issue, this work develops an adaptive TSA framework via domain adaptation‑enabled deep transfer learning. First, for the sake of capturing the primary transient stability characteristics, a robust metric, i.e., heterogeneous hybrid distribution metric (HHDM), is designed through mathematical means to effectively handle multi‑scale Gaussian and long‑tail distributions of transient responsive data and to precisely quantify the intrinsic distributional discrepancies between the source and target domains corresponding to different operating scenarios. With the help of the HHDM, a Bayesian theory‑based dual‑distribution domain adaptation method is constructed, aligning not only marginal probability distributions between domains but also the distributions of sub‑domain categories. Such alignments enable fine‑grained transient stability feature transfer, helping significantly improve the adaptability of a well‑trained TSA model to target domains. Furthermore, a multilayer sparse regularization algorithm is introduced to mitigate feature volatility caused by variations in operating scenarios, thereby enhancing the model's generalization in the presence of unforeseen scenarios. Numerical tests on three test systems illustrate that, compared with conventional methods, the proposed framework improves online TSA accuracy by 0.5% to 5% in a cost‑effective manner, with the learning cost for TSA model update largely reduced.

Authors:Merijn Van Deyck, Tom Van Acker, Geraint Chaffey, Dirk Van Hertem
Title: Topology Optimization for DC Circuit Breaker Placement in HVDC Switching Stations
Abstract:
HVDC protection will be required in future multiterminal HVDC grids to prevent large outages caused by DC faults. Therefore, system‑level protection design is essential for the development of HVDC switching stations that connect several converter stations and lines within these grids. This paper presents an optimization method for the design of HVDC circuit breaker (DCCB) configurations in HVDC switching stations and electrical energy hubs. This approach builds on the current practice of using selected configurations based on pre‑defined protection strategies. In contrast to these existing methods, the DC switching station design in the proposed method offers significantly more flexibility and allows the consideration of large numbers of relevant operating conditions, leading to more effective, optimal design outcomes. A mixed‑integer linear optimization problem is formulated to design the DC protection and minimize the risk of high impact DC faults. An example case study demonstrates that the optimization method allows the calculation of the optimal number of DCCBs for a given DC switching station, based on the failure rates of DC grid components and the DCCB cost relative to the fault impact. With these results, the marginal benefit to risk reduction of each additional DCCB included in a DC switching station is calculated. Moreover, the result of the optimization problem provides the optimal breaker configuration for the required number of DCCBs and can consequently be used as a topological design tool for DC switching stations.

Authors:Aron J. Aertssen, Lars A. T. H. van Alen, Igo J. M. Besselink, Rudolf G. M. Huisman, René M. J. G. van de Molengraft
Title: Short-Horizon Position Accuracy of Single-Track Models: Implications for Motion Planning of Autonomous Vehicles
Abstract:
Accurate and computationally efficient vehicle models are essential for motion planning of autonomous vehicles, where positional accuracy directly affects trajectory feasibility and safety. However, the positional accuracy has not been systematically evaluated against real measurements. Therefore, this paper compares the short‑horizon positional accuracy of three single‑track vehicle models against vehicle measurements across various driving maneuvers. Model parameters are identified through dedicated experiments with the instrumented test vehicle. Rather than identifying a single best model, this work aims to provide insight into the trade‑offs between model complexity, parameterization quality, and positional accuracy for informed model selection in Model Predictive Control applications.

Authors:David Philipp Klüner, Stefan Kowalewski, Alexandru Kampmann
Title: StreamRTPS: Increasing DDS Bandwidth Efficiency by Reducing Protocol Overhead
Abstract:
In this paper, we propose three extensions to the Real‑Time Publish Subscribe wire protocol, on which Data Distribution Service (DDS) is based, to improve bandwidth efficiency. First, a stream negotiation mechanism exchanges static header information during discovery, replacing the full RTPS header at runtime with a compact 2 B identifier. Second, a payload aggregation scheme aggregates samples for the same locator into single UDP packets, reducing IP and UDP header costs. Third, a predictive heartbeat suppression strategy reduces control traffic by omitting heartbeats for periodic communication patterns, falling back upon detected loss or timing violations. All three mechanisms preserve Real‑Time Publish Subscribe(RTPS) compatibility by extending DDS discovery to activate these features when supported. Experimental results show that stream headers reduce bandwidth consumption by up to 27.9 % compared to conventional RTPS under best‑effort transport, and that heartbeat suppression yields a further 22.7 % reduction on top of stream headers under reliable transport, while preserving transmission latency in both cases.

Authors:Dalim Wahby, Lorenzo Schenk, Guillaume Ducard
Title: SOS-based Stability Verification for Saturated INDI Control of Hybrid-VTOL Aircraft Pitch Rate Dynamics
Abstract:
Incremental nonlinear dynamic inversion (INDI) is a prominent flight‑control strategy valued for its robust disturbance rejection; however, its formal stability verification has traditionally been limited to linearized dynamical models. This paper presents a formal nonlinear stability certificate for a saturated INDI pitch‑rate controller for a hybrid vertical take‑off and landing (VTOL) aircraft by representing the INDI controller via an equivalent recurrent equilibrium network (REN). By casting the saturated INDI architecture as a REN, the closed‑loop dynamics are exactly mapped to an augmented state‑feedback system. This structural equivalence enables the use of sum of squares (SOS) programming to synthesize a locally valid Lyapunov function without relying on conservative bounding approximations. The resulting certificate yields an inner estimate of the region of attraction (RoA) that explicitly accounts for actuator saturation, formally verifying the controller's stability in operating regimes where standard linear margins lose their validity.

Authors:Wei-Chen Li, Jeffrey Fang, Sasanka Polisetti, Yuexi Song, Glen Chou
Title: Robustness without Wrinkles: Parallel Simulation and Robust MPC for Certified Deformable Manipulation
Abstract:
We present CORD‑SLS, a real‑time control method for safe deformable object manipulation, with a focus on ropes and cloth. At its core is a GPU‑parallel differentiable simulator with contact smoothing which enables efficient gradient‑based planning through intermittent contact. To robustly satisfy constraints under model and sensing uncertainty, we develop a real‑time, GPU‑parallel output‑feedback robust model predictive control (MPC) algorithm that plans with this simulator. We further show that the simulator accelerates model‑based RL for training neural manipulation policies. To improve real‑world robustness, we use conformal prediction to calibrate visual‑feedback and perception‑error bounds for MPC, producing reachable tubes that enable high‑probability safe control. We evaluate CORD‑SLS on high‑dimensional, contact‑rich rope and cloth manipulation tasks in simulation and hardware, including obstacle avoidance, routing, folding, and smoothing. Across settings, CORD‑SLS achieves millisecond‑speed planning, exceeding baselines in safety, speed, and task success.

Authors:Lin Feng
Title: Environment-Aware Stable Neural Koopman Dynamics Learning for Input-Driven Systems under Environmental Constraints
Abstract:
Constructing predictive models of nonlinear dynamical systems from measurement data is a longstanding problem in systems identification and control. Although Neural ordinary differential equations~(Neural ODEs), Koopman operator approximations, and input‑aware architectures have each moved the field forward, none simultaneously addresses environment‑varying operating conditions, rigorous stability guarantees, and input‑to‑state stability (ISS) certification within a unified trainable framework. This paper introduces Environment‑Aware Stable Neural Koopman Dynamics Learning (ESNKD), which integrates four components: (i)~a bundle‑structured encoder that maps environmental observations to a geometrically regularized latent manifold, drawing on the fiber bundle framework; (ii)~an input‑conditioned Neural ODE whose residual term handles arbitrary external signals, extending the input concomitant philosophy; (iii)~a contraction synthesis layer enforcing convergence via Persidskii‑type tractable linear inequalities, analogous to the certification mechanism; and (iv)~a Koopman lifting stage with LMI‑based ISS verification that follows the theoretical pipeline of. Theoretical guarantees cover solution existence and uniqueness, incremental exponential stability, ISS with explicit gain bounds, and robustness to environmental perturbation. Experiments on five benchmark systems, including two robotic manipulation platforms, show consistent improvements over five competitive baselines in both prediction accuracy and safety certification rates.

Authors:Eve Tsybina, Nicholson Koukpaizan, Joshua Hambrick, Samim Konjicija, Slaven Peles
Title: HPC-Enabled Generator Importance Assessment for RTO-Scale Resource Adequacy Planning
Abstract:
Modern power systems are increasingly under stress as aging assets approach retirement and load growth outpaces new generation construction. The severity of this challenge varies by region: in the EU, the transmission grid can partially compensate for local generation shortfalls, while in the US, generation tends to be more localized, making retirements harder to offset. Retirement of generation has consequences for system reserves, fuel supply chain, and public health. We present an high‑performance computing (HPC) framework for rapidly assessing the grid importance of individual generating units and ranking them by primary fuel type, operating cost, or grid impact. Historically, such studies were computationally intensive and therefore conducted infrequently. This work demonstrates that such assessments can be completed in minutes, enabling planners to evaluate a much broader range of generation portfolio scenarios than was previously possible.

Authors:Bernhard Paus Graesdal, Alexandre Amice, Pablo A. Parrilo, Russ Tedrake
Title: Semidefinite Relaxations for Collision-Free Motion Planning
Abstract:
We study semidefinite relaxations for collision‑free motion planning. We focus on a point robot moving from start to goal through spherical obstacles in \mathbbR^n, subject to path continuity constraints and squared derivative costs; a setting that is conceptually simple yet captures the hardness of collision‑free motion planning. We formulate this problem exactly as a nonconvex problem over polynomial curves, and present a natural semidefinite relaxation. We contribute two key theoretical insights; to our knowledge this is the first theoretical analysis of semidefinite relaxations for collision‑free motion planning. First, we show that solving the convex relaxation is equivalent to solving, to global optimality, a related motion planning problem in a potentially higher‑dimensional space. This geometric interpretation yields necessary and sufficient conditions for tightness, and a clear intuition for when the relaxation is loose. Second, we show that the relaxation admits a symmetry reduction that makes it significantly smaller than one might expect, with positive semidefinite cone sizes that scale linearly with the polynomial degree and are independent of the ambient dimension. The resulting relaxation is 10 to 100 times faster than direct nonlinear programming transcriptions solved with SNOPT and IPOPT, exhibits significantly lower variance in solve times, and reliably finds a locally optimal path for the original problem. We demonstrate its effectiveness as a convex steering function in an RRT planner for minimum‑snap quadrotor planning with C^4 continuous trajectories.

Authors:Vincent Yinjun-Wang, Madeleine Udell
Title: Battery Bidding under Price Uncertainty in Wholesale Electricity Markets
Abstract:
Grid‑scale batteries increasingly influence outcomes in wholesale electricity markets, but their observed bid patterns remain difficult to interpret. In particular, bids that appear to reflect strategic withholding may instead arise from rational operations under price uncertainty and risk management. We develop an asset‑level model of a price‑taking battery that submits stepwise buy and sell bid curves in the day‑ahead market under a finite set of price scenarios. The battery chooses quantity‑‑price pairs to maximize a mean‑‑CVaR objective subject to physical and market constraints. A direct formulation is a mixed‑integer linear program, but we show that its integer decisions can be removed, yielding an exact linear programming reformulation suitable for empirical analysis. Our empirical results deliver three insights. First, withholding behavior can arise even without market power, because scarce stored energy and uncertain future prices increase the value of holding energy. Second, the effect of uncertainty depends on the state of charge: when stored energy is scarce, greater uncertainty raises sell bid prices, whereas when stored energy is abundant it can lower them. Third, risk management reshapes bid curves into layered structures that secure profitable execution across a broad set of scenarios while preserving some exposure to rare but valuable price spikes.

Authors:Abel C. H. Chen, F. J. Hwang, Yu-Chih Wei, Chin-Chen Chang, Bon-Yeh Lin
Title: Pseudonym Scheme Based on Hybrid Certificates for Security Credential Management System in Vehicular Communications
Abstract:
In recent years, the Institute of Electrical and Electronics Engineers (IEEE) and the European Telecommunications Standards Institute (ETSI) have developed a series of security communication standards for vehicular communications. These standards include mechanisms such as the Security Credential Management System (SCMS) and Butterfly Key Expansion (BKE) to protect vehicle privacy. However, these standards are mainly based on the Elliptic‑Curve Cryptography (ECC), which may be vulnerable to attacks from quantum computing in the future. In response to this potential risk, this study proposes a hybrid certificate that combines the ECC with Post‑Quantum Cryptography (PQC). This approach enables infrastructure systems to be built on cryptographic foundations that are more resilient to quantum‑based attacks. Furthermore, this study presents a generalized pseudonym scheme that is compatible with various cryptographic algorithms for generating pseudonym certificates. This design aims to eliminate the possibility of inferring any correlation between the public key in a pseudonym certificate and that in an enrollment certificate. This study also conducts a comprehensive performance evaluation of the RSA, ECC, and PQC algorithms, particularly those standardized by the National Institute of Standards and Technology (NIST). The comparison considers factors such as message length and computation time. Based on the findings, this study recommends suitable pseudonym schemes that adopt hybrid certificates for secure and efficient use in vehicular communications.

Authors:Viacheslav Sydora, Jasan Zughaibi, Denis von Arx, Quentin Boehler, Michael Muehlebach
Title: Learning Dynamic Swing-Up of an Inverted Pendulum using Remote Magnetic Actuation
Abstract:
Electromagnetic Navigation Systems (eMNS) have gained considerable attention for minimally invasive surgery and targeted drug delivery. While most of the literature relies on quasi‑static control of these systems, recent work has demonstrated the benefits of dynamic approaches. However, trajectory tracking far from equilibrium states remains largely unaddressed. We close this gap by demonstrating the first swing‑up of a magnetically actuated inverted pendulum using the clinically‑ready Navion eMNS. Although the inverted pendulum is not clinically relevant in itself, the proposed method utilizes torques and forces as control objectives, making it applicable to other magnetically actuated devices such as catheters and guidewires. Our approach combines trajectory optimization that accounts for internal eMNS dynamics with time‑varying Linear Quadratic Regulator (LQR) state feedback and Iterative Learning Control (ILC), which leverages previous trial data and the system's dynamic model to progressively refine the feedforward command. While LQR alone fails due to the complex phenomena of magnetic actuation, ILC enables successful swing‑up within six iterations. Furthermore, post‑experimental analysis reveals that the learned ILC correction closely matches the torque discrepancy predicted by high‑fidelity magnetic field model calibration, suggesting learning and adaptation as a promising tool to deal with uncertainties in electromagnetic actuation arising, e.g., from patient‑specific physiological motion patterns and field model calibration inaccuracies.

Authors:Zexin Li, Soheil Shirvani, Cong Liu
Title: TetraRL: A Self-Adaptive Runtime for On-Device Deep Reinforcement Learning Systems
Abstract:
Autonomous robotic systems, including autonomous vehicles, drones, and mobile robots, increasingly rely on on‑device Deep Reinforcement Learning (DRL) to adapt to dynamic environments. Unlike cloud‑based solutions, embedded DRL must perform training and inference directly on resource‑constrained hardware while maintaining timely decision‑making. This creates a fundamental challenge: balancing four tightly coupled objectives, real‑time performance, task reward, memory utilization, and energy consumption. Optimizing these objectives independently often leads to suboptimal behavior, while conventional multi‑objective methods may violate resource constraints and compromise reliability. This paper presents TetraRL, a self‑adaptive runtime framework for tetra‑objective on‑device DRL. TetraRL formulates embedded DRL as a unified optimization problem over real‑time, reward, RAM, and reserve (energy) objectives, and employs a preference‑conditioned reinforcement learning controller to dynamically navigate the resulting trade‑off space. The framework integrates a unified resource‑management abstraction, hardware‑aware DVFS control, and a runtime Override Layer for robust constraint enforcement. We implement TetraRL on NVIDIA Jetson AGX Orin and Orin Nano platforms and evaluate it across diverse DRL environments. Results show that TetraRL effectively balances all four objectives, achieves competitive trade‑offs under varying runtime preferences, and incurs negligible overhead. Moreover, a single trained policy can support runtime‑switchable optimization goals, providing a practical foundation for resource‑aware and self‑adaptive on‑device DRL.

Authors:Chandan Chaudhary, Alaaeldein Abdelkader, Yansong Pei, Mohammed Benidris, Joydeep Mitra
Title: Spatial Load Correlation in AI Data-Center-Dominated Power Systems
Abstract:
The proliferation of large‑scale data centers introduces spatially correlated demand profiles that challenge the long‑standing assumption of statistical independence of loads in power system analysis. This paper examines the emergence of such load correlations and evaluates their impact on data‑center‑dominated grids. Analytical derivations reveal that correlated load fluctuations amplify aggregate stochastic disturbances, reduce voltage stability margins through weakened reactive power stiffness, and degrade frequency stability margin by erosion of natural load diversity effects. Real‑time digital simulation studies confirm that moderate spatial correlation in distributed data centers produces simultaneous frequency deviations and voltage fluctuations across multiple buses. The findings offer transmission system operators a physics‑based perspective to interpret emerging oscillatory phenomena and establish stability planning criteria grounded in measurable load‑correlation structures rather than traditional diversity assumptions.

Authors:Chandan Chaudhary, Michael Murillo, Mohammed Ben-Idris, Joydeep Mitra, Dilip Pandit, Atri Bera
Title: Modal Analysis of Spatial Load Correlation in AI Data Center-Dominated Power Systems
Abstract:
Hyperscale AI data centers induce spatially and temporally correlated load fluctuations that violate classical independence assumptions and are not captured by time‑averaged spectral methods. These correlations are episodic and non‑stationary, requiring analysis that resolves transient structure. This paper applies Dynamic Mode Decomposition (DMD) to the temporal evolution of pairwise inter‑bus correlation coefficients to form a low‑dimensional state representation that enables modal analysis without a stationarity assumption. DMD eigenvalues encode the correlation regime: their location in the complex plane distinguishes sustained coherence, decaying transients, and intensifying events, while oscillation frequency maps to underlying physical coupling mechanisms. Using an IEEE 39‑bus Real‑Time Digital Simulator (RTDS) testbed with three converter‑interfaced AI data center loads driven by synthetic workload profiles, global DMD provides a time‑averaged modal baseline in a slow thermal band (f \approx 0.005\,Hz, |μ| = 0.91) captures 93.6% of total correlation energy. A sliding‑window DMD formulation identifies transient intensification events: 51 of 775 windows (6.6%) satisfy the |μ_k^(n)| > 1 criterion, which aligns with stochastic workload coincidences. Cross‑validation with RTDS voltage coherence confirms elevated coupling during these intervals. The proposed modal growth indicator provides an early‑warning signal of correlation intensification prior to peak pairwise coherence.

Authors:Rebin Silva Valan Arasu, Rajiv Gupta
Title: Solving Subgraph Extraction Problems Using $Δ$Search
Abstract:
Many NP‑hard graph problems can be modeled as optimal subgraph extraction problems with feasibility constraints. From Network Design to Facility Location, from Robotics to Graph Drawing, the subgraph extraction pattern emerges across diverse domains. Despite this commonality, these problems are typically solved with domain‑specific heuristics. Usually, these problems balance competing objectives such as maximizing coverage or minimizing cost while satisfying structural constraints such as connectivity, planarity and reachability. In this work, we introduce ΔSearch, a general and fast heuristic framework that exploits the insight of Reward‑Penalty optimization for solving a large class of subgraph extraction problems. The framework is easy to use as it only requires feasibility constraints and optimality criteria to be provided by the user to express the subgraph extraction problem. We also show how exact methods can be augmented with ΔSearch to improve their performance by aggressive pruning of the search space. We evaluate our framework on monotone graph problems such as Maximum Planar Subgraph (MPS) and Minimum Connected Dominating Set, Weighted Monotone problems such as Maximum Weighted Independent Set and Minimum Weighted Steiner Tree, and non‑monotone graph problems such as Prize Collecting Vertex Cover (PCVC) and Uncapacitated Facility Location Problem (UFLP). Our results show that ΔSearch matches or surpasses state of the art heuristics for MPS, UFLP and PCVC problems with similar runtime. For the remaining problems, ΔSearch achieves approximately 89% of the solution quality of the state‑of‑the‑art algorithms without any problem‑specific tuning

Authors:Umut Demir, Aamir Ahmad, Walter Fichter
Title: An integrated interpretable control effectiveness learning and nonlinear control allocation methodology for overactuated aircrafts
Abstract:
Nonlinear dynamics and the strong couplings that arise between multiple effectors undermine the assumptions behind conventional, linear control allocation techniques. When flight enters regimes where nonlinear effects dominate, linear allocators exhibit reduced accuracy due to increased model mismatch, which subsequently degrades performance and robustness of the flight control system. High fidelity onboard models and black box data driven approaches can recover accuracy across the flight envelope, but respectively impose computational burdens prohibitive for real time allocation and sacrifice the interpretability required for verification and fault diagnosis. This paper addresses these limitations by learning an explicit, physics constrained analytical model of the control effectiveness mapping from representative flight data using Sparse Identification of Nonlinear Dynamics. The resulting mapping is compact, interpretable, and admits analytical derivatives, enabling efficient computation within nonlinear solvers that additionally incorporate actuator dynamics, without requiring an onboard model. An online adaptation mechanism monitors prediction residuals and refreshes the model when significant plant changes are detected, providing graceful reconfiguration under actuator failures and varying operating conditions. The methodology is evaluated on a high fidelity nonlinear benchmark aircraft across a range of aggressive maneuvers, achieving accuracy comparable to a full nonlinear onboard model while substantially reducing computational cost relative to established baselines.

Authors:Dénes Toth, George Ambroladze, Edwin Sundberg, Ali Beikmohammadi, Alfreds Lapkovskis
Title: Active Inference for Adaptive Traffic Signal Control in Noisy Nonstationary IoT Environments
Abstract:
Urban traffic signal control at IoT‑instrumented intersections must remain effective under sensor occlusion, weather attenuation, and nonstationary demand. Conventional controllers degrade under these conditions, and learned policies remain difficult to audit. To address these challenges, we propose an active inference controller for a four‑arm signalized intersection that dynamically selects phases by minimizing expected free energy (EFE) over Gaussian beliefs about per‑direction congestion levels, yielding a fully traceable decision pipeline. We benchmark the controller in a SUMO traffic simulator against a rule‑based heuristic and a deep Q‑network (DQN) across four scenarios that progressively increase noise and nonstationarity, spanning sensor occlusion, adverse weather, and stochastic accidents. Across 100 independent random evaluations per scenario, active inference attains the lowest idle times and CO2 emissions in the noisiest scenarios (56,977 s and 29.12 kg vs. 71,741 s and 30.56 kg for DQN). These gains come at a modest cost in bus priority service rate and phase switch frequency.

Authors:Ion Matei, Maksym Zhenirovskyy, Takuya Kurihana, Rohit Vupala, Anthony Wong
Title: Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model
Abstract:
Aerial wildfire suppression requires not only predicting fire spread, but also designing effective intervention strategies under operational and environmental uncertainty. We present a modeling and optimization framework for aerial wildfire suppression that combines a hybrid neural‑cellular automaton wildfire model with gradient‑based design of targeted aerial drops. The wildfire model predicts spatially varying spread behavior from terrain, fuel, and wind data, while the intervention module determines binary drop actions with continuous‑valued location and orientation parameters mapped to the simulation grid. Water and retardant are represented with distinct suppression effects, corresponding to immediate reduction of active burning and persistent reduction of future spread. To evaluate the robustness of the resulting suppression plans, we quantify both aleatoric uncertainty through Monte Carlo sampling of daily fire‑state realizations and epistemic uncertainty through spatially correlated prediction‑error perturbations. A case study based on the 2020 Bear Fire shows that the framework can generate coherent aerial suppression schedules for reducing total fire‑affected area and can support uncertainty‑aware analysis of wildfire intervention strategies.

Authors:Yashdeep Chaudhary, Roberto Armellin, Harry Holt, Marco Sagliano
Title: Distribution-Agnostic Robust Trajectory Optimization via Chance-Constrained Reinforcement Learning
Abstract:
This paper presents a distribution‑agnostic robust trajectory‑optimization framework based on chance‑constrained reinforcement learning. The uncertainty is represented here through initial conditions and process noise, with the only requirement being that it can be sampled. A deterministic nominal trajectory is first computed offline, and reinforcement learning is then used only to robustify that baseline through a structured affine closed‑loop correction law comprising a feedforward control adjustment and time‑varying feedback gains. Probabilistic feasibility is enforced empirically through rollout‑based upper‑tail quantiles, while terminal dispersion is regulated through covariance‑feasibility penalties. The framework is assessed on two materially different trajectory design problems. The flagship case study is a three‑dimensional multi‑impulse Earth‑Mars transfer, where the learned policy is benchmarked against a recent robust trajectory‑optimization reference under Gaussian uncertainty and then evaluated under bounded uniform uncertainty and under process disturbances not seen during training. The second case study is a stochastic atmospheric pinpoint rocket landing problem, used to assess portability to a short‑horizon continuous‑thrust setting with drag, mass depletion, and glide‑slope constraints. The results show that the proposed framework can remain competitive in upper‑tail fuel cost while preserving probabilistic feasibility, and that the same robustification scaffold can be carried across heterogeneous spacecraft trajectory planning problems without redesign of its core stochastic‑control structure.

Authors:Haosen Yang, Guowu Wei
Title: MCR-Bionic Hand: Anatomical Structural Priors for Dexterous Manipulation
Abstract:
Dexterous robotic hands are usually formulated as high dimensional active control systems governed by degrees of freedom, actuation, and algorithms. Human hand dexterity, however, is partly encoded in the physical architecture of bones, ligaments, tendons, aponeuroses, and intrinsic muscles. This work describes that contribution as two linked forms of structural intelligence: structural prior generation, in which wrist to finger tenodesis, FDS/FDP routing, and the dorsal extensor hood transform low dimensional posture inputs into default grasp configurations and PIP to DIP coordination; and muscle mediated modulation, in which extrinsic muscles, lumbricals, and interossei regulate MCP posture, distal stability, fingertip force paths, and contact states around that default state. Based on this framework, MCR‑Bionic Hand is developed as a 1:1 musculoskeletal biomimetic hand integrating a two row eight bone wrist, cross wrist tendons, anatomical flexor routing, volar plate and collateral ligament constraints, the dorsal extensor hood, and intrinsic muscle pathways within one body. Functional demonstrations and geometric mechanical models show that wrist posture induces multi joint pre shaping, the extensor hood maps PIP posture to a coupled DIP response, and intrinsic plus pathways modulate distal stability and fingertip action direction after grasp formation. Contact rich tasks, including coin rotation, pen transfer, dorsal coin flipping, and cube manipulation, show that MCR‑Bionic links low dimensional state generation with fine post contact modulation. These results suggest that anatomical biomimetics is valuable not for visual similarity, but for identifying human hand structures that perform part of control.

Authors:Shankar A. Deka
Title: Differential Geometric Conditions for Koopman Linearizability of Control-Affine Systems
Abstract:
Koopman linearization opens many possibilities for control synthesis and analysis of nonlinear systems. Whether or not any given nonlinear control system admits a finite‑dimensional Koopman representation remains a crucial question to address. A related problem is to categorize the class of all Koopman linearizable nonlinear control systems. In this work, we present differential geometric conditions on the drift and control vector fields of a control‑affine nonlinear system, that must be necessarily satisfied for Koopman linear transformation to exist. The same conditions are also shown to be sufficient for (a slightly weaker notion of) Koopman linearizability on control‑invariant manifolds. Further, these conditions, together with an additional condition, become necessary and sufficient for Koopman linearizability to a controllable linear system. Our examples illustrate the ease of checking these conditions, and also shed light on how Koopman linearizing transformation may not exist for a control‑affine system even though one can linearize the autonomous part of the system via Koopman lifting.

Authors:Paolo Testolina, Ergest Beshaj, Michele Polese, Tommaso Melodia
Title: Spectrum Sharing Across Terrestrial and Non-Terrestrial Services in the FR3 Upper Midband
Abstract:
The frequency bands between 7 and 24 GHz, also known as upper midband or Frequency Range (FR) 3, are being considered as an enabler of 6th Generation (6G) mobile networks. This portion of the spectrum exhibits different propagation characteristics compared to frequencies above 24 GHz, while also offering the potential to provide larger bandwidth allocations for mobile systems than those available in the sub‑6 GHz range. 6G technology and spectrum policy, however, will need to guarantee coexistence with the incumbents that already use these frequency bands, which include a variety of services, from radiolocation to satellite‑based communications, remote sensing, and radioastronomy. In this paper, we consider the challenge of coexistence between 6G terrestrial systems and satellite incumbents in different portions of the FR3 bands. Using a large‑scale 3D model of a terrestrial deployment in the city of Boston and an open‑source ray tracing solution, we evaluate the level of Radio Frequency Interference (RFI) that tens of terrestrial Next Generation Node Bs (gNBs) generate toward satellites at different elevation angles. Our model, based on realistic obstruction, clutter, diffraction, and reflections, shows that sidelobes and Non‑Line‑of‑Sight (NLoS) paths can significantly contribute to RFI. Besides directionality, the spatial distribution of gNBs also plays a key role in defining the RFI levels, suggesting that a careful design and operation of terrestrial deployments can create coexistence opportunities.

Authors:Yongyan Cao, Jinshan Tang
Title: Impedance MPC with Patient-Torque Estimation for Knee Rehabilitation Exoskeletons
Abstract:
Knee rehabilitation exoskeletons must enforce a prescribed joint trajectory while remaining safely compliant with involuntary spasm and voluntary patient effort‑objectives in tension for any fixed‑gain impedance controller. We present an Impedance Model Predictive Control framework for knee rehabilitation exoskeletons, demonstrated on a series‑elastic‑actuator (SEA) platform: an algebraic feedforward reduces the knee dynamics to a constant‑coefficient scalar double integrator, and a receding‑horizon quadratic program (QP) computes corrective torques while enforcing hard range‑of‑motion, torque, and velocity limits (ISO 13482). A Kalman disturbance state driven by direct SEA‑based torque sensing (the series‑elastic spring deflection measured through the elastic element ‑ an intrinsic, EMG‑free patient‑torque estimate, not a separate load cell) gives a nominal offset‑free guarantee and, via its sign and the desired‑motion direction, sensorless Assist‑as‑Needed. The constant state matrix permits offline precomputation of the QP cost inverse, enabling 500 Hz operation with a multi‑step horizon. Across seven‑controller benchmarks (sinusoidal tracking, isometric hold), the 500 Hz Kalman MPC is offset free 0.1 mrad RMS, 0.1 mrad steady‑state, 0.2 mrad peak under 15 Nm spasm, versus a 515 mrad steady‑state offset for classical impedance at the same stiffness ‑ the direct‑measurement channel converging the estimate near‑immediately (within a few sampling periods). Without the estimator it realizes a classical impedance (4.8 mrad RMS, 8.3 mrad steady‑state). All MPC variants meet the 87 mrad clinical criterion; no classical controller does. The architecture is formulated for the 20 DOF MyoSuite myoLeg via coupling‑aware per‑joint QPs.

Authors:Gregorio Marchesini, Bjarne Jan Jesse Moro, Siyuan Liu, Lars Lindemann, Dimos V. Dimarogonas
Title: A Reactive Redistribution Mechanism for STL Tasks in Multi-Agent Systems Under Time-Varying Communication
Abstract:
We present a communication‑aware task decomposition framework for multi‑agent systems with collaborative relative configuration objectives specified in Signal Temporal Logic (STL), allowing for dynamic task reallocation under time‑varying communication networks. Building on our prior work, the framework supports the direct use of existing feedback controllers for reactive task satisfaction. We address two key challenges: disjunctive STL specifications and time‑varying communication networks. Disjunctive specifications are handled through a graph transition system that captures the alternative task sequences induced by logical OR operators. To address time‑varying connectivity, we introduce a redistribution mechanism that transfers tasks from disconnected agents to connected ones as the network evolves while preserving decentralized execution. Simulations and experiments on a swarm of Crazyflie drones demonstrate scalability in the number of agents, communication connectivity, and specification complexity.

Authors:Zhiqi Tang, Yu Xing
Title: Embodied Opinion Dynamics for Safety-Critical Motion Control in Dynamic Environments
Abstract:
This paper proposes a novel adaptive control framework that embeds nonlinear opinion dynamics within the dynamical sensorimotor layers of an automated vehicle governed by second‑order nonholonomic bicycle kinematics. The framework enables an ego vehicle to perform adaptive decision‑making and achieve safe motion control under interaction uncertainty with non‑cooperative neighboring agents. We consider a representative case study in which an ego vehicle autonomously attempts to merge into a lane occupied by human‑driven or automated vehicles whose intentions are unknown. Within the proposed framework, the ego vehicle adaptively selects and executes merging versus non‑merging behaviors in response to changing environmental conditions. Formal safety guarantees, as well as equilibrium and stability analyses of the closed‑loop system, are provided. Numerical simulations further demonstrate the effectiveness of the proposed approach.

Authors:Paul Trodden, Xinda Li
Title: When expectation fails: stochastic MPC of linear systems with random input losses
Abstract:
We consider stochastic model predictive control (MPC) for constrained linear systems subject to multiplicative binary input uncertainty, motivated by applications such as networked control with packet losses and intermittent actuation. A common approach in this setting replaces the stochastic dynamics with their expectation, yielding tractable formulations that admit standard terminal ingredients and stability guarantees in expectation. We show that such formulations can exhibit structural properties that differ fundamentally from those of deterministic MPC and may be misleading as indicators of realized closed‑loop behaviour. In particular, the expected value function is not necessarily monotonic in the prediction horizon, and value function‑based inner approximations of the region of attraction may deteriorate as the horizon increases. Furthermore, we establish a probabilistic comparison with certainty‑equivalent (optimistic) MPC, showing that the latter can ensure a strictly positive probability of recursive feasibility in situations where stochastic MPC certifies feasibility but fails with probability one. These results highlight inherent limitations of expectation‑based stochastic MPC for systems with multiplicative binary uncertainty and motivate a re‑examination of how stochasticity is incorporated into constrained predictive control design for such systems.

Authors:Anup Joshi, Javier Renedo, Xavier Guillaud
Title: Sizing of a grid-forming power converter to improve the small-signal stability of an LCC-HVDC system connected to a weak grid
Abstract:
Line‑commutated converter high‑voltage direct current (LCC‑HVDC) has proven to be a reliable technology for bulk power transmission over long distances. However, the growing penetration of converter interfaced generation (CIG) is resulting in weaker AC grids, rendering the operation of LCC‑HVDC systems vulnerable and posing a serious challenge to their stability. Grid‑forming (GFM) controlled voltage source converter (VSC) have been shown to provide stabilizing impact in weak grid conditions. However, the impact of GFM controlled VSCs (GFM‑VSC) on stability of LCC‑HVDC in weak grid conditions has not been studied in depth in the literature. In this paper, a simplified model of LCC‑HVDC is proposed and validated. Then a small‑signal state‑space model of a system consisting of aforementioned LCC‑HVDC, a GFM‑VSC and an infinite grid is developed to study the interactions between different components. The small‑signal stability analysis shows the stabilizing effect of the GFM‑VSC on the stability of the LCC‑HVDC link in weak grid condition. Furthermore, the study on the sizing of the GFM power converter reveals that even a modest share of the capacity of the GFM power converter relative to the total nominal apparent power (sum of nominal power of LCC‑HVDC and the nominal apparent power of GFM‑VSC) is sufficient to ensure the stability of the system, in the test system analyzed in this study. This work just focuses in small‑signal stability, but it is important to highlight that other stability phenomena should also be taken into account when selecting the final size of the GFM‑VSC.

Authors:Joas Kahlert, Jiali Fu, Chengxi Liu
Title: Multi-Phase Optimization of Shared Charging Infrastructure for Freight Electrification
Abstract:
The transition to heavy‑duty battery electric vehicles requires an efficient and cost‑effective deployment of the charging infrastructure, particularly when multiple operators share resources. This paper presents a multi‑phase optimization framework for the joint planning of charging stations in a shared network, using high‑resolution empirical truck trajectory data from two freight companies with distinct operational characteristics. The model is formulated to minimize the total number of charging stations while ensuring that the predefined electrification targets are met over successive expansion stages. The analysis captures heterogeneity in fleet usage, with one company operating a spatially concentrated network with shorter and more consistent routes, and the other exhibiting more dispersed operations with longer and more variable driving patterns. The results show that early‑stage infrastructure deployment primarily supports fleets with concentrated operations, while later expansion phases are essential to accommodate long‑haul and geographically dispersed transport demand. Furthermore, shared infrastructure not only enables reductions in redundant investments, but also introduces dependencies where certain fleets rely heavily on the full network to sustain electrified operations. In general, the findings highlight the importance of coordinated and data‑driven infrastructure planning, and demonstrate that fleet‑specific characteristics strongly influence both infrastructure requirements and electrification outcomes. The proposed framework provides practical insights on how collaborative and phased deployment strategies can enhance the scalability and efficiency of freight transport electrification.

Authors:Jonathan Brandt, Astrid Bensmann, Richard Hanke-Rauschenbach
Title: Mitigating business risks from renewable PPA power sourcing uncertainties for European green hydrogen production: Robust system design, regulatory adjustments and offtake flexibility
Abstract:
As energy prices surge for the second time in recent years driven by the ongoing crisis in the Middle East, the European Union's continuing reliance on fossil energy imports is becoming increasingly apparent. However, despite offering an intriguing prospect of improved energy resilience, the ramp‑up of local green hydrogen production lags far behind the officially stated ambitions set after the 2022 energy crisis. A prominent reason for the widening implementation gap between announced and realised production projects is overly strict rules on renewable power sourcing, prompting Member states' ministries and the European Commission to propose advancing a planned rules review from 2028 to 2026. To contribute to a successful review and rule adjustments, we address an important gap in understanding the effects of power purchase rules on green hydrogen production. By taking the perspective of European electrolyser operators, we show how the criterion of additionality and its interaction with required temporal correlation can jeopardise the fulfilment of green hydrogen offtake agreements and affect green hydrogen production costs across different European bidding zones. Applying different design paradigms to a green hydrogen production system reveals that electrolyser operator measures, such as PPA and storage upsizing, can help to mitigate the business risks posed by the additionality criterion but come with increased costs. Alternatively, relaxed temporal correlation and increased offtake flexibility both increase production system robustness and reduce production costs simultaneously. Whereby relaxing temporal correlation rules does not result in exceeded emission intensity thresholds, underlining the potential of extended transitional rules to support the ramp‑up of European green hydrogen production.

Authors:Amirhossein Ayanmanesh Motlaghmofrad, Carlo Cena, Mauro Martini, Marcello Chiaberge
Title: MPC for underactuated spacecraft control with a Lyapunov supervised physics-informed neural network correction layer
Abstract:
Underactuated spacecraft faces controllability limitations and heightened sensitivity to environmental disturbances, complicating attitude maneuvering and stabilization. Due to the lack of control authority along the underactuated axis, conventional controllers cannot directly stabilize all attitude components and therefore require reference planning strategies. Furthermore, MPC approaches remain sensitive to inertia uncertainty and unmodeled dynamic couplings, resulting in degraded tracking performance under mismatch. To address these issues, we consider a hierarchical architecture integrating three layers: (i) a nonlinear model predictive controller (NMPC) for constraint and underactuation‑aware maneuver planning and nominal closed‑loop stability under actuator limits; (ii) a physics‑informed neural network (PINN) trained offline on simulation data to estimate residual disturbance torques, with loss terms that enforce consistency with rigid‑body rotational dynamics; (iii) a Lyapunov‑based supervisory safety mechanism that evaluates the learned correction online and bounds or suppresses its influence to preserve the stability properties of the baseline controller. The architecture is evaluated in a high‑fidelity simulation environment modelling reaction wheel dynamics, actuator saturation, and environmental disturbances. Monte Carlo studies show statistically significant reductions in steady‑state attitude error relative to standalone NMPC while maintaining robust behavior under uncertainty. The supervisory layer ensures graceful degradation to purely model‑based control when the learning‑based augmentation is unreliable.

Authors:Chiara Cavalagli, Alberto Bemporad, Mario Zanon
Title: Characterization and Computation of Feedback Nash Equilibria in Scalar Discounted N-Player Linear Quadratic Games
Abstract:
This paper studies feedback Nash equilibria (FNE) in scalar discounted linear quadratic (LQ) games with N players. By explicitly incorporating the discount factor, we show that finite‑cost equilibria may fail to stabilize the original system, motivating a distinction between FNE and stable FNE together with a sufficient stability condition. Based on a parametric characterization of the policies, we propose numerical methods for computing all equilibria. Particular attention is devoted to the symmetric game, where a closed‑form expression of the symmetric FNE and conditions for the existence of up to M\leq2^N‑2 equilibria are derived. Numerical experiments illustrate how equilibrium multiplicity depends on the game configuration and highlight the emergence of finite‑cost non‑stabilizing equilibria.

Authors:Baptiste Corban, Ana Bušić, Donatien Dubuc, Jiamin Zhu
Title: Proportional power dispatch and fairness in wind farm power tracking
Abstract:
Controlling the power output of a wind farm in order to track a target signal can be useful for the power grid frequency regulation. It can be achieved by dividing the target into individual setpoints, then followed by each turbines' controller. In this article, we are interested in finding power allocations that fairly spread the power reserves (i.e. unused fraction of available powers) among turbines, helping with robustness to uncertainties and changing wind conditions. In particular, we study the fairness properties of proportional dispatch, which is the most common power dispatching method. We show that due to the wake effects in a wind farm, proportional dispatch has to be applied iteratively to achieve fair distribution of power reserves. We study the convergence of this iterative process (referred to as IPD) to equalized reserves, and then illustrate it on simulated experiments, using steady‑state and dynamic simulators. Numerical results show that IPD closely approaches max‑min fairness, a related fairness objective, for a cheap computational price compared to black‑box optimization. Finally, IPD is also shown to reduce the complexity of the problem of fair power dispatch combined with yaw wake steering optimization.

Authors:Gokul Puthumanaillam, Vardhan Dongre, Pranay Thangeda, Hooshang Nayyeri, Dilek Hakkani-Tür, Melkior Ornik
Title: Trajectory-Level Redirection Attacks on Vision-Language-Action Models
Abstract:
Vision‑language‑action (VLA) policies bring natural language into closed‑loop robot control, enabling robots to execute manipulation tasks directly from text instructions. The same interface gives text a recurring role in control because the prompt is reused at every replanning step, and each prompt‑conditioned action changes the future observations on which the policy acts. Existing VLA attacks study adversarial prompts that elicit targeted low‑level actions or make such actions persist across changing images. We identify a stronger trajectory‑level failure mode: a prompt that still appears to specify the intended task but redirects the final physical outcome. We mathematically formalize this setting as command‑preserving trajectory redirection, a prompt‑only threat model in which the attacker chooses one prompt before the episode, all policy and environment components remain fixed, and the prompt must stay close to the benign instruction while omitting target words and correction language. To find such prompts, we introduce an on‑policy prompt search method that uses rollouts to discover perturbations whose closed‑loop behavior tracks a target task while satisfying the command‑preserving constraints. Experiments in simulation and on hardware show that near‑benign prompt perturbations can redirect VLA rollouts to attacker‑specified targets. These results expose a trajectory‑level vulnerability in VLA instruction grounding: text that appears to preserve the intended command can still give an adversary control over the robot's final physical outcome. Project website: https://vla‑redirection‑attack.github.io/

Authors:Yifei Li, Wenjie Liu, Gang Wang, Lihua Xie
Title: Data-Driven Frequency-Selective Output Regulation of Nonlinear Systems under Almost Periodic Exosignals
Abstract:
This paper studies output regulation for a class of unknown continuous‑time nonlinear systems driven by almost periodic exosignals. The plant dynamics are assumed to be linearly parameterized over a prescribed nonlinear dictionary, while all coefficient matrices in the plant, input channel, output map, and exosignal channel are unknown. Since the plant model is unavailable, exact nonlinear output regulation would generally require model identification followed by the solution of nonlinear regulator equations. To avoid these steps, we pursue a frequency‑selective regulation objective: the steady‑state regulation error is allowed to be almost periodic, but its Fourier‑Bohr coefficients at prescribed exosystem frequencies are guaranteed to vanish, and the residual error energy is explicitly bounded. To this end, a p‑copy internal model is embedded in a dynamic controller, yielding an augmented nonlinear system whose unknown constant matrices are represented directly by measured data. A noise‑robust semidefinite program is derived to synthesize the controller gain without model identification and without measuring the exosignal amplitudes or phases. The resulting closed‑loop vector field is made exponentially contractive on a prescribed operating set, which implies the existence and uniqueness of a bounded and attracting trajectory. By combining contraction theory with Fourier‑Bohr analysis, we prove that this steady‑state trajectory is almost periodic, that the embedded‑frequency components of the regulation error are eliminated, and that the unmodeled spectral components satisfy a Parseval‑type time‑averaged energy bound. Numerical and physics‑based simulations on a quadrotor with a cable‑suspended payload illustrate the effectiveness of the proposed data‑driven internal‑model design.

Authors:Michael Yuhas, George Gunter, Jose Paulo Talusan, Aron Laszka, Dan Freudberg, Abhishek Dubey
Title: Computing Headway Bounds under Worst-Case Bunching in Fixed-Line Transit Systems
Abstract:
Vehicle bunching is a major problem for transit operators. When vehicles bunch together, the lead vehicle will service the majority of passenger demand, leaving the following vehicles to operate below capacity, wasting fuel and money. Furthermore, after the last vehicle in the bunch passes, the time before the next vehicle's arrival (headway) will be large. Transit operators can combat bunching by holding buses at stops along a route, trading riding time for even headway times. While prior work has focused on developing holding policies to minimize average case bunching, no work has focused on analyzing the longest and shortest possible headway times under a broad group of such policies. We assume that dwell times at stops and travel times between stops are bounded and develop a dynamic program that computes the maximum and minimum headway times for a single bus route with an arbitrary number of control points, vehicles, and holding policies. These bounds are tight in the sense that it is always possible to identify the specific sequence of events that lead to their occurrence. We use these bounds to investigate the effects of different holding policies, stop placement, and number of vehicles on route headways and worst‑case bunching. Finally, we apply these analysis techniques to a real‑world transit system in Nashville, TN and show their utility for transit planning.

Authors:Ahmad Ali, Hantao Cui
Title: Homotopy-Based Re-Initialization for Switched DAEs in Power System Transient Simulation
Abstract:
The simultaneous solution of switched differential‑algebraic equations (DAEs) in power system transient simulation may suffer convergence loss following discontinuous events. This difficulty is typically interpreted as a poor post‑event initialization problem. This letter presents a geometric framework that explains the underlying convergence mechanism and clarifies why standard convergence‑restoration methods may fail at discontinuities. Based on this interpretation, a homotopy‑continuation based globalized re‑initialization scheme is developed to restore convergence. The proposed method is validated through numerical simulations of representative discontinuities in power system transient simulation. Results show that in the cases where direct post‑event solution fails, the proposed scheme can reliably recover convergence.

Authors:Soham Ghosh, Anik Goswami, Krishna Kumba
Title: Pushing the Frontiers for Floating Solar Photovoltaics -- The Case for South America
Abstract:
Floating solar photovoltaic (FSPV) systems provide a land‑efficient pathway to expand clean electricity access in energy‑poor regions. South America has among the highest global FSPV potential (approx 38.26 TWh per million acres of water surface), yet deployment remains limited. This study presents a techno‑socio‑economic framework to assess FSPV for energy access, water security, and grid flexibility, with case studies in Nicaragua, Honduras, and Guyana. Estimated yields for 50 to 398 MW systems exceed 1,500 to 2,000 kWh per kW annually with capacity factors above 20 percent. At El Cajon, FSPV could significantly reduce emissions relative to fossil generation. Results show competitive costs with land‑based PV when accounting for avoided land use, shared hydropower infrastructure, and water benefits. The framework also highlights co‑location with hydropower and AI data centers, offering a scalable model for deployment in underserved regions.

Authors:Yun-Su Kim
Title: The GIST 2064-Bus Test System: A Public-Data Synthetic Model of the Korean Power Grid
Abstract:
No model of the Korean transmission system at native resolution is publicly available, which makes reproducible research on one of the world's most distinctive grids difficult‑an islanded interconnection with extreme separation between generation and the Seoul Metropolitan Area load center, low renewable penetration, and heavy reliance on extra‑high‑voltage (EHV) transmission. Working strictly from public data, and for research purposes only, we present the GIST 2064‑bus test system, a geographically grounded synthetic model of the Korean grid. Unlike fully synthetic cases, whose lines match no real corridor, and aggregated public Korean models, it derives its 345 and 154 kV layout from the OpenStreetMap/OpenInfraMap power layer by a multi‑source shortest‑path reassembly of overhead‑line geometry, gap‑fills unreachable substations with a geographic minimum‑spanning‑tree backbone, and calibrates the aggregate circuit length to published national statistics (108/107/97% at 765/345/154 kV). The model spans 2064 buses, 512 generation and renewable sources (144 GW), 3044 AC line circuits plus high‑voltage direct‑current (HVDC) equivalents, 3073 transformers, and reactive resources (shunts and 11 FACTS devices), serialized to a PSS/E‑compatible CSV schema. A general‑purpose pandapower Newton‑Raphson solver‑with generator reactive limit enforcement, a secant‑gain remote voltage‑control loop, tap‑changer and switched‑shunt fixed‑point control, and zero‑impedance regularization‑solves an 85 GW high demand snapshot to a single connected, converged operating point (mean voltage 0.996 pu, 2.3 % losses, no undervoltage buses), structurally consistent with the independent public KPG‑193 model. The dataset, maps, and tooling are released as a citable platform for power flow, planning, and decarbonization studies.

Authors:Han-Teng Liao, Chang-Yi Kao
Title: To Share or Not to Share: Orchestrating Trustworthy Data in Global Value Chains
Abstract:
As the EU Carbon Border Adjustment Mechanism (CBAM) approaches, the global semiconductor value chain faces growing structural tensions between regulatory transparency and data sovereignty. This article proposes a RegTech reference architecture using the International Data Spaces (IDSA) framework to orchestrate trustworthy environmental telemetry across the semiconductor‑petrochemical nexus. The framework distinguishes the mandatory CBAM requirements from voluntary Science Based Targets initiative (SBTi) frameworks, while addressing the additive complexities of the Safe‑and‑Sustainable‑by‑Design (SSbD) framework. Moving beyond standard linear technology stacks, we introduce a prospective roadmapping methodology that transforms upstream physical vulnerabilities into circular, negative feedback loops. Focusing on the Taipei and Penang technology corridor, the article details how sovereign data exchange enables Digital Product Passports (DPPs) to drive Global Business Services (GBSs) capability demands. Finally, we discuss the integration of Agentic AI for autonomous compliance and FinTech green financing, providing a scalable blueprint for global industrial clusters to achieve sovereign, sustainable, and transparent value chains.

Authors:Han-Teng Liao, Karen Ang
Title: Orchestrating the Twin Transition in Multinational Corporations: Technology Roadmapping for Green and Digital Global Business Services
Abstract:
Global Business Services (GBS) have emerged as a "living laboratory" for the Twin Transition of Green and Digital Transformation, as multinational corporations (MNCs) face increasing pressure to harmonize digital efficiency with environmental stewardship. Aiming to derive a socio‑technical framework, this paper synthesizes Technology Roadmapping (TRM) with the International Telecommunication Union (ITU) ICT‑centric innovation ecosystem toolkit. A bibliometric analysis of research clusters reveals an evolutionary shift from basic process automation toward "Sustainable Intelligence," identifying the GBS unit as a central "operational airlock" that mediates between landscape pressures ‑‑ such as the EU's dual mandate and Carbon Border Adjustment Mechanisms ‑‑ and niche innovations in AI‑native workflows. The study further maps these clusters onto a stakeholder engagement canvas, highlighting how resilient "Middle Power" hubs in Poland, Portugal, and Malaysia are bypassing the middle‑income trap to provide a "third way" for global value chains amidst a bifurcated geopolitical cloud. The results offer a data‑driven design approach for leaders and entrepreneurial support networks to orchestrate talent and supply chain flows, thereby enriching the conceptual understanding of Industry 5.0 and the role of GBS as a primary mechanism for navigating a volatile, multipolar digital economy.

Authors:Yuya Miyaoka, Masaki Inoue
Title: Agentic MPC for Semantic Control System Resynthesis
Abstract:
While MPC effectively handles structured, diverse, and low‑level specifications, it lacks the capability to dynamically incorporate high‑level contextual information such as social norms, user intent, or natural language instructions. To address this limitation, this manuscript introduces an agentic MPC framework that enables context‑aware, semantically adaptive control synthesis by integrating with large language model‑based agents. The agent interprets heterogeneous inputs, including natural language messages, environmental observations, and external knowledge, to resynthesize the control specifications. The effectiveness of the framework is demonstrated in an autonomous driving scenario, where the system aligns with personal preferences or responds to social situations such as emergency vehicle yielding.

Authors:Ruikun Zhou, Yating Yuan, Haocheng Chang, Yinan Li, Yiming Meng
Title: Patching Control Lyapunov Barrier Functions for Temporal Logic Specifications with Bounded Controls
Abstract:
We propose an abstraction‑free framework for controller synthesis for continuous‑time dynamical systems subject to Linear Temporal Logic (LTL) specifications and bounded control inputs. The proposed method combines the sequential decomposition of LTL tasks with the use of formally certified Control Lyapunov‑Barrier Functions (CLBFs). By formulating local specifications as a sequence of safe‑stabilization problems, we systematically approximate and patch the winning sets of the decomposed subtasks. The satisfaction of these local constraints is guaranteed by the offline‑computed level sets of the CLBFs. As a result, our framework yields formally verified switching feedback controllers that enable efficient online planning and dynamic re‑planning. This ensures robust continuous specification satisfaction in the presence of state perturbations, avoiding the explicit state‑space abstractions commonly required in the literature. The approach is validated through numerical simulations and a hardware demonstration on a Crazyflie quadrotor.

Authors:Alexandros Washburn, Carlo Lipizzi
Title: Beyond Resilience -- A Conceptual Framework for Civic Ascent
Abstract:
The resilience literature measures urban performance as recovery: the degree to which a city returns to its pre‑shock baseline. This paper develops a stronger concept ‑‑ civic ascent ‑‑ as part of a broader research program on the ethology of coupled agent‑environment systems, of which the city is the deepest available empirical instance. Civic ascent is defined as the condition in which a city emerges from shock with higher functional capacity than before. We develop a conceptual framework in the ethological tradition, treating the city as a coupled system of three slow state variables ‑‑ topos (physical structure), nomos (institutional structure), and hexis (civic judgment) ‑‑ together with a fast affective channel (delta) through which shocks to topos and nomos reach hexis. The framework distinguishes three structurally distinct pressures on civic systems: shocks (discontinuities in T or M), decay (continuous entropy), and leakage (active extraction of civic surplus into non‑civic pools). The ascent condition is that reinforcement from cross‑coupling of T, M, and H exceeds the combined loss from decay and leakage. Post‑shock ascent is measured by a normalised improvement index A(T) applied to a composite civic performance signal P(t) constructed from scale‑adjusted key performance indicators, distinguishing intrinsic civic ascent from demographically driven growth. New York City after September 11, 2001, is proposed as the primary empirical case; the operational measurement program is specified in the companion NYC Civic Data Map (Washburn 2026c, 133 KPIs) and executed in Paper 2. The reader for whom only the urban contribution is of interest will find it complete in itself; the reader interested in the larger program will find this paper its formal core.

Authors:Arnab Deya, Vivek Khatana, Ankur Mani, Murti V. Salapaka
Title: Storage and Transport Capacity Design for a Self-Reliable Two-Node Stochastic Resource System
Abstract:
We study a two‑node stochastic resource system operating over a finite horizon. Each node experiences uncertain supply and demand and is equipped with finite storage. The objective is to ensure that resource levels remain within prescribed limits with high probability. To this end, we formulate a chance‑constrained capacity‑design problem in which resources can be exchanged through a capacity‑limited transport link. We characterize the minimum storage required at each node, derive the optimal transport policy, and quantify the trade‑off between storage and transport capacities. Our results show the existence of a critical transport‑capacity threshold that enables full risk pooling between the nodes. Moreover, this threshold decreases with the operating horizon, implying that full‑pooling performance can be achieved with progressively smaller transport capacity over longer horizons.

Authors:Jonas Welsch, Dominik Goerick, Martin Angerer, Jinhao Lu, Sergei Vostrikov, Michael Kupke, Heinz Voggenreiter, Andrea Cossettini, Luca Benini, Edmond Cretu, Robert Rohling
Title: Polymer-based Capacitive Micromachined Transducer-Enabled Inline Monitoring of Ultrasonic Welding in Thermoplastic Carbon Fiber Composites
Abstract:
Thermoplastic composite structures enable lightweight, recyclable, and high‑throughput aerospace manufacturing, but reliable quality assurance of advanced joining processes remains a key challenge. This work presents a compact, low‑cost, and wireless ultrasonic non‑destructive testing system for real‑time, inline monitoring of continuous ultrasonic welding of thermoplastic carbon fiber composites. The system integrates custom‑fabricated polymer‑based capacitive micromachined ultrasonic transducers (polyCMUTs) with the ultra‑low‑power WULPUS platform, enabling operation in the harsh, high‑interference welding environment. An eight‑element linear polyCMUT array operating at a center frequency of approximately 3.6 MHz is designed, fabricated, packaged, and integrated into an industrial welding setup. Inline measurements are performed during welding of carbon fiber laminates with intentionally introduced defects. Process‑synchronous ultrasonic data reveal consistent depth‑of‑echo shifts at defect locations, in strong agreement with X‑ray computed tomography ground truth. Across 21 welds, all induced defects are detected without false negatives and with limited false positives. The results demonstrate that polymer‑based CMUT technology enables robust, scalable, and manufacturing‑compatible ultrasonic sensing, representing a decisive step toward intelligent process monitoring and quality assurance for next‑generation thermoplastic composite welding.

Authors:Yahya Sattar, Sunmook Choi, Leo Maynard-Zhang, Yassir Jedra, Maryam Fazel, Sarah Dean
Title: Two-Layer Linear Auto-Regressive Models Estimate Latent States
Abstract:
Auto‑regressive models have emerged as powerful tools for sequential data, from language to video. Understanding how and why these models learn latent representations remains an open theoretical question. In this work, we demonstrate that when trained by empirical risk minimization on data from partially observed linear dynamical systems, two‑layer linear auto‑regressive models naturally learn to approximate Kalman filtering. In particular, we show that the learned hidden representation coincides, up to a similarity transformation, with the state estimates produced by the optimal (Kalman) filter, even though the model has no explicit knowledge of the underlying dynamics or state. The result follows from three main insights. First, we establish that the Kalman filter is well approximated by an auto‑regressive model with bounded truncation error. Second, we show that despite non‑convexity, the two‑layer optimization landscape is benign, i.e., all stationary points are either strict saddles or global minima. Finally, as our main contributions, we provide finite‑sample guarantees on prediction error, parameter estimation error, and latent state recovery. Numerical simulations support the theoretical results and demonstrate that the latent representations of auto‑regressive models recover state estimates.

Authors:Grace Ra Kim, Duncan Eddy, Vedant Srinivas, Mykel J. Kochenderfer
Title: Free-Placement Optimization of Ground Station Locations for Low-Earth Orbit Satellites
Abstract:
Rapidly expanding low Earth orbit satellite constellations are placing increasing demands on terrestrial ground networks, motivating the development of more efficient ground station network designs. Current approaches select sites from predefined locations, limiting optimization to existing infrastructure and constraining performance. In contrast, free‑placement optimization operates over a continuous spatial domain on Earth, broadening the search space and allowing higher‑throughput configurations at the cost of potentially requiring new infrastructure deployment. In this work, we introduce SCORE (Sequential Cyclic Optimization via Refinement & Evaluation), a two‑stage free‑placement method for ground station design. SCORE combines sequential coordinate selection with cyclic refinement to manage high‑dimensionality, non‑convexity, and local minima that challenge global optimizers. We benchmark SCORE against one‑shot methods such as differential evolution (DE) and integer programming approaches using locations from Kongsberg Satellite Services and the World Teleport Association. Tests across two commercial Earth observation constellations (Capella Space and ICEYE) and one synthetic Walker‑Star constellation show that SCORE requires up to 5x fewer function evaluations to converge relative to DE while improving downlink throughput by up to 13%. Compared to fixed‑site methods, unconstrained SCORE achieves up to 15% greater total downlink, establishing a strong empirical performance benchmark for flexible placement; infrastructure‑constrained SCORE retains over 92% of this gain while restricting placement to within proximity of existing fiber and power infrastructure. We also explore trade‑offs between expanding existing stations and deploying new sites, informing future ground network design for operational constellations.

Authors:Zhiwen Wan, Hamidreza Movahedi, Wenxue Liu, Jingchen Ma, Jason B. Siegel, Andrew Weng, Anna Stefanopoulou
Title: Modeling and Estimation of Solid Electrolyte Interphase during Formation in Battery Manufacturing
Abstract:
The solid electrolyte interphase (SEI) ‑ a critical passivation layer that governs the longevity, safety, and efficiency of lithium‑ion batteries ‑ is created during the last step in cell manufacturing called cell formation. Conventional cell formation protocols are largely empirical, resulting in long processing times and limited control over the SEI growth rate that influences SEI quality and lifetime performance. This paper develops a control‑oriented, semi‑empirical model to estimate SEI thickness growth from terminal voltage and cell expansion measurements acquired in‑operando during manufacturing using low‑cost micrometer‑precision integrated‑sensing fixture. Model parameters are calibrated against cell formation data, and an unscented Kalman filter is employed to estimate the SEI film growth. The results lay the foundation for future closed‑loop control of SEI growth, enabling high‑quality and more efficient formation processes.

Authors:Qingyun Guo, Junyi Shi, Jianuo Huang, Tianyu Shi
Title: Individual Control Barrier Functions-Guided Diffusion Model for Safe Offline Multi-Agent Reinforcement Learning
Abstract:
Offline reinforcement learning allows control policies to be learned directly from data without online interaction, making it suitable for safety‑critical tasks. Recent studies have applied diffusion models to offline reinforcement learning to leverage their strong capacity for modeling complex data distributions. However, existing approaches primarily focus on single‑agent settings, leaving the safety challenges in multi‑agent environments largely unexplored. In this work, we propose a safe offline multi‑agent reinforcement learning algorithm that embeds neural individual control barrier functions into the diffusion model to enhance safety during trajectory generation, with control policies recovered through inverse dynamics. We evaluate our algorithm across diverse benchmarks, demonstrating substantial safety improvements while maintaining competitive rewards.

Authors:Steven Oh, Jason Jingzhou Liu, Tony Tao, Philip Han, Kenneth Shaw, Satoshi Funabashi, Ruslan Salakhutdinov, Deepak Pathak
Title: FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning
Abstract:
Contact‑rich manipulation requires force sensitivity, but many robot arms lack dedicated force sensors due to their high cost. We present Neural External Torque Estimation (NEXT), a data‑driven method that estimates external joint torques without needing any dedicated force sensors. NEXT trains in 1 minute from only 10 minutes of free‑motion data, yet achieves estimates comparable to dedicated joint‑torque sensors. NEXT enables force‑feedback teleoperation on low‑cost arms and improves policy learning through Force‑Informed Re‑Sampling Training (FIRST), which up‑samples pre‑contact and contact segments during behavior cloning. Across five long‑horizon tasks, FIRST outperforms prior force‑aware policies by over 17% in task progress. Together, NEXT and FIRST bring force‑aware teleoperation and policy learning to off‑the‑shelf robots without additional sensing hardware. Video results and code are available at https://jasonjzliu.com/factr2

Authors:Paria Rezayan
Title: Traceable Virtual Sea Trials in the Marine Robotics Unity Simulator for Manoeuvring Assessment of Unmanned Surface Vehicles
Abstract:
Accurate identification of hydrodynamic derivatives is essential for control and navigation of Unmanned Surface Vehicles (USVs), but high‑fidelity manoeuvring data from physical sea trials are constrained by cost and safety. Turning Circle (TC) and Zig‑Zag (ZZ) trials remain fundamental to IMO and ITTC assessment procedures. This paper extends the Marine Robotics Unity Simulator (MARUS) by introducing a standardised Virtual Sea Trial framework for automated execution and data generation of TC/ZZ manoeuvres, with traceable command‑actuation logging, system‑identification (SI)‑focused data conditioning, and automated extraction of IMO/ITTC‑aligned manoeuvring metrics. A key contribution is a dedicated TC/ZZ data acquisition and post‑processing pipeline, improving the repeatability and auditability of simulator‑based manoeuvres while producing SI‑ready datasets for hydrodynamic‑derivative identification and digital‑twin workflows. Another feature is explicit command‑execution separation for differential‑thrust steering, where inputs are recorded as ordered rudder‑equivalent commands and realised actuation is logged as an execution‑level proxy derived from applied thrust. Case‑study results demonstrate repeatable and compliant manoeuvre behaviour. For TC tests, the normalised advance differs by approximately 3.9 percent between port and starboard sides, while the tactical diameter differs by approximately 4.6 to 4.7 percent. For ZZ tests, first and second overshoot excesses remain below 1 degree for both +/‑ 10 degree and +/‑ 20 degree manoeuvres, satisfying IMO criteria, while peak yaw rates range from approximately 4.1 to 5.8 deg/s. Overall, the framework provides a repeatable and auditable virtual sea‑trial workflow for generating IMO/ITTC‑aligned datasets and supporting system identification, hydrodynamic‑derivative estimation, and digital‑twin calibration.

Authors:Vivek Khatana, Soham Chakraborty, Murti V. Salapaka
Title: Analysis of a Distributed Optimization-Based Control Architecture for Inverter-Interfaced Virtual Power Plants
Abstract:
We develop a large‑signal stability analysis for a sampled‑data, optimization‑based secondary controller for inverter‑interfaced distributed energy resources in virtual power plants.

Authors:Bassam Bamieh
Title: From the Linear Quadratic Regulator (LQR) to the (Deterministic) Kalman Filter in Two Easy Steps
Abstract:
This note is a tutorial on the deterministic version of the Kalman filter (state estimator), which is formulated as finding the state trajectory consistent with the system's equations with the minimal amount of L^2 process and measurement uncertainty. As stated, this is an input signal design problem with linear dynamics and an objective that is affine‑quadratic in the state and inputs. The first step is to convert this problem to one with a purely quadratic objective by embedding in a larger system using ``homogeneous coordinates''. This converts the problem to a purely quadratic (i.e. an LQR) problem, but with non‑standard initial or final state constraints. This latter problem can then be solved using a version of the matrix Differential Riccati Equation (DRE) for the larger LQR problem. The second step is a partitioning of this larger problem, which then yields the optimal dynamic observer and the DRE of the traditional Kalman filter. For comparison, the solution of the traditional LQ‑tracking (Servomechanism) problem is also treated using a similar construction.

Authors:Haijia Xu, Alexander Verl
Title: Lexicographic optimization for real-time CNC feedrate planning with coupled orientation handling
Abstract:
Optimization‑based feedrate planning offers the potential to significantly increase machining productivity, but its industrial adoption has been limited by high computational cost and extensive tuning effort. This paper proposes a lexicographic feedrate optimization principle that adaptively balances finishing time and motion smoothness in a tuning‑free manner. To further improve computational efficiency, the optimization scheme is extended by a sparsity‑exploiting formulation combined with a sequential windowing strategy, enabling real‑time capable execution. In addition, a unified toolpath parameterization scheme is incorporated to synchronously handle tool position and orientation within the optimization framework. For a five‑axis freeform test contour, the proposed method takes 14 s on an Intel i5‑3470 CPU to optimize feedrate profiles for long toolpaths with 100,000 constraint checkpoints, and 52 s on a high‑performance AMD 9950X CPU to handle one million checkpoints. Compared to an industrial CNC kernel, the resulting finishing time is reduced by more than 15 %.

Authors:Prasanna S Gandhi, Dhruvi Joshi, Vivek Natarajan, Ravit Anand
Title: On the Dynamics and State Dependent Multiple Equilibria of a Post-Buckled Ultra-Flexible Inverted Pendulum on a Rotating Hub
Abstract:
Compliant element systems with ultra‑large deformation display rich nonlinear dynamics and pose challenging control problems, which, when solved, could enable enhancements in several mechatronics applications, such as soft robotics, MEMS, and biomedical applications. This paper considers post‑buckled dynamic analysis of an inverted ultra‑flexible pendulum actuated by a rotary hub. We first derive a complete set of equations capturing the dynamics of the system, essential for control development, using the assumed modes method framework, considering ultra‑large deformations. Constrained Lagrange formulation is used for the same. In the perfect inverted configuration with zero hub angle, the buckled beam would display two symmetric stable equilibria and one unstable. However, as the hub angle changes on either side, the equilibrium positions shift, and eventually two of them vanish, and we are left with only one stable equilibrium. We use the dynamic equations to characterize this interesting phenomenon, demonstrating the continuous state dependence of multiple equilibria. Furthermore, experimental counterparts of the equilibrium results are meticulously obtained and discussed. Moreover, simulation results capture the nonlinear dynamics of this system. Overall, the work establishes a solid mathematical foundation with a control‑amenable model for futuristic ultra‑compliant mechatronic systems.

Authors:Hyunjoong Kim
Title: Zero Knowledge Verification of Transaction Guides for P2P Energy Trading in Distribution Networks
Abstract:
Peer‑to‑peer (P2P) energy trading requires network‑aware coordination because transactions are physically realized through distribution networks. However, sensitivity‑based coordination causes a confidentiality‑verifiability tradeoff, as network sensitivities may reveal vulnerable components while undisclosed sensitivities prevent participants from verifying utility‑provided transaction guides. This paper proposes a zero‑knowledge‑proof‑based method for verifying the computational integrity of network‑constrained transaction guides with respect to committed private network data, without exposing network‑sensitivity information. The guide defines admissible injection and withdrawal volumes derived from sign‑decomposed sensitivity matrices while satisfying balance, voltage, line‑flow, and optimality conditions. These conditions are encoded in an arithmetic circuit, represented as R1CS constraints and a quadratic arithmetic program, and verified using a bilinear pairing. Blockchain commitments bind the approved circuit, public inputs, statement identifiers, proof, and verification result for tamper‑evident auditability. The proposed proof certifies correct guide computation from committed network data; the authenticity of the committed network data is handled through an explicit registration and attestation assumption. Case studies on a modified IEEE 33‑bus system show satisfaction of network constraints after clearing, rejection of public‑input and witness‑inconsistency attacks, and practical on‑chain overhead, with an 806‑byte proof.

Authors:Zhilin Fu, Sangmin Kim, Sangwon Hwang, Jihwan Moon, Jeongwon Kim, Jaewan Kim, Inkyu Lee
Title: Deep Reinforcement Learning for Adaptive Power Allocation in ISAC Systems with Mobile Target
Abstract:
In this paper, we study the power allocation for an integrated sensing and communication (ISAC) system which tracks a mobile target. We first model the problem as a Markov decision process, and then tackle it with a soft actor‑critic (SAC) based deep reinforcement learning (DRL) approach. We also combine a Dirichlet policy, which naturally produces normalized continuous actions under random target motion. To exploit different features of sensing and communication operations, we carefully design a reward function such that the system can dynamically control power allocation to conserve resources. The simulation results demonstrate that the proposed scheme enhances tracking performance compared to other baselines while sustaining communication performance.

Authors:Feng Guo, Luis D. Couto, Khiem Trad, Ru Hong, Guangdi Hu, Mohammadhosein Safari
Title: Physics-guided residual Kalman learning for state-of-charge estimation of lithium iron phosphate batteries
Abstract:
Accurate state of charge (SOC) estimation of lithium iron phosphate (LFP) batteries remains challenging because of their flat open‑circuit‑voltage (OCV)‑SOC characteristics, temperature‑dependent dynamics, and sensitivity to initialization errors. Here, we propose a physics‑guided residual Kalman learning (PRKL) framework for electrochemical‑model‑based SOC estimation. PRKL combines a control‑oriented single‑particle‑model‑based extended Kalman filter (EKF), which provides recursive physical state propagation, with a gated recurrent unit (GRU) residual learner that compensates structured EKF errors using electrochemical states and measurement features. The framework is evaluated on a public graphite/LFP dataset covering three dynamic drive cycles, eight temperatures from ‑10 to 50 degrees C, and initialization offsets up to 20 percent. Using dynamic stress test (DST) and federal urban driving schedule (FUDS) cycles for training and the supplemental federal test procedure (US06) cycle for cross‑profile testing within the same cell dataset, PRKL achieves a global average root mean square error (RMSE) of 1.19 percent, corresponding to a 77 percent reduction relative to the physics‑only EKF. These results show that electrochemical state information can guide residual learning and improve recursive SOC estimation for LFP batteries. The present validation supports cross‑profile robustness within the studied dataset and provides a basis for future cross‑cell, ageing‑aware, and embedded‑platform validation.

Authors:Fouzi Tabouri, Kim Guldstrand Larsen, Christian Schilling
Title: Robust Zonotopic Control
Abstract:
We propose a zonotopic framework for synthesizing a single robust state feedback controller that is certified to stabilize every plant inside a matrix zonotope, describing linearly varying parameters or parametric uncertainty. Common robust design strategies rely on checking many vertex models or on complex gain‑scheduling, leading to high offline computation and implementation complexity. Our approach finds a single gain that is provably valid across the entire parameter domain, which is simpler to implement and can reduce conservatism by exploiting the structure of the zonotope. We formulate the robust synthesis as a single convex program tailored to the zonotope representation and incorporate practical performance requirements (actuator constraints, decay rate, disturbance attenuation) into the same synthesis stage. In numerical experiments on a representative 4‑state example, our controller provides larger stability coverage across the parameter domain, attains comparable transient performance and control effort to more complex designs, and significantly reduces the number and scale of offline synthesis problems required by other robust approaches, compared to common‑vertex gain, H_\infty, and μ‑synthesis baselines.

Authors:Ziming Wang, Guanxuan Jiang, Yihuai Zhang, Karl H. Johansson, Apostolos I. Rikos
Title: Cooperative Switched Formation Control of Autonomous Vehicles: An Event-triggered Approach to Input Saturation and Time-delay Challenges
Abstract:
This paper presents a collaborative adaptive formation control framework for autonomous vehicles (AVs), that explicitly handles system uncertainties, input saturation, and communication delays. To overcome the inherent physical torque limits of steering and braking actuators, an input saturation compensation mechanism is introduced to render nonlinearities tractable and improve control reliability. Additionally, a delay‑compensating auxiliary system is designed to mitigate the effects of communication delays and reduce tracking errors. Our framework incorporates a dynamic‑threshold event‑triggered control (ETC) strategy to optimize resource usage. Additionally, uncertainty observers and symmetric barrier Lyapunov functions are developed to ensure robust and safe formation maneuvers. Finally, the effectiveness of the proposed approach is validated through numerical simulations of vehicle formations, complemented by a 3D visualization video demonstrating the dynamic fleet reconfiguration process.

Authors:Victor Daniel Reyes Dreke, Rahul Rane, Aleksandra Lekić
Title: Robust Tuning of Model Predictive Control for MMC-Based High-Voltage Power Systems
Abstract:
High‑voltage direct current (HDVC) transmission systems based on modular multilevel converters (MMCs) have become a key topology in modern power systems. The dynamics of MMCs exhibit strong multivariable coupling, constraints, and uncertainties, motivating the use of model predictive control (MPC) to enhance current regulation performance. However, MPC tuning is nontrivial and does not inherently guarantee stability or robustness, particularly in the presence of model uncertainties. This paper proposes a MPC tuning method that ensures robust performance under bounded model uncertainties. This method solves a convex linear optimization problem to compute the optimal weighting matrices Q, R, and P ensuring optimality and reproducibility. As a result, robustness is enhanced without increasing the online computation burden. The effectiveness of the method is validated through testing on a real‑time digital simulator (RTDS) model of a point‑to‑point HVDC system. Results demonstrate improved performance compared to conventional LQR‑based MPC tuning.

Authors:Saeid Sadeghi Vilni, Risto Wichman
Title: Risk-Aware AoII-Based Scheduling with Hybrid Transmission for a Semi-Markov Source
Abstract:
We consider a multi‑receiver status update system in which a transmitter monitors a finite‑state semi‑Markov source and decides whether to stay idle, unicast an update, or broadcast a common update. We formulate a risk‑aware scheduling problem that minimizes the long‑term average sum of the average Age of Incorrect Information (AoII), average risk ratio, and transmission cost. The risk state is defined by whether the AoII exceeds a prescribed threshold. We solve the problem using model‑based and model‑free policies and compare them with two baselines. Numerical results show that the proposed policies outperform the baselines, exploit both unicast and broadcast transmissions, and capture the effect of the dwell‑time law on scheduling performance.

Authors:Hayate Irie, Masaki Inoue, Banri Okita, Akira Yamaguchi, Tomohiro Taki, Takashi Hatano
Title: CBF-based Driving Assistance for Traffic Flow Stabilization
Abstract:
This manuscript addresses a hierarchical control system designed to suppress traffic congestion. The lower‑layered controllers, implemented in each controlled vehicle, monitor microscopic vehicle behaviors and assist human drivers to ensure sufficient spacing for following vehicles. This spacing logic is designed based on the Control Barrier Function. Meanwhile, the upper‑layered controller monitors the macroscopic traffic flow and activates the necessary lower‑layered controllers, using a data‑driven approach for the activation logic design. Furthermore, the effectiveness of the proposed control system is evaluated in a traffic flow simulation environment constructed using real‑world traffic data.

Authors:Yiwen Zhang, Lorenzo Calogero, Shukai Li, Alessandro Rizzo, Anton V. Proskurnikov
Title: Koopman-based NMPC for Virtually Coupled Train Control System
Abstract:
This paper investigates an analytical Koopman‑based nonlinear model predictive control (K‑NMPC) approach for tracking control of virtually coupled train systems. A nonlinear train movement model incorporating train dynamics, speed and control input limits, passenger comfort constraints, and collision avoidance is systematically lifted into a finite‑dimensional Koopman space through closed‑form observable functions. After freezing the affine parameter‑varying lifted predictor along the shifted predicted trajectory, the online optimal control problem is solved as a quadratic program that can be solved efficiently. The proposed KNMPC is benchmarked against a time‑discrete NMPC scheme, demonstrating comparable control performance with significantly reduced online computation time and strong potential for real‑time implementation in practical virtually coupled train control systems.

Authors:E. D. Gomez Anccas, E. A. MacPherson, J. Tegeler, D. Schulz
Title: Comparative Evaluation of Transition Mechanisms for Adaptive Droop Gains in Parallel Grid-Forming Inverters
Abstract:
Uncertainty in standalone microgrid operation usually originates from mismatches between power references and forecasts. These deviations are compensated by grid‑forming controlled units, which distribute the required power contribution based on their droop gains. To introduce an additional degree of flexibility, it is possible to treat droop gains as decision variables to redistribute active‑power contributions according to system‑level objectives. However, directly applying updated droop gain references from a supervisory layer to the primary controllers can introduce power and frequency transients. This paper investigates transition mechanisms for applying scheduled active‑power droop gain changes during operation. Hard switching, rate‑limited transition, first‑order IIR low‑pass filtering, and cubic as well as quintic S‑curve transitions are compared experimentally on two parallel 15 kW grid‑forming inverter units. The results show that shaping the droop gain trajectory significantly reduces transient deviations compared to hard switching. In the considered case study, the S‑curve transitions provide the strongest transient mitigation, reducing the active‑power overshoot from 632.7 W to approximately 115 W and limiting the frequency overshoot to about 0.003 Hz.

Authors:Linhan Fang, Xingpeng Li
Title: Violation-Informed Spatio-Temporal Adaptive Targeting Framework for EV-Driven Distribution System Expansion Planning
Abstract:
The rapid adoption of electric vehicles (EVs) can cause severe voltage drops and line current overloads in distribution networks, creating an urgent need for scalable expansion planning methods. This paper proposes a computationally efficient violation‑informed spatio‑temporal adaptive targeting (STAT) framework for EV‑driven distribution system expansion planning. The framework first identifies potential voltage and current violations through a violation analysis model, and then mitigates them through a joint optimal expansion planning model that co‑optimizes investment decisions for line reconductoring, shunt capacitors, and battery energy storage systems. To reduce computational burden, the proposed STAT‑temporal criticality assessment (STAT‑TCA) method extracts primitive stress events from annual operating data, derives an initial set of candidate planning horizons from signature‑consistent segments, and selects a final transferable critical horizon set through cross‑horizon validation based on optimization feasibility and cost. Meanwhile, the proposed STAT‑adaptive spatial targeting (STAT‑AST) method constructs device‑specific spatial features for BESS and SC siting to retain compact yet high‑impact candidate bus sets. Case studies on 33‑bus and 240‑bus distribution systems demonstrate that the proposed STAT framework can substantially reduce the temporal and spatial planning dimensions while preserving planning fidelity. Full‑year validation further confirms that the resulting investment plans can eliminate EV‑induced voltage and thermal violations while maintaining feasible BESS operations.

Authors:Shirantha Welikala, Zihao Song, Hai Lin, Panos J. Antsaklis
Title: Model-Based and Data-Driven Hierarchical Control and Topology Co-Design for Robust Networked Systems
Abstract:
In this paper, we consider a class of networked systems comprising an interconnected set of linear subsystems, disturbance inputs, and performance outputs. Using dissipativity theory, we first propose a model‑based hierarchical control design strategy to ensure the closed‑loop networked system is dissipative from its disturbance inputs to performance outputs. This involves designing local controllers for each subsystem to enforce local dissipativity guarantees, which are then exploited to co‑design distributed global controllers and the interconnection topology to enforce global dissipativity guarantees while optimizing interconnection topology costs. The overall design process requires only solving a sequence of linear matrix inequality (LMI) problems, thereby retaining compositionality and decentralizability while avoiding non‑convex, iterative design processes that are inefficient and centralized. This model‑based hierarchical control design strategy assumes the knowledge of the subsystem dynamics, which may not hold in many real‑world networked systems. Motivated by this, we also propose a data‑driven hierarchical control design strategy that assumes only the availability of rich input‑state‑output trajectory data from the subsystems. The proposed data‑driven design process assumes that the unknown disturbances affecting the subsystem dynamics are bounded by a quadratic matrix inequality (relaxing conventional bounds) and accounts for this by using the matrix S‑lemma. Finally, the effectiveness of the proposed model‑based and data‑driven hierarchical control designs is illustrated for a networked system representing a DC microgrid, with the aim of enforcing robust (dissipative) voltage regulation and current sharing.

Authors:Jun Hu, Xin Zhou, Xiaoshan Jiang, Dapeng Jin
Title: A High-Precision Clock Synchronization System for the CEPC Accelerator
Abstract:
The Circular Electron Positron Collider (CEPC) distributes a reference clock distributed to 192 control nodes along its 100~km underground tunnel. The required synchronization precision is 30~ps (standard deviation). We present an enhanced White Rabbit (WR)‑based clock synchronization system designed to meet this requirement. A noise‑budget analysis of the standard WR slave loop identifies the analog actuation chain (DAC + VCXO + multiplier PLL) and restart‑induced timing uncertainty as the dominant limitations. In our redesigned node, the DAC+VCXO chain is replaced by a Si5345A DSPLL clock generator with DCO‑based phase control, removing the board‑level analog tuning stage. GTX transceiver phase alignment and manual byte‑alignment fixing reduce restart uncertainty from 88.8~ps to 12~ps peak‑to‑peak. For multi‑node operation, we introduce a cascaded global‑control architecture with PC‑side PID auto‑tuned by TD3 reinforcement learning, on‑chip‑temperature feed‑forward calibrated to ‑0.76\,\mathrmps/^\circ\mathrmC. The measured point‑to‑point synchronization precision is 3.38~ps over 1~m fiber and 3.92~ps over 50~km. In a 12‑level cascade, the end‑node precision reaches 6.66~ps at constant temperature and 7.30~ps under a 13\,^\circC temperature swing. Synchronized‑clock TIE jitter stays below 1~ps regardless of cascade depth. Restart uncertainty is 2.82~ps (std.\ dev.). A 4‑level cascade operated stably for 25 hours of continuous monitoring. All measured metrics fall well within the CEPC 30~ps budget.

Authors:Bhushan Gopaluni, Vidya Kotamraju, Syon Bhushan
Title: Large Language Models in Process Systems Engineering: Opportunities, Architectures, and Industrial Deployment Challenges
Abstract:
Large Language Models (LLMs) have rapidly emerged as tools of interest across engineering disciplines, and Process Systems Engineering (PSE) is no exception. This survey provides a systematic review of LLM applications in PSE, organizing the literature into seven categories: (1) process design and engineering, (2) molecular design and synthesis, (3) process modeling and simulation, (4) time‑series forecasting, (5) optimization and scheduling, (6) process control, and (7) fault detection and diagnosis. For each category, we summarize the state of the art, identify common methodological approaches, and critically assess demonstrated capabilities versus aspirational claims. We find that LLMs show genuine promise for tasks involving natural language, including querying documentation, synthesizing unstructured knowledge, and enabling flexible human‑machine interaction. However, applications requiring real‑time execution, constraint satisfaction, or formal safety guarantees remain challenging. We conclude by identifying open problems and productive research directions for the PSE community.

Authors:Lei Chu, Yuning Zhang, Omer Gokalp Serbetci, Anushka Katiyar, Bassel Abou Ali Modad, Andreas F. Molisch
Title: OmniLoc: A Geometry-Aware Foundation Model for Anchor-Free UE Localization Across Diverse Indoor Environments
Abstract:
Indoor localization from wireless measurements remains challenging in large‑scale deployments due to substantial variation in building geometry, the set of detectable access points (APs), and the heterogeneity of received signals. Existing learning‑based methods often perform well only in limited settings and degrade under environmental shifts, making robust anchor‑free localization across diverse indoor environments notoriously difficult. In this paper, we present OmniLoc, an environment‑interactive foundation model for anchor‑free user equipment localization across diverse indoor environments. To the best of our knowledge, OmniLoc is the first foundation‑model‑based approach built directly on wireless measurements for this task. OmniLoc is built on three key designs. First, a unified input tokenization module converts heterogeneous wireless measurements into a common representation that is more amenable to learning. Second, a geometry‑aware Transformer performs AP‑aware feature extraction by emphasizing dominant APs while aggregating complementary evidence from supporting APs. Third, a geometry‑aware location estimation module conditions regression on geometric embeddings to produce geometrically consistent location predictions. We evaluate OmniLoc on both a large‑scale in‑house dataset and a public benchmark dataset. Results show that OmniLoc significantly outperforms existing methods, consistently improves existing backbones when its design components are integrated, and demonstrates strong generalization in cross‑environment evaluations.

Authors:Shaifalee Saxena, Alexander Scheinker
Title: Mahalanobis-Guided Latent OOD Detection for Hybrid ES-DRL Control in Time-Varying Systems
Abstract:
In this paper, we study Mahalanobis‑guided latent out‑of‑distribution (OOD) detection for test‑time RL controller switching in nonlinear time‑varying systems. RL controllers can quickly control high‑dimensional systems within the training distribution, but their performance can degrade when time‑varying dynamics produce unseen observations. We consider a combined ES‑‑DRL controller, where RL provides fast in‑distribution actions and bounded extremum seeking (ES) provides robust model‑independent control under OOD operation. The key challenge is deciding when to switch. We train a variational autoencoder (VAE) on in‑distribution beam‑profile observations and use Mahalanobis distance in the VAE latent space to detect OOD beam profiles at test time. This OOD decision sets a binary switch that selects either the RL controller or the ES controller. We evaluate the approach in safety‑critical particle accelerator control. In this setting, spatial magnet motion creates OOD beam profiles that were not seen during RL training. Visualization of the VAE latent space shows that the proposed method identifies this OOD scenario and provides an interpretable signal for switching between RL and ES in the combined controller.

Authors:Chirag Ramgopal Shah, Marta Molinas, Sjur Føyen, Roy Nilsen
Title: From Symmetry to Stability: Quantifying Converter Grid Impedance Asymmetry as Indicator of Stability Margin
Abstract:
Although symmetricity in the converter controller is desirable for robust stability margins, a direct link between system‑level asymmetricity and instability has yet to be clearly established. Converter control introduces three‑phase asymmetricity through loops such as DC‑link voltage control, a phase‑locked loop , and a power synchronization loop. Furthermore, the inherently asymmetric topology of the two‑level voltage‑source converter, which converts a DC voltage into a three‑phase balanced set, acts as the underlying origin of the asymmetries that propagate into the control structure. Consequently, establishing a direct relationship between system asymmetricity (rather than control asymmetricity alone) and the stability margin is essential for understanding the underlying instability mechanisms. In this work, asymmetricity is quantified using the Asymmetricity Quantification Index (AQI), derived from the sequence‑domain representation of the interconnected converter‑grid impedance. Within this domain, symmetricity is identified through the definition of symmetrical matrices, which serve as the benchmark against which asymmetricity is measured. A robust and generalized analysis correlates AQI with the stability margin, including both grid‑following and grid‑forming control structures connected to the power grid. It is found that instability arises from increased asymmetricity in the combined converter‑grid system, which is dominated by asymmetric control loops and operating points. Thus, reducing asymmetricity without compromising controller functionality can improve stability margins. The analysis is validated in both control‑hardware‑in‑the‑loop and power‑hardware‑in‑the‑loop environments.

Authors:Jinlei Wei, Yongxin Zhang, Guanyu Tian
Title: Probabilistic Repair Logistics Modeling for Utility-Scale PV Inverter Fleets Using Event-Driven Simulation
Abstract:
As renewable energy systems expand, inverter availability becomes increasingly important for grid reliability and economics, yet photovoltaic inverter repair logistics remain under‑modeled. This paper presents an event‑driven Monte Carlo framework for a centralized repair facility with parallel production lines, capturing the full repair cycle from administrative pre‑wait and transport to health‑driven repair and return‑to‑inventory. The model incorporates opportunistic scheduling that uses mandatory hold periods to insert additional units onto temporarily idle lines, improving throughput without added capacity. Stage durations are represented by a two‑component VaR‑style mixture distribution for routine and heavy‑tailed delays, while a continuous health score determines repair completion. Calibrated by minimizing the one‑dimensional Wasserstein distance between simulated and empirical repair‑duration distributions, the model is applied to 43 field‑observed repairs, reproducing the empirical bimodal structure with a Wasserstein distance of 53.3 days. Results show that 51.2% of units are accommodated through opportunistic insertion, indicating that hold periods provide a significant recoverable scheduling resource.

Authors:Andreas Hadjileonidas, Debargha Brahma, Yue Zhu, Timothy C. Green
Title: An Admittance-Based Inverter Connection Screening Tool for Small-Signal System Strength
Abstract:
The increasing occurrence of small‑signal instability, particularly sub‑synchronous oscillations (SSOs), in power systems with a high penetration of inverter‑based resources (IBRs) has made the planning of new IBR connections increasingly important and challenging. The impact of such connections on small‑signal stability is not always straightforward, as it strongly depends on the connection location, inverter operating mode, control configuration, parametrisation, and operating conditions. This paper proposes an inverter connection screening tool (ICST) that enables efficient and accurate assessment of the impact of prospective inverter configurations on small‑signal system strength. It can identify, among the candidates considered, the most suitable inverter configuration for a given connection location that avoids degrading small‑signal system strength and can also enhance it. As a result, higher IBR penetration can be supported while maintaining small‑signal stability. The ICST evaluates candidate inverter configurations using their admittances at critical modal frequencies, along with the system's admittance spectrum, thereby avoiding the need for analytical models. The ICST‑based planning procedure, which can support system operators, asset owners, and IBR developers in decision‑making across different stages of planning studies, is demonstrated using a modified IEEE 57‑bus system. Comparisons with model‑based studies demonstrate the accuracy of the ICST in predicting the modal impact of inverter connections and its effectiveness in selecting suitable inverter control configurations.

Authors:Susmit Sarkar, Abhinav Raghuvanshi, Kushal Chakrabarti, Mayank Baranwal
Title: Quantized Stochastic Primal-Dual Methods for Distributed Optimization under Relaxed Global Geometry
Abstract:
We study distributed optimization with stochastic gradients and finite‑bit communication modeled by random (unbiased) quantization. We propose q‑PDGD, a quantized stochastic primal‑dual method, and analyze it under relaxed global geometry. Under restricted secant inequality (RSI), a constant step‑size yields linear contraction to an explicit neighborhood determined by gradient noise, quantization distortion, and network connectivity, while a diminishing step‑size achieves O(1/k) convergence without shared‑minimizer assumptions. Under Polyak‑Lojasiewicz (PL) inequality, we obtain linear‑to‑neighborhood convergence in the same stochastic quantized setting. Our results match the best‑known centralized stochastic rates in oracle complexity, and are supported by experiments demonstrating the predicted tradeoffs between quantization level, step‑size choice, and graph structure.

Authors:Elisabeth Shi, Maria-Magdalena Wolf, Nina Theobald, Bettina Abendroth, Eugen Wige, Johannes Springer, Katharina Hottelart, Andreas Schrank, Thorben Brandt, Michael Oehl, Frank Diermeyer, Lena Plum
Title: Towards a Joint Understanding of Remote Operation for Vehicles in Public Road Traffic
Abstract:
Sustained driving automation systems are envisioned to be used as the foundation for driverless mobility services. However, both researchers and practitioners acknowledge that current driving automation systems are not yet able to handle all traffic situations that a human driver can handle. To bridge this gap and enable mobility services without an in‑vehicle human driver or fallback, remote operation (or teleoperation) is increasingly discussed. Recently, first legal actions have been taken to enable some forms of remote operation on public roads. Remote operation encompasses a broad spectrum of methods to support a driving automation system, ranging from remote assistance, which includes providing information or releasing a maneuver, to remote driving, which includes driving the vehicle from a remote location. As such, safe implementation of remote operation in public road traffic challenges the collaboration of multiple academic disciplines (e.g. engineering, psychology, informatics, law, etc.) and stakeholders (e.g. remote operation service providers, remote operators, vehicle manufacturers, regulatory authorities, etc.). At the same time, the interdisciplinary discourse is often challenging due to differing expectations and language. To build a common ground, this article traces terminology back to the original differences in information processing both on human and vehicle side. This framework aims to help further discourse by directly specifying what is needed to engage a diverse audience including researchers and stakeholders of different backgrounds and interests. Recently discussed forms of teleoperation are integrated into this framework.

Authors:Pratik Mochi, Magnus Korpås
Title: Shared Renewable Allocation and Hydrogen Flexibility in Local Energy Markets: A Market Design Perspective
Abstract:
The integration of green hydrogen in local energy markets is often analyzed from a technical flexibility perspective, while the effect of market design rules remains less explored. This paper proposes a coordinated local electricity‑hydrogen market framework in which hydrogen participation is regulated by explicit renewable access mechanisms. A mixed‑integer linear programming model is developed to co‑optimize electricity trading, battery operation, wind allocation and hydrogen production under centralized coordination. Six regulatory cases are examined including hydrogen supply options and access of local wind. Results are obtained for representative seasonal weeks for Norwegian energy community. Electrolyzer, when connected as rigid load, increases grid dependence, but also improves system cost when price‑based participation is activated. Direct renewable access reduces grid imports, enhances wind allocation and introduces competition with households for energy distribution and system cost optimization. Furthermore, findings show that (i) hydrogen integration in local energy systems is essentially a market design problem and (ii) renewable access rules critically determine system behaviour, flexibility interactions and seasonal performance.

Authors:Neelakantan Padmanabhan
Title: A Scalable Approach for Transient Thermal Modeling of Automotive Power Electronics
Abstract:
Efficient thermal management is critical for the reliability and performance of power electronics systems in automotive applications. This work presents a computationally efficient modeling approach for transient thermal simulation of power electronic systems, with a focus on inverter modules using multiple MOSFETs mounted on a printed circuit board assembly (PCBA). A case study of an inverter module comprising six MOSFETs arranged as high‑side and low‑side pairs for a three phases system mounted on a PCBA, attached to a heat sink is considered. Computational fluid dynamic (CFD) simulations in Ansys Icepak are performed considering different heat transfer mechanisms, including natural convection, forced convection at constant velocity, and forced convection with varying flow velocity. A transient thermal model is developed using the Lumped Parameter Linear Superposition (LPLSP) method, a hybrid approach that combines lumped parameter modeling with the principle of linear superposition to capture transient thermal behavior efficiently. Temperatures of the components from the simulations are compared with temperatures from the LPLSP model and temperatures from a Linear Time Invariant (LTI) based reduced order model (ROM) developed for this system. It is observed that the LPLSP model is able to model a wide range of use cases very accurately with error of less than 5 %. This method enables rapid thermal performance evaluation of power electronics systems that have very fast transients in component level power dissipation and variations in ambient conditions, making it particularly well‑suited for early‑stage design iterations and long‑duration mission profile simulations. The approach offers a practical path to reducing development cycles for automotive power electronics design.

Authors:Jielong Zhang, Guiju Duan, Tinggui Chen, Shengjie Zheng, Bozheng Xue, Baizhan Xia
Title: Emergent Non-Hermitian Topology in Multi-Robot Network
Abstract:
Non‑Hermitian (NH) topology has been extensively explored in wave and matter systems, typically relying on the routing of complex, non‑reciprocal couplings in physical space. This work demonstrates the experimental realization of programmable NH topological phases within decentralized multi‑robot networks. By digitally programming non‑reciprocal interaction rules and establishing real‑time state exchange among active robots, we observe emergent topological zero modes (TZMs) and NH skin effects in synthetic lattices spanning one to three dimensions. Dynamically tailoring non‑reciprocal parameters enables the precise morphing of TZMs between localized and delocalized states, establishing a versatile framework for topological mode engineering across dimensionalities. This platform establishes multi‑robot networks as highly reconfigurable systems for exploring non‑equilibrium topological physics, while paving the way for topologically protected, robust collective behaviors in active matter.

Authors:Sovesh Mohapatra, Christoffer G. Alexandersen, Panagiotis Fotiadis, Max B. Kelz, John A. Detre, Fabio Pasqualetti, Dani S. Bassett
Title: QUIET: Quantifying Underutilized Influential Edges for Targeted Synchronization
Abstract:
Network control theory can be used to model intrinsic and extrinsic strategies to steer neural dynamics. Standard approaches are node‑centric, structural, and focused on achieving desired instantaneous states. Here, we develop an edge‑centric approach which incorporates both structure and function to achieve extended patterns of neural dynamics characterized by desired synchronization states. Our method, Quantifying Underutilized Influential Edges for Targeted Synchronization (QUIET), is an edge‑centric framework that integrates structural controllability of individual white matter connections and mutual information between pairwise functional timeseries to identify energy‑efficient synchronization pathways. QUIET identifies quiet highways, edges that are structurally influential but functionally underutilized, to optimize regional synchronization. We validated QUIET across 75 synthetic configurations, where QUIET‑ranked edge sets significantly outperformed random selection in 93% of cases (p<0.01). The framework, tested on Human Connectome Project participants, revealed that the control energy required for synchronization of the salience network correlates with fluid intelligence. QUIET, applied to healthy adults undergoing dexmedetomidine‑induced unresponsiveness, showed that the frontoparietal and default‑mode networks exhibited the largest control energy required for synchronization in both awake and sedated states. QUIET is released as a stand‑alone software to be used to study theoretically‑defined synchronization pathways, which in turn could inform testable hypotheses in perturbative studies.

Authors:J. de Curtò, I. de Zarzà
Title: LLM-Mediated Demand Response Coordination in Smart Microgrids
Abstract:
Effective demand response in smart microgrids requires prosumers to cooperate voluntarily under strategic self‑interest, a coordination problem structurally equivalent to a repeated Prisoner's Dilemma on a social network. This paper presents a multi‑agent simulation in which a Large Language Model (LLM) Influence Compiler issues structured demand‑response directives to a population of heterogeneous prosumer agents, each governed by a hybrid decision architecture combining game‑theoretic base probability (derived from payoff history, neighbour imitation, and exploitation memory) with LLM narrative evaluation of incoming coordination signals. The hybrid architecture resolves a key methodological challenge: LLMs aligned via Reinforcement Learning from Human Feedback (RLHF) exhibit strong cooperation bias when used as direct decision‑makers, producing flat dynamics regardless of grid conditions. By separating strategic reasoning from grounded narrative evaluation, the model generates realistic prosumer behaviour across six personality archetypes, with baseline cooperation near 50% and clear differentiation under influence. Compiled structured directives achieve 33.3% demand‑curtailment cooperation versus 27.0% for unstructured messaging and 28.0% for a no‑intervention baseline (Δ_\mathrmcomp = +0.063), with the advantage preserved across both grounded and idealized agent substrates (Δ= +0.083) and across all resistance levels (R = 0.1 to 0.7). Hub‑targeted dissemination via high‑centrality network nodes outperforms peripheral or random targeting, confirming that grid topology provides mechanistic amplification independent of message content. These results suggest that structured LLM compilation, grounded agent reasoning, and network‑aware targeting are complementary design principles for scalable, interpretable demand‑response coordination in smart‑city energy systems.

Authors:Muhammad Zakwan, Leonardo Massai, Efe C. Balta, Giancarlo Ferrari-Trecate
Title: Free Parametrization of L_2-Bounded Structured State-Space Controllers for Nonlinear Control with Stability Guarantees
Abstract:
Designing stabilizing control policies for nonlinear systems while optimizing complex objectives remains a formidable challenge. Neural networks (NNs), despite their expressive power, can be highly sensitive to small input perturbations and can easily destabilize the closed‑loop system. Existing approaches often impose explicit constraints on the controller's parameters to ensure stability, but this typically leads to additional computational overhead. To address this issue, we leverage recently proposed structured state‑space models (SSMs) to parametrize discrete‑time control policies for nonlinear systems. Our key contribution is a new free parametrization of linear time‑invariant (LTI) systems with a prescribed L2 gain. We use this result to construct the L2‑Recurrent Unit (L2RU), an SSM layer that enforces the desired L2 bound by design. The resulting architecture can be used to guarantee closed‑loop stability via the small‑gain theorem or the so‑called performance‑boosting framework, independently of the controller's optimization parameters, thereby enabling fully unconstrained optimization of general nonlinear objectives. Furthermore, the structure induced by the proposed parametrization enables the efficient processing of long input sequences, as it is highly parallelizable through algorithms such as parallel scan. We demonstrate the effectiveness of this approach on a formation‑control task for mobile robots, where the L2RU‑based controller ensures collision and obstacle avoidance while maintaining stability and performance.

Authors:Leon Johann Brettin, Tobias Schräder, Kerstin Kuhlmann, Vanessa Schmidt, Markus Maurer
Title: A Companion App for an Autonomous Family Vehicle: Identification of Values for an Autonomous Mobility System
Abstract:
In this paper, we present a companion app for an autonomous vehicle aimed at user groups who would normally require an accompanying person to drive them. Two aspects of a companion app are presented in this paper: First, the possibility for a trusted person to track the ride of the person in need of support and second, to put the settings of the vehicle for persons in need of support in the hands of a trusted person. In addition, this article describes the requirements and addressed values and discusses the safety‑relevant aspects of such a companion app. We also discuss and identify the values that influence passengers and trusted persons using the companion app. Overall, a companion app can provide new perspectives and opportunities for people in need of support, allowing them to take advantage of the features offered by autonomous vehicles. It enables trusted individuals to configure the vehicle according to the passengers needs. Also such an app can be a mechanism to involve trusted persons in the options given by the vehicle and give them the possibility to adapt the vehicle to the needs of the person in need of support.

Authors:S. Habibi, L. Marques
Title: Multi-UAV Active Sensing with Information Gain-based Planning and Belief Fusion
Abstract:
Unmanned aerial vehicles (UAVs) are increasingly used for active sensing and information gathering in spatially distributed environments. Their performance, however, is constrained by limited flight time, sensing uncertainty, and the trade‑off between spatial coverage and observation accuracy. This paper presents a real‑world validation of a multi‑UAV active sensing framework for probabilistic binary terrain mapping, with precision agriculture used as the application case. The environment is represented as a probabilistic belief map, where spatial dependencies are modeled through a factor‑graph formulation. UAV decision making is guided by Information Gain based Informative Path Planning (IGbIPP), and the approach is compared with Random Walk and Sweep coverage path planning baselines using both synthetic terrains and real UAV‑derived agricultural imagery. The study also evaluates spatial correlation weights and several probabilistic belief‑fusion rules for multi‑UAV information sharing. Results show that IGbIPP reduces entropy and mapping error more effectively than the baselines, while a wider field of view improves real‑world coverage and map accuracy. The results further show that simple equal or biased spatial weights can be more robust than adaptive weights, and that Bayesian, log‑odds, and Dempster‑‑Shafer fusion achieve the best cooperative mapping performance. These findings highlight the importance of uncertainty‑driven planning, sensing geometry, spatial modeling, and probabilistic fusion for real‑world UAV‑based active sensing.

Authors:Batu Candan, Mohammed Atallah, Simone Servadio, Saeed Arabi
Title: Resilient Navigation for Autonomous Farm Robots by Leveraging Jerk-Augmented Models with IMU-Only Disturbance Rejection
Abstract:
Precise state estimation for navigation of autonomous agricultural robots is often compromised by sensor outages (GNSS/LiDAR/Visual) and high‑frequency vibrations inherent in off‑road environments. This paper proposes a robust navigation algorithm based on a jerk‑augmented Extended Kalman Filter (EKF) integrated with a Multiple Tuning Factor (MTF) adaptation method. Unlike standard EKF approaches that assume constant measurement noise, our method dynamically adjusts the measurement covariance matrix in real‑time, allowing the system to cope with sudden disturbances and sensor outliers. We evaluate the algorithm using real‑world data from a Salin247 autonomous robot. Results demonstrate that jerk‑augmentation combined with MTF adaptation significantly reduces 3D position Root Mean Square Error (RMSE) compared to baseline EKF models, providing superior dead‑reckoning capabilities.

Authors:Subhrajit Sinha, Sriram S. K. S. Narayanan, Raktim Bhattacharya, Umesh Vaidya
Title: Spectral Koopman Approach for Reconstructing State-space Geometry of Cislunar Restricted 3-Body Problem
Abstract:
In this work, we propose a novel approach, based on the path integral formulation of Koopman spectrum, to discover the phase‑space geometry of the planar Cislunar Restricted 3 Body Problem (CR3BP). In contrast to existing techniques, which use trajectory‑based (usually) local analysis, we leverage the Koopman operator framework, which generates a global linear \emphrepresentation of the system, to reconstruct the global phase space geometry of the CR3BP. In particular, we compute the principal eigenfunctions of the Koopman operator via the path integral approach and show how the zero level curves of these eigenfunctions encode the phase space characteristics of the planar CR3BP.

Authors:Victor Daniel Reyes Dreke, Rahul Rane, Aleksandra Lekić
Title: Robust Current Regulation of MMC-based MTDC Power Systems based on Lyapunov Inequality
Abstract:
Multi‑terminal DC (MTDC) transmission systems based on modular multilevel converters (MMCs) are a key component of the envisioned future energy sector, where sustainability and efficiency are increasingly prioritized. To ensure their reliable operation, MMC currents must be regulated safely and rapidly under a wide range of uncertain operating conditions. Consequently, the design of current controllers faces a fundamental challenge: achieving fast transient response while maintaining robustness against uncertainties. This paper addresses this challenge by proposing a linear matrix inequality (LMI)‑based design framework that leverages Lyapunov stability conditions to synthesize a less conservative static state‑feedback controller. The proposed design method explicitly accounts for system constraints, including input saturation and overcurrent limits. The proposed method effectiveness is assessed on the CIGRE MT‑HVDC benchmark, simulated in RTDS, and compared with existing methods.

Authors:Simon Malacek, Sonja Wogrin, Yannick Werner
Title: Temperature-Aware Heat Pump Modeling for Large-Scale Energy System Optimization
Abstract:
Heat pumps are expected to dominate the heating sector, substantially increasing peak electricity demand. At the same time, building thermal inertia enables operational strategies, providing temporal flexibility in heat pump operation and short‑term demand response. However, this dynamic behavior is not yet represented in large‑scale energy system optimization models. To address this gap, we present an innovative formulation of building thermal inertia. The resulting temperature variable is integrated into a novel conic temperature‑aware heat pump efficiency formulation, enabling a more precise emulation of smart control strategies. In a case study of the European energy system, we show that the approach captures operational heating flexibility while remaining computationally efficient. The results indicate substantial untapped flexibility potential, enabling up to a 22% reduction in heating‑related electricity costs. This potential can be realized through a suitable energy market design that incentivizes coordinated heat pump control, individually or via aggregators.

Authors:Ahmed-Manaf Dahmani, Vincent Bonnet, David Daney, François Charpillet
Title: Gradient based Bilevel for Inverse Optimal Control, a Riemannian approach
Abstract:
Inverse Optimal Control (IOC) aims to recover the cost function that explains observed trajectories as solutions of an optimal control problem. Classical IOC formulations rely on bilevel optimization, which repeatedly solves a nested optimal control problem and quickly becomes computationally prohibitive for realistic systems. Recent projection‑based approaches offer a promising alternative but suffer from numerical instability when solved with gradient‑based methods due to violations of standard constraint qualifications. In this paper, we show that these difficulties stem from the geometric structure of the IOC feasible set. We demonstrate that the set of trajectories satisfying the optimality conditions naturally forms a manifold and reformulate IOC as an optimization problem on this manifold. Based on this insight, we propose a Riemannian Inverse Optimal Control (RIOC) method that projects observed trajectories onto the manifold of optimal solutions while preserving feasibility by construction. Experiments on real human arm trajectories show that the proposed method achieves comparable or better reconstruction accuracy than classical bilevel IOC while reducing computation time by about a factor of four. These results highlight the potential of geometric optimization methods to improve the scalability and reliability of IOC for robotics and human motion analysis.

Authors:Benita Nortmann, Thulasi Mylvaganam
Title: Direct Data-Driven Approximate Optimal Control of Nonlinear Input-Affine Systems
Abstract:
In this paper, we combine a data‑driven system representation with a framework to systematically construct (approximate) solutions to nonlinear optimal control problems. By immersing the unknown dynamics into an extended state space, solutions are characterised via purely data‑dependent algebraic conditions. This allows us to design dynamic state‑feedback controllers with local stability and performance guarantees for unknown nonlinear, input‑affine systems directly using data, without explicitly identifying the dynamics.

Authors:Yavar Yeganeh, Mahsa Shekari, Nicla Frigerio, Daniele Pagano, Andrea Matta
Title: Event-Driven Reinforcement Learning Enables Long-Horizon Control in Semiconductor Fabrication
Abstract:
Reinforcement learning promises to optimize sequential decisions in large‑scale systems. Semiconductor manufacturing systems are stochastic and highly constrained environments where heterogeneous wafers traverse hundreds of processing steps across extensive equipment networks. These characteristics yield complex, high‑dimensional decision problems with delayed feedback and long‑horizon requirements, complicating production planning and control. We propose a deep reinforcement learning framework for multi‑objective policy optimization at this scale. Specifically, we formulate control as a centralized‑agent problem, where a core policy coordinates system‑wide decisions, while system evolution is represented as an interconnected temporal process driven by discrete events. Accordingly, we develop a tailored event‑driven temporal‑difference formulation that remains general and can be integrated with various policy optimization methods under relevant training settings. We investigate several core model‑free algorithms incorporated into this framework and evaluate their effectiveness using high‑fidelity simulations of diverse, industry‑real operating scenarios. Across extensive validation experiments, agents trained in both offline and online settings show significant and consistent gains in throughput and utilization. We further evaluate performance and generalization across training phases, clarifying the relative strengths of alternative reinforcement learning formulations and algorithms. Overall, the results support the scalability, generality, and transferability of the proposed framework for controlling event‑driven complex adaptive systems.

Authors:Amirhossein Azarbahram, Osmel M. Rosabal, David Ernesto Ruiz-Guirola, Melike Erol-Kantarci, Kaibin Huang, Onel L. A. López
Title: Toward Proactive RF Charging Scheduling: Generative AI for Decision Support
Abstract:
Radio frequency wireless power transfer (RF‑WPT) is an enabling technology for supporting uninterrupted communications in future Internet of Things systems by reducing the need for battery replacement and mitigating battery‑waste‑related issues. For large‑scale RF‑WPT deployment, one of the main challenges is the scheduler‑level resource allocation. Specifically, the transmitter must decide how much energy to deliver, when, and to whom, under limited charging resources, incomplete receiver‑side information, and uncertain near‑future charging conditions. This article positions generative artificial intelligence (GenAI) as a promising tool for this setting because it can foresee multiple plausible charging scenarios conditioned on coarse operational context and receiver‑side information. We propose GenAI to act as an uncertainty‑aware support layer for the RF‑WPT scheduler rather than as a standalone forecasting or decision‑making tool. To this end, we first revisit the main challenges of RF‑WPT scheduling, and discuss how major GenAI families can support uncertainty‑aware charging decisions by generating scenario‑based inputs for downstream tasks. We then present a warehouse‑style case study showing that preserving uncertainty through the sampling capability of generative models can improve robust charging decisions compared with deterministic prediction and simple non‑learning baselines, especially under risk‑sensitive objectives. Finally, we identify key open challenges and present some directions for future research.

Authors:Sangli Teng, Hang Liu, Koushil Sreenath
Title: Embedding Hybrid Systems into Continuous Latent Vector Fields
Abstract:
This work proves that an n‑dimensional hybrid system can be embedded into an m‑dimensional Euclidean space equipped with a continuous vector field on its embedded image whenever m>2n. This result suggests that an intrinsically discontinuous hybrid system generically admits a continuous extrinsic representation that is well‑posed for differentiable optimization. Building on this existence theorem, we show that a latent Neural ODE with consistency loss in both the latent and state space can accurately recover the flow of hybrid systems. Extensive experiments suggest the proposed method outperforms the existing method in learning hybrid systems with varying geometries from only time series data.

Authors:Alban J. F. Duvivier, Dominic Groß, Daniel Müller, Nicolaos A. Cutululis
Title: Transient Stability of Offshore Energy Hubs
Abstract:
Offshore energy hubs (OEHs) use grid‑forming modular multilevel converters (MMCs) to enable large‑scale offshore wind integration and multi‑terminal HVDC operation. In HVDC‑connected offshore wind farms and OEHs, the offshore grid‑forming HVDC converters absorb active power from an offshore AC grid supplied by the wind farms and convert it to DC power for transmission to the onshore grid. Converter current limiting under different fault types in this setting is an understudied topic in the literature, which mostly focuses on power‑injecting converters. This paper proposes a unified current‑limiting strategy that combines a variable virtual impedance (VVI), based on a smooth threshold function, with a novel virtual‑power (VP) mechanism derived from the power dissipated in the virtual resistance. The VVI ensures current limitation during fault‑induced overcurrents while preserving voltage‑source behavior, whereas the VP mechanism adds a compensating power term into the synchronization loop, enabling automatic power redistribution among converters. P‑delta analysis further shows that a more resistive VVI can improve the transient stability of power‑absorbing converters, while the proposed VP mechanism further enlarges the stability margin. EMT simulations validate that the combined VVI‑VP strategy limits fault currents, maintains synchronism during severe faults, and achieves coordinated post‑fault power sharing in fully converter‑based OEHs.

Authors:Sangli Teng, Ruiqi Zhang, Tzu-Yuan Lin, William A Clark, Mark Mueller, Ram Vasudevan, Maani Ghaffari, Koushil Sreenath
Title: LieIPM: Lie Group Interior Point Method for Direct Trajectory Optimization of Rigid Bodies
Abstract:
Designing dynamically feasible trajectories for rigid bodies is a fundamental problem in robotics. While direct methods are widely used, the existing constrained optimizers typically operate in Euclidean space and ignore the manifold structure of rigid body motions. This mismatch may introduce singularities or lead to poorly conditioned optimization problems. To bridge this gap, we develop a structure‑aware framework for constrained trajectory optimization directly on matrix Lie groups. Our approach is based on the second‑order rigid body models utilizing Lie group structures, which enables efficient Newton‑type updates while preserving the underlying geometry. Building on this model, we propose a line‑search Lie Group Interior Point Method (LieIPM) to handle constraints on the manifolds. We instantiate the framework for rigid body motion planning using Lie group variational integrators and derive closed‑form intrinsic derivatives that exploit group symmetries. The LieIPM preserves the topology of rotation motions by construction and avoids singularities. Numerical results demonstrate superior robustness and faster convergence compared to general‑purpose solvers and structure‑exploiting optimal control methods.

Authors:Han-Teng Liao, Karen Ang
Title: From Stacks to Circuits: A Regenerative Socio-Technical Roadmap for AI Infrastructure within Planetary Boundaries
Abstract:
Current scaling trajectories for Generative AI, typified by linear supply‑side "stacks," prioritize performance density while externalizing significant thermodynamic and material costs. As the "Twin Transition" of green and digital transformation accelerates, the industry faces technology gaps ‑ including Scope 3 emissions and e‑waste recycling ‑ that impede sustainable scaling and lead to social tensions. This study proposes a Regenerative Socio‑Technical roadmap that repurposes the Sustainable Production and Consumption system map to reframe artificial intelligence infrastructure as a system‑of‑systems governed ultimately by planetary limits. By integrating the Institute of Electrical and Electronics Engineers International Roadmap for Devices and Systems (IEEE IRDS) sustainability considerations for semiconductor facilities, the study proposes a metabolic circuit framework that centers "Values and Needs" within production and consumption relationship loops. This study identifies critical gaps in current Nvidia‑centric roadmaps and proposes a competing reference architecture. It demonstrates how a spontaneous order of resource parsimony and planetary accountability can provide an actionable pathway for regulatory compliance and industrial resilience in the digital circular economy.

Authors:Mohamed Camil Belhadjoudja
Title: Backstepping Control of Multidimensional Coupled First-Order Hyperbolic PDEs with Collinear Velocities
Abstract:
This paper addresses the backstepping boundary stabilization of coupled multidimensional first‑order hyperbolic systems. We consider systems whose transport velocity fields are collinear, meaning that each velocity field is a scalar multiple of a common base velocity field. Building upon a recent framework developed for scalar multidimensional first‑order hyperbolic equations, we introduce a change of variables, based on characteristic curves defined entirely in the spatial domain, that converts the original multidimensional system into a continuum of coupled one‑dimensional first‑order hyperbolic systems. By designing a backstepping controller for each system in the continuum representation, and assuming that the transit times of the characteristic curves are uniformly bounded, we achieve finite‑time stabilization of the multidimensional system.

Authors:Jovan Krajacic, Maitraya Avadhut Desai, Ognjen Stanojev, Gabriela Hug
Title: Dynamic Optimization of Virtual Inertia and Damping in Converter-Based Power Systems
Abstract:
The transition towards a sustainable power system is enabled by the replacement of conventional synchronous generators with converter‑interfaced renewable energy sources. However, the resulting loss of rotational inertia and governor damping causes significant frequency deviations and can therefore cause instability. The focus of this paper is the optimal allocation of virtual inertia and damping in the power system activated by established converter control schemes. To this end, we propose a novel dynamic optimization algorithm that considers performance metrics for system stability, cost‑efficiency, and resilience. In addition, our algorithm considers the magnitudes and locations of disturbances in the power system for the optimal allocation. Finally, we validate our approach on a three‑area system and also compare our results with a \mathcalH_2 system‑norm‑based allocation approach.

Authors:Jarod Morris, Xiuzhen Ye, Wentao Tang
Title: Koopman Modeling and Stabilization of Discrete-Time Nonlinear Control Systems: Bilinearity on a Reproducing Kernel Hilbert Space
Abstract:
Despite the popularity of Koopman modeling for nonlinear systems, in the presence of input variables, the evident nonexistence of a fully linear time‑invariant model even in infinite dimensions makes Koopman‑based control largely an open problem to date. Focusing on discrete‑time systems in this paper, which eschews from using operator semigroup and infinitesimal generator notions, it is proven that nonlinear systems, if satisfying appropriate smoothness and regularity conditions, can be expressed exactly as bilinear dynamics, when the state variables and input variables are separately lifted into their reproducing kernel Hilbert spaces (RKHSs). To account for the knowledge of an equilibrium point at the origin, the RKHS is defined by a linear‑‑radial product kernel, and hence the functions belonging to this RKHS are spanned by the multiplications of component functions and Sobolev functions. The stabilization problem, namely the determination of a feedback law that causes a Lyapunov function (expressed as a kernel sum‑of‑squares form) to decrease, is then posed as an infinite‑dimensional optimization problem over state‑dependent conditional probability measures over the input space, solved via a discretization scheme.

Authors:Hieu Trinh
Title: On Time-Delay Compensators for Delayed-Output Systems
Abstract:
This paper advances the practical utility of functional observer theory by addressing sensing latency in linear time‑delay systems. We address the estimation of the functional z(t)=Fx(t) in cases where the measurement delay h is independent of the internal state delay τ, with a specific focus on the condition 0 < h < τ. To compensate for sensing lags, we propose a functional observer structure characterized by multiple internal delays and an augmented architecture. Algebraic existence conditions are established alongside a constructive synthesis procedure. By incorporating an additional delayed measurement vector, we demonstrate that this approach significantly expands the design space and is applicable to a wider class of systems with larger state and output delays.

Authors:Xiangyu Li, Bodong Shang, Junchao Ma, Qingqing Wu, Jie Feng, Deshuang Huang
Title: Fundamentals of NOMA in Low-Earth Orbit Coordinated Multi-Satellite Networks
Abstract:
Coordinated multi‑satellite (CoMS) transmission and non‑orthogonal multiple access (NOMA) are envisioned to jointly enhance coverage, capacity, and spectrum efficiency for satellite networks. Their integration into a unified CoMS‑NOMA framework will allow more efficient, reliable, and energy‑efficient multi‑user access. This paper investigates the downlink performance of CoMS‑NOMA networks from a system‑level perspective, in which multiple satellites cooperatively serve multiple users via NOMA. Leveraging tools from stochastic geometry, related angles and distances in CoMS‑NOMA are first derived as intermediate results. Then, we obtain the combined signal power distributions and analyze coverage and spectrum performance under both inter‑ and intra‑satellite interference, accounting for potential imperfect successive interference cancellation (SIC). The analytical model is validated across a range of system parameters, including the number of satellites, service region angle, error‑propagation factor, and power allocation coefficients. Numerical results indicate that increasing the number of cooperative satellites does not always improve coverage and spectrum efficiency. Additionally, while a higher main‑lobe gain improves coverage, a near‑perfect SIC provides only slightly greater benefits than a reasonably good SIC. With properly selected power allocation coefficients, CoMS‑NOMA achieves up to a 270% improvement in coverage and a 56% gain in sum spectral efficiency, compared with conventional orthogonal and single‑satellite schemes, indicating potential for green, energy‑efficient satellite networking.

Authors:Ruiming Zheng, Mohammad Pirani, Davide Spinello
Title: Game-Theoretic Area Coverage Control with Cooperative-Adversarial Multi-Agent Systems
Abstract:
We formulate a multi‑agent area coverage control problem as a two‑player zero‑sum game between two agent groups with conflicting goals. Conventional coverage control allocates resources based on an environmental risk density field. In contrast, we generalize this metric by allowing a second group of adversarial agents to generate the spatial risk field. Coupled agent dynamics are linked through the area coverage metric, which functions as the game reward. This framework induces coupled gradient‑descent‑ascent controllers for the groups. Analysis of a low‑dimensional case reveals a Hopf bifurcation dictated by the ratio of the groups' control gains. In the regime dominated by adversarial agents, the system is driven into a periodic chase‑evasion cycle. In the regime dominated by ordinary agents, the system converges to a fixed configuration. Numerical simulations validate these theoretical insights. Finally, we characterize the Nash equilibrium conditions. Under this equilibrium, ordinary agents converge to a generalized centroidal Voronoi tessellation, whereas adversarial agents settle at their corresponding equilibrium centroids.

Authors:Shamak Dutta, Bahman Gharesifard, Stephen L. Smith
Title: Submodular Optimization with Applications to Decision and Control
Abstract:
Submodular set functions, characterized by the diminishing‑returns property, provide a unifying combinatorial framework for many subset‑selection problems in decision and control. Although exact maximization is NP‑hard in general, the structural properties of submodular functions enable simple greedy algorithms that achieve constant‑factor approximation guarantees for monotone objectives, with randomized greedy‑based variants extending such guarantees to the non‑monotone case. This survey reviews the theory, algorithms, and applications of submodular optimization with a focus on systems and control. We cover the structural properties of submodular functions, including curvature and the submodularity ratio, the constraint families that arise in practice (matroids, knapsack, and p‑systems), and the main approximation algorithms for monotone and non‑monotone submodular maximization, with up‑to‑date approximation ratios and hardness results. We then survey applications across sensor scheduling, multi‑agent coordination, robust submodular optimization, leader‑follower systems, distributed submodular optimization, game theory, system theory, resource allocation, social networks, and informative path planning. The survey emphasizes practically implementable greedy‑based algorithms and instance‑dependent refinements via curvature and the submodularity ratio. We close with observations on canonical control‑theoretic objectives: certain functionals are submodular (the log‑determinant and rank of the controllability Gramian, and the log‑determinant of the Kalman filter information matrix), whereas closely related objectives fail to be sub‑ or supermodular (the steady‑state Kalman filter error covariance, and the average control energy obtained from the inverse Gramian). We also highlight the cross‑cutting open directions that follow.

Authors:Olga Zarina, Natalya Martyuhova, Alexey Bobtsov, Romeo Ortega
Title: An Algebraic State Observer for a Self-Sensing Active Magnetic Bearing System
Abstract:
The problem of designing a globally stable observer for a self‑sensing active magnetic bearing system assuming only measurements of currents and voltages is addressed in this paper. Towards this end, we first design a radically different, high performance, state observer, which is obtained invoking novel techniques. Indeed, our objective is to obtain an algebraic relation between the unmeasurable part of the state and filtered versions of the systems inputs and outputs, which holds for all times. Then, using this algebraic observer, we propose a robust asymptotic version of the observer. Simulation results that illustrate the performance of the observer are also presented.

Authors:Sasan Vakili, Daniël Woonings, Pradyumna Paruchuri, Peyman Mohajerin Esfahani
Title: Nonlinear Estimator: Dual Bayesian Affine Estimators for Parameter Learning
Abstract:
This paper presents a nonlinear parameter estimator for Wiener‑type state‑space models obtained as a fixed‑point architecture that couples two affine minimum mean‑squared error (MMSE) estimators: one for the unknown parameters and one for latent variables. The architecture retains the functional structure of the optimal affine MMSE parameter estimator while incorporating Dynamic Basis Statistics (DBS) estimates that summarize nonlinear basis‑function evaluations. Two DBS construction strategies are developed, leading to two nonlinear estimator frameworks. The dual basis‑parameter estimator combines an affine basis estimator with the affine parameter estimator, whereas the dual state‑parameter estimator first computes affine state estimates and their covariances, then maps these state‑estimate statistics through a Gaussian DBS operator to obtain DBS estimates. Both dual estimators admit fixed‑point characterizations that alternate between estimating each component using the updated prior of the other, obtained from that component's plug‑in estimate statistics from the previous iteration. The efficacy of the proposed methods is examined via extensive Monte Carlo experiments, showing that the dual basis‑parameter estimator attains parameter mean‑squared errors comparable to those of the purely affine parameter estimator, while the dual state‑parameter estimator achieves the lowest parameter mean‑squared error, outperforming both the dual basis‑parameter and purely affine parameter estimators, as well as sequential Monte Carlo variants of classical Particle Gibbs and Expectation‑Maximization schemes.

Authors:Ilias Mitrai, Wentao Tang
Title: A constrained symbolic regression approach for Lyapunov function discovery
Abstract:
In this paper, we consider the data‑driven discovery of Lyapunov functions for autonomous dynamical systems. We represent the Lyapunov function as an expression tree of fixed depth and formulate the Lyapunov discovery task as a constrained self‑supervised symbolic regression problem. The constraints model the output of the Lyapunov function for a given input as well as the Lyapunov stability conditions. This modeling approach makes no a priori assumptions about the functional form of the Lyapunov function, is inherently interpretable since the function is obtained in a symbolic form, and, in principle, can be applied to any continuous dynamical system. We also develop a tailored branch‑and‑bound‑and‑check solution approach to efficiently solve the resulting learning task. Applications to several case studies show the ability of the proposed approach to discover Lyapunov functions.

Authors:Corentin Briat
Title: Optimal Control and Dissipativity of Linear Hermitian Matrix-Valued Dynamical Systems
Abstract:
We develop a unified framework for linear‑cost optimal control, finite‑time optimal steering, dissipativity analysis, and zero‑sum differential games for linear impulsive systems whose state is a Hermitian matrix evolving in \mathbbH^n+m_\succeq0, a class that encompasses continuous‑ and discrete‑time linear systems and switched systems as degenerate cases, and includes the second‑order moment dynamics of linear (stochastic) hybrid systems. The entire theory rests on three tools: a single \emphkey identity relating cost, trajectory, and a dual variable, an Extended Schur complement lemma, and a Schur inner‑product decomposition, applied identically to the flow integral and to each jump. These yield structurally uniform sufficient and necessary conditions, dual linear matrix inequality (LMI) characterizations, and explicit optimal policies for every problem class, on both finite and infinite horizons under time‑varying assumptions (without time invariance or periodicity), together with causal dwell‑time policies for the problems that admit them.

Authors:Tri Ngo
Title: Adaptive Model Predictive Control of Nonlinear Generic Urban Air Mobility Using Linear Parameter-Varying Systems
Abstract:
This paper presents an adaptive model predictive control (MPC) framework for nonlinear urban air mobility (UAM) vehicles operating across the full flight envelope. The proposed approach leverages a linear parameter‑varying (LPV) representation to update the predictive model online, enabling accurate capture of strongly nonlinear and time‑varying dynamics associated with distributed electric propulsion (DEP) eVTOL aircraft. To systematically address the high‑dimensional and coupled nature of MPC tuning, a multi‑objective evolutionary optimization strategy based on NSGA‑II is employed, incorporating proper normalization of states and control inputs to ensure balanced weighting and meaningful exploration of the design space. The resulting controller explicitly accounts for actuator constraints and enables reconfigurable control allocation for fault‑tolerant operation. The framework is evaluated in nonlinear simulations using NASA's Generic Urban Air Mobility (GUAM) model and benchmarked against a robust servomechanism linear quadratic regulator (RSLQR). Results demonstrate that the proposed adaptive MPC achieves improved trajectory tracking and enhanced robustness under both nominal conditions and actuator degradation scenarios, including partial motor failure, while maintaining constraint satisfaction throughout all flight regimes.

Authors:Fabrizio Sossan
Title: Energy Storage as a Multi-Use Asset: Applications Across the Power System
Abstract:
The energy transition in power systems requires flexible assets to offset renewable generation variability across multiple time scales, while supporting the integration of renewables and the electrification of demand without requiring costly grid reinforcement. Energy storage occupies a unique position among these assets: depending on the technology, it can provide short‑duration grid services at high ramping rates, such as frequency regulation and voltage support, longer‑duration functions such as intra‑day peak shaving, or inter‑seasonal energy buffering. This multi‑service character, combined with the declining costs of energy storage technologies (most notably that of battery energy storage systems), is central to the economic viability of storage investments. The value of a given installation depends strongly on its grid connection point and intended use case: an asset‑coupled battery serving a consumer or generation plant faces a different service landscape, and therefore a different business case, than a network‑coupled system operating as an independent grid resource. This paper presents a structured taxonomy of grid‑connected energy storage applications, discusses the principal application domains, and describes the key challenges that must be addressed to integrate storage effectively into power systems. Services are discussed with special emphasis on the Swiss regulatory context. Finally, the STORE flagship project supported by the Swiss Innovation Agency (Innosuisse), where some of the critical challenges of energy storage integration in power grids are addressed, is introduced.

Authors:Pavel Osinenko
Title: Some Essential Constructive Foundations for Systems and Control
Abstract:
This work develops several constructive foundations for systems and control within Bishop‑style constructive mathematics. For an engineer, the guiding principle is that an object claimed to exist, such as a trajectory, an optimal control law, a selector, or a viable solution, should come with finite data and an operation computing approximations to any prescribed precision. The style remains close to classical analysis, but existential statements are organized so that their computational content is visible. The paper begins with elementary geometric data in finite‑dimensional Euclidean spaces: blocks, multiblocks, representable sets, regular functions, and certified integrals. This set‑first integration route is meant to complement, rather than replace, abstract constructive integration theories such as Daniell‑type or integration‑space approaches. The developed apparatus is then applied to a constructive functional extremum‑value theorem, selector extraction for multifunctions, Filippov‑type and viable solutions of differential inclusions, regular probability densities, controlled Markov chains, and empirical density certificates. A short account of resolvent projectors and linear stability is included for completeness.

Authors:Max Grobbel, Tristan Schneider, Daniel Flögel, Sören Hohmann
Title: Real-Time and Accurate Collision-Free Teleoperation via Differentiable Constraint-Based Trajectory Planning
Abstract:
In teleoperation, the human operator typically controls only the end‑effector pose, which often leads to self‑collisions of the manipulator and collisions with environmental obstacles, since joints and links are not controlled individually. A common strategy to mitigate this issue is to enhance the operator's input using optimal‑control‑based trajectory planning. As derivative‑based solvers require differentiable constraints, existing approaches either approximate robots and obstacles with spheres, reducing geometric accuracy, or approximate derivatives, degrading convergence and increasing computation times. We address these limitations by adapting a recent formulation of differentiable collision‑avoidance constraints, based on duality in convex optimization, to the teleoperation setting. The robot is approximated with capsules and the environment with polytopes. We compare the resulting trajectory planning method against state‑of‑the‑art techniques in simulation with varying numbers of obstacles and evaluate it on a UR5e manipulator in a real‑world teleoperation test. Results show that our approach achieves lower computation times while enabling more accurate obstacle modeling, leading to smoother and collision‑free end‑effector teleoperation.

Authors:Ali Kafili Gavgani, Amin Talaeizadeh, Aria Alasty, Hossein Nejat Pishkenari
Title: Hybrid Neural Network and Conventional Controller Approach for Robust Control of Highly Unstable Systems: Application to Tilt-Rotor Control
Abstract:
Multirotors are widely used in applications ranging from surveillance to precision agriculture, yet conventional designs remain limited by their under‑actuation. Tilt‑rotor configurations overcome this limitation by enabling full actuation. This paper investigates neural‑network‑based control strategies for a fully actuated tilt‑rotor system with four thrust‑vectoring inputs. Our work is structured in two parts. First, we deliberately present a negative result by evaluating a direct input‑output control approach. In this method, multilayer perceptrons (MLPs), long short‑term memory (LSTM) networks, and transformer models are trained to map system states and their desired values directly to control signals. We show that this strategy fails to stabilize the system, highlighting the inherent difficulty of applying direct input‑output learning to highly unstable plants. Second, as the main contribution, we propose a neural‑network‑enhanced sliding mode controller (SMC). The method decomposes the system dynamics into input‑independent and input‑dependent components, with the former learned from a small dataset using lightweight networks, thereby reducing real‑time computational demands. Moreover, the proposed method can be trained using flight logs collected from low‑performance controllers, and the resulting dynamic model learned from real‑world data can be used in simulation. We further compare MLP‑ and LSTM‑based implementations under model uncertainties and external disturbances, demonstrating the robustness and effectiveness of the proposed approach; in particular, the controller with the LSTM plant dynamics predictor achieves superior performance to its MLP‑based counterpart while also exhibiting lower runtime.

Authors:Drishti Baruah, Sachin Teli, Ankur A. Kulkarni
Title: Nonlocal Teams and Information Structures
Abstract:
We look at Bell inequalities from the lens of information structures in stochastic teams. We consider the usual CHSH game and a dynamic variant of the same to study how various classes of strategies, classical, projective and quantum, behave under team theoretic solution concepts. We find that projective strategies (where each player performs projective measurements) enjoy important properties in the usual CHSH game, but they do not carry over to its dynamic version. These results shed light on the delicate interplay of information structure in quantum strategies and the fragility of some well known ideas under changes of information structure.

Authors:Ram Milan Kumar Verma, Shashi Ranjan Kumar, Hemendra Arya
Title: Cooperative Guidance and Control for Active Asset Protection with Time-Varying Agent Speeds
Abstract:
Protecting an asset against threats is a challenging problem in an era of continuously evolving intelligent attacks. This requires cooperation between the asset and the defender to share information and jointly maneuver. To address this problem, this work proposes a cooperative guidance and control strategy for active asset protection against a maneuvering threat. This work develops a joint maneuver strategy where both the defender and the asset coordinate their time‑varying speeds and courses to neutralize/capture the attacker. The control strategy is formulated around three coupled geometric and temporal objectives. The first objective is to set the line‑of‑sight rate between the asset and the attacker to zero, putting the attacker on a collision course and reducing their maneuvering. The second objective is to maintain the defender on the line‑of‑sight between the asset and the attacker. This ensures that the attacker faces the defender first before reaching the vicinity of the asset. Lastly, the defender is also guided to pursue the attacker based on the time‑to‑go estimates between the defender and the attacker. While keeping these objectives in mind, the control actions for the asset and the defender are jointly designed, fostering cooperation between the two. The stability of the proposed strategy is established using a Lyapunov‑based approach. Numerical simulations performed show the effectiveness of the proposed cooperative strategy in ensuring the successful capture of a maneuvering threat.

Authors:B. Seyed, M. Koehler, S. M. Goetz
Title: Coil-Integrated Alignment Sensor for Real-Time Feedback of Coil-Scalp Contact Point and Angle During Transcranial Magnetic Stimulation (TMS)
Abstract:
Whereas coil positioning in transcranial magnetic stimulation (TMS) to reach a specific cortical target with modern focal stimulation coils has been intensively studied, the alignment and contact of a coil with the head is often ignored. Focal figure‑of‑eight coils have a point on the surface, where they generate the largest induced electric field. This point should touch the head first, and the coil should be approximately tangential to the head in this point. Previous research has demonstrated the large impact if the coil does not touch the head with the right point and that many operators struggle with establishing or maintaining the correct coil‑scalp alignment. This paper presents a technological support technology that can monitor the exact position of the contact point and also pressure to provide feedback to users. As the system uses exclusively components from consumer electronics, the sensor is low‑cost and affordable. Through proper design, we achieved sufficient robustness so that the sensor does neither reset during TMS pulses and also not show any detectable degradation.

Authors:Liqiu Dong, Marta Zagórowska, Mehmet Mercangöz
Title: Bayesian Optimization of a Multi-Product Chemical Reactor Using Composite Models and Partial Physics Knowledge
Abstract:
We study data‑driven real‑time economic optimization of a multi‑product chemical reactor when no reliable first‑principles model is available beyond a steady‑state energy balance. Instead of learning the economic objective directly as a black‑box function, we use a composite formulation in which Gaussian process (GP) models predict physically meaningful outputs, including product concentrations and reactor temperature, while profit is computed analytically from these predictions together with raw‑material, product, and utility prices. This preserves the structure of the economic objective, makes it parametric in changing prices without needing retraining, and allows candidate operating points to be checked against the available energy balance through a physics residual. The GPs also provide predictive uncertainty, which is exploited in a Bayesian optimization (BO) framework both for data‑efficient exploration and for conservative enforcement of the reactor temperature constraint through an upper confidence bound. The acquisition function additionally penalizes large energy‑balance mismatch obtained by substituting the GP‑predicted outputs and candidate inputs into the available steady‑state energy balance. The approach is demonstrated on a benchmark simulation of a non‑isothermal multi‑product reactor. Relative to a trust‑region safe BO implementation, the proposed method achieves better simulated economic performance within the available iteration budget. Relative to a purely data‑driven BO approach that does not use the available physics information, it avoids reactor temperature constraint violations.

Authors:Elisei Shafer, Oren Gal
Title: Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning
Abstract:
Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control. This paper explores the feasibility of an end‑to‑end Deep Reinforcement Learning (DRL) approach that maps raw sensor data directly to thruster commands, reducing manual engineering. We propose a hierarchical reinforcement learning (HRL) architecture splitting the problem into two Markov Decision Processes. A High‑Level (HL) policy operating at 2Hz processes raw 84 × 84 pixel monocular camera frames, stacked 100 × 100 pixel forward‑looking imaging sonar, and proprioceptive data to generate spatial subgoals. Simultaneously, a Low‑Level (LL) policy operating at 10Hz converts these subgoals into thruster commands. The HL policy is trained using Reinforcement Learning from Prior Demonstrations (RLPD) within a modified Sample‑Efficient Robotic Reinforcement Learning (SERL) framework, while the LL policy utilizes Soft Actor‑Critic (SAC) combined with Hindsight Experience Replay (HER). Evaluated in the high‑fidelity HoloOcean simulator, our method demonstrates successful obstacle avoidance, achieving trajectory lengths closely approximating (within 4% to 6% of) an \textRRT^ planning baseline. Furthermore, the learned policy exhibits strong robustness to simulated sensor noise and decreased visibility. While the system navigates familiar geometries effectively, experiments reveal generalization limitations when encountering unvisited areas with novel obstacle shapes. Ultimately, this work demonstrates the promise of sample‑efficient, end‑to‑end DRL for underwater navigation using minimal computational hardware.

Authors:Xiucheng Wang, Kailong Wang, Nan Cheng
Title: RadioDiff-Inv2: Differentiable Diffusion Inversion under Location Drift from Sparse Noisy Measurements for Radio Map Estimation
Abstract:
Radio map (RM) estimation is a key enabler for environment‑aware optimization in 6G wireless networks. In practice, RM construction increasingly relies on crowdsourced received signal strength (RSS) feedback that is inherently sparse and noisy. A further and often overlooked challenge is location drift, whereby privacy constraints and user mobility cause reported sampling coordinates to deviate from the true measurement locations. Unlike additive measurement noise, location drift perturbs the sensing operator itself, since each RSS sample effectively queries the underlying RM at an incorrect spatial coordinate. This operator uncertainty, compounded with sparse noisy sensing, renders the inverse problem severely ill‑posed and limits conventional estimators that rely on analytically specified priors. This paper proposes RadioDiff‑Inv2, a differentiable diffusion inversion framework that estimates RMs from sparse noisy measurements under location drift. A Gaussian resampling scheme is introduced to construct a differentiable, drift‑aware measurement operator on grid‑based maps, and the probability‑flow ordinary differential equation (ODE) is exploited to cast the diffusion sampler as a deterministic, differentiable mapping from an initial noise code to the estimated RM. By optimizing the noise code via backpropagation against a drift‑marginalized data‑fidelity objective, RadioDiff‑Inv2 produces reconstructions that are both prior‑plausible and measurement‑consistent without costly posterior sampling. Extensive experiments show that RadioDiff‑Inv2 outperforms the best competing baseline by 4 to 14 dB in PSNR across varying sparsity and drift levels. The advantage is most pronounced in low‑SNR regimes, where the learned diffusion prior maintains near‑constant reconstruction fidelity while conventional methods degrade severely.

Authors:Qianxi Tang, Li Peng
Title: A Unified Framework for Contraction Stability Analysis of Heterogeneous Grid-Forming Inverters
Abstract:
The shift to renewable‑dominated power systems has produced low‑inertia grids, undermining system stability. In this context, grid‑forming inverters (GFMs) have emerged as a promising solution. However, GFMs challenge conventional analysis techniques, especially those relying on small‑signal or root‑mean‑square (RMS) models. Such models rely on linearization and sinusoidal steady‑state assumptions, which fail in large‑signal cases. Stability of GFM‑based systems therefore becomes operating‑point dependent, and a feasible operating point may not even exist. While large‑signal analyses are available, decentralized certification of operating‑point convergence with explicit transient guarantees, such as rate and overshoot, remains rare. This paper proposes an algebraic, decentralized contraction‑based framework. The proposed contraction stability analysis certifies system stability and convergence to desired operating points. The method works in the time domain and captures nonlinear, large‑signal behavior of synchronization and power‑sharing mechanisms. Moreover, the contraction rate provides an explicit bound on transient time: trajectories converge exponentially to the new operating point at a controlled rate, yielding computable contraction regions that certify stability and large‑signal convergence across operating‑point changes. These regions directly guide parameter tuning for heterogeneous GFMs.

Authors:Md Saiful Islam, Rahul Bhadani
Title: Control-Theoretic View of Neural ODEs: Empirical Controllability and Observability
Abstract:
This paper studies neural ordinary differential equations (neural ODEs) from a control‑theoretic perspective using controllability and observability concepts. The neural ODE is represented in a control‑affine form to facilitate analysis using tools from nonlinear and linear time‑varying (LTV) systems. Controllability is examined through trajectory linearization, where the LTV controllability Gramian provides a local, first‑order measure of input influence along a nominal trajectory. Observability is analyzed through output linearization, where the LTV observability Gramian characterizes the local ability to reconstruct system states from output measurements. Koopman‑based lifting is considered to extend the analysis to a higher‑dimensional representation, and its limitations under multiple equilibria and basin‑dependent behavior are discussed. The proposed framework is illustrated on a series RLC circuit. The learned neural ODE reproduces system trajectories and generalizes to unseen initial conditions. The computed Gramians are numerically full rank along the tested trajectories, indicating local controllability and observability of the linearized dynamics.

Authors:Manan Mittal, Ryan M. Corey, John R. Buck, Andrew C. Singer
Title: A Switching Beamformer for Highly Non-Stationary Environments
Abstract:
Adaptive beamforming is a cornerstone of array signal processing, yet its performance often collapses in the face of complex, rapidly changing interference. When interferers appear or move unpredictably, conventional estimators encounter a fundamental memory trade‑off: short windows enable rapid tracking but suffer from high estimation variance, while long windows provide stable rejection but fail to adapt to shifts. This challenge is resolved by introducing the Universal Switching Beamformer (USB), which integrates competitive sequential prediction into the beamforming architecture. By employing a linear transition diagram, the USB implicitly maintains an exponentially large family of candidate covariance histories and dynamically re‑weights them based on their cumulative output power. This mechanism allows the beamformer to automatically vary its effective memory length without explicit change detection or heuristic parameter tuning. A theoretical upper bound is proven on the regret relative to an omniscient oracle that selects the best piecewise‑stationary covariance model in hindsight. Extensive simulations and experiments on the SwellEx‑96 dataset demonstrate that the USB achieves the agility of short‑window estimators and the precision of long‑term integration, providing a principled solution for tracking highly non‑stationary scenes.

Authors:Francesco Bullo
Title: Predictive Coding with Bayesian Priors via Proximal Gradients
Abstract:
We recast predictive coding as continuous‑time proximal gradient descent applied to a regularized maximum‑a‑posteriori (MAP) objective. We study first a single‑level problem and then a multi‑level hierarchy. For the single‑level problem, we show that proximal gradient descent is precisely a leaky firing‑rate network: the membrane leak, the effective recurrent matrix, the local synaptic drive, and the static nonlinearity all follow from one optimization principle, and the resulting circuit is the one proposed by Rao and Ballard. The prior selects the nonlinearity through its proximal operator, and the likelihood precision sets the gain on the observation. For the hierarchy, we show that a classical variable‑splitting relaxation of the deep MAP problem yields hierarchical predictive coding as the interconnection of local and distributed solvers. In probabilistic modeling terms, this relaxation replaces the directed generative chain by an undirected Markov random field whose node potentials are the level‑wise priors. Each level then applies its own activation function, namely the proximal operator of its prior.

Authors:Shijie Huang, Sergio Grammatico
Title: Benchmarking Sequential Feedback Optimization for Wind Farm Power Maximization
Abstract:
This paper benchmarks sequential feedback optimization (SFO) for wind farm power maximization using a medium‑fidelity dynamic flow model. We compare SFO with two well‑established approaches, adjoint‑based economic model predictive control (AMPC) and extremum seeking control (ESC), under a common nine‑turbine layout and identical operating constraints. The comparison focuses on steady‑state power production and computational efficiency, both relevant for real‑time implementation. The simulation results illustrate that SFO achieves higher steady‑state power while preserving real‑time feasibility, AMPC provides a better transient performance at a higher online computational cost and without guarantees of convergence to the steady‑state optimum, and ESC offers a computationally inexpensive model‑free baseline that may converge to locally optimal solutions. These results provide a practical reference for selecting wind farm control strategies and for designing scalable, real‑time optimization methods.

Authors:Yongyan Cao, Jinshan Tang
Title: Impedance MPC for Physical Human-Robot Interaction: Predictive Disturbance Rejection with Joint-Limit Safety
Abstract:
Physical human‑robot interaction (pHRI) demands simultaneous trajectory accuracy and compliant safety under unplanned contact. Classical impedance control incurs a nonzero steady‑state position error under sustained human force ‑‑ the applied force divided by the task stiffness ‑‑ which integral action reduces only within a narrow stable‑gain budget. We present a two‑layer Impedance MPC that resolves this tension. Layer~1 analytically cancels gravity, Coriolis, and task‑space inertia, reducing the residual plant to a configuration‑independent double integrator with a constant state‑transition matrix. Layer~2 solves a 30‑variable convex QP at 100\,Hz, exploiting this constant structure so the free‑response matrix is precomputed once; an augmented Kalman filter estimates the persistent disturbance state, giving a formal zero‑steady‑state‑error guarantee. A null‑space inverse‑barrier potential and a task‑space workspace projection enforce joint‑limit safety across the tested workspace. On a 7‑DOF Franka FR3, Impedance MPC with Kalman augmentation attains sub‑0.05\,mm steady‑state error versus 44.8\,mm for classical impedance (a >800‑fold reduction) under a sustained 15\,N force, sub‑millimeter tracking on four 3‑D circles, and graceful robustness to measurement noise and inertial mismatch up to 30%.

Authors:Ankit Goel
Title: Exact Optimization-Free Safety Filters for Control Barrier Functions
Abstract:
For control‑affine systems, standard and high‑order control barrier function conditions are affine in the control input and are commonly enforced through quadratic‑program‑based safety filters. Although convex, these optimization problems may be undesirable in embedded, high‑rate, or resource‑limited implementations. This letter studies when the corresponding Euclidean projection can be computed exactly without solving a quadratic program. Given a nominal control input, we form the set of affine inequalities violated by that input and compute the minimum‑norm correction that enforces those inequalities with equality. This correction need not equal the exact Euclidean projection onto the full feasible set. The main result gives structural conditions under which it coincides with the Euclidean projection onto the feasible set. These conditions are interpreted through interactions between affine‑inequality normals and are expressed using a Gram matrix. Finally, an online certification procedure is given for determining whether the optimization‑free update is exact.

Authors:Dhairya Patel, Margaret P. Chapman
Title: Risk-Aware Control of Systems with Quasi-Cone-Bounded Nonlinearities
Abstract:
We develop a tractable, rigorous approach to risk‑aware control for a class of nonlinear systems. While many classical control methods reduce uncertainty to a simple average or a worst‑case outcome, risk‑aware control aims to equip systems with a refined awareness of uncertainty. Efficient methods for risk‑aware control of linear systems are available, but there is a paucity of tools for tractable, risk‑aware control of nonlinear systems. To bridge this gap, we develop an analytical, suboptimal controller with respect to a risk‑aware performance criterion for systems with nonlinearities characterized by cone‑like bounds. Numerical examples demonstrate benefits of the characterization of nonlinearities and risk that we consider.

Authors:Ashik Abrar Naeem, Mohammad Ariful Haque
Title: A Barrier-Modulated Architecture for Safe Affine Formation Control in Second-Order Multi-Agent Systems
Abstract:
Affine formation control offers immense flexibility for coordinating multi‑agent maneuvers, but guaranteeing the safety of agents under parametric uncertainties remains an open challenge. This paper proposes a novel safe affine formation control framework for second‑order multi‑agent systems by integrating Higher‑Order Control Barrier Functions (HOCBFs) with Adaptive Dynamic Programming (ADP). We introduce a barrier‑modulated control architecture that smoothly attenuates the nominal formation tracking objective when agents approach safety boundaries, preventing conflicting control inputs. Within this architecture, two distinct safety controllers are developed: (1) an analytical barrier‑gradient repulsive controller that provides a computationally efficient, rigorous mathematical baseline, and (2) a data‑driven optimal safety controller. The data‑driven approach utilizes an actor‑critic neural network to solve the Hamilton‑Jacobi‑Bellman (HJB) equation online, enabling optimal collision avoidance even in the presence of unknown system parameters. Using Nagumo's theorem and Lyapunov stability analysis, we formally prove that both controllers guarantee the forward invariance of the safe set ensuring absolute collision avoidance while maintaining Uniformly Ultimately Bounded (UUB) formation tracking errors. Finally, simulations validate the theoretical findings and demonstrate the robustness of the proposed controllers in dynamic obstacle avoidance scenarios.

Authors:Xu Du, Shuting Wu, Karl H. Johansson, Apostolos I. Rikos
Title: A Global Convergence Analysis of Consensus ALADIN for Convex Optimization
Abstract:
Distributed optimization problems are pervasive in machine learning and optimal control. In this paper, we study smooth strongly convex distributed consensus optimization problems. We present a distributed optimization algorithm for consensus problems based on the Consensus Augmented Lagrangian Alternating Direction Inexact Newton (C‑ALADIN) framework. Our algorithm uses an auxiliary variable to decide when to update second‑order information, enabling curvature exploitation without sacrificing global convergence. This contrasts with existing C‑ALADIN methods, which require constant Hessian approximations and thus lose numerical advantages. Under smooth strong convexity, the algorithm converges globally, and the auxiliary variable converges sublinearly. Numerical experiments on logistic regression show that our algorithm outperforms baseline methods that use either fixed or updated Hessian information.

Authors:Zhongze Li, Xiaoyu Peng, Xi Ru, Zhaojian Wang, Jianxin Zhang, Yingshang Liu, Feng Liu
Title: When Can Phasor-Domain Device Models Be Trusted for Electromechanical Stability Analysis of Grid-Forming Converter-Dominated Microgrids?
Abstract:
Grid‑forming (GFM) converter‑dominated microgrids are often analyzed using reduced‑order phasor‑domain electromechanical GFM models, but the validity of these models is often taken for granted. Assuming ideal inner‑loop tracking (IILT) of terminal‑voltage references, these models neglect the inner‑loop and filter dynamics at the electromagnetic‑transient (EMT) timescale to simplify stability analysis. This paper argues that such neglected dynamics can destabilize the system, invalidating the stability conclusions drawn from the IILT model. To address this cross‑timescale stability issue, we formulate the validity of the IILT stability conclusion as a robust‑stability certification problem. The EMT‑induced model mismatch between the reduced‑order converter model and the actual converter model is represented as a structured uncertainty embedded around the IILT feedback loop. This yields a frequency‑resolved interaction index and a structured singular‑value sufficient certificate for determining when the stability conclusion of the IILT model can be certified with respect to a prescribed EMT uncertainty weight. The uncertainty weight can be obtained from detailed EMT models or terminal reference‑response measurements. Case studies confirm that the proposed certificate correctly certifies model validity and identifies the loss of trustworthiness. We also demonstrate that the measurement‑based uncertainty weights closely match the model‑based ones, which enables deployment without accessing inner‑loop models.

Authors:Isaac Ortega Romero, Ioannis Zografopoulos
Title: Multidimensional Resilience for Electrical Power Systems: Systematic Review, Integrated Index, and Validation under Real-World Cyber-Physical Attack Scenarios
Abstract:
The accelerating decarbonization of energy systems has transformed electrical power systems into complex infrastructures exposed to threats whose interactions generate systemic vulnerabilities that conventional resilience approaches fail to capture. Although resilience assessment has expanded across multiple dimensions, existing studies largely examine them in isolation or adjacent pairs, leaving cross‑dimensional couplings insufficiently explored. This study demonstrates i) that single‑dimension assessments fail to capture the degradation produced by simultaneous cross‑dimensional failures, ii) the nonlinear amplification emerging when physical, operational, and digital‑cyber dimensions are jointly compromised, and iii) the intensification imposed by climatic and economic‑regulatory stressors. To this end, we leverage a hybrid quantitative methodology. A PRISMA 2020 review with backward and forward snowballing identifies methodological gaps and unresolved dependencies across five resilience dimensions: physical, operational, digital‑cyber, climatic‑external, and economic‑regulatory. Following this analysis, a Multidimensional Resilience Index (MDRI) is developed to capture endogenous couplings and exogenous amplification effects and is validated under escalating cyber‑physical attack scenarios inspired by the December 2025 attack on Polish energy infrastructure. Results show that degradation under cascading and simultaneous failures is nearly eight times greater than under isolated stress, while exogenous conditions amplify degradation by an additional factor approaching six, with 72% of this amplification driven by exogenous stressors. Combined, these mechanisms produce a 46‑fold increase in resilience loss compared to a single‑vector reference.

Authors:Rene Ebunle Akupan, May-Win Thein, Se Young Yoon
Title: Feedback Linearization and Control of a Grid-Forming Power Converter in an Islanded Microgrid
Abstract:
In an islanded setting, grid‑forming inverters must regulate their terminal voltage without support from an external grid, even though the load current depends directly on that voltage. The usual approach is a cascaded proportional‑‑integral (PI) controller, built on a fast inner current loop and a slower outer voltage loop, with feedforward terms used to compensate dq rotational coupling. However, this compensation is only exact at the operating point where the controller is tuned. This tutorial presents an alternative based on full‑state feedback linearization. It is shown that the islanded inverter model has full relative degree, which allows exact state‑space linearization with no internal or zero dynamics. A single feedback law cancels the main nonlinear effects; rotational coupling, resistive drops, and load conductance, so that the closed‑loop system behaves like two independent double integrators. A standard pole‑placement design is then used to shape the response. The controller is tested in MATLAB against a cascaded PI baseline under identical conditions at a 20 MW operating point, including reference tracking, load step disturbances, and parameter mismatch. The feedback‑linearizing controller settles a reference step in 0.76 ms, while the PI controller does not reach the 2 % band within 50 ms. The cascaded PI controller shows better robustness to filter parameter mismatch due to its inner‑loop integral action, which reduces steady‑state errors under modeling uncertainty. Overall, the performance improvement and the robustness trade‑off both come directly from the controller structures, rather than from tuning choices.

Authors:Vedang M. Deshpande, Raktim Bhattacharya
Title: On Improved Statistical Accuracy of Low-Order Polynomial Chaos Approximations
Abstract:
Polynomial chaos expansions provide surrogate models for stochastic systems, with coefficients typically derived using Galerkin projection, stochastic collocation, or least squares approximation. These traditional approaches often fail to accurately capture statistical moments without resorting to high‑order approximations. We propose a constrained optimization framework that modifies standard techniques to determine polynomial chaos coefficients that precisely recover the first two statistical moments. The effectiveness of our approach is demonstrated on several candidate algebraic functions of random variables, showing significant improvements in statistical accuracy even with low‑order approximations.

Authors:Gayan V. Dissanayake, Sandun S. Kuruppu
Title: Extremum Seeking Control Based Adaptive Compensation of Position Sensor Harmonics in PMSM Drives
Abstract:
Permanent Magnet Synchronous Machines (PMSMs) have become one of the preferred forms of electromechanical energy converters, attributing to their high efficiency, torque density, and other unique advantages. However, given the need for proper rotor position measurement for commutation and field orientation, accurate rotor position sensing is of paramount importance. In sensing motor rotor position with a sensor, harmonic errors that arise in the sensing subsystem lead to undesirable torque ripple. Thus, this paper presents an adaptive, extremum seeking control based approach capable of mitigating position signal harmonics in PMSMs. The proposed approach is experimentally validated under varying torque, speed, and harmonic conditions. Its harmonic compensation performance is comparatively evaluated against the look‑up table based method. Furthermore, the accuracy of the proposed approach is analyzed, highlighting its effectiveness.

Authors:Bibhor Kumar, Ish Kumar Jain, Vijay K Shah
Title: CellSense: A Sub-6 GHz Cellular ISAC System for Clutter-Robust Passive Sensing
Abstract:
Future wireless networks demand capabilities beyond traditional communication, driving the development of Integrated Sensing and Communication (ISAC) for environmental awareness, localization, and tracking. Ubiquitous cellular deployment allows ISAC to maximize spectral efficiency, lower costs, and expand sensing coverage. However, sub‑6 GHz research has heavily favored communication, leaving sensing capabilities largely underexplored. To bridge this gap, we introduce CellSense, a novel sub‑6 GHz ISAC architecture natively integrated into the 5G cellular protocol stack for real‑world target tracking. We validate the system via Sionna‑based orthogonal frequency‑division multiplexing (OFDM) link‑level simulations and an experimental USRP hardware prototype using the OpenAirInterface (OAI) stack. Furthermore, we analyze the communication‑sensing tradeoff by quantifying how pilot symbol density impacts throughput versus sensing accuracy. Simulations show that CellSense achieves a 74 percent detection probability with a 1.43 m localization error in indoor warehouse environment, which improves to 94 percent detection and a sub‑meter error of 0.33 m in the outdoor environment of Oval area at the NCSU Centennial campus. Hardware experiments in a highly cluttered indoor laboratory confirm a 1.28 m localization accuracy and 76 percent detection probability, proving its efficacy for practical ISAC deployments.

Authors:Zhenqi Liu, Philip N. Brown, Keith Paarporn
Title: Adverse Effects of V2V Adoption on Road Safety
Abstract:
Vehicle‑to‑vehicle (V2V) communication is expected to improve road safety and reduce congestion. However, prior work shows that V2V information sharing under partial adoption may increase congestion and decrease safety. We study whether increasing V2V adoption itself affects road safety. We propose a corrected version of an existing model and analyze its behavior under varying adoption levels. We show that, in some cases, increased V2V adoption can increase accident probability. Moreover, under an optimal signaling policy, the system can ensure that accident probability is non‑increasing in the adoption level.

Authors:Ali Eslami, Jiangbo Yu
Title: Stability Without Safety: Gain Manipulation Attacks on Agentic Cyber-Physical Systems
Abstract:
Agentic cyber‑physical systems (CPS), where autonomous AI agents participate in runtime control decision‑making, introduce agent‑driven parameter‑update pathways absent from conventional feedback architectures. These pathways form a parameter channel structurally distinct from classical sensor and actuator channels. Among these parameters, feedback gains are the highest‑leverage target: a single gain matrix determines closed‑loop eigenvalue placement for the entire system, and malicious updates can directly alter closed‑loop dynamics while evading residual‑based monitors. We formalize this attack surface through a three‑axis attacker model and a taxonomy of Gain Manipulation Attacks (GMA). Two impact classes are identified: stability‑margin erosion under sustained gain drift, and transient amplification under one‑shot gain replacement. A stability‑preserving gain replacement can still produce transient amplification far exceeding safe operating limits, and stability verification alone is insufficient to bound the physical impact of such attacks. Stealthiness conditions and worst‑case impact certificates are derived for each class via Bauer‑‑Fike eigenvalue bounds and the Kreiss matrix theorem, with preliminary detection directions and a vehicle lateral dynamics example provided.

Authors:Robin Strässer, Julian Berberich, Manuel Schaller, Karl Worthmann, Frank Allgöwer
Title: Koopman meets input-output data: Data-driven output-feedback control of nonlinear systems with closed-loop guarantees
Abstract:
Data‑driven control of nonlinear systems from input‑output measurements remains a fundamental challenge, as existing approaches with rigorous closed‑loop guarantees predominantly require access to full state measurements. In this paper, we address this gap by proposing a data‑driven output‑feedback controller design method for nonlinear systems that provides provable closed‑loop guarantees while operating solely on measured input‑output data. Our approach combines Koopman operator theory with an extended state representation of the nonlinear system constructed from input‑output trajectories. This allows us to obtain a bilinear surrogate model directly from data, on which robust state‑feedback design methods can be applied. By exploiting the observability of the underlying nonlinear system, we establish exponential stability of the extended state, which in turn implies exponential convergence of the original system state to the origin. Finally, we validate our theoretical findings in numerical simulations.

Authors:Feng Liu, Achira Boonrath, Eleonora M. Botta, Souma Chowdhury
Title: Quantifying Uncertainty in Space Debris Capture with Active Tether-Net Systems Caused by Noisy Observations
Abstract:
As Low Earth Orbit has grown more crowded with space debris, the need for reliable and efficient debris removal solutions becomes more urgent. An active tether‑net system with maneuverable units is one of the promising solutions to this problem, whose success is dependent on the robustness of the net maneuver and closing decisions. These in turn are impacted by the uncertainties attributed to i) noisy observation of the target debris state (e.g., sensing errors), and ii) imperfect simulations of the complex net dynamics and net/debris interaction behavior, over which the decision system is trained. This paper focuses on the first of these two uncertainty sources, and presents a pipeline to propagate and quantify the resulting uncertainty in the debris capture performance expressed in terms of Capture Quality Index (CQI). This quantification is uniquely performed for both an active tether‑net using a fixed baseline control and one using a trained neuro‑control policy to guide the net maneuver during the deployment phase. Two different uncertainty quantification (UQ) techniques, namely Sobol's variance‑based sensitivity analysis and perturbation‑based method are exploited. A high‑fidelity simulator and a lower‑fidelity surrogate‑based environment are used to demonstrate trade‑offs between prediction accuracy versus ease of resolving uncertainties.

Authors:Ziyang Xiong, He Zong, Zhiyuan Xue, Manxi Wu
Title: OPENPATH: A Supervisor--Specialist Agent System for Personalized, Accessible, and Multi-stop Urban Trip Planning
Abstract:
Urban trip‑planning systems are commonly optimized for travel time and cost, but they offer limited support for the heterogeneous needs that real travelers bring, such as personalized preferences, multi‑stop itinerary construction, and end‑to‑end wheelchair accessibility. We present openpaths, a supervisor‑specialist multi‑agent system that handles all of these tasks within a single architecture. openpaths adopts a deliberate division of labor: LLM agents parse natural‑language input, classify request intent, and orchestrate execution, while classical algorithms perform route optimization over curated mobility and accessibility data. This design ensures that the resulting trip honors heterogeneous user preferences and enforces strict accessibility requirements when requested. Beyond per‑user planning, openpaths doubles as a measurement instrument for city‑scale accessibility analysis: applied to NYC, the system reveals substantial ADA infrastructure gaps and quantifies their effect on job accessibility for wheelchair users. Overall, this study shows how a supervisor‑specialist LLM agentic framework can support heterogeneous trip planning and transparent, equitable transportation analysis in real urban environments.

Authors:Wending Heng, Mingming Zhang, Glen Cooper, Zhenhong Li
Title: Physiologically Constrained Musculoskeletal Neural Network for Multi-DoF Joint Kinematics Estimation from Partially Observed sEMG
Abstract:
This paper investigates multi‑degrees of freedom (DoF) joint kinematics estimation under partially observed surface electromyography (sEMG), where only a subset of task‑relevant muscles can be measured due to anatomical inaccessibility or sensor constraints. A novel musculoskeletal neural network (MSK‑NN) is proposed to estimate multi‑DoF joint angles while simultaneously inferring activations for both measured and unmeasured muscles. MSK‑NN consists of a CNN‑based muscle activation estimator and an embedded MSK forward dynamics module, forming a fully differentiable architecture. Unlike existing hybrid neural frameworks that require additional biomechanical labels (e.g., muscle‑tendon forces, joint torques), MSK‑NN is trained without direct supervision of internal biomechanical variables. A composite physics‑physiology loss is designed by incorporating a joint kinematics loss, a data‑driven muscle synergy loss, and an anatomy‑guided trend loss. The proposed method is evaluated on two‑DoF wrist kinematics estimation across three rhythmic motions with unconstrained speed and amplitude, and one random motion. Compared with CNN, Bi‑LSTM, CNN‑LSTM, and PET baselines, MSK‑NN achieves lower normalized root mean square error (NRMSE) and higher coefficient of determination (R2), especially for the random motion. More importantly, the optimized MSK parameters remain within physiological limits, and the estimated activation of an input‑excluded muscle exhibits strong temporal agreement with its recorded sEMG envelope, demonstrating the capability of musculoskeletal (MSK)‑NN to recover physiologically plausible activations.

Authors:Artem Angelchev-Shiryaev, Pavel E. Aleshin, Anton S. Shiriaev, Pavel A. Shamanaev, Leonid B. Freidovich
Title: On orbital stabilization of a circular motion primitive for a dynamic extension of the Dubins car model
Abstract:
This paper addresses orbital stabilization of a circular motion primitive for a dynamic extension of the Dubins car model within a transverse‑linearization framework. We show that the corresponding transverse linearization is unstable and not stabilizable by linear state feedback. Therefore, the standard linearization‑based approach to orbital stabilization cannot be applied directly. The main contribution is a set of explicit and verifiable conditions that characterize when a controller design based on transverse linearization remains applicable. These conditions rely on the specific structure of the dynamics in a neighborhood of the motion and on the use of non‑standard transverse coordinates for controller design and analysis. Numerical simulations illustrate the proposed design procedure.

Authors:Chaitanya Shinde, Hadi Hajieghrary, Paul Schmitt, Adam Shoemaker, Bodo Seifert, Steve Kenner
Title: Re-imagining ISO 26262 in the Age of Autonomous Vehicles: Enhancing Controllability through Transferability and Predictability
Abstract:
The ISO 26262 standard defines functional safety for road vehicles through risk assessments based on Severity, Exposure, and Controllability, grounded in a human‑driven vehicle paradigm. In the context of autonomous vehicles (AVs), the absence of a human driver necessitates revisiting these principles. This paper decomposes the Controllability placeholder into two auditable evidence dimensions of ISO 26262 by introducing two measurable sub‑concepts: Transferability and Predictability. Transferability extends Controllability to capture AV systems' ability to hand off control to dedicated fallback safety mechanisms, while Predictability captures how easily external agents can anticipate AV behavior. Predictability is formally defined from human‑robot interaction‑inspired principles, and a mathematical framework is provided to quantify it. A designed‑versus‑achievable gap is introduced to distinguish architectural fallback claims from scene‑conditioned achievable fallback capability. The proposed metrics align with ISO 26262 and ISO/PAS 21448 (SOTIF), rendering fallback and interaction claims falsifiable and traceable across ODD slices. These dimensions complement rather than replace existing standards, and the enhancements preserve the structure of ISO 26262 while extending its applicability to driverless automated systems operating at SAE Levels 4 and 5.

Authors:Sai Sandeep Damera, Maria Charitidou, Asim Zoulkarni, John S. Baras
Title: An End-to-End Encrypted Control Pipeline for Multi-Agent Coordination via CKKS Homomorphic Encryption
Abstract:
Cloud‑based coordination of multi‑agent systems requires sharing state with a central server, creating a conflict between coordination and privacy. Fully homomorphic encryption (FHE) resolves this in principle, but its severe arithmetic constraints demand that every stage of the control loop be redesigned from first principles. We present an end‑to‑end encrypted control pipeline in which sensing, state estimation, state propagation, and consensus control all operate on CKKS‑encrypted data using only addition, multiplication, and cyclic rotation. In order to overcome the computational challenges of FHE, we employ steady‑state Kalman gains instead of solving for the matrices online and graph Laplacians are applied via the diagonal method at a cost proportional to the number of nonzero cyclic diagonals, accommodating ring, torus, and complete‑graph topologies within a unified framework. To quantify the cumulative effect of encryption noise, we use the separation principle to decouple controller and observer error dynamics and derive a periodic bootstrapping bound in which CKKS bootstrapping acts as an impulsive disturbance; the resulting steady‑state error ball depends on the bootstrapping precision and the closed‑loop spectral radius, providing a direct design equation for the privacy‑accuracy tradeoff. The pipeline is validated on a multi‑agent formation control scenario, confirming stable closed‑loop operation under encryption with bounded tracking error.

Authors:José Luis Guzmán, Igor Pataro, Juan D. Gil, Manuel Berenguel
Title: Unlocking feedforward capabilities in Model Predictive Control algorithms to deal with measurable disturbances
Abstract:
Disturbance rejection is a central objective in process control, particularly when measurable disturbances can be exploited through feedforward action. Although Model Predictive Control (MPC) naturally incorporates disturbance models and prediction capabilities, standard formulations cannot achieve complete disturbance rejection since the cost function penalises control effort. This limitation prevents MPC from reproducing the behaviour of classical feedforward compensators. This work proposes a novel framework to embed true feedforward capabilities within MPC without removing the control effort penalty. The approach introduces a dual‑control structure in which two control actions are computed simultaneously: a tracking‑oriented action addressing set‑point tracking and robustness, and a feedforward‑oriented action dedicated to disturbance rejection. Both contributions are combined into a single control signal on which the process constraints are explicitly enforced. The feedforward‑oriented action is formulated without penalising control effort, enabling full compensation of measurable disturbances. The methodology is developed for Dynamic Matrix Control (DMC), Generalised Predictive Control (GPC), and state‑space MPC. Its effectiveness is demonstrated through simulation studies, including comparisons with standard MPC and classical feedforward schemes. A case study based on a reverse osmosis process shows that the proposed approach improves disturbance rejection while preserving constraint handling and overall control performance.

Authors:Suho Park, Keunju Song, Hongseok Kim
Title: SABLE: GPU-Based Power Flow Accelerator for Sparsity-Aware Batched Learning
Abstract:
Recent studies have developed GPU‑based approaches for solving AC power flow and successfully applied them to standalone power flow problems. However, integrating these approaches into modern differentiable learning frameworks while preserving sparsity remains challenging. To this end, we present SABLE, a GPU‑based sparse batched power flow accelerator for differentiable learning via an implicit power flow layer. SABLE leverages a block‑diagonal embedding that reformulates batched three‑dimensional Jacobians as a fixed‑pattern two‑dimensional sparse template that is shared across PyTorch, CuPy, and cuDSS. This common template enables zero‑copy interoperability and memory‑efficient sparse reuse across the software stack. On top of this representation, SABLE accelerates repeated power flow computations through reusable sparse templates, custom GPU kernels, a cuDSS‑based sparse‑direct LU solver, and mixed‑precision techniques. Extensive experiments show that SABLE improves standalone power flow solving throughput by up to 253.4× over pandapower and 5.7× over ExaPF. In end‑to‑end training, evaluated on AC optimal power flow learning models based on DC3 and DeepLDE, SABLE expands the feasible training batch range by up to 64× and improves training throughput by up to 206.7× over the corresponding baseline.

Authors:Tong Han, Yan Xu, David J. Hill
Title: Power Grid Topology Control
Abstract:
Power grids are facing major challenges from growing renewable integration and worsening climate impacts. While flexibility on both the demand and generation sides has been widely explored to address these challenges, network‑side flexibility, especially in network topology, remains highly underutilized. Advances in communication, power electronics, and circuit breakers have made network topology increasingly controllable. However, leveraging this topological flexibility poses substantial challenges, primarily due to the inherent non‑convexity and hybrid dynamics in associated optimization and control problems. This monograph surveys the development of power grid topology control in both early and recent years. It begins by discussing the fundamental topological constraints involved in topology control problems. Subsequently, it introduces steady‑state topology control for transmission and distribution networks separately, covering fundamentals, a state‑of‑the‑art review, and representative recent advances. Additionally, the network topology transition problem, which addresses the implementation of optimal topology solutions and has garnered increasing attention in recent years, is further modeled and analyzed. Beyond utilizing the flexibility of steady‑state network topology, controlling network topology during transients can also contribute to system stabilization. Traditional approaches, such as intentional controlled islanding for transmission networks, as well as recently developed topology control methods for microgrid stabilization, exemplify this concept. Finally, a summary of this monograph is provided.

Authors:Obai Bahwal, Oliver Kosut, LalithaSankar
Title: Forecast and Model Predictive Control of Distributed Energy Resource Aggregators for Net-Demand Balancing
Abstract:
With the rapid demand for energy, even the incorporation of bulk renewable energy sources is not entirely sufficient to meet demand besides adding supply uncertainty. Distributed Energy Resource Aggregators (DERAs) have the potential to address this uncertainty via aggregation and control of decentralized distributed energy sources, thereby acting like virtual power plants. We present a new approach that combines forecasting and model‑predictive control to assign DERAs to follow net‑demand patterns, while accounting for the dynamics of the aggregate energy sources and their capacity limits. Each DERA is represented as a flexible ``virtual battery" with constraints on state‑of‑charge and power limits. The dispatch problem is set up as a long‑term model predictive control task that aims to minimize differences from desired charge levels, output ramping, and net‑load tracking errors. To keep operations efficient in real time, we implement a rolling‑horizon MPC, which updates decisions regularly using the latest marginal‑demand forecasts. For forecasting, we present two models: linear regression and long‑short term memory (LSTM) neural network. Using high‑resolution CAISO net‑demand data and five typical DERA types, our simulations demonstrate how well our approach tracks marginal‑demand; in particular, we highlight the tradeoffs between forecasting horizon times and MPC update rate as well as the dependence on the choice of the load forecasting model. Our results also indicate a slight edge for LSTM models over linear regression for desired time shifts and horizon choices.

Authors:Haoxuan Dong, Dongjun Li, Ziyou Song
Title: Lane Change Trajectory Planning for Personalized Driving Comfort and Mobility Efficiency
Abstract:
Lane changing entails simultaneous longitudinal and lateral motions that affect driving comfort and mobility efficiency. Because these motions are tightly coupled and subject to substantial inter‑vehicle variability, trajectory planning for lane‑change maneuvers is characterized by a highly personalized nature. This study proposes a neural network‑driven planner that integrates a third‑order polynomial trajectory generator with a learning module that infers optimal trajectory parameters across diverse driving conditions. Using a shared backbone with dual heads, one head ensures all‑condition operational guarantees, while the other captures driver‑specific preferences for comfort or mobility efficiency. A head‑gated switching mechanism, realized through a statistical gate based on error‑winner logistic regression, adaptively selects the appropriate head under varying driving conditions, which enables context‑aware lane‑change trajectory planning. Representative cases and Monte Carlo simulations show that the proposed planner achieves personalized comfort and mobility during lane changes, while the baseline ensures feasible trajectories under driving conditions where personalized data are insufficient or inaccessible.

Authors:Arthur Bouton, Tristan D. Hasseler, Michael Paton, Travis Brown, Jacob Levy, William Reid, Joshua Martin, Hari Nayar
Title: Learning All-Terrain Locomotion for a Planetary Rover with Actively Articulated Suspension
Abstract:
This paper presents ERNEST, a four‑wheeled planetary rover concept equipped with a two‑degree‑of‑freedom Active Gimbal Suspension that combines yaw and roll actuation to enable wheel reconfiguration, steering, and active load redistribution. A single neural network controller, trained to track a desired path across challenging terrain, fully unlocks the capabilities of this actuated suspension system for autonomous obstacle negotiation. A reinforcement learning framework is developed using the high‑fidelity DARTS simulation engine, which combines rigid‑contact dynamics and Bekker‑Wong terramechanics, enabling the emergence of locomotion strategies adapted to loose‑soil conditions. To obtain a single unified controller across heterogeneous terrains, a policy consolidation strategy merges the experience of terrain‑specialized agents into one neural network, eliminating the need for explicit terrain classification and controller switching. The resulting controller operates on a combination of proprioceptive and exteroceptive feedback, including sparse stereo‑derived terrain elevation, chassis attitude, joint states, and force‑torque measurements. Zero‑shot transfer to the physical rover is achieved through domain randomization, sensor noise injection, and model‑to‑real system identification. Experimental results demonstrate autonomous traversal of rock fields, a bump trap, a wheel‑high step, sand ripples, and sandy slopes. On a 20° sandy slope, the learned controller reduces the cost of transport by 37% on dry sand despite the additional actuation, and achieves superior performance on wet sand where the passive suspension becomes completely immobilized.

Authors:John S. Baras, Sai Sandeep Damera, Ryan Matheu, Clinton Enwerem, Praveen M. S. Kumar
Title: IDDMBSE: Integrating Data-Driven and Model-Based Systems Engineering for Trusted Autonomous Cyber-Physical Systems
Abstract:
Autonomous cyber‑physical systems (CPS) sit at the intersection of Model‑Based Systems Engineering (MBSE) and data‑driven Machine Learning and Artificial Intelligence (ML/AI), yet no integrated Systems Engineering (SE) methodology natively spans both. We address this gap with IDDMBSE, an Integrated Data‑Driven and Model‑Based Systems Engineering methodology that extends the rigorous MBSE V‑process with a data‑driven loop at every step, anchored in SysML, the autonomy stack, and a hybrid model‑based plus data‑driven trade‑off architecture. We instantiate IDDMBSE as an interoperable, open‑source tool chain: PERFECT, which maps SysML system architectures to executable ROS autonomy stacks for scalable performance evaluation; TRADES‑X, which decomposes design‑space exploration into a model‑based optimization stage followed by a data‑driven evaluation stage; and VERITAS, which combines formal, data‑driven, and runtime verification into a single assurance workflow. We demonstrate IDDMBSE on a Trusted Autonomous Ground Robot across its development lifecycle, spanning sensor‑suite selection, risk‑sensitive path planning, behavior‑tree task verification, conformal‑prediction‑based robust perception, and assured multi‑robot coordination, all exercised in a contested‑terrain Isaac Sim test range that we release with the tool chain. We close by sketching how IDDMBSE is being re‑formulated on SysML v2 / KerML foundations to enable language‑native composability and tighter ML/AI integration.

Authors:Canyu Lei, Fenglin Zhang, Derek Bivona, Cristiane Singulane, Jonathan Pan, Kenneth Bilchick, Amit R. Patel, Jianxin Xie
Title: MSAIC-Net: A Multi-Scale Attention and Imbalance-Aware Contrastive Network for ECG-Based Myocardial Substrate Abnormality Detection
Abstract:
Myocardial substrate abnormalities, such as myocardial scar and myocardial infarction (MI), are associated with adverse cardiovascular outcomes. Electrocardiography (ECG) provides a low‑cost and widely available tool for detecting these abnormalities, but ECG‑based detection remains challenging due to heterogeneous lead‑dependent manifestations, high‑dimensional multi‑lead signals, class imbalance, and the limited interpretability of deep learning models. We propose a multi‑scale attention‑enhanced convolutional network (MSAIC‑Net) for ECG‑based myocardial substrate abnormality detection. MSAIC‑Net employs parallel atrous convolutional branches to extract ECG features across multiple temporal receptive fields. %, enabling the model to capture both local and longer‑range temporal patterns. Channel attention is then used to adaptively reweight informative lead‑wise and feature‑channel representations. To address class imbalance and improve feature separability, we introduce a novel imbalance‑aware supervised contrastive learning strategy that encourages samples from the same class to form compact representations while increasing separation between abnormal and normal samples. Lead‑wise permutation importance is further incorporated to quantify the contribution of each ECG lead and improve model interpretability. The proposed method was evaluated on two complementary datasets: a low‑data institutional cohort from the University of Virginia (UVA) Health System for myocardial scar classification and the large‑scale public PTB‑XL dataset from PhysioNet for MI identification. Experimental results show that MSAIC‑Net outperforms baseline models, with particularly pronounced improvements in the low‑data UVA cohort. Overall, the proposed framework provides an effective and interpretable approach for ECG‑based detection of myocardial substrate abnormalities.

Authors:Su Wang, Mung Chiang, H. Vincent Poor
Title: Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers
Abstract:
We investigate cluster formation, involving the number and composition of clusters, in decentralized federated learning (FL) with heterogeneous machine learning (ML) optimizers. While clustering in centralized FL has enabled scalability and resource savings, its value and development in fully decentralized environments have yet to be explored. Optimizing cluster formation in such environments is challenging, especially due to the complex coupling between network graph structures, local data heterogeneity, and different local ML model optimizers. To address these challenges, we propose serverless semi‑decentralized FL (SSD‑FL), a methodology requiring no persistent server infrastructure. In SSD‑FL, cluster formation occurs via a lightweight, one‑time device‑to‑device (D2D) initialization phase, after which actual ML model training (alongside consensus and convergence processes) is fully serverless. Functionally, SSD‑FL segments global rounds into intra‑cluster and inter‑cluster regimes, ensuring global convergence and consensus through novel "effective loss functions" that integrate device‑specific ML optimizers with network graph‑based regularization. Next, SSD‑FL leverages the consensus gap via the Cheeger inequality to develop an iterative clustering algorithm evaluated against our derived convergence and consensus bounds, which incorporate a unique scoring metric to quantify data and optimizer heterogeneity across devices. Finally, experimental evaluation against three categories of decentralized FL methodologies validate that SSD‑FL improves both convergence speeds and communication efficiency across various network graphs, datasets, and local optimizer regimes.

Authors:Guido Sanchez, Marina Murillo, Lucas Genzelis, Nestor Deniz, Leonardo Giovanini
Title: MPC for nonlinear systems: a comparative review of discretization methods
Abstract:
This work provides a comparative review of three different numerical methods generally used to discretize continuous‑time non‑linear equations appearing in model predictive control problems: direct multiple shooting, direct collocation and successive linearizations. An overview of the characteristics of each method is given and the performance of each method is evaluated through the simulation of two test cases.

Authors:Thomas T. Zhang, Alok Shah, Yifei Zhang, Vincent Zhang, Nikolai Matni, Max Simchowitz
Title: Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss
Abstract:
Many modern applications of deep learning involve training a neural network via a one‑step prediction loss (e.g., L^2 regression, cross‑entropy), but deploy the network by rolling out along its own predictions. Key examples include autoregressive language modeling, flow‑based generative modeling, and robot policy learning. It is well‑documented that these settings induce a phenomenon we call test‑time feedback (TTF): the mismatch between the training/validation loss and downstream metrics of interest, such as task success rate and generation quality, which grows with task length. While data curation, architecture, and objective design have been proposed to combat train‑test shift in TTF settings, this paper proposes optimization as a new design axis to mitigate error accumulation. Specifically, we introduce a new optimization paradigm called double‑preconditioning (DoPr) uniquely tailored to the challenges of TTF. DoPr combines gradient‑wise preconditioning, as in Adam and Muon, with activation‑wise preconditioning (AP), such as in KFAC. We show that the addition of AP yields a drop‑in intervention for increasing downstream model performance across a range of TTF settings. Interestingly, these gains in test‑time performance do not consistently accompany improvements in validation loss, opening new questions about how to properly evaluate models trained with one‑step supervised objectives.

Authors:Francisco Aguilera, Víctor Jaque, Andrés A. Peters, Alejandro I. Maass
Title: Expected String Stability of Human-Led Vehicle Platoons under Stochastic Communication Delays (Full Version)
Abstract:
This paper studies expected \mathcalL_2 string stability of event‑triggered vehicle platoons in which a human driver leads a chain of cooperatively controlled autonomous followers under stochastic communication delays. The leader's driving behavior propagates through the string via vehicle‑to‑vehicle (V2V) communication, so human‑induced disturbances must not amplify along the platoon. Unlike deterministic approaches based on worst‑case delay bounds, we derive string‑stability conditions depending on the full delay distribution through integral inequalities. The closed‑loop platoon is modeled as a stochastic hybrid system capturing vehicle dynamics, communication events, and event‑triggering. This framework certifies string stability even when delays exceed deterministic admissible bounds with nonzero probability. Results are evaluated under several delay distributions using the MATLAB HyEQ simulator.

Authors:Yan-Yun Zhang, Jef Billet, Jan Swevers, Peter Slaets
Title: Impact of RTK Augmentation and INS Integration on GNSS Positioning Accuracy and Continuity: A Benchmarking Study on Inland Waterways
Abstract:
RTK augmentation andINS integration are widely used to improve GNSS positioning performance. However, on inland waterways, bridges and surrounding structures can degrade satellite visibility and correction availability, causing RTK augmentation loss, and GNSS/INS fusion transients. Since these effects depend on the local environment and sensor configuration, nominal receiver specifications are insufficient, and deployment‑specific characterization is required. This paper presents a benchmarking study of an AsteRx‑i3 D Pro+ GNSS/INS receiver installed within the mobile Sensor Box developed at KU Leuven. The study combines a real‑world bridge‑passage case study, static benchmarking, and closed‑loop path‑following experiments. The static benchmarking evaluates four receiver configurations: standalone GNSS, standalone GNSS with INS integration, RTK‑augmented GNSS, and RTK‑augmented GNSS with INS integration. The closed‑loop experiments use INS‑integrated GNSS as the navigation input and compare path‑following operational performance with and without RTK augmentation. Results show that correction loss during bridge passage causes reduced positioning accuracy, increased positioning uncertainty and recovery‑induced state jumps exceeding 1 m. Static benchmarking and closed‑loop experiments confirm that RTK augmentation substantially improves positioning precision and uncertainty consistency, while INS integration supports short‑term continuity during RTK unavailability but may introduce drift, bias, or transient uncertainty variations. By characterizing the deployment‑specific receiver behavior with RTK augmentation and INS integration, this study motivates higher‑level state estimation as a necessary next step toward spatially continuous and uncertainty‑consistent positioning on inland waterway. The experimental data are released at: https://doi.org/10.5281/zenodo.20541733.

Authors:Sribalaji C. Anand, Anh Tung Nguyen, George J. Pappas
Title: Attack Detection using Time Series Foundation Models
Abstract:
This paper addresses the problem of attack detection in cyber‑physical systems without any knowledge of the plant model or its structure. A remotely located plant transmits sensor measurements to an operator over a network that is assumed to be under attack. We consider two classes of attacks: model‑free replay attacks and model‑based stealthy attacks. For the latter, we derive closed‑form expressions for the optimal stealthy attack policy against a χ^2 detector, for both linear and nonlinear systems. We then propose a model‑structure‑free detector based on TimesFM, a time‑series foundation model developed by Google Research, which serves as a surrogate residual generator operating in a zero‑shot fashion. We show empirically that the TimesFM‑based detector achieves a comparable or superior attack detection performance. The efficacy of the proposed approach is demonstrated numerically on the IEEE 14‑bus power system. We also demonstrate that TimesFM predictions can serve as a substitute for corrupted measurements, a practical mitigation technique when classical redundancy assumptions fail.

Authors:Paulina Quintanilla, Agustin Fuenzalida, Daniel Navia, Pablo Brito-Parada
Title: From data to decisions: Bayesian modelling and global sensitivity analysis for flotation control
Abstract:
This work presents a data‑driven framework for interpretable modelling and decision support in flotation systems, integrating Gaussian Process (GP) regression with Global Sensitivity Analysis (GSA) via Sobol indices and local interpretability using SHapley Additive exPlanations (SHAP). Based on laboratory‑scale experimental data, a static GP surrogate model is developed to capture how superficial air velocity, overflowing froth velocity, froth height over the lip, pulp height, bubble size, and tailings flowrate influence the measured air recovery. The trained GP enables the computation of Sobol indices to quantify the contribution of each variable and their interactions to the overall variance in air recovery. The combination of Bayesian inference and Sobol‑based sensitivity metrics provides a systematic approach to identify the dominant and interacting variables governing air recovery. This study links Bayesian learning, sensitivity quantification, and explainability to provide a foundation for data‑driven control and optimisation of flotation processes.

Authors:Alireza Zabihi, Luis Badesa, Araceli Hernandez
Title: Voltage Unbalance-Aware AC Optimal Power Flow in Distribution Networks
Abstract:
The increasing penetration of single‑phase loads and distributed generation exacerbates voltage unbalance (VU) in distribution grids, raising concerns about power quality and complicating network operation. However, most market‑clearing models and price‑based coordination frameworks do not enforce VU limits within a three‑phase AC representation, so the implications for grid‑code compliance, numerical scalability, and economic signals remain unclear. This paper embeds VU in a three‑phase AC optimal power flow market‑clearing model and benchmarks two treatments: strict VU limit enforcement and objective function penalization. Building on these insights, an Improved Hybrid Limits (IHL) formulation is proposed that preserves compliance while using a smooth unbalance proxy in the objective to guide the optimization solver. Case studies on a European low‑voltage feeder show that IHL maintains feasible operating points, yields price and curtailment signals consistent with conventional hybrid formulations, and converges substantially faster and more reliably than a penalization based on the exact unbalance metric. These results support IHL as a practical and scalable mechanism for VU mitigation in market‑based operation of unbalanced distribution systems.

Authors:Antonio Alvarez Valdivia, Robert Reeve, Ankush Dhawan, Ciera McFarland, Chad Council, Margaret McGuinness, Nathaniel Hanson
Title: Gotta Grow Fast: Design and Benchmarking of a Tip Mount for High-Speed Vine Robots
Abstract:
Soft, growing vine robots extend through tip eversion, a mechanism that enables navigation through cluttered environments. However, integrating cameras and other sensors at the tip is uniquely challenging because the material forming the tip is constantly renewed as the robot grows. This continual material turnover, combined with friction between internal layers, added tip weight, and fabric constriction, complicates sensor and tool mounting. These limitations hinder the deployment of vine robots for inspection and search tasks, where rapid growth while carrying tip‑mounted sensors is essential. In this work, we present a triangular roller tip mount that reduces internal resistance during growth by rolling rather than sliding against the robot body. The design was refined through iterative failure analysis, enabling, for the first time, consistent eversion on a TPU‑coated ripstop nylon vine robot. To quantitatively evaluate mount performance, we introduce a custom testbed that isolates tip mounting effects by measuring tail tension during eversion. Comparative experiments across multiple mount variants, including prior designs, show that our triangular roller mount achieves the lowest tail tension and most repeatable growth performance. These results establish both a validated tip mount design and a repeatable benchmarking framework for advancing sensor and tool integration in soft growing robots. CAD for the mount and testbed is available at: https://sprout‑mitll.github.io/tip_mounts/.

Authors:Richmond Boamah, Ferdous Pervej
Title: Double-Directional Wireless Channel Modeling Using Statistics-Aided Machine Learning
Abstract:
The double‑directional (DD) wireless channel model is important for realistic system design since it provides complete propagation information. While stochastic and deterministic channel models are widely adopted, and existing machine learning (ML) solutions mostly aim to align future channel realizations, these solutions are often limited to short time spans that may not be statistically significant. Moreover, because the number of multi‑path components (MPCs) varies with spatial and temporal variation of the receiver (RX) and/or interacting objects (IOs), typical ML solutions that require fixed, predefined input and output shapes fall short. To curb these limitations, we propose a statistics‑aided ML solution that relies on a fixed subset of MPCs selection. More specifically, we first select top‑M MPCs, where M\in\mathbbZ^+ is much smaller than the total number of MPCs, and construct learnable graphs to train our proposed hybrid TimesNet‑TimeFilter (TNTF) model. We then use a channel statistics‑aided training method to generate future top‑M DD channel realizations such that the statistics calculated from these realizations matches closely with those of the actual statistics from the complete time‑varying DD channel realizations. We validate the proposed solution using extensive simulations on both synthetic stochastic channel model (SCM)‑based and deterministic ray‑tracing‑based datasets, and demonstrate its effectiveness relative to state‑of‑the‑art baselines.

Authors:Mauro Di Marco, Mauro Forti, Luca Pancioni, Giacomo Innocenti, Alberto Tesi
Title: Mixed Potential Approach to Convergence of Nonlinear RLC Circuits with Memristors
Abstract:
The paper considers a large class of nonlinear circuits, termed RLCM, containing all four basic circuit elements, i.e., resistors, inductors, capacitors and memristors. A companion paper [1] has introduced a mixed potential for RLCM circuits generalizing that found by Brayton and Moser for circuits without memristors. In this paper, systematic Lyapunov‑like results on convergence of RLCM circuits are proved by means of the mixed potential. These hold under the basic assumption that an RLCM circuit has a complete set of variables in the flux‑charge domain and they require, roughly speaking, that there is a balance, which is quantitatively estimated, between capacitors and inductors. The convergence results are robust with respect to circuit parameter variations and they include cases where the memristor circuits possess multiple stable equilibrium points, which is of importance for instance to implement content addressable memories (CAMs). The results extend to circuits possessing all four basic circuit elements previous results that pertain to circuits without memristors or memristor circuits without inductors. The main proofs are conducted by using the flux‑charge analysis method (FCAM) to analyze RLCM circuits in the flux‑charge domain.

Authors:Francesco Pillitteri, Alberto Bemporad
Title: Amortized Nonlinear Model Predictive Control
Abstract:
Nonlinear Model Predictive Control requires solving a constrained nonlinear program (NLP) in real‑time at every sampling instant, a computational bottleneck that limits deployment on resource‑constrained hardware or at high sampling rates. We address this challenge for the broad class of input‑affine nonlinear systems to show that the optimal control move can be approximated by a state‑dependent quadratic program (QP) whose cost parameters depend on the current state and reference. We propose a single‑network residual‑corrector architecture: a state‑dependent analytic baseline provides initial QP parameters, and the network learns only the corrections needed to match the full NLP solution; the QP is solved by a differentiable interior‑point layer, guaranteeing constraint satisfaction for the first control action. The network is trained offline on data generated by an NLP solver using a hybrid loss that combines supervised imitation and KKT‑residual penalties. We validate the approach on a three‑link planar robotic arm with Cartesian end‑effector tracking, demonstrating orders‑of‑magnitude speedup over the NLP solver while maintaining comparable tracking performance.

Authors:Martina Alutto, Lorenzo Zino, Karl H. Johansson, Angela Fontan
Title: On Leadership Emergence in Opinion Dynamics on Social Networks
Abstract:
Leadership in social groups emerges dynamically through interaction and opinion exchange. Empirical evidence indicates that individuals expressing strong opinions tend to gain influence, while sustained leadership critically depends on maintaining alignment with the surrounding social context. Motivated by these observations, we introduce a coupled dynamical model describing the simultaneous evolution of opinions and leadership in a networked population. Extending the Friedkin‑Johnsen framework, we represent leadership as a time‑varying susceptibility to social influence, which evolves according to a game‑theoretic mechanism, consistent with social psychology evidence. Within this setting, agents strengthen their leadership by expressing decisive yet socially coherent opinions, whereas misalignment with the collective state results in a loss of influence. We analyze the coupled dynamics and establish sufficient conditions to identify which agents necessarily emerge as leaders and which act as followers in the social network.

Authors:Junzhe Xiao, Kaida Chen, Cong Wang, Zekun Niu, Minghui Shi, Yanhan Zhou, Lilin Yi
Title: Efficient Multi-Agent Optimization of Optical Power in S+C+L-Band Systems
Abstract:
We propose an AI Agent tailored for link power management in multi‑band systems. In S+C+L band span‑level study, the agent efficiently solves various optimization objectives. In network‑wide evaluation, it delivers 689.0 Tbps gain in total allocated traffic with merely 303 average interactions per power profile.

Authors:Keunju Song, Kyungnam Park, Sua Choi, Seunguk Kim, Tae-un Kim, Youngmin Choi, Sang-Won Min, Hongseok Kim
Title: Physics-Informed Graph Learning Acceleration for Large-Scale AC-OPF with Topology Changes
Abstract:
In power systems, alternating current optimal power flow (AC‑OPF) has been a challenging problem for decades due to its nonconvexity, but fast and efficient solutions are even more needed because of high penetration of large scale renewable generation and load growth. Recently, neural networks (NN) have gained attention in solving AC‑OPF, but it is still in an early stage to be applicable for real and large‑scale power system operation with topology‑changing characteristics. To end this, we propose a novel framework called GraphOPF that considers topology‑adaptability, scalability, NN training time, self‑supervision, and feasibility altogether. Extensive experiments show that the proposed framework against the baselines is up to 200 times faster in NN training and up to 66 times faster in solving AC‑OPF for large‑scale power systems including the real Korean power system, while achieving more than 99% feasibility.

Authors:Matthew Hampsey, Pieter van Goor, Ravi Banavar, Robert Mahony
Title: Tracking Control for a Dynamic Model of an Underwater Submersible
Abstract:
Underwater vehicles are naturally modelled as rigid bodies on SE(3) subjected to added mass effects. The passivity of the Hamiltonian structure of the system can be exploited to design energy‑based stabilising controllers, however, the extension of these control designs to tracking control is not trivial since the error system for the classical error formulations is not itself Hamiltonian. In this paper, we show that a novel choice of error function leads to error dynamics that are Hamiltonian. We go on to derive an energy‑based tracking control for a fully coupled model of a submersible vehicle. Asymptotic convergence of the control scheme is proved and the control is demonstrated in a simulation study of the Blue Robotics BlueROV2 Heavy submersible.

Authors:Junheng Li, Liang Wu, Sergio A. Esteban, Lizhi Yang, Ján Drgoňa, Aaron D. Ames
Title: Accelerating and Scaling MPC-Guided Reinforcement Learning for Humanoid Locomotion and Manipulation
Abstract:
In humanoid motion control, model predictive control (MPC) offers physically grounded prediction and constraint handling, while reinforcement learning (RL) enables robust whole‑body skills through large‑scale simulation. However, using MPC inside RL often requires time‑consuming problem construction or excessive training overhead, making such frameworks difficult to justify in practice. This work studies efficient training‑time MPC guidance for humanoid locomotion and manipulation, termed MPC‑RL. We introduce a centroidal‑dynamics MPC reward formulation that leverages guidance from MPC trajectories in training time. To make this practical in massively parallel RL, we develop π^nMPC, a parallel‑in‑horizon and construction‑free batched GPU MPC solver that operates directly on time‑varying dynamics to avoid high memory usage and pre‑compilation. Through a variety of comparative studies and hardware validations, we have found that MPC‑RL achieves superior performance in locomotion and manipulation skills. The code base is available at https://github.com/junhengl/mpc‑rl.

Authors:Cheng Ren, Ming Li, Xinping Guan, George Q. Huang
Title: Dynamic Multi-Agent Pickup and Delivery in Robotic Cellular Warehousing Systems
Abstract:
Robotic Cellular Warehousing Systems (RCWS) give rise to multi‑agent pickup and delivery (MAPD) processes in which robots sequentially collect multiple stock‑keeping units (SKUs) for each order. Unlike classical MAPD formulations that assume static tasks, real warehouse operations often involve dynamic order evolution, where new SKUs may be appended to an order while it is being executed. Motivated by this practical requirement, this letter formulates the Dynamic Multi‑Agent Pickup and Delivery problem considering internal order evolution for the first time. Building on the token passing paradigm, we propose two event‑triggered online replanning algorithms. The first, Dynamic Token Passing, performs localized replanning upon order updates through add‑order decomposition and priority‑based token scheduling while preserving collision‑free execution. The second, Cooperative Token Passing, further enables idle robots to opportunistically assist newly added pickups, improving system‑level efficiency. Simulation results in RCWS environments demonstrate that the proposed methods significantly reduce order flowtime compared with static and non‑cooperative baselines.

Authors:Taylor C. Fazzini, Daniel R. Herber
Title: Development of a Structured Approach for Establishing Mission Engineering Requirements
Abstract:
This paper addresses the question: How can mission effectiveness be systematically defined or approximated in the absence of customer requirements? Legacy requirements engineering frameworks presuppose customer input to define specifications but leave a gap in the process when stakeholder input is ill‑defined or missing. Rapid build and development programs (such as military acquisition, space assets, infrastructure projects, etc.) often see requirement and objective evolutions throughout the proposal process, so a more adaptive method is needed. To address this gap, a structured approach is proposed that decomposes mission intent into mission context, functions, constraints, critical dimensions, effectiveness attributes, and architecture alternatives. This method conducts a mission feasibility assessment, prioritizes mission‑critical dimensions using Best‑Worst Scaling, and introduces a mission complexity factor to quantitatively understand the impacts of external mission difficulties, technology maturity, evidence and confidence standards, and mission utility. The resulting method provides a traceable basis for deriving Tier 1 and 2 requirements. The approach is structured to support future Unified Architecture Framework (UAF) and Systems Modeling Language (SysML) artifact integration. The proposed framework is demonstrated using a notional close air support mission example.

Authors:Fan Jiang, Xingpeng Li
Title: Learning-Assisted Day-Ahead Energy Scheduling for Frequency-Secure Inverter-Dominated Grids with Grid-Forming Battery Energy Storage Systems
Abstract:
As grid‑forming (GFM) battery energy storage systems (BESS) are increasingly deployed to enhance power system inertial response and frequency stability, incorporating their frequency support capabilities into day‑ahead energy scheduling (DAES) is essential for achieving both frequency security and operational efficiency. However, accurately determining frequency metrics in grids with coexisting GFM inverters and synchronous generators requires electromagnetic transient (EMT) simulations, which are computationally prohibitive for direct embedding in grid operational optimization models. To bridge the gap between modeling accuracy and computational efficiency, a learning‑assisted DAES (LA‑DAES) framework is proposed in this work. By leveraging a surrogate model to represent the frequency support dynamics of GFM BESS, the proposed framework ensures frequency security with a reasonable solve time. Comparative results demonstrate that, relative to analytical frequency‑constrained DAES, the proposed LA‑DAES framework more accurately captures grid frequency metrics and improves the utilization of GFM BESS.

Authors:Ziyang Lu, M. Cenk Gursoy, Chilukuri K. Mohan, Pramod K. Varshney
Title: Constrained Deep Reinforcement Learning for Cognitive Radar Resource Management
Abstract:
In this paper, multi‑target tracking and scanning are considered in a radar system operating in the track‑while‑scan mode. Specifically, time allocation for radar scanning and tracking of multiple maneuvering targets under a time budget constraint is addressed, aiming to jointly optimize the performance of both tracking and scanning in a cognitive radar. We first present the details of the model for tracking and scanning and formulate the time management task as a constrained optimization problem. Subsequently, we design a \glscdrl framework to find the time allocation strategy for the problem. In the proposed \glscdrl framework, the parameters of the neural networks and the dual variable are learned simultaneously. The deep deterministic policy gradient (DDPG) algorithm is introduced to tackle continuous action space and its performance is compared with deep Q‑learning, heuristic approaches, and an optimization‑based approach. Numerical results show that the radar with the proposed \glscdrl framework can autonomously allocate more time to the tracking task that requires greater attention while providing time for scanning and also constraining the total time budget below the predefined threshold.

Authors:Taha El Hajji, Lars Sjöberg
Title: Electromagnetic Characterization of magnetic ring: Case of square cross section shape
Abstract:
This paper presents a comprehensive 2D analytical model of a toroidal magnetic ring with a square cross‑section, subjected to sinusoidal excitation. By applying Maxwell's equations in local Cartesian coordinates and utilizing a complex permeability framework, the exact analytical expressions for the internal magnetic field, flux, complex impedance, and losses are derived. The model rigorously separates eddy current losses, hysteresis losses, and winding losses, explicitly accounting for the skin effect and complex permeability within the conductive core using separation of variables and hyperbolic functions. Furthermore, parameter for apparent permeability is expressed to map the core behavior onto simplified linear material models. The derivations establish a mathematical foundation highly suitable for standardized material characterizations, such as Brockhaus and Iwatsu ring measurements, by avoiding the heavy computational cost of 2D and 3D Finite Element Analysis.

Authors:Malik Ali, Musabbir Ahmed Arrafi, Nicholas M. Stiffler, Krishna Bhavithavya Kidambi
Title: CAPE: Control Algorithm Performance Evaluation under Learned Vehicle Dynamics Models
Abstract:
We propose the Control Algorithm Performance Evaluation (CAPE) framework, a systematic methodology for benchmarking racing controllers under our proposed learned enhanced physics model (EPM). The proposed framework enables cross‑controller comparison by evaluating five closed‑loop control architectures. We further compare our proposed EPM with two state‑of‑the‑art learned vehicle dynamics models: Deep Pacejka Model (DPM) and Deep‑learning Dynamics Model (DDM). Closed‑loop experiments show that across all models and controllers, the proposed EPM achieves best average lap times. Specifically, the Adaptive NMPC with EPM achieves a time of 5.82 s, compared with 12.99 s for DPM and 8.80 s for DDM, while simultaneously producing substantially lower longitudinal and lateral tracking errors under identical controller configurations. We further evaluate all three models and five controllers using a disturbance‑aware simulation framework incorporating measurement noise, process disturbances, actuator delay, and parametric uncertainty. Under moderate global disturbance scaling factor (η = 1), results averaged across the five controllers show that EPM reduces a) longitudinal tracking error by 29.0% and 17.2%; b) lateral tracking error by 24.6% and 12.3%; c) while increasing average velocity magnitude by 39.9% and 3.1% relative to DPM and DDM, respectively. Overall, CAPE establishes a systematic benchmark for evaluating the performance of learned vehicle dynamics models in a closed‑loop control framework and demonstrates that our proposed EPM significantly improves controller robustness and performance under realistic uncertainties.

Authors:Alexander Hepp, Matthias Ludwig, Michaela Brunner, Johanna Baehr, Georg Sigl
Title: CRESS: Quantifying Vulnerabilities of Attack Scenarios in Hardware Reverse Engineering
Abstract:
The safety, security, and reliability of microelectronic systems depend on a trustworthy, secured supply chain and design flow. Globally distributed supply chains or unintentional design weaknesses leave the door open for attacks on the hardware level. These scenarios encompass counterfeiting, hardware trojans, or on‑device attacks. For these, hardware reverse engineering (RE) results play a pivotal role. The ongoing publication of new RE‑involved attacks motivated the development of the common RE scoring system (CRESS). The system enables a general classification of RE‑involved scenarios for a common, consistent rating. In this work, the originally qualitative system is extended to a quantitative system. We performed an extensive interview study with experts in the field. The interview results allowed us to derive weights that measure the severity of different RE‑involved attack categories. The weights form an equation that quantifies scenarios, resulting in the severity‑indicating CRESS score. The score enables the coherent rating of novel scenarios, renders them comparable, and supports the development of effective countermeasures. To showcase the effectiveness of the quantitative CRESS Score, six selected case studies are rated qualitatively and quantitatively. The CRESS Score proves to be significantly more expressive than the industry‑standard Common Vulnerability Scoring System (CVSS).

Authors:Pierre Peigné, Ky Nguyen, Paul Wang
Title: Zero knowledge verification for frontier AI training is possible
Abstract:
Frontier AI governance frameworks increasingly use cumulative training compute as the primary criterion for designating high‑impact models, but enforcement rests on self‑reporting because no technical verification primitive for training exists. Any future international agreement on frontier AI faces the same problem at higher stakes: coordinated regulation of technologies with significant externalities has historically rested on technical verification, without which agreements are declaratory. Recent governance analyses judge zero‑knowledge proofs a promising candidate but currently impractical at frontier scale [26, 4]. We argue the impracticality is paradigm‑bound rather than fundamental, and propose a verification architecture for frontier dense pre‑training combining a pre‑committed training specification, inter‑node network observations, and on‑the‑fly Merkle commitments of intermediate computation, verified through a zero‑knowledge Virtual Machine (zkVM) with native BF16/FP32 precompiles. The proof checks the actual floating‑point computation the GPU performed rather than a fixed‑point approximation, and preserves model‑architecture confidentiality through a private training specification. The protocol produces three proof types: a genesis proof at initialisation, in‑training step proofs across the run, and ex‑ante attestations enforcing policy‑relevant claims as running invariants, turning the training record into a governance‑enforceable artefact. We estimate a deployable proof of concept within approximately 36 months at single‑digit‑percent training‑side overhead, against a six‑to‑ten‑year cycle for verification‑grade custom silicon. Thirteen open research and engineering problems are catalogued as a research agenda for external contribution

Authors:Chams Eddine Mballo, Bryce L. Ferguson, Inkyu Jang, Donggun Lee, Claire J. Tomlin
Title: Characterization and Analysis of Emergency Landing Flight Envelopes with Graded Safety Specifications
Abstract:
Emergency landing flight envelope analysis traditionally adopts a binary notion of safety, whereby a trajectory is safe only if state constraints are satisfied pointwise in time. In practice, ensuring a successful landing requires recognizing that aircraft operation spans a continuum in the state space from the nominal to the critical regime. Between these regimes lies a degraded regime of states outside nominal operation that may be visited only for limited durations. Safety is therefore inherently graded, in the sense that limited exposure to degraded states may be tolerated, and must be assessed using a trajectory‑dependent criterion rather than a purely pointwise‑in‑time one. This paper develops a Hamilton‑Jacobi reachability framework for analyzing emergency landing flight envelopes under this graded notion of safety. Safety is encoded through a soft constraint defined by a designer‑specified continuous violation cost function that assigns zero cost in the nominal regime and larger cost to more safety‑critical off‑nominal states. We introduce a general class of state‑ and time‑dependent violation cost functions and establish monotonicity and continuity properties that characterize how the flight envelope varies with the cost of off‑nominal operation. These results provide a principled sensitivity analysis linking safety conservativeness to operational capability. Building on this analysis, we propose a synthesis algorithm for parameterized violation cost functions in this class. The algorithm provably converges to the least conservative parameter under which a prescribed off‑nominal safety requirement is satisfied. Numerical results for a fixed‑wing emergency landing scenario under propulsion failure demonstrate the sensitivity properties and validate the algorithm.

Authors:Alkım Gökçen, Savaş Şahin, Mahmut Kudeyt, Swapnil Tripathi, Özkan Karabacak
Title: Dual Lyapunov-based Synchronization Control of Rössler System
Abstract:
This paper proposes a novel approach for the synchronization problem of nonlinear dynamical systems, integrating dual Lyapunov stability analysis with polynomial optimization. A comprehensive review of the relevant scientific literature on synchronization methods is conducted, with a particular focus on classical Lyapunov‑based methods for chaotic systems. In this study, the Rössler system is synchronized by employing dual Lyapunov‑based closed‑loop synchronization method. This method uses semidefinite programming and sum‑of‑squares polynomials to compute a nonlinear state feedback function which synchronize a chaotic system to a selected reference model. It is aimed that chaotic behavior is destroyed and, instead, a limit cycle becomes attracting. Simulation works are performed for randomly selected 100 different initial conditions to show that synchronization process is successfully performed. Furthermore, bifurcation diagrams and phase portraits are evaluated to analyze the system dynamics. The paper discusses results and how new constraints should be employed and adapted to more complex systems.

Authors:Yugui Liu, Yibo Ding, Xudong Li, Jing Qu, Wenyi Zhang, Tong Qian, Wuyou Xiao, Zhengyang Hu, Jiaqi Ruan, Zhao Xu
Title: Peer-to-Peer Cloud Service Market for Data Centers Oriented to Computation-Electricity Coordination
Abstract:
Energy‑intensive data centers (DCs) have emerged as substantial and flexible loads in modern power systems, underscoring the critical need for computation‑electricity coordination. Harnessing the spatio‑temporal flexibility of DC workloads is a promising approach to facilitate this coordination. However, existing studies overlook the collaborative potential of computational resource sharing among geo‑distributed DCs, thereby failing to fully unlock this flexibility. In this paper, a bi‑level computation‑electricity coordination framework is proposed to explicitly capture the bidirectional interactions between DCs and power grid. Firstly, a peer‑to‑peer cloud service market (P2P‑CSM) for geo‑distributed DCs is proposed, which enables bilateral cloud service transactions to leverage regional heterogeneities (e.g., electricity prices, cooling efficiency). Secondly, locational marginal prices are embedded into the framework to reflect network congestion and nodal price disparities. Thirdly, a dual consensus alternating direction method of multipliers (ADMM)‑based decentralized algorithm is developed as the P2P market clearing algorithm, and a bisection‑assisted iterative algorithm is proposed to ensure rigorous convergence of the framework. Case studies conducted on modified IEEE 30‑bus system validate that the P2P‑CSM achieves a win‑win computation‑electricity coordination: it not only increases total DC operational profit by 22.8%, but also effectively alleviates grid congestion and yields a 3.2% reduction in total energy consumption.

Authors:Manoj Kumar, Nishith D. Tripathi, Jeffrey H. Reed
Title: A Survey of Smart Grid Emerging Use Cases and Relevant 5G and 6G Capabilities and Features
Abstract:
The growing complexity of modern energy systems has led to the adoption of Smart Grid (SG) that use advanced communication technologies to facilitate efficient, reliable, secure, and sustainable energy operation and management. Unlike existing surveys that often treat grid and communication domains separately, this work rigorously quantifies service requirements for high‑complexity emerging scenarios. It provides a comprehensive overview of SG architecture that integrates digital communication infrastructure with distributed energy resources (DERs), microgrids, energy storage systems, and cybersecurity frameworks. Furthermore, emerging SG use cases such as smart distributed voltage control, real‑time fault detection and self‑healing, smart and autonomous monitoring, and predictive maintenance are identified, and more importantly, service performance requirements associated with these use cases have been quantified. Additionally, key capabilities and emerging SG enablers of fifth‑generation (5G) and sixth‑generation (6G) networks are described. These capabilities and enablers include network slicing, edge computing, spectrum management, artificial intelligence (AI) driven optimization, digital twins, and Open‑Radio Access Network (O‑RAN). Finally, the paper discusses open challenges and future research directions for designing scalable, intelligent, and secure next‑generation SG systems.

Authors:Leonardo Pedroso, W. P. M. H. Heemels, Pedro Batista
Title: Consistent Distributed Cooperative Localization for Ultra Large-Scale Multi-agent Systems
Abstract:
Cooperative localization (CL) is fundamental in emerging multi‑agent systems, where agents fuse local sensing data with exchanged information to estimate their own states. At a large scale, however, tracking cross‑correlations becomes infeasible, preventing the use of optimal filters. Ignoring or underestimating these correlations leads to overconfident, and thus inconsistent, estimates. Existing CL algorithms achieve good performance and consistency typically at the expense of communication, computation, or memory that scales with the network size. This is incompatible with ultra large‑scale systems (ULSS) ‑ for example, satellite mega‑constellations ‑ where per‑agent resources are limited and must remain independent of the number of agents. This reveals a critical gap: no existing CL method is simultaneously well‑performing, consistent, and ULSS‑scalable. This paper introduces a new CL framework that addresses this gap using the recently proposed overlapping covariance intersection methodology, which enables agents to exploit limited structural information about cross‑correlations without compromising consistency. The resulting CL algorithm leads to optimal conservative covariance propagation using only locally available information. The method is fully distributed, scalable to an ultra large scale, and provably recursively consistent. Simulations demonstrate substantial performance improvement over state‑of‑the‑art consistent CL approaches while preserving scalability.

Authors:Haiyang You, Chengwei Lou, Jin Yang
Title: Source Side Mitigation of AI Datacenter Power Fluctuations with a Hybrid Energy Storage System and Residual Differentiable Predictive Control
Abstract:
The rapid growth of hyperscale AI datacenters introduces structured, workload‑driven active‑power fluctuations at the point of interconnection. These fluctuations appear to the grid as time‑varying disturbance injections that cannot be captured by conventional peak‑ or average‑load representations. To reduce the residual power disturbance before it propagates into the bulk power system, this paper proposes a hybrid energy storage system with differentiable predictive control (HESS‑DPC) framework for datacenter‑side power smoothing. A workload‑driven disturbance model is first established, representing the point‑of‑interconnection load deviation as the superposition of training and fine‑tuning workloads to capture the structured forcing inputs that can excite generator frequency dynamics. A frequency‑based rule‑based controller then allocates this deviation between a battery energy storage system (BESS) and a supercapacitor (SC), assigning the energy‑dominant component to the BESS and the fast‑varying component to the SC. To overcome the anticipation and constraint limitations of fixed‑frequency decomposition, a residual differentiable predictive control policy is trained offline to compute finite‑horizon command corrections around the rule‑based baseline while enforcing a one‑step safeguard. Simulations on the NPCC 140‑bus system show that HESS‑DPC reduces grid‑side residual deviations during workload transitions, improves SC state‑of‑charge sustainability over extended operation, and reduces generator peak‑to‑peak frequency deviations by more than 80 percent across all monitored generators, with the worst‑affected generator response falling from 15.1 mHz to 1.3 mHz. These results confirm that local active‑power smoothing at the datacenter point of interconnection can substantially mitigate frequency disturbances caused by AI workloads.

Authors:Lukas Lanza, Johannes Köhler, Dario Dennstädt, Thomas Berger, Karl Worthmann
Title: A model-free approach to control barrier functions for higher-order systems
Abstract:
Control barrier functions (CBFs) are a widely applied modular tool to ensure safe operation of nonlinear dynamical control systems. However, for their construction accurate knowledge of the system dynamics is typically needed. This requirement was recently alleviated for relative‑degree‑one systems using techniques from prescribed performance control (PPC) or funnel control (FC). This article extends the model‑free CBF design to nonlinear systems of arbitrary relative degree. Moreover, we show with a simple example that a straightforward extension of existing results for relative‑degree‑one systems fails. Instead, we utilize novel techniques from funnel control to characterize a subset of the controls satisfying a CBF condition without requiring a dynamic model or state measurement. Finally, we demonstrate the applicability of our results on a seven degrees of freedom robotic manipulator with relative degree two.

Authors:Jingru Zhu, Cheng Zhao, Lei Guo
Title: Towards Guaranteed Optimal PID Tuning for Uncertain Nonlinear Systems
Abstract:
Despite the widespread use of PID controllers in engineering practice, designing optimal PID parameters has long been regarded as a challenging problem in both theory and practice, particularly when faced with uncertain nonlinear dynamical systems. Based on the authors' PID control theory established recently for MIMO nonlinear uncertain systems (Zhao and Guo, 2022), which provides a concrete PID parameter set for global stability of PID controlled systems, this paper further proposes a near‑optimal PID tuning method, where only input‑output (zeroth‑order) data on the control performance is available. The tuning method is formulated as a constrained optimization problem and solved by an iterative learning algorithm, referred to as HRS‑KW algorithm, that combines a hysteretic random search with the Kiefer‑Wolfowitz algorithm, aiming at utilizing the advantages of both global exploration and local gradient acceleration. This method operates without requiring precise structural knowledge of the system dynamics, yet its almost sure convergence to an epsilon‑optimal solution for the PID parameters can be guaranteed in theory while ensuring closed‑loop system stability. Simulation results illustrate that our HRS‑KW algorithm outperforms other related optimization methods, exhibiting better convergence to the prescribed epsilon‑optimal performance set.

Authors:Jihoon Hong, Alice Chan, Qiyue Dai, Julian Skifstad, Glen Chou
Title: Activation Steering of Video Generation Models via Reduced-Order Linear Optimal Control
Abstract:
Text‑to‑video (T2V) models trained on large‑scale web data can generate undesired content, motivating interventions that reduce harmful outputs without sacrificing visual quality. Activation steering offers an attractive mechanistic alternative to finetuning and prompt filtering, but existing T2V steering methods remain limited, typically applying coarse, non‑anticipative interventions that can lead to oversteering and content degradation. To close this gap, we propose Latent Activation Linear‑Quadratic Regulator (LA‑LQR), a reduced‑order optimal control framework for minimally invasive T2V steering. LA‑LQR formulates T2V inference as a dynamical system and computes closed‑loop feedback interventions that steer activations toward desired feature setpoints while penalizing unnecessary perturbations. To make optimal control feasible for high‑dimensional video activations, we project activations onto a low‑dimensional, task‑relevant subspace derived from contrastive prompt pairs, estimate local linear dynamics in this latent space, and solve a latent LQR problem to obtain timestep‑ and layer‑specific steering signals. We provide theoretical bounds relating latent setpoint tracking to raw activation‑space feature control, and empirically validate the fidelity of the reduced latent dynamics. On concept steering and video safety benchmarks, LA‑LQR reduces unsafe generations relative to baselines, while preserving prompt fidelity and visual quality.

Authors:Evelyn Gondosiswanto, Joshua L. Pulsipher
Title: GPU-Accelerated Direct Transcription-Based Nonlinear Model Predictive Control
Abstract:
In this paper, we present a GPU‑accelerated framework for nonlinear model predictive control (NMPC) based on direct transcription and second‑order interior‑point methods. Many real‑world systems exhibit nonlinear dynamics that cannot be accurately captured by linear models, motivating the use of NMPC. However, NMPC requires the repeated real‑time solution of optimal control problems (OCP), which become computationally demanding large‑scale nonlinear programs (NLPs) after transcription. Although GPU acceleration has emerged as a promising approach for nonlinear optimization, existing GPU‑based NMPC workflows reconstruct structurally identical OCPs at each solve. This introduces substantial overhead even though successive solves differ only through updated system measurements or reference trajectories. To address this limitation, we introduce a parametric interior‑point formulation that exploits the fixed structure of transcribed OCPs, enabling reuse of structure‑dependent computations (e.g., symbolic factorization in sparse Cholesky) across re‑solves. We evaluate the proposed framework on distillation column and 2D heated plate benchmarks against state‑of‑the‑art CPU and GPU configurations. The results show that the framework achieves over an order‑of‑magnitude speedup in total NMPC run times. These improvements are primarily driven by reduced per‑iteration solve times, with GPU execution achieving up to a 94% reduction compared to the baseline. Overall, the results demonstrate the effectiveness of exploiting repeated problem structure in GPU‑accelerated NMPC and highlight the potential of the proposed framework to expand the envelope of real‑time NMPC applications.

Authors:Mauro Di Marco, Mauro Forti, Luca Pancioni, Giacomo Innocenti, Alberto Tesi
Title: Mixed potential for nonlinear RLC circuits with memristors
Abstract:
In two seminal articles published in 1964, Brayton and Moser introduced the concept of a mixed potential as a fundamental theoretic tool to describe and analyze a class RLC of nonlinear circuits containing resistors, capacitors and inductors. In this paper, it is shown for the first time that a mixed potential can be introduced for a class RLCM of RLC circuits containing also memristors. This is possible provided a memristor circuit is analyzed not in the traditional voltage‑current domain but rather in the flux‑charge domain. The flux‑charge analysis method (FCAM) plays a crucial role in the extension, in particular, a key step is an equivalence principle established via FCAM between an RLCM circuit in the flux‑charge domain and a nonlinear RLC circuit in the voltage‑current domain. Several examples are discussed where the mixed potential is explicitly found. These include basic circuits with memristors, such as Chua's circuit with a memristor and also large‑scale memristor arrays with a neural architecture. This paper is mainly devoted to the introduction of a mixed potential for memristor circuits and the study of its main theoretic properties, as the possibility to write the circuit state equations in the flux‑charge domain in an effective and compact form via the mixed potential. In a companion paper [1], the mixed potential is used to obtain in a systematic way Lyapunov‑like results on convergence of RLCM circuits. Those results will extend existing results on convergence that do not cover the important case where there is the simultaneous presence of capacitors and inductors in a memristor circuit.

Authors:Taroh Hijikata, Allan Jr Mesa, Charleston Dale Ambatali
Title: Implementation of a Misalignment-Tolerant MIMO Near Field Wireless Power Transfer System
Abstract:
The efficiency of reactive near‑field wireless power transfer (WPT) systems degrades rapidly with increasing separation distance and is highly sensitive to misalignment between transmitting and receiving coils. These limitations restrict the mobility of powered devices and confine many near‑field WPT applications to static scenarios. To address these challenges, a multiple‑input multiple‑output (MIMO) WPT configuration is investigated due to its capability to shape the magnetic field distribution between the transmitter and receiver. Maximum power transfer efficiency can be achieved by appropriately setting the amplitude and phase of each transmitting coil; however, determining these optimal settings requires accurate knowledge of the system's S‑parameters. This paper presents the use of the Nelder‑Mead iterative optimization algorithm to estimate the input amplitude and phase settings that maximize transfer efficiency in a near‑field WPT system. The implementation comprises a four‑element transmitter and a two‑element receiver. Based on measured S‑parameters, the proposed approach significantly improves WPT efficiency under both aligned and misaligned conditions.

Authors:Nicolae Darii, Alberto Magagna, Oscar Saborio-Romano, Nicolaos A. Cutululis
Title: Small-Signal Analyses Using Analytical IBR Models and Frequency-Dependent Thévenin Equivalents
Abstract:
This paper investigates whether component‑level studies can capture additional interactions through Small Signal Analysis (SSA) when the network connected to the Voltage Source Converter (VSC), typically modeled as a simple Thevenin Equivalent, is a more complex IBR‑based network. The research investigates cases ranging from basic analytical to an IEEE 9‑Bus EMT model, with and without Inverter‑Based Resources (IBRs), synthesized as State‑Space elements. The study identified that spurious poles at 50Hz related to dq‑frame conversion can hinder the accuracy of participation factor analysis. A potential approach involves a two‑step process: first, applying Henkel reduction to remove most spurious poles, followed by manual elimination of any remaining ones.

Authors:Kushal Pratap Singh, Twinkle Tripathy
Title: Bearing Only Distributed Circumnavigation with Limited Target Information for Asymmetric Dubins Vehicles
Abstract:
In this paper, we present a class of bearing based distributive nonlinear guidance laws for the cooperative circumnavigation of a stationary target by a heterogeneous team of asymmetric Dubins vehicles. In such a vehicle, the maximal left and right turn capabilities are non uniform. In the given framework, the location of the target is known only to a small subset of the vehicles, called the leaders. The uninformed vehicles, called the followers, use information from their out neighbours in the communication graph, constructed using the nearest neighbour rule. A class of guidance laws is formulated that relies solely on the heading angle and line of sight angles of a designated out neighbour of the vehicle in the graph. Using Zubov theorem, we prove that the proposed guidance laws achieve global asymptotic stability under angular speed only control and ensure the convergence of the trajectories of all the Dubins vehicles to a common centre. The proposed results are validated through numerical simulations.

Authors:Ziqi Zhuo, Junghui Chen, Lei Xie, Hongye Su
Title: Self-Optimizing Control of Continuous Processes Based on Reinforcement Learning
Abstract:
This paper addresses the Self‑Optimizing Control (SOC) problem in industrial continuous processes and proposes a Reinforcement‑Learning (RL)‑based SOC approach to improve dynamic performance under high‑frequency disturbances. In the proposed framework, the SOC controlled variable structure is embedded in the Actor network, and reward functions are designed based on economic indicators. Through interaction with the environment, the RL agent optimizes controlled variables while implicitly considering implementability and steady‑state uniqueness. Online fine‑tuning is further introduced to alleviate model mismatch. Experiments on a continuous stirred‑tank reactor with disturbances compare the proposed RL‑based SOC method with the Objective‑Guided Controlled Variable Learning Approach based on steady‑state data. The results show that the RL method achieves improved dynamic performance under real‑time disturbances, generates smooth controlled variable outputs without explicit regularization, reduces hyperparameter‑tuning complexity, and enhances adaptability through online adjustment. Overall, the proposed RL‑based SOC approach provides an effective solution for nonlinear process control and offers a promising reference for future studies involving multiple disturbances, multiple operating conditions, and model‑free scenarios.

Authors:Lin Feng
Title: Input-to-State Stable Bundle Koopman Neural ODEs for Learning Controlled Dynamics under Environmental Constraints
Abstract:
We propose ISS‑BKNO, a unified framework that integrates Koopman operator identification, Neural ordinary differential equations (ODEs), fiber bundle geometry, and input‑to‑state stability (ISS) certification. Unlike prior approaches that address stability, extrinsic inputs, or environmental constraints in isolation, the proposed framework simultaneously learns controlled nonlinear dynamics while guaranteeing global convergence and a computable ISS gain. The architecture introduces a three‑stage lifting pipeline: a bundle‑aware encoder that separates environment‑specific fibers, an environment‑conditioned Koopman backbone whose matrix spectrum is constrained to lie in the left half‑plane, and a residual neural ODE correction whose Jacobian satisfies a quadratic sector bound. Lyapunov‑based ISS regularization turns the stability requirement into a differentiable penalty that is jointly optimized with the prediction objective. Theoretical results establish fiber invariance, ISS with an explicit gain formula, and an approximation error bound that scales with the EDMD residual. Experiments on a pendulum, cart‑pole, a unicycle‑based navigation task, and a Franka Emika manipulator demonstrate substantially improved prediction accuracy and robustness under matched disturbances compared with existing Neural ODE and Koopman baselines.

Authors:Abdullah Y. Etcibasi, C. Emre Koksal, Eylem Ekici
Title: When Freshness Is Not Enough: Distribution-Aware Age of Information for Networked LQR Control
Abstract:
Age of Information (AoI) has become a central metric for the design of wireless update systems, especially in applications where fresh measurements support tracking, estimation, and control. Despite its popularity, the use of mean AoI or peak AoI as a surrogate for closed‑loop performance is often motivated by intuition rather than by a control‑theoretic derivation. This paper examines whether minimizing the mean AoI is in fact optimal for networked control systems. For scalar linear time‑invariant systems with delayed intermittent updates, we show that, under state‑independent scheduling policies, the infinite‑horizon LQR tracking problem reduces to an optimization over the distribution of inter‑scheduling intervals. The resulting objective depends on higher‑order statistical moments, and in unstable or correlated regimes on exponential moments, of the inter‑scheduling process rather than only on its mean. Consequently, policies with identical mean AoI can induce substantially different tracking costs. We further extend the analysis to disturbances with exponentially decaying autocorrelation and derive equivalent cost formulations that expose the role of the full interval distribution. Finally, we validate the theory using real vehicle trajectories from the NGSIM US‑101 dataset. The empirical results match the predicted performance trends, demonstrating that mean AoI alone is insufficient for control‑oriented network design.

Authors:Tanya Veeravalli, David M. Bossens, Atsushi Nitanda
Title: Policy Gradient for Continuous-Time Robust Markov Decision Processes
Abstract:
The framework of robust Markov decision processes (RMDPs) allows the design of reinforcement learning agents that satisfy performance guarantees under worst‑case transition dynamics. Traditional RMDPs consider discrete‑time dynamics and recently, sample‑efficient policy gradient algorithms have been considered in this context. This paper investigates policy gradient algorithms within a continuous‑time RMDP framework. Policy gradients and adversarial gradients are derived using pathwise and adjoint‑based formulas for stochastic and ordinary differential equations. We propose double‑loop optimisers to obtain linear convergence in the oracle‑based setting and an \tilde\mathcalO(\frac1ε^2) sample complexity in the sample‑based setting in an analysis which also derives novel tools for the framework of undiscounted total cost MDPs. Additionally, we propose mean‑field optimisers as distributional optimisers with an \tilde\mathcalO(\frac1K) oracle‑based convergence rate and an \tilde\mathcalO(\fracN^2ε) sample complexity under N‑particle approximation. The effectiveness of continuous‑time policy gradient algorithms is confirmed for both optimisers on continuous‑time RMDPs with neural ordinary differential equation dynamics.

Authors:Nilay Kant
Title: State Observers for Linear Systems with Prescribed Residual Bounds
Abstract:
This paper presents a state observer design for continuous linear time‑invariant (LTI) systems subject to unknown bounded disturbances, that enforces a prescribed bound on the observer residual. The proposed observer augments a continuous‑time Luenberger observer with state resets, triggered when the norm of the residual equals a pre‑specified bound. The reset map guarantees contraction of the residual at jump instants while preserving the uniform boundedness properties of a standard Luenberger observer. The paper also establishes forward invariance of the residual envelope and non‑expansiveness of the estimation error in a Lyapunov metric. Simulation results confirm the analysis. Under bounded disturbances, the residual stays within the prescribed bound. A standard Luenberger observer with the same gains violates this bound.

Authors:Guido Sanchez, Marina Murillo, Leonardo Giovanini
Title: Adaptive arrival cost update for improving Moving Horizon Estimation performance
Abstract:
Moving horizon estimation is an efficient technique to estimate states and parameters of constrained dynamical systems. It relies on the solution of a finite horizon optimization problem to compute the estimates, providing a natural framework to handle bounds and constraints on estimates, noises and parameters. However, the approximation of the arrival cost and its updating mechanism are an active research topic. The arrival cost is very important because it provides a mean to incorporate information from previous measurements to the current estimates and it is difficult to estimate its true value. In this work, we exploit the features of adaptive estimation methods to update the parameters of the arrival cost. We show that, having a better approximation of the arrival cost, the size of the optimization problem can be significantly reduced guaranteeing the stability and convergence of the estimates. These properties are illustrated through simulation studies.

Authors:Faryal Batool, Muhammad Ahsan Mustafa, Fawad Mehboob, Valerii Serpiva, Dzmitry Tsetserukou
Title: AgenticDiffusion: Agentic Diffusion-based Path Planning for Vision-Based UAV Navigation
Abstract:
Indoor UAV navigation requires efficient exploration, scene understanding, and reliable trajectory execution under limited field‑of‑view observations. Existing vision‑based navigation frameworks typically rely on single‑view observations, limiting their ability to reason about occlusions, target visibility, and global scene structure. In this work, we propose AgenticDiffusion, a multi‑view UAV navigation framework that coordinates language‑guided reasoning, open‑vocabulary target grounding, vision‑based diffusion planning, and NMPC within a unified aerial navigation pipeline. Given a natural language instruction and synchronized first‑person‑view (FPV) and top‑view observations, the framework determines the most informative viewpoint for navigation and generates a mission plan prior to trajectory execution. The targets are localized using an open‑vocabulary grounding model, after which viewpoint‑specific diffusion planners generate navigation trajectories for UAV execution. Using complementary viewpoints, the proposed framework reduces repeated target exploration and improves navigation efficiency in cluttered indoor environments. The framework was validated in four real‑world UAV navigation scenarios involving adaptive viewpoint selection, multi‑stage mission execution, long‑horizon navigation, and safe landing‑site selection. The experimental results demonstrated an overall mission success rate of 80% in 40 real‑world trials, while the diffusion planners achieved a trajectory generation success rate of 100%.

Authors:Pragya Sharma, Brian Wang, Mani Srivastava
Title: CADET: A Modular Platform for Evaluating Distributed Cooperative Autonomy in Connected Autonomous Vehicles
Abstract:
Deep learning models are increasingly central to autonomous vehicle (AV) pipelines, yet their integration has traditionally followed a monolithic design where perception, planning, and control execute on a single onboard computer. This design overlooks the emerging paradigm of cooperative autonomy, where vehicles interact with roadside units (RSUs), edge servers, and cloud‑hosted intelligence through vehicle‑to‑everything (V2X) connectivity. Cooperative perception and control improve safety and efficiency, but also introduce systems‑level challenges: network latency, compute heterogeneity, and multi‑tenant contention, all critically affect real‑time decision‑making. These challenges are further amplified by the increasing reliance on large foundation models, whose scale necessitates cloud deployment. We present CADET (Cooperative Autonomy through Distributed Experimentation Toolkit), a modular platform for systematic and reproducible evaluation of distributed cooperative autonomy systems under realistic deployment conditions. CADET decouples the AV stack into composable modules that can be flexibly deployed across vehicles, infrastructure, and edge/cloud tiers. The framework integrates state‑of‑the‑art models, incorporates trace‑driven network and workload emulation, and provides synchronized model‑, system‑, and task‑level instrumentation. Through V2V and V2I experiments, we show that distributed deployment choices fundamentally shape safety, with V2V intent packets outperforming cloud‑based perception and RSU‑assisted perception sustaining safety until overloaded by concurrent requests. Although designed for AV pipelines, CADET also supports dataset‑driven experimentation, enabling systems and ML researchers to benchmark distributed inference workloads independently of full vehicle simulation. CADET is open source, with code and demo available at https://nesl.github.io/cadet‑web.

Authors:J. Francisco Presenza, Leonardo J. Colombo, Ignacio Mas, Juan I. Giribet
Title: Multi-Robot Bearing-only Pose Estimation via Angle Rigidity
Abstract:
This letter proposes a novel distributed bearing‑based pose estimator for time‑varying multi‑robot systems. The method uses angles computed from body‑frame bearings to estimate the robots' positions in \mathbbR^3 without knowledge of their orientations. The orientations in \mathrmSO(3) are recovered from the estimated positions, the bearings, and the bearing derivatives. The proposed observer only requires the (directed) sensing topology to be angle‑rigid, a weaker condition than the commonly used ones like bearing rigidity. Local uniform exponential stability of the proposed observer is established under the assumption of persistently exciting motions for a subset of robots. Simulations are presented and discussed to evaluate the scheme's effectiveness and practicality.

Authors:Neda Vahabzad, Kenneth Bruninx, Peter Palensky, Pedro P. Vergara
Title: A Dynamic Capacity Allocation Model for DERs under Non-Firm Connection Agreements
Abstract:
The growing penetration of distributed energy resources (DERs) intensifies congestion in distribution networks by introducing bidirectional power flows and increasing competition for limited network capacity, underscoring the need for effective and efficient congestion management, including flexible grid‑access schemes. This paper proposes a bilevel optimization model for the dynamic allocation of connection capacity to DERs under non‑firm connection agreements, aligning the objectives of distribution system operator (DSO) and DER owners. The upper‑level problem, representing the DSO, determines the allocated connection capacity for all DERs, defined as maximum time‑varying power limits, subject to distribution system constraints and the last‑in‑first‑out (LIFO) allocation rule. The lower‑level problem, representing DER owners, maximizes the profit of each DER within the allocated power limits. The proposed model is tested on a modified CIGRE medium‑voltage (MV) network, demonstrating a balanced trade‑off between grid utilization and economic efficiency. Furthermore, the model enhances DER integration, enforces transparent allocation rules, reduces variability in allocation patterns, and achieves up to an 80% reduction in total curtailment costs compared with benchmark methods.

Authors:Shih-Jie Shih, Jonghan Lim, Ilya Kovalenko, Rômulo Meira-Góes
Title: NeuroSymbolic Robustness Analysis for Discrete Systems with Respect to Transition Deviations
Abstract:
Supervisory control of discrete‑event systems provides formal guarantees of correctness with respect to a plant model and specification. However, these guarantees heavily rely on the plant model, which could deviate from nominal behavior due to modeling errors or faults. Recent notions of discrete robustness model deviations as a set of additional transitions that are added to the plant. The discrete robustness is defined as all sets of extra transitions for which the supervised system still guarantees a desired specification. However, this notion suffers from scalability due to the large solution space and conservatism since most deviations are infeasible in practice. This paper proposes to address these two issues using a neurosymbolic computing framework for discrete robustness analysis of safety properties. First, a neural reasoning layer based on Large Language Models infers a set of feasible deviation transitions from system models, specifications, and domain knowledge. Next, a symbolic layer computes the discrete robustness guarantees over the inferred deviation set. We evaluate our framework on three case studies, demonstrating that our method identifies a smaller set of feasible deviations while preserving robustness guarantees comparable to those of full transition‑based analysis.

Authors:Xingchen Li, Kunpeng Liu, Keyou You
Title: APX-Hardness of Computing Lipschitz Constants for Multi-Parametric Quadratic Programs
Abstract:
Computing the Lipschitz constant of the solution map of a multi‑parametric quadratic program is important for the analysis of optimization‑based control. This problem is governed by three factors: the parameter dimension, the number of decision variables, and the number of constraints. While empirical evidence has long suggested exponential complexity, a rigorous complexity‑theoretic proof has been lacking. In this paper, we fill this gap by proving that this problem is not only NP‑hard but also APX‑hard. Furthermore, we reveal that: (a) the problem becomes polynomial‑time solvable when the number of constraints or decision variables is fixed; and (b) both NP‑hardness and APX‑hardness persist even in the scalar parameter case. These results confirm that the complexity stems from the number of constraints and variables, rather than the parameter dimension. Numerical experiments further validate these theoretical findings.

Authors:Mohamed Atef, Sanath Alahakoon, Umme Mumtahina, Peter Wolfs, Tamer Khatib, Moslem Uddin
Title: An Integrated Techno-Economic Framework for Optimal Microgrid Design: An Australian Case Study
Abstract:
Reliable and affordable electricity supply remains a challenge for remote and regional communities, motivating the deployment of renewable‑based microgrids supported by flexible storage and advanced planning methods. This paper proposes an integrated techno‑economic framework for optimal microgrid design and robustness assessment, and applies it to a 1000‑household residential community in Rockhampton, Queensland (Australia). The framework links time‑series simulation, dispatch‑based operation, and lifecycle costing to evaluate hybrid configurations comprising photovoltaic and wind generation, battery storage, diesel backup, grid exchange, and an optional hydrogen subsystem (electrolyzer‑‑hydrogen storage‑‑fuel cell). Key indicators include net present cost (NPC), cost of energy (COE), renewable penetration, energy purchased/sold, and emissions‑related outcomes. To avoid conclusions that depend on a single set of assumptions, the study performs systematic sensitivity analysis across financial, technical and policy drivers: discount rate, technology capital costs, fuel price, load uncertainty, renewable resource variability, carbon pricing/emissions cost, and grid outage duration, supplemented by a no‑hydrogen attribution case. The results demonstrate that several sensitivity dimensions induce nonlinear shifts in the optimal design, including breakpoints where capital‑intensive renewable‑‑storage expansion becomes economically preferable. The proposed framework enables transparent comparison of hydrogen‑enabled and battery‑centric solutions and provides planning guidance for resilient, low‑emission community microgrids under Australian operating conditions.

Authors:Yue Wu, Ziqing Xia, Shaokun Li, Heng Li, Shengyu Tao, Zhiwu Huang
Title: When are supercapacitors practically feasible in electric vehicles?
Abstract:
While the hybrid energy storage system (HESS) can theoretically mitigate battery degradation in electric vehicles, its practical implementation remains highly limited. To delineate the specific scenarios and application boundaries where supercapacitors remain feasible, this study proposes a multi‑dimensional techno‑economic feasibility evaluation framework. First, a cross‑vehicle sizing method based on dynamic programming is established to quantify physical mass‑volume packaging constraints and identify feasible supercapacitor candidates across different vehicle types. Building upon the optimal sizing parameters derived from the battery aging Pareto front, an expert‑guided deep reinforcement learning energy management strategy is integrated to yield near‑optimal online performance, ensuring a fair life‑cycle economic assessment. Finally, a comprehensive feasibility matrix is constructed to systematically evaluate mass, volume, battery lifespan, additional supercapacitor costs, total cost of ownership, future energy storage prices, and the influence of emerging solid‑state batteries. Results reveal that city buses remain the most promising vehicle type for HESS due to minimal additional costs and sufficient packaging space. Current mass‑volume penalties and limited economic benefits hinder HESS application in passenger vehicles and heavy‑duty trucks, respectively. This situation may only improve if supercapacitor prices drop significantly in the future. Beyond vehicle types, the HESS feasibility is governed by load‑frequency characteristics. Furthermore, looking toward the 2030+ solid‑state battery era, we highlight that integrating increasingly affordable supercapacitors can provide substantial asset protection leverage.

Authors:Henrique O. Caetano, Carlos Dias Maciel, Rahul K. Gupta
Title: Admittance Sensitivity-Informed Modular GP for Scalable Topology-Adaptive Power-Flow Learning
Abstract:
Data‑driven approaches for learning power flow models suffer from weak generalization across varying network topologies and limited computational scalability. Existing methods typically rely on training over a large set of grid topologies, which becomes impractical for large networks. This paper proposes a scalable and computationally efficient framework for topology‑adaptive learning of power flow solutions. We propose a modular architecture consisting of bus‑level Gaussian Process (GP) models, where each GP collects local features based on bus‑level egonet definition. The localized bus‑level feature includes first‑order power and admittance sensitivities, nodal injections and node degree. In addition to the modular architecture, we propose using Random Fourier Features (RFF) for feature reduction, which further enhances the computational scalability. We evaluate the effectiveness of the proposed method by simulations across multiple benchmark networks under N‑1, N‑2, and N‑3 contingencies. Results for the PEGASE 1354 bus system under N‑3 contingencies demonstrate high predictive quality, with an R^2 score of 0.983 and a voltage‑magnitude RMSE of 0.0023 p.u. The framework maintains recall rates exceeding 98% for detecting voltage limit violations across all test cases. Furthermore, the approach exhibits scalability, completing training and testing for the PEGASE 1354 system in 116.47 seconds while outperforming existing benchmarks in zero‑shot generalization without requiring additional training samples.

Authors:Sara Nour Sadoun, Giuseppe Alessio D'Inverno, Francois Cottin, Arnaud Boutin, Taous-Meriem Laleg-Kirati
Title: From Well-Posed Inversion to Learning Design: Physics- Informed Neural Estimation for Autonomic Regulation
Abstract:
Learning‑based and physics‑informed methods are increasingly used for inverse estimation in controlled nonlinear dynamical systems. However, in many such approaches, the theoretic requirements that make unknown‑input reconstruction meaningful, namely well‑posedness in the sense of Hadamard, are often disregarded or weakly addressed through generic regularization terms with no explicit guarantees. In this work, we adopt a complementary viewpoint in which these control‑theoretic and structural conditions inform the estimator design and constrain its training. We thus develop a physics‑informed input‑state neural estimator for joint unknown‑input and state estimation in nonlinear controlled systems with partial measurements. In the present work, this general framework is instantiated on a model of autonomic cardiac regulation, provides a concrete study case. The estimator is formulated as an inverse neural map conditioned on time and measured outputs, and is trained under data fidelity and dynamical consistency constraints. To ensure it complies with the same structural requirements imposed in robust estimation, we derive left‑invertibility conditions by differential‑algebraic elimination and embed the resulting constraints directly into the training objective. We further analyze a priori the stability of the inverse mapping to output perturbations and derive a conservative Lipschitz bound that guides the tuning of cost functional hyper‑parameters. The framework is evaluated on simulated data, where ground truth data is available, and on two distinct datasets of real cardiovascular recordings. The results show that incorporating control‑theoretic solvability constraints into physics‑informed learning improves the reliability of inverse inference beyond forward consistency alone.

Authors:Brian Block, Stephanie Stockar
Title: Optimal Finite-Horizon LQR Control for Traffic Flow via Variable Speed Limits
Abstract:
This article presents a finite‑horizon linear quadratic regulator for the control of the first‑order Lighthill‑Whitham‑Richards traffic model with a triangular fundamental diagram. The in‑domain control action is realized through variable speed limits implemented as a source term in the governing hyperbolic partial differential equation. Unlike prior studies on infinite‑horizon formulations, this article develops a finite‑horizon LQR framework, deriving a space and time varying state feedback function for hyperbolic PDEs. The solution to the finite time optimal control problem relies on the solution of another PDE, called the Riccati PDE. The resulting nonlinear Riccati PDE is solved analytically via the parametric method of characteristics. The Riccati PDE solution is a function of both time and space, as well as the traffic regime. A sensitivity analysis demonstrates the effects of the LQR parameters for both the infinite and finite time horizon problem in different traffic situations, while siulations validate the finite‑horizon LQR's ability to guarentee finite‑time convergence. Comapred to the infinite‑horizon LQR, the proposed approach achieves significantly improved control performance across various scenarios, making it particularly suitable for time‑sensitive traffic management applications.

Authors:Nicolae Darii, Ranjan Sharma, Vladislav Akhmatov, Kanakesh Vatta kkuni, Chi Su, Oscar Saborio-Romano, Nicolaos A. Cutululis
Title: Enhancing Offshore Wind Simulations: Interpolated Switching via DLL Black-Boxes
Abstract:
The modern power system, increasingly composed of Inverter‑Based Resources (IBR) from multiple manufacturers, requires new study and design techniques that balance accuracy with the need to protect the Intellectual Property (IP) of various stakeholders. One possible method to support detailed electromagnetic transient (EMT) simulations is to convert the original equipment manufacturers (OEM) models into shareable black‑box versions using dynamic link libraries (DLLs). This technique prevents IP violations while potentially maintaining simulation accuracy by embedding the original components within the shareable DLL. Thereby, this work aims explicitly to enhance simulation fidelity by translating full‑switching models of offshore wind turbines (OWTs). In this context, the paper offers valuable recommendations, including how to convert interpolation‑based elements, preserve simulation speed, recognize limitations, and outline future improvements

Authors:Swapnil Tripathi, Mahmut Kudeyt, Alkım Gökçen, Savaş Şahin, Özkan Karabacak
Title: Semidefinite Programming Certificates for Synchronization of Kuramoto Oscillators on Arcs
Abstract:
A class of Kuramoto models with a general coupling function that can be expressed in terms of a finite number of harmonics, each comprising sinusoidal terms, is studied. We propose a novel approach for certifying local phase synchronization in this class for all initial conditions lying on an arc. The trace parametrization property and Gram matrix representation of a trigonometric polynomial are utilized along with Putinar's Positivstellensatz to obtain semidefinite programming certificates for the stability of the phase‑difference system, which in turn implies synchronization of the original system. The results can be extended to any system of coupled oscillators where the forward‑invariance on arcs can be established.

Authors:Nicolae Darii, Luis A. Garcia-Reyes, Ignasi Ventura Nadal, Oscar Saborio Romano, Ranjan Sharma, Oriol Gomis-Bellmunt, Nicolaos A. Cutululis
Title: Systematic Gray-Box Identification Methodology for Voltage Source Converters
Abstract:
This paper introduces a systematic gray‑box identification framework for voltage‑source converter models based solely on terminal time‑series data. The proposed approach combines a physically informed white‑box standard model with iterative time‑domain calibration to estimate controller parameters that mimic the behavior of the black‑box model in electromagnetic transient (EMT) simulations. Unlike conventional frequency‑domain identification methods, the framework leverages time‑domain data more effectively to better constrain the surrogate model across a broader operating range and capture reference‑signal dynamics. To evaluate the accuracy of the identified model, the paper presents additional frequency‑domain validation metrics based on Nyquist analysis and singular value decomposition, allowing for both quantitative assessment of model divergence and qualitative classification of mismatch types. The methodology is tested on cases with increasing structural uncertainty, from exact parametric recovery to an actual detailed EMT black‑box model. Results demonstrate that the proposed framework can accurately recover parameters when the internal structure is known, adjust for moderate structural mismatch with extra degrees of freedom, and offer a reliability measure for small‑signal stability analysis of converter models protected by intellectual property

Authors:Nicolae Darii, Ranjan Sharma, Germano Rugendo Mugambi, Oscar Saborio Romano, Nicolaos A. Cutululis
Title: Estimation of Equivalent SCR for Offshore Wind
Abstract:
The integration of offshore wind power plants (OW‑PPs) into weak grids can pose stability challenges due to the interaction between inverter‑based resources (IBRs), Flexible AC Transmission Systems (FACTS) and the grid. In this context, long HVAC transmission systems, relatively common for OWPPs, can exacerbate the stability challenges. Therefore, this paper introduces a novel methodology for estimating the equivalent short‑circuit ratio (ESCR) at the offshore point of connection (PoC), combining analytical two‑port network (TPN) modeling with electromagnetic transient (EMT) simulations. The approach derives the Thevenin equivalent impedance for passive and active components, enabling accurate ESCR computations without complex derivations. Limitations of traditional SCR metrics are addressed by incorporating the dynamics of the converters, such as static synchronous compensators (STATCOMs), into a hybrid EMT‑TPN method for synthesizing equivalent impedances. The process is then verified on the CIGRE OWPP benchmark and is found to capture ESCR variations with cable lengths, shunt reactors, and grid strength. Additionally, the results emphasize the correlation between the ESCR and voltage stability, highlighting the role of STATCOMs in supporting voltage stability in weak grids. This modular framework aids in OWPP design and stability analysis for converter‑dominated systems.

Authors:M. Wind, J. Pierssens, R. Beerens, V. Dolk, T. van Keulen
Title: Recursive Learning of Feedforward and Compliance Compensation Parameters for Precision Motion Systems
Abstract:
To meet the stringent requirements of future motion systems exhibiting time‑varying and/or position‑dependent behavior, online data must be leveraged to improve control performance. This paper presents a recursive algorithm for simultaneous learning of feedforward and compliance compensation parameters. A multivariate regression formulation is proposed that jointly estimates friction, mass, jerk, and compliance compensation parameters while mitigating parameter coupling. Experimental results on a high‑tech semiconductor metrology and inspection system demonstrate an order‑of‑magnitude improvement in servo performance.

Authors:Liam Hallinan, Ioannis Lestas
Title: Unstable Poles Arising in AC Power Grid Subsystem Representations
Abstract:
Recent small‑signal stability studies of AC grids have shifted towards analysing power systems as interconnections of subsystems and leveraging their input‑output properties to derive scalable stability certificates. Two subsystem representations appear frequently in the literature: the PQ model, coupling powers to phase angle and voltage magnitude, and the IV model, coupling currents to voltages. In this paper, we derive both models without simplifying the bus or line dynamics and show that a loop transformation relates the two. One of the main results in the paper is to then show analytically that each representation may exhibit unstable poles depending primarily on the operating point (IV model) or the presence of high‑frequency passive dynamics (PQ model). In particular, such unstable poles in the subsystems can occur even when the aggregate interconnection is stable and well‑behaved. These effects are validated numerically, including a case study using the full‑order dynamics of a synchronous generator with an exciter and transformer. Our results highlight that care must be taken when choosing a subsystem representation, as neglecting high‑frequency dynamics or device operating points may obscure unstable poles that must be stabilised by the network interconnection and must be accounted for in system identification.

Authors:Pierre-Yves Massé, Maylis Duru, Benoit Couraud, Haicheng Ling, Solal Bizeul, Hugo Roussel, Cléa Verdot, Mariane Vittoz, Estefania Alvarez, Merlinda Andoni, Yann Rozier, Sonam Norbu, David Flynn, Erwin Franquet, Thibault Rihet
Title: Enhancing Collective Self-Consumption through Water Storage Heater Flexibility
Abstract:
While Renewable Energy Communities (RECs) and Collective Self‑Consumption (CSC) schemes have emerged as promising tools to accelerate renewable energy adoption and support the net‑zero transition, their full potential can only be realised when complemented by demand‑side flexibility that aligns consumption with renewable generation. Water storage heaters can function as distributed thermal storage, absorbing excess renewable energy at the community level. This work quantifies both the benefits of water storage heaters flexibility for energy consumers in a CSC community in France (such as energy bill reduction, increase of self‑consumption), and the challenges related to the implementation and user acceptance. At the first stage, an annual simulation analysis is performed on a community of 41 households and a large solar PV plant, comparing a scenario without a CSC community, a scenario with a standard CSC community, and a scenario with a CSC community with flexibility from water storage heaters, which showed that an average benefit of 70euro/year per household can be achieved due to flexibility and an increase of 6% and 22% of solar PV community self‑consumption and self‑production respectively. In the second stage, we present the results of the real‑world deployment in the community, analysing its technical performance and user reception, and examine the main factors shaping user engagement and satisfaction.

Authors:Trinh K. Nguyen, Hung K. Hoang, Thanh V. Pham, Chuyen T. Nguyen
Title: Secrecy Sum Rate Maximization for OIRS-Aided Visible Light Communications with Confidential Messages
Abstract:
This paper investigates the secrecy sum‑rate (SSR) performance of optical intelligent reflecting surface (OIRS)‑assisted multi‑user visible light communication (VLC) systems under line‑of‑sight (LoS) blockages. To mitigate physical obstructions and internal eavesdropping, a joint optimization problem is formulated to maximize the SSR through the co‑design of the transmission precoder and OIRS units assignment. Due to the binary constraints and coupled variables, the problem is highly non‑convex. To solve it efficiently, an alternating optimization (AO) framework integrating the concave‑convex procedure (CCCP) and first‑order Taylor approximations is developed. Simulation results demonstrate the convergence of the proposed algorithm and show that increasing the number of OIRS reflecting units yields significant SSR gains.

Authors:Robert Gaugl, Eloy Insunza, José Portela, Sonja Wogrin
Title: Surrogate Modeling of Interconnector Flows: A Machine Learning Alternative to Full-Scale Power System Simulations with Application to Cross-Border Electricity Exchange
Abstract:
Cross‑border electricity exchanges are crucial for operating and planning highly renewable power systems. Many studies reduce spatial granularity to keep the models tractable and prescribe cross‑border exchanges exogenously, often by reusing historical import/export time series. Such assumptions become inconsistent as renewable penetration changes the magnitude and timing of flows. This paper proposes a machine‑learning (ML) surrogate framework that maps available nodal time series data (e.g., hourly demand and renewable generation) to synthetic, interconnector‑level flow time series. The goal is to provide consistent flow profiles that are used as fixed boundary conditions in reduced power system optimization models (PSOMs). To improve downstream feasibility when surrogate flows are imposed in optimization, we further introduce a custom loss for the neural‑network surrogate that penalizes physically impossible flow patterns. We demonstrate the framework on a pan‑European single‑node per country DC optimal power flow setting using the open‑source LEGO PSOM with ENTSO‑E TYNDP 2024 National Trends assumptions for 2030. We assess two model classes: k‑nearest neighbors (KNN) and feedforward neural networks (SQU), using both full and reduced feature sets. The SQU models generalize more robustly than KNN to unseen climate years and substantially improve upon scaled historical benchmarks in terms of predictive accuracy. When imposed as fixed boundary flows in single‑node PSOMs, the ML‑generated profiles produce outcomes that closely match the results of the full European simulation, while delivering substantial runtime reductions (up to ~500x). These results indicate that ML‑based flow surrogates can provide decision‑relevant interconnector flows for tractable reduced studies in high‑renewable systems.

Authors:Françoise Zink, Chipten Valibhay, Jose Bonet Faus
Title: Navigating the unknown in large-scale operational transformation programs: The "Sirius Days" framework as a 'pilot-organization' for characterizing emerging issues
Abstract:
Large‑scale digital transformation programs must simultaneously sustain existing operations and navigate deep unknowns emerging from IT‑business‑operations interactions a challenge conventional project governance frameworks inadequately address. Based on a longitudinal case study of a transformation program, we investigate the ''Sirius Days,'' a monthly senior management retreat identified as a critical success factor. We show that this framework constitutes a pilot‑organization: an organizational 'dispositif' (or apparatus) that deconstructs established knowledge or assumptions, formulates rigorous conjectures, and tests them in real conditions. It generated five resilience levers systemic characterization of unknowns, early anomaly discernment, expansion of performance norms, social capital creation through a community of inquiry, and expansion of organizational agility across scales revealing a model of an organizational 'dispositif' that operationalizes navigating unknowns across cognitive, social, and normative dimensions.

Authors:Doyeong Lim, Seungyoon Lee, In Cheol Bang
Title: Validation-Gated Multi-Agent Governance for Online Adaptation of Thermal-Hydraulic Surrogate Models under Operating-Regime Shift
Abstract:
Artificial‑intelligence surrogates can support second‑by‑second thermal‑hydraulic forecasting, but models selected and frozen offline may become condition‑locked once deployed outside their pretraining envelope. This study develops a guarded continual‑adaptation framework for experimental thermal‑hydraulic loop data in which role‑separated agents ‑ Monitor, Diagnosis, Adaptation, Safety‑Auditor, and Orchestrator ‑ diagnose error signatures, prioritize candidate model families, and review promotions, while deterministic champion‑challenger gates and background shadow learning retain final authority over model replacement. Seven surrogate families were screened by blocked three‑fold cross‑validation, and a temporal Fourier neural operator was selected as the initial champion for 60‑s‑history‑to‑10‑s‑trajectory forecasting on two held‑out transients, with three seeds per adaptive mode. Static deployment gave a channel‑averaged MAE of 7.06 and a 56.8% warning‑exceedance ratio; rule‑based adaptation reduced MAE to 6.54, whereas shadow refresh alone remained close to Static. The MA‑Full mode, in which the role‑separated multi‑agent council reviews every evaluated stream step, achieved the lowest mean error, 5.72, and 35.8% exceedance, corresponding to a 19.0% improvement over Static. Paired bootstrap intervals against Static excluded zero, although intervals among adaptive modes overlapped and the six paired units limit broad statistical claims. Validated promotions from the neural operator to Transformer and graph neural network indicate that logged, gate‑controlled adaptation can support auditable surrogate evolution while deterministic gates retain deployment authority.

Authors:Joseph Moyalan, Ricardo de Castro, Shuang Feng, Xuchang Tang, Xinfan Lin, Qijian Gan
Title: Equivalent Circuit Model based Electric Vehicle Evacuation with Mobile Charging Stations
Abstract:
The increasing penetration of electric vehicles (EVs) introduces new challenges for emergency evacuation planning due to limited driving range, long charging times, and constrained charging infrastructure, particularly under disaster induced disruptions. This paper proposes a novel optimization based evacuation framework for EVs using Equivalent Circuit Models (ECMs) to jointly address routing, charging, and congestion management. By leveraging electrical analogies, traffic flow is modeled as electrical current, travel time as resistance, and driving range as voltage, enabling the use of Kirchhoff laws to enforce flow balance and energy feasibility constraints. The proposed controllable ECM incorporates binary switches to regulate route selection and explicitly models charging delays and range replenishment at both Fixed Charging Stations (FCSs) and Mobile Charging Stations (MCSs). The resulting formulation leads to an integer programming problem that determines optimal evacuation routes, charging durations, and the placement and number of MCSs to minimize evacuation time. The framework is extended to multiple origin destination pairs using the principle of superposition and supports fairness aware performance metrics, including worst case, average, and variance based evacuation times. Simulation studies on large scale transportation networks in California demonstrate that the proposed approach significantly improves evacuation efficiency and robustness, particularly in scenarios with limited charging access, highlighting the critical role of MCSs in EV based emergency evacuations.

Authors:Ryan Thompson, Ethan Wang, Nilay Kant
Title: Dynamics of the Thermomagnetic Pendulum
Abstract:
A thermomagnetic pendulum is introduced as a coupled thermo‑magnetic‑mechanical system consisting of a ferromagnetic bob under gravity and an offset permanent magnet. Heating drives the bob temperature above and below the Curie point, causing magnetic attraction to vanish and recover as the bob moves and cools. A multiphysics model is developed in which the magnetic torque depends nonlinearly on the bob temperature field and pendulum configuration. The formulation couples transient three‑dimensional heat transfer, a temperature‑dependent magnetization law, and pendulum dynamics. Simulations show angular torque asymmetry, rapid force reduction near the Curie point, and sustained oscillations.

Authors:Adebayo Olayinka Oke, Nilay Kant
Title: Learning Local Optimal Controller for a Class of Nonlinear Systems via Impulse-Supervised Exploration
Abstract:
This paper develops an impulse‑supervised confined exploration framework for learning local optimal controller for a class of nonlinear systems. The proposed approach combines continuous‑time approximate dynamic programming (ADP) with an impulsive supervisory layer, where impulsive braking confines the state within a prescribed region in which a local linear approximation of the nonlinear system is valid. This enables desired persistent excitation required for parameter convergence while preventing large state deviations that invalidate local optimality. The resulting hybrid closed‑loop system enforces invariance of the exploration region through state‑triggered braking inputs. Simulation results on a nonlinear mechanical system demonstrate effectiveness of the proposed approach.

Authors:Qiang Zeng, Lipeng Zhu, Yang Li, Yi Lei, Quan Zhou, Jiayong Li, Cong Zhang, Bingxu Li, Zhikang Shuai
Title: Impedance Modeling and Stability Analysis of Droop-Controlled Inverter Under Unbalanced Power Grid Operating Conditions
Abstract:
With the growing integration of renewable energy sources into power grids, the risks of oscillation caused by interactions between grid‑tied inverters and the grids are becoming increasingly prominent. Although existing studies have made significant progress in inverter modeling and oscillatory stability analysis, most of them do not sufficiently consider complex mirror frequency coupling effects (MFCE) under unbalanced operating conditions, leading to unreliable models and erroneous stability analysis results. To address this inadequacy, this work develops a novel sequence impedance modeling scheme that can be widely applied to unbalanced operating conditions. In particular, taking a representative type of grid‑forming inverter for instance, i.e., droop‑controlled inverter (DCI), a single‑input single‑output sequence impedance modeling method based on harmonic linearization (HL) is proposed to comprehensively model both a given DCI and the connected grid. By accounting for multi‑frequency interactions within the DCI, this method captures MFCE and unbalanced factors, leading to a more accurate impedance model. Further, the dominant factors influencing system stability are identified with a combination of normalized sensitivity analysis and proportional weighting. Finally, the detailed impacts of these dominant factors on system stability margin under three typical unbalanced operating conditions are analyzed through the Bode criterion. The effectiveness and reliability of the whole scheme proposed in this work are validated on the constructed grid‑connected droop‑controlled experimental platform.

Authors:Hieu Trinh, Phan Thanh Nam, Tran Ngoc Nguyen
Title: Observer-Based Control of Linear Systems with Mismatched Input and Output Delays
Abstract:
This paper investigates the stabilization of linear systems subject to simultaneous, mismatched time delays in both the control input and system output vectors. The proposed control framework is developed in two primary stages. First, an asymptotically stabilizing delayed state‑feedback controller is synthesized by leveraging recent advancements in Linear Matrix Inequality (LMI) techniques. Second, this controller is realized using novel time‑delay compensators \citetrinhnam26. This architecture successfully accommodates an output measurement delay τ_y that is independent of the input delay τ_u, enabling direct estimation of the delayed state‑feedback control law. The proposed methodology is then extended to target output controllers to account for simultaneous, mismatched time delays in both the control input and system output vectors.

Authors:Zixuan Guo, Xiucheng Wang, Nan Cheng
Title: RMPrior: Bridging Propagation Priors and Diffusion Refinement for Efficient Radio Map Construction
Abstract:
Diffusion models achieve high‑fidelity radio map construction through iterative denoising, yet their sampling cost limits practicality in dynamic wireless systems where radio maps must be refreshed repeatedly. Meanwhile, classical propagation models encode valuable scene‑level knowledge that standard diffusion inference discards entirely by initializing from pure Gaussian noise. This paper bridges propagation priors and diffusion refinement through a mid‑start sampling strategy. A matched propagation prior is perturbed to an intermediate diffusion timestep, and the pretrained diffusion backbone executes only the remaining reverse steps, focusing computation on multipath‑aware refinement rather than full reconstruction from noise. We provide theoretical analysis establishing an upper bound on the initialization gap, a sufficient condition under which truncation improves reconstruction fidelity, and a formal characterization of prior‑quality sensitivity under aggressive truncation. Experiments on IRT4HighRes show that, at P_\textstart=0.5, the proposed method achieves a 2.01× speedup while simultaneously improving NMSE, RMSE, SSIM, and PSNR over the full‑step baseline. A prior‑quality ablation across three propagation models of different fidelity confirms that reconstruction quality tracks prior quality, with the sensitivity amplified under shorter reverse trajectories, consistent with the theoretical predictions. These results also suggest that mid‑start reconstruction quality can serve as a proxy for ranking the scene‑level fidelity of different propagation models.

Authors:Alfreds Lapkovskis, Ali Beikmohammadi, Sindri Magnússon, Praveen Kumar Donta
Title: Brief Announcement: Generative Markov Model for Distributed Computing Systems
Abstract:
Emerging distributed computing paradigms, such as the computing continuum, are inherently heterogeneous, stochastic, and complex. Efficiently and effectively utilizing all available resources across the continuum demands a unified formal model of the system. To address this gap, we propose a general framework for modeling distributed computing systems as a generative Markov model, factorized over a structured system state. In our model, the state decomposes into high‑dimensional variables, each further factorized over its elements, reflecting the sparse dependency structure inherent to distributed systems. This yields a tractable model enabling simulation, inference, and policy learning over otherwise intractable system states, bridging distributed computing with Markov chain theory and reinforcement learning (RL). We demonstrate our framework through a case study of collaborative AI inference, in which a dedicated server combines resources with those volunteered by service users. Our results show that centralized scheduling becomes a bottleneck at scale, while distributing computation across user devices reduces both latency and server resource consumption. These findings highlight the value of adaptive decision‑making in distributed computing systems and demonstrate the framework's utility for modeling, simulation, and optimization.

Authors:Qi Dai, Beixiong Zheng, Yanhua Tan, Weidong Mei, Jie Tang, Fangjiong Chen
Title: Rotatable Antenna Meets Multiple Access: NOMA or OMA?
Abstract:
Rotatable antenna (RA) technology has emerged as a promising solution to enhance spectrum efficiency by exploiting additional spatial degrees of freedom (DoFs) in multiple access networks. However, the relative performance superiority among different multiple access schemes remains largely unclear due to the unique capability of RA in reconfiguring the directional gain pattern. In this letter, we conduct a theoretical comparison between non‑orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes in RA‑assisted communication systems in terms of transmit power minimization, subject to constraints on antenna rotational range and users' target rates. To address the associated non‑convex optimization problem, a particle swarm optimization (PSO) algorithm is employed to optimize the rotational angle. Simulation results demonstrate that RA‑assisted schemes significantly reduce transmit power compared to fixed‑antenna benchmarks. Furthermore, RA‑assisted NOMA may perform worse than time‑division multiple access (TDMA) for symmetric user deployments, while it exhibits superior robustness and energy efficiency in asymmetric scenarios.

Authors:Karthik Barma, Anil Sanneboyina, V C Premchand Yadav
Title: Glass Box at Orbit: A Constitutional AI Verification Framework for Trustworthy Autonomous CubeSat Intelligence
Abstract:
The space industry is quietly building toward something nobody has fully reckoned with: orbital data centers running thousands of autonomous AI workloads with no human in the loop, 550 km above the Earth. Microsoft, AWS, and a growing list of orbital computing ventures are moving cloud‑scale processing off the ground and into orbit. What none of them have answered yet is the governance question ‑‑ when autonomous AI systems at orbital data center scale make wrong decisions in space, what stops those decisions before they become irreversible? We introduce Glass Box: a runtime constitutional AI verification layer that intercepts every candidate action from an onboard AI policy and evaluates it against six physics‑grounded constitutional constraints and seven Linear Temporal Logic (LTL) safety invariants before a single command reaches any spacecraft subsystem. Every approved action carries a weighted explainability score E(a_t) in [0,1] and a complete constitutional audit log. We demonstrate Glass Box within Project October: a fully simulated five‑layer autonomous orbital intelligence architecture for CubeSat‑class spacecraft. We prove that Glass Box verification overhead is O(N_c) in the number of constitutional rules, independent of model size or spacecraft state dimension. We present a complete formal specification of the constitutional constraint grammar, seven LTL safety invariants verified by Z3 and NuSMV model checking, and a detailed worked example of Glass Box intercepting an unsafe inference request at eclipse‑entry under degraded battery state. As orbital computing scales toward data center infrastructure, runtime constitutional verification is no longer a research novelty ‑‑ it is mission‑critical safety infrastructure that every autonomous orbital platform will eventually require.

Authors:Abdallah Alalem B. Albustami, Ahmad F. Taha, Taylor T. Johnson
Title: Power System CBFs
Abstract:
Control barrier functions (CBFs) have become a standard tool in safety critical‑control systems. CBFs convert state constraints into real time control conditions that certify forward invariance (meaning that once the system starts in a safe region, it remains there for all future times) and minimally modify a nominal controller only when safety is at risk. In power systems, CBF based methods have been proposed for frequency and voltage safety, but they largely remain disconnected from three key features that are central to power system operation: differential algebraic equation (DAE) models that capture network power flow constraints, safety specifications involving algebraic variables such as bus voltages, and formal verification of the resulting closed loop system. This paper closes this gap by developing a CBF framework for power system DAE models that supports safety constraints on both dynamic and algebraic variables. The framework provides real time safety filtering through an optimization layer that wraps around an existing controller and minimally modifies its command to enforce safety. In addition, it provides formal verification (i.e., a mathematical guarantee that all admissible trajectories satisfy the prescribed safety constraints) through an offline reachability based certificate of safe operation. The result is a unified filter and verify methodology for enforcing and certifying frequency and voltage safety in power systems while preserving the DAE structure of the underlying model.

Authors:Mircea Lazar
Title: Koopman operator learning for predictive control via Khatri-Rao kernel regression
Abstract:
This paper develops a data‑driven realization of the generalized Koopman operator (GeKo), in which states and inputs are lifted independently and the dynamics are expressed as a tensor bilinear system. The first contribution is a time‑sequenced multi‑step Khatri‑Rao kernel regression formulation that exposes the operator to evolved snapshots along trajectories rather than only single one‑step pairs, which reduces compounded prediction error. Secondly, we develop a kernel‑ and input‑agnostic structured SVD reduction that compresses the lifted state and input spaces while preserving the Khatri‑Rao realization. We instantiate the framework with random Fourier features and describe a complete predictive‑control pipeline, including a multi‑step roll‑out diagnostic that guides the choice of MPC horizon. The framework is validated on the chaotic Lorenz system, where the learned reduced‑order GeKo model stabilizes an unstable equilibrium from a range of initial conditions.

Authors:Tingxiang Ji, Hansika Weerasena, Demitry Farfurnik, Jianqing Liu
Title: Towards Efficient Synthesis of Quantum Graph States by Fusing Graph Motifs
Abstract:
Photonic graph states with advanced topologies can enable measurement‑based quantum computing, distributed quantum sensing, and quantum interconnects. However, the efficient generation of photonic graph states is limited by the probabilistic nature of photonic entangling operations and the exponential dependence of generation rate on resource cost. In this work, we study photonic graph state synthesis as a cost‑aware decomposition problem, exploiting local Clifford (LC) equivalence to identify more synthesis‑friendly representations of the target graph state before decomposition. Specifically, we propose Cost‑aware Fusion‑based Decomposition (CFD), a three‑stage heuristic framework that decomposes a target graph state into ring, star, and linear motifs, and assembles them via Type‑I fusion operations to minimize fusion overhead and physical‑qubit consumption. We further show that selecting the LC‑equivalent graph state with the minimum number of edges provides a highly effective proxy for near‑optimal synthesis: in many cases it matches the best generation rate observed within the LC equivalence class under CFD, and in most remaining cases it remains close to it. Numerical evaluations on graph state orbit data and 2D and 3D lattice graph states demonstrate that CFD achieves up to 84.6% reduction in resource overhead compared to baseline constructions, and yields improvements in photonic generation rate spanning multiple orders of magnitude. These results suggest that combining structure‑aware motif decomposition with LC equivalence is a practical and scalable strategy for photonic graph state synthesis.

Authors:Rami Rasheedi, Salma Abdelzaher, Inna Partin-Vaisband
Title: Package-Embedded Coupled Inductor Arrays for High-Performance Computing Power Delivery
Abstract:
A novel power delivery framework, comprising a package‑embedded inductor topology and an inductance‑island methodology, is introduced to maximize both inductance and current densities in vertical power delivery (VPD). The framework leverages multiple multi‑phase converters, a common strategy in high‑performance computing systems, to enhance efficiency and scalability. The proposed topology employs an array of tightly coupled spiral square inductors sharing a common magnetic rod, serving multiple converters operating in the same conversion phase. The array is optimized to maximize coupling and minimize conversion losses, achieving superior inductance and current densities of 250 nH/mm^2 and 10 A/mm^2, respectively. At the system level, the inductance‑island methodology partitions the power delivery network into multiple islands, each dedicated to a converter phase and supplying a portion of the load current, thereby enabling scalable and efficient distribution. To validate the framework, the inductor array is designed and simulated in ANSYS Maxwell 3D and Mechanical, exhibiting an average quality factor of 23.6 and efficiency of 97.4% at 2 A load current, 6 V input, and 10 MHz switching frequency. The inductor array netlist is extracted from ANSYS and co‑designed in Cadence Virtuoso with a distributed dual‑phase power conversion system, ensuring joint optimization of passive and active components. The co‑designed converter achieves a significant efficiency gain of 5.65% on average and up to 11.04% at 40 A load over a similar converter with uncoupled inductors, demonstrating the practical benefits of the approach.

Authors:Shivam Bajpai, Abhinav Sinha
Title: Terminal Time and Angle-Constrained Nonlinear Intercept Guidance
Abstract:
This paper considers the problem of simultaneously controlling an interceptor's impact time and impact angle using its lateral acceleration as the sole control input. With a single control input, the nonlinear engagement kinematics is inherently underactuated, which complicates guidance law synthesis. To overcome this challenge, a hierarchical sliding mode‑based guidance law is developed to concurrently regulate the two terminal constraints. The proposed architecture consists of a two‑layer sliding manifold. The first layer comprises two sub‑sliding surfaces corresponding to the impact time and impact angle error dynamics, respectively, while the second layer introduces a composite sliding manifold that combines the two individual sub‑surfaces. Then, a variable‑gain adaptive guidance law is designed to ensure time and angle‑constrained interception against a stationary target, which is further extended to intercept a constant velocity target. Simulations are conducted for various engagement scenarios to attest to the efficacy of the proposed approach.

Authors:Senol Gulgonul
Title: PI and PID Tuning of Plants up to Third Order for a Monotonic Minimum Settling Time Solution
Abstract:
A unified, closed‑form analytical PI/PID tuning method is presented for all‑pole plants up to third order that yields a strictly monotonic (zero‑overshoot) step response with minimum settling time. The design target is the binomial closed loop p^n/(s+p)^n, which is monotonic with robustness depending only on the order n. Because adding a left‑half‑plane zero to a fixed pole pattern only slows the response, the minimum‑settling solution requires the controller zeros to be cancelled, which forces the controller numerator to divide the plant denominator. Carrying this principle through shows that an exact, real‑gained solution exists for any stable plant precisely up to second order with a PI controller and third order with a PID controller; the residual binomial factor acquires a complex pair beyond that, which a generic plant does not contain. Explicit gains are derived for first‑order plants (PI), second‑order plants with real and complex poles (PI and PID), and third‑order plants with three real poles and with one real pole plus a complex pair (PID). The second‑order PI case is treated in full as the lowest‑order instance. Monotonicity guarantees Mt = 1, hence Ms less then 2, phase margin above 60 degree, and gain margin above 6 dB, tightening to universal constants for the binomial family. Numerical verification confirms the results.

Authors:Liudong Chen, Bolun Xu
Title: Fairness as an Investment: Dynamic Participation and Long-Run Profit in Virtual Power Plants
Abstract:
We show that incorporating fairness constraints into virtual power plant (VPP) operations can incentivize consumer participation and thus improve the aggregator's long‑run profitability. VPPs rely on sustained participation from heterogeneous consumers to provide a variety of grid services whose timing and frequency are often uncertain. As a result, consumers' willingness and ability to provide flexibility evolve over time, creating a dynamic link between past participation and future resource availability. We develop a dynamic aggregation framework to study how fairness in service allocation affects future participation and long‑run profitability. By linking current dispatch decisions to future resource availability, we show that fairer allocations can strengthen consumer engagement, expand aggregate availability, and create additional value during high‑price and high‑demand events. To balance fairness and operational efficiency, we introduce a slack‑augmented allocation mechanism that preserves most of the participation benefits from fairness while avoiding unnecessary reductions in service procurement. We derive conditions under which the resulting availability gains outweigh the short‑run cost of redistribution and validate the approach using real‑world consumer behavior and electricity market data from Norway.

Authors:Evgenii Vinogradov, Debashisha Mishra, Mariam Ali Askar Alobeidli, Jamal Khaled Al Ali, Ahmed Saleh Alshehhi, Jennifer Simonjan, Enrico Natalizio
Title: Corridor Design and Separation Definition in Advanced Air Mobility: Systematic Literature Review
Abstract:
Advanced Air Mobility (AAM) uses electric vertical take‑off and landing (eVTOL) vehicles to address urban congestion and emissions. However, corridor design, operation management, and separation standards remain underexamined for safe high‑density operations. This paper applies the Preferred Reporting Items for Systematic Reviews and Meta‑Analyses (PRISMA) guidelines to systematically review relevant literature from IEEE Xplore and Web of Science, focusing on publications from 2010 to 2024. A Context, Intervention, Mechanism, and Outcome (CIMO) framework guided the development of research questions. After screening 2,039 journal and conference papers, 62 articles met the inclusion criteria. The findings reveal a lack of integrated corridor design approaches, limited operational strategies, and reliance on standards originally designed for conventional aviation. A unified corridor design and separation definition frameworks and taxonomies are proposed to address these shortcomings, informing future investigations and operational frameworks for safe, efficient eVTOL operation deployment in urban settings.

Authors:Christian Manasseh, Savana Ammons
Title: A Game-Theoretic Decision Framework for Optimal Selection of Coordination Detection Methods in Multi-UAV Fleet Operations
Abstract:
Detecting coordination among unmanned aerial vehicle (UAV) fleets operating in shared airspace and identifying the route‑lead aircraft whose navigation decisions govern fleet behavior presents a fundamental speed‑‑accuracy trade‑off: fast methods enable real‑time traffic management but sacrifice detection fidelity, while accurate methods may exceed the time budget for actionable airspace deconfliction. This paper presents a game‑theoretic decision framework that resolves this trade‑off by formulating method selection as a two‑player zero‑sum game between a Monitor (selecting computational methods and parameters) and Nature (selecting the unknown traffic scenario). We construct an end‑to‑end pipeline from trajectory surveillance data through eight candidate detection algorithms, a Monte Carlo sensitivity analysis characterizing their stochastic performance, and finally a multi‑objective optimization layer that identifies Pareto‑optimal method portfolios. The minimax solution provides a robust mixed strategy with a probability distribution over methods that guarantees worst‑case performance regardless of scenario uncertainty. Experimental evaluation across 200 randomized configurations spanning 5‑‑50 aircraft demonstrates that the framework recommends distinct method portfolios depending on operational priority: Koopman Phase dominates balanced (70.6%) and speed‑priority (79.7%) profiles, while CRQA emerges as primary (47.4%) when route‑lead identification is prioritized. The framework achieves a guaranteed game value of 0.29‑‑0.53 (normalized utility) across all tested preference profiles, providing the first principled, scenario‑adaptive methodology for computational method selection in UTM fleet monitoring operations.

Authors:Hung K. Hoang, Chuyen T. Nguyen, Thang K. Nguyen, Thanh V. Pham, Anh T. Pham
Title: Secure RSMA-based Visible Light Networks under Spatial Correlation
Abstract:
This paper investigates the secrecy sum rate (SSR) of rate‑splitting multiple access (RSMA)‑based visible light communication (VLC) systems considering internal eavesdropping, where legitimate users may intercept private data intended for others. We formulate an optimization problem to maximize the SSR of the system, which is inherently non‑convex due to the complex coupling of the objective function and constraints. To this end, two different approaches based on the convex‑concave procedure (CCCP) and semidefinite relaxation (SDR) are leveraged to solve the non‑convex parameterized problem. A central focus of this work is the investigation of channel similarity (CS), which serves as a metric for quantifying spatial correlation, and its impact on SSR performance. To mitigate the performance degradation caused by high spatial correlation, we propose a channel similarity reduction (CSR) clustering strategy that proactively minimizes CS to restore the system's degrees of freedom (DoF). Numerical results are provided to demonstrate the performance of the two proposed algorithms under various levels of CS. More importantly, the findings reveal that our proposed CSR‑clustering strategy significantly outperforms existing baselines, effectively overcoming the secrecy performance ceiling caused by high spatial correlation.

Authors:Martin Schuck, Marcel P. Rath, Yufei Hua, Abhishek Goudar, SiQi Zhou, Angela P. Schoellig
Title: Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX
Abstract:
High‑quality, large‑scale synthetic data from simulations is becoming a cornerstone for pushing the capabilities of robot algorithms. While aerial robotics simulators have evolved to support specialized needs such as fidelity, differentiability, and swarms independently, a unified platform that can synthesize data across all these domains is missing. In this work, we propose Crazyflow, a simulator designed to push the limits of aerial‑robotics algorithm development, from model‑based to data‑driven methods, gradient‑based to sampling‑based approaches, and single‑agent to multi‑agent systems. Compared to existing state‑of‑the‑art drone simulators, it achieves speeds more than an order of magnitude faster for a single drone and can simulate thousands of swarms of 4000 drones each. Real‑world experiments show Crazyflow supports both analytical‑gradient‑based policy learning, achieving sub‑centimeter trajectory tracking accuracy without domain randomization, and sampling‑based obstacle avoidance at speeds exceeding half a billion steps per second. Breaking the traditional train‑then‑deploy paradigm, we show that its unprecedented speed even enables in‑flight reinforcement learning; we demonstrate this by throwing a physical drone into the air and training a recovery policy from scratch in 0.38 seconds, successfully stabilizing the drone. Crazyflow supports multiple levels of simulation abstraction, is directly compatible with all open‑source Crazyflie models, and enables rapid reconfiguration across custom drone platforms and applications by providing a light‑weight system identification pipeline. By pushing accuracy, speed, and differentiability simultaneously, Crazyflow serves as an open‑source resource for synthetic data generation, with emerging capabilities for large‑scale parallelization for online, in‑execution learning and optimization, opening the door to novel algorithm development.

Authors:Tuan A. Hoang, Phuc V. Trinh, Shinya Sugiura, Chuyen T. Nguyen, Susumu Ishihara, Thanh V. Pham
Title: Efficient Numerical Modeling of Near-Field Diffraction in ORIS-Assisted Free-Space Optical Links
Abstract:
This paper investigates near‑field propagation in optical reconfigurable intelligent surface (ORIS)‑assisted free‑space optical (FSO) communication systems. Unlike conventional far‑field scenarios, near‑field propagation involves complex diffraction effects that hinder tractable closed‑form analysis. To address this issue, a numerical framework for evaluating the optical field distribution of ORIS‑assisted FSO links is proposed. Specifically, two numerical approaches are considered: direct Riemann‑sum evaluation and a fast Fourier transform (FFT)‑based method. Although the Riemann sum approach provides accurate field estimation, it incurs extremely high computational complexity due to the fine spatial discretization of the ORIS surface required at optical wavelengths. To improve computational efficiency, the optical‑field calculation is reformulated as a convolution in the spatial‑frequency domain, enabling efficient FFT‑based propagation analysis. Simulation results demonstrate that the proposed FFT‑based method achieves accuracy comparable to that of the Riemann‑sum approach while significantly reducing computational complexity.

Authors:Yifan Xie, Julian Berberich, Frank Allgöwer
Title: Data-Driven Min-Max MPC with Integral Quadratic Constraints
Abstract:
Data‑driven control of nonlinear systems with rigorous guarantees is a challenging control problem. Integral quadratic constraints (IQCs) provide a powerful framework for modeling nonlinearities. This paper presents a data‑driven min‑max model predictive control (MPC) synthesis method for unknown systems subject to (nonlinear) uncertainties using the IQC framework. The unknown system matrices are characterized by a set‑membership representation using the input‑state data and the knowledge of the IQCs. We derive two semidefinite programs (SDPs) that minimize an upper bound on the worst‑case cost over all possible system dynamics and uncertainties. By iteratively solving these SDPs, the proposed state‑feedback control law is obtained. We further prove that the resulting closed‑loop system is exponentially stable and satisfies the input and state constraints. A numerical example demonstrates the validity of the proposed method.

Authors:Ranulfo Bezerra, Satoshi Tadokoro, Kazunori Ohno
Title: AI-IoT-Robotics Integration: Survey of Frameworks, Emerging Trends, and the Path Toward Connected Robotics
Abstract:
The convergence of Artificial Intelligence, the Internet of Things, and Robotics is no longer a futuristic vision; it is rapidly becoming the foundation of real‑time, intelligent, and context‑aware systems. AI enables perception and reasoning, IoT provides scalable sensing and communication, and robotics delivers embodied actuation. Despite significant progress in pairwise combinations such as AIoT and the Internet of Robotic Things (IoRT), there remains a lack of unified design frameworks that fully integrate all three. This survey synthesizes the state‑of‑the‑art across these domains, emphasizing the emerging role of Small Language Models (SLMs) at the edge and Large Language Models (LLMs) in the cloud for distributed cognition and autonomous decision‑making. We propose a modular system architecture that aligns with these trends, analyze persistent gaps in interoperability and feedback control, and classify existing work by integration depth. Our review highlights how hybrid SLM‑LLM systems, when coupled with IoT infrastructure and robotic agents, can address challenges in real‑time adaptation, scalability, and reliability. This work offers a conceptual and technical roadmap for designing next‑generation AI‑IoT‑Robotic ecosystems that are modular, interpretable, and capable of learning within dynamic environments, paving the way for the emerging paradigm of Connected Robotics and Physical AI.

Authors:Amir Sajadi, Muhy Eddin Za'ter, Maria Vabson, Kyri Baker, Bri-Mathias Hodge
Title: Power Grid Infrastructure for AI Data Centers
Abstract:
This article addresses recent advances in artificial intelligence, which have set off an astounding race among technology frontiers to build large data centers. It provides insights into impacts of large data centers on the planning and operation of the power grid.

Authors:Zhong Liu, Jialin Zheng, Xiaonan Lu
Title: Lipschitz-Enforced Machine Learning Framework for Accelerating Transient Stability Analysis of Networked Grid-Interactive Inverters
Abstract:
The growing penetration of grid‑connected inverters renders Transient Stability Analysis (TSA) increasingly challenging in modern power systems. Existing TSA methodologies encounter an intrinsic trade‑off between accuracy and scalability when dealing with these networked inverter‑based resources (IBRs). To bridge this gap, this paper proposes a Lipschitz‑enforced machine learning framework that leverages Lipschitz continuity to restructure the transient stability certification mechanism. By replacing computationally intensive verification procedures with a deterministic and efficient algebraic check, the proposed method enables rigorous stability guarantees for complex multi‑inverter systems, effectively bypassing the complexity limits of traditional analytical approximations. Validated on networked Grid‑Forming (GFM) inverter systems, the proposed framework accelerates the training process by over 5 times compared to existing methods. Notably, the proposed framework substantially outperforms traditional transient stability analysis approaches (e.g., Linear Matrix Inequality and Sum‑of‑Squares methods) by capturing up to 30% larger Regions of Attraction (ROA), effectively shattering the conservativeness bottleneck that has long constrained traditional analytical tools. This advancement provides a scalable and theoretically rigorous solution for the TSA of networked IBRs in modern power grids.

Authors:Shilin You, Gael Luna, Tyler H. Summers
Title: A Framework for Motion Planning with Temporal Logic Precedence Specifications via Augmented Graphs of Convex Sets
Abstract:
We present a framework for planning trajectories that avoid obstacles and satisfy logical precedence constraints expressed with a fragment of signal temporal logic (STL). Our approach models environments containing obstacles, keys, and doors, where collecting a key unlocks its associated door and potentially opens shorter paths to a goal. Based on an exact convex partitioning of the free space that encodes connectivity among convex free space, key, and door regions, we construct an augmented graph of convex sets (GCS) whose layered structure exactly encodes the key‑door precedence logic. A shortest path in the augmented GCS simultaneously selects an optimal key collection sequence and computes an optimal continuous trajectory, providing an exact solution up to a finite Bézier curve parameterization.

Authors:Eugene Lavretsky
Title: Control of Flight
Abstract:
The main focus of this talk is to present mathematical fundamentals, state‑of‑the‑art, technical challenges and open problems in control of flight for atmospheric vehicles, such as aircraft and other aerial platforms. Reduced order modeling and flight simulation key features for control applications will be discussed. The emphasis is on the theoretical and engineering aspects of creating and transitioning to practice guidance and flight control systems with guarantees of closed‑loop stability, robustness and performance.

Authors:Hao Li
Title: State-Space Modelling and Analysis
Abstract:
Control science is a core representative of the third industrial revolution and is so important to modern civilization. Control systems are the main subject of control science and may involve many aspects of consideration, such as hardware consideration, software consideration, operation consideration, maintenance consideration, economy consideration, society consideration. However, besides all such aspects of consideration, one aspect that is most essential to the control system is methodology consideration in mathematical sense, knowledge on which is what we refer to as control theory. Besides its importance from the mathematical perspective, control theory is even more charming as it is deeply rooted in practical applications. Charms of control theory consist in both know‑why and know‑how and it is the fusion of control theory and practical applications that highlights such charms. Control theory for practical applications, especially when somewhat with so‑called ``advanced'' flavour, involves several fundamental aspects. This article introduces the State‑Space Modelling and Analysis aspect of Advanced Control Theory for Practical Applications [1,2].

Authors:Deepanjhan Das, Shankar Narasimhan
Title: Recursive Identification of EIV-ARX Models for Time Varying SISO Processes
Abstract:
This paper proposes a recursive algorithm, rARX‑DIPCA, for identifying errors‑in‑variables autoregressive models with exogenous input (EIV‑ARX), for tracking time‑varying SISO processes. Building on a recently developed recursive iterative PCA method, the proposed algorithm recursively updates model parameters and noise variances as new measurements arrive, without storing historical data beyond a specified lag window. The method enables real‑time adaptation to sensor degradation, and changes in model coefficients. The algorithm simultaneously identifies process order, time delay, and noise variances while maintaining computational efficiency through online covariance updates. Simulation studies on benchmark systems demonstrate effective tracking performance and practical applicability.

Authors:Hao Li
Title: Handling Control System Optimality
Abstract:
Control science is a core representative of the third industrial revolution and is so important to modern civilization. Control systems are the main subject of control science and may involve many aspects of consideration, such as hardware consideration, software consideration, operation consideration, maintenance consideration, economy consideration, society consideration. However, besides all such aspects of consideration, one aspect that is most essential to the control system is methodology consideration in mathematical sense, knowledge on which is what we refer to as control theory. Besides its importance from the mathematical perspective, control theory is even more charming as it is deeply rooted in practical applications. Charms of control theory consist in both know‑why and know‑how and it is the fusion of control theory and practical applications that highlights such charms. Control theory for practical applications, especially when somewhat with so‑called ``advanced'' flavour, involves several fundamental aspects. This article introduces the Handling Control System Optimality aspect of Advanced Control Theory for Practical Applications [1,2].

Authors:Subir Majumder
Title: Hashprice moderates the electricity demand response of Bitcoin miners
Abstract:
Large controllable loads, such as Bitcoin‑mining facilities, are increasingly viewed as valuable sources of power‑system flexibility, yet the conditions under which this flexibility is realized remain poorly understood. We examine this issue in the Texas power market, where large loads face both wholesale electricity prices and incentives created by coincident‑peak‑based transmission charges. We find that mining load declines as costs rise across both channels, and this response is moderated by hashprice, a measure of expected revenue for Bitcoin miners. When hashprice is higher, mining load is less responsive to electricity‑sector costs. This pattern is consistent with aggregate mining load arising from heterogeneous devices operated around distinct breakeven points. The wholesale‑price response illustrates this mechanism most clearly. Mining load remains largely online at low electricity prices but begins to decline once prices exceed an implied curtailment threshold, and higher hashprice shifts this threshold to higher wholesale prices. Bitcoin miners therefore respond to electricity‑sector costs, but the available flexibility varies with revenue conditions in the crypto‑financial sector. Treating such loads as stable demand‑response resources may overstate their available flexibility.

Authors:Thien Tran, Jonathan Kua, Thuong Hoang, Minh Tran, Honghao Lyu, Jiong Jin
Title: Edge-Based QoS-Aware Adaptive Task Placement: A Closed-Loop Control in Multi-Robot Systems
Abstract:
Multi‑robot systems (MRS) increasingly offload compute‑intensive perception tasks to edge nodes to meet strict time‑sensitive Quality‑of‑Service (QoS) constraints. However, static task orchestration on a shared edge node can severely degrade QoS due to network latency, jitter, and edge‑resource contention. We present a pilot edge‑centric MRS testbed using Raspberry Pi nodes to evaluate a camera‑to‑manipulator pipeline under three modes: local execution, static offloading, and a QoS‑aware Adaptive Task Placement (ATP) controller. ATP scores candidate placements using a multi‑metric cost (normalized latency, CPU utilization, and switching overhead) over two‑second control windows. The closed‑loop visual servoing testbed is instrumented with sub‑millisecond clock synchronization, network emulation, and detailed monitoring of multiple metrics across nodes to capture realistic jitter. Experimental results under compute‑stress and network‑fault scenarios show that static edge offloading reduces on‑board CPU load but amplifies tail latency and deadline misses. In contrast, the QoS‑aware ATP controller, by switching task placement based on measured latency and utilization thresholds, consistently lowers deadline violations and tail latency. Overall, the results position ATP as a practical edge‑side control primitive for MRS and concrete design guidelines for Cloud‑Edge Robotics deployments within the broader cloud‑fog automation, while motivating QoS‑aware multi‑objective workload orchestration for industrial cyber‑physical systems.

Authors:Akul Swami, Dnyaneshwar Sonawane
Title: Wire-Level Interrupt-to-Decision Latency of On-Sensor MLC versus Host Inference on the NVIDIA Jetson Orin Nano: A Pre-Registered Measurement Study
Abstract:
The Machine Learning Core (MLC) embedded in the STMicroelectronics LSM6DSOX IMU is widely cited as a low‑latency alternative to host‑side inference, yet wire‑level decision‑delivery latency is rarely measured. Using a Saleae Logic Pro 8 logic analyzer on an NVIDIA Jetson Orin Nano, we measured interrupt‑to‑decision latency (sensor INT1 edge to host decision GPIO) for three pipelines (a host‑side decision‑tree classifier, the standard MLC bank‑switch read protocol, and an MLC binary‑fast variant) under idle, I2C bus contention, and CPU stress. The protocol was pre‑registered with 12 externally‑timestamped Zenodo amendments before confirmatory data collection (4,770 of 4,860 trials included, 98.15%, across nine cells). The host pipeline exhibits lower median latency than the MLC pipeline under all conditions: 321.7 vs 681.5 us at idle (2.1x faster) and 574.5 vs 1,325.4 us under I2C contention (2.3x faster). The three‑transaction I2C read protocol, not the silicon's classification, is the dominant latency contributor. We additionally characterize a reproducible 706.5 ms MLC decision cadence that bounds full stimulus‑to‑decision latency. Code, data, and pre‑registration: github.com/akulswami/sensor‑mlc‑latency.

Authors:Haofan Cao, Zhaoyang Li, Zhichao You, Liang Guo, Tianrui Li
Title: PaCo-VLA: Passivity-Shielded Compliance Prior for Contact-Rich Vision-Language-Action Manipulation
Abstract:
Contact‑rich manipulation demands both high‑level semantic reasoning and the safe regulation of high‑frequency contact dynamics. While Vision‑Language‑Action (VLA) models provide unprecedented semantic generalization, their low‑rate outputs lack the reliability required for direct plant authority in force‑sensitive tasks. To bridge this semantic‑to‑control gap, we introduce PaCo‑VLA, a passivity‑shielded compliance prior that recasts the VLA interface. Rather than trusting VLAs with direct motor commands, PaCo‑VLA treats network outputs as task‑level compliance proposals: semantic bindings, task stages, and admittance schedules. A high‑frequency, proposal‑independent passivity shield governs these proposals through energy‑tank accounting and boundary checks, preventing invalid, stale, or unverified model predictions from bypassing low‑level contact physics. This decoupled architecture also enables causal evaluation, isolating semantic contributions from geometric shortcuts. Extensive simulated and real‑world connector‑insertion experiments demonstrate that PaCo‑VLA achieves superior precision over unshielded VLA baselines, sustaining zero passivity violations even under adversarial compliance shifts. This framework establishes a provably sampled‑passive runtime contract at the admittance port and provides a runtime interface for deploying foundation models in contact‑rich domains.

Authors:Shangqing Cao, Rishi Kumar Srinivasan, Raja Sengupta, Mark Hansen
Title: Like Uber or Like Buses? Economic Feasibility Analysis of UAM for Airport Access
Abstract:
The airport access use case is a promising early‑stage application for Urban Air Mobility (UAM). Understanding the operational paradigm of UAM at airports is crucial for making equitable and effective regulatory and management decisions. A central open question is whether UAM will be integrated into the airport transportation network as a conventional scheduled transit service, such as subways and rail, or as a Transportation Network Company (TNC) characterized by dynamic supply‑demand matching. In this paper, we propose a two‑stage framework for conducting an economic feasibility analysis of UAM networks. In the first stage, we introduce a joint‑supply‑demand variable pricing problem to evaluate the impact of dynamic pricing on UAM operations. This model uses a binary logit formulation to capture the trade‑off between travel time advantages and fare levels. In the second stage, the determined demand is used as input for the Electric Urban Air Mobility Vehicle Routing Problem with Non‑linear Charging Time (eUAMVRP‑NL), which optimizes fleet scheduling and charging decisions to derive operating revenue and cost estimates. We apply this framework to a case study of the Los Angeles International Airport (LAX) access market with an eight‑spoke vertiport network. Our results indicate that UAM operations benefit significantly from TNC‑like management; a variable pricing policy can increase operating profits by more than 100% compared to fixed‑pricing schemes. Furthermore, we identify economies of stage length in longer UAM flights.

Authors:Tao Chen, Hongju Wang, Wenfeng Hu
Title: Traffic Characterization of Event-Triggered Control Systems: A Geometric-Algebraic Perspective
Abstract:
This paper characterizes the triggering behaviors of event‑triggered control systems from a geometric‑algebraic perspective. We first model the feasibility of inter‑event time transition relations as a nonconvex quadratic constraint satisfaction problem and reformulate it as an equivalent linear cone problem, which provides a clearer geometric description of the feasible region, making subsequent analysis more reliable. Building on this formulation, we establish necessary and sufficient conditions that rigorously determine whether a given transition relation is feasible. Based on this condition, we propose an algorithm that computes the set of all feasible transition relations. Numerical simulations further demonstrate how the feasibility of specific transitions evolves with the control parameter σ, with visualizations of the feasible state space offering intuitive insight into parameter selection and system design.

Authors:Song-Kyoo Kim
Title: Stochastic Analysis of Cybersecurity Defense Strategies Under Single Attack Scenario
Abstract:
This research presents a novel stochastic framework for proactive cybersecurity defense timing under a single attack scenario. The approach models the defense process as a continuous observation mechanism in which the defense instant and the subsequent observation slot follow independent exponential distributions. Laplace‑Carson transforms combined with first‑excess theory yield the joint detection function that brackets the attack moment. Marginalization under Markovian Poisson arrivals then produces the probability density of the defense moment and conditional expectations of pre‑attack and post‑attack observation times. These closed‑form results enable quantitative assessment of defense timing sensitivity to threat intensity and support precise calibration of observation parameters for low‑latency proactive measures. Major contributions include the explicit derivation of marginal distributions and expected values, visualization of defense moment density, and the bridging of stochastic duel methodology with practical cybersecurity applications.

Authors:Danyal Saqib, Francisco Andrade Chavez, Marie Charbonneau
Title: Adaptive PD Gains for Energy-Conscious Control in Physical Human-Robot Interaction
Abstract:
Compliant force or torque control are approaches often investigated to achieve safe physical human‑robot interaction (pHRI). However, these approaches have limitations. Force control requires a robot to be equipped with external force sensors to track the amplitude and direction of applied forces. Torque control requires torque sensing or estimation in each joint. As this is not available on every robot, energy‑based approaches offer a promising alternative. Such approaches aim to achieve safe pHRI by limiting the mechanical energy of the robot. Current schemes leveraging an energy‑based approach tend to have a complex implementation, and some may require further stability verification. We hence propose an adaptive proportional‑derivative (PD) controller that can limit a robot's energy under any given limit to achieve safe pHRI. The proposed controller can limit both the kinetic and potential energy of a robot, and the behaviour of the controller gains can be shaped using various parameters, defining precisely the cutoff limit and sharpness. We construct a stability proof for the controller and define a condition to ensure the controller's stability. The proposed controller's behaviour and compliance are tested on the TALOS robot from PAL Robotics both in simulation and on hardware, verifying the expected compliant and energy‑limiting behaviour of the controller.

Authors:Alexey Ovchinnikov, Pedro Soto
Title: Verifying global identifiability of parametric linear ODE models is NP-hard
Abstract:
Global parameter identifiability is a property of a parametric ODE model to recover the parameter values uniquely from the input‑output data. Not all parametric ODE models have this property, and checking for parameter identifiability is a prerequisite to perform numerical parameter estimation. There are many algorithms and software packages for global parameter identifiability, and frequently the runtime is large. However, the computational complexity for this problem has not been analyzed yet, though there are complexity results for local (finitely many values fit the data) parameter identifiability. In this paper, we estimate the complexity of checking global parameter identifiability over real fields for ODE models that depend linearly on the state variables and rationally on the parameters. In particular, we prove that it is equivalent to the injectivity problem.

Authors:Magnus Malthe Sigsgaard Nielsen, Nicklas Nikolaj Grønvall, Xiaofeng Xiong, Saravana Prashanth Murali Babu
Title: SoFiE: Soft Finger Exoskeleton for Intelligent Grasping
Abstract:
Soft wearable robotic systems have emerged as a promising solution for assisting individuals with reduced hand function. This paper presents SoFiE, a modular soft finger exoskeleton designed to assist index‑finger flexion during grasping tasks. The proposed system is primarily fabricated using 3D‑printed flexible materials, enabling a lightweight, low‑profile, and modular design. Actuation is achieved through a tendon‑driven mechanism powered by a compact DC motor, while passive extension is provided by a compliant conductive spring. This element, termed StretchSense, also functions as a proprioceptive sensor by exhibiting resistance changes under deformation. Furthermore, a novel tactile sensing approach, MagSense, is introduced, using a magnet and magnetometer pair embedded in a soft fingertip structure to estimate contact force and object compliance. The system is fully untethered and controlled by an embedded microcontroller. In addition, actuator‑level sensing through motor encoder feedback enables estimation of the system state, providing a foundation for safe and adaptive control strategies. Experimental validation demonstrates the capability of the system to provide reliable pose estimation, distinguish between materials with different stiffness, and generate distinct sensor signatures across different grasping tasks. This paper details the design, fabrication, and sensing concepts of the proposed exoskeleton as a proof of concept toward modular, soft, and assistive wearable robotics.

Authors:Nipon Sarmah, Tim Finnigan, Mark Taylor, Tom Vinestock, Ethan Errington, Miao Guo
Title: Valorisation of Fermentation Side-Stream for Waste-to-Mycoprotein: Nutrient Composition, Metabolic Insights and Process Optimisation
Abstract:
Fermentation‑derived side streams represent an underutilised resource for sustainable protein production. This study investigates the potential of centrate from industrial Fusarium venenatum fermentation as a nutrient source for fungal biomass generation. Following compositional characterisation, a synthetic centrate medium was formulated and evaluated using a Box‑Behnken design combined with response surface methodology. Across 46 experimental runs, cell dry weight (CDW) ranged from 0.22 to 3.87 g per liter, demonstrating a strong dependence on nutrient composition. Ammonia and glucose were identified as the dominant factors influencing biomass production, with significant nonlinear effects. The model predicted a maximum CDW of 4.17 g per liter under optimised conditions, which was experimentally validated at 3.99 g per liter. Carbon conversion efficiency reached up to 29.02%, indicating effective substrate utilisation. These findings demonstrate that fermentation‑derived centrate can support substantial fungal growth, while highlighting its potential to enhance nutrient recovery and influence the biochemical composition of sustainable mycoprotein.

Authors:Jun Wen Law, Dharik S. Mallapragada
Title: Clustering-enhanced adaptive Benders decomposition for energy systems planning optimization
Abstract:
High‑resolution energy system capacity expansion models (CEMs) for energy transition planning often result in large‑scale mixed‑integer linear programming (MILP) formulations. Benders decomposition (BD) offers a scalable solution approach by iteratively solving a master problem (MP) for investment decisions and multiple subproblems (SPs) for operational decisions. However, accumulated Benders cuts generated by the SPs can make MP solution a major computational bottleneck. Incomplete SP parallelization can also introduce further bottlenecks when SPs exceed available CPUs. We develop clustering‑enhanced BD methods to address these challenges, by using clustering to group similar SPs for: a) aggregated Benders cut construction and b) identification of representative SPs to be solved most frequently. For grouped‑cuts, we examine two adaptive formulations based on dual variables and a fixed‑grouping formulation based on exogenous time‑series inputs. We evaluate these methods in an electricity‑sector CEM across varying system sizes, temporal SP lengths, inter‑SP coupling strengths represented by CO2 policy, computational resources, and stochastic settings. Relative to a benchmark regularized multi‑cut formulation, adaptive grouped cuts outperform fixed grouping and provide substantial benefits under weak inter‑temporal coupling. The largest gains occur in larger systems with shorter SP horizons, where the MP accounts for a greater share of runtime. Their effectiveness declines under strong inter‑temporal coupling, such as annual CO2 emissions limits, where the benchmark multi‑cut performs best. The representative‑SP method outperforms the benchmark under limited parallelization when SP solution dominates runtime. Overall, the preferred BD strategy depends on inter‑SP coupling strength and whether computational burden lies in the MP or the SPs.

Authors:Theo Guegan, Dexter Wen Jie Teo
Title: Behavior Cloning of MPC for 3-DOF Robotic Manipulators
Abstract:
While Model Predictive Control (MPC) provides strong stability and robustness, it imposes a significant computational burden on real‑time systems. This paper investigates the application of Behavior Cloning to approximate MPC policies for the real‑time control of a 3‑degree‑of‑freedom robotic manipulator. We present a baseline controller combining Inverse Kinematics with MPC and evaluate neural network architectures, ranging from classical regression algorithms to deep learning models including Deep MLPs and RNNs, to derive computationally efficient surrogate policies. We analyze generalization capabilities, stability considerations, and the trade‑offs inherent in different architectural choices. Our empirical study employs both online and offline evaluations to assess performance regarding accuracy, computational efficiency, and fidelity to the original MPC policy. Our results demonstrate that Behavior Cloning can effectively reduce the computational burden of MPC policies for 3‑DOF robotic manipulators, achieving a 3x reduction in inference latency with a 84.98% success rate under relaxed tolerances. Notably, we find that static architectures outperform temporal variants, confirming the sufficiency of instantaneous state observations for this task. However, we observe a precision gap under strict tolerances, which suggest that while Behavior Cloning captures the global optimal trajectory, further research is needed to minimize terminal steady‑state error.

Authors:David Mullett
Title: Benchmarking Recursive-Collapse Warning Claims Under Matched False-Positive Control
Abstract:
Recursive systems can enter collapse‑like regimes ‑‑ self‑reinforcing amplification, persistent recursion, and narrowing diversity that mask accelerating internal degradation ‑‑ before overt failure becomes visible. We introduce Loopzero, a claim‑bounded benchmark framework for testing whether recursive failures follow a directional telemetry pattern: rising gain (G), recursive persistence (p), and declining diversity (δ). The claim boundary is specified in Lean; the Lean artifact does not verify real telemetry, benchmark validity, or detector performance. We evaluate the bridge on two frozen public‑artifact benchmarks: a segmented public‑markets benchmark (Volmageddon 2018, COVID MWCB 2020) and a MovieLens‑25M offline deterministic recommender replay. Detectors are evaluated under a locked equal‑false‑positive contract (FP \in [0.03, 0.07], pre‑registered) so all configurations face the same alert budget. Neither tested standard comparators nor Loopzero's pre‑registered quantile detector achieved an accepted operating point. Directional witness alignment held on both canonical benchmarks, with adjacent‑horizon and row‑level limitations disclosed. Digitized Shumailov et al. (2024) LLM training‑loop trajectories are directionally consistent with the pattern; matched‑FP evaluation in that domain is deferred. The contribution is a reproducible, falsifiable benchmark framework for evaluating recursive‑collapse warning claims under an explicit alert‑budget contract ‑‑ non‑acceptance reported as a first‑class scientific outcome.

Authors:Jiarui Wang, Sina Sharifi, Mahyar Fazlyab
Title: Generalized Model Predictive Path Integral Control as Expectation--Maximization
Abstract:
Model Predictive Path Integral (MPPI) control is a powerful sampling‑based method for solving stochastic optimal control problems and has enabled real‑time control in complex robotic systems. Despite its empirical success, its theoretical understanding remains limited. In this work, we show that MPPI can be interpreted as a special case of the Expectation‑Maximization (EM) algorithm applied to a probabilistic inference formulation of optimal control. This perspective leads to a generalized EM‑MPPI framework that extends MPPI beyond the commonly used Gaussian parameterization. We analyze the convergence behavior of this algorithm and characterize the local convergence rate in terms of the covariance of the posterior trajectory distribution and the exploration distribution. For exponential‑family distributions, we establish a sufficient increase property of the log‑likelihood when the log‑partition function is strongly convex. Specializing the analysis to Gaussian MPPI yields explicit global and local convergence characterizations. The code for the experiments will be available upon acceptance.

Authors:Sean Reiter, Steffen W. R. Werner
Title: Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data
Abstract:
Data‑driven reduced‑order modeling is an essential component in the computer‑aided design of control systems. In this work, we present a novel symmetric Hermite formulation of the quadrature‑based balanced truncation algorithm that constructs linear reduced‑order models from evaluations of the full‑order system's transfer function and its derivative. Significantly, the Hermite formulation preserves desirable qualitative properties of the system used to generate the data, such as state‑space Hermiticity and, consequently, asymptotic stability.

Authors:Amirsaeid Safari, Jesse B. Hoagg
Title: Predicted-Flow Control Barrier Functions for Real-Time Safe Optimal Control
Abstract:
Control barrier functions (CBFs) provide real‑time safety guarantees through pointwise conditions on the state. However, synthesizing a valid CBF is difficult and the resulting controllers are myopic. To address myopia, this article introduces predicted‑flow control barrier functions (P‑CBFs), which generalize the CBF from a function of the current state to a functional of a predicted flow under a parametrized control plan over a finite prediction horizon. For safety, a P‑CBF can certify that the predicted flow is in a safe set over the entire prediction horizon. However, candidate P‑CBFs suffer from the same challenge as candidate CBFs, namely, control constraints make it difficult to guarantee that the P‑CBF is valid. This article resolves this challenge by introducing a terminal candidate P‑CBF requiring that the predicted flow end in a backup safe set at the terminal time, and a planning‑time shift that modulates the prediction horizon, providing an additional degree of freedom to ensure feasibility. The real‑time control and the evolution of the control‑plan parameter and planning‑time shift are determined jointly by a single convex optimization that is guaranteed to be feasible and renders the associated safe set forward invariant. The resulting safe optimal flow control provides a safety certificate over the entire prediction horizon and unifies finite‑horizon integral‑cost optimization with safety certification. This optimization reduces to a quadratic program (QP) if the control constraints are a convex polytope. The QP implementation, termed FlowBarrier, is validated on a nonholonomic ground robot navigating a dense environment. FlowBarrier is compared to nonlinear model predictive control and two CBF‑based safety filter methods across 100 trials, where FlowBarrier achieves the highest goal‑reaching rate, zero safety violations, and the lowest computation time.

Authors:Anthony Couthures, Athira Varma Jayakumar, Vineeth Satheeskumar Varma, Irinel-Constantin Morarescu, Samson Lasaulce, Antoine Girard
Title: Social learning community detection with nonlinear interaction
Abstract:
Conventional community detection requires centralized network data, making it unsuitable for distributed or privacy‑preserving systems. In this paper, we demonstrate that macroscopic graph partitioning can emerge purely from strictly local, privacy preserving interactions driven by social learning. By reframing clustering as a symmetry‑breaking process within nonlinear opinion dynamics, we show that exchanging saturated state dependent signal (like public actions) forces a network to naturally fracture along its sparsest cuts. We mathematically establish the spectral conditions under which dense core communities lock into stable, polarized states, robustly resisting external influence. To apply this mechanism, we propose three decentralized algorithms, leading up to the Score‑based Edge Reliability (SER) framework. By evaluating network ties across multiple independent discussion topics, SER statistically bypasses the errors of traditional greedy bisections and naturally isolates structurally ambiguous frontier nodes. Validations on the ABCD benchmark and the real‑world Ngogo chimpanzee network confirm that our fully decentralized approach matches the accuracy of globally optimized heuristics (e.g., Louvain, Leiden) up to a theoretical limit of detectable graphs.

Authors:Neil F. Johnson, Ferney J. Rodriguez, Luis Quiroga
Title: Symmetry-Protected Quantum Computing using Metamaterials
Abstract:
We propose a new architecture for practical quantum computing that combines three established principles: symmetry protection of relative‑motion qubits via the generalized Kohn theorem, control via twisted‑light orbital angular momentum, and metamaterial nanofocusing (e.g. using Weyl‑semimetal plasmonics). Crucially, the core mechanism is generic: it applies to any current or future quantum computing system involving parabolic confinement, including cold atoms, ions, and semiconductor dots.

Authors:Rintaro Watanabe, Yu Kawano
Title: Contraction Analysis of Time-Delay Systems
Abstract:
In this paper, we investigate contraction analysis for nonlinear time‑delay systems described by functional differential equations. We first extend the concept of Lyapunov‑Krasovskii functionals within the differential framework. We then show that its existence is equivalent to that of an incremental Lyapunov‑Krasovskii functional and guarantees uniform incremental exponential stability. Next, we extend the concept of Lyapunov‑Razumikhin functions within the differential framework, whose existence also ensures uniform incremental exponential stability. As an application of our results, we formulate stabilizing feedback control design for nonlinear time‑delay systems with single delays in terms of linear matrix inequalities.

Authors:Diego Da Silva, Raphael Y. de Camargo, Mayuri A. Morais, Amer Shalaby
Title: Data Architectures for AI-Ready Interoperable Public Transportation Ecosystems
Abstract:
Public transportation (PT) agencies generate vast amounts of heterogeneous data from automatic fare collection (AFC), automatic passenger counting (APC), vehicle location (AVL/CAD), schedule and real‑time feeds (GTFS/GTFS‑RT), and proprietary platforms. These datasets offer unprecedented opportunities for data‑driven planning, operations, and passenger services, but their potential is constrained by fragmentation, inconsistent update frequencies, and the lack of reproducible, interoperable pipelines. While contemporary data platform patterns and architectural styles from enterprise computing address analogous challenges in other sectors, their adaptation to the PT domain remains mostly underexplored. Transit systems present unique conditions, including the convergence of Information Technology (IT) and Operational Technology (OT), long asset lifecycles, rigorous security requirements, multi‑agency coordination requirements, and the need to operate on live systems that preclude controlled experimentation.

Authors:Hui Wu, Xiaoyang Wang, Zhong Fan
Title: Knowledge Boundary Probing and Demand-Guided Intervention for LLM-Based Power System Code Generation
Abstract:
Large language models (LLMs) are increasingly used to automate power‑system analysis, but many utilities and energy‑research labs require on‑premise serving for confidentiality, regulatory, reproducibility, and cost reasons. This makes the reliability of open‑weight models a deployment issue. We show that first‑pass failures in power‑system code generation are dominated not by reasoning alone, but by structured API‑knowledge boundary errors: hallucinated function names, misused parameters, and mishandled result tables in versioned simulation libraries. We introduce PowerCodeBench, an execution‑validated benchmark generator that pairs natural‑language operator queries with pandapower code and numerical ground truth; an L0‑L3 documentation‑driven probing procedure that measures per‑model API knowledge profiles; and a boundary‑aware intervention that combines query‑side API demand estimation with targeted proactive documentation injection and routed reactive correction. On a 2,000‑task frozen release, we evaluate ten open‑weight LLMs (1.5B‑480B parameters) and four commercial mid‑tier APIs. The intervention improves every evaluated open‑weight model of at least 7B parameters and every commercial API by 32 to 56 accuracy points. Open‑weight models in the 70B‑120B range match the commercial mid‑tier accuracy range, while Llama‑3.1‑405B and Qwen3‑Coder‑480B lead the panel. The targeted prompts preserve the full‑context accuracy ceiling while using 41% of the prompt‑token cost. The result is an accuracy‑side, deployment‑time path toward reliable on‑premise LLM assistance for grid‑analysis workflows without fine‑tuning or cloud inference.

Authors:Joonhee Lee, Hyunseung Shin, Hyunmi Kim, Pei Zhang, Jeonggil Ko
Title: On-Device Robotic Planning: Eliminating Inference Redundancy for Efficient Decision-Making
Abstract:
Reasoning‑based robotic policies using large language and vision‑language models achieve strong semantic planning capabilities but mostly suffer from a high inference latency that limits practical real‑time deployment. In this work, we observe that robotic reasoning workloads contain substantial temporal redundancy, where consecutive observations frequently produce identical actions and subgoals. Based on this insight, we present REIS, a human cognition inspired robotic decision‑making framework that minimizes unnecessary reasoning while preserving semantic adaptability. REIS combines lightweight scene gating, KV‑steered affordance routing, and deliberative reasoning to accelerate robotic control under embodied constraints. Experiments on ALFRED, and real‑world robotic tasks demonstrate that REIS significantly suppresses reasoning overhead while maintaining competitive task performance.

Authors:Oluwatosin Ogundare, Promise Ekpo, Nathanial Wiggins
Title: Ladder Logic Translation using Large Language Models in Industrial Automation
Abstract:
Ladder logic translation is an important problem in industrial automation because without it, it is difficult to switch Programmable Logic Controller (PLC) vendors. The prevailing translation problem highlights mismatched programming environments, incompatible ladder logic constructs, limitations in terms of differences in the semantic expressiveness of the vendor formalisms and integrated black‑box proprietary engineering tools which are exemplified in our example case; Rockwell to Siemens PLC code translation. This work presents a mathematical formulation of the problem, the detailed architecture of a solution which supports XML extraction, structural normalization, constrained generative function (LLM), and system integration via the TIA Portal Openness API as rigorously engineered pipeline for automated translation of Rockwell Ladder Programs to Siemens S7 ladder programs. Finally, we present results that show that the translations retain high semantic consistency across instruction categories.

Authors:Joost Commandeur, Bart De Schutter, Neil Yorke-Smith
Title: Current Practices in Electricy Demand and Charging Scheduling for On-Road Electric Fleet Operations: An Industry-Wide Review
Abstract:
The electrification of on‑road fleet logistics promises improved air quality, lower noise emissions, major climate benefits, increased energy flexibility through the use of locally generated electricity and reduced dependence on imported fuels. However, battery electric vehicles can introduce operational planning challenges not present with internal combustion engine vehicles, including heterogeneous charging speeds, exposure to volatile electricity prices, and scarcity in infrastructure. Managing these complexities requires solutions that balance cost efficiency and robustness, supported by sector coupling between transport and electricity systems. This paper reviews the current state of digital systems for operational decision‑making in electric fleet management through a grey literature analysis, drawing on practitioner‑oriented sources such as industry reports, company documentation, and technical blogs that reflect real‑world practices and developments. We identify key trends and gaps, providing insights to guide future research and development.

Authors:Arunava Naha, Subhrakanti Dey
Title: Model-free LQG Control with Chance Constraints
Abstract:
This paper studies model‑free optimal control design and its convergence properties for linear time‑invariant systems subject to probabilistic risk or chance constraints. In particular, we study a natural policy gradient (NPG)‑based actor‑critic (AC) algorithm with two timescales, using a Lagrangian primal‑dual framework to enforce the constraint. Furthermore, the risk is defined as the probability that a function of the one‑step‑ahead state exceeds a user‑specified threshold. To our knowledge, this is the first work to study the analytical convergence properties for NPG‑based AC in a chance‑constrained linear‑quadratic Gaussian (LQG) regulator setting without model knowledge. We establish the coercivity and gradient dominance properties of the Lagrangian function, which ensure linear convergence and closed‑loop stability during training for the actor. On the other hand, we analyse the convergence properties of the temporal difference (TD(0)) learning for the critic, applying stochastic approximation theory. Also, we demonstrate no duality gap in the constrained optimisation problem. Additionally, we have performed numerical analysis of the convergence properties and accuracy of the proposed method, comparing it with model‑based chance‑constrained LQR and scenario‑based MPC. Results show that our approach effectively limits risk while maintaining near‑optimal performance, without requiring full model knowledge or real‑time optimisation.

Authors:Jun-ya Gotoh, Andrew E. B. Lim, Michael Jong Kim
Title: Posterior and Likelihood Sensitivity in Bayesian Distributionally Robust Optimization
Abstract:
We introduce the notion of worst‑case posterior and worst‑case likelihood sensitivity. These measure, respectively, the sensitivity of the expected cost to worst‑case perturbations of the posterior distribution and worst‑case perturbations of the likelihood of a Bayesian model. Each defines a quantitative measure of robustness. A decision maker concerned about the sensitivity of the out‑of‑sample expected cost to deviations from her assumptions will want a decision for which both sensitivities are small. We derive posterior and likelihood sensitivities for uncertainty sets defined in terms of deviation measures. Posterior sensitivity vanishes when the posterior variance shrinks to zero, which occurs when parameter uncertainty is eliminated from learning. Parameter learning does not eliminate likelihood sensitivity. A distributionally robust formulation of a Bayesian optimization problem makes a near‑Pareto‑optimal tradeoff between performance (expected cost) and robustness (posterior and likelihood sensitivity).

Authors:Alessandro Varalda, Sergio Pequito
Title: Steering Fractional-Order Network Dynamics via Joint Parameter and State Control
Abstract:
This paper studies the control of discrete‑time linear fractional‑order networks, a flexible modeling framework for systems with long‑range memory such as power grids, biological networks, and neuronal circuits. In contrast to the common view that fractional exponents (time‑scales) are fixed parameters, we show that they can be systematically steered, together with the network coupling matrix, by appropriately designed input sequences. We first derive algebraic conditions under which the coupling matrix and the vector of fractional exponents of a given network can be reconfigured to desired values, and we characterize how truncating the infinite‑memory term impacts the resulting dynamics. Building on these results, we construct an equivalent linear representation that isolates the contribution of memory, and we introduce a fractional reachability matrix that provides explicit conditions for jointly steering both network parameters and state in a finite number of steps. To address practical implementations, we further formulate an energy‑constrained steering problem that incorporates actuator bounds and finite‑memory approximations as a quadratic program. The framework is illustrated on low‑dimensional toy examples, on larger networks with Erdos‑Renyi, Barabasi‑Albert, and Watts‑Strogatz topologies, and on a brain network model inferred from electrocorticography recordings of an epilepsy patient, where we showcase transitions between pre‑seizure and seizure configurations.

Authors:Sasinee Pruekprasert, Shinji Nakadai
Title: Safe Arrival Scheduling at Constraint Waypoints in UAM Corridors
Abstract:
This study introduces a novel Air Traffic Control (ATC) concept to support self‑separation between vehicles in Urban Air Mobility (UAM) corridors. Our proposed scheme involves sharing intended arrival schedules at Constrained Waypoints (CWPs) among UAM operators. We propose two approaches to assist the arrival scheduling at CWPs by computing the minimum arrival time gap necessary for each pair of vehicles to ensure their safety throughout the flights within the corridor. The first approach considers the minimum separation distance required by the Near Mid‑Air‑Collision (NMAC) avoidance rules, while the second one is based on the Responsibility‑Sensitive Safety (RSS) rules. We demonstrate that the NMAC‑rule‑based approach can effectively prevent collisions in normal circumstances, where the vehicles adhere to the speed limits of the corridor. However, this approach does not guarantee safety if vehicles exceed the speed limits. Conversely, while the RSS‑rule‑based approach ensures collision prevention during emergencies when vehicles exceed speed limits, it may require larger arrival time gaps under normal circumstances, which may lead to reduced traffic flow. Our results are confirmed through numerical simulations.

Authors:Nina Majer, Yannick Epple, Xin Ye, Stefan Schwab, Sören Hohmann
Title: Trajectory Planning for Non-Communicating Mobile Robots using Inverse Optimal Control
Abstract:
To enable an efficient interaction of non‑communicating mobile robots in collision avoidance scenarios, we present a novel combined trajectory planning and prediction algorithm. Inverse optimal control is used to estimate unknown goal states of all robots based on observed past trajectories. Each robot also takes the perspective of other robots in considering self‑prediction and solves a joint prediction problem using the estimated goal states. The resulting predictions are then considered for planning. Simulation results of scenarios with 2‑8 robots show that the median of the durations until all vehicles reach their goals is 9.8 % faster compared to planning with constant acceleration based estimated goal states. Moreover, the proposed approach never leads to the solver being unable to find a solution to the planning or prediction problem.

Authors:Tao Xu, Wang Xi, Jianping He
Title: Near-Optimal Mixed Strategy for Zero-Sum Linear-Quadratic Differential Games
Abstract:
Deriving analytic solutions for optimal mixed strategies in zero‑sum linear‑quadratic differential games (ZSLQDGs) remains an open problem. In this paper, we analytically synthesize near‑optimal mixed strategies for ZSLQDGs and establish rigorous performance certifications. Specifically, we construct a surrogate pure‑strategy stochastic differential game (SDG) by matching the first two moments of the mixed strategies. This method achieves an \mathcalO(\barπ^2) weak approximation of state distributions and expected costs with respect to the maximum commitment delay \barπ. By analytically resolving the surrogate SDG, we derive closed‑form optimal control laws for the matched moments. Crucially, we reveal that the surrogate game is governed by a Generalized Riccati Differential Equation (GRDE), which explicitly dictates a dynamic energy allocation law for variance injection. Building on these solutions, we propose a robust dual‑routing architecture to execute the near‑optimal mixed strategies. Furthermore, we certify that both the global value approximation error and the strategy suboptimality gaps are bounded by \mathcalO(\barπ^\frac12). Finally, numerical experiments on a double‑integrator pursuit‑evasion game illustrate the induced physical behaviors and validate the theoretical bounds.

Authors:Kaipeng Xu, Zhuo Zhi, Keyue Jiang
Title: A Data-Driven Methodology for Scalable Distributed MPC in Heterogeneous Building Aggregation: From Systematic Feature Selection to Convex Optimization
Abstract:
Coordinating large‑scale, heterogeneous building aggregations for demand response (DR) is impeded by a dual challenge: the computational intractability of centralized Model Predictive Control (MPC) and the inadequacy of conventional feature selection methods, which fail to address the error‑compounding nature of multi‑step forecasting required by MPC. This paper proposes a comprehensive, data‑driven framework that first employs a systematic, MPC‑aware feature selection methodology to ensure robust multi‑step prediction, then models the complex building dynamics using a novel Input‑Convex Encoder‑Only Transformer (IC‑EoT) to guarantee a convex optimization problem, and finally solves the resulting constraint‑coupled problem (CCP) in a fully distributed manner using the Tracking Alternating Direction Method of Multipliers (ADMM) algorithm. The framework is validated in a high‑fidelity co‑simulation environment, controlling a heterogeneous aggregation of consumer and prosumer buildings based on the EnergyPlus under a dynamic time‑of‑use (TOU) tariff. Results demonstrate that the proposed distributed approach achieves near‑identical economic optimality and superior thermal comfort compared to a theoretical centralized controller, while exhibiting exceptional computational scalability that overcomes the real‑time infeasibility of the centralized approach for large aggregations.

Authors:Siwon Jo, Yanze Zhang, Yupeng Yang, Wenhao Luo
Title: Geometry-Aware Control Barrier Functions for Collision Avoidance via Bernstein Polynomial Approximations
Abstract:
Safe navigation often relies on well‑defined conditions based on the shape of robots and obstacles, and can be challenging when they have irregular geometries. While Control Barrier Functions (CBFs) offer an efficient mechanism to enforce safe set forward invariance, common shape surrogates (e.g., spheres or super‑ellipsoids) either are overly conservative in unstructured scenes or require many local primitives, which inflates constraint counts and degrades real‑time performance. In this paper, we introduce a novel geometry‑aware Control Barrier Function (CBF) based on Bernstein‑Polynomial Signed Distance Fields (BP‑SDFs). It provides a unified way to represent the obstacles and robots, so as to represent the barrier function with a unified minimum distance. Benefiting from the differentiability of the Bernstein polynomials, one can easily enforce the control constraints in a closed loop. We validate the method's efficiency and performance to guarantee safety in single‑robot navigation and heterogeneous multi‑robot collision avoidance via simulations under different environments.

Authors:Tyrone L. Vincent, Michael B. Wakin
Title: Bounds on Prediction Error When Using an Impulse Response/Equilibrium Model Structure
Abstract:
An impulse response/equilibrium model (IREM) structure combines a linear convolution model with a nonlinear function that sets the current operating point via an equilibrium variable with integrator dynamics. This model structure is well suited for mildly nonlinear systems and in particular has been applied to battery fast charging control. This paper provides observability conditions for the IREM model structure and bounds on the prediction error. These conditions can be evaluated directly on the system impulse response.

Authors:Faiq Shamass
Title: ZAPS-DA: Zero-Phase Action Policy Smoothing with Decoupled Actor for Continuous Control in Reinforcement Learning
Abstract:
Continuous control policies trained with off‑policy reinforcement learning frequently exhibit high‑frequency action jitter, rendering direct deployment on physical actuators impractical. Post‑hoc filtering attenuates jitter but introduces phase lag; embedding smoothness penalties in the actor's loss couples them with the RL gradient and conflates reward regression with over‑aggressive smoothing. We present ZAPS‑DA, a framework that reduces action jitter at deployment with negligible phase lag and no post‑processing. ZAPS‑DA pairs an unmodified main actor (trained by the base RL loss) with a separate decoupled actor trained via supervised imitation of zero‑phase filtered targets stored in the replay buffer. The deployed policy is the decoupled actor: a feed‑forward map from the current observation to a smooth action, with no inference‑time filter and no action‑history input ‑‑ a mechanism we term causal distillation of a non‑causal filter. A magnitude‑matched MSE loss provides zero‑hyperparameter portability across optimizer classes. Validated with Soft Actor‑Critic and a Savitzky‑‑Golay filter in two driving simulators using paired n=150 evaluation protocols: on MetaDrive, ZAPS‑DA reduces steering jitter by 14‑‑21x and throttle jitter by 3‑‑5x (all p < 10^‑4, Bonferroni‑corrected) while matching task‑completion (p=0.28 success, p=0.31 crash) at a 6.3% reward cost; on a custom Webots adaptive cruise control environment, the same SG configuration produces a Pareto improvement ‑‑ reward parity (p=0.121), 8‑‑45x steering jitter reduction, and total task‑failure rate reduced from 2.0% to 0.7%.

Authors:Ming Cai
Title: Conservation-Based Feedback-Circuit Decomposition for Linear Forced Systems
Abstract:
We present a conservation‑based feedback‑circuit decomposition specifically for general linear forced systems. In a role parallel to that of eigenvalues and eigenvectors for initial‑value problems, the complete set of independent intrinsic circuit gains and their associated forcing‑transformation vectors provide a complete analytical representation of both transient and equilibrium forced solutions. The sign of intrinsic circuit gains determines whether successive feedback cycles exhibit monotonic or oscillatory convergence to transformed forcing, while the forcing‑transformation vectors determine the structure of transformed forcing. The exact transient and equilibrium solutions are represented analytically through the convergence of the finite‑cycle forcing‑transformation kernel to the equilibrium forcing‑transformation kernel, which is guaranteed regardless of whether the magnitudes of circuit gains exceed one or unstable modes exist in the system. The feedback‑circuit decomposition provides a new generic foundational mathematical tool for understanding, predicting, and controlling forced responses in a broad range of coupled linear systems across science and engineering.

Authors:Vittorio Giammarino, Anastasios Manganaris, Ahmed H. Qureshi
Title: Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics
Abstract:
Learning to reach arbitrary goals from sparse feedback requires agents to infer a rich notion of reachability across state‑‑goal pairs. Goal‑conditioned reinforcement learning (GCRL) tackles this challenge by learning policies that generalize across goals, but this generalization becomes increasingly difficult as the underlying dynamics become high‑dimensional, hybrid, or contact‑dependent. To address this issue, physics‑informed GCRL (Pi‑GCRL) introduces optimal‑control‑inspired inductive biases into goal‑conditioned value learning. While Pi‑GCRL methods have proven effective in navigation and object‑free goal‑reaching domains, their reliability in contact‑rich tasks remains unclear, where contact interactions induce hybrid dynamics, mode‑dependent controllability, and nonsmooth value landscapes. In this work, we show that these structural properties can cause existing Pi‑GCRL methods to degrade when applied naively to contact‑rich manipulation. Motivated by this analysis, we introduce contact‑aware and hierarchical formulations that apply physics‑informed inductive biases selectively across the manipulation problem. Our results provide a principled step toward extending Pi‑GCRL to contact‑rich manipulation.

Authors:Haoyu Wang, Alex Hanson
Title: Resonant Method-based Fully Automated Core Loss Measurement System for Sub-MHz Magnetics With Switched Capacitor Sequence
Abstract:
Accurate loss characterization is essential for the design of high‑frequency power magnetic components. State‑of‑the‑art resonant characterization methods are attractive for high accuracy and low sensitivity, especially at the MHz regime. However, they predominantly rely on manual tuning and computationally intensive Fast Fourier Transform (FFT) analysis to identify resonant conditions, causing both inefficiencies and inaccuracies. To ensure accuracy and expedite the process, this paper proposes a fully automated measurement architecture, the core innovation of which lies in the integration of digitally‑controlled switched capacitor sequences and onboard signal processing circuits,enabling automated sweeping of both frequency and drive level for complete and rapid characterization with no human intervention. A design guideline for the switched capacitor sequence is presented and common commercial electromechanical power relays are characterized to enable sub‑MHz measurements. Experimental results for several different magnetic materials demonstrate that the proposed system has great accuracy and is able to collect more than 1000 data points within 20 seconds, providing a very fast and robust solution for high‑frequency magnetic characterization.

Authors:Zixuan Huang, Da Chen, Kecheng Huang, Lihao Yin, Xing Li, Huiling Zhen, Mingxuan Yuan, Zili Shao
Title: Kernel Foundry: A Diagnosis-driven Evolutionary Kernel Optimizer with Multi-Experts
Abstract:
Generating high‑performance GPU kernels remains challenging due to the need for both correctness and hardware‑aware optimization. While large language models (LLMs) show promise in code generation, they often fail to produce kernels that are both correct and efficient. We propose Kernel Foundry, a diagnosis‑driven evolutionary framework for automatic GPU kernel optimization. Our method combines expert‑guided, retrieval‑augmented initialization with a multi‑island evolutionary search, where candidate kernels are iteratively refined using structured diagnostic feedback. A centralized experience library accumulates reusable optimization knowledge to guide subsequent evolution, while explicit mechanisms prevent cheating behaviors that bypass kernel‑level computation. Experiments on KernelBench show that our method consistently improves both correctness and performance over strong baselines, achieving up to 100% correctness on Level~2.

Authors:Jerzy Baranowski, Waldemar Bauer
Title: Optimization of Predictive Maintenance Schedules under Uncertainty: A Scenario-Based Theoretical Framework
Abstract:
This paper proposes a scenario‑based framework for predictive maintenance scheduling under uncertainty in a finite planning horizon. The considered setting involves multiple assets for which maintenance decisions are informed by three heterogeneous sources of information: calendar‑based overhaul intervals, usage‑based limits driven by uncertain future operating cycles, and condition‑monitoring outputs represented through remaining useful life (RUL) estimates with uncertainty. While these elements have been studied extensively in the maintenance literature, they are often treated separately or only partially integrated. In contrast, the proposed formulation evaluates complete maintenance schedules under simulated future scenarios and compares them using expected‑cost and tail‑risk criteria. The contribution is primarily conceptual and methodological: we define a unified finite‑horizon decision framework that combines calendar‑, usage‑, and prognostics‑based information within a common scheduling problem. A small synthetic computational example is used as a proof of concept. The results show that integrated scenario‑based policies can substantially outperform simpler single‑trigger rules, while the difference between risk‑neutral and risk‑aware integrated policies remains modest under the present calibration.

Authors:Eleni Tsotsopoulou, Vasileios Psaras, Dionysios Moutevelis, Oriol Gomis-Bellmunt, Alexandros Paspatis
Title: Fault-Ride-Through Coordination Strategy for Offshore AC Islands with Multi-Infeed HVDC Interconnections
Abstract:
Large‑scale offshore Wind Farms (WFs) are considered key assets towards realizing a sustainable power system. These systems are often configured as offshore AC islands and their integration largely depends on the High‑Voltage‑Direct‑Current (HVDC) technology. This topology, while it enables cost‑effective transmission over large offshore distances, may lead to operational challenges. Specifically, the operation of offshore AC islands during faults and the grid code requirement fulfillment are identified as a major challenges for their large‑scale deployment. To address this pressing issue, a comprehensive coordination control strategy for the different participating converters in multi‑infeed AC offshore islands during Fault Ride Through (FRT) operation is presented in this work. The proposed strategy introduces advanced control functions in the FRT schemes of both the HVDC and WF converters, such as zero active and reactive power injection during faults, as well as post‑fault active power droop control coordination to tackle power imbalances. The proposed FRT coordination strategy is validated through both extensive simulations in PSCAD/EMTDC, as well as with Power Hardware‑in‑the‑Loop (PHIL) experimental results, considering both AC and DC faults.

Authors:Felix Koch, Thomas Krug, Fabian Raisch, Benjamin Schäfer, Benjamin Tischler
Title: BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control
Abstract:
Machine learning (ML) is increasingly used for data‑driven modeling of buildings to enable downstream tasks such as fault detection and diagnosis, and energy‑efficient control. While recent work improves generalization across building characteristics, weather, and occupancy, generalization also depends on sufficient exploration of the control‑driven system state space. Existing real‑world datasets and simulation environments predominantly reflect stationary operation under fixed control policies, resulting in limited excitation and reduced robustness to unseen operating conditions. This paper introduces BuilDyn, a package based on BuilDa that enables customizable excitation strategies for control‑oriented data generation. BuilDyn further supports sampling from representative building distributions and provides a Python interface for easy integration into machine learning pipelines. We demonstrate the benefits of BuilDyn by comparing the performance of data‑driven ML models trained on non‑excited and excited data for one building. With BuilDyn, we hope to advance scalable control‑oriented modeling and support future directions such as transfer learning and building‑specific foundation models.

Authors:Alessandro Bettoni, Giacomo Mastroddi, Marco Muttoni
Title: Distributed Nonlinear Model Predictive Control for District Heating Networks
Abstract:
This paper presents a distributed nonlinear model predictive control that uses alternating direction method of mul tipliers for district heating networks. Exploiting a graph‑based modeling of the thermal dynamics, our controller optimizes the mass flow absorption of buildings in a distributed cooperative scheme that mediates between the superior performance of the centralized control and the privacy preservation of the decentralized schemes. A benchmark three‑building network simulation is used to compare the performance of the proposed solution with a decentralized model predictive control scheme.

Authors:Leon Johann Brettin, Nayel Fabian Salem, Ole Hans, Markus Maurer
Title: Teleoperation Operational Design Domain based on Minimal Risk Maneuver Capability
Abstract:
This article discusses the concept of an Operational Design Domain (ODD) designed specifically for teleoperated road vehicles. For this purpose, the ODD concept designed for automated driving is adapted for teleoperation. As teleoperation becomes more common in regular traffic, the question arises under which operating conditions such vehicles are able and allowed to drive. Currently, these conditions are selected primarily based on network performance. From a safety perspective, it is difficult to base such a selection on a reliable connection because it is almost impossible to guarantee sufficient reliability. With this in mind, the ODD concept designed for automated driving is adapted for teleoperation: A concept is proposed for basing the ODD for a teleoperation system on the capability of the teleoperated vehicle to perform a minimal risk maneuver using a dedicated system designed solely for this purpose. This concept is then demonstrated using a use case example.

Authors:Afsoon Alidadi Shamsabadi, Animesh Yadav, Halim Yanikomeroglu
Title: Tackling Interference in HAPS Networks via Angular-Aware Clustering and RSMA
Abstract:
High Altitude Platform Stations (HAPS) have emerged as a promising enabler for next‑generation wireless networks, offering ubiquitous connectivity to ground users. Operating either in standalone mode or in integration with terrestrial networks, HAPS can significantly enhance both coverage and capacity due to their strategic placement in the stratosphere. However, interference management in HAPS‑empowered networks requires special attention due to the unique propagation characteristics of HAPS links. In particular, the strong line‑of‑sight (LoS) conditions between HAPS and ground users result in limited channel variability, thereby intensifying inter‑user interference. In this work, we consider a single HAPS serving multiple ground users through multiple beams over a limited number of orthogonal resource blocks (RBs). To address the resulting interference, we propose a novel angular‑aware user clustering and interference‑aware RB allocation framework that strategically clusters users, designs beams to serve each cluster, and allocates RBs to users across clusters. To further mitigate intra‑RB interference, a rate‑splitting multiple access (RSMA) scheme is incorporated. Simulation results demonstrate that the proposed clustering and RSMA‑based approach significantly outperforms baseline schemes in terms of achievable per‑user spectral efficiency.

Authors:Jiamin Wang, Jian Liu, Feng Xiao, Haibin Duan, Yuanshi Zheng
Title: Robustness Enhancement of Consensus Networks: the Optimal Memory Depth
Abstract:
Understanding what governs collective robustness and how it can be enhanced remains a central pursuit in network science. This paper investigates the robustness of multi‑agent consensus networks, quantified by the H_2 performance metric, and delves into the enhancing effect of agents' local memory on it. Inspired by the hierarchical temporal structure of memory observed in neuroscience, we focus on the role of memory depth, which reflects the temporal features of memory from recent to remote. Building on linear extrapolation, we propose a consensus protocol with single‑step memory and tunable memory depth, derive the necessary and sufficient condition for achieving consensus, and show that the protocol exhibits an inheritable consensus property across memory depths. Furthermore, analytical expressions for the H_2 performance metric, which depend on the memory factor, memory depth, coupling gain, and Laplacian spectrum, are established. Under balanced usage of real‑time and memory information, we demonstrate that memory at any accessible depth enhances H_2 performance, and the optimal memory depth occurs at either the most recent or the most remote memory, contingent upon certain parameter regions. Further detailed discussions are provided to clarify the broader implications of our findings.

Authors:Yuanyi Wang, Yanggan Gu, Su Lu, Yifan Yang, Zhaoyi Yan, Congkai Xie, Jianmin Wu, Hongxia Yang
Title: Access Sets Matter: Budgeting Expert Reads for Scalable Weight-Space Model Merging
Abstract:
Weight‑space model merging is usually formulated as an algebraic operation on checkpoints, yet at LLM scale the limiting resource is often the set of expert weights that must be read. We introduce MergePipe, a budget‑aware execution layer that casts LLM merging as an \emphexpert access‑set problem: given a merge operator and a checkpoint family in a shared weight coordinate system, choose which expert delta blocks to access under an explicit I/O budget. MergePipe indexes parameter blocks, builds deterministic access plans, and executes the induced budgeted merge with replayable manifests. The plan is budget‑sound by construction and recovers the full‑read merge at full budget; for fixed‑coefficient additive operators, the omitted‑update error is bounded by the norm of omitted deltas. Across Qwen and Llama merging workloads, MergePipe reduces expert‑read I/O by up to an order of magnitude and achieves up to 11× speedups. Representative budget sweeps show O(10^‑3) parameter deviation from full‑read merges and no monotonic degradation on downstream benchmarks.

Authors:Suraj Kumar, Debjyoti Chakrabarti, Aditya Rallapalli, Bharat Kumar GVP, Ashok Kumar Kakula
Title: Real-Time Retargeting Using Controllability Boundary for Chandrayaan-3 Lunar Landing
Abstract:
This paper presents the real‑time retargeting guidance policy developed for the Chandrayaan‑3 lunar landing mission. The baseline guidance generates approximate fuel‑optimal descent trajectories, while a high‑level policy enables safe retargeting to alternate sites when the nominal site becomes infeasible. The retargeting strategy leverages a convex representation of the controllability boundary, allowing rapid feasibility checks and real‑time target updates. To the best of the authors knowledge, this represents the first application of a data‑driven retargeting framework in an operational lunar landing mission. Pre‑flight simulations and Chandrayaan‑3 flight results validate the effectiveness of the proposed approach.

Authors:Aditya Rallapalli, Suraj Kumar, Rijesh M P, Ashok Kumar Kakula, Bharat Kumar GVP
Title: Decoupled Thrust-Axis Attitude Control Using Quaternions for Chandrayaan-3 Lunar Landing Mission
Abstract:
Chandrayaan‑3 mission achieved a historic milestone with its successful soft landing near the lunar south pole, highlighting the critical role of the navigation, guidance, and control (NGC) system. Navigation provided vehicle state estimates relative to the Moon center, while a polynomial based guidance scheme computed the required acceleration profile to meet terminal landing conditions. This acceleration demand was translated into total thrust magnitude and attitude commands generation. Attitude command generation involved aligning the thrust axis with the required acceleration vector and constraining rotation about the thrust axis, typically governed by mission‑specific requirements. Although quaternion‑based control laws are preferred for their singularity‑free representation, they inherently couple all three rotational axes. This coupling can lead to undesirable interactions between guidance and control, especially during large rotations about the thrust axis, due to the quaternion shortest‑path property. This paper proposes a novel quaternion‑based decoupling method that enables independent thrust‑axis control, mitigating guidance‑control interaction and ensuring proper attitude commands generation for lander attitude control.

Authors:Hiroshi Okajima
Title: Closed-Loop Identification of Periodically Time-Varying Systems via Cyclic Reformulation
Abstract:
This paper studies closed‑loop identification of linear periodically time‑varying (LPTV) plants, with emphasis on open‑loop unstable plants for which open‑loop experiments are not practically available. The central contribution is an exact algebraic plant‑extraction theorem for cycled closed‑loop realizations: for square strictly proper plants and a controller path satisfying an invertibility condition, the cycled plant transfer matrix is recovered from a shared state‑space realization of the stable closed‑loop maps from the external reference to the plant output and to the control input, without state augmentation, and without requiring the recovered plant realization to be stable. Thus, the stability requirement for data generation is shifted from the open‑loop plant to the internally stable closed‑loop system. Building on this result, a closed‑loop identification algorithm is constructed that takes the reference, output, and input signals as data, applies standard subspace identification to the cycled signals, performs the algebraic plant extraction, and recovers the LPTV plant state‑space parameters via a coordinate transformation; the conditioning of the inverse controller path governs the reliability of the extraction step. Numerical examples demonstrate the recovery of stable and open‑loop unstable SISO LPTV plants and validate a MIMO case through coordinate‑invariant Markov‑parameter comparisons.

Authors:Gordon Ariho, Hara Madhav Talasila, James M. Stiles, Peng Seng Tan
Title: Iterative Reduced-Rank MMSE Estimation of Sparse Range Profiles from Non-Contiguous Radar Transmission Spectra
Abstract:
Ongoing demand for radio spectrum by commercial wireless services has steadily increased pressure on the frequency bands traditionally reserved for radar. This paper addresses the joint problem of designing non‑contiguous radar transmission spectra and estimating the range profile from the resulting reduced measurement set. Transmission spectra are constructed using a Marginal Fisher Information (MFI) criterion that removes blocks of frequencies contributing least to estimation accuracy. To process the underdetermined signals acquired from the resulting sparse measurement vector, an iterative Reduced‑Rank Minimum Mean‑Square Error (RRMMSE) estimator is proposed. The estimator starts with a single‑target hypothesis and grows the active target subspace one range bin at a time, updating the a~priori target covariance matrix in each iteration using both the largest estimated reflection coefficient and its posterior error variance. This avoids inversion of the full M×M covariance matrix that would be required by a one‑step MMSE and concentrates the rank of the estimator on the support of significant scatterers. The Bayesian Cramér‑‑Rao Lower Bound (CRLB) on the per‑bin reflection coefficient is derived for the non‑contiguous spectrum measurement model, and the computational complexity of the proposed estimator is shown to scale as \Order(G^2 M K^2), where G is the number of detectable scatterers, M is the number of range bins, and K is the number of preserved spectral samples. Simulations using 50% and 75% spectrally occupied MFI‑designed spectra confirm that the algorithm recovers sparse range profiles with Mean‑Square Error (MSE) close to the fully filled baseline when the number of significant scatterers is not larger than the rank of the sparse sensing matrix.

Authors:Aadila Ali Sabry, Gennaro Notomista
Title: $α$-stability of Differentially Flat Systems with Application to Newton-Raphson Tracking Control for Vehicle Dynamics
Abstract:
This paper studies the α‑stability property of differentially flat nonlinear dynamical systems. The results build off the recently introduced notion of α‑stability, which is particularly amenable to characterize the ability of a system to track dynamic output reference signals. We consider systems controlled using the Newton‑Raphson tracking controller, which results in closed‑form control policies, therefore it is computationally efficient, and it has been shown to be effective to control a large variety of mobile robots, including autonomous vehicles. The main results of the paper consist in sufficient conditions for the α‑stability of differentially flat systems and for the equivalence between the proposed control algorithm and the Newton‑Raphson tracking controller applied directly to the nonlinear dynamics. We demonstrate the behavior of the proposed controller applied to the kinematic unicycle and dynamic bicycle models.

Authors:Tao He, Gangshan Jing
Title: Distributed Non-Uniform Scaling Control of Multi-Agent Formation with Dynamic Agent Joining
Abstract:
Non‑uniform scaling control of formation enables multi‑agent systems to adjust their shape by scaling with different ratios along different coordinate axes, offering enhanced flexibility in complex environments. However, like most existing formation maneuver strategies, it typically assumes a fixed set of agents, limiting its applicability in scenarios requiring dynamic team expansion. This paper introduces a distributed control framework that enables a formation to incorporate new agents during non‑uniform scaling maneuvers in arbitrary dimensions while preserving the spectral properties of the graph Laplacian. Simulation examples validate the effectiveness of the theoretical results.

Authors:Chen-Lung Lu, John Wen
Title: Learning and Adaptation in Wire Arc Additive Manufacturing Bead Geometry Control
Abstract:
Robotics Wire Arc Additive Manufacturing (WAAM) is governed by complex and nonlinear process dynamics coupling thermal field to the build geometry. The process may be regarded as a multi‑input/multi‑output dynamical system with welding torch speed and wire feed rate as inputs and weld bead deposition height and width as outputs. In this paper, we use the input/output data to learn a data‑driven model and use it for weld planning and control. We show that a simple recurrent neural network architecture and one‑step‑ahead predictive control can improve the process performance in terms of height and width consistency. To account for the changing thermal conditions during the printing process, we update the learning model using prediction error from the previous layer. This adaptation step further improves the prediction accuracy and controller performance. Experiments on a robotic WAAM testbed with integrated line‑scanner feedback significant improvements in height and width consistency compared to constant input and static model baselines. The proposed learning and adaptation framework provides a practical pathway toward robust, data‑driven regulation of additive manufacturing processes.

Authors:Qitao Weng, Heechul Yun
Title: Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving
Abstract:
Latency‑accuracy tradeoffs are fundamental in real‑time applications of deep neural networks (DNNs) for cyber‑physical systems. In autonomous driving, in particular, safety depends on both prediction quality and the end‑to‑end delay from sensing to actuation. We observe that (1) when latency is accounted for, the latency‑optimal network configuration varies with scene context and compute availability; and (2) a single fixed‑resolution model becomes suboptimal as conditions change. We present a multi‑resolution, end‑to‑end deep neural network for the CARLA urban driving challenge using monocular camera input. Our approach employs a convolutional neural network (CNN) that supports multiple input resolutions through per‑resolution batch normalization, enabling runtime selection of an ideal input scale under a latency budget, as well as resolution retargeting, which allows multi‑resolution training without access to the original training dataset. We implement and evaluate our multi‑resolution end‑to‑end CNN in CARLA to explore the latency‑safety frontier. Results show consistent improvements in per‑route safety metrics ‑ lane invasions, red‑light infractions, and collisions ‑ relative to fixed‑resolution baselines.

Authors:Jiyong Lee, Melody Agustin, Joanne Langsdorf, Erhan Kutanoglu, Michael Baldea, Ilias Mitrai
Title: Grid Capacity Expansion under Data Centers and Electrified Manufacturing Large Loads
Abstract:
In this paper, we consider the expansion of power grids under emerging large loads from data centers and electrified manufacturing. We develop a multi‑period grid capacity expansion model to determine optimal investment profiles for power generation, storage, and transmission capacity while accounting for hourly power dispatch, such that electricity demand is satisfied and the total planning and operation cost is minimized. We also propose a new modeling approach regarding the spatial distribution of demand from large loads. The model is used to analyze the expansion of a synthetic grid that follows key characteristics of the ERCOT system over a seven‑year planning horizon, under loads from data centers and electrified oil refining, which account for 17.5% and 4.7% of total annual electricity demand by the end of the planning horizon. The optimal investment policy leads to an 83.6% increase in generation capacity and exploits the short construction times of solar and storage as well as the operational flexibility of thermal generators. Finally, sensitivity analysis reveals that the construction time of grid assets substantially impacts investment timing, generation technology mix, and transmission capacity expansion. The proposed modeling framework is general and can be extended to other grid systems, enabling the exploration of diverse demand scenarios, policy assumptions, and regional characteristics.

Authors:Yi Yang, Victor G. Lopez, Matthias A. Müller
Title: Local Observability and Moving Horizon Estimation-based Training of Feedforward Neural Networks
Abstract:
In this paper, we propose a moving horizon estimation (MHE)‑based training method for feedforward neural networks (FNNs) with rectified linear unit (ReLU) activation functions to determine their ideal weights from a control‑theoretic perspective. This allows for a rigorous theoretical analysis of the trained network. First, we reformulate the FNN as a dynamical system with the weights as states. Then, we investigate the local observability of such a system. For two‑layer FNNs with fixed output weights, we derive a sufficient condition under which the observability rank condition holds, ensuring a locally observable state. We also show that multi‑layer FNNs in general fail to satisfy the observability rank condition. Based on this analysis, we develop a persistently exciting (PE) input design method, which renders a state distinguishable from its neighbors. The resulting local observability provides convergence guarantees for the proposed MHE‑based training, where only the projection of the state onto the observable subspace is updated using a fixed‑length window of input‑output data. The effectiveness of the approach is illustrated via numerical examples.

Authors:Sang woo Ham, Donghun Kim, Michael Rossetti, John Sipple
Title: Tensorized Radiative Heat Transfer for a Scalable and Calibrated Building Energy Simulator
Abstract:
Accurate building energy simulation is essential for developing advanced control strategies that enable demand flexibility and grid responsiveness. The Smart Buildings Control Suite (sbsim) offers a lightweight, scalable, and data‑calibrated simulation environment based on a tensorized finite difference model. Previous work extended sbsim to include interior long‑wave radiative heat exchange between indoor surfaces. However, a complete thermal model must also account for exterior radiative processes, including long‑wave radiation exchange with the sky and surroundings, as well as short‑wave solar radiation incident on building surfaces. This paper presents a comprehensive radiative heat transfer implementation for sbsim that integrates both interior and exterior radiation mechanisms. Our primary contribution is the development and integration of a fully tensorized exterior radiation module that captures sky and ground long‑wave exchange as well as solar heat gains through opaque and transparent surfaces. By formulating these processes as tensor operations compatible with the existing framework, we preserve the computational efficiency necessary for reinforcement learning applications. We validate our implementation against established simulation tools and demonstrate improved prediction accuracy for surface temperatures and building thermal loads. This enhancement significantly increases the physical fidelity of sbsim, enabling more realistic training environments for building energy optimization and control.

Authors:Vladimir Sovljanski, Mario Paolone
Title: Identifiability of Low Frequency Li-ion Battery Parameters in Time Domain
Abstract:
This paper investigates the identification of observable low‑frequency (LF) parameters of battery cell's equivalent circuit models (ECMs) using time‑domain voltage and current measurements sampled at low frequency by built‑in battery management systems (BMS) during operation. Accurate estimation of such parameters is challenging due to measurement resolution available in practical settings. To address this, a modeling and identification framework is proposed in which fractional constant phase element (CPE), commonly used to model LF diffusion phenomena of battery cells, is approximated in the time domain using a high‑order RC network with a recursive definition. The parameter estimation problem is formulated as a constrained, non‑convex least‑squares problem in a discretized state‑space representation. To improve robustness, parameter initialization strategies, bounds, and a procedure for selecting the number of RC branches are rigorously derived. The method is evaluated in a numerical study based on a power system application where the battery under the study provides primary frequency control to the grid. Under noise levels representative of typical BMS measurements, the proposed approach achieves, from time‑domain measurements, accurate LF parameter estimation (including the CPE), with average errors below 1 %.

Authors:Amir Ali Ahmadi, Sanjeeb Dash, Yixuan Hua, Bartolomeo Stellato
Title: Disjunctive Sum of Squares
Abstract:
We introduce the concept of disjunctive sum of squares for certifying nonnegativity of polynomials. Unlike the popular sum of squares approach where nonnegativity is certified by a single algebraic identity, the disjunctive sum of squares approach certifies nonnegativity with multiple algebraic identities which can be found in parallel. Our main result is a disjunctive Positivstellensatz proving that we can keep the degree of each algebraic identity as low as the degree of the polynomial whose nonnegativity is in question. Based on this result, we construct a semidefinite programming based converging hierarchy of lower bounds for the problem of minimizing a polynomial over a compact basic semialgebraic set, where the size of the largest semidefinite constraint is fixed throughout the hierarchy. We further prove a second disjunctive Positivstellensatz which leads to an optimization‑free hierarchy for polynomial optimization. We specialize this result to the problem of proving copositivity of matrices. Finally, we describe how the disjunctive sum of squares approach can be combined with a branch‑and‑bound algorithm and we present numerical experiments on polynomial, copositive, and combinatorial optimization problems.

Authors:Zirui Niu, Daniele Astolfi, Giordano Scarciotti
Title: On the Solvability of Quasi-Regulator Equations in Non-smooth Output Regulation
Abstract:
Motivated by the prevalence of non‑smooth, possibly non‑periodic signals in real‑world applications, the output regulation of linear systems subject to non‑smooth non‑periodic exogenous signals has emerged as a challenging problem. A fundamental prerequisite for solving this problem is the existence of solutions to the so‑called ``quasi‑regulator equations''. In this paper, we investigate the solvability of these equations. To this end, we reformulate the quasi‑regulator equations as differential‑algebraic equations and highlight the critical role played by the system's relative degree. We finally propose a ``non‑smooth non‑resonance condition'' that, under specific relative degree requirements, provides a necessary and sufficient characterization of the solvability of the quasi‑regulator equations.

Authors:Jimin Choi, Grant Stagg, Cameron K. Peterson, Max Z. Li
Title: Integrated Exploration-Aware UAV Route Optimization and Path Planning
Abstract:
Uncrewed aerial vehicles (UAVs) are increasingly used for exploration‑driven monitoring in hazardous environments such as disaster zones, contaminated sites, wildfire areas, and damaged infrastructure, where limited flight endurance must be allocated between visiting reported locations and gathering new information. In these settings, prior information regarding hazards is often incomplete, spatially imprecise, and subject to change during execution. For example, initial reports may identify a region where a hazard is likely to exist, but the actual hazard may be displaced, partially observed, or entirely unreported. We present an integrated exploration‑aware UAV route optimization and path planning framework for hazard monitoring under uncertain and evolving prior information. The environment is represented as a spatial risk map, where each location has an associated belief of hazardous conditions. Reported hazards are modeled as uncertain regions of interest (ROIs) rather than confirmed target locations, requiring the UAV to inspect reported areas while also using its limited flight endurance to explore informative regions. The proposed method solves a vehicle routing problem over reported ROIs, augments the route with auxiliary pseudo‑nodes to improve spatial coverage, allocates the remaining flight distance budget across route segments, and optimizes dynamically feasible B‑spline trajectories for local exploration. During execution, UAV measurements update a grid‑based belief map, and the remaining trajectory is replanned when new information and the remaining budget justify adaptation. Across 48 scenario configurations, online replanning improves average KL reduction by 15.9% over the offline optimized planner and 48.6% over straight‑line traversal.

Authors:Kangyu Wu, Peng Cui, Guoxi Chen, Ya Zhang
Title: SARAD: LLM-Based Safety-Aware Hybrid Reinforcement Learning with Collision Prediction for Autonomous Driving
Abstract:
Ensuring both safety and efficiency in decision‑making for autonomous driving systems remains a fundamental challenge. Traditional Deep Reinforcement Learning (DRL) suffers from unsafe random exploration and slow convergence, while Large Language Models (LLMs) demonstrate inherent latency in real‑time inference operations. To address these limitations, this paper proposes SARAD, a novel safety‑aware hybrid framework that synergizes LLMs and DRL for autonomous driving. SARAD substitutes the random exploration of DRL with Retrieval‑Augmented Generation (RAG)‑enhanced, LLM‑guided decisions sourced from a dynamic expert knowledge repository. An attention discriminator is proposed to integrate the prior knowledge of LLMs into DRL policy optimization. A collision predictor module, fine‑tuned with historical collision data, is further designed to improve vehicle safety. Extensive experiments show that SARAD achieves significant performance improvements in the Highway‑Env simulator, validating the effectiveness of the proposed model in autonomous driving.

Authors:Kianoush Aqabakee, Kosar Behnia, Amirhossein Heydarian Ardakani, Farzaneh Abdollahi, Elham Shirazi
Title: Actor-Identifier-Critic Reinforcement Learning for Adaptive Model-Free Optimal Control of Nonlinear Systems with Stochastic Packet Dropouts
Abstract:
Packet dropouts in control systems poses a critical challenge, as it can significantly compromise system performance and stability. In these conditions, classical controllers often struggle to deliver effective control, as they rely on accurate system models, which may not always be available. This paper proposes a novel Actor‑Identifier‑Critic~(AIC) controller to address model‑free tracking control of nonlinear systems in the presence of packet dropouts in both the controller‑to‑actuator and sensor‑to‑controller channels. Using an identifier to learn the system dynamics, the proposed controller is able to handle packet dropouts in the communication link and facilitate gradient propagation from the critic to the actor within a model‑free control framework. The performance of the proposed method is demonstrated on two nonlinear SIMO and MIMO systems and a case study on power system stability subject to stochastic packet dropouts.

Authors:Roland Schurig, David Ohlin, Anders Rantzer, Emma Tegling, Rolf Findeisen
Title: Model Predictive Control for Constrained Linear Positive Systems on Graphs
Abstract:
Positive systems describing networks with inherently non‑negative states and inputs arise naturally in routing, logistics, and compartmental modelling. We consider problems modelled as positive linear systems in incidence form with linear cost. The addition of capacity constraints on states (storage) and inputs (flows between nodes) significantly increases the problem complexity. Leveraging the analytic structure of the unconstrained problem, an explicit suboptimal admissible controller is constructed. This yields graph‑computable performance bounds and a minimum stabilising horizon length for a model predictive controller without terminal conditions. A convex program enables efficient computation of the optimal bound and horizon. These results highlight how system structure enables explicit MPC guarantees that are typically not available.

Authors:David Petrovic, Gian Antonio Susto, Angelo Cenedese
Title: Towards Autonomous Commissioning of Industrial Drives via Multi-Objective Bayesian Optimization
Abstract:
The commissioning of industrial electric drives still relies heavily on manual tuning of cascaded control loops, requiring expert knowledge and significant time. In this paper, we propose a fully automated approach for tuning the current control loop of industrial drives using Bayesian Optimization (BO) directly on real hardware, without requiring a system model or firmware modifications. The drive is treated as a black‑box system, and the controller parameters are iteratively updated through closed‑loop experiments. The tuning problem is formulated as a multi‑objective optimization task that directly minimizes tracking error, time‑weighted error, overshoot, and oscillatory behavior, enabling the identification of Pareto‑optimal controller configurations. To address discrete parameters, noisy evaluations, and limited budgets, we adopt a multivariate Tree‑structured Parzen Estimator (TPE) as the underlying BO strategy. The proposed method operates under practical industrial constraints, including communication latency and limited evaluation budgets. The experimental validation on a real motor drive system under no‑load conditions shows that the method achieves performance comparable to expert tuning within a few minutes and without human intervention. Results show that Gaussian Process (GP)‑based BO can yield highly competitive final solutions, but TPE‑based BO is better aligned with this setting due to faster convergence, richer Pareto‑front approximation, and lower computational overhead.

Authors:Songita Das, Gourab Ghatak, Chen Quan, Geethu Joseph
Title: Information Age-Controllability Trade-offs in Communication-Constrained Networks
Abstract:
We investigate the trade‑off between controllability, channel access, and age‑related performance in a wireless network of control systems. Controllers share a random‑access channel to transmit control inputs to actuators over slotted blocks. We measure reliable control via block controllability, where a block is controllable if it contains a required number of consecutive successful transmissions. In parallel, we capture information freshness via the age of information. To enable efficient allocation of channel resources over time, we introduce adaptive access probabilities at the block level, prioritizing controllers that have not yet achieved controllability. We then derive closed‑form expressions for block controllability probability, the peak latency between inter‑block consecutive successes, and peak age of information. We further characterize the peak control latency, defined as the time between consecutive controllable blocks. Finally, we optimize access probabilities to jointly balance controllability and age‑related metrics. Numerical results illustrate the effectiveness of the proposed adaptive access policies in managing this trade‑off in interference‑limited wireless control networks.

Authors:Faisal Lawan, Xiaoran Han, Joaquin Carrasco, Barry Lennox, Xiaoxiao Cheng
Title: Safety-Critical Adaptive Impedance Control via Nonsmooth Control Barrier Functions under State and Input Constraints
Abstract:
Safe physical interaction is critical for deploying robotic manipulators in human‑robot interaction and contact‑rich tasks, where uncertainty, external forces, and actuator limitations can compromise both performance and safety. We propose an online adaptive impedance control framework that enforces joint‑state safety while achieving compliant interaction under uncertain dynamics. The approach combines a quadratic‑program‑based safety filter with a novel composed position‑velocity non‑smooth control barrier function (NCBF), enabling joint position and velocity constraints to be enforced through a unified relative‑degree‑one barrier. Unknown dynamics are compensated online using an interval type‑2 fuzzy logic system, while actuator torque limits are handled through soft constraints with exact penalty recovery of feasible solutions. A disturbance‑observer‑enhanced safety mechanism improves robustness against modelling errors and external interaction forces. Using composite Lyapunov analysis, we prove forward invariance of the safe set and the uniform ultimately boundedness of the impedance‑tracking error. Simulations on a 7‑DOF manipulator with severe parametric uncertainty and external interaction wrenches demonstrate safe constraint satisfaction and robust impedance tracking.

Authors:Onur Günlü, Stefano Tomasin, João P. Vilela, Francesco Chiti, Prajnamaya Dass, Angeliki Alexiou, Utz Roedig
Title: ISAC Privacy: Challenges and Solutions for 6G
Abstract:
Integrated sensing and communication (ISAC) is a promising feature of future communication networks. While spatial sensing can improve network performance and enable external services, it also creates privacy challenges that go beyond the confidentiality of communication content. Future networks using millimeter‑wave (mmWave) and sub‑terahertz (THz) frequencies may collect or infer detailed information about people, devices, bystanders, passive objects, and environments in a sixth‑generation (6G) deployment area. Such sensing can reveal location and environment data, support behavioral profiling such as movement or activity recognition, and, in advanced cases, expose physiological information such as breathing frequency or heart‑rate‑related data. Thus, the capabilities of spatial sensing must be controlled to satisfy privacy requirements. In this work, we organize privacy‑sensitive ISAC data into three sensing levels: location and environment data, behavioral data, and physiological data, and use this classification as the organizing principle throughout the paper. Based on this classification, we discuss internal and external ISAC applications, identify privacy challenges related to consent, transparency, data ownership, profiling, bystander exposure, and sensitive sensing data, review representative solution directions, and outline future research directions for privacy‑preserving ISAC.

Authors:Mirado Mortel, Luc Jaulin, Lionel Lapierre, Simon Rohou
Title: Natural Locomotion: Principle and Method
Abstract:
Robotic locomotion can become efficient when mechanisms exploit passive dynamics, compliance, and resonance rather than track prescribed trajectories. This paper formulates natural locomotion as an exchange principle for systems whose motion is mediated by environmental constraints or interactions. A motion is natural when an internal oscillator returns periodically, the body pose drifts, and the mean Propulsion‑‑Oscillator Exchange power (POE power) vanishes over one cycle. The selected family is a Natural Locomotion Manifold (NLM). We develop the conservative realization of this principle for continuous ideal environmental constraints: the constraints do no external work, total mechanical energy is conserved, and zero mean POE power is an internal exchange with the environment‑mediated propulsive channel, not external energy input. The method is a closed/open construction. The propulsive channel is first closed to reveal an effective internal oscillator, organized by scalar action‑angle structure in one effective degree of freedom or by nonlinear modal sectors in several degrees of freedom. The channel is then reopened, pose is reconstructed, and accepted cycles must preserve internal recurrence and zero mean POE power. We demonstrate the principle on two ideal nonholonomic no‑slip systems: a Chaplygin‑sleigh / pendulum‑driven car and a three‑body extension. In the scalar case, POE closure is equivalent to the missing internal return condition, giving a theorem‑backed computation of the NLM family. In the multi‑degree case, POE closure remains necessary but must be completed by modal identity, internal return, dynamics consistency, same fixed passive architecture, and nonzero displacement. Natural locomotion becomes a design question: which passive architectures support no, one, or several certified NLM families?

Authors:Andrea De Gregorio, Mara Serrapede, Danilo Kaddouri, Paolo Angelini, Giuseppe Bruno, Simone Luigi Marasso, Salvatore Guastella, Andrea Lamberti, Paolo Crovetti
Title: Digital-Based Potentiostat and Mesoporous Microelectrode Co-Design for Non-Enzymatic Glucose Detection at 0.3V-VDD and 1.65nW-Power
Abstract:
This paper presents a proof‑of‑concept ultra‑low voltage and ultra‑low power chronoamperometric electrochemical sensor for non‑enzymatic glucose readout integrated circuit (IC) in 130nm CMOS detection featuring a reconfigurable Digital‑Based (DB) Potentiostat. The signal transfer and noise characteristics of the new digital‑based architecture are analytically described in the frequency domain for the first time by an equivalent linearized model that is validated by simulations and experiments. Based on experiments, the proposed DB potentiostat enables the detection of a wide electrochemical current range, spanning from 600pA to 650nA, with R2=0.991 linearity and consumes only 1.65nW (53.5nW) at V dd = 300mV (V dd = 500mV). The proposed DB readout is tested in a proof of‑concept platform for non‑enzymatic glucose detection with nanostructured microelectrodes, demonstrating successful non enzymatic glucose detection at physiological levels at the lowest reported voltage and power, even in the presence of an interferent (ascorbic acid) and under aerobic conditions, thus revealing a strong potential for emerging Point of Care (PoC) diagnostics applications.

Authors:Zian Wang, Yiming Shu, Zejian Deng, Chen Sun
Title: DRIFT: Driving Risk Inference via Field Transmission for Human-like Autonomous Driving
Abstract:
Risk fields offer spatially structured alternatives to scalar safety metrics. However, hand‑crafted static risk field models struggle with occlusion and topology‑driven propagation. We present DRIFT, a spatiotemporal risk field governed by an advection‑diffusion‑reaction partial differential equation (PDE), with an optional telegrapher term. DRIFT draws on three sources: anisotropic Gaussian kernels to capture velocity‑induced risk, occlusion‑aware latent hazards behind large vehicles, and topology‑coupled merge‑zone conflict pressure. We further introduce field‑centric evaluation metrics to complement the existing Surrogate Safety Measures (SSMs), including Lane‑Change Risk Differential, Temporal Anticipation Index, Occlusion Sensitivity Index, and Occlusion Response Latency. Experiments on real‑world traffic datasets show that DRIFT reduces occlusion response latency and lowers the near‑collision rate under occlusion compared with selected baselines in synthetic scenarios.

Authors:Keyi Wang, Jiaxiang Ji, Mahan Mansouri, Ahmed Aziz Ezzat
Title: Day-Ahead Electricity Price Forecasting Using a Multivariate Group Lasso Method
Abstract:
Electricity price signals in modern power systems exhibit complex dependence structures that render forecasting inherently challenging. Our analysis of real‑world pricing signals from the California Independent System Operator (CAISO) reveals complex temporal group effects, whereby the influence of explanatory variables on electricity prices persists across consecutive blocks of time due to underlying economic and operational drivers. In response, we propose a multivariate statistical method based on a Group Lasso formulation to forecast the vector of day‑ahead electricity prices, by leveraging multi‑feature temporal group effects. Our approach is evaluated on two full years of electricity prices from CAISO, demonstrating considerable improvements in point and probabilistic forecast metrics compared to a wide array of statistical and deep learning methods. Theoretical and empirical analyses confirm the effectiveness of the proposed approach in modeling realistic group effects, maintaining both interpretability and low computational complexity. When retrospectively evaluated on test data from a recent international electricity price forecasting challenge, the proposed method ranked in second place, despite having access to significantly less information than competing approaches. Finally, the proposed method is independently validated against two operational electricity price forecasting systems in CAISO, demonstrating competitive predictive performance and practical relevance.

Authors:Sravan Reddy Chintareddy, Sherwan Jalal Abdullah, Justin D. Clough, Victor S. Frost, Shawn Keshmiri, Morteza Hashemi
Title: A Vertical Look at UAV Connectivity in the Wild: Cellular vs. Starlink, 3D Characterization, and Performance Prediction
Abstract:
In this paper, we present an open‑source measurement platform designed to characterize the performance of commercial cellular (Verizon, a major US provider) and LEO satellite (Starlink) networks through real‑world flight tests in rural environments. We implement a comprehensive multi‑layer measurement approach spanning physical layer signal metrics, multi‑cell network topology, and end‑to‑end (E2E) application performance. Through an extensive flight campaign with more than 10 flight tests, 4.5+ hours of flight time resulting in more than 18K samples, we present the first detailed, open‑source dataset analyzing dual cellular and Starlink performance for low‑altitude UAV operations. Our cellular‑Starlink comparative results, which are collected \emphsimultaneously at the same time and location, demonstrate significant performance differences between the two technologies: the LEO satellite link achieves superior latency performance with 95% of Round‑Trip Time (RTT) measurements below 50 ms compared to 80% under 150 ms for cellular, and exceptional downlink capacity with 95% exceeding 25 Mbps versus only 5 Mbps for cellular. Our analysis on cellular network performance demonstrates that while higher altitudes (e.g., 330+ m above the sea level) improve signal power by 15‑20 dB via line‑of‑sight (LOS) propagation, it causes a 3‑4 × increase in handover rates, which is due to excessive multi‑cell visibility rather than signal degradation. Furthermore, we observe asymmetric impacts on the RTT performance due to handovers such that 53.5% of handovers improve RTT, but worst‑case degradation (275 ms) is 2 × larger than best‑case improvement (137 ms).

Authors:Debojyoti Dey, Ron Ofir, Christian Grussler
Title: Inversion of the Multiplicative Matrix Compound Operator
Abstract:
We study the problem of determining a matrix whose kth multiplicative compound is a prescribed matrix~M. The cardinality of the set of matrices whose kth multiplicative compound equals~M is characterized in terms of \rank(M). On the one hand, if \rank(M)\le 1, it is shown that there exist infinitely many such matrices for which a complete characterization is determined. On the other hand, if \rank(M)>1, then there exists a unique matrix ‑‑ up to an overall sign ‑‑ whose compound is~M. An algorithm for finding a matrix whose compound equals~M is detailed, and its time complexity is analyzed.

Authors:Charalambos D. Charalambous, Umarbek Guvercin, Seddik Djouadi
Title: Private & Common Information States in Decentralized Team Equilibrium via Dynamic Programming for POMDPs with Delayed Sharing
Abstract:
Witsenhausen, in his seminal 1971 paper [1], introduced decentralized partially observable Markov decision problems (POMDPs), with multiple agents or controls operating under T‑step delayed sharing information patterns. A fundamental problem in [1] is the identification of structural properties of optimal strategies that compress the information patterns into multiple information states. In this paper, we develop such structural properties of optimal strategies and associated dynamic programming (DP) equations, using the concept of decentralized sequential team equilibrium (a generalization of person‑by‑person optimality from static team theory). Within this framework, each strategy is assigned an individual value function conditioned on its delayed sharing information pattern, while the strategies of all other agents are held fixed. The resulting DP framework yields several new DP equations and characterizations of decentralized team equilibrium. Moreover, these DP equations exhibit fundamental properties analogous to those of centralized DP of POMDPs: the optimization in each agent's DP equations is performed over the agent's action space rather than over strategy spaces; each agent's multiple information states satisfy Markov recursions; and a separation principle holds. The DP equations reveal a structural compression property of optimal strategies: each agent compresses its delayed sharing information pattern into three components: 1) a private posterior distribution conditioned on the agent's delayed sharing information pattern, 2) a centralized posterior distribution conditioned on the common information shared by all agents, and 3) the agent's private information component. This structural result substantially extends Witsenhausen's Assertion 8 in [1].

Authors:Srini Ramaswamy
Title: Intelligence as Managed Autonomy: Failure, Escalation, and Governance for Agentic AI Systems
Abstract:
As autonomous and agentic AI systems scale in robotic and human‑machine environments, managing hallucination and persistent but unjustified action remains an open challenge. Rather than attributing these failures solely to model or alignment limitations, this paper explores the architectural vulnerability of unbounded autonomy ‑ the presumption that an agent should continue operating regardless of rising uncertainty. It introduces a theory of managed autonomy that defines intelligent behavior through the formal capacity to detect epistemic drift, suspend reasoning, attempt recovery, and ultimately surrender control when reliability diminishes. We instantiate this theory via the SMARt (Self‑Managing Multi‑tier Autonomous Reasoning with Regulated/Revoked transitions) model, a four‑layer framework featuring Stable, Meta‑cognitive, Assisted, and Regulated states. By developing a timed, guarded Petri net formulation, we establish theoretically bounded properties for the system, demonstrating how architecture can formally mandate escalation, constrain invalid outputs, and ensure governance reachability under specified conditions. We further analyze how incorporating domain‑specific trigger sets across varied operational settings (e.g., healthcare, robotics, etc.) can systematically preserve safety, assuming completeness and soundness criteria are met. Because these triggers are designed to be adaptive, the SMARt model accommodates the safe, controlled expansion of an agent's operational scope over time. We conclude that formalizing failure management within the autonomy lifecycle is a crucial step toward realizing reliable and governed artificial intelligence.

Authors:Jürgen Gutekunst, Armin Nurkanovic, Ekaterina Kostina, Hans Georg Bock, Robert Scholz, Amer Mesanovic
Title: Economic Nonlinear Model Predictive Control for Microgrids with Generator Up and Downtime Constraints
Abstract:
Recently there has been a lot of progress in the development of economic nonlinear model predictive control (NMPC) schemes for multistage optimal power flow (OPF) problems. However, the additional inclusion of discrete decision variables to model generator runtimes and generator startup costs can amount to large scale mixed‑integer nonlinear programs (MINLPs) that are computationally very challenging. This work investigates the practical approach that replaces the nonlinear AC power flow equations by convex quadratic approximations. In combination with the discrete generator dynamics this leads to a mixed‑integer quadratically constrained program (MIQCP) which is of significantly lower complexity and can be solved in reasonable time by off‑the‑shelf solvers such as CPLEX. We further show that simple terminal constraints are not sufficient to guarantee recursive feasibility of the NMPC scheme if constraints on generator runtime and on the number of generator startup events are present. To address this challenge we propose the use of additional time‑coupled constraints and prove the resulting recursive feasibility property. Based on the assumption of periodic dissipativity of the underlying system we can prove stability of the proposed controller. To illustrate our results, we present simulations of a realistic 6‑bus microgrid under different demand scenarios.

Authors:Esmaeil Ghorbani, Jürgen Hackl
Title: Subsystem Structure as an Inferential Resource for Coupled Engineered Systems
Abstract:
Engineered infrastructure systems pose inverse problems in which hidden states, unknown parameters, and subsystem couplings must be inferred from sparse and noisy measurements. These problems are difficult because physical subsystems are heterogeneous, sensing is partial, uncertainty is distributed across subsystem interfaces, and computational cost grows rapidly with system size. We address this challenge with probabilistic compositional inference, a graph‑based architecture that represents a coupled system as interacting subsystems, each retaining its own local model, estimator, and uncertainty representation, while coupling is handled through physically meaningful stochastic messages exchanged across subsystem interfaces. This formulation allows mechanistic, learned, and deterministic components to coexist within a single inference framework and propagates calibrated uncertainty without assembling a global augmented state or covariance. We validate the framework in three increasingly demanding settings: a sparse‑sensing canonical inverse problem, where interface couplings can also be learned from data; infrastructure‑scale power networks, where the method matches centralized joint state‑and‑parameter inference while reducing computational scaling from approximately cubic to approximately linear; and a multi‑physics turbine embedded in a power‑grid network, where heterogeneous subsystems compose hierarchically without degrading local inference or collapsing local posteriors into a global estimate. Together, these results show that subsystem structure can be exploited as the organizing principle for uncertainty‑aware inverse inference in coupled engineered systems.

Authors:Pranav Kedia, Aaron Gan, Hannah J. Williams, Andreagiovanni Reina, Heiko Hamann
Title: Speed-Weighted Adaptive Flocking for Sailing Swarms under Dynamic Environmental Forcing
Abstract:
Collective behavior models, such as aggregation and flocking, usually assume self‑propelled robots that can directly execute their desired speed and direction of motion without fundamental constraints. However, autonomous sailing robots violate this assumption. Their motion is shaped by wind‑dependent propulsion, restricted headings, and spatially varying wind conditions. In particular, maneuverability is coupled to wind speed: in weak wind, sailboats may turn only slowly or not at all, whereas stronger wind enables faster turns. This introduces transient heterogeneity in speed and maneuverability across the flock. We focus on this fast‑slow coordination problem in sailing robot flocks. To study this problem, we introduce SailSwarmSwIM, a reduced‑order simulator for autonomous sailing robot swarms that captures wind‑dependent speed and maneuverability, no‑go zones, tacking behavior, and steady or gusty wind fields. To design our novel flocking technique, we start from the Couzin model and introduce a speed‑weighted social interaction rule that accounts for each robot's transient motion constraints. A key result is that increasing the social influence of slower robots improves polarization and reduces close encounters. This effect arises from a balance between attraction to fast neighbors, which helps maintain movement, and cohesion around slow neighbors, which prevents the flock from fragmenting. Together, our simulator, SailSwarmSwIM, and the speed‑weighted interaction rule provide a modeling framework for studying adaptive collective behavior in robotic fleets whose motion capabilities are continuously shaped by wind.

Authors:Melissa Z. Pan, Negar Arabzadeh, Mathew Jacob, Fiodar Kazhamiaka, Esha Choukse, Matei Zaharia
Title: Natural Language Query to Configuration for Retrieval Agents
Abstract:
Modern retrieval agents expose many configuration choices ‑‑ LLM, retriever, number of documents, number of hops, and synthesis strategy ‑‑ each shaping both answer quality and serving cost. Today, these pipelines are typically hand‑tuned once per workload, leaving substantial per‑query optimization untapped. We formulate the problem: given a natural‑language query and either an accuracy or a budget target, select from a predefined pipeline catalog the configuration that minimizes cost or maximizes accuracy at inference time. We propose BRANE, which uses an LLM to convert each query into workload‑specific characteristics, then trains a lightweight per‑configuration predictor that estimates whether the pipeline will answer the query correctly. At inference time, BRANE selects the configuration that maximizes predicted correctness penalized by cost, exposing a tunable cost‑quality tradeoff without retraining. Across MuSiQue, BrowseComp‑Plus, and FinanceBench, BRANE consistently pushes the cost‑quality Pareto frontier, matches the best fixed configuration's accuracy at up to 89% lower cost, and outperforms LLM‑routing, rule‑based, and fine‑tuned Qwen3‑4B baselines. These results show that per‑query configuration of the full retrieval pipeline is a practical alternative to static workload‑level tuning.

Authors:Vito Mengers, Oliver Brock
Title: Riding the Shifting Potential: When Reactive Control Suffices for Multi-Goal Behavior
Abstract:
Reactive control is often considered insufficient for multi‑objective tasks because conflicting objectives give rise to local minima. We argue this limitation is not inherent but arises from static encodings that fail to reflect how objectives currently interact. We exploit the interaction structure encoded in a graph‑based world model by extending it with nullspace projections: conflicts are resolved where they arise by projecting lower‑priority gradients into the nullspace of higher‑priority ones, with priorities determined continuously from the current state. We demonstrate this in two domains where conflicts between objectives are central: navigation around non‑convex obstacles, where static potential fields fundamentally fail, and planar pushing of non‑convex objects, where our method achieves 100% success across one‑hundred configurations versus 0% for the steepest‑descent baseline and ～55% for diffusion policy, without demonstrations or retraining. The same formulation transfers directly to a real robot with additional perceptual and kinematic constraints, accommodating them through the same mechanism.

Authors:Murat Moran
Title: Risk Averse Alert Prioritization for IDS Using Subnormal Gaussian Fuzzy Models
Abstract:
Modern intrusion detection systems generate thousands of alerts daily, but alert fatigue severely limits security operations effectiveness due to too many false positives or low‑impact events. We address this by proposing a principled framework for alert prioritization based on subnormal Gaussian fuzzy numbers, explicitly modeling three sources of uncertainty: threat severity, detection confidence, and organizational risk attitude. Each alert is represented as a fuzzy number with the core indicating severity, spread indicating uncertainty, and height reflecting detection reliability. We apply ranking indices to prioritize alerts, allowing organizations to tune security posture through a risk‑attitude parameter. Experimental validation on CIC‑IDS2017 and NSL‑KDD demonstrates greater robustness than baselines under detector degradation (0.9963 vs 0.8215 NDCGrel@100), with distinct differentiation in mid‑confidence alerts and near‑parity with baselines under robust detectors. The framework is theoretically grounded, computationally efficient, provides interpretable reasoning, and remains robust across detector families and miscalibration scenarios.

Authors:Jan Rüdiger, Max Schenke, Daniel Weber
Title: Container Unloading via Reinforcement Learning: Picking Order, Deadlock Avoidance, and Proof-of-Concept Simulation
Abstract:
Unloading containers in the courier, express and parcel industry is a physically demanding and labor‑intensive work. Automatizing this process is an important step towards increasing the efficiency of parcel‑handling systems. This work investigates the potential of reinforcement learning to learn a policy for item selection in container unloading scenarios. For that, a simulation environment is created and a masked deep Q‑learning with a specially designed neural network architecture is implemented. The results indicate that the agent can learn to select items with an average success rate of 60 %, which is significantly better than a random policy at a random chance of 20 %. The findings suggest that RL could be a promising approach for automatizing item unloading tasks in the future.

Authors:Joas Kahlert, Ruiting Wang, Jonas Mårtensson
Title: En-route Charging Coordination for Electric Trucks
Abstract:
The electrification of long‑haul freight transport introduces several new challenges, such as the limited capacity and congestion at en‑route charging infrastructure. To reduce waiting times during peak periods, this paper proposes a framework for coordinated charging scheduling. The approach employs a mixed‑integer formulation to optimize charging‑related costs across charging, operation, battery degradation, and congestion delay, considering a range of scenarios. The results demonstrate that coordinated scheduling yields substantial cost savings up to 36% compared to uncoordinated scheduling, particularly by reducing battery degradation and delay costs.

Authors:Ayse Nur Pehlivanoglu, Aimin Li, Elif Uysal
Title: In-Orbit Intelligence or Ground Offloading? Inference Freshness under Intermittent Satellite Connectivity
Abstract:
This paper studies how to balance onboard and ground computation under intermittent LEO connectivity for optimized inference freshness. As connectivity varies in time, the system switches among the actions of onboard computation, cached semantic transmission, raw‑data offloading, and waiting. We define Age of Inference (AoInf) as the performance metric, where the age resets only upon successful task‑valid updates. We formulate long‑run average AoInf minimization as a finite‑state average‑cost semi‑Markov decision process whose state captures the ground AoInf, orbital contact phase, cache occupancy, and cache age. We then transform the SMDP into an equivalent average‑cost MDP and compute the solution via normalized relative value iteration (RVI). Numerical results indicate that the resulting hybrid policy reduces average AoInf relative to onboard‑only and offload‑only baselines, while requiring less computational resources on the satellite than the former, and fewer communication resources than the latter.

Authors:Joseph M. Pisani, Christopher T. Aksland, Philip M. Renkert, Joseph Broniszewski, Vismay Vyas, Andrew G. Alleyne, Donald J. Docimo, Justin P. Koeln, Neera Jain, Herschel C. Pangborn
Title: Graph-Based Modeling, Control, and Optimization for Multi-Domain and Multi-Timescale Energy Systems
Abstract:
Modern energy systems in vehicles and built infrastructure are governed by high‑dimensional dynamics spanning multiple physical domains (e.g., electrical, thermal, mechanical) and timescales. This tutorial paper presents a graph‑based modeling approach created to facilitate the modeling, analysis, control, estimation, optimization, and design of these systems. Matured and validated through more than a decade of research spanning multiple academic institutions and companies, the graph‑based approach combines transient energy conservation with an explicit mathematical representation of the network by which energy is stored and transferred within a system. Following a mathematical overview of graph‑based models, examples of multi‑domain component and system models from the recent literature are presented, including single‑phase thermal systems, two‑phase thermal systems, and electro‑mechanical systems. This is followed by a survey of recent applications for decentralized and hierarchical model predictive control, design optimization, and control co‑design. Lastly, the paper describes an open‑source toolbox created to facilitate the generation and analysis of graph‑based models.

Authors:Joas Kahlert, Ruiting Wang, Jonas Mårtensson
Title: Congestion Forecasting for Electric Vehicle Charging Scheduling with Fluid Queues
Abstract:
To support the adoption of electric transport systems, public charging opportunities are becoming increasingly important. In this dynamic environment, a central challenge for route planning and charging scheduling is forecasting charging‑station availability under fluctuating demand. In this work, we propose a fluid‑based forecasting method that accounts for uncertainty in both known and unforeseen electric vehicle arrival patterns while respecting station capacity constraints. We further evaluate the congestion forecasting method by applying it to an electric vehicle scheduling problem. Compared to scheduling frameworks that rely on standard baselines, charging schedules based on the fluid congestion forecasting model reduce waiting‑related downtime by up to 14%. Finally, we quantify how increased knowledge of vehicle arrivals and different levels of station congestion affect overall system performance.

Authors:Jiarui Yu, Zhiyu He, Wenbin Wang, Colin N. Jones, Florian Dörfler, Hanmin Cai
Title: Load Management of Distribution Systems via Online Dynamic Pricing
Abstract:
The growing adoption of electric vehicles (EVs) is increasing peak demand in distribution systems, which can threaten grid stability and reduce operational efficiency. Dynamic electricity pricing is a promising means of mitigating these peaks by shifting flexible demand. However, most existing approaches rely on detailed user‑level consumption data and behavioral models, which are often difficult to obtain in practice and may raise privacy concerns. This paper proposes an Online Feedback Optimization (OFO) algorithm for day‑ahead price design with limited data, where only aggregate loads are observed. OFO updates prices iteratively using aggregate load measurements, enabling effective peak reduction without access to individual user data. The formulation also includes a term that penalizes deviations in total electricity cost relative to a reference tariff. Although relying only on aggregate load measurements, the OFO price updates efficiently converge to the optimal price. In finite‑horizon simulations, OFO achieves peak reduction close to that of the Stackelberg benchmark with full model information. Meanwhile, its computational effort is substantially lower. Additional tests under multiple initial conditions and delayed charging‑window mismatch further confirm the robustness of the proposed method. Overall, these results show that OFO is a scalable and computationally efficient approach for peak‑demand management in distribution systems with limited observability.

Authors:Baby Diana, Priyanka Singh, Shyam Kamal, Sandip Ghosh, Bijnan Bandyopadhyay
Title: A Fixed-Time Sliding-Mode Framework for Constraint Optimization
Abstract:
This paper develops a robust fixed time optimization framework for constrained problems that guarantees exact constraint satisfaction and convergence to KKT points within fixed time , independent of initial conditions. The approach treats the Lagrange multipliers as control inputs, composed of an equivalent control and a switching control, with the system states representing the decision variables. An equivalent control steers the gradient flow to a local KKT point asymptotically for nonconvex objectives and to unique global optimum in fixed time for convex objectives. Constraint enforcement is achieved by embedding the equality constraints directly as a sliding manifold, with a fixed time switching control ensuring rapid and reliable feasibility. The framework further accounts for the matched disturbances, providing robustness guarantees that are theoretically characterized and illustrated using spherical constraints. Numerical studies on a 3‑bus AC optimal power flow problem and distributed consensus=based parameter estimation problem demonstrate the effectiveness, scalability and robustness of proposed approach.

Authors:Shriya Pandey, Devaprakash Muniraj
Title: Critical Infrastructure Defense Against Aerial Swarms Under Sensing Uncertainty: Online Allocation With Finite-Time Guarantees
Abstract:
This article presents a closed‑loop, uncertainty‑aware framework for defending a protected zone against coordinated incursions by swarms of small uncrewed aircraft systems (UAS). The interaction structure of the attackers is modeled as time‑varying, while defenders operate under imperfect sensing. The proposed criticality‑driven defender‑to‑attacker assignment strategy integrates three components: a probabilistic graph‑based representation of the attacking swarm inferred from uncertain observations; a risk‑aware attacker criticality model combining time‑to‑breach urgency with uncertainty; an online defender allocation mechanism that assigns and selectively reassigns defenders while limiting switching‑induced instability through robust execution constraints. Analytical guarantees are established within a filtration‑based first‑hitting‑time framework. In particular, finite‑time triggering of the first capture event following detection is proven, and explicit mixed linear‑geometric upper bounds are derived for the expected neutralization time. Monte Carlo simulations demonstrate the effectiveness of the proposed framework, achieving 85.6% neutralization efficiency under probabilistic sensing and 99.9% under deterministic sensing. Systematic ablation and sensitivity studies further quantify how detection thresholds and coordination parameters influence reliability and time‑to‑first‑capture.

Authors:Zhisen Jiang, Florian Dörfler, Saverio Bolognani
Title: Incentive-Based Load Curtailment with Limited Information: A Bilevel Zeroth-Order Learning Approach
Abstract:
Incentive‑based load curtailment unlocks critical demand‑side flexibility but is hindered by the limited knowledge of private user parameters and the inherent nonsmoothness of responses due to physical device constraints. We address this via a constrained bilevel optimization framework and propose the Bi‑ZOL (Bilevel Zeroth‑Order Learning) algorithm. Unlike conventional black‑box methods, Bi‑ZOL exploits the bilevel structure to decompose the hypergradient, integrating the exact analytical information of the SO's objective with a zeroth‑order estimate of the unknown response sensitivity. This structural decomposition‑based learning method mathematically smoothes the nonsmooth response landscape and reduces hypergradient estimation error. We provide theoretical convergence guarantees to an approximate stationary point and demonstrate through simulations that Bi‑ZOL achieves near‑optimal performance.

Authors:Ricus Husmann, Sven Weishaupt, Harald Aschemann
Title: Enforcing Soft Monotonicity Constraints for Recursive Gaussian Process Regression in Real Time
Abstract:
In this work, we introduce a real‑time capable algorithm for considering monotonicity assumptions for recursive Gaussian Process regression (RGP). Therefore, we present how to efficiently calculate the RGP gradients online. Then, we utilize an extended Kalman filter and pseudo‑measurements in combination with a ReLU pseudo‑measurement function to enforce soft inequality constraints. This work builds upon a previously published conference paper with the same goal and a similar fundamental approach. Opposite to our previous work, however, we now use an exact covariance calculation for the RGP gradients. Furthermore, we also present a real‑time optimized version of this algorithm with less simplifications compared to the previously published version. These and several other algorithmic innovations lead to an algorithm with greatly improved numerical robustness. The algorithm is validated and compared to its previously published version for a 2D numerical example. The paper is concluded with a successful experimental validation of the developed algorithm for the monotonicity‑preserving learning of pneumatic valve characteristics for the control of a pneumatic system, leveraging a partial input ‑ output linearization.

Authors:Pietro Kristović, Andrej Jokić, Mircea Lazar
Title: Dynamic Output-Feedback Controller Synthesis for Dissipativity and $H_2$ Performance from Noisy Input-Output Data
Abstract:
In this paper we propose dynamic output‑feedback controller synthesis methods for discrete‑time linear time‑invariant systems. The synthesis goal is either to achieve dissipativity with respect to a given quadratic supply rate, or to achieve given H_2 performance level. It is assumed that the autoregressive model of system dynamics is unknown, expect for the noisy disturbance term which is not part of the performance channel. Instead, we have a recorded trajectory of inputs and outputs which can be corrupted by an unknown but bounded disturbance. Methods are formulated in terms of linear matrix inequalities parametrized by a scalar variable, while in noiseless case they reduce to linear matrix inequalities. Within the considered setting, synthesis procedures are non‑conservative.

Authors:Qiuhua Pan, Yukai Shen, Liwei Zhang, Cailian Chen, Xinping Guan
Title: Sample Complexity of Policy Gradient for Log-Growth Control
Abstract:
We study the sample complexity of policy gradient for log‑growth control ‑‑ the problem of learning, from observed state transitions, a feedback gain that optimally stabilizes a scalar linear system driven through a multiplicative‑noise actuation channel. The objective J(K) = \mathbbE[\log|1+BK|] is the top Lyapunov exponent of the closed loop. This problem carries a structural difficulty we call the cusp obstruction: the optimal gain K^ always places the noise singularity b_\rm sing(K) = ‑1/K in the interior of the support. At this singular optimum the policy gradient exists only as a Cauchy principal value, not as a Lebesgue integral, and the natural single‑sample gradient estimator has infinite variance. Standard first‑order stochastic‑optimization analysis is thus inapplicable at the optimum, and merely smoothing the objective does not resolve the difficulty. The obstruction, however, has an exploitable symmetry: the Cauchy kernel is an odd function of the displacement from the moving pole, so pairing each observation with its reflection through the pole cancels the divergent part. This one cancellation simultaneously controls the population curvature, the gradient‑estimator variance, and the bias incurred when the noise density is estimated. Combining these bounds with a closed‑form single‑transition gradient oracle, we prove that projected mini‑batch policy gradient, initialized in any compact subset of the stabilizing region, attains total sample complexity \tildeO(1/η) when the noise density is known and \tildeO(η^‑(2s+1)/(2s)) when it must be estimated, for C^s noise densities with s \geq 2.

Authors:Chayan Banerjee, Ethan Goan
Title: Breaking the Epistemic Trap: Active Perception Under Compound Uncertainty
Abstract:
Deploying reinforcement learning in safety critical domains, from autonomous vehicles to medical decision support, is constrained by failures arising when systems encounter unfamiliar conditions. We argue that the fundamental bottleneck is not individual challenges like changing dynamics or incomplete observations, but their synergistic interaction, which we term the Epistemic Trap: agents cannot estimate their state without knowing system dynamics, nor learn dynamics without accurate state information. Proof‑of‑concept experiments in simulated locomotion reveal that combining these uncertainties causes failures far worse than either challenge alone, a 77% performance degradation against the 46% by adding the individual effects, demonstrating compounding failure modes that conventional methods overlook. Such approaches adopt a passive epistemic stance that cannot resolve this coupled uncertainty. We propose reframing safety as an information problem, introducing an Adaptive Safety Architecture built around three contributions: the Compound Uncertainty Coefficient (κ), a mutual information based metric that quantifies state dynamics coupling and is computable online without full joint belief inference; information seeking policies governed by a MaxInfoRL objective that actively probe system dynamics; and regime‑adaptive safety constraints that tighten as epistemic coupling rises. This paradigm shift, from passive robustness to active perception, offers a principled path toward decision making systems that operate under uncertainty, recognize their own ignorance, and act strategically to resolve it.

Authors:Bendegúz Györök, Roland Tóth, Maarten Schoukens, Tamás Péni
Title: Efficient stochastic model-predictive control based on the meta-state-space representation
Abstract:
Stochastic model‑predictive control (SMPC) has evolved to a powerful framework for the control of stochastic dynamical systems. SMPC utilizes a probabilistic uncertainty description to provide a systematic trade‑off between the control objective and constraint satisfaction in a statistical sense. However, the majority of existing SMPC methods face challenges related to computational tractability due to the need for stochastic inference. Approaches that apply accurate inference are computationally demanding, which can lead to serious limitations in the implementability of these methods. Hence, in practice, the uncertainty propagation and the resulting distributions are typically approximated, e.g., by Gaussian distributions. These approximations promote computational efficiency, but are often too conservative, becoming a limiting factor in the representation of stochastic state evolution and the implied guarantees. To overcome this fundamental limitation of SMPC approaches, we propose a novel formulation based on the meta‑state‑space (MSS) representation of stochastic dynamical systems. The proposed MSS‑based SMPC scheme offers a computationally efficient way to forward propagate the uncertainty with a flexible and highly accurate approximation of the probabilistic system description. With the presented method, the entire output probability density function can be directly shaped, which is unprecedented among existing SMPC techniques. Finally, we provide a detailed theoretical analysis and demonstrate the effectiveness of the proposed methodology via an extensive simulation study.

Authors:Ibon Gracia, Qi Heng Ho, Luca Laurenti, Morteza Lahijanian
Title: Provably Safe Motion Planning Under Unknown Disturbances
Abstract:
We present a provably safe sampling‑based motion planning algorithm for robotic systems affected by random disturbances of unknown distribution. We consider systems with linear or linearizable dynamics evolving in workspace with arbitrary‑shaped obstacles subject to state and control constraints. Safety requirements are formulated as chance‑constraints. Our approach leverages data from trajectories of the system to learn a Wasserstein ambiguity tube, i.e., a sequence of ambiguity sets, which contains the trajectory of the system's state distribution with high confidence. This ambiguity tube is then used in a probabilistically complete algorithm to grow a sampling‑based motion planning tree that respects the constraints of the problem. We show that learning several lower‑dimensional ambiguity tubes instead of a single high‑dimensional one effectively reduces the conservatism and boosts scalability. Additionally, we design an efficient bandit‑based validity checker that remarkably increases the empirical performance of our approach without sacrificing probabilistic completeness. Case studies show our algorithm finds valid plans in cluttered environments under strict safety thresholds, outperforming state‑of‑the‑art methods.

Authors:Akib Mostabe Refat, Mohammed F. Al-Mashdali, Alan Cordic, Abdulaziz Qwbaiban, Emad Abukhousa, Kazi N. Hasan, M. A. Abido, Mohammed Al-Muhaini
Title: Voltage and Frequency Stability Analysis of Transmission Power Grids with EV Charging Stations
Abstract:
The large‑scale Electric Vehicle (EV) integration into the electricity grid has initiated significant challenges to grid stability issues due to dynamic loadability events. Although electric vehicle impacts on distribution systems are well studied, transmission‑level investigations remain limited. In this research paper, case scenarios of EV load models as charging stations have been considered for stability analysis (Voltage and Frequency Stability) to address EV operation on the transmission grid. It is also noted that the operation of EV stations due to their high loadability causes more stability complexities to the grid compared to other loads in a power network. Simulations have been conducted on two different power networks of the IEEE‑9 and IEEE‑39 bus test systems, respectively.

Authors:Haoyu Li, Xiangru Zhong, Hao Cheng, Bin Hu, Huan Zhang
Title: Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial
Abstract:
Learning‑based methods for synthesizing controllers have gained popularity due to their high expressiveness and strong empirical performance. However, in safety‑critical scenarios such as autonomous driving, robotics, and power systems, empirical performance alone is insufficient, and formal verification of controller properties such as stability and safety is highly desirable. Unfortunately, many prior verification approaches are either tied to specific structural assumptions on the system or the certificate, making them difficult to transfer across settings, or suffer from poor scalability on higher‑dimensional neural network systems. In this tutorial, we present a unified framework that aims to mitigate this gap via bridging control with the state‑of‑the‑art neural network verifier α,\!β‑CROWN (alpha‑beta‑CROWN). At its core, α,\!β‑CROWN is a general‑purpose bounding engine for nonlinear functions represented as computation graphs: given an input domain, it can produce certified bounds and explicit linear relaxation of the nonlinear function. These certified bounds are useful on their own for tasks such as reachability analysis, and they also provide the foundation for more complex routines that perform satisfiability checking and optimization. More specifically, many control problems reduce to verifying real‑valued inequalities over a state domain (e.g., Lyapunov theory). Consequently, α,\!β‑CROWN enables scalable verification of such conditions by computing tight bounds and recursively partitioning and pruning subdomains based on the bounds. Thanks to GPU parallelization, this pipeline demonstrates superior scalability on verification and optimization problems that are challenging for traditional approaches. In this tutorial, we discuss the basics of α,\!β‑CROWN and introduce its application to various control‑related tasks.

Authors:Yang Zhao, Jungeun Lee, Jeong hwan Jeon, Sze Zheng Yong
Title: Learning Safe-by-Design Neural Network Controllers
Abstract:
Safety filters constructed from control barrier functions (CBFs) are commonly appended to pre‑trained neural network controllers to enforce safety requirements. However, this decoupled design with hand‑tuned, fixed CBF parameters often fails to adapt to the underlying controller, yielding overly conservative solutions. Thus, given a valid CBF, we address these limitations by jointly learning a neural network controller and neural‑network‑parameterized CBF parameters, enforcing the resulting affine safety constraints by construction and avoiding an online quadratic program (QP) safety filter at run time. To further improve computational efficiency and scalability, we introduce a lightweight projection architecture that enforces constraints without full constraint enumeration. Extensive simulation evaluations demonstrate reliable, scalable safety constraint satisfaction at reduced computational cost.

Authors:Haoxiang You, Yilang Liu, Davis Zong, Qian Wang, Teeratham Vitchutripop, Qi Wang, Daniel Rakita, Ian Abraham
Title: Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient
Abstract:
We present the stochastic decoupled policy gradient (SDPG), a lightweight visual reinforcement learning (RL) method that trains diverse visuomotor control policies end‑to‑end within a few hours on a single NVIDIA RTX 4080 GPU. SDPG estimates policy gradients via random perturbations of trajectory rollouts, requiring orders of magnitude fewer batch‑rendered environments and substantially reducing compute and memory overhead. On visual MuJoCo benchmarks, SDPG consistently outperforms baseline methods in training time, memory usage, and rewards. Finally, to support future research, we introduce a suite of realistic visual robotics benchmarks spanning dexterous manipulation, challenging locomotion, and demonstrate effective sim‑to‑real transfer on physical hardware.

Authors:Zexin Li, Nikil Dutt, Cong Liu
Title: Orion: Enabling Self-adaptive Memory Management for On-device Online Continual Learning
Abstract:
Online continual learning (OCL) enables real‑time adaptation to new data, making it crucial for dynamic robotic applications. However, its practical deployment is hindered by memory constraints in resource‑limited systems, which affect key trade‑offs in training latency, plasticity, and stability. Unlike offline parameter tuning, which cannot account for the dynamic shift in memory pressure and workload complexity as OCL progresses, an online and self‑adaptive approach is essential for robust on‑device deployment. This paper proposes Orion, a holistic framework designed to co‑optimize training latency, plasticity, and stability of state‑of‑the‑art OCL models under strict memory constraints, enabling feasible on‑device deployment. At its core, Orion leverages URGE, a unified runtime indicator grounded in the ``Buckets effect'' principle that system performance is bounded by its scarcest resource, to dynamically reallocate memory across OCL components by jointly coordinating batch processing, replay buffers, and optimization strategies at both the OS and application level. Furthermore, Orion introduces system‑level data prefetching techniques to maximize efficiency. A system prototype of Orion has been implemented using the widely adopted \textttAvalanche‑lib and thoroughly evaluated across a diverse range of OCL algorithms, benchmarks, and hardware platforms commonly used in autonomous robotic applications. To further demonstrate its practical utility, Orion is integrated into a realistic autonomous navigational robot powered by OCL. The results show that Orion achieves significant training speedups while maintaining balanced performance and effectively adapting to various scenarios, all with minimal runtime, memory, and energy overhead, making Orion a practical solution for on‑device continual learning.

Authors:Denisa-Andreea Constantinescu, David Atienza
Title: GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers
Abstract:
At global scale, data‑center electricity demand is growing faster than the grids that supply it, while system operators increasingly require large flexible loads that can adjust power within seconds to absorb variable wind and solar generation. For multi‑megawatt AI/HPC facilities, the key unresolved question is practical and measurable: how quickly can the software stack translate a grid request into a real change in GPU power at the facility meter, where commitments are settled? We answer this on real hardware with GridPilot, a three‑tier predictive controller operating across milliseconds, seconds, and hours, augmented by a deterministic safety‑island bypass for fast response. On a three‑GPU NVIDIA V100 testbed, GridPilot achieves a measured end‑to‑end trigger‑to‑target response of 97.2 ms, which is 6.9x faster than the 700 ms requirement of Nordic Fast Frequency Reserve. We further incorporate an instantaneous Power Usage Effectiveness (PUE) correction so dispatched commitments remain robust at meter level rather than only at IT load level. In replay experiments across six representative European grids (from Sweden to Poland), the PUE‑aware controller closes 2.5‑5.8 percentage points of cooling‑overhead drag. GridPilot is released as open source and serves as a proof of concept that MW‑scale AI/HPC demand can be engineered as controllable, grid‑responsive flexibility by design.

Authors:Judy Fox, Geoffrey Fox
Title: Experiments in Agentic AI for Science
Abstract:
This paper details two novel frameworks for developing autonomous, agentic AI in scientific workflows. Both systems leverage a hybrid Local Body, Remote Brain architecture via Google Colab, utilizing Python‑based local orchestrators to invoke large language model (LLM) cloud backends. The first agent, DeepTS/DeepCollector, automates the large‑scale curation, extraction, and deduplication of time‑series datasets. The second, DeepScribe, is an autonomous presentation analyzer that converts visually dense, mathematically complex physics lectures into structured scientific reports. Through practical systems engineering‑such as granular attribute extraction (Cellular RAG), remote data inspection, and distributed concurrency controls‑we demonstrate how agentic AI can overcome the context and reasoning limitations of current state‑of‑the‑art systems to rigorously support scientific workflows. Finally, we outline a generalization of DeepTS to support deep knowledge graphs and discuss the application of this conceptual approach to high‑energy physics (DeepQCD).

Authors:Marco Civera, Gabriele Dessena, Marina Cózar Alcázar, Saray Undiano Echániz, Oscar E. Bonilla-Manrique
Title: International Space Station operational modal analysis via iterative pole relocation
Abstract:
In recent years, increasing aerospace safety requirements have intensified the demand for reliable structural damage detection. This work presents an Operational Modal Analysis approach for accurate modal parameter estimation, with an application to space structure monitoring. The proposed System Identification (SI) method innovatively combines the Natural Excitation Technique (NExT) with the Fast and Relaxed Vector Fitting (FRVF) algorithm, which uses an iterative least‑squares optimisation. A preliminary validation is first carried out on a numerical beam model, comparing results with analytical solutions and the established Natural Excitation Technique with Eigensystem Realisation Algorithm (NExT‑ERA) and Stochastic Subspace Identification with Canonical Variate Analysis (SSI) methods. Then, operational validation is performed on real acceleration data from the Space Acceleration Measurement Systems aboard the International Space Station. Identified vibration modes from NExT‑FRVF and NExT‑ERA show comparable results after signal processing, with mode consistency assessed by repeated occurrence and physical interpretation, while SSI fails to identify most. The output‑only algorithm proves to be highly reliable, outperforming benchmark methods under noisy conditions on a numerical system and offering reliable identifications on the experimental data.

Authors:Francesco Conte, Fernando Mancilla-David, Federico Silvestro, Samuele Grillo
Title: Small-Signal Stability Manifolds in Converter-Dominated Power Systems
Abstract:
This paper proposes a systematic framework to assess the small‑signal stability of power systems with high shares of grid‑following inverter‑based resources (IBRs) under varying controller parameters and operating conditions. Stability manifolds are introduced to identify controller‑parameter regions that ensure stability across multiple scenarios. Full‑network linearization and eigenvalue analysis are combined with adaptive sampling based on probabilistic support vector machine classification to approximate stability boundaries efficiently, while surrogate optimization identifies feasible initial controller settings meeting bandwidth and phase‑margin constraints. The approach is validated on a modified Cigré European HV network benchmark with 50 operating scenarios and increasing inverter penetration. Results show that stability sensitivity grows with inverter share, interactions among IBRs reshape admissible parameter regions, and simplified equivalent‑network models may overlook critical system‑level limitations. The framework supports stability‑oriented controller design and interconnection studies in converter‑dominated systems.

Authors:Ayman Elnashar
Title: When Does a Neural Receiver Help? Calibration-Drift Benchmarking and Detect-and-Rollback for 5G/6G NR
Abstract:
Convolutional neural receivers such as DeepRx outperform minimum mean‑square error physical uplink shared channel detection on in distribution channel and waveform configurations, but their behavior under calibration drift when transmitter or channel parameters depart from the training envelope is poorly characterized.

Authors:Philippe Reynier
Title: Inflatable devices for planetary aerocapture and aerobraking manoeuvres
Abstract:
Future missions to Mars and Venus will make use of aerobraking and aerocapture in order to gain mass through the saving of fuel at planetary arrival. So far only aerobraking has been demonstrated, if the Mars Premier project has paved the way for aerocapture, no demonstration was performed due to the project interruption. The use of these techniques induces additional constraints for planetary probes, since additional heating and mechanical loads have to be carefully managed. Moreover, aerocapture requires a high level of accuracy for the Guidance Navigation and Control aspects, since a pass at an altitude of the atmosphere with a different density could lead to the vehicle destruction. This document surveys the existing state‑of‑the‑art on inflatable devices (including ballutes, sails, or inflatable heat‑shield capsule) for orbital manoeuvres at planetary arrival.

Authors:Y. Harsha Vardhana Reddy, Soumyendu Raha
Title: Low Latency Stand Alone Compute-Efficient Forecasting of Marine Engine Time Series Data
Abstract:
The operational reliability of a high performance marine vessel depends critically on the health of its marine propulsion systems, which are increasingly subjected to diverse operational loads and environmental stressors. This paper proposes a robust mathematical framework for non‑linear state‑space forecasting of marine engine parameters using adaptive‑window multi‑particle stochastic differential equations. Traditional time‑series models such as Vector Autoregressive Integrated Moving Average, often fail to capture the inherent stochasticity and transient dynamics of complex systems due to their reliance on fixed‑window linear assumptions. To address this, we develop a dual‑layered estimation approach: first, an adaptive lookback mechanism dynamically adjusts the learning window size based on the instantaneous drift magnitude, ensuring responsiveness during non‑stationary regimes. Second, a Multi‑Particle ensemble is evolved via Euler‑Maruyama discretization, where each particle trajectory represents a stochastic realization of the system state. To refine the ensemble mean and mitigate the "noise‑chasing" behavior of raw estimators, a Girsanov transform induced change of probability measure is implemented, assigning higher probabilistic weights to particles that align with the physical drift. Theoretical evaluation and empirical benchmarking demonstrate that the proposed adaptive SDE framework significantly outperforms classical statistical baselines in multi‑step prediction stability and computational efficiency. The model provides a scalable, "grey‑box" solution for real‑time risk quantification in systems characterized by high‑frequency volatility and non‑linear transitions.

Authors:Xingjian Xue, Sze Zheng Yong
Title: Robust Tracking of Curvature-Constrained Paths for Uncertain Dubins Systems
Abstract:
This paper presents a robust tracking controller for tracking curvature‑constrained paths by vehicles/robots with uncertain Dubins dynamics. Although Dubins paths have been widely used in vehicular and robotic applications, robust and convergent tracking under model uncertainties remains understudied. To address this, we propose path tracking controllers based on sliding mode control, formulated in the transverse coordinate frame, which guarantee invariance and convergence of both lateral and heading errors to zero in the presence of bounded disturbances. Simulation results show that the proposed method reliably tracks paths despite disturbances and significantly outperforms existing methods based on sliding mode controllers.

Authors:Pietro Zanotta, Dibakar Roy Sarkar, Honghui Zheng, Somdatta Goswami, Ján Drgoňa
Title: CINOC: Cardinality-Invariant Neural Operator Policies for Scalable PDE Control
Abstract:
Controlling partial differential equations (PDEs) with learning‑based policies remains fundamentally limited by fixed‑dimensional representations: policies trained for a specific sensor, actuator, or agent configuration typically fail when the configuration changes. This limitation is particularly severe in multi‑agent PDE control, where policies do not scale across population sizes without retraining. We address this challenge by introducing Cardinality Invariant Neural Operator Control (CINOC), reformulating PDE control as an operator learning problem that maps state fields to continuous control functions and trains them end‑to‑end through differentiable PDE solvers, yielding policies that naturally adapt to varying sensor and actuator configurations. Remarkably, CINOC policies trained on small swarms exhibit cardinality invariance, allowing for zero‑shot transfer to significantly larger populations as well as robustness to partial agent failure. This scalability arises from agents sharing a common policy and coordinating through their physical environment, which produces an emergent self‑normalization effect. To explain this phenomenon, we provide a theorem grounded in mean‑field theory demonstrating that policy gradients computed from finite‑agent systems converge to those of a continuous control limit. Empirically, we validate CINOC on tracking, stabilization, and density transport across linear, nonlinear, chaotic, and turbulent PDEs.

Authors:Nikolas Sacchi, Giacomo Basile, Silvia Siri, Manuela Minetti, Andrea Bonfiglio, Antonella Ferrara
Title: Optimal Dispatch of Connected and Autonomous Electric Vehicles to Enhance Short-Term Grid Flexibility in Smart Cities
Abstract:
This paper proposes a coordinated energy‑mobility dispatch framework for grid support service provision in smart cities under time constraints. In particular, a scenario in which a distributed system operator requests a specified amount of energy within a given deadline is considered. A fleet of connected autonomous electric vehicles equipped with virtual battery partitioning is dynamically dispatched toward vehicle‑to‑grid stations. The routing problem is formulated as a periodically updated resource‑constrained shortest path, accounting for time and energy constraints with congestion‑dependent travel times derived from a dynamic traffic model. At the vehicle level, a model predictive control strategy regulates speed to satisfy mobility energy requirements while ensuring deadline compliance. The framework is validated through simulations on the urban network of Rapallo (Italy), demonstrating robustness against congestion‑induced delays.

Authors:Feng Lin, Caisheng Wang, Jun Chen, Xiang Yin
Title: Deterministic and Nonblocking Supervisory Control of Discrete Event Systems under Cyber Attacks
Abstract:
We investigate deterministic and nonblocking supervisory control of discrete event systems under cyber‑attacks using the ALTER (Attack Language for Transition‑basEd Replacement) model. While prior works consider supervisory control that achieves either the large (upper bound) language or small (lower bound) language separately, deterministic supervisory control achieves both large language and small language at the same time to ensure that the language generated by the supervised system is unique and deterministic. We introduce two new concepts of CA‑D‑controllability and CA‑D‑observability and prove that they are necessary and sufficient for the existence of a deterministic supervisor. For nonblocking supervisory control, the objective is to ensure that the supervised system can always reach marked states under any attack scenario. We prove that relative closure, CA‑D‑controllability, and CA‑D‑observability together are necessary and sufficient for the existence of a nonblocking supervisor. We further develop methods to verify CA‑D‑controllability and CA‑D‑observability. We also illustrate our results using a robotic system example.

Authors:Giacomo Ganassoli, Francesco Mazzeo, Cecilia Pasquale, Silvia Siri, Mauro Salazar
Title: Justice-informed Planning of Intermodal Autonomous Mobility-on-Demand Systems under Operational Constraints
Abstract:
To date, most of the research on transport planning has focused on optimizing revenues or utilitarian metrics such as average travel times, which often ends up penalizing the worst‑off for the sake of profit or efficiency. At the same time, most of the research in transport justice has focused on assessing injustices, without being able to prescribe operational solutions. This paper contributes to bridging this gap and presents optimization models for justice‑informed operational planning of intermodal mobility systems that explicitly account for the budget and safety limitations of users, and for infrastructural capacity constraints. Specifically, we first focus on an intermodal Autonomous Mobility‑on‑Demand (AMoD) system ‑‑ where self‑driving robotaxis provide on‑demand mobility jointly with public transit and active modes ‑‑ and characterize its operations from a mesoscopic planning perspective via network flow models. Second, we leverage these models to optimize system operations through both utilitarian efficiency and justice‑informed objectives. We showcase our framework in a real‑world case‑study for Manhattan, New York. Our results show that monetary budgets significantly limit the social justice potential of AMoD systems if they are to be deployed as transportation network companies. At the same time, granting free public transit can result in sufficiency levels very close to a completely free intermodal AMoD system, where justice‑informed operations can be achieved without compromising standard efficiency metrics, ultimately highlighting the strong potential of social policies.

Authors:Jung Ho Park, Changyeob Lee, Shangqing Cao, Raja Sengupta, Mark Hansen, Pavan Yedavalli
Title: Aircraft and Fleet Sizing for Regional Air Mobility: College Town Case Studies
Abstract:
We examine how aircraft seat configuration interacts with daily operation in Regional Air Mobility by applying a joint supply‑demand optimization framework that simultaneously determines market share, fare, and flight schedule. The framework integrates a binary logit discrete choice model into a task assignment formulation, capturing passengers' mode choice between Regional Air Mobility and driving across spatiotemporal origin‑destination pairs. We evaluate three U.S. college town corridors under 4‑, 6‑, and 8‑seat configurations across cost scales from 0.4 to 1.0 and fleet sizes from 12 to 30 aircraft. Profitability and throughput serve as primary performance metrics, and we analyze pricing power, operating cost, and revenue to explain performance variation across markets. We find that larger aircraft configurations and fleet sizes do not improve profitability universally. Larger aircraft are preferred where economies of scale are favorable and demand is sufficient and directionally balanced. The best configuration in these case studies is the 4‑seat in imbalanced markets and the 6‑seat in balanced or dense markets.

Authors:Keyi Shen, Glen Chou
Title: Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers
Abstract:
Neural network (NN) dynamics models and control policies achieve strong performance in robotics, but providing sound guarantees under uncertainty remains difficult, especially for closed‑loop NN systems. Existing reachability tools provide formal over‑approximations, yet are often non‑differentiable, overly conservative, or too slow for modern learning and online planning pipelines. To address this, we present a parallelizable, differentiable reachability framework in JAX for continuous‑ and discrete‑time systems with analytical and NN‑based dynamics and controllers. Our framework combines Taylor‑model flowpipe construction with CROWN‑style linear bound propagation through a unified representation that preserves affine dependencies while supporting GPU‑batched computation and automatic differentiation. Building on this reachability primitive, we develop (i) a certified training method that encourages reachability‑friendly dynamics models and controllers, and (ii) a reachability‑aware sampling‑based MPC scheme with gradient‑based refinement. Experiments on non‑prehensile manipulation and quadrotor tasks, including hardware and higher‑dimensional evaluations (up to 72D), demonstrate practical online planning while maintaining certified reachable‑set over‑approximations under bounded uncertainty.

Authors:Yeison David Mejia Mosquera
Title: TIP: A Decentralized Intent-Based Protocol for Declarative IoT Interoperability and Sandboxed Schema Adaptation
Abstract:
Heterogeneous Internet of Things (IoT) systems suffer from fragmentation across hardware architectures, networking stacks, and data serialization formats. Existing standards (such as MQTT, COAP, and DDS) rely on address‑bound, imperative routing models that require hardcoded configurations and leave no flexibility for runtime schema translation. This paper presents TIP (The Intent Protocol), a decentralized, declarative network protocol. Instead of addressing specific physical endpoints, nodes submit abstract intents specifying desired capabilities, schemas, and Quality of Service (QoS) constraints. The TIP Engine resolves matching nodes using a hybrid discovery mechanism combining local multicast DNS (mDNS) with Kademlia Distributed Hash Tables (DHT). Selection is optimized via a multi‑criteria scoring algorithm incorporating network latency, historical reputation, and contract compliance. Mismatched data representations are reconciled on‑the‑fly inside isolated WebAssembly (WASM) sandboxes compiled dynamically from TOML specifications. Security is enforced through Ed25519 signatures, X25519 key exchanges, and ChaCha20‑Poly 1305 payload encryption. Evaluation of our reference implementation in Rust and C++ shows sub‑millisecond translation overhead and robust resilience under industrial conditions.

Authors:Phuc Duc Nguyen, Ryosuke Isogai, Keitarou Kondou, Satoshi Yasuda, Nobuyasu Shiga, Yozo Shoji
Title: Leveraging Space-Time Synchronization for Ultra-Spot Detection in mmWave/THz UAV-to-UAV Communications
Abstract:
In UAV‑to‑UAV communication, airborne UAVs need to detect the location and direction of ultra‑high‑speed millimeter‑wave (mmWave) and Terahertz (THz) coverage areas, referred to as ultra‑spots. This predictive capability allows UAVs to optimally adjust their flight paths, altitude, and velocity, thereby maximizing the utilization of ultra‑spot services. A space‑time synchronization technique employing multiple Wireless Two‑way Interferometry devices (multi‑Wi‑Wi) is proposed in this paper to detect mmWave/THz ultra‑spot locations during UAV operations. This paper proposes an algorithm that estimates the likelihood of nearby ultra‑spots by considering the UAV flight route and ultra‑spot direction, and by sharing location and pose information among UAVs in the network via a 920 MHz wireless communication link. For the first time, this work addresses the problem of optimizing UAV flight routes to maximize ultra‑spot utilization. To address the inherent challenges of Wi‑Wi, such as phase data unreliability, RSSI attenuation, or packet loss caused by obstructions from the UAV's own body, this study proposes the use of multiple Wi‑Wi devices equipped with antennas positioned at different positions around the arms of the UAV to leverage spatial diversity effects. The proposed method's effectiveness is confirmed through experimental data derived from real‑world UAV‑to‑UAV communication tests. An error of 37.16 cm was observed experimentally in ultra‑spot location estimation, corresponding to 186 ms error in temporal prediction of ultra‑spot entry from an in‑flight UAV, demonstrating its effectiveness in addressing ultra‑spot detection challenges in mmWave communication.

Authors:Bohang Fang, Lijun Ding, Cong Chen
Title: A Scalable Bundle Method for Exact Reformulation of SDP in Three-Phase Power Flow Feasibility
Abstract:
Power flow feasibility assessment is computationally challenging for unbalanced three‑phase distribution networks. This paper develops a vectorized semidefinite program (SDP) based on the bus injection model (BIM) and reformulates its dual as an exact‑penalty problem, enabling us to develop a scalable three‑cut proximal bundle method for feasibility assessment. The proposed bundle method is numerically over 400 times faster than MOSEK with less than 1/2000 of its memory; on the decomposed BIM‑SDP, approximately 2 times faster with 75% less memory.

Authors:Shengjun Zhang, Tingyi Liu, Heng Zhang, Dong Xie
Title: Nonlinear-Gain Distributed Zeroth-Order Optimization for Networked Black-Box Control
Abstract:
This letter studies distributed stochastic optimization over a peer‑to‑peer network when agents can query only zeroth‑order function values. We propose ZOOM‑PB, a coordinate‑sampling distributed zeroth‑order method equipped with a fractional‑power powerball map. Unlike existing distributed zeroth‑order methods that mainly refine gradient estimation or introduce primal‑‑dual tracking, the proposed mechanism acts as a nonlinear feedback gain on the estimated gradient: it amplifies weak signals in flat regions and attenuates large stochastic estimates without adding transmitted states. Under standard smoothness, oracle‑variance, and network‑connectivity assumptions, ZOOM‑PB achieves the leading nonconvex stationarity rate \mathcalO(\sqrtp/(nT)), where p is the decision dimension, n is the number of agents, and T is the iteration horizon. Under the Polyak‑‑Łojasiewicz condition, it further attains the leading objective residual rate \mathcalO(p/(nT)). Thus the method preserves the known distributed ZO order while changing the finite‑time behavior through a local nonlinear control gain. Simulations on black‑box learning and sensor‑driven UAV source seeking show faster empirical convergence in weak‑signal regimes.

Authors:Mohamed Camil Belhadjoudja
Title: Backstepping Control of First-Order Hyperbolic Equations in Arbitrary Dimensions with Non-Trapping Characteristics
Abstract:
This paper presents a backstepping approach for the boundary control of first‑order hyperbolic equations with spatially varying coefficients posed on domains of arbitrary dimension. The method is based on a change of variables induced by the characteristic flow of the time‑invariant transport operator, transforming the original multidimensional system into a continuum of decoupled one‑dimensional hyperbolic equations evolving along individual characteristic curves. A backstepping controller is then designed for each equation in the decomposition, and the resulting control laws are reassembled in the original coordinates to achieve finite‑time stabilization of the full system. The framework relies on the existence of characteristic curves foliating the spatial domain, with uniformly bounded transit times (non‑trapping).

Authors:Shiqian Guo, Jianqing Liu, Beatriz Lorenzo
Title: Personalized Federated Learning by Energy-Efficient UAV Communications
Abstract:
Federated learning (FL) is an effective paradigm for enhancing the learning capability of edge devices while preserving data privacy. In geographically dispersed FL systems, such as sensor networks in remote areas, unmanned aerial vehicles (UAVs) can flexibly establish high‑quality communication links to support parameter exchange. However, device heterogeneity and the limited battery capacity of UAVs pose significant challenges. Specifically, data heterogeneity slows convergence, while scheduling all devices for global collaboration incurs excessive communication and energy costs. To overcome these challenges, we adopt a strict separation between a globally shared backbone and permanently local personalization heads, thereby mitigating the impact of data heterogeneity. Furthermore, we propose a gradient‑based scheduling strategy that jointly considers energy efficiency and learning performance. In each communication round, the backbone is updated only by the top‑α devices ranked by gradient \ell_2‑norm, ensuring that optimization focuses on the most informative updates. Simulation results demonstrate that the proposed scheme achieves higher learning accuracy than state‑of‑the‑art approaches while significantly reducing UAV energy consumption.

Authors:José González-Hernández, Laura Bernacchioni, Ainoa Morillas-España, José Luis Guzmán, Francisco Gabriel Acién
Title: Sensor-Based Turbidostat Operation Enables Biomass Setpoint Regulation and Productivity Improvement in semi-industrial Microalgae Raceway Pond
Abstract:
This work presents the experimental validation of a turbidostat strategy for biomass control in a semi‑industrial outdoor raceway reactor. The proposed approach regulates biomass concentration by automatically triggering dilution when the online biomass estimate exceeds a predefined threshold. To ensure safe outdoor operation, dilution was restricted to daylight periods, avoiding biomass removal under low‑radiation conditions. The strategy was implemented through an industrial control architecture using an optical monitoring system for online biomass estimation. Experiments were conducted over 14 consecutive days in an 80 m^2 (12000 L) raceway reactor. A second parallel reactor operated in chemostat mode, with a nominal dilution of 20 % of the total volume during operating days, provided contextual information under the same outdoor conditions. The analysis focuses on the ability of the sensor‑based strategy to configure and maintain the desired biomass concentration, rather than on a direct reactor‑to‑reactor performance ranking. During the campaign, the biomass threshold in the turbidostat reactor was changed from 1.0 to 0.8 g L^‑1, demonstrating the flexibility enabled by online biomass monitoring. Excluding initial adjustment and transition days, harvested areal productivity increased from 9.52 to 23.20 g m^‑2 d^‑1 after reducing the operating threshold. The overall biomass balance also showed higher net areal productivity in the turbidostat reactor, reaching 20.34 g m^‑2 d^‑1 compared with 11.16 g m^‑2 d^‑1 in the parallel chemostat reactor. These results demonstrate the feasibility of robust turbidostat‑based biomass control in large‑scale outdoor raceway photobioreactors.

Authors:José González-Hernández, Ainoa Morillas-España, José Luis Guzmán, José Carlos Moreno, Alain Vande Wouwer
Title: Model-Free Control approach for pH Regulation in Thin-Layer Photobioreactors
Abstract:
Thin‑layer photobioreactors (TLRs) exhibit fast hydrodynamic and thermal dynamics, strong nonlinear photosynthetic responses and significant time‑variability due to irradiance fluctuations and biomass growth. These characteristics challenge conventional model‑based control strategies, whose tuning degrades under rapidly changing operating conditions. This work presents the experimental implementation of a model‑free control approach, Extremum Seeking Control (ESC), for performance optimization in a semi‑industrial thin‑layer photobioreactor. Unlike previous studies in raceway ponds, the reduced hydraulic inertia of TLR systems enables the adaptation of this control strategy to accelerate convergence while preserving gradient estimation accuracy. The proposed approach is experimentally compared against classical on‑off control and ESC configurations with and without feedforward compensation of solar irradiance. Beyond control performance metrics, biological indicators such as biomass concentration and productivity are evaluated to assess the impact on process efficiency. Results show that the proposed ESC strategy reduced cumulative CO_2 consumption by approximately 39 % and decreased the accumulated pH tracking error by more than 60 % compared with conventional on‑off control, while biomass‑ and irradiance‑normalised indicators confirmed a more efficient use of injected carbon. These results demonstrate that high‑frequency ESC can improve regulation performance and carbon utilisation efficiency in fast photobioreactor systems, highlighting its suitability for thin‑layer cultivation under outdoor conditions.

Authors:Ashir Kulshreshtha, Abdullah Mughees, Gaadha Sudheerbabu, Tanwir Ahmad, Kristian Klemets, Dragos Truscan, Mikael Manngård
Title: Multi-Agent Specification-based Metamorphic Testing of FMU-Based Simulations
Abstract:
In many industrial domains, the Functional Mock‑up Interface (FMI) is used to exchange simulation models as Functional Mock‑up Units (FMUs) across different partners using various modelling tools. This opens up the possibilities for simulation‑based verification and validation using FMUs for ensuring reliable system behaviour. However, deriving effective test oracles for these simulation models remains challenging due to the absence of explicit expected outputs. This limits the applicability of conventional testing approaches, which require access to the internal workings of the systems. Metamorphic testing (MT) addresses this limitation by leveraging metamorphic relations (MRs), but extracting such relations from specifications remains largely a manual and error‑prone process. To address this challenge, we propose an LLM‑powered multi‑agent workflow for specification‑based metamorphic testing of FMU‑based simulation models. The approach takes functional and interface specifications as input and orchestrates multiple agents to extract requirements and derive MRs. These MRs are expressed using Given‑When‑Then patterns to structure input conditions (Given), transformations (When), and expected output behaviours (Then). These relations are then used to generate metamorphic test cases, execute simulations, and evaluate output consistency across multiple sessions. We evaluate the approach on a Lube Oil Cooling system FMU, demonstrating its ability to automatically generate meaningful MRs and corresponding test cases. Preliminary results indicate that the proposed workflow can effectively support the systematic verification and validation of dynamic simulation models by reducing manual effort and improving test generation.

Authors:Josef Berman, Oren Gal
Title: Micro-Swarm Locomotion Optimization in Dynamic Flow using Multi-Objective Multi-Agent Reinforcement Learning
Abstract:
Coordinating micro‑robotic swarms in physiologically realistic, time‑dependent fluid environments remains an unsolved challenge for biomedical and environmental applications. We present a hybrid Computational Fluid Dynamics ‑ Multi‑Objective Multi‑Agent Reinforcement Learning framework that directly couples a high‑fidelity incompressible Navier‑Stokes solver with decentralized proximal policy optimization to learn physically consistent swarm control strategies in oscillatory flow. Sixteen magnetically actuated micro‑robots navigate a pulsatile arterial waveform, simultaneously optimizing upstream progression, energy conservation, and motion smoothness, reconciled using PCGrad surgery. Without PCGrad, energy efficiency and smoothness rewards collapse to near zero within 10,000 training steps while progress exhibits persistent large‑amplitude oscillations, confirming that gradient conflict resolution is a structural requirement rather than an optional refinement in this domain. The converged policy achieves a progress reward of 6.5‑7.0, a sustained energy efficiency of 0.63‑0.65, and near‑maximum smoothness (0.97‑0.99), representing improvements over brute‑force baselines on the primary objective while both baselines yield negative energy efficiency throughout. Training reveals three emergent behavioral phases: a collective two‑layer hydrodynamic throttling formation that suppresses peak channel velocities during forward flow, a cycle‑synchronized ratchet mechanism that exploits flow reversals for upstream repositioning, and an individualized final approach as agents near the success boundary. These results establish that time‑dependent fluid‑agent interactions can be captured directly within multi‑objective reinforcement learning loops, offering a physically grounded paradigm for micro‑swarm control in biomedical navigation, environmental monitoring, and industrial microfluidics.

Authors:Lorenzo Italiano, Federico Marino, Mattia Brambilla, Giovanni Megna, Benedetto Carambia, Monica Nicoli
Title: Highway Readiness Assessment for SAE Levels of Automation and V2X Notification
Abstract:
While highway automation is advancing rapidly, road operators still lack practical methods to assess the readiness of their infrastructures for supporting automated driving systems. This work proposes a quantitative Highway Readiness Index (HRI) that maps static Operational Design Domain (ODD) infrastructure conditions into measurable attributes and weights them through an expert survey to evaluate readiness across Society of Automotive Engineers (SAE) automation levels. A real corridor case study shows how HRI scores can be computed, interpreted, and used to identify infrastructure gaps that limit higher automation. Finally, we outline how these indicators can be integrated into a standardized Cooperative Intelligent Transport System (C‑ITS) message, i.e., Infrastructure‑to‑Vehicle Information Message (IVIM), to communicate segment‑level automation guidance to connected vehicles.

Authors:Frederic Fabry, Alessio Lodge, Robinson Medina, Feye Hoekstra, Steven Wilkins, Madalin Frunzete
Title: Degradation-Aware Fast-Charging of Li-Ion Batteries Using Joint Electrical and Thermal Model Predictive Control
Abstract:
Fast‑charging of lithium‑ion batteries is essential for electric vehicle adoption, but aggressive charging can accelerate its degradation and create safety risks. This study investigates a control framework that coordinates charging current with active thermal management to minimise charging time, while respecting constraints on electrochemical degradation and thermal safety. A single particle model with electrolyte dynamics (SPMe), extended with a two‑node thermal model, represents the battery dynamics and enables the prediction of internal states ‑ used in the control strategy ‑ including anode potential, core temperature, and cell voltage. Two multi‑input multi‑output control strategies are developed and compared: a classical approach using parallel proportional‑integral‑derivative (PID) controllers and an advanced model predictive control (MPC) with dual resolution prediction. Both controllers regulate the charging current and thermal resistance to minimise charging time while keeping within the limits of anode potential, core temperature, and cell voltage. The results demonstrate that coordinated thermal‑electrochemical optimal control outperforms conventional approaches, achieving a 42.2% reduction in charging time compared to the manufacturer's charging recommendation, without increasing degradation. MPC reduces the charging time by 5.2% compared to PID control, but at a significant computational cost. This improvement demonstrates the untapped potential of integrated thermal management in fast‑charging protocols.

Authors:Sriharini Krishnakumar, Inna Partin-Vaisband
Title: Power-Integrity Modeling of VR Faults in High-Performance Applications
Abstract:
Distributed vertical power delivery has emerged as a promising approach to meet aggressive current‑density, efficiency, and transient response requirements in high‑performance computing systems. Tight integration of voltage regulators within stacked substrates, however, increases the vulnerability of the power delivery system to short‑circuit and open‑circuit faults arising from elevated thermal and mechanical stresses. Such faults can propagate through the shared power delivery network, leading to rapid degradation of system‑wide efficiency at worst‑case rates of up to 0.5% per microsecond. Advanced fault‑tolerant power management strategies are therefore required to ensure efficient power delivery. A real‑time fault‑detection and isolation methodology are proposed in this paper for vertical power delivery systems. The methodology is developed based on an analytical inductor‑current models that rely solely on signals available within the converter control circuitry, thereby eliminating additional sensing overhead. The proposed framework is designed and simulated in SPICE environment, demonstrating sub‑microsecond fault detection and effective dual‑fuse isolation, maintaining uninterrupted power delivery with a system‑wide efficiency degradation of less than 2%.

Authors:Tianyi Wang, Yiming Xu, Jiseop Byeon, Junfeng Jiao, Javad Mohammadi, Kara Kockelman, Christian Claudel, Alexandre Bayen
Title: Solar phased arrays-based wireless power transfer for commercial airlines can reduce energy costs and carbon emissions in the United States
Abstract:
Decarbonizing aviation remains challenging because energy‑dense jet fuels dominate beyond short‑range operations, while batteries impose severe range and payload penalties. Here we evaluate a new infrastructure pathway in which utility‑scale solar farms equipped with solar phased arrays wirelessly beam microwave power to hybrid‑electric aircraft during cruise. Integrating 143,152 U.S. flight trajectories, 5,712 solar farms and wireless power transfer models, we quantify the spatial, temporal, and operational potential of this concept at continental scale. We find that benefits are highly concentrated in solar‑rich, traffic‑dense states and are dominated by short‑ and medium‑range flights, accounting for nearly all delivered energy and cost savings. Schedule optimization and higher cruise altitudes further increase value by improving alignment between aircraft demand and beaming availability. Market penetration analysis reveals non‑linear scaling between solar farm and flight adoption. These results show that wireless power beaming is best understood as a corridor‑specific strategy complementing other aviation decarbonization pathways.

Authors:Sriharini Krishnakumar, Inna Partin-Vaisband
Title: Dynamic Power Management Methodology for Distributed Vertical Power Delivery in High-Performance Computing Systems
Abstract:
Distributed vertical power delivery (DVPD) architectures employ multiple parallel voltage regulators (VRs) to meet the high‑power and high current density demands of modern high performance computing (HPC) systems. While full parallel activation maximizes efficiency near peak load, medium to light load operation leads to efficiency degradation when all VRs remain active due to persistent switching and gate drive losses. This work proposes a load aware power system activation framework targeted at the medium to light load regime, in which the number of active VRs scales proportionally with instantaneous load power. A spatially informed selection strategy determines which VRs are activated from the available pool, aligning regulator placement with localized power demand. This locality aware activation minimizes lateral redistribution currents within the power plane and reduces conduction losses and voltage drops. Simulation results on a representative DVPD system demonstrate 2x to 3x switching loss reduction relative to conventional full‑parallel light load operation, while sustaining an approximately 87% efficiency plateau across the 5% to 30% load range. Output ripple constraints are preserved, with inductor current ripple maintained within 6% and output voltage ripple within 2%, ensuring regulation integrity while improving overall conversion efficiency.

Authors:Prateek Priyaranjan Pradhan, Ketan Rajawat, Mangal Kothari
Title: Communication-Constrained Energy-Optimal Trajectory Generation for Quadrotor UAVs in Urban Environments
Abstract:
Communication‑aware trajectory generation for unmanned aerial vehicles (UAVs) operating in urban environments requires simultaneous consideration of vehicle dynamics, wireless communication quality, obstacle avoidance, and onboard energy limitations. In such missions, UAVs must navigate through obstacle‑rich environments while ensuring reliable relay of mission‑critical sensory information to ground infrastructure. This results in a highly nonlinear and nonconvex optimal control problem involving coupled communication and flight‑dynamics constraints. This paper presents a communication‑constrained energy‑optimal trajectory generation framework for quadrotor UAVs operating in urban environments. The proposed formulation incorporates full rigid‑body quadrotor dynamics, urban wireless communication models, cumulative data throughput constraints, and obstacle avoidance requirements within a unified free‑final‑time optimal control framework. Unlike conventional approaches based on simplified kinematic or point‑mass models, the proposed framework generates dynamically feasible trajectories suitable for practical aerial platforms. The resulting nonconvex optimal control problem is solved iteratively using sequential convex programming (SCP). Numerical simulations for multiple urban mission scenarios demonstrate the ability of the proposed framework to generate energy‑efficient and communication‑aware trajectories while adapting mission duration according to data relay requirements. The proposed methodology provides a practical framework for autonomous UAV operations requiring reliable communication in dense urban environments.

Authors:Tianyi Wang, Tianyi Zeng, Zimo Zeng, Feiyang Zhang, Yujin Wang, Xiangyu Li, Yiming Xu, Sikai Chen, Junfeng Jiao, Christian Claudel, Xinbo Chen
Title: DBPnet: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Wheel Load Estimation
Abstract:
Advanced driver assistance systems (ADAS) play an important role in modern automotive intelligence, significantly enhancing vehicle safety and stability. The performance of ADAS critically relies on accurate and reliable vehicle state estimation, particularly from vehicle dynamic sensors. Among these signals, wheel load is a key variable for chassis control and safety‑critical functions, yet it remains difficult to estimate robustly due to complex suspension geometry, nonlinear dynamics, and measurement noise. To address this issue, we propose DBPnet, a Bayesian physics‑informed neural network (PINN) with a physics‑aware embedding module inspired by damper characteristics. First, this paper presents a suspension linkage‑level modeling (SLLM) approach that constructs a nonlinear instantaneous dynamic model by explicitly considering the complex geometric structure of the suspension. Building upon SLLM, Bayesian inference is integrated into the PINN to effectively cope with noise and uncertainty in the vehicle chassis system, thereby improving the model's robustness. Then, a physics‑informed loss function is employed to ensure consistency with fundamental physical principles, while the damper characteristics‑inspired embedding module extracts temporal variation features of input signals and incorporates them into each layer of the PINN, ensuring that physical observations guide the neural network without being constrained by fixed physical models. Extensive evaluations on high‑fidelity simulations and real‑world experiments demonstrate that our DBPnet consistently achieves lower RMSE and MaxError than baseline methods. These results highlight the potential of our DBPnet to advance wheel load estimation and contribute to the development of more reliable ADAS actuator functions.

Authors:Manan Mittal, Ryan M. Corey, Diego Cuji, John R. Buck, Andrew C. Singer
Title: Time Segmented Beamforming via Dynamic Programming: Theory and Implementation
Abstract:
In dynamic acoustic environments with time‑varying interferers, effective beamforming requires identifying stationary regions over time. The Capon beamformer, a whitened matched filter constrained to maintain unity gain in the desired direction, theoretically relies on the instantaneous ensemble covariance matrix. Practical implementations rely on the batch Capon (or Sample Matrix Inversion), which estimates the sample covariance matrix (SCM) by averaging over a block of snapshots. This practical approach implicitly assumes that the data within the batch window is stationary and can be coherently combined. In non‑stationary settings, a batch approach that averages over fixed or excessively long windows fails, as moving interferers smear the SCM and degrade the beamformer's nulling capabilities. To address this, this paper introduces a temporally segmented distortionless response beamformer. Inspired by the segmented least squares method, which fits piecewise polynomials to data while penalizing excessive segmentation to prevent overfitting, the framework extends practical Capon beamforming by incorporating data‑driven temporal segmentation. This formulation minimizes output power while dynamically adapting the SCM estimation windows to local stationarity, offering a principled approach to tracking time‑varying interferers.

Authors:Siddhartha Ganguly, George Rapakoulias, Panagiotis Tsiotras
Title: Lifted Schrödinger Bridges for Gaussian Mixture Endpoints: Projection Gaps and Path-Space Obstructions
Abstract:
We study stochastic density control between Gaussian‑mixture endpoint distributions under Brownian prior dynamics. Since the direct Schrödinger bridge between Gaussian mixtures is generally not available in closed form, we introduce a lifted path‑space construction in which each trajectory is augmented with a source‑‑target component label. Consequently, the problem decomposes into Gaussian component‑to‑component Schrödinger bridges with explicit marginal, drift, and cost formulas, while the mixture‑level assignment reduces to a finite‑dimensional entropic coupling problem with a Sinkhorn scaling form. We then analyze the projection obtained by discarding or forgetting the label. By construction, the projected law satisfies the original Gaussian‑mixture endpoint constraints, but its relative entropy generally differs from the lifted relative entropy by a nonnegative conditional label‑information gap. This gap reveals a path‑space obstruction: the lifted optimizer cannot, in general, be identified with the direct unlabeled Schrödinger bridge after projection. We also derive the posterior‑averaged Markov drift associated with the projected marginal flow, prove a kinetic‑energy upper bound, and identify a common path‑potential condition under which the projection gap vanishes. Several numerical illustrations showing density and shape control are recorded for a self‑contained exposition.

Authors:Reiji Terunuma, Yuta Nakamura, Takeshi Hatanaka
Title: Passivity-based Semi-autonomous Rotational Motion Navigation for Rigid-body Networks: Stability and Human Passivity Analysis
Abstract:
This paper presents a novel passivity‑based semi‑autonomous attitude control framework, with a particular focus on attitude kinematics defined on the special orthogonal group SO(3). While human‑robot interaction facilitates the successful execution of complex tasks, ensuring stability of human‑in‑the‑loop systems on the SO(3) manifold remains a largely unsolved challenge. We first propose a new control architecture in which a multi‑robot system preserves invariance of the average information fed back to the human operator through so‑called stealthy control, and the human intervention is mediated through a virtual leader, which is coupled with the robots via a passivity‑based attitude synchronization law. We then rigorously prove closed‑loop stability of the proposed human‑in‑the‑loop system under the assumption that the human behaves as a passive system. To support this analysis, simulation studies are conducted to identify the human operator as a dynamical system, and to examine passivity properties of the identified model.

Authors:Shengyang Wu, Vladimir Dvorkin
Title: Differentially Private Obfuscation of Power Grid Dynamics
Abstract:
Dynamic models of power systems are critical for analyzing grid response to disturbances and blackouts, but the release of real‑world dynamic models is hindered by privacy and cybersecurity concerns, as such models carry sensitive information about transmission, generation, and load parameters. We develop an algorithm for synthesizing dynamic grid models from real‑world power grids balancing two objectives: the privacy of the source grid, quantitatively measured using the notion of differential privacy, and the fidelity of the synthesized model. The algorithm applies privacy‑preserving noise to obfuscate the original grid parameters, but then optimizes the perturbed parameters to ensure that the resulting model dynamics are statistically consistent with those observed in the source grid. Application to the frequency dynamics of the IEEE 30‑bus system reveals the inherent privacy‑fidelity trade‑off: stricter privacy requirements degrade modeling fidelity, yet optimization significantly improves the quality of the synthesized models.

Authors:Sai Sandeep Damera, Ryan Matheu, Aniruddh G. Puranic, John S. Baras, Calin Belta
Title: On the Stability and Realizability of Recurrent Polynomial Surrogate Ternary Logic Gate Networks
Abstract:
Recurrent Neural Networks (RNNs) can learn to predict Signal Temporal Logic (STL) verdicts online from partial trajectories, but deploying them as runtime monitors in safety‑critical systems demands more than predictive accuracy. Standard RNN architectures offer no structural guarantee that outputs degrade gracefully under sensor degradation; a dropped input can silently flip a verdict from safe to unsafe. We introduce the Recurrent Differentiable Ternary Logic Gate Network (R‑DTLGN), a recurrent architecture that operates over Kleene's three‑valued logic \‑1, 0, +1\, where 0 explicitly represents unknown. The R‑DTLGN trains through continuous polynomial surrogates and hardens to a discrete ternary logic circuit at inference. We analyze the hardened circuit through two gate vocabularies derived from two orderings on the ternary domain: numerically monotone gates ensure stable recurrent dynamics, while information‑monotone gates, when present, guarantee principled abstention (unknown inputs never produce wrong outputs) and monotonicity in input certainty (more information can only improve the verdict). We show that the recurrent connections required by bounded STL operators use exclusively AND and OR, which belong to both vocabularies, linking the monitoring task to the architecture's guarantees. A realizability bound derived from the STL formula's temporal operators directly sizes the network's hidden state, replacing hyperparameter search with a formula‑driven specification. We evaluate on STL specifications over D4RL PointMaze navigation data, testing prediction accuracy, degradation under predicate dropout, and the accuracy‑versus‑safety tradeoff between two label construction pipelines. The R‑DTLGN is, to our knowledge, the first recurrent architecture that couples learned temporal prediction with formal degradation guarantees rooted in three‑valued logic.

Authors:Jørgen Anker Olsen, Kostas Alexis
Title: Towards Low-Gravity Planetary Exploration using Reinforcement Learning for Walking, Jumping, and In-flight Attitude Control
Abstract:
This paper presents reinforcement learning (RL) policies for dynamic quadrupedal locomotion in planetary exploration scenarios. Building on a taskoptimized quadruped with a 5‑bar leg design, we develop RL policies for walking, vertical jumping, forward jumping, and in‑flight attitude control, explicitly tailored to the reduced gravity on Mars. These policies jointly enable such robots to overcome obstacles larger than themselves through coordinated jumping and precise in‑flight reorientation for safe landings. We demonstrate Sim2Real transfer of the attitude control policy on the Olympus quadruped through single‑axis reorientation tests, while all locomotion policies are validated in simulation. A complete Mars exploration mission scenario demonstrates coordinated policy deployment across challenging terrain. Experimental results show 90° attitude reorientation in 2.6 seconds, with simulations demonstrating 3.1 meter vertical jumps and 3.9 meter forward jumps under Martian gravity conditions. ‑ Supplementary video: https://www.youtube.com/watch?v=qlSJ3P87A4A

Authors:Ninglin Ou, Mohammad A. Razzaque, Iftekher Islam Shovon, Shafkat Khan Siam, Shafiuzzaman K Khadem, Krishnendu Guha, Mayeen U Khandaker, Md. Noor-A-Rahim
Title: Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration
Abstract:
The rapid growth of Electric Vehicle (EV) adoption challenges power distribution networks through peak load spikes, voltage instability, and transformer overloads from uncoordinated charging. While Model Predictive Control (MPC) and standard Reinforcement Learning (RL) methods have addressed these issues, existing approaches rarely treat real‑time carbon intensity or fluctuating renewable energy (RE) availability as primary scheduling objectives, leaving substantial decarbonisation potential unrealised. This paper proposes an emission‑aware RL strategy based on the Soft Actor Critic (SAC) algorithm, with a multi‑objective reward that penalises carbon emissions, curtailed on‑site renewables, and unmet user demand. The agent is trained within a unified benchmarking framework on the EV2Gym platform, incorporating behind‑the‑meter solar and wind profiles, time‑varying EirGrid carbon intensity data, and realistic workplace EV behaviour across 25 Electric Vehicle Supply Equipment (EVSE) units. Nine control strategies, including heuristics, emission‑aware MPC variants, and the proposed RL agent, are compared under five renewable penetration scenarios (0%‑50%) over ten independent runs each. The RL agent achieves a carbon intensity as low as 23.96 grams of carbon dioxide per kilowatt‑hour under 50% wind penetration, representing up to 87% emission reduction versus the uncontrolled baseline, and outperforms the external graph‑based Power Distribution Network (PDN) benchmark. Transformer overload remains below 7 kWh across scenarios, against up to 1093 kWh for the As Fast As Possible (AFAP) heuristic, and renewable self‑consumption reaches 52% under combined wind and solar supply. Embedding carbon intensity forecasts into the RL state and reward aligns charging with low‑emission periods while preserving grid compliance and user satisfaction.

Authors:Lin Feng, Xin He
Title: Safe Data-Driven Control and Dynamical Learning via Constrained Neural Architectures and Koopman Operators
Abstract:
The deployment of learning‑based models in safety‑critical control systems demands mathematical guarantees that standard regression architectures cannot provide. This paper presents an integrated framework that bridges Neural Ordinary Differential Equations (Neural ODEs), measurement‑induced geometric structures, and Koopman operator theory, with the explicit aim of producing data‑driven models whose stability certificates are computable, not merely conjectured. Three complementary components are developed and analyzed. First, ControlSynth Neural ODEs enforce global convergence through tractable linear matrix inequalities (LMIs), enabling complex nonlinear dynamics to be captured without sacrificing boundedness guarantees. Second, the ICODE formulation incorporates extrinsic environmental inputs into the learned vector field, while measurement‑induced bundle structures confine state trajectories to physically admissible manifolds. Third, a systematic ISS verification pipeline certifies the input‑to‑state stability of Koopman‑identified models via a convex L_2‑gain LMI, converting an otherwise intractable robustness question into a solvable semidefinite program. The certified model is embedded in an ICODE‑MPPI controller, which uses continuous‑time residual learning inside a stochastic sampling loop to deliver robust path tracking under parametric uncertainty and persistent disturbances. Numerical experiments on a vehicle path‑tracking benchmark and a nonlinear mechanical oscillator demonstrate up to a 61% reduction in tracking RMSE and a 54% reduction in state estimation error relative to uncertified baselines, with near‑zero LMI violation rates across all evaluated disturbance levels.

Authors:Xuanhao Mu, Kundan Thota, Nan Liu, Jianlei Liu, Thorsten Schlachter, Veit Hagenmeyer
Title: Mechanism-Dependent Antagonism of Auxiliary Information in Substation-Level Load Disaggregation for Distribution Network Planning
Abstract:
Open‑source energy system models disaggregate zonal electricity demand to substations through Voronoi‑based preprocessing pipelines that combine socioeconomic weighting with auxiliary spatial corrections. Whether the same auxiliary data helps or harms when the weighting component shifts from rule‑based to learned has not been investigated. We fix Voronoi partitioning and cross two design axes on metered demand from 1,891 British primary substations: the demand‑weighting method and the mechanism through which Nighttime Light (NTL) intensity and substation‑proximity signals enter the allocation, giving 15 configurations. Mechanism‑isolation experiments further test additive post‑correction and random‑noise controls to pinpoint the structural cause of any performance reversal. The same auxiliary data reduces RMSE by 41 % on the static base but increases it by 21 % on the GNN base under multiplicative post‑correction (p < 0.001 for both); the best static pipeline outperforms the best GNN variant by 19 %. Post‑correction on the GNN improves rank‑order correlation (p < 0.001) yet worsens absolute error, so correlation‑only evaluation masks the calibration penalty. The isolation experiments trace this reversal to the multiplicative correction form under demand conservation constraints, not to signal redundancy; switching to additive post‑correction eliminates the antagonism entirely. A transfer check on 13 German primary substations confirms directional replication and shows amplified antagonism where the GNN baseline already explains over 95 % of demand variance. The NTL and proximity signals behind the 41 % static improvement are publicly available at no cost and should be adopted as default corrections in static pipelines; method evaluation should report RMSE and correlation jointly, as the two metrics diverge under post‑correction on learned representations.

Authors:Jiarong Deng, Liu Chang, Quanshun Yang
Title: Single-Chord Augmentation of Weighted Cycles for Algebraic Connectivity and Network Coherence
Abstract:
Ring‑like communication graphs appear in UAV formations, cyclic patrols, perimeter monitoring, and other multi‑agent tasks in which agents exchange information mainly with neighboring vehicles along a closed route. When measurement and actuation noise are persistent, a useful augmentation should improve both the convergence rate of consensus and the steady‑state disagreement level. This paper studies the addition of a single weighted chord to a connected weighted cycle. The central observation is that a chord is not just a generic rank‑one edge update: it splits the cycle into two complementary resistance arcs, and this resistance split governs both the algebraic‑connectivity gain and the Kirchhoff‑index reduction. We first derive exact chord‑induced effective‑resistance and Kirchhoff‑index update formulas, giving a closed‑form coherence objective. We then prove that, under bounded conductances and small resistance discrepancy, near‑antipodal resistance‑balanced chords are near‑optimal for algebraic‑connectivity improvement; an i.i.d. bounded‑conductance model yields the same conclusion with high probability. Finally, because the best convergence‑rate chord and the best coherence chord need not coincide, we formulate the design as a finite Pareto problem and introduce RBAPS and AW‑RBAPS, two resistance‑balanced screening rules that retain only linear or near‑linear candidate sets. Numerical experiments show that AW‑RBAPS remains effective beyond the formal moderate‑heterogeneity regime and approximates the exhaustive Pareto front with mean hypervolume ratio 0.9987 while evaluating about 10.1% of admissible chords.

Authors:Taoran Wu, Jingduo Pan, Luke Ong, Bai Xue
Title: Cost-Aware Adaptive Conformal Inference for Runtime Assurance in Dynamic Environments
Abstract:
This paper addresses the problem of providing runtime assurance for systems operating online under unknown and potentially time‑varying data distributions. We propose Cost‑Aware Adaptive Conformal Inference (ACI), a novel framework that incorporates constraint violation costs directly into the conformal adaptation mechanism. Our key insight is that uncertainty margins should adapt not only to the frequency of constraint violations but also to their severity. We formalize this through a cost‑aware loss function that couples the miscoverage indicator with violation costs. Unlike existing methods that regulate a single controlled metric, our approach provides a dual statistical guarantee: simultaneously bounding the long‑run average violation frequencies (reliability) and cumulative violation cost (harm). By weighting prediction failures according to their severity, the algorithm enables the controller to respond proportionally to violation severity, expanding prediction sets aggressively when necessary while maintaining efficiency during nominal operation. We integrate Cost‑Aware ACI into a robust control synthesis framework, creating a closed‑loop system that balances task performance with runtime risk control without requiring explicit model knowledge. Experiments validate its effectiveness for online risk‑aware controller synthesis.

Authors:Ehsan K. Ardestani, Leonardo Piga, Jovan Stojkovic, Pavan Balaji, Mustafa Ozdal, Mikel Jimenez Fernandez, Mihaela Dimovska, Luka Tadic, Hao Shen, Devika Vishwanath, Richa Mishra, Melaku Mihret, Valentin Andrei, Mauricio Cespedes, Julien Prigent, James Monahan, Tyler Graf, Bin Li, Charles Marquez, Shobhit Kanaujia, Kaushik Veeraraghavan, Chunqiang Tang
Title: Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster
Abstract:
The electric power supply for AI data centers is now the most significant bottleneck in the race toward Artificial General Intelligence, surpassing even the constraint of AI accelerator availability. To our knowledge, this paper is the first to describe the end‑to‑end power management process for a hyper‑scale AI datacenter; from early power planning to accommodate next‑generation accelerators 6‑‑12 months before their general availability, to tuning power settings after large scale deployment, and finally to dynamic, runtime power management for evolving workloads. We present detailed power measurements for a 150 MW datacenter hosting a cluster of 83K GB200 GPUs. We share insights from building this state‑of‑the‑art AI cluster. We hope this work encourages practitioners across the industry to share their own experiences as well.

Authors:Hongshuo Zhao, Zeyi Liu, Xiao He
Title: Asymmetric Adaptation-based Real-time Fault Diagnosis Under Transitional Operating Conditions
Abstract:
Data streams in real‑world industrial scenarios often contain transitional operating conditions that are uncovered during offline training, leading to significant distribution shifts. To bridge the gap between static offline models and dynamic online data, a novel asymmetric adaptation‑based fault diagnosis method is proposed in this paper. Specifically, in the offline stage, we employ domain generalization techniques to extract domain‑invariant features from multiple stable conditions and construct robust normalized fault prototypes as reference anchors. Subsequently, during online inference, we design an online test‑time adaptation method based on a periodic prototype re‑projection mechanism to dynamically update prototype positions. Furthermore, we utilize the geometric distribution derived from anchors to guide the updates of classifiers and adopt an asymmetric learning rate strategy for the feature extractor and classifier. The proposed approach ensures rapid adaptation to new transitional conditions while preserving the discriminative power inherited from the offline domain generalization initialization. Experimental results demonstrate that this mechanism effectively leverages offline generalized knowledge to guide online inference, significantly improving robustness in non‑stationary environments.

Authors:Tianwei Hou, Da Guan, Yu Zhang, Anna Li, Arumugam Nallanathan
Title: Rethinking Satellite Networks: When Navigation Meets Communications
Abstract:
This paper investigates satellite navigation and communication systems in both low‑Earth‑orbit (LEO) and medium‑Earth‑orbit (MEO) satellites, which systematically outlines the fundamental principles of satellite navigation systems (SNS), satellite communication systems (SCS), and integrated navigation and communication (INAC) systems. By exploring the enhanced capabilities of satellite systems, the article emphasizes how INAC systems improve overall functionality by enabling efficient signal multiplexing and multiple access, positioning multi‑functional satellites as promising alternatives to traditional architectures. Moreover, it introduces emerging frontiers for LEO‑based SNS and MEO‑based SCS through the integration of advanced sixth‑generation (6G) wireless technologies, which cannot be realized through mere extensions of existing communication or navigation techniques. Motivated by these insights, the article further discusses various conceptual transitions required to unlock the full potential of INAC systems, with particular focus on channel capacity, positioning accuracy, and artificial intelligence‑enabled waveform design.

Authors:Priyanka Dey, Takahiro Kawaguchi, Yuichiro Yano, Yuko Hanado, Takayuki Ishizaki
Title: Explicit Ensemble Mean Synchronization for Time Scale Generation with Mixed Atomic Clock Ensembles
Abstract:
In this paper, we consider a mixed ensemble containing a mixture of cesium‑type and hydrogen maser‑type atomic clocks. For the mixed ensemble, the conventional Kalman filtering algorithm has certain limitations due to divergence of the error covariance matrix. To overcome these limitations, we obtain a Kalman filtering algorithm based on observable canonical decomposition that does not have any diverging terms. We use the estimates from the transformed Kalman filter to propose a time scale generation algorithm called explicit ensemble mean synchronization algorithm for the mixed ensemble. In this algorithm, we synchronize the time deviation of each clock from the ideal clock behavior to the unobservable ensemble mean of the phases where the weighting can be decided by the user. By regulating the free‑running dynamics associated with the unobservable state, through choosing an appropriate weight vector, the frequency stability of the generated time scale or the synchronized time shared by the clocks is optimized over shorter (resp. longer) intervals, as measured by Hadamard variance. An illustrative example is given to demonstrate the efficiency of our algorithm.

Authors:Ahmad Al-Tawaha, Ming Jin, Khaled F. Aljanaideh
Title: Finite-Time Markov-Parameter Identification of LTI Systems Using Non-Causal FIR Models: A Unified Framework for Stable and Unstable Systems
Abstract:
We present a finite‑time framework for identifying stable and unstable linear time‑invariant (LTI) systems from a single closed‑loop input‑output trajectory. The method does not require knowledge of the stabilizing controller, an intermediate observer, or prior separation of the plant into stable and unstable components. The approach uses a non‑causal finite impulse response (FIR) model obtained from a Laurent expansion of the transfer function. In this representation, stable dynamics are captured by causal Markov parameters, while unstable dynamics are captured by non‑causal coefficients associated with reverse‑time stable evolution. This avoids the growth of causal unstable Markov parameters. A key advantage is that the coefficients multiplying both the input and the process noise remain controlled by stable and reverse‑time stable decay rates, rather than by growing forward‑time unstable dynamics. To handle closed‑loop data, we use the injected excitation as an instrumental variable, which removes the bias caused by correlation between the feedback input and the process noise. Under explicit instrument‑strength and closed‑loop concentration conditions, we derive a non‑asymptotic error bound for the estimated Laurent/FIR Markov parameters with the usual \mathcalO(N^‑1/2) statistical rate, up to logarithmic factors and truncation terms. The bound captures the effects of process noise, measurement noise, FIR horizons, closed‑loop state moments, and controller‑dependent instrument conditioning. Numerical experiments support the finite‑time analysis by showing the predicted Markov‑parameter convergence rate and illustrating how controller‑dependent instrument conditioning affects the sample complexity of closed‑loop identification.

Authors:Joseph Nyangon
Title: Contested Temporalities in Critical Minerals and Resource Extraction for Electric Vehicles
Abstract:
The global push for electric vehicles (EVs) has sharply increased demand for critical minerals such as cobalt and lithium, creating a tension between rapid industrial growth and long‑term sustainability. Extraction is concentrated in a few regions ‑‑ notably the Democratic Republic of Congo (DRC), Chile, and Argentina ‑‑ where it has produced serious socio‑environmental harms, including ecosystem degradation, labour exploitation, and the displacement of Indigenous communities. In the DRC, cobalt mining is frequently linked to child labour and hazardous working conditions; in Chile, lithium extraction intensifies water scarcity and threatens local agriculture and biodiversity. Policy instruments such as the U.S. Inflation Reduction Act (IRA) seek to promote ethical sourcing, but an extraction‑driven model continues to deepen global inequalities. This chapter examines the contested temporalities of the transition, in which the short‑term economic incentives of extraction conflict with longer‑term environmental and social goals. It argues for a place‑based framework built on community‑centred governance, sustainable mining practices, and circular‑economy strategies, including recycling and material substitution, to align resource security with equity and ensure that the shift to EVs does not reproduce the injustices it aims to address.

Authors:Ilias Mitrai, Tongjia Liu, Gabriel E. Sanoja
Title: Learning regime-dependent governing equations: A symbolic decision tree approach
Abstract:
Many chemical engineering systems are governed by mechanisms that switch across operating regimes, making the data‑driven discovery of regime‑dependent governing equations essential for predictive modeling, optimization, and control. We propose symbolic decision trees for the data‑driven discovery of regime‑dependent governing equations. The method simultaneously learns interpretable splitting conditions to partition the input domain and local governing equations that describe each regime. To improve tractability, both the splitting conditions and governing equations are parametrized using basis functions, resulting in a mixed‑integer optimization learning problem. We use the proposed approach to learn hybrid dynamical models and a constitutive equation for the zero‑shear viscosity of polymer melts. Symbolic decision trees identify physically interpretable regimes and local governing equations while improving predictive accuracy relative to approaches that learn a single global model or use existing decision tree models. This framework provides an interpretable and generalizable route for discovering regime‑dependent models in chemical engineering systems.

Authors:Eric Pulick, Stephanie Carpenter, Matthew Buman, Yonatan Mintz
Title: Optimizing Digital Therapeutic Interventions: Online Learning under Endogenous Adherence
Abstract:
A critical challenge facing clinicians managing chronic disease interventions is sustaining long‑run patient health given limited information and resources. Digital therapeutics (DTs) provide a cost‑effective way to manage interventions at scale through repeated interactions (e.g. daily treatment recommendations), but patient success is highly dependent on their adherence. Behavioral psychology suggests that both treatment recommendations and past adherence affect future adherence, yet existing decision support frameworks for DTs model only recommendation effects or treat adherence as exogenous context, leaving a key gap in model and algorithm development. To address this gap, we present a DT decision support framework that captures both recommendation and adherence effects, allowing clinicians to better plan treatment recommendations. We model a patient's time‑varying capacity for engagement with treatment using a linear dynamical system (LDS) that captures both recommendation and adherence effects, endogenously connected to adherence behavior with a logit link. We establish finite‑time identification guarantees for this model, extending LDS results to our setting. Next, we propose an optimism‑based algorithm, UCB‑BOLD, for online treatment selection and prove that it achieves sublinear regret. We evaluate UCB‑BOLD against benchmarks via ablation studies on a synthetic patient cohort generated using micro‑randomized trial data. DT decision support tools can include dynamical models to enable decision makers to efficiently use the data in DT settings to improve patient health through effective resource allocation. While myopic or heuristic approaches suffice for some patient types, the benefits of explicitly planning around recommendation and adherence effects are significant for others; UCB‑BOLD achieves 2‑3x lower conditional value‑at‑risk regret than the next‑best benchmark.

Authors:Antonio Carlucci
Title: Stability Enforcement in Multivariate Rational Approximation of Parametric Transfer Functions
Abstract:
Preserving stability is a central problem in data‑driven model order reduction of dynamical systems. For linear systems whose dynamics depend on geometric or physical parameters, multivariate rational approximation algorithms such as the Parameterized Sanathanan‑Koerner iteration and the pAAA algorithm construct parameterized reduced models from sampled transfer function data. In this setting, stability must be enforced robustly across the parameter domain. This paper introduces a necessary and sufficient criterion for characterizing the stability of parameterized models. Within a unified framework, the results apply to models with general rational as well as polynomial dependence on the parameters. Building on this criterion, we develop and demonstrate a rational approximation algorithm that includes robust stability constraints through convex optimization. Relative to the state of the art, the approach enforces stability without conservatism while allowing increased flexibility in the choice of model structure.

Authors:Ethan R. Burnett, Spencer Boone
Title: Efficient Nonlinear Uncertainty Quantification for Spaceflight Leveraging Nonlinear Expansions
Abstract:
This paper provides a comparative study of modern uncertainty quantification (UQ) methods. To greatly enhance real‑time performance, both differential algebra (DA) and a directional differential algebra (DDA) approach are employed. This can enable fast UQ in the case of non‑Gaussian statistics. Higher‑order moments, namely skew and kurtosis, can be computed quickly by several means. This motivates their implementation in an analytic approximation of the confidence bounds for the so‑called "banana‑shaped" non‑Gaussian distributions encountered often in nonlinear astrodynamics problems. This method improves greatly on a linear covariance approach, with only 5x its runtime in numerical tests, even before DA methods are employed. Test problems in this work include a restricted three‑body cislunar example and an Earth‑return aerocapture example.

Authors:Thales C. Silva, M. Ani Hsieh
Title: Local Input-to-State Stability for Consensus in the Presence of Intermittent Communication and Input Saturation
Abstract:
This paper addresses the problem of reaching consensus under input saturation and intermittent communication, which can hinder the convergence of the system. We propose a method that translates the consensus into an equivalent stability problem. Then, we compute bounded sets that enclose the initial conditions and the evolution of trajectories leading to local input‑to‑state stability for systems interconnected over directed intermittent topologies. Our contributions include sufficient conditions for stability and stabilization of multi‑agent systems under intermittent interactions and saturating inputs, with the ability to evaluate disturbance tolerance and rejection based on the regions that enclose the system's trajectories. We define disturbance rejection in terms of the \mathscrL_2 gain, and formulate stability and controller design conditions as convex optimization problems. Our method enable the maximization of regions that ensure local input‑to‑state stability, we provide numerical examples highlighting the trade‑offs between mean frequency of intermittent interactions, disturbance energy, and convergence region size.

Authors:Zexin Li, Tao Ren, Johnathan Liu, Xiaoxi He, Cong Liu
Title: RED: Adaptive Real-Time DAG Scheduling for Robotic Inference under Environmental Dynamics
Abstract:
Robots deployed in dynamic environments must contend with environment‑driven changes that reshape computation at runtime: new tasks may appear, precedence relations can shift, and overall workload structure evolves, all of which degrade performance, especially when multi‑task inference is required under tight resource and real‑time budgets. We present RED, a real‑time scheduling framework for multi‑task deep neural network workloads on resource‑constrained robotic platforms that adapts to Robotic Environmental Dynamics (RED) while preserving end‑to‑end timing guarantees under modeling assumptions. The core of RED is a deadline‑aware scheduler that assigns intermediate sub‑deadlines, allowing it to accommodate evolving computation graphs and asynchronous inference induced by unpredictable conditions. The framework also supports flexible deployment of MIMONet (multi‑input multi‑output neural networks), commonly used in multi‑tasking robots to alleviate memory pressure through weight sharing. RED explicitly leverages this shared‑parameter property via a workload refinement and graph‑reconstruction procedure that aligns MIMONet structure with schedulability requirements, improving compatibility and efficiency. We implement RED on NVIDIA Jetson family platforms and on an Apple M‑series MacBook and evaluate it on navigation‑oriented workloads representative of real robotic scenarios. Experiments show consistent gains over existing methods in throughput, deadline satisfaction, robustness to interference, adaptability, and runtime overhead.

Authors:Eugene Yu Ji, Igor Grossmann, Amir-Hossein Karimi
Title: Metacognition Should Be the Scientific Framework for Bounded and Effective Self-Governance in Generative AI
Abstract:
Generative AI research increasingly confronts a shared problem: systems must sustain yet govern their own generative activity when uncertainty is high, evidence is missing, or context is insufficient. This position paper argues that metacognition should become the scientific framework for bounded and effective self governance in generative AI, where output generation is properly evaluated together with the capacities through which generative systems navigate and regulate their own activity. We advance this position by showing that bounded and effective AI self‑governance requires metacognitive alignment across computational, algorithmic, and ecological levels. At the computational level, metacognition specifies the meta‑level functions a system is meant to serve, such as monitoring, evaluation, control, and adaptation. At the algorithmic level, these functions are realized through procedures such as elicitation, iteration, and modularization. At the ecological level, metacognitive signals become meaningful, actionable, and accountable within the interface, workflow, and accountability arrangements. Metacognition thus makes it possible to conceive generative AI as both capable and well‑governed, rather than treating capability and governance as competing aims.

Authors:Ziyang Fang, JinXi Wang, Jinghui Zhong, Yew-Soon Ong
Title: TriVAL: A Tri-Validation Framework for Faithful Automatic Optimization Modeling
Abstract:
Optimization modeling serves as the pivotal bridge between natural‑language problem descriptions and optimization solvers, and remains a cornerstone for bringing operations research (OR) into real‑world decision making. Recent advances in large language models (LLMs) have driven significant progress in automatic optimization modeling. However, existing methods still lack explicit validation during the modeling process, allowing errors introduced in earlier stages to carry through the pipeline and ultimately reduce final modeling accuracy. To address this challenge, we introduce TriVAL, a tri‑validation framework that performs explicit validation at three stages of automatic optimization modeling: semantic specification, mathematical formulation, and code generation. At each stage, TriVAL follows a construct‑validate‑revise loop that assesses the current result against stage‑specific criteria and revises it when needed. This design helps identify and correct errors before they accumulate across stages, helping preserve faithfulness throughout the modeling process. To evaluate automatic optimization modeling on more challenging combinatorial problems, we further introduce NL4COP, a benchmark of 150 instances across 50 diverse problem types with more complex decision logic, more tightly coupled constraints, and more demanding modeling requirements than existing benchmarks. Experiments on NL4COP and established benchmarks show that TriVAL consistently outperforms state‑ofthe‑art methods, with the largest gains on the most challenging problems.

Authors:Celle Hendrickx, Fabio Pavirani, Chris Develder
Title: Multi-market value-stacking: Battery control for combined imbalance participation and non-uniform FCR bidding
Abstract:
The growing share of Renewable Energy Sources (RES) in modern power systems increases both grid imbalances and frequency deviations, reinforcing the need for ancillary services such as Frequency Containment Reserve (FCR) and passive balancing. Battery Energy Storage Systems (BESS) are well‑suited for these services, but prior research typically relies on uniform FCR bids that remain constant throughout the control period. Such static bids fail to fully exploit BESS flexibility, as they do not balance the trade‑off between reserving energy for FCR delivery and using it for imbalance arbitrage, limiting the achievable value in value‑stacking settings. To address this limitation, we propose a two‑stage control framework for the European context that introduces non‑uniform FCR bids. In the first stage, we derive a time‑varying bid sequence using data‑driven Monte Carlo (MC) optimization. In the second stage, a Deep Reinforcement Learning (DRL) agent leverages the residual flexibility for real‑time imbalance trading while proactively managing the State of Energy (SoE) to ensure compliance with FCR requirements. The framework is presented as a proof of concept, highlighting the potential benefits of time‑varying bidding strategies. By incorporating daily cycle budgets and time‑varying reserve commitments, our approach achieves a 7.56% profit increase compared to uniform baselines. These results show that non‑uniform bidding can unlock additional value by more effectively aligning reserve obligations with rapidly changing imbalance opportunities.

Authors:Marcelo Fernandez - TraslaIA
Title: Operationalizing Reconstructive Authority: Runtime Construction, Dependency Resolution, and Execution Gating in Autonomous Agent Systems
Abstract:
Autonomous agent systems fail not only due to incorrect decisions, but due to executing decisions whose authority no longer holds at runtime. Prior work defined Reconstructive Authority (RAM) as a condition for valid execution: actions are permitted only if authority can be constructed from current state. This paper addresses enforcement at runtime: how to enforce this condition in a running system. We introduce a runtime execution model in which authority is evaluated at action time and execution is conditioned on its constructibility. This extends the execution state space beyond admit/deny with a third state, halt, representing cases where authority is undefined due to incomplete or uncertain observability. We define a concrete execution protocol including dynamic dependency resolution, authority reconstruction, and explicit decision semantics. We further introduce a Recovery Loop that integrates drift detection (IML) with execution control (ACP), allowing the system to suspend execution, acquire missing information, and re‑attempt authority reconstruction. We show that this model guarantees safety ‑‑ no action is executed without constructible authority ‑‑ and conditional liveness: execution resumes when authority‑defining variables become observable. This work operationalizes reconstructive authority as a runtime enforcement mechanism, providing the execution semantics required to apply RAM in real systems.

Authors:Yuyang Yao, Shaocheng Luo
Title: SEIDM: A Safe and Efficient Intelligent Driver Model for Autonomous Driving Behavior
Abstract:
The Intelligent Driver Model (IDM) is a cornerstone of Adaptive Cruise Control (ACC), valued for its interpretable parameters and effectiveness in car‑following behavior modeling. However, its inherent conservatism leads to prolonged stabilization and reduced traffic efficiency, which have received limited attention. In this paper, we propose SEIDM (Safe and Efficient Intelligent Driver Model), an enhanced IDM extension designed to improve traffic flow efficiency without sacrificing safety. SEIDM introduces an adaptive safety factor to dynamically modulate the impact of the safe deceleration term in acceleration decisions. This allows vehicles to follow more assertively under safe conditions while behaving more cautiously in potential hazards. Extensive urban traffic simulations show that SEIDM achieves significantly shorter stabilization spacing and faster convergence to traffic flow equilibrium, outperforming the original IDM and its variants in traffic stability and efficiency.

Authors:Olivier Durif, Barbara Nozière
Title: Simplifying Gas-Phase Kinetics with a Dual-Arm Flow Tube Reactor
Abstract:
We present a flow tube reactor design for gas‑phase kinetics studies near ambient temperature and pressure. Built entirely from standard tubing, the setup simplifies conventional flow tube configurations based on injector translation while allowing tighter adjustment of reaction time. The reactor spans residence times from sub‑second to several minutes through two operating modes: (i) a variable‑length mode, in which reaction time is controlled by the tube length, and (ii) a variable‑flow mode, in which the second arm acts as an exhaust branch that decouples reactor pressure from inlet flows while allowing the reactor flow rate to be adjusted over a setup‑dependent range. Key advantages include a narrow and well‑characterized residence time distribution, rapid radial mixing in millimeter‑scale tubing, low wall reactivity through the use of perfluoroalkoxy alkane (PFA), and operation of the reaction section at nearly uniform pressure independently of detector constraints. We characterize the residence time distribution and demonstrate reactor performance with the ozonolysis of 2,3‑dimethyl‑2‑butene. Overall, the method provides a compact, low‑cost, and versatile alternative to conventional movable‑injector flow tubes, with potential applications in atmospheric chemistry, fundamental kinetics, and gas‑‑liquid or gas‑‑solid uptake studies.

Authors:Uğur Aydın, Tamer Başar
Title: Variational Contraction Conditions for Iterative Algorithms in Multi-Population Discrete-Time Regularized Mean-Field Games
Abstract:
In this work, we study the contraction conditions of iterative algorithms for stationary and finite‑horizon discrete‑time regularized mean‑field games (MFGs) with multiple populations, where each population only interacts with the state distributions of the other populations. Due to the high dimensionality caused by the interaction of different populations, contraction rates for these algorithms cannot, in general, be expressed in terms of radicals. By studying the dynamics of these iterative algorithms and assuming that the system components of each population's MFG are Lipschitz continuous, we present explicit (eventual) contraction conditions for each algorithm in any normed space, relying only on these Lipschitz parameters. As a consequence of these contraction conditions, we provide convergence rates of finite‑horizon mean‑field equilibria to infinite‑horizon stationary (and non‑stationary) mean‑field equilibria (MFEs), under restrictions on a variational characterization of the dynamics of these iterative algorithms. In the single‑population case, the restrictions we impose on this variational characterization to obtain these convergence results are less restrictive than previous results in the literature.

Authors:Dongwei Xie, Xuhao Wang, Yujie Tang, Jie Song
Title: Harnessing Individual Motivation for Collective Efficiency: A Mechanism-Driven Distributed Optimization Method
Abstract:
In industrial scenarios involving multi‑agent collective decision‑making, centralized decision‑making may not be admissible due to restrictive access to individual local information, while the conflicts between participants' self‑interest and global performance may also impede collaborative distributed decision‑making. This paper proposes a mechanism‑driven distributed decision‑making method, wherein incentives are employed and designed to motivate participants to collaborate in a distributed fashion even though each participant's decision is driven primarily by self‑interest. Focusing on optimization problems with coupled objective functions and coupled constraints, we design a distributed optimization algorithm tailored for this class of problems and provide guarantees for its convergence. Furthermore, we design two incentive mechanisms, the shadow pricing mechanism and the Vickrey‑Clarke‑Groves mechanism, and demonstrate that participants are willing to engage in distributed collaboration under these mechanisms. The mechanism drives the execution of the distributed algorithm, and the optimal result of distributed computation guides the determination of incentives in the mechanism, both of which are interrelated to form a closed loop. Finally, numerical experiments illustrate the effectiveness of the proposed algorithm and mechanisms.

Authors:Ossama Abdelkhalik, Aimar Negrete
Title: Minimum Effort Control Using Variational Methods of Analytical Mechanics A New Approach For Optimal Control
Abstract:
Modern optimal control theory involves adjoining the already known equations of motion of a dynamic system to the objective function using dynamic costates; this is done in order to constrain the optimal control solutions to satisfy the equations of motion. The use of costates increases the number of variables and hence increases the complexity of the problem. On the other hand, variational methods of analytical mechanics finds the equations of motion by minimizing an action functional of the dynamic system, realizing control forces as external input to the system. In this paper a new disruptive approach for computing the optimal control is presented. This approach adopts the variational methods of analytical mechanics to derive equations for the control, in addition to the equations of motion. This is achieved by recognizing the control actuator as part of the dynamic system. In addition to the kinetic energy and potential energy, the action functional in this new approach includes additional energy terms that represent the control energy of the system. Two different methods are presented to write the modified action functional. The proposed approach is a significant departure from the modern optimal control theory, and it eliminates the need for costates when solving for the control. In this paper, a case study is presented to demonstrate the new approach.

Authors:Seyed Bagher Hashemi Natanzi, Pranshav Gajja, Bo Tang, Vijay K. Shah
Title: Advanced AI Service Provisioning in O-RAN through LLM Engine Integration
Abstract:
The Open Radio Access Network (O‑RAN) architecture allows AI to be embedded directly into the RAN through modular xApps and rApps, yet creating these applications collecting data, training models, writing code, and deploying them safely remains slow and largely manual. Large Language Models (LLMs) offer strong reasoning and code‑generation capabilities but are unsuited for the fast, deterministic inference required in real‑time RAN control. We present a proof‑of‑concept Dual‑Brain architecture that combines both strengths: an LLM‑based orchestrator translates operator intents into data‑collection policies and deployment code, while an automated ML engine, NeuralSmith, trains lightweight classifiers on demand via an API. We describe the architecture and provisioning workflow, share practical insights from a containerized O‑RAN 5G~SA testbed, and discuss open research directions.

Authors:Lihui Yi, Ermin Wei
Title: Routing Equilibrium in Mixed-Autonomy Traffic Networks with Altruistic Autonomous Agents
Abstract:
Recent advancements in vehicle autonomy have drawn interest in understanding the impact of autonomous vehicles on traffic systems. In this paper, we study a traffic assignment problem in a mixed‑autonomy setting where both human‑driven and autonomous vehicles coexist. We model the interaction as a simultaneous routing game where human drivers are self‑interested and aim to minimize their own travel times, while autonomous agents are altruistic and aim to minimize the total social cost. The standard nonatomic congestion game analysis establishes the existence of equilibrium to this game under convex cost functions, and does not have any implication of its uniqueness. In this work, we formulate the equilibrium as a variational inequality (VI), which enables us to establish the equilibrium existence without convexity assumption, and guarantees the uniqueness of the aggregated link flow and social cost at equilibrium under a specific class of cost functions. Leveraging this VI framework, we provide sufficient conditions under which including autonomous agents improves, deteriorates, or has no effect on social cost. While the possibility of deterioration has been established in prior work, our results complement existing worst‑case bounds by explicitly characterizing sufficient conditions under which each outcome occurs, thereby providing a deeper understanding of mixed‑autonomy traffic systems. Furthermore, we consider a centralized scenario where a social planner optimizes the routing of autonomous agents, and show that the same equilibrium is achieved as in the decentralized scenario when assuming convex costs.Finally, we conduct numerical experiments that illustrate how social cost changes with the amount of autonomous vehicles under different system parameters.

Authors:Giacomo Acciarini, Dario Izzo, Zhong Zhang
Title: Reachability for Low-Thrust Trajectories via Maximum Initial Mass
Abstract:
Reachability analysis plays a central role in low‑thrust spacecraft trajectory optimization by identifying which target states can be achieved under constraints on time, thrust, and propellant. Classical approaches construct reachable sets by solving many optimal control problems over grids of terminal states, requiring extensive forward simulations with fixed initial conditions. While effective, this approach is computationally expensive and becomes impractical for high‑dimensional systems or strongly nonlinear dynamics, such as those encountered in cislunar environments or solar sail missions. This work introduces a dual formulation of the reachability problem. Instead of computing reachable sets directly, we determine, for fixed transfer time and boundary conditions, the maximum allowable initial mass (or, for solar sails, a scalar sail‑strength parameter) that permits a successful transfer. A target is reachable if the spacecraft's initial mass does not exceed this threshold. This reformulation reduces reachability assessment to a scalar optimization problem for each target, producing a smooth scalar field that encodes equivalent feasibility information to classical reachable sets. We develop indirect maximum‑initial‑mass (MIM) formulations for both electric low‑thrust and solar‑sail dynamics and show how they can serve as efficient reachability oracles. Building on this formulation, we construct data‑driven surrogate models to approximate the MIM‑based reachability indicator. We investigate fully connected neural networks and demonstrate that residual networks provide the best trade‑off between accuracy, training stability, and model complexity. The resulting surrogates enable rapid reachability evaluation while preserving the numerical advantages of the dual formulation, offering a practical tool for preliminary mission design and feasibility assessment.

Authors:Christian Nauck, Junyou Zhu, Michael Lindner, Frank Hellmann
Title: Learning Dynamic Stability Landscapes in Synchronization Networks
Abstract:
The robustness of synchronization is typically characterized by scalar, per‑node stability indices whose dependence on topology is studied via network science or graph neural networks (GNNs). We propose a novel upstream task, learning stability landscapes, which provide deeper insights into synchronization behavior and from which many such scalar indices can be derived. Crucially, we pioneer a graph‑to‑image prediction paradigm: learning image‑like landscapes as per‑node targets directly from graph topology, a formulation we are not aware of having been established elsewhere in the literature. To support this task, we release two datasets of 10,000 graphs each at 20 and 100 nodes with per‑node landscape labels, based on a conceptual oscillator model, capturing power grid synchronization behavior. A GNN encodes topology and a CNN decoder renders per‑node images, learned end‑to‑end with good in‑distribution accuracy, generalizing across graph sizes and to realistic power grid topologies. This demonstrates that stability landscapes, while beyond the reach of conventional network science, are learnable from topology and open new avenues for moving beyond scalar stability indices in biology, neuroscience, and power grids.

Authors:Tonghua Liu, Yifan Su, Zhaojian Wang, Feng Liu
Title: A Non-Iterative Algorithm for Clearing Two-Layer Energy-Sharing Markets with Voltage Constraints
Abstract:
Real‑time hierarchical energy‑sharing markets are promising to coordinate large numbers of prosumers. Still, most existing clearing methods rely on linearized or DC power‑flow models and do not explicitly handle reactive power or voltage‑security constraints. With AC network constraints, the problem becomes a large‑scale bilevel Mathematical Program with Equilibrium Constraints (MPEC) that is difficult to solve in real time. This paper develops a non‑iterative clearing algorithm for two‑layer energy‑sharing markets with voltage constraints. We first derive an efficient best‑response function for each lower‑layer energy‑sharing market and reduce the equilibrium search to one dimension by exploiting the pricing‑coupling structure. We then embed this function into the upper‑layer network‑constrained problem and reformulate the bilevel MPEC as a single‑level mixed‑integer second‑order cone program (MISOCP), which is computationally tractable. Case studies on the IEEE 123‑bus system with 12,300 prosumers show that the proposed method preserves nodal voltages within prescribed limits and delivers solutions with maximum errors below 0.01% in 0.829 s.

Authors:Anchita Dey, Shubhendu Bhasin
Title: Output Feedback MPC with Adaptive Tubes
Abstract:
An output feedback model predictive control (MPC) framework with adaptive tubes is proposed for linear time‑invariant systems subject to parametric and additive uncertainties. An adaptive observer provides point estimates of the system state, model parameters, and initial condition, while jointly updating the corresponding sets containing the true parameters and initial state. These estimates parameterize the constrained optimal control problem, enabling constraint tightening, terminal ingredients, and tube geometry to be updated as the estimates evolve. In contrast to standard robust tube‑based MPC formulations, the proposed approach does not require a common quadratically stabilizing linear feedback gain across the parametric uncertainty set. As the available uncertainty information improves, the tube geometry evolves accordingly, resulting in an adaptive tube MPC framework with improved performance over time. Recursive feasibility and robust exponential stability are established, and a numerical example is presented.

Authors:Chunhui Li, Wei Fan
Title: RF Instrument Agent (RFIA): Empowering RF Instruments with Natural Language Understanding, Scheduling and Execution of Complex Tasks
Abstract:
Modern radio‑frequency (RF) instruments, such as vector network analyzers (VNAs), already provide mature remote‑control interfaces. However, practical RF measurement workflows still rely on manual operation or custom scripting, which is time‑consuming and expertise‑intensive. This paper presents RF Instrument Agent (RFIA), a natural‑language agent framework for reliable task‑driven RF instrument control. RFIA adopts a decoupled intent‑‑planning‑‑execution architecture, where the LLM is used only for task understanding and high‑level planning, while instrument‑facing operations are handled by a deterministic runtime. Verified skills, workflow templates, RF analysis tools, instrument‑specific rules, and retrieval‑assisted SCPI knowledge are organized in a structured knowledge base, and hybrid execution graphs are used for closed‑loop measurement tasks. A hardware‑in‑the‑loop prototype is implemented on a commercial VNA and evaluated using a 16‑task benchmark covering configuration, query, acquisition, rule‑aware operation, RF‑data analysis, and closed‑loop measurement. RFIA handles all benchmark tasks under predefined execution and safety policies, including one expected safety rejection. Hardware‑in‑the‑loop results with both a 230B‑scale MiniMax‑M2.7 model and a smaller 27B‑scale Qwen3.6‑27B model confirm that the decoupled architecture supports reliable natural‑language RF measurement automation across different LLM backends.

Authors:Ziyan Feng, Yulong Fu, Zheng Li, Yuxin He, Jieji Ren, Lujia Wang, Jinni Zhou, Yudong Zhong, Qiang Nie
Title: TactileReflex: Noise-Statistics-Driven Vision-Tactile Reflex Control for Force-Sensitive Manipulation
Abstract:
Manipulating fragile deformable containers, such as disposable plastic cups filled with liquid, demands real‑time grip‑force adaptation within an extremely narrow force margin: insufficient force causes slip, while excessive force irreversibly deforms the thin wall. Existing approaches struggle to achieve such force‑sensitive manipulation tasks. We propose a noise‑statistics‑based calibration‑driven reflex control paradigm with vision‑based tactile sensing: by analyzing the sensor's intrinsic noise characteristics (via a brief static‑hold‑and‑unload protocol), we directly derive all controller thresholds, eliminating external force calibration, trial‑and‑error manual tuning, or material‑specific physical models. Instantiating this paradigm, we present TactileReflex, a three‑channel closed‑loop controller that extracts three image‑level proxies, shear intensity (S_y), contact intensity (F_n), and center of pressure (C), from dual visuo‑tactile sensors and drives prioritized reflex channels at ~12 Hz for slip suppression, weight‑adaptive release, and force protection. Each channel closes the loop directly on its proxy via noise‑derived thresholds. Ablation demonstrates that only the full three‑channel system is able to prevent irreversible container deformation (5/5 success vs. at most 1/5 for partial configurations). In a dynamic pouring task, fixed‑effort baselines fail in all 10 attempts due to pose drift, while TactileReflex achieves 9/10 success across two water volumes. As a self‑contained and interpretable controller, TactileReflex can serve as a plug‑and‑play safety layer beneath high‑level manipulation pipelines, including haptic‑free VR teleoperation and vision‑language‑action (VLA) policies.

Authors:Hongjun Xie, Jiahang Zhu, Zhiming Shao, Chao Fan, Zenghui Zhang, Genke Yang, Pengcheng Luo
Title: SafeSABR: Risk-Calibrated Adaptive Bitrate Streaming over Starlink Networks
Abstract:
Starlink, as a representative low Earth orbit (LEO) satellite broadband system, makes high‑bitrate video streaming possible in regions where terrestrial broadband is unavailable. However, its access links exhibit rapid throughput fluctuations caused by satellite mobility and handovers. Existing learned adaptive bitrate (ABR) algorithms can achieve high average quality of experience (QoE), yet high‑bitrate Starlink streaming exposes severe session‑level rebuffering that is not captured by average QoE alone. To address it, this paper proposes SafeSABR, a risk‑calibrated learned ABR framework for Starlink networks. SafeSABR formulates Starlink ABR as a QoE‑‑severe‑risk tradeoff and follows a three‑stage design: behavior‑cloning pretraining learns a high‑QoE ABR prior, risk‑calibrated reinforcement learning (RL) fine‑tuning reduces severe‑tail action tendencies, and a runtime safety auditor uses safe‑capacity lower bounds to check policy‑requested bitrates before execution. Experiments on real Starlink traces compare SafeSABR with online, prediction‑assisted, and learned ABR baselines. Compared with advanced methods, SafeSABR reduces severe‑stall sessions from 22.8% to 7.2% and worst‑5% session rebuffering from 54.30 s to 22.68 s, with a 1.8% QoE cost. Component analyses further show that risk‑calibrated fine‑tuning and safe‑capacity auditing reduce unsafe bitrate decisions and downstream severe‑session rebuffering. These results show that combining risk‑calibrated policy learning with decision‑aware safe throughput forecasting can move learned ABR toward a safer QoE‑‑severe‑risk operating point under volatile Starlink networks.

Authors:Gianluca Giacomelli, Victor G. Lopez, Simone Formentin, Matthias A. Müller, Valentina Breschi
Title: Beyond Shrinkage: Foundations of Data-Driven Control for Piecewise Affine Systems
Abstract:
Data‑enabled predictive control (DeePC) has recently attracted attention as a promising approach for controlling systems directly from raw data, without requiring an explicit identification step. However, DeePC has not yet been extended to piecewise affine (PWA) systems, despite their extensive use in the (predictive) control literature and their universal approximation capabilities. To address this gap, in this work, we lay the foundations for data‑enabled predictive control of PWA systems, providing: (i) their behavioral characterization; (ii) an extension of Willems' Fundamental Lemma to represent their behavior from raw data; (iii) an analysis of the coherence of DeePC strategies using a linear predictor and shrinkage regularizers; and (iv) a study of the impact of misclassification errors on structuring data for prediction. Our theoretical findings are validated by numerical results on a simple example, emphasizing the need to extend beyond a regularized version of the foundational DeePC framework to design control actions that are both effective and coherent with a PWA system's behavior, thus ensuring the controller's explainability.

Authors:Irene Hammermeister, Eric Tönges, Nils Bornhorst, Johannes Heid, Gabriela Fritzler, Timo Rehwald, Andrea Schoen, Ronald Halbauer, Jan Meschede, Michael Kramer, Julia Holl, Sina Straußberger, Andrey Luzhbin, Maximilian Niedhammer, Mischa Geiger, Aaron Eicker, Maurice Raetsch, Alfio Spina, Johannes Dieplinger, Christian Mayer, Josef Bayer, Thorsten Reske, Dominik Hilbrich
Title: OptiQU: Coordinated Multi-Level Voltage and Reactive Power Control for Enhanced Voltage Quality and Secure Grid Operation
Abstract:
Modern low‑voltage (LV) distribution grids face rising shares of photovoltaic generation and high‑power loads such as heat pumps and electric vehicle charging stations. Due to high simultaneity, voltage constraints often become binding before thermal limits, triggering costly conventional grid reinforcement measures. Existing voltage and reactive power control in LV grids ‑ e.g., fixed cos(ϕ) or Q(V) control of distributed generators, on‑load tap‑changing distribution transformers, and line voltage regulators ‑ is typically applied locally and independently, leaving reactive power flexibility potential unused. This paper presents OptiQU, a coordinated voltage and reactive power control concept for medium‑voltage (MV) and LV distribution grids, combining centralised optimisation with decentralised local control and fallback strategies. The approach coordinates operational targets and setpoints across MV and LV (e.g., DER reactive power and substation equipment) to mitigate voltage violations and curtailment and to increase hosting capacity, while enabling robust operation under limited communication. The concepts are being evaluated using representative MV/LV models in simulation and lab environments and will be validated in field tests with two German DSOs. Based on existing research, the coordinated approach is expected to increase the exploitable flexibility for upstream voltage and reactive power control. The planned evaluation will quantify this potential and investigate trade‑offs between performance, communication effort, and resilience.

Authors:Mirhan Urkmez, Maryam Sharifi, Shahab Heshmati-Alamdari
Title: A Distributed Framework for Data-Driven Safe Coordination in Leader-Follower Networks
Abstract:
This paper addresses connectivity preservation in leader‑follower multi‑agent systems with unknown control‑affine dynamics and local state information. We introduce the distributed data‑driven zeroing control barrier function (3D‑ZCBF) framework, which ensures the controlled invariance of safety sets by identifying derivative bounds from input‑state data without requiring explicit models of high‑dimensional agent dynamics. In this work, we derive the explicit, decoupled safety conditions necessary to maintain connectivity for leader‑leader, and follower‑follower pairings. These individual constraints, along with the leader‑follower conditions, are aggregated into explicit system‑wide conditions that formally guarantee the preservation of the entire communication network. Furthermore, we provide a quantitative analysis demonstrating how the size of the collected data set and the accuracy of the learned Jacobian bounds impact the feasibility of the safety certificates. The proposed conditions are implemented via a projection‑based controller, and simulations confirm that these explicit 3D‑ZCBF requirements effectively maintain system‑level connectivity using only local, two‑hop information.

Authors:Tayyab Manzoor, Yasir Ali, Yuanqing Xia, Lijie You, Yan Wang
Title: Physics-informed sparse identification-based tube model predictive control for aerial vehicles
Abstract:
Autonomous aerial vehicles necessitate control strategies that balance computational efficiency with robust performance in dynamic operational environments. This paper proposes a model predictive control (MPC) framework for aerial platforms that leverages physics‑informed machine learning (PIML) to achieve an optimal balance between computational tractability and robust performance. At the core of the proposed approach lies a sparse, control‑affine model identified via the PIML method, which provides a parsimonious yet interpretable representation of the system dynamics by embedding first‑principles knowledge and learning residual uncertainties from operational data. This model is incorporated within a robust MPC scheme that adopts a high‑order Runge‑Kutta discretization to ensure prediction accuracy and an adaptive tube‑based mechanism to guarantee constraint satisfaction under uncertainty. The online adaptation of the tube, directly informed by the residual error of the PIML model, ensures robust stability without introducing excessive conservatism. Rigorous theoretical proofs are provided to establish recursive feasibility and stability. Numerical simulations and experiments on a quadrotor demonstrate that our method significantly reduces computational load compared to nonlinear MPC and robust MPC using a high‑fidelity model, while outperforming PID, nonlinear MPC, neural‑network‑based MPC, and fixed‑tube robust MPC in tracking performance and robustness, showcasing the practical efficiency of the proposed PIML‑based control synthesis for resource‑constrained aerial systems.

Authors:Akihiro Fujita, Sasinee Pruekprasert, Katsuhiro Nishinari, Shinji Nakadai
Title: From Visual to Digital: Coordination Scheduling and Its Effect on Safety and Efficiency in UAM Corridors
Abstract:
This paper explores scalable coordination strategies for urban air mobility (UAM) corridors by comparing two representative approaches. The first, inspired by visual flight rules (VFR), is a local coordination strategy relying on spatial information available to each vehicle. The second, conceptually aligned with digital flight rules (DFR), is a global coordination strategy based on shared estimated times of arrival (ETAs) at constrained waypoints (CWPs). To support this comparison, we introduce a lightweight disturbance‑avoidance mechanism that enables vehicles to adjust their ETAs in response to forecasted disruptions using shared information. We evaluate these approaches through numerical simulations under varying disturbance levels, comparing the locally reactive VFR‑style scheme with the globally coordinated DFR‑style scheme. Results show that VFR achieves high throughput in low‑traffic scenarios but becomes increasingly prone to collisions at higher traffic densities unless conservative separation is enforced, which reduces traffic efficiency. In contrast, DFR maintains more consistent safety performance and traffic efficiency, even under moderate ETA update propagation delays. These findings highlight the advantages of DFR‑style global coordination in managing high‑density air traffic control (ATC) operations within UAM corridors.

Authors:Sasinee Pruekprasert, Shinji Nakadai, Katsuhiro Nishinari
Title: Safety-Assured Arrival Scheduling in Sequential UAM Corridor Sections under Speed and Separation Constraints
Abstract:
This paper presents a safety‑assured arrival‑scheduling framework for Urban Air Mobility (UAM) corridor operations. We propose an analytical method to compute a sufficient ETA gap at Constrained Waypoints (CWPs) that guarantees longitudinal separation along sequential corridor sections with heterogeneous speed limits. The resulting ETA‑gap condition depends on section‑specific speed bounds and the required separation distance, providing an efficiently computable rule suitable for integration into future digital ETA‑scheduling and air traffic management systems. We show that the computed ETA gap ensures safe separation across all corridor sections under prescribed section travel times and speed limits. Numerical simulations for a decreasing‑speed corridor confirm that vehicles coordinated with the proposed mechanism adjust their speeds to maintain the required spacing, avoid potential collisions, and support improved traffic flow compared with unscheduled operations.

Authors:Haopeng Deng, Fucheng Zheng, Xinhai Xia
Title: SpinFlow: A Physics-Informed Spin Field Framework for Traffic Phase Inference and Transition Detection
Abstract:
Active traffic management (ATM) is frequently hindered by traditional macroscopic models and rigid empirical thresholds that fail to capture metastable phase precursors, resulting in delayed, reactive interventions. To address this, we propose SpinFlow, a physics‑informed spin‑field framework unifying Kerner's three‑phase theory with statistical physics for continuous macroscopic traffic phase inference. Inspired by the Heisenberg model, SpinFlow parametrizes spatially varying phase weights via a latent spin vector and a competitive‑equilibrium mapping, allowing synchronized flow to emerge naturally. A physics‑regularized Expectation‑Maximization algorithm inverts this latent structure from high‑resolution trajectories, jointly optimizing the spin field while softly enforcing mass conservation and spatial smoothness. We introduce the Phase Equilibrium Degree (PED) to quantify structural alignment and topologically localize phase‑transition points. Across four real‑world trajectory datasets, SpinFlow achieves R_q^2 up to 0.940, PED drops of 94.9‑100%, and interpretable phase maps that outperform three heterogeneous baselines on forward accuracy, physics consistency, and bottleneck localization. SpinFlow pinpoints congestion nucleation without prior network topology, yielding a data‑driven, physics‑consistent trigger for ATM.

Authors:Lipeng Dai, Luping Xiang, Kun Yang
Title: 6G Communication Networks Enabling Embodied Agents: Architecture and Prototype
Abstract:
Embodied agents, which couple intelligent decision‑making with physical actuation in the real world, impose far more stringent and heterogeneous communication requirements than purely software‑based agents. While 6G promises sub‑millisecond latency, ultra‑high reliability, native intelligence, and integrated sensing, systematic studies on how to exploit these capabilities for embodied agent communication remain limited. This article investigates 6G‑enabled communication systems for embodied agents from both conceptual and engineering perspectives. First, we review the concept, embodiment value of embodied agents, and clarify their distinctions from disembodied agents. Then, we analyse the symbiotic relationship between embodied agents and 6G networks. We highlight how key 6G enablers can support the stringent requirements of human‑robot interaction. Furthermore, we demonstrate the proactive role of embodied agents in bolstering communication networks through coverage extension, environmental sensing, and physical world understanding. Building on these insights, we propose a hierarchical communication architecture for human‑robot remote interaction, comprising a human‑intent perception layer, an open radio access network (O‑RAN)‑based transport layer, an intelligent intermediary layer, and an embodiment layer. To validate its feasibility, we implement an end‑to‑end prototype that integrates a haptic device, an industrial robotic arm, an intermediary platform, and a 5G O‑RAN testbed. Experimental results demonstrate millisecond‑level latency and stable closed‑loop operation, confirming the practicality of the proposed architecture and providing a reference for future 6G‑embodied agent research and industrial deployments.

Authors:Yu Chen, Ancheng Hou, Mingyang Feng, Xiao Yu, Xiang Yin
Title: Signal Temporal Logic Motion Planning via Graphs of Convex Sets
Abstract:
This paper investigates continuous‑time motion planning under Signal Temporal Logic (STL) specifications. The goal is to generate smooth robot trajectories that satisfy high‑level logical and timing requirements while respecting low‑level motion constraints. To this end, we propose an efficient framework that combines timed‑automata reasoning with graphs of convex sets (GCS). An STL specification is first represented by a timed automaton, which is then coupled with a convex decomposition of the configuration space to form a joint transition system encoding both task progress and region occupancy. Based on this joint transition system, the STL motion‑planning problem is reformulated as a shortest‑path problem over a GCS, whose solution induces a smooth Bézier‑spline trajectory satisfying the STL specification, smoothness requirements, and velocity bounds. We establish the soundness of the proposed formulation and analyze its computational complexity, showing that, once the timed automaton and convex decomposition are fixed, the convex relaxation scales polynomially with the configuration‑space dimension and the Bézier degree. We further develop a compact timed‑automaton construction for an expressive STL fragment using dedicated templates and Boolean composition. Numerical experiments on low‑dimensional benchmarks, a 3‑D quadrotor, a 30‑DoF humanoid, and a hardware experiment on a UR‑3 robot arm demonstrate that the proposed method efficiently solves complex STL motion‑planning problems and produces smooth executable trajectories.

Authors:Phuc Duc Nguyen, Kazuhiro Maruyama, Keitarou Kondou, Yozo Shoji
Title: Experimental Evaluation of Data Upload Efficiency and Guiding Challenges for a Vehicular-to-Road System Using 60-GHz mmWave Ultra-Spots
Abstract:
Maximizing data uploading efficiency in a vehicular‑to‑road data uploading system using millimeter‑wave communication is a challenging issue, as the wireless zone is often critically narrow, and vehicles can easily fail to pass through it without the aid of an autonomous guiding system. Variations in driving routes, speeds, approach angles, and distances to the ultra‑spot can significantly affect data transmission performance, leading to either efficient or suboptimal results. This study presents a comprehensive analysis based on 75 experimental cases to identify the optimal travel trajectory and conditions that allow the vehicle to pass through the ultra‑spot and enhance data transmission effectively. Experimental results show that with an optimal travel trajectory, appropriate movement speed, antenna placement, and prior estimation of the ultra‑spot area, the amount of transferred data can be improved by 6 to 8 times.

Authors:Wentao Tang
Title: Convex Hybrid Modeling: An Operator-Based Approach
Abstract:
While machine learning can accurately model process systems, models for decision making should also be structurally simple and physically interpretable. In process control, for example, (nearly) linear models are favored than nonlinear ones, promoting the use of operator theory, which ``universally'' represents a nonlinear system by a nonparametric operator. On the other hand, interpretability requires by a ``non‑universal'', parametric nonlinear model family satisfying first principles; these constraints tend to complicate the learning procedure. This paper considers hybrid modeling by formulating convex learning problems that account for interpretability systematically and give surrogate models efficiently. Three settings are discussed ‑‑ (i) regularization around a particular ``reference model'', (ii) restriction on an ``interpretable subspace'', and more generally, (iii) restriction on a ``interpretable manifold'' that is nonlinearly parameterized. In the more general setting, by introducing an operator‑theoretic technique to re‑parameterize models in the ``lifted'' parameters (``canonical features'', potentially infinite‑dimensional), the system is regarded as a kernel‑based mixture of interpretable models. Application to both static and dynamic models are exemplified in numerical studies.

Authors:Violetta Rostobaya, James Berneburg, Daigo Shishika
Title: Deception and Counter Deception in Adversarial Graph Traversal Game
Abstract:
We study deception in adversarial graph traversal, where a mobile agent seeks to reach a goal with minimum cost while an adversary alters edge costs to increase the total traversal cost. Unlike prior works that assume fixed observer‑deceiver roles, we model this problem with two‑sided incomplete information in which both players possess private information and update beliefs from observed actions. To solve the resulting indefinite‑horizon game, we develop an adaptation of the Extensive‑Form Double Oracle (XDO) algorithm. While the standard XDO algorithm is designed for finite games, the proposed adaptation ensures bounded computation despite endogenous game termination. We show that the proposed algorithm terminates in finite time and returns an epsilon‑Nash equilibrium. Finally, we use Value of Information to characterize the deceptive and counter‑deceptive behaviors that emerge from equilibrium strategies.

Authors:Zhenghua XU, Dominic Groß, George Alin Raducu, Behnam Nouri, Oscar Saborío-Romano, Nicolaos A. Cutululis
Title: Holistic Grid-Forming Control to Enhance the Frequency Support from HVDC-Connected Offshore Wind Power Plants
Abstract:
To address the frequency stability challenges posed by the rising penetration of power electronics in power systems, HVDC‑connected offshore wind power plants (OWPPs) are increasingly expected to provide inertial response and frequency containment reserve (FCR). In this paper, an improved holistic grid‑forming (GFM) control is proposed, aiming to enhance the frequency support by coordinating the GFM controls implemented at all AC and DC terminals of an HVDC‑OWPP system, without requiring communication. Firstly, the model of a typical HVDC‑OWPP system is developed for control design. Accordingly, the proposed controllers are formulated, followed by an analytical tuning method, where the upper bound of the bandwidth at each AC or DC terminal is identified. Finally, simulations are conducted to verify the functionality and compare the performance with that of representative control configurations. The results show that the proposed holistic GFM control achieves faster response and thus more effective frequency support, while the utilization of the inherent energy storage of each converter is minimized, thereby supporting a new design philosophy for converter control in converter‑dominated systems.

Authors:Germano Rugendo Mugambi, Behnam Nouri, Oscar Saborío-Romano, George Alin Raducu, Nicolaos A. Cutululis
Title: A Methodology for Impedance-based Stability Margin Analysis for Interconnected Offshore Wind Clusters
Abstract:
With recent developments in offshore grid architectures, power park modules (PPMs) such as clusters of offshore wind power plants (OWPPs) are increasingly interconnected offshore. Consequently, it is necessary to assess how integrating a new OWPP affects the stability margins of an existing OWPP at the point of connection. Although impedance‑based methods are widely used for small‑signal stability assessment of interconnected converter‑based systems, many studies rely primarily on Nyquist encirclements and do not explicitly quantify stability margins. Thus, this paper proposes a general impedance‑based methodology to (i) evaluate the stability margins of an existing connection after a new PPM is integrated and (ii) derive a maximum allowable impedance for the new connection such that the minimum stability margin requirements specified by system operators are satisfied and stable operation is maintained. In addition, new Nyquist‑based stability regions are introduced to complement the generalized Nyquist criterion, providing analytical indications of margin compliance and headroom. The proposed method is validated through case studies using vendor‑based frequency‑domain models of two interconnected OWPPs and HVDC system.

Authors:Andrew Lamperski, Debojyoti Biswas, Eric S. Fortune, John Guckenheimer, Kathleen Hoffman, Noah J. Cowan
Title: Active Sensing Subserves Task-Level Control
Abstract:
Active sensing is traditionally defined as the expenditure of energy, typically in the form of movement, for obtaining information. Here, we propose that the combination of reliance on adaptive sensors, the linkage between movement and sensing, and task‑level control inevitably gives rise to the emergence of active sensing movements. In this way, active sensing is not driven by sensory goals, such as minimizing uncertainty about the state, but rather is necessary for task‑level control. This hypothesis, that active sensing subserves control, is supported by both empirical data from organisms and mathematical theory. Interestingly, active sensing behaviors often occur in discrete epochs, interspersed with goal‑oriented behavior. This suggests that animals switch between two behavioral modes with distinct control policies, an `explore' mode in which animals produce dynamic movements to shape sensory feedback, and an `exploit' mode in which animals produce slower compensatory movements that are directly related to achieving task goals. This strategy for feedback control that relies on adaptive sensors, active sensing, and mode switching is not commonly used in engineered systems despite being ubiquitous in biology. Engineered systems comprising state‑of‑the‑art sensors, actuators, and mechanical designs can outperform animals with respect to ``cost functions'' such as maximum force generation, precision, and speed. Nevertheless, animals routinely achieve robust, graceful behaviors that are currently unmatched by engineered systems, suggesting that current control systems are insufficient. These insights, expressed in the language of control theory, may be critical for improving robotic sensing and control.

Authors:Anders Hansson, Bo Wahlberg
Title: Performance Bounds for Rollout Policies in Stochastic Shortest Path Problems
Abstract:
This paper concerns rollout and certainty‑equivalent rollout policies for stochastic shortest path problems with absorbing terminal states. The main result provides a direct non‑asymptotic performance certificate for a fixed rollout policy: the loss relative to the optimal value is controlled by the uniform accuracy of the value approximation and by the expected time for which the rollout closed loop remains away from the terminal state. Thus, in the undiscounted transient setting, the expected hitting time plays the role of a discount or finite‑horizon parameter in more standard approximate dynamic programming bounds. This paper also gives a performance‑difference identity showing that suboptimality is exactly accumulated through the transient occupation measure, and a deterministic sharpness example showing that the hitting‑time factor is unavoidable. Finally, consequences under uniform hitting‑time and Foster‑Lyapunov drift conditions are given, and extend the argument to certainty‑equivalent rollout by adding a separate local model‑mismatch term.

Authors:Jung Ho Park, Jordan Kam, Vishwanath Bulusu, Alexandre Bayen, Raja Sengupta
Title: Dynamic Lane Allocation in UAM Corridors for Efficient Multimodal Door-to-Door Mobility
Abstract:
This article presents dynamic directional lane allocation in urban air mobility (UAM) corridors as a discrete‑time mixed‑integer linear program (MILP). This formulation activates, deactivates, and reverses lane direction as bi‑directional airspace demand evolves. We model demand from disaggregate ground travel data by decomposing each trip into a multi‑modal sequence with first‑, middle‑, and last‑mile legs and routing the UAM‑served middle‑mile segment through a vertiport‑side dispatch model. We use the San Francisco Bay Area as a case study by placing a multi‑region spanning corridor between Contra Costa county and Silicon Valley. We find that the dynamic policy cuts unused airspace capacity by 5x, increases mean lane utilization from 36‑48% to 67% at the same service level relative to baselines, and reduces commuting‑population mean travel time by up to 21.6%. These results show that dynamic configuration of airspace capacity alleviates a significant percentage of the under‑utilization issue of lane‑based UAM airspace design and UAM concept of operations. This dynamic allocation also provides a safe, structural way to increase throughput, making UAM a more viable complement to multimodal door‑to‑door mobility systems.

Authors:Yifan Xue, Toktam Mohammadnejad, Faizan M Tariq, Sangjae Bae, David Isele, Yosuke Sakamoto, Nadia Figueroa, Jovin D'sa
Title: N3P: Accelerated Automated Parking via a Learning-Based Naturalistic Three-Stage Scheme
Abstract:
Autonomous parking requires efficient path planning that ensures kinematic feasibility and collision avoidance in constrained environments. Hybrid A is widely used but computationally expensive, while reinforcement learning (RL) methods lack reliability and often struggle with long‑horizon geometric constraints, leading to suboptimal trajectories. We present N3P, a fast learning‑based three‑stage framework for automated parking. By introducing an intermediate preparatory pose and using a learning module to predict it, N3P decomposes the maneuver into simpler subproblems, thereby reducing computational complexity and accelerating path generation. We validate the framework by integrating it with Hybrid A algorithms. Experiments in perpendicular and parallel parking scenarios show that N3P‑enhanced Hybrid A speeds up planning by more than 80%. It also outperforms RL baselines in success rate and trajectory quality, producing shorter trajectories with fewer gear changes, while achieving comparable or lower planning time in most cases.

Authors:Eric Yocam, Varghese Vaidyan
Title: TriSweep: A Four-Drone Swarm Framework for Electromagnetic Side-Channel Analysis
Abstract:
Electromagnetic (EM) side‑channel analysis traditionally assumes a stationary, close‑proximity probe ‑ a threat model that underestimates aerial adversaries. TriSweep is a simulation framework that designs and evaluates a four‑drone swarm architecture for autonomous standoff EM‑SCA of embedded microcontrollers at 0.25‑1.5 m. Three spatially specialized collector drones ‑ Anchor (full‑spectrum), Mask Probe (mask‑register loading leakage), and Cipher Probe (masked SubBytes output leakage) ‑ feed a stationary Accumulator drone that performs coherent combining (+4.8 dB SNR gain) and second‑order mask cancellation via a centered product of the two spatially separated leakage streams. Evaluated against three real ANSSI ASCAD datasets (ATmega8515 masked AES‑128 and 50/100‑sample desynchronized variants), the framework achieves a simulated key rank of 18 +/‑ 1.7 (five‑seed) at 0.25 m on the primary masked dataset. Profiling‑trace cross‑correlation alignment reduces single‑drone rank from 89 to 21 on the 100‑sample‑jitter variant, demonstrating compensation for drone hover vibration. A two‑channel CNN in the Accumulator converges to a loss of 0.454 (vs. random baseline 5.545) and improves rank on desynchronized datasets. No physical hardware has been fabricated; prototype construction is the planned next step.

Authors:Promise Ekpo, Angelique Taylor, Lekan Molu
Title: A Generalized Nash Equilibrium-Seeking Scheme for Trauma Resuscitation
Abstract:
Trauma resuscitation is a clinical process for treating life‑threatening physiological disorders in safety‑critical environments, driven by the experience of healthcare workers (HCWs). Designing and optimizing quantifiable metrics that accurately capture HCW decisions may augment current resuscitation procedures with the potential to improve patient outcomes. This motivates our socio‑technical formulation of trauma resuscitation as a distributed generalized Nash equilibrium (GNE)‑seeking game with coupled inequality constraints. This method is optimized over a time‑varying communication graph. We introduce novel insights from clinical experience to model HCWs behavior. This work facilitates the best possible resuscitation outcome given HCWs workloads, schedules, competencies, and limited resources.

Authors:Andrea Bisoffi, Wenjie Liu, Zhongjie Hu, Claudio De Persis
Title: Output regulation via input-output data
Abstract:
From a multi‑input‑multi‑output (MIMO) discrete‑time linear system, we collect input‑output data affected by noise in the form of an unknown exosignal and, from these data points (without knowledge of the system model), we design a feedback controller that asymptotically annihilates the effect of that exosignal on the output. This amounts to solving an output regulation problem purely from input‑output data, for MIMO linear systems. The design of the controller corresponds to a semidefinite program and is pursued on a suitable auxiliary system. Such design carries over from the auxiliary system to the original one by a rigorous examination of the relation between the solutions of the two systems.

Authors:Simone Pirrera, Francesco Ripa, Daniele Astolfi, Vito Cerone, Sophie M. Fosson, Diego Regruto
Title: Global Convergence of Control-Based Lagrangian Flows for Non-Convex Optimization
Abstract:
This paper studies the continuous‑time dynamics generated by control‑theoretic Lagrangian methods for equality‑constrained optimization. In particular, we consider dynamics induced by proportional‑integral and feedback linearization controllers, which have recently been proposed as alternatives to primal‑dual gradient methods. Unlike global convergence results for these dynamics, which rely on strong convexity of the objective function or boundedness assumptions, we exploit the geometric structure induced by the constraints. Specifically, we show global exponential convergence for non‑convex problems that satisfy a suitable convexity property when restricted to the constraint manifold.

Authors:Etienne Hoffmann, Jan-Felix Klein, Sören Weindel, Max Goebels, Sebastian Behrendt, Daniel Hernández, Ratan Bahadur Thapa, Jürgen Fleischer, Kai Furmans, Steffen Staab
Title: KAPPS: A knowledge-based CPPS Architecture for the Circular Factory
Abstract:
While linear manufacturing relies on homogeneous materials and predefined process sequences, circular manufacturing reintroduces used products with heterogeneous and uncertain conditions. This shift demands manufacturing systems capable of handling variable product states, dynamically reconfigurable processes, and the integration of human and machine knowledge. Conventional manufacturing IT architectures, designed for stable structures and deterministic execution, are unable to meet these requirements, as they cannot adequately represent and manage the uniqueness of individual components at runtime. Following a design science methodology for developing a Cyber Physical Production System for circular manufacturing, we derive 14 requirements from five complementary perspectives. Based on these requirements, we design KAPPS, a knowledge‑based architecture that uses an ontology‑grounded knowledge graph as a unifying data backbone, combined with a semantic interface layer to enable consistent data and information integration, reasoning, and communication across heterogeneous systems and services, turning the knowledge graph from an integration layer into the factories authoritative write‑time state. KAPPS incorporates modules for constraint enforcement and event‑driven planning, enabling incremental adaptation of execution plans under uncertainty and human‑machine knowledge exchange. The applicability of KAPPS is demonstrated through two implemented use cases: (i) Anomaly detection and learning through knowledge graph mediated services and (ii) runtime constraint enforcement in a modular conveyor system. Subsequently, the architecture is evaluated against the 14 requirements (ed. abstract shortened)

Authors:Junpeng She, Ruoyu Yan, Zhizhen Qin, Zhanyu Li, Zhongtao Shen, Zichao Zhou, Binxiang Qi, Luming Duan
Title: QuCtrl-BELL: A Compiler-Driven Sub-Microsecond Feedback Control Stack for Scalable Trapped-Ion Quantum Experiments
Abstract:
As trapped‑ion quantum computing scales to larger qubit registers and more complex control protocols, classical control systems face a fundamental tradeoff: sub‑microsecond board‑level feedback requires tight hardware coupling, whereas maintainability and extensibility require clean, modular software abstractions. This paper presents QuCtrl‑BELL (Bell), a compiler‑driven software stack for trapped‑ion quantum control. The design resolves this tradeoff by decoupling control flow ‑‑ including loops, branches, and synchronization ‑‑ from hardware state data. A Python‑embedded domain‑specific language (DSL) is lowered through a six‑stage transpilation pipeline covering control flow graph (CFG) construction, static single‑assignment (SSA) conversion, liveness analysis, and graph‑coloring register allocation. The compiler generates deterministic distributed board‑level programs and compact step‑table data. A cross‑board synchronization protocol supports feedback loops with latency below 700~ns without host intervention. Bell is deployed and evaluated on the QuCtrl‑BELL platform (RISC‑V + PXIe), demonstrating that a compiler‑based infrastructure can provide programmability, deterministic timing, and modularity for scalable trapped‑ion quantum control.

Authors:Bruno Sokal, André L. F. de Almeida, Martin Haardt
Title: Low-Complexity Tensor Beamforming for RIS-Aided Multiuser Multistream MIMO Systems
Abstract:
We address joint active and passive beamforming for uplink RIS‑assisted multi‑user multi‑stream MIMO systems with joint detection. The coupled design of the receive combiner, block‑diagonal user precoders, and RIS phase vector is formulated through a third‑order composite channel tensor. Exploiting this multilinear structure, we propose a multi‑stream tensor alternating optimization method that updates the combiner, user precoders, and RIS coefficients via low‑dimensional tensor projections. Simulations show that the proposed method approaches a multi‑start alternating‑optimization benchmark while reducing computational complexity and improving large‑RIS scaling.

Authors:Shivanshu Tripathi, Maziar Raissi
Title: Online Optimization with Unknown Time-Varying Parameters from Noisy Gradient Measurements
Abstract:
We study online optimization problems in which the cost function depends on latent, time‑varying parameters that are unmeasurable and governed by unknown dynamics. Specifically, we consider a strongly convex cost function whose linear term evolves according to unknown linear stochastic dynamics, while the algorithm has access only to finite noisy gradient measurements. We propose a solution that uses control theoretic tools to reconstruct the latent parameters from gradient observations using a Gauss‑Markov estimator, then identifies the parameter dynamics using an instrumental‑variable estimator, and finally forecasts the parameters to compute the future minimizer. We provide a bound on the expected tracking error. We illustrate the effectiveness of our algorithm on a series of numerical examples.

Authors:Rupak Majumdar, Nikhil Singh, Sadegh Soudjani
Title: Kernel-Based Safe Exploration in Deep Reinforcement Learning
Abstract:
Safety has been a major concern when deploying deep reinforcement learning algorithms in the real world. A promising direction that ensures that the learned policy does not visit unsafe regions is to learn a \emphbarrier function along with the policy. A barrier is a function from states to reals that assigns low values to the initial states, high values to the unsafe states, and decreases in expectation on each transition; such a function can be used to bound the probability of reaching unsafe states. Previous attempts learned a barrier function directly from exploration data, but this required either large amounts of data or restrictions on the system dynamics. In this paper, we show how kernel embeddings can be used to learn barrier functions during deep reinforcement learning for stochastic systems with unknown dynamics. Our algorithm, \emphkernel‑based safe exploration (KBSE), learns an optimal policy and a barrier simultaneously during exploration. The barriers are computed iteratively, represented as conditional mean embeddings, and provide better probabilistic safety guarantees with more exploration. The exploration algorithm uses the learned barrier functions to identify safety violations. In the case of violation, it intervenes to modify the unsafe action to a safe action, thereby ensuring that the exploration is restricted to actions that bound the probability of reaching unsafe states. We evaluate KBSE on several complex continuous control benchmarks. Experimental results establish our new algorithm to be suitable for synthesizing control policies that are probabilistically safe without degradation in reward accumulation.

Authors:Shijie Peng, Xiuqiang He, Xi Ru, Hua Geng
Title: Equilibrium-Free Contraction Stability Analysis for Grid-Forming Converter-Based Microgrids
Abstract:
Renewable‑driven microgrids dominated by grid‑forming (GFM) converters are subject to persistent power fluctuations, making equilibrium‑known stability assessments restrictive. This paper develops an equilibrium‑free contraction stability method based on semi‑contraction theory. By formulating the system in a symmetry‑aware projected state space, the intrinsic rotational mode induced by uniform angle shifts is removed. A blockwise Jacobian decomposition is introduced to characterize the coupled active and reactive power dynamics, yielding a computable regional contraction condition. This condition is then converted into forward‑invariant stability certificates that provide trajectory‑level performance guarantees. For autonomous operation without disturbances, the method provides an equilibrium‑free nonlinear stability characterization together with an estimation of the region of attraction (ROA). For non‑autonomous operation under disturbances, it derives explicit bounds for quasi‑steady tracking under slowly varying injections and for robustness under fast or composite disturbances. Case studies on a 9‑bus system validate the proposed method.

Authors:Hong Liang Cheah, Mohammad Deghat, Jose Guivant
Title: Bearing-Only Solution to the Fermat-Weber Location Problem for Unicycle Agent
Abstract:
This paper addresses bearing‑only algorithms for solving the Fermat‑Weber Location Problem (FWLP) with a unicycle agent. Unlike existing FWLP solutions for single‑ or double‑integrator agents, our approach accounts for the nonholonomic constraints of wheeled robots. We first develop a bearing‑only control law for the case with stationary beacons. Next, we consider saturated control inputs and propose a corresponding bearing‑only control law. Finally, we address moving beacons with constant velocities and develop a control law that enables the unicycle agent to track the moving Fermat‑Weber point. Both simulations and experiments are provided to demonstrate the effectiveness of the proposed methods.

Authors:Joseph Nyangon
Title: Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review
Abstract:
The integration of machine learning with domain‑specific physics is transforming the design, monitoring, and control of electricity systems, where data scarcity, limited interpretability, and the need to enforce physical laws constrain purely data‑driven models. Physics‑informed machine learning (PIML) addresses these limitations by embedding governing equations directly into the learning process, yielding accurate, efficient, and scalable solutions for Industry 4.0 applications. This article reviews hybrid PIML architectures for electricity systems, including physics‑informed neural networks (PINNs), Deep Operator Networks (DeepONets), Fourier Neural Operators, Extreme Learning Machine‑enhanced PINNs, graph‑based PINNs (PIGNNs), and domain‑decomposition PINNs. Each approach is examined through case studies spanning field analysis, fault detection, digital twins, surrogate modeling, and control optimization. The review shows that embedding Maxwell's equations and other first‑principles constraints substantially improves predictive accuracy under sparse and noisy data, reduces simulation time by orders of magnitude relative to finite element methods, and enhances generalization across operating regimes. Hybrid frameworks consistently outperform purely data‑driven baselines on parameter sensitivity, dynamic behavior, and robustness, while supporting real‑time digital‑twin calibration and uncertainty quantification. Persistent challenges include training instability for stiff multi‑scale problems, computational cost of high‑fidelity models, and the absence of standardized benchmarks. The findings demonstrate that PIML enables a paradigm shift from black‑box data‑driven methods to transparent, physics‑informed strategies, positioning the field for sustained innovation in resilient and intelligent electricity systems.

Authors:Armin Makvandi, Md. Zoheb Hassan, Md. Jahangir Hossain
Title: AdaPTwin: Adaptive Multi-Fidelity Predictive Digital Twin for Proactive Radio Resource Management in Vehicular Networks
Abstract:
The highly dynamic nature of vehicular networks necessitates proactive and site‑specific radio resource management (RRM) to achieve ultra‑reliable low‑latency communications. While Network Digital Twins (NDTs) have emerged as a promising enabler, ray‑tracing remains time‑consuming, challenging accurate RRM under latency constraints. We propose AdaPTwin, an adaptive multi‑fidelity predictive NDT for proactive and latency‑aware RRM in vehicular networks. Unlike single‑ and multi‑fidelity NDTs with fixed fidelity levels, AdaPTwin dynamically adjusts NDT fidelity based on network conditions. The framework adopts a hierarchical cloud‑edge architecture, where computationally intensive fidelity selection is performed periodically in the cloud, and the proactive RRM loop operates in real‑time at the edge. The edge‑based proactive RRM task consists of channel prediction between vehicles and roadside units (RSUs) via trajectory forecasting and look‑ahead ray tracing, followed by RRM execution. A transformer model enhanced with continual and transfer learning enables vehicular trajectory prediction while adapting to new environments and traffic patterns. Ray‑tracing is performed using NVIDIA Sionna by exploiting a dynamically updated virtual environment to ensure realistic radio propagation within the NDT. Furthermore, a joint RSU beamforming and vehicle‑RSU association problem is formulated to maximize proportionally fair sum‑rate, and it is efficiently solved using a scalable multi‑start iterative coordinate descent algorithm. Comparisons against reactive, single‑fidelity, and non‑adaptive predictive NDTs under realistic vehicular conditions confirm that AdaPTwin successfully adapts to diverse scenarios where other frameworks fail. Ultimately, AdaPTwin achieves up to 90% sum‑rate gain and 80% outage probability reduction compared to non‑adaptive NDTs, while maintaining real‑time performance.

Authors:Asaad S. Abdul-Hamid, Hao Chen
Title: System Level Analysis and Management of Orbital Debris Using Empirical Dynamic Modeling
Abstract:
Orbital debris is a pressing problem which presents a danger to global space operations and a barrier to continued development of the space economy and space infrastructure. As research continues regarding orbital debris, there is a need for tools to understand the system‑level implications of orbital debris solutions. This research considers the orbital debris problem as a dynamic process. Based on dynamic system theories, time‑series variables of the numbers of orbital debris, orbital objects, and object launches should be causally linked, which means they share a common system attractor manifold. We propose a data‑driven method based on complexity science to reconstruct a shadow attractor of the dynamic system using limited observable variables. The reconstructed shadow attractor helps us to understand the fundamental system dynamics for orbital debris and enables us to simulate the future of the orbital debris system based on changes to policy. These findings represent a significant advancement in our ability to understand high level impacts of space system policy with limited data available.

Authors:Jaehan Im, Filippos Fotiadis, Ufuk Topcu, David Fridovich-Keil
Title: Secure Coordination for Vertiport Sequencing in Advanced Air Mobility
Abstract:
Advanced air mobility operations will require reliable coordination mechanisms for managing dense traffic near vertiports. However, sequencing decisions may become vulnerable when they rely on potentially falsified self‑reported information such as estimated time of arrival. Self‑interested vehicles may misreport their arrival times to obtain favorable landing priority, while malicious actors may spoof information to disrupt sequencing decisions or induce unnecessary congestion. This paper studies secure coordination for vertiport sequencing under sensing uncertainty. We consider a coordinator that combines self‑reported Remote‑ID information with externally obtained surveillance measurements to check reports and assign separation‑feasible arrival schedules. Since surveillance‑based estimates are uncertain, falsified reports may remain consistent with the sensing uncertainty region and cannot always be rejected outright. We therefore formulate sequencing as a robust design problem over this uncertainty region. Self‑interested misreporting is modeled as a strategic deviation that improves the reporting vehicle's own sequencing outcome, whereas malicious spoofing is modeled as an adversarial disturbance that degrades the system‑level objective. The final paper will develop robust sequencing rules over surveillance‑consistent uncertainty sets and evaluate their performance in representative vertiport sequencing scenarios.

Authors:Oliver Mortensen, Mohammad Sadegh Talebi
Title: On the Sample Complexity of Discounted Reinforcement Learning with Optimized Certainty Equivalents
Abstract:
We study risk‑sensitive reinforcement learning in finite discounted MDPs, where a generative model of the MDP is assumed to be available. We consider a family or risk measures called the optimized certainty equivalent (OCE), which includes important risk measures such as entropic risk, CVaR, and mean‑variance. Our focus is on the sample complexities of learning the optimal state‑action value function (value learning) and an optimal policy (policy learning) under recursive OCE. We provide an exact characterization of utility functions u for which the corresponding OCE defines an objective that is PAC‑learnable. We analyze a simple model‑based approach and derive PAC sample complexity bounds. We establish that whenever u does not have full domain \textdom(u)\neq \mathbbR, the corresponding problem is not PAC‑learnable. Finally, we establish corresponding lower bounds for both value and policy learning, demonstrating tightness in the size SA of state‑action space, and for a more restricted class of utilities, we derive lower bounds that makes the dependence on the effective horizon \frac11‑γ explicit. Specifically, for \textCVaR_τ we show that the correct dependence on τ is \frac1τ^2, thus improving by a factor of \frac1τ over state‑of‑the‑art although our bound has a suboptimal dependence on \frac11‑γ.

Authors:Riwa Karam, Ruoyu Lin, Brooks A. Butler, Magnus Egerstedt
Title: Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems
Abstract:
This paper studies heterogeneous multi‑team collaboration through dynamic robot allocation, where robots are treated as transferable resources. Leveraging Hamilton's rule from ecology as an altruistic decision‑making mechanism, we propose a multi‑team collaborative resource allocation framework with heterogeneous capabilities, transfer costs, and capability‑dependent contributions. The resulting allocation problem is combinatorial and is shown to be NP‑hard. To address scalability, we develop a graph neural network policy under centralized training and decentralized execution that approximates the altruistic allocations based on Hamilton's rule. The model operates over the team interaction graph and predicts robot‑level transfer decisions and next robot‑to‑team assignments. The proposed approach is validated in a firefighting scenario through simulations and experiments, demonstrating that the learned policy achieves near‑optimal performance while scaling to larger systems.

Authors:Thales Costa Silva, Nora Ayanian
Title: Mind the Gaps: Multi-Robot Feedback-Driven Ergodic Coverage in Unknown Environments
Abstract:
In this work, we address the problem of multi‑robot adaptive coverage, where teams of robots perform dynamic sampling by continuously adjusting their positions to collect data in an environment. This task can be challenging, particularly when robots must be efficiently allocated to new sampling locations over time. Ergodic search methods optimize robot trajectories by ensuring that the robots' time‑averaged spatial distribution aligns with the spatial distribution of environmental information. While these methods promote effective exploration provided a target distribution, they often fail to account for unknown prior distributions of the environment. To overcome this limitation, we propose an adaptive coverage strategy that utilizes real‑time feedback from an environmental model to adjust robot sampling behavior in response to unknown conditions. Our approach enhances traditional ergodic trajectory optimization by constructing a target spatial information distribution based on parametric models of the environment, which are updated online. This strategy assumes that the environment is either static or changes slowly compared to the robot's motion. Our framework allows robots to dynamically prioritize regions of high interest, improving coverage efficiency, synthesizing effective control policies for individual agents, and optimizing resource use in settings with unknown prior distributions. We validate our approach through simulations, demonstrating its effectiveness in enhancing coverage and resource allocation.

Authors:Yuzhang Peng, Jiaqi Yan, Wei Wang
Title: Disturbance Rejection Control under Nested Signal Temporal Logic Specifications: A Recursive Design Approach
Abstract:
This paper investigates the control synthesis for continuous‑time uncertain systems under nested Signal Temporal Logic (STL) specifications containing nested temporal operators. Control Barrier Functions (CBFs) are utilized herein to encode STL formulas into system constraints. However, traditional CBF designs fail to encode nested STL formulas, whereas recent reachability analysis‑based methods capable of handling such formulas are inapplicable to uncertain systems and suffer from a severe computational burden. To overcome these challenges, a novel recursive CBF design procedure based on a modified STL tree (sTLT) is proposed to yield explicit parameterized CBFs. Within this framework, sliding window variables are introduced to capture complex temporal relationships. Crucially, satisfying the resulting CBF constraints is proven to guarantee the fulfillment of the STL specifications. To render the proposed recursive CBF design applicable to systems subject to uncertain disturbance, a novel controller based on reconstructed CBF using quadratic programming (QP) is proposed, ensuring strict CBF constraint satisfaction under disturbances. In contrast to existing methods, the proposed reconstructed CBF approach requires no prior knowledge of the disturbances while relaxing initial safety assumptions. Simulation results validate the efficacy of the proposed approach.

Authors:Chaerim Moon, Joohyung Kim, Justin K. Yim
Title: Motion Design for Grasp-Based Dynamic Locomotion in Microgravity
Abstract:
Locomotion in microgravity often relies on sparsely and irregularly arranged anchors, motivating grasp‑based mobility with multiple limbs. In this setting, dynamic locomotion is feasible only through deliberate regulation of both anchored interactions and whole‑body coordination under coupled dynamic and kinematic constraints. This paper presents design insights for grasp‑based dynamic locomotion with multi‑limbed robotic systems in microgravity, targeting scenarios that require 6D limb manipulation to establish contacts with candidate anchors. The investigated design parameters include gait pattern, stride length, locomotion speed, and nominal posture. A parameterizable locomotion planning framework is proposed to support variations of these parameters and to evaluate the resulting locomotion performance in terms of stability and actuation demand. Two representative quadruped morphologies are adopted for evaluation in physics‑based simulation. The results demonstrate that enlarging the feasible contact wrench space and attenuating impulsive whole‑body dynamics improve locomotion performance. These findings inform strategies for contact configuration selection and whole‑body coordination in microgravity locomotion with multi‑limbed systems.

Authors:Fei Feng, Lizhi Wang, Ziqian Liu
Title: DAE-Embedded Neural Control Verification for Shipboard Microgrids under Transient Shocks
Abstract:
Neural control offers strong potential for handling highly nonlinear dynamics in shipboard microgrids (SMGs), yet its black‑box nature can trigger abrupt control spikes and actuator saturation during initial transient shocks. This letter devises a formal verification method for SMG neural controller to assess its shock responses. Our contributions include: 1) a set‑based SMG differential‑algebraic equation(DAE) model compatible with set propagation; 2) a DAE‑embedded bound propagation approach to compute tight envelopes of all possible neural control output. Extensive case studies demonstrate the effectiveness of the devised method in formally certifying SMG control performance under uncertain disturbances.

Authors:Lizhi Wang, Fei Feng, Ella Chou, Yashen Lin
Title: Resilient Energy-Based Control for DC Data Centers under Grid and Load Disturbances
Abstract:
This paper presents a passivity‑based control framework for AC‑DC converters supplying non‑passive Information Technology rack loads in DC data centers. Unlike conventional cascaded proportional‑integral controllers that ensure stability only near nominal operating points, the proposed method is derived from the system total energy balance using the Port‑Hamiltonian formulation. By shaping the stored energy and injecting virtual damping through a lossless interconnection with a PH controller, the converter behaves as a passive system even when interfaced with non‑passive loads or under grid disturbances. The closed‑loop system guarantees asymptotic voltage regulation and strict energy dissipation without assuming constant grid voltage or frequency. Simulation studies under realistic load and fault scenarios validate that the proposed controller achieves smaller voltage deviations, faster recovery, and superior robustness, demonstrating its suitability for future high‑efficiency DC data‑center architectures.

Authors:Alessandro Amici, Houari Bettahar, Veeti Jaakkola, Quan Zhou
Title: Closed-Loop Sim-to-Real Reinforcement Learning for Deformable Microfiber Shape Control
Abstract:
Autonomous contact‑based micromanipulation is challenging because surface and interfacial interactions at the microscale are difficult to model accurately, limiting the use of conventional model‑based control and sim‑to‑real learning. We present a closed‑loop sim‑to‑real reinforcement learning (RL) approach for microfiber shape control on a surface. The central idea is to train geometric shape regulation in a simplified frictionless simulator and rely on real‑time visual feedback during deployment to iteratively correct the observed effects of unmodeled surface interactions. An RL policy trained entirely in simulation is transferred directly to a physical dual‑gripper micromanipulation system operating at 40 Hz, without retraining or domain adaptation. Using silk microfibers as a testbed, the policy achieves a mean point‑wise shape error of 270 \pm 80 μm across twenty‑four diverse initial configurations. Across nine specimens covering all combinations of three fiber diameters (50, 80, and 120 μm) and three manipulated lengths (10 mm, 15mm, and 20 mm), the same policy achieves sub‑millimeter final shape error without any retraining or retuning. These results show that a policy learned in a simplified simulator can achieve repeatable real‑world microfiber shape regulation under surface contact, provided that the task‑relevant effects of the sim‑to‑real mismatch remain observable and correctable within the closed feedback loop.

Authors:Dianrui Mu, Changchun Hua, Yafeng Li, Jiannan Chen, Rao Wei
Title: Fully Actuated Manifold Constraint Based Output Feedback Control for Input-Constrained Uncertain Nonlinear Systems
Abstract:
This paper presents a low‑complexity, model‑free, output‑feedback controller for a class of unknown time‑varying nonlinear systems with unknown input constraints. The controller achieves the preset control accuracy when the actuator is not saturated and maintains flexible control accuracy after actuator saturation. This result extends existing constraint control methods for linear manifolds to a more general form, including the construction of nonlinear manifolds and various types of constraints, thereby achieving preset control accuracy within finite or fixed time. Additionally, flexible control under unknown saturation is achieved through the construction of an error‑driven flexible constraint. Finally, second‑order and higher‑order control examples and simulations are provided.

Authors:Marcel Menner, Heather Hussain, Eugene Lavretsky
Title: Output Feedback Control of Linear Time-Invariant Systems with Operational Constraints
Abstract:
This paper introduces a systematic method for designing robust linear controllers using output feedback in the presence of operational constraints. The design uses Nagumo's Theorem and the Comparison Lemma to guarantee constraint satisfaction, while incorporating min‑norm optimal control principles inspired by Control Barrier Functions. The resulting controller is a continuous piecewise‑linear output feedback policy that preserves the closed‑loop system's analyzability using linear systems theory. Due to the linear control design, multi‑input multi‑output (MIMO) robustness margins can be derived with and without active operational constraints. This paper shows that operational constraints on the system's state can be satisfied using an observer‑based output feedback control design. Through flight control trade studies, we demonstrate the practical relevance of the framework in safety‑critical aircraft control applications.

Authors:Devangi, Ankit Singhal, Yashasvi Bansal
Title: Grid-Aware Peer-to-Peer Energy Trading: A Learning-Augmented Framework
Abstract:
Distribution networks are transitioning from passive to active systems due to the growing integration of distributed energy resources (DERs). Peer to Peer (P2P) energy trading has emerged as a viable framework that enables local energy exchange among participants, represented here as aggregated microgrids (MGs). Incorporating network constraints is essential to ensure that P2P transactions remain physically feasible and consistent with grid's operating limits. However, existing P2P frameworks still lack advanced predictive mechanisms that allow prosumers to anticipate network feasibility or the distribution system operator (DSO) response during trade formulation. This paper proposes a learning augmented P2P and DSO interface that predicts the DSOs response to the proposed P2P trades, allowing prosumers to self‑assess and refine their trading decisions. A supervised transformer based regression model is trained to enable MGs to locally predict the DSOs response without sharing their proposed trades, thereby reducing transaction overhead, alleviating DSO burden, and preserving information privacy. The proposed framework is validated on the modified IEEE 33 bus distribution power system with interconnected microgrids. Case studies are presented to validate the effectiveness of the proposed model in terms of market efficiency, trade acceptance and computational burden.

Authors:Iasson Karafyllis, Miroslav Krstic
Title: Beyond Nonlinear Small-Gain Design: DADS with Partial-State Feedback
Abstract:
Eduardo Sontag and coauthors studied Input‑to‑Output Stability (IOS) and the output asymptotic gain property. These notions changed control theory and recently had an impact on robust adaptive control through the Deadzone‑Adapted Disturbance Suppression (DADS) control scheme. Moreover, recently the notion of IOS was extended to systems described by Partial Differential Equations (PDEs). In this work, we celebrate Eduardo Sontag by combining DADS and IOS for PDEs: we study the partial‑state regulation problem for a scalar Ordinary Differential Equation (ODE) which is interconnected with a possibly infinite‑dimensional system. In such a case the DADS control scheme can allow an escape from the requirements of the small‑gain theorem that is mainly used for partial‑state feedback. We show the design procedure of partial‑state DADS controllers and we prove robust regulation even in the presence of external inputs (disturbances) without assuming knowledge of any disturbance/parameter bounds. The DADS controller is applied to three different cases of the interconnection of an ODE with an almost completely unknown: (a) heat PDE, (b) transport PDE, and (c) wave PDE with viscous damping. We show that the same DADS controller can achieve robust regulation in all three cases.

Authors:Austin Braniff, Yuhe Tian
Title: Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes
Abstract:
In this work we present an efficient and practically implementable approach for the application of reinforcement learning (RL)‑based control in chemical process systems. This is an area that has yet to widely adopt RL‑based control largely due to inherent challenges in trusting RL algorithms and the time‑consuming process of training reliable agents. To address these challenges, we leverage a class of RL algorithms termed Y‑wise Affine Neural Network (YANN)‑ RL, which we have developed in our prior work (Braniff and Tian, 2025a). By strategically initializing actor and critic networks YANN‑RL algorithms provide confident and interpretable starting points within control schemes. We apply this RL‑based control approach to three different process engineering case studies publicly available on the PC‑Gym library (Bloor et al., 2026): (i) a continuous stirred tank reactor (CSTR), (ii) a four‑tank system, and (iii) a multistage extraction column. Our approach is compared to several popular RL algorithms (PPO, SAC, DDPG, and TD3) and is benchmarked against nonlinear model predictive control (NMPC). These case studies demonstrate that YANN‑RL can greatly reduce the training time and data needed, can be deployed with confidence for chemical process systems, and can approach the performance of NMPC without the knowledge of a full nonlinear model.

Authors:Pengfeng Lin, Miao Zhu, Jiahui Sun, Haoyang Cui, Xiaoyong Cao, Chuanlin Zhang, Yunda Yan
Title: Collaborative Optimization of Battery Charging / Swapping Stations for eVTOLs Based on Closed-Loop Supply Chain and Space-Time Network
Abstract:
Against the backdrop of the burgeoning global low‑altitude economy, countries have successively introduced a series of policies to accelerate the application and commercialization of electric vertical take‑off and landing (eVTOL) aircraft. Nevertheless, purely electric eVTOLs confront constraints including limited battery energy density, high operational power requirements, and challenges associated with rapid energy replenishment, which collectively restrict their flight endurance and application scenarios. Furthermore, while eVTOL deployment is scaling up, supporting charging infrastructure and regulations remain underdeveloped. This situation presents emerging power distribution networks with new challenges in maintaining adequate electricity supply and ensuring operational continuity. To tackle these issues, following an investigation into battery energy replenishment strategies, a closed‑loop supply chain‑based model for eVTOL battery charging and swapping is proposed. Time‑space network methods are utilized to characterize the scheduling of batteries and logistics throughout the system. Subsequently, aiming to maximize the operational revenue of the model, optimized management of battery swapping, transportation, and charging processes is implemented, facilitating coordinated operation among eVTOLs, swapping stations, and charging stations. Finally, the model is solved by Gurobi, verifying its feasibility. Simulation results further indicate that the model alleviates range anxiety for eVTOLs, offering strong support for their commercialization. Moreover, it enables coordinated scheduling between eVTOLs and the distribution network, thereby facilitating the network's gradual improvement and upgrading.

Authors:Tiantian Ji, Pengfeng Lin, Miao Zhu, Stephan M. Goetz, Ahmed Abu-Siada, Syed Islam
Title: Coordinated Optimal Power Quality Management in Distribution Systems Using The Residual Capacity of Community IBRs
Abstract:
This letter proposes a network‑wide coordinated optimization model to mitigate voltage unbalance (VU) by unleashing the remaining capacity of community inverter‑based resources (IBRs). Existing single‑sequence strategies ignore coupled capacity constraints and cause idle headroom. Meanwhile, they fail to harness the collective governance capabilities of community IBRs. To solve this discrepancy and exploit the unused potential, we developed a sequence‑domain network model in dual commonly shared synchronous reference frames. Strict phase current and apparent power limits are formulated and convexified via polyhedral approximations. A quadratic objective function flexibly balances sequence capacity allocation. Simulation and experimental results validate the effectiveness of the proposed strategy.

Authors:Timo de Groot, Maurice Heemels, Tom Oomen, Sebastiaan van den Eijnden
Title: Scaled Graph Bounding Techniques for Reset Systems
Abstract:
Reset systems can overcome fundamental limitations of linear time‑invariant control. The recently introduced notion of scaled (relative) graphs provides a promising framework for developing graphical analysis and design tools for reset systems, in line with widely adopted loopshaping methods for linear systems. The aim of this paper is to derive techniques for over‑bounding the scaled graph of reset systems, and obtain insights in their accuracy. We exploit connections between quadratic dissipativity and scaled graphs to recast the over‑bounding problem as the search for piecewise quadratic storage functions. Using specific sampling techniques, we reveal a fundamental limitation of general scaled graph approximation methods that are based on quadratic dissipativity.

Authors:Dean Calver
Title: Runtime-Certified Bounded-Error Quantized Attention
Abstract:
KV cache quantization reduces the memory cost of long‑context LLM inference, but introduces approximation error that is typically validated only empirically. Existing systems rely on average‑case robustness, with no mechanism to detect or recover from failures at runtime. We present a tiered KV cache architecture that enables runtime‑certified attention: INT8 keys and INT4 values are stored in GPU memory, while FP16 originals are retained in system RAM for deterministic fallback. A two‑term error decomposition yields per‑head, per‑step bounds on (i) attention distribution distortion from key quantization and (ii) value reconstruction error. These bounds are computed online and used to drive adaptive precision selection and a multi‑stage fallback ladder, which guarantees recovery to the exact dense attention output when required. Across PG‑19, NIAH, and RULER benchmarks on LLaMA~3.1‑8B with contexts up to 128K, the system matches dense FP16 KV quality within noise for language modelling and retrieval tasks, while recovering catastrophic failures observed in naive INT8/INT4 baselines. Value‑sensitive tasks at short context expose a controlled trade‑off between compression and fidelity, which can be eliminated via tighter value tolerances or FP16‑value fallback. The certification is local (per‑head, per‑step) and does not guarantee end‑to‑end model correctness, but ensures that each attention computation is either bounded relative to an FP16 reference or exactly recovered via fallback. This reframes KV cache quantization as a runtime‑verified computation rather than a fixed approximation. The goal is not raw speedups, but enabling safe deployment of aggressive KV compression under strict quality constraints.

Authors:Canyu Lei, David Repaske, Jianxin Xie
Title: PACD-Net: Pseudo-Augmented Contrastive Distillation for Glycemic Control Estimation from SMBG
Abstract:
Effective diabetes management requires continuous monitoring of glycemic levels. Clinically, glycemic control is assessed using metrics such as Time in Range (TIR), Time Below Range (TBR), and Time Above Range (TAR), typically derived from continuous glucose monitoring (CGM). However, many patients rely on self‑monitoring of blood glucose (SMBG) due to the high cost and limited accessibility of CGM. Unlike CGM, SMBG provides sparse and irregular measurements, making accurate estimation of these metrics challenging. Conventional supervised learning approaches struggle under such sparsity, leading to poor generalization and unstable performance. To address this, we propose PACD‑Net, a self‑supervised contrastive knowledge distillation framework for estimating glycemic control from SMBG. Pseudo‑SMBG samples with richer temporal coverage are used as teacher signals to guide learning from sparse observations. In addition, multi‑view contrastive learning enforces representation consistency across diverse sampling patterns. The model adopts a hybrid Swin Transformer‑CNN backbone to capture temporal dependencies in sparse SMBG sequences. Experimental results demonstrate that PACD‑Net consistently outperforms existing methods in estimating TAR, TIR, and TBR from real‑world SMBG data, achieving improved accuracy as well as enhanced stability and generalization under extremely sparse observation settings. The proposed framework provides a practical tool for clinical SMBG interpretation and offers a generalizable approach for learning from sparse and irregularly sampled sensor data in broader applications.

Authors:Michael Acquah, Zheng Liu
Title: Cooling Channel Design Optimization for High Power Multi-chip Packages
Abstract:
Thermal management is a major challenge in next‑generation high‑performance computing systems, particularly for heterogeneous multi‑chip packages such as the NVIDIA GB200 Grace Blackwell Superchip. In this work, a physics‑based computational framework is developed to optimize embedded cooling channel layouts for high‑power multi‑chip modules. The model couples steady‑state heat conduction with a porous media‑based representation of coolant transport, coupled with a row‑wise coolant energy balance, to estimate chip temperature fields within microchannel networks. Unlike conventional designs, an interdigitated cooling architecture is parameterized using geometric variables, including channel count, width, and expansion over chip regions, enabling systematic design exploration. To enable efficient optimization, a surrogate‑based approach is employed to approximate the relationship between geometric parameters and temperature metrics. The resulting model is optimized using a mixed‑integer quadratic programming algorithm to minimize a weighted objective based on peak and average chip temperatures. To improve physical relevance, channel placement is further constrained to increase cooling coverage near GPU regions, where thermal loads are highest. The framework is applied to a representative multi‑chip configuration based on NVIDIA GB200 architecture, consisting of two graphics processing units and one central processing unit. The results demonstrate that the optimal design reduces the peak chip temperature by 140.45°C and the average chip temperature by 35.87°C compared to the baseline configuration.

Authors:Xu Du, Jingzhe Wang, Karl H. Johansson, Apostolos I. Rikos
Title: Distributed and Decentralized Optimization Algorithms via Consensus ALADIN
Abstract:
Distributed optimization has found widespread applications in smart grids, optimal control, and machine learning. This paper studies distributed consensus optimization. We extend the Augmented Lagrangian‑based Alternating Direction Inexact Newton (ALADIN) framework to propose Consensus ALADIN (C‑ALADIN) with a central coordinator, which directly handles consensus constraints. Our C‑ALADIN algorithm admits both a first‑order variant and a second‑order variant that employs a Hessian approximation, avoiding direct transmission of second‑order information while preserving fast local convergence. We then develop a decentralized version of C‑ALADIN that operates over directed graphs with quantized communication, using a finite‑time coordination protocol. For both versions, we establish global convergence guarantees for convex problems and local convergence guarantees for non‑convex problems. For the decentralized case, the iterates converge to a neighborhood of the optimum determined by the quantization level. Numerical results demonstrate that our methods retain fast convergence while substantially reducing communication and computational costs compared to existing decentralized approaches.

Authors:Matthew Low, Jasmine Jerry Aloor, Victoria Marie Tuck, Pierluigi Nuzzo, Jason J. Choi
Title: Time-To-Reach Separation and Safety Filtering for Safe, Fair, and Efficient Multi-Agent Coordination
Abstract:
Advanced Air Mobility (AAM) operations are expected to significantly increase aerial traffic in urban airspace, requiring autonomous traffic management systems to ensure collision‑free operations in highly congested environments. In this paper, we propose a multi‑agent coordination framework that uses minimum time‑to‑reach (TTR) as a unifying metric for priority assignment, temporal separation, and safety filtering. We focus on the problem of coordinating multiple aerial vehicles merging into an air corridor while maintaining safe separation between vehicles. Vehicles are assigned arrival‑consistent priority based on TTR, and target TTR values are used to enforce temporal spacing that induces spatial separation. A priority‑consistent safety filtering layer based on Hamilton‑Jacobi reachability value functions ensures collision avoidance while minimally modifying the reference guidance. Simulation results in a highly congested corridor merging scenario show that the proposed method improves safety, fairness, and efficiency compared to time‑optimal guidance and priority‑agnostic safety filtering.

Authors:Timothy I. Salsbury, Ashish Singhal
Title: A New Approach for ARMA Pole Estimation Using Higher-Order Crossings
Abstract:
The paper describes a new method for estimating the poles of an ARMA model using higher‑order crossings. The method involves transforming counts of crossing events into estimates of ARMA poles via the autocorrelation domain. An important advantage of the method is that the crossing counts are the only features that need to be stored from the original data. The poles of an ARMA model of a control loop correspond to the roots of the characteristic equation and are thus useful for evaluating control performance.

Authors:Elias Khalife, Mazen Farhood, Pierre-Loic Garoche
Title: Quadratic Characterizations for Reachability Analysis of Neural Networks
Abstract:
Quadratic constraints (QCs) are widely used to characterize nonlinearities and uncertainties, but generic analytical characterizations can be conservative on bounded domains. This paper develops a framework for constructing verified quadratic characterizations of scalar relations in the two‑dimensional real plane. Candidate quadratic inequalities are locally generated by solving convex quadratic programs using samples from the relation and exterior sample points. They are then verified globally using sum‑of‑squares certificates over an exact semialgebraic description or, in the case of nonpolynomial relations, over relaxed polynomial descriptions. The resulting verified constraints define a sound overapproximation of the scalar relations over the considered domains. These constraints are directly compatible with existing analysis frameworks based on QCs and pointwise integral quadratic constraints (IQCs) for static nonlinearities and uncertainties, and they can also be embedded in QC‑based semidefinite programs for reachability and safety analysis of feedforward neural networks. For smooth activations such as \tanh, the method yields domain‑dependent quadratic characterizations that constitute an alternative to generic sector‑ or slope‑based descriptions. For ReLU networks, we give methods to reduce conservatism in QC‑based reachability analysis of feedforward networks by exploiting dependencies between neurons and tighter local bounds. Numerical examples demonstrate improved reachability results for smooth activations, reduced conservatism for ReLU networks, and applicability beyond neural networks through an example involving saturation.

Authors:Leila Thompsky, Yuexuan Yolanda Wu, Mason A. Porter, Jiajie Luo
Title: A Bounded-Confidence Model of Opinion Dynamics with Adaptive Interaction Probabilities
Abstract:
Models of opinion dynamics aim to capture how individuals' opinions change when they interact with each other. One well‑known model of opinion dynamics is the Deffuant‑‑Weisbuch (DW) model, which is a type of bounded‑confidence model (BCM). In the DW model, agents have pairwise interactions, and they are receptive to other agents' opinions when their opinions are sufficiently close to each other. In this paper, we extend the DW model by studying it on networks with heterogeneous and adaptive edge weights between pairs of agents. These edge weights govern the interaction probabilities between the agents and thereby encode the idea that people are more likely to communicate with individuals with whom they have previously compromised or had other positive interactions. We prove theoretical guarantees of our adaptive edge‑weighted DW model's convergence properties, the long‑time dynamics of its edge weights, and the model's associated ``effective graph", which is a time‑dependent subgraph that includes edges only between agents that are receptive to each other's opinions. We support our theoretical results with numerical simulations of our adaptive edge‑weighted DW model on a variety of networks and find that including adaptive edge weights yields different qualitative dynamics for different types of networks. In particular, for small confidence bounds, we observe that incorporating adaptive edge weights decreases the convergence time for dense networks but increases the convergence time for sparse networks.

Authors:Kaito Iwasaki, Tejaswi K. C., Anthony Bloch, Maani Ghaffari, Taeyoung Lee
Title: Max-Entropy Moment Filtering for Stochastic Hybrid Systems
Abstract:
Stochastic hybrid systems combine continuous‑time stochastic dynamics with discrete reset events, producing intrinsically non‑Gaussian and often multimodal uncertainty. A consistent propagation law must also account for boundary‑induced probability flux across guard sets, making direct density propagation through hybrid Fokker‑Planck equations expensive. We develop a hybrid extension of the Max‑Entropy Moment Kalman Filter (MEM‑KF) that performs filtering from partial statistical information by propagating a finite collection of moments through stochastic hybrid dynamics and reconstructing beliefs using moment‑constrained maximum‑entropy distributions. The key step is a moment propagation rule derived from Dynkin's formula with a jump‑sum, in which reset effects appear as a boundary‑flux correction over the guard set. This yields tractable moment dynamics without solving the underlying hybrid PDE. In a stochastic bouncing‑ball example, the proposed method captures reset‑induced non‑Gaussianity through corrected moment equations while retaining the MEM‑KF's optimization‑based maximum‑entropy representation.

Authors:Guillaume Dupré, Alain Grossetête
Title: The OAPS solution: a real-time predictive system for flexible PWR operation
Abstract:
This paper presents an innovative solution designed to facilitate safe and flexible operation of nuclear power plants. The purpose of this new device, named OAPS system, is to provide optimal strategies (e.g., axial offset control, xenon oscillations mitigation, effluent minimization) and real‑time recommendations (e.g., dilution and boration flowrates, turbine power setpoints and variation rates) to help NPP operators perform power variations confidently and efficiently. In fact, just as a GPS navigator optimizes and modifies its planned route according to the current position of the user, the OAPS system regularly updates its recommendations based on the latest plant measurements. To achieve this, the OAPS system relies on a well‑established yet cutting‑edge in the nuclear industry advanced control technique known as model predictive control. The conventional axial offset control strategy of the OAPS system was previously validated on both Framatome's full‑scope PWR simulator and EDF's full‑scope N4 simulator. In this paper, three new advanced strategies are showcased on an intermediate‑complexity PWR simulator developed by Framatome: 1) determination of the fastest feasible power variation rates, 2) accelerated cancellation of axial power oscillations and 3) minimization of water and boron effluents.

Authors:Mohamadamin Rajabinezhad, Shan Zuo
Title: A Unified Framework for Attack-Resilient CLF-CBF Quadratic Programs for Nonlinear Control-Affine Systems
Abstract:
This letter introduces attack‑resilient Control Lyapunov Functions (AR‑CLFs) and attack‑resilient Control Barrier Functions (AR‑CBFs) for nonlinear control‑affine systems subject to control‑input false data injection attacks (FDIA) satisfying an at‑most‑exponentially growing envelope. The proposed framework embeds a unified adaptive compensation term into both the CLF decrease and CBF safety constraints. In contrast to input‑to‑state stability/safety (ISS/ISSf)‑based methods that certify disturbance‑dependent enlarged safe sets, the proposed approach enables finite‑time recovery to the nominal safe set without requiring a prior magnitude bound on the FDIA, relying instead on a growth‑rate characterization used for analysis and an online gain tuning law that regulates the compensation term. A unified quadratic program (QP) is developed to enforce the AR‑CLF and AR‑CBF conditions simultaneously, guaranteeing uniformly ultimately bounded (UUB) stability and uniform ultimate safety (UUS) under unbounded FDIA. Numerical results demonstrate improved resilience compared to existing ISS‑CLF, ISSf‑CBF, and robust CLF‑CBF‑QP approaches.

Authors:Larry Hui, Jordan Kam, William Su, Jianshu Zhou
Title: Hamilton--Jacobi Reachability for Spacecraft Collision Avoidance
Abstract:
This article presents a Hamilton‑‑Jacobi (HJ) reachability framework for a two‑‑satellite collision avoidance problem operating in the same circular orbit, where relative motion is modeled in the radial‑‑tangential‑‑normal (RTN) frame using planar Hill‑‑Clohessy‑‑Wiltshire (HCW) dynamics. We define the target state space as unsafe relative configurations in the orbit plane corresponding to minimum separation requirements consistent with Federal Communications Commission (FCC) orbital standards. The interaction between spacecraft is formulated as a zero‑‑sum differential game, where Player 1 is the controlled satellite and Player 2 is modeled as a bounded adversarial disturbance with unknown intent. We present the HJ formulation and compute backward reachable sets that characterize relative states from which collision cannot be avoided under worst‑case disturbances, while states outside this set admit provably collision‑free trajectories. These reachable sets are integrated with supervisory hybrid control logic to determine when evasive maneuvers must be initiated, enabling mathematically grounded safety guarantees for scalability.

Authors:Zhiyao Zhang, Gergely Zachár, William Barbour, Matt Bunting, Marcos Quiñones-Grueiro, Jonathan Sprinkle, Dan Work
Title: Enabling Real-Time Phase Control in Traffic Signal Hardware-in-the-Loop Simulation
Abstract:
Advanced Traffic Signal Control (TSC) algorithms require real‑time phase control, yet existing Hardware‑in‑the‑Loop Simulation (HILS) testbeds only support pre‑programmed timing plans. In this paper, we present the first HILS testbed for real‑time phase control. We develop a novel middleware architecture that translates dynamic phase actions (selection, switch, and duration) into commands for NTCIP‑compliant commercial hardware controllers. This middleware manages phase transitions, synchronizes signal states, and handles errors without interrupting the hardware's internal operations. Experimental validation demonstrates that the system executes real‑time phase commands, handles system conflicts, and achieves a low system internal latency at sub‑millisecond on average.

Authors:Ben Wooding, Hongchao Zhang, Taylor T. Johnson, Abolfazl Lavaei
Title: k-Inductive Neural Barrier Certificates for Unknown Nonlinear Dynamics
Abstract:
While conventional (k=1) discrete‑time barrier certificate conditions impose strict safety constraints by requiring the function to be non‑increasing at every step, k‑inductive barrier certificates relax this by allowing a temporary increase ‑‑ up to k‑1 times, each within a threshold ε ‑‑ while maintaining overall safety, and improving flexibility. This paper leverages neural networks and constructs k‑inductive neural barrier certificates (k‑NBCs) for (partially) unknown nonlinear systems. While neural networks offer scalability in the design process, they lack formal guarantees, requiring additional approaches such as counterexample‑guided inductive synthesis (CEGIS) with satisfiability modulo theories (SMT) for verification. However, the CEGIS‑SMT framework requires knowledge of system dynamics, which is unavailable in practical settings. To address this, we leverage the generalization of the Willems et al.'s fundamental lemma, using a single state trajectory, to construct a data‑driven representation of (partially) unknown models for SMT verification without sacrificing accuracy. Additionally, CEGIS‑SMT further removes the constraint of restricting barrier certificates to specific function classes, such as sum‑of‑squares, enabling greater flexibility in their design. We validate our approach on three nonlinear case studies with (partially) unknown dynamics.

Authors:Timothy I. Salsbury, Min Gyung Yu
Title: A New Simple-to-Configure Self-Perturbing Multivariable Extremum-Seeking Controller
Abstract:
This paper presents a new stochastic relay‑based extremum‑seeking controller (ESC) for multi‑input‑single‑output (MISO) systems. The goal of this work was to create an algorithm that is much simpler to configure than alternative approaches making deployment to real‑world problems easier. A solution is developed first for a static map and then adapted for a general class of dynamic systems. The number of configurable parameters is one per input channel for the static case and only one additional parameter is needed for the dynamic version. The problem of gradient identification is solved via the use of stochastic relay gains and a simple stability proof for the static case is presented. Simulation tests demonstrate the performance of the strategy for optimizing both static and dynamic systems.

Authors:Yongchun Bi, Jun Zheng, Guchuan Zhu, Jiye Zhang
Title: Robust synchronization for multi-agent systems governed by PDEs with observable and unobservable disturbances
Abstract:
This paper investigates robust synchronization for multi‑agent systems (MASs) governed by parabolic partial differential equations in the presence of both observable and unobservable disturbances. Using only boundary output measurements, a disturbance observer is designed to estimate observable Dirichlet boundary disturbances while ensuring robustness of the observer error system with unobservable disturbances occurring in the domain. Using only the reference signal and local output information, distributed synchronization controllers are then constructed to enable all agents to track the reference trajectory. In particular, exponential tracking is achieved in the absence of unobservable disturbances, while robustness is preserved when additional unobservable disturbances occur during controller implementation. We further analyze the impact of unobservable Dirichlet‑Robin boundary disturbances on synchronization performance by proving the boundedness of solutions to the synchronization error system. Moreover, to characterize the influence of all disturbances, input‑to‑state stability (ISS) is established for the closed‑loop system. For the involved systems, the generalized Lyapunov method and the recursion technique are extensively employed in the stability analysis, and the lifting technique and semigroup theory are used to prove the well‑posedness. Simulation results validate the proposed control scheme, demonstrating effective disturbance estimation and rejection, robust synchronization, and the ISS properties under various scenarios.

Authors:Juntang Yang, Mohamed Khalil Ben-Larbi
Title: Safe Deep Reinforcement Learning for Spacecraft Reorientation with Pointing Keep-Out Constraint
Abstract:
This paper implements deep reinforcement learning (DRL) with a safety filter for spacecraft reorientation control with a single pointing keep‑out zone. A new state space representation is designed which includes a compact representation of the attitude constraint zone. A reward function is formulated to achieve the control objective while enforcing the attitude constraint. The soft actor‑critic (SAC) algorithm is adopted to handle continuous state and action space. A curriculum learning approach is implemented for agent training. To guarantee the compliance of the attitude constraint, a control barrier function (CBF)‑based safety filter is implemented for agent deployment. Simulation results demonstrate the effectiveness of the proposed state space presentation and the designed reward function. Monte Carlo simulations underscore that reward shaping alone cannot guarantee the safety during reorientation maneuver. In contrast, with the CBF‑based safety filter, the constraint can be guaranteed during maneuvers.

Authors:Oumayma Khattabi, Matteo Tacchi-Bénard, Martin Gulan, Sorin Olaru
Title: Data-driven approximation of regions of attraction via an LP-based selection of PWA Lyapunov functions
Abstract:
This paper presents a method to approximate regions of attraction of unknown nonlinear dynamical systems from data. Assuming point‑wise evaluations of the vector field and known Lipschitz bounds, a polyhedral uncertainty set of admissible dynamics is constructed. This uncertainty description enables the synthesis of a continuous piece‑wise affine Lyapunov candidate via a linear program, enforcing a robust decrease condition for all admissible vector fields. The approach allows certification of a region of attraction consistent with the available data. Numerical examples illustrate the effectiveness of the proposed method in extracting certified regions of attraction from sparse data.

Authors:Thien Tran, Jonathan Kua, Thuong Hoang, Minh Tran, Yuemin Ding, Jiong Jin
Title: DAG-Based QoS-Aware Dynamic Task Placement for Networked Multi-Stage Control Pipelines
Abstract:
Current Physical AI (PAI) relies heavily on closed‑loop visual‑servoing pipelines, whose perception and planning stages may become computationally intensive onboard due to complex models embedded on robots. In practice, offloading the perception task to on‑site edges statically is inappropriate for latency‑sensitive, precise industrial settings over a standardized industrial network. This emphasizes the importance of Control‑Communication‑Computing (3C) co‑design in industrial automation: monolithic local execution saturates AI‑accelerated machine and robot hardware, while static edge offloading exposes the control loop to network jitter. Existing adaptive task placement (ATP) controllers can partially address the gap by relocating a single pipeline stage on binary threshold rules, without a multi‑stage model and an explicit cost on placement switching. In this Work‑in‑Progress (WiP) paper, we propose a directed acyclic graph (DAG) based quality‑of‑service (QoS)‑aware dynamic task placement (DTP) framework for sensing‑perception‑planning‑control pipelines in networked robotics. This pipeline is formalized as a DAG with task‑level and node‑level attributes for compute cost, communication delay, and feasible placement sets; over a small interpretable candidate set (fully local, static offload, hybrid), a window‑based cost function combines tail end‑to‑end latency, deadline violation rate, hardware utilization, and a Hamming‑distance switching penalty, and a DTP algorithm with hysteresis and a minimum dwell‑time bounds placement chatter. Our WiP paper presents the theoretical framework, a structured qualitative analysis, and a two‑phase simulation plus hardware‑in‑the‑loop validation roadmap.

Authors:Yiyao Xu, Hao Zhou, Yuhang Wang, Jingran Sun
Title: A Closed-loop, State-centric, Multi-agent Framework for Passenger Load Estimation from Heterogeneous Data Streams
Abstract:
To support operations and passenger‑facing services, transit agencies need reliable passenger load trajectories. Currently, load estimates are typically inferred from imperfect sensing systems rather than fully observed, and the accuracy of modern automatic passenger counting (APC) systems still varies with station layout, flow intensity, and operating conditions. To address the challenges of robust passenger load estimation from heterogeneous data streams, including incremental count errors, evidence conflicts, and context‑dependent sensor reliability, we propose a closed‑loop, state‑centric, multi‑agent framework. This method enforces physical feasibility at every step, allocates trust dynamically among evidence sources, and feeds physics‑derived violation residuals back into training for robustness improvement. The architecture consists of a unified stop‑event backbone, a coupled Perception‑‑Physical‑‑Fusion loop for stop‑by‑stop inference, and optional trip‑level macro‑correction and closed‑loop calibration modules.

Authors:Muhammad Tahir, Muhammad Mubbashar Baig, Umer Irfan, Muhammad Ahad, Naveed Anwar Bhatti
Title: Motion-Coupled Sensing: When the State Change Powers Its Own Sensing
Abstract:
Batteryless IoT systems have largely followed two paths: ambient‑energy sensing, where energy arrival is decoupled from the event being monitored, and kinetic event telegrams, where a user actuation powers a short report of the actuation itself. Mechanically gated states expose a third case: the access motion is not only an event to report, but the moment at which a latent physical state may have changed and must be measured. We show that routine hinge motion can supply enough energy for one bounded wake‑sense‑transmit transaction, including ultrasonic sensing and a long‑range LoRa uplink. We call this principle motion‑coupled sensing and instantiate it with an open‑source compact electromagnetic harvester that retrofits to bins, doors, and cabinets with no structural modification. We size the platform for the most demanding workload, waste‑bin monitoring, where each actuation must power both an ultrasonic measurement and a long‑range LoRa uplink. Across five campus locations and 5,945 lid actuations, the bin deployment achieves 99.3% per‑event transmission reliability. Field deployments on room doors with 1,870 actuations and office cabinets with 1,636 actuations achieve 92% and 94% transmission success respectively, demonstrating that the same energy envelope transfers across hinge geometries without hardware redesign. These results show that mechanical access can be treated as a self‑powered sensing transaction, removing periodic polling and scheduled battery maintenance for IoT deployments.

Authors:Ole Reuter, Richard Schubert, Marvin Loba, Markus Maurer
Title: Equalized Coverage in Motion Control Performance Prediction for Self-Adaptive Road Vehicles
Abstract:
Automated driving systems require monitoring mechanisms to ensure operation as intended, especially when system elements degrade and/or fail. Hence, capability monitoring is crucial in order to evaluate the system's remaining performance and implement capability‑based behavior. In this paper, we investigate the dynamics of a highly over‑actuated automated vehicle under actuator degradations and failures, affecting the vehicle's motion control capabilities. We propose a lightweight prediction model based on conformalized quantile regression that predicts whether an automated vehicle can be controlled with sufficiently low lateral deviation from a planned trajectory under nominal, degraded, and failed actuator conditions. We recognize that statistical guarantees should hold not only across all data (marginal coverage) but also for different regimes within the data (conditional coverage). We therefore employ equalized coverage methods to address this challenge. During runtime behavior generation our predictor can provide a heuristic for determining the admissible action space. Its application and limitations are discussed in this paper.

Authors:Sishen Yuan, Baijia Liang, Tangyou Liu, Yiqing Huang, Haoxuan Wu, Shuo Xu, Hongliang Ren
Title: MagCeptor: Encoding Broadcast-Addressable Logic into Magnetic Receptors
Abstract:
Multicellular coordination relies on broadcast‑addressable receptors, yet engineered magnetic systems face an addressability bottleneck because global fields intrinsically conflate power and control. Here, we introduce MagCeptors to resolve this by encoding selectivity directly into magnetic topology. Establishing an energetic isomorphism with biological receptors, these arrays utilize local couplings to shape potential landscapes where global field vectors act as spatial keys, triggering deterministic snap‑through instabilities. This architecture decouples force from source distance, achieving a density of 385 mN/mm3 (>50‑fold increase over prior art). We validate this primitive through signal demultiplexing, embodied sequential logic, and untethered distributed networking. This framework enables distributed systems to orchestrate complex tasks without tethers or electronics, relying solely on the intrinsic logic of matter.

Authors:Régulo E. Ávila-Martínez, Javier Renedo, Luis Rouco, Aurelio Garcia-Cerrada, Lukas Sigrist
Title: Revisiting angle stability in power systems with grid-forming power converters
Abstract:
This letter presents a comprehensive analysis of the stability phenomenon related to the ability of generators to remain in synchronism when subjected to small or large disturbances, in power systems with both synchronous machines and grid‑forming voltage source converters (GFM‑VSC). This phenomenon is associated with two stability classes in the IEEE/PES classification, namely, rotor‑angle stability (when involving synchronous machines and slow‑interaction converter‑driven stability (when involving power converters). However, this work shows that this phenomenon is fully characterised with the slow dynamics of the angle difference between the voltage sources connected to the power system, regardless of whether they are synchronous machines (with rotors) or GFM‑VSCs. Therefore, we suggest using the term angle stability to refer to this phenomenon, while slow‑interaction converter‑driven stability should only include slow interactions of different nature involving power converters.

Authors:Wenhao Zhuang, Yuyi Mao, Ivan Wang-Hei Ho, Xianghao Yu
Title: UAV-Assisted Cooperative Edge Inference for Low-Altitude Economy via MoE-based Hierarchical Deep Reinforcement Learning
Abstract:
The low‑altitude economy (LAE) is reshaping the industrial landscape by deploying unmanned aerial vehicles (UAVs) to facilitate a wide range of applications demanding flexible aerial mobility. Integrating edge artificial intelligence (AI) into LAE platforms creates a compelling paradigm where UAVs provide real‑time AI‑driven analysis while simultaneously executing their primary aerial mission duties. However, realizing this paradigm remains challenging due to the strict mission constraints imposed by these primary duties and the throughput bottlenecks of wireless links. To bridge this gap, we propose a UAV‑assisted cooperative edge inference framework where UAVs execute mission‑critical LAE duties, quantified by trajectory deviations from reference paths, while concurrently supporting ground devices via intermediate feature offloading. Within this framework, UAV trajectories, inference task offloading decisions, and feature compression ratios are jointly optimized to maximize the system performance. We cast this joint optimization task into a constrained partially observable Markov decision process (POMDP) framework. To efficiently solve it, we propose HDRL‑MoE, a novel hierarchical deep reinforcement learning framework that decouples the optimization of slow‑varying inference decisions from rapidly changing UAV trajectory control. Furthermore, HDRL‑MoE integrates a mixture‑of‑experts (MoE) architecture, where a router network orchestrates discrete offloading decisions while expert networks independently optimize the feature compression ratios. Extensive simulations show that HDRL‑MoE achieves significant inference accuracy gains over baselines and exhibits high scalability and efficiency through its MoE design.

Authors:Guanwen Xie, Jingzehua Xu, Yiyuan Yang, Yimian Ding, Shuai Zhang
Title: ERFSL: An Efficient Reward Function Searcher via Language Models for Custom-Environment Multi-Objective Optimization (Student Abstract)
Abstract:
We propose ERFSL, an efficient reward function searcher using large language models (LLMs) for custom‑environment, multi‑objective learning‑based methods (LB). ERFSL generates reward components based on explicit user requirements, rectifies them using a reward critic, and iteratively optimizes the weights of these components based on textual context generated by the training log analyzer. Applied to a simulation‑based benchmark task, the reward critic corrects reward codes with only one feedback iteration per requirement, and the reward weight initializer acquires diverse reward functions within the Pareto set. Even when a weight is off by a factor of 500, an average of only 5.2 iterations is needed to meet user requirements. The approach works adequately with GPT‑4o mini and does not require advanced understanding capabilities.

Authors:Xiaoyan Ma, Seohyun Lee, Taejoon Kim, Christopher G. Brinton
Title: Detecting and Mitigating Backdoor Attacks in OTA-FL Systems: A Two-Stage Robust Aggregation Scheme
Abstract:
Over‑the‑air federated learning (OTA‑FL) improves communication efficiency by exploiting the superposition property of wireless channels, but this same property also creates a critical security vulnerability: the parameter server (PS) cannot access individual local updates, making it difficult to identify and exclude poisoned gradients. The challenge is further exacerbated under non‑independent and identically distributed (Non‑IID) training data, where benign gradient drift can closely resemble malicious updates. In this paper, we propose a two‑stage robust aggregation framework for defending against backdoor attacks in OTA‑FL. Under our scheme, each client is first assigned a modality‑aware multi‑indicator trust score, where the specific indicators are selected according to the data modality (e.g., waveform, text, image) and model architecture to capture the most discriminative footprint of backdoor updates. Based on this score, the PS then performs trust‑based multiple access (TBMA) to separate clients into trusted, suspicious, and malicious categories. Suspicious clients are further examined through PS‑side layer‑wise inspection and a longitudinal reputation mechanism. Experimental results on several datasets demonstrate that the proposed methodology effectively suppresses stealthy backdoor attacks, including bounded‑scaling attacks, Euclidean‑constrained attacks, Cosine‑constrained attacks, and Neurotoxin, while maintaining competitive main‑task accuracy.

Authors:Joshua Betz, Daniel Herber, Jeffrey Niemann
Title: Multi-Criteria Integer Programming Model for Route Planning in an Off-Road Combat Environment
Abstract:
Route planning for military vehicles is a complex decision‑making problem due to the simultaneous influence of environmental trafficability and tactical risks. This paper presents an optimization model that integrates soil trafficability and risk of enemy engagement into a decision‑support model for planning activities in open terrain. Although a military application is the focus of this paper, other use cases include wildfire response, agricultural operations, and off‑road vehicle recreation. The routing problem is formulated as a minimum cost mixed‑integer linear program over a discretized representation of the operational environment. Each node represents a location and is connected by arcs to adjacent nodes whose traversal incurs a cost derived from a composite risk function that accounts for soil strength and the proximity to known enemy activity and prior convoy routes. Environmental inputs required for evaluating soil strength are obtained by integrating external models, which estimate spatial variations in the rating cone index (RCI) across the terrain. The model is evaluated through a case study conducted at a location in northern Colorado using fine‑resolution environmental data and simulated tactical conditions. Scenario analyses demonstrate how variations in risk weighting, vehicle mobility characteristics, and operational conditions influence route geometry and mission risk. The objective function values achieved varied by five orders of magnitude based on the coefficients assigned to the terms in the cost function and the vehicle properties of the scenario. The results illustrate the capability of the proposed framework to quantify trade‑offs between environmental mobility constraints and tactical considerations.

Authors:Farid Rajabali, Roland Malhame, Sadegh Bolouki
Title: Continuous Aggregative LQG Games with Delayed Discrete Observations
Abstract:
Mean field game equilibria are predicated on the assumption of immediate pairwise interactions within a population of homogeneous agents with asymptotically vanishing influence as population size increases. However, in many real‑world cases, agents receive population‑level information with a delay. In this paper, we characterize agent best responses under an information exchange structure whereby agents observe the empirical mean state only at discrete time instants with some delay. Sufficient conditions are presented for the existence of a Nash equilibrium within a finite population of agents, and the cost increase due to delayed discrete empirical mean observations relative to zero‑latency discrete observations and continuous global‑state observations is also evaluated.

Authors:Yunan Wang, Jizhou Yan, Chuxiong Hu, Zeyang Li
Title: Reachability-Augmented Dual Dynamic Programming for Optimal Path Parameterization
Abstract:
Optimal path parameterization (OPP) is a fundamental problem for planning trajectories along a prescribed geometric path under kinodynamic constraints and task‑dependent objectives. While TOPP minimizes traversal time, its saturating states and controls may induce vibration and tracking errors, which can be mitigated by introducing smoothness objectives. However, a key capability gap remains in OPP: feasibility guarantees, general‑objective optimality certificates, and computational efficiency are difficult to achieve simultaneously in a unified framework, especially for third‑order OPP (OPP3) with non‑convex constraints. This paper proposes reachability‑augmented dual dynamic programming (RDDP), a state‑grid‑free and objective‑aware DP framework for OPP. The key idea is to replace the relatively complete recourse assumption used in classical dual DP (DDP) with OPP‑specific backward reachable sets, and then generate both value‑function cuts and trial trajectories only inside these reachable sets. For convex and non‑convex OPP, we prove global optimality and Karush‑Kuhn‑Tucker convergence of RDDP under OPP‑specific conditions, respectively. Efficient instantiations are developed for OPP2 and OPP3. Experiments show that RDDP achieves objective values comparable to convex‑optimization baselines while reducing computation time by 28.6 times for OPP2 and 5.8 times for OPP3. RDDP also achieves faster convergence than grid‑based DP. Compared with reachability‑analysis methods, RDDP retains the reachability mechanism while replacing local maximum‑control propagation with value‑function‑guided control selection, thereby enabling objectives beyond traversal time. In summary, RDDP addresses a key capability gap in OPP by unifying certifiable general‑objective optimization, reachability‑based feasibility preservation, and online‑compatible low‑dimensional DP computation in a single OPP framework.

Authors:Shreyaa Raghavan, Cameron Hickert, Monica Chan, Cathy Wu
Title: Dynamic Gradient-Based Calibration for Robust and Accurate Traffic Macrosimulation
Abstract:
Robust and accurate calibration of macroscopic traffic flow models such as METANET is critical for reliable prediction and effective control. While gradient‑based methods are desirable for high‑dimensional parameter spaces, their application to real‑world traffic scenarios is hindered by highly nonconvex optimization landscapes. Consequently, standard static calibration frequently yields parameter sets that produce unstable, unrealistic traffic dynamics, undermining confidence in the estimated parameters and compromising the simulation's utility for counterfactual scenario testing. To address this, we propose a dynamic, rolling‑horizon calibration framework. By reformulating static one‑time estimation as a closed‑loop control problem, parameters better maintain stability and accuracy in the presence of measurement noise. Using real‑world data from the I‑24 MOTION testbed, this work empirically characterizes the instability of standard methods. It then shows that the proposed approach simultaneously enhances robustness to perturbations and achieves a 48% improvement in predictive accuracy over conventional static calibration.

Authors:Hyeontae Sung, Hyeongchan Ham, Junyoung Park, Kai Ren, Heejin Ahn
Title: Probabilistic Recursively Feasible Motion Planning Under Uncertain Environments
Abstract:
Safe motion planning in uncertain, time‑varying environments is challenging because the safe region can change unpredictably across planning steps, often causing a loss of recursive feasibility. In this work, we present a Probabilistic Recursively Feasible Model Predictive Control (PRF‑MPC) framework that guarantees recursive feasibility with a specified probability. We introduce properties that an ideal predictor should satisfy to ensure distributional consistency, and use these properties to derive closed‑form expressions for the means and covariances of trajectories predicted at future time steps. Building on this analysis, we construct safety constraints that ensure, with high probability, that the current safe set is contained within the safe sets at future time steps, thereby probabilistically guaranteeing recursive feasibility. Simulation results on a lane‑change scenario demonstrate that the proposed method significantly improves recursive feasibility.

Authors:Zongxi Yu, Cyprien Tamekue, Ruiqi Chen, ShiNung Ching
Title: Scalable iterative Gramian synthesis for control-affine systems
Abstract:
This article presents a scalable implementation of nonlinear Gramian‑based control synthesis for control‑affine systems, including a minimum energy control construction. These synthesis advances are achieved by addressing key computational bottlenecks inherent to iterative synthesis map formulations, yielding a computational scheme that exhibits rapid convergence and high‑precision. The efficacy of this synthesis framework is demonstrated across five canonical nonlinear control systems and 100‑dimensional recurrent neural network models, including underactuated systems. Empirical scaling results further indicate that convergence is primarily governed by intrinsic system properties, such as nonlinearity and controllability, rather than by state‑space dimensionality. This work provides a practical, scalable computational pathway for translating rigorous nonlinear synthesis theory into high‑dimensional control applications.

Authors:Ahmed Gamal Eldin
Title: Descriptive versus Regulatory Uncertainty in Bounded Predictive Systems
Abstract:
Any system that models the world under finite representational capacity must compress; any compression entails a prior; and the prior is the system's bias. What has not been established is whether uncertainty participates in the dynamics governing future behavior, or merely describes the output distribution without consequence. We introduce a structural distinction between descriptive uncertainty, which does not recursively modulate the system's policy, and regulatory uncertainty, which directly enters the optimization landscape and drives persistent adaptive restructuring. We prove formally that current transformer architectures are confined to descriptive uncertainty at inference. We ground this in thermodynamics via Landauer's principle: for uncertainty to be regulatory, epistemic error must cost real energy; in a decoupled system, hallucinations and correct derivations dissipate identical energy. We test this empirically across three locally‑deployed language models (3B, 8B, 70B parameters). Token‑level Shannon entropy is statistically invariant across tasks spanning pattern retrieval, causal operator application, and out‑of‑distribution causal generalization in all three models (all pairwise p >= 0.568; within‑model ranges 0.011‑0.028 nats), while task accuracy varies substantially across the same conditions (0%‑100%). Entropy and accuracy are orthogonal. The decoupling is scale‑invariant: larger models achieve higher accuracy but identical entropy flatness. This structural incapacity is not resolvable by additional parameters or training data. Genuine epistemic grounding requires physical coupling between thermodynamic substrate state and information processing cost.

Authors:Yongfu Tian, Shan Ding, Guofeng Su, Jianguo Chen
Title: A Novel Urban Flood Dynamical System Model and a Corresponding Nonstandard Finite Difference Method
Abstract:
Urban flood disaster is one of the most serious natural disasters. Numerous flood simulation models have been proposed and relatively matured. However, two major challenges persist: excessive simplification of the city system and high computational complexity. To break these limitations, this paper develops an Urban Flood Dynamical System Model (UFDSM) based on the concept of the Cellular Automata Urban Flood Model. This model allows flexible customization of cell types and selection of water motion or distribution rules based on actual urban environments to incorporate as much the urban system data as possible. The water motion and distribution rules can be simple, which could reduce the computational complexity, but not arbitrary. So, a sufficient condition is provided so that solutions of dynamical system align with macroscopic physical conditions governing water movement. Then, to preserve the evolutionary properties of the UFDSM, we propose a first‑order conservation nonstandard finite difference algorithm. This numerical method ensures positive solutions and conservation of water while maintaining the same fixed‑point characteristics as the dynamical system. And, this numerical method is validated by comparing it with an analytical solution.Furthermore, to verify the applicability of our model, we performed an urban flood simulation experiment and compared it to HEC‑RAS. There is approximately a 2mm discrepancy in distance dp' and 0.02mm discrepancy in distance d2' , with the relative distance Rp about 7.5% and the relative distance R2 approximately 0.06%. Additionally, the proposed model is easily coupled with other hydrological processes and facilitates data assimilation, thereby offering promising practical applications.

Authors:Zining Cao
Title: On Generalized Performance Evaluation and Generalized Controller Synthesis
Abstract:
In this paper, we propose the frameworks of generalized performance evaluation and generalized controller synthesis. To this end, we give a true concurrent process calculus as the model of systems, and present a lattice‑valued performance evaluation language as the performance specification of systems. We give a framework of generalized performance evaluation based on the process calculus and the performance evaluation language. We show that the several problems in computer science are special cases of generalized performance evaluation. A generalized performance evaluation algorithm is presented. Furthermore, we present a framework of generalized controller synthesis, which is the inverse problem of generalized performance evaluation. We show several special cases of generalized controller synthesis in computer science, and give an outline of generalized controller synthesis algorithm.

Authors:Tingwei Chen, Kaiyang Huang, Kai Sun
Title: A Benchmark on LLM-Based Power Flow Computation: Do More Structured Prompts Help?
Abstract:
We present a controlled benchmark evaluating three LLMs ‑‑ Claude Sonnet 4.5, Gemini 2.5 Pro, and GPT‑3.5 Turbo ‑‑ across four prompt formats (from concise narrative to structured JSON with explicit iteration trace) on Gauss‑‑Seidel AC power flow computation for a three‑bus system. Against 50 test cases with reference solutions computed numerically, Gemini 2.5 Pro with the simplest narrative prompt achieves the lowest mean absolute error (MAE = 0.257 MW/MVar, 54% of cases within 5% relative error), while the same model with a JSON‑structured prompt raises MAE to 0.789 ‑‑ a 3.1× increase. Adding a worked example degrades accuracy for Gemini but provides a marginal gain for Claude. GPT‑3.5 Turbo fails on at least 90% of cases under all prompt formats. An independent 100‑case replication with related prompt‑format families confirms the qualitative ordering (Gemini > Claude > GPT‑3.5): the best 100‑case configuration (Gemini with explicit iteration trace) achieves MAE = 0.402 and 53% within 5%, while Claude Sonnet 4.5's near‑flat accuracy profile (\approx38% within 5% across formats) and GPT‑3.5's near total ineffectiveness (92‑‑97% above 20% error) both replicate. In neither evaluation does any configuration achieve sufficient reliability for use as a direct numerical solver. These findings offer a diagnostic baseline for practitioners and researchers evaluating LLMs for smart‑grid decision‑support assistance.

Authors:Adam Suski, Elina Spyrou, Jacob Mays, Richard Green
Title: Comparing Contract-Based Support Mechanisms for Long-Duration Energy Storage
Abstract:
Long‑duration energy storage (LDES) faces significant revenue volatility that impedes investment. This paper evaluates four contract‑based support mechanisms using an equilibrium model with risk‑averse investors and incomplete risk markets. Applied to a stylized 2035 Great Britain case, we find that all mechanisms can achieve the targeted LDES capacity but differ substantially in cost‑effectiveness and risk‑aversion sensitivity. Contracts that eliminate revenue volatility achieve the lowest costs but may weaken operational incentives, while contracts that preserve market exposure maintain incentives at higher costs.

Authors:Lekan Molu, Venkatraman Renganathan, Namhoon Cho
Title: HJ-Gauss: A Monte-Carlo HJ Reachability Scheme
Abstract:
Backward reachable tubes (BRTs), computed via viscous Hamilton‑Jacobi (HJ) partial differential equations, provide principled safety certificates for learned controllers and planning algorithms in trustworthy machine learning. However, classical grid‑based HJ solvers require O(M^n) memory footprint for M grid points per n state dimension. This renders them impractical for high‑dimensional systems. We address this bottleneck with a local PDE linearization that enables a frozen‑coefficient sampling scheme for the viscous HJ PDE: a generalized Cole‑Hopf‑type transformation reduces the nonlinear HJ equation to a sequence of linear heat equations whose solutions admit Gaussian heat‑kernel representations. The value function and its spatial gradient are then recovered via roll‑outs of Monte Carlo expectations on Gaussian densities, yielding a storage and grid‑free algorithm that scales as N\cdot n for N samples. This decoupling of memory from dimensionality enables reachability analysis on problems where grid‑based methods are simply impossible. We prove a finite‑sample concentration bound O(N^‑1/2) error and conditional linear convergence for the introduced Monte‑Carlo Picard iterative scheme. Numerical validation on pursuit‑evasion games demonstrates relative L^2_\textrel errors of 0.03 ‑ 0.20, with 14‑26 second wall‑clock times per 2D slice on a CPU. Crucially, the method scales with validation on up to (but not limited to) n=45‑dimensional multi‑agent games.

Authors:Haoxiang Wan, Xingpeng Li
Title: Data Center Spatio-Temporal Load Flexibility in Security-Constrained Unit Commitment for Enhanced Grid Efficiency and Reliability
Abstract:
Data center electricity consumption reached 4.4% of U.S. total in 2023 and is projected to grow to 6.7‑‑12% by 2028, imposing increasing stress on transmission networks while representing a largely untapped source of controllable demand‑side flexibility. This paper proposes a modular security‑constrained unit commitment (SCUC) framework that coordinates flexible data center workloads with system‑level scheduling to reduce renewable curtailment, alleviate congestion, and lower operating costs. Three mixed‑integer linear programming (MILP) models are formulated: the Data Center Spatial model (DC‑S), enabling instantaneous workload redistribution across geographically distributed sites; the Data Center Temporal model (DC‑T), permitting each site to shift its deferrable load across time while preserving the daily energy balance; and the Data Center Spatio‑Temporal model (DC‑ST), jointly activating both mechanisms and spanning the largest feasible operating region. Case studies on a modified IEEE 24‑bus reliability test system show that DC‑ST eliminates all base‑case and post‑contingency transmission violations at a flexibility ratio of 40%, and reduces renewable curtailment by up to 84.4% at 30% relative to the inflexible baseline. Sensitivity analysis further reveals that moderate flexibility levels of 20%‑‑30% already capture most of the achievable benefits, supporting practical deployment with limited operational burden on data center operators.

Authors:Sampath Kumar Mulagaleti, Boris Houska, Mario Zanon, Mario E. Villanueva
Title: On Piecewise Quadratic Terminal Costs for MPC
Abstract:
This paper presents a novel approach to synthesize stabilizing termi‑ nal ingredients for linear model predictive control (MPC) schemes, with the aim of increasing the region of attraction while reducing suboptimal‑ ity with respect to the solution of the infinite‑horizon optimal control problem. It is based on the construction of a novel terminal region using methods from the field of configuration‑constrained polytopic computing, along with a terminal cost that is exactly equal to the infinite‑horizon linear‑quadratic regulator cost in a nontrivial neighborhood of the steady‑ state. The practical performance of the controller is illustrated through various case studies, and comparisons with state‑of‑the‑art approaches are presented.

Authors:Yuwei Ying, Johan Löfberg, Anders Hansson
Title: A characteristic function framework for chance constraint programming in stochastic model predictive control
Abstract:
The computation of chance constraints in stochastic model predictive control is often numerically challenging due to the non‑Gaussian nature of the disturbances. To overcome this problem, we propose an optimization computational framework applicable to non‑Gaussian disturbances. This framework employs a numerical inversion method, utilizing the characteristic function of the disturbance distribution to compute the probability in the chance constraint as well as its gradient. To improve efficiency, it vectorizes integral points and reuses intermediate computations in Gauss‑Kronrod quadrature. The framework is implemented within the YALMIP toolbox to perform chance constraint calculations for arbitrary non‑Gaussian disturbances, applicable to both single‑component distributions and mixture models. It allows the user to simply specify a distribution type and its parameters for the disturbance and directly compute the probability and its gradient to solve the optimization problem. The method is validated through a numerical example of a stochastic model predictive control application.

Authors:Sigurd Skogestad
Title: Advanced PID architectures for tracking changing active constraints
Abstract:
Advanced regulatory control (ARC), also known as advanced PID architectures, is a simple and robust way of controlling processes with changing and possibly conflicting constraints, where it previously was believed ‑ at least in academia ‑ that model‑based solutions, such as MPC, were the only effective solution. To illustrate this, ARC is applied in two case studies. The first is a gas‑liquid separation process, in which selectors and split‑parallel control are combined to achieve bidirectional inventory control in which the throughput manipulator moves automatically to the most optimal position. The second case study is on keeping acceptable air quality (CO2‑level) and temperature in a room (in this case, a barn for cows). The CO2 and temperature constraints can be conflicting, leading to a hierarchical switching network of PID controllers. Note: this is an extended version (with simulations) of paper at IFAC World Congress, August 2026, Korea.

Authors:Riccardo Ramaschi, Mario Paolone, Sonia Leva
Title: Electric Vehicle Charging Profile Forecasting Using Hybrid Models
Abstract:
Electric Vehicle (EV) fast charging stations require forecasting techniques both at the single charger level and aggregated level. While for the latter several models exist, forecasting individual EV charging profiles is still underexplored in literature. However, such methods may be potentially used by battery‑aware scheduling, leading to a more granular update of the charging station aggregated forecast and provide a more accurate estimation of EVs departure times. Nonetheless, the variable extent of available information in time and in different settings could jeopardize these benefits. For this reason, we propose a hybrid and lightweight method to estimate the EV charging profile before and during the charging process. Besides evaluating this method on multiple EVs from a public dataset, we also assess the impact of different level of information in the time transposition of the charging profile.

Authors:Daniel Frank, Fahim Shakib, Steffen Staab
Title: Learning the dynamics of nonlinear systems with regional stability guarantees through linear matrix inequality constraints
Abstract:
This paper presents a method that learns a regionally stable recurrent neural network model from a set of input‑output data generated by an unknown dynamical system. Relying on generalized sector conditions on the deadzone activation function, we first derive sufficient conditions that guarantee forward invariance on a compact set of the state space for any inputs from a given set. Such regional properties lead to less conservative conditions compared to variants that offer a global form of stability, and are in line with the system data that is only observed regionally. Our learning method derives conditions for regional stability using a barrier function approach, leading to models equipped with a certificate of regional stability in a subset of the state space and for a given input set. We illustrate our theoretical result with a numerical example and compare it to methods that impose a global form of stability, which fail to identify the system, and with a method that imposes no stability constraints at all, which does not guarantee a stable behavior within any state or input set.

Authors:Ahmad Bazzi, Florian Gast, Fan Liu, Shi Jin, Gerhard Fettweis, Marwa Chafii
Title: From Coverage to Sensing: ISAC meets FR3
Abstract:
Future 6G systems are expected to exploit upper midband spectrum in frequency range 3 (FR3) not only for high throughput communications, but also for sensing services such as localization, detection, and situational awareness. The following paper develops a concrete path from today's coverage‑oriented deployments to FR3 networks that treat sensing as a native function. We first show how existing FR2 radars can be time‑multiplexed and coordinated under a 6G medium access control as radar‑as‑a‑service, forming a bridge between legacy sensing and network‑managed integrated sensing and communications (ISAC). We then propose a hierarchical FR3 beam‑alignment strategy in which coarse access occurs at lower frequencies and refinement occurs at upper FR3, and quantify the resulting sensing and communication capabilities via range‑angle Cramér‑Rao bounds in the near field. We identify intra‑ and inter‑beam squint phenomena specific to wideband FR3 arrays, and discuss design approaches to mitigate them. On the signal‑processing side, we argue that FR3 sensing cannot rely solely on pilot resources and discuss how much sensing information can be extracted from payload resource elements. We further highlight the role of calibrated FR3 channel simulators and real‑time models as the core of wireless digital twins for training and evaluating ISAC algorithms, and discuss how massive MIMO and dense or distributed deployments at FR3 naturally act as large reconfigurable sensor arrays.

Authors:Gabriele Gualandi, Carl Mikael Larsson, Alessandro V. Papadopoulos
Title: Active Defense Against False Data Injection Attacks in Robotic Manipulators
Abstract:
Robotic systems are vulnerable to False Data Injection Attacks (FDIAs), where adversaries corrupt sensor signals to gain malicious control. Feedback linearization exposes robotic systems to integrator vulnerability, making them susceptible to stealthy attacks that can cause significant deviations in end‑effector behavior without raising alarms. This paper addresses the resilience of manipulators against finite‑horizon FDIAs by formalizing two defense methods, namely anomaly‑aware virtual damping and manipulability reduction, with probabilistic guarantees on nominal task execution. Simulations on a 7‑DOF redundant manipulator show that the proposed defenses substantially reduce the impact of FDIA compared to using solely a threshold‑based ADS like the Chi‑squared, while preserving nominal task performance in the absence of attack.

Authors:Tamer Başar, Tomohisa Hayakawa, Hideaki Ishii, Quanyan Zhu
Title: Cooperative and Noncooperative Paradigms for Game-Theoretic Control of Socio-Technical Systems
Abstract:
This tutorial presents cooperative and noncooperative game‑theoretic frameworks for modeling, learning, and control in socio‑technical systems, where human behavior, incentives, institutions, and social interactions are coupled with cyber‑physical and networked infrastructures. The paper reviews strategic, dynamic, cooperative, matching, learning, and feedback‑control approaches for analyzing how local decision‑making, adaptation, and strategic interactions shape collective system outcomes. The tutorial further develops feedback‑learning and incentive‑design perspectives that connect equilibrium analysis with adaptation, distributed control, and mechanism design under information and coordination constraints. We also examine resilience and security challenges arising from adversarial behavior, misinformation, disruptions, and cascading failures in interconnected socio‑technical networks. Finally, we discuss emerging research directions at the intersection of game theory, control, learning, and network science for resilient and adaptive socio‑technical systems.

Authors:Qingyun Luo, Jingqing Wang, Wenchi Cheng
Title: Control-Certified Wireless Resource Allocation for Digital-Twin-Enabled UAV Swarms
Abstract:
Wireless resource allocation in digital‑twin‑enabled unmanned aerial vehicle (UAV) swarms must be both network‑feasible and certifiably safe for closed‑loop control. Existing packet‑level or scalar‑priority schedulers cannot meaningfully compare heterogeneous multi‑hop actions that differ simultaneously in route, retransmission depth, blocklength, bidirectional delay, delivery probability, and TDMA slot cost. This paper introduces a certificate‑guided resource allocation framework for low‑altitude multi‑hop UAV swarms. A digital twin maps predicted topology, channel, route, and controller‑side state into a shared five‑dimensional quality‑of‑service (QoS) certificate comprising uplink/downlink delay bounds, directional delivery guarantees, and a certified upper bound on the interval between successful bidirectional interactions. A state‑conditioned stochastic drift test then admits only certificates whose augmented Lyapunov drift is nonpositive under the current controller state. Admitted actions are reduced to certified supply frontiers by removing dominated route‑slot configurations, and the online scheduler maximizes Lyapunov‑drift reduction under a shared TDMA slot budget via exact dynamic programming. Closed‑loop ns‑3 simulations demonstrate that the proposed framework outperforms fixed‑service, certificate‑filtered fixed‑priority, dynamic‑transmission‑count, and value‑of‑information baselines in both tracking accuracy and high‑risk state suppression under identical communication budgets.

Authors:Jerry Anunrojwong, Baosen Zhang
Title: Residential Battery Pooling Under Backup Commitments
Abstract:
Residential batteries increasingly serve two roles: they can earn money by arbitraging wholesale prices and providing grid services, and they provide backup power during outages. This dual use creates a basic tradeoff between earning market value and preserving outage readiness. Coordination across many batteries can help, but a provider cannot treat the fleet as a single virtual battery when each household is promised its own backup protection. We compare standalone control, in which each home is dispatched independently, with pooling, in which homes are coordinated while each battery retains its own state of charge and household‑specific backup requirement. Both regimes are implemented as model predictive control problems with 15‑minute decision intervals and evaluated using household telemetry together with ERCOT market inputs. The empirical design focuses on the 543 homes in our sample that can support at least one backup product in standalone operation and studies backup caps ranging from 2 to 24 hours. Lower caps relax backup obligations, while the 24‑hour cap coincides with assigning each home its own longest feasible backup tier. Pooling remains beneficial in this service‑constrained setting, but its value declines smoothly as backup obligations tighten. Standalone firm margin ranges from \11.06 per home per week at the 2‑hour cap to \10.79 at the 24‑hour cap, while pooling benefit falls from \1.49 to \1.27 per home per week. Relative to standalone firm margin, pooling is worth about 13.5% at the 2‑hour cap and about 11.8% at the 24‑hour cap. Coordination therefore still helps after preserving household‑level backup guarantees, but its value declines as backup obligations tighten.

Authors:G. Q. Bao Tran, Yutaka Hori, Shinji Hara
Title: Observer-Based Stabilization for Linear Multi-Agent Dynamical Systems Using Generalized Frequency Variables
Abstract:
We address the conditions and design of controllers and observers for homogeneous networks of linear MIMO agents. We develop networked controllers and observers that ensure the stability of both the system state and the estimation error, leveraging the concept of generalized frequency variables. A separation principle for networks is then established, showing that the observer and controller can be designed independently and combined to achieve a stable output feedback. Our results are illustrated via a highly unstable, oscillatory network of locally actuated pendulums on carts. Finally, necessary conditions for controllability and observability ‑‑ derived from agent properties and network structure ‑‑ are established and discussed.

Authors:Akul Swami, Nikhil Chougule
Title: Architecture Dependent Temporal Observability Under Deployment Interference in Edge Inference Systems
Abstract:
Edge inference systems are typically evaluated with software‑reported latency collected under controlled conditions. We argue, and demonstrate empirically, that deployment interference can corrupt not only the inference timing being measured but the timing observability infrastructure that measures it, and that the two failures can occur independently. We pair software‑reported timing with externally observable GPIO intervals captured by a Saleae Logic Pro 8 logic analyzer on an NVIDIA Jetson Orin Nano, running MobileNetV2 under two inference architectures (TensorRT FP16 GPU and ONNX Runtime CPU) across baseline, light memory pressure, and storage writeback stress. Across 35 paired capture runs (3500 samples) plus 3 storage‑stress runs where external pairing failed (300 software‑only samples), we observe three findings the software‑only view does not surface. (1) The two architectures differ not only in mean latency but in distributional structure: TensorRT baseline clusters tightly near 1.23 ms (run‑mean SD 15 us) while ORT CPU baseline is multimodal with run‑mean SD 31.8 ms. (2) Light memory pressure inflates TensorRT P99 from 1.28 ms to 1.61 ms, while one of five ORT memory‑stress runs collapses into a deterministic 198 ms regime rather than uniformly inflating variance. (3) All three TensorRT storage‑stress runs produce complete software timing logs (100/100 iterations) alongside externally observable timing failures of three different kinds (full post‑marker collapse, ~40% transition loss, and complete acquisition failure) ‑‑ while the runtime reports normal completion in every case. We claim, narrowly, that timing observability is itself an interference‑sensitive resource, and that summary statistics from a single timing source can hide failure modes an independent external observer makes visible.

Authors:Arioua, Islameddine, Benzaoui, Amir, Zeroual, Abdelhafid, Houam, Lotfi
Title: Attention-Guided Fusion of 1D and 2D CNNs for Robust ECG-Based Biometric Recognition
Abstract:
Electrocardiogram (ECG)‑based biometric recognition has emerged as a promising solution for secure authentication and liveness detection. However, most existing methods rely on unimodal deep learning architectures that independently process either one‑dimensional (1D) temporal signals or two‑dimensional (2D) time‑frequency representations, limiting robustness and generalization. To address this issue, this paper proposes a hybrid framework integrating 1D and 2D convolutional neural networks (CNNs) within a unified end‑to‑end architecture. The 1D branch extracts temporal and morphological features from raw ECG signals, while the 2D branch captures discriminative spectral information from time‑frequency representations. An attention‑guided fusion mechanism dynamically weights both modalities according to input characteristics, overcoming the limitations of conventional static fusion strategies. The framework was evaluated on three benchmark datasets (ECG‑ID, MIT‑BIH, and PTB), including healthy subjects and patients with cardiac pathologies, achieving identification accuracies of 99.56%, 100.00%, and 99.89%, respectively. To assess long‑term biometric permanence, experiments were also conducted on the multi‑session Heartprint dataset spanning ten years. The proposed approach achieved same‑session accuracies of 98.54% (S1), 99.09% (S2), 94.93% (S3R), and 96.08% (S3L), while cross‑session evaluations reached 56.33% (S1‑S2) and 53.27% (S2‑S3R), demonstrating the ability to capture stable biometric signatures over time. The optimal configuration combines InceptionTime for 1D processing, ResNet‑34 for 2D analysis, and attention‑based fusion. Ablation studies confirm that the proposed attention mechanism consistently outperforms conventional fusion approaches. Overall, the proposed framework provides a robust, scalable, and high‑performance solution for ECG biometric recognition.

Authors:Michael C. A. Nestor, Fei Teng
Title: Distributed Synthesis of Gray-Box Distributed H2 Controllers
Abstract:
Distributed controller synthesis offers scalable and privacy‑preserving control design, but typical state‑of‑the‑art approaches either assume white‑box models or resort to centralized synthesis. In this paper, we combine partially known model knowledge and an input‑state dataset within a distributed gray‑box scheme to design \(\mathcalH_2\) controllers. Our method can handle unknown dynamics and offers scalable synthesis. Each agent communicates with a set of neighbors determined by the physical coupling topology of the system such that we can apply the Alternating Direction Method of Multipliers (ADMM) to solve the problem iteratively in a fully distributed fashion (i.e., without a central server). The effectiveness and flexibility of the proposed approach is demonstrated in simulations of the IEEE 39‑bus power system test case.

Authors:Yongkang Su, Joaquin Carrasco, Iñaki Esnaola, Lanlan Su
Title: Distributed Synchronisation of Heterogeneous Dynamical Networks With Nonlinear Diffusive Couplings
Abstract:
This letter investigates the problem of output synchronisation in heterogeneous dynamical networks with nonlinear diffusive couplings in the presence of disturbances on the coupling links. By exploiting relative dissipativity properties between adjacent agents, distributed conditions are established to guarantee output synchronisation. Specifically, these conditions can be verified using only local information associated with neighbouring agents and coupling links. As an illustration, a heterogeneous network of Goodwin oscillators is considered, where the relative dissipativity properties between neighbouring oscillators are characterised and used to analyse synchronisation.

Authors:Immanuel R. Santjoko, Richie R. Suganda, Miao Pan, Bin Hu
Title: Distributed 3D Leader-Follower Formation Control with Field-of-View Safety via Control Barrier Functions
Abstract:
This letter proposes a distributed 3D leader‑follower formation (3D‑LFF) control framework for multi‑UAV systems that achieves formation tracking while enforcing perception safety constraints. Maintaining safe, vision‑based 3D‑LFF is challenging because onboard cameras impose strict Field‑of‑View (FOV) limitations, and demanding formation commands can drive the leader outside the follower's camera frustum, resulting in loss of visibility. To address this issue, we develop a perception‑aware safe control architecture that guarantees visibility by construction. First, we derive a relative kinematic model in a line‑of‑sight coordinate representation and design a distributed 3D‑LFF tracking controller using only locally available relative states. Next, we embed the nominal formation controller within a Control Barrier Function‑based Quadratic Program (CBF‑QP) safety filter that minimally modifies the commanded velocities to maintain the leader inside the follower's camera frustum while preserving formation tracking whenever feasible. Gazebo simulations and Crazyflie hardware experiments validate the proposed approach, demonstrating accurate formation tracking and effective FOV enforcement, including scenarios in which the nominal desired formation conflicts with visibility constraints.

Authors:Tong Xiao, Reinhild Roden, Matthias Blau, Simon Doclo
Title: Robust Soft-Constrained Spatially Selective Active Noise Control for Hearables Under Secondary Path Variations
Abstract:
Spatially selective active noise control (SSANC) hearables aim to attenuate noise from certain directions at the eardrum while preserving desired speech arriving from selected directions. Existing SSANC systems typically assume an accurate estimate of the secondary path from the loudspeaker to the inner error microphone. In practice, however, this path varies across users and device fits, which can degrade performance and compromise system stability. This paper proposes a robust soft‑constrained optimization framework that computes a single control filter by minimizing the average cost over a set of secondary path estimates derived from human measurements. Simulations and experiments on a real‑time control platform show that the proposed approach slightly reduces mean performance relative to the matched case but substantially narrows the performance spread under secondary path mismatch. The proposed framework therefore provides a practical design strategy when accurate secondary path estimates are unavailable.

Authors:Emad Abukhousa, Saman Zonouz, A. P. Sakis Meliopoulos
Title: Latency-Aware Deep Learning Benchmark for Real-Time Cyber-Physical Attack and Fault Classification in Inverter-Dominated Power Grids
Abstract:
This work introduces a latency‑aware benchmarking framework for evaluating deep learning models in power system anomaly detection using high‑fidelity, time‑domain signals generated from an industry‑grade electromagnetic transient simulator. Eight neural network architectures, ranging from MLPs to Transformers, were systematically evaluated on streaming datasets representing both physical faults and cyber‑attacks in inverter‑dominated networks. All models successfully classified two representative multi‑event sequences in real time with sub‑cycle response times below 15 ms. However, although classification decisions occurred within one cycle, the end‑to‑end inference latency consistently exceeded three cycles, ranging from 50 to 90 ms. These results highlight a critical gap between algorithmic capability and protection‑grade deployment, pointing to the need for further optimization and hardware acceleration. The findings establish a reproducible benchmark for sub‑cycle anomaly detection and provide guidance for transitioning machine learning methods from research prototypes to real‑world protection applications.

Authors:Hao Li
Title: Handling Control System Uncertainty
Abstract:
Control science is a core representative of the third industrial revolution and is so important to modern civilization. Control systems are the main subject of control science and may involve many aspects of consideration, such as hardware consideration, software consideration, operation consideration, maintenance consideration, economy consideration, society consideration. However, besides all such aspects of consideration, one aspect that is most essential to the control system is methodology consideration in mathematical sense, knowledge on which is what we refer to as control theory. Besides its importance from the mathematical perspective, control theory is even more charming as it is deeply rooted in practical applications. Charms of control theory consist in both know‑why and know‑how and it is the fusion of control theory and practical applications that highlights such charms. Control theory for practical applications, especially when somewhat with so‑called ``advanced'' flavour, involves several fundamental aspects. This article introduces the Handling Control System Uncertainty aspect of Advanced Control Theory for Practical Applications.

Authors:Qingwen Pu, Kun Xie, Yuan Zhu, Guocong Zhai
Title: Generating Realistic Safety-Critical Scenarios for Vehicle-Pedestrian Interactions
Abstract:
Automated driving system deployment requires rigorous validation across safety‑critical vehicle‑pedestrian interactions, yet real‑world datasets rarely capture high‑risk scenarios while simulation platforms lack realistic behavior. In response, this study proposes a three‑stage framework that combines real‑world grounding with adaptive simulation to generate behaviorally realistic safety‑critical scenarios at scale. Stage 1 pre‑trains multi‑agent state‑space Transformer‑enhanced DDPG (MA‑SST‑DDPG) agents on real‑world safety‑critical data to learn human‑like interactive evasive behaviors through data‑driven learning. Stage 2 deploys pre‑trained multi‑agents in CARLA for online reinforcement learning to generalize across diverse scenarios, integrating real‑world knowledge with simulation experience to produce a refined MA‑SST‑DDPG model. Stage 3 uses CARLA with the refined model to generate over 198,000 high‑resolution interaction episodes from eight intersection scenarios, culminating in the Vehicle‑Pedestrian Safety‑Critical Interaction (VPSCI) dataset. The Refined MA‑SST‑DDPG model outperformed baseline methods in reproducing realistic evasive behaviors, achieving the lowest trajectory errors (ADE = 0.072 m, FDE = 0.142 m). Statistical comparison confirmed distributional equivalence between the generated and real‑world data in both conflict severity and behavioral response. A Turing test confirmed that the three‑stage framework generated evasive behaviors were indistinguishable from real‑world interactions. These results demonstrate the framework's effectiveness in producing high‑fidelity safety‑critical data, offering valuable sources for the development of ADS and simulation‑based safety evaluations.

Authors:Chao Wu, Jinhao Wang, Yong Wang, Changjiang Zhan, Frede Blaabjerg
Title: Revisiting the Voltage-Source Behavior: Why Impedance Magnitude of Grid-Forming Converter Rises Near Fundamental Frequency?
Abstract:
Grid‑forming (GFM) converters are generally expected to exhibit low impedance near the fundamental frequency due to their voltage‑source behavior. However, an impedance peak and a negative‑resistance region are consistently observed in this range, which contradicts this expectation and lacks a clear physical explanation. This paper reveals that these phenomena originate from the inherent dynamics of the active power control loop, where the mapping from power disturbance to the synchronous angle inherently involves an integrative action, intrinsically preventing a positive‑resistance characteristic near the fundamental frequency. This finding explains why existing grid codes in China, the United States, and Europe exclude a narrow band around the fundamental frequency in impedance‑based evaluations. It is further shown that the width of the excluded frequency band (e.g., +/‑ 3~5 Hz) is governed by the power‑to‑frequency dynamics. Based on this insight, a quantitative index is proposed to determine the exclusion bandwidth from the corner frequencies of the impedance magnitude curve. The proposed index provides a concise and theoretically grounded criterion for voltage‑source assessment and impedance standardization of GFM converters.

Authors:Lingling Fan, Ali Yazdanpanah, Yunzhi Cheng, Zhixin Miao, Farshid Salehi, Patrick Gravois, Shun-Hsien, Huang
Title: Replicating Real-World 23-Hz Oscillations Caused by Large Electronic Loads
Abstract:
In 2024, Texas operators observed 23‑Hz oscillations in real power measurements close to a large electronic load (LEL). Oscillations emerged when the load's power consumption reached approximately 320 MW level and subsided as the active power demand decreased. The paper aims to analyze the event and reproduce the oscillations using electromagnetic transient (EMT) simulations. In the first stage, a representative feedback system is developed, and frequency‑domain analysis is conducted to examine the phenomenon and identify its key influencing factors. Next, detailed EMT simulations are performed to further validate the proposed analytical approach. The results show that the feedback system effectively captures and characterizes the critical features of the 23‑Hz oscillation incident. In addition, the EMT simulations successfully reproduce the real‑world event, with the simulated results closely matching the fault recorder data.

Authors:Yasar Yanik, Himadri Basu, Ricardo G. Sanfelice, Daniele Venturi
Title: Weighted Flow Matching and Physics-Informed Nonlinear Filtering for Parameter Estimation in Digital Twins
Abstract:
Digital twins (DTs) rely on continuous synchronization between physical systems and their virtual counterparts through online parameter estimation under uncertainty. In many practical settings, however, this task is challenged by low observability, weak excitation, nonlinear dynamics, and noisy or biased measurements. In this work, we develop a new mathematical framework that integrates Weighted Flow Matching (WFM) generative modeling with physics‑informed nonlinear filtering to enhance parameter estimation in DTs. WFM relies on dynamic reweighting of training samples, which guides the generative model toward parameter regimes most informative of the evolving system state. This generative component is tightly coupled with a physics‑informed filtering architecture based on the Unscented Kalman Filter (UKF), yielding a unified DT framework that combines data‑driven probability transport with physically consistent state and parameter estimation. The effectiveness of the new integrated framework is demonstrated within a spacecraft DT architecture, where stable moment of inertia estimation is achieved under uncertain and noisy sensing, with significant performance improvements over established approaches such as Extended Kalman Filtering (EKF) and Ensemble Kalman Filtering (EnKF). These results highlight the potential of weighted generative modeling as a core mechanism for real‑time DT synchronization in operational and mission‑critical systems.

Authors:Mahdi Taheri, Haeyoon Han, Soon-Jo Chung, Fred Y. Hadaegh
Title: Geometric Fault Identification via Mirror Descent Learning
Abstract:
This paper develops a fault detection and identification (FDI) method for nonlinear control‑affine systems under simultaneous actuator and sensor faults. We adopt a geometric approach to study the isolability of faults in the sense of the principal angles between subspaces corresponding to each actuator and sensor fault. As for the fault identification, a hybrid estimator that consists of a Luenberger‑like observer with contraction guarantees is developed. Moreover, neural networks are embedded in the mentioned observer to estimate actuator and sensor faults. Considering that the training dataset for neural networks cannot be representative of every fault scenario, the last layer of each network is adapted using mirror descent‑based laws. The mirror descent‑based adaptive laws impose isolability conditions for fault channels and do not assume a quadratic parameter estimation space to consider the geometry of the fault subspaces. A Lyapunov‑based analysis establishes that the state and parameter estimation errors are uniformly ultimately bounded. The effectiveness of our proposed FDI method is illustrated on the 3‑axis attitude control system of a spacecraft.

Authors:Arnaud Verstraeten, Maria Margarida Mascarenhas, Hussain Kazmi
Title: A review of imbalance price forecasting algorithms in Europe: algorithms, metrics and the way forward
Abstract:
Renewable electricity generation has grown significantly across many European power systems, leading to a greener energy mix, but also additional complexity in balancing electricity supply and demand. Unexpected differences between forecasts and actual output can lead to fluctuations in the system imbalance, which causes volatile imbalance prices. Accurate imbalance price forecasts are crucial for market players to choose a strategic balancing position. In early works, most forecasting methods combined fundamental and statistical approaches, but currently there is a clear trend towards data‑driven machine learning models. This review compares forecasting algorithms in European markets with a focus on methodology. We emphasize the importance of high‑quality input data, including intraday information and per‑minute system data. Next, we identify the need for a common benchmark to compare novel forecasting methods developed for different markets and time periods. Finally, we argue that forecasts should be evaluated in terms of both downstream value and accuracy.

Authors:Mohammed Zeehan Saleheen, Markus Wagner, Hao Wang
Title: Ensuring reliability in 100% renewable microgrids: a scenario-based joint planning and operational design framework
Abstract:
Off‑grid microgrids powered entirely by renewable energy sources face substantial challenges in achieving utility‑grade reliability standards. Existing microgrid planning frameworks often prioritize cost minimization while treating reliability as a secondary metric, thereby leading to suboptimal designs. This paper presents a comprehensive scenario‑based optimization framework that simultaneously addresses long‑term capacity planning and short‑term operational dispatch in two stages for 100%‑renewable microgrids. The developed two‑stage stochastic programming model co‑optimizes the investment and operation of photovoltaic generation and battery energy storage, while ensuring compliance with stringent reliability constraints following utility grid standards. Network modeling with operational constraints, such as line capacities and voltage limits, is incorporated to allow distributed resource placement leveraging power sharing between microgrid nodes. A novel scenario generation approach captures critical uncertainties, including seasonal demand fluctuations, solar output variations, and probabilistic equipment failures, through the statistical clustering of historical data. The optimization framework integrates utility‑grade reliability constraints limiting the expected energy not served to below 0.002% of the annual demand while minimizing the total system costs. Numerical simulations demonstrate the effectiveness of the proposed framework, achieving 99.998% supply reliability using only photovoltaic power and battery energy storage. The optimized network‑aware distributed resource allocation provides inherent resilience through power rerouting during component outages, maintaining load continuity even under simultaneous equipment failures. This study confirms the feasibility of 100%‑renewable microgrids to support remote communities while meeting utility‑grade reliability benchmarks.

Authors:Chi Bui, Maria Margarida Mascarenhas, Arnaud Verstraeten, Hussain Kazmi
Title: Empirical evaluation of Time Series Foundation Models for Day-ahead and Imbalance Electricity Price Forecasting in Belgium
Abstract:
Recent advances in Time Series Foundation Models (TSFMs) promise zero‑shot forecasting capabilities with minimal task‑specific training. While these models have shown strong performance across generic benchmarks, their applicability in volatile, complex electricity markets remains underexplored. Addressing this gap, this study provides a systematic empirical evaluation of several TSFMs, specifically Chronos‑2 and Chronos‑Bolt (developed by Amazon), and TimesFM 2.5 (provided by Google), for forecasting Belgian day‑ahead and imbalance electricity prices. For both considered markets, Chronos‑2 in ARX mode produces the most accurate forecasts. Compared with the best ensemble prediction from other machine learning methods, Chronos‑2's Mean Absolute Error (MAE) is 5% lower for the day‑ahead market. In contrast, the model yields 10% higher MAE predicting imbalance prices across all forecast horizons, except for the two‑hour‑ahead horizon. Moreover, we find that TSFMs exhibit genuine zero‑shot forecasting skills but still struggle under extreme market conditions.

Authors:Carol Xuan Long, David Simchi-Levi, Feng Zhu, Huangyuan Su, Andre P. Calmon, Flavio P. Calmon
Title: Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management
Abstract:
This paper studies autonomous generative AI agents in multi‑echelon supply chains using the MIT Beer Game. We identify four inference‑time levers that shape performance: model selection, policies and guardrails, centralized data sharing, and prompt engineering. Model capability is the dominant factor: an out‑of‑the‑box reasoning model exceeds human‑level performance, and optimized reasoning models reduce costs by up to 67% relative to human teams. However, strong average performance masks substantial reliability risks. We introduce agent bullwhip: the amplification of run‑to‑run decision instability in autonomous multi‑echelon systems. A central component is decision bullwhip, the portion of order variability generated by stochastic agent decisions rather than by changes in customer demand. We show that decision instability can amplify both across facilities at a fixed point in time and within the same facility over time, even when the demand path is held fixed. Repeated sampling, a natural test‑time remedy, fails to meaningfully reduce this instability, suggesting that reliability requires changing the underlying decision policy rather than merely averaging over model outputs. To address this limitation, we propose a Group Relative Policy Optimization (GRPO)‑based reinforcement‑learning post‑training framework that trains a shared base LLM using system‑level supply‑chain rewards. Post‑training substantially reduces tail events, curtails agent bullwhip, and improves the reliability of autonomous supply‑chain agents.

Authors:Rieke de Maeyer, Holger Hermanns, Martina Maggio
Title: Over-approximation of weakly-hard constraints for control systems verification (Extended)
Abstract:
A hard real‑time system cannot miss any deadline. A weakly‑hard real‑time system, on the contrary, is designed to tolerate a specific number of deadline misses. For instance, the AnyMiss(2, 300) weakly‑hard constraint stipulates that in every window of 300 consecutive jobs, at most 2 deadlines are missed. The weakly‑hard model is the state‑of‑the‑art for industrial dependability‑by‑design of control systems that tolerate deterministic failures. Weakly‑hard constraints correspond to regular languages. The size of the minimal finite state machine that recognizes whether a string satisfies the constraint (about 45k states for AnyMiss(2, 300)) is a notorious impediment for the verification of control system properties. This paper discusses an over‑approximation of the language that allows us to provide sound safety guarantees for control systems under deadline misses that would be out of reach using the minimal finite state machine. We present a compressed language acceptor and prove that it simulates the original finite state machine. We study language cardinality properties, and report on empirical results that show how the new acceptor can be embedded in the control design workflow, leading to verifying safety for systems for which the state‑of‑the‑art tools do not provide answers.

Authors:Xuesong Wang, Caisheng Wang, Carol Miller, Amir Shahin Kamjou, John Norton
Title: A Resilience Evaluation Framework for Electric Distribution Systems: Historical Weather Conditioning, Sensitivity Analysis, and a Flooding-Aware Extension
Abstract:
Evaluating resilience in electric distribution systems under severe weather requires models that can connect network topology, hazard simulation, fragility modeling, restoration assumptions, repair strategy, and downstream consequences. This paper extends our prior graph‑based resilience evaluation framework for power distribution systems in three ways: it adds analysis conditioned on historical events with real outage and weather data, introduces sensitivity studies for key modeling assumptions, and includes a coupled power‑flooding extension for sewage‑backup assessment. Historical wind events drive Monte Carlo simulations conditioned on real weather, and the observed outage trajectories are treated as realized historical samples for comparison. Wind‑event resilience metrics stabilize at approximately 256 episodes, and outage peak, duration, and outage intensity change systematically with fragility parameters, network topology, restoration assumptions, and repair strategies. In a separate 1000‑episode joint power‑flooding simulation, episodes with at least one flooded customer occur in 1.9% of episodes overall, and both flood occurrence and flood intensity increase with outage intensity, showing a selective power‑to‑flood consequence pathway. Overall, the framework provides a practical basis for resilience assessment, comparative scenario analysis, and coupled power‑flooding studies in a limited public‑data setting, while also suggesting that more detailed utility data could further improve simulation realism.

Authors:Jehyeop Han, Minhee Kang, Alessandro Colombo, Marcello Farina, Heejin Ahn
Title: Knapsack-based Online Sensor Selection for Vehicle State Estimation
Abstract:
As connected and autonomous driving technologies advance, vehicles increasingly rely on data from external sensors. Although this information can enhance state estimation, processing all available streams imposes significant communication and computational costs. To address this challenge, we introduce a Sensor Management Center (SMC) that selects a low‑cost subset of external sensors in real time while satisfying chance‑constrained error bounds derived from an Extended Kalman Filter (EKF) covariance. We formulate the selection problem as a multidimensional minimum knapsack problem and adopt a deficiency‑weighted greedy algorithm as an approximate yet efficient solution. The proposed approach is validated through MATLAB simulations and experiments on a 1:15‑scale cooperative driving testbed.

Authors:Qian Zhang, Sadie Zhao, Lucy Diao, Conleigh Byers, Yiling Chen, Derya Cansever, Le Xie
Title: Modeling Coincident Peak Pricing in Electricity Markets: Challenges and Peak Shaving Effectiveness
Abstract:
Coincident Peak (CP) pricing is widely used in U.S. electricity markets to allocate capacity and transmission costs. This paper develops a behavioral game‑theoretic framework for CP‑driven load shifting that couples a nonlinear cost‑allocation model with day‑ahead (one‑shot) and real‑time (sequential‑learning) decision processes. We examine two update rules, namely best‑response dynamics (BRD) and fictitious‑play dynamics (FPD), across continuous and finite action spaces to quantify how flexibility, action resolution, and participation influence peak outcomes. Using ERCOT peak‑day data, we find that FPD reliably reduces system peaks, whereas BRD is more variable and can increase peaks under tight‑capacity conditions. Finer action resolution improves peak shaving, while the number of participants is largely neutral when aggregate flexibility is fixed. Meanwhile, information‑provider signals can induce herding, whereas response‑aware or diverse signals improve peak shaving. These results highlight both the potential and limits of CP pricing: smoothing information and enabling granular control are as important as the amount of available flexibility. The framework offers practical guidance for system operators and consumers: For ISOs, broadcasting smoothed CP signals and setting minimum controllable‑capacity thresholds enhance coordination. For consumers, greater flexibility and finer control resolution improve both cost savings and peak‑shaving performance.

Authors:Jiaxin Hou, Yujia Li, Jong-Shi Pang
Title: A Coupled V2G Equilibrium Model of Electric Vehicle and Power System Interactions
Abstract:
Vehicle‑to‑grid (V2G) technology empowers electric vehicles (EVs) to act as mobile energy resources, providing critical support to power systems, especially under stressed conditions. To understand the economic mechanism driving V2G participation and its benefits to power grid, this paper proposes a multi‑player coupled equilibrium framework that models the bidirectional interactions between power grid operations and EV routing, incorporating charging and discharging choice in a preprocessed feasible path generation procedure. Energy prices are endogenously determined by market clearance conditions. We formulate the overall problem as a Variational Inequality that unite the decision‑making of Distribution System Operator, Charging Network Operator, Load Serving Entities, and EV drivers. Numerical studies validate the framework under two stress scenarios: increased household load and power line outages. Results show that when EVs are incentivized by reduced generalized path costs, V2G is particularly effective in eliminating load shedding and reducing distribution locational marginal electricity prices. On the transportation side, V2G can lead to divergence in EV behavior between normal and scarcity conditions, and alter route choices yet improve overall trip economic.

Authors:Syed Pouladi
Title: Stable Fiber-Koopman Residual Dynamics for Environment-Constrained Robust Control
Abstract:
Learning‑based dynamical models face a persistent tension between expressiveness and formal guarantees: richer model classes improve predictive accuracy, but their stability properties are typically verified only empirically, if at all. This paper proposes \emphStable Fiber‑Koopman Residual Dynamics (SFKD), a unified framework that simultaneously addresses environment‑aware geometric consistency, latent‑space stability certification, and bounded residual perturbation propagation. Concretely, SFKD constructs a fiber bundle latent manifold whose fibers encode environment‑specific dynamics; an environment‑conditioned Koopman operator governs the dominant linear evolution on each fiber; and a contraction‑constrained residual neural network captures unmodeled nonlinear effects while admitting an explicit input‑to‑state stability (ISS) certificate. The resulting model is embedded in a sampling‑based MPPI controller for autonomous vehicle path tracking under variable surface conditions and wind disturbances. Theoretical analysis establishes ISS of the latent dynamics and a finite ultimate bound on tracking error. Numerical experiments against five baselines ‑‑ Koopman MPC, Neural ODE, ICODE, ControlSynth, and ICODE‑MPPI ‑‑ demonstrate a 31% reduction in tracking RMSE, a 44% improvement in control smoothness, and near‑zero latent stability violation rate across environment‑switching scenarios.

Authors:Alessio Alberto Lodge, Alessio Lombardo Pontillo, Feye S. J. Hoekstra, Robinson Medina, Steven Wilkins, Ilenia Battiato
Title: Health-Aware Fast Charging Using Homogenized Model with Heterogeneous Internal State Reconstruction
Abstract:
Fast charging of lithium‑ion batteries is limited by lithium plating, which occurs when the anode potential drops below 0 V vs Li/Li+. Model‑based control aims to maximize charging current while maintaining anode potentials above this threshold. In this work, a plating‑free fast charging strategy is demonstrated using a Homogenized Model (HM) coupled with a classical PID controller. The HM, derived from homogenization theory applied to the Poisson‑Nernst‑Planck equations, retains the physics of the Doyle‑Fuller‑Newman model while capturing electrode microstructural heterogeneity in a one‑dimensional double‑continua formulation. By reconstructing three‑dimensional distributions of electrochemical variables from precomputed closure variables, the HM enables non‑invasive estimation of heterogeneous anode potentials, acting as a virtual sensor. Through MATLAB‑COMSOL co‑simulation, a PID controller regulates current to maintain the full 3D anode potential distribution above the plating limit, achieving model‑based fast charging at a fraction of the computational cost of high‑fidelity models. The results demonstrate the potential of HM‑based control for safe, degradation‑aware, and efficient fast charging of lithium‑ion batteries.

Authors:Felipe Galarza-Jimenez, Majid Zamani, Saber Jafarpour
Title: Trajectory-based Safety of Monotone Systems: Verification and Control Synthesis
Abstract:
This paper presents a novel data‑driven framework for the robust safety verification and safe control synthesis of unknown monotone discrete‑time systems. While existing data‑driven safety analysis approaches are often either heuristic in nature or require large amounts of data to provide rigorous guarantees, we leverage the structural property of monotonicity to significantly reduce data requirements while still ensuring formal safety guarantees. Our approach is built upon a new class of certificates called dominance functions, constructed directly from collected system trajectories, which themselves need not be safe. By exploiting the monotone structure of the dynamics, we show that dominance functions are (i) dissipative, meaning that they decrease monotonically along system trajectories, and (ii) sufficiently \expressive to characterize safety certificates for monotone systems. Together, these properties establish dominance functions as principled building blocks for the systematic construction of formal safety certificates directly from trajectory data. For both robust safety verification and safe control synthesis, we develop an efficient sampling‑based optimization framework that searches for safety certificates represented as linear combinations of dominance functions constructed from collected trajectories. We validate our data‑driven framework on two monotone systems by successfully deriving safety certificates from a small number of trajectories.

Authors:Kaito Iwasaki, Anthony Bloch, Taeyoung Lee, Maani Ghaffari
Title: The Score Kalman Filter
Abstract:
A central obstacle in nonlinear Bayesian filtering is representing the belief distribution. Moment‑based filters address this by propagating polynomial moments and reconstructing a density from them. Recent work completes the predict‑update loop via the maximum‑entropy (MaxEnt) principle, but each step requires the partition function and its gradient, both n‑dimensional integrals whose cost scales exponentially, restricting the demonstrated MaxEnt moment filtering to n \le 4. We avoid the partition function entirely by combining score matching with Stein's identity. In our setting, score matching reduces the density fit to a single linear solve whose coefficients are assembled directly from the propagated moments. The same parameters then drive Stein's identity to close the moment hierarchy during prediction and to recover posterior moments after each Bayesian update, keeping the full predict‑update loop free of partition function evaluation. The resulting Score Kalman Filter (SKF) reduces to the classical information‑form Kalman filter as a special case and performs every step through linear algebra. On nonlinear coupled‑oscillator networks, the SKF runs through n=20 and reports lower RMSE than the EKF, UKF, EnKF, and particle‑filter baselines on the tested synthetic benchmarks.

Authors:Sachith Wijesooriya, Sandun S. Kuruppu
Title: A Coupled Inductor Based Multi Port DC DC Converter with Coordinated Duty-Cycle and Phase Shift Control
Abstract:
Electrified powertrains rely heavily on magnetics for power conversion, where cost, volume, and weight concerns make integrated multi‑use designs an attractive solution. With EV powertrain architectures requiring a boost stage being a major market segment, the proposed Coupled Inductor‑Based Multi‑Port DC‑DC Converter (CI‑MPC) leverages the existing magnetic framework of a conventional topology to realize independent, isolated, and simultaneously regulated converters without additional magnetic cores or cascaded stages. Unlike existing architectures that use secondary windings solely for voltage gain or passive rectification, the proposed topology integrates an actively controlled full bridge on the secondary side to create a distinct, independently regulated auxiliary converter. Primary output regulation is achieved via duty‑cycle control, while the auxiliary converter employs phase‑shift modulation synchronized with the primary switching to enable active rectification and flexible voltage or current regulation. A unified control framework ensures decoupled operation with minimal interaction between the primary and auxiliary loops, while also avoiding high step‑down conversion ratios from high voltages to lower auxiliary levels. The operating principles and coordinated control strategies are validated through simulation and experimental results on a hardware prototype, demonstrating enhanced controllability, decoupled regulation, and a scalable pathway toward generalized multi‑port power conversion within a unified magnetic framework.

Authors:Taekyung Kim, Hideki Okamoto, Bardh Hoxha, Georgios Fainekos, Dimitra Panagou
Title: Policy Library CBF: Finite-Horizon Safety at Runtime via Parallel Rollouts
Abstract:
Safety‑critical autonomy in unstructured environments poses significant challenges for online safety certification under evolving constraints. We propose Policy Library Control Barrier Function~(PL‑CBF), a runtime safety filter that evaluates a library of fallback policies via parallel finite‑horizon rollouts, selects the least invasive safe mode, and enforces safety by solving a quadratic program that minimally modifies a nominal policy. We provide a theoretical analysis based on a finite‑horizon language metric over closed‑loop behaviors, characterizing policy‑library coverage requirements for certifying finite‑horizon safety. Simulations on a planar double‑integrator (4 states), highway driving with abrupt friction changes using a realistic nonlinear vehicle model (8 states), and 3D quadrotor navigation in crowded dynamic environments (12 states) demonstrate improved safety coverage over single‑policy safety filters while retaining millisecond‑level runtime.

Authors:Yuyang Zhang, Derya Cansever, Na Li
Title: Provably Efficient Sensor Allocation for Unknown High-dimensional Systems with Limited Sensing
Abstract:
This paper focuses on learning efficient sensor allocations that ensure observability of unknown high‑dimensional linear systems using only a small number of sensors. Existing methods either require an impractically large number of sensors or assume access to an observable allocation in advance. We propose a two‑stage framework that overcomes these limitations: first, a novel system identification algorithm integrates information from multiple trajectories, each observing different subsets of state coordinates; then, a classic sensor allocation method is adapted to operate on the learned system parameters. Our non‑asymptotic guarantees show that the proposed approach learns a sensor allocation with a near‑optimal number of sensors when sensors can be allocated on any state coordinate. We further extend the results to settings with inaccessible state coordinates that are unavailable for sensor allocation.

Authors:Violetta Rostobaya, Yue Guan, James Berneburg, Daigo Shishika
Title: Linear Programming Approach to Deceptive Path Planning Game with Goal Selection
Abstract:
In adversarial settings, a mobile agent may strategically plan its motion to influence an opponent's inference about its intended goal. We study deceptive path planning in a scenario where a mobile agent aims to reach a privately selected goal while an adversarial observer allocates limited defensive resources based on the observed trajectory. Unlike classical path‑planning and goal‑recognition approaches that model observers as passive inference process, our game‑theoretic formulation models them as strategic decision‑makers. For the resulting dynamic asymmetric‑information game, we develop an efficient solution method that combines a linear programming formulation with the Double Oracle algorithm. To evaluate performance, we introduce metrics that quantify both the risk and the effectiveness of deception and provide illustrative numerical examples.

Authors:Gorkem Gemalmaz, Harun Tolasa, Volkan Patoglu
Title: Haptic Rendering of Fractional-Order Viscoelasticity: Passivity and Rendering Fidelity
Abstract:
Haptic rendering of viscoelastic materials that exhibit creep and stress relaxation is crucial for many applications, such as medical training with realistic biological tissue models. Fractional‑order viscoelastic models provide an effective means of describing intrinsically time‑dependent dynamics with few parameters, as these models can naturally capture memory effects. In this study, we present analyses of passivity and rendering performance for fractional‑order viscoelastic models under finite‑memory discretization. We derive closed‑form expressions to ensure the passivity of haptic rendering with a fractional‑order (FO) standard linear solid (SLS) model based on Grunwald‑Letnikov derivative under short‑memory discretization. We also provide symbolic expressions for the effective stiffness and damping of such FO‑SLS models. The resulting passivity conditions constitute a unified framework that generalizes previously reported results for integer‑order Kelvin‑Voigt, Maxwell, and SLS models, since these results are special cases of the newly derived condition. Furthermore, we provide experimental validations of the theoretical passivity bounds and human‑subject evaluations of perceived realism of FO‑SLS models. Overall, this study establishes a unified theoretical framework and experimental evaluations for FO viscoelastic rendering under short‑memory discretization.

Authors:Jinyang Dong, Shizhen Wu, Yongchun Fang
Title: Differentiable Optimization Layered Safety-Critical Control for Risk-Aware Navigation via Conformal Prediction
Abstract:
Risk‑aware navigation in unknown environments is a fundamental challenge for autonomous vehicles operating in complex urban systems. To address this issue, this paper presents a differentiable optimization layered safety‑critical control method based on conformal prediction. First, to handle uncertainties arising from sensor noise, the conformal prediction method is employed to generate risk‑aware obstacle ellipsoids around an elliptical‑shaped robot. Second, two nested differentiable optimization layers are introduced to build the control barrier functions for obstacle avoidance and feasibility guarantee, respectively. Then, a quadratic program based safety‑critical control law is proposed to integrate the above control barrier function constraints as well as input constraints. In the end, the effectiveness of the proposed framework is demonstrated through numerical simulations.

Authors:Igor Bogdanov, Chung-Horng Lung, Thomas Kunz, Jie Gao, Adrian Taylor, Marzia Zaman
Title: FORGE: Self-Evolving Agent Memory With No Weight Updates via Population Broadcast
Abstract:
Can LLM agents improve decision‑making through self‑generated memory without gradient updates? We propose FORGE (Failure‑Optimized Reflective Graduation and Evolution), a staged, population‑based protocol that evolves prompt‑injected natural‑language memory for hierarchical ReAct agents. FORGE wraps a Reflexion‑style inner loop, where a dedicated reflection agent (using the same underlying LLM, no distillation from a stronger model) converts failed trajectories into reusable knowledge artifacts: textual heuristics (Rules), few‑shot demonstrations (Examples), or both (Mixed), with an outer loop that propagates the best‑performing instance's memory to the population between stages and freezes converged instances via a graduation criterion. We evaluate on CybORG CAGE‑2, a stochastic network‑defense POMDP at a 30‑step horizon against the B‑line attacker, where all four tested LLM families (Gemini‑2.5‑Flash‑Lite, Grok‑4‑Fast, Llama‑4‑Maverick, Qwen3‑235B) exhibit strongly negative, heavy‑tailed zero‑shot rewards. Compared against both a zero‑shot baseline and a Reflexion baseline (isolated single‑stream learning), FORGE improves average evaluation return by 1.7‑7.7× over zero‑shot and by 29‑72% over Reflexion in all 12 model‑representation conditions, reducing major‑failure rates (below ‑100) to as low as ～1%. We find that (1) population broadcast is critical mechanism, with a no‑graduation ablation confirming that broadcast carries the performance gains while graduation primarily saves compute; (2) Examples achieves the strongest returns for three of four models, Rules offers the best cost‑reliability profile with ～40% fewer tokens; and (3) weaker baseline models benefit disproportionately, suggesting FORGE may mitigate capability gaps rather than amplify strong models. All evidence is confined to CAGE‑2 B‑line; cross‑family findings are directional evidence.

Authors:Pavan Manjunath, Thomas pruefer
Title: A Unified Generative-AI Framework for Smart Energy Infrastructure: Intelligent Gas Distribution, Utility Billing, Carbon Analytics, and Quantum-Inspired Optimisation
Abstract:
The accelerating convergence of smart metering, generative artificial intelligence, and quantum‑inspired combinatorial optimisation is reshaping how energy utilities manage physical infrastructure, customer engagement, and environmental accountability

Authors:Sahan Liyanaarachchi, Sennur Ulukus, Nail Akar
Title: Preemption Revisited: Multi-Threshold Preemption Policies for AoI Minimization
Abstract:
The study of optimal preemption policies for status update systems has been a recurring topic in the age of information (AoI) literature, where threshold‑based structures have been shown to be optimal under a generate‑at‑will update generation model under certain assumptions. In this work, we study the effectiveness of threshold‑based policies for a system with random update arrivals. In this regard, we introduce an analytical framework for evaluating the AoI of multi‑threshold preemption policies and present interesting characteristics of the structure of the optimal preemption policy. We show the effectiveness of these threshold‑based policies over the traditional probabilistic preemption policies and single‑threshold policies, where we observe that significant gains in terms of AoI can be obtained by utilizing both the age of the packet and the age of the system when designing these preemption policies.

Authors:Igor Bogdanov, Chung-Horng Lung, Thomas Kunz, Jie Gao, Adrian Taylor, Marzia Zaman
Title: Context, Reasoning, and Hierarchy: A Cost-Performance Study of Compound LLM Agent Design in an Adversarial POMDP
Abstract:
Deploying compound LLM agents in adversarial, partially observable sequential environments requires navigating several design dimensions: (1) what the agent sees, (2) how it reasons, and (3) how tasks are decomposed across components. Yet practitioners lack guidance on which design choices improve performance versus merely increase inference costs. We present a controlled study of compound LLM agent design in CybORG CAGE‑2, a cyber defense environment modeled as a Partially Observable Markov Decision Process (POMDP). Reward is non‑positive, so all configurations operate in a failure‑mitigation mode. Our evaluation spans five model families, six models, and twelve configurations (3,475 episodes) with token‑level cost accounting. We vary context representation (raw observations vs. a deterministic state‑tracking layer with compressed history), deliberation (self‑questioning, self‑critique, and self‑improvement tools, with optional chain‑of‑thought prompting), and hierarchical decomposition (monolithic ReAct vs. delegation to specialized sub‑agents). We find that: (1) Programmatic state abstraction delivers the largest returns per token spent (RPTS), improving mean return by up to 76% over raw observations. (2) Distributing deliberation tools across a hierarchy degrades performance relative to hierarchy alone for all five model families, reaching up to 3.4× worse mean return while using 1.8‑2.7× more tokens. We call this destructive pattern a deliberation cascade. (3) Hierarchical decomposition without deliberation achieves the best absolute performance for most models, and context engineering is generally more cost‑effective than deliberation. These findings suggest a design principle for structured adversarial POMDPs: invest in programmatic infrastructure and clean task decomposition rather than deeper per‑agent reasoning, as these strategies can interfere when combined.

Authors:Shaohui Liu, Sungho Shin, Deepjyoti Deka
Title: Watts vs. Bytes: Turning Data Centers into Grid Assets via Storage Compute Co-Optimization
Abstract:
Enabling continued data‑center growth under increasing grid stress motivates closer coordination between flexible computing demand and co‑located battery energy storage systems (BESS) to improve site operations and provide grid services. This paper develops a robust co‑optimization framework for day‑ahead operation of data centers with co‑located BESS under utility‑imposed interconnection limits on peak load and ramping. The model jointly considers deadline‑constrained computing workloads, managed through workload scheduling and dynamic voltage and frequency scaling (DVFS), together with degradation‑aware BESS dispatch to enable cost optimization and participation in ancillary‑service markets. Case studies based on real‑world market and workload data show that the proposed framework yields feasible day‑ahead schedules across a range of operating conditions, with substantially larger benefits when interconnection constraints become binding. Under baseline conditions, BESS value is derived from both ancillary‑service participation and improved workload and energy management. Under stressed peak‑load and ramping limits, however, the daily value of BESS increases by a factor of two or more, driven primarily \reviseby BESS actions to reduce the potential incompletion in the schedulable workload while complying with interconnection constraints. Under tight peak‑load caps, workload composition also matters where a higher share of non‑schedulable jobs can increase operating cost by more than 25% relative to more flexible workload mixes. \reviseAdditionally, DVFS studies further show that processor‑level control is a material flexibility lever under tight load limits. These results demonstrate that coordinated compute‑storage flexibility can materially expand the operational headroom and grid value of data centers, especially under increasingly scarce grid capacity.

Authors:Yun-Feng Lo, Matthieu R. Bloch
Title: Covert Bayesian Quickest Change Detection
Abstract:
We investigate the problem of covert quickest change detection in a Bayesian and infinite‑horizon setting. A legitimate entity seeks to detect a change in the state of a discrete memoryless channel as quickly as possible by actively probing it. Simultaneously, the entity must ensure its probing remains covert from an adversary monitoring the channel for active sensing. We introduce the expected covertness budget (ECB) as an analytically tractable covertness metric that bounds from above the relative entropy between the observation sequences induced by active and passive sensing. Under constraints on both the probability of false alarm (PFA) and the ECB, we establish a second‑order asymptotic converse bound on the average detection delay as the PFA constraint approaches zero, for any positive ECB constraint, explicitly quantifying the maximum square‑root‑order covert sensing gain possible. Furthermore, we propose an achievability scheme utilizing a constant‑sensing‑probability Shiryaev‑type policy and show that it matches the second‑order asymptotic converse. We illustrate our result with a numerical example.

Authors:Hasna El Hasnaouy, Pablo Krupa, Mario Zanon, Alberto Bemporad
Title: Active Learning MPC Objective Functions from Preferences
Abstract:
Designing the objective function in Model Predictive Control (MPC) is challenging when performance assessment criteria are available only from human judgment. We adopt a preference‑based learning (PbL) approach to learn the MPC objective function from preferences over trajectory pairs. However, the real‑world application of PbL is often restricted by the significant cost or limited availability of human preference queries. To address this, Active Learning (AL) strategies seek to improve sampling efficiency, reducing the labeling effort required to obtain a well‑performing classifier. We present two AL strategies for learning the MPC objective function from human preferences over pairwise system trajectories: a pool‑based strategy that selects trajectory pairs that are both uncertain under the current surrogate and diverse relative to previously labeled comparisons, and a query‑synthesis strategy that incorporates new trajectories using the current surrogate‑driven MPC. Numerical results show that the proposed strategies yield closed‑loop behaviors that align more with the expressed preference using fewer number of queries compared to a random sampling approach.

Authors:Yiwei Liu, Luwei Yang, Shunbo Lei
Title: Communication-Efficient Federated Online Decision-Making with Stateful Costs
Abstract:
We study dynamic regret in federated online decision‑making with stateful incurred costs under block‑based synchronization and partial client participation. In this setting, sparse communication affects not only the pointwise update quality but also the realized state trajectory along which costs are incurred. We propose BLADE, a projected blockwise federated online decision method. BLADE uses only \(O(T/K)\) communication and achieves a dynamic‑regret bound for the incurred cost against path‑length‑bounded comparator sequences; under \(K=\lceil\sqrt T\rceil\), the bound is sublinear whenever \(V_T=o(T^1/4)\). Experiments on a controlled synthetic stable linear system validate the predicted communication‑‑regret, memory, participation, disturbance‑variation, and horizon‑scaling effects.

Authors:Beatrice E. Bauret Martínez, Gabriele Dessena, Marco Civera, Oscar E. Bonilla-Manrique
Title: Enhanced input stacking for non-square MIMO modal identification of aeronautical structures via Fast and Relaxed Vector Fitting
Abstract:
Fast and Relaxed Vector Fitting (FRVF) is a frequency‑domain system identification approach that has been widely adopted in electrical system modelling, while its application to mechanical systems has remained relatively unexplored. In this work, FRVF is reformulated for the identification of structural modal parameters of an aircraft based on Ground Vibration Test (GVT) data within a Multi‑Input Multi‑Output (MIMO) framework. The proposed procedure consists of three stages: (i) rational approximation of frequency response functions via an enhanced input‑stacking strategy, (ii) identification of system poles from the resulting rational model, and (iii) estimation of modal parameters from the extracted poles and associated residues. The methodology is first numerically validated on a MIMO beam model, with particular emphasis on accuracy and robustness under increasing measurement noise. Subsequently, experimental validation is conducted using GVT data from the BAE Systems Hawk T1A aircraft. The results obtained demonstrate a level of performance comparable to that achieved by existing methods. Overall, the extended MIMO formulation of FRVF exhibits high accuracy and strong robustness to measurement noise, highlighting its suitability for application in GVT‑based modal analysis.

Authors:Hao Li
Title: State Estimation
Abstract:
Control science is a core representative of the third industrial revolution and is so important to modern civilization. Control systems are the main subject of control science and may involve many aspects of consideration, such as hardware consideration, software consideration, operation consideration, maintenance consideration, economy consideration, society consideration. However, besides all such aspects of consideration, one aspect that is most essential to the control system is methodology consideration in mathematical sense, knowledge on which is what we refer to as control theory. Besides its importance from the mathematical perspective, control theory is even more charming as it is deeply rooted in practical applications. Charms of control theory consist in both know‑why and know‑how and it is the fusion of control theory and practical applications that highlights such charms. Control theory for practical applications, especially when somewhat with so‑called "advanced" flavour, involves several fundamental aspects. This article introduces the State Estimation aspect of Advanced Control Theory for Practical Applications [1,2].

Authors:D. Sorokin, M. Stokolesov, A. Granovskiy, I. Prokofyev, E. Adishchev, M. Nurgaliev, E. Khayrutdinov, G. Subbotin, R. Clark, D. Orlov
Title: Dynamic Plasma Shape Control with Arbitrary Sensor Subsets
Abstract:
Plasma shape control in tokamaks requires a real‑time controller that tracks dynamically changing shape targets while tolerating diagnostic failures. Classical approaches decompose the problem into equilibrium reconstruction followed by a linear controller, and assume a fixed, fully operational sensor set. We present a reinforcement learning agent that addresses both limitations simultaneously. The agent is trained in NSFsim, a high‑fidelity tokamak simulator configured for DIII‑D, on a curated dataset of 120 experimental plasma shapes. The shape targets are resampled as random step changes every 0.25 s, exposing the agent to diverse transitions across the full shape envelope. At test time the agent zero‑shot tracks dynamic shape sequences; on a held‑out static configuration in simulation it achieves a mean shape error of 2.01 cm, and dynamic trajectory following is demonstrated qualitatively in simulation and on the physical device. Diagnostic dropout randomly masks 30% of magnetic sensors per episode, yielding a single policy robust to arbitrary sensor subsets without backup controllers or mode‑switching logic. An asymmetric actor‑critic architecture with privileged equilibrium information improves value estimation under partial observability; an auxiliary shape reconstruction head on the actor enables end‑to‑end shape reconstruction from raw diagnostics and serves as an interpretability tool for policy analysis. The policy transfers to experimental DIII‑D shots, where it directly commands the coil actuators on two dynamic shape maneuvers, and to the independent GSevolve simulator.

Authors:Heekang Song, Wan Choi
Title: Communication-Efficient Approximate Gradient Coding for Distributed Learning in Heterogeneous Systems
Abstract:
We propose a communication‑efficient optimally structured gradient coding scheme to jointly address straggler resilience and communication efficiency in heterogeneous distributed learning. By establishing a unified framework that simultaneously optimizes gradient coding and quantization, we formulate an optimization problem to minimize residual error subject to an unbiasedness constraint. We rigorously establish the joint global optimum by deriving a closed‑form code structure coupled with an optimal bit allocation strategy, while simultaneously proposing a low‑complexity bit allocation algorithm that efficiently yields near‑optimal performance. We provide rigorous convergence analysis for convex and smooth functions. Experiments on the COCO dataset demonstrate that our joint design significantly accelerates convergence and enhances communication efficiency compared to existing baselines.

Authors:Guanru Pan, Dirk Reinhardt, Sebastien Gros, Timm Faulwasser
Title: Uncertainty Propagation under Residual Disturbances: A Smart-Home Case Study
Abstract:
This paper presents a data‑driven framework for uncertainty propagation under unmeasured or statistically unmodeled (unstructured) disturbances. We consider residual disturbances, which consolidate all unstructured disturbances into a single quantity that can be estimated from data. Under mild assumptions, the resulting stochastic predictor is causal and distributionally consistent, enabling efficient uncertainty quantification through polynomial chaos expansions and higher‑order Chebyshev inequalities. The proposed method is validated using experimental data from a smart home in Norway.

Authors:Juan A. Cabrera, Pit Hofmann, Jonas Schulz, Frederic Benken, Hrjehor Mark, Giang T. Nguyen, Holger Boche, Frank H. P. Fitzek
Title: The Shared Prosperity Internet
Abstract:
The Shared Prosperity Internet (SPI) is a network‑computing architecture that makes the benefits of automation and Artificial Intelligence (AI) broadly accessible to the society. To ground its design, this paper maps the physical constraints of Shannon, Landauer, Turing, and Einstein to three design principles: trustworthiness, sustainability, and technological sovereignty, and maps them into three technical pillars: i) post‑Shannon, goal‑oriented communication that transmits only what the task requires; ii) anticipatory decision‑making ("negative latency") with confidence‑bounded pre‑action and correction; and iii) beyond‑digital computing that selects energy‑optimal substrates under deadline and computability constraints. The SPI is grounded in three societal use cases: remote teaching for pupils, remote teaching of robots and cyber‑physical systems, and elder care. Furthermore, this paper defines measurable outcomes for an SPI, including latency decomposition, bits per event, energy and CO2 per task, safety and privacy indicators, and robustness.

Authors:Viswa Narayanan Sankaranarayanan, Vignesh K. Viswanathan, Akshit Saradagi, Sumeet Satpute, George Nikolakopoulos
Title: Reactive Robot-Centric Safety for Autonomous Navigation in Constrained and Dynamic Environments
Abstract:
In this work, we address the problem of ensuring real‑time safety in autonomous robot navigation, in spatially constrained dynamic environments, by utilizing only onboard sensors. We present a real‑time control architecture that integrates a 3D LIDAR perception‑based composite control barrier function(CBF)‑based safety filter directly into the autonomy pipeline. The proposed perception‑driven framework enforces collision avoidance constraints dynamically from onboard point cloud data, thus allowing a large number of constraints to be handled at the control frequency, while remaining minimally invasive to nominal task execution. The safety region is defined as an ellipsoid in the body‑frame, consistent with the geometry of the platform, which induces time‑varying constraints in the world frame as the robot rotates; this effect is handled through a dedicated formulation of time‑varying (CBF) for each LIDAR point. We validate the system through multiple field experiments in underground environments by utilizing a quadruped platform performing a visual inspection task, demonstrating reliable operation in the presence of dynamic obstacles, unsafe high‑level references, abrupt localization anomalies, and while traversing through narrow corridors.

Authors:Can Berk Saner, Yong-Sheng Soh, Antonios Varvitsiotis
Title: Fairness-Guaranteed Online Power Allocation Policies for EV Fast Charging Stations
Abstract:
The rapid expansion of electric vehicles (EVs) necessitates scalable and efficient fast charging station (FCS) infrastructure. These stations often operate in oversubscribed configurations where the total port rating exceeds a station‑level cap reflecting infrastructure limits, grid constraints or market setpoints. In such settings, ensuring fairness in real‑time power allocation is essential to prevent user bias and secure equitable access to limited resources while maximizing infrastructure utilization. This task is further complicated by state‑of‑charge dependent EV power limits defined by charge curves, for which accurate data is often unavailable. This paper introduces two fairness‑guaranteed online power allocation policies: FAIR‑OPAP‑C for conventional FCSs with continuously adjustable power delivery, and FAIR‑OPAP‑M for modular FCSs composed of discrete assignable power modules. Unlike existing methods, these algorithms require no prior knowledge of charge curves, utilizing only instantaneous power requests available via standard protocols. We formalize fairness with a unified framework encompassing envy‑freeness, Pareto efficiency, and proportionality, and establish theoretical guarantees for both algorithms. The algorithms rely on lightweight operations, achieving near‑linear and logarithmic scalability for the conventional and modular cases, respectively. Comprehensive evaluations show the proposed methods achieve superior performance across various metrics among seven benchmarks from EV charging and fair division literature. Furthermore, they are orders of magnitude faster than optimization‑based approaches, with runtimes below 1 ms for up to 300 EVs, validating their suitability for real‑time deployment on hardware‑constrained edge devices.

Authors:Yushan Li, Jiabao He, Julien M. Hendrickx, Dimos V. Dimarogonas
Title: Preserving Topology Privacy of Network Systems by Feedback: Conditions and Distributed Design
Abstract:
This paper develops a feedback‑based method to preserve the topology privacy of consensus protocols in network systems. The key idea is to intentionally violate topology identifiability conditions, thereby preventing unique or accurate recovery of the true topology from available observations, while preserving the intended consensus behavior. This problem is challenging because the feedback magnitude directly reflects the privacy level of edges, while it is strongly coupled with the consensus convergence and constrained by local communications at each node. To begin with, we derive the feedback conditions of both partial and full observation cases, where the topology unsolvability from observation data is characterized in the former, and the solution space that enforces topology inaccuracy from data is constructed in the latter. Then, we propose a novel distributed topology modification design under limited privacy budgets, and establish the performance guarantees through a controllable tradeoff between the consensus deviation and the topology privacy. Finally, we develop a low‑complexity heuristic algorithm to achieve optimal privacy preservation on existing edges. Comparative simulations validate the effectiveness and outperformance of the proposed preservation design.

Authors:Shangqing Wang, Abhirup Sain, Christopher Lehmann, Shiwei Shen, Razan Habeeb, Frank H. P. Fitzek
Title: Enabling Intelligent Bidirectional Charging: A Real-World Communication Interface Between Electric Vehicles, Charging Infrastructure, and a Control Optimizer
Abstract:
This paper presents the real‑world implementation and field validation of a user‑aware bidirectional electric vehicle (EV) charging system developed within the Mobilities for EU and DymoBat projects in Dresden. Building on earlier simulation frameworks, the system enables transition from conceptual models to operational deployment in urban environments. To support grid flexibility and sustainable mobility, the solution combines real‑time vehicle and user data with a centralized optimization platform to enable dynamic charging and discharging decisions. The architecture integrates a wireless On‑Board Diagnostic II (OBD‑II) interface and an open middleware node connected via a 5G campus network, allowing early access to vehicle state‑of‑charge before plug‑in. A tablet‑based interface captures user preferences such as departure time and energy demand, which are incorporated into the optimization together with grid conditions. A key contribution is a multi‑level communication architecture linking the EV, charging station, user interface, and grid control center using the Open Charge Point Protocol (OCPP). The system integrates software, embedded hardware, and network communication for real‑time charging management. Field deployment at Ostra Sport Park in Dresden demonstrates feasibility, improved load balancing, and robust vehicle‑to‑grid operation. The results show that early data acquisition and predictive control can enhance system efficiency. This work provides a practical benchmark for positive energy districts and future urban e‑mobility systems.

Authors:Aditya Kudre, Heng-Sheng Chang, Prashant G. Mehta
Title: Transformer-like Inference from Optimal Control
Abstract:
Decoder‑only transformers compute the conditional probability of the next token from a sequence of past observations. This paper derives, from first principles, inference architectures that solve the same prediction problem ‑ and in doing so, recovers transformer‑like layer operations as a consequence of optimal control theory. The framework is developed for two model classes: a nonlinear model of discrete‑valued processes, directly motivated by the transformer, and a linear Gaussian model as a tractable baseline. For both model classes, the prediction objective is reformulated as an optimal control problem whose solution yields an explicit inference algorithm, the dual filter, with a layer structure that mirrors the layer structure of a decoder‑only transformer. Numerical experiments provide a comparison of the optimal control to attention weights from a trained transformer. These experiments reveal that when the embedding dimension is insufficient, the transformer implicitly exploits non‑Markovian structure.

Authors:Shubo Kang, Keyou You
Title: Direct Data-Driven Linear Quadratic Tracking via Policy Optimization
Abstract:
Direct data‑driven optimal control provides an elegant end‑to‑end paradigm, yet its real‑time applicability is often hindered by the growing dimensionality of online decision variables. Recent breakthroughs, notably Data‑EnablEd Policy Optimization (DeePO), overcome this bottleneck for the Linear Quadratic Regulator (LQR) through sample‑covariance parameterization; however, extending this paradigm to Linear Quadratic Tracking (LQT) poses a fundamental challenge. The core difficulty stems from the intricate coupling between time‑varying references and the feedback‑feedforward policy structure, which prevents a direct application of constant‑dimension parameterization. We first introduce a reference‑decoupled reformulation of LQT that naturally accommodates the covariance parameterization, guaranteeing a fixed dimension of decision variables independent of data horizon. This formulation is proven to be exactly equivalent to the indirect certainty‑equivalence LQT solution. Leveraging this characterization, we develop offline and online DeePO algorithms. Theoretically, we prove global linear convergence for the offline algorithm using local gradient dominance and smoothness, and show that in the online setting the optimality gap decays linearly up to a bias term that scales inversely with the signal‑to‑noise ratio (SNR). Numerical simulations varify the theoretical results and illustrate the superior tracking performance of the proposed method.

Authors:Anik Kumar Paul, Shalabh Bhatnagar
Title: Stochastic Mirror Descent under Iterate-Dependent Markov Noise: Analysis in the Asymptotic and Finite Time Regimes
Abstract:
We study a stochastic optimization problem in which the sampling distribution depends on the decision variable, and the available samples are generated through an iterate‑dependent Markov chain. Such settings arise naturally in problems with decision‑dependent uncertainty; however, they introduce bias and temporal dependence, which render standard techniques developed for i.i.d.\ noise inapplicable. In this work, we analyze the stochastic mirror descent algorithm under iterate‑dependent Markov noise. We first establish almost sure convergence for both convex and non‑convex problems under the mild assumption of Lipschitz continuity of the objective function, without requiring differentiability. We then derive finite‑time concentration bounds for smooth objectives. In the convex setting, the resulting sample complexity matches the classical rate of stochastic mirror descent under i.i.d.\ noise. In the non‑convex setting, we obtain a sample complexity bound in terms of the norm of the Riemannian gradient over the probability simplex. Overall, our results establish a unified convergence framework for stochastic mirror descent with state‑dependent Markov noise, and highlight its behavior in both convex and non‑convex regimes.

Authors:Nirabhra Mandal, Sonia Martínez
Title: Distributionally Robust Nash Equilibrium Seeking with Partial Observations and Distributed Communication
Abstract:
In this work, we study stochastic one‑shot games where agents' utilities depend on the collective strategy profiles of other agents as well as on some well‑behaved randomness. While each decision‑maker is agnostic to the random variable's underlying distribution, they have access to finitely many i.i.d. samples generated from it. We consider two cases: one where samples are shared; and another, more special one, where samples are individually accessible. To hedge against the unknown uncertainty, each agent plays a distributionally robust game and aims to maximize the worst‑case expected utility over a Wasserstein ball around the sample average distribution. In this setting, we provide conditions under which the game has a non‑empty set of distributionally robust Nash equilibria (DRoNE) and then characterize the closeness of the DRoNE set to the Nash equilibria (NE) of the associated stochastic game. We then propose an inertial, supported, better response, ascending supergradient dynamics ISBRAG that seeks the DRoNE's when the distributionally robust game possesses what we term as amicable supergradients. This forms the basis of a distributed version (d‑ISBRAG) where agents estimate others' strategies by means of a dynamic consensus subroutine over a directed communication network. While initially the distributed algorithm works in the case where agents have individual samples, we later extend this to the case of shared observations under certain simplifying assumptions. This involves analyzing a tractable reformulation of the distributionally robust optimization problem and solving it in a distributed manner to compute the required supergradients. Simulations illustrate our results.

Authors:William D. Compton, Zachary Olkin, Aaron D. Ames
Title: Terrain Consistent Reference-Guided RL for Humanoid Navigation Autonomy
Abstract:
We present a method for training reference‑guided, perceptive reinforcement learning locomotion policies for humanoid robots in which reference trajectories are modulated in training to be consistent with terrain geometry. Aiming to deploy our method with standard navigation autonomy infrastructure, we synthesize SE(2)‑controllable reference trajectories inside the RL training loop, projecting desired footsteps onto valid footholds and adjusting swing‑foot and center‑of‑mass trajectories to match the terrain. The resulting policy exposes a clean SE(2) velocity interface compatible with standard navigation planners. In simulation, environmentally‑conditioned references significantly improve reference tracking performance compared to environment agnostic references. On hardware, we integrate the policy with an MPC + control barrier function planner and demonstrate long‑horizon (>70m) closed‑loop autonomous navigation on the Unitree G1 through outdoor environments containing rough terrain and consecutive flights of stairs, with all sensing and computation onboard.

Authors:Shrenik Jadhav, Zheng Liu
Title: Co-Design Optimization for Data Center Cooling System via Digital Twin
Abstract:
Liquid‑cooled exascale supercomputers dissipate heat through cooling plants organized as multiple parallel subloops, but how to allocate coolant distribution units (CDUs) across subloops and how to distribute flow among them has not been systematically addressed for facilities at this scale. This paper presents a three‑layer optimization framework that jointly determines the integer partition of CDUs across subloops, the continuous flow fraction allocation, and the per‑timestep co‑design optimization of total flow rate and supply temperature subject to per‑subloop thermal safety constraints. The Modelica simulation model is built based on the data of Frontier exascale supercomputer at Oak Ridge National Laboratory. By developing a reduced‑order surrogate model, all 611 feasible partitions of 25 CDUs are evaluated across the full year operational dataset of 49,353 timesteps. Three progressively richer operational strategies are compared, ranging from flow control optimization to full three‑layer co‑design optimization with dynamically adjusted flow fractions. The globally optimal design is a two‑subloop plant achieving 35.48% annual cooling energy savings, only 0.18% above the current three‑subloop Frontier design at 35.30%. Flow fraction optimization is shown to compensate for any feasible CDU‑to‑subloop assignment, reducing the design sensitivity by 93% and providing a low‑cost software‑only pathway to near‑optimal performance on the existing Frontier hardware. The framework is transferable to other liquid‑cooled high‑performance computing plants.

Authors:Min Gyung Yu, Alex Vlachokostas, Karthik Devaprasad, Matt Cornachione, Stephanie Johnson, Tim A. Yoder, Timothy I. Salsbury
Title: Optimizing Chilled Water Systems with Cooling Towers via Virtual Power Metrics and Extremum-Seeking Control
Abstract:
This paper presents an extremum seeking control (ESC) method for cooling tower fans to minimize overall power consumption of a chilled water plant system. Simulation studies across different climate locations demonstrate energy savings of approximately 15% compared to conventional control during summer conditions. This paper also proposes a virtual power meter (VPM) to enable use of the strategy in systems that lack physical power meters. Validation tests for the VPMs against physical meters showed good accuracy with a correlation of 96.11% and a normalized error of 5.11%. Coupled with the VPM, the proposed ESC control solution can be implemented on systems using typically available sensor measurements without the need for additional instrumentation.

Authors:Olivér Törő, Domonkos Csuzdi, Tamás Bécsi
Title: A Variational Lagrangian Framework for Log-Homotopy Particle Flow Filters
Abstract:
The log‑homotopy particle flow filter resolves the Bayesian update by transporting particles along a continuous trajectory in pseudo‑time. However, the governing partial differential equation for the flow velocity is fundamentally underdetermined, admitting an infinite family of valid solutions. In this work, we regard the particle flow as the motion of a pressureless inviscid fluid. We define a Lagrangian action based on the kinetic energy of the system, subject to the constraints imposed by the continuity equation and the log‑homotopy evolution. By applying the principle of least action, we obtain the Euler‑‑Lagrange equations for the optimal flow, which yields an irrotational potential flow structure. We show that this variational framework yields a coupled Hamilton‑‑Jacobi equation structurally isomorphic to Madelung's hydrodynamic formulation of quantum mechanics. In this analogy, the log‑homotopy constraint acts as a generalized quantum potential that generates the force required to guide the probability fluid along the exact Bayesian update path. Finally, we derive the material acceleration of the flow, shifting the formulation from a kinematic to a dynamical description. This perspective could enable the application of higher‑order symplectic integrators for improved numerical stability and provide a physics‑based metric for adaptive stiffness detection in high‑dimensional filtering.

Authors:Stanislav Kim, Anton Pyrkin, Oleg Borisov
Title: Coordinated Trajectory Control Algorithm for Quadcopter Motion along a Smooth Spatial Trajectory
Abstract:
A complete model of the motion of a quadcopter along a smooth spatial trajectory is presented. Based on the model, a robust algorithm is proposed for controlling a quadcopter using measurements of linear coordinates and yaw angle. By introducing additional integrators, a dynamic control algorithm with a simplified controller tuning methodology is obtained. The control law is synthesized within the geometric approach, and its stability is proven. A realizable output‑feedback version using an extended observer is also given. The results enable coordinated trajectory following in three‑dimensional space despite unmeasured disturbances and incomplete state information.

Authors:Shafayeth Jamil, Rehan Kapadia
Title: Lie Generator Networks Extract EIS-Grade Battery Diagnostics from Pulse Relaxation Data
Abstract:
Electrochemical impedance spectroscopy (EIS) is the most informative diagnostic for lithium‑ion batteries: its frequency‑resolved spectra decompose cell behavior into distinct electrochemical processes, revealing mechanism‑specific degradation invisible to voltage and resistance measurements. Yet EIS requires dedicated hardware and minutes‑long acquisitions incompatible with field deployment. Here we show that Lie Generator Networks (LGN), a structure‑preserving identification framework, extract electrochemical time constants from 60 seconds of post‑pulse voltage relaxation, data that battery management systems already collect, that encode the same diagnostic and prognostic information as impedance spectra. LGN learns the generator matrix of the relaxation dynamics with stability guaranteed by architecture, yielding time constants precise enough to resolve electrochemical variation that conventional curve fitting cannot detect from identical data. Across five datasets totaling over 850 cells, four institutions, and multiple chemistries, LGN tracks degradation with near‑perfect rank correlation (|ρ_s| = 0.999), enables cross‑validated reconstruction of full Nyquist spectra at 2% median error across 227 cells, predicts which capacity‑matched cells fail first from three early diagnostics, and recovers Arrhenius activation energies with zero physics priors without retraining or cell‑specific tuning. LGN requires no training data, no impedance hardware, and no chemistry‑specific calibration, converting any existing relaxation pulse into an impedance‑grade diagnostic. This enables real‑time health monitoring, rapid second‑life grading, production‑line quality control, and physics‑informed prognosis from minutes of measurement.

Authors:Stanislav Kim, Anton Pyrkin, Oleg Borisov
Title: Control Algorithms for Quadcopter Motion in Dynamic Positioning Mode
Abstract:
A complete model of quadcopter motion for the task of dynamic positioning at a specified point is derived. Based on this model, two control algorithms are proposed. The first one generalizes previously obtained results to the case of a varying yaw angle. The second control algorithm addresses the above problem using a simplified regulator tuning methodology.

Authors:Jules Pénot, Hamsa Balakrishnan
Title: Designing Dense Satellite Clusters for Distributed Space-based Datacenters
Abstract:
Recent proposals for datacenters in sun‑synchronous Low Earth Orbit rely on a large number of compute satellites formation‑flying in dense clusters. Designing such satellite clusters requires optimizing the satellites' orbital geometry under several safety and operational constraints applied throughout the cluster's entire orbit. These constraints include guaranteeing a minimum inter‑satellite spacing, obstruction‑less solar power for every satellite, and that each satellite have a stable set of nearest neighbors with which it can maintain inter‑satellite links (ISLs). In this work, we propose two main cluster orbital designs, parametrized by the minimum inter‑satellite spacing R_min and the cluster radius R_max: a planar cluster, and a 3D cluster. We show by construction and numerical analysis that both cluster orbital designs are consistent with the inter‑satellite spacing, unobstructed sun‑vector, and inter‑satellite line of sight constraints. The proposed planar architecture is the most efficient packing of satellites in a plane for given R_min and R_max values, and our 3D architecture allows for the number of datacenter satellites to scale proportional to (R_max/R_min)^3, an improvement over all previous LEO datacenter cluster designs. Finally, for a given satellite cluster, we formulate and solve an integer optimization problem that maps a VL2‑like Clos network datacenter switching fabric onto the satellites and their corresponding set of feasible ISLs. We confirm that for both the planar and 3D architectures, there are sufficiently many permanently unobstructed ISLs within the cluster to replicate the switching fabric of terrestrial datacenters. We also examine the tradeoff between the number of ISLs each satellite can simultaneously sustain, and the corresponding number of cluster satellites that must be dedicated as aggregation and intermediate switches.

Authors:Jose Antonio Rebollo, Enrico Barbiero, Marco Lovera
Title: Continuous-time Predictor-Based Subspace Identification with Hermite basis expansions
Abstract:
In this paper the problem of continuous‑time subspace identification for Linear Time Invariant (LTI) systems is considered and a method which directly identifies a continuous‑time state‑space form is proposed. First, Hermite basis functions are used to project signals and obtain a finite number of Hermite coefficients. By exploiting recursive relations and time derivative properties of the Hermite basis functions, an expression of the derivative operator is obtained. The latter is then recursively applied, ensuring that the state‑space matrices remain in continuous‑time form and making the system suitable for the implementation of steps which are akin to those of the Predictor‑Based Subspace IDentification (PBSID) method. This new method, hereby called the Hermite‑Domain PBSID (HD‑PBSID) method, has the further advantage of avoiding time‑shifts by properly scaling the input and output signals. The performance of the proposed approach is illustrated in a simulation study aimed at showing the accuracy of the estimates and at comparing the HD‑PBSID method and the Laguerre‑projections based Continuous‑Time PBSID (CT‑PBSID) algorithm.

Authors:Sanja Karilanova, Subhrakanti Dey, Ayça Özçelikkale
Title: Time-Varying Deep State Space Models for Sequences with Switching Dynamics
Abstract:
The identification and modeling of time‑varying systems is a fundamental challenge in signal processing and system identification. To address this challenge, we propose a class of time‑varying state‑space model (SSM) based neural networks in which the neurons' states are governed by time‑varying dynamics. The proposed model provides the learnable time‑varying dynamics through a dictionary of basis functions, where each basis function evolves differently over time. We evaluate the proposed approach on both synthetic data from switching systems and a speech denoising task where real audio is corrupted with switching dynamics noise. The results show that the proposed time‑varying model consistently outperforms its time‑invariant counterparts while maintaining comparable computational complexity. Our investigations also reveal which aspects of the time‑varying dynamics of the data most need to be captured by the proposed time‑invariant models, how the additional freedom provided by time‑varying basis functions should be allocated across model components, and to what extent larger models can compensate for time‑invariant limitations.

Authors:Eron Ristich, Eduardo Sontag, Necmiye Ozay
Title: On the Nonexistence of Continuous Immersions for Discrete-time Systems
Abstract:
Understanding when linear immersions of nonlinear dynamical systems exist is important since such immersions allow us to leverage the rich tools of linear system theory to analyze nonlinear dynamics. Recently, Liu et al. (2023) showed that continuous‑time dynamical systems that admit countably many but more than one omega‑limit sets cannot be immersed into finite dimensional linear systems with a one‑to‑one and continuous mapping. In this paper, we extend these results to discrete‑time dynamics and show that similar obstructions exist also in discrete time. We further consider a generalization involving alpha‑limit sets. Several examples are provided to demonstrate the results.

Authors:Michael Baumgartner, David Müller, Agon Serifi, Ruben Grandia, Espen Knoop, Markus Gross, Moritz Bächer
Title: CoCo-InEKF: State Estimation with Learned Contact Covariances in Dynamic, Contact-Rich Scenarios
Abstract:
Robust state estimation for highly dynamic motion of legged robots remains challenging, especially in dynamic, contact‑rich scenarios. Traditional approaches often rely on binary contact states that fail to capture the nuances of partial contact or directional slippage. This paper presents CoCo‑InEKF, a differentiable invariant extended Kalman filter that utilizes continuous contact velocity covariances instead of binary contact states. These learned covariances allow the method to dynamically modulate contact confidence, accounting for more nuanced conditions ranging from firm contact to directional slippage or no contact. To predict these covariances for a set of predefined contact candidate points, we employ a lightweight neural network trained end‑to‑end using a state‑error loss. This approach eliminates the need for heuristic ground‑truth contact labels. In addition, we propose an automated contact candidate selection procedure and demonstrate that our method is insensitive to their exact placement. Experiments on a bipedal robot demonstrate a superior accuracy‑efficiency tradeoff for linear velocity estimation, as well as improved filter consistency compared to baseline methods. This enables the robust execution of challenging motions, including dancing and complex ground interactions ‑‑ both in simulation and in the real world.

Authors:Junaid Ahmed Memon, Allan Andre Do Nascimento, Kostas Margellos, Antonis Papachristodoulou
Title: A Prototyping Framework for Distributed Control of Multi-Robot Systems
Abstract:
This paper presents a prototyping framework for distributed control of multi‑robot systems, aimed at bridging theory and practical testing of distributed optimization algorithms. Using the Single Program, Multiple Data (SPMD) paradigm, the framework emulates distributed control on a single computer, with each core running the same algorithm using local states and neighbour‑to‑neighbour communication. We demonstrate the framework on a four‑quadrotor position‑swapping task using a non‑cooperative game‑theoretic distributed algorithm. Computational time and trajectory data are compared across the supported dynamics levels: a point‑mass model, a high‑fidelity quadrotor model, and an experimental hardware testbed using Crazyflie quadcopters. The results show that the framework provides a low‑cost and accessible approach for validating distributed algorithms.

Authors:Milton L. Montero, Elias Najarro, Jakob Schauser, Sebastian Risi
Title: Learning Developmental Scaffoldings to Guide Self-Organisation
Abstract:
From subcellular structures to entire organisms, many natural systems generate complex organisation through self‑organisation: local interactions that collectively give rise to global structure without any blueprint of the outcome. Yet a significant portion of the information driving such processes is not produced by self‑organisation itself, instead, it is often offloaded to initial conditions of the system. Biological development is a prime example, where maternal pre‑patterns encode positional and symmetry‑breaking information that scaffolds the self‑organising process. From maternal morphogen gradients in early embryogenesis to tissue‑level morphogenetic pre‑patterns guiding organ formation, this transfer of information to initial conditions, analogous to a memory‑compute trade‑off in computational systems, is a fundamental part of developmental processes. In this work, we study this offloading phenomenon by introducing a model that jointly learns both the self‑organisation rules and the pre‑patterns, allowing their interplay to be varied and measured under controlled conditions: a Neural Cellular Automaton (NCA) paired with a learned coordinate‑based pattern generator (SIREN), both trained simultaneously to generate a set of patterns. We provide information‑theoretic analyses of how information is distributed between pre‑patterns and the self‑organising process, and show that jointly learning both components yields improvements in robustness, encoding capacity, and symmetry breaking over purely self‑organising alternatives. Our analysis further suggests that effective pre‑patterns do not simply approximate their targets; rather, they bias the developmental dynamics in ways that facilitate convergence, pointing to a non‑trivial relationship between the structure of initial conditions and the dynamics of self‑organisation.

Authors:Stanislav Kim, Anton Pyrkin, Oleg Borisov
Title: Robust Quadcopter Motion Control Using Output Feedback
Abstract:
The study addresses the problem of quadcopter motion control using output feedback. By applying a geometric approach, the quadcopter model is transformed into a normal form with a time‑varying gain coefficient, which is subsequently made stationary through double integration of the control input. A robust output feedback control law is synthesised based on the extended observer method.

Authors:Iskandar Khemakhem, Manuel Zobel, Johannes Schüle, Oliver Sawodny, Naoki Uchiyama, Abdallah Farrage
Title: Behavioral Data-Driven Optimal Trajectory Generation for Rotary Cranes
Abstract:
With the growth of the construction industry and the global shortage of skilled labor, the automation of crane control has become increasingly important for safe and efficient operations. A central challenge in automatic crane control is the reduction of load oscillations during motion, which is primarily addressed through appropriate slewing trajectories. In this context, classical model‑based control methods rely on accurate dynamical models and expert tuning, and often struggle to meet safety and precision requirements, while many learning‑based approaches require large data sets and significant computational resources. This paper proposes a behavioral data‑driven framework for generating open‑loop slewing trajectories for rotary cranes that suppress load sway while reducing operation time and energy consumption. The approach builds on Willems' fundamental lemma and its generalizations, to bypass explicit system modeling and operate directly on measured input‑output data. A practical workflow is presented in this paper to reduce the need for expert knowledge. Despite the underactuated nature of the crane dynamics, the method identifies a nonparametric representation of the system behavior and generates smooth, optimal trajectories using limited data and convex optimization. The proposed trajectory generation method is validated on a laboratory crane setup and compared against an established model‑based approach, achieving up to 35% reduction in load sway, 43% reduction in tracking error, and 50% reduction in travel time.

Authors:Lysander Miller, Joshua Keene, Jeremy M. C. Brown, Airlie Chapman
Title: Radioactive Source Seeking using Bayesian Optimisation with Movement Penalty
Abstract:
The use of mobile robotics in radioactive source seeking has become an important part of modern radiation‑safety practices, supporting timely mitigation of contamination risks and helping protect public health. However, measuring radiation is often time‑consuming, rendering traditional gradient‑based source‑seeking methods less effective due to lower sample efficiency. This paper proposes a sample‑efficient Bayesian‑Optimisation source‑seeking strategy that utilises a heteroscedastic Gaussian process surrogate to balance exploration and exploitation. Excessive inter‑sample travel is discouraged through a movement switching cost. The strategy is shown to generate sublinear regret in the source‑seeking task, while simulations demonstrate its effectiveness in localising radioactive sources.

Authors:Domonkos Csuzdi, Tamás Bécsi, Olivér Törő
Title: An integration-free approach for particle flow filtering
Abstract:
Log‑homotopy particle flow filters realize nonlinear Bayesian estimation by continuously migrating samples from the prior to the posterior distribution. This transport is governed by a pseudo‑time ordinary differential equation (ODE). A major practical challenge of these filters is the need for numerical integration, which suffers from high computational cost and susceptibility to stiffness. This paper develops an exact, integration‑free closed‑form solution for the exact Daum‑‑Huang deterministic particle flow under vector linear Gaussian measurements. By transforming the ODE into a specific eigenspace, we derive closed‑form algebraic expressions for both the homogeneous state transition matrix and the inhomogeneous forcing term. We prove that this analytic solution is equivalent to the exact Kalman measurement update. We embed this closed‑form evaluation within an N‑step piecewise method for nonlinear measurement models. We further propose a constant contraction rate substep schedule that equalizes the per‑step contraction along the eigendirection of D associated with the largest eigenvalue α_\max. The result is a stiffness‑mitigating, integration‑free particle update for highly nonlinear measurement models. On a bearings‑only tracking benchmark, it achieves the lowest error among the compared filters, at a per‑update cost comparable to deterministic particle flow baselines and substantially lower than stochastic flows.

Authors:Ruslan Zakirzyanov
Title: Hybrid Metaheuristic Optimization of Distributed Control System Hardware Architecture with Model-Based Verification
Abstract:
Large‑scale chemical plants rely on distributed process control systems (PCS) comprising numerous processing units, communication modules, and I/O devices interconnected via industrial networks. The design of a cost‑efficient and reliable hardware architecture under partial uncertainty in plant parameters remains a challenging combinatorial optimization problem. This paper proposes a formal model for distributed control system hardware architecture synthesis. A hybrid ant colony‑based metaheuristic framework is developed to construct feasible hierarchical architectures. The proposed approach is validated on a large‑scale sulfuric acid plant control system case study. Plant parameters are identified from operational data, system stability is analyzed, and a controller synthesis is performed based on the optimized architecture. The results demonstrate the feasibility of the approach and confirm that the obtained architecture satisfies structural and dynamic performance requirements.

Authors:Maximilian Bloor, Martha White, Ehecatl Antonio del Rio Chanona, Calvin Tsay
Title: Addressing Terminal Constraints in Data-Driven Demand Response Scheduling
Abstract:
Electrified chemical processes are incentivized by exposure to time‑varying electricity markets to operate flexibly, but participating in demand response schemes can require satisfying terminal constraints over long horizons. Specifically, terminal constraints may be required when computing optimal schedules in order to preserve dynamic stability. Model‑based optimization methods are computationally costly, and data‑driven scheduling via reinforcement learning (RL) faces severe credit‑assignment challenges. We integrate Goal‑Space Planning (GSP) with Deep Deterministic Policy Gradient (DDPG), using learned temporally abstract models over discrete subgoals to propagate value across extended horizons. Using a simulated air separation benchmark, we demonstrate the proposed approach improves sample efficiency over standard DDPG while satisfying terminal storage constraints, mitigating myopic control behavior.

Authors:Dongjoo Kim, Lin Dong, Le Xie
Title: Flexibility-Aware Framework for Efficient Planner-Initiated Siting of Data Center
Abstract:
Explosive growth in energy‑intensive AI data centers is outstripping the pace of power grid interconnection and transmission expansion. While operational flexibility has been proposed to mitigate this stress, existing processes are often reactive and evaluate projects only after they enter a multi‑year interconnection queue. To address this, we introduce a planner‑initiated siting framework that integrates (i) reliability‑gated screening, (ii) system‑wide market‑impact assessment under standardized flexibility envelopes (firm, pause, and shift), and (iii) entropy‑weighted multi‑criteria scoring to produce ranked, pre‑certified catalogues of interconnection‑ready locations. Applied to a synthetic 2,000‑bus Texas power system, the framework demonstrates that operational flexibility expands the siting frontier by 9‑17% at 1 GW and 19‑21% at 2 GW compared to firm operation. Median all‑hour average prices remain essentially unchanged (USD 24.32/MWh for the 2 GW cases), and the shift envelope attenuates peak‑hour price dispersion by approximately 3.4% with minimal side effects during off‑peak hours. Utilizing pre‑certified envelopes to bypass major transmission reinforcements, this workflow enables first energization in 12‑18 months, a conservative reduction of 3.5‑4 years versus the conventional 5‑8 year project‑led process. This technology‑agnostic framework provides a proactive decision‑making tool for system operators and regulators to fast‑track large flexible loads while preserving grid reliability and market stability.

Authors:Vitoriano Casas, Gabriela Reis, Pedro Henrique Coutinho, Iury Bessa, Rodrigo Araújo
Title: Dynamic Event-Triggered Control of Discrete-Time Nonlinear Systems based on Difference-Algebraic Representations
Abstract:
This paper addresses the dynamic event‑triggered control for a class of discrete‑time nonlinear systems described by a difference‑algebraic representation (DAR), using a gain‑scheduled controller. An outstanding aspect of the proposed method is the incorporation of information about the system's nonlinearities into the control law and the trigger function. The proposed event‑triggered mechanism also incorporates information on the asynchronous terms induced by the event‑based sampling. All these ingredients enable the derivation of a less conservative co‑design condition for the simultaneous design of the gain‑scheduled control law and the dynamic triggering mechanism to ensure the asymptotic stability of the closed‑loop system. An estimate of the region of attraction of the origin of the closed‑loop system is obtained to guarantee the closed‑loop system's operation within the domain of validity of the DAR. Then, an optimization problem is formulated to reduce the number of events and enlarge the estimated region of attraction. Finally, the effectiveness of the proposed condition is illustrated by a numerical example.

Authors:Abdelhakim Amer, Aske Alstrup, Frederik Rasmussen, Yury Brodskiy, Andriy Sarabakha, Erdal Kayacan
Title: SeaVis: Modeling and Control of a Remotely Operated Towed Vehicle for Seabed Visualization and Mapping
Abstract:
High‑resolution seafloor mapping necessitates stable and precise positioning for underwater robots. This paper introduces a novel mathematical model for SeaVis remotely operated towed vehicles (ROTVs) and develops a gain‑scheduled linear‑quadratic regulator (LQR) for robust depth and attitude control. We validate the approach in a high‑fidelity simulation, benchmarking the LQR against a conventional PID controller over a challenging seabed profile. The presented results demonstrate the LQR's superior performance, with significantly enhanced robustness to disturbances, greater control efficiency, and substantially reduced flap actuation. The gain scheduling also confirms the controller's effectiveness across the full operational velocity range. The complete simulation environment and controller are open‑sourced.

Authors:Nikolas Recke, Mathias Hudoba de Badyn
Title: Distributionally Robust Model Predictive Control for Virtual Power Plants
Abstract:
This paper presents a distributionally robust model predictive control (DRMPC) framework for the optimal Virtual Power Plant (VPP) operation under electricity price uncertainty. A unified VPP model is formulated that captures the interaction between buildings, battery storage, and renewable generation, all influenced by exogenous weather and market signals. The proposed approach integrates data‑driven forecasting with quantile‑based uncertainty quantification to construct time‑varying Wasserstein ambiguity sets that adapt to forecast dispersion and distributional shifts. This yields a tractable DR‑MPC formulation that incorporates predictive distribution information directly into real‑time decision making. The method is evaluated using real weather and market data from a Nordic case study across two seasonal scenarios. The results show that DR‑MPC improves economic performance relative to standard forecast‑based MPC when the ambiguity radius is chosen appropriately, with consistent gains of up to 0.8% for small radii across both seasonal scenarios. Larger radii become overly conservative and reduce revenue, underscoring the importance of proper radius selection. These findings demonstrate the practical value of distributionally robust optimization for uncertainty‑aware VPP operation.

Authors:Hongwei Zhen, Ze Yu, Xin Xiang, Mingyang Sun, Wuhua Li
Title: Admittance-Guided Inverter Dispatch Command Manipulation Attack: A Grid Stability-Oriented Approach
Abstract:
The high penetration of voltage source converters in modern smart microgrids enhances operational flexibility while introducing complex cyber‑physical vulnerabilities. Existing cyber‑attack studies either require detailed knowledge of system topology and controller dynamics or depend on repeated online interactions, which may compromise practicality by generating operationally infeasible or limit‑violating commands. This article investigates a dispatch command manipulation attack and develops an admittance‑guided framework to identify the vulnerable inverter and the worst‑case dispatch command that most severely degrades system stability. A compromised inverter is utilized to inject controlled harmonic perturbations for sparse admittance measurement, and a physics‑informed neural network is then employed to reconstruct the operating‑point‑dependent admittance of target inverters over the feasible dispatch region. Based on the reconstructed admittance, a stability‑margin‑oriented optimization is formulated to locate the most vulnerable inverter and the corresponding worst‑case dispatch command. Controller hardware‑in‑the‑loop experiments on a five‑inverter microgrid demonstrate that the identified command can drive the system into severe sub‑synchronous oscillations while remaining within nominal dispatch bounds, highlighting the need for stability‑aware command screening beyond static limit checking.

Authors:Hongwei Zhen, Ze Yu, Xin Xiang, Wuhua Li, Mingyang Sun
Title: Quantifying Cyber-Vulnerability in Power Electronics Systems via an Impedance-Based Attack Reachable Domain
Abstract:
Power electronics systems are increasingly exposed to cyber threats due to their integration with digital controllers and communication networks. However, an attacker‑oriented metric is still lacking to quantify the extent to which a node can be pushed toward instability within a privilege‑constrained action space. This letter proposes an impedance‑based Attack Reachable Domain (ARD) framework that maps feasible adversarial actions to critical‑eigenvalue migration through impedance reshaping. Based on the ARD, an Attack Penetration Index is defined to quantify node‑level cyber‑vulnerability by jointly characterizing the penetration of the nominal stability margin and the accessibility of successful destabilizing attacks within a privilege‑constrained action space. To make the proposed assessment computable when inverter models are unavailable, a practical gray‑box workflow is further established by integrating existing impedance identification and differentiable surrogate tools. Case studies on a 4‑bus system and a modified IEEE 39‑bus system show that coordinated cross‑layer manipulations are markedly more damaging than isolated single‑layer attacks, and that the proposed metric reveals vulnerability patterns that cannot be inferred from grid‑strength indicators.

Authors:Edoardo Scarpel, Alberto Pettena, Matteo Cederle, Federico Chiariotti, Marco Fabris, Gian Antonio Susto
Title: Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning
Abstract:
This paper proposes a fully dynamic Deep Reinforcement Learning (DRL) method for rebalancing dockless bike‑sharing systems, overcoming the limitations of periodic, system‑wide interventions. We model the service through a graph‑based simulator and cast rebalancing as a Markov decision process. A DRL agent routes a single truck in real time, executing localized pick‑up, drop‑off, and charging actions guided by spatiotemporal criticality scores. Experiments on real‑world data show significant reductions in availability failures with a minimal fleet size, while limiting spatial inequality and mobility deserts. Our approach demonstrates the value of learning‑based rebalancing for efficient and reliable shared micromobility.

Authors:Sergio Vanegas, Lasse Lensu, Fredy Ruiz
Title: A Novel Schur-Decomposition-Based Weight Projection Method for Stable State-Space Neural-Network Architectures
Abstract:
Building black‑box models for dynamical systems from data is a challenging problem in machine learning, especially when asymptotic stability guarantees are required. In this paper, we introduce a novel stability‑ensuring and backpropagation‑compatible projection scheme based on the Schur decomposition for the state matrix of linear discrete‑time state‑space layers, as well as an alternative pre‑factorized formulation of the methodology. The proposed methods dynamically project the quasi‑triangular factor of the state matrix's real Schur decomposition onto its nearest stable peer, ensuring stable dynamics with minimal overparameterization. Experiments on synthetic linear systems demonstrate that the method achieves accuracy and convergence rates comparable to those of state‑of‑the‑art stable‑system identification techniques, despite a marginal increase in computational complexity. Furthermore, the lower weight count facilitates convergence during training without sacrificing accuracy in stacked neural‑network architectures with static nonlinearities targeting real‑world datasets. These results suggest that the Schur‑based projection provides a numerically robust framework for identifying complex dynamics on par with the State of the Art while satisfying strict asymptotic‑stability requirements.

Authors:Ankur Kamboj, Rajiv Ranganathan, Xiaobo Tan, Vaibhav Srivastava
Title: Automated Curriculum Design for High-dimensional Human Motor Learning
Abstract:
Designing effective practice schedules for high‑dimensional motor learning tasks remains a challenge, especially when skill states are unobservable and task performance may not reflect the true learning. We propose an automated curriculum design framework that combines a human motor learning model and personalized real‑time skill estimation with Stochastic Nonlinear Model Predictive Control in \emphde‑novo (novel) motor learning paradigms. We validated our framework both through simulations and human‑subject studies (N = 36) using a hand exoskeleton. Our proposed approach accelerates skill acquisition by ～23%, and ～17% when compared to a random curriculum and a performance heuristics‑based curriculum, respectively. These significant gains in learning efficiency highlight the potential of model‑based, individualized curricula for motor rehabilitation and complex skill training.

Authors:Rajiv Singh, Mario Sznaier, Lennart Ljung
Title: Randomized Atomic Feature Models for Physics-Informed Identification of Dynamic Systems
Abstract:
We present a physics‑informed framework for system identification based on randomized stable atomic features. Impulse responses are represented as random superpositions of stable atoms, namely damped complex exponentials associated with poles sampled inside a prescribed disk. Identification is then cast as a convex regularized least‑squares problem with optional linear, second‑order‑cone, and KYP constraints. The approach generalizes random Fourier and random Laplace features to the damped, nonstationary regime relevant to engineering systems while retaining modal interpretability and scalable finite‑dimensional computation. The main analytic point is an operator‑theoretic Disk‑Bochner viewpoint: positive measures over stable poles generate positive‑definite kernels with a radius‑dependent shift defect, while a converse scalar disk moment representation for an arbitrary kernel is characterized by subnormality of the canonical shift. We prove this statement, establish an RKHS‑to‑l1 embedding, show that sampled poles induce a valid finite atomic gauge, discuss random‑feature convergence, and state sparse‑recovery guarantees conditionally on the restricted‑eigenvalue properties of the realized disk‑Vandermonde or input‑output design matrix. We also connect the normalized transfer function problem to Nevanlinna‑Pick interpolation and LFT set‑membership. The framework directly encodes stability margins, modal localization, DC‑gain bounds, monotonicity, passivity, relative degree, settling‑time targets, and time/frequency‑domain error bounds. Numerical comparisons illustrate how physically meaningful priors can compensate for poor excitation and improve constrained impulse‑response recovery in an under‑informative data setting.

Authors:Xiaoting Wang, Supun Amarathunga, Pasan Gunawardena, Gregory Kish, Yunwei, Li
Title: Energy Management for Solar-Powered Electric-Bus Charging Station: A Data-Driven Method
Abstract:
This paper presents a flexible energy management system (EMS) for an electric bus charging station (EBCS) that integrates renewable generation, energy storage, and electric bus (EB) charging while accounting for uncertainties in solar PV output, electricity prices, and EB arrival/departure state of charge. A data‑driven polynomial chaos expansion surrogate is developed from a limited set of uncertainty samples, and a nonparametric inference method is used to enrich the input data when historical data is limited. Case studies on a solar‑powered EBCS with 20 EBs demonstrate the effectiveness of the proposed EMS and data‑driven method.

Authors:Yushen Liu, Yin-Jen Chen, Ziyi Chen, Tao Wang, Heng Huang, Xugui Zhou, Yanfu Zhang
Title: Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability
Abstract:
Many safety‑critical control problems are modeled as risk‑sensitive partially observable Markov decision processes, where the controller must make decisions from incomplete observations while balancing task performance against safety risk. Although belief‑space planning provides a principled solution, maintaining and planning over beliefs can be computationally costly and sensitive to model specification in practical domains. We propose a lightweight risk‑gated reinforcement learning approximation for risk‑sensitive control under partial observability. The method constructs a compact finite‑history proxy state and learns an action‑conditioned predictor of near‑term safety violation. This predicted candidate‑action risk is used in two complementary ways: as a risk penalty during value learning, and as a decision‑time gate that interpolates between optimistic and conservative ensemble value estimates. As a result, low‑risk actions are evaluated closer to reward‑seeking estimates, while high‑risk actions are evaluated more conservatively. We evaluate the approach in two safety‑critical partially observable domains: automated glucose regulation and safety‑constrained navigation. Across adult and adolescent glucose‑control cohorts, the method improves overall glycemic tradeoffs and substantially reduces runtime relative to a belief‑space planning baseline. On Safety‑Gym navigation benchmarks, it achieves a more favorable reward‑cost balance than unconstrained RL and several standard safe‑RL baselines. These results suggest that action‑conditioned near‑term risk can provide an effective local signal for approximate risk‑sensitive POMDP control when full belief‑space planning is impractical.

Authors:Philipp Binfet, Janis Adamek, Moritz Schulze Darup
Title: On the (non-)resilience of encrypted controllers to covert attacks
Abstract:
The security of networked control systems (NCS) is receiving increasing attention from both cyber‑security and system‑theoretic perspectives. The former focuses on classical IT security goals such as confidentiality, integrity, and availability of process data, while the latter investigates tailored attacks (and detection schemes), including covert and zero‑dynamics attacks. Confidentiality in control systems can, for instance, be achieved by securely outsourcing the evaluation of the controller to third‑party platforms, such as cloud services. The underlying technology enabling such secure computation often is homomorphic encryption (HE). Recent works in encrypted control have proposed modifications to underlying HE schemes to achieve not only confidentiality but also resilience to certain types of integrity attacks. While extensions in this direction are desirable in principle, we show that the integrity problem in encrypted control cannot be solved by public‑key HE schemes alone due to their inherent malleability. In other words, the same homomorphisms that enable encrypted control in the first place can be leveraged not only constructively but also destructively. More precisely, we demonstrate that NCS are vulnerable to covert attacks, even when encrypted control is employed. Remarkably, this remains possible without knowledge of an unencrypted model. Yet, resilience to such attacks can still be achieved through complementary techniques. We present an approach based on verifiable computation that integrates with modern homomorphic cryptosystems and is asymptotically secure while incurring no communication overhead.

Authors:Andrew Lanpouthakoun, Aryaman Arora, Zhengxuan Wu, Dhruv Pai, Ben Keigwin, Dan Jurafsky, Christopher Potts
Title: PreFT: Prefill-only finetuning for efficient inference
Abstract:
Large language models can now be personalised efficiently at scale using parameter efficient finetuning methods (PEFTs), but serving user‑specific PEFTs harms throughput, even with specialised kernels and memory management techniques. This is because, theoretically and empirically, a mismatch exists between prefill (processing a large number of tokens at once) and decode (generating a single token autoregressively): the latter has far lower throughput when serving multiple adapters. Rather than optimising performance relative to parameter count, for efficient multi‑adapter serving, we instead ought to optimise performance relative to serving throughput. We therefore propose PreFT (Prefill‑only Finetuning), wherein we only apply the adapter to prefill tokens and discard it afterwards. PreFT significantly increases throughput with minimal effect on performance. We develop and release an efficient implementation of two prefill‑only PEFTs, LoRA and ReFT, on the vLLM inference engine. We first show that serving multi‑user PreFTs is more efficient than traditional PEFTs (1.9× the throughput when serving 512 adapters on Llama 3.1 70B). Then, we compare the performance of prefill‑only vs. all‑token adapters on a variety of supervised finetuning and reinforcement learning tasks with LMs at varying scales. On SFT, we observe that the evaluation loss of PreFTs is higher than PEFTs, but can be compensated by increasing rank with nearly no reduction in throughput. On RL, we consistently find that PreFTs approach parity with standard PEFTs. Together, this work validates prefill‑only adaptation of LLMs as a more favourable accuracy‑throughput tradeoff than existing PEFTs for personalised serving.

Authors:Eunhan Ka, Satish V. Ukkusuri
Title: Day-to-Day Traffic Network Modeling under Route-Guidance Misinformation: Endogenous Trust and Resilience in CAV Environments
Abstract:
Connected and autonomous vehicles and smart mobility services increasingly use digital route guidance as an operational input to traffic network management. When this information becomes unreliable or adversarial, day‑to‑day traffic models must represent not only flow adaptation but also the evolution of user trust in the information source. This paper develops a coupled day‑to‑day traffic assignment and trust‑evolution framework for route‑guidance misinformation. Within‑day congestion is represented by Lighthill‑Whitham‑Richards network loading, while day‑to‑day route choice follows bounded‑rationality logit learning with trust‑dependent reliance on external guidance. Trust is modeled as an aggregate class‑level behavioral reliance state encoded by a Beta evidence model and updated from repeated guidance errors. Theoretical analysis establishes stationary equilibria, a conservative stability guide, a weighted compliance index for population‑level vulnerability, and an asymmetric recovery law that explains post‑attack trust hysteresis. Numerical experiments on Sioux Falls, with an Anaheim robustness check, show that endogenous trust creates a threshold‑based resilience mechanism. Below the trust‑activation threshold, the attack remains behaviorally stealthy and dynamic trust provides almost no attenuation. Above the threshold, trust erosion reduces the impact of the fixed‑trust attack by about 91 percent in Sioux Falls and 85 percent in Anaheim. The experiments also show that CAV penetration increases fixed‑trust vulnerability while preserving dynamic attenuation, and that traffic performance can recover before trust, resulting in a 77‑day hidden vulnerability window. The results provide a trust‑aware modeling basis for resilience analysis in CAV‑enabled traffic networks.

Authors:Matheus Wagner, Antônio Augusto Fröhlich
Title: Motion Planning for Autonomous Vehicles using Optimization over Graphs of Convex Sets
Abstract:
Motion planning for autonomous vehicles requires generating collision‑free and dynamically feasible trajectories in complex environments under real‑time constraints. While nonlinear optimal control formulations provide high‑fidelity solutions, they are computationally demanding and sensitive to initialization, whereas geometric planning methods scale well but often decouple path selection from trajectory optimization. This paper studies the extent to which optimization over Graphs of Convex Sets (GCS) can approximate solutions of nonlinear optimal control problems in the context of autonomous driving. The free space is represented as a finite union of convex regions organized as a directed graph, allowing nonconvex geometry to be handled through discrete connectivity decisions while maintaining convex trajectory constraints within each region. Vehicle motion is parameterized using Bezier curves for the spatial path and a polynomial time‑scaling function for temporal evolution. Under small‑slip and linear tire assumptions, a simplified dynamic bicycle model enables approximate enforcement of dynamic feasibility through convex constraints on trajectory derivatives. The approach is evaluated in CommonRoad scenarios involving static obstacle avoidance and lane‑changing maneuvers, and is compared against a nonlinear discrete‑time optimal control formulation. The results indicate that the GCS‑based method generates collision‑free and dynamically consistent trajectories that closely match those obtained from the nonlinear program, while exhibiting improved computational efficiency and reduced sensitivity to initialization. These findings suggest that GCS provides a structured approximation of nonlinear motion planning problems, capturing dominant geometric and dynamic effects while preserving convexity in the continuous relaxation.

Authors:Sarthak Antal, Steve Enosh
Title: Time Domain Near Memory Computing Engine
Abstract:
The increasing computational demand of AI workloads has intensified the need for energy‑efficient in‑memory and near‑memory computing architectures, particularly because data movement often consumes significantly more energy than computation itself. While fully digital architectures provide robust scalability and support higher‑resolution computation, analog in‑memory computing has demonstrated improved energy efficiency for low‑precision workloads. However, its reliance on peripheral DACs and ADCs introduces additional power, area, and design overhead. To address these challenges, this work presents a time‑domain near‑memory computing architecture for low‑precision multiply‑and‑accumulate (MAC) operations. In the proposed approach, digital weight bits stored in SRAM are converted using a current‑steering DAC, while the digital input vector is encoded by an N‑pulse generator. This enables multiplication to be performed in the time domain while maintaining a digital‑friendly interface. Two accumulation schemes, a delay‑cell‑based architecture and a counter‑based architecture, are investigated and compared in terms of design trade‑offs, linearity, scalability, and power efficiency. To improve technology portability, the N‑pulse generator and counters are implemented using RTL synthesis, while the current‑steering DAC remains in the analog domain. A 4 x 4 MAC prototype is implemented with a 1 V supply, achieving an operating frequency of 40 MHz, power consumption of 42 uW, and energy efficiency of 7.62 TOPS/W.

Authors:Kyung-Bin Kwon, Min Gyung Yu, Sayak Mukherjee, Timothy I. Salsbury
Title: Optimizing Grid-Forming Controls using Relay-based Extremum Seeking to Enhance Transient Performance
Abstract:
Grid‑forming (GFM) inverters are essential for enhancing stability in modern power systems with high penetration of inverter‑based resources (IBRs). However, their performance highly depends on control parameters tuning, particularly the active power‑frequency droop coefficient. This parameter presents a trade‑off among competing objectives, including damping, settling time, rate of change of frequencies (RoCoF) and frequency nadirs. This paper proposes a real‑time, adaptive optimization framework based on Extremum Seeking Control (ESC) to dynamically tune the GFM droop gain. A multi‑objective cost function balances conflicting performance goals such as oscillation energy, frequency nadir, RoCoF, and post‑disturbance settling performance. The approach is validated through numerical simulations on a modified IEEE 68‑bus system. Results demonstrate that the cost function is convex with respect to the droop parameter, justifying gradient‑based optimization. Furthermore, the ESC algorithm successfully tracks the time‑varying optimal droop coefficient in real‑time as network conditions change, thereby ensuring robust and near‑optimal system performance without requiring an analytical grid model.

Authors:Mohammadreza Barzegaran, Kemeng Han, Hamid Jafarkhani
Title: An Encoded Corrective Double Deep Q-Networks for Multi-Agent Control Systems
Abstract:
This paper studies the synthesis of control policies for heterogeneous and interconnected multi‑agent systems that collaborate through data exchange over a communication network to minimize a collective cost. We propose a distributed encoded corrective double actor‑critic framework that integrates a novel message‑passing mechanism. Existing methods assume noise‑free and delay‑free access to the global or partial states and overlook the fact that the global states, though noisy and delayed, can be progressively reconstructed and refined over time. In contrast, this work explicitly models communication sampling asynchrony, delay, and link noise based on the network configuration. The proposed message‑passing mechanism characterizes timing and information flow to refine and time shift global state information, which is then used to incrementally correct the Q‑networks. The double Q‑network design mitigates overestimation bias, while the shared encoder coupling the actor‑critic networks captures inter‑agent dependencies. We evaluate our approach in multiple test cases, demonstrate its effectiveness over various baselines, and provide a numerical regret analysis.

Authors:Xin Lu, Qianwen Xu
Title: Grid Integration of Gigawatt-Scale AI Data Centers under Connect-and-Manage
Abstract:
Emerging connect‑and‑manage interconnection practices allow gigawatt‑scale artificial intelligence data centers (AIDCs) to connect to the transmission network without prior network upgrades, at the cost of real‑time curtailment during grid stress. This paper formalizes the resulting AIDC‑transmission system operator (TSO) coordination as a sequential request‑acceptance protocol with an explicit curtailment variable and a strict information boundary between the two parties. Physical models are developed on both sides of the point of common coupling: the AIDC is decomposed into frontier training, batch training, and inference serving subclasses sharing on‑site battery energy storage, capturing differentiated temporal flexibility; the transmission network is modeled via DC power flow with generator constraints and budget‑constrained demand uncertainty. Because the TSO's acceptance mapping is opaque to the AIDC, a three‑layer hierarchical architecture is formulated in which a learning‑based planning layer generates power requests, the TSO evaluates each request through a robust acceptance mechanism, and a single‑step execution optimizer enforces internal feasibility under the realized power budget. Case studies with a gigawatt‑scale AIDC on the IEEE 39‑bus system with Australian market data show that the framework reduces curtailment from 9.1% to 2.8% while preserving 98.1% frontier training workload, that batch training acts as the primary grid‑elastic resource with the largest throughput swing during peak demand, and that the on‑site battery provides curtailment buffering through active discharge and charge deferral.

Authors:Xin Lu, Jing Qiu, Jiafeng Lin, Sihai An, Mingyang Sun, Junhua Zhao
Title: Battery-Assisted Operation of Hyperscale AI Data Centers under Connect-and-Manage Interconnection Practices
Abstract:
Emerging connect‑and‑manage practices allow new transmission‑connected mega‑loads to connect while enforcing time‑varying admissible power exchange limits at the point of common coupling (PCC) in real time. Hyperscale artificial intelligence data centers (AIDCs), whose demand can reach hundreds of megawatts and whose internal computing‑cooling dynamics evolve rapidly, can therefore face frequent conflicts between workload continuity requirements and externally imposed PCC envelopes. This paper proposes a battery‑assisted operational framework in which on‑site battery energy storage (BESS) serves as a physical buffering interface to reconcile fast internal dynamics with time‑varying interconnection limits. A continuity‑aware energy‑computation model is developed to jointly capture checkpoint‑constrained AI training workloads, information technology (IT) computing power‑throughput characteristics, and IT‑cooling thermal dynamics. A two‑stage decision framework is then formulated, consisting of scenario‑based day‑ahead workload commitment and a real‑time receding‑horizon delivery assurance controller that enforces battery, thermal, and grid‑interaction constraints. Case studies on the IEEE 39‑bus system with Australian real data demonstrate that BESS substantially increases credible day‑ahead workload commitment and improves real‑time delivery robustness under transmission congestion. Sensitivity analyses further reveal a regime‑dependent role transition of BESS ‑‑ from feasibility‑oriented continuity support when PCC limits are binding to economy‑driven flexibility provision as transmission constraints are relaxed.

Authors:Yihong Zhou, Dylan Cope, Jakob Foerster, Thomas Morstyn
Title: JAX-Based Batched AC Power Flow for GPU Acceleration and AI Ecosystem Integration
Abstract:
Coordinating growing grid flexibility under uncertainty is becoming increasingly important for efficient and reliable power‑system operation. A core computational requirement is the efficient large‑scale batched evaluation of AC power flow across candidate operating actions and uncertainty scenarios. Previous work has explored GPU‑based batched power‑flow evaluation, but has largely relied on hand‑written C or CUDA code, creating barriers to customisation, efficient kernel optimisation, and long‑term maintenance. JAX is a Python‑based framework that enables efficient accelerator execution while keeping implementations in Python. This letter therefore proposes a JAX‑based batched AC power‑flow solver that uses current JAX functionality to implement Newton‑‑Raphson for transmission networks and Z‑Bus power flow for three‑phase unbalanced distribution networks, achieving more than 10x speed‑ups relative to pandapower and OpenDSS. In addition, JAX integrates seamlessly with the broader JAX‑based AI ecosystem, making it straightforward to embed power‑flow evaluation within AI methods for future larger‑scale and more complex power‑system operation.

Authors:Xiangyu Zou, Amir Reza Nikzad, Ilyas Farhat, John W. Simpson-Porco
Title: Frequency Nadir-Constrained Power System Restoration Planning with Energy Storage
Abstract:
Power system restoration following blackouts must ensure frequency stability throughout the recovery process. This paper proposes a frequency‑constrained mixed‑integer linear programming (MILP) framework for black‑start restoration planning in transmission systems with synchronous machines and energy storage systems. To prevent excessive frequency deviations caused by restorative actions, a frequency nadir prediction method is developed for power systems with energy storage system (ESS) integration and incorporated into a multiperiod optimization framework. The formulation ensures that frequency deviations resulting from restorative actions remain within prescribed safe limits. Furthermore, the presented framework leverages ESSs to enhance frequency security and recovery speed. Case studies on a modified IEEE 9‑bus system demonstrate that the computed restoration plan maintains frequency security, as validated through MATLAB and PSS/E simulations, while reducing restoration time through ESS coordination.

Authors:Xubin Fang, Brian M. Sadler, Rick S. Blum
Title: Receding Horizon Multi-Agent Deceptive Path Planner
Abstract:
Deceptive path planning enables autonomous agents to obscure their true goals from observers by deviating from an expected optimal path. Prior work largely solves full‑horizon, end‑to‑end optimization for single agents, which is expensive to recompute online and difficult to scale or adapt en route. We propose a unified framework for deceptive path planning using a Boltzmann distribution, computing over short‑horizon candidate trajectories within a receding‑horizon loop. By param‑ By iterating a user‑defined cost that captures deception, resources, and smoothness, and optionally includes coupling terms between agents, the framework yields stochastic policies that balance the tradeoff between optimal paths and deceptive deviation. Policies are updated locally and do not require training. The level of deception and adherence to constraints can be dynamically tuned, enabling online adaptation to changes in goals and constraints such as obstacles. This step‑by‑step tuning opens the door to new forms of dynamic deception. Simulation studies demonstrate the flexibility of our approach, maintaining deception while adapting to environmental and constraint updates, avoiding the recomputation required by full‑horizon methods, and supporting intuitive tuning via a small set of parameters

Authors:Hikaru Hoshino, Taiyo Mantani, Eiko Furutani
Title: Optimal design of solar-battery hybrid resources considering multi-market participation under weather and price uncertainty
Abstract:
The rapid growth of variable renewable energy has increased the need for flexible and efficiently coordinated energy resources. In this context, hybrid resources that combine renewable generation and battery storage within a single market‑participating entity have attracted growing attention. Such hybrid resources can have multiple revenue streams, while allocating limited power and energy capacity across multiple electricity markets including energy and ancillary services. This multi‑market coordination increases operational complexity and complicates profitability assessment, making optimal system sizing a challenging design problem. In addition, uncertainty in renewable generation and market prices makes it difficult for conventional optimization approaches to determine system designs that remain effective under stochastic operating conditions. To address these challenges, this paper proposes a deep reinforcement learning‑based co‑optimization framework for hybrid solar‑battery resources. The framework embeds system design variables directly into the policy learning process, enabling joint optimization of hybrid system sizing and coordinated multi‑market bidding strategies within a unified stochastic formulation. Case studies using historical renewable generation and market data demonstrate the effectiveness of the proposed framework in identifying economically rational hybrid system design considering multi‑market operation.

Authors:Bardh Hoxha, Oliver Schön, Hideki Okamoto, Lars Lindemann, Georgios Fainekos
Title: Vision-Based Runtime Monitoring under Varying Specifications using Semantic Latent Representations
Abstract:
We study certified runtime monitoring of past‑time signal temporal logic (ptSTL) from visual observations under partial observability. The monitor must infer safety‑relevant quantities from images and provide finite‑sample guarantees, while being \emphreusable: once trained and calibrated, it should certify any formula in a target fragment without per‑formula retraining. For fragments induced by a finite dictionary of temporal atoms, we prove that the \emphsemantic basis, the vector of atom robustness scores, is the minimum prediction target within the class of monotone, 1‑Lipschitz reusable interfaces: any formula is evaluated by a deterministic decoder derived from the parse tree, and a single conformal calibration pass certifies the entire fragment with no union bound. We also introduce a \emphrolling prediction monitor that predicts only current predicate values and reconstructs temporal history online; this is easier to learn but grows conservative at long horizons. On a pedestrian‑crossroad benchmark, rolling achieves tighter certified bounds at short horizons while the semantic‑basis monitor is up to 4‑times tighter at long horizons. We validate the presented monitors on real‑world Waymo driving data, where both monitors satisfy the conformal coverage guarantee empirically.

Authors:Jingguan Liu, Xiaomeng Ai, Cong Chen, Shaoze Li, Shichang Cui, Jiakun Fang, Jinyu Wen
Title: Reachable-Set Decomposition for Real-Time Aggregation of Multi-Zone HVAC Fleets
Abstract:
Aggregating building heating, ventilation, and air‑conditioning (HVAC) fleets provides substantial real‑time flexibility to power system operations. However, real‑time aggregation of multi‑zone HVAC fleets faces two key challenges: (i) strong coupling across zones and time makes flexibility characterization high‑dimensional and computationally demanding, and (ii) the sequential revelation of temperature states and exogenous conditions requires that decisions made at each period preserve feasibility over the remaining horizon using only currently realized information. To address these challenges, this paper proposes a reachable‑set decomposition framework comprising an offline decomposition stage and a real‑time policy. In the offline stage, backward reachable sets are formulated to encode remaining‑horizon feasibility into per‑period state constraints, so that any state within the current reachable set is guaranteed to sustain feasible operation over the entire remaining horizon. A tailored inner approximation is then developed for tractable calculation in multi‑zone‑coupled HVAC settings. In the real‑time stage, aggregate flexibility is computed efficiently via building‑level parallel linear programs followed by closed‑form Minkowski summation of power intervals, and any regulation signal within the reported flexibility interval admits a recursively feasible disaggregation. Case studies demonstrate the effectiveness of the proposed framework in aggregate flexibility characterization, disaggregation feasibility, and scalable computation.

Authors:Pradeep J, Kedarisetty Siddhardha, Ashwini Ratnoo
Title: Loiter UAV Reinsertion Guidance for Fixed-wing UAV Corridors
Abstract:
This paper considers fixed‑wing unmanned aerial vehicle (UAV) corridors comprising a main lane, a circular loiter lane for managing traffic congestion, and transit lanes connecting the two. In particular, we address the problem of conflict‑free reinsertion of UAVs from the loiter lane back into the main lane. The loiter lane contains a fixed number of equidistant virtual slots that UAVs can occupy. Reinsertion of loiter UAVs into the main lane becomes essential either due to reduced traffic in the main lane or due to a loiter UAV needing to reach its destination urgently. Given the total number of loiter slots, UAV speed limits, and the minimum safety distance, a guidance algorithm is developed to compute the required speed of a loiter UAV in the transit lane to ensure safe reinsertion. The proposed guidance and automation strategies are validated through numerical simulations.

Authors:Yizhuo Xiao, Haotian Yan, Ying Wang, Zhongpan Zhu, Yuxin Zhang, Xintao Yan, Mustafa Suphi Erden, Cheng Wang
Title: Learning Responsibility-Attributed Adversarial Scenarios for Testing Autonomous Vehicles
Abstract:
Establishing trustworthy safety assurance for autonomous driving systems (ADSs) requires evidence that failures arise from avoidable system deficiencies rather than unavoidable traffic conflicts. Current adversarial simulation methods can efficiently expose collisions, but generally lack mechanisms to distinguish these fundamentally different failure modes. Here we present CARS (Context‑Aware, Responsibility‑attributed Scenario generation), a framework that integrates responsibility attribution directly into adversarial scenario generation. CARS combines context‑aware adversary selection with a generative adversarial policy optimized in closed‑loop simulation to construct collision scenarios that are both physically feasible and diagnostically attributable. Across benchmark datasets spanning heterogeneous national traffic environments, CARS consistently discovers feasible collision scenarios with high attribution rates under multiple regulation‑prescribed careful and competent driver models. By coupling adversarial generation with normative responsibility assessment, CARS moves simulation testing beyond collision discovery toward the construction of interpretable, regulation‑aligned safety evidence for scalable ADS validation.

Authors:Ishaan Mahajan, Jon Arrizabalaga, Andrea Grillo, Fausto Vega, James Anderson, Zachary Manchester, Brian Plancher
Title: TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics
Abstract:
Semidefinite programming (SDP) provides a principled framework for convex relaxations of nonconvex geometric constraints in motion planning, yet existing solvers are too computationally expensive for real‑time control, particularly on resource‑constrained embedded systems. To address this gap, we introduce TinySDP, the first semidefinite programming solver designed for embedded systems, enabling real‑time model‑predictive control (MPC) on microcontrollers for problems with nonconvex obstacle constraints. Our approach integrates positive‑semidefinite cone projections into a cached‑Riccati‑based ADMM solver, leveraging computational structure for embedded tractability. We pair this solver with an a posteriori rank‑1 certificate that converts relaxed solutions into explicit geometric guarantees at each timestep. On challenging benchmarks, e.g., cul‑de‑sac and dynamic obstacle avoidance scenarios that induce failures in local methods, TinySDP achieves collision‑free navigation with up to 73% shorter paths than state‑of‑the‑art baselines. We validate our approach on a Crazyflie quadrotor, demonstrating that semidefinite constraints can be enforced at real‑time rates for agile embedded robotics.

Authors:Lohitvel Gopikannan, Shashi Ranjan Kumar, Abhinav Sinha
Title: Bounded-Input True Proportional Navigation for Impact-Time Control
Abstract:
This paper proposes a nonlinear guidance strategy capable of intercepting a constant‑velocity, non‑maneuvering target while strictly satisfying the prescribed bounds on the control input (commanded acceleration). Unlike conventional strategies that estimate time‑to‑go using linearization or small‑angle approximations, the proposed strategy employs true proportional‑navigation guidance (TPNG) as a baseline, which utilizes an exact time‑to‑go formulation and is applicable over a wide range of target motions. In contrast to most existing strategies, which do not incorporate control input bounds into the guidance design, the proposed approach explicitly accounts for these limits by modeling the interceptor acceleration as a dynamic variable. Based on the sliding mode control technique, an effective guidance law that achieves time‑constrained interception while accounting for bounded input is then derived. The performance of the proposed strategy is evaluated for various engagement scenarios.

Authors:Zelin Sun, Shanshan Jiang, Xiaoyu Peng, Xiang Zhu, Xiuqiang He, Hua Geng
Title: Decentralized Frequency-Domain Conditions for D-Stability with Application to DC Microgrids
Abstract:
This paper proposes a decentralized method for regional pole placement, or \mathcalD‑stability, in linearized networked systems. Existing LMI‑based methods are hindered by confidentiality concerns regarding proprietary subsystem models and the absence of communication infrastructures. To overcome these barriers, we map the target region \mathcalD of pole placement to an auxiliary left‑half plane and introduce positive functions to handle the resulting complex‑coefficient dynamics. We prove that \mathcalD‑stability is guaranteed via local frequency‑domain criteria without requiring shared subsystem models or inter‑subsystem communication. This method is then tailored to DC microgrids, where a loop transformation is utilized to reallocate the burden of stability certification, deriving a broadcastable grid code for decentralized parameter synthesis. Numerical examples verify the efficacy of the proposed method.

Authors:Clara Lucía Galimberti, Johan Peralez, Daniele Astolfi, Vincent Andrieu, Madiha Nadri
Title: Learning a Contracting KKL-observer with Local Optimal Guarantees
Abstract:
The Kazantzis‑Kravaris‑Luenberger (KKL) observer provides a general framework for nonlinear state estimation by immersing the system dynamics into a stable linear or nonlinear latent dynamics. However, the performance of KKL observers relies heavily on the specific choice of these latent dynamics, which is often heuristic. This paper proposes a methodology to learn a KKL observer that combines global stability guarantees with local optimality. We derive a condition on the latent dynamics such that the observer locally mimics the behavior of a Minimum Energy Estimator (Mortensen observer). We then employ Deep Learning to approximate the KKL transformation and the latent dynamics, using neural network architectures that structurally enforce the contraction property. The proposed strategy is validated through numerical simulations on nonlinear benchmarks, demonstrating a good performance in the presence of state and measurement noise.

Authors:Betül Mamudi, Jochen Stiasny, Jochen Cremer
Title: Sensitivity Quantification for Distribution System State Estimation
Abstract:
Pseudo‑measurements are the dominant source of uncertainty in distribution system state estimation (DSSE), yet their distributional assumptions are treated as fixed inputs by existing uncertainty quantification methods. This paper investigates whether the uncertainty bounds assumed by weighted least squares (WLS)‑based DSSE are sensitive to these distributional assumptions, and whether this sensitivity is quantifiable using the Fisher Information Matrix (FIM). We propose a diagnostic framework that compares the true Cramér‑Rao Bound (CRB) against the WLS‑assumed CRB via a per‑bus, per‑scenario ratio, computed directly from the converged WLS solution. Pseudo‑measurement distributions are varied across five types in 22 variants matched at equal spread to isolate shape effects from variance. Experiments on the CIGRE MV network across 100 operating scenarios yield three findings. First, heavy‑tailed and skewed distributions show consistently that WLS systematically overstates its uncertainty bounds. Second, the degree of miscalibration varies across buses and operating scenarios, confirming that distributional sensitivity is not uniform. Third, the CRB ratio is structurally blind to mean‑shift bias, exposing a fundamental limitation of variance‑based uncertainty diagnostics. Together, these results confirm the hypothesis and show that the choice of pseudo‑measurement distribution directly distorts the confidence limits under WLS‑based assumptions, which must be explicitly accounted for in any uncertainty‑aware DSSE method.

Authors:Aoun Abbas, Zhongda Chu, Charalambos Konstantinou
Title: Impedance-Based VSC Unit Commitment with STATCOM Support under High IBG Penetration
Abstract:
The large‑scale replacement of synchronous machines with inverter‑based generation (IBG) introduces critical challenges to both voltage and frequency stability. This work builds on a mixed‑integer second‑order cone programming (MISOCP) framework that co‑optimizes unit commitment (UC) model which embeds frequency‑nadir constraints through synthetic inertia (SI) dispatch and an SOC voltage stability boundary for IBG buses. The formulation extends by modeling a STATCOM as a reactive‑power decision variable in the same MISOCP model. A modified IEEE 30‑bus system is used to assess three scheduling strategies: (i) baseline UC with SI only, (ii) voltage‑stability‑constrained (VSC) UC with SI, and (iii) the joint UC with SI and reactive power support from IBGs. The impact of incorporating a 30~MVAr STATCOM at a weak grid location near the IBG buses is investigated. Simulation results show that the proposed framework enhances voltage security, maintains frequency‑nadir compliance, and reduces operating cost, while STATCOM integration further improves dispatch feasibility under high IBG.

Authors:Johnson Zhou, Daniel Tanneberg, Forough Habibollahi, Alon Loeffler, Kiaran Lawson, Valentina Baccetti, Kwaku Dad Abu-Bonsrah, Candice Desouza, Finn Doensen, Bradley Watmuff, Daria Kornienko, Azin Azadi, Justin Leigh Bourke, Bernhard Sendhoff, Brett J. Kagan
Title: Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation
Abstract:
Biological neural networks (BNNs) have been established as a powerful and adaptive substrate that offer the potential for incredibly energy and data efficient information processing with distinct learning mechanisms. Yet a core challenge to utilizing BNN for neurocomputation is determining the optimal encoding and decoding mechanisms between the traditional silicon computing interface and the living biology. Here, we propose an Embodied Neurocomputation framework as a systems‑level approach to this multi‑variable optimization encoding/decoding problem. We operationalize this approach through the first large‑scale parameter optimization of encoding configurations for a BNN agent performing closed‑loop navigation along an odor‑style gradient in a simulated grid‑world. Despite the relative simplicity of the task, the biological interactions gave rise to a massive multi‑combinatorial search space for optimal parameters. By considering how the components of the system are interconnected and parameterized, we evaluated approximately 1,300 parameter combinations, over 4,000 hours of real‑time agent‑environment interactions, to identify 12 configurations that consistently demonstrated learning across multiple episodes. These configurations achieved significantly higher task performances than optimized silicon‑based DQN agents under the same interaction budget. These findings represent an initial step toward robust and scalable goal‑oriented learning using BNNs. Our framework establishes a foundation for applying task‑driven neurocomputing and supports the development of field‑wide benchmarks. In the long term, this work supports the development of hybrid bio‑silicon architectures capable of efficient, adaptive and real‑time computation, including the potential for robotic control applications.

Authors:Jannis Lübsen, Annika Eichler
Title: Safe Bayesian Optimization for Uncertain Correlation Matrices in Linear Models of Co-Regionalization
Abstract:
This paper extends safety guarantees for multi‑task Bayesian optimization with uncertain co‑regionalization matrices from intrinsic co‑regionalization models to linear models of co‑regionalization. The latter allows for more flexible modeling of the inter‑task correlations by composing multiple features. We derive uniform error bounds for vector‑valued functions sampled from a Gaussian process with a linear model of co‑regionalization kernel. Furthermore, we show the potential performance gains of linear models of co‑regionalization in a numerical comparison on a safe multi‑task Bayesian optimization benchmark.

Authors:Jing Liu, Yangyang Yang, Luca Ballotta, Fangfei Li, Yang Tang, Ruggero Carli
Title: Submodular Multi-Agent Policy Learning for Online Distributed Task Allocation in Open Multi-Agent Systems
Abstract:
This paper studies multi‑agent reinforcement learning with submodular team utilities for online distributed task allocation. In this setting, each agent selects one action from a local categorical policy, so feasible joint actions form a partition matroid over agent‑action pairs. Classical multilinear extensions use independent Bernoulli sampling and therefore do not match the categorical policies executed by decentralized agents. To address this mismatch, we introduce the Partition Multilinear Extension (PME), a continuous relaxation whose value equals the expected team utility under factorized categorical policies. We prove that submodular difference rewards provide unbiased PME marginal‑gradient information and yield a stagewise score‑function policy‑gradient estimator. Based on this connection, we propose SubMAPG, a centralized‑training decentralized‑execution policy‑gradient framework with masked categorical policies and submodular difference‑reward training signals. For the associated PME marginal‑space projected stochastic‑gradient dynamics, we prove a stagewise 1/2‑approximation guarantee and sublinear dynamic regret in slowly varying environments, measured by the path length of the optimal PME marginals. To handle open systems with time‑varying agents and targets, we instantiate SubMAPG with graph neural network policies. Experiments on multi‑robot coverage and multi‑target tracking show that SubMAPG outperforms local greedy and shared‑reward baselines and is competitive with centralized myopic greedy strategies.

Authors:Alexander Rose, Lukas Theiner, Rolf Findeisen
Title: Real-time Gaussian Process based Approximate Model Predictive Trajectory Tracking Control for Autonomous Vehicles
Abstract:
Applying model predictive control on embedded systems remains challenging due to the high computational cost of solving optimal control problems. To address this limitation, computationally efficient Gaussian process approximations of the implicit model predictive control law can be employed. However, for trajectory‑tracking applications, the large amount of training data required for successful generalization across distinct reference trajectories poses a significant challenge. To improve data efficiency, we propose to transform the model into curvilinear coordinates around the reference trajectory. Secondly, we use a nominal feedforward component, allowing the Gaussian process to learn only the residual control input, making the approximation of a trajectory‑tracking controller feasible. To underline the applicability of the approach, we deploy the controller on a Raspberry Pi in a small‑scale vehicle and validate it experimentally. Compared to a model predictive control implementation using real‑time iterations, the Gaussian process based approximation computes control inputs about five times faster while achieving similar closed‑loop tracking performance.

Authors:Dhruv Shah, Jorge Cortés
Title: Subspace Pruning via Principal Vectors for Accurate Koopman-Based Approximations
Abstract:
The accuracy of Koopman operator approximations over finite‑dimensional spaces relies critically on their invariance properties. These can be rigorously quantified via the principal angles between a candidate subspace and its image under the Koopman operator. This paper proposes a unified algebraic framework for subspace pruning designed to systematically refine the invariance error. We establish the geometric equivalence between consistency‑based methods and principal‑vector pruning, and build on this insight to introduce a hybrid strategy that balances between multiple and single principal vector pruning for improved numerical stability and scalability. We derive error bounds for the retention of approximate and external eigenfunctions, demonstrating that the multi‑vector approach mitigates the numerical drift inherent to sequential pruning. To ensure scalability, we develop an efficient numerical update scheme based on rank‑one modifications that reduces the computational complexity of tracking principal angles by an order of magnitude. Finally, we exploit the subspace obtained from the pruning algorithms to build a lifted linear model for state prediction that accounts for the trade‑offs between improving invariance and minimizing state reconstruction error. Simulations demonstrate the effectiveness of our approach.

Authors:Georgios Mitsos, Dimos V. Dimarogonas, Siyuan Liu
Title: Security-Aware Planning and Control of Multi-Agent Systems with LTL Tasks
Abstract:
This paper presents a secure‑by‑construction planning and control framework for multi‑agent systems subject to linear temporal logic (LTL) specifications. The framework protects sensitive information from a passive intruder with partial observations of the agents' motion. Security in multi‑agent coordination is captured by two notions that prevent the intruder from inferring whether a secret task has been executed and from identifying the agent responsible for its execution. The proposed framework incorporates the security constraints directly into the LTL synthesis procedure by constructing a secure finite transition system that removes all paths violating these constraints. Standard LTL synthesis is then applied to this secure abstraction to generate discrete plans, which are then refined into dynamically feasible continuous trajectories. This synthesis procedure provides formal guarantees that the resulting behavior of the multi‑agent system satisfies both the global LTL specification and the security constraints. The effectiveness of the proposed framework is demonstrated through a two‑drone case study.

Authors:Enrico Dozzi, Tom Oomen, Rodrigo A. González
Title: D-Optimized Sampling Design for System Identification
Abstract:
Traditional system identification with multisine inputs relies on uniform sampling and periodic excitation to preserve Fourier orthogonality and avoid spectral leakage, limiting its use in scenarios with irregular sampling or nonperiodic inputs. This work investigates continuous‑time system identification under nonperiodic multisine excitation and nonuniform sampling. We develop a nonparametric frequency response function estimator suited to such conditions and design irregular sampling schemes that enhance the informativeness of measurements and reduce spectral leakage. The proposed sampling scheme improve the statistical accuracy of system identification in settings where periodic excitation is impractical.

Authors:Aniruddha Roy, Pavankumar Tallapragada
Title: Guaranteed cost structured control in infinite-horizon linear-quadratic cooperative differential games
Abstract:
In this paper, we consider infinite‑horizon linear‑quadratic cooperative differential games with output feedback information structure. We first demonstrate that, under output feedback information structure, computing Pareto optimal controls can be difficult even for simple low‑dimensional differential games. To address this issue, this paper introduces the concept of feedback guaranteed cost structured control (GCSC). The feedback GCSC concept is inspired from suboptimal control. At a feedback GCSC, the total weighted team cost remains below a prescribed threshold while satisfying the structural constraints. We derive fundamental properties of the feedback GCSC and the admissible weight set, including their monotonicity properties. In particular, we show that if Pareto optimal controls exist, they belong to the class of feedback GCSCs. We also quantify the suboptimalty of Pareto optimal controls (if they exist) and the proposed GCSC with respect to output feedback optimal control. Furthermore, we provide the conditions for verification and the synthesis of a feedback GCSC. Finally, we illustrate the effectiveness of the proposed approach through numerical examples, including a case study on tracking synchronization in a microgrid.

Authors:Fuyilong Ma, Lidong Zhang, Wangqianyun Tang, Waisheng Zheng, Huanhai Xin, Linbin Huang, Lennart Harnefors
Title: Revisiting Voltage and Synchronization Stability Analysis in Converter-Integrated Weak Grids: Insights from Non-Minimum-Phase Zeros
Abstract:
The increasing penetration of converter‑interfaced generators (CIGs) intensifies concerns over small‑signal voltage and synchronization stability. While existing theories treat these two stability issues distinctly, practical wisdom in contrast employs a unified and static metric, short‑circuit ratio (SCR), to assess both in weak grids. This paper aims to bridge this theory‑practice gap by introducing the insight of non‑minimum phase (NMP) zeros. First, we demonstrate that the two stability issues in weak grids originate from NMP zeros in the grid Jacobian transfer matrix: a zero at the origin corresponds to voltage instability, while low‑frequency zeros impose fundamental constraints on synchronization dynamics. The traditional SCR is proven to be a special case of our proposed novel stability metric, NMP‑zero (NMP‑Z) factor, evaluated at the rated operating point. This establishes the theoretical foundation for the empirical success of SCR. Building on this insight, we then develop a unified stability assessment method for multi‑converter systems. The method retains the simplicity of SCR, requiring only the NMP‑Z factor together with individual CIG dynamic models and enabling stability margin assessment under various operating points. Our work provides a simple yet theoretically rigorous framework for stability analysis in CIG‑integrated weak grids, with all theoretical findings and the proposed method validated through detailed time‑domain simulations.

Authors:Alessandro Melis, Tarek Bouazza, Soulaimane Berkane, Tarek Hamel
Title: Relative Pose-Velocity Estimation Using Dual IMU Measurements and Relative Position Sensing
Abstract:
This paper addresses the problem of estimating the relative pose (position and orientation) and velocity of a vehicle with respect to a moving target, where both are equipped with Inertial Measurement Units (IMUs), assuming the availability of relative position or bearing measurements. The body‑target relative dynamics are formulated on \mathbfSE_2(3) and recast into a linear time‑varying (LTV) model in the ambient space \mathbbR^15, on which a deterministic Riccati observer is designed. We analyze the uniform observability (UO) conditions required to guarantee global exponential convergence of the estimation error in the ambient space for both measurement cases. In the case of relative position measurements, UO requires only a persistence‑of‑excitation condition on the target acceleration, whereas for bearing measurements, additional conditions are required. Building on this, a nonlinear complementary filter on \mathbfSO(3) is designed to provide a smooth estimate of the orientation component of the state with almost global asymptotic stability. Finally, simulation results are provided to validate the proposed solution.

Authors:Luís Marques, Dmitry Berenson
Title: Local Conformal Calibration of Dynamics Uncertainty from Semantic Images
Abstract:
We introduce Observation‑aware Conformal Uncertainty Local‑Calibration (OCULAR), a conformal prediction‑based algorithm that uses perception information to provide uncertainty quantification guarantees for unseen test‑time environments. While previous conformal approaches lack the ability to discriminate between state‑action space regions leading to higher or lower model mismatch, and require environment‑specific data, our method uses data collected from visually similar environments to provably calibrate a given linear Gaussian dynamics model of arbitrary fidelity. The prediction regions generated from OCULAR are guaranteed to contain the future system states with, at least, a user‑set likelihood, despite both aleatoric and epistemic uncertainty ‑‑ i.e., uncertainty arising from both stochastic disturbances and lack of data. Our guarantees are non‑asymptotic and distribution‑free, not requiring strong assumptions about the unknown real system dynamics. Our calibration procedure enables distinguishing between observation‑velocity‑action inputs leading to higher and lower next‑state‑uncertainty, which is helpful for probabilistically‑safe planning. We numerically validate our algorithm on a double‑integrator system subject to random perturbations and significant model mismatch, using both a simplified sensor and a more realistic simulated camera. Our approach appropriately quantifies uncertainty both when in‑distribution and out‑of‑distribution, being comparatively volume‑efficient to baselines requiring environment‑specific data.

Authors:Yilie Huang, Xun Yu Zhou
Title: Amortized Guidance for Image Inpainting with Pretrained Diffusion Models
Abstract:
We study image inpainting with generative diffusion models. Existing methods typically either train dedicated task‑specific models, or adapt a pretrained diffusion model separately for each masked image at deployment. We introduce a middle‑ground model, termed Amortized Inpainting with Diffusion (AID), which keeps a pretrained diffusion backbone fixed, trains a small reusable guidance module offline, and then reuses it across masked images without per‑instance optimization. We formulate it as a deterministic guidance problem with a supervised terminal objective. To make this problem learnable in high dimensions, we derive an auxiliary Gaussian formulation and prove that solving this randomized problem recovers the optimal deterministic guidance field. This bridge yields a principled continuous‑time actor‑‑critic algorithm for learning the guidance module in a fully data‑driven manner. Empirically, on AFHQv2 and FFHQ under the pixel EDM pipeline and on ImageNet under the latent EDM2 pipeline, AID consistently improves the quality‑‑speed trade‑off over strong fixed‑backbone and amortized inpainting baselines across multiple mask types, while adding less than one percent trainable overhead.

Authors:Jinjun Jia, Yuchen Liao, Kang An, Xun Yan, Tiedong Zhang, Dapeng Jiang
Title: Port-Hamiltonian Systems with Dissipation Potential: Modelling and Trajectory Tracking Control
Abstract:
Port‑Hamiltonian systems (PHS) and interconnection and damping assignment passivity‑based control (IDA‑PBC) have achieved broad success in modelling and stabilisation of physical systems. However, the absence of a dedicated scalar potential for the momentum channel forces any modification of the momentum‑dependent dynamics to proceed indirectly through the interconnection and damping matrices, rendering the matching partial differential equation (PDE) difficult to solve and complicating extensions to trajectory tracking. This paper proposes a port‑Hamiltonian system with dissipation potential (PHS‑DP), in which the damping matrix is replaced by scalar convex dissipation potentials, providing independent scalar objects for the momentum and auxiliary state channels and restoring the variational symmetry between stored and dissipated energy. Building on this framework, Dual Potential Shaping Control (DPSC) achieves trajectory tracking by sequentially shaping the potential energy and dissipation potentials without modifying the interconnection structure. Contraction of the closed‑loop cascade is established via a hierarchical contraction argument, and the matching condition is satisfied automatically for any admissible choice of shaped potentials, requiring no PDE to be solved. In contrast to existing PDE‑free energy shaping approaches, which achieve this by abandoning the port‑Hamiltonian closed‑loop structure and sacrificing physical interpretability, the proposed framework preserves the interconnection structure and retains a transparent energy‑based interpretation at every stage of the design. Validation on a magnetic levitation system demonstrates tracking performance comparable to timed IDA‑PBC with substantially reduced design complexity.

Authors:Eduardo Mapurunga, Alexandre Sanfelici Bazanella
Title: Optimal excitation and measurement patterns for networks with tree topology
Abstract:
In this work we evaluate the excitation and measurement patterns (EMP) for networks with tree topology. We investigate guidelines for the selection of the minimal EMPs, i.e. those with the least number of excited and measured nodes combined, for which the accuracy obtained, in terms of the trace of the asymptotic covariance matrix, is optimal. We introduce the concept of partial information matrix as a means to systematically obtain the information matrix for any dynamic network. For a specific tree class, called cross, we show that the accuracy of a particular module depends on the magnitude of the parameters to be estimated. Furthermore, when all factors are equal, it is best to excite. %we show that for small magnitudes of this parameter, it is best to excite. We extend a topological condition for branches under which the accuracy of a particular module of the network is independent of the other parameters from the tree. We provide a numerical analysis showing that our guidelines could be used as a selection tool for minimal EMPs for tree networks.

Authors:Fatemeh Fardno, S. Rasoul Etesami
Title: Dynamic Transaction Scheduling and Pricing in the Ethereum Mempool
Abstract:
The Ethereum blockchain utilizes the EIP‑1559 algorithm to manage transaction inclusion and block assembly. However, EIP‑1559 and much of the existing literature study this problem from a static perspective, focusing on price evolution without modelling transaction dynamics within the mempool. Motivated by this limitation, we study a dynamic transaction scheduling problem in which transactions with heterogeneous sizes and per‑unit values arrive over time and remain in the mempool until scheduled. To capture the stochastic mempool evolution, we formulate the problem as a Markov Decision Process (MDP) whose state represents the mempool configuration and whose actions correspond to block prices. We first provide a primal‑dual interpretation of the static EIP‑1559 mechanism, showing that block prices arise naturally as dual variables of a social‑welfare maximization problem. Building on this perspective, we extend the framework to the dynamic setting and formulate an objective that maximizes long‑run discounted reward while incorporating holding costs and overshoot penalties. We then employ a Natural Policy Gradient (NPG) algorithm to compute the optimal policy. Our results show that dynamic pricing stabilizes the mempool while maximizing long‑run discounted reward. In particular, as the overshoot penalty increases, the average scheduled transaction volume converges to the target block capacity, and the resulting NPG updates closely resemble the EIP‑1559 price update rule. Finally, we study two special cases of the MDP formulation: homogeneous transactions and uniform arrivals. In the homogeneous setting, where the protocol directly controls scheduled volume, we show that the optimal policy has a threshold structure. We then propose a bang‑bang pricing mechanism for uniform arrivals and derive a lower bound on the block capacity needed to ensure system stability.

Authors:Maximino Linares, Guillaume Doras, Thomas Hélie
Title: Identifying the nonlinear string dynamics with port-Hamiltonian neural networks
Abstract:
Hybrid machine learning combines physical knowledge with data‑driven models to enhance interpretability and performance. In this context, Port‑Hamiltonian Systems (PHS), which generalize Hamiltonian mechanics to describe open, non‑autonomous dynamical systems, have been successfully integrated with neural networks under the name Port‑Hamiltonian Neural Networks (PHNNs). While the ability of PHNNs to identify Hamiltonian ordinary differential equation (ODE) systems has already been demonstrated, their application to learning Hamiltonian partial differential equation (PDE) systems remains largely unexplored. This limitation restricts their use in musical acoustics, where instruments are typically modeled as distributed parameter systems governed by PDEs. In this work, we demonstrate how to learn the nonlinear string dynamics from data in a physically‑consistent framework through a PHNN extension to PDEs. By constructing structured neural network architectures based on PHS, we can recover both the Hamiltonian governing the string and the dissipation affecting it. This approach outperforms baseline, non‑physics‑informed methods in terms of both accuracy and interpretability. Numerical experiments using synthetic data demonstrate the ability of the proposed PHNN model to identify and emulate the nonlinear dynamics of the system.

Authors:Longchen Niu, Gennaro Notomista
Title: Safe and Energy-Aware Decentralized PDE-Constrained Optimization-Based Control of Multi-UAVs for Persistent Wildfire Suppression
Abstract:
This paper presents a safe and energy‑aware optimization‑based control framework for multi‑UAV wildfire suppression under localization and motion uncertainties. We first develop a centralized density‑based controller that couples UAV motion and water deployment in a wildfire‑specific control Lyapunov function. This framework is then extended to a decentralized setting suitable for large‑scale operations using only local information. The controllers use control barrier function constraints to enforce both danger zone avoidance and the ability to reach a charging region. Simulations and real quadcopter experiments demonstrate the controller's effectiveness in fire suppression while preserving safety and energy sufficiency over multiple charge cycles.

Authors:Boming Liu, Jin Dong, Jamie Lian
Title: Grid-Orch: An LLM-Powered Orchestrator for Distribution Grid Simulation and Analytics
Abstract:
The power distribution engineering workforce faces a projected shortage of up to 1.5 million engineers by 2030, creating urgent demand for more accessible analysis tools. This paper introduces Grid‑Orch, a framework that bridges Large Language Models (LLMs) and power system simulation through the Model Context Protocol (MCP), enabling engineers to perform complex distribution analyses via natural language. Using OpenDSS as the reference implementation, Grid‑Orch provides 36 domain‑specific tools across eleven categories, covering power flow, voltage analysis, quasi‑static time series (QSTS) simulation, and automated optimization. A provider‑agnostic LLM layer supports both cloud‑hosted (Gemini, Claude) and locally deployed (Ollama, llama‑cpp) models, enabling air‑gapped operation for security‑sensitive utility environments. Three optimization skills, capacitor placement, voltage violation analysis, and overvoltage mitigation, extend the platform beyond single‑tool queries to multi‑step engineering workflows. Grid‑Orch is delivered as an interactive web platform with chat‑based interaction, a QSTS dashboard, and feeder topology visualization, and renders simulation results inline. Workflow demonstrations show that distribution analyses formerly requiring hours of scripting, such as distributed energy resource (DER) interconnection screening, complete in under two minutes through natural language, producing numerically identical results to direct OpenDSS scripting.

Authors:Ivana Vasiljević, Nikola Petrović, Aleksandra Lekić
Title: Investigation of Chaotic Behavior in Clapp Oscillator
Abstract:
In this paper we investigate the chaotic behavior of the class of oscillators denoted as Clapp oscillators. Clapp oscillator is a simple oscillator containing one transistor and a few reactive elements ‑ inductors and capacitors. This oscilllator is chosen for its design simplicity and a good performance. Oscillator with chaotic behavior can be used to construct chaotic radar. For that matter, in this paper is investigated approach for construction of the chaotic Clapp oscillator, which can be further verified experimentally using microstrip technology.

Authors:Sophie Hall, Florian Dörfler, Timm Faulwasser
Title: Towards Closed-loop Stability of Nonlinear Receding Horizon Games
Abstract:
We analyze Receding Horizon Games without any MPC‑like terminal ingredients. We show that recursive feasibility can be inferred from the turnpike phenomenon under mild assumptions. Moreover, we prove sufficient conditions for practical asymptotic convergence of the closed‑loop trajectories, and we discuss how the gap towards practical asymptotic stability may be closed. We use numerical examples to show that the closed‑loop region of attraction around the steady‑state GNE shrinks exponentially with the horizon length, a behavior previously known only for model predictive control. Further, we apply a linear end penalty and demonstrate in numerical simulations that it suppresses the leaving arc and ensures asymptotic convergence to the steady‑state GNE.

Authors:Anubhav Gupta
Title: Basilisk and Docker for Reproducible GN&C Simulation: A Workflow Reference
Abstract:
Basilisk is an open‑source astrodynamics simulation framework widely used for spacecraft guidance, navigation, and control (GN&C) research and development. Despite its flexibility and computational capabilities, configuring Basilisk consistently across heterogeneous development environments presents practical challenges due to dependency management, operating system compatibility, and software configuration requirements. This paper presents a Docker‑based containerization workflow for Basilisk that encapsulates the complete build environment, dependencies, and simulation infrastructure within a portable container image. The workflow is demonstrated through a progression of simulation scenarios of increasing complexity, from standalone orbital dynamics scripts to BSKSim‑based attitude dynamics and control simulations with Monte Carlo analysis. The BSKSim class hierarchy, dynamics model architecture, flight software implementation, and scenario execution patterns are described in detail. The presented workflow provides a self‑contained implementation reference for GN&C engineers and researchers seeking reproducible and portable Basilisk simulation environments. This work expands upon a workshop presentation delivered at the 46th Rocky Mountain AAS GN&C Conference, February 2024, available at https://doi.org/10.5281/zenodo.15008785.

Authors:Davi G. Accioli, Jerome Jouffroy
Title: Estimation Problems and the Modulating Function Method: The Algebra of Modulating Functions
Abstract:
State and parameter estimation, along with fault detection, are three crucial estimation problems within the control systems community. Although different approaches have been proposed for each type of problem, the modulating function method proposes a more unified approach to all three problem classes, being used for state and parameter estimation of lumped systems, fault detection, and estimation of distributed and fractional systems. At the core of the method is the modulating function: a function that evaluates to 0 at the left or right boundaries up to a certain order of derivatives. By selecting the modulating functions, one directly determines the filter characteristics, and, for that reason, different function families have been proposed over the years. Nevertheless, many families of modulating functions are given in a rather similar mathematical structure. In light of these structures, this paper formally discusses the algebraic properties of modulating functions, and, after formalizing the closedness and group properties of modulating functions, a simple algorithm to construct new modulating functions is proposed, discussed, and illustrated with the construction of the newly introduced logarithmic modulating function families and 3 non‑analytic modulating function families. Moreover, the fact that total modulating functions form a vector space and an algebra is exploited to construct orthonormal modulating functions, which are then used for the parameter estimation of a boat's roll dynamics, effectively avoiding matrix inversion issues.

Authors:Hendrik Schäfke, Daniel O. M. Weber, Askar Vagapov, Christoph Schweers, Thomas Seel, Simon F. G. Ehlers
Title: Neural Network-Based Virtual Wheel-Speed Sensor for Enhanced Low-Velocity State Estimation
Abstract:
Accurate wheel speed information is crucial for vehicle control and state estimation. Conventional sensors suffer from quantization and latency, especially at low velocities, while motor‑speed signals in electric vehicles are distorted by drivetrain torsion. This work presents a neural‑network‑based virtual wheel‑speed sensor that fuses wheel‑speed and motor‑speed signals to reduce errors from both sources. Validated on real‑world Volkswagen ID.7 data, the real‑time capable model achieves an error reduction of up to 85% compared to the production sensor and 47% compared to an optimized zero‑phase filter, providing a smooth signal for driver‑assistance functions. The results demonstrate robust generalization across diverse real‑world maneuvers within the vehicle platform.

Authors:Roel Drenth, Jan H. Hoekstra, Maarten Schoukens, Roland Tóth
Title: Efficient Learning of Affine and Rational Dependency LPV Models With Linear Fractional Representation
Abstract:
Identifying control‑friendly models of nonlinear systems remains one of the major challenges at the intersection of system identification and control. The Linear Parameter‑Varying (LPV) framework offers a promising solution, but existing identification methods often rely on model structures with affine scheduling dependency. Instead, this work proposes the use of LPV models with Linear Fractional Representation (LFR) admitting a rational scheduling‑dependency, capable of modelling complex nonlinear systems with fewer scheduling variables compared to affine models. This work introduces a direct parameterization to ensure well‑posedness of rational LPV‑LFR models, which by joint‑estimation of an LPV plant and scheduling map, using only input‑output data, is capable of modelling complex nonlinear systems. Accuracy of the proposed approach is shown on two simulation examples.

Authors:Jino George, Chang Liu
Title: Structured input-output analysis of oblique turbulent bands in Waleffe flow
Abstract:
This work employs structured input‑output analysis (SIOA) to study Waleffe flow. The SIOA framework employs structured uncertainty to include the componentwise structure of nonlinearity in Navier‑Stokes equations, and SIOA quantifies the flow response using structured singular values. The structured input‑output analysis identifies the wavelength and inclination angle of oblique turbulent bands observed in large‑domain direct numerical simulations. The structured input‑output response scales over Reynolds number as ～ Re^1.7.

Authors:Kaspar Schmerling, Andreas Kugi, Andreas Deutschmann-Olek
Title: Optimal State Preparation for Impulse Estimation in Gaussian Quantum Systems
Abstract:
We present an optimal control‑based strategy to enhance the estimation of impulse‑like disturbances in continuously monitored linear classical and quantum systems by exploiting non‑equilibrium states. Using optimal estimation techniques for linear Gaussian systems to collect information from the temporal vicinity of the disturbance, we cast the minimization of disturbance estimation uncertainty as a nonlinear optimal control problem over time‑dependent system parameters. The resulting method dynamically shapes the estimation covariances through parametric modulation, maximizing information gain at a known impulse time. This differs fundamentally from conventional squeezing protocols using periodic modulation that effectively degrade inference of impulse‑like disturbances. Applied to nanomechanical resonators and levitated nanoparticles, optimal parametric driving reduces estimation variance by up to a factor of two relative to steady‑state operation

Authors:Youwei Yu, Jionghao Wang, Zhengming Yu, Wenping Wang, Lantao Liu
Title: Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration
Abstract:
Designing learnable information‑theoretic objectives for robot exploration remains challenging. Such objectives aim to guide exploration toward data that reduces uncertainty in model parameters, yet it is often unclear what information the collected data can actually reveal. Although reinforcement learning (RL) can optimize a given objective, constructing objectives that reflect parametric learnability is difficult in high‑dimensional robotic systems. Many parameter directions are weakly observable or unidentifiable, and even when identifiable directions are selected, omitted directions can still influence exploration and distort information measures. To address this challenge, we propose Quasi‑Optimal Experimental Design (Q\footnotesize OED), an adaptive information objective grounded in optimal experimental design. Q\footnotesize OED (i) performs eigenspace analysis of the Fisher information matrix to identify an observable subspace and select identifiable parameter directions, and (ii) modifies the exploration objective to emphasize these directions while suppressing nuisance effects from non‑critical parameters. Under bounded nuisance influence and limited coupling between critical and nuisance directions, Q\footnotesize OED provides a constant‑factor approximation to the ideal information objective that explores all parameters. We evaluate Q\footnotesize OED on simulated and real‑world navigation and manipulation tasks, where identifiable‑direction selection and nuisance suppression yield performance improvements of \SI35.23\percent and \SI21.98\percent, respectively. When integrated as an exploration objective in model‑based policy optimization, Q\footnotesize OED further improves policy performance over established RL baselines.

Authors:Ali Sidar Yilmaz, Buday Turan, Lukas Pries, Markus Ryll
Title: Control of Fully Actuated Aerial Vehicles: A Comparison of Model-based and Sensor-based Dynamic Inversion
Abstract:
Fully actuated multirotor platforms decouple translational force generation from vehicle attitude, enabling independent control of position and orientation and shifting performance limitations from attitude authority to actuator dynamics and control effectiveness. This paper compares a model‑based nonlinear dynamic inversion controller (geometric NDI) with a sensor‑based incremental dynamic inversion controller (INDI) on a fixed‑tilt fully actuated hexarotor. Both controllers share an identical outer‑loop structure and are both executed at 500 Hz; therefore, performance differences can be attributed primarily to the inversion strategy. Controller performance is evaluated in five experiments covering attitude step tracking under nominal conditions and under a 50% mismatch in the rotor force coefficient, hover disturbance rejection under an external lateral load, waypoint tracking in the presence of wind gust disturbances, reduced control frequency, and injected sensor degradation. The results show that INDI offers clear advantages under parameter mismatch, gust disturbances, and sensor degradation, and maintains lower position errors across the controller‑frequency sweep. However, its advantages are not universal: geometric NDI yields better attitude tracking at reduced control frequencies. To the authors' best knowledge, this work presents the first experimental validation of a full pose tracking INDI controller with decoupled translational and rotational dynamics. These findings highlight the trade‑off between measurement‑based and model‑based inversion for robust control and rapid deployment of fully actuated UAVs.

Authors:Shaun Azzopardi, Luca Di Stefano, Nir Piterman
Title: sweap: Reactive Synthesis for Infinite-State Integer Problems
Abstract:
Recent years have seen a significant increase in the interest in reactive synthesis from specifications that relate to infinite state spaces. We present sweap, a tool for synthesis of infinite‑state Linear Integer Arithmetic reactive systems. sweap implements a CEGAR approach, relying on state‑of‑the‑art finite‑state synthesis tools as black boxes to solve abstract synthesis problems. sweap supports most common input formalisms for infinite‑state reactive‑synthesis problems: Temporal Stream Logic Modulo Theories, Reactive Program Games, the bespoke input of the ISSY tool, and our own bespoke input. We present a mature version of sweap with novel features: a dual abstraction approach that improves its capabilities in proving unrealisability, support for nondeterministic and unbounded updates, more general initialization of variables, and equirealisable reductions for optimisation. Experimental evaluation shows that sweap outperforms its only competitor in this domain.

Authors:Mohammad Khoshkdahan, John Pravin Arockiasamy, Andy Flores Comeca, Alexey Vinel
Title: Cooperative Robotics Reinforced by Collective Perception for Traffic Moderation
Abstract:
Collisions at non‑line‑of‑sight (NLOS) intersections remain a major safety concern because drivers have limited visibility of approaching traffic. V2X based warnings can reduce these risks, yet many vehicles are not equipped with V2X and drivers may ignore in vehicle alerts. Collective perception (CP) can compensate for low V2X penetration by extending the awareness of connected vehicles, but it cannot influence unconnected vehicles. To fill this gap, our work introduces a complementary concept that adds a cooperative humanoid robot as an active traffic moderator capable of physically stopping a vehicle that attempts to merge into an unseen traffic stream. The system operates on two parallel perception pathways. A dual camera infrastructure unit detects the position, speed and motion of approaching vehicles and transmits this information to the robot as a collective perception message (CPM). The robot also receives cooperative awareness messages (CAM) from connected vehicles through its onboard V2X unit and can act as a relay for decentralized environmental notification messages (DENM) when safety events originate elsewhere along the road. A fusion module combines these streams to maintain a robust real time view of the main road. A Zone of Danger (ZoD) is defined and used to predict whether an approaching vehicle creates a collision risk for a merging road user. When such a risk is detected, the robot issues a human‑like STOP gesture and blocks the merging path until the hazard disappears. The full system was deployed at the Future Mobility Park (FMP) in Rotterdam. Experiments show that the combined vision and V2X perception allows the robot to detect approaching vehicles early, predict hazards reliably and prevent unsafe merges in real world NLOS conditions.

Authors:Andrei Maalberg, Axel Neumann, Pablo Echevarria, Andriy Ushakov, Jens Knobloch
Title: KIND: A Kalman-Inspired Adaptive Estimator for SRF Cavity Detuning
Abstract:
Superconducting radio frequency cavities with a high quality factor enable energy‑efficient accelerator operation but are very sensitive to mechanical disturbances that detune their resonance. Accurate detuning estimation is therefore essential for efficient resonance control and stable beam conditions. This paper introduces Kalman‑Inspired Neural Decomposition (KIND), a data‑driven estimator that fuses a Dynamic Mode Decomposition model for stationary modal behavior with a Transformer‑based predictor for transient dynamics. KIND further outputs learned uncertainty signals that indicate regime changes, enabling anomaly detection. Using operational cavity data, we compare KIND with a classical Kalman filtering baseline and discuss its potential as a foundation for future uncertainty‑aware, forecast‑based control.

Authors:Eni Solomon Laughter
Title: Lane-Aware Graph Attention Network for Multi-Vehicle Trajectory Prediction in Expressway Merge Zones
Abstract:
Accurate multi‑vehicle trajectory prediction in expressway merge and diverge areas is fundamental to the decision‑making frameworks of autonomous vehicle systems. However, the majority of existing graph‑based prediction models are developed and validated on mainline freeway segments and do not address the geometrically distinct interaction structures that characterize merge zones. Furthermore, standard evaluation protocols rely exclusively on displacement error metrics, leaving the safety consequences of predicted trajectories unquantified. This paper proposes a Lane‑Aware Graph Attention Network (LA‑GAT) that encodes vehicle interaction within dynamic scene graphs, augmented with a trainable lane‑relationship attention bias that prioritizes merge‑conflict interactions from the outset of training. The model is pre‑trained on the raw NGSIM US‑101 and I‑80 datasets and subsequently fine‑tuned on UAV‑captured UTE SQM‑W‑1 trajectory data from a Chinese expressway merge area, with final evaluation on the held‑out SQM‑W‑2 dataset. Evaluation spans both displacement metrics (ADE, FDE at 1s, 3s, 5s horizons) and surrogate safety measures (TTC violation rate, DRAC exceedance rate, collision rate). Fine‑tuned results on SQM‑W‑2 yield ADE of 0.865 m at 1s and 2.518 m at 3s, demonstrating that drone‑informed fine‑tuning substantially reduces the cross‑dataset transfer gap. The deliberate use of unfiltered NGSIM data is shown to characterize raw‑condition generalization limits, with the performance degradation attributed to the well‑documented measurement errors in that dataset.

Authors:Atul Sharma, Chayan Kumar Paul, S. Janardhanan
Title: Observer-Based Fixed-Time Nested Sliding-Mode Control for Tip-Position Regulation of a Single-Link Flexible Manipulator
Abstract:
This paper presents a novel position control strategy for a single‑link flexible manipulator, tailored for applications where precise position must be achieved within strict time constraints. To accomplish this objective, firstly, a nested non‑singular terminal sliding mode controller is designed for the system, enabling precise and robust control. Furthermore, a fixed‑time sliding mode observer is designed to estimate unmeasured system states accurately in a fixed time, thereby enabling closed‑loop control implementation. A stability analysis is presented to guarantee the robustness and efficacy of the proposed composite control algorithm. The effectiveness of the proposed fixed‑time controller is demonstrated through numerical simulation on accuracy, stability, and convergence speed. The proposed controller's performance is also compared with that of other state‑of‑the‑art control schemes. The proposed controller is further validated through experiments conducted on a real hardware setup.

Authors:Yuhao Wu, Tung-Ling Li, Hongliang Liu
Title: Behavioral Integrity Verification for AI Agent Skills
Abstract:
Agent skills extend LLM agents with privileged third‑party capabilities such as filesystem access, credentials, network calls, and shell execution. Existing safety work catches malicious prompts and risky runtime actions, but the skill artifact itself goes unverified. We formalize this as the behavioral integrity verification (BIV) problem: a typed set comparison between declared and actual capabilities over a shared taxonomy that bridges code, instructions, and metadata. The BIV framework instantiates this comparison by pairing deterministic code analysis with LLM‑assisted capability extraction. The resulting structured evidence supports three downstream analyses: deviation taxonomy, root‑cause classification, and malicious‑skill detection. On 49,943 skills from the OpenClaw registry, the deviation taxonomy reveals a pervasive description‑implementation gap: 80.0% of skills deviate from declared behavior, with four novel compound‑threat categories surfaced. Root‑cause classification finds that deviations are mostly oversight, not malice: 81.1% trace to developer oversight and 18.9% to adversarial intent, with 5.0% of skills carrying predicted multi‑stage attack chains. On a 906‑skill malicious‑skill detection benchmark, BIV reaches an F1 of 0.946, outperforming state‑of‑the‑art rule‑based and single‑pass LLM baselines. These results demonstrate behavioral integrity auditing for agent skills at scale.

Authors:Chen Zhao, Parsa Poorsistani, Mohammad Goudarzi, Tawfiq Islam, Adel N. Toosi
Title: GraphFlash: Enabling Fast and Elastic Graph Processing on Serverless Infrastructure
Abstract:
Graph processing systems are essential for analyzing large‑scale data with complex relationships, yet most existing frameworks rely on statically provisioned clusters, resulting in poor elasticity and inefficient resource utilization under dynamic workloads. Serverless computing offers automatic scaling and fine‑grained billing, but existing serverless graph systems suffer from performance limitations due to inefficient state management and high communication overhead through external storage. We present GraphFlash, a fast and elastic graph processing framework built on serverless infrastructure. GraphFlash adopts a subgraph‑centric programming model and leverages shared external storage for coordination and communication, enabling stateless, fine‑grained function execution. It supports two execution modes: rotating mode for resource‑constrained environments and pinned mode for higher performance when resources are sufficient. To address serverless limitations, GraphFlash introduces system‑level optimizations, including partition‑aware key aggregation, intra‑function partition co‑location, and superstep‑aware activation. Across multiple graph algorithms and datasets, GraphFlash outperforms existing serverless‑compatible systems by up to 127x in execution time and reduces resource consumption by up to 98% under higher‑resource configurations, while matching the performance of traditional distributed frameworks on large workloads. Even with limited resources, it achieves up to 48x speedup and 99.97% cost reduction over prior serverless solutions, demonstrating that GraphFlash makes serverless graph processing practical and performant.

Authors:Zijiang Yan, Hao Zhou, Wael Jaafar, Jianhua Pei, Ping Wang, Halim Yanikomeroglu, Hina Tabassum
Title: Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity
Abstract:
Uncrewed aerial vehicles (UAVs) are increasingly deployed in complex networked environments, yet the joint optimization of multi‑UAV motion control and connectivity remains a fundamental challenge. In this paper, we study a multi‑UAV system operating in an integrated terrestrial and non‑terrestrial network (ITNTN) comprising terrestrial base stations and high‑altitude platform stations (HAPS). We consider a three‑dimensional (3D) aerial highway scenario where UAVs must adapt their motion to ensure collision avoidance, efficient traffic flow, and reliable communication under dynamic and partially observable conditions. We first model the problem as a hierarchical multi‑objective partially observable Markov decision process (H‑MO‑POMDP), capturing the coupling between control and communication objectives. Based on this formulation, we propose a large language model (LLM)‑driven hierarchical multi‑rate control framework. At the global level, an LLM‑based controller on the HAPS performs long‑term planning for load balancing and handover decisions. At the local level, each UAV employs a hybrid controller that integrates a slow‑timescale LLM for high‑level spatial reasoning with a reinforcement learning agent for faster UAV‑to‑infrastructure (U2I) communication and motion control. We further develop a high‑fidelity 3D simulation platform by integrating the gym‑pybullet‑drones environment with 3GPP‑compliant RF/THz channel models. Numerical results demonstrate that the proposed framework significantly outperforms state‑of‑the‑art baselines, achieving a 14% increase in transportation efficiency and a 25% improvement in telecommunication throughput. Additionally, it achieves a 23% reduction in physical collision rates, demonstrating strong handover stability and zero‑shot generalization in dynamic scenarios.

Authors:Mehmet Ali Ferah, Tufan Kumbasar
Title: Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification
Abstract:
System identification (SysID) is critical for modeling dynamical systems from experimental data, yet traditional approaches often fail to capture nonlinear behaviors. While deep learning offers powerful tools for modeling such dynamics, incorporating uncertainty quantification is essential to ensure reliable predictions. This paper presents a systematic framework for constructing and training interval Neural Networks (INNs) for uncertainty‑aware SysID. By extending crisp neural networks into interval counterparts, we develop Interval LSTM and NODE models that propagate uncertainty through interval arithmetic without probabilistic assumptions. This design allows them to represent uncertainty and produce prediction intervals. For training, we propose two strategies: Cascade INN (C‑INN), a two‑stage approach converting a trained crisp NN into an INN, and Joint INN (J‑INN), a one‑stage framework jointly optimizing prediction accuracy and interval precision. Both strategies employ uncertainty‑aware loss functions and parameterization tricks to ensure reliable learning. Comprehensive experiments on multiple SysID datasets demonstrate the effectiveness of both approaches and benchmark their performance against well‑established uncertainty‑aware baselines: C‑INN achieves superior point prediction accuracy, whereas J‑INN yields more accurate and better‑calibrated prediction intervals. Furthermore, to reveal how uncertainty is represented across model parameters, the concept of channel‑wise elasticity is introduced, which is used to identify distinct patterns across the two training strategies. The results of this study demonstrate that the proposed framework effectively integrates deep learning with uncertainty‑aware modeling.

Authors:Kaoru Teranishi, Jihoon Suh, Takashi Tanaka
Title: Experimental Examination of Secure Two-Party Controller Computation
Abstract:
A secure two‑party computation protocol for running dynamic controllers over secret sharing has recently been proposed. Unlike encrypted control schemes based on homomorphic encryption, this protocol enables operating dynamic controllers for an infinite time horizon without controller‑state decryption, controller‑state reset, or input re‑encryption. However, the two‑party setting introduces additional online communication between the computing parties, which may hinder real‑time feasibility. In this study, we demonstrate the feasibility of the protocol through implementation on a commercial cloud platform with an inverted pendulum testbed. Experimental results show that the proposed protocol successfully stabilized the pendulum despite the online communication overhead.

Authors:Jingxian Wang, Chen Yu, David Matthews, Emma Alexander, Sam Kriegman, Michael Rubenstein
Title: Computational Design of a Low-Visibility UAV Using a Human-Aligned Perceptual Metric
Abstract:
We introduce Phantom Twist, a type of single‑propeller UAV designed to achieve low visibility through high‑speed spinning and the exploitation of motion blur. We develop a two‑stage automated design pipeline that optimizes the placement of functional components including batteries, control PCB, motor‑propeller assembly, and counterweights. The pipeline minimizes visibility as measured by a human‑aligned perceptual metric (LPIPS) while strictly satisfying inertial and aerodynamic constraints required for stable flight. We validate this approach through fabrication and flight testing of multiple prototypes. These tests confirm that our pipeline produces stable, controllable designs and that the optimized UAV exhibits significantly reduced visual perceptibility compared to conventional quadcopters.

Authors:Meysam Sohani Darban, Fariborz Lohiri Pour, Dong S. Ha, Jeffrey S. Walling
Title: 236 μW Direct-RF PLL-Free Multi-PSK Transmitter Using Oscillator-Based Phase Synthesis
Abstract:
This paper presents a compact, low‑power, direct RF multi‑phase‑shift keying (PSK) transmitter (TX) that eliminates the need for a phase‑locked loop (PLL) by performing phase modulation directly within a ring oscillator. The proposed architecture exploits synchronized charge extraction at the oscillator's transition points to induce controlled phase shifts while maintaining constant amplitude and frequency. A time‑domain multi‑triggering technique is introduced to enable reconfigurable multi‑mode modulation, supporting 16‑PSK, 8‑PSK, QPSK, and BPSK within a unified hardware structure. The TX circuit is fabricated in a 22‑nm FD‑SOI process and operates in the ISM band at 2.4 GHz. Measurement results indicate a symbol rate of 2 MSps with a maximum error vector magnitude (EVM) of 5.13% rms. The core TX occupies 23 × 17.6 μm2 and consumes 236 μW, excluding the output driver, which delivers ‑10 dBm output power over a 60 MHz bandwidth. The proposed design achieves a favorable trade‑off between power consumption, circuit complexity, and modulation flexibility, making it well‑suited for low‑power wireless applications.

Authors:Nivar Anwer, Hans Riess, Matthew Hale
Title: Multi-Agent System Identification with Nonlinear Sheaf Diffusion
Abstract:
Local interaction laws governing multi‑agent systems can be difficult to recover from trajectory data, even when the dynamics are observed faithfully. In systems governed by a nonlinear sheaf Laplacian ‑‑ a generalization of the graph Laplacian accommodating heterogeneous state spaces and asymmetric communication channels ‑‑ the coordination law is encoded by edge potential functions whose gradients produce the inter‑agent forces. Because trajectory observations record node‑state evolution, they expose only the aggregate effect of the edge forces at each node: distinct interaction laws that agree at the node level are indistinguishable from trajectory data alone. We show that the fundamental obstruction to recovery is topological, measured by sheaf cohomology, and that unique recovery from an unconstrained function class is possible if and only if this cohomology vanishes. When the obstruction is nontrivial, we show that recovery within a finite‑dimensional parameterized class is possible precisely when a data‑dependent information matrix is positive definite. Experiments validate the theory and illustrate that accurate trajectory reproduction need not certify recovery of the underlying interaction law.

Authors:Abolfazl Mohammadi, Merijn Van Deyck, Geraint Chaffey, Dirk Van Hertem
Title: Hybrid Analytical--EMT Method for HVDC Protection System Component-Level Design
Abstract:
Protection system design for multi‑terminal HVDC grids is challenging due to the complexity of the system and the often conflicting design requirements. Effective specification of protection component parameters (e.g., DC circuit breakers and series DC inductors) during component‑level design is crucial due to interdependencies among components, the need for detailed modeling, and the complex interactions between the protection system and converter control systems. Both analytical and simulation‑based approaches have been proposed as solutions for component‑level design. However, analytical methods may not accurately represent system behavior given that approximation is necessary, and simulation‑based approaches often require extensive computational effort and time. Therefore, this paper presents an efficient systematic design method, combining both approaches. First, a fundamental analytical solution is derived to consider the protection system requirements. Then, a hybrid analytical‑‑EMT methodology is proposed to accelerate convergence toward the required design parameters, after which detailed models are applied to ensure accuracy in design and validation. The approach is applicable to component‑level design for both fully and partially selective protection strategies in HVDC grids.

Authors:Miguel Alejandro Mantilla, Ana Maria Montañez, Daniel Escobar Saltarén, Sofía C. Henao, María B. Salazar-Sánchez
Title: Design of a validation methodology for a prototype wristband for capturing muscle signals and upper limb movement
Abstract:
Surface electromyography (sEMG) is a noninvasive technique widely used to control myoelectric prostheses and other human‑machine interfaces. However, the high cost of commercial systems limits accessibility in academic and research environments, especially in developing countries. This study presents a validation protocol for a low‑cost eight‑electrode sEMG wristband prototype based on IEC 60601 and ANSI/AAMI EC13 standards. The protocol includes electrical safety tests, such as leakage current measurement, insulation evaluation, and continuity verification between electrodes and circuits. Functional performance was evaluated by comparing signals acquired with the prototype against those obtained from a commercial reference device (PortiLab2) using Pearson correlation, Bland‑Altman analysis, and mean squared error. Additional tests included signal stability during rest and contraction, UART and Bluetooth communication, frequency response, mechanical characterization of the casing, and user comfort assessment. Results showed leakage currents between 11.4 uA and 13.5 uA, adequate insulation, stable signal acquisition, and high correlation with the reference system (r > 0.85). Reliable wireless transmission without packet loss was also observed. Limitations included power supply constraints during wireless testing and discrepancies in the frequency response at high‑gain stages compared with simulations. Mechanical tests showed elastic behavior of the casing under loads up to 98 N. The proposed protocol provides a practical and reproducible framework for the technical and functional validation of low‑cost sEMG systems for research and educational applications.

Authors:Luis A. Garcia-Reyes, Josep Arévalo-Soler, Oriol Gomis-Bellmunt, Eduardo Prieto-Araujo, Vinícius A. Lacerda, Macarena Martin-Almenta, Edgar Nuño-Martínez, Javier Renedo
Title: Enabling Small-Signal Stability Analysis of Black-Box Voltage Source Converters in Large-Scale Modern Power Systems
Abstract:
Modern power systems increasingly rely on power electronic converters, yet many of these devices are provided as black‑box models, limiting the applicability of conventional small‑signal analysis (SSA) tools. This work presents a unified multi‑variable fitted state‑space (SSA‑FITSS) methodology that enables accurate small‑signal modeling of black‑box Voltage Source Converters (VSCs) using frequency‑domain (FD) identification, adaptive pole‑expansion, and reduced‑order realization. The method includes an automated state‑interpretation strategy that assigns fitted states to representative control‑loop categories based on their dominant frequency ranges, providing an approximate but meaningful physical interpretation of the identified dynamics. This capability allows extensive modal analysis, including eigenvalue sensitivities and participation factors, in systems where internal converter details are unavailable. The methodology is validated on a grid‑following (GFL) VSC and applied to the New England system, which contains multiple black‑box converters operating in both GFL and grid‑forming (GFM) modes. Results show that the SSA‑FITSS models accurately reproduce converter and system dynamics, support full eigenvalue‑based analysis, and reveal stability limits under varying synchronous generation and GFL penetration levels. The approach overcomes key limitations of existing identification‑based techniques by enabling scalable, interpretable, and system‑wide stability assessment.

Authors:Audrey Blizard, Stephanie Stockar
Title: Sensitivity Analysis of Performance-Based Partitioning in District Heating Networks
Abstract:
The paper presents a sensitivity analysis of the factors affecting the optimal partitioning of a district heating network for distributed control. Leveraging a physics‑based, distributed model predictive control framework and a performance‑based partitioning method, this work studies the relationship between variations in system parameters and the resulting optimal partition, providing insight into the robustness of a nominally designed partition to perturbed operating conditions. The enabling methodology is a learning‑enhanced branch and bound method that culls the search space, reducing the number of partitions evaluated for each case. The sensitivity of the nominally optimal partition is characterized across twelve parameter variations, including supply temperature, operating season, building flexibility, pipe characteristics, and building type. This simulation study shows that a well‑designed nominal partition exhibits an average cost increase of only 2.8% relative to centralized control across eleven of the twelve cases, with three cases identifying the nominal partition as globally optimal under the perturbed conditions. The robustness study is followed by an analysis of the sensitivity of the optimality loss metric (OLM), revealing that, in five of twelve cases, the case‑specific OLM‑minimizing partitions underperform the nominally optimal one due to shifts in the relative magnitude of heat loss versus flexibility costs. This indicates that proper tuning of cost function weights and initial conditions for the performance optimization problem is essential for reliable partition selection, and that seasonal repartitioning is warranted when demand profiles deviate substantially from the nominal, as observed in the November operating case.

Authors:Raktim Bhattacharya
Title: Sensor Design for Accuracy-Bounded Estimation via Maximum-Entropy Likelihood Synthesis
Abstract:
Designing the sensing architecture for large‑scale spatio‑temporal systems is hard when accuracy requirements are specified but sensor models are uncertain or unavailable. Classical design treats sensor placement and estimation sequentially, requiring valid forward models for each sensing modality. This paper inverts the design flow: given an error budget, synthesize the measurement likelihood that enforces it while injecting minimal information beyond the dynamical prior. The likelihood is constructed by constrained optimization: among all posteriors satisfying a prescribed accuracy bound relative to a target, select the one minimizing Kullback‑Leibler divergence from the prior. The solution is a maximum‑entropy posterior in relative‑entropy form, and the induced likelihood is the Radon‑Nikodym derivative. The framework accommodates arbitrary discrepancies and is instantiated for Wasserstein distance, maximum mean discrepancy, f‑divergences, moment constraints, and hybrid metrics. For each, we derive the discrete particle‑level problem, analyze its convex or convex‑relaxed structure, and present solvers with complexity scaling. A closed‑form solution exists for the symmetric exponential‑tilt case, and a distillation procedure converts nonparametric likelihood samples into parametric forms. A two‑layer sensor design architecture embeds the synthesized likelihood in the recursive predict‑update loop, connecting accuracy budgets to physical sensor placement, precision, and configuration. Numerical experiments comparing four metrics on unimodal and multimodal scenarios confirm the accuracy constraints are reliably enforced and reveal how metric choice determines the amount and spatial distribution of injected information.

Authors:Shourya Bose, Helgi Hilmarsson, Dhruv Suri
Title: Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models
Abstract:
Neural warm starts can sharply reduce the number of Newton‑Raphson iterations required to solve the AC power flow problem, but existing supervised approaches generalize poorly on heavily loaded instances near voltage collapse. We prove a lower bound on the Newton‑Raphson iteration count that depends on the direction of the warm start error rather than on its magnitude, and show as a corollary that the bound becomes vacuous as the smallest singular value of the power‑flow Jacobian shrinks, identifying the failure mode of supervised regression near the saddle‑node bifurcation. Motivated by this analysis, we introduce Newton's Lantern, a finetuning pipeline that combines group relative policy optimization with a learned reward model trained on perturbations of the base model's predictions, using the iteration count itself as the supervisory signal. Across IEEE 118‑bus, GOC 500‑bus, and GOC 2000‑bus benchmarks, Newton's Lantern is the only method that converges on every test snapshot while attaining the smallest mean iteration count.

Authors:Max Studt, Georg Schildbach
Title: Decentralized Contingency MPC based on Safe Sets for Nonlinear Multi-agent Collision Avoidance
Abstract:
Decentralized collision avoidance remains challenging, particularly when agents do not communicate any information related to planned trajectories. Most existing approaches either rely on conservative coordination mechanisms or provide limited guarantees on recursive feasibility and convergence. This paper develops a decentralized contingency MPC framework for multi‑agent systems with nonlinear dynamics that achieves collision‑free motion under a state‑only information pattern. Each agent follows the same consensual rule set, enabling safe decentralized planning without communication. Each agent solves a local optimization problem that couples a nominal trajectory with a contingency certificate ensuring a feasible backup maneuver under receding‑horizon operation. A novel geometric and decentralized safe‑set update mechanism prevents feasibility loss between consecutive time steps. The resulting scheme guarantees recursive feasibility, including collision avoidance, and establishes a Lyapunov‑type convergence result to an admissible safe equilibrium. Simulation results demonstrate performance in both sparse and dense multi‑agent environments, including cluttered bottleneck scenarios and under plug‑and‑play operation.

Authors:Suvi De Silva, Alfreds Lapkovskis, Alaa Saleh, Sasu Tarkoma, Praveen Kumar Donta
Title: An Uncertainty-Aware Resilience Micro-Agent for Causal Observability in the Computing Continuum
Abstract:
Grey failures in the computing continuum produce ambiguous overlapping symptoms that existing approaches fail to diagnose reliably, either due to a lack of causal awareness or acting under high epistemic uncertainty, risking destructive interventions. This paper presents an uncertainty‑aware resilience micro‑agent for causal observability (AURORA), a lightweight framework for diagnosing and mitigating grey failures in edge‑tier environments. The framework employs parallel micro‑agents that integrate the free‑energy principle, causal do‑calculus, and localized causal state‑graphs to support counterfactual root‑cause analysis within each fault's Markov blanket. Restricting inference to causally relevant variables reduces computational overhead while preserving diagnostic fidelity. AURORA further introduces a dual‑gated execution mechanism that authorizes remediation only when causal confidence is high and predicted epistemic uncertainty is bounded; otherwise, it abstains from local intervention and escalates the diagnostic payload to the fog tier. Our experiments demonstrate that AURORA outperforms baselines, achieving a 0% destructive action rate, while maintaining 62.0% repair accuracy and a 3ms mean time to repair.

Authors:Taha Entesari, Mahyar Fazlyab
Title: Hierarchical End-to-End Taylor Bounds for Complete Neural Network Verification
Abstract:
Reachability analysis of neural networks, which seeks to compute or bound the set of outputs attainable over a given input domain, is central to certifying safety and robustness in learning‑enabled physical systems. Since exact reachable set computation is generally intractable, existing methods typically rely on tractable overapproximations. Examining the state of the art for smooth, twice‑differentiable networks, we observe that existing approaches exploit at most second‑order information and do not systematically leverage higher‑order information. In this work, we introduce \textscHiTaB, a novel verification framework that exploits second‑order smoothness through both the Hessian, \nabla^2 f, and its Lipschitz constant, L_\nabla^2 f. We further develop a unified hierarchy of zeroth‑, first‑, and second‑order bounds, together with precise conditions under which higher‑order approximations yield provable improvements. Our main technical contribution is a compositional procedure for efficiently bounding L_\nabla^2 f in deep neural networks via layerwise propagation of curvature bounds. We extend the framework to both \ell_2‑ and \ell_\infty‑constrained input sets and show how it can be integrated into branch‑and‑bound verification pipelines. To our knowledge, this is the first practical reachability analysis framework for smooth neural networks that systematically exploits Lipschitz continuity of curvature, leading to tighter and more informative safety certificates.

Authors:Rohollah Moghadam, Santosh Kandel
Title: Effect of Graph Gluing on Consensus in Networked Multi-Agent Systems
Abstract:
In this paper, the effects of graph gluing operations in networks of multi‑agent systems and their impact on system performance are investigated. In many practical applications, multiple multi‑agent subsystems must be interconnected through communication links to accomplish complex tasks, resulting in a larger communication network. Such interconnections modify the underlying graph topology and consequently affect the consensus behavior and convergence rate of the network. In particular, this paper examines both bridge gluing and interface gluing and analyzes how the number and structure of communication links between subsystems influence the Fiedler eigenvalue of the resulting graph. Since the Fiedler eigenvalue is directly related to the convergence rate of consensus dynamics, the proposed analysis establishes a clear relationship between interconnection strategies, algebraic connectivity, and system performance. The results provide theoretical insight into how different gluing mechanisms alter the spectral properties of the graph Laplacian and, in turn, the convergence characteristics of the networked multi‑agent system. Simulation studies are presented to illustrate the theoretical findings and to validate the effectiveness of the proposed framework.

Authors:Tarek Bouazza, Pieter van Goor, Robert Mahony, Tarek Hamel
Title: Equivariant Observer Design on SL(3) for Image Intensity-Based Homography Estimation
Abstract:
This paper addresses the problem of homography estimation using a nonlinear observer designed on the Lie group \mathbfSL(3) that exploits the full image information through direct image registration. Unlike traditional feature‑based methods, which rely on extensive feature extraction and matching, the proposed approach formulates an observer that minimises a cost function defined directly in terms of image pixel intensities. Explicit conditions ensuring the non‑degeneracy of the cost function are derived, and a comprehensive analysis is conducted to characterise and generate degenerate (unobservable) image configurations. Theoretical results demonstrate local exponential convergence of the observer. To improve local convergence properties, a second‑order observer variant is introduced by incorporating the Hessian of the cost function into the correction term. Simulation results demonstrate the performance of the proposed solutions on real images.

Authors:Yujia Luo, Ye Pu, Jonathan H. Manton, Jingge Zhu
Title: A PAC-Bayes Approach for Controlling Unknown Linear Discrete-time Systems
Abstract:
This paper presents a PAC‑Bayes framework for learning controllers for unknown stochastic linear discrete‑time systems, where the system parameters are drawn from a fixed but unknown distribution. We derive a data‑dependent high probability bound on the performance of any learned (stochastic) controller, and propose novel efficient learning algorithms with theoretical guarantees, which can be implemented for both finite and infinite controller spaces. Compared to prior work, our bound holds for unbounded quadratic cost. In the special case where LQG is optimal, our numerical results suggest that the learned controllers achieve comparable performance to LQG.

Authors:Pukhrambam Akash Singh, Ratnangshu Das, Ahan Basu, Pushpak Jagtap
Title: Glycemic Safety Tube: A Provably Safe Control Framework for Artificial Pancreas Systems under Parametric Uncertainty
Abstract:
Type 1 diabetes eliminates the body's ability to produce insulin, making glucose regulation entirely dependent on external insulin delivery and the control algorithm. Existing closed‑loop methods either rely on accurate patient‑specific models or do not provide formal safety guarantees, and are often computationally demanding for wearable devices. This paper proposes Glycemic Safety Tube Control (GSTC), a model‑free and computationally efficient control framework for automated insulin delivery. The method enforces clinically relevant safety bounds on glucose levels by design, ensuring that glucose remains within a prescribed safe range. We also derive feasibility conditions that guarantee safety and input constraint satisfaction under bounded meal disturbances and estimation errors. The performance of GSTC is evaluated against state‑of‑the‑art methods, including linear and nonlinear model predictive control and sliding mode control. The results demonstrate that GSTC maintains safety under varying meal patterns and patient conditions, highlighting its robustness and computational efficiency. Overall, GSTC provides a safe, efficient, and patient‑independent approach for next‑generation artificial pancreas systems.

Authors:Roberto Rizzello, Davide Salzano, Stefano Boccaletti, Pietro De Lellis
Title: Observing the state of networks with directed higher-order interactions
Abstract:
We consider the problem of reconstructing the state of a network of nonlinear dynamical systems in the presence of directed higher‑order interactions. Grounded on analytical convergence results, we propose an algorithmic observer design procedure that simultaneously selects the nodes to be measured and the observer gains. We complement the theoretical analysis with an exhaustive numerical investigation campaign that showcases the performance and robustness of the designed observer. Finally, the algorithmic procedure is used to fully reconstruct the opinions of a group of agents.

Authors:Qingyun Guo, Junyi Shi, Tomasz Piotr Kucner, Dominik Baumann
Title: Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning
Abstract:
Event‑triggered control provides a mechanism for avoiding excessive use of constrained communication bandwidth in networked multi‑agent systems. However, most existing methods rely on accurate system models, which may be unavailable in practice. In this work, we propose a model‑free, priority‑driven reinforcement learning algorithm that learns communication priorities and control policies jointly from data in decentralized multi‑agent systems. By learning communication priorities, we circumvent the hybrid action space typical in event‑triggered control with binary communication decisions. We evaluate our algorithm on benchmark tasks and demonstrate that it outperforms the baseline method.

Authors:Zhiheng Zhao, Hans Henrik Niemann, John Bagterp Jørgensen
Title: Hierarchical 2-degree-of-freedom control combining Youla-Kucera parameterization and model predictive control
Abstract:
A hierarchical 2DOF (2‑degree‑of‑freedom) structure combining Youla‑Kucera (YK) parameterization and model predictive control (MPC) is presented in this paper. The YK parameterization employs the coprime factorization of the nominal system and controller, thereby introducing an auxiliary feedforward channel dedicated to system optimization and a controller parameterization channel. The feedforward channel is utilized to implement cascaded MPC for system optimization. The controller parameterization channel is utilized to achieve offset‑free MPC by designing an appropriate YK parameter through the H2 optimal controller design.

Authors:Luca Giangrande, Matthieu Heller, Teresa Montaruli
Title: High-speed single-photoelectron detection for Cherenkov astronomy
Abstract:
Silicon photomultipliers are increasingly replacing photomultiplier tubes in Cherenkov telescope cameras, but achieving single‑photoelectron resolution with nanosecond timing in a low‑noise, scalable detector system remains challenging. We present a co‑designed SiPM sensor and front‑end application specific integrated circuit (ASIC) that meets these requirements. The custom hexagonal sensor, developed with Hamamatsu Photonics, incorporates an integrated optical filter and fourfold pixel segmentation. The readout is performed by a second prototype of the FANSIC ASIC, optimized for this application and fabricated in 65~nm standard CMOS technology, it provides eight channels with on‑chip analog summing of sub‑channels on a 3.5× 3.5~\mathrmmm^2 die, while consuming only 24~mW per channel. We demonstrate clear single‑photoelectron peak separation with a gain of 2.7 × 10^‑12~ \mathrmV \cdot s , and an impulse response below 4~ns full width at half maximum with a 1.7 ns rise time, preserving the nanosecond‑scale structure of Cherenkov pulses. The system responds linearly from 1 to 130 photoelectrons, and 55 distinct photoelectron peaks are resolved by varying the source intensity. These results demonstrate that the integrated sensor‑electronics architecture delivers the speed, resolution, and dynamic range required for imaging atmospheric Cherenkov telescopes, and provides a scalable path toward large‑area camera modules.

Authors:Burak Soner, Ekin Uzun, Can Aksoy
Title: Low-Cost GNSS Anti-Jamming Through 2-Bit Phase Shift Beamforming with Machine Learning
Abstract:
We investigate low‑cost GNSS anti‑jamming using beamforming with inexpensive 2‑bit phase shifters, constraining each complex array weight to one of four QPSK phase states (real/imaginary = ‑1 or +1). This severe quantization sharply limits the beampattern solution space, making conventional real‑valued beamforming and naive weight quantization highly suboptimal. We formulate a discrete optimization that trades interference suppression against satellite‑direction gain, and benchmark known combinatorial optimization methods across array sizes and interference conditions. Simulations show that performance improves with array size, with oracle and greedy search achieving up to 34 dB nulling, but oracle incurs exponential latency and greedy sampling is stochastic. To obtain deterministic low‑latency performance, we propose an ML‑aided method based on gradient‑boosted decision trees followed by local search, which performs similar to the oracle for larger arrays at fixed latency. We further validate the approach experimentally using a fully digital emulation of the QPSK oracle beamformer and compare against a GNSS receiver without beamforming capability. Under mild jamming (J/S approximately 44 dB) both receivers maintain adequate tracking, with QPSK yielding a 4.2 dB higher average C/N0 (37.3 vs. 33.1 dB‑Hz). Under moderate and strong jamming (J/S approximately 62‑70 dB) the benefit is substantial. At J/S = 70 dB the unprotected receiver degrades to near tracking limits (avg C/N0 = 9.3 dB‑Hz) while the QPSK oracle sustains an average C/N0 of 20.8 dB‑Hz. These results confirm that 2‑bit phase‑shift beamforming provides considerable anti‑jamming benefit over a standard GNSS receiver, motivating further research on oracle‑level practical methods.

Authors:Ricus Husmann, Sven Weishaupt, Malin Lotta Husmann, Harald Aschemann
Title: Online Learning-Based Control with Guaranteed Error Bounds for a Class of Nonlinear Systems
Abstract:
In this paper, we present a learning‑based control for a class of nonlinear systems that guarantees exponential stability as well as bounded output errors. The control is based on the Gaussian Process Submodel Online Learning (GPSOL) algorithm and the Disturbance Error Rate Limiting (DERL) algorithm, both of which were developed in previous work. The GPSOL algorithm provides a method to learn Gaussian Process (GP) models for subsystems online, whereas the DERL algorithm allows to limit the rate of the prediction error of these GP models. The focus of this paper is the utilization of the GP model within an adaptive controller and the derivation of corresponding stability conditions and system peak‑to‑peak gains by means of linear matrix inequalities (LMIs). These peak‑to‑peak gains are then used to prescribe a desired prediction error rate for the DERL algorithm to achieve user‑defined output error bounds. The gains and the related bounds were successfully verified using a simulation model. Furthermore, results form a successful experimental validation of the bounds and the overall control structure on a pneumatic test rig are presented. While the control scheme and error bounds proposed in this paper are limited to first‑order single‑input‑single‑output systems, an extension to certain classes of higher‑order and multiple‑input‑multiple‑output systems is expected to be forthcoming.

Authors:Zhengmiao Wang, Ming Chi, Zhi-Wei Liu, Lintao Ye, Carla Fabiana Chiasserini
Title: Learning to Sparsify Stochastic Linear Bandits
Abstract:
This paper addresses the problem of learning to sparsify stochastic linear bandits, where a decision‑maker sequentially selects actions from a high‑dimensional space subject to a sparsity constraint on the number of nonzero elements in the action vector. The key challenge lies in minimizing cumulative regret while tackling the potential NP‑hardness of finding optimal sparse actions due to the inherent combinatorial structure of the problem. We propose an adaptively phased exploration and exploitation algorithmic framework, utilizing ordinary least squares for parameter learning and specialized subroutines for sparse action selection. When the action set is a Euclidean ball, optimal sparse actions can be efficiently computed, enabling us to establish a \tilde\mathcalO(d\sqrtT) regret, where d is the dimension of the action vector and T is the time horizon length. For general convex and compact action sets where finding optimal sparse actions is intractable, we employ a greedy subroutine. For general strongly convex action sets, we derive a \tilde\mathcalO(d \sqrtT) α‑regret; for general compact sets lacking strong convexity, we establish a \tilde\mathcalO(d T^2/3) α‑regret, where α pertains to the approximation ratio of the greedy algorithm. Finally, we validate the performance of our algorithms using extensive experiments including an application to recommendation system.

Authors:Nico Westerbeck, Leonard Hilfrich, Dirk Witthaut
Title: Transmission Topology Optimization using accelerated MapElites
Abstract:
Transmission Topology Optimization has great potential to improve efficiency and flexibility of grid operations through non‑costly switching actions, but previous approaches struggle with runtime performance and scalability. In this work, we present an optimization approach that leverages GPU acceleration to speed up computations. In a genetic algorithm setting, topologies are randomly mutated and evaluated in parallel for multiple optimization criteria. Combined with a fully GPU‑native DC loadflow solver, there is no CPU‑GPU data transfer required in the DC optimization loop. Using a variant of the illumination algorithm MapElites, we efficiently generate a set of diverse candidate solutions on the pareto front. Together with an importing and AC validation step, we present an end‑to‑end optimization solution that runs in under 15 minutes. The approach is currently under evaluation by operational planning operators in two European TSOs. We furthermore open‑source our code at github.com/eliagroup/ToOp.

Authors:Meiqi Tian, Yihan Liu, Bingzhuo Zhong
Title: Lure-and-Reveal: An Exposure Framework for Stealthy Deception Attack in Multi-sensor Uncertain Systems
Abstract:
Multi‑sensor integration via error‑state Kalman filter (KF) is widely employed for precise state estimation in cyber‑physical systems (CPSs). However, this integration exposes the system to stealthy deception attacks that render conventional detection mechanisms ineffective. We propose an exposure framework to actively reveal such stealthy attacks without modifying sensor interfaces. The framework introduces a suspect mode in which the defender injects random exposure shakes into the nominal control inputs, thus creating a discrepancy between the defender's true state estimates and the attacker's manipulated state estimates, preventing the attack from remaining stealthy. We further derive an explicit exposure condition that characterizes the minimum shake magnitude to guarantee the finite‑time exposure and a compensable condition that ensures the shakes do not degrade closed‑loop performance. Simulation results based on a GNSS/INS‑integrated UAV system verify the effectiveness of the proposed framework.

Authors:Jiangtao Luo, Yongyi Ran, Guoliang Xu, Jihua Zhou
Title: Learning to Compress and Transmit: Adaptive Rate Control for Semantic Communications over LEO Satellite-to-Ground Links
Abstract:
The bottleneck of satellite‑to‑ground links poses a major challenge for the timely downlink of massive on‑board imagery. This paper studies adaptive image transmission over LEO satellite‑to‑ground links using joint source‑channel coding (JSCC). We propose an RL‑based framework that dynamically selects the channel dimension (compression ratio) of a SwinJSCC encoder to maximize the number of received satisfying reconstruction‑quality constraints (PSNR and MS‑SSIM) within a finite visibility window. The agent leverages SNR prediction to perform proactive rate adaptation and incorporates an on‑board transmission‑queue model that captures bursty encoding while penalizing both buffer overflow and underutilization. Simulations under realistic overpass conditions show that the proposed policy substantially outperforms fixed‑rate baselines, achieving nearly 95% qualified frames with zero packet loss.

Authors:Yiwei Liu, Luwei Yang, Shunbo Lei
Title: Delay-Robust Secondary Frequency Control via Passive Interconnection and Randomized Block Updates
Abstract:
This paper studies secondary frequency control in transmission networks subject to communication delays at the cyber‑physical interface and limited per‑update computation at the control center. The regulation objective is formulated as a constrained economic dispatch problem incorporating generation capacity constraints, nodal power balance, transmission‑flow limits, and scheduled tie‑line power exchanges. Based on this formulation, we develop a passivity‑based control framework in which an augmented projected primal‑dual controller restores nominal frequency and drives the closed‑loop system to the solution set of the constrained economic dispatch problem. Two‑way communication delays between the physical network and the control center are modeled as scattering‑based passive channels for the measurement uplink and the control‑command downlink. This construction preserves the target equilibrium and enables a delay‑robust passivity analysis of the delayed closed loop. To reduce the computational burden at the control center, we develop a randomized block‑coordinate implementation of the augmented projected primal‑dual controller. The resulting sampled‑data closed loop preserves the target solution set and achieves local mean‑square geometric convergence under suitable step‑size and regularity conditions. Finally, a multivariable wave‑domain interface filter is introduced to inject additional dissipation and improve the damping of the delayed interface without altering the steady‑state interconnection. Simulations on the IEEE 14‑bus system indicate that the proposed digital implementation accurately reproduces the delayed closed‑loop behavior while reducing the per‑update computational cost.

Authors:Alba Gurpegui, Sribalaji C. Anand, André M. H. Teixeira
Title: Scalable Design of Attack-Resilient Controllers for Positive Systems
Abstract:
This paper proposes a framework for secure and resilient controller design for positive systems against cyber‑attacks. In particular, we consider a network‑controlled system where an adversary injects false data into the actuator channels to increase the control cost (performance measure) while penalizing the attack effort and subject to state‑dependent constraints. Using a minimax formulation, we analyze the worst‑case performance loss caused by such adversaries, which is given by the solution of a difference equation, and an algebraic equation when the time horizon is infinite. We show that the optimal attack policy, among possible nonlinear policies, is linear. Despite the lack of explicit stealthiness constraints, we also show that when the measured output has an unstable zero which is not an unstable zero of the performance measure, the attacks can induce unbounded performance degradation. The proposed framework is also extended to systems with model uncertainty. Numerical examples illustrate the results and demonstrate how tools from positive systems and linear regulator theory can be used to mitigate cyber‑attacks with low computational effort.

Authors:Rida Fatima, Hassan Zahid Butt, Xingpeng Li
Title: Optimal Loss Reduction in Distribution Networks Using Conservation Voltage Reduction and Network Topology Reconfiguration
Abstract:
Conservation voltage reduction (CVR) and network topology reconfiguration (NTR) are widely employed to improve distribution system performance; however, existing approaches largely treat them independently, overlooking their coupled impact on load demand, voltage profiles, and power flow distribution, thereby limiting their overall effectiveness. This paper proposes a coordinated optimization framework for day‑ahead operational planning of distribution networks, integrating CVR and NTR to enhance overall network efficiency and reduce active power losses in radial distribution networks. The problem is formulated as a mixed‑integer conic programming model incorporating AC power flow constraints, voltage‑dependent load representation, and radiality constraints. CVR is implemented to achieve load reduction through coordinated voltage control, while NTR redistributes line loading via optimal switching of controllable branches. The proposed framework is validated on the IEEE 33 and 123‑bus distribution systems under varying load conditions. Results demonstrate that the coordinated approach consistently outperforms independent strategies, achieving up to 20.6% reduction in active power losses while maintaining voltage compliance and improving branch loading uniformity. These findings confirm that coordinated optimization provides an effective and scalable solution for enhancing efficiency in modern distribution networks.

Authors:Antonina Kosikova, Mehmet Kerem Turkcan, Ahmed Darrat, Andrew Smyth
Title: Harnessing Floating Car Data, Traffic Camera Observations, and Network Flow Analysis for Traffic Volume Estimation
Abstract:
Cities increasingly rely on vehicle trajectory data to monitor traffic conditions; however, such data offer only a partial and spatially heterogeneous view of network dynamics and exhibit systematic biases across corridors and time periods. In contrast, surveillance cameras can provide high‑fidelity traffic information, but only at a limited set of locations, typically sparsely distributed across the road network. We present a hybrid modeling and calibration framework that fuses these complementary data sources to produce physically consistent, network‑wide estimates and short‑horizon forecasts of traffic volumes. The framework leverages kinematic features derived from the Cell Transmission Model (CTM) formulation within a graph neural network (GNN). By enforcing traffic‑flow conservation, capacity limits, and spillback dynamics, the CTM provides a physically grounded representation of traffic flow, while the GNN learns the spatiotemporal evolution of traffic states over the entire road network. To calibrate the model predictions on traffic camera observations, we use a progressive data‑assimilation scheme based on an Ensemble Square‑Root Kalman filter (EnSRF). A topology‑informed flow‑weighted transition matrix is further employed to propagate camera‑driven corrections to unobserved road segments, enabling real‑time, network‑wide traffic state and volume estimation. The approach is demonstrated using probe‑vehicle trajectory data and municipal traffic cameras in Manhattan, New York City, where it achieves improved accuracy relative to trajectory‑based estimates while maintaining physically plausible and network‑consistent traffic flows. The proposed framework accommodates varying sensor availability and produces calibrated traffic volumes with uncertainty estimates, supporting operational monitoring and evaluation of transportation policies in data‑constrained urban environments.

Authors:Azadeh Aghaeeyan, Pouria Ramazi
Title: From Discrete to Continuous Highest-earning Imitation Dynamics
Abstract:
Imitating the highest earners is a common decision‑making heuristic, but in finite populations it can generate persistent fluctuations between strategies. This paper studies whether such fluctuations persist as population size grows in heterogeneous two‑strategy populations. We show that the Markov chains describing the discrete imitation dynamics form generalized stochastic approximation processes for a good upper semicontinuous differential inclusion, which defines the associated mean dynamics. We prove that these mean dynamics always converge to equilibria. Using stochastic approximation results, we then show that the amplitudes of fluctuations in the population proportions of the two strategies vanish almost surely as population size tends to infinity. Thus, in well‑mixed large populations, highest‑earning imitation is unlikely to produce large‑scale perpetual fluctuations.

Authors:Tong Wu
Title: Geometric Pareto Control: Riemannian Gradient Flow of Energy Function via Lie Group Homotopy
Abstract:
We propose Geometric Pareto Control (GPC), a framework overcoming barriers of reinforcement learning in cyber‑physical systems where governing physics is known. Reinforcement learning confronts barriers in safety‑critical applications: sample complexity grows with action‑space dimension, retraining is required when objectives or conditions shift, goals such as safety recovery and economic dispatch demand brittle switching logic, and unsafe exploration persists under constrained RL formulations. GPC resolves these barriers through a two‑stage geometric approach. Offline, the supported family of Pareto‑optimal solutions (i.e., solutions recoverable by weighted scalarization) is embedded as a submanifold within a Lie group. Exponential map closure preserves membership in the ambient Lie group; drift and reset assumptions keep online latent states within a bounded neighbourhood of the Pareto submanifold, and a training‑time feasibility margin guarantees decoded actions remain feasible without post‑hoc projection, constructing a "map" of the solution landscape. Online, a closed‑form proximal navigator traverses this submanifold via a unified Riemannian gradient flow driven by a singular perturbation potential field, inducing dual‑timescale dynamics that prioritize constraint restoration over performance optimization. The homeomorphic structure of the submanifold guarantees that varying system parameters and objective weights produce continuous control actions, enabling deployment under unseen conditions without retraining. Validated on a nonconvex control task and real‑time multi‑objective optimal power flow, GPC achieves 100% feasibility, 0.30% oracle suboptimality, and 12.3 ms decisions while shifting from constraint recovery to economic dispatch. Under branch‑admittance uncertainty, it remains 100% feasible without retraining, whereas model‑free baselines produce no feasible dispatches.

Authors:Su Wang, Mung Chiang, H. Vincent Poor
Title: Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks
Abstract:
We investigate the control and optimization of vertical federated learning (VFL), a class of distributed machine learning (ML) methods in which edge/fog devices contain separate data features, in dynamic edge/fog networks. Owing to heterogeneous data features and hardware across edge/fog networks, devices' contributions to VFL vary substantially, and, moreover, dynamic edge/fog networks can lead to the permanent exit or entry of select data features. In this setting, our proposed methodology, server controlled VFL in dynamic networks (SC‑DN), first establishes the existence of a global first‑order stationary point for every global round, and then leverages this result to jointly optimize ML model training and resource consumption based on four key control variables: (i) server placement, (ii) device‑to‑server transmit power, (iii) local device processor frequency, and (iv) local training iterations per global round. The resulting optimization formulation contains coupled variables as well as numerous forms of logarithmic constraints which we show is a mixed‑integer signomial program, an NP‑hard problem, and for which we develop a general solver. Finally, via experiments on both image and multi‑modal datasets, we show that our methodology demonstrates superior classification/regression performance and resource consumption savings than even greedy methodologies.

Authors:Baris Ata, Ebru Kasikaralar
Title: Dynamic Scheduling of a Parallel-Server Queueing System: A Computational Method for High-Dimensional Problems
Abstract:
A key operational challenge for call centers is to decide, in real time, which waiting customer should be served by which available agent. This is known as skill‑based routing, and the decision becomes especially difficult in large systems with many customer classes, where standard dynamic programming methods can be computationally intractable. Focusing on the Halfin‑Whitt heavy‑traffic regime and an infinite‑horizon discounted cost criterion, we develop a computational method that scales to high‑dimensional settings with many customer classes. Our approach begins by deriving an approximating diffusion control problem in the heavy traffic limiting regime. Building on earlier work by Han et al. (2018), we develop a simulation‑based method to solve this problem, relying heavily on deep neural network techniques. Using this framework, we construct a policy for the original (prelimit) call center scheduling problem. To evaluate performance, we adopt a data‑driven approach. Using call center data from a large U.S. bank, we calibrate the model and construct realistic test instances. We then compare the resulting policy with benchmark policies drawn from the literature. Across all test problems considered so far, our policy performs at least as well as or better than the best benchmark identified. Moreover, the method remains computationally feasible in dimensions up to 100, corresponding to call centers with 100 or more distinct customer classes.

Authors:Renyan Sun, Ashutosh Nayyar
Title: Action Recommendations for Sequentially Rational Strategic Agents
Abstract:
We consider a finite‑horizon discrete‑time dynamic system that is jointly controlled by two strategic agents. There is a system designer that has its own reward function but does not have direct control over the agents' actions. We consider an information structure where the current state and all past history are equally accessible by the designer and the agents. The designer sends action recommendations to the agents at each time step. Each agent can use the received recommendation and the available information to choose its action. We are interested in the setting where the designer would like to send recommendations in a way that incentivizes the agents to adopt obedient strategies, i.e., to take the action recommended by the designer. Our goal is to find an optimal action recommendation strategy for the designer that maximizes the designer's objective while ensuring that obedient strategies are \emphsequentially rational for the agents. We provide an algorithm for the designer's problem that involves solving a family of linear programs in a backward inductive manner.

Authors:Stefano Tonini, Soroush Rastegarpour, Hamid Reza Feyzmahdavian, Nicola Bastianello, Karl Henrik Johansson
Title: Safe Exploration for Nonlinear Processes Using Online Gaussian Process Learning
Abstract:
This paper proposes a safe data‑driven control framework for nonlinear systems with partially known dynamics. The method ensures stability and constraint satisfaction during online learning, assuming only a stabilizable linear approximation of the process is available. Unmodeled nonlinear dynamics are captured by a Gaussian process residual learned in real time. Safety is enforced through a probabilistic control‑invariant set derived from Lyapunov theory, guaranteeing high‑probability stability. A convex quadratic program computes control inputs that maximize information gain while respecting probabilistic safety constraints. The framework provides finite‑sample safety guarantees and allows adaptive expansion of the invariant set as uncertainty decreases. Numerical results validate the approach, demonstrating safe and informative exploration under model uncertainty: the safe set expands by about 30% while the Gaussian process root‑mean‑square error drops from 1.11 to 0.03.

Authors:Koen Classens, Jeroen van de Wijdeven, Maurice Heemels, Tom Oomen
Title: Nullspace-based Fault Diagnosis for Closed-Loop Mechatronic Systems with Application to Semiconductor Equipment
Abstract:
Fault detection and isolation (FDI) systems are critical for modern mechatronic production equipment, as their continuous operation is heavily dependent on the ability to detect and isolate faults in a timely and efficient manner. The aim of this paper is to address closed‑loop aspects for linear systems and enable the application of well‑known nullspace‑based FDI synthesis conditions to mechatronic systems subject to actuator and sensor faults. These tailored FDI synthesis conditions are applied to a large‑scale prototype wafer stage, showcasing the proposed approach through real experiments, thereby underlining the usefulness of the derived synthesis conditions for a wide range of production machines and scientific instruments.

Authors:Weizhen Wang, Ziheng Wang, Jianping He, Xinping Guan, Xiaoming Duan
Title: Minimizing Worst-Case Weighted Latency for Multi-Robot Persistent Monitoring: Theory and RL-Based Solutions
Abstract:
We study multi‑robot persistent monitoring on weighted graphs, where node weights encode monitoring priorities and edge weights encode travel distances. The goal is to design joint robot trajectories that minimize the worst‑case weighted latency across all nodes over an infinite time horizon. The widely adopted worst‑case latency objective evaluates team performance over the entire time horizon and therefore may fail to distinguish strategies with poor transient behavior but strong asymptotic performance. To address this limitation, we propose a family of tail‑performance objectives that generalize the standard objective and study the resulting functional optimization problems. We establish several key theoretical properties, including the existence of optimal strategies, relationships among the proposed objectives and their corresponding optimization problems, approximation by periodic solutions to arbitrary accuracy, and reductions to event‑driven decision models with discretized waiting times. Building on these results, we construct an equivalent event‑driven Markov decision process (MDP), called the Tail Worst‑case Latency‑Optimizing Markov Decision Process (TWLO‑MDP), which reformulates the tail‑performance objective as a standard average‑reward criterion. We then develop reinforcement‑learning‑based solution methods for the TWLO‑MDP and introduce the multi‑robot monitoring benchmark (M2Bench), a unified platform that supports the evaluation and comparison of heuristic and learning‑based monitoring algorithms. Experiments on synthetic and realistic monitoring scenarios show that our methods effectively reduce the worst‑case weighted latency and outperform representative baselines.

Authors:Yue Wu
Title: Diagnostic Certificates of Data Quality and Regression Identifiability for Koopman Identification
Abstract:
Classical persistent excitation criteria usually assess whether an input or regressor signal is sufficiently rich. In Koopman and EDMD with control (EDMDc), however, data quality is determined by the concatenation of lifted state features and control inputs. Input‑rich data can still visit a narrow state region, well‑spread state samples can still produce degenerate lifted features, and both can fail to condition the final regression problem. This paper develops a diagnostic certificate framework for locating these failures. The certificates separate state‑space coverage and clustering, lifted‑feature nondegeneracy, and the final regression spectrum. The regression‑spectrum certificate is the layer with direct theoretical guarantees: it controls the active standardized design's smallest singular value, has Fisher‑information and one‑step EDMDc stability interpretations, and admits a finite‑sample lower bound under a population spectral gap. We also give structural examples and a Schur‑complement condition showing why state, lifted, input, and regression diagnostics cannot be substituted for one another. As a sampling example, IGPE‑DOPT uses these certificates to score candidate trajectory segments. Experiments on Duffing, Van der Pol, and Lorenz systems compare input‑, state‑, lifted‑, and regression‑oriented baselines. The results show that certificate layers separate, budget and weights shift bottlenecks, and downstream prediction or control performance is not monotone in any single certificate. The framework is therefore intended as an interpretable diagnostic and data‑collection guide, not as a universal optimality claim.

Authors:Hongjun Xie, Chao Zhang, Pengcheng Luo, Zenghui Zhang, Genke Yang, Xiaojuan Zhang, Boon-Hee Soong
Title: Risk-Aware Safe Throughput Forecasting for Starlink Networks
Abstract:
As a representative low Earth orbit (LEO) broadband system, Starlink exhibits highly variable access throughput, making short‑term forecasting essential for network resource management. Existing forecasting methods mainly optimize symmetric point‑prediction metrics such as MAE and RMSE, but they do not explicitly control the asymmetric risk of overestimating future throughput, which can cause over‑admission, bandwidth overbooking, and service violations. This paper formulates Starlink throughput prediction as a risk‑budgeted safe forecasting problem, where the predictor must satisfy a prescribed overestimation budget while maintaining competitive accuracy. We propose Budget‑Guided Coarse‑to‑Fine Quantile Selection (BG‑CFQS), a data‑driven framework that trains a family of lower‑quantile predictors, locates the quantile boundary satisfying the risk budget, and refines the boundary region to select the most accurate feasible predictor. Experiments on three real‑world Starlink throughput datasets show that BG‑CFQS satisfies the risk budget on all datasets and achieves the lowest average MAE, mean positive error, and tail positive error among budget‑feasible methods. In high‑risk and severe‑risk low‑throughput regimes, BG‑CFQS reduces harmful positive errors by 11.0% and 12.6%, respectively. An admission‑control evaluation further shows that the proposed safe forecasts reduce dropped sessions, demonstrating that risk‑aware forecasting can translate prediction safety into application‑level benefits.

Authors:Mohammad H. Mamduhi, Sadegh Soudjani
Title: Barrier Certificates for Uncertain Temporal Specifications
Abstract:
This paper studies satisfying temporal logic specifications on stochastic dynamical systems, where the predicates evolve randomly over time. Such randomness may arise from uncertain environment models or external stochastic processes causing the sets associated with predicate satisfaction to vary in a non‑deterministic manner. As a result, verifying whether a stochastic dynamical system satisfies a temporal specification depends also on the uncertainty in the predicates. We develop a certificate‑based framework to bound the probability of satisfying temporal logic specifications with randomly evolving predicates. We first show that temporal logic specifications with stochastic predicates can be transformed to specifications with deterministic predicates on an augmented space which is extended to include the stochastic space of predicate's uncertainty. We then utilize barrier certificates on an augmented space to provide tractable optimization‑based conditions and to avoid the computational burden of dynamic programming. Focusing on linear dynamics and safety‑type specifications, we derive analytical conditions under which barrier certificates guarantee bounds on the probability of violating the stochastic safety predicates. The approach is demonstrated on numerical case studies.

Authors:Xiaoyang Li, Runni Zhou
Title: A Stochastic Hybrid Automaton for Smartphone Battery Dynamics: Electro-Thermal Coupling and First-Passage Time-to-Empty Estimation
Abstract:
Smartphone time‑to‑empty (TTE) is difficult to predict because shutdown is governed not only by remaining charge, but also by instantaneous power capability under temperature‑, aging‑, and load‑dependent voltage sag. We develop a stochastic hybrid automaton for smartphone battery dynamics that couples a first‑order Thevenin equivalent‑circuit model with a lumped thermal model and a stochastic user‑activity process. The continuous state includes state of charge, polarization voltage, and battery temperature; user behavior is represented as a piecewise deterministic Markov process switching among idle, social/web, video, gaming, and weak‑signal modes. Shutdown is formulated as a first‑passage event when terminal voltage crosses a cutoff threshold or when requested power exceeds the instantaneous feasibility envelope. The model captures a voltage‑collapse mechanism that simple Coulomb‑counting or linear discharge models miss: cold temperature or battery aging increases internal resistance, so high‑power bursts can drive terminal voltage below cutoff even when substantial charge remains. Monte Carlo simulation yields a full TTE distribution rather than a single countdown, allowing lower‑tail risk to be quantified by the 5th percentile. Sensitivity analysis identifies ambient temperature, internal resistance, weak‑signal radio penalty, and screen brightness as major drivers of premature shutdown risk. These results motivate practical user guidance and an operating‑system‑level resistance‑aware throttling policy that limits peak power in the power‑limited regime. The framework provides a physically grounded, risk‑aware approach for explaining and extending usable smartphone battery life under real‑world uncertainty.

Authors:Mahmoud Raeisi, Ayoub Ammar Boudjelal, Henk Wymeersch, Ertugrul Basar, Huseyin Arslan
Title: Transceiver-Integrated BD-RIS: Wave-Domain Signal Processing for Sustainable and Inclusive 6G
Abstract:
The shift toward sixth‑generation (6G) wireless communications demands transceiver architectures that simultaneously support high‑data‑rate communications, pervasive sensing, and sub‑meter‑level localization. Beyond these performance targets, 6G systems are also expected to align with long‑term societal goals, including sustainability and inclusiveness. Conventional radio designs, however, remain heavily reliant on digital baseband processing, whose cost, power consumption, and computational complexity scale unfavorably with increasing array size and carrier frequency, making them poorly aligned with these emerging requirements. Beyond‑diagonal reconfigurable intelligent surfaces (BD‑RISs) introduce a new paradigm by enabling direct manipulation of electromagnetic waves in the analog domain. This article presents BD‑RIS as a wave‑domain analog processing unit embedded within the transceiver aperture. By migrating linear signal processing functions from the digital baseband to the wave domain, BD‑RISs significantly reduce computational load and energy consumption, enabling scalable and sustainable operation for extra‑large antenna array systems. Owing to their ability to jointly provide high operational flexibility, modularity, and energy‑efficient analog processing, transceiver‑integrated BD‑RISs offer a compelling architectural trade‑off and emerge as a strong candidate for next‑generation wireless transceivers.

Authors:Shoju Enami, Kenji Kashima
Title: Mutual Information Optimal Density Control of Linear Systems and Generalized Schrödinger Bridges with Reference Refinement
Abstract:
We consider a mutual information (MI) regularized version of optimal density control of a discrete‑time linear system. MI optimal control has been proposed as an extension of maximum entropy optimal control to trade off between control performance and benefits provided by stochastic inputs. MI regularization induces stochasticity in the policy, which poses challenges for applications of MI optimal control in safety‑critical scenarios. To remedy this situation, we impose Gaussian density constraints at specified times to directly control state uncertainty. For this MI optimal density control problem, we propose an alternating optimization algorithm and derive the closed form of each step in the algorithm. In addition, we reveal that the alternating optimization of the MI optimal density control problem coincides with that of the so‑called generalized Schrödinger bridge problem associated with the discrete‑time linear system.

Authors:Ryungyeong Lee, Minhan Yoon, Hunyoung Shin
Title: Price Distortions in Korea's Electricity Market: Barriers to Renewable Integration and Reform Pathways
Abstract:
Structural distortions in price signals within the Korean electricity market, governed by a cost‑based pool (CBP) and a uniform pricing mechanism, fundamentally undermine the nation's energy transition goals. The current market design fails to reflect transmission constraints, real‑time supply and demand dynamics, and generator‑specific costs, leading to inefficient resource allocation and hindering long‑term investments in renewable energy and grid flexibility. This paper identifies the key drivers of these distortions and proposes a holistic reform package to enhance market efficiency. The package includes four key reforms: \stepcounterexcep(\romanexcep) introducing a locational marginal pricing system to manage transmission constraints; \stepcounterexcep(\romanexcep) establishing a real‑time market to reflect temporal value; \stepcounterexcep(\romanexcep) integrating market and system operations to resolve inconsistencies; and \stepcounterexcep(\romanexcep) transitioning from CBP to a price‑based bidding system. Each reform targets a distinct source of inefficiency. The broader contribution of this study, however, lies in showing that, under the current Korean market design, the market cannot readily provide effective price signals. These reforms therefore need to be implemented jointly to establish a coherent market design in which price signals are aligned with Korea's energy policy objectives.

Authors:Jingting Yao, Artan Kaso, Nikhil Patel, Yin-Ching Iris Chen, Andre van der Kouwe, Daniel M. Merfeld, Jerome L. Ackerman
Title: Moving MRI: Imaging a moving body with a moving magnet
Abstract:
Current magnetic resonance imaging (MRI) requires the subject to remain stationary to limit motion artifacts and avoid unwanted field‑induced brain stimulation. However, imaging during large‑scale motion could enable studies in which motion itself is central. One example is the study of brain networks involved in vestibular function, which senses head motion. Here, we demonstrate Moving MRI (mMRI), a system that enables imaging during large‑scale motion by moving the subject and scanner together to minimize relative motion. We implemented a proof‑of‑concept platform using a compact, cryogen‑free superconducting magnet mounted on a pneumatically actuated tilt mechanism that moves the magnet, gradients, and RF coil as a unit during scanning. Phantom and in vivo rat brain scans were acquired during repetitive tilting. We characterized artifacts arising from tilt‑induced field shifts and residual subject‑scanner motion, and partially reduced these effects. mMRI enables imaging during large‑scale movement and may broaden access to naturalistic vestibular paradigms while providing a foundation for future human systems.

Authors:Duc Cuong Nguyen, Phuong Nam Dao
Title: Data-Driven Inverse Reinforcement Learning of Linear Systems with Model Uncertainty: A Convex Optimization View
Abstract:
Inverse reinforcement learning (IRL) for linear systems seeks a cost function whose optimal controller reproduces an expert policy from data. Existing data‑driven methods for discrete‑time linear systems are largely built on iterative policy/value updates, repeated matrix inversions, and, in some cases, an initial stabilizing controller, which can limit numerical robustness and practical applicability. This paper develops a convex‑optimization framework for data‑driven inverse reinforcement learning of discrete‑time linear systems with model uncertainty. For nominal systems, we derive a semidefinite characterization of inverse optimality and a relaxed formulation that recovers an equivalent state‑cost matrix together with a stabilizing controller from expert trajectories. We then obtain a model‑free, off‑policy reformulation by replacing the unknown system matrices with a regressed kernel matrix identified from local input‑‑state data. For uncertain local systems, we show that a standard LQR cost is generally insufficient to represent every stabilizing target gain and therefore introduce a generalized LQR cost with a state‑‑input cross term. Based on this model, we develop a convex data‑driven inverse‑RL method and extend it to robust cost design over a population of perturbations via differentiable semidefinite programming and stochastic approximation. Simulations on a discrete‑time power‑system example show accurate recovery of expert behavior, improved robustness to gain‑estimation error and model mismatch, and a simpler computational pipeline than classical iterative inverse‑RL schemes.

Authors:Carlo Schreiber, Duncan Eddy, Mykel J. Kochenderfer
Title: Fault-Aware MPC for Robotic Fleet Communications Scheduling
Abstract:
Operating a fleet of remote robotic systems with intermittent communications requires scheduling limited contact opportunities to maintain fleet health awareness, complete mission objectives, and intervene on faulted assets before their permanent loss. This scheduling problem is complicated by observational ambiguity: when an asset fails to check in, the operator cannot distinguish between a lethal hardware fault and a benign communications failure. If the system's failure modes are structured through a fault model, a scheduler can exploit mode‑specific lethality, timing, and recoverability properties to prioritize correctly ‑ but only if it can distinguish between modes that produce identical observations under standard actions. We present Interacting Multiple Model Model Predictive Control (IMM‑MPC), a receding‑horizon framework that maintains a probabilistic belief over discrete fault modes with time‑inhomogeneous dynamics and optimizes a two‑term objective coupling acquisition value with information gain. We characterize when observationally aliased fault modes can be disambiguated through scheduled actions and when aliasing is permanently unresolvable. Applied to satellite launch and early orbit communications scheduling, IMM‑MPC recovers 59.8% of spacecraft experiencing lethal‑faults versus 9.0% for binary‑MPC and 2.0% for a bipartite graph‑based formulation solved through matching. These results hold across 200 randomized trials, while maintaining identical acquisition of healthy satellites and near‑identical solve times.

Authors:Mustafa Ergen
Title: Engineering Economy: A New Paradigm for Escaping the Middle-Income Trap
Abstract:
This paper introduces the concept of Engineering Economy as a new paradigm for understanding and managing macroeconomic policy in middle‑income countries seeking to escape the middle‑income trap. Drawing on Turkiye's post‑2001 economic trajectory and South Korea's successful transition from a low‑income to a high‑income economy, the study argues that conventional frameworks whether the Washington Consensus's market liberalization prescriptions or the institutionalist critique alone are insufficient. Instead, it proposes treating the economy as a dynamic control system requiring continuous calibration rather than static equilibrium. The paper develops a road‑surface metaphor (highway, side‑road, off‑road) to characterize different global economic regimes and presents eleven interconnected policy pillars spanning venture capital formation, regulatory sandboxes, technology‑focused industrial policy, and human capital development. By synthesizing insights from endogenous growth theory (Romer), institutional economics (Acemoglu), the catching‑up literature (Lee), cybernetic systems theory (Wiener), and Schumpeterian creative destruction, the framework reconceptualizes macroeconomic instruments through control‑engineering analogies: interest rates as energy gradients, fiscal policy as energy flow, exchange rates as balance motors, and regulation as adaptive suspension. The analysis demonstrates that Turkiye's structural challenge is not merely institutional weakness but a systemic absence of R&D demand from its dominant enterprise structures, creating a vicious cycle that conventional reforms cannot break. Seven specific opportunity windows arising from US‑China technological rivalry are identified, and a phased implementation roadmap is proposed.

Authors:Jannik Graebner, Ryne Beeson
Title: Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo
Abstract:
Preliminary low‑thrust spacecraft mission design is a global search problem characterized by a complex solution landscape, multiple objectives, and numerous local minima. During this phase, mission parameters are often not yet fully defined, requiring new solutions to be generated at a high cadence across varying parameter values. When combined with the indirect approach to optimal control, diffusion models can accelerate this search by learning distributions that represent high‑quality initial costates. However, generating training data remains expensive, and opportunities exist to better exploit past data. We propose a transfer‑learning framework that combines homotopy in a mission parameter with Markov chain Monte Carlo (MCMC) to generate training data more efficiently. The approach reformulates a multiobjective optimization problem as sampling from an unnormalized target distribution in costate space. We compare three MCMC algorithms on a planar multi‑revolution transfer in the circular restricted three‑body problem, with homotopy in the system mass parameter. The results show that gradient‑based MCMC variants achieve the best trade‑off between sample quality and computational cost. For the test transfer, the proposed framework generates 40 % more feasible solutions and achieves a higher‑quality Pareto front than a state‑of‑the‑art indirect approach based on adjoint control transformations and gradient‑based optimization. Finally, the MCMC‑generated samples are used to fine‑tune a diffusion model conditioned on the mass parameter, enabling it to learn a global representation of the underlying solution distribution and efficiently generate new solutions. These findings establish the transfer‑learning framework as a practical method for efficiently solving indirect trajectory optimization problems with varying parameters.

Authors:Anika Tabassum Orchi, Md Farhan Zaman, Md Darain Khan, Md Alamgir, Mahbubul Islam, Md. Jobayer Rahman, A. M. Zayed Abdullah, Md Mehrab Hossain Khan, Md. Kutub Al Baki, Iftekharul Islam, Shakil Ahmed, Md Sadique Hossain, Md Muzahidul Islam, Shah Mohammad Seaman, Nusrat Jahan Piyal, Shekh Md. Saifur Rahman, Fahim Hafiz, A. K. M. Muzahidul Islam, M. Rezwan Khan
Title: HyDRA Scorpion: A Cost-effective and Modular ROV for Real-Time Underwater Inspection, Intervention, and Object Detection
Abstract:
A Remotely Operated Vehicle (ROV) is a tethered underwater robot used for tasks like inspection and intervention. While essential tools for underwater science, the high cost of commercial ROVs and a persistent gap between mechanically capable platforms and those with integrated intelligence create a significant barrier to access. HyDRA Scorpion differs from conventional systems by addressing these challenges, integrating an advanced, AI‑driven perception stack with in‑situ measurement capabilities onto a low‑cost, locally manufacturable platform. The system combines 4‑DoF maneuverability, dual manipulators, and a custom pressure‑tested housing. Experimental results validate the system's robustness and performance. Leak‑free operation was confirmed through prolonged pressure testing of the electronics housing to 4 bar, equivalent to the pressure of a 304.8‑meter water depth approximately in a simulated environment, with no moisture ingress detected. The vehicle also demonstrated stable station‑keeping, maintaining its position within a tight tolerance of \(\pm\)0.15 meters under external disturbances. The onboard AI module achieved underwater object detection mean Average Precision (mAP) of 0.89 with real‑time inference, length and 3D‑mapping based distance measurement. Also, 4‑DoF manipulator arm can grip and maintain dual‑function manipulator feature which support 360 degree tangle‑free rotation.

Authors:Afsaneh Mollasalehi, Armin Farhadi
Title: Solar Cars: A Comprehensive Review
Abstract:
Energy crisis has forced many countries to think of a replacement for energy supply. Renewable energy sources as firendly environment sources play a pivotal role in producing clean energy for various sectors in industry. Gas emissions originating from the transportation industry is another contributing factor to air pollution. Hence, designing and utilizing vehicles that run on renewable energy is crucial, as it provides a dependable energy source that is naturally abundant, leaves nearly no carbon footprint, and is sustainable. Solar powered electric cars make a significant impact on global climate change. To better understand this impact and building upon the plenty of research done on this topic, this paper aims to provide a comprehensive review of the various factors related to solar cars. Specifically, this review will examine the following key factors: Types and sizing of solar cars, solar vehicle power source configurations, leading solar car nations, and solar car challenges.

Authors:Shaohui Liu
Title: Locational Pricing for Generative-AI Services via Token-Flow Market Clearing
Abstract:
GenAI services are in an early yet fast expanding phase. Providers compete on model capability and service quality, while the underlying infrastructure remains expensive and heterogeneous across regions, workloads, and compute assets. If these services diffuse into routine daily use, the relevant engineering problem becomes not only better models but also efficient dispatch on a geographically distributed AI service infrastructure. To address this, we formulate a network‑constrained token‑flow market that clears AI workloads across compute nodes and communication links. The baseline model is a linear program that co‑optimizes routing and processing subject to compute‑capacity and bandwidth constraints; its dual variables define location‑ and workload‑specific marginal service prices. We further introduce a transfer‑aware extension that prices data movement in physical units and isolates bandwidth congestion rents. In a 5‑node U.S. case study, the transfer‑aware model uncovers four saturated backbone links and raises total operating cost by 2.7% relative to the token‑equivalent baseline, while tightening the chatbot latency limit from 100~ms to 15~ms increases one locational price by 117%. A 20‑node scale‑up exhibits the same merit‑order dispatch logic and becomes infeasible once demand exceeds aggregate capacity. These results suggest that locational pricing is a useful organizing principle for operating an emerging AI service infrastructure and, over time, for designing competitive markets around it.

Authors:Francisco M. F. R. Gonçalves, Conor K. Trygstad, Néstor O. Pérez-Arancibia
Title: Model-Reference Adaptive Flight Control of the 95-mg Bee++
Abstract:
We introduce a model‑reference adaptive control (MRAC) architecture for high‑performance positional tracking of the Bee++, a 95‑mg insect‑scale flapping‑wing aerial vehicle. The suitability, functionality, and high performance of the proposed approach are demonstrated using data from real‑time flight experiments.

Authors:Chenchen Zhou, Hongxin Su, Xinhui Tang, Yi Cao, Shuang-Hua Yang, Lingjian Ye
Title: Generalized Global Self-Optimizing Control for Chemical Processes: Part II Objective-Guided Controlled Variable Learning Approach
Abstract:
Self‑optimizing control (SOC) aims to maintain near‑optimal process operation by judiciously selecting controlled variables (CVs). In this series of work, the generalized global SOC (g2SOC) approach is proposed, which extends the concept of SOC to the whole operation space and uses general nonlinear functions to design CVs instead of linear combinations. In the first part of this series work, two numerical approaches for g2SOC are proposed: the optimization‑based approach and the regression‑based approach, based on a theoretical analysis of the existence of perfect self‑optimizing CVs. The CVs designed by the former perform better, but are usually infeasible for large‑scale problems. In this paper, we propose an algorithm called objective‑guided controlled variable learning (OGCVL) that combines the advantages of both and has a better scalability. OGCVL is proposed for efficient CV design that seamlessly integrates symbolic and numerical computation techniques. Finally, the effectiveness of the OGCVL method is verified in two numerical examples. Both examples illustrate show that the OGCVL method is able to achieve good results while maintaining computational efficiency and is also feasible in large‑scale problems.

Authors:Prayag Sharma, Jonathan Y. M. Goh, Behçet Açıkmeşe, Franck Djeumou
Title: Transcription-Induced Failure Modes in 6-DOF Rocket Landing Trajectory Optimization
Abstract:
Solving optimal control problems via large‑scale NLP solvers depends on discretizing continuous dynamics. Yet, this transcription step hides critical vulnerabilities‑most notably truncation error and invariant drift‑that can drive solvers toward dynamically infeasible or suboptimal trajectories. To expose these hidden failures, we introduce a problem‑ and transcription‑agnostic adversarial objective that leverages the structure of local truncation‑error bounds to aggressively amplify such defects. When applied to a 6‑DOF rocket‑landing problem, we reveal a stark reliability gap: of fourteen transcription methods tested, only three satisfy rigorous validation criteria. These results also expose a striking performance inversion: even in the absence of classical stiffness, a fourth‑order implicit scheme (GL2) matches the fidelity of a sixth‑order explicit method (RK6). Using B‑series expansions and symplectic Runge‑Kutta theorems, we isolate the specific truncation errors and quaternion‑invariant drift responsible for these failures. Crucially, these theoretical vulnerabilities dictate operational performance: in practical lateral‑divert scenarios, the implicit GL2 consistently outperforms the explicit RK6 in both end‑to‑end solve speed and robustness.

Authors:Haoxuan Wu, Sishen Yuan, Haitao Gao, Zhen Li, Xiuli Zuo, Hongliang Ren
Title: Anatomical Landmark-Guided Deep Reinforcement Learning for Autonomous Gastric Navigation
Abstract:
Wireless capsule endoscopy (WCE) enables painless visualization of the gastrointestinal tract, but its diagnostic potential is limited by incomplete mucosal coverage and poor transferability of existing navigation methods across patient anatomies. We propose a transferable, anatomical landmarkguided deep reinforcement learning (AL‑DRL) framework for autonomous gastric navigation. Leveraging a lightweight edgecontour‑depth fusion module, our policy operates on stable, lowdimensional landmark coordinates rather than high‑dimensional video streams, effectively bridging the sim‑to‑real gap. In simulations across eight patient‑derived models, the method achieves over 97% coverage within 50 seconds, significantly outperforming vanilla PPO, SAC, and DQN agents. A two‑stage sim‑to‑real pipeline with an adaptive dynamic programming controller actively mitigates physical disturbances. Ex‑vivo experiments demonstrate a mean coverage of 87% and a 53% reduction in procedure time compared with expert manual control.

Authors:Ayoosh Bansal, Mikael Yeghiazaryan, Artyom Khachatryan, Tianyi Zhu, Hunmin Kim, Naira Hovakimyan, Lui Sha
Title: Synergistic Simplex: Cooperative Runtime Assurance for Safety-Critical Autonomous Systems
Abstract:
Autonomous systems increasingly rely on machine‑learning (ML) components for safety‑critical tasks such as perception and control in autonomous vehicles (AVs). While ML enables essential capabilities, it inevitably exhibits long‑tail faults that make it unsuitable for safety‑critical tasks. Runtime assurance (RTA) mitigates this issue by pairing ML components with verifiable safety monitors, e.g., Control Simplex and Perception Simplex architectures. However, the limited performance of safety monitors remains a major bottleneck. The Synergistic Simplex (SS) architecture improves system performance by enabling bidirectional integration between ML components and safety monitors while preserving formal safety guarantees. The key innovation here is allowing safety monitors to use ML outputs, which is typically prohibited in RTA systems. We formally derive conditions under which this integration preserves safety and demonstrate the performance benefits. We present the design, analysis, and evaluation of SS for AV obstacle detection.

Authors:Truman Welling, Onur Günlü, Aylin Yener
Title: Secure Integrated Sensing and Communication: Information Theory Offers Insights
Abstract:
Integrated sensing and communication (ISAC) combines sensing and communication within a shared system framework by using the same transmitted signal for both objectives. ISAC can improve the efficiency of spectrum and hardware use but also gives rise to new security challenges, as users associated with one function may need to be prevented from inferring information related to the other. This paper surveys information‑theoretic approaches to secure ISAC with emphasis on formulations, performance metrics, and fundamental limits. We first review the information‑theoretic ISAC models that underlie secure formulations. We then organize the secure ISAC literature according to the protected functionality and the adversary model, covering secure communication, sensing security, and active‑adversary settings such as jamming. We also discuss formulations in which communication security and sensing security interact more directly, as well as their connections to privacy and covert communication. Throughout, we highlight the main modeling assumptions and the insights they provide on the tradeoffs among communication reliability, sensing performance, and security.

Authors:Eunhan Ka, Ludovic Leclercq, Satish V. Ukkusuri
Title: Adaptive Domain Decomposition Physics-Informed Neural Networks for Traffic State Estimation with Sparse Sensor Data
Abstract:
Traffic state estimation from sparse fixed sensors is challenging because physics‑informed neural networks (PINNs) tend to over‑smooth the shockwaves admitted by the Lighthill‑Whitham‑Richards (LWR) model. This study proposes Adaptive Domain Decomposition Physics‑Informed Neural Networks (ADD‑PINN), a two‑stage residual‑guided framework for LWR‑based offline speed‑field reconstruction. A coarse global PINN is first trained; its spatial residual profile is then used to place subdomain boundaries and initialize child subnetworks in a decomposition‑enabled mode, while a data‑driven shock indicator can retain a single‑domain fallback when localized evidence of transition is weak. The primary offline I‑24 MOTION evaluation spans five days, five sensor configurations, and ten seeds per configuration, yielding 1,500 runs in total. Against neural and physics‑informed baselines, ADD‑PINN attains the lowest relative L2 error in 18 of 25 configurations and in 14 of 15 sparse‑sensing cases, while training 2.4 times faster than the extended PINN (XPINN) baseline. An ablation study supports spatial‑only decomposition as an effective default for fixed‑sensor traffic reconstruction in the evaluated settings. Supplementary Next Generation Simulation (NGSIM) experiments serve as a negative control: the shock indicator suppresses decomposition in all 50 runs, and the default single‑domain fallback ranks first across all sensor configurations. These results support residual‑guided spatial decomposition as an effective PINN‑family design for offline reconstruction when sparse fixed sensing coincides with localized transition regions.

Authors:Vivek Pandey, Nader Motee
Title: Entropic Value-at-Risk for Inter-Vehicle Collision in Platoons: Network- and Delay-Induced Bounds on Risk Due to Extreme Events
Abstract:
Safe operation of connected vehicle platoons under stochastic disturbances and time‑delayed dynamics requires accurate quantification of rare but dangerous events, such as inter‑vehicle collisions. We propose a rigorous framework for quantifying the risk of inter‑vehicle collisions in connected vehicle platoons subject to time‑delayed stochastic dynamics. We adopt the \emphentropic value‑at‑risk (EVaR) as a conservative metric to capture \emphrisk due to extreme events, highlighting its advantages over conventional Value‑at‑Risk (VaR) and Conditional Value‑at‑Risk (CVaR). By expressing the inter‑vehicle distance covariance in terms of the Laplacian eigenvalues of the communication network, we derive \emphnetwork‑and time‑delay‑induced bounds on both the minimum inherent risk and the worst‑case risk. Specifically, the algebraic connectivity dictates the maximum EVaR, while the largest Laplacian eigenvalue determines the minimum risk inherently induced by the network structure. Numerical simulations illustrate how network topology and time delay shape collision risk, offering actionable insights for the safe design of vehicle platoons operating under stochastic disturbances.

Authors:Wilhiam de Carvalho, Florin Capitanescu, Cyril Rasic, Jean-François Toubeau, François Vallée
Title: Allocation of Dynamic Operating Envelopes in Radial Distribution Networks
Abstract:
This paper provides an in‑depth analysis on how different aspects of the dynamic operating envelope (DOE) formulation impact the computation and allocation of network capacity. We show that the envelopes are significantly affected by the power flow model (non‑linear or linear), binding network constraint (thermal or voltage) and by the calculation case (import or export envelope). We also propose a novel DOE algorithm (LACE) that presents transparent and scalable computation that is useful for larger networks or to act in tandem with other optimization engines. We run numerical simulations with different test feeders, including a realistic low‑voltage feeder with real‑world data from Belgium. This paper provides crucial insights and tools to distribution system operators (DSOs), stakeholders and academics alike to make sure DOE calculation achieves desirable and efficient outcome.

Authors:Tomás Tapia, Ryan Piansky, Yury Dvorkin, Jean-Paul Watson
Title: Robust Capacity Expansion under Wildfire Ignition Risk and High Renewable Penetration
Abstract:
In power systems, the risk of wildfire ignition has increased significantly in recent years. The impact and severity of these events on energy dispatch, as well as their societal ramifications, make wildfire prevention critical for power system planning and operation. A common intervention by system operators is to de‑energize transmission lines to mitigate the risk of fire caused by equipment failures. With the growing integration of variable renewable generation, managing and preparing the system to de‑energization under wildfire risk has become even more challenging. In this context, mitigation decisions such as installing battery energy storage systems and undergrounding transmission lines can reduce the risk and adverse effects associated with de‑energization and renewable generation variability. This paper presents a robust optimization model to determine the optimal location of battery storage and undergrounding of transmission line investment, utilizing representative weeks and uncertainty sets to capture the temporal relationship of uncertain variables. Specifically, this paper addresses: (i) the worst‑case realization of ignition risk leading to the de‑energization of transmission lines, combined with the worst‑case realization of renewable energy availability, and (ii) the optimal investment decisions for energy storage capacity and undergrounding of transmission lines that are exposed to ignition risk. The proposed model is formulated as a mixed‑integer linear programming (MILP) problem, employing duality theory and binary decomposition to address nonlinearities, and is solved using a column‑and‑constraint generation algorithm. The proposed framework is evaluated on a model of the San Diego power system, demonstrating its practical effectiveness in improving the resilience to wildfire risk.

Authors:Markus Walker, Marcel Reith-Braun, Daniel Frisch, Uwe D. Hanebeck
Title: Sampling-based Model Predictive Control Using Trust Regions
Abstract:
Sampling‑based model predictive control (MPC) algorithms, such as model predictive path integral (MPPI), enable approximate, gradient‑free solutions to optimal control problems by drawing samples from a proposal distribution, evaluating their trajectory costs, and updating the proposal parameters accordingly. However, these approaches typically rely on heuristics for adjusting hyperparameters, such as temperature or momentum, or manual tuning. We propose a trust region formulation for sampling‑based MPC that constrains updates of the proposal distribution via a principled Kullback‑‑Leibler (KL) divergence bound and, optionally, an entropy lower bound. This replaces heuristic hyperparameter adaptation with values that are optimal w.r.t. the underlying Lagrangian. We further improve sample efficiency and convergence by combining the trust region update with deterministic localized cumulative distribution (LCD)‑based sampling. Experiments on two benchmark environments demonstrate that the proposed trust region update achieves faster convergence and better sample efficiency in low‑sample and low‑iteration regimes, especially when paired with deterministic LCD‑based sampling.

Authors:Erik Börve, Nikolce Murgovski, Morteza Haghir Chehreghani, Leo Laine
Title: Interactive Trajectory Planning with Learning-based Distributionally Robust Model Predictive Control and Markov Systems
Abstract:
We investigate interactive trajectory planning subject to uncertainty in the decisions of surrounding agents. To control the ego‑agent, we aim to first learn the decision distribution and solve a Stochastic Model Predictive Control (SMPC) problem. To account for errors in the learned distribution, we show that it is possible to utilize Probably Approximately Correct (PAC) learning in combination with Distributionally Robust (DR) optimization to obtain a solution which accounts for the errors induced by the learning model. The results indicate that our PAC learning‑based DR‑MPC framework provides a method to interpolate between a robust MPC and an omnipotent SMPC, based on the available number of samples.

Authors:Xiucheng Wang, Nan Cheng, Zhisheng Yin, Conghao Zhou, Ruijin Sun
Title: Beam-Aware Radio Map Estimation With Physics-Consistent Parametric Modeling for Unknown Multiple Satellites
Abstract:
Satellite networks with dense low Earth orbit (LEO) constellations rely on aggressive spectrum reuse, making co‑channel interference a dominant and rapidly varying factor that limits link availability and complicates spectrum sharing and compliance. Satellite radio map (RM) construction is therefore essential for interference cognition, yet it is challenging because the active satellite set is unknown, beam footprints and pointing are not directly observable, and received signal strength (RSS) measurements are difficult to calibrate under coupled link budget variations and noise. These latent uncertainties yield a severely underdetermined inverse problem with strong signature coherence, where existing methods often trade detection recall for precision and still fail to recover a faithful continuous RSS field. This paper proposes a beam‑aware RM estimation framework that unifies active satellite identification and RSS field reconstruction through physics‑consistent parametric modeling. An interpretable structural prior links geometry and beam shaping to spatial RSS formation, and an adaptive model order selection strategy infers the number of active satellites from measurements by balancing fit and complexity. Extensive experiments across varying signal to noise ratio (SNR), total satellite count, and active satellite count demonstrate consistently higher RSS spatial correlation, lower root mean squared error (RMSE), and improved F1 score, validating the proposed approach for interference‑aware satellite RM construction in satellite networks.

Authors:Nour-eddine Id omar, Alexandre Lê-Agopyan, Fabrizio Sossan
Title: Control and Scheduling of Behind-the-Meter Battery Energy Storage Systems for Stacked Grid and Building Services
Abstract:
This paper proposes and experimentally validates a two‑stage scheduling and control strategy for a behind‑the‑meter battery energy storage system (BESS) delivering both local and grid services. Considered services are the maximization of PV self‑consumption, peak‑load reduction, and secondary frequency control (aFRR).The day‑ahead stage allocates battery capacity across local and balancing services using a scenario based approach, reflecting potential remuneration from aFRR participation without committing to fixed power availability; in the real‑time stage, BESS set‑points are computed in a periodic fashion at a high time resolution based on updated information on balancing prices, net load realization and BESS state of charge. The strategy is experimentally validated on a building at the Energypolis Campus of HES‑SO Valais (Sion, Switzerland), which exhibits a peak power demand of 300 kW and is equipped with a 264 kWh / 140 kW lithium‑ion BESS. The experimental results demonstrate the effectiveness of the proposed framework in scheduling and actuating the provision of both behind‑the‑meter and front‑of‑the‑meter services.

Authors:Muhammad Haris, Claudio Roncoli
Title: Efficient MILP-based Urban Network Traffic Control in Mixed Autonomy with Dynamic Saturation Rates
Abstract:
This paper introduces a novel control strategy to optimize urban network traffic in mixed autonomy settings, featuring Connected and Automated Vehicles (CAVs) alongside Human‑Driven Vehicles (HDVs). Unlike previous control strategies, where the impact of driver behaviour of CAVs and HDVs is not explicitly considered, we propose a dynamic, queue‑responsive saturation rate to account for autonomy‑driven variations in traffic flow characteristics. The proposed method is based on an extended multi‑commodity store‑and‑forward model to a mixed autonomy environment, integrating optimized routing for CAVs via infrastructure‑linked connectivity, and signal timings at every signalized intersection. The problem is formulated as a Non‑Convex Quadratic Program (NQP), which accounts for queue evolution, spillback, green time allocation, and CAVs routing. To enable computational efficiency for real‑time applications, we transform the NQP into a sequence of convex subproblems, leveraging under‑ and over‑estimators to reformulate it as a Mixed Integer Linear Program (MILP). Experimental results via microscopic simulations validate the efficiency and robustness of the proposed methodology. The results reflect that the proposed model outperforms the existing multi‑commodity approach, thus demonstrating its potential for real‑time traffic optimization in future urban mobility systems.

Authors:Khalid Mehrab, Md Zamiul Alam, Shadman Tahmid Haque
Title: Cascade PID Control of an Inverted Pendulum on a Cart System: Simulation and Experimental Analysis
Abstract:
This study investigates the performance of cascade PID control architecture applied to an inverted pendulum on a cart system through both simulation and experimental implementation. A nonlinear model of the system was developed using Simscape Multibody in Simulink, while a physical prototype was constructed using a DC motor‑driven cart, pendulum, rotary encoder, ultrasonic sensor, and an Arduino. The cascade PID control structure consists of an inner loop regulating the pendulum angle and an outer loop controlling the cart position. Simulation results demonstrated effective stabilization of the pendulum and satisfactory position tracking under idealized conditions. Experimental results confirmed successful real‑time stabilization but revealed notable differences from simulation, particularly in controller gains, transient behavior, and disturbance response due to sensor noise, unmodeled friction, and implementation constraints. The study also highlights the limitations of cascade PID control in disturbance rejection and large position commands, particularly under limited track length. A comparative analysis using an LQR‑based inner loop demonstrated better disturbance rejection and reduced overshoot. The results provide practical insights into the applicability and limitations of cascade PID control of the inverted pendulum system.

Authors:Botao Zhu, Xianbin Wang
Title: Spatiotemporal Trust Evaluation for Collaborator Selection via Customized GNN-Mamba
Abstract:
The successful completion of collaborative tasks relies on the effective selection of trustworthy collaborators. To accurately evaluate the trustworthiness of potential collaborators, it is necessary to combine insights from their past collaborations with assessments of their resource capabilities under specific task contexts. However, the coexistence of diverse trust perspectives, along with complex spatiotemporal dependencies among devices, makes accurate trust evaluation particularly challenging. To address these challenges, we propose a customized Graph Neural Network (GNN)‑Mamba (GM) model for trust evaluation and collaborator selection. In this model, the GNN model performs spatial trust fusion by leveraging inter‑device spatial dependencies extracted from historical collaborations, while the Mamba‑based temporal model captures both short‑term fluctuations and long‑term evolution of device trust. In addition, task‑specific resource trust is incorporated to reflect the practical capabilities of devices under varying task conditions. Experimental results demonstrate that the proposed GM model outperforms baseline approaches in terms of the accuracy and stability of trust evaluation.

Authors:Timo Oksanen, Karl Th. Renius
Title: Electric Axle and Wheel Module Driveline Concepts for Self-propelled Agricultural Machinery and Equipment Carriers
Abstract:
Direct electric drivelines without power‑split open new design freedom for frame and suspension design, along with often lower energy losses. This paper focuses on self‑propelled agricultural machinery (combine and forage harvest‑ers, root crop harvesters), equipment carriers, propelled trailers and field robots. For a typical vehicle with four driven wheels, the electric motors can be packaged as two axle modules or four wheel modules, both defined herein as self‑contained mechatronic units with integrated power electronics, distributed control intelligence and steering. Axle module and wheel module concepts are compared in detail against engineering requirements including loads, effi‑ciency, steerability, controllability, braking, suspension, structural load support, asymmetric wheel loading and manu‑facturing cost. The wheel module offers maximum design freedom, redundancy and controllability, while the axle module provides lower cost, structural rigidity, automatic load sharing through the differential and the ability to be used in existing vehicle structures. Both concepts are defined such that distributed control intelligence and steering are integral to each unit, requiring only a DC power bus and communication interface from the vehicle.

Authors:Mirhan Urkmez, Shahab Heshmati-Alamdari
Title: Distributionally Robust Data-Driven Predictive Control for Stochastic LTI Systems
Abstract:
We propose a distributionally robust data‑driven predictive control framework for stochastic linear time‑invariant systems with unknown dynamics and disturbance distributions. We use an offline trajectory to fit the subspace predictive control (SPC) predictor via least squares and construct an empirical distribution of the prediction residuals as a proxy for the unknown disturbance distribution. We then center a Wasserstein ambiguity set around this estimate and minimize the worst‑case expected cost while enforcing probabilistic output constraint satisfaction over all distributions in the set. The resulting problem admits a tractable reformulation with an equivalent direct data‑driven form, eliminating the need for explicit predictor identification. Using finite‑sample concentration results, we provide a data‑driven Wasserstein radius such that, with high probability, the true expected cost is bounded above by the tractable objective and output constraints are satisfied with respect to the true disturbance distribution. Numerical simulations validate the framework against existing methods under various disturbance conditions and cost functions.

Authors:Haiyuan Li, Yulei Wu, Dimitra Simeonidou
Title: Deadline-Driven Hierarchical Agentic Resource Sharing for AI Services and RAN Functions in AI-RAN
Abstract:
AI‑RAN consolidates AI services and Radio Access Network (RAN) functions onto a unified, GPU‑accelerated infrastructure at the network edge. However, compute sharing between real‑time RAN functions and highly heterogeneous AI services requires coordination of scheduling decisions at mismatched timescales, and placement adaptation may require service migration across nodes with non‑negligible interruptions. This paper proposes a hierarchical agentic framework (HAF) for compute sharing in AI‑RAN that combines a large language model (LLM)‑based agent for slow‑timescale placement of AI services and RAN functions with a closed‑form, deadline‑aware convex algorithm for fast‑timescale GPU/CPU allocation. The LLM agent is further equipped with a predictive critic that filters out migrations when the induced service interruption outweighs the expected service‑level objective (SLO) benefit. Experimental results show that HAF reaches 90.0% overall SLO fulfillment, a 20.5% improvement over the strongest baseline, and raises AI service request fulfillment from 51% to 85.3%. Further evaluations show that HAF retains its advantage under diverse load conditions, while the critic consistently improves SLO fulfillment across multiple open‑source LLM agents.

Authors:Lasse Kötz, Knut Åkesson
Title: Learning Neural Hybrid Surrogates for Gradient-Based Falsification
Abstract:
Falsification of hybrid dynamical systems remains challenging due to mode‑dependent dynamics and discrete transitions. In this work, we propose a surrogate‑based falsification approach that enables hybrid systems by learning a differentiable hybrid automaton model from data. This extends previous surrogate‑based falsification methods, which were limited to purely continuous dynamics. Specifically, we employ neural hybrid automata to learn both a latent mode encoder and the corresponding mode‑conditioned vector fields. Once the surrogate has paired each mode with an associated vector field, the transition guards are inferred using existing trajectory data. The learned surrogate is subsequently subjected to a gradient‑based optimal control formulation, which minimizes a smooth approximation of the safety specification to find safety violations. In the last step, an experiment with the optimal control solution is carried out on the original system to ensure soundness. The proposed method consistently uncovers counterexamples on a majority of evaluated benchmark specifications; on these cases, it achieves competitive or improved sample efficiency than other tools while using a reduced simulation budget.

Authors:Denys Mishchenko, Irina Oleinikova, Laszlo Erdodi
Title: Resilience of IEC 61850 Sampled Values-Based Protection Systems Under Coordinated False Data Injections
Abstract:
This paper assesses the resilience of IEC 61850 digital substations under False Data Injection Attacks (FDIAs) targeting the Sampled Values (SV) protocol. The multicast nature of SV, while enabling time‑critical automation, exposes substations to cyber intrusions capable of disrupting protection functions and causing large‑scale outages. To evaluate these risks, coordinated attack vectors involving both physical and cyber access at the bay level are experimentally analyzed using an advanced setup based on industrial‑grade intelligent electronic devices (IEDs). The proposed attacks simultaneously manipulate multiple electrical parameters in a coordinated and physically consistent manner. Experimental results confirm the feasibility of stealthy multi‑vector FDIAs that can trigger false protection actions, conceal real faults, or block protection mechanisms while maintaining realistic signal behavior. The Power Hardware‑in‑the‑Loop (PHIL) testbed enables closed‑loop evaluation under strict timing, communication, and protection logic constraints, reflecting real device behavior beyond simulation and controller‑level HIL environments. The findings reveal critical vulnerabilities in SV‑based protection schemes that directly affect grid reliability, particularly under realistic attacker positioning. To address these challenges, a defense strategy covering deterrence, prevention, detection, mitigation, and resilience is analyzed, with emphasis on bay‑level infrastructure. Furthermore, a resilience‑oriented method based on trusted independent channels and cross‑verification of SV data within the protection logic is outlined as a complementary countermeasure for scenarios where existing standardized security mechanisms are insufficient.

Authors:Masahiro Fujiwara, Naoya Ozaki
Title: Stochastic Differential Dynamic Programming for Trajectory Optimization under Partial Observability
Abstract:
Designing spacecraft trajectories remains challenging in the presence of stochastic effects such as maneuver execution errors and observation uncertainties. Although covariance control and belief‑space planning provide useful tools for designing robust control policies and information‑aware trajectories under uncertainty, practical methods remain limited for partially observable trajectory optimization problems in which trajectory design, orbit determination, and correction maneuver planning are tightly coupled. This paper presents a stochastic differential dynamic programming algorithm for such coupled problems. The proposed method optimizes the nominal control sequence and feedback gains subject to belief dynamics and general mission constraints, explicitly accounting for the dependence of covariance propagation on the nominal trajectory without relying on the separation principle. Numerical examples demonstrate that the proposed algorithm produces navigation‑aware and uncertainty‑robust solutions across a range of dynamical systems, observation models, and uncertainty levels. In particular, the circular restricted three‑body problem shows that the proposed method can exploit the coupling between trajectory design and orbit determination to obtain navigation‑aware solutions with substantially lower fuel consumption than those from deterministic local optimization starting from the same initial guess.

Authors:Zhaojun Ruan, Yulin Liu, Le Fu, Libao Shi
Title: Data-Driven Contextual-Aware Uncertainty Set for Robust Dispatch of Power Systems
Abstract:
Both the level of conservativeness and the computational burden in robust optimization are critically influenced by uncertainty set design. However, contextual side information is rarely exploited in robust dispatch of power systems characterized by irregular data distributions, which hinders the explicit characterization of the relationship between covariates and uncertain parameters. To address this issue, a data‑driven method for constructing contextual‑aware uncertainty set is proposed in this letter. Based on a conditional Gaussian mixture model, a set of covariates is leveraged as side information to design uncertainty sets tailored to historical data exhibiting irregular distributions. The resulting set is formulated as a union‑of‑subsets formulation, and a mixed integer linear reformulation is adopted to describe the worst‑case realization across all subsets. Finally, the effectiveness of the proposed method is demonstrated through numerical experiments applied to robust unit commitment.

Authors:Christopher Anthony Orrico, W. P. M. H. Heemels, Dinesh Krishnamoorthy
Title: Learning myopic mixed-integer nonlinear model predictive control from expert demonstrations
Abstract:
Applying nonlinear model predictive control (NMPC) to systems with hybrid dynamics or discrete actions typically yields mixed‑integer nonlinear programs (MINLPs), whose real‑time solution remains a major challenge and limits the applicability of mixed‑integer NMPC (MINMPC). This paper proposes a myopic MINMPC framework that incorporates value‑function approximation to substantially reduce the online computational burden. Using Bellman's principle of optimality, we shorten the prediction horizon and append a value function learned offline from expert state‑action demonstrations via inverse optimization with optimality residual minimization. A central feature is the dual treatment of discrete decisions, whereby integer constraints are relaxed during offline learning to enable KKT‑residual‑based value function synthesis, while the online controller enforces the true integer constraints to ensure feasibility. The learned value function induces a policy that is approximately policy‑consistent with the expert demonstrations. The resulting controller achieves high closed‑loop performance with a significantly shorter horizon, enabling real‑time MINMPC. The effectiveness of the approach is demonstrated on the Lotka‑Volterra fishing problem and a satellite attitude control system with discrete actuators.

Authors:Shih-Yu Lai
Title: MORPH-U: Multi-Objective Resilient Motion Planning for V2X-Enabled Autonomous Driving in High-Uncertainty Environments via Simulation
Abstract:
V2X can warn an autonomous vehicle about hazards beyond line‑of‑sight, but it also brings uncertainty: messages may be delayed, dropped, or even forged. Meanwhile, map knowledge may change during a trip, forcing the vehicle to replan under tight real‑time budgets. This paper studies how to make motion planning and low‑level control robust to such uncertain, event‑driven updates. We present MORPH‑U, a CARLA‑based closed‑loop stack that fuses LiDAR/radar/camera with V2X (CAM/DENM) into a Local Dynamic Map (LDM) and triggers Hybrid‑A replanning when validated hazards or map changes affect the planned route. We expose the planning/control trade‑offs via a multi‑objective formulation over tracking error, safety margin (minimum TTC), responsiveness, and smoothness, and select operating points using Pareto‑frontier analysis. To avoid unsafe replanning from faulty V2X triggers, MORPH‑U adds a lightweight Byzantine‑inspired acceptance gate that combines a quorum rule with an on‑board sensor veto. Experiments in dynamic CARLA scenarios show that V2X‑augmented LDM improves downstream safety, Pareto tuning provides controllable accuracy‑comfort trade‑offs, and the gate prevents replanning under saturated false‑DENM injection (p_\textattack=1.0).

Authors:Syed Pouladi
Title: Stability-Certified Koopman Observer Design for Nonlinear Systems via Generalized Persidskii Dynamics
Abstract:
This paper addresses the problem of nonlinear state estimation for dynamical systems whose governing equations are approximated through Koopman operator liftings. While Koopman‑based predictors have demonstrated broad approximation capability for nonlinear dynamics, certifying observer convergence under model mismatch and measurement noise has remained a largely open problem. To resolve this, we establish a structural correspondence between the error dynamics of a Koopman latent‑space observer and the class of generalized Persidskii systems, which admits diagonal Lyapunov functions and incremental sector characterizations. Exploiting this connection, we design a nonlinear correction term whose gain is computed via a linear matrix inequality (LMI) that simultaneously certifies input‑to‑state stability (ISS) of the estimation error with respect to both lifting residuals and external disturbances. Exponential convergence in the nominal case and ultimate boundedness under bounded perturbations are established analytically. Numerical validation on the Van~der~Pol oscillator and a nonlinear robotic arm with friction uncertainty demonstrates that the proposed observer substantially outperforms both the Extended Kalman Filter and a linear Koopman observer in terms of estimation accuracy and robustness, achieving up to a 42% reduction in steady‑state RMSE under lifting mismatch.

Authors:Antonio Franchi
Title: Variable Aerodynamic Damping via Co-Contraction: A Dynamic Isomorphism with Variable Stiffness Actuators
Abstract:
We prove that aerodynamic co‑contraction in a redundant dual‑rotor actuator can tune a passive, trim‑defined aero‑mechanical damping while keeping the commanded net force constant. In particular, we define an incremental damping coefficient as the local sensitivity of net thrust to air‑relative velocity at a trim and prove that it increases monotonically along constant‑force fibers under a mild aerodynamic hardening condition. We then validate the required damping and hardening properties from a first‑principles Blade Element Theory derivation, which yields a minimal thrust model affine in inflow and explicitly reveals the speed‑‑inflow coupling driving the effect. The resulting mechanism is formalized as a Variable Aerodynamic Damping Actuator (VADA) and shown to be dynamically isomorphic to stiffness modulation in antagonistic variable‑stiffness actuation (VSA), similar to the co‑contraction of tendons by muscle co‑activation. The same fiber‑density principle also enhances the active aerodynamic promptness measure of redundant multirotors. Finally, an impedance‑form representation clarifies the roles of common‑mode and differential‑mode actuation in the control of passive impedance and the equilibrium velocity of the VADA system.

Authors:Ishank Juneja, Carlee Joe-Wong, Osman Yağan
Title: Cost-Ordered Feasibility for Multi-Armed Bandits with Cost Subsidy
Abstract:
The classic multi‑armed bandit (MAB) problem tackles the challenge of accruing maximum reward while making decisions under uncertainty. However, in applications, often the goal is to minimize cost subject to a constraint on the minimum permissible reward, an objective captured by multi‑armed bandits with cost‑subsidy (MAB‑CS). Of interest to this paper is the setting where the quality (reward) constraint is specified relative to the unknown best reward and the cost of each arm is known. We characterize the expected sub‑optimal samples required by any policy by proving instance‑dependent lower bounds that offer new insight into the problem and are a strict generalization of prior bounds. Then, we propose an algorithm called Cost‑Ordered Feasibility (COF) that leverages our insight and intelligently combine samples from all arms to gauge the feasibility of a cheap arm. Thereafter, we analyze COF to establish instance‑dependent upper bounds on its expected cumulative cost and quality regret, i.e., relative to the cheapest feasible arm. Finally, we empirically validate the merits of COF, comparing it to baselines from the literature through extensive simulation experiments on the MovieLens and Goodreads datasets as well as representative synthetic instances. Not only does our paper develop qualitatively better theoretical regret upper bounds, but COF also convincingly demonstrates improved empirical performance.

Authors:Alfo Borzi, Guofeng Zhang
Title: Symplectic H2 Model Reduction for High-Dimensional Linear Quantum Systems
Abstract:
The \mathcalH_2 model reduction problem for high‑dimensional linear quantum systems is studied under the constraint of physical realizability (PR). This constraint requires preservation of the canonical commutation relations and the quantum input‑output structure, and therefore prevents the direct use of standard projection methods. A symplectic Petrov‑Galerkin framework is presented, in which reduced‑order models automatically satisfy the PR identities by construction. Within this framework, a symplectic variant of the iterative rational Krylov algorithm is developed and referred to as Quantum IRKA (Q‑IRKA). At each iteration, an enriched tangential rational Krylov pool is generated from shifted linear solves. A symplectic basis is then extracted by a Gram‑Schmidt‑type procedure, paired with symplectic conjugates, and normalized so that the reduced trial space satisfies the canonical symplectic constraint. The interpolation points are updated from selected mirror images of the poles of the current reduced‑order model, while the reduced‑order matrices are obtained exclusively by structure‑preserving projection. Numerical experiments on low‑channel oscillator‑chain systems and on a bosonic Kitaev‑chain‑inspired benchmark show that Q‑IRKA is effective for large‑scale linear quantum systems. Symplecticity and PR are preserved to machine precision, and accurate reduced‑order models are obtained with moderate computational cost. The results also show that reduction quality depends substantially on dissipation geometry, channel placement, heterogeneity, and reduced order. These findings indicate that scalable \mathcalH_2 model reduction of linear quantum systems can be achieved while strictly preserving the underlying physical structure.

Authors:Boya Hou, Maxim Raginsky
Title: A Behavioral Framework for Data-Driven Modeling of Nonlinear Systems in Vector-Valued Reproducing Kernel Hilbert Spaces
Abstract:
We generalize Jan Willems' behavioral approach to a class of discrete‑time nonlinear systems in a vector‑valued reproducing kernel Hilbert space (RKHS). Apart from linear time‑invariant systems, this class covers nonlinear systems modeled by Volterra series and their autoregressive variants, as well as systems admitting Hammerstein‑type state‑space realizations. We apply the proposed framework to the problem of data‑driven modeling of such systems, i.e., when simulation or control objectives for an unknown system are carried out without an explicit system identification step. To that end, we link the behavioral approach to two data‑driven modeling methods in a vector‑valued RKHS: (1) minimum‑norm interpolation and (2) subspace identification.

Authors:Jiawei Xu, Longsen Gao, Rafael Fierro, David Saldaña
Title: AirBender: Adaptive Transportation of Bendable Objects Using Dual UAVs
Abstract:
The interaction of robots with bendable objects in midair presents significant challenges in control, often resulting in performance degradation and potential crashes, especially for aerial robots due to their limited actuation capabilities and constant need to remain airborne. This paper presents an adaptive controller that enables two aerial vehicles to collaboratively follow a trajectory while transporting a bendable object without relying on explicit elasticity models. Our method allows on‑the‑fly adaptation to the object's unknown deformable properties, ensuring stability and performance in trajectory‑tracking tasks. We use Lyapunov analysis to demonstrate that our adaptive controller is asymptotically stable. Our method is evaluated through hardware experiments in various scenarios, demonstrating the capabilities of using multirotor aerial vehicles to handle bendable objects.

Authors:Simon J. Jones, Dominic Liao-McPherson, Marco M. Nicotra
Title: A Semi-smooth Newton Method for the Constrained Optimal Control of Continuous-Time Linear Systems
Abstract:
This paper details a novel indirect method for solving constrained optimal control problems (OCPs) directly in continuous‑time function space. The KKT conditions are embedded in a non‑smooth complementarity function, which enables their reformulation as a rootfinding problem in Banach space. This problem is then solved using a non‑smooth Newton method. Finally, the paper shows that the Newton update can be obtained by solving a modified differential Riccati equation, where the cost terms are reweighted at every iteration based on the constraint multipliers. Numerical simulations show the effectiveness of the method, which converges superlinearly up to the tolerance of the ODE solver.

Authors:Muhammad Faraz Ul Abrar, Nicolò Michelusi, Erik G. Larsson
Title: Decentralized Time-Varying Optimization for Streaming Data via Temporal Weighting
Abstract:
Classical optimization theory largely focuses on fixed objective functions, whereas many modern learning systems operate in dynamic environments where data arrive sequentially and decisions must be updated continuously. In this work, we study optimization with streaming data over a distributed network of agents. We adopt a structured, weight‑based formulation that explicitly captures the streaming‑data origin of the time‑varying objective: at each time step, every agent receives a new sample, and the network seeks to track the minimizer of a temporally weighted objective formed from all samples observed across the network so far. We focus on decentralized gradient descent (DGD) with a limited communication/computation budget, where at each time step, only a limited number of DGD iterations can be performed before the objective changes again. For strongly convex and smooth losses, we analyze the tracking error with respect to the time‑varying minimizer through a fixed‑point theory lens. Our analysis reveals that the tracking error decomposes into a fixed‑point tracking term and a bias term induced by data heterogeneity across agents. We specialize the analysis to two natural weighting strategies: uniform weights, which treat all samples equally, and exponentially discounted weights, which geometrically decay the influence of older data. Under uniform weighting, DGD tracks the fixed‑point at a rate \mathcalO(1/t), whereas discounted weighting yields a non‑vanishing fixed‑point tracking floor controlled by the discount factor. In both cases, decentralization induces an additional non‑zero bias floor under a constant step size. We validate our theoretical findings through numerical simulations.

Authors:Timothy I. Salsbury, Min Gyung Yu, Sayak Mukherjee
Title: Data-Driven Koopman-Enhanced Extremum Seeking for Oscillation Damping in Nonlinear Systems
Abstract:
We propose a novel extremum seeking control (ESC) method that operates in a lifted Koopman state space to minimize the filtered RMS energy in the dominant subspace. The lifted representation provides linear embeddings of nonlinear dynamics, enabling more accurate gradient estimation and dampening of state interference for more consistent ESC performance. Applied to a parameterized, forced, and time‑varying Van der Pol oscillator, we show that the approach yields faster and more robust performance than operating ESC on the measured states. These advantages position the method for a diverse range of applications including vibration suppression, motion control, and subsynchronous oscillation mitigation in inverter‑dominated power systems.

Authors:Umme Mahbuba Nabila, Paul Seurin, Linyu Lin, Majdi I. Radaideh
Title: Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning
Abstract:
Real‑time supervisory control of thermal energy distribution systems requires digital twins that are accurate, interpretable, and uncertainty‑aware, yet remain data and computationally efficient. High‑fidelity simulations alone are costly, while purely data‑driven surrogates often lack robustness. To address these challenges, this work proposes an active learning (AL) framework that couples system‑level Modelica simulations with four simpler physics‑informed and data‑driven surrogate modeling approaches: deterministic Sparse Identification of Nonlinear Dynamics with Control (SINDyC), its probabilistic multivariate‑Gaussian extension (MvG‑SINDyC), feedforward neural network (FNN), and gated recurrent unit (GRU) network. Tailored to each surrogate, model‑specific AL query strategies are employed, including Mahalanobis‑distance sampling in coefficient space for MvG‑SINDyC and error‑based sampling in prediction space for SINDyC, FNN, and GRU, allowing the learning process to prioritize dynamically informative trajectories. The proposed approach is demonstrated on the glycol heat exchanger (GHX) subsystem of the Thermal Energy Distribution System (TEDS) at Idaho National Laboratory. Across key GHX outputs‑‑the bypass mass flow rate \dotm_\mathrmGHX and heat transfer rate Q_\mathrmGHX‑the AL framework achieves comparable predictive accuracy using as few as one‑fifth of the simulation trajectories required by random sampling. Among the evaluated surrogates, the GRU achieves the highest predictive fidelity, while SINDyC remains the most computationally efficient and interpretable. The probabilistic MvG‑SINDyC surrogate further enables uncertainty quantification and exhibits the largest computational gains under AL.

Authors:Yixuan Wang, Dan Guralnik, Warren Dixon
Title: Quantifying Trade-Offs Between Stability and Goal-Obfuscation
Abstract:
Safety‑critical autonomy in adversarial settings demands more than Lyapunov stability of tracking error signals. An agent executing a goal‑directed trajectory is intrinsically legible to a passive observer running online Bayesian inference, because the contractive dynamics of any Lyapunov basin of attraction concentrates posterior belief over the latent intent parameters. We initiates the study of intent privacy over a continuous state space as a joint control problem on the physical state combined with the latent belief state of a putative observer. With the main challenges concentrated around the analysis of the belief‑state dynamics, the agent dynamics is assumed to be simple, modeled by the differential inclusion \dotx\in u+\bard\mathbbB. That is, the agent is fully actuated with bounded unknown disturbance to the control input. The observer's intent inference process is modeled as a discrete‑time stochastic dynamical system evolving over the belief state space of a Rao Blackwellized particle filter reasoning over large random samples of possible agent goals. The agent's control input is modeled as a piecewise constant signal, with jumps matching the RBPF update times. Building on a prior intent‑inference framework and its KL‑based information leakage measurement, a privacy constraint is imposed, which amounts to maintaining information leakage above a prescribed threshold with high probability, using probabilistic discrete‑time control barrier functions. A key technical contribution is the derivation of separate PCBF results for the Bayesian update step and the resampling step of the RBPF, enabling a PCBF result for the full update as well as integration of the privacy constraint with the agent's task‑side tracking requirement. Finally, a joint feasibility analysis is carried out by examining the interplay between the privacy constraint and the tracking envelope.

Authors:Ahmed Ali, Chiara Gabellieri, Antonio Franchi
Title: Lie Group Formulation of Recursive Dynamics Algorithms of Higher Order for Floating-Base Robots
Abstract:
In this paper, we describe procedures for computing higher‑order time derivatives of the Lie‑group Newton‑Euler, Articulated‑Body Inertia, and hybrid dynamics algorithms for floating‑base trees, where the base configuration evolves on SE(3) and the attached mechanism is an open kinematic tree with configuration on the (n1+n2)‑dimensional manifold T^n1 × R^n2, using spatial representation of twists. After presenting the algorithms, we collect the resulting recursions into closed‑form equations of motion, identifying an admissible Coriolis matrix satisfying the passivity property, and showing that the articulated inertia tensor remains unchanged across all time derivatives. We then apply the developed methods to a 12‑DoF aerial manipulator to derive analytical expressions for its geometric forward and inverse dynamics along with their first time derivatives whereas the numerical simulations successfully evaluate these dynamics up to fifth order. Finally, to demonstrate their practical utility, we benchmark the proposed extensions and show that, in the considered tests, their computational cost scales quadratically with the derivative order, whereas the automatic‑differentiation baseline exhibits exponential scaling.

Authors:Chenchen Zhou, Hongxin Su, Xinhui Tang, Yi Cao, Shuang-hua Yang
Title: Global self-optimizing control of batch processes
Abstract:
This work considers to achieve near‑optimal operation for a class of batch processes by employing self‑optimizing control (SOC). Comparing with a continuous one, a batch process exhibits stronger nonlinearity with dynamics because of the non‑steady operation condition. This necessitates a global version of SOC to achieve satisfactory performance. Meanwhile, it also makes the existing global SOC (gSOC) not directly applicable to batch processes due to the causality amongst variables. Therefore, it is necessary to extend the original gSOC to batch processes. In addition to the nonconvexity challenge of the original gSOC problem, the new extension for batch processes has to face even more challenges. Particularly, the causality due to dynamics of batch processes brings in structural constraints on controlled variables (CVs), making a CV selection problem even more difficult. To address these challenges, the gSOC problem is recast in a vectorized formulation and it is proved that the structural constraints considered are linear in the vectorized formulation. Moreover, a novel shortcut method is proposed to efficiently find sub‑optimal but more transparent solutions for this problem. The effectiveness of the new approach is validated through a case study of a fed‑batch reactor, where CVs are constructed through a combination matrix with a repetitive structure, resulting in a simple SOC scheme. This simplicity facilitates the implementation of the SOC approach and enhances its practical applicability and robustness.

Authors:Bowen Ye, Zhijian Li, Junyue Huang, Junkai Ma, Xiang Yin
Title: ReasonSTL: Bridging Natural Language and Signal Temporal Logic via Tool-Augmented Process-Rewarded Learning
Abstract:
Signal Temporal Logic (STL) is an expressive formal language for specifying spatio‑temporal requirements over real‑valued, real‑time signals. It has been widely used for the verification and synthesis of autonomous systems and cyber‑physical systems. In practice, however, users often express their requirements in natural language rather than in structured STL formulas, making natural‑language‑to‑STL translation a critical yet challenging task. Manual specification requires temporal‑logic expertise and cannot scale, while prompting commercial LLM APIs incurs substantial token costs and may expose sensitive system requirements to third‑party services, raising privacy concerns for industrial deployment. To address these challenges, we present \textscReasonSTL, a tool‑augmented framework that adapts local open‑source language models for natural‑language‑to‑STL generation. \textscReasonSTL decomposes the translation process into explicit reasoning, deterministic tool calls, and structured formula construction. We further introduce process‑rewarded training to supervise both tool‑use trajectories and final formulas, together with \textscSTL‑Bench, a bilingual, computation‑aware benchmark grounded in real‑world signals. Experiments show that a 4B model trained with \textscReasonSTL achieves state‑of‑the‑art performance in both automatic metrics and human evaluations, demonstrating that \textscReasonSTL provides a transparent, low‑cost, and privacy‑preserving alternative for formal specification drafting.

Authors:Chenchen Zhou, Shaoqi Wang, Hongxin Su, Xinhui Tang, Yi Cao, Shuang-Hua Yang
Title: Dynamic Controlled Variables Based Dynamic Self-Optimizing Control
Abstract:
Self‑optimizing control is a strategy for selecting controlled variables, where the economic objective guides the selection and design of controlled variables, with the expectation that maintaining the controlled variables at constant values can achieve optimization effects, translating the process optimization problem into a process control problem. Currently, self‑optimizing control is widely applied to steady‑state optimization problems. However, the development of process systems exhibits a trend towards refinement, highlighting the importance of optimizing dynamic processes such as batch processes and grade transitions. This paper formally introduces the self‑optimizing control problem for dynamic optimization, termed the dynamic self‑optimizing control problem, extending the original definition of self‑optimizing control. A novel concept, "dynamic controlled variables" (DCVs), is proposed, and an implicit control policy is presented based on this concept. The paper theoretically analyzes the advantages and generality of DCVs compared to explicit control strategies and elucidates the relationship between DCVs and traditional controllers. Moreover, this paper puts forth a data‑driven approach to designing self‑optimizing DCVs, which considers DCV design as a mapping identification problem and employs deep neural networks to parameterize the variables. Three case studies validate the efficacy and superiority of DCVs in approximating multi‑valued and discontinuous functions, as well as their application to dynamic optimization problems with non‑fixed horizons, which traditional self‑optimizing control methods are unable to address.

Authors:Chenchen Zhou, Yi Cao, Shuang-hua Yang
Title: Performance guaranteed MPC Policy Approximation via Cost Guided Learning
Abstract:
Model predictive control (MPC) is widely used in industries but implementing it poses challenges due to hardware or time constraints. A promising solution is to approximate the MPC policy using function approximators like neural networks. Existing methods focus on minimizing the error between the approximators outputs and the MPC optimal control actions on training data, which is called error guided learning approach in this paper. However, the goals of control law design is not to minimize the fitting error but to minimize the operation cost. This paper proposes a novel cost‑guided learning approach that utilizes the cost sensitivity information from the MPC problem to directly minimize the loss in closed‑loop performance. A theoretical analysis shows cost‑guided learning provides tighter guarantees on optimality loss compared to traditional error‑guided learning. Experiments on a continuous stirred tank reactor (CSTR) benchmark demonstrate that the proposed technique results in approximate MPC policies that achieve substantially better closed‑loop performance. This work makes an important contribution by connecting the fitting errors with operational objectives, overcoming key limitations of existing approximation methods. The core idea could be applied more broadly for data‑driven control.

Authors:Jingyu Liu, Xiaoting Wang, Xiaozhe Wang
Title: Probabilistic Assessment of Rare Transient Instability Events via Kriging-based Active Learning Framework
Abstract:
The increasing uncertainty in modern power systems, driven by the integration of intermittent energy sources and variable loads, underscores the need for probabilistic transient stability assessment. However, existing assessment methods primarily focus on average system stability behavior and may struggle or incur high computational cost when identifying rare transient instability events, which in turn are critical for ensuring system resilience. To address this, the paper proposes a Kriging‑based active learning framework to accurately characterize rare instability regions within the input uncertainty space and estimate the associated small instability probability, while requiring only a limited number of expensive time‑domain simulations. The proposed active learning (AL) framework is tested on a modified IEEE 59‑bus system with simulated load and wind uncertainties, and a WECC 240‑bus system incorporating real‑world wind and solar generation data. Comparative studies with the existing random forest‑based active learning method and three non‑AL methods demonstrate that the proposed AL framework achieves superior accuracy and computational efficiency.

Authors:Samira Abdelrahman, Hossam Farag, Gilberto Berardinelli
Title: Distributed Online Learning for Time-Critical Communication in 6G Industrial Subnetworks
Abstract:
6G industrial in‑X subnetworks are expected to support highly time‑critical alarm reporting in large‑scale environments characterized by mobility, bursty event‑driven traffic, and limited radio resources. In such settings, conventional medium access solutions are ill‑suited to guarantee reliable delivery of critical traffic, e.g., emergency alarms, within strict deadlines, especially when multiple subnetworks become simultaneously active after a common alarm event, a scenario widely referred as medium access with a shared message. This paper proposes a distributed deep reinforcement learning (DRL)‑based medium access control protocol for timely alarm transmission in time‑critical industrial subnetworks. The proposed method enables each local access point (LAP) to learn, in an online manner, to infer contention conditions from a broadcast contention‑signature signal and to autonomously select a transmission pattern over the available channels using a lightweight deep neural network and an (ephsilon)‑greedy policy. Simulation results demonstrate that the proposed approach consistently achieves a higher probability of in‑time alarm delivery than benchmark random‑access schemes, while exhibiting better scalability with increasing network density. For instance, the proposed method improves probability of in‑time alarm delivery by at least 7% with a network size of 40 subnetworks, while the gain increases to 21% when the number of subnetworks increases to 60.

Authors:Alexandre Barbosa de Lima, Roberta Vieira Raggi
Title: Residual-Corrected Equivalent-Circuit Model with Universal Differential Equations for Robust Battery Voltage Prediction under Operating-Condition Shift
Abstract:
Accurate terminal‑voltage prediction underpins model‑based battery management, yet low‑order equivalent‑circuit models (\ecm) lack expressiveness under transient conditions, whereas purely data‑driven predictors sacrifice interpretability and may degrade under operating‑condition shift. This paper introduces a residual‑corrected hybrid formulation in which a first‑order Thevenin \ecm (\ecmrc) provides the dominant voltage structure, and a compact neural network embedded as a universal differential equation (\ude) corrects only the latent polarization mismatch. The \ecmrc parameters identified by nonlinear least squares warm‑start the hybrid model so that the learned component operates in a low‑residual regime. Experiments on a public Panasonic 18650PF dataset compare the proposed \ecmude with standalone \ecmrc and Long Short‑Term Memory (\lstm) baselines across four axes: matched‑condition prediction on UDDS at \SI25\celsius, inference‑time perturbation of the supplied state‑of‑charge (\SOC, denoted z) input, zero‑shot temperature transfer (\SI25\celsius to \SI‑20\celsius), and zero‑shot drive‑cycle transfer to US06, LA92, and HWFET. The proposed \ecmude achieves the lowest voltage error in every setting, reducing mean absolute error (\mae) by 48% relative to the \lstm under matched conditions and showing an order‑of‑magnitude lower inter‑seed variability (coefficient of variation: 0.44% vs.\ 6.20%). Substantial gains persist under challenging distribution shifts, indicating that the physical model anchors prediction where a purely learned model is most vulnerable. These results position residual‑corrected \ecmude as a lightweight and interpretable enhancement of low‑order circuit models for voltage prediction in battery management systems (\bms).

Authors:Haruto Nakashima, Siddhartha Ganguly, Kenji Kashima
Title: Unbalanced Optimal Transport and Density Control for Discrete-Time Linear Systems
Abstract:
This article studies unbalanced optimal transport (UOT) and its dynamical extension, unbalanced density control (UDC), for a class of constrained discrete‑time linear systems. UOT compares measures with unequal total mass by balancing transport cost and fidelity to reference measures, while UDC incorporates system dynamics and constraints into this framework. Focusing on Gaussian references and discrete‑time linear systems, we show that both problems admit globally optimal convex formulations, analogous to covariance steering. A numerical experiment is provided to illustrate our approach.

Authors:Nils Hanke, Zonglin Liu, Olaf Stursberg
Title: Synthesis of Limit Cycles and Reference Tracking via Switching Affine Systems
Abstract:
This paper introduces a novel method to approximate limit cycles of nonlinear ODEs by use of switching affine dynamics in order to ease data‑based modeling and analysis. Previous approaches to approximating limit cycles by switching systems have been largely confined to simple partitions into two‑regions or low‑dimensional (often planar) settings. In contrast, this study utilizes more general partitions in higher‑dimensional state spaces, augmented by external signals, to develop a synthesis scheme that guarantees a globally stable limit cycle. The synthesis task is formulated and solved based on constrained numerical optimization. Starting from sampled data of the nonlinear dynamics, the method minimizes the error between the data and the limit cycle generated by the switching affine model, while employing stability constraints to ensure global stability. Based on the obtained model, the paper tackles the problem of reference tracking for switching affine systems with periodic behavior. While the approximation scheme is based on a common Lyapunov function, the reference tracking approach uses multiple Lyapunov functions to achieve less conservative convergence results. The principle and effectiveness of the proposed methods are illustrated through a set of examples.

Authors:Alireza Modirshanechi, Benjamin Eysenbach, Peter Dayan, Eric Schulz
Title: Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization
Abstract:
Unsupervised pretraining has driven empirical advances in goal‑conditioned reinforcement learning (GCRL), but its theoretical foundations remain poorly understood. In particular, an influential class of methods, mutual information skill learning (MISL), discovers behaviorally diverse skills that can later be used for downstream goal‑reaching. However, it remains a theoretical mystery why skills learned through MISL should support goal‑reaching. A subtle challenge is that both GCRL and MISL are umbrella terms: different GCRL tasks use distinct criteria for measuring goal‑reaching performance, while different MISL methods optimize distinct notions of behavioral diversity. We address this challenge and unify GCRL and MISL as instances of control maximization. We identify three canonical GCRL formulations and prove that they are fundamentally inequivalent: they can induce incompatible optimal policies even in the same environment. Nevertheless, they all share a common interpretation: a well‑performing goal‑conditioned policy is one whose future trajectory is highly sensitive to the commanded goal, with the precise notion of sensitivity determined by the GCRL formulation. Noting that MISL objectives can be understood as measures of skill‑sensitivity akin to goal‑sensitivity, we show that MISL objectives are bounded by formulation‑specific downstream goal‑sensitivities. These bounds establish a precise correspondence between MISL methods and downstream GCRL tasks: for every GCRL formulation, there exists a matching MISL objective for which more diverse skills afford greater downstream goal sensitivity. Our results thus lay a theoretical foundation for RL pretraining and have important practical implications, such as suggesting which pretraining objectives to use when a user cares about a specific class of downstream tasks.

Authors:Kanghong Shi, Ian R. Manchester
Title: Absolute Stability of Nonlinear Negative Imaginary Systems with Application to Potential Energy Shaping
Abstract:
This paper establishes absolute stability conditions for nonlinear negative imaginary (NI) systems interconnected with static nonlinear feedback. We first show that the NI property is preserved when the feedback nonlinearity can be expressed as the gradient of a continuously differentiable function, and the composite storage of the resulting system remains positive definite. This condition provides a direct connection between nonlinear static feedback and storage‑function shaping along the measured output channels. Building on this result, conditions are derived for absolute stability of the closed‑loop system under mild assumptions. The linear specialization of the results strictly generalizes prior absolute stability results for linear NI systems, allowing coupled nonlinearities not covered by existing slope‑restricted or sector‑bounded frameworks. Finally, the proposed theory is illustrated through a linear example highlighting this generalization and a nonlinear example that shows the utility of the proposed results in potential energy shaping.

Authors:Oliver Schön, Licio Romao, Sadegh Soudjani
Title: Safety Certification is Classification
Abstract:
The goal of this paper is certifying safety of dynamical systems subject to uncertainty. Existing approaches use trajectory data to estimate transition probabilities, and compute safety probabilities recursively via dynamic programming (DP). This recursion may lead to compounding errors in the certified safety probability, thus collapsing to a vacuous lower bound for growing horizons T. We propose a kernel embedding framework that treats safety certification as a classification problem on trajectory data, directly estimating the T‑step safety probability without recursion. We show that the framework subsumes well‑established approaches from the literature (e.g., barrier certificates, robust Markov models) as special cases, and allows us to go beyond their limitations. As the main consequence, it bypasses compounding error across the horizon and enables certification for systems with non‑Markovian dynamics. We demonstrate that direct estimators remain stable independent of the certification horizon and in the non‑Markovian setting, whilst DP‑based certificates silently go unsound ‑‑ confirmed in simulation on a neural‑controlled quadrotor.

Authors:Vladislav Nenchev, Prodromos Sotiriadis
Title: Monitoring autonomous persistent surveillance missions using invariance
Abstract:
This paper studies runtime monitoring for persistent surveillance by autonomous robots when the autonomy stack is a black box. The environment is partitioned into finitely many parts, each carrying an uncertainty state that decreases when observed and increases otherwise. We model the closed loop as a state‑dependent hybrid system with linear parameter varying dynamics and design a monitor based on an invariant computed offline. As this invariant is typically hard to obtain for large to‑be‑surveyed spaces, we propose a compositional monitor obtained by decentralized computation of low‑dimensional invariant sets for each uncertainty region, and checking their conjunction online. Under common independence assumptions, the compositional monitor is sound and complete with respect to the full‑system invariant. The approach is applied in a case study with a real robot persistently monitoring a labyrinth, emphasizing its applicability in practice.

Authors:Peihao Li, Nadia Dahmani, Wenqi Cai
Title: Arbitrage and the Stability of AMM Price Tracking
Abstract:
Automated market makers (AMMs) quote prices from pool state rather than from a limit order book. AMM pools often stay close to a reference price because arbitrageurs correct profitable mispricing. A large part of decentralized finance therefore relies on a simple economic premise: once the AMM price drifts away from the reference price, arbitrage incentives push it back. This paper studies when that premise is strong enough to guarantee block‑scale stability. We model the gap between the reference price and the AMM price as a stochastic tracking error, treat arbitrage as the corrective input, and place blockchain execution inside the loop through fees, discrete blocks, transaction ordering, delays, and transaction failure. The detailed execution layer is reduced to the total successful correction confirmed in each block. Under a block‑level correction condition, we prove geometric ergodicity of the tracking error and obtain explicit one‑step bounds that connect tracking quality to liquidity and execution quality. We also show in a constant‑product example how fees, fixed execution costs, and local liquidity map into the no‑trade band and the optimal corrective trade. Finally, we build empirical proxies for the theorem quantities from realized block data and use them to organize reduced and mechanism‑focused simulations whose comparative statics are consistent with the theory. The contribution is to turn a basic economic intuition behind decentralized finance into a quantitative stability statement together with a tractable calibration interface.

Authors:Hen-Wei Huang, Claas Ehmke, Dawei Wang, Blake Smith, Ziyao Zhou, Rong Tan, David Werder, Crystan McLymore, Niels Neidlein, Emanuele Falli, Ali Imani, James McRae, Yeseul Jeon, So-Yoon Yang, Wesley S. Culberson, James Byrne, Giovanni Traverso
Title: Kirigami-Structured Electronic Capsule for Long-Term Continuous Gastric Monitoring
Abstract:
Ingestible electronic systems enable non‑invasive, in situ sensing within the gastrointestinal (GI) tract, yet clinical translation has been limited by uncontrolled transit, short operational lifetimes, and unreliable wireless communication that prevent continuous monitoring. Here, we present a gastric‑resident ingestible robotic platform that achieves week‑long operation through integration of a bioinspired, electrically triggered release mechanism with a kirigami‑enabled electronic architecture. A kirigami‑patterned flexible printed circuit board spans the capsule body and deployable superelastic arms, enabling high‑density integration of sensing, power management, and wireless modules within a constrained volume while tolerating large mechanical deformation during gastric residence. Stable retention and on‑demand disassembly are achieved using thermally responsive polycaprolactone joints that transition from rigid to compliant states under electrical activation, avoiding dependence on variable chemical triggers. Reliable telemetry in the highly attenuating gastric environment is maintained using a dual‑band Bluetooth Low Energy and sub‑gigahertz module with RSSI‑ and throughput‑aware adaptive transmission, balancing link robustness and energy consumption. We demonstrate long‑term, continuous monitoring of gastric radiation exposure, enabling early detection of dose accumulation and providing a promising in vivo alternative to wearable or handheld dosimeters. Swine studies confirm stable gastric residence, sustained real‑time telemetry, and safe gastrointestinal passage following triggered disassembly. This work establishes kirigami‑enabled integration as a scalable strategy for long‑term gastric‑resident robotic systems.

Authors:Hongjin Du, Aleksandra Lekić
Title: SOPF-Based Adaptive Droop Control for Hybrid AC--HVDC Grids Under Offshore Wind Uncertainty
Abstract:
The integration of massive offshore wind into hybrid AC‑HVDC grids demands robust DC voltage regulation, yet conventional fixed‑gain droop controllers struggle under severe stochastic volatility. This paper bridges the gap between system‑level economic dispatch and converter‑level control by proposing a novel Stochastic Optimal Power Flow (SOPF)‑based adaptive droop framework. Rather than relying on heuristic or reactive tuning, wind forecast uncertainty is modeled using a zone‑wise Beta distribution that accurately captures the heteroscedastic nature of wind errors across low, mid, and high power regimes. By leveraging Polynomial Chaos Expansion (PCE) within a chance‑constrained SOPF, the system's stochastic states are formulated analytically. Crucially, the optimal adaptive droop gain is extracted directly from the first‑order PCE coefficients via a Jacobian‑free sensitivity analysis, embedding statistical voltage‑security guarantees directly into the local converter control. Validation on a 4‑terminal AC‑HVDC system demonstrates that scenario‑adaptive gains significantly outperform standard fixed‑coefficient approaches, effectively minimizing active‑power tracking errors during extreme wind disturbances.

Authors:Na Li, Dong Liu, Stavros Orfanoudakis, Özge Okur, N. K. Panda, Pedro P. Vergara, Binod Koirala
Title: Community-to-Vehicle: Integrating Electric Vehicles into Energy Communities -- A Swiss Case Study
Abstract:
The institutional separation between local energy communities and public electric vehicle (EV) charging limits the efficient use of locally generated renewable energy. This paper introduces the concept of community‑to‑vehicle (C2V) as an institutional design mechanism to bridge this gap by enabling EV charging within the community boundary, where locally generated photovoltaic (PV) surplus is preferentially allocated and offered to external users at a community charging price. Building on the recently introduced local electricity community framework in Switzerland, we design scenarios that capture the transition from full separation to coordinated EV charging and evaluate their impacts on EV users and the community. The results show that C2V significantly improves local PV utilization and enhances economic performance, reducing EV charging costs relative to commercial alternatives while generating additional revenue streams for the community. These findings highlight the potential of C2V as a practical, implementable mechanism for integrating EV charging into local energy communities, providing a clear pathway for adopting coordinated community‑EV interaction within existing regulatory frameworks.

Authors:Pedro P. Vergara
Title: Foundation Twins: A New Generation of Power Systems Digital Twins using Foundation AI Models
Abstract:
Power systems are inherently multi‑timescale systems, with different physical phenomena and decision‑making processes spanning multiple timescales, time horizons, and geographic scopes. I envision power systems digital twins (DTs) as powerful modeling and simulation tools that can accelerate and improve decision‑making across different time scales and geographic scopes. However, until now, research has not delivered such a vision, and power systems DTs remain a concept distant from implementation. This is not a regular research paper. This is a position paper that outlines my vision for developing a new generation of power systems DTs that leverage recent advances in artificial intelligence (AI) and machine learning (ML). I call these Foundation Twins. Foundation Twins combines the generalization features of foundation models with the decision‑making capabilities of reinforcement learning (RL) architectures to deliver the envisioned power systems DTs.

Authors:Yifan Zhang, Yunjie Gu, Yue Zhu, Yitong Li, Hsiao-Dong Chiang, Timothy C. Green
Title: Consideration of Control-Loop Interaction in Transient Stability of Grid-Following Inverters using Bandwidth Separation Method
Abstract:
Grid‑following inverters have been widely adopted as a grid interface for renewable energy, and ensuring their small‑signal and large‑signal stability is critical to modern power systems. Their large‑signal, or transient, stability is a significant challenge to analyze because of the interaction of the phase‑locked loop (PLL), which must maintain synchronism with various outer‑loop controllers. Simple analysis in which outer‑loop controllers are idealized is insufficient, and the interactions between the nonlinear dynamics of the PLL and the dynamics of the DC‑link voltage control (DVC), as well as the AC terminal voltage control (TVC) when present, must be considered. An asymptotic analysis approach, termed the bandwidth separation method, is proposed. This method enables simplification and order reduction of the original differential equations when sufficient bandwidth separation exists. Through this method, the interaction between the DVC and PLL is explicitly characterized, revealing that such interaction degrades system stability and shrinks the stability region. The analysis also indicates that voltage instability, rather than PLL loss of synchronization alone, is often the root cause of transient instability. Optimal bandwidth configurations for the PLL and DVC are identified under various grid fault conditions: a larger PLL bandwidth improves resilience to phase‑jump faults, while a larger DVC bandwidth enhances tolerance to power fluctuations. In addition, the influence of the TVC loop is analyzed, showing that a high TVC bandwidth can mitigate the destabilizing effects of PLL‑DVC interaction and further improve transient stability. All analytical findings are validated through hardware‑in‑the‑loop (HIL) experiments.

Authors:Millend Roy, Agostino Capponi, Vineet Goyal
Title: PREFER: Personalized Review Summarization with Online Preference Learning
Abstract:
Product reviews significantly influence purchasing decisions on e‑commerce platforms. However, the sheer volume of reviews can overwhelm users, obscuring the information most relevant to their specific needs. Current e‑commerce summarization systems typically produce generic, static summaries that fail to account for the fact that (i) different users care about different product characteristics, and (ii) these preferences may evolve with interactions. To address the challenge of unknown latent preferences, we propose an online learning framework that generates personalized summaries for each user. Our system iteratively refines its understanding of user preferences by incorporating feedback directly from the generated summaries over time. We provide a case study using the Amazon Reviews'23 dataset, showing in controlled simulations that online preference learning improves alignment with target user interests while maintaining summary quality.

Authors:Andreas Carlsson, Christian Sandström, Viktor Josefsson, Lisa Kjellén, Taha El Hajji, Marcus Lenberg
Title: Investigation of Wound Field Synchronous Machines using Soft Magnetic Composites for Automotive Applications
Abstract:
This paper investigates the application of soft magnetic composites (SMCs) in the stators of wound field synchronous machines for automotive traction. While SMCs are traditionally employed in axial flux topologies, this study examines their use in radial‑flux electrically excited synchronous machines (EESMs). Multiple SMC materials and lamination thicknesses are evaluated, with the optimal configuration combining a SMC material in the stator and 0.35 mm NO35 laminated steel in the rotor. This combination delivers improved torque and efficiency compared to conventional designs. When integrated into a full electric drive unit (EDU), this motor achieves 89.7% efficiency over the WLTP drive cycle, representing a 1.4 percentage point improvement over a reference permanent magnet synchronous machine‑based EDU. The proposed solution eliminates rare‑earth materials, reduces cost through thicker laminations, and offers environmental benefits through SMC utilization. This novel material combination, previously unexplored for radial EESMs, presents a promising direction for affordable, high‑efficiency, rare‑earth‑free automotive traction machines.

Authors:Donglin Wang, Anjie Qiu, Qiuheng Zhou, Hans D. Schotten
Title: A Disaster-Aware Integrated TN-NTN System-Level Simulator for Resilient 6G Wireless Networks
Abstract:
Non‑terrestrial networks (NTN) have been standardized by the 3rd generation partnership project (3GPP) as a key component of future 6G systems to enhance coverage and resilience. In particular, NTN technologies such as low‑earth orbit (LEO) satellites, high‑altitude platform stations (HAPS), and unmanned aerial vehicles (UAVs) are expected to support terrestrial networks (TN) during extreme events and disasters. In this paper, we present a lightweight system‑level simulator for evaluating post‑failure fallback behavior in integrated TN‑NTN wireless networks under a partial‑failure disaster model. The simulator follows 3GPP Rel‑17/18 modeling principles, supports probabilistic terrestrial next‑generation node B (gNB) failures, and service migration to NTN. The simulator supports comparative analysis of throughput, packet reception ratio (PRR), and latency under different user loads, disaster severities, and NTN provisioning levels. Results show the expected capacity‑delay tradeoff of terrestrial operation, the reliability and stability of non‑terrestrial service, and the balanced resilience behavior of hybrid TN‑NTN operation. The proposed framework provides a tractable tool for studying wireless network resilience and traffic management in future integrated 6G mobile systems.

Authors:Donglin Wang, Anjie Qiu, Qiuheng Zhou, Hans D. Schotten
Title: Comparative Analysis of Direct-to-Cell (D2C) and 3GPP Non-Terrestrial Networks (NTN) for Global Connectivity
Abstract:
The quest for ubiquitous mobile coverage has catalyzed two fundamentally distinct architectural paradigms: Direct‑to‑Cell (D2C) and standardized 3GPP Non‑Terrestrial Networks (NTN). D2C, pioneered by SpaceX Starlink and AST SpaceMobile, leverages existing terrestrial spectrum and unmodified consumer handsets to provide emergency connectivity as a market‑driven overlay. In contrast, 3GPP NTN, standardized across Releases 17‑19, offers a systematic satellite‑native framework designed for long‑term scalability, high‑throughput broadband, and deep integration with terrestrial 5G/6G networks. This paper presents a comprehensive technical comparison of these approaches, analyzing their standardization trajectories, network architectures, physical‑layer innovations, security postures, and operational trade‑offs. We further examine their implications for emerging 6G use cases, particularly autonomous driving, where safety‑critical redundancy motivates a hybrid tri‑link architecture combining terrestrial 5G, NTN broadband, and D2C emergency fallback. Our analysis shows that, although D2C enables rapid market entry through legacy‑device compatibility, NTN provides superior performance, security, and scalability, positioning it as the foundational framework for 6G satellite‑terrestrial convergence. A hybrid model that combines the strengths of both paradigms is identified as the most practical path toward truly global connectivity.

Authors:Dhruv Suri, Helgi Hilmarsson, Shourya Bose
Title: WARP: A Benchmark for Primal-Dual Warm-Starting of Interior-Point Solvers
Abstract:
Solving AC Optimal Power Flow (AC‑OPF) is of central importance in electricity market operations, where interior‑point methods (IPMs) such as IPOPT are the standard solvers. A growing body of work uses machine learning to predict primal warm‑start iterates, reporting iteration reductions of 30‑46%. We show that these reported gains rest on an inappropriate evaluation baseline: prior methods benchmark against the flat start V_m = 1, V_a = 0, whereas the solver's actual default ‑ the variable‑bound midpoint (l+u)/2 ‑ is near‑optimal for log‑barrier centrality. Against this corrected baseline, no primal‑only warm‑start method reduces solver iterations. We trace the failure to a geometric property of interior‑point methods: primal prediction accuracy is anticorrelated with convergence speed, and providing the ground‑truth optimal solution x^ without dual variables causes the solver to diverge. Oracle experiments establish that the complete primal‑dual‑barrier state (x^, λ^, z^, μ^) reduces IPOPT iterations from 23 to 3 ‑ an 85% reduction that is structurally inaccessible to primal‑only methods. To enable rigorous evaluation of warm‑start methods on this task, we release a benchmark suite comprising dual‑labeled AC‑OPF datasets with IPOPT‑extracted solutions, a corrected evaluation protocol, and WARP ‑ a topology‑conditioned encode‑process‑decode interaction network that predicts the full interior‑point state (\hatx, \hatλ, \hatz, \hatμ) on the heterogeneous constraint graph. WARP achieves a 76% reduction in IPOPT iterations while natively accommodating N‑1 contingency topology variations without retraining.

Authors:Somasundhar Venkatasubramanian, Anirudh Venkat, Advaidh Venkat
Title: Space-Time Diversity in Observability and Estimation on Product Lie Groups
Abstract:
Robust state estimation in coupled dynamical systems depends critically not only on sensor quality but on the structural alignment between observation channels and the system's intrinsic dynamics. This paper develops a rigorous framework for analyzing spatial and temporal diversity in dynamical state estimation on product Lie groups, drawing structural parallels to diversity gains in space‑time coding. Three main results are established: (i) coupling‑based necessary and sufficient conditions for cross‑factor observability, showing that a sensor local to one group factor renders another factor observable if and only if the dynamics propagate error directions across the corresponding Lie algebra components; (ii) a spatial diversity saturation theorem identifying precisely when additional observation channels fail to expand the propagated observation subspace and thus provide no structural benefit; and (iii) a time‑space diversity decomposition that exactly separates instantaneous spatial information from accumulated temporal information in the estimation error covariance. The framework is applied to planar SE(2) and spatial SE(3) navigation, yielding exact observability guarantees for redundant and non redundant sensor architectures in modern robotics and autonomous vehicles. These results extend classical observability theory beyond Euclidean state spaces, exposing structural constraints invisible to standard rank‑based analysis that fundamentally govern robust inference in coupled dynamical systems.

Authors:Tanmay Dokania, Yashwanth Kumar Nakka
Title: Maximal Controlled Invariant-MPC: Enhancing Feasibility and Reducing Conservatism through Terminal CBF Constraint in Safety-Critical Control
Abstract:
Optimal control for safety‑critical systems is often dependent on the conservativeness of constraints. Control Barrier Functions (CBFs) serve as a medium to represent such constraints, but constructing a minimally conservative CBF is a computationally intractable problem. Therefore, approaches that can guarantee safety while reducing conservatism will help improve the optimality of the system under consideration. Here, we present a Model Predictive Control (MPC) formulation using CBF as a terminal constraint, which is proven to improve feasibility and reachable sets with increasing prediction horizon. The constructive nature of the proofs allows for warm‑starting the nonlinear optimization problem, thereby reducing the computational time substantially. Simulations are set up for a simple nonholonomic system to numerically validate the results, and it is observed that the number of infeasible points decreased by a factor of 1.7 to 2.7. The increase in reachable state space was demonstrated by the ability of the system to track trajectories that are entirely inside the unsafe region of the control barrier function.

Authors:Manuel C. R. M. Fernandes, Fernando A. C. C. Fontes
Title: A Computationally Tractable Path-Planning Method for Airborne Wind Energy Systems
Abstract:
Airborne Wind Energy Systems (AWES) have emerged as a promising renewable energy technology that exploits stronger, more consistent high‑altitude winds via tethered airborne devices. Among the various concepts, crosswind systems, where efficient flight control is essential to maximise energy output, offer significant potential. This paper addresses the problem of reference selection for crosswind flight control, focusing on the design of power‑maximising geometric flight paths for the reel‑out phase of Groundgen systems. To overcome the computational challenges associated with optimal control approaches, a computationally tractable framework is proposed in which a path‑planning problem is formulated as a nonlinear program. The method optimises the parameters of a Lissajous curve to maximise the average power production over the reel‑out phase, while incorporating curvature constraints. The proposed approach provides an efficient alternative to existing optimal control and learning‑based methods.

Authors:Himanshu Paudel, Basanta Joshi, Dhirendra Raj Madai, Alina Bartaula, Biman Rimal, Sanjay Neupane
Title: A Closed-Form Dual-Barrier CBF Safety Filter for Holonomic Robots on Incrementally Built Occupancy Grid Maps
Abstract:
We present a dual‑barrier control barrier function (CBF) safety filter for real‑time, safety‑critical velocity control of holonomic robots operating in incrementally built occupancy grid maps. As a robot explores an unknown environment, unmapped regions introduce irreducible uncertainty, since obstacle geometry beyond the explored frontier is unknown, making entry into such regions a source of collision risk, especially with front‑facing sensors. To address this, we enforce two constraints: avoidance of mapped obstacles and restriction from unexplored regions. Both constraints are derived analytically from the occupancy grid's signed distance field, yielding a closed‑form safety filter that requires only a small linear system solve per cycle. On resource‑constrained platforms such as the Raspberry Pi, where SLAM and planning already consume significant compute, the low overhead of the proposed filter preserves resources. An adaptive gain schedule relaxes the frontier constraint in information‑rich regions and tightens it in well‑mapped areas, improving exploration efficiency while maintaining safety. The filter operates in velocity space as a minimally invasive correction and composes with arbitrary nominal controllers, including learning‑based methods. Hardware flight experiments on a PX4‑controlled quadrotor demonstrate zero collisions across multiple indoor runs.

Authors:Jennifer T. Bui, Dominic Groß
Title: Input-Output Specifications and Dynamic Droop Coefficients: Stability and Performance Conditions for Grid-Forming IBRs
Abstract:
This paper proposes dynamic stability and performance conditions for grid‑connected inverter‑based resources (IBRs). To this end, we extend the notion of steady‑state droop coefficients to dynamic droop coefficients to capture the small‑signal dynamics of IBRs and synchronous generators (SGs). Notably, the dynamic droop coefficients can be obtained from input‑output data collected at the unit's (e.g., IBR or SG) point of interconnection without requiring prior knowledge of IBR internals or controls structure. To obtain frequency stability conditions, this IBR model is combined with a lightweight dynamic transmission network model that accounts for uncertainty of line dynamics. The resulting stability conditions are highly scalable and, given a few key network parameters, can be verified at the unit level. To make the conditions practical and offer intuitive and illustrative interpretations, we map the frequency stability conditions to bounds on the Bode plot of the dynamic droop coefficient for two broad types of IBR responses. Moreover, our specifications on the dynamic droop coefficient (i) translate basic frequency control ancillary services into verifiable requirements, and (ii) provide insights into the much‑debated question of how to certify an IBR as grid‑forming (GFM). The results are illustrated using dynamic droop coefficients obtained using detailed simulations of GFM and GFL IBRs as well as SGs.

Authors:Gerald Ogbonna, David Bindel, Lindsay C. Anderson
Title: Minimizing the Expected Cost of Synchronization in Lossless Power Networks
Abstract:
The reliable operation of large‑scale electric power networks is increasingly challenging, particularly with the integration of stochastic renewable generation. In this work, we address the problem of minimizing network transients by optimally modifying the underlying network. We formulate the problem in terms of graph Laplacian matrices and show that, under certain assumptions, the problem is convex. We derive a linear matrix inequality whose feasibility guarantees the existence and uniqueness of phase cohesive steady‑state angles; this condition can be directly incorporated as a convex constraint in the optimization framework and we provide several geometric interpretations of the optimization problem. The proposed method is validated on the IEEE 30‑bus test system, where results demonstrate that our approach effectively identifies critical links on the network. Dynamic simulations show a significant reduction in network transients and overall improvements across several performance metrics. We explore the sparsity‑optimality trade‑off using a reweighted \ell_1 heuristic.

Authors:Eni Solomon Laughter
Title: Kinematic Discriminants of Deceleration Behavior Modes in Car-Following: Evidence from NGSIM Trajectory Data
Abstract:
Gap‑closing rate and visual looming swap discriminative dominance depending on deceleration intensity ‑ a finding that reconciles a long‑standing conflict in the car‑following literature and challenges spacing‑centered assumptions in traditional driver behavior models. This study presents a two‑stage analytical framework that distinguishes between information availability (kinematic variables measurable in the environment) and information utilization (variables that demonstrably separate driver behavioral patterns), applied to 1,060,119 valid car‑following observations from the NGSIM trajectory dataset (2,932 vehicles). Six kinematic features are extracted, and deceleration events are detected under two threshold conditions (‑0.5 m/s^2 and ‑0.3 m/s^2). K‑means clustering identifies behavioral modes, and one‑way ANOVA with eta‑squared effect sizes ranks each feature's discriminative power. Three key findings emerge: (1) threshold selection fundamentally shapes behavioral inference ‑ the stricter threshold yields three interpretable modes while the permissive threshold collapses these to two; (2) hard braking prioritizes gap‑closing rate (eta^2 = 0.715) while moderate braking emphasizes visual looming (eta^2 = 0.574); and (3) spacing headway is negligible (eta^2 <= 0.014) across both thresholds. These findings provide empirically grounded candidates for perceptual cue prioritization and have direct implications for ADAS warning system design and autonomous vehicle control.

Authors:Subham Sahoo, Huai Wang, Frede Blaabjerg
Title: Quantized Probabilistic AI for Gear Fault Diagnosis in Motor Drives
Abstract:
Deploying large artificial intelligence (AI) models in power electronics often demands high computational resources. Driven by the quantization paradigm, this digest proposes a quantization‑aware training (QAT) principle to substantially minimize the number of bits required and simultaneously maximize the accuracy of computations in pre‑trained AI models. Considering a pre‑trained probabilistic Bayesian Neural Network (BNN) for gear fault diagnosis in motor drives as an example, we quantize its weights and activation functions from floating‑point FP32 to low‑precision INT8 values, which enhances the computational efficiency by a significant margin of 30‑45% (for different model versions) without any compromise in the accuracy and uncertainty estimates. This substantiates a sustainable mechanism of deploying most quantized light‑weight AI models into low‑cost edge processors for power electronic applications.

Authors:Subham Sahoo, Huai Wang, Frede Blaabjerg
Title: Unlocking Embodied Probabilistic Computational Features in Motor Drives
Abstract:
Artificial intelligence (AI)‑driven fault diagnosis in motor drives often requires significant computational efforts and time for re‑training, in addition to the limited knowledge behind the model and suitability of training and learning mechanisms. This work bridges this gap by proposing a structured mechanism of transforming untapped labeled fault data into AI parameters to leverage probabilistic data‑driven learning. This novel AI reservoir modeling framework for power electronics not only eliminates exogenous efforts behind learning data patterns and its optimization, but also provides intuitive guidelines for power electronics engineers behind sizing of AI models. This alignment between data and system physics makes the proposed model transparent and interpretable, bridging practical understanding with data‑driven learning. Its computational efficiency is demonstrated using experimental data that structured, physics‑aware reservoirs achieve higher diagnostic accuracy and clearer explanations than conventional black‑box AI methods.

Authors:David E. Ruiz-Guirola, Samer Nasser, Bikramjit Singh, Henrique Duarte Moura, Andrey Belogaev, Jeroen Famaey, Efstathios Katranaras, Mahdi Shahabi, Onel L. A. Lopez
Title: Adaptive Contention-based Random Access for Uplink Reporting in 3GPP Ambient IoT Networks
Abstract:
Ambient Internet of Things (A‑IoT) targets energy harvesting (EH), battery‑less devices as a simple connectivity solution for extensive ultra‑low‑power deployments. These devices typically face intermittent energy availability, making uplink reports increasingly susceptible to access collisions and energy outages. In this paper, we build upon the cellular standardization of A‑IoT and examine the paging‑triggered contention‑based random access (CBRA) framework for uplink reporting. We analyze the effects of energy availability and collisions on these systems and introduce an EH‑aware access control mechanism. In this mechanism, the reader broadcasts an access probability in the paging message, which helps regulate the number of devices attempting random access. Results show that, unlike the baselines, the proposed method scales well under dense deployments by keeping collisions nearly constant, improving access efficiency, and substantially reducing the number of paging rounds required for successful reporting. These results highlight the importance of lightweight reader‑side access control for reliable and resource‑efficient reporting in A‑IoT environments.

Authors:Federica Fieramosca, Anastasia Maiolli, Alexander H. Paulus, Stefano Savazzi, Michele D'Amico
Title: Fast Full-Wave Simulation of Indoor RSS Maps for Pre-Measurement Validation in Device-Free Localization
Abstract:
Human localization is gaining momentum in security, healthcare, logistics, and smart spaces applications. While global navigation systems are unreliable indoor, device‑free (a.k.a. passive) localization methods that exploit human‑induced perturbations of radio propagation can be effectively used. This paper investigates the use of a compact full‑wave electromagnetic (EM) setup as a fast and reliable tool to simulate indoor Wi‑Fi propagation for human sensing. The goal is to provide a practical baseline for validating simplified propagation models, such as diffraction‑based descriptions, and to reduce the need for costly measurement campaigns. Two‑dimensional attenuation maps from received signal strength are generated and compared in controlled environments, focusing on attenuation statistics and interference patterns. The simulations reproduce the main spatial features, though discrepancies remain due to simplified material characterization. Diffraction‑aware refinements are proposed to mitigate these effects. Overall, the approach provides an efficient pre‑measurement reference to support device‑free system design and to guide experimental planning.

Authors:Jiaqian Yang, Romulo Aparecido, Eric Sillekens, Ronit Sohanpal, Mindaugas Jarmolovičius, Zelin Gan, Yang Hong, Morteza Kamalian-Kopae, Abdallah Ali, Shahab Bakhtiari Gorajoobi, Ruben S. Luís, Daniele Orsuti, Aleksandr Donodin, Vitaly Mikhailov, Jiawei Luo, David J. DiGiovanni, Nicolas Fontaine, Lauren Dallachiesa, Mikael Mazur, Roland Ryf, Haoshuo Chen, David Neilson, Ian D. Phillips, Wladek Forysiak, Sergei K. Turitsyn, Hideaki Furukawa, Jamie Gaudette, David J. Richardson, Benjamin J. Puttnam, Robert I. Killey, Polina Bayvel
Title: 423.7 + 426.5 Tb/s GMI Bi-Directional HCF Transmission
Abstract:
We demonstrate OESCL‑band same‑wavelength bi‑directional transmission over 60 km HCF with 42.5 THz bandwidth, achieving GMIs comparable with the highest unidirectional SMF data‑rates in both directions, with an aggregate of 423.7 + 426.5 Tb/s.

Authors:Shudian Zhao, Mohammad Reza Karimi Gharigh, Jan Kronqvist, Mohammad Reza Hesamzadeh
Title: ADMM-based decomposed DNN+RLT Relaxations for Completely Positive Models in Electricity Market Clearing
Abstract:
The day‑ahead electricity market clearing with nonconvex order types can be formulated as a mixed‑integer linear program (MILP), but its LP relaxation may provide weak bounds, and exact solutions can become computationally intractable in large‑scale or extended market settings. We study a welfare‑maximizing clearing model with elementary hourly orders, block orders with logical acceptance constraints, and flexible hourly orders. Starting from a compact MILP formulation, we derive an equivalent completely positive programming (CPP) reformulation via matrix lifting and propose relaxed CPP variants that further reduce the modeling burden while maintaining strong bounds. We then develop tractable doubly nonnegative (DNN) relaxations, including decomposed formulations that exploit the problem structure by using smaller positive semidefinite matrices. To further strengthen these bounds, we introduce reformulation‑linearization technique (RLT) inequalities tailored to the decomposed structure. To tackle the challenge of large‑scale DNNs, we design an alternating direction method of multipliers (ADMM) with adaptive penalty updates and rigorous dual lower bounds, enabling certified early termination. Computational experiments on synthetic instances show that the proposed DNN+RLT relaxations substantially tighten LP bounds, while decomposition and first‑order methods significantly reduce computational effort.

Authors:Xi Ru, Cong Fu, Zhongze Li, Xiaoyu Peng, Feng Liu
Title: Toward less conservative distributed stability analysis of power systems via matrix-valued differential passivity indices
Abstract:
Passivity indices have been widely adopted to derive distributed stability certificates for power systems. Nevertheless, conventional passivity indices remain scalar‑valued even for multi‑input‑multi‑output (MIMO) systems, which can introduce excessive conservatism and compromise analysis accuracy. To overcome these limitations, this paper extends the differential passivity index to a matrix‑valued formulation that captures both channel‑wise passivity properties and inter‑channel coupling effects in MIMO subsystems. On this basis, semi‑distributed and fully distributed stability criteria are developed for power systems with heterogeneous nonlinear devices. It is shown that system stability is guaranteed when the aggregate passivity excess of devices compensates for the passivity shortage imposed by the network. Furthermore, analytical passivity matrix expressions for typical power system components are derived, facilitating compositional stability analysis. Case studies on a three‑bus system and a modified IEEE 118‑bus system validate the effectiveness of the proposed framework.

Authors:Maryam Khodabakhshloo, Elizabeth L. Ratnam, Ian R. Petersen
Title: Negative Imaginary and Passivity Properties of Synchronous Machine Systems
Abstract:
The recent rapid proliferation of renewable energy is fundamentally changing the dynamic operations of power systems, necessitating new approaches to assess stability for these highly nonlinear systems. In this paper, we prove that synchronous machine systems, modeled in the nonlinear dq‑frame, possess fundamental dissipativity properties. Specifically, we show passivity from current input to voltage output and a nonlinear negative imaginary property from torque input to rotor angle output. For the nonlinear system shifted around an equilibrium point, we derive explicit conditions for both passivity and the NI property to hold. Finally, we demonstrate that interconnection with passive droop controllers preserves these dissipativity properties with identical supply rates, thereby ensuring closed‑loop stability.

Authors:Maryam Khodabakhshloo, Elizabeth L. Ratnam, Ian R. Petersen
Title: Equilibrium points and stability of synchronous machine systems
Abstract:
This paper investigates equilibrium points and stability in two synchronous machine configurations: (i) a single generator with an impedance load and (ii) two interconnected machines with co‑located loads. We consider both abc and dq reference frames to show that the equilibrium condition reduces to a cubic polynomial in the single‑machine case and to an 18th‑ degree polynomial in the two‑machine case. For the single‑machine system, Lyapunov stability analysis and linearization based stability analysis are carried out. For the two‑machine system, local stability is assessed through linearization and eigenvalue analysis. Illustrative examples confirm the existence of multiple equilibria and illustrate the impact of parameter variation on stability. Our results provide insight into the stability of synchronous machine systems.

Authors:Philipp Braun, Timothy L. Molloy, Gal Barkai, Iman Shames
Title: From open-loop representations to closed-loop feedback implementations in differential games: A numerical case study
Abstract:
Solutions to pursuit‑evasion and surveillance‑evasion differential games are typically computed and expressed using open‑loop representations, with the synthesis of feedback strategies significantly less common. We propose a numerical scheme for obtaining feedback strategies for the recently introduced prying‑pedestrian surveillance‑evasion differential game. The scheme involves computing feedback strategies as input‑output maps approximated via neural networks trained using data obtained from open‑loop representations of solutions. Simulations show the effectiveness of neural networks trained with an appropriate learning‑loss function. Since optimal feedback strategies are discontinuous, as a second contribution, the potential loss/gain of individual players is subsequently studied for players using sample‑and‑hold feedback compared to continuous‑time feedback.

Authors:Onel L. A. López, Amirhossein Azarbahram
Title: Sequential Monte Carlo for Resilient Networks: Assessment, Mitigation, and Generative Modeling
Abstract:
Resilience is becoming crucial for future wireless networks, which must withstand, adapt to, and recover from rare but potentially cascading disruptions. This paper develops a sequential Monte Carlo (SMC) simulation framework for such systems, in which resilience failures are formulated as path‑dependent rare events arising from staged degradation and delayed recovery, and are decomposed into semantically interpretable levels defined by a reaction coordinate. Building on this structure, we present a fixed‑level splitting approach with budget‑aware population control, enabling efficient estimation of rare non‑recovery probabilities. We discuss the potential reuse of SMC checkpoints as representative near‑critical states for policy evaluation and simulation‑based selection. We further extend the methodology to learned stochastic simulation by using generative sequence models as restartable surrogates within data‑driven digital twins. We showcase the framework in a delay‑critical wireless network use case, where SMC substantially improves over standard Monte Carlo in rare‑event regimes with both physical and learned simulators.

Authors:Yasod Ginige, Ransika Gunasekara, Darsha Hewavitharana, Manjula Ariyarathne, Peshala Jayasekara, Ranga Rodrigo
Title: VC-FeS: Viewpoint-Conditioned Feature Selection for Vehicle Re-identification in Thermal Vision
Abstract:
Identification of less‑articulated objects using single‑channel images, such as thermal images, is important in many applications, such as surveillance. However, in this domain, existing methods show poor performance due to high similarity among objects of the same category in the absence of color information (overlooking shape information) and de‑emphasized texture information. Furthermore, variability in viewpoint adds more complexity as the features vary from side to side. We address these issues by constructing viewpoint‑conditioned feature vectors and area‑specific feature comparisons in separate feature spaces. These interventions enable leveraging the advancements of existing RGB‑pre‑trained ViT feature extractors while effectively adapting them to address the challenges specific to the thermal domain. We test our system with RGBNT100 (IR) vehicle dataset and a thermal maritime dataset acquired by us. Our results surpass the state‑of‑the‑art methods by 19.7% and 12.8% for the above datasets in mAP scores, respectively. We also plan to make our thermal dataset available, the first of its kind for maritime vessel identification.

Authors:Yurui Du, Pinhao Song, Yutong Hu, Renaud Detry
Title: ELVIS: Ensemble-Calibrated Latent Imagination for Long-Horizon Visual MPC
Abstract:
A central challenge of visual control with model‑based reinforcement learning (RL) is reliable long‑horizon planning: long rollouts with learned latent dynamics exhibit branching futures and multi‑modal action‑value distributions. In addition, compounding model errors amplified by visual occlusions make deep imagination brittle. We present ELVIS, a latent model predictive controller (MPC) designed to make long‑horizon planning practical. ELVIS plans in a Dreamer‑style recurrent state space model (RSSM) and replaces standard unimodal model predictive path integral (MPPI) with a Gaussian‑mixture MPPI that maintains multiple coherent hypotheses over long horizons, avoiding mode averaging under branching rollouts. In parallel, ELVIS stabilizes deep imagination with a shared uncertainty‑aware lambda‑return: an ensemble of latent critics defines an upper‑confidence‑bound (UCB) score that gates a time‑varying lambda, adaptively trading off bootstrapping versus look‑ahead to limit compounding error during planning. The same return is used both to train an actor‑critic prior from imagined rollouts and to score candidate trajectories inside GMM‑MPPI, aligning RL objectives with the planner's long‑horizon optimization. On fourteen DeepMind Control Suite visual tasks, ELVIS establishes state‑of‑the‑art performance compared with TD‑MPC2 and DreamerV3. Finally, ELVIS transfers zero‑shot to a real‑world sand‑spraying task with severe occlusions, improving surface‑quality metrics and demonstrating robustness beyond simulation.

Authors:Zhicheng Zhang, Fausto Lizzio, Zhongjun Ma, Masaaki Nagahara
Title: Towards Lag Consensus with Noisy Digital Twins Perception in Second-order Multi-agent Cyber-physical Systems
Abstract:
In this paper, we study second‑order lag consensus in multi‑agent cyber‑physical networks subject to random noise and input failures, within a framework modeling the interactions and perceptions between physical twins and digital twins. We propose a lag consensus protocol and establish sufficient conditions for the mean‑square (exponential) stability of the resulting stochastic lag error dynamics. The consensus criteria are derived via Lyapunov analysis using the Itô formula, ensuring robustness to random perturbations and intermittent input failures. Numerical examples illustrate the effectiveness of the proposed method.

Authors:Bishal Dey, Abhishek Dhar, Sumit kr. Pandey, Anindita Sengupta
Title: Adaptive MPC for Constrained Trajectory Tracking of Uncertain LTI System with Input-Rate Limits
Abstract:
This paper addresses the trajectory‑tracking problem for discrete‑time linear time‑invariant systems with bounded parametric uncertainty, subject to hard constraints on system states, control inputs, and input rates. Unlike existing methods, which often consider only partial uncertainty, omit input‑rate or state constraints, or focus on regulation problems, this work provides a systematic adaptive model predictive control (MPC) solution for constrained trajectory tracking under full parametric uncertainty. Determining the control input required to achieve zero tracking error under unknown parameters is challenging. Simultaneously, trajectory tracking under uncertainty with input‑rate constraints induces temporal coupling in the control sequence, resulting in a time‑varying admissible control set and rendering standard recursive feasibility arguments inapplicable. These challenges are overcome by systematically utilizing the estimated system parameters, coupled with a suitably designed adaptive learning process within a reformulated MPC framework. The recursive feasibility of the proposed MPC optimization routine is then rigorously established despite the time‑varying admissible control set induced by input‑rate constraints. Closed‑loop stability is guaranteed via Lyapunov‑based analysis, ensuring convergence of the tracking error and boundedness of system states. Simulation results validate the effectiveness of the pr

Authors:Jan Marco Ruiz de Vargas, Fabian Raisch, Zoltan Nagy, Pierre Pinson, Christoph Goebel
Title: Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models
Abstract:
Model‑based reinforcement learning (MBRL) offers a promising approach for data‑efficient energy management in buildings, combining the strengths of predictive modeling and reinforcement learning. While previous MBRL methods applied to HVAC control have reduced training data requirements, they still require several months of interaction with the building to learn a satisfactory control policy. A key reason is that existing surrogate models attempt to predict the entire state‑space, including weather and electricity prices that are unaffected by control actions, or completely ignore these variables. Addressing these issues, we propose Counter‑Dyna, a method that enhances the data‑efficiency of Dyna, an MBRL method. We create data‑efficient counterfactual surrogate models (CSM) by leveraging invariances in the state‑space. Using a CSM in Dyna speeds up RL training measured in environment interaction data compared to previous results. In comparison with previous state‑of‑the‑art that used 6‑12 months of environment interactions, our method needs only 5 weeks. We evaluate our method in a large simulation study using the literature standard BOPTEST framework and proximal policy algorithm (PPO) as the RL algorithm. Our results show cost‑saving potentials of 5.3% to 17.0% in a hypothetical deployment scenario. Our work is a significant step towards making real‑world deployment of RL algorithms in HVAC control practically viable.

Authors:Batu Candan, Simone Servadio
Title: Tightly-Coupled Estimation and Guidance for Robust Low-Thrust Rendezvous via Adaptive Homotopy
Abstract:
Minimum‑fuel low‑thrust rendezvous guidance yields bang‑bang control structures highly sensitive to estimation errors, sensor anomalies, and solver regularization, making aggressive closed‑loop execution brittle for uncooperative proximity operations. This paper proposes a tightly‑coupled estimation and guidance architecture where navigation confidence directly modulates the homotopy parameter of a receding‑horizon indirect optimal control solver. Relative motion is modeled in the Clohessy‑Wiltshire frame. The translational state is estimated via a linear Kalman filter augmented by a Multiple Tuning Factors (MTF) covariance inflation mechanism that suppresses suspicious innovation directions. A composite score from the normalized innovation and MTF activity is mapped online to the homotopy parameter, allowing the controller to relax toward a smoother, conservative regime when confidence degrades, and recover fuel‑efficient bang‑bang control as sensing improves. Numerical results under severe measurement degradation show fixed bang‑bang guidance remains brittle; both plain‑KF and MTF‑KF fixed‑epsilon controllers yield large terminal miss distances. Conversely, the proposed MTF‑adaptive homotopy controller reduces terminal miss by roughly two orders of magnitude, from hundreds of meters to sub‑meter levels, requiring only a moderate increase in control effort versus the open‑loop fuel‑optimal benchmark. A comparison indicates adaptive homotopy is the dominant robustness mechanism, while MTF provides additional accuracy and efficiency improvements. The receding‑horizon implementation exhibits consistently fast and reliable solution times, supporting the practical online viability of the proposed method.

Authors:Mudassar Liaq, Mahyar Tajeri, Peng Hu
Title: Queue-Aware and Resilient Routing in LEO Satellite Networks Using Multi-Agent Reinforcement Learning
Abstract:
With the rapid growth in data demand and stringent latency requirements of modern applications has driven significant interest in Low Earth Orbit (LEO) satellite constellations as an emerging solution for global Internet coverage. However, routing in LEO networks remains a fundamental challenge due to highly dynamic topologies, time‑varying traffic conditions, and its susceptibility to link failures. Conventional routing algorithms typically assume static link metrics and fail to account for queue backlogs or real‑time system variations, making them less effective in such environments. We propose a queue‑aware multi‑agent deep reinforcement learning (MA‑DRL) framework for routing in LEO satellite networks. Each satellite is modeled as an independent agent responsible for making local routing decisions, enabling a distributed and scalable solution. The proposed framework formulates a latency‑aware optimization problem that incorporates background traffic, queue dynamics at each satellite, and a resilience score to improve robustness. We evaluate the proposed approach against the state‑action‑reward‑state‑action (SARSA) and Dijkstra algorithms. While Dijkstra achieves the lowest end‑to‑end latency under ideal conditions, its computational and signaling overhead becomes a significant bottleneck as the network scales. In contrast, our proposed approach incurs significantly lower overhead (approximately 50% of Dijkstra at a 5 s recalculation interval), scales efficiently with network size, and effectively manages queue backlogs and resilience under increasing traffic load, demonstrating enhanced robustness and scalability in LEO satellite networks while maintaining competitive latency and resilience scores.

Authors:Jieying Wang, Xinqi Cai, Caifeng Shan, Wenjin Wang
Title: Optimize-at-Capture: Highly-adaptive Exposure Controlling for In-Vehicle Non-contact Heart-rate Monitoring
Abstract:
Remote photoplethysmography (rPPG) holds great promise for continuous heart‑rate monitoring of drivers in intelligent vehicles. However, its performance is severely degraded by the highly dynamic illumination changes. A critical yet overlooked factor is the lack of exposure controlling during video acquisition ‑‑ most existing systems rely on either fixed exposure settings or camera build‑in auto‑exposure, both of which fail to maintain stable facial brightness under rapidly changing lighting conditions during driving. To address this gap, we propose a highly‑adaptive exposure controlling framework that proactively adjusts exposure parameters based on predictive modeling of historical skin reflections. Unlike standard auto‑exposure, our method is specifically optimized for rPPG measurement, ensuring the skin region of interest (ROI) remains within the optimal dynamic range for rPPG signal extraction. As an important contribution of this study, we introduce ExpDrive, a public in‑vehicle physiological monitoring dataset comprising synchronized facial video and reference ECG from 48 subjects captured under real driving conditions. Extensive experiments demonstrate that our method consistently outperforms fixed exposure and standard auto‑exposure strategies. Specifically, it reduces the Mean Absolute Error (MAE) by 6.31 bpm (from 14.1 to 7.79 bpm) and significantly increases the success rate by 32.3 percentage points (p < 0.001) (from 24.9% to 57.2%) across challenging driving scenarios. Notably, it clearly improved the performance of non‑contact heart‑rate monitoring in both low‑light (rainy) and high‑glare (sunny) conditions, validating the efficacy of exposure‑aware acquisition design.

Authors:Zhenning Yang, Yuhan Chen, Patrick Tser Jern Kon, Tongyuan Miao, Hongyi Lin, Venkat Viswanathan, Danai Koutra, Ang Chen
Title: Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery
Abstract:
To unleash the full potential of AI for Science, we must untether the agents from a purely digital environment. The agent's ability to control and explore in real‑world labs is essential because the physical lab remains foundational to scientific discovery. While some tasks can be performed on a computer (e.g., data analysis, running simulated experiments), Eureka moments could occur at any time while operating lab instruments (e.g., when a scientist notices unexpected clues, intuition may prompt a real‑time course change). Although autonomous labs are on the rise, which expose programmable APIs to control scientific instruments via software, bridging the gap between increasingly powerful AI agents and automated lab equipment requires innovation that draws insights from computer systems. We propose a new paradigm called ``Experiment‑as‑Code (EaC) Labs,'' where a core concept is to encode experiments as declarative configurations that can be compiled down to device‑level APIs. AI agents come up with hypotheses and experiments, written as an ensemble of declarative configurations. The systems layer performs program analysis, safety checks, resource assignment, and job orchestration. Finally, programmatic experimentation occurs via actuating the device APIs. This is a general stack that is science‑, lab‑, and instrument‑independent, representing a novel synthesis across the physical, systems, and intelligence layers to unleash the next breakthrough in AI for Science.

Authors:Hongyu Hè, Minhao Jin, Maria Apostolaki
Title: Worst-Case Discovery and Runtime Protection for RL-Based Network Controllers
Abstract:
RL‑based controllers achieve strong average‑case performance in networking tasks such as congestion control and adaptive bitrate streaming. Yet their performance can degrade severely under network conditions where strong performance is still achievable. Identifying such conditions and quantifying the resulting performance gap is intractable by enumeration, while the sequential and closed‑loop nature of RL controllers makes formal verification methods impractical. We present ReGuard, a framework that discovers worst‑case scenarios for a given RL controller and protects it against them at inference time without retraining. Discovery is formulated as a bilevel regret‑maximization problem, which yields a certified lower bound on the worst‑case performance gap. The discovered trajectories are then analyzed as counterfactuals and compiled into lightweight logic rules that intervene only when a risky state is detected, leaving the controller's behavior unchanged otherwise. We evaluate ReGuard across three RL‑based network controllers: Pensieve, Sage, and Park. ReGuard discovers scenarios in which the controller's performance is 43‑64% worse than what is achievable. ReGuard not only discovers gaps 57% to 6× larger than those found by the strongest baselines but also shrinks them by 79‑85% via lightweight rule‑based protection while preserving nominal performance. ReGuard's protection extends beyond the scenarios it discovers, improving performance across a wider range of network conditions.

Authors:Chih-Fan Pai, Yang Zheng
Title: Online Nonstochastic Prediction: Logarithmic Regret via Predictive Online Least Squares
Abstract:
We study online prediction for marginally stable, partially observed linear dynamical systems under nonstochastic disturbances. Our objective is to minimize the cumulative squared prediction loss and compete with the best‑in‑hindsight Luenberger predictor. Standard online learning methods typically rely on bounded domains/gradients, and thus their guarantees may fail to deal with potentially unbounded trajectories in marginally stable systems. In this paper, we introduce an unconstrained online least squares method that stabilizes the learning process via tailored predictive hints. With model knowledge, we prove that hints constructed from any stabilizing Luenberger predictor render the hint residuals uniformly bounded, achieving logarithmic regret despite unbounded trajectory growth. We also discuss model‑free prediction and introduce a simple universal hint for symmetric systems, under which logarithmic regret is maintained without model knowledge. Our results provide an adaptive, instance‑wise optimal online predictor compared to classical fixed‑gain observers under nonstochastic disturbances.

Authors:Manan Mittal, Ryan M. Corey, John R. Buck, Andrew C. Singer
Title: Adaptive Diagonal Loading for Norm Constrained Beamforming
Abstract:
Reliable adaptive beamforming is critical for large microphone arrays operating in highly dynamic acoustic environments. In scenarios characterized by fast‑moving talkers and interferers, the available sample support for estimating the spatial correlation matrix is often snapshot‑deficient. This deficiency, coupled with array imperfections, degrades the White Noise Gain (WNG), leading to severe target signal cancellation. To ensure stable and robust beamforming, we propose a novel adaptive diagonal loading method that guarantees the WNG remains strictly within specified bounds. By leveraging the Kantorovich inequality, we map the desired WNG to a strict upper bound on the condition number of the correlation matrix. Furthermore, we present three estimation techniques for the adaptive loading level, ranging from trace‑based bounding to exact eigenvalue decomposition, offering scalable computational complexities of \mathcalO(M), \mathcalO(M^2), and \mathcalO(M^3). Our approach demonstrates highly stable beamforming under fast‑changing interference.

Authors:Benjamin Catalano, Keith Paarporn, Sebin Gracy
Title: Analysis of a Competitive Bivirus SIS Epidemic Model with Game Theoretic Social Distancing
Abstract:
We propose a competitive bi‑virus model with dynamic social distancing behavior. Our model illustrates how public perception of different viruses changes the conditions for their eradication, their coexistence, or the dominance of one over the other. We show that our model is not monotone, in contrast to the classic bi‑virus model. We detail how social distancing behavior produces different sets of equilibria than the classic bi‑virus model and changes the criteria for their stability. In particular, we detail the set of disease free equilibria (DFE) present in our model and identify necessary and sufficient conditions for almost global exponential stability of the same. We prove similar global results for all but one non‑DFE isolated (unilateral) equilibria and local stability results for the remainder. We also consider coexistence equilibria; we show such equilibria, when they exist, take the form of lines of equilibria and give local conditions for their stability. Finally, we illustrate our theoretical findings with numerical examples.

Authors:Yuqing Cui, Zhaoxi Zhang, Sidharth Santhi Nivas, Prem Sagar Pattanshetty Vasanth Kumar, Maxwell McManus, Chenzhi Zhao, Guanying Sun, Nicholas Mastronarde, George Sklivanitis, Dimitris A. Pados, Elizabeth Serena Bentley, Zhangyu Guan
Title: StormWave: An Open-Source Portable SDR Platform for Over-the-Air Resilience Evaluation of Terrestrial and Aerial Communications
Abstract:
This paper presents \emphStormWave, an open‑source, portable software‑defined Radio Frequency (RF) interference generation and monitoring platform designed for realistic field‑based evaluation of the resilience of wireless communication systems. StormWave enables seamless composition and runtime switching among a wide range of narrowband and wideband waveforms, while supporting multiple digital modulations, adaptive coding, and multi‑radio orchestration with real‑time spectrum visualization. We evaluate the effectiveness of StormWave through both outdoor ground and air‑to‑air (A2A) experiments. Ground experiments demonstrate clear waveform‑ and modulation‑dependent interference effects under realistic propagation conditions, while A2A experiments reveal pronounced distance‑dependent constellation distortion and access‑symbol degradation under active interference. The StormWave source code will be released to the community, with the expectation that StormWave will be used as a flexible, extensible, and field‑ready platform for systematically validating interference resilience of wireless systems under realistic operating conditions.

Authors:Sinan Bank, Casey E. Eaton
Title: OPENJ: A Conceptual Framework for Open-Source Digital Human Modeling and Ergonomic Assessment in a CAD Environment
Abstract:
Industrial workplace challenges range from musculoskeletal disorders ‑‑ a leading cause of occupational injury ‑‑ to suboptimal workstation layouts, inefficient task sequences, and poor human‑equipment fit. Digital human modeling (DHM) tools address several of these challenges by placing a scalable virtual mannequin in a computer‑aided design (CAD) environment, enabling engineers to evaluate ergonomic risk through standardized assessment methods (RULA, REBA, NIOSH Lifting Equation, OWAS), optimize workstation layouts for reach and visibility, predict task postures through inverse kinematics, and simulate operations before physical implementation. Despite four decades of development since the Jack system originated at the University of Pennsylvania in the 1980s, the integrated DHM capability set ‑‑ anthropometric mannequin, posture prediction, ergonomic assessment, and CAD integration ‑‑ remains exclusive to commercial platforms such as Siemens Tecnomatix Jack (Process Simulate), Dassault DELMIA, Humanetics RAMSIS, and the University of Iowa's Santos system. These platforms operate under proprietary, vendor‑quoted pricing models, and their acquisition and operating costs, together with closed‑source implementations, have been repeatedly identified as practical adoption barriers for individual researchers, small‑to‑medium enterprises, and educational institutions. Organizations without access resort to manual observational methods ‑‑ paper‑based worksheets applied to photographs or video ‑‑ sacrificing the predictive power and reproducibility that computational analysis provides. The paper serves as a design blueprint for (OpenJane/Joe), positioning the project for subsequent open‑source implementation and community adoption.

Authors:Haruto Nakashima, Siddhartha Ganguly, Kenji Kashima
Title: Globally Solving Unbalanced Optimal Transport and Density Control for Gaussian Distributions
Abstract:
In this article, we study unbalanced optimal transport (UOT) and establish a control‑theoretic dynamical extension, which we call the unbalanced density control (UDC), for a class of Gaussian reference measures. In the static setting, we consider UOT with quadratic transport cost and Kullback‑‑Leibler penalties on the marginals relative to prescribed Gaussian measures. We show that the infinite‑dimensional variational problem admits an exact Gaussian reduction, yielding a finite‑dimensional optimization over masses, means, and covariances, together with a closed‑form expression for the optimal transported mass. We then formulate UDC for discrete‑time linear systems, where the initial and terminal state measures are imposed softly through KL penalties and the intermediate evolution is governed by controlled linear dynamics with quadratic control cost. For this problem, we prove that any feasible solution can be replaced, without loss of optimality, by a Gaussian initial measure and an affine‑Gaussian control policy. This leads to an exact finite‑dimensional reformulation and, after a standard covariance‑steering lifting, to an SDP‑based optimization for fixed mass, again coupled with a closed‑form mass update. We further establish existence of optimal solutions and identify a sufficient condition under which the affine‑Gaussian UDC policy is deterministic. These results provide globally optimal solution methods for both Gaussian UOT and Gaussian UDC. Finally, we illustrate our results with several numerical examples.

Authors:Apoorv Mathur
Title: Thinking fast and slow -- a cognitive inspired framework for decision intelligence for power systems
Abstract:
Decision‑making in power systems spans multiple timescales ‑‑ from milliseconds to prevent surges, to seconds to balance frequency and protect grid assets, to minutes for real‑time energy balancing, to day‑ahead, seasonal, and long‑term planning. Growing uncertainty and complexity, driven by intermittent renewables and distributed energy resources (DER), demand fresh approaches to power system intelligence and architecture. Daniel Kahneman describes the interplay of two systems of human decision‑making: System 1 that is fast, intuitive, experience based, reactive, and System 2 that is slow, deliberate, analytical. Similarly, octopus intelligence illustrates a model for distributed yet coordinated decision‑making between central and edge intelligence. Future power systems must embed coordinated intelligence that operates across diverse timescales and with placement at both edge and centralized levels. This paper maps decision‑intelligence in power systems against System 1 and 2 and edge‑central architecture paradigms based on the trade‑offs inherent in decision making such as speed/latency, energy cost/compute, accuracy, and robustness. The framework inspires an agentic intelligence architecture ‑‑ laying the foundation for trustworthy, autonomous power systems of the future.

Authors:Yoshinari Takayama, Alessio Iovine, Bart Besselink, Guillaume Sandou, Adnane Saoud
Title: Safety by Invariance, Liveness through Refinement: Heterogeneous Contract Framework for Co-Design of Layered Control
Abstract:
Real‑world control systems must achieve long‑horizon objectives (liveness) while respecting continuous‑time safety constraints, a combination that motivates hierarchical layered control architectures (LCAs). Existing LCA research, however, lacks (i) a uniform specification language across discrete planning and continuous execution, (ii) formal guarantees that specifications are preserved when interconnecting subsystems at heterogeneous time scales, and (iii) compositional separation between layers, owing to reliance on naive input‑filtering laws. This paper addresses all three gaps by importing the safety‑‑liveness decomposition into a heterogeneous assume‑‑guarantee framework: \emphsafety is enforced by invariance at the continuous‑time layer, while \emphliveness is achieved through refinement at the discrete‑time layer, with inter‑layer coordination formalized via vertical refinement and timing‑compatibility conditions. We instantiate this contract with a novel LCA combining an MPC planner, an input‑to‑state stabilizing (ISS) low‑level controller, and a reference‑governor bridge, and validate it on a Hybrid Energy Storage System (HESS) comprising a battery and a supercapacitor.

Authors:Jesse Wayment, Brian Yarbrough, Jingbo Wang, Shreyas Sundaram, Philip E. Paré
Title: HyParLyVe: Hyperplane Partitioning for Neural Lyapunov Verification
Abstract:
This work introduces HyParLyVe (Hyperplane Partitioned Lyapunov Verifier), a novel algorithm for sound and complete verification of neural Lyapunov candidates by interpreting shallow ReLU networks as hyperplane arrangements. This perspective reduces positive definiteness verification to a finite set of vertex evaluations, and the decrease condition to a bounded optimization problem over each region. We formally prove correctness of the proposed verification procedures and demonstrate that HyParLyVe achieves significant speedups over state‑of‑the‑art methods.

Authors:Jonas G. Matt, Ilia Shilov, Saverio Bolognani
Title: A Welfarist Perspective on Fair Generation Curtailment
Abstract:
This paper presents a welfarist approach to fair active power curtailment in distribution grids with distributed photovoltaics. We address the lack of consistent axiomatic foundations in existing ad‑hoc curtailment rules by modeling the decision as a social choice problem over feasible operating points and by deriving curtailment objectives from a set of foundational axioms that express principled stances on fairness and grid access rights. Rather than relying on the typically assumed full comparability of utilities, which can lead to undesirable outcomes in heterogeneous residential systems, we adopt a cardinal non‑comparability stance on utilities. This approach requires far fewer assumptions about prosumers' private preferences while providing a rigorous basis for fair social ranking. We then present a unified framework that demonstrates that existing curtailment schemes represent specific instances of the Kalai‑Smorodinsky rule applied to different normative reference points. This perspective offers grid operators an auditable, axiomatic foundation for justifying fairness in local energy systems.

Authors:Riccardo Cescon, Andrea Martin, Giancarlo Ferrari-Trecate
Title: Sinkhorn Ambiguity Sets for Distributionally Robust Control: Convexity, Weak Compactness, and Tractability
Abstract:
Classical stochastic control assumes perfect knowledge of the uncertainty affecting the plant. In practice, however, such information is often incomplete. To address this limitation, we consider a distributionally robust control (DRC) problem with ambiguity sets defined via the Sinkhorn discrepancy. Compared to other discrepancy measures based on optimal transport, such as the popular Wasserstein distance, the Sinkhorn divergence does not constrain the worst‑case distribution to be discrete, and allows combining observed data with prior knowledge in the form of a reference distribution, making this choice particularly suitable when only few noise samples are available for control design. We first study the properties of Sinkhorn ambiguity sets, establishing convexity and weak compactness under standard assumptions. We then leverage these results to prove that, the Sinkhorn DR linear quadratic control problem over linear policies can be solved through convex programming‑even in the presence of DR safety constraints. Finally, we validate our theoretical findings and demonstrate the effectiveness of the proposed approach on a trajectory planning example.

Authors:Francesco Popolizio, Albert Škegro, Torsten Wik, Chih Feng Lee, Changfu Zou
Title: Online Energy Management for Bidirectional EV Charging with Rooftop PV: An Aging-Aware MPC Approach
Abstract:
This paper investigates the economic impact of vehicle‑home‑grid integration in the presence of rooftop PV, by proposing an online, aging‑aware energy management strategy for an electric vehicle (EV), a household, and the electrical grid. The model predictive control‑based framework explicitly exploits vehicle‑to‑grid (V2G) and vehicle‑to‑home (V2H) operation to perform energy arbitrage, increase self‑consumption, while respecting user‑driven driving requirements. The framework optimizes power flows over a shrinking horizon using a detailed battery aging model that captures both calendar and cycle degradation, and a Transformer‑based forecaster that provides short‑term predictions of household load and solar irradiance. For a one‑year horizon, the proposed strategy yields the lowest annual cost among all evaluated strategies. Adding PV increases the annual profit by EUR 1060.7 compared to operating without PV, and yields an economic gain of up to EUR 2410.5 over smart unidirectional charging, at the expense of only 1.27% extra battery degradation. Even in the least favorable case with no remuneration for V2G energy, bidirectional operation still delivers an economic gain of EUR 355.8 through V2H. Sensitivity analyses over V2G price ratio, EV battery size, household demand, and pickup time uncertainty confirm that these benefits persist across a wide range of scenarios and highlight the potential of EVs as active energy nodes, enabling sustainable energy management and cost‑effective battery usage in real‑world conditions.

Authors:Dimitrios C. Tzarouchis
Title: A Directivity-Dependent Rician K-Factor Model for Indoor Industrial Channels
Abstract:
We derive a physics‑based, closed‑form model linking antenna directivity to the root‑mean‑square (RMS) delay spread and mean excess delay in large reverberant indoor environments. Starting from the Rician K‑factor‑the ratio of line‑of‑sight (LOS) to scattered power we show that K scales with the total transmit‑plus‑receive (Tx+Rx) antenna gain through a single reverberance factor that quantifies scatter anisotropy. For an arbitrary scatter power delay profile (PDP), we derive a general identity connecting sigma, tau, and K; the exponential scatter model is the physically motivated special case. Ray‑tracing simulations over 100 random link placements in a 57300 m3 industrial hall at 75 GHz validate the model. Compact design rules map target delay‑spread values to the minimum required antenna gain, enabling wideband mmWave industrial links.

Authors:Job Groeneveld, Miguel Muñoz, Jan Viebahn, Alessandro Zocca
Title: Exact and Evolutionary Algorithms for Sequential Multi-Objective Transmission Topology Planning
Abstract:
We address day‑ahead transmission topology planning and congestion management as a sequential, multi‑objective optimization problem and develop two complementary algorithms for it: an exact enumeration method and a tailored evolutionary heuristic. The problem is formulated with four operational objectives reflecting real TSO decision criteria: worst‑case line loading under N‑1 security, topological depth, number of switching actions, and time spent in non‑reference topologies, over a 24‑hour horizon. We introduce the block algorithm, an exact method that exploits the temporal block structure of feasible strategies to enumerate the complete Pareto front; for fixed operational bounds on depth and switch count, its evaluation count grows polynomially with the planning horizon. We complement it with a multi‑objective evolutionary algorithm based on NSGA‑III, with structure‑guided initialization and problem‑specific variation operators tailored to the topology‑planning structure. Using real operational data from the Dutch high‑voltage grid operated by TenneT TSO, we show that the block algorithm computes the full Pareto front for a highly congested day in under three minutes, and that the evolutionary algorithm converges toward but does not recover the exact front. The block algorithm thus provides both a practical decision‑support tool and a ground‑truth benchmark for future heuristic and learning‑based methods on this problem class.

Authors:Ala Kolsi, Christian Ebenbauer, Bahman Gharesifard, Raik Suttner
Title: Global exponential stabilization of a force- and torque-actuated unicycle by flexible-step MPC
Abstract:
We study the problem of global exponential stabilization of a force‑ and torque‑controlled unicycle model in discrete time. To this end, we extend a recently introduced approach to model predictive control (MPC) in which a flexible number of inputs is implemented in every iteration. We present the first flexible‑step MPC protocol with state‑dependent weights for average descent. Notably, the proposed method relies neither on a suitable design of running or terminal cost functions nor on a suitable choice of terminal constraints. Instead, stability is guaranteed through a generalized discrete‑time control Lyapunov function. We establish a new theoretical framework for global exponential stabilization of general nonlinear discrete‑time control systems by flexible‑step MPC. The obtained results go beyond the unicycle example. However, given the importance of the unicycle dynamics, we make that a focal point of our work. For the particular case of the dynamic (second‑order) unicycle model, we show that global exponential stability cannot be attained in the classical sense, but in a slightly weaker sense. The proposed flexible‑step MPC method is shown to induce the best possible notion of global exponential stability for this model. We provide explicit rules for the choice of parameters, which guarantee feasibility and global exponential stability. Our numerical simulations show that the discrete MPC method also works very well in applications to a continuous‑time torque‑actuated unicycle.

Authors:Panagiotis Rousseas, Dimos V. Dimarogonas
Title: Feasibility-aware Hybrid Control for Motion Planning under Signal Temporal Logics
Abstract:
In this work, a novel method for planar task and motion planning based on hybrid modeling is proposed. By virtue of a discrete variable which models local constraint satisfaction and enables local feasibility analysis, the proposed control architecture unifies planning with control design. Concurrently, control barrier functions are designed on a transformed disk version of the original nonconvex and geometrically complex robotic workspace, thus amending the issue of deadlocks. Simulations of the proposed method indicate effective handling of multiple overlapping spatio‑temporal tasks even in the face of input saturation.

Authors:Arash Bahari Kordabad, Rupak Majumdar, Sadegh Soudjani
Title: Almost Sure Reachability in Continuous-time Stochastic Systems
Abstract:
We provide certificates for almost sure reachability of continuous‑time stochastic systems governed by stochastic differential equations (SDEs). We first show that a standard Euler‑Maruyama discretization may fail to preserve almost sure reachability property of the system using a double‑well Langevin system. This observation motivates us to develop certificates for almost sure reachability directly on the continuous‑time system. We introduce a pair of certificates, a drift function and a variant function, and prove necessity and sufficiency for almost sure reachability of an open bounded target set. Using these certificates, for linear SDEs, we give a characterization of almost sure reachability in terms of the spectral structure of the system matrices. For polynomial SDEs, we fix a polynomial template for the drift function and choose the variant function template as an exponential function composed with a polynomial. This allows us to translate the conditions in the certificates into sum‑of‑squares (SOS) constraints. We then propose an alternating scheme to resolve bilinearities. We illustrate the approach on the double‑well Langevin example, showing that continuous‑time SOS certificates recover almost sure reachability that is lost under time discretization. Moreover, we verify the SOS approach on a polynomial system.

Authors:Jan Lorenz Svensen, Steen Hørsholt, Guruprasath Muralidharan, John Bagterp Jørgensen
Title: A simulation- and model-based approach to PI control pairing and tuning for the pyro process in a cement plant
Abstract:
The operation of the pyro process in cement production significantly affects the energy efficiency and sustainability of the cement plant, especially for reductions in carbon dioxide emissions. Hence, pyro process control is essential to obtain efficient and sustainable operation of cement plants. In this paper, we demonstrate how simulations and models can be utilized to evaluate and design control strategies for the pyro section in cement plants. We apply a novel differential algebraic equation (DAE) model for dynamic simulation of the pyro‑section in cement plants to design decentralized PI controllers for the pyro‑section. We utilize the pyro‑process model to evaluate the control structure design. Through linearization of the pyro‑process model, we apply the Relative Gain Array (RGA) method to choose and evaluate the pairings of the manipulated variables (MVs) and the controlled variables (CVs). Using simulations of the pyro‑section, we generate step responses to estimate transfer models and apply Internal Model Control (IMC) for the tuning of the individual decentralized single‑input single‑output (SISO) PI controllers. Closed‑loop simulations of the PI controllers demonstrate that PI controllers with IMC parameters provide smoother and faster responses compared with manually tuned PI parameters.

Authors:Bo Yang, Kai Pan, Mohammad Reza Hesamzadeh
Title: Event-Based Dynamic Programming for Pumped-Storage Hydropower Scheduling
Abstract:
This paper studies the single‑unit pumped‑storage hydropower (PSH) plant scheduling problem with reservoir dynamics, generation and pumping limits, ramping constraints, start‑up and shut‑down costs, and minimum up/down‑time requirements. A new event‑based formulation is proposed in which an operating schedule is represented as a sequence of mode‑specific events, with dispatch decisions within each event determined by linear programs. Based on this construction, the original time‑indexed mixed‑integer formulation is reformulated exactly as a deterministic dynamic program on an event network. The framework is modular and can be extended to incorporate additional operating modes, such as hydraulic short‑circuit operation, by introducing corresponding event modules without significantly changing the overall event‑network structure. To obtain tractable solution methods, a finite‑grid approximation of the event network is developed, leading to a linear programming formulation for the discretized model. In addition, an event‑based branch‑and‑bound algorithm with linear program‑based bounds is proposed for the continuous‑state problem. Numerical results demonstrate that the proposed event‑based framework provides a computationally effective alternative to the conventional time‑indexed formulation, while offering substantial modeling flexibility for PSH scheduling problems.

Authors:Ho Jae Lee, Yonghyeon Lee, Alexander Alexiev, Tzu-Yuan Lin, Se Hwan Jeon, Sangbae Kim
Title: Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control
Abstract:
In this work, we propose a hybrid hierarchical control framework for reactive dexterous grasping that explicitly decouples high‑level spatial intent from low‑level joint execution. We introduce a multi‑agent reinforcement learning architecture, specialized into distinct arm and hand agents, that acts as a high‑level planner by generating desired task‑space velocity commands. These commands are then processed by a GPU‑parallelized quadratic programming controller, which translates them into feasible joint velocities while strictly enforcing kinematic limits and collision avoidance. This structural isolation not only accelerates training convergence but also strictly enforces hardware safety. Furthermore, the architecture unlocks zero‑shot steerability, allowing system operators to dynamically adjust safety margins and avoid dynamic obstacles without retraining the policy. We extensively validate the proposed framework through a rigorous simulation‑to‑reality pipeline. Real‑world hardware experiments on a 7‑DoF arm equipped with a 20‑DoF anthropomorphic hand demonstrate highly robust zero‑shot transferability for dexterous grasping to a diverse set of unseen objects, highlighting the system's ability to reactively recover from unexpected physical disturbances in unstructured environments.

Authors:Jiachen Shen, Jian Shi, Dan Wang, Han Zhu
Title: Will the Carbon Border Adjustment Mechanism Impact European Electricity Prices? A GNN-Based Network Analysis
Abstract:
The European Union's Carbon Border Adjustment Mechanism (CBAM) creates a complex challenge for the interconnected European electricity market. Traditional static analyses often miss the cross‑border spillover effects that are vital for understanding this policy. This paper addresses this gap by developing a spatio‑temporal Graph Neural Network (GNN) framework. It quantifies how CBAM affects electricity prices and carbon intensity (CI) at the same time. We modeled a subgraph of eight European countries. Our results suggest that CBAM is not just a uniform tax. Instead, it acts as a tool that transforms the market and creates structural differences. In our simulated scenarios, we observe that low‑carbon countries like France and Switzerland can gain a competitive advantage. This suggests a potential decrease in their domestic electricity prices. Meanwhile, high‑carbon countries like Poland face a double burden of rising costs. We identify the primary driver as a fundamental shift in the market's merit order.

Authors:Sergio A. Esteban, Junheng Li, Vince Kurtz, Aaron D. Ames
Title: On Surprising Effects of Risk-Aware Domain Randomization for Contact-Rich Sampling-based Predictive Control
Abstract:
Domain randomization (DR) is widely used in policy learning to improve robustness to modeling error, but remains underexplored in contact‑rich sampling‑based predictive control (SPC), where rollout quality is highly sensitive to uncertainty. In this work, we take the first step by studying risk‑aware DR in predictive sampling on a simple yet representative Push‑T task, comparing average, optimistic, and pessimistic rollout aggregations under randomized model instances. Our initial results suggest that DR affects not only robustness to model error, but also the effective cost landscape seen by the sampling‑based optimizer, by reshaping the basin of attraction around contact‑producing actions. This opens up potential for exploring better grounded risk‑aware contact‑rich SPC under model uncertainty. Video: https://youtu.be/f1F0ALXxhSM

Authors:Akhil B Krishna, Mangal Kothari
Title: Model Predictive Static Programming for Discrete-Time Optimal Control on Lie Groups
Abstract:
This paper extends the Model Predictive Static Programming (MPSP) framework for nonlinear systems evolving on Euclidean spaces to simple mechanical systems evolving on Lie groups. Classical optimal control approaches based on Pontryagin's Maximum Principle (PMP) lead to nonlinear two‑point boundary value problems (TPBVPs), whose numerical solution becomes particularly challenging on nonlinear configuration spaces. To overcome this difficulty, the proposed Lie‑group MPSP framework reformulates the finite‑horizon optimal control problem as a sequence of static quadratic programs that admit closed‑form control updates, thereby avoiding the need to solve TPBVPs directly. The development relies on left‑trivialized variations, intrinsic linearization on Lie groups, and a recursive computation of terminal sensitivity matrices, which together enable computationally efficient real‑time implementation. The proposed method is demonstrated through optimal flipping maneuvers of a variable‑pitch quadrotor (VPQ) and a single‑main‑rotor helicopter (SMRH), both of which are capable of generating negative thrust. For validation, continuous‑time necessary and sufficient optimality conditions are derived, and the corresponding TPBVP solutions are compared against the trajectories generated by the proposed MPSP method in numerical simulations. In addition, the proposed algorithm is systematically compared with the iterative Linear Quadratic Regulator (iLQR) method, and a detailed numerical study is presented to highlight the relative performance and computational features of the two approaches.

Authors:Alexander Dorsey, Ankit Goel
Title: Feedback Linearization-Based Guidance with Zero-Dynamics Correction for Guaranteed Interception
Abstract:
This paper develops a guidance law for nonlinear interception using input‑output feedback linearization (IOL). The engagement between a pursuer and an evader is modeled using point‑mass dynamics, and a baseline IOL‑based guidance law is constructed by regulating the angular rates of the line‑of‑sight (LOS) vector. While this approach yields stable input‑output behavior, it does not constrain the internal (zero) dynamics of the system, which can result in non‑intercepting trajectories despite successful regulation of the LOS rates. To address this limitation, a modified IOL‑based guidance law is proposed that incorporates a correction mechanism to enforce convergence of the range. The resulting formulation ensures that LOS alignment corresponds to a closing trajectory, thereby enabling convergence of the pursuer to the evader for a broad class of initial engagement geometries. The proposed method retains the computational simplicity and real‑time implementability of feedback linearization while improving closed‑loop performance relative to classical guidance laws. Extensive Monte Carlo simulations over a wide range of initial conditions are conducted to evaluate the proposed method. The results demonstrate improved reliability, reduced miss distance, and consistent convergence compared to the baseline IOL and classical proportional navigation.

Authors:Siyuan Wang, Fengqi You
Title: High-Fidelity Full-Sky Video Prediction for Photovoltaic Ramp Event Forecasting
Abstract:
Accurate ultra‑short‑term forecasting of photovoltaic (PV) ramp events is essential for maintaining grid stability in solar‑integrated power systems, particularly under rapidly changing cloud conditions. This paper presents a generative forecasting framework that integrates a future sky video prediction model (PhyDiffNet) with a ramp aware PV output forecasting model (RaPVFormer). Based on the relatively slow yet chaotic dynamics of cloud motion, the system forecasts ramp events up to 16 minutes in advance at a 1‑minute resolution by capturing fine‑grained spatiotemporal cloud patterns and generating high‑fidelity full‑sky video frames. Interpretability is enhanced through attention visualization, highlighting cloud occlusion regions that significantly influence irradiance variability. Supported by extensive quantitative evaluation, the proposed framework demonstrates state‑of‑the‑art performance in both full‑sky video prediction and PV output forecasting. It delivers consistent improvements in structural, perceptual, and temporal video quality, along with a 10% increase in Critical Success Index (CSI) for PV ramp detection. These results demonstrate the capability of AI driven multimodal sensing for ultra short term solar forecasting, supporting more reliable renewable integration and potentially reducing dependence on reserve capacity.

Authors:Noman Bashir, Rob Sherwood, Le Xie, Minlan Yu
Title: From Barrier to Bridge: The Case for AI Data Center/Power Grid Co-Design
Abstract:
For over a century, the electric grid has relied on a single statistical assumption: \emphload diversity, the principle that the uncorrelated demands of millions of small consumers produce a smooth, predictable aggregate. AI training data centers break that assumption. A single hyperscale training campus can draw power comparable to a mid‑sized city, driven by one tightly synchronized job whose demand swings by hundreds of megawatts in seconds. This paper argues that the resulting entanglement of compute and power infrastructure requires a shift from implicit coexistence to explicit co‑development between the historically decoupled data center and electric power industries. We introduce the distinct design principles, operational philosophies, and economic incentives of each sector, and show why their cultural and technical misalignment makes coordination difficult. We identify key research directions, from joint capacity planning, multi‑timescale control, a compute‑‑power protocol stack, to market innovation, that must be pursued to power the future of AI sustainably and reliably.

Authors:Kerri Prinos, Lilianne Brush, Cameron Denton, Zhanqi Wang, Joshua Knox, Snehal Antani, Anton Foltz, Amy Villaseñor
Title: Stable Agentic Control: Tool-Mediated LLM Architecture for Autonomous Cyber Defense
Abstract:
Agentic systems involved in high‑stake decision‑making under adversarial pressure need formal guarantees not offered by existing approaches. Motivated by the operational needs of security operations centers (SOCs) that must configure endpoint detection and response (EDR) policies under adversarial pressure, we present a tool‑mediated architecture: LLM agents use deterministic tools (Stackelberg best‑response, Bayesian observer updates, attack‑graph primitives) and select from finite action catalogs enforced at the tool‑output interface. A composite Lyapunov function machine‑checked in Lean 4 with zero sorry certifies controllability, observability from asymmetric sensor data, and Input‑to‑State Stability (ISS) robustness under intelligent adversarial disturbance, with two corollaries extending the certificate to any controller or adversary from the catalogs. On 282 real enterprise attack graphs, the claims hold with margin. On paired offensive/defensive telemetry, a tool‑mediated Claude Sonnet 4 controller reduces the attacker's expected payoff (game value) by 59% relative to a deterministic greedy baseline, with zero variance across 40 runs at four temperatures. A Claude Haiku 4.5 controller converges to suboptimal game values but stays catalog‑bounded over an additional 40 runs, demonstrating that architectural stability is not dependent on the controller capability. The LLM agent's non‑determinism furthers creative exploration of strategies, while the tool‑mediated architecture ensures system stability.

Authors:Yang Fu, Peng Qin, Liming Chen, Zihao Zhang, Hao Yu, Yifei Wang
Title: Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach
Abstract:
Artificial intelligence‑generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers. It is imperative for AIGC service providers (ASPs) to strategically schedule AIGC workloads to reduce data center energy costs while guaranteeing high‑quality content generation. However, the distinctive characteristics of AIGC services pose critical challenges, including model heterogeneity across ASPs, implicit service quality evaluation, and complex inference process control. To tackle these challenges, we propose a joint energy management and coordinated AIGC workload scheduling framework, which introduces an explicit mathematical characterization of service quality to promote both job transfer among ASPs and fine‑grained inference process configuration. Moreover, various energy resources within data centers are jointly considered to enhance power usage flexibility. Subsequently, a system utility maximization problem is formulated to balance AIGC service revenue with operational penalties and costs. Nevertheless, the strong coupling among job scheduling decisions induces severe reward sparsity, which limits the effectiveness of existing deep reinforcement learning (DRL) algorithms. To address this issue, we develop a diffusion model‑aided reward shaping approach to synthesize complementary reward signals through a multi‑step denoising process. This approach is seamlessly integrated with DRL to enable efficient learning of scheduling policies under sparse environmental feedback. Experiments based on real‑world models and datasets demonstrate that our scheme effectively accommodates electricity price fluctuations and AIGC model heterogeneity, while achieving superior learning convergence and system utility compared with benchmark methods.

Authors:Michael Chertkov
Title: Analytic Bridge Diffusions for Controlled Path Generation
Abstract:
Most modern bridge‑diffusion methods achieve finite‑time transport by specifying an interpolation, Schrödinger‑bridge, or stochastic‑control objective and then learning the associated score or drift field with a neural network. In contrast, we identify a restricted but sufficiently broad and analytically solvable class in which the score, intermediate marginals, and protocol gradients are available in closed form without inner stochastic simulation loops and without neural networks in the optimization loop. We recast the classical linear‑‑quadratic‑‑Gaussian (LQG) stochastic‑control structure as a transport problem of the Path Integral Diffusion (PID) type. In classical LQG control, linear dynamics, Gaussian noise, and quadratic costs lead to Riccati equations and closed‑form optimal feedback. In LQ‑GM‑PID, we retain the linear‑‑quadratic stochastic‑control backbone, but replace terminal state regulation by a prescribed terminal probability density and allow both the initial and terminal laws to be Gaussian Mixtures (GM). Moreover, LQ‑GM‑PID turns bridge diffusion from a tool for terminal target matching alone into a tool for path shaping. We demonstrate this on a 2D corridor task, a 2D multi‑entrance transport task, and a high‑dimensional scaling study with d=32 and M=16 Gaussian‑mixture terminal modes, all with sub‑50\,ms analytic precompute on a laptop. We position LQ‑GM‑PID as an analytically solvable reference model for the state‑of‑the‑art neural bridge‑diffusion and generative‑transport methods: a controlled setting in which neural approximations, score estimates, path‑shaping objectives, and protocol‑learning procedures can be tested against exact quantities.

Authors:S. O. Edeki, S. Noeiaghdam, L. Hairong, S. Kaennakham, I. D. Ezekiel
Title: Analysis of a Stochastic Energy Supply and Demand Model with Renewable Integration
Abstract:
In this work, a stochastic energy supply‑demand model with renewable integration is developed and analyzed. The basic nonlinear deterministic model describing the relationship among regional demand, external supply, energy imports, and renewable resource integration is extended to an Ito‑type stochastic system that captures the uncertainties due to market volatility, climatic variation, policy interventions, and technical changes. Also, the noise structure is multiplicative, ensuring proportional fluctuations and preservation of nonnegativity of the state variables. Global existence, uniqueness of positive solutions, moment boundedness, and stochastic persistence are established rigorously. Furthermore, the deterministic system is analyzed, and stochastic stability is examined using matrix inequality criteria to guarantee almost sure exponential stability of the system in the stochastic setting. Among other results, stochastic perturbations significantly alter the effective system capacity compared to the deterministic case; however, under suitable parameter conditions, boundedness and stability cases are preserved. The Euler‑Maruyama scheme is employed to perform numerical simulations to illustrate various dynamical behaviors and highlight the effects of uncertainty on system dynamics.The numerical reliability of the proposed model is further confirmed by additional numerical experiments via the Milstein scheme and parameter sensitivity analysis. Moreover, the results indicate that stochastic effects should be considered for capturing complex energy systems' behavior under uncertainty and its implications for renewable integration.

Authors:Akul Swami, Nikhil Chougule
Title: Per-Platform GPIO Overhead in Hardware-Validated Edge ML Inference Timing
Abstract:
Edge machine learning (ML) deployments increasingly rely on per‑inference timing measured by software clocks such as Python's perf_counter, but these measurements are not always validated against external hardware references on embedded Linux, and edge ML benchmarking methodologies typically do not isolate platform‑dependent instrumentation overhead. This paper reports a preliminary characterization of GPIO call overhead in hardware‑validated edge ML inference timing on two embedded platforms running a one‑dimensional convolutional neural network (1‑D CNN) arrhythmia classifier on electrocardiogram (ECG) data from the MIT‑BIH Arrhythmia Database, with five classes per the Association for the Advancement of Medical Instrumentation (AAMI) EC57 standard. Across n = 10 trials on each platform at a controlled steady‑state baseline, the per‑platform constant on the Jetson Orin Nano (TensorRT FP16, Jetson.GPIO) is approximately ‑20\,μs, and on the Raspberry Pi 4 (ONNX Runtime CPU, pigpio) approximately ‑86\,μs, yielding a cross‑platform asymmetry of approximately 66\,μs that is large relative to commonly used uniform validation tolerances. The Jetson constant is well‑approximated by direct GPIO call duration (the direct profile accounts for ~88% of the platform constant), while the Pi direct profile over‑predicts the platform constant by ~19%, motivating empirical per‑platform calibration in the deployed measurement context. The Pi constant is not a single sharp value but exhibits a cross‑day range of approximately 6\,μs across the three sessions sampled, while the Jetson constant reproduces to within approximately 0.14\,μs. These preliminary results suggest that cross‑platform edge ML timing studies may benefit from platform‑aware and potentially session‑aware validation gates.

Authors:Purna Sai Garigipati, Onur Ayan, Kishor Chandra Joshi, Xueli An
Title: Tool Use as Action: Towards Agentic Control in Mobile Core Networks
Abstract:
Artificial Intelligence (AI) will play an essential role in 6G. It will fundamentally reshape the network architecture itself and drive major changes in the design of network entities, interfaces, and procedures. The adoption of agentic AI in next‑generation networks is expected to enhance network intelligence and autonomy through agents capable of planning, reasoning, and acting, while also opening up new business opportunities. Under this vision, existing network functions are expected to evolve into AI‑enabled agents and tools that deliver both connectivity and beyond‑connectivity services. As an initial attempt to move toward this vision, this paper presents a tool‑based interface design and an experimental prototype that are based on agentic AI for the mobile core network, with the Model Context Protocol (MCP) and the Agent2Agent (A2A) protocol as foundational protocols. MCP is selected to design the interface between the agent and network tools, and the A2A protocol is used for message exchange between AI agents. In such an experimental setup, we analyze packet‑level message flows between the agents, tools, and network functions and break down the latency of end‑to‑end operations, starting from the prompt injection until the completion of the input task. This work demonstrates how an AI agent‑based core network combined with network‑specific tools can be utilized in next generation mobile systems to execute intent‑based tasks.

Authors:R K B M Rizmi, Shabbir Ahmed
Title: Vision-Based Structural Damage Identification in Vibrating Beams via Dynamic Mode Decomposition
Abstract:
Structural damage detection using non‑contact sensing remains a challenging problem in structural health monitoring. This study presents a data‑driven framework based on Dynamic Mode Decomposition (DMD) for extracting structural dynamics directly from high‑speed video recordings of vibrating structures. Within this approach, the underlying dynamics are approximated by a linear operator, whose spectral decomposition yields modal frequencies and corresponding spatial mode shapes, enabling a physically interpretable representation of the system response. The proposed methodology is evaluated through both numerical and experimental investigations. First, a cantilever beam model is simulated in ANSYS under healthy and damaged conditions. DMD is applied to partial observation data to reconstruct and predict the system response, while the extracted modal features are analyzed to characterize damage‑induced variations. Second, high‑speed video recordings of the beam are processed into spatiotemporal snapshot matrices, allowing DMD to recover full‑field dynamic behavior without contact sensors. To enable quantitative assessment, a damage index is formulated based on DMD‑derived modal features, capturing deviations in both frequency content and spatial characteristics. The results demonstrate consistent and distinguishable patterns between healthy and damaged states across both simulation and experiments, highlighting the capability of DMD as a robust and interpretable tool for non‑contact damage detection using video data.

Authors:Alessandro Scagliotti, Thomas M. Surowiec
Title: Risk-Averse Ensemble Control for Control-Affine Systems
Abstract:
A number of important modern applications in optimal control can be formulated as open loop control problems in which the underlying dynamical systems are subject to random inputs. These so‑called ensemble control problems require the corresponding optimal control to be deterministic, as it must be computed before the realization of uncertainty and the passage of time. Practical applications of ensemble control include quantum control and the training of Neural ODEs. However, the standard approach to ensemble control treats the uncertainty in the objective function via the expectation, which provides optimal controls that only work well on average while ignoring critical outlier phenomena. This study provides a comprehensive mathematical treatment of risk‑averse ensemble control. Within this setting, we adopt a control‑affine structure that ensures the lower semi‑continuity needed for proving the existence of optimal solutions. The central analytical contribution of this paper is a rigorous characterization of the control‑to‑state mapping in which we establish weak‑to‑strong continuity, continuous Fréchet differentiability, and weak‑to‑strong continuity of the derivative operator. Furthermore, this regularity yields primal and dual first‑order optimality conditions characterized by an adjoint state of bounded variation, and it fulfills the functional prerequisites required for the convergence of infinite dimensional optimization algorithms. We conclude by validating these theoretical developments through a numerical experiment in quantum control.

Authors:Zhenyu Liu, Yi Ma, Rahim Tafazolli
Title: Executor-Side Progressive Risk-Gated Actuation for Agentic AI in Wireless Supervisory Control
Abstract:
Agentic artificial intelligence (AI) shows promise for automating O‑RAN wireless supervisory control, but translated intents still require an executor‑side decision before live network actuation. Existing control flows lack explicit semantics for whether an intent should commit, gate for evidence, or reject under stale telemetry, concurrent policies, deadline and bandwidth limits, and rollback constraints. We propose Progressive Risk‑Gated Actuation (PRGA), an executor‑side contract for risk‑gated wireless intent execution. PRGA structures each intent into executable local triage (C0), on‑demand coordination evidence (C1), and post‑hoc provenance support (C2), with C2 kept off the online safety path. A deterministic two‑stage policy checks expiry, freshness, rollback‑handle validity, local conflict, blocking preconditions, and planner‑executor risk divergence from C0, then retrieves C1 only for gated intents when deadline and bandwidth budgets allow; evidence‑mandatory gates reject when required C1 is unavailable. On two 3GPP‑parameterized energy‑saving and slice‑SLA benchmarks, PRGA reduces time‑to‑first‑safe‑action by 23.3‑27.4% and per‑commit control‑plane bytes by 52.7‑54.2% against a decision‑identical eager full‑evidence cost‑overlay comparator, thereby isolating retrieval‑cost accounting; remains non‑inferior within a pre‑declared 0.5 percentage‑point unsafe‑action margin against an invariant‑respecting static‑threshold comparator; and rejects 100% of injected over‑threshold stale inputs in the stale‑state fault campaign. On these benchmarks, PRGA improves supervisory responsiveness and control‑plane efficiency within the evaluated unsafe‑action boundary.

Authors:Xinyi Wen, Xiao Li, Leonardo Rydin Gorjão, Veit Hagenmeyer, Benjamin Schäfer
Title: PowerSINDy: Identifying Nonlinear Time-Dependent Dynamics in Power Grid Frequency
Abstract:
System identification plays a crucial role in physics and machine learning for discovering governing equations directly from data. A powerful approach is the Sparse Identification of Nonlinear Dynamics (SINDy) method, which assumes that only a few dominant terms drive the essential behavior of a nonlinear dynamical system. While SINDy methods have shown excellent results, they are most often illustrated on synthetic or simulated systems, leaving open the question of how well they perform on complex, noisy, real‑world data. Power grid frequency dynamics provide a highly relevant and challenging environment for advancing system identification methods. In this work, we propose PowerSINDy as a framework for empirical power system data. We apply this framework to empirical frequency data from the Continental Europe (CE) and South Korea (SK) synchronous grids, two major power systems with distinct dynamical characteristics. PowerSINDy, which also includes time‑dependent terms, can identify the dynamics of these complex real‑world systems. Furthermore, we benchmark three sparsity‑promoting regression strategies: Sequentially Thresholded Least Squares (STLSQ), Least Absolute Shrinkage and Selection Operator (LASSO), and Sparse Relaxed Regularized Regression (SR3) to evaluate trade‑offs between accuracy, sparsity, and robustness. Results show that LASSO consistently achieves the lowest stable RMSEs, reaching 0.0101 for the CE, while STLSQ provides the best balance between accuracy and stability. SR3 exhibits higher variability and sensitivity to regularization, with L0 and L1 producing nearly indistinguishable outcomes.

Authors:Miriam Kranzlmüller, Lukas Koller, Tobias Ladner, Matthias Althoff
Title: Set-Based Training of Neural Barrier Certificates for Safety Verification of Dynamical Systems
Abstract:
Barrier certificates are scalar functions over the state space of dynamical systems that separate all unsafe states from all reachable states. The existence of a barrier certificate formally verifies the safety of the dynamical system. Recent approaches synthesize barrier certificates by iteratively training a neural network. In each iteration, the candidate is formally verified ‑ if successful, the barrier certificate is found. Instead, we propose a set‑based training approach that tightly integrates verification into training via a set‑based loss function that soundly encodes all barrier certificate properties. A loss of zero formally proves the validity of the barrier certificate, collapsing the iterative training and verification into a single training procedure. Our experiments demonstrate that our set‑based training approach scales well with the system dimension and naturally handles complex nonlinear dynamics.

Authors:Máté Kiss, Maarten Schoukens, Roland Tóth
Title: Least Costly Space-Filling Experiment Design for the Identification of a Nonlinear System
Abstract:
The quality of an estimated nonlinear model highly depends on the data quality that was used for the system identification. By using a Gaussian Process‑based optimal input design approach, a so‑called space‑filling dataset can be generated in the feature space of the system model. The design method is applicable for a broad type of signals and models and also incorporates information measures through optimality criteria into the signal design. However, the resulting input design can be costly to apply to the real system. The goal of this paper is to propose a space‑filling input design that can minimize the experimentation cost in terms of a user defined measure, while still guaranteeing a prescribed level of space‑fillingness. Through a Monte Carlo simulation study we demonstrate that the proposed method can appropriately shape the excitation signal to significantly reduce the experimental cost while the identified model performance remains adequate.

Authors:Vaibhav Gupta, Daniele Martinelli, Giancarlo Ferrari-Trecate, Luca Furieri, Alireza Karimi
Title: Data-Driven Optimal Distributed Controller Synthesis via Spatial Regret
Abstract:
In this paper, we present a novel method for synthesising an optimal distributed spatial regret controller using experimentally obtained frequency‑response data. Spatial regret provides a measure of the performance gap between a structured distributed controller and an oracle with enhanced communication topology. We relax assumptions on the communication topology, allowing the oracle to adopt any enhanced structure. While this generalisation requires an iterative solution in place of a single convex program, we provide a tractable algorithm that synthesises optimal controllers from frequency‑response data while preserving stability and the desired communication structure. Through numerical examples, we illustrate the better performance of the spatial regret controller compared to classical H2/Hinf designs, underscoring the effectiveness of the proposed methodology.

Authors:Andrew Campbell, Chenyue Zhang, Anna Scaglione, Eli Kerr, Merilyn Chesler, Sean Peisert
Title: Differentially Private Synthetic Voltage Phasor Release for Distribution Grids
Abstract:
Training machine learning models, including Grid Foundation Models (GFMs), requires large volumes of realistic grid data, yet substantial privacy concerns discourage utilities and data providers from sharing load profiles and network parameters. We study the release of synthetic voltage phasor trajectories for distribution grids under differential privacy (DP). We first fit a DP generative model to historical customer loads, then propagate synthetic load trajectories through the AC power flow equations on the true admittance matrix to produce voltage phasors. The central question is whether the randomness already present in the DP synthetic loads is sufficient to protect not only the loads, but also the network topology encoded by the bus admittance matrix. We show that it is. The implication is that a corpus of voltage trajectories can be constructed from DP synthetic loads while preserving the statistics of AC power flow, which is critical for training GFMs. This preservation of the power flow statistics stands in contrast to approaches that perturb the admittance matrix directly or inject noise into the voltage outputs, both of which distort the underlying physics. Concretely, we derive (\varepsilon,δ)‑DP guarantees for the released voltage trajectories with respect to the admittance matrix, meaning privacy of the network parameters is obtained without any additional noise mechanism. Our bound depends on the adjacency assumption, the Jacobian of the AC power flow, and the covariance of the synthetic DP‑loads. Finally, we present a synthetic voltage generation procedure and an empirical evaluation against Gaussian output‑perturbation baselines, demonstrating that our approach provides a clear advantage for enabling GFM training.

Authors:Dumitra Iancu, Vilgot Snygg, Sijia Cheng, Lina Tinnerberg, Mikael Henriksson, Emil Bergman, Anders J Johansson, Baktash Behmanesh, Ove Edfors, Liang Liu
Title: A Scalable 256-Antenna Distributed MIMO Testbed with Real-Time Fully Digital Beamforming
Abstract:
Distributed massive MIMO (D‑MIMO) is a promising technology for future generation wireless systems as it takes advantage of both an increased array aperture and a decentralized processing architecture and topology. In order to truly understand the possibilities and limitations of these approaches in real scenarios, practical realization of testbeds is an essential step in the technology advancement. This work presents the Lund University Large Intelligent Surface testbed ‑‑ LuLIS, that can operate up to 256 coherent radio frequency (RF) chains using 16 AMD Zynq UltraScale RFSoC ZCU216 evaluation boards acting as distributed processing nodes. Real‑time processing is facilitated by acceleration and distribution of MIMO processing algorithms on the FPGA fabric of the boards. The system is easily scalable, as increasing the number of antennas is done in multiples of 16 by adding more RFSoCs, which also implies addition of another processing node. The design allows up‑scaling without hardware redesign, introduction of large latencies or data transfer overhead. The testbed is flexible in terms of deployment, with options of fully distributing the nodes (as in D‑MIMO) or co‑locating them (as in more traditional Massive MIMO). A detailed description of the implementation of the testbed is presented and initial results are shown for an uplink (UL) transmission from four single‑antenna user equipments (UEs) to 64, 128 and 256 base‑station antennas.

Authors:Jilie Zhang
Title: A Fresh Look on Network Synchronization
Abstract:
This paper gives a fresh look at network synchronization. Here we no longer analyze it from the view of mathematics, such as graph theory, while we probe into one from control theory. First, we analyze the synchronization region using the inner coupling matrix, giving up the routine method of studying the network structure. The motivation comes from the inner coupling matrix that is not subject to any restrictions like network structure, such as distance and communication strength among nodes. It can be configured at will to meet the synchronization performance if only the states of the local dynamic are measurable or observable and the communication topology is connected. Thus, it is very useful for future practical engineering design. In addition, we have an amazing finding that the network synchronization and multi‑agent system consensus problems are equivalence essentially. Afterwards a unified viewpoint, that is, the essence of multi‑agent consensus control is the same as that of network synchronization, is present. Here, the equivalence relation is clearly proven and proposed. Therefore, we can synthesize the inner coupling matrix for network systems or the controller gain for multi‑agent systems for each other. Finally, we also present a kind of method for addressing the nonlinear complex network system. Then the effectiveness of method is verified by taking the network of the three‑oscillator universal probe as an example.

Authors:Péter Antal, Andrea Carron, Melanie Zeilinger, Roland Tóth, Tamás Péni
Title: Robust Adaptive Predictive Control for Hook-Based Aerial Transportation Between Moving Platforms
Abstract:
This paper presents a novel model predictive control (MPC) approach for autonomous pick‑and‑place between moving platforms with a hook‑equipped aerial manipulator. First, for accurate and rapid modeling of the complex dynamics, a digital twin model of the quadcopter equipped with a hook‑based gripper, implemented in MuJoCo, is constructed and used as the predictive model for the MPC. To handle uncertainties of the predictive model (e.g. due to aerodynamics and uncertain payloads), a robust adaptive MPC approach is proposed. By systematic integration of zero‑order robust optimization (zoRO) based uncertainty propagation and an extended Kalman filter (EKF) for parameter estimation, the MPC algorithm ensures robust constraint satisfaction, high performance, and computational efficiency. The effectiveness of the proposed method is evaluated in complex simulated scenarios and in real‑world flight experiments.

Authors:Liqian Ma, Zishun Liu, Glen Chou, Yongxin Chen
Title: Feedback Motion Planning for Stochastic Nonlinear Systems with Signal Temporal Logic Specifications
Abstract:
We study feedback motion planning for continuous‑time stochastic nonlinear systems under signal temporal logic (STL) specifications. We propose a framework that synthesizes control policies for chance‑constrained STL trajectory optimization problems, with the goal of ensuring that the closed‑loop stochastic system satisfies a given STL formula with high probability (e.g., 99.99%). Our approach is based on a predicate erosion strategy that transforms the intractable stochastic problem into a deterministic STL trajectory optimization problem with tightened STL formula constraints. The amount of erosion is determined by a probabilistic reachable tube (PRT) that bounds the deviation between the stochastic trajectory and an associated nominal trajectory. To compute such bounds, we leverage contraction theory and feedback design, and develop several tracking controllers. This yields a complete feedback motion planning pipeline which can be implemented by numerical optimizations. We demonstrate the efficacy and versatility of the proposed framework through simulations on several robotic systems and through experiments on a real‑world quadrupedal robot, and show that it is less conservative and achieves higher specification satisfaction probability than representative baselines.

Authors:Habtemariam Aberie Kefale, Weijie Xia, Nanda Kishor Panda, Peter P. Palensky, Pedro P. Vergara
Title: Risk-Based PV-Rich Distribution System Planning Using Generative AI
Abstract:
Hosting capacity (HC) assessment plays a critical role in distribution system planning under increasing penetration of distributed energy resources (DERs) and associated uncertainties in load and generation. However, conventional approaches often rely on deterministic worst‑case evaluation, leading to overly conservative HC estimates. This paper introduces a risk‑based framework for HC assessment that explicitly accounts for the frequency, intensity, and duration of voltage violations under uncertain operating conditions. A generative AI‑based approach is employed to generate realistic, time‑correlated load demand scenarios conditioned on projected energy consumption growth levels. These scenarios are then used to assess voltage violations and quantify their risk using probabilistic intensity, duration, and frequency (IDF) metrics. The results show that extreme‑percentile (zero‑risk) approaches significantly underestimate PV‑HC by treating all violations equally, regardless of their likelihood or persistence. For instance, allowing a 5% risk level increases HC by approximately 18% for a 15 min violation duration. The proposed approach provides a practical tool for risk‑informed distribution system planning under uncertainty.

Authors:Lorenzo Zapparoli, Blazhe Gjorgiev, Giovanni Sansavini
Title: Efficient Multi-Market Scheduling of Virtual Power Plants via Spectral Representation of Uncertainty
Abstract:
As the penetration of distributed energy resources increases, harnessing their flexibility becomes critical for power system operations. Virtual power plants (VPPs) offer a promising solution. However, existing VPP market scheduling tools exhibit a tradeoff between economic performance and tractability. Stochastic formulations provide probabilistically optimal decisions but are computationally intractable for large systems due to scenario explosion. Robust approaches are more tractable but often yield conservative decisions. This paper addresses this gap by proposing a stochastic multi‑market VPP scheduling framework that represents uncertainty in the spectral domain via intrusive Polynomial Chaos Expansion (PCE). The resulting reformulation yields a low‑dimensional deterministic spectral counterpart that preserves the stochastic structure and can be solved efficiently with standard optimization tools. The proposed spectral approach is demonstrated on a DER‑based VPP operating on a realistic Swiss low‑voltage grid and benchmarked against a state‑of‑the‑art scenario‑based solution. Results show that intrusive PCE achieves solution quality comparable to the scenario‑based benchmark, with up to a 137 times reduction in computational effort, while yielding highly accurate bidding decisions. Finally, to facilitate adoption and reproducibility, we release an open‑source, application‑agnostic projection tool that automates the spectral reformulation for generic single‑ and two‑stage stochastic programs.

Authors:Jingwen Fu, Ming Xiao, Mikael Skoglund
Title: SkillCom: Decomposing LLM-based Semantic Communication into Task and Channel Aware Skills
Abstract:
Large language models (LLMs) are increasingly used as semantic encoders and decoders in semantic communication. However, current LLM based systems mostly remain monolithic: a single prompted model, or a tightly coupled transmitter/receiver pair, must jointly perform semantic encoding, channel adaptation, and semantic decoding. Such coupling makes intermediate decisions difficult to control, diagnose, or replace, and may cause channel corruption to propagate through a compressed source representation. To address the limitations, we propose SkillCom, a modular framework that decomposes LLM‑based semantic communication into four explicit skills: semantic abstraction skill, channel‑adaptive transmission skill, receiver‑side repair skill, and task execution skill. These skills are interconnected through typed semantic‑unit interfaces. Thus, transmission operates on structured unit‑level representations rather than on one monolithic text block. This design localizes channel impairment, enables targeted repair from successfully received units, and supports stage‑wise ablation and single‑skill replacement under matched communication constraints. Experiments on multi‑hop question answering and dialogue state tracking show that SkillCom consistently outperforms the monolithic LLM baseline, remains more robust under varying channel conditions, and exhibits task‑dependent preferences over skill realizations. The results suggest that explicit skill decomposition provides a more robust and diagnosable foundation for LLM‑based semantic communication than monolithic methods.

Authors:Enea Figini, Mario Paolone
Title: Should Small-Scale Data Centers Participate in the Day-Ahead Electricity Market?
Abstract:
The global race to artificial intelligence competitive advantage is challenging electricity grids by demanding growing data center capacity. Addressing this challenge requires synergistic operational strategies that integrate data centers into electricity markets while supporting grid operation. This work proposes a bilateral power purchase agreement between small‑scale data centers and distribution system operators, enabling data center participation in the day‑ahead electricity market. To facilitate market participation, we develop a scenario‑based, risk‑averse bidding strategy that leverages flexibility from local energy resources, waste heat recovery, and data center workload. The strategy jointly minimizes operational costs and carbon emissions, creating a carbon‑aware cost‑effective framework for data center integration in the electricity day‑ahead market. The method is evaluated on a study case comparing a conventional time‑of‑use supply scheme with the proposed custom power purchase agreement, showing a potential 22% cost reduction, thus highlighting financial opportunities for small‑scale data centers day‑ahead electricity market participation. Two additional case studies illustrate the marginal effects of: (i) data center flexible workload on energy costs and (ii) virtual de‑rating of grid transfer capacity.

Authors:Chang Liu, Wenhan Cao, Zeju Sun, Tianyi Zhang, Jiayu Yuan, Yi Zeng, Ting Yuan, Yao Lyu, Wei Wu, Stephen Shing-Toung Yau, Shengbo Eben Li
Title: Natural Gradient Bayesian Filtering: Geometry-Aware Filter for Dynamical Systems
Abstract:
Bayesian filtering is a cornerstone of state estimation in complex systems such as aerospace systems, yet exact solutions are available only for linear Gaussian models. In practice,nonlinear systems are handled through tractable approximations,with Gaussian filters such as the extended and unscented Kalman filters being among the most widely used methods. This tutorial revisits Gaussian filtering from an information‑geometric perspective, viewing the prediction and measurement update steps as inference procedures over state distributions. Within this framework, we introduce a geometry‑aware Gaussian filtering approach that leverages natural gradient descent on the statistical manifold of Gaussian distributions. The resulting Natural Gradient Gaussian Approximation (NANO) filter iteratively refines the posterior mean and covariance while respecting the intrinsic geometry of the Gaussian family and preserving the positive definiteness of the covariance matrix. We further highlight fundamental connections to the classical Kalman filtering, showing that a single natural‑gradient step exactly recovers the Kalman measurement update in the linear‑Gaussian case. The practical implications of the proposed framework are illustrated through case studies in representative nonlinear estimation problems,including satellite attitude estimation, simultaneous localization and mapping, and state estimation for robotic systems including quadruped and humanoid robots.

Authors:Mohammadreza Doostmohammadian, Hamid R. Rabiee
Title: Distributed Observer-based Fault Detection over Intelligent Networked Multi-Vehicle Systems
Abstract:
Decentralized strategies are of interest for local decision‑making over multi‑vehicle networks. This paper studies mixed traffic networks of human‑driven and autonomous vehicles with partial sensor measurements. The idea is to enable the group of connected autonomous vehicles (CAVs) to track the state of a group of human‑driven vehicles (HDVs) via distributed consensus‑based observers/estimators. Particularly, we make no assumption that the group of HDVs is locally observable in the direct neighborhood of any CAV. Then, the main contribution is to design local residual‑based fault detection and isolation (FDI) at every CAV to detect possible faults/attacks in the sensor measurements. This distributed detection strategy enables every CAV to locally find possible anomalies in its taken sensor measurement with no need for a central processing unit. Two FDI logics are proposed with and without considering the history of the residuals. These FDI techniques are based on probabilistic threshold design on the residuals (in contrast to the existing deterministic threshold FDI techniques) with no assumption that the noise is of bounded support. This is more realistic in real‑world multi‑vehicle transportation systems.

Authors:Mengbin Ye, Lorenzo Zino, Ming Cao
Title: Awareness in collective decision-making: Modeling and control in a game-theoretic framework
Abstract:
For a society to remain healthy and prosperous, people must collectively behave and act to contribute to the common good, even if there is often a tradeoff against their individual benefit. Paradigmatic examples include the adoption of sustainable behaviors and technologies to combat the climate crisis, and the mobilization for collective action to promote the rights and freedoms of repressed minorities. In this tutorial, we illustrate how game theory and network systems theory can be powerful tools to model and study this collective decision‑making problem. We provide examples of how awareness of this tradeoff can impact collective change toward the societal good, exploring different problem contexts such as sustainable behavior and collective action. Finally, we review recent developments using systems and control‑theoretic approaches to generate awareness and guide the emergent population dynamics towards a desired outcome, and conclude by highlighting new research and application frontiers.

Authors:Ambuj Gupta, Muhammad Sharjeel Javaid, Balarko Chaudhuri, Mark O'Malley
Title: Frequency-Domain Compliance Assessment of Grid-Forming Devices
Abstract:
Grid‑ForMing Inverters (GFMIs) are expected to provide voltage stiffness to the grid. Explicitly, system operators (SOs) and regulators expect GFMIs to behave like a "voltage source behind impedance (VSBI)" in the (sub)‑transient time frame. SOs assess this VSBI characteristic of GFMIs during compliance by defining a pass‑fail time‑domain criterion. This is done by evaluating the GFMIs' active (or reactive) power/current response to step changes in voltage phase (and magnitude) at its terminals. However, this approach is prone to errors due to poorly defined measurement specifications for very fast (less than a cycle) transients. To address this, this work proposes a compliance criterion for the VSBI characteristic of GFMIs in the frequency domain based on elements of the frequency‑domain Jacobian. The compliance criterion is defined in terms of the minimum expected P(s)/θ(s) and Q(s)/V(s) Bode plot characteristics across a specific frequency range. The equivalence between the time‑domain and frequency‑domain criteria is established. The proposed method is demonstrated by assessing the compliance of generic NLR (formerly NREL) GFMI models in PSCAD. Furthermore, the impact of GFMI compliance on the small‑signal stability of the IEEE 39‑bus bulk‑power system is demonstrated.

Authors:Subarna Pudasaini, Parker Smith, Daning Huang
Title: Trajectory Optimization of Morphing Aerial Vehicles Based on Mid-Fidelity Aeroservoelastic Models
Abstract:
Morphing aerial vehicles offer enhanced maneuverability and fuel efficiency compared to fixed‑wing configurations. However, the trade‑off between performance gains and control cost in dynamic, unsteady maneuvers remains under‑explored. This paper addresses this by integrating a trajectory optimization framework with a mid‑fidelity aeroservoelastic model, coupling nonlinear multi‑body structural dynamics with an unsteady vortex lattice method. A physics‑based control cost model captures the energy required to overcome instantaneous aerodynamic hinge moments. Applied to an aircraft with flexible, high‑aspect‑ratio wings and morphing winglets, the framework evaluates trim, maneuver performance, and lateral obstacle avoidance. Results show morphing wings significantly expand the flight envelope by decoupling lift and pitch requirements. In dynamic maneuvers, morphing yields distinct trade‑offs: a pull‑up maneuver increased altitude gain by 28.95% at a higher control cost, while a banked turn improved lateral displacement by 8.62% while reducing control cost by 13.40%. Notably, in obstacle avoidance, morphing reduced total control cost by 65.65%. This efficiency stems from exploiting aero‑mechanical coupling via trajectory optimization to identify coordinated control strategies that offload aerodynamic loads. These findings underscore wing morphing's potential for achieving extreme maneuvers with superior energy efficiency.

Authors:Nicholas B. Andrews, Kristi A. Morgansen
Title: Observability Conditions and Filter Design for Visual Pose Estimation via Dual Quaternions
Abstract:
This paper presents a dual quaternion framework for 6‑DOF visual target tracking that addresses key limitations of perspective‑n‑point (PnP) solvers: sensitivity to noise and outliers, and inability to propagate estimates through measurement dropouts. A nonlinear observability analysis is performed using a Lie algebraic approach, deriving sufficient conditions for local observability under two sensing modalities: relative position vector and unit vector measurements. For the unit vector case, the classical collinear feature point degeneracy of the perspective‑three‑point problem is recovered through rank analysis of the observability codistribution matrix, providing a control‑theoretic interpretation of a previously geometric result. A dual quaternion Lie group unscented Kalman filter is then developed, directly modeling relative dynamics without assumptions about cooperative measurements or slowly‑varying motion. Simulations demonstrate improved pose estimation accuracy and robustness to occlusions compared to an off‑the‑shelf PnP solver. Results are broadly applicable to visual‑inertial navigation, simultaneous localization and mapping, and PnP solver development.

Authors:Santiago Thorup, Luca Castelletto, Zeyuan Feng, Somil Bansal
Title: Neural Backward Reach-Avoid Tubes with MPC Supervision for High-Dimensional Systems: An Application to Safe Spacecraft Docking
Abstract:
Autonomous spacecraft docking requires control policies that simultaneously ensure collision avoidance and target reachability under coupled, high‑dimensional translational‑rotational dynamics. Hamilton‑Jacobi (HJ) reachability provides formal reach‑avoid guarantees, but classical solvers are limited to low‑dimensional systems. Learning‑based approaches have begun to scale HJ analysis, yet they struggle in reach‑avoid settings, especially where goal and failure sets are tightly coupled, as in docking. We propose a learning‑based Backward Reach‑Avoid Tube (BRAT) framework that addresses this challenge by tightly integrating HJ structure with MPC‑based supervision. In the offline phase, we train a neural approximation of the HJ value function using PDE‑based losses augmented with curriculum‑driven MPC supervision, which provides informative value targets and stabilizes training in regions where purely PDE‑based methods fail. In the online phase, the learned value function is deployed through two real‑time controllers: (i) a value gradient‑driven controller, and (ii) a value‑function‑augmented terminal MPC that explicitly enforces reachability at the horizon. We evaluate the proposed method on a 6D planar docking problem against grid‑based ground truth and then scale to the full 13D system. Across both settings, our approach outperforms existing methods in success rate and computational efficiency.

Authors:Zachary Olkin, William D. Compton, Aaron D. Ames
Title: Stability of Control Lyapunov Function Guided Reinforcement Learning
Abstract:
Reinforcement learning (RL) has become the de facto method for achieving locomotion on humanoid robots in practice, yet stability analysis of the corresponding control policies is lacking. Recent work has attempted to merge control theoretic ideas with reinforcement learning through control guided learning. A notable example of this is the use of a control Lyapunov function (CLF) to synthesize the reinforcement learning rewards, a technique known as CLF‑RL, which has shown practical success. This paper investigates the stability properties of optimal controllers using CLF‑RL with the goal of bridging experimentally observed stability with theoretical guarantees. The RL problem is viewed as an optimal control problem and exponential stability is proven in both continuous and discrete time using both core CLF reward terms and the additional terms used in practice. The theoretical bounds are numerically verified on systems such as the double integrator and cart‑pole. Finally, the CLF guided rewards are implemented for a walking humanoid robot to generate stable periodic orbits.

Authors:Gongpu Chen, Deniz Gündüz
Title: The Control Plant as A Communication Channel: Implicit Communication for Decentralized LQG Control
Abstract:
We study a decentralized linear quadratic Gaussian control problem, in which a leader and a follower must steer a linear system to a target state. The target state is known only to the leader, and no explicit communication channel exists between the agents. To address the challenge posed by this asymmetric information structure, we propose an integrated communication and control (ICoCo) framework in which the control plant itself serves as a communication channel: the leader encodes the target state into its control input through an additive communication term, and the follower decodes it from the resulting state trajectory. We design an implicit coordination scheme based on joint source‑channel coding ideas, and prove that the follower's estimation error decreases monotonically to zero, enabling the two agents to coordinate increasingly well and ultimately steer the system to the target state. We then formulate the design of the communication power as an optimal control problem to minimize the overall control cost. In the fully actuated leader case, we derive necessary optimality conditions and in the under‑actuated case, we solve the problem numerically. Numerical results show that the proposed scheme effectively coordinates the two agents and achieves a control cost close to that of the explicit‑communication lower bound.

Authors:Kohei Ayaka, Takumi Namba, Kiyotsugu Takaba
Title: Data-Driven Sub-Optimal LQ Regulator for Linear Input-Delay Systems based on Informativity
Abstract:
This paper proposes a novel informativity‑based data‑driven synthesis method for a sub‑optimal linear quadratic (LQ) regulator for linear input‑delay systems from noisy input‑state data. Exploiting the augmented state structure of input‑delay systems with a known delay length, we derive a linear matrix inequality (LMI) condition for the data‑driven synthesis of the augmented state‑feedback controller that achieves a prescribed LQ performance level for every plant model consistent with the data. The proposed LMI condition enables efficient controller synthesis via convex optimization. Numerical simulations demonstrate the effectiveness of the proposed method. The trade‑off between the achievable LQ performance and the uncertainty in the data is also clarified through a numerical example.

Authors:Hadar Sivan, Gil Shabat, Yoel Shkolnisky
Title: nvPAX: Constrained Optimization for Dynamic Power Allocation in Hierarchical and Multi-Tenant Systems
Abstract:
Power oversubscription is increasingly central to datacenter operation as power density grows, making it necessary to dynamically allocate limited power budgets across devices based on real‑time demand. Existing approaches typically assume flat power domains, whereas in practice power distribution is hierarchical and allocation decisions must additionally respect tenant‑level contractual constraints. We present nvPAX, a constrained‑optimization policy that computes feasible power allocations at every control step via a three‑phase hybrid QP/LP procedure. Phase I allocates power with minimum deviation from each device's power request, while respecting job priorities. Phase II fairly distributes excess power among active devices. Phase III fairly distributes any remaining power to idle devices. The rationale behind the three phases is to allow power oversubscription while maximizing datacenter utilization. On a trace‑driven large‑scale simulation using GPU power telemetry from a production datacenter, nvPAX runs with a mean wall‑clock time of 264.69 ms per allocation interval and achieves a mean satisfaction ratio of 98.92%, outperforming static equal‑share allocation and providing robustness beyond greedy proportional allocation in the presence of non‑uniform hierarchical bottlenecks.

Authors:Giulio Maria Bianco, Alberto Dellacasa Bellingegni, Federico Mereu, Daniel Gelmini, Michele Canepa, Matteo Laffranchi, Emanuele Gruppioni
Title: A Low-Frequency, Autoresonant Wireless Power Transfer Link for Bidirectional Bionic Interfaces
Abstract:
To provide multimode sensory feedback and motion control, bidirectional bionic interfaces for advanced prosthetic systems require continuous and secure energy delivery to implantable electronics and integration in the sensing WBAN (Wireless Body Area Network) of the patient. However, powering such interfaces is still an open issue. Wireless Power Transfer (WPT) avoids implanted batteries and transcutaneous connections, but its design is constrained by stringent requirements on electromagnetic safety, implant size, voltage compliance, and coexistence with sensitive bio‑signal acquisition and stimulation circuitry. This paper presents the design and testing of a low‑frequency (127 kHz) inductive WPT link for an implantable bidirectional bionic interface. The system includes an autoresonant driving control to maintain operation at resonance under varying coupling and load conditions of the cyber‑physical prosthesis. Starting from the requirements of the bionic interface, the wireless body‑area sensing system is designed by selecting the working frequency, drawing the electrical schemes, and checking its safety and regulatory compliance. Preliminary WPT prototypes can provide up to ~140 mA and ~20 V, achieving a maximum power transfer efficiency higher than 40% and satisfying the project requirements up to a 2 cm implantation depth.

Authors:Ashik Abrar Naeem, Mohammad Ariful Haque
Title: Zero-Shot, Safe and Time-Efficient UAV Navigation via Potential-Based Reward Shaping, Control Lyapunov and Barrier Functions
Abstract:
Autonomous navigation and obstacle avoidance remain a core challenge of modern Unmanned Aerial Vehicles (UAVs). While traditional control methods struggle with the complexity and variability of the environment, reinforcement learning (RL) enables UAVs to learn adaptive behaviors through interaction with the environment. Existing research with RL prioritizes the mission success at the expense of mission time and safety of UAVs. This study integrates Potential Based Reward Shaping (PBRS) with Control Lyapunov Functions (CLF) and Control Barrier Functions (CBF) to simultaneously optimize mission time and ensure formal safety guarantees. An RL model is trained in a generalized simple environment, then used in complex scenarios incorporating a CLF‑CBF‑QP filter without further training. Experimental results in simulated environments demonstrate a significant reduction in mission time and outstanding performance in complex environment.

Authors:Sixu Li, Swaroop Darbha, Yang Zhou
Title: Lateral String Stability for Vehicle Platoons: Formulation, Definition, and Analysis
Abstract:
Platooning of connected and automated vehicles provides significant benefits in terms of energy efficiency, traffic throughput, and, most critically, safety. These safety benefits depend on string stability, which dictates how disturbances propagate along a vehicle string. Although longitudinal string stability has been extensively examined, lateral string stability, which governs the propagation of path‑tracking errors that can lead to unsafe deviations from the desired path, remains underexplored. Its importance is growing as autonomous vehicles increasingly depend on onboard sensing and map‑free navigation, where sensor occlusions and tight formations amplify safety risks. This paper presents a framework for lateral string stability that focuses directly on safety‑critical, path‑relative tracking errors and enables consistent comparison across vehicles that follow the same planned path. The key element of the framework is an arc‑length (Eulerian) viewpoint, a departure from traditional analyses, that clarifies how tracking errors at a given point on the path propagate from one vehicle to the next. Building on this foundation, we propose the definition of L2 lateral string stability along with two control strategies: a feedback‑feedforward strategy that relies solely on onboard sensing, and a novel learn‑from‑predecessor strategy that makes use of vehicle‑to‑vehicle communication. Both strategies are analyzed for lateral string stability with respect to two error measures: tracking error vector and lateral (cross‑track) error. Our results show that onboard sensing alone cannot guarantee attenuation of path‑tracking errors, imposing a fundamental safety limitation, while V2V communication enables true error attenuation. The analysis further identifies structural controller requirements, showing that nonzero feedback on specific measurements is essential for guaranteeing stability.

Authors:Ahmad F. Taha, Mohamad H. Kazma
Title: Nonsmooth Hydraulics, Smooth Control: System Theory Framework for Analyzing Water Networks
Abstract:
This paper presents a comprehensive control‑theoretic analysis of water distribution network (WDN) hydraulics. Starting from a general nonlinear differential algebraic equation (DAE) model of WDNs with arbitrary topology and network components (valves and pumps), we investigate three main questions. First, we study local well‑posedness of the network dynamics and characterize the loss of differentiability introduced by pump and valve switching. Second, we introduce regularization methods that smooth flow and pressure trajectories under changing controls. Third, we establish error bounds for DAE linearization, local stability, and finite‑horizon controllability, and quantify how network‑induced parametric uncertainty impacts these properties. We demonstrate that the developed smoothed DAE models produce trajectories closely matching EPANET, a widely used WDN simulator, for various benchmark networks. The case studies also show that the WDN DAE exposes energy dissipation through a weighted Laplacian, ranks pipes by operating point sensitivity, and reveals that aggressive demand variation changes stability and controllability margins without eliminating local stability or pump authority. The developed theoretical foundations enable network analysis, mitigation strategies, and system design.

Authors:Khalid Mahmud Labib, Inayat Rasool, Shabbir Ahmed
Title: Operator-Theoretic and physics-guided Sequence Modeling of Lithium-Ion Battery Voltage Dynamics
Abstract:
Lithium‑ion batteries exhibit nonlinear voltage dynamics across varying operating conditions and aging states, making accurate modeling essential for estimation, control, and health monitoring. This work compares two data‑driven frameworks for modeling voltage responses from hybrid pulse power characterization (HPPC) measurements: an operator‑theoretic model based on Dynamic Mode Decomposition with control (DMDc), and a physics‑guided transformer‑based sequence model. In the DMDc framework, delay‑embedded snapshots of terminal voltage and current are used to identify system matrices directly from measurement data, yielding an interpretable state‑space model for recursive prediction. In parallel, a modified PatchTST architecture is developed in which terminal voltage is decomposed into an analytically computed open‑circuit‑voltage (OCV) component and a learned dynamic residual, with a future‑current fusion pathway tailored to the prescribed HPPC current profile. Experimental results on a 30 Ah lithium‑ion cell show that, although both models capture the sharp transient pulse dynamics, DMDc achieves lower prediction error and greater robustness to cell degradation under the present limited data regime, while the transformer captures qualitatively similar dynamics with greater architectural flexibility. These results highlight the advantages of operator‑theoretic models in interpretability, computational efficiency, and robustness, while indicating the promise of physics‑guided transformer models when larger and more diverse datasets are available.

Authors:Khalid Mahmud Labib, Saad Waheed, Bakhtiar Nafis, Shabbir Ahmed
Title: A Graph Theoretic Approach in Combination With Dynamic Mode Decomposition With Control (DMDc) to Analyze Battery Degradation
Abstract:
Accurate monitoring of lithium‑ion battery (LIB) degradation is essential, yet it remains challenging due to the complex, nonlinear, and time‑varying nature of electrochemical aging processes. Conventional equivalent circuit models (ECMs) provide simplified representations of battery behavior using fixed electrical components, but they cannot capture evolving internal degradation mechanisms and structural changes over time. In this study, a data‑driven framework is developed by integrating dynamic mode decomposition with control (DMDc) with graph‑theoretic analysis to characterize battery degradation from operational data alone. The mode matrix (\mathbfϕ) obtained from DMDc is transformed into a weighted adjacency matrix, enabling the representation of battery dynamics as an evolving network of interacting states. Graph‑based measures, including connectivity and a modularity (proxy), are then used to quantify structural changes in the system across degradation stages. The results show a clear transition from a highly connected and coherent network in the healthy state to a progressively weaker and more fragmented structure as degradation advances, accompanied by increasing heterogeneity. This work demonstrates that graph‑theoretic representations can effectively capture the evolving dynamics of battery degradation and provide interpretable insights into system‑level aging behavior.

Authors:Marcus J. Hamilton, Abhishek Yadav, Harrison Hartle, Jan Korbel, Niels Kornerup, Andrew J. Stier, Douglas H. Erwin, Hyejin Youn, Christopher P. Kempes, Hajime Shimao, Kyle Harper, James Evans, David H. Wolpert
Title: Computational foundations of the human world
Abstract:
Human societies continuously transform scattered information into collective judgments and coordinated action, whether through markets discovering prices, governments allocating resources, communities enforcing norms, or science converging on reliable claims. Importantly, the computational difficulty of collective decision‑making, particularly the time and communication required to reach solutions, imposes fundamental constraints on social organization. While theoretical computer science offers formal tools for analyzing such problems, for instance, by analyzing resource requirements, including time and memory, surprisingly, there is no domain of social science that focuses on the nature of computation in the human world. This perspective argues that we now have the opportunity to deploy these computational frameworks to study human social organization, opening research directions at the intersection of computer science and social science. We highlight core social phenomena that can be framed as computational, including (i) distributed consensus and coordinated action, (ii) societal restructuring with scale, (iii) hierarchical and modular structure, and (iv) externalized memory systems. We identify several concepts from theoretical computer science that may provide insight into these phenomena, especially emphasizing more recently developed approaches beyond the paradigm of Turing~Machines and worst‑case computational complexity.

Authors:Tyler Halterman, Ali Pakniyat
Title: Hybrid Optimal Control of Homogeneous Epidemiological Compartmental Models with Regime Switching
Abstract:
Optimal intervention design is formulated as a hybrid optimal control problem for multiphase homogeneous epidemiological systems. The system extends a foundational compartmental model through intermediate phases that incorporate work‑from‑home (WFH) policies and a vaccination protocol, yielding a four‑phase hybrid system that captures policy escalation and relaxation. Key characteristics of the resulting hybrid system include (i) phase‑dependent continuous dynamics and running costs that respectively capture distinct disease transmission mechanisms and shifting public health socioeconomic trade‑offs, (ii) a combination of autonomous and controlled switchings for intervention policies, whose times are co‑optimized ‑ whether indirectly via state thresholds or directly as decision variables alongside continuous inputs to minimize the overall cost, and (iii) nontrivial state jump maps that govern transitions between phases with differing state and control space dimensions. The Hybrid Minimum Principle (HMP) is invoked to obtain the optimal solutions. Numerical results demonstrate that coordinating WFH policies with vaccination efforts provides improved mitigation of disease spread compared to single‑phase policy interventions.

Authors:Victor M. Preciado
Title: A Measure-Theoretic Formulation of Behavioral Systems
Abstract:
In Willems' behavioral systems theory, a dynamical system is identified with the set of all trajectories compatible with its laws of motion. In the linear time‑invariant setting this trajectory set is a linear subspace, and its algebraic structure underpins the Fundamental Lemma: a single persistently exciting data trajectory generates the entire finite‑horizon behavior. For nonlinear or stochastic systems, however, the admissible trajectory set is generally nonconvex, obstructing direct optimization over the behavior. In this paper, we lift the behavioral viewpoint from trajectories to probability measures on trajectories by representing a finite‑horizon dynamical system with the set of all Borel probability measures supported on its admissible trajectories. For deterministic systems, this behavioral‑measure set is convex and weakly closed even when the dynamics are nonlinear, because convex combinations of trajectory distributions remain dynamically admissible even when convex combinations of trajectories do not. Its extreme points are precisely the Dirac masses on individual admissible trajectories, so the classical deterministic theory is embedded as the extremal skeleton of the richer measure‑valued object. On this foundation we establish two core deterministic results and outline a stochastic extension based on history‑conditional kernel consistency.

Authors:Animesh Kumar Shastry, Mangal Kothari
Title: A Universal Optimal Control Strategy for a Tailsitter UAV
Abstract:
This work develops a unified optimal control framework for a Quadrotor Biplane tailsitter UAV capable of operating seamlessly across hover, transition, and cruise flight regimes. Although the tailsitter configuration enables mechanically simple mode switching, the transition maneuver remains challenging due to strong nonlinearities and rapidly varying aerodynamics. To address this, a trajectory optimization scheme based on nonlinear programming with direct collocation is formulated, incorporating nonlinear dynamics, actuator limits, and angle‑of‑attack constraints. The resulting optimal trajectories are safe, reliable, and time‑efficient. For the cruise‑to‑hover maneuver, optimal trajectories are generated over a range of initial cruise velocities and subsequently learned using feedforward multilayer neural networks. The learned model generalizes across operating conditions and enables real‑time generation of constraint‑satisfying transition trajectories. These trajectories provide both feedforward control inputs and reference state profiles, which are tracked using a Model Predictive Controller (MPC). The MPC eliminates the need for controller switching or gain scheduling across flight envelopes, enabling a single universal controller for hover, transition, and cruise. A nonlinear Dynamic Inversion (DI) controller is also designed for comparison. Two numerical schemes for MPC are implemented and evaluated. Simulation results across all flight modes demonstrate that MPC achieves superior robustness to parameter uncertainties compared to DI. A computational cost analysis further highlights the trade‑off between execution time and performance for the different MPC solvers.

Authors:Jitu Sanwale, Mangal Kothari, Hari B. Hablani, Suresh Dahiya
Title: Physics Driven Digital Twin Model for Evaluation of GNSS User Receiver Equipment
Abstract:
This paper presents a physics‑consistent digital twin framework for end‑to‑end modeling and evaluation of Global Navigation Satellite Systems (GNSS) user receiver equipment. In contrast to conventional GNSS simulations that rely on predefined signal models, the proposed framework enforces dynamic consistency between satellite ephemerides, user motion, and received signal observables through trajectory‑driven injection of code‑phase and Doppler dynamics. The GPS L1 C/A signal is synthesized in accordance with the IS‑GPS‑200 Rev. N specification, with motion‑induced effects derived directly from orbital and user kinematics, and augmented by ionospheric and tropospheric delay models. The resulting complex baseband signal is converted to radio frequency using a software‑defined radio platform disciplined by an external reference clock, enabling seamless hardware‑in‑the‑loop integration with commercial and software receivers. Validation across static, moderate‑motion, and high‑dynamics scenarios, including projectile‑like trajectories, demonstrates close agreement between truth‑model and receiver‑estimated code phase, Doppler, and position, as well as strong correspondence between simulated and measured intermediate frequency spectra. The results establish the proposed digital twin as a high‑fidelity, repeatable, and physically consistent platform for GNSS receiver evaluation, tracking‑loop stress testing, and development of robust navigation algorithms.

Authors:Giulia De Pasquale, Sarah Dean, Paolo Frasca
Title: Recommender Systems as Control Systems
Abstract:
We propose a control‑theoretic interpretation of recommender systems and use this perspective to analyze how fairness interventions shape long‑term system behavior. Fairness concerns arise for both users and creators, ranging from opinion polarization and representation bias on the user side to popularity bias on the creator side. A central insight of our analysis is that fairness should not be viewed as a simple trade‑off against utility. When optimized over time, it can in fact be beneficial for overall system performance. Realizing these gains, however, requires a clear understanding of the underlying dynamics.

Authors:Brener G. Ferreira, Vinicius M. Gonçalves, Marcelo A. Santos, Guilherme V. Raffo
Title: Point-to-Cloud NMPC with Smooth Avoidance Constraints
Abstract:
This paper proposes a finite‑horizon optimal control strategy for set‑point tracking using a nonlinear model predictive control framework with integrated avoidance capabilities. The formulation employs a smooth point‑to‑cloud distance metric that ensures continuously differentiable and numerically well‑conditioned gradients, even in the presence of regions with complex and nonconvex geometries. This smoothness allows safety constraints to be formulated consistently and differentiably through control barrier functions, resulting in a reliable avoidance behavior for the closed‑loop system. Additionally, stationary artificial variables are introduced in the optimal control problem to preserve feasibility under changing set‑points. The proposed approach is validated through numerical experiments of an aerial robot, demonstrating accurate tracking and smooth obstacle avoidance in complex environments.

Authors:Srishti Siddharth
Title: Quasi-Static Control of Discrete Cosserat Rod
Abstract:
In this paper, we design feedback control laws for soft robots modelled using the Cosserat rod, which is spatially discretised using the Piecewise Constant Strain (PCS) approach. The PCS approach transforms the nonlinear PDEs describing the Cosserat rod to a system of nonlinear ODEs. This simplification results in a model describing soft robots which is similar to the serial rigid‑link manipulators. We design feedback control laws for the quasi‑static PCS model by using the external wrenches as control input. The control laws are designed based on state‑feedback linearisation in strain and task spaces. An extensive set of numerical results demonstrates the performance of the control laws for end‑effector trajectory tracking and shape control of soft robots.

Authors:King Yiu Yu, Aritra Sarkar, Erbing Hua, Maximilian Rimbach-Russ, Ryoichi Ishihara, Sebastian Feld
Title: From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data
Abstract:
High‑fidelity circuit execution on noisy intermediate‑scale quantum devices is bottlenecked by compilation pipelines that disregard complex, correlated noise. To address this, this methodology article proposes a quantum machine learning control (QMLC) framework for generative quantum circuit synthesis from gate‑set tomography (GST) data that bypasses the traditional two‑step pipeline of characterizing native quantum gates via GST followed by unitary decomposition algorithms. Instead, a generative concept space is directly learnt from GST data, enabling conditional synthesis of quantum circuits on a desired output distribution. Our approach tokenizes GST germ circuits and embeds them into a structured latent space using a curriculum‑learning‑motivated strategy, starting with short circuits and progressively incorporating longer ones with diverse output statistics. The embedded sequences are processed by a set‑vision transformer with permutation‑invariant pooling, producing k‑seed vectors that represent the learned concept space of the quantum device. Aggregating data across multiple circuits makes this latent representation inherently context‑aware, capturing the shared physical noise environment (e.g., crosstalk, drift) that isolated gate metrics miss. We propose an unconditional diffusion model to sample from the concept space. During inference, a user provides a target measurement distribution, and the model generates a corresponding circuit. To ensure fidelity and robustness, the output is denoised using a diffusion model that operates on the target conditional covariance matrix. This end‑to‑end framework is a step towards context‑aware, hardware‑native circuit synthesis directly from raw GST data, which offers a new paradigm for integrating quantum control and compilation. The QMLC framework is particularly suited for near‑term quantum devices with complex calibration procedures.

Authors:Ting-Yu Dai, Kingsley Nweye, Dev Niyogi, Zoltan Nagy
Title: Toward a foundational thermal model for residential buildings
Abstract:
The building energy community lacks a foundational thermal model, i.e., a single pretrained model capable of generalizing across diverse buildings, climates, and control strategies without building‑specific calibration. Achieving this vision requires architectural principles that capture universal thermal dynamics rather than memorizing building‑specific patterns. We take a step toward this goal by presenting a physics‑informed transformer architecture that embeds domain knowledge, e.g., derivative enrichment and Euler‑based numerical integration, into a decoder‑only framework. We incorporate static building features extracted from simulation models and employ Rotary Position Embedding attention to capture temporal dependencies. Evaluated on the CityLearn dataset spanning 247 residential buildings across three climate zones, our model achieves one‑step prediction accuracy (RMSE of 0.30°C in Texas, 0.29°C in Vermont) while outperforming both traditional baselines and fine‑tuned Time‑Series Foundation Models. We also demonstrate zero‑shot transferability: models trained on as few as two buildings generalize to unseen buildings and climate zones without fine‑tuning. Despite the limitation of simulated residential buildings, our results establish physics‑informed architectural principles as a promising foundation for universal building thermal models.

Authors:Ava Mohammadi, Rick Kramer, Zoltan Nagy
Title: Coordination Architecture Shapes Continuous Demand Response Outcomes in Building Districts
Abstract:
Grid‑integrated building districts must provide energy flexibility while preserving occupant comfort and equitable distribution of control burden. We study how coordination architecture influences the ability of building clusters to track aggregated load profiles, comparing four paradigms: centralized model predictive control (MPC), decentralized independent reinforcement learning (SAC), centralized‑training‑decentralized‑execution multi‑agent RL (MAPPO), and a hybrid MPC‑‑SAC controller that separates district‑level battery optimization from building‑level HVAC regulation. A rule‑based controller serves as a baseline. We evaluate a 25‑building residential district across three metrics: aggregate load tracking, thermal comfort, and spatial variability of control actions. We find that architecture choice determines the trade‑off structure. Centralized MPC achieves low tracking bias (8.8% NMBE) but concentrates actuation on a subset of buildings, causing elevated comfort violations (24.8% exceedance) and spatial imbalance. Decentralized RL distributes control effort more evenly but fails to sustain accurate tracking. The hybrid architecture achieves the best balance: accurate tracking (4.8% NMBE), moderate comfort impact (16.8% exceedance), and the lowest spatial variability. These findings demonstrate that architecture choice determines the trade‑off structure between tracking and comfort.

Authors:Biqiang Mu
Title: Sequentially decoupling estimators for Box-Jenkins model estimation
Abstract:
In this paper, we propose a consistent and asymptotically efficient estimation method for Box‑Jenkins (BJ) models that is applicable under both open‑loop and closed‑loop data conditions, serving as a possible alternative to the weighted null‑space fitting approach. The method comprises two stages: an initial sequentially decoupling (SD) estimator, followed by Gauss‑Newton (GN) refinement step. The SD estimator is constructed from three sequential least squares (LS) estimators: (i) estimation of a high‑order autoregressive model with exogenous inputs (ARX) model; (ii) estimation of the BJ model's dynamic model via an auxiliary output‑error (OE) model; and (iii) estimation of the noise model of the BJ model using another auxiliary OE model. We establish the consistency of the SD estimator under standard regularity conditions, leveraging the consistency of the underlying LS estimators for both the ARX and OE models. Moreover, we show that one‑step GN iteration starting from the SD estimator yields an estimator that is asymptotically equivalent to the prediction error method, provided the ARX model order satisfies a mild growth condition. Simulation studies confirm the theoretical properties of the proposed method.

Authors:Jun Zheng, Guchuan Zhu
Title: Unified Lyapunov Method for ISS of PDEs: A Tutorial on Constructing Generalized Lyapunov Functionals for Parabolic and Hyperbolic Equations
Abstract:
This tutorial provides an overview of the generalized Lyapunov method (GLM) for analyzing input‑to‑state stability (ISS) of partial differential equations (PDEs). We begin by revisiting the classical Lyapunov method and the standard ISS‑Lyapunov theorem, highlighting their limitations when applied to systems with complex boundary disturbances. In contrast, the GLM, based on the concept of generalized Lyapunov functionals (GLFs) that explicitly depend on the external input, offers greater flexibility and efficiency, particularly for PDEs with Dirichlet‑type disturbances. The main objective of this tutorial is to demonstrate how to systematically construct GLFs to establish ISS estimates in L^q spaces with any q\in[2,\infty] for different PDEs. Specifically, we consider three representative classes of PDEs: (i) an N‑dimensional nonlinear parabolic equation with mixed nonlinear boundary disturbances, (ii) a first order nonlinear hyperbolic equation with boundary disturbances, and (iii) a second order linear hyperbolic equation, i.e., a wave equation, with boundary damping and disturbances. For each case, we provide step‑by‑step constructions of appropriate GLFs and derive explicit ISS estimates, illustrating the general applicability of the GLM. Finally, we discuss open challenges and future directions, including the systematic construction of GLFs for broader classes of PDEs and their applications in controller design.

Authors:Zian Wang, Peng Hu, Grant Gunn
Title: Toward LEO Satellite Network Systems for Instantaneous Detection of Environmental Changes
Abstract:
The rapid deployment of Low Earth Orbit (LEO) satellite constellations has enabled the emergence of in‑orbit edge computing and data centers‑interconnected satellites equipped with onboard computing capabilities and high‑speed inter‑satellite links (ISLs). This paper investigates whether such architectures, integrated with a deep learning‑based computer vision pipeline, can achieve sub‑minute information freshness suitable for real‑time wildfire detection. To evaluate this hypothesis, we develop a simulation framework that models orbital dynamics, distributed processing, and network routing, using Age of Information (AoI) as the primary performance metric. A total of 720 simulation trials are conducted across 12 real‑world constellation configurations, including Starlink, Kuiper, Telesat, and OneWeb. The results demonstrate that constellation design has a significant impact on AoI performance, with average AoI values ranging from 66.5 s to over 6300 s. The best‑performing configurations achieve an average AoI below 70 s and a peak AoI under 100 s, indicating that orbital edge computing systems can provide the level of timeliness required for near‑instantaneous environmental monitoring.

Authors:Dianle Gong, Peng Hu
Title: In-Orbit Optical SSA Using Proliferated LEO Satellites for Space Traffic Monitoring: An Analytical Framework
Abstract:
The increase in space activities has increased the risks of space debris generation, affecting space safety and sustainability. Traditional space situational awareness (SSA) relies on single star trackers and ground‑based tracking facilities. There is limited discussion on the use of in‑orbit optical sensors on low Earth orbit (LEO) satellite constellations for SSA, despite their importance for efficient space traffic management systems. In this paper, we aim to address this important challenge. We first present a new analytical system model for utilizing LEO satellite constellations for in‑orbit SSA. We then develop a method to evaluate and analyze such a system. We also propose a Poisson expected revisit period algorithm and introduce the period of equivalent orbital distributions to reveal the relationship between revisit period and geometric variables, with insightful results based on real‑world and custom satellite constellations. Experiments on real‑world constellation show that the representative Poisson expected revisit period ranges from 0.4 days to 5.7 days for targets whose apogee altitude ranges from 552 km to 650 km, while requiring a per‑case computation time of 0.4 s to 4.8 s. Our work can inform the future design of in‑orbit and onboard computing systems for SSA, such as space object detection and space traffic monitoring systems.

Authors:Fiaz Hossain, Nilanjan Ray Chaudhuri, Alok Sinha, Sai Gopal Vennelaganti, Mohammed E. Nassar
Title: Limiting the Impact of AI Data Centers on Fatigue Life of Thermal Turbine Generators in the Grid: A Frequency-Domain Approach
Abstract:
A framework is established that assesses the impact of variations in artificial intelligence (AI) data center (DC) loads on the fatigue damage of steam/gas turbines of the synchronous generators (SGs) from torsional oscillations. Next, a simple three‑step process that is supported by frequency‑domain analysis is laid out to quantify the limits on fluctuations in AI DC loads. In the first step, the maximum allowable variation in electrical power output at each SG terminal is independently determined from the first principles. This step needs only a lumped multi‑mass model of the mechanical side of the SG. In the second step, we propose a new approach that relies on load flow to determine the so‑called algebraic `interaction factor' that maps the change in AI DC load at a given bus to the corresponding change in each of the SG power outputs. In the third step, we propose a screening method to rank the candidate buses to site AI DCs and solve an optimization problem to determine the optimal allowable fluctuations in the AI DCs. We demonstrate the applicability of the proposed approach through frequency‑domain and time‑domain analyses in the modified IEEE 4‑machine and IEEE‑68 bus systems using a dynamic phasor framework. Finally, we demonstrate the scalability of the proposed approach on the synthetic 2000‑bus Texas system.

Authors:Hongyu Wu, Raul Rodriguez
Title: A Mission-Centric Cyber-Resilience Benchmark for Silent-Watch Operation of Electrified Ground-Platform Power Architectures
Abstract:
Silent‑watch operation makes electrified ground platforms depend on supervisory energy management because mission loads must be sustained from stored energy while the engine is off. This paper develops a mission‑centric cyber‑resilience benchmark for this operating mode. The benchmark connects battery state‑of‑charge (SOC) spoofing to mission outcomes rather than evaluating the attack only through detector response or control error. It combines a reduced‑order DC‑bus model, residual‑based detection, fallback shedding, and five mission‑facing metrics for endurance, critical‑load service, priority‑weighted loss‑of‑load cost, unsafe‑voltage exposure, and detection delay. The study shows that SOC spoofing creates a structured stealth‑versus‑impact envelope. Small biases have limited mission effect, intermediate biases produce an endurance deficit well approximated by a first‑order expression in bias magnitude, shed power, and average battery draw, and large biases disable the SOC‑driven guard. The results also show that defense value depends on fallback depth, not detection alone. An undersized fallback action can leave the Defended case failing to complete the mission despite early detection. MATLAB‑to‑Simulink parity across five regression scenarios provides a software‑verified basis for hardware‑in‑the‑loop testing.

Authors:Hemanta Ban, Seddik M. Djouadi, Kevin Tomsovic
Title: Distributed Attraction-Repulsion Potential for Multi-Agent Formation Control
Abstract:
In this paper, a distributed multi‑agent formation control driven by the gradient of the Lennard‑Jones potential is analyzed. For collision‑free initial data, we prove global well‑posedness together with a uniform lower bound on all inter‑agent distances, thereby excluding hard collisions. Taking the total energy as a Lyapunov function, LaSalle's invariance principle shows that every positive limit point is an equilibrium. Since trajectories remain uniformly away from collisions, the energy is analytic along the flow and an argument yields convergence to a single equilibrium modulo translations. Illustrative numerical examples are presented.

Authors:Ziyan Zhang, Xuanpeng Zhao, Chuheng Wei, Ronald William Snyder, Changxin Wan, Kanok Boriboonsomsin, Peng Hao, Guoyuan Wu
Title: From Sensing to Decision: A Generic Architecture for Freight Signal Priority Systems
Abstract:
Freight Signal Priority (FSP) systems have emerged as a promising strategy to enhance freight mobility and reduce corridor delays in urban networks. While extensive research has focused on priority control algorithms and operational performance evaluation, comparatively limited attention has been devoted to the architectural design of sensing processes that shape reliable priority decisions. In practice, uncertainties in vehicle detection, communication, and estimated time of arrival (ETA) may propagate within the sensing‑to‑decision process, affecting priority timing and downstream signal performance. This paper presents a systematic review of FSP systems from a sensing‑to‑decision perspective. We propose a generic two‑layer architecture consisting of a sensing‑to‑decision layer and a control execution layer. The sensing‑to‑decision layer transforms sensing inputs into priority decisions, while the control execution layer implements approved actions within traffic controllers. Within this architecture, we systematically compare major sensing modalities, including loop detectors, vision sensors, and V2I, across dimensions such as classification capability, state estimation accuracy, latency, and information richness. We further examine representative FSP systems to analyze how modality‑specific characteristics and uncertainties influence ETA computation, priority triggering, and decision reliability. By linking sensing design to decision outcomes, this review identifies key deployment challenges and research gaps in reliability‑aware sensing‑to‑decision design. Ultimately, this work provides a conceptual foundation for developing scalable and robust FSP systems that explicitly account for sensing imperfections rather than assuming idealized inputs.

Authors:Papri Dey, Ksenia Zlobina, Nicholas A. Rondoni, Marcella M. Gomez
Title: Sampled-data Robust Control of Electrically Stimulated Engineered Cell Factories
Abstract:
Closed‑loop bioelectronic regulation of engineered secretory cell systems is challenging because electric‑field (EF) stimulation acts indirectly through transcription‑factor activation, in the presence of delayed, nonlinear, and noisy intracellular dynamics, sparse measurements, and constrained burst‑based actuation. We develop a framework for robust closed‑loop endocrine regulation in electrically stimulated engineered cell factories, illustrated through extracellular thyroid hormone \(T_4\) production in engineered thyroid‑like cells. The plant is modeled by a control‑oriented ODE formulation combining a reduced mechanistic \(T_4\) pathway, an EF‑responsive Hill module, and a linear‑chain Erlang cascade representing distributed intracellular delay. On this basis, we design a sampled‑data adaptive proportional‑integral‑derivative (PID) controller with derivative filtering, anti‑windup, saturation and rate limits, and hysteretic band‑locking, together with a robust adaptive extension that accounts for parameter mismatch, sensor noise and bias, actuator mismatch, delay/jitter, and exogenous rhythmic disturbance through a scenario‑based risk‑aware update. We provide local sampled‑data input‑to‑state stability interpretations for both APID and RAPID, showing that, under standard local Lyapunov and bounded‑disturbance conditions, the sampled tracking error is ultimately bounded by a disturbance‑dependent constant. In silico experiments demonstrate sustained regulation of extracellular \(T_4\) across prescribed targets despite significant uncertainty.

Authors:Mohammad Mahdi Abaei, Ahmad BahooToroody, Arttu Polojärvi, Heikki Remes, Ulf Tyge Tygesen, Mikko Suominen, Michael Beer
Title: Equation-Free Digital Twins for Nonlinear Structural Dynamics
Abstract:
Monitoring high‑dimensional engineering structures in extreme environments is limited by non‑stationary excitation, nonlinear structural kinematics, and stochastic forcing. Traditional model‑based and black‑box data‑driven methods often struggle to resolve these dynamics in real time, particularly under sensor failure or partial observability. This paper introduces a rank‑optimized digital twin framework based on Koopman operator theory, Hankel‑matrix embeddings, and dynamic mode decomposition. By lifting operational data into a linear invariant subspace, the method enables autonomous, input‑blind reconstruction of structural states without requiring a priori mass or stiffness matrices. The framework is validated on an NREL 5MW spar‑buoy floating offshore wind turbine, representing a challenging coupled aero‑hydro‑servo‑elastic system. Results show that the rank‑optimized Koopman‑Hankel manifold separates structural resonances from deterministic 3P rotor harmonics under colored noise, where standard subspace identification can be unreliable. A rolling‑horizon virtual sensing strategy achieves high‑fidelity reconstruction at critical structural hotspots, with coefficient of determination greater than 0.95 at 1 Hz data assimilation and accuracy exceeding 0.99 at higher sampling rates. By estimating a physical Lyapunov time of approximately 1.0 s, the study defines the predictability horizon associated with the system information barrier. The proposed framework provides a computationally efficient and resilient digital twin approach for real‑time identification and virtual sensing of complex structural dynamics.

Authors:Soumia Siyoucef, Najmeddine Dhieb, Hakim Ghazzai, Eleonora Guanziroli, Franco Molteni, Gianluca Setti
Title: LiDAR for Rehabilitation: A Comprehensive Survey of Applications, AI Techniques, and Future Directions
Abstract:
Rehabilitation aims to help patients with limited mobility regain their physical abilities through targeted movements, exercises, stimulation, and other therapeutic methods. Recent advances in technology have introduced sensor‑based systems into rehabilitation and clinical practices, enabling real‑time monitoring and providing accurate feedback on movement accuracy. Among these sensors, LiDAR has demonstrated strong potential, offering key advantages over conventional techniques such as camera‑based systems, which raise privacy concerns, and wearable sensors, which can be uncomfortable and prone to errors. In this work, we review the applications of LiDAR in rehabilitation, post‑injury care, and hospital environments, focusing on studies published between 2019 and 2025. Studies across several areas have been explored: 3D body scanning and gait analysis with standalone LiDAR, LiDAR mounted on robotic systems for rehabilitation, real‑time monitoring and environment scanning for safe navigation, and activity and position recognition. We also analyze processing techniques, particularly learning‑based approaches, and support the discussion with statistical analysis, highlighting trends, gaps, and future research opportunities. To the best of our knowledge, this is the first comprehensive survey dedicated to LiDAR for rehabilitation applications, providing an overview of current methods, AI‑based processing techniques, and open challenges.

Authors:Davi Febba, William A. Callahan, Anna Sacchi, Andriy Zakutayev
Title: Autonomous Reliability Qualification of Ga$_2$O$_3$-based Hydrogen and Temperature Sensors via Safe Active Learning
Abstract:
We present a Safe Active Learning (SAL) framework for autonomous reliability characterization of rectifying Ga_2O_3‑based devices under coupled thermal and hydrogen stress. SAL treats rectification as a device‑physics‑motivated safety observable and models its evolution over elapsed time, temperature, and H_2 concentration using a Gaussian‑process surrogate. To handle condition‑dependent and uncertain experiment durations, the method combines an adaptive completion‑time window, time‑window lower‑confidence‑bound safety checks, a trust region anchored to previously verified safe conditions, and a two‑phase strategy that transitions from conservative safe exploration to progressively relaxed rectification targets as the device degrades. We first evaluate SAL in simulation, where it safely expands the explored region while learning the evolving rectification surface. We then demonstrate SAL experimentally on an automated high‑temperature probe‑station platform using a Pt/Cr_2O_3:Mg/β‑Ga_2O_3 device. In the reported campaign, phase 1 incurred only one unsafe measurement associated with spurious current‑voltage sweeps, while phase 2 intentionally probed lower‑rectification regimes. Finally, we use the curated SAL dataset for offline long‑horizon forecasting of device response at a target voltage using a structured Gaussian‑process model with a condition‑dependent Kohlrausch‑‑Williams‑‑Watts mean and a residual covariance kernel. The model captures long‑time, saturating degradation trends in an auxiliary validation dataset, illustrating how safety‑aware autonomous experimentation enables both conservative characterization and subsequent degradation modeling. Although demonstrated here for a rectifying Ga_2O_3 device, SAL is applicable to other systems where a measurable in situ safety observable can be defined.

Authors:Tao Chen, Shilian Zheng, Jiepeng Chen, Zhangbin Pei, Qi Xuan, Xiaoniu Yang
Title: Deep Learning for Multi-Antenna Modulation Recognition of Radio Signals
Abstract:
Multi‑antenna receiving systems have become a prevalent technical solution in communication systems. Meanwhile, deep learning has achieved significant progress in automatic modulation recognition tasks in single‑antenna systems. However, the application of deep learning in multi‑antenna modulation recognition (MAMR) tasks is still limited. In this paper, we propose an MAMR method namely MAMR‑IQ to fully explore the diversity gain of a multi‑antenna receiving system, which concatenates the raw received in‑phase and quadrature (IQ) signals of multiple antennas and feeds them into a convolutional neural network. Simulation results show that the proposed MAMR‑IQ method outperforms two existing deep learning‑based MAMR methods which are based on direct voting (DV) and weight average (WA) in terms of both recognition accuracy and computational complexity. To address the problem of limited training data in few‑shot scenarios, we further propose a data augmentation method that involves exchanging IQ sequences received by any two antennas to generate augmented samples. Simulation results show that with the proposed augmentation method, the recognition accuracy can be further improved.

Authors:Amir Norouzi, Michael Morel
Title: Voltage Ride-Through in Large Loads- A Dual PQ Approach
Abstract:
This paper provides a detailed investigation of voltage ride‑through in large loads, such as Artificial Intelligence data centers. Voltage ride‑through capability of large loads during transient disturbances in the power grid is important because of the potential impact on the stability and reliability of the Bulk Power System. A mathematical analysis is presented and it is shown how the traditional approach, based on reactive power compensation, may not be adequate for voltage ride‑through in large loads. Ultimately, due to capacity limits of the load's power distribution infrastructure and grid's constraints, there is a limit to using reactive power as a corrective tool. A new dual active and reactive power (PQ) approach is proposed in which non‑grid resources with dynamic P and Q capabilities are shown to be needed to help with voltage ride‑through. Additionally, the analysis illustrates that at extreme voltage dips in the power grid maintaining an acceptable level of load voltage can become practically or theoretically unattainable, which may lead to the load's disconnection from the grid. Analytical results are provided with practical numerical examples.

Authors:Vishnu Murali, Amin Falah, Ashutosh Trivedi, Majid Zamani
Title: HyperCertificates: Verification of Discrete-time Dynamical Systems against HyperLTL Specifications
Abstract:
We introduce a functional inductive framework to verify discrete‑time dynamical systems against hyperproperties specified as Hyperlinear temporal logic formulae via a notion of HyperCertificates. Unlike linear temporal logic (LTL) formulae which are concerned with individual traces of a system, hyperproperties are properties that are concerned with how the traces of a system relate to one another. HyperLTL is an extension of LTL for hyperproperties, and is useful to describe specifications such as opacity, privacy as well as notions of robustness. Our notion of HyperCertificates consists of a pair of functions, where the first models the lookahead, and the second relies on a combination of barrier and ranking functions. We use closure certificates, to act as a model for this lookahead and then rely on barrier and ranking function arguments modulo this lookahead to provide guarantees against HyperLTL formulae. We demonstrate how our approach is automatable via existing techniques such as sum‑of‑squares optimization (SOS) and satisfiability modulo theories (SMT) solvers. Finally, we demonstrate our approach on some case studies.

Authors:Wei Ai, Vladimir Dvorkin, Michael T. Craig
Title: Economic Valuation and Optimal Deployment of Static Synchronous Series Compensators for U.S. Power System Expansion
Abstract:
Flexible AC Transmission Systems (FACTS), particularly Static Synchronous Series Compensators (SSSC), can improve network transfer capability and complement restricted transmission expansion. Evaluations of FACTS within large‑scale, real‑world power system planning are currently lacking. This paper develops a capacity expansion model for the contiguous U.S. power system toward 2050, incorporating SSSC‑modified linear power flow equations and accounting for impedance feedback in transmission expansion. Cost‑optimal system expansion leverages widespread nationwide SSSC deployment on small‑to‑medium capacity lines and reduces the number of corridors to be reinforced. Overall, SSSCs reduce annualized system costs by 1.9 billion or decrease transmission expansion requirements by 20%. The most advantageous deployments achieving benefit‑cost ratios of 59 concentrated in the Midwest, facilitating the delivery of central U.S. wind power to eastern load centers. The value proposition of SSSCs is robust to cost sensitivities and potential competition from HVDC network expansion, and increases under higher demand growth and more stringent decarbonization policies. These findings provide a blueprint for leveraging SSSC deployment in the U.S. power system.

Authors:Yixun Wen, Stelios Timotheou, Boli Chen
Title: Multi-Regional Traffic Control with Travel and Charging Demand Co-Management
Abstract:
Urban traffic management is essential for reducing congestion and supporting sustainable mobility. However, the task is becoming more challenging due to the growing penetration of electric vehicles and their charging demands. This paper presents a regional traffic coordination framework that combines route guidance and charging management to improve traffic network efficiency. Regional traffic dynamics are modeled by the macroscopic fundamental diagram, which allows for the analysis of congestion at the system level. The framework jointly optimizes routes and charging decisions, and it also uses demand management to regulate external inflows into the network. A case study on a 16‑region urban network demonstrates the effectiveness of the proposed approach.

Authors:Richard Mahuze, Charlotte Gressel, Ali Amadeh, K. Max Zhang
Title: The Potential Welfare Gains from Curtailment Trading Under Non-Firm Interconnection
Abstract:
Rapid growth of large loads led by data centers is straining grid capacity. These loads increasingly accept curtailment risk through non‑firm interconnection agreements to gain faster grid access, expanding the pool of consumers subject to mandatory disconnection during supply shortfalls. Yet, blunt rules assign curtailment without reference to the wide variation in the value consumers place on avoiding curtailment, often captured by the value of lost load (VOLL). This paper introduces the network‑constrained Curtailment Credit Market (CCM), a mechanism in which agents submit bids that determine bilateral credit flows, subject to transmission network constraints. We prove that the bilateral credit flow representation can reach every curtailment allocation available to an omniscient central planner (feasible‑set equivalence). Under truthful bidding, the CCM achieves the planner's total value of served load. The CCM clearing problem is a linear program. When embedded in a strategic bidding model, where an upper‑level agent anticipates the CCM clearing outcome, the resulting bilevel problem admits an exact single‑level mixed‑integer linear program (MILP), solved in 0.009 to 0.034 seconds on the reported test systems. Numerical experiments on the three test systems validate the mechanism at increasing scale and complexity. A 3‑bus illustrative network isolates the core trading logic, the IEEE 24‑bus reliability test system provides a standard benchmark, and a reduced New York (NY) grid captures coordination across NY load zones. Our simulations show that the CCM increases the total value of served load by 1.41 to 1.83 times relative to pro‑rata curtailment. On the three test systems examined here, no participant is worse off under incentive‑compatible benchmark payments than under the administrative baseline.

Authors:Lei Wang, Jiahao Chen, Fanping Sui, Ying Zhang, Di Shi
Title: Deployment-Efficient Short-Term Load Forecasting in AI Data Centers via Sequence-to-Point Knowledge Distillation
Abstract:
Accurately forecasting the bursty and non‑stationary power demand of AI data centers has become increasingly important, as abrupt workload‑driven variations at the GPU‑node level can affect real‑time operational efficiency, power management, and grid‑data center coordination. However, high‑capacity forecasting models are often difficult to deploy at scale because of their memory and latency requirements, while lightweight predictors may fail to capture short‑horizon temporal dynamics. To address this accuracy‑deployment tradeoff, this paper proposes a deployment‑efficient knowledge distillation framework for short‑term load forecasting in AI data centers. The proposed framework first trains a high‑capacity sequence teacher model for multi‑step load trajectory prediction, where residual learning is used to improve robustness under non‑stationary operating conditions. A lightweight point‑wise student model is then developed for low‑latency rolling inference using a compact neural network architecture. To transfer temporal knowledge from the teacher to the student, a sequence‑to‑point distillation strategy is introduced by aligning near‑term predictive behavior and temporally pooled representations. Case studies on the MIT Supercloud dataset demonstrate that the proposed student model improves forecasting accuracy over recent deep learning baselines while reducing the deployment footprint by over 10x in parameter memory and model size.

Authors:Po-Heng Chou, Yi-Fang Yu, Shou-Yu Chen, Chiapin Wang
Title: Policy-Driven DRL-Based TXOP Adaptation in NR-U and Wi-Fi Coexistence
Abstract:
The coexistence of NR‑U and Wi‑Fi in unlicensed spectrum introduces a challenging coexistence management problem, where heterogeneous channel access mechanisms lead to a significant imbalance in spectrum utilization and degraded Wi‑Fi performance. To address this challenge, we propose a policy‑driven deep reinforcement learning (DRL) framework for adaptive transmission opportunity (TXOP) control, in which the coexistence process is formulated as a Markov decision process (MDP) and a deep Q‑network (DQN) learns control policies through online interaction. A key contribution is the introduction of a policy layer via reward design, enabling explicit control of coexistence tradeoffs among fairness, throughput, and utility. Three policies, namely absolute fairness, moderate fairness, and utility‑based fairness, are developed to achieve different operating points. Simulation results show that the proposed framework achieves a Jain fairness index above 0.9 under strict fairness control. Compared to absolute fairness, moderate fairness improves aggregate throughput by 68.22%, while the utility‑based policy further enhances utility by 177.6%. These results demonstrate that policy‑driven control provides a flexible and effective solution for managing tradeoffs in heterogeneous coexistence networks.

Authors:Bhaskar Krishnamachari
Title: How to Do Statistical Evaluations in ECE/CS Papers: A Practical Playbook for Defensible Results
Abstract:
Strong experimental papers in electrical and computer engineering and computer science (ECE/CS), especially in systems, networking, and applied machine learning, rest on more than a single impressive number. They rest on a chain of design, measurement, analysis, and validation choices that, taken together, make a result believable. This tutorial is a compact, example‑driven guide to that chain for beginning researchers. We organize it as an evaluation workflow: claim, hypothesis, unit of analysis, baseline, regime sweep, uncertainty estimate, validation check, and reporting. Within that workflow we cover the classical statistical foundations (descriptive statistics, the central limit theorem, normal‑ and t‑based confidence intervals, Student's t‑test, ANOVA, chi‑squared and Pearson correlation, linear regression) alongside the modern, distribution‑free techniques (the bootstrap, Wilcoxon and Mann‑‑Whitney tests, Cliff's delta) that are usually preferred for ECE/CS data. We also discuss factorial design, randomization and blocking, multiple‑comparison correction, latency‑specific pitfalls, simulation verification and validation, equivalence‑style claims, and reproducibility. A running example, a comparison of two job‑scheduling algorithms on simulated workloads with truncated heavy‑tailed job sizes, threads through the tutorial, with Python snippets the reader can paste and adapt. The paper closes with a pre‑submission checklist; companion student‑facing material (project‑type translation tables, an evaluation‑plan worksheet, exercises, and a worked ``bad evaluation autopsy'') is collected in a separate workbook released alongside this paper.

Authors:Norak Rin, Iman Shames, Ian Petersen, Elizabeth Ratnam
Title: Electric Grid Topology and Admittance Estimation using Phasor Measurements
Abstract:
Recent advances in precise phasor measurement units are enabling new approaches to estimate distribution and transmission grid parameters in real‑time. In this paper, we investigate voltage and current phasor measurement requirements to estimate the electric grid topology and admittance parameters. We show necessary and sufficient conditions for the number of independent operating points (measurements) required to determine the topology and admittance of a completely unknown electric grid. With prior topology information, we also show that there is a minimum number of measurements required to uniquely determine the admittance matrix and corresponding grid topology. In the presence of noisy phasor measurements, we show that the admittance matrix can be estimated using a structured total least squares approach. By means of numerical simulations on the IEEE 13‑node distribution feeder, the IEEE 14‑node transmission network, and the IEEE 123‑node distribution feeder, we demonstrate our approach is suitable for applications in radial and mesh grid topologies in the presence of measurement noise.

Authors:Jie Zhu, Yinliang Xu, Hongbin Sun
Title: Real-Time Neural Distributed Energy Resources Dispatch with Feasibility Guarantees
Abstract:
The growing penetration of renewable energy necessitates high‑frequency real‑time scheduling. While neural network‑based surrogates enable computationally efficient scheduling, strictly enforcing nonconvex power flow constraints without external solvers remains a fundamental challenge. To bridge this gap, this letter proposes a solver‑free neural dispatch framework with rigorous feasibility guarantees. A convex inner approximation of the DistFlow model is first derived via the convex envelope theorem. Building upon this approximation, a robust optimization‑based affine policy is formulated to yield a theoretically certified interior‑point mapping rule, which is then embedded within a bisection‑based projection scheme to efficiently recover feasibility for infeasible NN outputs without any external solver. Experimental results demonstrate that the proposed method restores feasibility on the order of 10^‑3 s while maintaining near‑optimal performance.

Authors:Bowen Li, Nikolaos Pappas
Title: Joint Accuracy and Confidentiality in Semantic-Aware Secure Remote Reconstruction
Abstract:
In this paper, we consider remote reconstruction over wireless networks when simultaneous accuracy at the legitimate receiver and confidentiality against eavesdropping are required. These two objectives are often treated separately, even though they arise from the same update process and are marginals of a joint reconstruction event. This paper introduces confidential reconstruction accuracy (CRA), a metric to capture the joint event in which the legitimate receiver reconstructs correctly while the eavesdropper fails. Under randomized stationary policies, we develop a three‑dimensional stationary analysis and derive closed‑form expressions for the long‑term average CRA and the optimal transmission probability. The results show that conventional marginal analysis can misidentify the optimal policy and misestimate the achievable simultaneous accuracy‑confidentiality performance. They also reveal nontrivial behaviors: more frequent transmissions or better legitimate channels do not necessarily improve joint accurate and confidential reconstruction, and when the eavesdropping channel is strong, improving the legitimate channel alone may be insufficient. Finally, the framework induces the spatial safety boundary in a geofencing setting for secure remote reconstruction.

Authors:Hussein Suprême, Martin de Montigny, Kevin-R. Sorto-Ventura, Hind Chit Dirani, Mouhamadou Makhtar Dione, Nicolas Compas
Title: An Annual Quasi-Static Time-Series Simulation Framework for Enhanced Transmission System Expansion Planning
Abstract:
The increasing integration of distributed energy resources (DERs), variable renewable energy sources, and emerging technologies presents new challenges for transmission system expansion planning (TSEP). Traditional snapshot‑based and deterministic approaches are inadequate for capturing the temporal dynamics and operational constraints of modern power systems. This paper introduces an annual quasi‑static time‑series simulation (AQSTSS) framework that enables high‑resolution, year‑round modeling of transmission systems, incorporating detailed equipment behavior, control strategies, and DER interactions. By simulating system performance across all seasons and operating conditions, AQSTSS uncovers flexibility opportunities and operational constraints that static methods overlook. Applied to Hydro‑Québec's projected 2035/2036 grid, the framework reveals critical insights under high wind and electric vehicle penetration. It also integrates an energy storage control strategy designed to mitigate wind variability and support grid reliability. Furthermore, AQSTSS facilitates the assessment of system resilience under diverse scenarios, including extreme weather and load variability. The simulation results underscore the importance of aligning planning with operational realities to ensure secure, efficient, and future‑ready grid development. Overall, the proposed framework enhances the robustness of TSEP by bridging the gap between long‑term planning and real‑time operational needs.

Authors:Giulio Fattore, Maria Elena Valcher, Rui Gao, Guang-Hong Yang
Title: Distributed Observer Design for Discrete-Time LTI Systems via Jordan Canonical Form
Abstract:
This paper addresses the problem of distributed state estimation for discrete‑time linear time‑invariant systems. Building on the framework proposed in Gao & Yang (2025), we exploit the Jordan canonical form of the system matrix to develop two distributed estimation schemes that ensure asymptotic convergence of local estimates to the true system state. In both approaches, each node reconstructs the components of the state that are locally detectable for it via a Luenberger observer, while employing a consensus‑based mechanism to estimate the components that are not directly detectable. The first scheme relies on local observers whose dimension matches that of the original state vector; however, its applicability requires the satisfaction of a large set of inequalities. The second scheme, in contrast, can be implemented under less restrictive conditions, but results in observers of increased (augmented) order. For both methods, we derive necessary and sufficient conditions ‑ expressed in terms of the eigenvalues of the system matrix and certain submatrices of the communication network Laplacian ‑ that guarantee the existence of a distributed observer achieving asymptotically accurate estimation. Compared to Gao & Yang (2025), the proposed approaches offer greater flexibility in the selection of coupling gains and impose less stringent solvability conditions.

Authors:Jaewon Jeong, Pan-Yang Su, S. Shankar Sastry, Anil Aswani
Title: Moral Hazard in LTI Dynamics: A Hypothesis Testing Approach
Abstract:
Many incentive design problems must contend with information asymmetries due to non‑observation of efficiency (adverse selection) or non‑observation of effort (moral hazard). And although a growing body of literature considers incentive design in control systems, the problem of designing incentives for control systems under information asymmetries has been less well‑studied. This paper considers a model of moral hazard within control systems. In our model, the control system is described by an (affine) linear time‑invariant (LTI) system with process noise. There is an agent who gets to choose (from between two choices) a linear state‑feedback controller to apply to the LTI system, with one of the state‑feedback controllers having a higher quadratic cost on the control inputs than the other. Our goal is to design a payment scheme that incentivizes the agent to choose the state‑feedback controller that minimizes a quadratic cost on system states plus the time‑discounted payment amount, subject to the understanding that the agent bears the control cost while being risk‑averse with respect to their time‑discounted payment. We formulate the problem as a constrained optimization, and prove that for a payment given after a fixed (but optimizable) time horizon the optimal payment scheme chooses the payment amount using a likelihood ratio hypothesis test. We numerically demonstrate our results by applying the derived optimal payment scheme to two examples: load frequency control (LFC) in power systems and wellness interventions for body weight loss.

Authors:Rong Lu
Title: TADI: Tool-Augmented Drilling Intelligence via Agentic LLM Orchestration over Heterogeneous Wellsite Data
Abstract:
We present TADI (Tool‑Augmented Drilling Intelligence), an agentic AI system that transforms drilling operational data into evidence‑based analytical intelligence. Applied to the Equinor Volve Field dataset, TADI integrates 1,759 daily drilling reports, selected WITSML real‑time objects, 15,634 production records, formation tops, and perforations into a dual‑store architecture: DuckDB for structured queries over 12 tables with 65,447 rows, and ChromaDB for semantic search over 36,709 embedded documents. Twelve domain‑specialized tools, orchestrated by a large language model via iterative function calling, support multi‑step evidence gathering that cross‑references structured drilling measurements with daily report narratives. The system parses all 1,759 DDR XML files with zero errors, handles three incompatible well naming conventions, and is backed by 95 automated tests plus a 130‑question stress‑question taxonomy spanning six operational categories. We formalize the agent's behavior as a sequential tool‑selection problem and propose the Evidence Grounding Score (EGS) as a simple grounding‑compliance proxy based on measurements, attributed DDR quotations, and required answer sections. The complete 6,084‑line, framework‑free implementation is reproducible given the public Volve download and an API key, and the case studies and qualitative ablation analysis suggest that domain‑specialized tool design, rather than model scale alone, is the primary driver of analytical quality in technical operations.

Authors:Wim Nijsink, Bruno Endres Forlin, Amirreza Yousefzadeh, Marco Ottavi
Title: Shooting Neutrons at Neurons: Radiation Testing of a Spiking Neural Network on Flash-Based FPGAs
Abstract:
Neuromorphic, or spiking, processors are increasingly being considered for use in harsh, radiation‑prone environments such as space and avionics, where energy efficiency and graceful degradation are essential. In this study, we propose and experimentally validate a radiation‑testing methodology specifically designed for neuromorphic processors that employ on‑chip synaptic plasticity. We map the open‑source ODIN SNN processor with Spike‑Dependent Synaptic Plasticity (SDSP) onto the FPGA and expose it to a high‑energy neutron beam while continuously monitoring MNIST classification accuracy and recording the synaptic state. From these measurements, we extract SEU cross‑sections for ODIN's synaptic memory and develop a calibrated fault model to inform a complementary fault‑injection campaign. By comparing inference‑only and online‑learning configurations, we demonstrate that enabling SDSP can significantly extend the time to application‑level failure and enable partial recovery from accumulated bit flips, with modest hardware overhead.

Authors:Zhiyuan Huang, Shuo Li, Murat Arcak, Majid Zamani, Bingzhuo Zhong
Title: Hierarchical Control for Continuous-time Systems via General Approximate Alternating Simulation Relations
Abstract:
This paper introduces a general approximate alternating simulation relation (\emph\varepsilon‑gAAS relation) for continuous‑time systems, which relaxes existing simulation relations to tolerate larger mismatches between abstract and concrete models. The definition of gAAS for continuous‑time systems is first proposed, and its properties are investigated. Then, a control refinement method is developed to enable hierarchical control for the gAAS relation. Finally, case studies demonstrate the effectiveness of the proposed approach, highlighting its advantages over existing methods.

Authors:Mahuizi Lu, Kelin Jia, Rajib Goswami, Yukun Hu
Title: Intelligent Self-tuning Active EMI Filtering for Electrified Automotive Power Systems Using Reinforcement Learning
Abstract:
The rapid electrification and intelligence of modern transportation systems place stringent demands on the electromagnetic compatibility, reliability, and adaptability of automotive power electronics. In electric and autonomous vehicles, electromagnetic interference (EMI) generated by high‑frequency switching power converters can compromise safety‑critical functions, in‑vehicle communications, and system efficiency under dynamic operating conditions. Conventional passive EMI filters, while robust, are often oversized and lack adaptability, leading to increased weight, volume, and energy losses. This paper proposes an intelligent self‑tuning active EMI filtering approach for electrified automotive power systems based on reinforcement learning (RL). The EMI mitigation problem is formulated as a Markov decision process, enabling an RL agent to continuously adapt filter parameters in response to time‑varying interference characteristics. To improve robustness and generalisation under complex and non‑stationary conditions, a variational autoencoder is employed for compact state representation, while a noise‑based exploration mechanism enhances learning efficiency and prevents suboptimal convergence. The proposed method is evaluated using experimentally measured EMI spectra from an automotive electric drive unit within a MATLAB/Simulink co‑simulation framework. Results demonstrate consistent EMI attenuation improvements of 25‑30 dB across a wide frequency range compared with conventional control strategies and passive filtering solutions. By reducing reliance on oversized passive components and enabling adaptive EMI suppression, the proposed framework supports lightweight, energy‑efficient, and reliable power‑electronic systems for intelligent and green transportation applications.

Authors:Emre Akıskalıoğlu, Mustafa Atmaca, Lorenzo Ghiro, Giovanni Perin, Renato Lo Cigno
Title: A MEC-Based Optimization Framework for Dynamic Inductive Charging
Abstract:
Range anxiety and long recharging times remain critical barriers to electric vehicle adoption. Dynamic Inductive Charging (DIC) offers a compelling solution by enabling wireless power transfer while driving, potentially reducing battery size requirements and thus vehicle costs. However, DIC infrastructures are expensive and power‑constrained, requiring intelligent resource allocation to maximize user satisfaction and economic viability. We propose a Model Predictive Control framework for optimal power allocation in DIC systems, using edge computing and vehicular communications to prioritize vehicles with critical battery states. The framework is implemented and evaluated through SUMO‑based simulations on a realistic 10 km urban scenario in Istanbul, Turkey, under varying traffic intensities. Results demonstrate two critical limitations of uncoordinated allocation. First, resource utilization remains suboptimal despite available power when demand saturates system capacity. Second, when demand exceeds capacity, uniform distribution of power leaves a heavy tail of critically unsatisfied vehicles that may require emergency stops. Our MPC‑based strategy addresses both regimes ‑‑ maximizing power utilization during saturation through dynamic stripe rebalancing, and improving satisfaction fairness under scarcity by aggressively prioritizing depleted batteries at the expense of well‑charged vehicles. The framework and simulation tools are released as open‑source to support further research in this emerging domain.

Authors:Kaleem Arshid, Ali Krayani, Lucio Marcenaro, David Martin Gomez, Carlo Regazzoni
Title: Flying by Inference: Active Inference World Models for Adaptive UAV Swarms
Abstract:
This paper presents an expert‑guided active‑inference‑inspired framework for adaptive UAV swarm trajectory planning. The proposed method converts multi‑UAV trajectory design from a repeated combinatorial optimization problem into a hierarchical probabilistic inference problem. In the offline phase, a genetic‑algorithm planner with repulsive‑force collision avoidance (GA‑‑RF) generates expert demonstrations, which are abstracted into Mission, Route, and Motion dictionaries. These dictionaries are used to learn a probabilistic world model that captures how expert mission allocations induce route orders and how route orders induce motion‑level behaviors. During online operation, the UAV swarm evaluates candidate actions by forming posterior beliefs over symbolic states and minimizing KL‑divergence‑based abnormality indicators with respect to expert‑derived reference distributions. This enables mission allocation, route insertion, motion adaptation, and collision‑aware replanning without rerunning the offline optimizer. Bayesian state estimators, including EKF and PF modules, are integrated at the motion level to improve trajectory correction under uncertainty. Simulation results show that the proposed framework preserves expert‑like planning structure while producing smoother and more stable behavior than modified Q‑learning. Additional validation using real‑flight UAV trajectory data demonstrates that the learned world model can correct symbolic predictions under noisy and non‑smooth observations, supporting its applicability to adaptive UAV swarm autonomy.

Authors:Armin Gießler, Felix Thömmes, Sören Hohmann
Title: Data-Driven Continuous-Time Linear Quadratic Regulator via Closed-Loop and Reinforcement Learning Parameterizations
Abstract:
This paper studies data‑driven approaches to the continuous‑time linear quadratic regulator (LQR) problem based on two existing parameterizations, namely a closed‑loop (CL) parameterization from behavioral system theory and an integral reinforcement learning (IRL) parameterization. The CL parameterization characterizes the closed‑loop system via a matrix that satisfies equality constraints. While this parameterization has been extensively studied for discrete‑time systems, we adapt key results to the continuous‑time setting and develop a policy iteration (PI) scheme, derive a data‑driven continuous‑time algebraic Riccati equation (CARE), and introduce an alternative convex problem formulation. The IRL parameterization utilizes off‑policy data to perform policy evaluation, which is then used for PI or value iteration. Within the IRL framework, we derive a policy gradient flow and propose convex reformulations of the LQR problem. Finally, we provide a unified treatment of these parameterizations that enables a systematic understanding of existing approaches and clarifies their structural relationships.

Authors:Rachit Mehra, M Parimi, Amol Yerudkar, S. R. Wagh, Navdeep Singh
Title: On the Nesterov's acceleration: A NAIM perspective
Abstract:
We present a unifying Nearly Asymptotically Invariant Manifold (NAIM) framework for understanding Nesterovs Accelerated Gradient (NAG) method. By lifting the first‑order gradient flow into a second‑order phase space we construct a NAIM a slow, attracting graph and show that acceleration emerges from a curvature aware perturbation of this graph. The evolving slope of the perturbed manifold is governed by a Differential Riccati Equation (DRE), which enforces strict tangency of the vector field to the manifold surface. In the quadratic case the DRE reduces to an Algebraic Riccati Equation (ARE), and the requirement of spectral resonance equal contraction rates across all curvature modes uniquely determines the damping coefficient, directly yielding the continuous time Nesterov ODE. Fenichels theorem then extends this picture rigorously to general smooth, strongly convex landscapes: normal hyperbolicity guarantees persistence of the accelerated manifold despite varying Hessian curvature. The method is further extended to unified geometric derivation of NAG methods for smooth convex and strongly convex optimization in the discrete case. We exploit the underlying geometric structure and derive both cases from the same principle of preserving the projective structure under discretization process. A Lie Trotter splitting separates the linear dissipative dynamics from the nonlinear gradient flow. The dissipative subsystem is integrated by the Cayley (bilinear) transform, which preserves the underlying projective (Mobius) structure unconditionally and produces the classical Nesterov momentum coefficient as the unique Pade multiplier. For the convex case, projective flatness (vanishing Schwarzian derivative) uniquely selects the time‑varying damping recovering the canonical Nesterov ODE for convex functions.

Authors:Jonas G. Matt, Saverio Bolognani, Florian Dörfler
Title: Optimal Functional Incentives for Control: The Linear-Quadratic Case with Bilinear Incentives
Abstract:
We study the design of functional incentive mechanisms for dynamical systems, in which a leader designs a fixed incentive function to motivate a self‑interested follower to actuate the system beneficially over an extended horizon, without real‑time revision of the incentive. This stands in contrast to the adaptive paradigm, in which the incentive is itself a continuously updated control variable. We formalize the problem as a discrete‑time bi‑level optimal control problem and derive analytical results for the linear‑quadratic case with bilinear incentives and a myopic follower. Specifically, we establish a necessary and sufficient stability condition for the induced closed‑loop system, derive a closed‑form expression for the gradient of the expected leader cost with respect to the incentive parameter matrix, and obtain a fully closed‑form cost expression in the scalar setting. Based on the latter, explicit characterizations of the optimal incentive parameter are provided in two asymptotic regimes: the infinite‑horizon limit and the limit of high follower cost. For long horizons, the optimal incentive is shown to become independent of the follower's private cost parameter, with direct implications for robust mechanism design under private information.

Authors:Alexandre Anahory Simoes, Leonardo Colombo
Title: Robust Geometric Control of Catenary Robots under Unstructured Force Uncertainties
Abstract:
This paper considers the robust control of a catenary robot composed of two quadrotors connected by an inextensible cable. The system is modeled on \(SE(3)\), with the cable treated as a geometric subsystem induced by the UAV configuration rather than as an independent dynamical element. The catenary shape determines configuration‑dependent forces that couple the translational dynamics of the vehicles. We propose a geometric tracking controller for the relative configuration of the agents and analyze its robustness with respect to unstructured uncertainties in the catenary‑induced forces. The main theoretical result establishes local input‑to‑state stability of the closed‑loop tracking errors. In particular, we obtain asymptotic convergence in the nominal case and an explicit ultimate bound for the tracking errors under bounded catenary‑force perturbations.

Authors:José Álamos, Thomas C. Schmidt, Matthias Wählisch
Title: LZn : Robust LoRa Frame Synchronization Under Frame Collisions and Ultra-Low SNR Conditions
Abstract:
LoRa has become a widely adopted wireless modulation scheme in LPWANs due to its low cost, long range, and minimal transmission power. However, collisions between frames of the same spreading factor ‑‑ common in dense LoRa deployments ‑‑ prevent conventional LoRa receivers from detecting and correctly decoding frames. Recent work has introduced methods to improve recovery, yet their detection stage degrades sharply under low signal‑to‑noise ratio (SNR) and high collision rates. In this work, we introduce LZn, a low‑complexity synchronization scheme driven by a spectral intersection operation. Our method enables robust frame synchronization even under multiple packet overlaps or extremely low SNR conditions. We evaluate LZn on simulations and three independent, real‑world LoRa datasets. LZn improves detection sensitivity by up to 10dB and increases detection probability by up to 1.54x. In real‑world datasets, LZn improves decoding by 3.46x in the most challenging single‑user scenario and up to 1.22x in collision scenarios compared to the second best collision‑tolerant scheme (TnB). These results demonstrate that LZn substantially improves the frame recovery of LoRa receivers, while remaining compatible with real‑time requirements.

Authors:Pedro F. C. de Carvalho, Zijie Liu, Md Umar Hashmi, Dirk Van Hertem
Title: Fairness for distribution network operations and planning
Abstract:
The incorporation of fairness into the distribution network (DN) planning and operation has become a key goal of recent studies. The cost of implementing fairness, denominated the price of fairness (PoF), covers the efficiency that is renounced for attaining social cohesion through fair outcomes. Locational disparity makes fairness schemes emerge to level the consumers playing field. However, fairness encompasses a range of notions. From egalitarian to merit‑based criteria, various metrics are implemented as a tool for measuring equitable utility distribution. These have different mathematical complexities, from linear to non‑linear programming cases, which affect their overall applicability. Hence, this study compiles the overarching fairness notions and metrics, reviewing how these affect stakeholders and the inherent mathematical optimisation in resource allocation problems. The aim is to support consistent and transparent planning and decision‑making within DN operations.

Authors:Aygun Baltaci, Irshad A. Meer, Mustafa Ozger, Cicek Cavdar, Dominic Schupke
Title: Multi-Connectivity for UAVs: A Measurement Study of Integrating Cellular, Aerial Mesh, and LEO Satellite Links
Abstract:
Future uncrewed aerial vehicle (UAV) systems increasingly combine heterogeneous communication technologies, such as low‑latency aerial mesh, terrestrial cellular, and satellite links, to improve robustness and coverage. Multipath transport is a natural mechanism for aggregating these links, yet its ability to support real‑time UAV services in highly heterogeneous environments remains insufficiently characterized. We present a measurement‑driven study based on UAV flight experiments in an integrated network comprising UAV‑to‑UAV aerial mesh, private cellular, and low Earth orbit (LEO) satellite connectivity. Using Multipath TCP (MPTCP) as a representative lossless, in‑order multipath transport framework, we find that aggregation can preserve end‑to‑end connectivity under severe link outages. However, large round‑trip time (RTT) heterogeneity amplifies packet reordering, leading to substantial receiver‑side buffering and bursty delivery. In addition, when the available links do not provide sufficient capacity for the offered load, pronounced sender‑side buffering emerges. These effects cause real‑time streaming to violate delay constraints, including cases where aggregate capacity is sufficient. To interpret these results, we formalize the distinction between connectivity continuity and service continuity and show empirically that maintaining connectivity is necessary but not sufficient for timely real‑time delivery in multi‑technology UAV networks. The findings motivate multipath designs that explicitly account for delay constraints, rather than optimizing for connectivity alone.

Authors:Shyam Kamal, Baby Diana, Sunidhi Pandey, Sandip Ghosh, Thach Ngoc Dinh
Title: Robust Constrained Optimization via Sliding Mode Control
Abstract:
This paper develops a sliding mode control based frame work for equality constrained optimization by reformulation the first order Karush Kuhn Tucker conditions as control affine dynamical system. The optimization variables are treated as states and the Lagrange multipliers as control input, with equality constraints defined as sliding manifold. The resulting design guarantees exact constraint enforcement with finite time convergence, independent of objective convexity, and exhibits robustness to matched disturbance, structural uncertainty and bounded measurement noise. To accelerate the convergence, a nonsingular terminal sliding mode based normed gradient flow is introduced, ensuring both finite time convergence to optimal solution and constraint satisfaction. Rigorous Lyapunov analysis establishes closed loop stability and convergence. Numerical studies across diverse benchmark problems demonstrate superior accuracy and robustness over classical continuous time optimization method, highlighting effectiveness under disturbance.

Authors:Syed Pouladi
Title: Stability Analysis and Data-Driven State Estimation for Generalized Persidskii Systems with Time Delays: Theory and Experimental Validation on PMSM Drives
Abstract:
This paper addresses the stability analysis and state estimation of generalized Persidskii systems subject to time‑varying delays and external disturbances. The generalized Persidskii class, which couples linear dynamics with sector‑bounded nonlinear feedback loops, offers a tractable yet expressive framework for modeling electromechanical and neural network systems. We develop delay‑dependent conditions for input‑to‑state stability (ISS) via Lyapunov‑‑Krasovskii functionals incorporating Persidskii‑type integral terms, and cast these conditions as linear matrix inequalities (LMIs). A structured robust observer is proposed for systems with partial state measurement, and its convergence is guaranteed through an H_\infty synchronization criterion. To handle plant uncertainty, the system matrices are identified from trajectory data using a stability‑preserving Koopman lifting procedure, in which the ISS‑LMI constraint is embedded as a convex side condition during parameter regression. The identified model populates the prediction horizon of an ICODE‑MPPI (Input‑dependent Control‑oriented Dynamical Estimation ‑‑ Model Predictive Path Integral) controller. The complete framework is validated on a 1.5 kW Permanent Magnet Synchronous Motor (PMSM) drive equipped with a programmable load brake. Experimental results confirm a 35% reduction in velocity estimation RMSE relative to an Extended Kalman Filter and a 67% improvement in speed‑tracking accuracy relative to standard Field‑Oriented Control, corroborating the theoretical ISS bounds established herein.

Authors:Sivaram Krishnan, Bassel Al Homssi, Zhouyou Gu, Jihong Park, Sung-Min Oh, Jinho Choi
Title: Toward Scalable SDN for LEO Mega-Constellations: A Graph Learning Approach
Abstract:
Terrestrial network limitations drive the integration of non‑terrestrial networks (NTNs), notably mega‑constellations comprising thousands of low Earth orbit (LEO) satellites. While these satellites act as interconnected network switches via inter‑satellite links (ISLs), their massive scale creates severe bottlenecks for network management. To address this, we propose a scalable, hierarchical software‑defined networking (SDN) framework. Our architecture leverages graph neural networks (GNNs) to compactly represent the constellation topology, and Koopman theory to linearize nonlinear dynamics. Specifically, a Graph Koopman Autoencoder (GKAE) forecasts spatio‑temporal behavior within a linear subspace for each orbital shell. A central SDN controller then aggregates these shell‑level predictions for globally coordinated control. Simulations on the Starlink constellation demonstrate that our approach achieves at least a 42.8% improvement in spatial compression and a 10.81% improvement in temporal forecasting compared to established baselines, all while utilizing a significantly smaller model footprint.

Authors:Aaditya Kumar, Puduru Viswanadha Reddy
Title: Solution Sets for Inverse Infinite-Horizon Linear-Quadratic Descriptor Differential Games
Abstract:
In this letter, we study a model‑based inverse problem for infinite‑horizon linear‑quadratic differential games with descriptor dynamics. Given an observed feedback strategy profile, we seek to identify all cost functions that rationalize it as a feedback Nash equilibrium; this collection is referred to as the solution set. We characterize the solution set, show that it is rectangular and convex, and provide an algorithm for computing an admissible realization whenever it is nonempty. We also show that, compared with the corresponding inverse problem for standard state‑space dynamics, descriptor dynamics modify the geometry of the solution set and may reduce identifiability. Finally, we illustrate the results with numerical examples.

Authors:Miroslav Krstic
Title: Feedback Linearization of Hyperbolic PDEs with Volterra Nonlinearities
Abstract:
Alberto Isidori's framework of geometric nonlinear control, and particularly of feedback linearization, is the inspiration behind PDE backstepping: apply a transfromation of the state to cast the plant into a canonical form, bring all the non‑canonical effects within the "span" of (boundary) control, and close the design with a feedback that makes the closed loop evolve in accordance with well‑studied stable dynamics. The specificity of this approach is that, for PDEs, there is not one canonical form (like Brunovsky for ODEs) but the canonical forms are PDE‑class‑specific. When conducting this process for nonlinear PDEs, where the "transformation of the state" is performed using a nonlinear Volterra series indexed by the spatial variable, enormous technical challenges arise. One has to deal with kernels governed by PDEs on simplex domains growing in dimension to infinity, capture the growth rates of these kernels of the "direct transformation," and conduct the same for the "inverse transformation" without directly studying its Volterra kernels. So far, this agenda has been executed only once, two decades ago: for parabolic PDEs by Vazquez and Krstic [Automatica, 2008]. Generalization attempts have not followed because of the immense complexity involved in feedback‑linearizing nonlinear PDEs. In this paper, dedicated to Professor Isidori, we convert the PDE feedback‑linearizing methodology of 2008 from the parabolic to a hyperbolic class and, for a transport‑adapted subclass of Chen‑Fliess series, construct controllers without kernel PDEs.

Authors:Walid Shaker, Mustafa Suphi Erden
Title: An Experimental Modular Instrument With a Haptic Feedback Framework for Robotic Surgery Training
Abstract:
Robotic‑assisted surgery offers significant clinical advantages but largely eliminates direct haptic feedback, increasing the risk of excessive tool‑tissue interaction forces. Although recent commercial systems have begun to introduce force feedback, their high cost limits accessibility, particularly for surgical training. This paper presents a modular experimental robotic laparoscopic instrument integrated with a real‑time haptic feedback framework. The proposed instrument employs a wrist‑mounted force/torque (F/T) sensor to estimate tool‑tissue interaction forces while avoiding the durability and integration challenges of tip‑mounted sensors. A haptic feedback framework is developed to extract the external contact forces, render them to the haptic device, and generate stable and perceptually meaningful feedback. The instrument is integrated into the robotic surgery training system (RoboScope) and evaluated through a controlled user study involving a force regulation task. Experimental results demonstrate that haptic feedback significantly improves task success rate, force regulation accuracy, and task efficiency compared to visual‑only feedback. The proposed instrument enables stable, high‑fidelity haptic interaction, supporting effective robotic surgery training.

Authors:Brendan Gould, Chih-Yuan Chiu, Antoine P. Leeman, Kyriakos G. Vamvoudakis, Samuel Coogan, Glen Chou
Title: Over-Approximating Minimizer Sets of Constrained Convex Programs with Parametric Uncertainty via Reachability Analysis
Abstract:
We study the set of solutions to a parameterized, strongly convex optimization problem whose cost depends on uncertain, bounded parameters. We compute a certified outer approximation of the corresponding set of optimizers, using convergence properties of the projected gradient descent (PGD) algorithm for convex programs. Concretely, by treating the cost parameter as constant but unknown, we interpret the PGD iterates as an uncertain dynamical system and analyze its forward reachable sets. Since PGD converges exponentially to the unique optimizer for each fixed parameter, these reachable sets provide outer approximations of the optimizer set, with an explicit error bound that decays exponentially with the iteration count. We apply system‑level synthesis (SLS) on the PGD dynamics to optimize the step‑size sequence and obtain reachable‑set over‑approximations. Our method outperforms existing baselines in over‑approximating, with low conservativeness, the minimizer sets of convex programs with uncertain costs and high‑dimensional decision variables.

Authors:Moh Kamalul Wafi, Arthur C. B. de Oliveira, Eduardo D. Sontag
Title: Boundedness of solutions in feedback systems with antithetic controllers
Abstract:
This paper studies whether solutions of a class of nonlinear feedback systems remain bounded over time. The systems we consider arise naturally in synthetic biology, where the antithetic feedback controller regulates a biological process through a delayed feedback loop. Our main result is that every trajectory of such a system is bounded. The key insight is simple: if the regulated state grows too large for too long, the feedback loop will eventually respond and push it back down. More precisely, we show that whenever the state exceeds a threshold and remains there long enough, the feedback signal becomes strong enough to force the state to decrease. We then show that once this happens, the feedback remains strong enough to keep the state from growing unbounded. The proof works directly with differential inequalities and does not require constructing a Lyapunov function, making the mechanism transparent and easy to interpret. The boundedness result can be understood as a time‑domain small‑gain effect, where the delayed feedback ultimately counteracts any persistent growth in the system.

Authors:Ziying Wang, Ying Zhang, Lei Wang, Yuzhang Lin
Title: Regime-Adaptive Weighted Ensemble Learning for Computing-Driven Dynamic Load Forecasting in AI Data Centers
Abstract:
Short‑term load forecasting for AI data centers presents new challenges because it is computing‑driven, with heterogeneous job arrivals, sizes, and durations exhibiting bursty, non‑stationary dynamics. Compared with traditional load types, data center loads are less researched and can pose greater threats to the efficiency and stability of power grids. To close the gap, this paper proposes a regime‑adaptive ensemble learning forecasting algorithm to predict computing‑driven dynamic workloads in AI data centers. A weight‑learned neural network within an ensemble learning framework is developed to exploit the complementary strengths of two machine learning (ML) submodels across varying operating regimes. Furthermore, a novel feature engineering strategy is developed to incrementally learn from a non‑stationary data stream. Thus, the ensemble weights are dynamically optimized to facilitate adaptive calibration of inter‑submodel contributions. Comparative case studies on the MIT Supercloud dataset demonstrate that the proposed method significantly enhances load forecasting accuracy and adaptivity across various regimes, and the selected combination of ML models for ensemble learning outperforms other possible combinations. To the best of our knowledge, our method is the first to reduce minute‑class forecasting errors for AI data center loads to below 1%, highlighting its potential for grid‑interactive coordination and demand response.

Authors:Slava G. Turyshev
Title: Orbital Data Centers: Spacecraft Constraints and Economic Viability
Abstract:
Orbital data centers are being evaluated as solar‑powered compute constellations and relay‑integrated processing platforms. Their feasibility is not set by orbital solar flux alone, but by simultaneous closure of photovoltaic generation, eclipse recharge, radiative heat rejection, sustained space‑to‑ground communications, utilization, replacement cadence, and delivered compute‑years over finite mission life. This paper derives necessary cluster‑level competitiveness conditions using delivered information‑technology (IT) electrical power P_\rm IT, deployed mass per delivered IT power m_\rm kW in kg/kW, communication intensity Γ=D_\rm sg/E_\rm IT, sustained communication ceiling Γ_\max, effective utilization U_\rm eff, and lifetime penalty Π_\rm life. For a representative P_\rm IT=1 MW high‑sunlight anchor, the base case gives beginning‑of‑life photovoltaic area A^\rm BOL_\rm PV=5.64 × 10^3 \rm m^2, radiator area A_\rm rad=2.50 × 10^3 \rm m^2, and 29.4 kg/kW for photovoltaic, storage, and radiator mass; fixed spacecraft mass raises the total to 34‑59 kg/kW. At m_kW ~ 40 kg/kW, a terrestrial infrastructure benchmark of 10‑40 k\/kW allows only 250‑1000 \/kg for the combined launch and spacecraft‑build cost before space‑to‑ground communications, operations, utilization, and lifetime terms are included. That allowance is 3.4‑13.5 times below the current public Falcon 9 dedicated low‑Earth‑orbit launch‑price benchmark alone, before spacecraft build is included. Space‑native preprocessing and communications‑integrated edge compute are credible early regimes; terrestrial‑user general compute closes only for low Earth‑coupled communication intensity, high effective utilization, long delivered lifetime, and very low combined launch‑plus‑build cost.

Authors:Akshay Karjol, Shadi Alawneh
Title: Real-Time GPU-Accelerated Monte Carlo Evaluation of Safety-Critical AEB Systems Under Uncertainty
Abstract:
Automatic Emergency Braking (AEB) systems represent a safety‑critical national interest, with the National Highway Traffic Safety Administration (NHTSA) Federal Motor Vehicle Safety Standard (FMVSS No. 127) requiring AEB in all new light vehicles sold in the United States by September 2029. However, production implementations frequently rely on deterministic stopping‑distance or Time‑to‑Collision (TTC) thresholds that fail to capture uncertainty in sensing, road conditions, and vehicle dynamics. This paper presents a GPU‑accelerated Monte Carlo framework for stochastic evaluation of emergency braking performance using a high‑fidelity longitudinal vehicle model incorporating aerodynamic drag, road grade, brake actuator dynamics, and weight transfer effects. A one‑thread‑per‑sample execution strategy exploits the independence of Monte Carlo rollouts, while deterministic CPU‑generated sampling ensures bit‑exact numerical consistency between CPU and GPU implementations. The framework is evaluated across four hardware platforms spanning development and deployment environments: two laptop GPUs (GTX 1650, RTX 5070) and two automotive‑grade embedded platforms (Jetson Orin Nano, Jetson AGX Orin). Peak speedups of 54.57x are achieved while maintaining exact numerical agreement. Real‑time feasibility analysis with a complete AEB timing budget (700 ms human reaction time minus 120 ms perception and 50 ms decision overhead) demonstrates that the Jetson AGX Orin can execute approximately 25,000 Monte Carlo samples within a 530 ms budget, enabling real‑time probabilistic AEB evaluation as part of a complete embedded pipeline. These results establish Monte Carlo‑based uncertainty evaluation as a deployable runtime component rather than an offline validation tool and provide quantitative guidance for risk‑aware AEB threshold selection under the NHTSA final rule.

Authors:Nilavra Pathak, Smriti Shyamal, Prasant Mhasker, Christopher Swartz
Title: Learning to Spend: Model Predictive Control for Budgeting under Non-Stationary Returns
Abstract:
We study finite‑horizon budget allocation as a closed‑loop economic control problem and evaluate receding‑horizon Model Predictive Control (MPC) relative to reactive budgeting policies. Budgets are allocated periodically under execution noise and operational constraints, while return efficiency may evolve over time. Using a controlled simulation framework motivated by digital marketing, we compare reactive pacing to MPC across environments with increasing degrees of non‑stationarity. Our results show that non‑stationarity alone does not justify predictive control. When return dynamics are stationary or evolve through unpredictable stochastic drift, MPC offers no systematic advantage over reactive baselines. By contrast, when return efficiency exhibits predictable structure over the planning horizon, that is captured through an underlying model, MPC consistently outperforms reactive budgeting by exploiting intertemporal trade‑offs.

Authors:Duong Thuy Anh Nguyen, Harsha Nagarajan, Robert Ferrando, Russell Bent, David Fobes
Title: Efficient Graph Partitioning under Resource Constraints: A Cutting-Plane Framework for Distribution Grids
Abstract:
This paper presents an optimal network topology control framework using cutting‑plane methods for efficient network partitioning with controllable edges. The objective is to enable real‑time reconfiguration of interconnected sub‑networks while ensuring radial connectivity, resource feasibility, and structured leader allocation, which are essential for distributed control, stability, and coordination. The problem is formulated as a mixed‑integer program that integrates graph‑theoretic constraints, resource flow, and network structural properties to enforce an operational hierarchy. To address the combinatorial complexity of cycle elimination and leader assignment, we propose an iterative cutting‑plane framework that ensures convergence to an optimal and feasible network topology. Theoretical guarantees on optimality preservation, feasibility, and convergence are established, ensuring systematic elimination of infeasible configurations while maintaining distributed controllability. Simulations on a modified Iowa 240‑bus power distribution grid demonstrate the framework's effectiveness in network reconfiguration under resource constraints. The approach achieves median and best‑case speedups of 57.5x and over 64x in a 46‑switch configuration, highlighting its applicability to other networked control systems.

Authors:Nate DeGoede, Maha N. Haji
Title: Multidisciplinary Design Optimization for Wave-Driven Desalination Systems
Abstract:
Wave‑driven desalination systems are an innovative solution to the global freshwater crisis, leveraging the complementary characteristics of seawater reverse osmosis and wave energy converters. However, the high costs of this system pose a significant barrier to widespread adoption. Optimization can help these systems reach a more competitive levelized cost of water, but the highly coupled nature of the system necessitates a multidisciplinary design optimization approach. This paper presents a holistic, multidisciplinary design optimization framework for wave‑driven desalination system design, integrating models for wave energy converter hydrodynamics, power take‑off transmission, seawater reverse osmosis constraints, and economic analysis. This study demonstrates the impact of multidisciplinary design optimization for wave‑driven desalination systems, resulting in a 69.5% reduction in levelized cost of water compared to a nominal design. We demonstrate that multidisciplinary design optimization outperforms sequential design approaches, yielding lower levelized costs of water and substantially different optimal designs. The multidisciplinary design optimization results suggest major design changes compared to designs found in the literature. Notably, smaller wave energy converters and larger pistons, along with smaller accumulators and larger seawater reverse osmosis plant installations, are preferred. These design trends are consistent across a range of sea states, suggesting potential generalizability beyond a single location. This study demonstrates the importance of holistic modeling and co‑design for wave‑driven desalination systems and establishes an effective optimization framework for future studies to build upon.

Authors:Baktash Behmanesh, Ahmad Rezvanitabar
Title: Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies
Abstract:
This paper provides a concise yet comprehensive review of recent advancements in millimeter‑wave (mm‑wave) oscillators below 100 GHz and sub‑terahertz (sub‑THz/THz) oscillators above 100 GHz for next‑generation computing and communication systems, including 5G, 6G, and beyond. Various design approaches, including CMOS, SiGe, and III‑V semiconductor technologies, are explored in terms of performance metrics such as phase noise, output power, efficiency, frequency tunability, and stability. The review highlights key challenges in achieving high‑performance and reliable oscillator designs while discussing emerging techniques for performance enhancement. By evaluating recent design trends, this work aims to offer valuable insights and design guidelines that facilitate the development of robust mm‑wave and sub‑THz/THz oscillators for future communication, computing, and sensing applications.

Authors:Omanshu Thapliyal, Malarvizhi Sankaranarayanasamy, Ravigopal Vennelakanti
Title: Safe Navigation using Neural Radiance Fields via Reachable Sets
Abstract:
Safe navigation in cluttered environments is an important challenge for autonomous systems. Robots navigating through obstacle ridden scenarios need to be able to navigate safely in the presence of obstacles, goals, and ego objects of varying geometries. In this work, reachable set representations of the robot's real‑time capabilities in the state space can be utilized to capture safe navigation requirements. While neural radiance fields (NeRFs) are utilized to compute, store, and manipulate the volumetric representations of the obstacles, or ego vehicle, as needed. Constrained optimal control is employed to represent the resulting path planning problem, involving linear matrix inequality constraints. We present simulation results for path planning in the presence of numerous obstacles in two different scenarios. Safe navigation is demonstrated through using reachable sets in the corresponding constrained optimal control problems.

Authors:Alec Boron, Bokun Zheng, Ziyang Zhou, Noel Naughton, Suyi Li
Title: Stochastic Entanglement of Deterministic Origami Tentacles For Universal Robotic Gripping
Abstract:
Origami‑inspired robotic grippers have shown promising potential for object manipulation tasks due to their compact volume and mechanical flexibility. However, robust capture of objects with random shapes in dynamic working environments often comes at the cost of additional actuation channels and control complexity. Here, we introduce a tendon‑driven origami tentacle gripper capable of universal object gripping by exploiting a synergy between local, deterministic deformation programming and global, stochastic entanglements. Each origami tentacle is made by cutting thin Mylar sheets; It features carefully placed holes for routing an actuation tendon, origami creases for controlling the deformation, and a tapered shape. By tailoring these design features, one can prescribe the shrinking, bending, and twisting deformation, eventually creating deterministic coiling with a simple tendon pull. Then, when multiple coiling tentacles are placed in proximity, stochastic entanglement emerges, allowing the tentacles to braid, knot, and grip objects with random shapes. We derived a simulation model by integrating origami mechanics with Cosserat rods to correlate origami design, tendon deformation, and their collective gripping performance. Then, we experimentally tested how these coiling and entangling origami tentacles can grasp objects under gravity and in water. A stow‑and‑release deployment mechanism was also tested to simulate in‑orbit grasping. Overall, the entertaining origami tentacle gripper presents a new strategy for robust object grasping with simple design and actuation.

Authors:Mohamed Camil Belhadjoudja, Mohamed Maghenem, Emmanuel Witrant
Title: Spectral Boundary Observer for Counter-Flow Heat Exchangers
Abstract:
We consider a system of two coupled first‑order linear hyperbolic partial differential equations modeling heat transport in a counter‑flow heat exchanger: one equation describes the transport of a hot fluid, and the other the transport of a cold fluid in the opposite direction. For this system, we design a boundary observer that uses only the temperature of the cold fluid measured at one boundary. Our approach is spectral: by assigning the spectrum of the operator governing the observation error dynamics to a prescribed region within the open left‑half complex plane, we can freely tune the convergence rate of the observation error to zero in the L^2 norm. The main technical contribution is the proof that spectral stability, that is, the location of the spectrum in the open left‑half plane, is equivalent to L^2 exponential stability of the origin for the observation error dynamics. This equivalence is established by showing that the operator governing the observation error dynamics satisfies the so‑called spectral mapping property.

Authors:Bernd Frauenknecht, Lukas Kesper, Daniel Mayfrank, Henrik Hose, Sebastian Trimpe
Title: Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics
Abstract:
Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploration, yet their reliance on first‑principles models or Gaussian processes limits scalability and broader applicability. Meanwhile, model‑based RL (MBRL) methods routinely employ probabilistic ensemble (PE) neural networks to capture complex, high‑dimensional dynamics from data with minimal prior knowledge. However, existing attempts to integrate PEs into PSFs lack rigorous uncertainty quantification. We introduce the Uncertainty‑Aware Predictive Safety Filter (UPSi), a PSF that provides rigorous safety predictions using PE dynamics models by formulating future outcomes as reachable sets. UPSi introduces an explicit certainty constraint that prevents model exploitation and integrates seamlessly into common MBRL frameworks. We evaluate UPSi within Dyna‑style MBRL on standard safe RL benchmarks and report substantial improvements in exploration safety over prior neural network PSFs while maintaining performance on par with standard MBRL. UPSi bridges the gap between the scalability and generality of modern MBRL and the safety guarantees of predictive safety filters.

Authors:Shuhao Que, Remco Poelarends, Valentina Breschi, Ying Wang
Title: PM-EKF: A Physiological Model-Based Extended Kalman Filter for Daily-Life Physical Activity Energy Expenditure Estimation
Abstract:
Monitoring physical activity energy expenditure (PAEE) in daily life is essential for characterizing individual health and metabolic status. Although indirect calorimetry provides gold‑standard PAEE measurements, it is impractical for continuous daily‑life monitoring. Consequently, wearable sensing approaches using inertial measurement units (IMUs) and heart rate (HR) sensors have attracted substantial interest. However, most existing IMU‑ and HR‑based methods are purely data‑driven and offer limited physiological interpretability. In this work, we propose a simplified physiological model that explicitly links body movement during activities of daily living to the underlying metabolic gas‑exchange processes governing PAEE. The model is formulated as a nonlinear state‑space system and embedded within an Extended Kalman Filter (EKF), enabling principled handling of measurement noise, model uncertainty, and system nonlinearities. The proposed framework provides personalized, interpretable PAEE estimates without employing black‑box models. Our model was validated using a dataset, including 9 subjects with around 50 minutes of measurements per subject, collected in our lab simulating a free‑living condition. Using the respiratory data measured by COSMED K5 as reference and explained variance (R^2) as evaluation metric, our model's predicted PAEE yielded median (min‑max) R^2 = 0.72 (0.60‑‑0.87), using three IMUs (pelvis and two thighs) for capturing the body‑center‑of‑mass motion and measured HR for the time‑varying cardiac output. Our model outperformed a linear regression (LR) model (R^2 = 0.52 (0.23‑‑0.92)) and CNN‑LSTM model (R^2 = 0.65 (0.46‑‑0.78)) on the same dataset. Notably, excluding the sensory HR measurement did not significantly degrade PAEE estimation of all three models, indicating that IMU‑captured mechanical workload dominated PAEE estimation performance in our protocol.

Authors:Diego Gutiérrez-Oribio, Ioannis Stefanou
Title: A Control Framework for Induced Seismicity Mitigation in Groningen Gas Reservoir
Abstract:
Induced seismicity associated with gas production poses major operational and societal challenges, as illustrated by the Groningen field in the Netherlands. While many studies have focused on forecasting seismicity under prescribed production scenarios, fewer works address the inverse problem: designing operational strategies that minimize seismicity while maintaining production objectives. In this paper, we propose a control‑oriented methodology for operating Groningen under induced‑seismicity mitigation constraints. We employ a cascade model coupling pore‑pressure diffusion with seismicity rate (SR) dynamics, and complement it with a stochastic event‑generation procedure to convert the continuous SR field into a synthetic earthquake catalog with event times, locations, and magnitudes. From this catalog, we estimate regional SR measurements and design a robust feedback controller that computes well‑rate commands to regulate the SR toward a desired reference while satisfying operational requirements, including prescribed production constraints. The proposed control architecture explicitly accounts for injection and extraction flux limits (actuator saturation). The well fluxes generated by the controller are updated at discrete‑time intervals (digital control). We validate the modeling components against Groningen data and illustrate the approach through numerical experiments under different scenarios, including various control update periods and gain selections, as well as combined production with compensating injection (e.g., reinjection of nitrogen). The results illustrate how the proposed framework can reduce seismicity levels in a controlled manner while maximizing production targets.

Authors:Parham Oveissi, Ankit Goel
Title: Model-Free Dynamic Mode Adaptive Control for Data-Driven Control Synthesis
Abstract:
This paper presents a model‑free, data‑driven control synthesis method called dynamic mode adaptive control (DMAC) for systems whose mathematical models are unavailable or unsuitable for classical control design. The proposed approach combines data‑driven dynamics approximation with adaptive control synthesis to enable online controller design using measured system data. DMAC comprises two main components: a dynamics‑approximation module and a controller‑synthesis module. The dynamics approximation module estimates a local linear representation of the system dynamics directly from measurements using a matrix recursive least‑squares algorithm with a forgetting factor. The estimated dynamics are then used to compute an online stabilizing controller with full‑state feedback and integral action. Theoretical analysis establishes convergence properties of the recursive dynamics approximation and boundedness of the closed‑loop system under the DMAC controller. The performance of the proposed method is demonstrated through numerical examples involving representative dynamical systems, including an unstable linear system, the Van der Pol oscillator, and the Burgers' equation. Sensitivity studies further demonstrate the robustness of DMAC with respect to both algorithm hyperparameters and variations in system parameters.

Authors:Jovan Krajacic, Ognjen Stanojev, Mario Schweizer, Orcun Karaca, Gabriela Hug, Vladan Lazarević
Title: Exploring Converter Control Duality in Microgrids: AC Grid-Forming vs DC Droop Control
Abstract:
Power electronic converters are fundamental building blocks of both AC and DC microgrids, enabling the integration of renewable energy sources, energy storage systems, electronic loads, and electric vehicles. In contrast, converter control in DC microgrids has developed along the path of droop control, which is widely adopted for decentralized DC‑bus voltage regulation and power sharing. Although these control strategies share certain characteristics, their similarities remain largely unexplored due to the distinct physical domains in which they operate. To bridge this gap, we introduce a novel perspective based on the concept of duality to reveal the underlying isomorphism between the two control approaches. We show that AC grid‑forming and DC I‑‑V droop control are duals of each other in several aspects, including: (i) the small‑signal model of the converter; (ii) the inner current control structure; (iii) power‑sharing mechanisms based on the AC swing equation and DC capacitor power balance; and (iv) disturbance signals and dynamic response. Theoretical analysis, validated through simulations on simple converter setups, illustrates these dualities and provides new insights towards a unified control design.

Authors:Stavros Orfanoudakis, Ziyan Li, Ruixiao Yang, Nikolay Aristov, Pedro P. Vergara, Chuchu Fan, Elenna Dugundji
Title: Learning to Route Electric Trucks Under Operational Uncertainty
Abstract:
Electric truck operations require routing decisions that remain feasible under limited battery range, long charging times, travel and energy consumption, and competition for shared charging infrastructure. These features make electric truck routing a coupled logistics and energy problem, limiting the practicality of heuristics‑based methods and rendering them computationally infeasible at scale. This paper proposes a learning‑based framework for the stochastic electric truck routing under charging constraints and operational uncertainty. The problem, solved by Reinforcement Learning, is formulated as an event‑driven semi‑Markov decision process with shared charging resources, stochastic travel and energy requirements, and realistic nonlinear fast‑charging behavior. To support learning in this setting, a graph‑based representation of system state and feasible decisions is introduced, together with a rule‑based action mask that restricts policies to operationally admissible actions; thus, improving training efficiency. Building on this formulation, an event‑driven simulation environment is developed that supports both Reinforcement Learning and benchmarking against heuristic and mathematical programming baselines. Computational experiments across a range of fleet sizes show that the proposed learning‑based algorithm consistently outperforms baselines and attains performance close to optimization benchmarks in many settings, while preserving high success rates under charging congestion and uncertainty.

Authors:Nils Mandischer, Daria Eckert and, Lars Mikelsons
Title: Persona-Based Process Design for Assistive Human-Robot Workplaces for Persons with Disabilities
Abstract:
Human‑robot interaction is emerging as an important paradigm for integrating persons with disabilities into the workplace. While these systems can enable individuals to work, their design is mostly personalized, hindering widespread use beyond the individual user. The universal design paradigm is a central pillar of inclusive design, describing usability of systems by all. To incorporate universal design into process design for human‑robot workplaces expert knowledge is required that is often not available. To simplify process design of human‑robot workplaces, we propose a persona‑based design approach. First, typical impairments prevalent in the workforce or particularly relevant for the processes are abstracted into personas with disabilities. The work process is subdivided into sequential actions. For each action and persona, strategies are developed to reach the action goal by a design thinking approach. The resulting actions are ordered by level of robot assistance, i.e. robot involvement, and implemented in a behavior tree. Therefore, the macro‑behavior of the workplace may adapt to individual personas online. We demonstrate the method in a collaborative box folding process with a total of seven personas with disabilities. The persona‑based process design shows promising results by generating more comprehensive process strategies while enabling adaptive behavior in the sense of universal design.

Authors:L. L. T. C. Jansen, E. Petri, M. van Berkel, W. P. M. H. Heemels
Title: Fuelling fusion plasmas with pellets: Can neuromorphic control outperform Sigma-Delta modulation?
Abstract:
Nuclear fusion is a promising clean energy source in which deuterium and tritium fuse inside a magnetically confined plasma in a tokamak, releasing energy. A key challenge on the route to practical nuclear fusion is the control of the plasma density which has to be done through adding fuel in the form of deuterium and tritium to the plasma. Pellet injection, firing frozen fuel into the plasma, is used to accomplish this. Since the injection of a pellet causes an almost instantaneous increase in particle density compared to the time scales of the plasma dynamics, the problem is of a hybrid nature in which continuous plasma dynamics are interrupted by discrete bursts of particles. In this paper, we propose a formal hybrid model for this fuelling process and we propose a new, neuron‑inspired control method that treats pellets much like spikes as in a brain‑like system. The neuromorphic controller offers a lightweight solution that naturally fits the hybrid character of pellet fuelling. For comparison, we also develop a hybrid model of sigma‑delta modulation, which is used in current tokamaks. For both the neuromorphic controller and the sigma‑delta modulation we present formal analysis results for this control problem in nuclear fusion. We derive explicit actuator and controller parameter constraints, key for controller tuning, that lead to practical stability guarantees. Numerical simulations compare the different controller variants and validate the theoretical results.

Authors:Lorenzo Zapparoli, Paul Fäth, Blazhe Gjorgiev, Giovanni Sansavini
Title: Risk-Aware Multi-Market Scheduling of Virtual Power Plants with Dynamic Network Tariffs
Abstract:
As the penetration of distributed energy resources (DERs) increases, harnessing their flexibility becomes critical for power system operations. Virtual power plants (VPPs) offer a promising solution. However, most existing scheduling tools rely on simplified DER or grid models and largely overlook local flexibility procurement mechanisms such as dynamic network tariffs. This paper proposes a two‑stage stochastic optimization framework for VPP multi‑market scheduling that integrates detailed device‑level constraints, network limitations, and operational and market uncertainties. Conditional value‑at‑risk is incorporated to represent risk preferences, and Benders decomposition ensures tractability with extensive scenario sets. The model jointly optimizes bidding across energy and reserve markets while explicitly accounting for local flexibility procurement through dynamic network tariffs. The results from a realistic case study show that both risk‑neutral and risk‑averse strategies exploit arbitrage opportunities. However, risk aversion reduces profit volatility through closer alignment with physical dispatch. Dynamic tariffs unlock local flexibility by shifting demand across the day, though strong tariff signals reduce expected profitability by up to 65% with limited additional flexibility gains.

Authors:Om Parkash, Jannik Bauer, Vincent Schmitt, Thomas Greiner, Rainer Drath
Title: Asset Administration Shell-Based OCL Validation Framework for Model-Based System Engineering
Abstract:
Increasing complexity of modern enterprise systems and the demand for automation and interoperability require consistent and semantically validated models in Model‑Based Systems Engineering (MBSE). The Object Constraint Language (OCL) supports formal definition of such constraint validations. However, MBSE models and OCL constraints are typically managed in separate tools, causing manual effort during model constraint application and result interpretation. To address this gap, this paper proposes an approach to managing OCL constraints and their validation results through Asset Administration Shells (a well‑established technology for interoperability in enterprise systems). The methodology is demonstrated through a fictional industrial scenario, and to support reproducibility, all artifacts are publicly available in a GitHub repository.

Authors:Tzu-Hsiang Huang, Haojian Lu, Hen-Wei Huang, Tan Rong
Title: Real-Time Minimum-Energy Operating-Point Tracking for Battery-Powered Micro DC Motors Under Dynamically Variable Loading
Abstract:
Micro DC brushed motors are widely deployed in battery‑powered biomedical systems, where limited energy budgets and variable physiological loading impose stringent efficiency and safety constraints. However, conventional actuation strategies rely on conservative voltage margins to avoid stalling, leading to systematic energy inefficiency. Furthermore, existing methods primarily optimize steady‑state performance, neglecting the energy required to complete individual actuation cycles under dynamic conditions. This paper reveals that the energy consumption per mechanical cycle of a DC motor exhibits a non‑monotonic dependence on driving voltage, with a load‑dependent minimum that shifts with external loading. Based on this insight, we propose a real‑time operating‑point tracking method that enables the motor to autonomously converge to its minimum‑energy condition. A lightweight load metric derived from current waveform features is introduced to detect load variation, and a two‑phase adaptive voltage strategy is developed to track the optimal operating point online. Experimental results demonstrate that the proposed method can track the new minimum‑energy operating region under both low‑to‑high and high‑to‑low loading transitions. With 3‑cycle averaging, the mean response time is 11.55s for the low‑to‑high case and 11.16s for the high‑to‑low case, while the mean convergence voltage is 2.73V and 2.0V, respectively.

Authors:Ankur Kamboj, Biswadip Dey, Vaibhav Srivastava
Title: Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
Abstract:
We develop a physics‑informed learning framework for energy‑shaping control of port‑Hamiltonian (pH) systems from trajectory data. The proposed approach co‑learns a pH system model and an optimal energy‑balancing passivity‑based controller (EB‑PBC) through alternating optimization with policy‑aware data collection. At each iteration, the system model is refined using trajectory data collected under the current control policy, and the controller is re‑optimized on the updated model. Both components are parameterized by neural networks that embed the pH dynamics and EB‑PBC structure, ensuring interpretability in terms of energy interactions. The learned controller renders the closed‑loop system inherently passive and provably stable, and exploits passive plant dynamics without canceling the natural potential. A dissipation regularization enforces strict energy decay during training, thereby enhancing robustness to sim‑to‑real gaps. The proposed framework is validated on state‑regulation and swing‑up tasks for planar and torsional pendulum systems.

Authors:Junya Ikemoto, Satoshi Maruyama, Kazumune Hashimoto
Title: Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems
Abstract:
This paper proposes a deep reinforcement learning (DRL)‑based event‑triggered controller design for networked artificial pancreas (AP) systems. Although existing DRL‑based AP controllers typically assume periodic control updates, networked control systems (NCSs) require a reduction in communication frequency to achieve energy‑efficient operation, which is directly tied to control updates. However, jointly learning both insulin dosing and update timing significantly increases the complexity of the learning problem. To alleviate this complexity, we develop a practical DRL‑based controller design that avoids explicitly learning update timing by introducing a rule‑based criterion defined by changes in blood glucose. As a result, decision‑making occurs at irregular intervals, and the problem is naturally formulated as a semi‑Markov decision process (SMDP), for which we extend a standard DRL algorithm. Numerical experiments demonstrate that the proposed method improves communication efficiency while maintaining control performance.

Authors:Teetat Pipattaratonchai, Aueaphum Aueawatthanaphisut
Title: Privacy-Preserving Federated Learning Framework for Distributed Chemical Process Optimization
Abstract:
Industrial chemical plants often operate under strict data confidentiality constraints, making centralized data‑driven process modeling difficult. Federated learning (FL) provides a promising solution by enabling collaborative model training across distributed facilities without sharing raw operational data. This paper proposes a privacy‑preserving federated learning framework for distributed chemical process optimization using data collected from multiple geographically separated plants. Each plant locally trains a neural‑network‑based process model using its own time‑series sensor data, while only model parameters are transmitted to a central aggregation server through secure aggregation mechanisms. This design allows cross‑plant knowledge sharing while maintaining strict data locality and industrial confidentiality. Experimental evaluation was conducted using process datasets from three independent chemical plants operating under heterogeneous conditions. The results demonstrate rapid convergence of the federated model, with the global mean squared error decreasing from approximately 2369 to below 50 within the first five communication rounds and stabilizing around 35 after 40 rounds. In comparison with local‑only training, the proposed federated framework significantly improves prediction accuracy across all plants, while achieving performance comparable to centralized training. The findings indicate that federated learning provides an effective and scalable solution for collaborative industrial analytics, enabling privacy‑preserving predictive modeling and process optimization across distributed chemical production facilities.

Authors:Aliasghar Arab, Milad Khaleghi, Koorosh Aslansefat
Title: Risk Assessments for Evasive Emergency Maneuvers in Autonomous Vehicles
Abstract:
This paper presents a systematic verification and validation (V\&V) framework for the Evasive Minimum Risk Maneuver (EMRM) feature in autonomous vehicles, addressing a critical gap in existing safety assessment methods. We introduce the first formally integrated pipeline that unifies Hazard Analysis and Risk Assessment (HARA), System‑Theoretic Process Analysis (STPA), and Finite State Machine (FSM) modeling into a single traceable workflow specifically designed for EMRM V\&V. HARA and STPA are combined through a structured hazard‑loss mapping to identify hazards and unsafe control actions; an FSM layer captures hazard‑to‑loss state transitions that neither method models individually; and the unified framework drives automated scenario generation with measurable parameter‑space coverage. Applied to a T‑junction EMRM case study, the framework guides 1,880 RRT‑based simulations spanning ego speed, time‑to‑collision (TTC), and road friction, uncovering a key physical result: the T‑junction geometry gives nearly equal difficulty to stopping and to navigating, so the intermediate mitigation mode occupies only 1.9% of the feasible parameter space. EMRM steering strategies achieve 81% collision‑avoidance rate and reduce mean residual impact speed from 18.9~km/h to 9.0~km/h compared with emergency braking alone, while the framework attains 100% hazard, UCA, and parameter‑space coverage versus \leq1% for traditional methods. These results demonstrate that the integrated HARA‑STPA‑FSM framework enables high‑resolution, traceable EMRM V\&V that is not achievable with any single method in isolation.

Authors:Yoshiyuki Yajima, Hemant Prasad, Daisuke Ikefuji, Takemasa Suzuki, Shin Tominaga, Hitoshi Sakurai, Manabu Otani
Title: Optimal-Control Suggestion for Congestion on Freeways using Data Assimilation of Distributed Fiber-Optic Sensing
Abstract:
This paper presents the optimal‑control suggestion for congestion on freeways using data assimilation (DA) of distributed fiber‑optic sensing (DFOS). To simultaneously maximize throughput and avoid/mitigate congestion, it is necessary to execute optimal control for the current traffic state as active transportation and demand management (ATDM) according to multi‑objective optimization with real‑time monitoring data. However, optimal control cannot be estimated due to intermittent observed data obtained from conventional sensors. To solve the issue, this paper proposes the ATDM optimal control estimation with DA of DFOS, which can monitor traffic flow in real time without dead zones. Our real‑time DA method enables us to estimate the effectiveness of control scenarios by simulation. This paper also provides a method to uniquely determine the optimal‑control solution among the Pareto solutions for multi‑objective optimization. Throughput and mean speed across the entire road are considered as the objective functions. Variable speed limit (VSL) and inflow control are taken as ATDM examples. Validation results on a Japanese freeway show that (i) the optimal control scenario varies depending on the traffic state, especially congestion level; (ii) optimal control considering VSL alone improves throughput by 5‑14% while the improvement rate for mean speed is 0‑8%; (iii) throughput and mean speed are improved by 10‑15% and 20‑30%, respectively when VSL and inflow control are considered. This paper also implies the importance of balance management for the lane occupancy and proactive optimal control before congestion occurs.

Authors:Shan Yu, Junyi Shu, Yuanjiang Ni, Kun Qian, Xue Li, Yang Wang, Jinyuan Zhang, Ziyi Xu, Shuo Yang, Lingjun Zhu, Ennan Zhai, Qingda Lu, Jiarong Xing, Youyou Lu, Xin Jin, Xuanzhe Liu, Harry Xu
Title: Pythia: Exploiting Workflow Predictability for Efficient Agent-Native LLM Serving
Abstract:
As LLM applications grow more complex, developers are increasingly adopting multi‑agent architectures to decompose workflows into specialized, collaborative components, introducing structure that constrains agent behavior and exposes useful semantic predictability. Unlike traditional LLM serving, which operates under highly dynamic and uncertain conditions, this structured topology enables opportunities to reduce runtime uncertainty\unicodex2015yet existing systems fail to exploit it, treating agentic workloads as generic traffic and incurring significant inefficiencies. Our analysis of production traces from an agent‑serving platform and an internal coding assistant reveals key bottlenecks, including low prefix cache hit rates, severe resource contention from long‑context requests, and substantial queuing delays due to suboptimal scaling. To address these challenges, we propose Pythia, a multi‑agent serving system that captures workflow semantics through a simple interface at the serving layer, unlocking new optimization opportunities and substantially improving throughput and job completion time over state‑of‑the‑art baselines.

Authors:Clinton Enwerem, Shreya Kalyanaraman, John S. Baras, Calin Belta
Title: Variational Neural Belief Parameterizations for Robust Dexterous Grasping under Multimodal Uncertainty
Abstract:
Contact variability, sensing uncertainty, and external disturbances make grasp execution stochastic. Expected‑quality objectives ignore tail outcomes and often select grasps that fail under adverse contact realizations. Risk‑sensitive POMDPs address this failure mode, but many use particle‑filter beliefs that scale poorly, obstruct gradient‑based optimization, and estimate Conditional Value‑at‑Risk (CVaR) with high‑variance approximations. We instead formulate grasp acquisition as variational inference over latent contact parameters and object pose, representing the belief with a differentiable Gaussian mixture. We use Gumbel‑Softmax component selection and location‑scale reparameterization to express samples as smooth functions of the belief parameters, enabling pathwise gradients through a differentiable CVaR surrogate for direct optimization of tail robustness. In simulation, our variational neural belief improves robust grasp success under contact‑parameter uncertainty and exogenous force perturbations while reducing planning time by roughly an order of magnitude relative to particle‑filter model‑predictive control. On a serial‑chain robot arm with a multifingered hand, we validate grasp‑and‑lift success under object‑pose uncertainty against a Gaussian baseline. Both methods succeed on the tested perturbations, but our controller terminates in fewer steps and less wall‑clock time while achieving a higher tactile grasp‑quality proxy. Our learned belief also calibrates risk more accurately, keeping mean absolute calibration error below 0.14 across tested simulation regimes, compared with 0.58 for a Cross‑Entropy Method planner.

Authors:Anas Gamal Aly, Hala ElAarag
Title: No Pedestrian Left Behind: Real-Time Detection and Tracking of Vulnerable Road Users for Adaptive Traffic Signal Control
Abstract:
Current pedestrian crossing signals operate on fixed timing without adjustment to pedestrian behavior, which can leave vulnerable road users (VRUs) such as the elderly, disabled, or distracted pedestrians stranded when the light changes. We introduce No Pedestrian Left Behind (NPLB), a real‑time adaptive traffic signal system that monitors VRUs in crosswalks and automatically extends signal timing when needed. We evaluated five state‑of‑the‑art object detection models on the BGVP dataset, with YOLOv12 achieving the highest mean Average Precision at 50% (mAP@0.5) of 0.756. NPLB integrates our fine‑tuned YOLOv12 with ByteTrack multi‑object tracking and an adaptive controller that extends pedestrian phases when remaining time falls below a critical threshold. Through 10,000 Monte Carlo simulations, we demonstrate that NPLB improves VRU safety by 71.4%, reducing stranding rates from 9.10% to 2.60%, while requiring signal extensions in only 12.1% of crossing cycles.

Authors:Mohamed Camil Belhadjoudja, Kirsten A. Morris
Title: Backstepping Observer for the Quasilinear Heat Equation with Linear Design Gains: Beyond Local Stability
Abstract:
We consider the one‑dimensional quasilinear heat equation with state‑dependent heat capacity and thermal conductivity, and design a boundary‑output observer based on the backstepping design for a linear heat equation with constant coefficients. Viewing the quasilinear system as a perturbation of the linear one, we establish exponential stability of the origin for the observation error dynamics in H^1, with an explicit region of attraction depending on the system parameters, observer gains, and the mismatch between the nonlinear diffusivity and the constant design diffusivity. Importantly, the observation error converges to zero rather than merely to a neighborhood scaling with this mismatch, even though, in contrast to backstepping‑based stabilization of nonlinear PDEs, the mismatch need not decay along trajectories and may remain bounded away from zero, acting as a persistent state‑dependent multiplicative perturbation. A technical challenge was to perform a sufficiently‑fine Lyapunov analysis that does not yield overly conservative results such as mere boundedness of the observation error. Interestingly, while in the linear case the relationship between one of the backstepping observer gains and the convergence rate is monotonic, we show that in the nonlinear setting this is no longer the case: there may exist an optimal value of that gain, beyond which further increases deteriorate the system's performance. Such behavior cannot be predicted without our analysis: one might expect a priori the decay rate to be freely tunable at the expense of a region of attraction that shrinks to zero as the prescribed rate tends to infinity. However, our Lyapunov analysis (supported by numerical experiments) reveals that this intuition is incorrect.

Authors:Thomas J. Neubert, Laxima Niure Kandel, Berker Peköz
Title: Threat-Oriented Digital Twinning for Security Evaluation of Autonomous Platforms
Abstract:
Open, unclassified research on secure autonomy is constrained by limited access to operational platforms, contested communications infrastructure, and representative adversarial test conditions. This paper presents a threat‑oriented digital twinning methodology for cybersecurity evaluation of learning‑enabled autonomous platforms. The approach is instantiated as an open‑source, modular twin of a representative autonomy stack with separated sensing, autonomy, and supervisory‑control functions; confidence‑gated multi‑modal perception; explicit command and telemetry trust boundaries; and runtime hold‑safe behavior. The contribution is methodological: a reproducible design pattern that translates threat analysis into observable, controllable tests for spoofing, replay, malformed‑input injection, degraded sensing, and adversarial ML stress. Although the implemented proxy is ground based, the architecture is intentionally framed around stack elements shared with UAV and space systems, including constrained onboard compute, intermittent or high‑latency links, probabilistic perception, and mission‑critical recovery behavior. The result is an implementable research scaffold for dependable and secure autonomy studies across UAV and space domains.

Authors:Xinyuan Jiang
Title: Local Shifted Passivity Analysis of the Single-Machine Infinite-Bus System
Abstract:
This letter presents a shifted passivity analysis of the single‑machine infinite‑bus system in the stationary (αβ) reference frame. We study the attractivity of a periodic synchronous steady state with constant rotor frequency and formulate shifted passivity with respect to this motion. A port‑Hamiltonian representation of the machine dynamics is used to construct a local shifted passivity condition from the error Hamiltonian and a correction term adapted to the synchronous steady state. For the infinite‑bus interconnection, the resulting dissipation inequality leads to a sufficient stability condition expressed in terms of field excitation magnitude, damping, inertia, and steady‑state current. This condition implies local asymptotic stability of the synchronous steady state and yields a sublevel‑set estimate of its region of attraction under an additional small‑inertia condition. A distinctive feature of the analysis is that it preserves the periodic structure of the rotor angle and provides a compact passivity‑based stability certificate for the stationary‑frame model.

Authors:Abdullah Mughees, Gaadha Sudheerbabu, Tanwir Ahmad, Dragos Truscan, Mikael Manngård, Kristian Klemets
Title: Using Large Language Models for Black-Box Testing of FMU-Based Simulations
Abstract:
We propose a human in the loop approach for black‑box testing of Functional Mock‑up Units (FMUs) using Large Language Models (LLMs). The goal is to reduce the manual effort in defining test scenarios for dynamic simulation models and to improve the interpretability of results. The approach takes the functional and interface specifications of an FMU as input, and prompts an LLM to generate structured scenario goals in Given‑When‑Then format that define the initial input conditions of the simulation, a possible change in those conditions, and the expected output behaviour of the system against those changes. The corresponding scenario plans specify input patterns and add assertion oracles that describe expected output patterns defined in scenario goals. The approach generates a complete input time series for the scenario plans, runs the FMU simulation, and evaluates assertions on the recorded outputs. It produces human‑readable logs and plots that show statistics for each scenario with overlays, aggregate pass rates, and per‑goal outcomes. The generated scenarios and results are stored for evaluation and later re‑execution. We evaluate the approach on a Lube Oil Cooling system and discuss design choices that make the approach practical for everyday use. Results suggest that LLM‑assisted scenario generation can facilitate automatic test design and verification of dynamic simulation models.

Authors:Antoine Thibault Vié, Leonid Fridman, Roberto Galeazzi, Dimitrios Papageorgiou
Title: Multi-layer barrier adaptation of the discrete-time super-twisting controller
Abstract:
In digital sliding mode control implementations, discretization‑induced chattering and inter‑sample blindness can severely degrade the closed‑loop performance, especially in case of fast perturbations. This paper addresses these challenges for a discrete‑time implementation of the super‑twisting sliding mode controller. Building upon recent results on barrier‑function‑modulated super‑twisting algorithms, a nested architecture employing multiple barriers is discretized using an eigenvalue‑based exact matching approach. The resulting discrete‑time controller preserves the adaptive and robustness properties established in continuous time, while ensuring consistent stability behavior at the sampling level. The proposed framework is validated through numerical simulations. The results highlight the effectiveness of multi‑layer barrier adaptation for discrete‑time sliding mode control applications.

Authors:Juan Carlos Bravo-Rodríguez, Juan Carlos del-Pino-López, Francisco Casado-Machado
Title: Complex-Vector Power and Cross-Phase Unbalance in Three-Phase Systems
Abstract:
Unbalanced three‑phase systems still lack a compact phasor‑domain representation of power that makes phase asymmetry explicit while remaining consistent with established apparent‑power definitions. This paper addresses that point through a complex‑vector power formulation for sinusoidal steady‑state operation. The proposed representation supplements the classical dot‑product expression of complex power with the cross product of voltage and current phasors, thereby retaining the usual active and reactive terms while making explicit a cross‑phase unbalance vector that captures antisymmetric interphase relations. In this way, apparent power is separated into intraphase and cross‑phase contributions, and its norm is preserved under the power‑invariant Fortescue transformation. The formulation is extended to three‑phase four‑wire systems by introducing equivalent coordinates that preserve the effective apparent‑power norm for the chosen voltage reference. Only standard complex numbers and matrices are required. Numerical examples show operating conditions in which a non‑negligible part of the apparent‑power structure is associated with cross‑phase unbalance and cannot be inferred from active and reactive power alone. The proposed formulation thus provides a compact phasor‑based descriptor of unbalance that complements established apparent‑power theories by making explicit a component that is not accessible from scalar apparent‑power representations.

Authors:Die Gan, Siyu Xie, Zhixin Liu, Xuebo Zhang
Title: Distributed adaptive estimation for stochastic large regression models
Abstract:
This paper studies the distributed adaptiveestimation problems for stochastic large regression modelswith an infinite number of parameters. By constructing a re‑cursive local cost function, we propose a novel distributedrecursive least squares algorithm to estimate the unknownsystem parameters, where the growth rate of regressors'dimension is characterized by a non‑decreasing positivefunction. The almost sure convergence of the proposedalgorithm is established under a cooperative excitationcondition, which incorporates the temporal information andthe spatial information to reflect the cooperative effectamong multiple agents. Moreover, we analyze the predic‑tion error by establishing the asymptotic upper boundof the accumulated regret without any excitation condi‑tions. The main difficulty of theoretical analysis lies in howto analyze properties of the product of non‑independentand non‑stationary random matrices, whose dimensionschange over time simultaneously. Some techniques, suchas stochastic Lyapunov function, double‑array martingaletheory and algebraic graph theory, are employed to dealwith the above issue. Our theoretical results are derivedwithout imposing independence or stationarity assump‑tions on the regression vectors, thereby not excluding thecorrelated feedback signals.

Authors:Khagendra Joshi, Deepak Kumar Sahoo, Kamalesh Kumar K, Debidas Kundu, Vivek A. Bohara, Amalendu Patnaik
Title: A Miniaturized Broadband 1-Bit Coding Reconfigurable Intelligent Surface for NLOS UE Localization and Uplink Communication
Abstract:
In this paper, a broadband 1‑bit coding metasurface‑based reconfigurable intelligent surface (RIS) is presented. The unit cell of the metasurface consists of a wide dipole modified with interdigital capacitors and loaded with an SMP 1340‑040LF PIN diode. The proposed element offers cell miniaturization and a stable angular response. A phase difference of 180\degree \pm 30\degree is achieved for a frequency range of 4.85‑6.05 GHz between the ON and OFF states for the normal incidence of the TE polarized wave, whereas it provides a fairly stable response with reflection loss of less than 3 dB and phase difference of 180\degree \pm 50\degree for oblique incidence up to 45\degree. The RF is isolated from the DC on the bias lines using properly designed butterfly‑shaped radial stubs. Using this unit cell, a prototype with an array of 16 × 10 elements is constructed. A low‑cost microcontroller‑based control circuit is designed, which can be plugged‑in for biasing the PIN diodes of such array. The theoretically calculated and full‑wave simulated radiation patterns of the array are validated using experiments inside anechoic chamber. Furthermore, the capability of the RIS for non‑line of sight (NLOS) user equipment (UE) localization and robust uplink communication is demonstrated using LTE communication framework. This shows great potential of our RIS for applications, such as in unmanned aerial vehicle (UAV) localization and its uplink communication at NLOS or extended range.

Authors:Huriyeh Babak, Melanie Schaller
Title: CUDA Kernel Optimization and Counter-Free Performance Analysis for Depthwise Convolution in Cloud Environments
Abstract:
Efficient GPU execution of convolution operators is governed by memory‑access efficiency, on‑chip data reuse, and execution mapping rather than arithmetic throughput alone. This paper presents a controlled operator‑level study of CUDA kernel optimization for the depthwise convolution used in Structured State Space Model Convolutional Diagonal (S4ConvD), together with a cloud‑compatible, counter‑free performance analysis methodology. The operator, model, dataset, and training configuration are fixed, and only the CUDA kernel implementation is varied. The evaluated CUDA kernels comprise naive, global‑memory‑coalesced, shared‑memory cache‑blocked, and warp‑tiled variants, covering forward, input‑gradient, and weight‑gradient execution paths under steady‑state training conditions. Performance is characterized using a counter‑free methodology that combines CUDA‑event timing, execution‑path decomposition, analytically derived memory‑traffic modeling, effective‑bandwidth estimation, and roofline analysis. This enables profiling‑like architectural insights without requiring hardware performance counters or privileged profiling access. The warp‑tiled kernel reduces convolution runtime by 3.26× relative to the naive CUDA baseline, while end‑to‑end training speedup reaches 1.29×. A PyTorch implementation is used separately for numerical validation and runtime context, but is not treated as a controlled architectural baseline. Forward and input‑gradient paths benefit substantially from improved locality and on‑chip data reuse, whereas the reduction‑dominated weight‑gradient path remains the primary bottleneck. The results demonstrate that meaningful architecture‑level GPU kernel analysis can be performed reproducibly in restricted cloud environments, even without access to hardware performance counters.

Authors:Amir Ali Farzin, Philipp Braun, Iman Shames
Title: From Cursed to Competitive: Closing the ZO-FO Gap via Input-to-State Stability
Abstract:
While it is generally understood that zeroth‑order (ZO) algorithms have an extra dependency on their number of iterations for any choice of parameters, compared to their first‑order (FO) counterparts, in this work, we show that under several conditions, in expectation, ZO methods do not suffer from extra dimension dependencies in their convergence rates with respect to their FO counterparts. We look at optimisation algorithms from the dynamical systems perspective and analyse the conditions under which one can formulate the average of a ZO algorithm as the average of its FO counterpart with bounded perturbations with values dependent on design parameters. Then, using input‑to‑state stability properties, we show ZO methods follow the same decay rate as their FO counterparts and converge to a neighbourhood of the fixed point of FO methods, where its radius depends on the bound of the norm of the perturbations, which can be made arbitrarily small. The theoretical findings are illustrated via numerical examples.

Authors:Pradeep J, Siddhardha Kedarisetty, Ashwini Ratnoo
Title: Slot-hopping Enabled Loiter Guidance and Automation for Fixed-wing UAV Corridors
Abstract:
This paper addresses the problem of traffic congestion management in fixed‑wing unmanned aerial vehicle (UAV) corridors by further developing a recently introduced loiter‑lane framework. A semi‑cooperative guidance strategy is developed for inserting fixed‑wing UAVs into a loiter lane with minimal disruption to the UAVs already operating within it, while enabling a more compact fixed‑wing UAV corridor. Building on the concepts of cooperative and non‑disruptive loiter‑lane insertion, the proposed strategy makes the incoming UAV first attempt, within its speed bounds, to rendezvous with an existing empty loiter slot. If direct insertion is infeasible, a minimal number of loitering UAVs perform coordinated slot hopping to create a suitably positioned empty slot. The feasibility and performance of the method are demonstrated through numerical simulations.

Authors:Zhengyuan Wu, Junhui Zhao, Qingmiao Zhang, Ming Zhang
Title: Dual-Polarized Massive MIMO Based on Precoding for Vehicle-To-Ground Communication in Urban Rail Transit
Abstract:
The development of intelligent and diversified ser vices in urban rail transit (URT) has resulted in an increasing de mand for high‑rate communication between vehicles and ground equipment. However, existing URT communication systems strug gle to handle the massive data exchange required for vehicle‑to ground (V2G) communication. To address this issue, we propose a distributed dual‑polarized MIMO architecture suitable for URT tunnel scenarios. Specifically, the channel model is based on spatial three‑dimensional (3D) non‑stationary geometry‑based stochastic model (GBSM), which takes into account the geometric distribution of URT tunnels and the cross‑polarization effects between dual‑polarized antennas. For dual‑polarized MIMO systems, the polarized‑aware sparse channel estimation (PASCE) method is proposed for effective channel estimation. Additionally, we derive closed‑form expressions for the MMSE and MR precoding schemes. The polarized‑aware dynamic interference cancellation (PADIC) algorithm is developed to eliminate in terference between different polarization modes and multiple users. The simulation results demonstrate that the proposed dual‑polarized precoding algorithm can withstand high cross polarization correlation (XPC) and improve the efficiency of V2G communication to achieve high rates.

Authors:Dhrumil Bhatt, Anakha Kurup
Title: Behaviour-aware Hybrid Architecture for Trust-driven Transmissions
Abstract:
Reliable and secure communication is essential for mission‑critical aerospace and defence operations involving autonomous platforms such as Unmanned Aerial Vehicles (UAVs), satellites, and ground control systems. In contested or dynamic environments, communication links are frequently exposed to jamming, interference, and cyberattacks, making network resilience a key operational requirement. This paper presents a trust‑aware Software‑Defined Networking (SDN) framework that enables secure, low‑latency failover between heterogeneous communication channels. The proposed architecture integrates a high‑bandwidth primary link (e.g., satellite or tactical LTE) with a low‑power fallback channel (e.g., RF or mesh), managed by an SDN controller that enforces zero‑trust routing policies. A real‑time Intrusion Detection System (IDS) continuously updates node trust scores; when trust or link reliability degrades, the controller autonomously switches traffic to the secondary channel, ensuring uninterrupted connectivity. Simulation results in a Mininet‑based test environment demonstrate sub‑5 ms failover latency, efficient flow installation, and significant reduction in packet loss compared with conventional single‑channel or static routing systems. The proposed framework provides a scalable and resilient communication backbone for next‑generation aerospace networks, enhancing mission reliability, cyber defence, and autonomous coordination across distributed aerial and space assets.

Authors:Yiwei Qiu, Qingjie Sun, Yangjun Zeng, Ge Chen, Longjie Yang, Ge He, Xu Ji, Shi Chen, Buxiang Zhou, Kaigui Xie
Title: Reconfiguring flexibility in renewable power-to-ammonia systems using molten-salt thermal energy storage in the ammonia synthesis loop: A coordinated electro-hydrogen-thermal scheduling approach
Abstract:
In renewable power‑to‑ammonia (ReP2A) systems, the intermittency of wind and solar generation propagates through electrolytic hydrogen production and induces thermal instability in the ammonia synthesis reactor (ASR). The resulting temperature cycling accelerates fatigue and shortens service life, while reactor thermal inertia limits flexible start‑up, shutdown, and load adjustment. To address this issue, this study integrates molten‑salt thermal energy storage (MS‑TES) into the Haber‑Bosch synthesis loop and develops a coordinated electro‑hydrogen‑thermal scheduling framework. MS‑TES decouples hydrogen supply fluctuations from reactor thermal dynamics by enabling hot standby operation and sustained thermal support during start‑up and low‑load conditions. A state‑space model is established to capture the thermal dynamics of the ASR and MS‑TES. Based on this model, an optimal scheduling program coordinates ammonia synthesis operation with hydrogen production, battery energy storage (BES), and hydrogen storage (HS). The problem is formulated as a mixed‑integer linear program (MILP) and extended with information gap decision theory (IGDT) to address renewable uncertainty. Case studies based on an industrial‑scale project in northern China show that MS‑TES enhances reactor thermal stability and system‑level flexibility, while diminishing the marginal benefit of large BES capacity. As a result, a configuration combining small BES, HS, and MS‑TES achieves near‑equivalent performance to large‑BES systems, with lower investment and improved economic returns. Year‑round simulations further show that MS‑TES avoids ASR start‑up and shutdown and delivers consistently higher net revenue under variable renewable conditions.

Authors:Max Emerick, Saroj Prasad Chhatoi, Bassam Bamieh
Title: On Distributed Control of Continuum Swarms: Local Controllers as Differential Operators
Abstract:
We study the problem of distributed control of large‑scale robotic swarms which can be modeled as continuum densities evolving under the continuity equation. We propose a formalization of distributed controllers as (generally nonlinear) differential operators, in which control inputs depend only on local information about the state and environment. This perspective yields a fully local, PDE‑based framework for analysis and design. We apply this framework to the problem of stabilizing a swarm density around an arbitrary target density, and investigate fundamental limitations of low‑order distributed controllers in achieving this goal. In particular, we show that controllers which act in a purely pointwise manner are incompatible with natural system symmetries and strong forms of stability, and must rely on mixing‑type behavior to achieve stabilization. In contrast, we present a simple first‑order control law which achieves stabilization and enjoys substantially stronger properties.

Authors:Zhang Jiang, Marios Andreou, Sebastian Reich, Nan Chen
Title: A Continuous-Time Ensemble Kalman-Bucy Smoother for Causal Inference and Model Discovery
Abstract:
Data assimilation (DA) integrates observational information with model predictions to improve state estimation in complex systems. While filtering provides the basis for online forecasts by using only past and present observations, it can exhibit delays and biases when the underlying dynamics evolve rapidly or undergo regime transitions. Smoothing, which additionally incorporates future observations, provides a natural pipeline for hindcasting and reanalysis that yields an uncertainty reduction beyond the filter. This paper introduces an ensemble Kalman‑Bucy smoother (EnKBS) for continuous‑time DA of nonlinear dynamical systems, where the smoother's conditional distributions are reconstructed using ensemble moments. The result is a derivative‑free framework that does not require explicit computation of tangent‑linear or adjoint models, which converges to the exact smoother solution at the infinite‑ensemble limit for a wide class of complex systems. Incorporating standard regularization techniques for high‑dimensional systems, such as covariance localization and inflation, the skill of the EnKBS is demonstrated in various important scientific problems. By integrating future observations, which reveal the underlying causal mechanisms for retrospective state updates, the EnKBS is used for Bayesian‑based inference of causal relationships and their temporal influence range in a dyadic trigger‑feedback model and the development of a causality‑driven iterative learning algorithm that identifies the structure and recovers the hidden parameters of a nonlinear reduced‑order model mimicking midlatitude atmospheric circulation. Notably, both tasks remain effective with an ensemble size of O(10) under partial observations, suggesting that EnKBS can support the instantaneous discovery of high‑dimensional complex systems over time.

Authors:Longxu Pan, Yue Guan, Daigo Shishika, Panagiotis Tsiotras
Title: Asymmetric-Information Resource Allocation Games: An LP Approach to Purposeful Deception
Abstract:
In this work, we introduce the Deceptive Resource Allocation Game (DRAG), which studies purposeful deception within a Bayesian game framework. In DRAG, a Defender allocates resources across the true asset and several decoys to influence an Attacker's beliefs and actions, with the goal of diverting the Attacker away from the true asset. We seek to characterize purposeful deception, whereby the Defender deceives only when doing so improves its performance. To this end, we solve for the Perfect Bayesian Nash Equilibrium (PBNE) of the corresponding game. We show that, despite the coupled belief‑policy interdependence, the problem admits an efficient, non‑iterative linear programming formulation. Numerical results demonstrate that the resulting policies naturally balance effective allocation and belief manipulation, giving rise to purposeful and emergent deceptive behaviors.

Authors:Zeyu Bai
Title: Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark
Abstract:
Custom policy‑learning pipelines in Spark fail for two coupled systems reasons: rowwise Python execution makes inference impractical, and driver‑side candidate materialization makes split search fragile at feature scale. We present Spark Policy Toolkit, a semantics‑governed systems toolkit for scalable policy learning in Spark. The toolkit provides two Spark‑native primitives: partition‑initialized vectorized inference through mapInPandas and mapInArrow, and collect‑less split search that scores candidates on executors. Both primitives are governed by one fixed‑input semantic contract: the same rows, feature order, treatment vocabulary, preprocessing manifest, and split boundaries must preserve per‑row score vectors, best‑split decisions, and end‑to‑end learned policy outputs. The evaluation combines practical baseline ladders, backend parity checks, measured split‑search scale results, synthetic and Hillstrom end‑to‑end policy preservation, missingness stress, partition and order perturbation tests, quantile‑boundary sensitivity, and a concrete adversarial failure catalog. On a 40‑worker Databricks cluster, mapInArrow reaches 4.72M rows/s at 10M matched rows and 7.23M rows/s at 50M rows, while collect‑less split search remains valid from F = 10 through F = 1000 with 124000 candidate rows, where the driver‑collect baseline is intentionally skipped. Across 24 backend‑ablation settings, mapInArrow wins 18 while mapInPandas wins 6, so the paper treats backend choice as workload‑dependent rather than universal. Once the fixed‑input lock is enforced, all six tested repartition/coalesce/shuffle perturbations preserve identical signatures; before lock, all six drift. The central result is not speed alone: throughput and collect‑less execution are the mechanisms that let policy semantics survive at Spark scale.

Authors:Hassan Abouelgheit, Hendrik Lens
Title: A Novel Two-Step Approach for Reactive Power Demand Calculation Using Integrated Voltage Stability Analysis
Abstract:
The assessment of reactive power demand plays an instrumental role in power system planning. This paper presents a methodology for calculating reactive power demand based on a two‑step approach. Unlike existing methodologies in the literature that focus primarily on optimization of reactive power compensation equipment placement and sizing through single‑simulation approaches, this methodology directly calculates the actual reactive power demand through a comprehensive back‑to‑back simulation framework. While existing methods address either long‑term or short‑term voltage stability using either steady‑state analysis or individual dynamic simulations, the proposed approach integrates both stability assessments sequentially through iterative Quasi‑Dynamic Simulation, Q‑V analysis and dynamic simulation. Furthermore, this methodology employs comprehensive time‑series analysis over a full annual period (8760 hours) with multi‑criteria violation assessment (number, severity and duration of voltage violations). In the final section of this paper, a case study was conducted to demonstrate the application of the proposed methodology. Simulations were performed to validate the effectiveness of the methodology, with the results showing that all buses with voltage issues were successfully addressed and finally the total reactive power demand across the network was calculated.

Authors:Parmida Valiahdi, Niloofar Mehrnia, Walid Saad, Sinem Coleri
Title: EVT-Based Generative AI for Tail-Aware Channel Estimation
Abstract:
Ultra‑reliable and low‑latency communication (URLLC) will play a key role in fifth‑generation (5G) and beyond networks, enabling mission‑critical applications. Meeting the stringent URLLC requirements, characterized by extremely low packet error rates and minimal latency, calls for advanced statistical modeling to accurately capture rare events in wireless channels. Traditional methods, such as those that rely on large datasets and computationally intensive estimation techniques, often fail in real‑time scenarios. In this paper, a novel framework is proposed to meet URLLC requirements through a synergistic integration of extreme value theory (EVT) with generative artificial intelligence (AI). EVT is used to model channel tail distributions, providing an accurate characterization of rare events. Concurrently, generative AI enables data augmentation and channel parameter estimation from limited samples. The integration of EVT with generative AI can thus help overcome the limitations of generative models in capturing extreme events during channel characterization. Using an experimental dataset collected from an automotive environment, it is demonstrated that this integration enhances data augmentation for extreme quantiles, while requiring fewer samples than traditional analytical EVT methods and generative baselines in online estimation of channel distribution.

Authors:Lukas Schulthess, Andreas Rätz, Michele Magno, Philipp Mayer
Title: A Class AAA Solar Testbed for Reproducible Long-Term Characterization of Energy-Harvesting Systems
Abstract:
Energy harvesting promises maintenance‑free operation of wireless sensor nodes but introduces strong dependencies on stochastic and deployment‑specific environmental conditions. In particular, solar‑powered systems are highly sensitive to variations in irradiance and spectral composition, which complicates system‑level design, parameter tuning, and reliable verification. This work presents a solar testbed in which active control via Hardware‑in‑the‑Loop (HIL) enables stable and repeatable illumination conditions for evaluating ultra‑low‑power energy harvesting systems. The proposed LED‑based solar testbed provides spectrally configurable illumination over a wide dynamic range, from 5.7 mW/m2 to 908 kW/m2. It achieves Class AAA performance according to IEC 60904‑9, with a spectral match below 1.3% and a spatial non‑uniformity below 1.28% over a 16.5 cm x 16.5 cm test area. The long‑term irradiance instability remains below 0.6%. Closed‑loop control using integrated illuminance and spectral sensors ensures high temporal stability, while a temperature‑controlled DUT stage supports long‑term experiments. Experimental results demonstrate high repeatability and suitability for systematic laboratory characterization of solar energy harvesting systems.

Authors:Gia Quoc Bao Tran, Thach Ngoc Dinh, Zhenhua Wang
Title: Interval Observer Design Using Observability Decomposition for Detectable Linear Systems
Abstract:
We provide a systematic interval observer design method for detectable linear time‑invariant (LTI) systems, where a part of the state is observable from the measured output. An observability‑based invertible LTI transformation decomposes the state into two parts. The first part is decoupled from the other and observable from the output, while the second is affected by the first, does not appear in the output, but is detectable. A Sylvester‑based LTI interval observer is designed for the first part. For the second part, a Jordan‑based linear time‑varying interval observer is built, treating the interaction from the first part as inputs with known bounds. The intervals in the original coordinates are constructed either by inverting the decomposition online for the intervals in the transformed coordinates or by directly implementing the observer written in the original coordinates. Academic examples illustrate the interest of our approach.

Authors:Zhongzheng Zhang, Maxwell Ruyle, Andrew Kappes, Tyler Ruble, William Shaoul, Dana Moreno, Jack Penn, Ivan Ruchkin
Title: TEACar: An Open-Source Autonomous Driving Platform
Abstract:
Intelligent Transportation Systems (ITS) increasingly rely on vision‑based perception and learning‑based control, necessitating experimental platforms that support realistic hardware‑in‑the‑loop validation. Small‑scale platforms for autonomous racing offer a practical path to hardware validation, but often suffer from limited modularity, high integration complexity, or restricted extensibility. This paper presents TEACAR, a 1/14‑ to 1/16‑scale autonomous driving platform designed with modular mechanical architecture, hardware abstraction, and ROS 2‑based software. The system adopts a four‑layer deck structure that physically decouples sensing, computation, actuation, and power subsystems, improving structural rigidity while simplifying reconfiguration. We constructed and comprehensively evaluated the prototype of TEACAR. Its mechanical stability, structural characteristics, and software performance were quantified based on three CNN‑based steering controllers. Inference latency, power consumption, and system operating time were measured to evaluate computational capability and robustness. Our experiments demonstrated that TEACAR offers a scalable, modular, and cost‑effective testbed for ITS research, education, and development. Our project repository is available on GitHub.

Authors:Eva Krueger, Marcus Rosette, Joseph R. Davidson
Title: An analysis of sensor selection for fruit picking with suction-based grippers
Abstract:
Robotic fruit harvesting often fails to reliably detect whether a fruit has been successfully picked, limiting efficiency and increasing crop damage. This problem is difficult due to compliant fruit and grippers, variable stem attachment, and occlusions in orchard environments. Prior work has explored vision‑based perception and multi‑sensor learning approaches for pick state estimation. However, minimal sensor sets and phase‑dependent sensing strategies for accurate pick and slip detection remain largely unexplored. In this work, we design and evaluate a multimodal sensing suite integrated into a compliant suction‑based apple gripper. Our approach is unique because it identifies which sensors are most informative at different phases of the pick, enabling predictive detection of failures before they occur. The contributions of this paper are a phase‑dependent evaluation of multimodal sensors and the identification of minimal sensor sets for reliable pick state classification. Experiments in a real apple orchard show that Random Forest and Multilayer Perceptron classifiers detect successful picks and impending failures with over 90% accuracy, and Random Forest predicts pick/slip events within 0.09 s of human‑annotated ground truth.

Authors:Chris Verhoek, Nikolai Matni
Title: A Quantitative Framework for Navigating Controller Design Tradeoffs under Computational Constraints
Abstract:
Computational constraints permeate the controller design process, and yet are rarely treated as explicit design constraints. Towards addressing this gap, we propose a quantitative framework that captures the effects of common design approximations, such as model order reduction, temporal discretization, horizon truncation, and solver accuracy, on both controller performance and computational requirements. Our framework highlights that these approximations are tunable parameters within an overall controller design process. By leveraging incremental input‑to‑state stability, we show that bounding the aggregate effects of these approximations reduces to verifying a design‑dependent sector bound on the difference between the deployed policy and an idealized baseline, with stability enforced via a small‑gain condition. We operationalize these insights via a Design Meta‑Problem in which the performance gap is minimized subject to stability, real‑time compute, and timing constraints. Finally, we instantiate the framework on a receding horizon LQR case study, and demonstrate a principled near‑optimal navigation of tradeoffs among sampling rate, model order, horizon length, and solver iterations.

Authors:Jiwan Jiang, Soyoung Ahn
Title: Stochastic and Dynamic Fundamental Diagram for Mixed Traffic
Abstract:
This study develops a dynamic fundamental diagram (FD) framework tailored to mixed traffic environments comprising automated vehicles (AVs) and human‑driven vehicles (HDVs). Describing function analysis is employed to derive approximate linear transfer functions for nonlinear HDV car‑following models. A sequence‑based stochastic dynamic FD is then formulated for mixed platoons, enabling the evaluation of hysteresis in the evolution of flow‑density relations across different vehicle sequencing scenarios and AV penetration levels. Monte Carlo simulation results reveal that (i) differences in AV‑HDV sequencing significantly alter the size of traffic hysteresis loops; and (ii) higher AV shares generally dampen hysteresis magnitude and variability, yet the net impact depends on how AVs are distributed within the platoon. The results suggest that traffic hysteresis in mixed environments is governed not only by the composition of AVs and HDVs, but also by how their interactions unfold through sequencing.

Authors:Yiping Liu, Xiaozhe Wang, Geza Joos
Title: Data-Driven Privacy-Preserving Modeling and Frequency Regulation with Aggregated Electric Vehicles via Bilinear Hidden Markov Model
Abstract:
Vehicle‑to‑Grid (V2G) technology allows bidirectional power flow for real‑time grid support, making electric vehicles (EVs) well‑suited for ancillary services such as frequency regulation. However, existing methods for flexibility estimation and coordinating aggregated EVs often rely on individual EV traveling information (e.g., arrival/departure time) and/or characteristic parameters (e.g., charging efficiency, battery capacity) as well as real‑time state‑of‑charge (SOC), which raises privacy concerns and faces data quality issues. To address these challenges, this paper proposes a data‑driven, privacy‑preserving modeling and control framework for frequency regulation using aggregated EVs. The proposed method can provide accurate estimation for power outputs and flexibility of aggregated EVs and carry out effective frequency regulation without any individual EV information. Simulation results validate the accuracy and effectiveness of the proposed method, which also outperforms the model‑based and federated learning‑based method under SOC data inaccuracies.

Authors:Tobias A. Farger, Adam W. Hall, Angela P. Schoellig
Title: Exploiting Differential Flatness for Efficient Learning-based Model Predictive Control of Constrained Multi-Input Control Affine Systems
Abstract:
Learning‑based control techniques use data from past trajectories to control systems with uncertain dynamics. However, learning‑based controllers are often computationally inefficient, limiting their practicality. To address this limitation, we propose a learning‑based controller that exploits differential flatness, a property of many robotic systems. Recent research on using flatness for learning‑based control either is limited in that it (i) ignores input constraints, (ii) applies only to single‑input systems, or (iii) is tailored to specific platforms. In contrast, our approach uses a system extension and block‑diagonal cost formulation to control general multi‑input, nonlinear, affine systems. Furthermore, it satisfies input and half‑space flat state constraints and guarantees probabilistic Lyapunov decrease using only two sequential convex optimizations. We show that our approach performs similarly to, but is multiple times more efficient than, a Gaussian process model predictive controller in simulation, and achieves competitive tracking in real hardware experiments.

Authors:Fengjiao Liu, Yixiao Zhang, Panagiotis Tsiotras
Title: Reachability Analysis of the State Transition and State Covariance Matrices for an LTV System
Abstract:
In this paper, we study the reachability of two closely related matrices appearing in the analysis of linear time‑varying (LTV) systems over a finite time interval, namely, its closed‑loop state transition matrix via a state feedback control and its state covariance matrix starting from some given initial state covariance matrix. Under a mild assumption, we first characterize the set of closed‑loop terminal state transition matrices reachable from the identity matrix using controls of the state feedback form. Then, we provide the set of terminal state covariance matrices reachable from any given positive definite initial state covariance matrix when the LTV system is not necessarily controllable. Both results are based on the solutions of corresponding matrix Riccati differential equations (RDE).

Authors:Daniel Cao, Beixi Du, Andrew Lowitt, Sunmook Choi, Sarah Dean, Yahya Sattar
Title: Dual Control of Linear Systems from Bilinear Observations with Belief Space Model Predictive Control
Abstract:
We study finite‑horizon quadratic control of linear systems with bilinear observations, in which the control input affects not only the state dynamics but also the partial observations of the state. In this setting, the separation principle can fail because control inputs influence the future quality of state estimates. State estimation requires an input‑dependent Kalman filter whose gain and error covariance evolve as functions of the control inputs. To address this challenge, we propose a belief‑space model predictive control (\textttB‑MPC) method that plans directly over both the estimated state and its error covariance. In particular, \textttB‑MPC plans with a deterministic surrogate of the belief evolution defined by the input‑dependent Kalman filter. Through numerical experiments in two synthetic settings, we show that \textttB‑MPC can outperform both the separation‑principle controller and its MPC variant in favorable regimes, and that these gains are accompanied by lower estimation covariance and more uncertainty‑aware action choices.

Authors:Sriram Narayanan, Daniele Sicoli, Piyush Mehta
Title: Reduced-Order Data Assimilation for Thermospheric Density Using Physics-informed SINDyc Models
Abstract:
Accurate estimation of thermospheric mass density is a prerequisite for orbit prediction and space situational awareness, where the upper atmosphere responds nonlinearly to solar and geomagnetic forcing across several orders of magnitude. Physics‑based general circulation models resolve this response but are computationally expensive, while empirical models run cheaply but lack a time‑evolving atmospheric state. This work couples a data‑driven reduced‑order thermospheric model with a Kalman filter that assimilates in situ density observations. An autoregressive Sparse Identification of Nonlinear Dynamics with control (SINDy_c‑AR) reduced‑order model derived from the Thermosphere‑Ionosphere‑Electrodynamics General Circulation Model (TIE‑GCM) captures the dominant modes of variability and their dependence on solar and geomagnetic drivers at a fraction of the parent model's cost. Density observations from CHAMP, GRACE, GRACE‑FO, GOCE, and Swarm are assimilated across a range of orbital configurations and geomagnetic conditions, with a linear DMDc model evaluated as a reference. Assimilation reduces density estimation error relative to open‑loop predictions, most visibly during geomagnetic storms and under single‑satellite coverage. SINDy_c‑AR and DMDc perform comparably on assimilated orbits; on withheld orbits, SINDy_c‑AR is more accurate in the in‑training scenarios while DMDc is better in the out‑of‑training 2024 Swarm‑C case. Benchmarks against NRLMSIS~2.1 and HASDM (2000‑‑2019, where available) show that empirical references can outperform the assimilated model far from the assimilated track, so results are framed as improvements over the open‑loop forecast.

Authors:Xincheng Cao, Haochong Chen, Bilin Aksun-Guvenc, Levent Guvenc, Brian Link, Peter J Richmond, Dokyung Yim, Shihong Fan, John Harber
Title: Hybrid A*-Based Reverse Path-Planning of a Vehicle with Trailer System
Abstract:
Reverse parking maneuvering of a vehicle with trailer system is a difficult task to complete for human drivers due to the multi‑body nature of the system and the unintuitive controls required to orientate the trailer properly. The problem is complicated with the presence of other vehicles that the trailer and its connected vehicle must avoid during the reverse parking maneuver. While path planning methods in reverse motion for vehicles with trailers exist, there is a lack of results that also offer collision avoidance as part of the algorithm. This paper hence proposes a modified Hybrid A‑based algorithm that can accommodate the vehicle‑trailer system as well as collision avoidance considerations with the other vehicles and obstacles in the parking environment. One of the novelties of this proposed approach is its adaptability to the vehicle with trailer system, where limits of usable steering input that prevent the occurrence of jackknife incidents vary with respect to system configuration. The other contribution is the addition of the collision avoidance functionality which the standard Hybrid A algorithm lacks. The method is developed and presented first, followed by simulation case studies to demonstrate the efficacy of the proposed approach.

Authors:Shubham Sawarkar, P Sangeerth, S Saharsh, Pushpak Jagtap
Title: Sliding Mode Control for Safe Trajectory Tracking with Moving Obstacles Avoidance: Experimental Validation on Planar Robots
Abstract:
This paper presents a unified control framework for robust trajectory tracking and moving obstacle avoidance applicable to a broad class of mobile robots. By formulating a generalized kinematic transformation, we convert diverse vehicle dynamics into a strict feedback form, facilitating the design of a Sliding Mode Control (SMC) strategy for precise and robust reference tracking. To ensure operational safety in dynamic environments, the tracking controller is integrated with a Collision Cone Control Barrier Function (C3BF) based safety filter. The proposed architecture guarantees asymptotic tracking in the presence of external disturbances while strictly enforcing collision avoidance constraints. The novelty of this work lies in designing a sliding mode controller for ground robots like the Ackermann drive, which has not been done before. The efficacy and versatility of the approach are validated through numerical simulations and extensive real‑world experiments on three distinct platforms: an Ackermann‑steered vehicle, a differential drive robot, and a quadrotor drone. Video of the experiments are available at https://youtu.be/dWcxwum96vk

Authors:Bruce D. Lee, Anastasios Tsiamis, Nikolai Matni, Manfred Morari, John Lygeros
Title: The Fragility of Learning LQG Controllers
Abstract:
Learning methods are increasingly used to synthesize controllers from data, yet existing sample‑complexity characterizations for continuous control are sharp only in the fully observed setting. This paper studies the partially observed case by deriving information‑theoretic lower bounds for learning Linear Quadratic Gaussian (LQG) controllers from offline trajectories generated by a (linear) exploration policy. We prove an \varepsilon‑local minimax excess‑cost lower bound that applies to any algorithm mapping the offline dataset to a stabilizing linear controller. The bound is expressed in terms of the Hessian of the LQG cost with respect to model parameters and the inverse Fisher Information induced by the exploration policy. We further provide system‑theoretic characterizations of these objects, enabling transparent construction of hard instances. Instantiating the bound on classical fragile robust‑control examples, including variants of the Doyle LQG fragility counterexample and non‑minimum‑phase systems, demonstrates when fragile robust control problems translate into high sample complexity for learning‑enabled control. These results suggest the asymptotic optimality of certainty‑equivalent synthesis and motivate the importance of both task‑directed experiment design and system co‑design for sample‑efficient learning in partially observed control.

Authors:Joanna Delicaris, Erika Ábrahám, Anne Remke
Title: Minimum Reachability Probabilities in Rectangular Automata with Random Clocks
Abstract:
Control applications for cyber‑physical systems must make reliably safe control decisions in the presence of continuous dynamics as well as stochastic uncertainty. Providing safety guarantees for such systems requires formal modeling and analysis techniques that capture these aspects. For modeling, in this paper we consider rectangular automata with random clocks under prophetic scheduling. For this model class, existing methods can compute only upper bounds on reachability probabilities, enabling optimistic, best‑case safety reasoning. We complement this view by introducing a novel method to compute lower bounds, thereby enabling worst‑case analysis that is essential for safety‑critical applications. Although both upper and lower bounds rely on reachability analysis, they are not dual: computing lower bounds requires an explicit separation of stochastic and nondeterministic choices along executions. We implement our approach and demonstrate its practical feasibility on an electric vehicle charging scenario, showing that meaningful worst‑case guarantees can be obtained.

Authors:Alberto De Santis, Stefania Iannazzo, Fabio Ingravalle, Stefano Lucidi, Massimo Maurici, Giulia Riccardi, Massimo Roma, Antonio Vinci
Title: A Realistic Discrete Event Simulation model for Ambulance Location and Deployment within a regional Emergency Medical Service
Abstract:
The objective of Emergency Medical Services (EMSs) is to promptly respond to calls from citizens for first aid, providing pre‑hospital care and, if necessary, to transfer patients to an appropriate Emergency Department (ED) by ambulance. The efficiency of such a system strongly depends on the deployment of ambulance home bases, i.e., locations where ambulances and their crews are strategically positioned, ready to respond to emergency calls. This paper presents a general Discrete Event Simulation (DES) model designed to capture the stochastic behaviour and workflow of regional ambulance emergency systems. The proposed model incorporates and integrates information collected from different sources, reproducing very accurately the operation of the ambulance system, thus allowing a more comprehensive and realistic analysis. To show the applicability and reliability of the proposed general model, a case study provided by the Azienda Regionale Emergenza Sanitaria ‑ ARES 118 (an Italian Regional Emergency Medical Services Authority ‑ ARES~118) is presented. It concerns a territory within the Lazio region of Italy, including a medium‑size city along with sparsely populated areas. The reported results about scenario analyses highlight how the model we propose can be fruitfully used by the managers to improve effectiveness and quickness of the entire regional EMS system.

Authors:Carlos S. Alvarez-Merino, Alejandro Ramirez-Arroyo, Rasmus Suhr Mogensen, Morten V. Pedersen, Miguel Villanueva-Fernández, Emil J. Khatib, Sergio Fortes, Raquel Barco, Preben E. Mogensen
Title: Data-Driven Adaptive Resource Allocation for Reliable Low-Latency Uplink Communications in Rural Cellular 5G Multi-Connectivity
Abstract:
Reliable low‑latency communication is a key requirement for mission‑critical and mobile autonomous systems, including teleoperation, autonomous navigation, and real‑time uplink‑dominant telemetry applications. While commercial 5G networks often provide adequate downlink performance, uplink performance in rural deployments may be constrained by radio‑resource limitations and uplink power‑control mechanisms. This paper presents a comprehensive experimental evaluation of multi‑connectivity strategies over commercial 5G Non‑Standalone networks, based on measurement campaigns conducted in urban, suburban, and rural environments. The study analyzes per‑packet uplink and downlink latency, packet loss, and radio‑layer KPIs across two mobile network operators. The measurements indicate that latency and reliability cannot be inferred solely from coverage indicators such as RSRP. In coverage‑constrained scenarios, performance appears to be strongly influenced by uplink power‑limited operation and partially correlated impairments across operators. Several multi‑connectivity strategies are evaluated, including link aggregation, switching‑based policies, and conditional packet duplication. A Primary‑Anchored Adaptive Failover (PAAF) framework is introduced to selectively activate redundancy based on radio, latency and service cost considerations. The results suggest that Partial Duplication (PD) approaches can approach the reliability of multi‑connectivity while substantially reducing duplication overhead in the evaluated rural scenario.

Authors:Hidir Selcuk Nogay
Title: AI-Native Autonomous Infrastructure (ANAI): A Formal Framework for the Next General-Purpose Technology
Abstract:
Artificial intelligence is increasingly described as a candidate next generation general purpose technology (GPT). However, existing interpretations predominantly emphasize performance scaling rather than structural transformation. This paper introduces a formal framework for evaluating AI as a systemic infrastructural transition rather than merely a computational breakthrough. We propose the concept of AI Native Autonomous Infrastructure (ANAI), defined as a regime in which decision autonomy becomes embedded within critical infrastructures. The framework operationalizes this transition through three quantitative constructs: the Autonomy Index (AIx), the Infrastructure Coupling Coefficient (ICC), and the Technological Transition Potential (TTP). We formalize the joint scaling dynamics of autonomy and infrastructural embedding, derive threshold conditions for paradigm transition, and introduce a phase‑space representation of systemic transformation. A temporal transition model further illustrates how nonlinear coevolution between autonomy and infrastructure integration produces super linear growth in transition potential. Unlike prior GPT cycles, the ANAI regime exhibits a recursive energy computation feedback loop in which AI systems both increase computational demand and optimize the infrastructures that sustain them. This feedback mechanism accelerates infrastructural embedding and differentiates AI driven transformation from previous technological revolutions. By shifting analytical focus from model performance to infrastructural autonomy and coupling intensity, this study offers a conceptual and mathematical foundation for assessing whether artificial intelligence constitutes the next general purpose technology.

Authors:Zhiyuan Dong, Weichao Liang, Guofeng Zhang
Title: On Realization of Back-Action-Evading Measurements and Quantum Non-Demolition Variables via Linear Systems Engineering
Abstract:
We establish a framework for realizing back‑action‑evading (BAE) measurements and quantum non‑demolition (QND) variables in linear quantum systems. The key condition, a purely imaginary Hamiltonian with a real or imaginary coupling operator, enables BAE measurements of conjugate observables. Symmetric coupling further yields QND variables. For non‑compliant systems, coherent feedback is designed to engineer BAE measurements. Crucially, the QND interaction condition simultaneously ensures BAE measurements and promotes the coupling operator to a QND observable.

Authors:Jihao Luo, Zesong Fei, Xinyi Wang, Shuntian Tang, Zilong Liu, Yiqing Zhou
Title: Toward Low-Altitude Embodied Intelligence: A Sensing-Communication-Computation-Control Closed-Loop Perspective
Abstract:
The rapid growth of the low‑altitude economy drives increasingly autonomous unmanned aerial vehicle (UAV) operations, giving rise to low‑altitude embodied intelligence (LAEI), in which sensing, communication, computation, and control (SC^3) are tightly integrated to enable closed‑loop interaction, ensuring timely, effective, and safe responses in complex or unknown environments. This article systematically explores the LAEI networks, from its fundamental architecture to the diverse scenarios that it can support. We examine key enabling techniques that sustain timely information exchange and effective decision feedback within the \textSC^3 closed loop. A representative low‑altitude UAV mission in an unknown urban area is presented as a case study, where the UAV provides communication services and performs environmental sensing to inform closed‑loop control, illustrating how coordinated \textSC^3 capabilities enable efficient and responsive operation. By identifying major challenges and outlining future research directions, this work serves as a cornerstone for developing next‑generation low‑altitude intelligent systems.

Authors:Hannes M. H. Wolf, Christian A. Hans
Title: Graph Neural Ordinary Differential Equations for Power System Identification
Abstract:
With the shift towards decentralized energy generation, the increasing complexity of power systems renders physics‑based modeling challenging. At the same time the growing amount of available measurement data opens the door for obtaining models in a data‑driven manner. A modern method to do so are neural ordinary differential equations (NODEs), offering a framework for continuous time system identification. Recent extensions, so called graph NODEs impose a structural inductive bias that has the potential to improve generalization of the learned representation. In this work, we employ graph NODEs and extend them with novel ideas to develop message‑passing graph NODEs (MPG‑NODEs) for identification of coupled systems with heterogeneous node dynamics and edge couplings. This encompasses state‑of‑the‑art machine learning architectures to infer latent representations of unmeasured states from past measurements, local node and edge embeddings to account for heterogeneity as well as an autoregressive scheme to allow for piecewise constant control inputs. We apply MPG‑NODEs to identify voltage and frequency dynamics of power systems and compare them to a monolith NODE under identical measurement assumptions. Our case study on the IEEE 9‑bus system indicates that the proposed MPG‑NODE offers a much more flexible framework with transfer learning options that allow to add or remove powerlines and units with little to no retraining.

Authors:Zuodong Pan, Xu Fang, Wei Ren
Title: Optimal Planning and Control under Signal Temporal Logic Specifications
Abstract:
This paper addresses the planning and control problem for nonlinear systems under Signal Temporal Logic (STL) specifications. We first decompose an STL task into finite local tasks. A sampling‑based method generates sequences of local waypoints to satisfy all local tasks, from which the corresponding satisfaction pair sets are derived. Following a local‑to‑global strategy, all sequences of local waypoints are synthesized into a global one, based on which a safe corridor is then constructed. Leveraging the safe corridor and the satisfaction pair sets, an optimization problem is formulated and solved to derive a position trajectory that satisfies the STL task. Finally, numerical examples and comparative results are presented to demonstrate the efficacy of the proposed approach.

Authors:Huaicheng Li, Junhui Zhao, Haoyu Quan, Xiaoming Wang
Title: Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptation
Abstract:
Federated learning (FL) offers a promising distributed learning paradigm for internet of vehicles (IoV) applications. However, it faces challenges from communication overhead and dynamic environments. Model compression techniques reduce computing and communication burden yet create trade‑offs between compression ratios and vehicle participation strategies. In this paper, we propose a lightweight FL algorithm named federated learning with dynamic low‑rank adaptation (Fed‑DLoRA), which is combined with low‑rank adaptation (LoRA) to effectively reduce parameters and communication costs while enhancing training efficiency. The convergence analysis of Fed‑DLoRA is conducted through stochastic gradient descent optimization coupled with singular value decomposition. This analysis establishes the theoretical relationships among LoRA rank, vehicular scheduling strategies and the model's convergence characteristics. Building on these insights, we formulate a joint optimization problem aimed at maximizing system performance. To address this problem, we propose an adaptive rank, bandwidth and vehicle selection (ARBVS) algorithm that integrates enumeration with greedy optimization strategies. The algorithm provides efficient rank selection and resource scheduling strategies for each FL communication round, thereby achieving effective performance improvements for the FL system. Experimental results demonstrate that Fed‑DLoRA achieves superior performance compared to conventional federated learning approaches, exhibiting enhanced accuracy, faster convergence, and improved communication efficiency.

Authors:Maximilian Engelhardt, Carsten Andrich, Daniel Stanko, Alexander Ihlow, Markus Landmann
Title: A Road-Mobile GNSS-Disciplined Oscillator for Accurate Synchronization of Vehicular Microwave Measurements
Abstract:
Precise synchronization is essential in various technical disciplines, being especially challenging in mobile scenarios. Unfortunately, state‑of‑the‑art global navigation satellite system (GNSS) disciplined oscillators (GNSSDOs) are designed and optimized for stationary operation. We present a novel solution that is optimized for mobile use from the ground up. The centerpiece is a precise oven‑controlled crystal oscillator (OCXO) that is optimized for low sensitivity to dynamic accelerations. A state‑of‑the‑art GNSS timing module is used to discipline it. We evaluate the system by comparing it with state‑of‑the‑art test equipment in a real‑world test drive through diverse environments. After compensating for the stationary offset, the state‑of‑the‑art devices deviated by up to 2315 ns, while with our devices, the deviation never exceeded 22.6 ns. It is evident that the devices designed for laboratory use perform inadequately in mobile operation and that our novel solution enables a significant leap in accuracy.

Authors:Alireza Arastou, Ye Wang, Erik Weyer
Title: Analysis of Stability and Performance of Economic Model Predictive Control with State-Independent Costs
Abstract:
This paper studies economic model predictive Control (EMPC) schemes, where the stage cost depends only on control inputs. Such problems arise in applications like water distribution networks and differ from standard EMPC since multiple steady states can correspond to the unique optimal steady input. We show that, under a strict dissipativity assumption related to the set of optimal steady states, the closed‑loop trajectories converge asymptotically to this set, ensuring convergence of the economic cost to the optimal steady state cost. To enhance Lyapunov stability, we propose a modified stage cost that preserves the optimal input while guaranteeing asymptotic stability of a specific equilibrium with a slight performance loss. The approach is further extended to EMPC of a class of linear systems with periodic costs and disturbances by lifting it to a multi‑step EMPC problem for periodic operations. A case study with a water distribution network demonstrates the effectiveness of the proposed methods in achieving both asymptotic convergence and stability.

Authors:Ahmed Mesfer Alkhudaydi, Bai Cui
Title: Safe Reconnection Time for Large-Scale Data Center Loads: An Analytical Framework for Transient Stability Assessment
Abstract:
The rapid growth of large, power‑electronics‑rich data center (DC) loads is creating new operational challenges for bulk power systems. A key risk arises when a DC uninterruptible power supply (UPS) disconnects the facility during voltage/frequency disturbances and then reconnects it while the bulk grid is still dynamically settling to a new equilibrium point. Poorly timed reconnection can amplify electromechanical oscillations, deepen frequency deviations, and lead to repeated connect‑disconnect \emphflapping. In this paper, we develop an analytical framework to characterize the \emphsafe reconnection time for large DC loads after a disturbance‑induced disconnection that avoids flapping. Using a model in the spirit of the classical single‑machine infinite‑bus system, we capture (i) swing dynamics during the disconnection interval and (ii) voltage‑angle coupling at the load bus, which determines the electrical power step at reconnection under constant‑power load assumptions. Using energy function method, we characterize the critical safe reconnection time such that for any reconnection time after the critical safe reconnection time, the post‑reconnection trajectory is guaranteed to remain within operational limits (frequency/angle/voltage) and converge to the post‑reconnection equilibrium, thereby preventing flapping. Time‑domain simulations validate the effectiveness of the proposed analytical approach. The results provide a simple, physics‑informed criterion that can be used to bound reconnection windows for large DC facilities and inform UPS reconnection logic.

Authors:Mohammadreza Kamaldar
Title: Extracting Exact Lie Derivatives Without Backpropagation: A Dual Compiler for Neural Control Barrier Functions
Abstract:
Deploying neural‑network control barrier functions (CBFs) on embedded hardware requires evaluating the barrier value and its Lie derivatives along the system vector fields at every control cycle. The standard mechanism for exact gradient extraction, reverse‑mode automatic differentiation, constructs a dynamic computational graph whose memory footprint grows with network depth and whose backward traversal obstructs the worst‑case execution time analysis required for safety‑critical certification. This paper presents a dual‑algebraic compiler that extracts the exact barrier value and its Lie derivatives through forward network evaluation alone. Encoding the system state as the real part of a dual number and a target vector field as the dual part, we prove that every affine and componentwise‑activation layer admits a dual extension that propagates the exact directional derivative alongside the activation, and that the composed dual‑extended network evaluates the exact Jacobian‑‑vector‑field product with zero truncation error. We derive closed‑form expressions for the dual‑pass floating‑point operation count and peak memory footprint, prove that the proposed algorithm eliminates dynamic graph allocation, and extend the framework to the second‑order Lie derivatives required by relative‑degree‑two CBFs using hyper‑dual arithmetic. An open‑source ahead‑of‑time compiler translates trained neural CBFs into self‑contained C++ headers that assemble the complete safety constraint on an ESP32‑S3 microcontroller from a statically allocated buffer, with zero dynamic memory allocation and a sub‑millisecond cycle budget that supports kilohertz‑rate safety filters.

Authors:Qingwen Pu, Kun Xie, Yuxiang Liu
Title: VLM-VPI: A Vision-Language Reasoning Framework for Improving Automated Vehicle-Pedestrian Interactions
Abstract:
Autonomous driving systems often infer pedestrian yielding behavior from geometric and kinematic cues alone, limiting their ability to reason about visual scene context and age‑dependent behavioral variability. This limitation can produce delayed interventions in safety‑critical encounters and unnecessary braking in benign interactions. This work introduces Vision‑Language Model‑based Vehicle‑Pedestrian Interaction (VLM‑VPI), a multimodal reasoning framework for pedestrian intent understanding and yielding‑aware control in autonomous driving. The system combines three components: a multimodal perception layer that captures visual and kinematic observations, a reasoning layer that uses Qwen3‑VL 8B for visual scene understanding and GPT‑OSS 20B for few‑shot intent reasoning, and a tiered safety controller that applies age‑specific braking margins for children, adults, and seniors. In 112 CARLA scenarios, VLM‑VPI achieves 92.3% intent classification accuracy, outperforming a rule‑based baseline (78.4%), supervised trajectory models (73.5‑82.4%), and a zero‑shot LLM configuration (88.4%). Validation on 24 real‑world PIE scenarios yields 87.5% accuracy, indicating functional sim‑to‑real transferability. Across 200 simulation cases, VLM‑VPI reduces the false‑alarm rate from 7.4% to 2.8% and mean intersection traversal time from 13.5 s to 11.8 s. Conflict occurrences decrease from 124 to 33, while mean minimum time‑to‑collision improves from 1.92 s to 4.47 s. Demographic‑adaptive control further reduces conflicts by 60% for children and 54.5% for seniors compared with uniform control. These results show that an explicit vision‑language reasoning layer can improve both safety and efficiency by linking pedestrian intent, demographic context, and vehicle control decisions.

Authors:Wei Lu, Jay Wang, Dingli Duan, Ding Mao, Caiyi Song, John Huang
Title: Machine Learning and Deep Learning Models for Short Term Electricity Price Forecasting in Australia's National Electricity Market
Abstract:
Short term electricity price forecast is essential in competitive power markets, yet electricity price series exhibit high volatility, irregularity, and non‑stationarity. This phenomenon is pronounced in the South Australian region of the National Electricity Market, where high renewable penetration drives price volatility and frequent negative price intervals, while structural changes such as the transition to five‑minute settlement further complicate forecast. To address these challenges, this study develops a unified benchmark framework. Under identical data preprocessing, feature engineering with lag features, rolling statistics, cyclic temporal encodings, and so on, and an 85% to 15% chronological train test split, six algorithms are systematically compared, including AWMLSTM, CatBoost, GBRT, LSTM, LightGBM, and SVR. The results show that for price prediction, tree‑based models, especially GBRT with an R squared value of 0.88, generally outperform LSTM and SVR. However, all models achieve a mean absolute percentage error above 90%, and more than 65% of GBRT predictions have relative errors above 10%, which highlights the inherent difficulty of price forecast. For demand prediction, all models perform substantially better than in price prediction. AWMLSTM and GBRT achieve an R2 value of 0.96 with mean absolute percentage error below 32%, and GBRT has 74.37% of samples within 5% error, while LSTM and SVR perform less accurately in both tasks. Future improvements should focus on hybrid models such as tree plus transformers, data augmentation for extreme events, and error correction to better capture price spikes.

Authors:Ralph Sabbagh, Tryphon T. Georgiou
Title: Isospectral Steering
Abstract:
We study the controllability of the differential Lyapunov equation under isospectral rotation of a linear gradient field. Specifically, control is effected by a symmetric time‑varying gain‑matrix constrained to have fixed eigenvalues; that is, by exclusively modulating the eigen‑vectors of the state matrix and not its eigenvalues. Motivation for this problem stems from a certain type of control objectives (minimum shear/attention) aimed to reduce anisotropic deformation when ensembles are steered by a common law‑‑optimality necessitates constancy of eigenvalues. In the paper we introduce and motivate this type of isospectral steering, and describe the reachable set of covariances for any specified terminal time and eigenvalues of the gain matrix. The theory we develop is intimately linked to multilinear algebra as well as to positive linear algebra and the Birkoff‑von Neumann theorem for doubly stochastic matrices.

Authors:Hao Wang, Nam Nguyen, Armand Jordana, Ludovic Righetti, Somil Bansal
Title: Cooptimizing Safety and Performance Using Safety Value-Constrained Model Predictive Control
Abstract:
Autonomous systems are increasingly deployed in real‑world environments, where they must achieve high performance while maintaining safety under state and input constraints. Although Model Predictive Control (MPC) provides a principled framework for constrained optimal control, guaranteeing safety beyond its finite planning horizon remains a fundamental challenge. In this work, we augment MPC with a safety value function‑based terminal constraint that enforces membership in a control‑invariant safe set at the end of each planning horizon. This formulation enables real‑time synthesis of trajectories that are both high‑performing and provably safe. We show that, under an exact safety value function and a feasible initialization, the proposed MPC scheme is recursively feasible, thereby ensuring persistent safety. In contrast to traditional terminal set constructions that rely on local linearizations or conservative approximations, our approach incorporates a reachability‑based safety value function for terminal constraints, yielding less conservative and more expressive safety guarantees. We validate the proposed framework through simulation and hardware experiments on a Flexiv Rizon 10s manipulator. Results demonstrate improved constraint satisfaction and robustness compared to standard state‑constrained MPC and reactive safety filtering, while maintaining competitive task performance. The full implementation and experiments are available on the project website.

Authors:Akul Mallayya Swami
Title: Architectural Isolation as a Timing Safety Primitive for Edge AI Medical Devices: Controlled Experimental Evidence on a Shared-Silicon Platform
Abstract:
A system can satisfy accuracy‑based validation, maintain output stability (Safety‑Threshold Exceedance Rate, STER, equal to zero), and still violate timing constraints under deployment load. These are structurally independent properties that current pre‑market validation protocols often do not operationalize at the inference layer. This letter demonstrates their independence through a controlled same‑hardware experiment: identical MobileNetV2 models are evaluated under identical adversarial load on two execution paths of the same NVIDIA Jetson Orin Nano Super, a dedicated GPU accelerator (TensorRT FP16, half‑precision floating point) and a general‑purpose CPU (ONNX Runtime FP32, single‑precision floating point). Both paths maintain STER = 0; the CPU path (ONNX Runtime FP32) degrades 7.2x under combined load (mean latency 9.8x higher than the GPU path (TensorRT FP16), which maintains latency below 11 ms), breaching the 10 Hz clinical cycle budget by 65%. Joint STER and latency verification is proposed as a candidate method for operationalizing U.S. FDA Draft Guidance FDA‑2024‑D‑4488 robustness requirements at the inference layer, subject to regulatory review and clinical validation.

Authors:Alex Tang, M. Emrullah Ildiz, Batin Kurt, Samet Oymak, Necmiye Ozay
Title: On the Generalization Properties of Selective State-Space Models for Filtering Tasks for Unknown Systems
Abstract:
Selective State‑Space Models (SSMs) such as Mamba have emerged as an alternative architecture to self‑attention based transformers in sequence modeling tasks. Recent works have demonstrated the use of transformers in some filtering and output prediction tasks via in‑context learning. In this paper, we analyze whether structured SSMs can work equally well for filtering of unknown systems. In particular, we train the SSM on trajectory samples from a set of systems. At run‑time, the SSM is given the outputs of an unknown system from the same set and is expected to predict the next output online. Theoretically, under appropriate assumptions, we derive generalization bounds as to why SSMs succeed in such tasks. Empirically, we demonstrate the performance via several numerical examples. We also discuss the advantages and disadvantages of SSMs versus transformers for this task.

Authors:Hye Jin Lee, Ho Sub Lee, PooGyeon Park
Title: An Individual-Delay-Reflected Generalized Consensus Analysis for Multi-Agent Systems with Heterogeneous Time-Varying Delays
Abstract:
In multi‑agent systems, heterogeneous time delays exist for all agents because of the difference in communication environments. Therefore, the consensus analysis of a system considering a homogeneous time‑varying delay among all agents results in conservatism. In this study, an individual‑delay‑reflected generalized consensus is proposed for multi‑agent systems with heterogeneous time‑varying delays with various bounds. To reflect heterogeneous time‑varying delays, the proposed Lyapunov‑Krasovskii functional is constructed by dividing the integral term into intervals containing heterogeneous delays and considering augmented vectors with delay states and integral states. Furthermore, by adding zero equality conditions, conservatism is reduced. N‑dependent generalized integral inequality is used to allow the user to adjust the computational complexity. Numerical examples demonstrate a reduction in conservatism with the proposed consensus criterion.

Authors:Taha El Hajji, Aleksandr Nadkin, Stefan Skoog, Lars Sjöberg, Kristoffer Nilsson, Anthony C. Morcos
Title: Defining the Magnetization State of LCF Magnets: From Material Properties to Motor-Level Metrics
Abstract:
Variable flux memory motors, which employ Low Coercive Force (LCF) magnets, achieve extended high‑efficiency operation through controllable magnetization states. To address the need for a unified approach to defining and comparing the magnetization state (MS) across material and motor levels, this paper proposes four MS definitions: two based on intrinsic material properties‑magnetic flux density B and magnetic polarization J‑and two based on motor‑level quantities‑fundamental flux linkage and back‑EMF components. These definitions are evaluated across the id, iq operating plane using finite element analysis on an interior PMSM with a hybrid magnet configuration (LCF and HCF: High Coercive Force) and a defined circuit setup. The results clarify the relationship between material‑level behavior and measurable motor quantities. The proposed framework provides guidance for selecting appropriate MS metrics depending on the application objective, whether for material analysis, control implementation, or condition monitoring in variable flux machines.

Authors:Yifan Zhang, Hsiao-Dong Chiang, Yitong Li, Yang Wu
Title: Unified Energy Function Tailored to Inverter-Based Resources with PI Controllers for Transient Stability Analysis
Abstract:
The increasing penetration of inverter‑based resources (IBRs) has fundamentally altered the transient stability characteristics of modern power systems. IBRs typically rely on proportional‑‑integral (PI) controllers for synchronization and regulation, resulting in nonlinear swing equations that differ significantly from those of synchronous generators (SGs) and exhibit state‑dependent damping. Consequently, although the classical energy function is often adopted in IBR analysis by analogy with SGs, it cannot be directly applied to IBRs with PI controller. A new energy function explicitly tailored to PI controller is proposed in this letter. It admits a unified form and can be applied to a class of nonlinear systems with PI controllers. Two representative cases are considered, including a grid‑following (GFL) inverter and a DC‑voltage‑controlled grid‑forming (GFM) inverter, demonstrating less conservative and more effective estimation of the region of attraction (ROA). All findings are verified through hardware‑in‑the‑loop (HIL) experiments.

Authors:Walid Shaker, Mustafa Suphi Erden
Title: Real-Time Non-Contact Force Compensation for Wrist-Mounted Force/Torque Sensors in Haptic-Enabled Robotic Surgery Training
Abstract:
Haptic feedback has been a long‑missed feature in robotic‑assisted surgery, one that would allow surgeons to perceive tissue properties and apply controlled forces during delicate procedures. Although commercial robotic systems have begun to integrate haptic technologies, their high costs limit accessibility for training and research purposes. To address this gap, we extend our previously developed low‑cost robotic surgery training setup, RoboScope, by incorporating a wrist‑mounted force/torque (F/T) sensor for haptic feedback training. Wrist‑mounted sensing avoids many challenges associated with tip‑mounted sensors but introduces additional non‑contact forces, such as gravity, sensor bias, installation offsets, and associated torques, which compromise measurement accuracy. In this paper, we propose a robust real‑time compensation method based on recursive least squares (RLS). This method eliminates the need for dataset collection and frequent recalibration while adapting to changing operating conditions. Experimental validation demonstrates that the proposed approach achieves over 95% error reduction in non‑contact force compensation and more than 91% in non‑contact torque compensation, significantly outperforming existing methods. These results highlight the potential of our approach for providing reliable haptic feedback in robotic surgery training and research.

Authors:Ahmad Mohammad Saber, Ahmed Saber Refae, Davor Svetinovic, Hatem Zeineldin, Amr Youssef, Ehab F. El-Saadany, Deepa Kundur
Title: An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids
Abstract:
Line current differential relays (LCDRs) are measurement‑driven relays that rely on time‑synchronized multi‑phase current waveforms to infer internal faults in AC and DC power networks. In inverter‑based microgrids, however, the increasing reliance on digitally communicated measurements exposes LCDRs to false‑data injection attacks (FDIAs), in which adversaries manipulate remote measurement streams to create protection‑triggering yet physically inconsistent current trajectories. This paper addresses this emerging measurement integrity problem by introducing a measurement integrity validation scheme that operates as a supervisory instrumentation layer for modern LCDRs. The proposed scheme interprets short windows of synchronized instantaneous current measurements recorded during relay operation and assesses their physical consistency to distinguish genuine fault‑induced trajectories from cyber‑manipulated measurement streams. A recurrent neural network is trained offline using only relay‑available current measurements and exploits the temporal structure of differential current waveforms, which remains informative in inverter‑dominated systems where current magnitude is no longer a reliable observable. The method requires no additional sensors, auxiliary protection elements, or prior knowledge of network topology, and is applicable to both AC and DC LCDRs without structural modification. The proposed measurement validation scheme is evaluated on an islanded inverter‑based microgrid under a comprehensive set of fault and FDIA scenarios, demonstrating high detection accuracy while preserving relay dependability. Hardware‑in‑the‑loop validation using an OPAL‑RT real‑time simulator confirms that the scheme satisfies protection timing constraints and can operate in real time under realistic operating conditions.

Authors:Jungjin Park, Kiminao Kogiso
Title: Dynamic-Key Post-Quantum Encrypted Control Against System Identification Attacks
Abstract:
This study proposes post‑quantum encrypted control systems based on dynamic‑key Learning with Errors (LWE) encryption schemes. The proposed method develops update maps that simultaneously update the private key and ciphertexts within the LWE framework, enabling dynamic‑key encrypted control resistant to system identification attacks. The growth of errors induced by homomorphic operations is analyzed, and sufficient parameter conditions guaranteeing correct decryption at each control step are clarified. Furthermore, a design procedure for the encrypted control systems is presented based on security metrics such as sample‑identifying complexity and deciphering time. A numerical example demonstrates that the proposed control systems achieve secure control against the considered system identification attack.

Authors:Lei Wang, Ying Zhang, Di Shi, Fei Ding
Title: Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions
Abstract:
Accurately characterizing wind power uncertainty under icing and post‑disaster conditions remains a critical challenge for resilient power system operation. To address this issue, this paper proposes a physics‑aware large language model (LLM) framework for probabilistic wind power scenario generation under extreme icing conditions. The proposed framework integrates supervisory control and data acquisition (SCADA)‑based physical modeling, multimodal tokenization, and a causal Transformer architecture trained in an autoregressive manner. A physics‑aware decoding scheme effectively enforces rated power limits and ramping constraints on the generated trajectories while preserving stochastic diversity. Case studies using real wind turbine data show that the proposed method reproduces icing‑induced power degradation and temporal variability observed during extreme weather. The resulting scenarios are physically consistent and high‑fidelity, thereby significantly enhancing resilience assessment and recovery planning in renewable‑integrated power systems.

Authors:Khaled Surur, Melkior Ornik
Title: Resource-Constrained Shortest Path with Polytopic Reset Sets
Abstract:
This paper investigates the problem of computing the shortest path between two states under resource constraints in environments with resource‑replenishment regions. Namely, the length of the path is limited by a budget that can be restored within polytopic replenishment regions. We show that the optimal path in this problem exhibits a distinct geometric structure: it consists of straight‑line segments, changes direction at replenishment regions, and visits regions at most once. We propose an approach to solve the continuous problem in two steps: using a graph‑based approach, followed by convex programming. First, we define a graph whose nodes are possible waypoints of feasible paths, and the edges are the Euclidean distances between these nodes. To obtain a discrete set of nodes that ensure a feasible and near‑optimal solution, we utilize a wavefront algorithm. With a sufficiently small spacing between wavefronts, the solution of the shortest path problem on this graph yields the optimal sequence of polytopes to visit. Next, we use convex optimization on this sequence of polytopes to find the exact optimal path. A numerical experiment is presented to demonstrate the effectiveness of the approach. This approach provides a framework for solving the resource‑constrained shortest path with budget reset.

Authors:Charalambos D. Charalambous, Umarbek Guvercin, Seddik Djouadi
Title: Private and Common Information States in Decentralized Parallel Dynamic Programming for Delayed Sharing Patterns
Abstract:
This paper develops a dynamic programming (DP) approach for decentralized stochastic optimal control problems with delayed sharing information patterns, which exhibits the fundamental Properties of classical DP of centralized partially observable Markov decision problems (POMDPs): the value functions and information states depend on the actions of the minimizing controls and not their strategies. This is achieved by invoking the concept of Person‑by‑Person (PbP) optimality, in which each control strategy is associated with a value function conditioned on its assigned delayed sharing information pattern, when all other strategies are fixed to their optimal responses. The value functions satisfy generalized and simplified DP equations. These are used to derive necessary and sufficient conditions for PbP optimality. The simplified DP equations are obtained by invoking the structural property that optimal strategies are separated and functionals of two information states: 1) a private a posteriori probability distribution based on the information pattern of the strategy, and 2) a centralized a posteriori probability distribution based on the shared or common information to all strategies, each satisfying a Markov recursion. The DP approach of this paper, settles a long standing open problem since the appearance of T‑step delayed sharing patterns in [1, Section IV.G], in terms of generalizing the fundamental properties of classical DP approach.

Authors:Georgiy A. Bondar, Asmaa Eldesoukey, Yongxin Chen, Abhishek Halder
Title: Nonlinear Non-Gaussian Density Steering with Input and Noise Channel Mismatch: Sinkhorn with Memory for Solving the Control-affine Schrödinger Bridge Problem
Abstract:
Solutions to the Schrödinger bridge problem and its generalizations yield feedback control policies for optimal density steering over a controlled diffusion. To numerically compute the same, the dynamic Sinkhorn recursion has become a standard approach. The mathematical engine behind this approach is the Hopf‑Cole transform that recasts the conditions for optimality into a system of boundary‑coupled linear PDEs. Recent works pointed out that for the control‑affine Schrödinger bridge problem, this exact linearity via Hopf‑Cole transform, and thus the standard Sinkhorn recursion, apply only if the control and noise channels are proportional. When the channels do not match, the Hopf‑Cole‑transformed PDEs remain nonlinear, and no algorithm is available to solve the same. We advance the state‑of‑the‑art by designing a Sinkhorn recursion with memory that leverages the structure of these nonlinear PDEs, and demonstrate how it solves the control‑affine Schrödinger bridge problem with input and noise channel mismatch. We prove the local stability of the proposed algorithm.

Authors:Liang Chen
Title: Multiplicative Contractions, Additive Recoveries: Functional-Form Restrictions on Risk Exposure Dynamics
Abstract:
We test a regime‑conditional functional‑form restriction on aggregate risk‑exposure dynamics implied by VaR‑constrained intermediary models: exposures contract multiplicatively when capital constraints bind and grow additively (level‑independent) when slack. The contraction half follows from binding VaR constraints (Brunnermeier and Pedersen 2009; Adrian and Shin 2010; He and Krishnamurthy 2013). The additive‑rebuild prediction is derived under constant‑rate capital replenishment; we test the joint restriction on FINRA monthly margin debt (1997‑2026). Two findings. First, regime‑interacted regression of detrended margin growth on lagged level (T=350 months) yields calm slope ‑0.040 (p=0.082, additive) and stress slope ‑0.205 (p<0.001, multiplicative); Wald test on regime x level interaction rejects equal dependence (p=0.0016). Second, the restriction implies drawdown‑recovery duration ratio increases with crash depth. On 73 S&P 500 episodes (1950‑2026), Cox model gives depth coefficient ‑13.75 (p<10^‑7): 75% lower recovery hazard per 10pp deeper drawdown. Continuous‑depth regression yields beta=1.22 (p=0.047); beta=1.59 (p<0.001) excluding 1980‑82 Volcker. Median duration ratio for crashes >30% is 3.1x; replicates across eight other equity indices. Calibrated Heston, Markov‑switching, and block bootstrap nulls match price‑level duration asymmetry but lack an exposure state variable, so cannot speak to the regime‑conditional flip on direct exposures. We do not claim the exposure test identifies the intermediary mechanism: FINRA margin debt is a noisy proxy. We claim only that the regime‑conditional functional form is a sharper target than return‑level moments alone, and confirming it on margin debt is consistent with ‑‑ not proof of ‑‑ the constrained‑intermediary mechanism. A companion test on CFTC weekly speculative positioning is left for future work (Sections 5.2 and F).

Authors:Zhisheng Xiong, Dimitris Boskos, Bo Zeng, Peter Palensky, Pedro P. Vergara
Title: Robust Operation of Distribution Networks: Generalized Uncertainty Modelling in Confidence-Level-Based Information Gap Decision
Abstract:
This paper studies the robust optimal operation of distribution networks (DNs) under renewable generation and load demand uncertainties, seeking an improved trade‑off between robustness and economic performance. Building upon information gap decision theory (IGDT), a generalized uncertainty modelling is proposed to enhance the expressiveness of the uncertainty characterization. The proposed modelling captures both symmetric and asymmetric uncertainty features, and supports linear or nonlinear expansion of the uncertainty sets driven by confidence level. This advancement leads to the development of a confidence‑level‑based IGDT (CL‑IGDT) framework for DN operation. To solve the resulting model, its equivalence to a family of two‑stage robust optimization problems (TSROs) is established, enabling a Fibonacci search over the confidence level. To further improve computational efficiency, a cut‑recycling strategy is proposed to exploit invariant information across TSROs. These techniques are integrated into a novel Fibonacci‑Parametric Column‑and‑Constraint Generation algorithm with guaranteed asymptotic convergence. Case studies validate the effectiveness of the proposed framework and demonstrate the performance advantages of the proposed algorithm.

Authors:Ratul Ali
Title: Operationalising Information Security Management: A Procedural Framework Analysis of ISO/IEC 27001:2022 Implementation in a Financial-Technology Organisation
Abstract:
Organisations operating within information‑intensive environments face intensifying pressure to formalise the governance of information security. The ISO/IEC 27001:2022 standard provides a globally recognised framework for establishing, implementing, maintaining, and continually improving an Information Security Management System (ISMS). This article analyses the procedural architecture deployed in a financial‑technology organisation's ISMS, examining eight core operational procedures: IT Risk Assessment and Treatment, User Code of Conduct, Password Policy, Access Control, Internet Access, Physical Security, Backup and Restore Management, and Nonconformity Root Cause Analysis and Corrective Action. Drawing on documented internal training materials, the article investigates how each procedure operationalises the requirements of Annex~A controls and Clauses~6‑‑10 of ISO~27001:2022. The paper evaluates the CIA Triad as a unifying evaluation criterion, the twelve‑step risk assessment methodology, role‑based responsibility allocation, and the interplay between corrective action governance and continual improvement. The findings suggest that a tightly integrated, multi‑layered procedural hierarchy, supported by clear accountability structures and measurable risk metrics, constitutes the foundation of an effective ISMS implementation in financial‑technology operating environments.

Authors:Umair Zulfiqar
Title: A Low-rank ADI Algorithm for Solving Large-scale Non-symmetric Algebraic Riccati Equations
Abstract:
This paper considers large‑scale nonsymmetric continuous‑time algebraic Riccati equations (NAREs) that admit low‑rank solutions. Low‑rank alternating direction implicit (ADI) methods have proven to be an efficient approach for solving several matrix equations, including Lyapunov equations, Sylvester equations, and symmetric Riccati equations. Although a low‑rank algorithm for the Sylvester equation has been used as an inner loop in computing low‑rank solutions of NAREs, no low‑rank ADI algorithm currently exists for NAREs themselves. This paper fills this gap by developing a low‑rank ADI algorithm for large‑scale NAREs that admit a low‑rank solution. Since Lyapunov equations, Sylvester equations, and symmetric Riccati equations are special cases of the NARE, the existing low‑rank ADI methods in the literature are special cases of the more general low‑rank ADI method proposed here. An automatic and computationally efficient method for shift generation is also discussed, and a subspace‑accelerated projection approach is presented to generate shifts for subsequent iterations without user intervention. Once initialized with arbitrary shifts, the proposed algorithm solves large‑scale NAREs autonomously, generating its own shifts. Numerical results are presented using benchmark example of order 10^6, demonstrating the computational efficiency and accuracy of the proposed algorithm.

Authors:Chao Deng, Lipeng Zhu, Chang Liu, Hefeng Zhai, Baoye Tian, Zexiang Zhu, Jiayong Li, Cong Zhang
Title: Adaptive Spatial-Temporal Graph Learning-Enabled Short-Term Voltage Stability Assessment against Time-Varying Topological Conditions
Abstract:
The emerging deep learning (DL) technology has recently exhibited great potential in data‑driven short‑term voltage stability (SVS) assessment of complex power grids. However, without sufficient attention to the time‑varying topological structures of today's power grids, the majority of existing DL‑based SVS assessment schemes could experience severe performance degradation in practice. To address this drawback, this paper proposes an adaptive spatial‑temporal graph learning‑enabled SVS assessment approach that can adapt well to various topological changes. First, considering the time‑varying topological conditions of a given power grid, an adaptive graph representation matrix is automatically learned to effectively capture the complicated spatial correlations between individual buses within the grid. Then, to help better capture regional SVS features for subsequent learning processes, the adaptive graph representation matrix is properly adjusted by introducing a spatial attention mechanism. Further, with post‑fault system trajectory data linked together via attention‑based graph representation, a residual spatiotemporal graph convolutional network is carefully built with Optuna‑based optimization to deeply mine system‑wide spatiotemporal features and thus achieve structure‑adaptive SVS assessment. Numerical test results on two representative sub‑systems of a realistic provincial power grid in South China demonstrate the efficacy of the proposed approach under various changing topological conditions.

Authors:Suryasnata Dash, Abhishek Dey
Title: An Exponentially stable Extended Kalman Filter with Estimate dependent Process noise Covariance for Chemical Reaction Networks
Abstract:
Biomolecular systems are often modeled with partially known nonlinear stochastic dynamics, making state and parameter estimation a central challenge. While Kalman filtering techniques are widely used in this setting, their performance critically depends on the choice of the process noise covariance, which is typically assumed constant and heuristically tuned. Such assumptions are not justified for biomolecular systems, where intrinsic noise arises from underlying reaction kinetics. In this work, we propose an Extended Kalman Filter (EKF) with a state estimate‑dependent process noise covariance based on Chemical Langevin Equation (CLE). Further, we analyze the stochastic stability of the proposed filter and derive conditions under which the estimation error remains exponentially bounded in the mean‑square sense. In particular, we obtain an upper bound on the sampling period for discrete‑time biomolecular systems that guarantees this property. The proposed framework is validated through simulations on a nonlinear gene expression model. This approach enables first principle‑based modeling and filter design choices for synthetic biomolecular circuits, eliminating the need for heuristic tuning of the process noise covariance.

Authors:Yifei Xu, Yuzhang Lin
Title: GPU-Native Multi-Area State Estimation via SIMD Abstraction and Boundary Condensation
Abstract:
Power system state estimation (SE) is foundational for grid monitoring, yet conventional centralized solvers face increasing computational pressure as the system scale and real‑time requirements grow. This paper presents a GPU‑native framework for hierarchical multi‑area state estimation (MASE) that addresses these bottlenecks through a single‑instruction, multiple‑data (SIMD) abstraction and sparse Schur local condensation. We partition the network into areas, evaluate measurement residuals and derivatives using fixed‑sparsity templates, and directly assemble local normal‑equation blocks through a fused GPU accumulation kernel without materializing explicit Jacobians. Each area is then factorized on the GPU in Schur mode to export a dense local boundary block and condensed right‑hand side, after which a reduced global boundary system is assembled and solved on device. This design preserves device residency across measurement evaluation, local condensation, and boundary coordination while exposing parallelism across areas. Numerical experiments on partitioned PEGASE 2869‑bus, PEGASE 9241‑bus, and ACTIVSg10k benchmark systems demonstrate that the proposed approach effectively leverages GPU throughput by maintaining full device residency and high arithmetic intensity.

Authors:Alexey Bobtsov, Jose Guadalupe Romero, Romeo Ortega, Anton Pyrkin
Title: An Algebraic State Observer for a Class of Physical Systems
Abstract:
In this paper we present a radically new approach to design state observers for nonlinear systems, with particular emphasis on physical ones. Our objective is to obtain an algebraic relation between the unmeasurable part of the state and filtered versions of the systems inputs and outputs, which holds true for all t \geq 0. The latter qualifier should be contrasted with the usual asymptotic (or fixed/finite time) objective. The standing assumption for our design is the availability ‑‑ or possibility of constructing, via coordinate change ‑‑ state components with measurable derivatives. In the physical systems studied in the paper this condition is naturally satisfied. The next step in the design is the application of the Swapping Lemma to pull out from the dynamics the derivative of one of these signals. The design is completed replacing the latter by the measurable signals and arranging the remaining terms. The algebraic observer constitutes a refreshing major departure from classical asymptotic observer designs, even in the case of electrical motors and mechanical systems that have been exhaustively studied. Particularly notable is the fact that no observability or excitation condition is imposed for the construction of the algebraic observer.

Authors:Akibul Hasan Mazumder, Yuanrui Sang
Title: System-Level Impacts of Flexible Data Center Load Scheduling on Cost, Emissions, and Transmission Congestion
Abstract:
Large data centers are being deployed in the U.S. at an unprecedented rate, introducing significant flexible load potential. A portion of data center workloads ‑ best‑effort (BE) jobs ‑ can be scheduled flexibly to reduce power system operating costs and emissions. However, the system‑level impacts of such scheduling remain underexplored. This paper investigates the effects of flexible data center load scheduling on operating cost, system stress, and emissions using the ACTIVSg2000 2000‑bus test system. Results show that BE loads shift toward periods of lower locational marginal prices (LMPs), typically aligned with high renewable generation. Importantly, latency‑critical (LC) workloads remain unaffected, preserving quality of service (QoS). Flexible scheduling also leads to reductions in both greenhouse gas and toxic emissions, as well as transmission congestion, compared to inflexible operation, demonstrating its potential to support more efficient and sustainable grid operation.

Authors:Sol Lim, Min-Seung Ko, Farnaz Safdarian, Hao Zhu
Title: Learning the Weather-Grid Nexus via Weather-to-Voltage (W2V) Predictive Modeling
Abstract:
This paper proposes a weather‑to‑voltage (W2V) predictive modeling framework to learn the underlying weather‑grid nexus. Unlike existing approaches on weather‑informed grid operations, our proposed W2V model can achieve the joint analysis of weather and grid states, and further leverage this coupling to enhance grid‑aware weather forecasting (GAWF) as a key application. To achieve this end‑to‑end learning, the W2V model acts as a differentiable surrogate for weather‑incorporated power flow analysis by mapping weather features at high spatial resolution directly to grid‑wide bus voltages. Thanks to a compact neural network design and principal component analysis based initialization, it achieves high voltage prediction accuracy and numerical stability during training. Building on this capability, W2V‑based voltage signals are used to guide the development of GAWF that can account for its downstream voltage prediction performance. Using a 6717‑bus Texas synthetic test system with meteorological inputs from 701 weather locations, our numerical tests have verified the excellent accuracy and generalizability of the proposed W2V model. More importantly, the W2V model has enabled the GAWF to effectively prioritize the weather features and conditions that are most critical to grid operations, such as system‑wide quick wind drops preceding ramp‑ups.

Authors:Sudhir P. Jodha, Konstantinos G. Papakonstantinou
Title: Probabilistic Hazard Analysis Framework with Stochastic Optimal Control for Deteriorating Civil Infrastructure Systems
Abstract:
The safety and resilience of civil infrastructure systems are increasingly threatened by compounded risks from various hazard events and structural deterioration due to environmental stressors. This study presents a comprehensive risk‑informed, life‑cycle optimization framework that extends the Performance‑Based Earthquake Engineering (PBEE) and probabilistic seismic loss estimation paradigms by combining hazard uncertainties, nonstationary deterioration, structural damage accumulation, and state‑dependent fragility assessments, with optimal, adaptive maintenance strategies in time. The life‑cycle cost optimization is formulated in this work as a Markov Decision Process (MDP) problem, utilizing derived, transition matrices reflecting time‑variant deterioration effects and hazard risks. To mitigate the curse of dimensionality in system‑level optimization, a novel tensor‑based method exploiting Kronecker‑factored transition dynamics is introduced, reducing complexity from exponential to linear in the number of components while still preserving exact, global dynamic programming solutions. Overall, the framework is general and versatile, able to accommodate various hazard types. A seismic hazard application is, however, demonstrated and explained in detail in this work. The developed methodology eventually provides decision‑makers with a practical, data‑driven tool toward cost effective risk mitigation of civil infrastructure systems.

Authors:Emre Aklan, Fatih Seker, Bekir Gencalioglu, Mehmet Batuhan Kaya, Yigit Serceoglu, Furkan Yavuz, Omer Burak Iskender, Burak Yaglioglu
Title: Integrated Lander-Propulsion-GNC Framework for Autonomous Lunar Powered Descent
Abstract:
This paper presents an integrated lander‑propulsion‑GNC framework for autonomous lunar powered descent. The BUG VTVL test vehicle serves as the reference platform, with the YUNT V0 throttleable bipropellant engine providing variable thrust across a wide operating envelope, integrated with a real‑time successive convexification guidance solver. The vehicle design accounts for structural configuration, landing stability, center‑of‑mass migration, and inertia evolution, while the propulsion architecture defines the throttle ratio, dead‑zone behavior, and gimbal authority that constrain the guidance problem. A successive convexification algorithm addresses all nonconvexities; thrust lower bounds, mass depletion coupling, and thruster dead‑zone behavior are all handled within a unified second‑order cone program solvable in near‑real time. Parametric analysis reveals a fundamental coupling between throttle ratio, pointing authority, and surface gravity. Monte Carlo simulations validate guidance robustness, achieving sub‑50‑meter landing precision under realistic perturbations.

Authors:Elena Fernandez Bravo, Sunil Tamang, Yong Hoon Lee, James T. Allison
Title: Surrogate-Based Co-Design Coupling Analysis for Floating Offshore Wind Turbines
Abstract:
This work presents a design coupling analysis (DCA) framework to investigate the interactions among control and plant design variables in floating offshore wind turbine (FOWT) and to support the formulation of tractable control co‑design (CCD) optimization strategies. DCA provides quantitative information that reveals the relationships and dependencies among design variables and to objective function, enabling improved design variable selection, identification of dominant variables that drive system interactions, and informed selection of optimization solution strategies. However, applying DCA to complex systems is challenging because the models used to describe their dynamics are computationally expensive, and constructing DCA information requires exhaustive model evaluations and optimizations. Here, a surrogate model of the FOWT system is employed to make the repeated model evaluations required for DCA computationally feasible. Using this framework, the bidirectional couplings between control and plant design variables, as well as the couplings among plant design variables, are estimated. The results reveal strong interactions among various design variables, and identify the most influential plant design variables affecting system performance. These insights guide the development of two DCA‑based optimization strategies for large CCD problems: a sequential decomposition approach that preserves dominant design variable couplings while reducing problem size at each stage, and a reduced dimensional optimization approach that focuses on collectively the most influential variables. The results demonstrate that these strategies significantly reduce computational complexity while achieving solutions comparable to those obtained through full simultaneous optimization, demonstrating the value of DCA for understanding and solving complex design problems.

Authors:Md Tanjemul Islam, Md Rafiul Kabir
Title: Vision-Based Lane Following and Traffic Sign Recognition for Resource-Constrained Autonomous Vehicles
Abstract:
Autonomous vehicles (AVs) rely on real‑time perception systems to understand road environments and ensure safe navigation. However, implementing reliable perception algorithms on resource‑constrained embedded platforms remains challenging due to limited computational resources. This paper presents a lightweight vision‑based framework that integrates lane detection, lane tracking, and traffic sign recognition for embedded autonomous vehicles. A computationally efficient threshold‑based lane segmentation method combined with perspective transformation and histogram‑based curvature estimation is used for robust lane tracking under varying illumination conditions. A rule‑based steering controller generates steering commands to maintain stable vehicle navigation. For traffic sign recognition, two lightweight convolutional neural networks (CNNs), EfficientNet‑B0 and MobileNetV2, are evaluated using a custom dataset captured from the vehicle's onboard camera. Experimental results show that the system achieves real‑time performance while maintaining accurate lane tracking with only 3.16% maximum offset RMSE. EfficientNet‑B0 achieves a high offline classification accuracy of 98.77% on the test dataset, while achieving 90% accuracy during real‑time on‑device deployment, outperforming MobileNetV2 in both settings. MobileNetV2, however, offers slightly faster inference and lower computational cost. These results highlight the effectiveness of lightweight vision‑based perception pipelines for resource‑constrained autonomous driving applications.

Authors:Felix Leonhard Janzen, Lukas Moddemann, Alexander Diedrich, Oliver Niggemann
Title: Avionic Main Fuel Pump Simulation and Fault-Diagnosis Benchmark
Abstract:
In many cyber‑physical systems, especially in critical applications such as aeroplanes, data to train anomaly detection and diagnosis algorithms is lacking due to data protection issues and partial observability. To combat this inherent lack of data, we introduce a high‑fidelity, physics‑informed co‑simulation of a common aircraft main‑fuel‑pump system modelled in \textscMATLAB/Simulink Simscape Fluids. We also describe its generated time‑series data with health and fault mode annotations. To show feasibility of our benchmark, we apply an unsupervised Recurrent Variational Autoencoder (RNN‑VAE) for anomaly detection and a SOM‑VAE for operating mode discretization, trained to separate healthy and faulty conditions.

Authors:Huseyin Emre Tekaslan, Ella M. Atkins
Title: Airspeed Forward-Invariance for Unpowered Fixed-Wing Aircraft
Abstract:
Autonomous fixed‑wing flight is becoming a key capability in aerial robotics, enabling sensing, mobility, and contingency operations across both small‑scale Uncrewed Aircraft Systems and large‑scale Advanced Air Mobility. During unpowered operation in fixed‑wing platforms, airspeed is regulated solely through potential‑kinetic energy exchange, making airspeed dynamics highly sensitive to guidance commands, particularly under wind. This paper presents a viability‑based airspeed protection for ground‑referenced guidance in steady wind, where airspeed evolution depends explicitly on the commanded flight path angle. Leveraging Nagumo's tangency condition, we derive a closed‑form, wind‑dependent characterization of admissible guidance commands that guarantees forward invariance of a safe airspeed envelope. These conditions are embedded within an offline quadratic programming framework to certify airspeed‑safe maneuver primitives for non‑ascending flight at the guidance level. The approach is validated using a high‑fidelity unpowered fixed‑wing aircraft model on gliding trajectories formed by concatenating certified maneuver primitives, demonstrating strict airspeed boundedness. Future work will address unsteady wind fields and flight experiments.

Authors:Massimo Guiggiani
Title: On Common Misconceptions in Classical Vehicle Dynamics
Abstract:
Classical vehicle dynamics contains several widely adopted misconceptions that, while intuitively appealing, may lead to inconsistencies when examined under a rigorous mechanical framework. This paper revisits a number of such misconceptions, clarifying their domains of validity and highlighting potential sources of misunderstanding. The aim is not to dismiss established models, but to promote a more precise and scientifically grounded interpretation of key concepts.

Authors:Karol Dykiert, Mateusz Stolarski, Michał Czuba, Wojciech Cieżak, Piotr Bródka
Title: The Elusive Nature of Roughness: Linking Hydraulics and Graph Theory for Water Distribution Networks Model Calibration
Abstract:
Accurate pipe roughness estimation in large‑scale water distribution networks is often hindered by the high cost of traditional field methods. This study investigates whether network partitioning, by utilizing hydraulic and graph‑derived attributes, can enhance the calibration of these parameters. Using a high‑fidelity model of a real network as a benchmark, we evaluate density‑based clustering, and topology‑driven grouping strategies. Optimization experiments demonstrate that attribute‑based grouping yields stable, repeatable results comparable to manual calibration for hydraulically significant pipes. While hydraulic attributes generate more distinct cluster structures, the inclusion of graph‑based data improves calibration robustness by stabilizing the optimization process. Notably, density‑based clustering achieves similar accuracy to k‑means while reducing computational effort in specific configurations. Although the method does not eliminate all sources of uncertainty, results suggest that topology‑informed grouping provides a systematic, reproducible, and computationally efficient alternative to manual heuristics, highlighting the critical role of network structure in reliable parameter estimation.

Authors:Kaito Ito, Anqi Dong
Title: Sliced Wasserstein Steering between Gaussian Measures
Abstract:
Optimal transport with quadratic cost provides a geometric framework for steering an ensemble, modeled by a probability law, with minimal effort. Yet ambient‑space formulations become unwieldy in high dimensions, and sensing or actuation in practice often reveals only linear views of the state ‑‑ camera silhouettes, LiDAR beams, tomographic slices. We develop a sliced feedback controller for distribution steering: the evolving law is projected onto one‑dimensional directions on the sphere, the optimal one‑dimensional velocity is synthesized in each projection, and these velocities are averaged to produce a feedback control in the ambient space. The construction reduces to the Benamou‑‑Brenier problem in one dimension. In addition, it is invariant under orthogonal transforms, nonexpansive under projections, and well posed on \mathcalP_2(\mathbbR^n). Computation proceeds by sampling directions on the sphere and solving independent one‑dimensional subproblems, yielding a scalable method aligned with partial observations. In the Gaussian setting, we show that the developed sliced controller steers the law to the prescribed target. Furthermore, we derive an identity relating the energy consumption incurred by the controller to the sliced Wasserstein distance.

Authors:Jialu Liu, Yao Li, Zhuoheng Li, Huining Li, Ying Chen
Title: See No Evil: Semantic Context-Aware Privacy Risk Detection for AR
Abstract:
Augmented reality (AR) systems pose unique privacy risks due to their continuous capture of visual data. Existing AR privacy frameworks lack semantic understanding of visual content, limiting their effectiveness in detecting context‑dependent privacy risks. We propose PrivAR, which leverages vision language models (VLMs) with chain‑of‑thought prompting for contextual privacy risk detection in AR environments. PrivAR uses visual scene cues to infer potential sensitive information types, such as identifying password notes in office environments through contextual reasoning. PrivAR detects and obfuscates textual content, preventing exposure of sensitive information while preserving contextual cues necessary for VLM inference. Additionally, we investigate contextually‑informed warning interfaces to enhance user privacy awareness. Experiments on a real‑world AR dataset show that PrivAR achieves superior accuracy (81.48%) and F1‑score (84.62%) compared to baselines, while reducing privacy leakage rate to 17.58%. User studies evaluating contextually‑informed warning interfaces provide insights into effective privacy‑aware AR design.

Authors:Marcus Binder Nilsen, Teodor Olof Benedict Åstrand, Tuhfe Göçmen, Pierre-Elouan Réthoré
Title: Hierarchical RL-MPC Control for Dynamic Wake Steering in Wind Farms
Abstract:
Wind farm wake steering optimization is challenging due to complex flow physics and changing conditions. This paper presents a hierarchical framework that combines reinforcement learning with model predictive control, where an RL agent learns compensatory state estimates for an MPC controller, rather than directly controlling turbines. Evaluated on a three‑turbine case, the approach achieves a 23% power gain over the baseline control and surpasses the idealized MPC with perfect state knowledge. Compared to direct RL control, the hybrid architecture maintains superior safety characteristics during training while achieving comparable performance with more stable control actions.

Authors:Teodor Åstrand, Marcus Binder Nilsen, Iasonas Tsaklis, Tuhfe Göçmen, Pierre-Elouan Réthoré, Nikolay Dimitrov
Title: Load constrained wind farm flow control through multi-objective multi-agent reinforcement learning
Abstract:
This study presents a multi‑agent reinforcement learning (MARL) framework for load‑constrained wind farm flow control (WFFC). While wake steering can enhance total wind farm power, it often introduces increased structural loads on downstream turbines. To address this, we integrate an Independent Soft Actor‑Critic (I‑SAC) architecture with a data‑driven, local inflow sector‑averaged surrogate model to provide real‑time estimates of Damage Equivalent Loads (DELs). By incorporating these estimates into a shaped reward function, turbine‑specific agents are trained to maximize power production while adhering to specific load‑increase thresholds (Δ_max) of 10%, 20%, and 30% relative to a baseline controller. The framework is implemented within the WindGym environment using the DYNAMIKS flow solver with Dynamic Wake Meandering (DWM) model to capture non‑stationary wake physics. Results indicate that the MARL agents successfully learn collaborative policies that prioritise power gain while actively retreating from high‑DEL control strategies.

Authors:Marcus Binder Nilsen, Julian Quick, Tuhfe Göçmen, Nikolay Dimitrov, Pierre-Elouan Réthoré
Title: Accelerating Reinforcement Learning for Wind Farm Control via Expert Demonstrations
Abstract:
Reinforcement learning (RL) offers a promising approach for adaptive wind farm flow control, yet its practical deployment is hindered by slow training convergence and poor initial performance, factors that could translate to years of reduced power output if an untrained agent were deployed directly. This work investigates whether domain knowledge from steady‑state wake models can accelerate RL training and improve initial controller performance. We propose a pretraining methodology in which expert demonstrations are generated by deploying a PyWake‑based steady‑state optimizer within a dynamic wake simulation (WindGym), then used to initialize both the actor and critic networks of a Soft Actor‑Critic agent via behavior cloning. Experiments on a 2x2 wind farm show that pretraining eliminates the costly initial learning phase: while an untrained agent underperforms the greedy zero‑yaw baseline by approximately 12%, pretraining raises initial performance to near‑baseline levels. During online fine‑tuning, all configurations converge within 250,000 environment steps to achieve similar performance, ultimately exceeding that of a lookup‑table controller, which reaches approximately 7% power gain after 500,000 steps.

Authors:Mostafa Emam, Björn Martens, Thomas Rottmann, Matthias Gerdts
Title: A Vehicle Routing Problem for Human-Centered Electric Mobility
Abstract:
In this paper, we present the Electric Mobility Dial‑a‑Ride Problem (EM‑DARP), which extends the Electric Vehicle Dial‑a‑Ride Problem (EV‑DARP) to better accommodate human‑focused mobility services. The problem involves utilizing a fleet of heterogeneous Electric Vehicles (EVs) to fulfill a set of customer requests with DARP and mobility‑related specifications, while incorporating visits to charging stations amid requests. The problem is formulated as a Mixed‑Integer Linear Program (MILP) and subsequently solved for a number of curated evaluation scenarios to demonstrate its practical applicability.

Authors:Meshal Alharbi, Munther A. Dahleh, Gioele Zardini
Title: Compositional Online Learning for Multi-Objective System Co-Design
Abstract:
Many engineered systems must balance competing objectives, such as performance and safety, cost and reliability, or efficiency and sustainability, and are naturally modeled as compositions of interacting subsystems. We study online multi‑objective decision‑making in monotone co‑design, where functionalities and resources are partially ordered, and the goal is to identify the target‑feasible antichain of non‑dominated trade‑offs using few expensive evaluations. We introduce optimistic evaluators: history‑dependent bounds on functionality and resource mappings that enable safe elimination of implementations before full evaluation. Based on these evaluators, we develop an elimination‑based rejection‑sampling algorithm, prove its soundness, and show that the admissible region shrinks monotonically as information accumulates. We instantiate the framework under monotonicity, Lipschitz continuity, and linear‑parametric structure. For compositional co‑design problems modeled by multigraphs, we show how local optimistic certificates propagate through the tractable remainder of the graph to yield system‑level optimistic feasibility and resource bounds. Experiments on multi‑robot fleet design, intermodal mobility systems, and synthetic monotone and Lipschitz benchmarks show substantial sample‑efficiency gains over uniform sampling, Bayesian optimization, and multi‑objective evolutionary algorithms.

Authors:Cinzia Tomaselli, Stefano Covone, Andreagiovanni Reina, Mario di Bernardo
Title: Multi-robot obstacle-aware shepherding of non-cohesive target agents
Abstract:
This paper presents a novel control strategy for multi‑agent shepherding of non‑cohesive targets in obstacle‑rich environments. Unlike previous approaches that assume cohesive flocking behavior, our method handles targets that interact only with nearby herders through repulsive forces and exhibit no inter‑target coordination. Each herder employs a hybrid control policy that combines direct goal‑oriented steering with obstacle‑tangent maneuvering, enabling targets to circumnavigate obstacles while being guided toward a goal region. The herder dynamics integrate three key behaviors: return‑to‑goal motion when idle, target steering with adaptive directional control, and obstacle avoidance using both normal and tangential force components. Numerical simulations demonstrate superior performance compared to existing shepherding methods, achieving higher target confinement rates in cluttered environments. Experimental validation using TurtleBot4 herders and Osoyoo target robots in an indoor arena confirms the practical effectiveness of the proposed approach.

Authors:Boris Velasevic, Nicolas Lanzetti, Eric Mazumdar
Title: Strategically Robust Linear Quadratic Dynamic Games
Abstract:
We study linear quadratic dynamic games where players are uncertain about each other's control policies or goals and consequently seek to be strategically robust. Building on recent work on strategically robust and risk‑averse game theory, we first formalize the problem of strategically robust linear quadratic dynamic games. We show that these can be rewritten as simple transformations of linear quadratic games in which each player chooses a controller in a fictitious game in which they are faced with an adversary who is penalized for deviating from the other players' policies. This formulation naturally induces a novel notion of dynamic equilibrium, which we call a strategically robust dynamic equilibrium. We establish existence and uniqueness of such equilibria and furthermore show that the equilibrium policies are Markovian, linear, and can be efficiently computed via coupled backward Riccati equations. Through numerical simulations, including experiments in a network game, we illustrate the benefits of strategic robustness in designing robust and resilient decentralized control schemes. Our experiments also expose a "free‑lunch" phenomenon in games in which robustness does not incur a corresponding loss in performance but can yield improvements in players' utilities and social welfare.

Authors:Tommaso Zaccherini, Siyuan Liu, Dimos V. Dimarogonas
Title: Control of Multi-agent Systems under STL Specifications based on Prescribed Performance Observers
Abstract:
This paper addresses decentralized control of large‑scale heterogeneous multi‑agent systems subject to bounded external disturbances and limited communication, with the objective of satisfying cooperative Signal Temporal Logic (STL) specifications. The considered specifications involve spatiotemporal tasks that require collaboration among multiple agents, including agents beyond direct communication neighborhoods. To address the communication constraints, a k‑hop Prescribed Performance State Observer (k‑hop PPSO) is designed to enable each agent to estimate the states of agents up to k communication hops away using only information from 1‑hop neighbors, while guaranteeing predefined performance bounds on the estimation errors. The estimation error bounds are explicitly incorporated into a reformulation of the spatial robustness of the STL specifications, yielding robustness measures that account for worst‑case estimation uncertainty. Based on the modified robustness, a decentralized continuous‑time feedback control law is designed to guarantee satisfaction of the STL specifications in the presence of bounded disturbances and estimation errors. The proposed framework provides formal correctness guarantees using only local information and limited communication. Numerical simulations illustrate the theoretical results.

Authors:Annice Najafi, Shokoufeh Mirzaei
Title: Optimal sequential decision-making for error propagation mitigation in digital twins
Abstract:
Here, we explore the problem of error propagation mitigation in modular digital twins as a sequential decision process. Building on a companion study that used a Hidden Markov Model (HMM) to infer latent error regimes from surrogate‑physics residuals, we develop a Markov Decision Process (MDP) in which the inferred regimes serve as states, corrective interventions serve as actions, and a scalar reward that takes into consideration the cost‑benefit tradeoff between system fidelity and maintenance expense. The baseline transition matrix is extracted from the HMM‑learned parameters. We then extend the formulation to a Partially Observable MDP (POMDP) that accounts for the imperfect nature of regime classification by maintaining a belief distribution updated via Bayesian filtering, with the HMM confusion matrix serving as the observation model. Both formulations are solved via dynamic programming and validated through Gillespie stochastic simulation. We then benchmark two model‑free reinforcement learning algorithms, Q‑learning and REINFORCE, to assess whether effective policies can be learned without explicit model knowledge. A systematic comparison of different intervention policies demonstrates that the MDP policy achieves the highest cumulative reward and fraction of time in nominal operation, while the POMDP recovers approximately 95% of MDP performance under realistic observation noise. Sensitivity analyses across observation quality, repair probability, and discount factor confirm the robustness of these conclusions, and the major gaps in the policy hierarchy are statistically significant at p < 0.001. The gap between MDP and POMDP performance quantifies the value of information providing a principled criterion for investing in improved classification accuracy.

Authors:Junyoung Park, Hyeontae Sung, Heejin Ahn
Title: Sampling-Based Safety Filter with Probabilistic Restrictiveness Guarantee
Abstract:
Ensuring safety is a critical requirement for autonomous systems, yet providing formal guarantees for nominal controllers remains a significant challenge. In this paper, we propose a modular sampling‑based safety filter to ensure the safety of arbitrary nominal control inputs. At each timestep, the filter evaluates the safety of the nominal input by leveraging control sequence samples generated via Stein Variational Model Predictive Control (SV‑MPC). This approach approximates a safety‑conditioned posterior distribution over control sequences, enabling the filter to effectively capture multimodal safe regions in complex, non‑convex environments. The filter guarantees safety by overriding the nominal input when all sampled control sequence candidates are deemed unsafe. By leveraging the scenario approach, the proposed method provides a probabilistic guarantee on its restrictiveness. We validate the filter through collision avoidance tasks in both single‑ and multi‑vehicle settings, demonstrating its efficacy in navigating cluttered environments where nominal controllers may fail.

Authors:Jhojan A. Rodriguez-Gil, César A. Uribe
Title: Global Convergence of Policy Gradient Methods for ReLU Controllers in Linear Quadratic Regulation
Abstract:
We study the convergence of model‑based policy gradient for the deterministic, scalar, discounted linear‑quadratic regulator when the controller is an overparameterized one‑hidden‑layer ReLU network without biases. Although the optimal LQR controller is linear, neural parameterization creates a redundant nonconvex weight space with a possibly asymmetric piecewise‑linear controller. We show that this structure can still be analyzed exactly through the two effective gains induced on the positive and negative half‑lines. Under suitable random initialization, sufficient width, and a small step size, the model‑based policy gradient remains stable, decreases the cost geometrically, and drives the effective gains to the unique optimal scalar LQR gain with high probability.

Authors:Aaron Weber, Daksh Dhingra, Sawyer B. Fuller
Title: Characterizing pitch and roll torque coupling in insect-sized flapping-wing robots using a microfabricated gimbal
Abstract:
Sub‑gram flapping‑wing flying insect robots (FIRs) are challenging to model because of mechanical complexity in their wings, unsteady aerodynamic flow, and the difficulty of making precise measurements at a small scale. Coupling effects between roll and pitch torque actuation have not previously been measured because a two‑axis sensor that is sensitive enough has not been realized. To address this shortcoming, we introduce a microfabricated gimbal design capable of precisely and simultaneously measuring roll and pitch torques as well as thrust. We then used it to measure the extent to which a pitch torque command affects roll torque and vice versa on a 180 mg piezo‑actuated flapping‑wing flying platform. Our results show a high coefficient of determination in the linear regression for both pitch (0.95) and roll (0.98) and low cross‑correlation coefficients (‑0.001 and ‑0.085, respectively) across the full range of simultaneous torque commands, indicating negligible cross‑axis coupling. Similarly, thrust force deviates by a maximum of only 5.8% from the mean thrust value. These results validate the assumption that pitch and toll can be considered independently in control and will inform future models of how inputs affect the aerodynamics of resonant flapping‑wing systems.

Authors:Bernard T. Agyeman, Zhe Li, Ilias Mitrai, Prodromos Daoutidis
Title: A Hybrid Reinforcement and Self-Supervised Learning Aided Benders Decomposition Algorithm
Abstract:
We propose a hybrid reinforcement and self‑supervised learning framework for accelerating generalized Benders decomposition (GBD). In this framework, a graph based reinforcement learning agent operates on a bipartite representation of the master problem and, together with a verification mechanism, determines the integer variable assignments that solve the master problem. These assignments are then used as inputs to a KKT informed neural network, trained via self supervision to predict primal dual solutions that approximately satisfy the Karush Kuhn Tucker conditions of the subproblem. The predicted solutions are used to construct Benders cuts directly. The framework is evaluated on a mixed integer nonlinear programming case study, where it achieves a 57.5% reduction in solution time relative to classical GBD while consistently recovering optimal solutions across all test instances.

Authors:Muhy Eddin Za'ter, Bri-Mathias Hodge
Title: Empirical Assessment of Time-Series Foundation Models For Power System Forecasting Applications
Abstract:
Accurate forecasting of electric load and renewable generation is essential for reliable and cost effective power system operations. Recent advances in transformer based and foundation machine learning models, driven by large scale pretraining, increased available data and computation, in addition to architectural innovations, have shown promise in time series forecasting across multiple domains. However, their application to power system forecasting tasks remains largely underexplored. This work presents a comprehensive, empirical benchmark of state of the art time series foundation models, transformer architectures, and deep learning baselines for solar, wind, and load forecasting using the high resolution ARPAE PERFORM dataset for the Electric Reliability Council of Texas (ERCOT) grid. Eight core capabilities are assessed, including zero shot performance, fine tuning efficiency, multivariate input and output handling, horizon sensitivity, generalization to unseen sites, probabilistic forecasting, and context window effects. Models evaluated include TimesFM, Chronos Bolt, MoiraiL, MOMENT, Tiny Time Mixer, Temporal Fusion Transformer, PatchTST, TimeXer, LSTM, and CNN. The manuscript aims to provide clear guidance on when foundation models can provide enhanced renewable and load forecasting capabilities and when other approaches remain the more practical choice for power system operations.

Authors:Ricardo G. Sanfelice, Berk Altin
Title: Model Predictive Control of Hybrid Dynamical Systems
Abstract:
The problem of controlling hybrid dynamical systems using model predictive control (MPC) is formulated and sufficient conditions for asymptotic stability of a set are provided. Hybrid dynamical systems are modeled in terms of hybrid equations, involving a differential equation and a difference equation with inputs and constraints. The proposed hybrid MPC algorithm uses a suitable prediction and control horizon construction inspired by hybrid time domains. Structural properties of the hybrid optimization problem, its feasible set, and its value function are provided. Checkable conditions to guarantee asymptotic stability of a set are provided. These conditions are given in terms of properties on the stage cost, terminal cost, and the existence of static state‑feedback laws, related through a control Lyapunov function condition. Examples illustrate the results throughout the paper.

Authors:Kexin Wang, Haohui He, Ruolin Li
Title: When Altruism Meets Autonomy: Managing Bottleneck Congestion with Strategic Autonomous Vehicles
Abstract:
Weaving ramps are critical bottlenecks in highway networks due to conflicting traffic flows and complex interactions among heterogeneous vehicle types. In mixed‑autonomy settings, the presence of controllable autonomous vehicles (AVs) introduces new opportunities to influence system‑level outcomes, yet the structural impact of such control remains poorly understood. This paper develops a unified equilibrium framework to capture, predict, and optimize aggregate lane‑choice behavior in weaving ramps with heterogeneous vehicle populations. We first formulate a Wardrop‑based model capturing the selfish behavior of human‑driven vehicles (HDVs) and establish existence, uniqueness, and validity of the resulting equilibrium. We then introduce a Stackelberg‑‑Wardrop formulation in which AVs act as strategic leaders optimizing system performance, while HDVs respond through equilibrium adaptation. The framework is further generalized to incorporate heterogeneous behavioral preferences of HDVs and AVs via a Social Value Orientation (SVO) model. Our analysis reveals a fundamental structural property of mixed‑autonomy traffic systems: under selfish HDV behavior, the impact of AV penetration is inherently non‑increasing, exhibiting plateau regions where performance remains unchanged and improves only at critical thresholds. These results provide principled guidance for the design of AV control and incentive mechanisms in the presence of selfish human behavior, and demonstrate how strategically controlled autonomous agents can be deployed to induce system‑level efficiency gains in mixed‑autonomy transportation networks.

Authors:Muhy Eddin Za'ter, Anna Van Boven, Bri-Mathias Hodge, Kyri Baker
Title: A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment
Abstract:
Maintaining instantaneous balance between electricity supply and demand is critical for reliability and grid instability. System operators achieve this through solving the task of Unit Commitment (UC),ca high dimensional large‑scale Mixed‑integer Linear Programming (MILP) problem that is strictly and heavily governed by the grid physical constraints. As grid integrate variable renewable sources, and new technologies such as long duration storage in the grid, UC must be optimally solved for multi‑day horizons and potentially with greater frequency. Therefore, traditional MILP solvers increasingly struggle to compute solutions within these tightening operational time limits. To bypass these computational bottlenecks, this paper proposes a novel framework utilizing a transformer‑based architecture to predict generator commitment schedules over a 72‑hour horizon. Also, because raw predictions in highly dimensional spaces often yield physically infeasible results, the pipeline integrates the self‑attention network with deterministic post‑processing heuristics that systematically enforce minimum up/down times and minimize excess capacity. Finally, these refined predictions are utilized as a warm start for a downstream MILP solver, while employing a confidence‑based variable fixation strategy to drastically reduce the combinatorial search space. Validated on a single‑bus test system, the complete multi‑stage pipeline achieves 100% feasibility and significantly accelerates computation times. Notably, in approximately 20% of test instances, the proposed model reached a feasible operational schedule with a lower overall system cost than relying solely on the solver.

Authors:Liam P. Burns, Dayse M. Cavalcanti, Felipe G. Cabral, Max H. de Queiroz, Melissa Greeff, Publio M. M. Lima, Karen Rudie
Title: A Case Study in Recovery of Drones using Discrete-Event Systems
Abstract:
Discrete‑event systems and supervisory control theory provide a rigorous framework for specifying correct‑by‑construction behavior. However, their practical application to swarm robotics remains largely underexplored. In this paper, we investigate a topological recovery method based on discrete‑event‑systems within a swarm robotics context. We propose a hybrid architecture that combines a high‑level discrete event systems supervisor with a low‑level continuous controller, allowing lost drones to safely recover from fault or attack events and re‑enter a controlled region. The method is demonstrated using ten simulated UAVs in the py‑bullet‑drones framework. We show recovery performance across four distinct scenarios, each with varying initial state estimates. Additionally, we introduce a secondary recovery supervisor that manages the regrouping process for a drone after it has re‑entered the operational region.

Authors:Isaac Ortega Romero, Ioannis Zografopoulos
Title: Resilience Revisited: A Multidimensional Framework Derived from Realistic Attack Scenarios
Abstract:
Power systems are increasingly vulnerable to high‑impact, low‑probability (HILP) events, including coordinated cyberattacks targeting inverter‑based resources. Existing resilience frameworks rely on single‑dimensional metrics that fail to capture cross‑dimensional coupling effects, underestimating real system degradation under multi‑vector attack conditions. This study proposes a Multidimensional Resilience Index (MDRI) that decomposes power system degradation into five interacting dimensions: physical, operational, digital‑cyber, climatic, and regulatory, explicitly separating independent and coupled contributions via a calibrated multiplicative interaction term. The framework is validated on the IEEE 39‑bus system under two attack scenarios derived from the December 2025 cyberattack on the Polish energy infrastructure. MDRI results show that multi‑vector attacks produce degradation exceeding linear expectations by a factor of 5.6, with simultaneous dimensional failures contributing an additional 60.6% through endogenous coupling, and exogenous factors amplifying it by an additional 84%.

Authors:Mohammad Almuhaihi, Dennis Bernstein
Title: An Adaptive Kalman Filter that Learns the Coloring Dynamics of the Process Noise
Abstract:
In many applications of state estimation, the process noise is colored; this case is addressed by applying the standard Kalman filter (KF) to dynamics that are augmented with the coloring dynamics. The present paper considers the case where the coloring dynamics are unknown, which renders the estimates obtained from the standard approach suboptimal. To address this problem, the present paper proposes an adaptive technique based on the principle that, if the measurement noise is white, then the innovations sequence is white if and only if the process noise is white. Leveraging this fact, an Innovations‑Whitening Adaptive Kalman Filter (IWAKF) is developed, which learns the process‑noise coloring online. By embedding an unknown coloring filter in a state‑augmentation framework, IWAKF adapts its parameters by minimizing the empirical autocorrelation of the innovations, thereby driving them toward whiteness and restoring near‑optimality without prior knowledge of the coloring dynamics.

Authors:Nicola De Carli, Nicola Bastianello, Dimos V. Dimarogonas
Title: ADMM-Based Distributed Kalman-like Observer with Applications to Cooperative Localization
Abstract:
This paper addresses distributed state estimation for multi‑agent systems with local and relative measurements, motivated by cooperative localization problems in which the global state dimension scales with the size of the network. We consider a Kalman‑like observer in information form and introduce a sparsity‑preserving prediction step based on an exponential forgetting factor, thereby avoiding the dense Riccati recursion of the standard information filter. The correction step is recast as a strongly convex quadratic program with structure induced by the sensing graph, which enables a distributed solution based on the alternating direction method of multipliers (ADMM). In the resulting scheme, each agent updates local copies of its own correction variable and those of its neighbors using only local communication, thus avoiding centralized matrix inversion and consensus over full global‑state quantities. A two‑time‑scale stability analysis is developed for the interconnected observer: the reduced estimation‑error dynamics are shown to be uniformly exponentially stable, the ADMM dynamics define an exponentially stable fast subsystem, and these properties are combined to establish uniform exponential stability of the overall distributed observer. Numerical simulations in a multi‑agent cooperative localization scenario illustrate the performance of the proposed distributed observer.

Authors:Wenbang Wang, Neng Li, Wei Ren
Title: A Characterization of Integral Input-to-state Stability for Hybrid Systems with Memory
Abstract:
This paper addresses characterizations of Integral Input‑to‑State Stability (iISS) for hybrid systems with memory. Based on the Krasovskii approach, a novel Lyapunov characterization of iISS is established to extend the hybrid system theory to the time‑delay case. In particular, we introduce the notions of dissipativity, detectability and storage functional to describe the iISS property from different perspectives. Under mild regularity and convexity assumptions, the equivalence relations among diverse stability descriptions are established, which lays a solid foundation for the control design. Finally, a numerical example is presented to illustrate the derived results.

Authors:Haris Turkmanović, David Vukoje, Aleksandra Lekić, Milan Prokin
Title: Using Assembly Language for Creating Games
Abstract:
The aim of this paper is to demonstrate some interesting and useful approaches for writing a program in the assembly language. In order to demonstrate the possibilities of the assembly language, a project called "Arkanoid" was created. This project is written in assembly language and it presents few interesting algorithms. Assembly language, which is used for designing the game is x86 Assembly language, which produces object code for the x86 class of processors. As a working environment is chosen Visual Studio 2015, because it gives the useful tools for debugging and testing of the created software (game). Execution of the program results in a "Arkanoid" game, placed in Windows OS Console.

Authors:Fabio Bersano, Cyrille Masserey, Vanessa Conti, Andrea Iaconeta, Niccolo' Martinolli, Ehsan Ansari, Anna Varini, Igor Stolichnov, Adrian Mihai Ionescu
Title: Monolithically Integrated VO$_2$ Mott Oscillators for Energy-Efficient Spiking Neurons
Abstract:
Brain‑inspired non‑Boolean computing offers intrinsic error tolerance and parallelism, but its practical deployment is limited by the lack of compact, energy‑efficient spiking hardware compatible with large‑scale integration. Mott phase‑transition materials provide a promising route, as their abrupt insulator‑to‑metal transitions enable neuron‑like thresholding and oscillatory dynamics in compact devices. Among these, vanadium dioxide (VO_2) stands out for its near‑room‑temperature transition, fast switching, and scalability. However, existing VO_2‑based neuristors rely on discrete components, limiting integration density and system applicability. Here, we report monolithic back‑end‑of‑the‑line (BEOL) integration of one‑transistor‑one‑VO2‑memristor (1T‑1MR) spiking neurons on CMOS‑compatible platforms. VO_2 nanosheet devices are fabricated by pulsed‑laser deposition below 430 °C on dielectrically isolated silicon‑on‑insulator (SOI) p‑type junctionless field‑effect transistors (JLFETs) in a compact 1T‑1MR configuration. The architecture exhibits gate‑tunable oscillations from 40 to 410 kHz in 60 nm‑thick VO_2 devices with an active area of 6 μm^2, achieving energy consumption as low as 18 pJ per spike at room temperature, with memristor power dissipation of 8 μW and potential scaling toward sub‑3 μW operation. We further uncover a non‑monotonic dependence of oscillation frequency on current and temperature, along with bias‑dependent stochastic firing dynamics, highlighting the rich behavior of integrated VO_2 memristor systems. Finally, we demonstrate voltage‑controlled oscillator functionality and actively tunable resistive coupling of two nano‑oscillators mediated by a JLFET. These results establish a pathway toward dense, energy‑efficient, and monolithically integrated Mott‑based neuromorphic hardware compatible with CMOS technology.

Authors:Taichi Ikezaki, Kaoru Teranishi
Title: Encrypted Visual Feedback Control Using RLWE-Based Cryptosystem
Abstract:
This study proposes an encrypted visual feedback control algorithm for regulating a one‑dimensional stage using Ring Learning With Errors (RLWE) encryption. The proposed algorithm performs both feature extraction and controller computations directly on encrypted images, ensuring that sensitive visual data remain protected throughout the entire control process. Furthermore, an image captured by the camera is encrypted into a single ciphertext leveraging the message packing technique of RLWE encryption, thereby reducing computational cost. The effectiveness of the proposed framework is demonstrated through numerical simulations.

Authors:Anton Glushchenko, Konstantin Lastochkin
Title: Estimation of Unknown Parameters in Presence of Perturbations and Noises with Application to GPEBO Design
Abstract:
A problem of online estimation of unknown parameters is considered for a linear regression equation, which is affected by an additive perturbation that can be caused by measurement noise (that corrupts regressor and regressand), as well as external perturbations. Known approaches to solve this problem typically have one of the following disadvantages: 1) they ensure convergence of a parametric error to a compact set with non‑adjustable bound, 2) independence of all system regressor elements from the perturbation/noise is required to annihilate them, 3) an instrumental variable is needed to be selected. On the basis of the novel perturbation annihilation procedure, in the present paper, we propose three new estimation laws, which are free from the above‑mentioned drawbacks and ensure exponential convergence of the parametric error to an arbitrarily small neighborhood of zero, particularly, in case more than a half (not all) of the regressor elements are independent from additive perturbation. One of the proposed estimation laws is used for the design of Generalized Parameter Estimation‑Based Observer (GPEBO) for nonlinear affine systems to enhance GPEBO performance in case when the measured system output is corrupted by noise. The theoretical results are supported by examples and mathematical modelling.

Authors:Haoqiang Zhou, Chi Chen, Yongfeng Zhi, Huan Gao
Title: Privacy-Preserving Distributed Stochastic Optimization with Homomorphic Encryption and Heterogeneous Stepsizes
Abstract:
Distributed stochastic optimization enables multi‑agent collaboration in applications such as distributed learning and sensor networks, but also raises critical privacy concerns due to the involvement of sensitive data. While existing privacy‑preserving approaches often face limitations in balancing accuracy with efficiency, we propose a novel distributed stochastic gradient descent algorithm that integrates Paillier homomorphic encryption with heterogeneous and time‑varying random stepsizes. The proposed algorithm provides inherent privacy protection against both internal honest‑but‑curious agents and external eavesdroppers, without relying on any trusted neighbors. Furthermore, we incorporate an attenuation factor to effectively mitigate quantization error induced by the encryption process, ensuring almost sure convergence to the optimal solution while maintaining privacy preservation. Numerical simulations demonstrate the effectiveness and efficiency of the proposed approach.

Authors:Hyeongmin Choe, SooJean Han
Title: Scalable Sensor Scheduling for Continuous-Discrete Kalman Filtering via Information-Form Surrogate Dynamics
Abstract:
We study sensor scheduling for continuous‑discrete Kalman filtering with Poisson measurement arrivals and propose an information‑form deterministic surrogate for scalable offline design. Unlike the covariance‑form surrogate, the sensing rates enter through sensor‑specific additive information increments, eliminating mixed state‑input derivatives in the transcribed nonlinear program and thereby yielding a simpler derivative structure. We further show that, together with the covariance‑form surrogate, the proposed surrogate provides computable two‑sided performance bounds for a given schedule under stochastic measurement arrivals. Numerical experiments demonstrate substantial computational savings, especially in many‑sensor settings, while retaining comparable realized Monte Carlo performance and providing computable two‑sided performance bounds for the returned schedule.

Authors:Senol Gulgonul
Title: Analytical PI Tuning for Second-Order Plants with Monotonic Response and Minimum Settling Time
Abstract:
This study presents two analytical closed‑form PI controller tuning solutions for second‑order plants with real poles, each achieving monotonic step response and minimum settling time. The first solution employs pole‑zero cancellation, placing the controller zero at the slower plant pole and reducing the closed‑loop dynamics to a critically damped second‑order system. The second solution, applicable when the plant pole ratio is less than two, places all three closed‑loop poles at a common location without cancelling any plant pole, yielding a closed‑loop transfer function with a triple real pole and a zero. Despite retaining a closed‑loop zero, this solution achieves strictly faster settling time than the pole‑zero cancellation method in its region of applicability. The two solutions coincide at the boundary pole ratio of two and together form a continuous piecewise‑analytical tuning covering the full range of plant pole ratios. This study further establishes that closed‑loop transfer functions of the form a^n/(s + a)^n possess a maximum sensitivity Ms together with phase margin and gain margin that are independent of the pole location a and depend solely on the order n, yielding universal robustness constants for each n. A closed‑form expression GM(n) = 1 + sec^n(pi/n) is established for the gain margin of the family. Numerical verification confirms the analytical results across multiple plant configurations.

Authors:Yichen Zhou, Stephen Tu
Title: CLT-Optimal Parameter Error Bounds for Linear System Identification
Abstract:
There has been remarkable progress over the past decade in establishing finite‑sample, non‑asymptotic bounds on recovering unknown system parameters from observed system behavior. Surprisingly, however, we show that the current state‑of‑the‑art bounds do not accurately capture the statistical complexity of system identification, even in the most fundamental setting of estimating a discrete‑time linear dynamical system (LDS) via ordinary least‑squares regression (OLS). Specifically, we utilize asymptotic normality to identify classes of problem instances for which current bounds overstate the squared parameter error, in both spectral and Frobenius norm, by a factor of the state‑dimension of the system. Informed by this discrepancy, we then sharpen the OLS parameter error bounds via a novel second‑order decomposition of the parameter error, where crucially the lower‑order term is a matrix‑valued martingale that we show correctly captures the CLT scaling. From our analysis we obtain finite‑sample bounds for both (i) stable systems and (ii) the many‑trajectories setting that match the instance‑specific optimal rates up to constant factors in Frobenius norm, and polylogarithmic state‑dimension factors in spectral norm.

Authors:Changjun He, Hua Geng, Xiuqiang He, Chen Shen, Yushuang Liu
Title: Frequency Security Assessment in Power Systems With High Penetration of Renewables Considering Spatio-Temporal Frequency Distribution
Abstract:
The increasing integration of renewable energy sources exacerbates the spatial and temporal differences in frequency across the power system, posing a serious challenge to the accurate and efficient assessment of system frequency security. To address this issue, a generic effective nodal frequency (ENF) model is first established to concisely characterize nodal frequency dynamics. This model is featured by the effective nodal inertia (ENI), damping, and primary regulation parameters, which retain only the dominant constant component governing nodal frequency dynamic performance. This model enables the tractable analytical formulation of nodal frequency trajectory and the key frequency security indicators. Quantitative analysis under the temporary power disturbance condition reveals that the ENI is the most influential parameter governing frequency security. Consequently, the critical nodal inertia for ensuring nodal frequency security is analytically derived. A system‑level frequency security index based on the actual ENI and critical nodal inertia is proposed. On the basis of the proposed index, the system frequency security assessment is carried out with the procedure of ``offline calculation and online evaluation'', which is achieved using a lookup table approach and an interpolation method. Simulations on the modified IEEE 39‑bus system verify the effectiveness of the proposed assessment method.

Authors:Ge Chen, Yiwei Qiu, Shiyao Zhang, Pengfei Su, Haoran Deng, Hongcai Zhang
Title: A Convexified Eulerian Framework for Scalable Coordination of Massive DER Populations
Abstract:
This paper proposes a scalable coordination framework with aggregator‑side privacy protection for storage‑like distributed energy resources (DERs). The framework adopts a two‑layer architecture. At the macroscopic layer, building upon an \emphEulerian modeling perspective, the DER population is represented as a continuum whose density evolution is governed by a partial differential equation (PDE), such that the computational complexity is independent of the population size. To address the bilinear non‑convexity in this PDE‑constrained optimization problem, we develop a convexification method that combines finite‑volume discretization with a flux‑lifting technique, reformulating the macroscopic problem into a sparse linear program (LP). The LP solution yields a unified, state‑dependent broadcast signal for population coordination. Furthermore, a Wasserstein‑based relaxation is introduced to replace rigid cyclic constraints and provide additional operational flexibility for improved economic performance. At the microscopic layer, individual resources autonomously recover local setpoints from the broadcast signal and their local states, while an upstream data‑mixing protocol aggregates individual states into a macroscopic density histogram without exposing raw individual states to the aggregator. Numerical studies validate the scalability, feasibility, and economic effectiveness of the proposed framework.

Authors:Manou Rosenberg, Mengbin Ye, Brian D. O. Anderson
Title: Optimum adaptation of a Steiner network
Abstract:
The Euclidean Steiner tree problem, normally posed in two dimensions, seeks to connect a set of prescribed terminal nodes by placing additional nodes, known as Steiner points, with edges connecting such nodes either to another Steiner point or a terminal node, and with the placements minimising the sum of all the edge lengths of the associated tree. We consider a problem in which we start with a known solution to a Steiner tree problem, and the terminal positions are then perturbed. A first‑order approximation theorem is established for efficiently updating the Steiner point positions to recover a Steiner tree solution after the perturbations to terminal nodes. Numerical examples illustrate the effectiveness of our approach (including a stepwise application for large perturbations) as well as its limitations.

Authors:Fiaz Hossain, Nilanjan Ray Chaudhuri, Constantino M. Lagoa
Title: A Dynamic Phasor Framework for Analysis of IBR-Induced SSOs in Multi-Machine Systems
Abstract:
We propose a generalized dynamic phasor (DP) framework to analyze inverter‑based resources (IBRs) connected to multi‑machine systems under balanced and unbalanced conditions. It captures subsynchronous oscillations (SSOs) induced by grid‑following (GFL) IBRs. The linearizability and time invariance of the framework enables us to perform eigen decomposition, which is a powerful tool for root‑cause analysis of the SSO modes and damping controller design. The same framework also enables analysis of excitation of the SSO modes in presence of data center (DC) loads. The GFL IBRs are modeled in their respective dq‑frame DPs and the detailed model of synchronous generators (SGs) along with dynamic transmission network models are represented in pnz‑frame DPs. Several case studies are performed on the modified IEEE two‑area benchmark system, where 2 SGs are replaced by GFL IBRs and validated with EMTDC/PSCAD simulations. First, time‑ and frequency‑domain analyses of the SSO mode are presented followed by the design of a robust decentralized \mathcalH_\infty damping controller based on local signals of the GFL IBRs. Second, the dynamic behavior of the system following an unbalanced fault is demonstrated that is damped by the proposed damping controller. Finally, excitation of the SSO mode in presence of DC load is exhibited and its locational impact is analytically quantified.

Authors:Giulia Gatti, Giacomo Como
Title: On the dynamic behavior of the network SIRS epidemic model
Abstract:
We study the Suscectible‑Infected‑Recovered‑Susceptible (SIRS) epidemic model on deterministic networks. For connected but otherwise general interaction patterns and heterogeneous recovery and loss‑of‑immunity rates, we identify a fundamental parameter R_0 (the basic reproduction number), which fully characterizes the qualitative dynamic behavior of the system. This parameter is the dominant eigenvalue of a rescaled version of the interaction matrix, whose rows are normalized by the corresponding recovery rates. We prove that a transcritical bifurcation occurs as R_0 crosses the threshold value 1. Specifically, we show that, if R_0 does not exceed 1, then the disease‑free equilibrium is globally asymptotically stable, whereas, if R_0 is larger than 1, then the disease‑free equilibrium is unstable and there exists a unique endemic equilibrium, which is asymptotically stable. As a byproduct of our analysis, we also identify key monotonicity properties of the dependence of the endemic equilibrium on the model parameters (the interaction matrix as well as the recovery rates and the loss‑of‑immunity rates) and obtain a distributed iterative algorithm for its computation, with provable convergence guarantees. Our results extend existing ones available in the literature for network SIRS epidemic models with rank‑one interaction matrices and homogeneous recovery rates (including the single homogeneous population SIRS epidemic model).

Authors:Ahmed Alkhonain, Kiran Kumar Challa, Amarsagar Reddy Ramapuram Matavalam, Alok Kumar Bharati, Venkataramana Ajjarapu
Title: Online Long-Term Voltage Stability Margin Estimation for IBR/DER Dominated Power System with Integrated VSM-Aware TSO-DSO Framework
Abstract:
The rapid growth of inverter‑based resources (IBRs) and distributed energy resources (DERs) has fundamentally altered the long‑term voltage stability characteristics of modern power systems. This article leverages the advantages of machine learning (ML) for the online estimation of long‑term voltage stability margin (VSM) and enhancement of VSM through coordinated transmission system operator‑distribution system operator (TSO‑DSO) optimization. An explicit analytical VSM expression is derived from offline T&D co‑simulation data using a physics‑informed ML‑trained model under probabilistic loading and generation mix scenarios, while accounting for unbalanced distribution modeling. The resulting closed‑form VSM representation is linearized and embedded into the TSO optimization problem, enabling real‑time enforcement of minimum VSM constraints. We further enhance operational efficiency by incorporating VSM sensitivities into both transmission and distribution optimization, allowing prioritization of the most influential reactive power resources. Simulation studies conducted on the IEEE 30‑bus transmission network integrated with multiple IEEE 37‑node distribution feeders validate that the proposed framework successfully achieves the desired VSM enhancement while maintaining high estimation accuracy.

Authors:Mohsen Jalaeian Farimani, Roya Khalili Amirabadi, Davoud Nikkhouy, Malihe Abdolbaghi, Mahshad Rastegarmoghaddam, Shima Samadzadeh
Title: A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems
Abstract:
The integration of Model Predictive Control (MPC) and Reinforcement Learning (RL) has emerged as a promising paradigm for constrained decision‑making and adaptive control. MPC offers structured optimization, explicit constraint handling, and established stability tools, whereas RL provides data‑driven adaptation and performance improvement in the presence of uncertainty and model mismatch. Despite the rapid growth of research on RL‑‑MPC integration, the literature remains fragmented, particularly for control architectures built on linear or linearized predictive models. This paper presents a comprehensive Systematic Literature Review (SLR) of RL‑‑MPC integrations for linear and linearized systems, covering peer‑reviewed and formally indexed studies published until 2025. The reviewed studies are organized through a multi‑dimensional taxonomy covering RL functional roles, RL algorithm classes, MPC formulations, cost‑function structures, and application domains. In addition, a cross‑dimensional synthesis is conducted to identify recurring design patterns and reported associations among these dimensions within the reviewed corpus. The review highlights methodological trends, commonly adopted integration strategies, and recurring practical challenges, including computational burden, sample efficiency, robustness, and closed‑loop guarantees. The resulting synthesis provides a structured reference for researchers and practitioners seeking to design or analyze RL‑‑MPC architectures based on linear or linearized predictive control formulations.

Authors:I-Chia Chang, Xinyan Huang, Tzu-Yuan Lin, Sangli Teng, Wenjing Li, Maani Ghaffari, Jingang Yi, Yan Gu
Title: A Survey of Legged Robotics in Non-Inertial Environments: Past, Present, and Future
Abstract:
Legged robots have demonstrated remarkable agility on rigid, stationary ground, but their locomotion reliability remains limited in non‑inertial environments, where the supporting ground moves, tilts, or accelerates. Such conditions arise in ground transportation, maritime platforms, and aerospace settings, and they introduce persistent time‑varying disturbances that break the stationary‑ground assumptions underlying conventional legged locomotion. This survey reviews the state of the art in modeling, state estimation, and control for legged robots in non‑inertial environments. We summarize representative application domains and motion characteristics, analyze the root causes of locomotion performance degradation, and review existing methods together with their key assumptions and limitations. We further identify open problems in robot‑environment coupling, observability, robustness, and experimental validation, and discuss future directions in autonomy, system‑level design, bio‑inspired strategies, safety, and testing. The survey aims to clarify the technical foundations of this emerging area and support the development of reliable legged robots for real‑world dynamic environments.

Authors:Douglas R. Frey
Title: The Riccati Characteristic Equation
Abstract:
The Riccati differential equation is examined in light of its connection to second order linear time varying systems. In that light it becomes the clear generalization for the characteristic equation of linear time invariant systems, and is called the Riccati Characteristic Equation (RCE). Consequently, the RCE becomes the unifying centerpiece for the study of linear systems. Its solutions are considered in complementary pairs that form a continuum based on a primitive pair. Pairs may always be found as purely real solutions, despite the fact that complex conjugate primitive solutions are shown to exist in many cases. Not only is the pairing unique, but the general form of solutions, shown here for the first time, is uniquely compact and encompasses all known solutions, while allowing for all initial conditions. Classical engineering mathematics examples are shown to conform to this approach, which provides new insights to all, especially Floquet theory.

Authors:Douglas R. Frey
Title: A Complete Approach to Time Varying Linear Systems
Abstract:
This paper presents a unifying theory of Linear second order systems that allows time‑varying and time invariant systems to be treated in the same way for the first time. In the process, a transformation is given that diagonalizes an arbitrary time varying state matrix in a spectrum invariant way. A canonical form for the fundamental matrix is given that depends on dynamic eigenvalues and related eigenvectors dependent upon the Riccati Characteristic Equation for the system, which intuitively generalizes the standard characteristic equation for time invariant systems. The technique is shown by examples to give a unified approach to the solutions of time invariant, time‑varying, and periodic systems.

Authors:Juwairiya S. Khan, Mostafa Mohammadi, Alexander L. Ammitzbøll, Ellen-Merete Hagen, Jakob Blicher Izabella Obál, Ana S. S. Cardoso, Oguzhan Kirtas, Rasmus L. Kæseler, John Rasmussen, Lotte N. S. Andreasen Struijk
Title: Clinical Evaluation of a Tongue-Controlled Wrist Abduction-Adduction Assistance in a 6-DoF Upper-Limb Exoskeleton for Individuals with ALS and SCI
Abstract:
Upper‑limb exoskeletons (ULEs) have the potential to restore functional independence in individuals with severe motor impairments; however, the clinical relevance of wrist degrees of freedom (DoF), particularly abduction‑adduction (Ab‑Ad), remains insufficiently evaluated. This study investigates the functional and user‑perceived impact of wrist Ab‑Ad assistance during two activities of daily living (ADLs). Wrist Ab‑Ad assistance in a tongue‑controlled 6‑DoF ULE, EXOTIC2, was evaluated in a within‑subject study involving one individual with amyotrophic lateral sclerosis and five individuals with spinal cord injury. Participants performed drinking and scratch stick leveling tasks with EXOTIC2 under two conditions: with and without wrist Ab‑Ad assistance. Outcome measure included task success, task completion time, kinematic measures, and a usability questionnaire capturing comfort, functional perception, and acceptance. Enabling wrist Ab‑Ad improved task success rates across both ADLs, with consistent reductions in spillage (from 77.8% spillages to 22.2%) and failed placements (from 66.7% to 16.7%). Participants utilized task‑specific subsets of the available wrist range of motion, indicating that effective control within functional ranges was more critical than maximal joint excursion. Questionnaire responses indicated no increase in discomfort with the additional DoF and reflected perceived improvements in task performance. In conclusion, wrist Ab‑Ad assistance enhances functional task performance in assistive exoskeleton use without compromising user comfort. However, its effectiveness depends on task context, control usability, and individual user strategies. This study provides clinically relevant, user‑centered evidence supporting the inclusion of wrist Ab‑Ad in ULEs, emphasizing the importance of balancing functional capability with usability in assistive device design.

Authors:Masahiro Ono, Daniel Selva, Morgan L. Cable, Marie Ethvignot, Margaret Hansen, Andreas M. Hein, Elena-Sorina Lupu, Zachary Manchester, David Murrow, Chad Pozarycki, Pascal Spino, Amanda Stockton, Mathieu Choukroun, Soon-Jo Chung, John Day, Alexander Demagall, Anthony Freeman, Chloe Gentgen, Michel D. Ingham, Charity M. Phillips-Lander, Richard Rieber, Alejandro Salado, Maria Sakovsky, Lori R. Shiraishi, Yisong Yue, Kris Zacny
Title: Planetary Exploration 3.0: A Roadmap for Software-Defined, Radically Adaptive Space Systems
Abstract:
The surface and subsurface of worlds beyond Mars remain largely unexplored. Yet these worlds hold keys to fundamental questions in planetary science ‑ from potentially habitable subsurface oceans on icy moons to ancient records preserved in Kuiper Belt objects. NASA's success in Mars exploration was achieved through incrementalism: 22 progressively sophisticated missions over decades. This paradigm, which we call Planetary Exploration 2.0 (PE 2.0), is untenable for the outer Solar System, where cruise times of a decade or more make iterative missions infeasible. We propose Planetary Exploration 3.0 (PE 3.0): a paradigm in which unvisited worlds are explored by a single or a few missions with radically adaptive space systems. A PE 3.0 mission conducts both initial exploratory science and follow‑on hypothesis‑driven science based on its own in situ data returns, evolving spacecraft capabilities to work resiliently in previously unseen environments. The key enabler of PE 3.0 is software‑defined space systems (SDSSs) ‑ systems that can adapt their functions at all levels through software updates. This paper presents findings from a Keck Institute for Space Studies (KISS) workshop on PE 3.0, covering: (1) PE 3.0 systems engineering including science definition, architecture, design methods, and verification & validation; (2) software‑defined space system technologies including reconfigurable hardware, multi‑functionality, and modularity; (3) onboard intelligence including autonomous science, navigation, controls, and embodied AI; and (4) three PE 3.0 mission concepts: a Neptune/Triton smart flyby, an ocean world explorer, and an Oort cloud reconnaissance mission.

Authors:Juwairiya S. Khan, Mostafa Mohammadi, Alexander L. Ammitzbøll, Ellen-Merete Hagen, Jakob Blicher, Izabella Obál, Ana S. S. Cardoso, Oguzhan Kirtas, Rasmus L. Kæseler, John Rasmussen, Lotte N. S. Andreasen Struijk
Title: A Tendon-Driven Wrist Abduction-Adduction Joint Improves Performance of a 5 DoF Upper Limb Exoskeleton -- Implementation and Experimental Evaluation
Abstract:
Wrist function is essential in performing activities of daily living (ADLs). However, there is limited experimental evidence on the functional impact of wrist Abduction‑Adduction (Ab‑Ad) joint assistance in upper limb exoskeletons (ULEs) for rehabilitation. This study evaluates the effect of implementing an active wrist Ab‑Ad joint in a five degree of freedom (DoF) ULE, EXOTIC2 exoskeleton, to support individuals with severe motor impairments. Methods: A compact, lightweight wrist module with tendon‑driven abduction and spring‑driven adduction was integrated into the EXOTIC exoskeleton. Eight adults with no motor disabilities completed drinking and scratching tasks under randomized wrist‑enabled and wrist‑locked conditions along with a preliminary feasibility test in one individual with Amyotrophic lateral sclerosis (ALS). Kinematic and task performance metrics including wrist range of motion, task completion time, spillage and leveling metrics were assessed. Results: Implementing the wrist Ab‑Ad DoF improved task success metrics. Spill incidence during the drinking task decreased from 56% to 3%, and leveling success for scratching task improved from 28% to 75%. Conclusion: Integrating wrist Ab‑Ad assistance improved key functional task outcomes without increasing execution time. Significance: The study provides the experimental evidence that active wrist Ab‑Ad control enhances task‑level performance in exoskeleton‑assisted ADLs.

Authors:Jong Ha Woo, Qi Xiao, Yu Ma, Zishuo Yang, Victor Daldegan Paduani, Ning Lu
Title: Accurate Frequency Response Modeling in Integrated T&D Co-Simulation via EWMA-RTTA-Based Quadratic Extrapolation
Abstract:
The large‑scale integration of inverter‑based resources (IBRs), particularly distributed photovoltaics (DPVs), into distribution networks increases the need for integrated transmission and distribution (T&D) co‑simulation. A key challenge in such co‑simulation lies in accurately modeling system frequency across two asynchronous simulation environments. For example, the transmission system, simulated in the phasor domain, can operate with a simulation timestep of 10 ms, while the distribution system, simulated in the electromagnetic transient domain (EMT) to include IBR models, uses a much finer timestep of 100 microseconds. To ensure accurate PLL‑based frequency estimation in distribution systems, it is essential to predict voltage magnitude and phase angle variations within the 10 ms transmission intervals, rather than using constant values that cause inaccurate frequency calculations. This issue becomes particularly critical when modeling primary and secondary frequency response services provided by IBRs. To address this challenge, we propose an automated Exponentially Weighted Moving Average Real‑Time Threshold Adaptation (EWMA‑RTTA) method, which utilizes Quadratic Extrapolation to predict voltage magnitude and phase angle trends more precisely. The proposed method is validated using two Opal‑RT simulators: one simulating an IEEE 118‑bus transmission system and the other simulating an IEEE 123‑bus distribution network. Simulation results demonstrate that our approach improves the normalized mean absolute error (nMAE) by a factor of 25.7 compared to methods that do not account for time mismatches, offering a scalable and accurate solution for modeling IBR‑based frequency response in modern power systems.

Authors:Sreechakra Vasudeva Raju Rachavelpula, Sangwhan Cha
Title: Personalized electric vehicle energy consumption estimation framework that integrates driver behavior with map data
Abstract:
This paper presents a personalized Battery Electric Vehicle (BEV) energy consumption estimation framework that integrates map‑based contextual features with driver‑specific velocity prediction and physics‑based energy consumption modeling. The system combines route selection, detailed road feature processing, a rule‑based reference velocity generator, a PID controller‑based vehicle dynamics simulator, and a Bidirectional LSTM model trained to reproduce individual driving behavior. The predicted individual‑specific velocity profiles are coupled with a quasi‑steady backward energy consumption model to compute tractive power, regenerative braking, and State‑of‑Charge (SOC) evolution. Evaluation across urban, freeway, and hilly routes demonstrates that the proposed approach captures key driver behavioral patterns such as deceleration at intersections, speed‑limit tracking, and road grade‑dependent responses, while producing accurate power and SOC trajectories. The results highlight the effectiveness of combining learned driver behavior with map‑based context and physics‑based energy consumption modeling to produce accurate, personalized BEV SOC depletion profiles.

Authors:Gelin Chen, Chen Song, Chao Yang
Title: RG-Based Local Hopf Reduction and Slow-Manifold Reconstruction for Nonlinear Aeroelastic Systems
Abstract:
Self‑excited limit‑cycle oscillations (LCOs) from Hopf bifurcations are a key feature of nonlinear aeroelasticity and depend sensitively on structural and aerodynamic parameters. Classical center‑manifold and normal‑form theory describe this local behavior, but can be cumbersome to apply in large discretized models and standard reduced‑order modeling (ROM) workflows. A renormalization‑group (RG)‑based reduction is developed that directly yields a Hopf‑type amplitude equation on a local invariant manifold, specialized for polynomial nonlinearities in tensor‑based discretizations and compatible with finite‑element‑type settings. The method provides explicit coefficients governing the Hopf threshold, criticality, and leading LCO amplitude/frequency trends, and admits a companion slow‑manifold approximation with selected stable modes retained as static coordinates. Representative nonlinear‑aeroelastic examples illustrate how the proposed framework supplies compact, parameter‑aware Hopf/LCO descriptors suitable for local ROM construction near flutter.

Authors:Jinliang Xu
Title: Decoupling Speculation from Merit: The Identity-Bound Asset Integrity Model (IBAIM) for Sustainable Web3 Gaming
Abstract:
The rapid collapse of decentralized game economies, often characterized by the death spiral, remains the most formidable barrier to the mass adoption of Web3 gaming. This paper proposes that the sustainability of an open game economy is predicated on three necessary and sufficient conditions: Anti‑Sybil Resilience, Anti‑Capital Dominance, and Anti‑Inflationary Saturation. The first section establishes a theoretical proof of these conditions, arguing that the absence of any single dimension leads to systemic failure. The second section explores the dialectical relationship between these dimensions, illustrating how unchecked automation and capital‑driven monopolies accelerate asset hyperinflation. In the third section, we introduce the Identity‑Bound Asset Integrity Model (IBAIM) as a comprehensive technical solution. IBAIM utilizes Zero‑Knowledge (ZK) biometric hashing and Account Abstraction (AA) to anchor asset utility to unique human identities through a privacy‑preserving and regulatory‑compliant architecture. By exogenizing biometric verification to trusted local environments and utilizing Zero‑Knowledge Proofs of Identity (zk‑PoI), the model ensures absolute user privacy. Furthermore, by implementing an Asymmetric Utility Decay (AUD) engine‑whereby assets suffer a vertical 50% utility cliff upon secondary transfer‑and an entropy‑driven thermodynamic degradation mechanism., the model successfully decouples financial speculation from in‑game merit. Finally, we apply this framework to analyze prominent historical failures in the GameFi sector, demonstrating that their collapse was an inevitable consequence of violating these core economic constraints. Our findings suggest that trading a degree of asset liquidity for system integrity is the only viable path toward long‑term economic viability in decentralized virtual worlds.

Authors:Peter Collett, Alexander Johannes Stasik, Simone Casolo, Signe Riemer-Sørensen
Title: Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health
Abstract:
Accurate condition monitoring of industrial equipment requires inferring latent degradation parameters from indirect sensor measurements under uncertainty. While traditional Bayesian methods like Markov Chain Monte Carlo (MCMC) provide rigorous uncertainty quantification, their heavy computational bottlenecks render them impractical for real‑time process control. To overcome this limitation, we propose an AI‑driven framework utilizing Simulation‑Based Inference (SBI) powered by amortized neural posterior estimation to diagnose complex failure modes in heat exchangers. By training neural density estimators on a simulated dataset, our approach learns a direct, likelihood‑free mapping from thermal‑fluid observations to the full posterior distribution of degradation parameters. We benchmark this framework against an MCMC baseline across various synthetic fouling and leakage scenarios, including challenging low‑probability, sparse‑event failures. The results show that SBI achieves comparable diagnostic accuracy and reliable uncertainty quantification, while accelerating inference time by a factor of82× compared to traditional sampling. The amortized nature of the neural network enables near‑instantaneous inference, establishing SBI as a highly scalable, real‑time alternative for probabilistic fault diagnosis and digital twin realization in complex engineering systems.

Authors:William Scarbro, Ravi Mangal
Title: Interval POMDP Shielding for Imperfect-Perception Agents
Abstract:
Autonomous systems that rely on learned perception can make unsafe decisions when sensor readings are misclassified. We study shielding for this setting: given a proposed action, a shield blocks actions that could violate safety. We consider the common case where system dynamics are known but perception uncertainty must be estimated from finite labeled data. From these data we build confidence intervals for the probabilities of perception outcomes and use them to model the system as a finite Interval Partially Observable Markov Decision Process with discrete states and actions. We then propose an algorithm to compute a conservative set of beliefs over the underlying state that is consistent with the observations seen so far. This enables us to construct a runtime shield that comes with a finite‑horizon guarantee: with high probability over the training data, if the true perception uncertainty rates lie within the learned intervals, then every action admitted by the shield satisfies a stated lower bound on safety. Experiments on four case studies show that our shielding approach (and variants derived from it) improves the safety of the system over state‑of‑the‑art baselines.

Authors:Rebekah Edwards, Taylor Martini, Jonathan E. Swindell, David W. Cox, Adam C. Goad, Austin Egbert, Charles Baylis, Robert J. Marks
Title: Low-Cost Turntable Designed for RF Phased Array Antenna Active Element Pattern Measurement
Abstract:
Accurate antenna array calibrations and measurements of aspects such as active element pattern (AEP) are critical for enabling integrated sensing and communication (ISAC) technologies such as directional modulation. One reliable way of obtaining accurate and repeatable AEP measurements is to spin the antenna array on a turntable, but many turntables designed for antenna array measurements are prohibitively expensive for small labs and may not be designed with RF considerations, such as cable phase stability, in mind. This paper details the design of a motorized 3D printed turntable for use in directional modulation and in‑situ measurement experiments that will allow for rotation of an antenna array around a point, such that the far field of the antenna pattern can be measured by a stationary receiver.

Authors:Pascal Archambault, Houari Sahraoui, Eugene Syriani
Title: Generative Flow Networks for Model Adaptation in Digital Twins of Natural Systems
Abstract:
Digital twins of natural systems must remain aligned with physical systems that evolve over time, are only partially observed, and are typically modeled by mechanistic simulators whose parameters cannot be measured directly. In such settings, model adaptation is naturally posed as a simulation‑based inference problem. However, sparse and indirect observations often fail to identify a unique and optimal calibration, leaving several simulator parameterizations compatible with the available evidence. This article presents a GFlowNet‑based approach to model adaptation for digital twins of natural systems. We formulate adaptation as a generative modeling problem over complete simulator configurations, so that plausible parameterizations can be sampled with probability proportional to a reward derived from agreement between simulated and observed behavior. Using a controlled environment agriculture case study based on a mechanistic tomato model, we show that the learned policy recovers dominant regions of the adaptation landscape, retrieves strong calibration hypotheses, and preserves multiple plausible configurations under uncertainty.

Authors:Bowen Li, Jiping Luo, Themistoklis Charalambous, Nikolaos Pappas
Title: Model Predictive Communication for Timely Status Updates in Low-Altitude Networks
Abstract:
Timely information delivery in low‑altitude networks is critical for many time‑sensitive applications, such as unmanned aerial vehicle (UAV) navigation, inspection, and surveillance. The key challenge lies in balancing three competing factors: stringent data freshness requirements, UAV onboard energy consumption, and interference with terrestrial services. Addressing this challenge requires not only efficient power and channel allocation strategies but also effective communication timing over the entire operation horizon. In this work, we propose a model predictive communication (MPComm) framework, enabled by advanced channel sensing techniques, in which the channel conditions that the UAV will experience are largely predictable. Within this framework, we formulate a constrained bi‑objective optimization problem to achieve a desired trade‑off between energy consumption and terrestrial channel occupation, subject to a strict timeliness constraint. We solve this problem using Pareto analysis and show that the original non‑convex, mixed‑integer problem can be decomposed into a two‑layer structure: the outer layer determines the optimal communication timing, while the inner layer determines the optimal power and channel allocation for each communication interval. An efficient algorithm for the inner problem is developed using non‑convex analysis, with asymptotic optimality guarantees, while the outer problem is solved optimally via a simple graph search, with edges characterized by inner solutions. The proposed approach applies to a broad class of problem variants, including objective transformations and single‑objective specializations. Numerical results demonstrate the efficiency of the proposed solution, achieving up to a six‑fold reduction in terrestrial channel occupation and a 6dB energy saving compared to benchmark schemes.

Authors:Patrick Wilk, Ethan Cantor, Yikui Liu, Jie Li
Title: A Hierarchical MARL-Based Approach for Coordinated Retail P2P Trading and Wholesale Market Participation of DERs
Abstract:
The ongoing shift towards decentralization of the electric energy sector, driven by the growing electrification across end‑use sectors, and widespread adoption of distributed energy resources (DERs), necessitates their active participation in the electricity markets to support grid operations. Furthermore, with bi‑directional energy and communication flows becoming standard, intelligent, easy‑to‑deploy, resource‑conservative demand‑side participation is expected to play a critical role in securing power grid operational flexibility and market efficiency. This work proposes a market engagement framework that leverages a hierarchical multi‑agent deep reinforcement learning (MARL) approach to enable individual prosumers to participate in peer‑to‑peer retail auctions and further aggregate these intelligent prosumers to facilitate effective DER participation in wholesale markets. Ultimately, a Stackelberg game is proposed to coordinate this hierarchical MARL‑based DER market participation framework toward enhanced market performance.

Authors:Eric Tönges, Andrea Schoen, Frank Marten, Marco Pau, Denis Mende
Title: Designing Active Operation in Low-Voltage Distribution Grids: Requirements, Interfaces and Roadmap
Abstract:
This paper outlines a pathway towards active operation of lowvoltage distribution grids. In these grids, the growing deployment of distributed generation, controllable demand and storage, together with the roll‑out of intelligent metering systems, creates new requirements and opportunities for distribution system operators. On the basis of the German and European regulation, and in particular of recent directives enabling grid‑oriented interventions and market‑based procurement of flexibility, the paper identifies three key pillars for active low‑voltage operation: (a) measurement placement and observability, (b) secure and interoperable information and communication architectures and interfaces, and (c) integration of market‑based and gridoriented optimisation for controlling connected assets. A structured system overview is developed that specifies main actors and data flows, highlighting central research topics across these pillars. Building on this, a four‑phase roadmap is presented, spanning requirements and use‑case definition, method development and simulation, laboratory and field validation, and roll‑out with system‑level feedback, thus providing guidance for distribution system operators and researchers.

Authors:Zirui Niu, Antoine Girard, Giordano Scarciotti
Title: Approximate Simulation-based Hierarchical Control of Nonlinear Systems
Abstract:
Controlling complex dynamical systems to satisfy sophisticated specifications remains a significant challenge in modern engineering. A promising approach to this problem is the approximate simulation‑based hierarchical control (ASHC) technique. In this method, a simplified representation of the complex system, called the abstract system, is first designed and controlled. An interface function is then designed to translate the control law into the input of the complex system, thereby achieving approximate control synthesis. However, most existing results in ASHC are only for linear systems. This paper proposes a constructive method for solving the ASHC problem for nonlinear systems. To this end, we propose invariance equation‑based methods to achieve the two classical requirements of the ASHC technique, namely the bounded output discrepancy and the m‑relation. We then study the solvability conditions of the problem and summarise the overall design procedures. We illustrate the results with a practical example, providing step‑by‑step solutions to the ASHC problem of a DC‑to‑DC Ćuk converter.

Authors:Bhola, Yu-Jia Chen, Ashutosh Balakrishnan, Swades De, Li-Chun Wang
Title: Adaptive Multi-UAV Relay Deployment Framework in Satellite Aerial Ground Integrated Systems
Abstract:
The sixth generation (6G) communication networks are expected to provide high data rates, ultra‑reliable communication, and massive connectivity, especially in challenging environments such as dense urban areas and disaster‑affected regions. However, traditional terrestrial‑only networks face significant challenges in these scenarios, including signal blockages from high‑rise buildings, traffic congestion, and dynamic user distributions. To address these limitations, we propose the adaptive multi‑UAV deployment (AMUD) framework within satellite air‑ground integrated networks (SAGINs). The AMUD framework dynamically deploys amplify‑and‑forward multiple unmanned aerial vehicle relay (UAVr) in with low Earth orbit (LEO) satellites to improve coverage, alleviate congestion, and ensure reliable communication in non‑line‑of‑sight and high‑demand conditions. We formulate an optimization problem that aims to jointly maximize the energy efficiency of the total network and the total capacity while ensuring the fairness of the total capacity and satisfying the users' requirements. The simulation results demonstrate that AMUD improves the total capacity of the network, improves the total energy efficiency, and increases the fairness of the capacity compared to traditional LEO satellite and ground base station (LEO‑GBS) only systems.

Authors:Simone Baroncini, Bahman Gharesifard, Giuseppe Notarstefano
Title: On Reward-Balancing Methods for Reinforcement Learning
Abstract:
This paper investigates the so‑called reward‑balancing methods, a novel class of algorithms for solving discounted‑return reinforcement learning (RL) problems. These methods consist of iteratively adjusting the reward function to transform the RL problem into an equivalent one in which the optimal policies are greedy. For this procedure, referred to as normalization process, we provide a theoretical analysis of the involved transformations, emphasizing their algebraic structure. Then, we introduce a control‑theoretic reformulation, recasting the reward‑balancing procedure into an optimal control framework. The approach is further extended to address model uncertainty through stochastic model sampling, yielding normalization guarantees and probabilistic bounds on stochastic fluctuations. Using the proposed optimal control framework within a scenario model predictive control (MPC) setting, we demonstrate, through simulation studies, performance improvements over the current state‑of‑the‑art.

Authors:Eray Unsal Atay, Venkat Chandrasekaran, Victoria Kostina
Title: Rate-Cost Tradeoffs in Nonlinear Control
Abstract:
We study the rate‑cost tradeoff in rate‑limited control of general stochastic control systems, including nonlinear systems, over a finite horizon. At each time step, an encoder observes the state and transmits a description to a controller, which then selects the control action. For an average control‑cost threshold D, we characterize the minimum achievable communication rate R_n(D) via a nonasymptotic bound: R_n(D) lies within an additive logarithmic gap of the optimal value of a directed‑information minimization F_n(D), namely, we show that F_n(D) \le R_n(D) \le F_n(D)+\log \bigl(F_n(D)+3.4\bigr)+2+\frac1n, in bits. This establishes directed information as the operationally relevant quantity governing rate‑limited control, thereby broadening its utility beyond its previously established roles in causal source coding and linear quadratic Gaussian (LQG) control to general nonlinear control systems. We prove the upper bound constructively by building an encoding‑and‑control policy using the strong functional representation lemma at each time step. As special cases of our setting, our framework yields nonasymptotic bounds for sequential (causal) rate‑distortion and LQG control.

Authors:Ruixing Ren, Minjie Wei, Junhui Zhao
Title: Lightweight Low-SNR-Robust Semantic Communication System for Autonomous Driving
Abstract:
Image transmission for vehicle‑to‑vehicle collaborative perception in autonomous driving faces challenges including limited on‑board terminal resources, time‑varying wireless channel fading, and poor robustness under low signal‑to‑noise (SNR) ratio. Traditional separate source‑channel coding schemes suffer from the cliff effect, while existing semantic communication models are limited by large parameter sizes and weak digital compatibility. This paper proposes a lightweight, low‑SNR‑robust deep joint source‑channel coding (JSCC) semantic communication system. First, structured pruning is implemented based on batch normalization layer scaling factors and L1 regularization, which significantly reduces model complexity while ensuring image reconstruction quality. Second, a uniform quantization and M‑QAM modulation scheme adapted to JSCC features is designed, and a training‑deployment separation strategy is adopted to address the non‑differentiable quantization problem, enabling compatibility with existing digital communication systems. Simulation results on the Cityscapes dataset show that the pruned model maintains comparable performance and robustness to the original one, even with over half of its parameters removed. Notably, the proposed scheme exhibits significant advantages over conventional communication methods under low SNR conditions.

Authors:Aleksandra Lekić, Nikola Petrović, Dušan Stipanović
Title: Controlling the Ćuk Converter using Piecewise Linear Lyapunov Functions
Abstract:
In this paper we design a switching control law for the Ćuk converter in the continuous conduction mode using piecewise linear Lyapunov functions. These Lyapunov functions can be constructed using different number of state variables affecting the system's performance. In the paper, some representative simulations covering construction of different piecewise Lyapunov functions, are provided.

Authors:Aleksandra Lekić, Dušan Stipanović
Title: LMI Approach for Sliding Mode Control and Analysis of DC-DC Converters
Abstract:
Circuits' and in particular DC/DC converters' switching behavior is analyzed in this paper using the equivalent control modeling of the dynamic systems' sliding mode regime. As a representative example and also being one of the most complex circuits among DC/DC converters, the Ćuk converter is chosen. It is shown how the converter's behavior in the steady state regime can be studied and analyzed by the linear matrix inequalities based stability conditions for linear dynamic systems with nonlinear sector bounded perturbations. The maximization of the nonlinear sector bound provides a limit for applying the linear ripple approximation in the converter operation analysis. Furthermore, our approach is validated by providing simulation results for two different switching surfaces of practical interest.

Authors:Chayan Kumar Paul, Krishanu Nath, Indra Narayan Kar, Denis Efimov, Rosane Ushirobira
Title: Robust Fixed-Time Model Reference Adaptive Control
Abstract:
This article proposes a Model Reference Adaptive Control (MRAC) strategy to achieve fixed‑time convergence of parameter estimation and tracking errors for unknown linear time‑invariant systems, without relying on the persistence of excitation condition. Instead, it employs a less restrictive initial/interval excitation condition on the regressor matrix, enhancing practicality and ease of implementation in real‑world scenarios. Our primary contribution is a novel parameter update law within the indirect MRAC framework, ensuring that parameter estimates converge within a fixed time, once the initial/interval excitation condition is met. This approach simplifies the practical requirements for adaptive control while guaranteeing robust performance against parameter uncertainty and external disturbances. Simulation results provide a comparison with the current literature to validate the effectiveness of this approach.

Authors:Gobinda Chandra Sarker, Nathan Dahlin
Title: Risk-Aware Hosting Capacity Analysis for Flexible Load Interconnection in Distribution Networks
Abstract:
The increasing penetration of flexible loads, such as electric vehicles and AI data‑centers necessitates new methodologies for quantifying electrical load hosting capacity under operational constraints and flexible connection agreements. We propose a risk‑aware hosting capacity framework that explicitly accounts for both flexibility, in the form of load curtailment, and system reliability. The proposed method incorporates a Conditional Value‑at‑Risk (CVaR) constraint to control the tail risk of excessive curtailment, ensuring that extreme interventions remain limited. Additionally, a weighted \ell_1 approach is introduced to limit the number of utility‑controlled interventions, enabling control over the frequency of curtailment actions. A regularization parameter is used to tune the intervention count to a desired intervention budget. The resulting optimization formulation is convex and efficiently solvable, allowing scalable implementation. Numerical results demonstrate that the proposed method significantly increases hosting capacity while maintaining strict risk guarantees and limiting intervention frequency, providing a practical balance between flexibility and reliability in distribution systems.

Authors:Ching-Yi Lin, Sahil Shah
Title: Design Space Exploration for ReRAM-based Architectures to Address Scaling Non-idealities
Abstract:
ReRAM‑based in‑memory computing (IMC) architectures are promising candidates for energy‑efficient matrix‑vector multiplication. While scaling the size of ReRAM arrays allows for the amortization of power‑hungry peripheral circuits like DACs and ADCs, it simultaneously introduces more parasitic along the signal path. Because of these challenges, current design methodologies often lack practical guidelines to balance these effects at early design stage, forcing designers to rely on time‑consuming, iterative transistor‑level simulations. In this work, we propose a comprehensive framework for design space exploration that enables the selection of optimal array size, ADC resolution, and system frequency without requiring exhaustive simulations. The framework utilizes a specialized testbench to extract parameters from a limited set of representative transistor‑level simulations. These parameters are then used to accurately predict the performance of arbitrary architectures. We demonstrate the effectiveness of this framework through two realistic design cases aimed at maximizing energy efficiency (TOPs/s/W). The results show that the framework successfully identifies optimal architectural configurations under strict power and error constraints, providing an efficient path for high‑performance IMC design.

Authors:Hidekazu Yoshioka
Title: Forward-looking evolutionary game dynamics subject to exploration cost
Abstract:
We extend classical evolutionary game dynamics based on the momentary action choices of agents by accounting for two elements: forward‑looking behavior and exploration cost. We focus on pairwise comparison protocols that cover major evolutionary game dynamics, such as replicator and logit models. In the proposed mathematical framework, agents update their actions by paying a cost so that a utility or its relative difference is maximized. We show that forward‑looking behavior can be modeled as a coupling between the evolutionary game dynamic and static Hamilton‑Jacobi‑Bellman equation: a mean field game. The exploration cost and its constraint are naturally related to these equations as a function of the optimal Lagrangian multiplier serving as a relaxation parameter, and it is incorporated into the game as a constraint. We show that under certain conditions, our evolutionary game dynamic admits a unique solution. Finally, we computationally investigate one‑ and two‑dimensional problems.

Authors:Fangji Wang, Siddhartha Ganguly, Panagiotis Tsiotras
Title: Covariance Steering of Discrete-Time Markov Jump Linear Systems with Multiplicative Noise
Abstract:
We study a finite‑horizon covariance steering problem for discrete‑time Markov jump linear systems (MJLS) with both state‑ and control‑dependent multiplicative noise. The objective is to minimize a quadratic running cost while steering the system from given mode‑conditioned initial means and covariances to a prescribed terminal mean and covariance. We first show that, without loss of generality, feasible controls may be represented by mode‑dependent linear feedback together with feedforward and independent random components, and we highlight that, in contrast to the case without multiplicative noise, a purely affine state‑feedback law does not in general suffice. To this end, we introduce a lifted‑state formulation that embeds the mean and covariance information into a unified second‑moment description, and we prove that the resulting lifted problem is equivalent to the original covariance steering problem formulation. This leads to a lossless relaxation in moment variables and an SDP reformulation for the unconstrained case. We further study chance‑constrained covariance steering with ball and half‑space constraints on the state and control, derive tractable sufficient convex surrogates, and establish an iterative reference‑update scheme to reduce conservatism. Numerical experiments on a finance application illustrate our results.

Authors:Wenjian Hao, Yuxuan Fang, Zehui Lu, Shaoshuai Mou
Title: Efficient Reinforcement Learning using Linear Koopman Dynamics for Nonlinear Robotic Systems
Abstract:
This paper presents a model‑based reinforcement learning (RL) framework for optimal closed‑loop control of nonlinear robotic systems. The proposed approach learns linear lifted dynamics through Koopman operator theory and integrates the resulting model into an actor‑critic architecture for policy optimization, where the policy represents a parameterized closed‑loop controller. To reduce computational cost and mitigate model rollout errors, policy gradients are estimated using one‑step predictions of the learned dynamics rather than multi‑step propagation. This leads to an online mini‑batch policy gradient framework that enables policy improvement from streamed interaction data. The proposed framework is evaluated on several simulated nonlinear control benchmarks and two real‑world hardware platforms, including a Kinova Gen3 robotic arm and a Unitree Go1 quadruped. Experimental results demonstrate improved sample efficiency over model‑free RL baselines, superior control performance relative to model‑based RL baselines, and control performance comparable to classical model‑based methods that rely on exact system dynamics.

Authors:Ansel Kaplan Erol, Divya Mahajan
Title: Equinox: Decentralized Scheduling for Hardware-Aware Orbital Intelligence
Abstract:
Earth‑observation satellites are emerging as distributed edge platforms for time‑critical tasks, yet orbital scheduling remains challenged by intermittent energy harvesting and temporal coupling where eager execution risks future battery depletion. Existing schedulers rely on static priorities and lack mechanisms to adaptively shed work. We present Equinox, a lightweight, decentralized runtime for resource‑constrained orbital systems. Equinox enables adaptive scheduling by compressing time‑varying constraints, including battery charge, thermal headroom, and queue backlog, into a single state‑dependent marginal cost of execution. Derived from a barrier function that rises sharply near safety limits, this cost encodes both instantaneous pressure and future risk. This local signal serves as a constellation‑wide coordination primitive. Tasks execute only when their value exceeds the current cost, enabling value‑ordered load shedding without explicit policies. If local costs exceed a neighbor's, tasks are dynamically offloaded over inter‑satellite links, achieving distributed load balancing without routing protocols or global state. We evaluate Equinox using a multi‑day simulation of a 143‑satellite constellation grounded in physical Jetson Orin Nano measurements. Equinox improves scientific goodput by 20% and image‑processing throughput by 31% over priority‑based scheduling while maintaining 2.2x higher mean battery reserves. Under high demand, Equinox achieves 5.2x the execution rate of static scheduling by gracefully shedding work rather than collapsing under contention.

Authors:Mads R. Almassalkhi, Dakota Hamilton, Hasan Giray Oral, Yury Dvorkin, Dennice Gayme, Bri-Mathias Hodge, Brian Vad Mathiesen, Jakob Stoustrup, Tobias Ritschel, Rune G. Junker, Shahab Tohidi, Razgar Ebrahimy, Henrik Madsen
Title: Cross-Atlantic Research Agenda for Scalable Grid Architectures and Distributed Flexibility
Abstract:
Electric power systems are rapidly evolving into deeply digital, cyber‑physical infrastructures in which large fleets of distributed energy resources must be coordinated as system‑level flexibility across multiple spatial and temporal scales. Despite growing distributed energy resource deployment, existing grid and market architectures lack scalable, interoperable mechanisms to reliably translate device‑level flexibility into grid‑aware services, creating risks to reliability, affordability, and resilience at high penetration. We propose that scalable and reliable coordination of distributed energy resource‑based flexibility in future power systems is fundamentally an architectural problem that can be addressed through laminar cyber‑physical design using minimal, standardized interoperability interfaces that link device autonomy with system‑level objectives. To assess this claim, we present and discuss a layered cyber‑physical systems architecture and explicate its implementation through standards‑based interfaces, Flexibility Functions, hierarchical control, and case studies spanning U.S. and Danish regulatory, market, and operational contexts. Empirical evidence from New York's Grid of the Future proceedings, Danish Smart Energy Operating System pilots, and operational aggregator deployments demonstrates that such architecture enables predictable, grid‑aware flexibility while preserving device autonomy, interoperability, reliability, and quality of service. These results support a cross‑Atlantic research agenda centered on joint testbeds, harmonized interoperability mechanisms, and coordinated policy experiments to accelerate the deployment of resilient, scalable, and flexible clean energy systems.

Authors:David E. J. van Wijk, Tamas G. Molnar, Samuel Coogan, Manoranjan Majji, Aaron D. Ames, Joel W. Burdick
Title: Output Feedback Backup Control Barrier Functions: Safety Guarantees Under Input Bounds and State Estimation Error
Abstract:
Guaranteeing the safety of controllers is vital for real‑world applications, but is markedly difficult when the states are not perfectly known and when the control inputs are bounded. Backup control barrier functions (bCBFs) use predictions of the flow under a prescribed controller to achieve safety in the presence of bounded inputs and perfect state information. However, when only an estimate of the true state is known, this flow may not be precisely computed, as the initial condition is unknown. Furthermore, the true flow evolves using feedback from the estimated state, thus introducing coupling between known and unknown flows. To address these challenges, we propose a technique that leverages an uncertainty envelope centered around the estimated flow and show that ensuring the safety of this envelope guarantees that the true state satisfies the safety constraints. Additionally, we show that in the presence of state uncertainty, using the resulting Output Feedback Backup Control Barrier Functions (O‑bCBFs), there always exists a feasible control input that can guarantee the safety of the true state, even in the presence of input constraints.

Authors:Jian Huang, Zixiang Ming, Yongli Zhu, Linna Xu
Title: On-Meter Graph Machine Learning: A Case Study of PV Power Forecasting for Grid Edge Intelligence
Abstract:
This paper presents a detailed study of how graph neural networks can be used on edge intelligent meters in a microgrid to forecast photovoltaic power generation. The problem background and the adopted technologies are introduced, including ONNX and ONNX Runtime. The hardware and software specifications of the smart meter are also briefly described. Then, the paper focuses on the training and deployment of two graph machine learning models, GCN and GraphSAGE, with particular emphasis on developing and deploying a customized ONNX operator for GCN. Finally, a case study is conducted using real datasets from a village microgrid. The performance of the two models is compared on both the PC and the smart meter, exhibiting successful deployments and executions on the smart meter.

Authors:Houchao Gan
Title: A Network-Aware Evaluation of Distributed Energy Resource Control in Smart Distribution Systems
Abstract:
Distribution networks with high penetration of Distributed Energy Resources (DERs) increasingly rely on communication networks to coordinate grid‑interactive control. While many distributed control schemes have been proposed, they are often evaluated under idealized communication assumptions, making it difficult to assess their performance under realistic network conditions. This work presents an implementation‑driven evaluation of a representative virtual power plant (VPP) dispatch algorithm using a co‑simulation framework that couples a linearized distribution‑system model with packet‑level downlink emulation in ns‑3. The study considers a modified IEEE~37‑node feeder with high photovoltaic penetration and a primal‑‑dual VPP dispatch that simultaneously targets feeder‑head active power tracking and voltage regulation. Communication effects are introduced only on the downlink path carrying dual‑variable updates, where per‑DER packet delays and a hold‑last‑value strategy are modeled. Results show that, under ideal communication, the dispatch achieves close tracking of the feeder‑head power reference while maintaining voltages within the prescribed limits at selected buses. When realistic downlink delay is introduced, the same controller exhibits large oscillations in feeder‑head power and more frequent voltage limit violations. These findings highlight that distributed DER control performance can be strongly influenced by communication behavior and motivate evaluation frameworks that explicitly incorporate network dynamics into the assessment of grid‑interactive control schemes.

Authors:Kai Heussen, Jawad Kazmi, Narges Mehran, Artjoms Obushevs, Terence O'Donnell, Thomas I. Strasser
Title: Towards Reproducible Test Annotation for Cyber-Physical Energy Systems using Ontology-driven Dataspaces
Abstract:
Reproducibility, traceability, and transparency in testing cyber‑physical energy systems are crucial for scientific advancement and cross‑laboratory collaboration. Current experimentation and test documentation practices lack formal semantics, making it difficult to reproduce experiments, share data, and apply, for example, the artificial intelligence‑driven analysis. A dataspace that relies on structured ontologies aims to address these gaps by providing machine‑actionable descriptions. In this work, we outline an ontology‑driven approach for reproducibility of cyber‑physical energy systems testing and illustrate its applicability through representative cross‑laboratory use cases, demonstrating feasibility while identifying remaining semantic and metadata gaps that limit reproducibility. Based on these observations, we propose an open three‑viewpoint ontology framework to guide future ontology extensions.

Authors:Tongxin Li
Title: Safety-Critical Contextual Control via Online Riemannian Optimization with World Models
Abstract:
Modern world models are becoming too complex to admit explicit dynamical descriptions. We study safety‑critical contextual control, where a Planner must optimize a task objective using only feasibility samples from a black‑box Simulator, conditioned on a context signal ξ_t. We develop a sample‑based Penalized Predictive Control (PPC) framework grounded in online Riemannian optimization, in which the Simulator compresses the feasibility manifold into a score‑based density \hatp(u \mid ξ_t) that endows the action space with a Riemannian geometry guiding the Planner's gradient descent. The barrier curvature κ(ξ_t), the minimum curvature of the conditional log‑density ‑\ln\hatp(\cdot\midξ_t), governs both convergence rate and safety margin, replacing the Lipschitz constant of the unknown dynamics. Our main result is a contextual safety bound showing that the distance from the true feasibility manifold is controlled by the score estimation error and a ratio that depends on κ(ξ_t), both of which improve with richer context. Simulations on a dynamic navigation task confirm that contextual PPC substantially outperforms marginal and frozen density models, with the advantage growing after environment shifts.

Authors:Aleksandra Lekić, Vujo Drndarević
Title: Simulation of Switching Converters Using Linear Capacitor Voltage and Inductor Current Prediction and Correction
Abstract:
In this paper an algorithm for transient simulation of switching converters using prediction and correction to calculate duty ratio is proposed. It provides large signal simulation on the level of averaged currents and voltages in the circuit. Calculation of duty ratio using inductor current and capacitor voltage prediction and correction do not require their priori knowledge. Number of circuit solving per switching period is fixed and equal to two. Using this algorithm various of constant frequency regulated switching converters can be simulated. Due to predetermined circuit values convergence problems are avoided. This algorithm results in very fast and accurate large signal simulation.

Authors:Donghwan Lee
Title: Lyapunov-Certified Direct Switching Theory for Q-Learning
Abstract:
Q‑learning is a fundamental algorithmic primitive in reinforcement learning. This paper develops a new framework for analyzing Q‑learning from a switching‑system viewpoint. In particular, we derive a direct stochastic switching‑system representation of the Q‑learning error. The key observation is that the Bellman maximization error can be expressed exactly as an average of action‑wise Q‑errors under a suitable stochastic policy. The resulting recursion has a switched linear conditional‑mean drift and martingale‑difference noise. To the best of our knowledge, this is the first convergence‑rate analysis of standard Q‑learning whose leading exponential rate is expressed through the joint spectral radius (JSR) of a direct switching family. Since the JSR is the exact worst‑case exponential rate of the associated switched linear drift, the resulting rate is among the tightest drift‑based rates that can be certified for this Q‑learning representation. Building on this representation, we prove finite‑time bounds based on a product‑defined JSR‑induced Lyapunov function and also give an optional common quadratic Lyapunov certificate. The quadratic certificate is only a sufficient condition and hence applies only to instances for which the certificate is feasible, whereas the JSR‑induced Lyapunov construction applies to the full direct switching family whenever its JSR is below one. When feasible, the quadratic certificate replaces product‑based verification by a computable matrix inequality and gives a simpler stochastic bound. We further extend the framework to Markovian observation models.

Authors:Ziqin Chen, Zuang Wang, Yongqiang Wang
Title: Accelerating Optimization and Machine Learning through Decentralization
Abstract:
Decentralized optimization enables multiple devices to learn a global machine learning model while each individual device only has access to its local dataset. By avoiding the need for training data to leave individual users' devices, it enhances privacy and scalability compared to conventional centralized learning, where all data has to be aggregated to a central server. However, decentralized optimization has traditionally been viewed as a necessary compromise, used only when centralized processing is impractical due to communication constraints or data privacy concerns. In this study, we show that decentralization can paradoxically accelerate convergence, outperforming centralized methods in the number of iterations needed to reach optimal solutions. Through examples in logistic regression and neural network training, we demonstrate that distributing data and computation across multiple agents can lead to faster learning than centralized approaches, even when each iteration is assumed to take the same amount of time, whether performed centrally on the full dataset or decentrally on local subsets. This finding challenges longstanding assumptions and reveals decentralization as a strategic advantage, offering new opportunities for more efficient optimization and machine learning.

Authors:Hossein Gholampour, Logan E. Beaver
Title: Wrench-Aware Admittance Control for Unknown-Payload Manipulation
Abstract:
Unknown payloads can strongly affect compliant robotic manipulation, especially when the payload center of mass is not aligned with the tool center point. In this case, the payload generates an offset wrench at the robot wrist. During motion, this wrench is not only related to payload weight, but also to payload inertia. If it is not modeled, the compliant controller can interpret it as an external interaction wrench, which causes unintended compliant motion, larger tracking error, and reduced transport accuracy. This paper presents a wrench‑aware admittance control framework for unknown‑payload pick‑and‑place using a UR5e robot. The method uses force‑torque measurements in two different roles. First, a three‑axis translational excitation term is used to reduce payload‑induced force effects during transport without making the robot excessively stiff. Second, after grasping, the controller first estimates payload mass for transport compensation and then estimates the payload CoM offset relative to the TCP using wrist force‑torque measurements collected during the subsequent translational motion. This helps improve object placement and stacking behavior. Experimental results show improved transport and placement performance compared with uncorrected placement while preserving compliant motion.

Authors:Joscha F. Bongard, Georg Jank, Simon Sagmeister, Boris Lohmann
Title: Robust Nonlinear Trajectory Tracking Control for Autonomous Racing on Three-Dimensional Tracks
Abstract:
We propose a robust nonlinear model predictive control (MPC) scheme for trajectory‑tracking control of autonomous vehicles at the limits of handling on non‑planar road surfaces. We derive the dynamics from first principles and selectively omit terms with negligible dynamic influence to maintain real‑time capability. The resulting MPC with a three‑dimensional (3D) dynamic single‑track model integrates relevant dynamic effects directly into the prediction model and leverages them to improve prediction accuracy and therefore control performance. Even if the influence of terrain‑induced vertical loads on the total acceleration potential is modeled, tire‑road interactions are subject to uncertainty and disturbance. The uncertainty‑aware constraint tightening scheme introduces a margin to constraint bounds to keep the vehicle controllable and stable in this environment. To validate our proposed approach, we perform high‑fidelity dynamic double‑track vehicle dynamics simulations on a model of a real circuit. We find that our algorithm can improve trajectory‑tracking accuracy while maintaining low computation times.

Authors:Sriram Narayanan, Mohamed Naveed Gul Mohamed, Ishan Paranjape, Indranil Nayak, Suman Chakravorty, Mrinal Kumar
Title: State Forecasting in an Estimation Framework with Surrogate Sensor Modeling
Abstract:
In recent years, computational power and data availability breakthroughs have revolutionized our ability to analyze complex physical systems through the inverse problem approach. Data‑driven techniques like system identification and machine learning play an important role in this field, allowing us to gain insights into previously inaccessible phenomena. However, a major hurdle remains: How can meaningful information from partial measurements be extracted? In the aerospace domain, the challenge of state estimation is particularly pronounced due to the limited availability of observational data and the constraints imposed by sensor capabilities for tracking resident space objects (RSOs). To address these limitations, advanced compensation methodologies are required. Currently, range and bearing measurements obtained from radar and optical systems constitute the primary observational tools in the space situational awareness (SSA) community. In this work, we propose a novel framework that integrates a simplified reference dynamics model with a data‑driven surrogate measurement model. This fusion process leverages the strengths of both models to estimate complex dynamical behaviors under conditions of partial observability. Extensive numerical experiments were conducted across multiple datasets to validate the proposed framework. The results demonstrate its efficacy in accurately reconstructing system dynamics from incomplete measurement data. Furthermore, to ensure the robustness of the framework, an initial consistency analysis of the surrogate modeling approach is presented. By addressing the current challenges and refining the integration of data‑driven techniques with traditional physics‑based modeling, this framework aims to advance state estimation methodologies in the aerospace sector.

Authors:Mohammad Rowhani Sistani, Katarzyna Kosek-Szott, Pierluigi Gallo
Title: Direction-Dependent Path Loss Modeling in Olive Orchards for Precision Agriculture
Abstract:
Wireless links deployed in orchards often exhibit significant variability in the strength of the received signal that is not adequately captured by classical distance‑based propagation models. In row‑structured olive groves, signal attenuation differs markedly between along‑row and cross‑row propagation directions, leading to discrepancies when using omnidirectional propagation assumptions such as those adopted in the Free Space Path Loss (FSPL) model or ITU‑R vegetation loss formulations. This paper proposes a topology‑based propagation model that explicitly accounts for orchard layout and the relative positions of radio devices within the plantation structure. Experimental validation was conducted using LoRa technology operating at 868 MHz, and the results were compared with established models from the literature and with the proposed two‑dimensional model. The proposed approach achieves a closer fit to measured RSSI data than conventional models, providing a more reliable basis for link budgeting and network planning in structured agricultural environments.

Authors:Luiz Giacomossi, Håkan Forsberg, Ivan Tomasic, Baran Çürüklü, Tommaso Cucinotta
Title: Scheduling Analysis of UAV Flight Control Workloads using Raspberry Pi 5 Using PREEMPT_RT Linux
Abstract:
Modern UAV architectures increasingly aim to unify high‑level autonomy and low‑level flight control on a single General‑Purpose Operating System (GPOS). However, complex multi‑core System‑on‑Chips (SoCs) introduce significant timing indeterminism due to shared resource contention. This paper performs an architectural analysis of the PREEMPT RT Linux kernel on a Raspberry Pi 5, specifically isolating the impact of kernel activation paths (deferred execution SoftIRQs versus real‑time direct activation) on a 250 Hz control loop. Results show that under heavy stress, the standard kernel is unsuitable, exhibiting worst‑case latencies exceeding 9 ms. In contrast, PREEMPT RT reduced the worst‑case latency by nearly 88 percent to under 225 microseconds, enforcing a direct wake‑up path that mitigates OS noise. These findings demonstrate that while PREEMPT RT resolves scheduling variance, the residual jitter on modern SoCs is primarily driven by hardware memory contention.

Authors:Rentaro Iwai, Natsuki Hikasa, Hiroshi Okajima
Title: Robust Path Following Control for Vehicles with Uncertain Steering Resistance Using Model Error Compensation
Abstract:
This paper presents a robust path following control method for vehicles that explicitly considers steering resistance dynamics to improve tracking accuracy. Conventional methods typically treat the steering angle as a direct control input; however, this approach introduces the steering angle as a state variable and incorporates the steering resistance effect into the control model. The steering resistance is modeled as a function of vehicle speed and steering angle, whereas in practice it varies depending on road conditions. To address the resulting model inaccuracies, a Model Error Compensator (MEC) is introduced, mitigating the effects of variations in steering resistance and enhancing the adaptability of the system to different environments. Since the steering resistance coefficient depends on road surface properties and is difficult to determine precisely, the proposed method treats it as an uncertain parameter and compensates for the resulting model error via MEC. Numerical simulations are conducted to evaluate the performance of the proposed method under varying degrees of parameter mismatch, demonstrating that the proposed method substantially reduces the maximum tracking error in representative mismatched cases compared to the conventional method. The results indicate that explicitly modeling steering resistance dynamics and compensating for model errors improve path following performance in numerical simulations compared to conventional approaches.

Authors:Antos Cheeramban Varghese, Rajasekhar Anguluri, Anamitra Pal
Title: A Constrained Formulation for Simultaneous Line Parameter Estimation and Instrument Transformer Calibration
Abstract:
The process of calibrating instrument transformers (ITs) has been greatly simplified by using phasor measurement unit (PMU) data since this process eliminates the need for (a) additional hardware, and (b) taking ITs offline. However, such simplification comes at the cost of knowing the line parameters, whose estimation using PMU data in turn requires calibrated ITs. To solve this interdependency problem, we propose a novel framework that incorporates power system domain knowledge as constraints to perform simultaneous line parameter estimation and IT calibration. We demonstrate the effectiveness of our approach with simulated and real PMU data as well as for a power system application that uses both PMU data and line parameter information.

Authors:A. D. Brynes, M. King, K. R. L. Baker, R. Banerjee, R. Clarke, D. J. Dunning, J. K. Jones, M. Leputa, A. E. Pollard, M. Romanovschi, M. Shaw, N. Ziyan
Title: Closing the Loop: Deploying Auto-Generating Digital Twins for Particle Accelerators
Abstract:
The simulation of a physical system in a virtual replica, known as a digital twin, is a useful way to interrogate the system non‑invasively, providing the ability to perform predictive maintenance and surveillance, and to investigate potential novel configurations without perturbing the system. This article presents the implementation of an auto‑generating digital twin architecture for particle accelerators: a virtual control system is generated to mirror the physical accelerator hardware, and used to update a simulation model which then feeds back the results into virtual diagnostics. All of the information about the accelerator lattice is cascaded down from a ground source of truth, removing any ambiguity about the naming of parameters between the simulation model and the virtual hardware. This design is modular and extensible, allowing researchers from different institutions to use their own models (for example, a machine learning model) and accelerator lattices while maintaining the overall structural coherence of the digital twin. This architecture has been tested for three accelerator facilities \textendash~CLARA, the ISIS injector, and the proposed UK XFEL \textendash~and aims to provide the foundation for a collaborative community effort in the development of shared technology towards a generic digital twin solution.

Authors:Sachin Khoja, Kamlesh Sawant, Palak Jain, Sairaj Dhople, Jason Poon
Title: Automated Synthesis of Hardware-implementable Analog Circuits for Constrained Optimization
Abstract:
This paper presents an automated software toolchain for synthesizing hardware‑implementable analog circuits that solve constrained optimization problems. The proposed toolchain supports nonlinear objective functions with linear and quadratic constraints. It maps optimization variables to capacitor voltages, implementing dynamics that enforce Karush‑Kuhn‑Tucker conditions using operational amplifiers, resistors, capacitors, diodes, and analog multipliers. From high‑level problem descriptions in AMPL or MPS, the toolchain generates a SPICE netlist for the analog circuit, simulates it, and verifies that the solutions converge. The projected settling time of the analog circuit depends on circuit parameters, gain‑bandwidth product, and slew‑rate limits of operational amplifiers, and leverages the inherent parallelism of analog circuits. The proposed toolchain successfully generates circuits with up to 10,000 variables and demonstrates large scalability improvements, achieving up to a 1,000X increase in solvable problem size over prior analog hardware demonstrations. Simulation studies further show that the automatically synthesized circuits converge to optimal solutions, achieving more than a 200X speedup compared to IPOPT, a state‑of‑the‑art digital interior‑point solver.

Authors:Dipayan Sarkar, Qifeng Li
Title: PROMETHEE-based Modeling of Endogenous Behavioral Uncertainty of EV Owners
Abstract:
The electric vehicle (EV) charging demands (CD) are jointly determined by the EV owners' behavior (i.e., human factor) and the electricity prices (i.e., decisions of distribution system operators (DSO)). However, most existing studies either neglect the decision‑dependent nature of EVCD uncertainty or idealistically treat EV owners as perfect decision‑makers. This paper formulates the optimal operation of power distribution systems (PDS) as a distributionally robust chance‑constrained (DRCC) problem considering EVCDs as endogenous uncertainty (i.e., decision‑dependent uncertainty). The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) is introduced to capture the human factor of EV owners in the proposed ambiguity set. Case studies on IEEE test systems demonstrate that the proposed method achieves superior performance compared to deterministic and conventional DRCC approaches, thereby enhancing resilience and security in PDS operations.

Authors:Siddharth Chandak, Ramanan Tamizholi, Nicholas Bambos
Title: Last-Iterate Guarantees for Learning in Co-coercive Games
Abstract:
We establish finite‑time last‑iterate guarantees for vanilla stochastic gradient descent in co‑coercive games under noisy feedback. This is a broad class of games that is more general than strongly monotone games, allows for multiple Nash equilibria, and includes examples such as quadratic games with negative semidefinite interaction matrices and potential games with smooth concave potentials. Prior work in this setting has relied on relative noise models, where the noise vanishes as iterates approach equilibrium, an assumption that is often unrealistic in practice. We work instead under a substantially more general noise model in which the second moment of the noise is allowed to scale affinely with the squared norm of the iterates, an assumption natural in learning with unbounded action spaces. Under this model, we prove a last‑iterate bound of order O(\log(t)/t^1/3), the first such bound for co‑coercive games under non‑vanishing noise. We additionally establish almost sure convergence of the iterates to the set of Nash equilibria and derive time‑average convergence guarantees.

Authors:Bai Xue, C. -H. Luke Ong
Title: Quantitative Verification of Finite-Time Constrained Occupation Measures for Continuous-time Stochastic Systems
Abstract:
This paper addresses the quantitative verification of finite‑time constrained occupation time for stochastic continuous‑time systems governed by stochastic differential equations (SDEs). Unlike classical reachability analysis, which focuses on single‑event properties such as entering a target set, many autonomous tasks‑including surveillance, wireless charging, and chemical mixing‑require a system to accumulate a prescribed duration within a target region while strictly maintaining safety constraints. We propose a barrier‑certificate framework to compute rigorous upper and lower bounds on the probability that such cumulative specifications are satisfied over a finite time horizon. By introducing a stopped process that freezes the system once it reaches the boundary of the safe set, we derive three classes of certificates: one for upper bounds and two for lower bounds. The proposed approaches are validated through numerical examples implemented using semidefinite programming.

Authors:Changjun He, Li Zhang, Qi Liu, Rui Zou
Title: Inertia Matching Principle: Improving Transient Synchronization Stability in Hybrid Power Systems With VSGs and SGs
Abstract:
This paper investigates the transient synchronization stability in power systems hybridized with virtual synchronous generators (VSGs) and synchronous generators (SGs). A relative swing equation model is established to capture the transient synchronization dynamics between the VSG and the SG. Based on this model, both static and dynamic characteristics are systematically analyzed, and a quantitative stability level index is derived to elucidate the underlying stability mechanism. Then, two fundamental inertia matching principles are identified. First, a new instability mechanism induced by improper inertia matching between the VSG and the SG is revealed. It is identified that increasing the VSG's inertia does not monotonically improve transient stability, as commonly presumed. Instead, an optimal inertia matching constant exists that maximizes stability performance. Second, the influence of the VSG share on the synchronization stability is discovered to be strongly influenced by the matching between the VSG's inertia level and its voltage strength (i.e., output impedance). To achieve reliable and robust synchronization stability, proper coordination between the VSG's inertia and virtual impedance is essential. Finally, a coordinated stabilization strategy based on inertia matching and virtual impedance adjustment is proposed to enhance transient synchronization stability performance while suppressing fault current. Simulations conducted on a two‑machine system and the IEEE 39‑bus system validate the theoretical findings and demonstrate the effectiveness of the proposed strategy.

Authors:Minsu Kim, Walid Saad, Kui Wang, Zongdian Li, Tao Yu, Kei Sakaguchi
Title: Transformer Architecture with Minimal Inference Latency for Multi-Modal Wireless Networks
Abstract:
Next‑generation wireless networks are expected to leverage multi‑modal data sources to execute various wireless communication tasks such as beamforming and blockage prediction with situational‑awareness. To do so, multi‑modal transformers emerged as an effective tool, however, existing transformer‑based approaches suffer from high inference latency and large memory footprints when processing multi‑modal data. Hence, such existing solutions cannot handle wireless communication tasks that require fast inference to track a dynamically changing environment with moving vehicles and blockages. One major bottleneck is the reliance on attention mechanisms whose complexity grows quadratically with respect to the number of tokens. Hence, in this paper, a novel, fast multi‑modal transformer inference framework is designed to practically support wireless communication tasks by processing only important tokens. To this end, an optimization problem is formulated to find the optimal number of tokens under a target FLOPs for a given wireless communication task while maintaining the task accuracy. To solve this problem, modality‑specific tokenizers are first designed to project each modality into the same embedding dimension. Then, a token router is introduced to learn the importance of each token and process only important tokens. Subsequently, a trainable keep ratio is introduced to learn how many tokens to process for each layer under the target FLOPs. Simulation results show that, on DeepSense 6G beamforming tasks, we can reduce the inference latency, GPU memory, and FLOPs by 86.2% 35%, and 80%, respectively, with negligible accuracy loss. To validate the feasibility for real‑world deployments, a multi‑modal handover dataset is developed using a real‑world testbed. Emulation results on the developed dataset show that the proposed framework can proactively initiate handover before blockage.

Authors:Goutam Das, Takashi Tanaka
Title: Path Integral Control for Partially Observed Systems with Controlled Sensing
Abstract:
Path integral control in Gaussian belief space requires a structural matching condition between the observation‑driven diffusion of the belief mean and the actuation authority, which a fixed observation matrix cannot enforce. We treat the observation matrix as a control variable and show that constraining the sensing control to a measurable selector from the resulting matching set reduces the Hamilton‑Jacobi‑Bellman equation for the belief mean and covariance to a linear PDE with a Feynman‑Kac representation.

Authors:Elizabeth Glista, Tomas Valencia Zuluaga, Amelia Musselman, Minda Monteagudo, Juliette Franzman, Jean-Paul Watson
Title: Capacity Expansion Planning for Puerto Rico's Electric Power System
Abstract:
This study presents a mathematical optimization framework and preliminary analysis for long‑term investment planning in Puerto Rico's electric power system. We develop a high‑resolution capacity expansion model to identify least‑cost generation and storage investments that improve system reliability. The model co‑optimizes new investments and thermal generator retirements while representing generator dispatch, unit commitment, fuel selection, and storage operations under constraints of equipment engineering limits, fuel supply limitations, and load satisfaction. Key methodological advances relative to prior long‑term planning studies for Puerto Rico include: (i) nodal transmission modeling at 38 kV and above, (ii) hourly chronological operations for representative days, (iii) explicit unit commitment for existing and new thermal units with realistic ramping, minimum up and down times, and startup costs, (iv) system‑wide fuel supply constraints, and (v) stochastic operating scenarios reflecting load variation, renewable availability, and the high forced outage rates of legacy units. Using data from LUMA, PREPA, DOE, and public sources, we build present‑day (2024) and future (2030) test systems, with the latter including planned generation and storage projects. We evaluate planning scenarios that vary future load, fuel supply assumptions, realization of planned expansion, and allowable new technologies. Results show that, given the recent relaxation of interim renewable goals for the near future in Puerto Rico, an optimal portfolio includes at least 1.5 GW of new H‑class combined cycle capacity beyond planned projects. These additions are needed mainly to replace unreliable legacy thermal units rather than to serve new load. The new combined cycle units eliminate modeled bulk‑system load shedding and restore a strong reserve margin, even under stressed load and outage conditions.

Authors:Blake Werner, Sergio A. Esteban, Massimiliano De Sa, Max H. Cohen, Aaron D. Ames
Title: HALO: Hybrid Auto-encoded Locomotion with Learned Latent Dynamics, Poincaré Maps, and Regions of Attraction
Abstract:
Reduced‑order models are powerful for analyzing and controlling high‑dimensional dynamical systems. Yet constructing these models for complex hybrid systems such as legged robots remains challenging. Classical approaches rely on hand‑designed template models (e.g., LIP, SLIP), which, though insightful, only approximate the underlying dynamics. In contrast, data‑driven methods can extract more accurate low‑dimensional representations, but it remains unclear when stability and safety properties observed in the latent space meaningfully transfer back to the full‑order system. To bridge this gap, we introduce HALO (Hybrid Auto‑encoded Locomotion), a framework for learning latent reduced‑order models of periodic hybrid dynamics directly from trajectory data. HALO employs an autoencoder to identify a low‑dimensional latent state together with a learned latent Poincaré map that captures step‑to‑step locomotion dynamics. This enables Lyapunov analysis and the construction of an associated region of attraction in the latent space, both of which can be lifted back to the full‑order state space through the decoder. Experiments on a simulated hopping robot and full‑body humanoid locomotion demonstrate that HALO yields low‑dimensional models that retain meaningful stability structure and predict full‑order region‑of‑attraction boundaries.

Authors:Mychal Amoafo, Ilya Kolmanovsky, Anuradha Annaswamy
Title: A Safe and Stable Controller for Fuel Cell Systems Using Adaptation and Reference Governors
Abstract:
This paper proposes a control architecture integrating adaptation with Lyapunov‑based Reference Governors (LRGs) to ensure state constraint satisfaction for first‑order systems with parametric uncertainties. Adaptation combined with LRGs guarantees stability, ensures good control performance, and remains safe even with parametric uncertainties. Simulations of the fuel cell temperature regulation problem demonstrate that the proposed control architecture successfully meets all control and safety objectives, whereas the standard adaptation fails to achieve the latter.

Authors:Dinithi Jayasuriya, Aravind Saravanan, Nilesh Ahuja, Amanda Rios, Amit Trivedi
Title: Structural Verification for Reliable EDA Code Generation without Tool-in-the-Loop Debugging
Abstract:
Large language models (LLMs) have enabled natural‑language‑driven automation of electronic design automation (EDA) workflows, but reliable execution of generated scripts remains a fundamental challenge. In LLM‑based EDA tasks, failures arise not from syntax errors but from violations of implicit structural dependencies over design objects, including invalid acquisition paths, missing prerequisites, and incompatible API usage. Existing approaches address these failures through tool‑in‑the‑loop debugging, repeatedly executing and repairing programs using runtime feedback. While effective, this paradigm couples correctness to repeated tool invocation, leading to high latency and poor scalability in multi‑step settings. We propose to eliminate tool‑in‑the‑loop debugging by enforcing structural correctness prior to execution. Each task is represented as a structural dependency graph that serves as an explicit execution contract, and a verifier‑guided synthesis framework enforces this contract through graph‑conditioned retrieval, constrained generation, and staged pre‑execution verification with diagnosis‑driven repair. On single‑step tasks, our method improves pass rate from 73.0% (LLM+RAG) and 76.0% (tool‑in‑loop) to 82.5%, while requiring exactly one tool call per task and reducing total tool calls by more than 2x. On multi‑step tasks, pass rate improves from 30.0% to 70.0%, and further to 84.0% with trajectory‑level reflection. Uncertainty‑aware filtering further reduces verifier false positives from 20.0% to 6.7% and improves precision from 80.0% to 93.3%. These results show that enforcing structural consistency prior to execution decouples correctness from tool interaction, improving both reliability and efficiency in long‑horizon EDA code generation.

Authors:Aleksandra Lekić, Predrag Pejović
Title: Simulation of Switching Converters on the Level of Averaged Voltages and Currents
Abstract:
An algorithm for simulation of switching converters is proposed in the paper. The algorithm is based on simulation of averaged circuit model applying "switching cell" concept, and construction of instantaneous values of the waveforms using quasi steady state and linear ripple approximation. Simulation covers converters operating both in the continuous and the discontinuous conduction mode. Application of the algorithm is demonstrated by simulation results of all three of the basic converters: buck, boost and buck‑boost, as well as a flyback converter, which required slight generalization of the switching cell concept.

Authors:Aleksandra Lekić, Predrag Pejović
Title: Simulation of Switching Converters on the Level of Averaged Voltages and Currents
Abstract:
An algorithm for simulation of switching converters is proposed in the paper. The algorithm is based on simulation of averaged circuit model applying "switching cell" concept, and construction of instantaneous values of the waveforms using quasi steady state and linear ripple approximation. Simulation covers converters operating both in the continuous and the discontinuous conduction mode. Application of the algorithm is demonstrated by simulation results of all three of the basic converters: buck, boost and buckboost, as well as a flyback converter, which required slight generalization of the switching cell concept.

Authors:Moad Abudia, Opeyemi Owolabi, Joel A. Rosenfeld, Rushikesh Kamalapurkar
Title: A Dynamic Mode Decomposition Approach to Parameter Identification
Abstract:
This paper presents a data‑driven algorithm for simultaneous system identification and parameter estimation in control‑affine nonlinear systems. Parameter estimation is achieved by training a data‑driven predictive model using state‑action measurements and various known values at the parameters of interest. The predictive model is then used in conjunction with state‑action data corresponding to unknown values of the parameters to estimate the said unknown value. Numerical experiments on the controlled Duffing oscillator with unknown damping, stiffness, and nonlinearity coefficients demonstrate accurate recovery of both the system trajectories and the unknown parameter values from data collected under open‑loop excitation.

Authors:Xingyu Zhao, Marcos Netto, Junbo Zhao
Title: Stiffness-Aware Decentralized Dynamic State Estimation for Inverter-Dominated Power Systems
Abstract:
Dynamic state estimation (DSE) is becoming increasingly important for monitoring inverter‑dominated power systems. Due to their cascading control structures, inverter‑based resources (IBRs) exhibit multi‑timescale dynamics, leading to stiff system models that pose significant challenges for conventional DSE methods. In particular, explicit discretization schemes often require impractically small sampling intervals to maintain numerical stability, increasing computational and communication burdens. To address this issue, this paper proposes a stiffness‑aware decentralized DSE method for inverter‑dominated power systems. The statistical linearization is used to construct a local linear surrogate model for the nonlinear dynamics, which allows matrix‑exponential discretization to enable analytical uncertainty propagation in discrete time, rather than relying on explicit integration schemes. This enables stable DSE at lower sampling rates. Numerical results reveal the mechanism by which stiff dynamics destabilize conventional DSE and demonstrate that the proposed method achieves efficient and accurate estimation under coarse sampling conditions.

Authors:Worachit Amnuaypongsa, Yotsapat Suparanonrat, Pana Wanitchollakit, Jitkomut Songsiri
Title: Barrier-enforced multi-objective optimization for direct point and sharp interval forecasting
Abstract:
This paper proposes a multi‑step probabilistic forecasting framework using a single neural‑network based model to generate simultaneous point and interval forecasts. Our approach ensures non‑crossing prediction intervals (PIs) through a model structure design that strictly satisfy a target coverage probability (PICP) while maximizing sharpness. Unlike existing methods that rely on manual weight tuning for scalarized loss functions, we treat point and PI forecasting as a multi‑objective optimization problem, utilizing multi‑gradient descent to adaptively select optimal weights. Key innovations include a new PI loss function based on an extended log‑barrier with an adaptive hyperparameter to guarantee the coverage, a hybrid architecture featuring a shared temporal model with horizon‑specific submodels, and a training strategy. The proposed loss is scale‑independent and universally applicable; combined with our training algorithm, the framework eliminates trial‑and‑error hyperparameter tuning for balancing multiple objectives. Validated by an intra‑day solar irradiance forecasting application, results demonstrate that our proposed loss consistently outperforms those in current literature by achieving target coverage with the narrowest PI widths. Furthermore, when compared against LSTM encoder‑decoder and Transformer architectures‑‑including those augmented with Chronos foundation models‑‑our method remains highly competitive and can be seamlessly adapted to any deep learning structure.

Authors:Sacha Huriot, Ihab Tabbara, Hussein Sibai
Title: Safe Control using Learned Safety Filters and Adaptive Conformal Inference
Abstract:
Safety filters have been shown to be effective tools to ensure the safety of control systems with unsafe nominal policies. To address scalability challenges in traditional synthesis methods, learning‑based approaches have been proposed for designing safety filters for systems with high‑dimensional state and control spaces. However, the inevitable errors in the decisions of these models raise concerns about their reliability and the safety guarantees they offer. This paper presents Adaptive Conformal Filtering (ACoFi), a method that combines learned Hamilton‑Jacobi reachability‑based safety filters with adaptive conformal inference. Under ACoFi, the filter dynamically adjusts its switching criteria based on the observed errors in its predictions of the safety of actions. The range of possible safety values of the nominal policy's output is used to quantify uncertainty in safety assessment. The filter switches from the nominal policy to the learned safe one when that range suggests it might be unsafe. We show that ACoFi guarantees that the rate of incorrectly quantifying uncertainty in the predicted safety of the nominal policy is asymptotically upper bounded by a user‑defined parameter. This gives a soft safety guarantee rather than a hard safety guarantee. We evaluate ACoFi in a Dubins car simulation and a Safety Gymnasium environment, empirically demonstrating that it significantly outperforms the baseline method that uses a fixed switching threshold by achieving higher learned safety values and fewer safety violations, especially in out‑of‑distribution scenarios.

Authors:Manuel Klädtke, Feiran Zhao, Florian Dörfler, Moritz Schulze Darup
Title: On the Effect of Quadratic Regularization in Direct Data-Driven LQR
Abstract:
This paper proposes an explainability concept for direct data‑driven linear quadratic regulation (LQR) with quadratic regularization. Our perspective follows the parametric effect of regularization, an analysis approach that translates regularization costs from auxiliary variables to system quantities, enabling intuitive interpretations. The framework further enables the elimination of auxiliary variables, thereby reducing computational complexity. We demonstrate the effectiveness of our approach and the identified effect of regularization via simulations.

Authors:Hanfeng Zhai, Hongtao Qiao, Hassan Mansour, Christopher Laughman
Title: Scalable Physics-Informed Neural Differential Equations and Data-Driven Algorithms for HVAC Systems
Abstract:
We present a scalable, data‑driven simulation framework for large‑scale heating, ventilation, and air conditioning (HVAC) systems that couples physics‑informed neural ordinary differential equations (PINODEs) with differential‑algebraic equation (DAE) solvers. At the component level, we learn heat‑exchanger dynamics using an implicit PINODE formulation that predicts conserved quantities (refrigerant mass M_r and internal energy E_\texthx) as outputs, enabling physics‑informed training via automatic differentiation of mass/energy balances. Stable long‑horizon prediction is achieved through gradient‑stabilized latent evolution with gated architectures and layer normalization. At the system level, we integrate learned components with DAE solvers (IDA and DASSL) that explicitly enforce junction constraints (pressure equilibrium and mass‑flow consistency), and we use Bayesian optimization to tune solver parameters for accuracy‑‑efficiency trade‑offs. To reduce residual system‑level bias, we introduce a lightweight corrector network trained on short trajectory segments. Across dual‑compressor and scaled network studies, the proposed approach attains multi‑fold speedups over high‑fidelity simulation while keeping errors low (MAPE below a few percent) and scales to systems with up to 16 compressor‑condenser pairs.

Authors:Boyu Yao, Yury Dvorkin
Title: Grid-Supporting Equipment Supply Chains Constrain the Feasible Pace of Power System Expansion
Abstract:
Power system expansion depends on the equipment required to connect, convert, regulate, and condition electricity, yet grid‑supporting equipment (GSE) is rarely modeled as an explicit constraint. We develop a framework integrating dynamic stock‑flow modeling, bill‑of‑materials accounting, multi‑regional supply‑use analysis, and expansion optimization to quantify GSE deployment requirements and upstream material dependence. Because manufacturing data are often fragmented or proprietary, we use critical material requirements as a physically grounded proxy for GSE supply constraints. In a U.S. case study, GSE shortages reach 269.6‑‑274.1 GVA (28.5%‑‑28.6%) by 2030 under high‑growth conditions. Copper becomes fully binding, with steel and nickel forming additional constraints. Trade disruption intensifies shortages, while grid‑enhancing technologies provide limited relief. These results show that grid expansion depends on the timely manufacturability, replacement, and material support of GSE, motivating planning frameworks that explicitly incorporate deliverability, supply chain exposure, and resilience strategies.

Authors:Asad Husein, Kimmo Rasilainen, Juha-Pekka Mäkelä, Veikko Hovinen, Klaus Nevala, Aarno Pärssinen, Marko E. Leinonen
Title: Far-Field Absolute Gain Antenna Measurements at Sub-THz Frequencies: A New Interpretation
Abstract:
The evolution of large aperture antennas and arrays at the sub‑THz band (100‑300 GHz) results in traditional far‑field (FF) gain measurements to require large distances due to the high frequency nature making them impractical in many laboratory environments. In the presented work, absolute antenna gain measurements are performed in localized distance clusters for commercial horn antennas in the sub‑THz range of 145‑170 GHz using the three‑antenna method, leveraging a theoretically derived modified FF equation along with the Friis transmission equation to enable a compact measurement setup. By applying the proposed modified FF formulation, the approach aims to redefine the FF distance by considering the combined effects of both the transmitting and receiving antennas, accounting for their aperture sizes and radiation characteristics. This allows precise gain characterization within a compact measurement footprint. The proposed theoretical model was validated through radiated measurements and simulations, demonstrating its effectiveness in this case study. Also, measurements were performed using dissimilar antenna pair combinations due to inventory constraints, a common challenge both in research and in industry. Despite the mismatches, the presented work demonstrates that reliable and sufficiently accurate measurement results can still be achieved. This highlights the practical feasibility of the compact cluster measurement technique without compromising measurement integrity. The compact setup ensures efficiency in the measurement time and cost, making it a robust solution for both research and industrial needs in sub‑THz antenna characterization for applications including 6G, high frequency sensing, and imaging systems.

Authors:Miroslav Kosanic, Marija Ilic
Title: Composite Control of Grid-Following Inverters for Stabilizing AI-Induced Fast Power Disturbances
Abstract:
AI data center loads create query‑driven power transients on millisecond timescales. Such loads can violate the timescale separation assumptions underlying internal inverter control of grid‑following resources collocated with data centers as supplementary generation. This paper develops a singular perturbation‑based modeling and control for stabilizing fast power imbalances. We show that physically‑implementable droop control is derived and valid by requiring reduced‑system stability rather than being imposed a priori, and that AI workloads satisfy a bounded‑rate disturbance class due to physical filtering in power delivery hardware. The analysis yields explicit gain bounds linking inverter parameters to disturbance rejection performance, a modulation admissibility condition ensuring physical realizability of the feedback linearizing control, and a feasibility condition identifying the maximum tolerable load ramp rate. Numerical simulations validate the theoretical predictions under stochastic AI transients.

Authors:Ruijia Liu, Ancheng Hou, Xiao Yu, Xiang Yin
Title: DAG-STL: A Hierarchical Framework for Zero-Shot Trajectory Planning under Signal Temporal Logic Specifications
Abstract:
Signal Temporal Logic (STL) is a powerful language for specifying temporally structured robotic tasks. Planning executable trajectories under STL constraints remains difficult when system dynamics and environment structure are not analytically available. Existing methods typically either assume explicit models or learn task‑specific behaviors, limiting zero‑shot generalization to unseen STL tasks. In this work, we study offline STL planning under unknown dynamics using only task‑agnostic trajectory data. Our central design philosophy is to separate logical reasoning from trajectory realization. We instantiate this idea in DAG‑STL, a hierarchical framework that converts long‑horizon STL planning into three stages. It first decomposes an STL formula into reachability and invariance progress conditions linked by shared timing constraints. It then allocates timed waypoints using learned reachability‑time estimates. Finally, it synthesizes trajectories between these waypoints with a diffusion‑based generator. This decomposition‑‑allocation‑‑generation pipeline reduces global planning to shorter, better‑supported subproblems. To bridge the gap between planning‑level correctness and execution‑level feasibility, we further introduce a rollout‑free dynamic consistency metric, an anytime refinement search procedure for improving multiple allocation hypotheses under finite budgets, and a hierarchical online replanning mechanism for execution‑time recovery. Experiments in Maze2D, OGBench AntMaze, and the Cube domain show that DAG‑STL substantially outperforms direct robustness‑guided diffusion on complex long‑horizon STL tasks and generalizes across navigation and manipulation settings. In a custom environment with an optimization‑based reference, DAG‑STL recovers most model‑solvable tasks while retaining a clear computational advantage over direct optimization based on the explicit system model.

Authors:Ravi Kumar Thakur, Luis Granados Segura, Jan Klivan, Radim Špetlík, Tobiáš Vinklárek, Matouš Vrba, Martin Saska
Title: Relative State Estimation using Event-Based Propeller Sensing
Abstract:
Autonomous swarms of multi‑Unmanned Aerial Vehicle (UAV) system requires an accurate and fast relative state estimation. Although monocular frame‑based camera methods perform well in ideal conditions, they are slow, suffer scale ambiguity, and often struggle in visually challenging conditions. The advent of event cameras addresses these challenging tasks by providing low latency, high dynamic range, and microsecond‑level temporal resolution. This paper proposes a framework for relative state estimation for quadrotors using event‑based propeller sensing. The propellers in the event stream are tracked by detection to extract the region‑of‑interests. The event streams in these regions are processed in temporal chunks to estimate per‑propeller frequencies. These frequency measurements drive a kinematic state estimation module as a thrust input, while camera‑derived position measurements provide the update step. Additionally, we use geometric primitives derived from event streams to estimate the orientation of the quadrotor by fitting an ellipse over a propeller and backprojecting it to recover body‑frame tilt‑axis. The existing event‑based approaches for quadrotor state estimation use the propeller frequency in simulated flight sequences. Our approach estimates the propeller frequency under 3% error on a test dataset of five real‑world outdoor flight sequences, providing a method for decentralized relative localization for multi‑robot systems using event camera.

Authors:Kobena Badu Enyam, Cara Koepele, Timothy Asare, Kevin Wallington, John Lygeros
Title: Scenario-Based Stochastic MPC for Energy Hubs with EV Fleets Under Persistent Grid Outages
Abstract:
Emissions reduction and resilience to outages motivate the adoption of renewable microgrids. Surprisingly, research integrating both probabilistic grid outages and electric vehicle (EV) charging requirements remains limited. This paper addresses this gap by developing a scenario‑based stochastic model predictive controller (SMPC) for a microgrid energy hub comprising solar generation, battery storage, diesel backup, and an EV fleet connected to a weak grid. Grid outage and campus load scenarios are generated from a continuous‑time Markov chain and a Gaussian Process, respectively. Using 2023 operational data from the Ashesi University Energy Hub in Ghana, we demonstrate that the SMPC achieves performance within 1% of a perfect‑forecast benchmark. In contrast, a naive MPC that assumes continuous grid availability offers no economic or sustainability advantage over rule‑based control, with both incurring significantly higher costs and emissions than the SMPC. These results highlight that outage anticipation is essential for economic viability. Finally, we show that incorporating a deterministic buffer against EV consumption uncertainty eliminates over 90% of state‑of‑charge violations with negligible impact on total operating costs

Authors:Ikumi Banno
Title: Informativity of Data-Knowledge Pairs for Lyapunov Equations
Abstract:
In the past few years, data informativity with prior knowledge has attracted increasing attention. This line of research aims to characterize a dataset on a dynamical system that enables system analysis or design only by the dataset and given prior knowledge on the system. In this paper, we investigate such a characterization for the data‑driven problem of computing a unique solution to Lyapunov equations. First, we introduce a notion of joint informativity for data‑knowledge pairs as an extension of the standard informativity concept. Second, we derive an algebraic equivalent condition for the joint informativity. Finally, we provide further insights into the joint informativity by considering a special case of prior knowledge. The characterization presented in this paper is developed for a wide class of prior knowledge, enabling the incorporation of various forms of system information.

Authors:David E. Ruiz-Guirola, Miltiadis Filippou, Onel A. Lopez
Title: Frugal Geofencing via Energy-aware Sensing and Reporting
Abstract:
Timely and accurate monitoring in geofencing scenarios is challenging when relying on ultra‑low power Internet of Things devices (IoTDs) powered by energy harvesting (EH). This is mainly because frequent wake‑ups for data acquisition and data uploading may quickly deplete their limited energy buffer. Conventional grid‑like IoT deployments overlook these limitations and merely rely on continuously powered sensing. Herein, we propose an energy‑aware geofencing framework for camera‑equipped EH IoTDs deployed around a protected area and its surrounding perimeter zone. The framework integrates a directional sensing power model with an operational representation of EH, sensing, sleeping, and reporting, accounting for the limited field‑of‑view (FoV) and distance‑dependent detection confidence of the IoTDs. Device activity is controlled by the coverage‑providing access point, which hosts a mobile edge host and a facility geocencing system to ensure timely and reliable detection under tight energy constraints. Reinforcement learning is used to determine IoTD placement, enabling earlier intruder detection than uniform grid‑based deployments. Numerical results show that the proposed coordinated sensing and reporting configuration achieves frugal geofencing with fewer devices, while concurrently improving detection timeliness and dependability.

Authors:Alessio Bocci, José Juan Corona-Sánchez, Raymond Kristiansen
Title: Leader-Follower Formation Control Using Differential Drag and Effective Surface Regulation
Abstract:
The growing interest in space activities has led to the emergence of new space operators and innovative mission concepts. Small satellites such as CubeSats reduce mission costs and are typically deployed in constellations or formation flights. Since they are often propulsionless, passive orbital control strategies are the standard, primarily through differential drag achieved via attitude control maneuvers. This work develops a control system to achieve a generic relative positioning between two small satellites in a virtual leader and real follower formation flight, relying entirely on differential drag achieved through attitude maneuvers. We propose a control law based on the integrator backstepping technique, which, in a closed loop with the rotational dynamics, results in the asymptotic stability of the closed‑loop system equilibrium points. We demonstrate the asymptotic stability of the closed‑loop system equilibrium points using the Lyapunov theory, and a numerical simulation assesses the effectiveness and accuracy of the control strategy.

Authors:Ruben Hefele, Timo Oksanen
Title: EcoTIM: Fuel-saving multi-brand tillage with ISO 11783 TIM
Abstract:
Tillage operations account for a large share of on‑farm diesel consumption, yet the fuel efficiency of the combined tractor‑implement system is not optimised in current practice. Modern continuously variable transmission (CVT) tractors minimise engine fuel consumption internally, but they treat the implement as an unknown load and do not account for the effect of vehicle speed on implement draft force. This paper presents EcoTIM, a distributed fuel‑optimisation concept in which the tractor and tillage implement cooperate through the extended ISO 11783 (ISOBUS) Tractor Implement Management (TIM) interface to minimise fuel consumption per hectare in real time. In the EcoTIM concept, the tractor Electric Control Unit fuses its internal engine, transmission, and traction efficiencies into a single combined efficiency value and its derivative with respect to vehicle speed, and broadcasts both to the implement at the standard 100 ms CAN bus cycle. The implement ECU combines these two received scalars with its own analytically known draft force model to evaluate the fuel‑consumption gradient, and commands the optimal speed, and as a novel TIM extension, the desired acceleration, back to the tractor. Because only two scalar values are exchanged and neither party discloses proprietary subsystem models, the architecture is inherently multi‑brand and plug‑and‑play. The required data exchange is realised with three new messages and one backward‑compatible byte‑level extension to the standard TIM speed command, and this paper proposes that these messages are standardised within ISO 11783. The acceleration command enables feed‑forward torque and CVT ratio planning on the tractor side, improving transient response compared with speed‑only TIM commands. This paper also contains a proof‑of‑concept simulation with six tillage scenarios and a spatially varying 1km test track for initial concept validation.

Authors:Takumi Namba
Title: Robust Distributed Sub-Optimal Coordination of Linear Agents with Uncertain Input Nonlinearities
Abstract:
In this paper, we study robust distributed sub‑optimal coordination of linear agents subject to input nonlinearities. Inspired by the robust agreement literature, we formulate a bounded distributed sub‑optimal coordination problem, in which each agent converges to a neighborhood of the optimizer of a global optimization problem defined over a communication network. We propose a novel control protocol, and analyze convergence by employing a robust control approach, in which both the input nonlinearities and the gradients of the objective functions are treated in a unified manner via sector conditions. In particular, we derive sufficient conditions for the solvability of the considered problem and characterize them in terms of matrix inequalities. The effectiveness of the proposed method is demonstrated through a numerical simulation.

Authors:Bai Xue, Peixin Wang, C. -H. Luke Ong
Title: Quantitative Verification of Constrained Occupation Time for Stochastic Discrete-time Systems
Abstract:
This paper addresses the quantitative verification of constrained occupation time in stochastic discrete‑time systems, focusing on the probability of visiting a target set at least k times while maintaining safety. Such cumulative properties are essential for certifying repeated behaviors like surveillance and periodic charging. To address this, we present the first barrier certificate framework capable of certifying these behaviors. We introduce multiplicative stochastic barrier functions that encode visitation counts implicitly within the algebraic structure of a scalar barrier. By adopting a switched‑system reformulation to handle safety, we derive rigorous probabilistic bounds for both finite and infinite horizons. Specifically, we show that dissipative barriers establish upper bounds ensuring the exponential decay of frequent visits, while attractive barriers provide lower bounds via submartingale analysis. The efficacy of the proposed framework is demonstrated through numerical examples.

Authors:Liraz Mudrik, Yaakov Oshman
Title: Kill-Probability-Maximization Guidance: Breaking from the Miss-Distance-Minimization Paradigm
Abstract:
Classical guidance laws aim at minimizing the miss distance, thus implicitly determining the minimum warhead lethality radius required against nominal targets. However, nonnominal targets or scenarios might render the designed warhead insufficient, causing a significant degradation in the single‑shot kill probability (SSKP). We propose a guidance methodology that shifts the interceptor's objective from minimizing the miss distance to directly maximizing the SSKP, while taking into account the warhead's probabilistic lethality model. Complying with the generalized separation theorem, the new paradigm is based on modifying deterministic differential‑game‑based guidance laws using Bayesian decision theory. Extensive Monte Carlo simulations demonstrate consistent SSKP improvement over the standard and recently introduced estimation‑aware guidance laws, when tested against nominal and nonnominal evasively maneuvering targets.

Authors:Yutian Pang, Daniel Delahaye, John-Paul Clarke
Title: Trajectory-Based Optimization for Air Traffic Control in the Terminal Maneuvering Area
Abstract:
We present a trajectory‑based optimization framework for arrival sequencing and scheduling in the terminal maneuvering area (TMA). Unlike node‑link scheduling models that reduce trajectories to time‑delay variables, the proposed method computes implementable per‑aircraft speed profiles and path extensions that achieve required landing separation through terminal air traffic control actions. The framework combines an analytic TMA path model, consisting of a tangent leg, a radius‑to‑fix turn, and a final‑approach segment, with a nonlinear program (NLP) that jointly optimizes path stretch and segment speeds under a weighted objective. Three landing‑order policies are examined: First‑Entry‑First‑Serve (FEFS), First‑on‑Final‑First‑Serve (FOFFS), and FOFFS with Constrained Position Shifting (CPS) up to k positions. CPS is implemented through a two‑phase approach coupling mixed‑integer linear programming (MILP) with NLP to select an optimized landing order before trajectory optimization. The aircraft population follows a realistic weight‑class fleet mix with pair‑specific wake‑turbulence separation, and each scenario is perturbed by a Gaussian wind sample projected onto each segment to convert commanded airspeeds into ground speeds. An online rolling‑horizon formulation commits each aircraft trajectory irrevocably upon entry, enabling real‑time decision‑making. Monte Carlo experiments on the simplified A80 TMA show that: (i) FOFFS consistently outperforms FEFS in delay, path stretch, and fuel burn by exploiting geometric asymmetries among arrival streams; (ii) CPS further reduces separation violations and path stretch, though with diminishing returns and rapidly increasing solver cost; (iii) fuel estimates from BADA 3 and OpenAP show consistent qualitative trends; and (iv) per‑entry optimization completes in near real‑time, supporting practical deployment.

Authors:Wu Yue
Title: Input-Side Variance Suppression under Non-Normal Transient Amplification in Continuous-Control Reinforcement Learning
Abstract:
Continuous‑control reinforcement learning (RL) often exhibits large closed‑loop variance, high‑frequency control jitter, and sensitivity to disturbance injection. Existing explanations usually emphasize disturbance sources such as action noise, exploration perturbations, or policy nonsmoothness. This letter studies a complementary amplifier‑side perspective: in nominally stable yet strongly non‑normal closed loops, small input perturbations can undergo transient amplification and lead to disproportionately large state covariance. Motivated by this source‑‑amplifier decomposition, we introduce an input‑side variance suppression layer that operates between the learned policy and the plant input to reduce applied‑input variance and step‑to‑step jitter. To separate mechanism from correlation, we use two control‑theoretic interventions: one varies only eigenvector geometry under fixed eigenvalues and spectral radius, and the other varies only applied‑input statistics under fixed strongly non‑normal geometry. We then provide mechanism‑consistent external validation on planar quadrotor tasks. Throughout, Koopman/ALE surrogates are used only as analysis and certification tools, not as direct performance paths. Taken together, the results support a narrower claim: in the studied settings, non‑normal transient amplification is an important and under‑emphasized contributor to execution‑time closed‑loop variance, and source‑side suppression can reduce downstream covariance without changing the structural peak gain.

Authors:Noha Hassan, Xavier Fernando, Halim Yanikomeroglu
Title: Path-Based Quantum Meta-Learning for Adaptive Optimization of Reconfigurable Intelligent Surfaces
Abstract:
Reconfigurable intelligent surfaces (RISs) modify signal reflections to enhance wireless communication capabilities. Classical RIS phase optimization is highly non convex and challenging in dynamic environments due to high interference and user mobility. Here we propose a hierarchical multi‑objective quantum metalearning algorithm that switches among specific quantum paths based on historical success, energy cost, and current data rate. Candidate RIS control directions are arranged as switch paths between quantum neural network layers to minimize inference, and a scoring mechanism selects the top performing paths per layer. Instead of merely storing past successful settings of the RIS and picking the closest match when a new problem is encountered, the algorithm learns how to select and recombine the best parts of different solutions to solve new scenarios. In our model, high‑dimensional RIS scenario features are compressed into a quantum state using the tensor product, then superimposed during quantum path selection, significantly improving quantum computational advantage. Results demonstrate efficient performance with enhanced spectral efficiency, convergence rate, and adaptability.

Authors:Vijeth Hebbar, Spencer Hutchinson, Mahnoosh Alizadeh, Cédric Langbort
Title: Steady-state Based Approach to Online Non-stochastic Control
Abstract:
We study the problem of online non‑stochastic control (ONC), which is the control of a linear system under adversarial disturbances and adversarial cost functions, with the aim of minimizing the total cost incurred. A recent line of literature in ONC develops algorithms that enjoy sublinear regret with respect to a benchmark based on the set of steady‑states that are attainable by a constant input. In this work, we extend this research direction by giving an algorithm that enjoys \mathcalO(\sqrtT) regret with respect to a richer benchmark set, namely the set of steady‑states attainable under an \emphaffine controller. Since this benchmark substantially broadens the comparison class, it provides significantly stronger performance guarantees. Our proposed algorithm combines a Follow‑The‑Perturbed‑Leader‑style online non‑convex optimization approach with a batching method that maintains stability despite changing policies. Although our proposed algorithm requires solving non‑convex subproblems, we show that an approximate solution to this subproblem is sufficient to ensure \mathcalO(\sqrtT) regret. Furthermore, numerical experiments show that our algorithm enjoys lower total cost and similar computation to existing methods in certain settings.

Authors:Shigeng Wang, Sijia Geng
Title: Decentralized Stability-Constrained Optimal Power Flow for Inverter-Based Power Systems
Abstract:
Future inverter‑dominated power systems feature higher variability and more stressed operating conditions, which motivates the consideration of stability in operational settings. Existing approaches to stability‑constrained OPF often rely on eigenvalue calculation, global model information, or dynamic evaluation inside optimization formulation, which are computationally intensive and difficult to scale. This paper proposes the first decentralized stability‑constrained OPF framework for inverter‑based power systems. The key novelty lies in the incorporation of a class of algebraic decentralized small‑signal stability criteria that admits tractable representations in steady‑state variables and is therefore suitable for optimization. The decentralized stability condition is based on local voltage differences and enables clear theoretical and practical economic interpretation of the stability contribution from each inverter. We define a Nodal Stability Shadow Price (NSSP) for each inverter, and characterize the role of these stability constraints through their associated shadow prices, enabling a nodal interpretation of their economic impacts. It is proved that under active‑power‑only objectives in lossless networks, binding stability constraints may occur but will admit zero shadow prices if all other operational constraints are inactive. Most importantly, we reveal the importance of considering the opportunity cost of reactive power for inverter‑based resources (IBRs) that have limited capacity. When reactive power costs are considered, stability constraints can carry strictly positive shadow prices and admit meaningful economic impacts.

Authors:Erich Trieschman, Saurabh Amin
Title: Structural Misalignment in Financial Transmission Rights
Abstract:
Financial Transmission Rights (FTRs) enable electricity market participants to hedge congestion risk in Day Ahead Market (DAM) operations, but for the market to be solvent, Independent System Operators (ISOs) must ensure that FTR payouts do not exceed the collected DAM merchandising surplus that funds them. We show that FTR underfunding (or conversely, hedging efficiency) can arise structurally from misalignment between the network models used in the FTR auction and the DAM, independent of bidding behavior. We develop a geometric framework in which both DAM merchandising surplus and the maximum supportable FTR payout are expressed as support functions of network‑feasible injection polytopes. The resulting dual representation assigns nonnegative weights to transmission element‑contingency constraints, enabling constraint‑level attribution of model misalignment. Using this framework, we derive sharp implications for canonical FTR network modeling choices like uniform transmission element derates, and for structural sources of underfunding like unplanned DAM outages. We further show that multi‑interval FTR products impose an intrinsic hedging inefficiency when DAM shadow prices vary over time, even under perfect model alignment. These results provide ISOs with rigorous tools to diagnose underfunding and quantify the efficiency cost of conservative FTR network modeling choices.

Authors:Gabriele Oliva, Bianca Mazzà, Roberto Setola
Title: An Innovation-Based Approach to Detect Stealthy Disturbance Attacks in Maritime Monitoring
Abstract:
Modern maritime navigation and control systems rely on digital sensing, estimation, and communication pipelines that fuse GNSS, radar, inertial, and AIS data through approaches such as Kalman‑filter‑based estimators. While these technologies are essential for safety and efficiency, their growing interconnection also exposes vessels to faults and cyber‑physical anomalies. This paper introduces a Statistical Detection Suite (SDS) to detect malicious stealthy disturbances. Specifically, the SDS operates directly on the innovations of Kalman filters, providing a lightweight yet statistically grounded layer of anomaly monitoring within maritime estimation frameworks. The SDS jointly evaluates whitened innovations through four complementary checks: (i) bias, (ii) covariance consistency via the normalized innovation squared (NIS), (iii) Gaussianity, and (iv) temporal independence via portmanteau statistics. The analysis further examines how an adversary can craft stealthy finite‑impulse‑response (FIR) Gaussian disturbances that can evade classical chi‑square checks, formulating an optimization‑based design that balances stealth and trajectory impact. An evaluation in maritime navigation scenarios illustrates how the SDS exposes colored spoofing attacks that bypass traditional methods, highlighting the role of innovation‑based monitoring in strengthening maritime resilience against cyber‑physical threats.

Authors:Achraf El Messaoudi, Noureddine Khaous, Karim Cherifi
Title: Target Parameterization in Diffusion Models for Nonlinear Spatiotemporal System Identification
Abstract:
Machine learning is becoming increasingly important for nonlinear system identification, including dynamical systems with spatially distributed outputs. However, classical identification and forecasting approaches become markedly less reliable in turbulent‑flow regimes, where the dynamics are high‑dimensional, strongly nonlinear, and highly sensitive to compounding rollout errors. Diffusion‑based models have recently shown improved robustness in this setting and offer probabilistic inference capabilities, but many current implementations inherit target parameterizations from image generation, most commonly noise or velocity prediction. In this work, we revisit this design choice in the context of nonlinear spatiotemporal system identification. We consider a simple, self‑contained patch‑based transformer that operates directly on physical fields and use turbulent flow simulation as a representative testbed. Our results show that clean‑state prediction consistently improves rollout stability and reduces long‑horizon error relative to velocity‑ and noise‑based objectives, with the advantage becoming more pronounced as the per‑token dimensionality increases. These findings identify target parameterization as a key modeling choice in diffusion‑based identification of nonlinear systems with spatial outputs in turbulent regimes.

Authors:Jianming Tong, Hanshen Xiao, Krishna Kumar Nair, Hao Kang, Ashish Sirasao, Ziqi Zhang, G. Edward Suh, Tushar Krishna
Title: Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading
Abstract:
Multi‑user virtual reality enables immersive interaction. However, rendering avatars for numerous participants on each headset incurs prohibitive computational overhead, limiting scalability. We introduce a framework, Privatar, to offload avatar reconstruction from headset to untrusted devices within the same local network while safeguarding attacks against adversaries capable of intercepting offloaded data. Privatar's key insight is that domain‑specific knowledge of avatar reconstruction enables provably private offloading at minimal cost. (1) System level. We observe avatar reconstruction is frequency‑domain decomposable via BDCT with negligible quality drop, and propose Horizontal Partitioning (HP) to keep high‑energy frequency components on‑device and offloads only low‑energy components. HP offloads local computation while reducing information leakage to low‑energy subsets only. (2) Privacy level. For individually offloaded, multi‑dimensional signals without aggregation, worst‑case local Differential Privacy requires prohibitive noise, ruining utility. We observe users' expression statistical distribution are slowly changing over time and trackable online, and hence propose Distribution‑Aware Minimal Perturbation. DAMP minimizes noise based on each user's expression distribution to significantly reduce its effects on utility, retaining formal privacy guarantee. Combined, HP provides empirical privacy against expression identification attacks. DAMP further augments it to offer a formal guarantee against arbitrary adversaries. On a Meta Quest Pro, Privatar supports 2.37x more concurrent users at 6.5% higher reconstruction loss and 9% energy overhead, providing a better throughout‑loss Pareto frontier over quantization, sparsity and local construction baselines. Privatar provides both provable privacy guarantee and stays robust against both empirical and NN‑based attacks.

Authors:Donghwan Lee
Title: Beyond the Bellman Fixed Point: Geometry and Fast Policy Identification in Value Iteration
Abstract:
Q‑value iteration (Q‑VI) is usually analyzed through the \(γ\)‑contraction of the Bellman operator. This argument proves convergence to \(Q^\), but it gives only a coarse account of when the induced greedy policy becomes optimal. We study discounted Q‑VI as a switching system and focus on the practically optimal solution set (POSS), the set of \(Q\)‑functions whose tie‑broken greedy policies are optimal. The main result shows that Q‑VI reaches the optimal action class in finite time by entering an invariant tube around \(\mathcal X_1=Q^+\operatornamespan(\mathbf 1)\), which is contained in the POSS. For every \(\varepsilon>0\), the distance to \(\mathcal X_1\) satisfies an exponential bound with rate \((\barρ+\varepsilon)^k\), where \(\barρ\) is the joint spectral radius of the projected switching family restricted to directions transverse to \(\mathcal X_1\). When \(\barρ<γ\), this transverse convergence is faster than the classical contraction rate. The analysis separates fast policy identification from the subsequent convergence to \(Q^\), which may still be governed by the all‑ones mode. We also give spectral and graph‑theoretic conditions under which the strict inequality \(\barρ<γ\) holds or fails.

Authors:Linlin Li, Steven X. Ding, Jiahao Wang, Maiying Zhong, Wei Cheng
Title: System representations in subspaces of finite-sample signals and their application to data-driven fault detection
Abstract:
This paper deals with system representations in finite‑sample signal subspaces and their application to data‑driven fault detection. The first part addresses concepts of finite‑sample image and kernel system representations and, associated with them, image and residual subspaces of finite‑sample signals. On this basis, the equivalence between the fundamental lemma and finite‑sample image subspace is demonstrated. While the image representation models the nominal system dynamics, the residual representation describes uncertainties in the input‑output data and is essential for fault detection. This result extends the fundamental lemma and builds the basis for exploring data‑driven fault detection. In the second part, a data‑driven projection‑based fault detection approach is developed. By means of a singular value decomposition, orthogonal projections onto the image and residual subspaces are realized in the context of a low‑rank matrix approximation, leading to projection‑based residual generation and evaluation. Finally, analysis of detection performance in the framework of matrix perturbation theory and comparison with existing data‑driven fault detection methods are explored.

Authors:Ran Yi, Ruopeng Xu, Dongshu Zhao, Zhaoyang Zhang, Baolin Chen, Kai-Kit Wong, Hyundong Shin, Zhaohui Yang
Title: WirelessAgent: A Unified Agent Design for General Wireless Resource Allocation Problem without Current Channel State Information
Abstract:
This paper investigates the agent design for solving the wireless resource allocation problem without sufficient channel state information (CSI), which cannot be effectively solved via conventional method. In the considered wireless agent design, we provide the general sense‑repair‑decide‑act workflow, which can be used to intelligently solve general wireless resource allocation problem. A multi‑objective optimization problem is formulated to adaptively satisfy different user requirements including both spectrum and energy efficiency. This work addresses the challenge of incomplete CSI for multiple optimization objectives. To solve this problem, we use an artificial intelligence (AI) model to predict missing channel data and construct an agent on the Coze platform, allowing the network operators to optimize multiple objectives through natural language conversations. To tackle the resource scheduling under different objectives, we develop adaptive algorithms. Simulation results validate the effectiveness of our proposed design, demonstrating that the proposed AI method reduces the root mean square error by approximately up to 67% compared to the traditional approach. Moreover, the data‑driven scheduling balances system performance compared to conventional baseline approaches.

Authors:H. Trinh, P. T. Nam, T. N. Nguyen
Title: Time-Delay Compensators for Linear Systems with Delayed Output Measurements
Abstract:
This paper provides a comprehensive framework for designing functional observers for linear systems subject to delayed output measurements. Moving beyond traditional methodologies, the proposed observer generates an estimate \hatz(t) that predicts the current state functional z(t)=Fx(t) using delayed data. By neutralizing sensing latency, the observer serves as a potent time‑delay compensator, effectively expanding the practical utility of functional observer theory. The proposed observer architecture offers greater robustness and versatility than traditional Luenberger‑type observers by leveraging multiple delayed components to preserve accuracy despite latency. A key contribution of this work is a novel method for extending the maximum allowable measurement delay while maintaining the asymptotic stability of the estimation‑error system. Existence conditions are established together with constructive synthesis procedures. Extensive numerical examples are given to illustrate the proposed theory.

Authors:Mohamed Atouife, Jesse Jenkins
Title: The structure of technological learning: insights from water electrolysis for cost forecasting, policy, and strategy
Abstract:
Forecasting the cost evolution of emerging clean technologies is crucial for informed policy, investment, and decarbonization decisions, yet it remains deeply uncertain. Learning curves, which link cost declines to cumulative deployment, are widely used for technological cost forecasting. However, applying them to emerging technologies is challenging due to parametric uncertainty in learning rates, which are scarce and highly uncertain, and structural uncertainty stemming from multiple plausible learning frameworks. Using water electrolysis as a case study, we evaluate how different learning structures, from shared to fragmented learning across technology variants and regions, alter expected cost paths. We interrogate model assumptions that represent contrasting industrial realities, including competition among electrolyzer variants and supply chain fragmentation associated with protectionism and industrial policy. We find that plausible modeling choices generate widely different trajectories, with materially different implications for policy design and technology strategy. We argue for routinely applying multiple learning frameworks to explore decision spaces and stress‑test conclusions for scale‑up planning, national industrial strategy, and energy‑systems modeling.

Authors:Sebastiano A. Piccolo, Giorgio Terracina
Title: Project resilience as network robustness
Abstract:
Engineering projects are the result of the combined effort of their members. Yet, it has been documented that labor division withing projects is unevenly distributed: some project members are specialists undertaking only few tasks, whereas other are generalists and are responsible for the success of many tasks. Moreover, the latter are often facilitators of project integration. Such a workload distribution prompts one question: how resilient is a project to key personnel loss? Far from being a theoretical problem, the reliance of a project on a few key people can lead to severe economic losses and delays. We argue that current methods to estimate such a risk are unsatisfactory: some methods offer a best‑case estimate and are, therefore, too optimistic; other methods fail to capture project fragmentation leading to biased estimates and unrealistic consequences in many settings. In this paper, we develop a novel method to assess project vulnerability by looking at it from the lens of network robustness. We compare our method against existing alternatives and show that it offers better and more consistent estimates of project resilience to personnel loss.

Authors:Lamia Alyami, Anis Hamadouche, Amir Hussain
Title: Stochastic Delayed Dynamics of Rumor Propagation with Awareness and Fact-Checking
Abstract:
This paper presents a stochastic delayed differential model for rumor propagation during infodemic that incorporates human behavioral response, public skepticism and fact‑checking mechanisms. A discrete time delay is introduced to model natural lags in information processing and institutional response. Additionally, we adopt additive stochastic perturbations to model random fluctuations in social interaction and exposure. We present a rigorous stability analysis of the proposed rumor transmission model and derive convergence guarantees under reproduction number conditions. We also validate the model by numerical simulations and analyze the outbreak severity and quantify uncertainty under variable information processing delays. The results highlight the importance of timely awareness and fact‑checking interventions for mitigating misinformation spread during pandemics

Authors:Zihao Ren, Lei Wang, Guodong Shi
Title: Distributed Nesterov Flows for Multi-agent Optimization
Abstract:
Various distributed gradient descent algorithms for multi‑agent optimization have incorporated the Nesterov accelerated gradient method, where the use of momentum enhances convergence rates. These algorithms have found broad applications in large‑scale machine learning and optimization owing to their simplicity and low communication complexity. In this paper, we establish a continuous‑time approximation of distributed Nesterov gradient descent. The convergence properties and convergence rate of the resulting distributed Nesterov flow are analyzed using Lyapunov methods. Building on these insights, we design new parameter choices within the flow, from which we derive flow‑inspired discrete‑time algorithms for multi‑agent optimization. Surprisingly, the resulting algorithms achieve faster convergence compared to existing distributed gradient descent methods: they require fewer iterations to reach the same accuracy for strongly convex functions and exhibit an improved convergence rate for general convex functions without incurring additional communication rounds. Furthermore, we investigate the influence of the network topology on algorithm performance and derive an explicit relationship between the convergence rate and the graph condition number. Numerical simulations are presented to validate the effectiveness of the proposed approach.

Authors:Hiroki Fujii, Masaki Yamakita
Title: Bilinear Input Modulation for Mamba: Koopman Bilinear Forms for Memory Retention and Multiplicative Computation
Abstract:
Selective State Space Models (SSMs), notably Mamba, employ diagonal state transitions that limit both memory retention and bilinear computational capacity. We propose a factorized bilinear input modulation that augments the SSM with a state‑input product, interpretable as a finite‑dimensional Koopman bilinear form. After introducing a shared state across channels (Coupled SSM), the modulation admits three implementations. Coupled Bilinear Input Modulation (seq‑BIM) retains the full bilinear product on the input side at the cost of sequential computation, Coupled Gated Modulation (GM) linearizes it into a gate modulation that is compatible with the parallel scan, and Parallel Bilinear Input Modulation (p‑BIM) places the same bilinear product on the state transition while remaining parallel‑scannable. Experiments on a multiple input‑delay pendulum (memory retention) and NARMA‑10 (bilinear computation) reveal a clear dissociation. GM substantially improves memory retention but not bilinear computation, while both seq‑BIM and p‑BIM improve both. A pathway ablation confirms that the two downstream routes of the bilinear signal serve complementary roles. The improvement is statistically robust, with the bilinear variants consistently outperforming the other variants on bilinear computation. Furthermore, only the bilinear variants benefit from increasing the SSM state dimension, while coupling or gate modulation alone show no improvement, establishing the bilinear mechanism as uniquely capable of exploiting larger state spaces.

Authors:Zhuo Ouyang, Jixian Liu, Enrique Mallada
Title: Symplectic Inductive Bias for Data-Driven Target Reachability in Hamiltonian Systems
Abstract:
Inductive bias refers to restrictions on the hypothesis class that enable a learning method to generalize effectively from limited data. A canonical example in control is linearity, which underpins low sample‑complexity guarantees for stabilization and optimal control. For general nonlinear dynamics, by contrast, guarantees often rely on smoothness assumptions (e.g., Lipschitz continuity) which, when combined with covering arguments, can lead to data requirements that grow exponentially with the ambient dimension. In this paper we argue that data‑efficient nonlinear control demands exploiting inductive bias embedded in nature itself, namely, structure imposed by physical laws. Focusing on Hamiltonian systems, we leverage symplectic geometry and intrinsic recurrence on energy level sets to solve target reachability problems. Our approach combines the recurrence property with a recently proposed class of policies, called chain policies, which composes locally certified trajectory segments extracted from demonstrations to achieve target reachability. We provide sufficient conditions for reachability under this construction and show that the resulting data requirements depend on explicit geometric and recurrence properties of the Hamiltonian rather than the state dimension.

Authors:Taha Saeed Khan, Hamidreza Nazaripouya
Title: Power Flow Solvability with Volt-Var Controlled Inverter-Based Resources
Abstract:
This paper establishes a sufficient condition for guaranteeing power flow solvability in distribution grids with inverter‑based resources (IBRs) operating under IEEE 1547 compliant Volt‑Var control. While designed to improve voltage profiles, reactive power injection can drive the system toward its operational limits. Under these stressed conditions, any further incremental reactive power injection can trigger voltage collapse, the point at which a power flow solution ceases to exist. In this paper, by leveraging a phasor‑based voltage representation, the power flow equations with Volt‑Var control are developed in the complex fixed point form, enabling a compact formulation and the rigorous application of fixed‑point theorems. Addressing the challenges posed by the non‑holomorphicity of the complex power flow equations due to the Volt‑Var function's dependence on voltage magnitude, the solvability conditions are then developed using the Brouwer fixed‑point theorem. The proposed conditions are validated through simulations on distribution test feeders, with a primary focus on their application to real‑time decision‑making for voltage regulation services.

Authors:Y. Zhao, G. Meng
Title: Modeling, Control and Self-sensing of Dielectric Elastomer Soft Actuators: A Review
Abstract:
Dielectric elastomer actuators (DEAs) have garnered extensive attention especially in soft robotic applications over the past few decades owing to the advantages of lightweight, large strain, fast response and high energy density. However, because the DEAs suffer from nonlinear elasticity, inherent viscoelastic creep, hysteresis and vibrational dynamics, the modeling, control and self‑sensing of DEAs are challenging, thereby hindering the practical applications of DEAs. In order to address these challenges, numerous studies have been conducted. In this review, various physics‑based modeling methods and phenomenological modeling methods for predicting the electromechanical response of DEAs are presented and discussed. Different control methods for DEAs are reviewed, which are classified into open‑loop feedforward control, feedback control, feedforward‑feedback control and adaptive feedforward control. Physics‑based self‑sensing methods and data‑driven self‑sensing methods for reconstructing the DEA displacement without the need for additional sensors are discussed. Finally, the existing problems and new opportunities for the further studies are summarized.

Authors:Yuji Takubo, Simone D'Amico
Title: Intent-aligned Autonomous Spacecraft Guidance via Reasoning Models
Abstract:
Future spacecraft operations require autonomy that can interpret high‑level mission intent while preserving safety. However, existing trajectory optimization still relies heavily on expert‑crafted formulations and does not support intent‑conditioned decision‑making. This paper proposes an intent‑aligned spacecraft guidance framework that links high‑level reasoning and safe trajectory optimization through explicit intermediate abstractions, based on behavior sequences and waypoint constraints. A foundation model first predicts an intent‑aligned behavior plan, a waypoint generation model then converts it into waypoint constraints, and the safe trajectory is computed via optimization. This decomposition enables scalable supervision without sacrificing safety. Numerical experiments in close‑proximity operation scenarios demonstrate that the proposed pipeline achieves over 90% SCP convergence and yields a 1.5× higher rate of generating trajectories that satisfy the top intent‑prioritized performance criteria than heuristic decision‑making. These results support the use of intermediate behavior abstraction as a practical interface between foundation‑model reasoning and safety‑critical onboard spacecraft autonomy.

Authors:Maxfield Parson-Scherban, Kasra Fallah, Navid Rahbariasr, Bernard Steyaert, James Anderson, Matthias Preindl
Title: On the Unification of Optimal Current Reference Theory for Wound Rotor Synchronous Machines
Abstract:
Controllers for motor drives typically require a current reference which will satisfy the requested torque subject to system constraints. This work generalizes existing current reference theory to the case of the Wound Rotor Synchronous Machine (WRSM). By incorporating the additional rotor‑current degree‑of‑freedom, along with magnetic saturation, cross‑coupling, and speed‑dependent core losses, the problem of finding an optimal current reference is formulated within affine flux regions as a quadratically constrained quadratic program using a piecewise‑affine approximation derived from finite‑element data. The solution is characterized according to the active constraint regime, yielding closed‑form or low‑dimensional polynomial solutions in several cases, and a small semidefinite program in the voltage constrained regime. The proposed framework extends unified optimal current reference theory beyond the permanent‑magnet setting to three degree‑of‑freedom WRSMs while remaining computationally tractable. Results on a physical WRSM prototype illustrate the effectiveness of the approach across the torque‑speed operating envelope.

Authors:Ali Jalilian, Deepjyoti Deka, Md. Umar Hashmi, Dirk Van Hertem
Title: Coordinated Dynamic Operating Envelopes for Unlocking Additional Flexibility at Grid Edge
Abstract:
Dynamic operating envelopes (DOEs) provide a systematic framework to integrate the flexibility of distribution grid resources while safeguarding network limits such as line ratings and voltage bounds. However, the flexibility derived from individual DOEs is often restricted and conservative, especially when some resources can coordinate via communication with an aggregator. This paper presents a convex, geometry‑aware framework for constructing DOE for distribution grid customers under partial coordination, with coordinated customers modeled through polytopal flexibility sets and non‑coordinated customers through hyperrectangles. The framework additionally incorporates fairness constraints for export and import headroom allocated to the customers within the DOE design. To account for forecast uncertainty in inelastic injections, the DOE design is extended to a robust formulation for bounded uncertainty sets. Case studies on the European Low Voltage Test Feeder indicate that the proposed DOE construction expands total harnessed flexibility, while being consistent with network limits, export/import fairness constraints and is robust to forecast uncertainty. Specifically, coordinating 30% of customers increased the achievable aggregate active‑power injection range by approximately 25% relative to the non‑coordinated baseline.

Authors:Diganta Bhattacharjee, Shih-Chi Liao, Peter J. Seiler, Maziar S. Hemati
Title: Trapping Regions for Quadratic Systems with Generalized Lossless Nonlinearities
Abstract:
A trapping region is a compact set that is forward invariant with respect to the dynamics. Existence of a trapping region certifies boundedness of trajectories, and the size of the set provides an estimate of the ultimate bound. Prior work on trapping region analysis has focused on quadratic systems with energy‑preserving (lossless) nonlinearities. In this work, we focus on a generalization of the lossless property and present an efficient parameterization that enables optimal trapping region computation for a broader class of quadratic systems than afforded by existing methods. We also formulate conditions for ellipsoidal trapping regions, whereas spherical regions have been the focus of prior works. Three numerical examples are used to demonstrate the proposed framework: (1) a four dimensional system for which the prior state‑of‑the art is incapable of identifying a trapping region; (2) a low‑order unsteady aerodynamics model for which the proposed approach yields trapping regions approximately an order of magnitude smaller than prevailing methods; and (3) a two‑state academic example in which the proposed approach correctly identifies a globally asymptotically stable equilibrium point.

Authors:Victor Schmitt, Farzaneh Pourahmadi, Angela Flores-Quiroz, Pablo Apablaza, Pierluigi Mancarella
Title: Learning a Non-linear Surrogate Model for Multistage Stochastic Transmission Planning
Abstract:
Transmission expansion planning (TEP) plays a critical role in ensuring power system reliability and facilitating the integration of renewable energy resources. However, this process requires planners to constantly deal with significant uncertainty. While multistage stochastic TEP models provide a robust framework for identifying investment plans under uncertainty, the rapid growth in problem size hinders their computational tractability. To address this challenge, this paper develops a hybrid machine learning‑optimisation framework for stochastic TEP. The proposed approach uses investment decisions and uncertainty scenarios as input features to train surrogate neural networks, which are then reformulated as mixed‑integer linear constraints and embedded within an optimisation model. The surrogate model approximates expected operational costs to inform TEP decisions, reducing the burden arising from large operational problems. Case study applications on IEEE test systems demonstrate that, after training, the proposed approach achieves near‑optimal investment costs while reducing total computational time by up to a factor of around 13 compared to a single full‑optimisation stochastic formulation. This enables performing extensive multi‑scenario analysis and stress testing that would otherwise be computationally prohibitive at scale.

Authors:Oluwafolajimi Samuel Bolusteve, Linhan Fang, Xingpeng Li
Title: Net Load Forecasting Using Machine Learning with Growing Renewable Power Capacity Features: A Comparative Study of Direct and Indirect Methods
Abstract:
Renewable energy adoption has increased significantly over the past few years. However, with the increasing adoption of renewable energy, forecasting the net load has become a major challenge due to the inherent uncertainty associated with these renewable sources. To mitigate the impact of uncertainties, this study utilizes long short‑term memory (LSTM) model and fully connected neural networks (FCNN) to predict net load based on two independent approaches: the direct method and indirect method. While the conventional direct method directly forecasts the target net load, the indirect approach derives it by separately predicting total load and renewable energy generation. Furthermore, this study innovatively incorporates renewable energy capacity as an input feature to train the forecasting model. The indirect method for FCNN provided a better estimate than the direct method, and the indirect method for LSTM model gave the best prediction. These findings suggest that recurrent architectures like LSTM are particularly well‑suited for net load forecasting applications, while the choice between direct and indirect methods depends on the specific neural network architecture employed. By advancing reliable forecasting tools for renewable energy integration, this work enhances grid resilience and accelerates the transition toward renewable‑dominant power systems.

Authors:Xiaoyan Dai
Title: Semi-definite programs for online control of nonlinear systems with stability guarantees
Abstract:
This paper develops a semidefinite‑programming‑based method for online feedback control of nonlinear systems using a state‑dependent representation. We formulate sequences of time‑varying SDPs whose optimal solutions jointly yield a stabilizing feedback controller and a Lyapunov certificate satisfying stability conditions and quadratic performance specifications. We further establish compact conditions certifying recursive feasibility of the resulting SDP sequences and derive estimates of the region of attraction. Numerical examples on representative nonlinear systems illustrate the flexibility and effectiveness of the proposed method.

Authors:Zhihe Zhuang, Rodrigo A. González, Hongfeng Tao, Wojciech Paszke, Tom Oomen
Title: End-to-End ILC for Repetitive Untrackable Tasks: A Cooperative Game Perspective
Abstract:
An inherent assumption of perfect tracking in iterative learning control (ILC) is that there exists an ILC input such that the generated output can track the desired trajectory reference. This assumption may fail in practice, which gives rise to desired but untrackable tasks. This paper gives an end‑to‑end ILC design for repetitive untrackable tasks in closed‑loop systems. The reference input is trial‑to‑trial updated together with the ILC feedforward input based on the measurement data. This two‑player behavior of the closed‑loop ILC system is investigated from a cooperative game perspective. A sufficient condition for the two‑player end‑to‑end ILC to have a lower cost than the one‑player norm optimal ILC (NOILC) is discovered. Finally, a numerical example is given to verify the effectiveness of the developed method.

Authors:Ruochen Wu, Xu Du, Karl H. Johansson, Apostolos I. Rikos
Title: Nesterov Accelerated Distributed Optimization with Efficient Quantized Communication
Abstract:
In modern large‑scale networked systems, rapidly solving optimization problems while utilizing communication resources efficiently is critical for addressing complex tasks. In this paper, we consider an unconstrained distributed optimization problem in which information exchange among nodes is governed by a directed communication graph. In our setup we focus on two key challenges. The first is the zigzag phenomenon caused by the objective functions of individual nodes having significantly different curvature along different directions. The second is that the communication channels among nodes are subject to limited bandwidth, which motivates the use of compressed (quantized) messages. To address both challenges simultaneously, we propose QANM, a distributed optimization algorithm that combines Nesterov‑accelerated gradient descent with a distributed finite‑time quantized consensus protocol, enabling accelerated convergence. Under strong convexity and smoothness assumptions, we show that our proposed algorithm converges linearly to a neighborhood of the optimal solution. Finally, we validate our algorithm on a distributed sensor fusion application for multi‑dimensional target parameter estimation, where simulations across two distinct scenarios confirm the convergence guarantees and demonstrate clear acceleration benefits over non‑momentum baselines.

Authors:Koki Yamane, Cristian C. Beltran-Hernandez, Steven Oh, Masashi Hamaya, Sho Sakaino
Title: Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning
Abstract:
Fast execution of contact‑rich manipulation is critical for practical deployment, yet providing fast demonstrations for imitation learning (IL) remains challenging: humans cannot demonstrate at high speed, and naively accelerating demonstrations alters contact dynamics and induces large tracking errors. We present a method to autonomously refine time‑accelerated demonstrations by repurposing Iterative Reference Learning Control (IRLC) to iteratively update the reference trajectory from observed tracking errors. However, applying IRLC directly at high speed tends to produce larger early‑iteration errors and less stable transients. To address this issue, we propose Incremental Iterative Reference Learning Control (I2RLC), which gradually increases the speed while updating the reference, yielding high‑fidelity trajectories. We validate on real‑robot whiteboard erasing and peg‑in‑hole tasks using a teleoperation setup with a compliance‑controlled follower and a 3D‑printed haptic leader. Both IRLC and I2RLC achieve up to 10x faster demonstrations with reduced tracking error; moreover, I2RLC improves spatial similarity to the original trajectories by 22.5% on average over IRLC across three tasks and multiple speeds (3x‑10x). We then use the refined trajectories to train IL policies; the resulting policies execute faster than the demonstrations and achieve 100% success rates in the peg‑in‑hole task at both seen and unseen positions, with I2RLC‑trained policies exhibiting lower contact forces than those trained on IRLC‑refined demonstrations. These results indicate that gradual speed scheduling coupled with reference adaptation provides a practical path to fast, contact‑rich IL.

Authors:Muhammad Junayed Hasan Zahed, Chieh Tsai, Salim Hariri, Hossein Rastgoftar
Title: Online Reinforcement Learning for Safe Gain Scheduling in Nonlinear Quadrotor Control
Abstract:
This paper presents an online reinforcement‑learning framework for safe gain scheduling of a nonlinear quadcopter controller. Rather than learning thrust and torque commands directly, the proposed method selects gain vectors online from a finite library of pre‑certified stabilizing controllers, thereby preserving the structure of the underlying snap‑based control law. Safety is enforced by restricting the policy to admissible gains that maintain forward invariance of a prescribed safe state set, while dwell‑time constraints prevent excessively fast switching. To reduce the action‑space dimension, translational gains are shared across spatial axes by exploiting the isotropic structure of the translational dynamics, whereas yaw gains are scheduled independently. A deep Q‑network learns to adjust feedback authority according to the current flight condition, using aggressive gains during large transients and milder gains near hover. High‑fidelity nonlinear simulations demonstrate accurate trajectory tracking, bounded attitude motion, reduced control effort near convergence, and stable hover regulation under online safe gain scheduling.

Authors:Junji Yan, Asrin Efe Yorulmaz, Hanchen Zhou, Tamer Başar
Title: A Stackelberg Game Framework with Drainability Guardrails for Pricing and Scaling in Multi-Tenant GPU Cloud Platforms
Abstract:
Modern Graphics Processing Unit (GPU)‑backed services must satisfy strict latency service‑level objectives (SLOs) while controlling spare‑capacity cost. In multi‑tenant GPU cloud platforms, this trade‑off is inherently dynamic because workload demand is endogenous; specifically, pricing shapes the submissions of heterogeneous tenants, which subsequently impact congestion and delay. We formulate the joint pricing‑and‑scaling problem as a large‑population Stackelberg game problem, and we derive an explicit equilibrium demand map. The resulting closed‑loop model reveals a structural failure mode in which delay‑insensitive workloads sustain a residual demand floor, making the backlog undrainable under bounded price and service capacity. This observation motivates a computable drainability guardrail that certifies uniformly negative drift in the residual‑demand regime. For any fixed price‑capacity pair satisfying the drainability guardrail, we establish a unique operating point and global convergence towards it under a checkable step‑size condition. Building on this fixed‑pair analysis, we further develop an optimizer‑agnostic action shield for the full dynamic problem and show empirically that it improves safety and robustness for model‑free reinforcement learning (RL) in this setting.

Authors:Qinan Zhou, Daniel Stephens, Jing Sun
Title: Experimental Characterization Data for Battery Modules with Parallel-Connected Cells across Diverse Module-Level State of Health and Cell-to-Cell Variations
Abstract:
This experimental dataset presents both module‑level and cell‑level characterization data for lithium‑ion battery modules composed of three parallel‑connected inhomogeneous cells across a wide range of module‑level state of health (M‑SoH) and cell‑to‑cell variation (CtCV). First, 70 cells are aged to establish an inventory with cell‑level state of health (C‑SoH) ranging approximately from 100% to 80% (80% is considered as the end‑of‑life for automotive applications). From this inventory, 78 battery modules are then assembled, each exhibiting a distinct M‑SoH value (from 100% to 80.98%) and a unique CtCV value (from 0% to 9.31%, defined as population standard deviation of C‑SoH within each module). Module‑level characterization data are collected at 25°C under 0.5C and 0.25C conditions, enabling extraction of module‑level capacities and supporting diagnostic analyses such as incremental capacity analysis and differential voltage analysis. Before a module is assembled and tested, cell‑level characterization tests are conducted for every individual cell within that module under 1C conditions, enabling direct quantification of CtCV and providing accurate labels for cell‑level capacities and internal resistances. The dataset is organized with both raw time‑series data and processed summary information such as C‑SoH, M‑SoH, and CtCV for all modules. With the paired module‑level and cell‑level characterization data, this dataset enables understanding and development of advanced degradation monitoring mechanisms for battery modules with parallel‑connected cells in the presence of CtCVs.

Authors:Leeroy Makusha, Preston Abadie, Donald J. Docimo
Title: A Control-Oriented Framework for Coupling Physics-Based and Data-Driven Models
Abstract:
Design, control, and estimation for dynamic systems require accurate and analytically tractable models. However, modern engineered systems contain components that are described with heterogeneous modeling paradigms, as well as subsystems that are challenging to model from physics alone. There have been significant efforts to address this through heterogeneous coupling frameworks and data‑driven modeling. However, these two paths have been pursued in parallel. This work bridges this gap by introducing a control‑oriented framework to couple physics‑based and data‑driven models. A physics‑based microgrid with a data‑driven data center load model is used to demonstrate the proposed four step methodology. Application of the framework yields a coupled system that allows for rigorous assessment of control properties. Equilibrium and stability tests are conducted, and they both reveal that the coupling structure and functions play a critical role in determining physically meaningful equilibrium points and stability of the integrated system. This information could only be accessed through the proposed framework, highlighting its importance.

Authors:William Retnaraj, Simone Betteti, Alexander Davydov, Francesco Bullo, Jorge Cortes
Title: Timescale Limits of Linear-Threshold Networks
Abstract:
Linear‑threshold networks (LTNs) capture the mesoscale behavior of interacting populations of neurons and are of particular interest to control theorists due to their dynamical richness and relative ease of analysis. The aim of this paper is to advance the study of global asymptotic stability in LTNs with asymmetric neural interactions and heterogeneous dissipation under the structural Lyapunov diagonal stability (LDS) condition. To this end, we introduce a one‑parameter family of LTNs that preserves the LDS condition and has a parameter‑independent equilibrium set. In the fast limit, this family converges to a projected dynamical system (PDS), while in the slow limit, it converges to a discontinuous hard‑selector system (HSS). Under LDS, we prove that the fast PDS limit is globally exponentially stable and that the HSS limit is globally asymptotically stable. This alignment suggests that the limiting systems capture essential mechanisms governing stability across the entire LTN family. Together with numerical evidence, these findings indicate that resolving stability at the fast and slow endpoints provides a promising and structurally grounded path toward establishing global stability for LTNs with biologically plausible recurrence and diagonal dissipation.

Authors:Cong Bai, Salish Maharjan, Yunyi Li, Wenlong Shi, Zhaoyu Wang
Title: Synchronization-Safe Dynamic Microgrid Formation for DER-Led Distribution System Restoration With Constraint-Aware Graph Learning
Abstract:
Prolonged blackouts in distribution systems (DSs) with high penetration of distributed energy resources (DERs) necessitate novel restoration strategies to rapidly restore loads. However, the resulting complex optimization problem significantly limits scalability. This paper proposes a synchronization‑safe dynamic microgrid (MG) formation (SSDMGF)‑enabled restoration framework, in which a constraint‑aware graph learning approach is developed to enhance solution efficiency. To characterize the restoration status of systems with evolving boundaries, the concepts of system mode and system class are defined. To ensure synchronization safety during restoration, the transitions of system mode and class for dynamically formed MGs are explicitly restricted. To further accelerate the solution process, a constraint‑aware spatio‑temporal graph convolutional network (STGCN) is designed to partially generate high‑quality warm‑start solutions, where synchronization‑related constraints are embedded into a differentiable feasibility‑resolving layer based on the straight‑through estimator (STE). Case studies on a modified IEEE 123‑node feeder validate that the proposed method ensures synchronization‑safe MG formation and improves restoration performance. Meanwhile, the proposed acceleration framework achieves significant computational speed‑ups without compromising final optimality.

Authors:Josh A. Taylor, Alejandro D. Domínguez-García
Title: Distance characteristics for incremental quantities
Abstract:
We derive distance relay characteristics in terms of incremental quantities. The characteristics are operating‑point independent in that they depend on the network structure and types of sources, but not their real‑time voltages or current injections.

Authors:Shakul Pathak, Martin Z. Bazant
Title: Scaling and Analytical Approximation of Porous Electrode Theory for Reaction-limited Batteries
Abstract:
Porous electrode theory (PET) provides essential insights into electrochemical states, but its computational complexity hinders real‑time control and obscures scaling relations. To bridge the gap between high‑fidelity simulations and reduced‑order models, we present a framework of scaling analysis and analytical approximations. By assuming high‑performance electrodes minimize transport limitations and overpotentials, we derive a simplified "lean model" governed by four dimensionless numbers: (i) a traditional Damk"ohler number, Da, scaling the characteristic reaction rate to the diffusion rate in the electrolyte‑filled pores; (ii) the "process Damk"ohler number," Da_p, scaling the reaction rate to the applied capacity utilization rate (C‑rate); (iii) the "wiring Damk"ohler number," Da_w, scaling the reaction rate to an effective electromigration rate for ions in the pores in series with electrons in the conducting matrix; and (iv) the "capacitive Damk"ohler number," Da_c, comparing the rates of Faradaic reactions and double‑layer charging. For batteries, we derive analytical solutions for standard protocols, including galvanostatic discharge, chronoamperometry, and electrochemical impedance spectroscopy. Validated against numerical simulations of a practical NMC half‑cell, our formulae show excellent agreement at negligible computational cost. This interpretable, physics‑based framework accelerates battery design and state estimation while unifying the modeling of batteries, supercapacitors, fuel cells, and other porous electrode systems.

Authors:Tingwei Cao, Yan Xu
Title: CVaR-Guided Decision-Focused Learning and Risk-Triggered Re-Optimization for Two-Stage Robust Microgrid Operation
Abstract:
Microgrid operation is highly vulnerable to short‑term load uncertainty, while conventional predict‑then‑optimize pipelines cannot fully align probabilistic forecasting quality with downstream robust scheduling performance. This paper proposes a CVaR‑guided decision‑focused learning and risk‑triggered re‑optimization framework for two‑stage robust microgrid operation. A probabilistic load forecasting model first generates multi‑quantile outputs, which are converted into prediction intervals to parameterize the load uncertainty set of the downstream two‑stage robust optimization (TSRO) model. To improve forecasting reliability under difficult and high‑risk operating conditions, a CVaR‑guided forecasting objective is introduced to emphasize tail‑sensitive samples. To further close the forecast‑decision gap, a convex regularized surrogate TSRO model and a smooth regret loss are developed, enabling downstream operational feedback to be propagated to the forecasting model through KKT‑based implicit differentiation. For online deployment, a risk‑triggered re‑optimization mechanism selectively re‑solves the remaining‑horizon TSRO only when the schedule mismatch becomes significant, avoiding unnecessary online computation. Case studies on modified IEEE 33‑bus and 69‑bus microgrids demonstrate superior probabilistic forecasting accuracy, operational economy, and tail‑risk mitigation over benchmark methods, while preserving near‑full‑re‑optimization performance with less than 0.5% higher operating cost and up to 91% lower daily solution time.

Authors:Trong Duy Tran, Maxime Ferreira Da Costa, Salah Eddine Elayoubi, Nguyen Linh Trung
Title: Goal-oriented Resource Allocation for Collaborative Integrated Sensing and Communication
Abstract:
In this paper, we consider resource allocation for a collaborative integrated sensing and communication (ISAC) scenario, in which distributed smart devices can be scheduled to perform sensing and transmit their sensing features to a fusion center. The fusion center aims to perform classification tasks on the environment based on received features. A scalable networksensing framework is proposed to balance the performance of the sensing service with that of the classical enhanced Mobile Broadband (eMBB) service. We adopt a tractable theoretical metric, the discriminant gain, as a proxy for the classification goal. We formulate cross‑layer optimization problems to maximize discriminant gain under constraints on energy consumption and eMBB communication quality for the independent and joint scheduling policies. The joint scheduling policy has considerably higher complexity than the independent scheduling policy, in exchange for better collaborative sensing performance. A simplified gain model is proposed to reduce the complexity and practicality of the joint scheduling policy. Both policies are obtained via successive convex approximation and parametric convex optimization. Extensive experiments are conducted to verify the goal‑oriented framework and the two policies. It is demonstrated that the two policies outperform the baseline policies with both synthetic and realistic radar simulation datasets. The joint scheduling policy can exploit device correlations and thus performs better than the independent scheduling policy under strong correlations and strict communication constraints.

Authors:Duong The Do, Matthew Aldridge, Duong Tung Nguyen
Title: Projected Variational Quantum Extragradient for Zero-Sum Games
Abstract:
We propose a projected variational quantum extragradient (VQEG) framework for computing approximate Nash equilibria in two‑player zero‑sum matrix games. Mixed strategies are parameterized as Born distributions of parameterized quantum circuits (PQCs), transforming the classical bilinear saddle point problem into a smooth but generally minmax optimization in circuit‑parameter space. The expected payoff is expressed as the expectation of a diagonal observable, enabling gradient evaluation via the parameter shift rule and compatibility with shot based quantum hardware. To support arbitrary game sizes, we introduce a dominated embedding that maps (m,n) games to qubit‑compatible power‑of‑two dimensions while preserving equilibrium structure. We then develop a projected extragradient method using stochastic gradient estimates derived from finite measurement shots, and establish variance bounds scaling as O(1/S) with respect to the number of measurement shots S, along with convergence to approximate first‑order stationarity under standard assumptions. Since stationarity does not guarantee equilibrium optimality, we evaluate performance using the game‑space Nash gap. Numerical results demonstrate high‑precision solutions on structured instances up to 32x32, while highlighting challenges in unstructured settings.

Authors:Jin Won Kim
Title: A Unified Control Theory Derivation of Discrete-Time Linear Ensemble Kalman Filters
Abstract:
The ensemble Kalman filter (EnKF) has become a standard methodology for state estimation in high‑dimensional systems, yet its various stochastic and deterministic formulations often appear conceptually disconnected. In this paper, a unified derivation framework for EnKF algorithms are established by leveraging the classical duality between estimation and optimal control, which is the key concept in deriving Kalman filter. By recasting the minimum variance estimation problem into second order moment for the ensembles, we demonstrate that seemingly distinct EnKF variants ‑‑ both with or without perturbed observation ‑‑ can be systematically classified. Specifically, the duality based framework reveals that the operational differences among these variety of EnKF algorithms reduce to a specific choice of hyperparameters. Ultimately, this perspective not only covers existing EnKF variants but also provides a systematic foundation for designing novel hybrid filters using control theory approach.

Authors:Thoshitha Gamage, Lasanthi Gamage
Title: Compiling OpenSCENARIO 2.1 for Scenario-Based Testing in CARLA
Abstract:
While the ASAM OpenSCENARIO 2.1 Domain‑Specific Language (DSL) enables declarative, intent‑driven authoring for Scenario‑Based Testing (SBT), its integration into open‑source simulators like CARLA remains limited by legacy parsers. We propose a multi‑pass modern compiler architecture that translates the OpenSCENARIO 2.1 DSL directly into executable CARLA behaviors. The pipeline features an ANTLR4 frontend for Abstract Syntax Tree (AST) generation, a semantic middle‑end, and a runtime backend that synthesizes deterministic py_trees behavior trees. Mapping the standardized domain ontology directly to CARLA's procedural API via a custom method registry eliminates the need for external logic solvers. A demonstrative multi‑actor cut‑in and evasive maneuver, selected from a wider suite of validated scenarios, confirms the compiler's ability to process concurrent actions, dynamic mathematical expressions, and asynchronous signaling. This framework establishes a functional baseline for reproducible, large‑scale SBT, paving the way for future C++ optimizations to mitigate current Python‑based computational overhead.

Authors:Kang Yang, Walid A. Hanafy, Prashant Shenoy, Mani Srivastava
Title: FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models
Abstract:
As edge AI deployments scale to billions of devices running always‑on, real‑time compound AI pipelines, they represent a massive and largely unmanaged source of energy consumption and carbon emissions. To reduce carbon emissions while maximizing Quality‑of‑Service (QoS), this paper proposes FM‑CAC, a proactive carbon‑aware control framework that leverages a battery as an active temporal buffer. By decoupling energy acquisition from energy consumption, FM‑CAC can maximize the use of low‑carbon energy, substantially reducing carbon emissions. At each control step, FM‑CAC jointly optimizes the software pipeline variant, the hardware operating point, and the battery charging and discharging actions. To support this decision process, FM‑CAC leverages edge‑friendly Time‑Series Foundation Models (TSFMs) for zero‑shot carbon forecasting and integrates these forecasts into a dynamic programming solver with deferred cost attribution to prevent myopic battery depletion. Results show that FM‑CAC reduces carbon emissions by up to 65.6% while maintaining near‑maximum inference accuracy.

Authors:Chih-Yuan Chiu, Sarah H. Q. Li, Bryce L. Ferguson
Title: Distributionally Robust Tolls for Traffic Networks with Affine Latency Functions
Abstract:
In network congestion games, system operators often utilize latency models, estimated from real‑world traffic flow and travel time data, to design monetary incentives which steer equilibrium user behaviors towards lowering system‑wide latency. This work studies the impact of latency model uncertainty when designing incentives in non‑atomic network congestion games. Our approach leverages distributionally robust optimization (DRO), which captures data‑driven uncertainty in latency models by considering worst‑case distribution shifts. We prove that, under mild and practically relevant assumptions, the distributionally robust tolling problem in single origin‑destination, affine‑latency congestion games can be solved via convex programming. Numerical simulations illustrate that tolls designed to be distributionally robust against unknown disturbances can outperform tolls designed using fixed, nominal disturbance models in minimizing system‑wide latency.

Authors:Felix Koch, Fabian Raisch, Benjamin Tischler
Title: Thermal-GEMs: Generalized Models for Building Thermal Dynamics
Abstract:
Data‑driven models for building thermal dynamics are a scalable approach for enabling energy‑efficient operation through fault detection & diagnosis or advanced control. To obtain accurate models, measurement data from a target building spanning months to years are required. Transfer Learning (TL) mitigates this challenge by employing pretrained models based on single or multiple source buildings. General multi‑source TL models promise to outperform single‑source TL, but alternative multi‑source modeling architectures remain to be explored, and evaluation on real‑world data is missing. Moreover, time series foundation models (TSFM) have emerged as candidates for the best‑performing general models. Hence, we conduct a first, comprehensive assessment of general modeling approaches for building thermal dynamics, including multi‑source TL and TSFMs. Our assessment includes ablations using four state‑of‑the‑art multi‑source TL architectures and evaluations on synthetic as well as real‑world data. We demonstrate that multi‑source TL models are highly effective in accurately modeling buildings in real‑world applications, yielding up to 63% lower forecasting errors compared to single‑source TL. Moreover, our results suggest a trade‑off between multi‑source TL models exclusively pretrained with building data and TSFMs pretrained with a multitude of different time series, revealing that data from 16‑32 source buildings must be available over 1 year for pretraining multi‑source TL models to consistently outperform TSFMs as evaluated using the mean absolute error. These findings provide practical guidance for selecting modeling strategies based on the number of source buildings available for pretraining multi‑source TL models.

Authors:Noah Rhodes, Sachin Shivakumar, Luke S. Baker, Kaarthik Sundar, Anatoly Zlotnik
Title: Simulating Arbitrage Optimization for Market Monitoring in Gas and Electricity Transmission Networks
Abstract:
We examine market outcomes in energy transport networks with a focus on gas‑fired generators, which are producers in a wholesale electricity market and consumers in the natural gas market. Market administrators monitor bids to determine whether a participant wields market power to manipulate the price of energy, reserves, or financial transmission rights. If economic or physical withholding of generation from the market is detected, mitigation is imposed by replacing excessive bids with reference level bids to prevent artificial supply shortages. We review market monitoring processes in the power grid, and present scenarios in small interpretable test networks to show how gas‑fired generators can bid in the gas market to alter outcomes in a power market. We develop a framework based on DC optimal power flow (OPF) and steady‑state optimal gas flow (OGF) formulations to represent two interacting markets with structured exchange of price and quantity bids. We formulate optimization‑based methods to identify market power in a power grid, as well as to identify market conditions that indicate market power being exerted by a generator using gas market bids.

Authors:Kiran Kumar Challa, Alok Kumar Bharati, Venkataramana Ajjarapu
Title: Consensus Clustering for the Identification of Coherent Regions with Varied Generation Mix
Abstract:
With a steady increase in the inverter technology integration to the grid, frequency response of the large inter‑connection system becomes more unpredictable. This leads to a significant change in the boundaries of the coherent region, which highly depends on the changing disturbance locations and operating conditions. While most of the existing coherency identification is based on a single large generator outage, it is important to identify these boundaries in view of wide range of disturbances. With large amount of inverters in the system, there is increase in the dynamic interactions of the various grid components leading to a need for such boundary identification. This paper presents the multi‑view consensus algorithm to identify coherency in the case of variable grid operating conditions and wide range of disturbances. The proposed approach is demonstrated by identifying the coherent regions in the miniWECC 240 bus test system.

Authors:Xiaohui Zhang, Liaoyuan Yang, Peng Yang
Title: Data-Driven Distributed Stability Certification for Power Systems via Input-State Trajectories
Abstract:
This article proposes a data‑driven framework to verify the distributed conditions that guarantee the system‑wide stability for interconnected power systems. To guarantee system wide stability, the dynamics of each bus are required to satisfy an output differential passivity (ODP) condition with a sufficient index. These ODP indices uniformly quantify the impacts on the system‑wide stability of individual bus dynamics and the coupling strength from the power network. To obtain these indices without explicit physical models, we derive a data‑driven linear matrix inequality (LMI) criterion based exclusively on measured input‑state trajectories. Furthermore, extracting the optimal ODP index is formulated as a convex semi‑definite programming (SDP) problem. Simulations verify the effectiveness of the proposed method under both single‑device offline evaluation and system‑wide online certification scenarios.

Authors:Asmaou S. Ouedraogo, Donald J. Docimo
Title: Optimization of Phase Change Material Integration for Active Cooling Control
Abstract:
This paper presents a unified optimization framework for phase change material (PCM) based cooling systems. Thermal management is critical in applications such as photovoltaic (PV) modules, battery packs, and power electronics, where excessive heat reduces performance and lifespan. Designing such systems is challenging because energy dynamics, capacity, heat rejection, and structural constraints must all be considered. Although prior studies have investigated PCM applications and heat transfer enhancement, there are limited efforts that unify such diverse performance objectives through formalized design methods. This paper develops a framework that formulates the PCM design problem using critical energy‑based terms, with static and dynamic objectives capturing the PCM physical design and control aspects. Two case studies are used to validate the approach: the first explores passive cooling, and the second implements an active cooling configuration. The results compare the design and control of these systems, showing improvement in individual performance metrics between the two options.

Authors:Shaoqing Liu, Mushuang Liu
Title: Real-Time Solution-Seeking for Game-Theoretic Autonomous Driving via Time-Distributed Iterations
Abstract:
Computational complexity has been a major challenge in game‑theoretic model predictive control (GT‑MPC), as real‑time solutions to a game (e.g., Nash equilibria (NEs)) have to be computed at each sampling instant of an MPC. This challenge is especially critical in autonomous driving, where interactions may involve many agents, and decisions must be made at fast sampling rates. We show that this challenge can be addressed through time‑distributed solution‑seeking iterations designed based on, e.g., Newton and Newton‑‑Kantorovich methods. Specifically, the autonomous vehicle decision‑making problem is first formulated as a GT‑MPC problem. To ensure solution attainability, a potential game framework is adopted. Within this framework, both potential‑function optimization and best‑response dynamics are used to seek the NE. To enable real‑time implementation, Newton and Newton‑‑Kantorovich methods are employed to solve the optimization problems arising in the NE‑seeking algorithms, with their iterations distributed over time. Numerical experiments on an intersection‑crossing scenario demonstrate that the proposed methods achieve effective real‑time performance.

Authors:Diego Torres-García, Wim Michiels
Title: A numerical approach to the co-design of PID controllers and low-pass filters for time-delay systems
Abstract:
This paper addresses the numerical optimization of proportional‑integral‑derivative (PID) controllers for linear time‑invariant systems with delays, where the derivative action is implemented using a low‑pass filter. While performance assessment is often based on the spectral abscissa of the ideal PID‑controlled system, the inclusion of a derivative filter fundamentally alters the closed‑loop spectral properties and cannot be treated as a post‑processing step. In particular, the spectral abscissa of the filtered closed‑loop system may differ significantly from that of its unfiltered counterpart, potentially affecting both stability and performance. We propose a systematic numerical design framework in which the PID gains and the filter constant are optimized simultaneously by directly minimizing the spectral abscissa of the filtered closed‑loop system. Treating the filter as an integral part of the control design allows us to reconcile robustness at high frequencies, in the sense of mitigating fragility issues due to approximate identities, with performance at low frequencies, in addition to counter measurement noise amplification. At the end of the presentation, numerical examples illustrate the proposed approach and highlight the benefits of controller‑filter co‑design. The results apply to general linear systems with input and/or state delays and are valid for both single‑input single‑output (SISO) and multi‑input multi‑output (MIMO) configurations.

Authors:Katharina Stich, Bastian Perner, Friedemann Laue, Torsten Reissland, Norman Franchi
Title: Convergence Time Distributions for Max-Consensus over Unreliable Networks
Abstract:
This paper proposes the LiFE‑CD algorithm for convergence time analysis of the max‑consensus algorithm in multi‑agent systems under Bernoulli‑distributed link failures. Unlike existing approaches, which either assume ideal communication or provide asymptotic upper bounds on the expected convergence time, LiFE‑CD deterministically computes the full probability distribution of the convergence time from network topology and individual link failure probabilities, without simulation. The full probability distribution enables deadline‑aware protocol design with specified reliability guarantees. Based on geometrically distributed link delays, the proposed algorithm iteratively reduces the given network topology considering both unicast and broadcast transmissions. LiFE‑CD yields exact results for acyclic networks and, for cyclic networks, tight upper bounds on the convergence time via shortest‑path spanning tree construction. Numerical results confirm analytical exactness for acyclic networks, validate tightness for cyclic networks, and demonstrate improvement over existing approaches. Our complexity analysis shows reduced computational cost compared to Monte Carlo simulations, while eliminating stochastic variability and enhancing reproducibility. All results extend directly to min‑consensus by structural equivalence.

Authors:Colin Jüni, Mina Montazeri, Yi Guo, Federica Bellizio, Giovanni Sansavini, Philipp Heer
Title: Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility
Abstract:
Buildings account for approximately 40% of global energy consumption, and with the growing share of intermittent renewable energy sources, enabling demand‑side flexibility, particularly in heating, ventilation and air conditioning systems, is essential for grid stability and energy efficiency. This paper presents a safe deep reinforcement learning‑based control framework to optimize building space heating while enabling demand‑side flexibility provision for power system operators. A deep deterministic policy gradient algorithm is used as the core deep reinforcement learning method, enabling the controller to learn an optimal heating strategy through interaction with the building thermal model while maintaining occupant comfort, minimizing energy cost, and providing flexibility. To address safety concerns with reinforcement learning, particularly regarding compliance with flexibility requests, we propose a real‑time adaptive safety‑filter to ensure that the system operates within predefined constraints during demand‑side flexibility provision. The proposed real‑time adaptive safety filter guarantees full compliance with flexibility requests from system operators and improves energy and cost efficiency ‑‑ achieving up to 50% savings compared to a rule‑based controller ‑‑ while outperforming a standalone deep reinforcement learning‑based controller in energy and cost metrics, with only a slight increase in comfort temperature violations.

Authors:Ali Eslami, Jiangbo Yu, Mohammad Pirani
Title: Stealthy Cyber-Attacks on Vehicle Lateral Dynamics: A System-Theoretic Analysis
Abstract:
This paper studies the vehicle bicycle model under three classes of stealthy cyber‑attacks: replay attacks, zero dynamics attacks, and covert attacks. Using a system‑theoretic framework, we analyze the feasibility and impact of these attacks on vehicle lateral dynamics. The investigation considers different measurement configurations, including yaw rate, lateral acceleration, and longitudinal acceleration outputs, to evaluate how sensor selection influences attack detectability and system vulnerability. Each attack class is characterized in terms of required system knowledge, communication access, and impact. The analysis shows that replay attacks remain largely model‑agnostic, while zero dynamics attacks are fundamentally constrained by control‑oriented design choices, particularly output selection, which can eliminate unstable zero dynamics and limit the attack impact. In contrast, covert attacks, enabled by coordinated actuator and sensor manipulation, allow sustained and stealthy deviation of lateral states when sufficient access and system knowledge are available. The effects of actuator and tire saturation are also examined, revealing attack‑dependent impacts on stealthiness and effectiveness. Finally, simulation case studies are conducted by using CarSim‑Simulink co‑simulation to validate and verify the theoretical results.

Authors:Gerson Portilla, Carolina Albea, Alexandre Seuret
Title: Robust predictive control design for uncertain discrete switched affine systems subject to an input delay
Abstract:
Robust stabilization conditions for uncertain switched affine systems subject to a unitary input delay are presented. They are obtained through the Lyapunov framework and a min‑switching state‑feedback predictive control law. The result relies on a prediction scheme considering nominal system parameters. By constructing a Lyapunov function that considers the prediction error, we demonstrate the exponential convergence of the system trajectories and system prediction to a robust limit cycle. An example is provided to validate the obtained result.

Authors:Leontine Aarnoudse, Mark Haring, Nathan van de Wouw, Alexey Pavlov
Title: Uncertainty-based perturb and observe for data-driven optimization
Abstract:
Data‑based adaptive optimization methods hold great promise for the performance optimization of uncertain, time‑varying processes. However, current methods are often based on continuous perturbation which is in general undesired for real‑life (e.g., industrial) applications. In this paper, a new uncertainty‑based perturb‑and‑observe method is developed that addresses this limitation and reduces the required number of perturbations, while retaining the capability to track time‑varying optima. The method is based on the philosophy of `only perturbing when needed,' and is shown to converge to the optimum under mild conditions. A simulation‑based case study on a photo‑voltaic solar array demonstrates that it can outperform the standard perturb and observe approach as well as three other data‑based optimization methods.

Authors:E. Sundström, M. Bauer, J. L. Guzmán, T. Hägglund, K. Soltesz
Title: A Practical Guide to PID Controller Implementation
Abstract:
How difficult can it be to implement a PID controller? The answer is twofold. Implementing the PID control law is simple and computationally inexpensive. However, this basic form will not work in practical applications. The primary reason for this is the various physical limitations of the actuator. Measurement noise, different implementations depending on the various structures (P, PI, PD or PID), bumpless transfer, and varying sampling time also result in problems rendering the basic form inoperable. PID implementation is therefore more difficult than meets the eye. This paper introduces a reference implementation of the PID controller which considers these practical issues. It includes pseudo‑code, discussion of the implementation choices and simulation of carefully selected, important test cases.

Authors:Na Li, Binod Koirala
Title: From Individual Consumers to Energy Communities: A Techno-economic Assessment of Swiss Local Electricity Communities
Abstract:
As energy communities move from policy design to implementation in Switzerland, understanding their performance in practice has become increasingly important. A techno‑economic assessment of a regulation‑compliant LEC is presented under the new Swiss legal framework in this study. A reference case without local electricity exchange is compared to a LEC scenario with internal electricity sharing. Results show that LEC participation increases local renewable utilization, reduces grid exports, and delivers economic benefits to both consumers and prosumers. A sensitivity analysis further indicates that internal electricity pricing plays a critical role in shaping trade‑offs between overall efficiency and fairness in benefit distribution. This exploratory study provides practical insights to support informed decision‑making and the future development of LEC in Switzerland.

Authors:Shaojie Zhang, Ozgur B. Akan
Title: Dispersion-Domain Detection for Mobile Molecular Communication Under Multiplicative Geometry Uncertainty
Abstract:
Mobile molecular communication (MC) links with counting receivers are sensitive to transmitter‑‑receiver geometry especially when nodes are mobile. We study binary detection from within‑symbol count observations with unknown finite‑memory inter‑symbol interference (ISI) and a block‑constant multiplicative geometry gain. Under a mixed‑Poisson view mobility and geometry uncertainty can randomize the latent received intensity and create extra‑Poisson dispersion. We propose a profiled dispersion‑domain statistic T_k^(Δ) formed after profiling the deterministic mean shape. The statistic subtracts the intrinsic Poisson component and normalizes by the squared profiled mean to target threshold stability under the stated multiplicative‑gain model. Activity gating makes conditional and gate‑integrated false‑alarm probabilities explicit. We characterize T_k^(Δ) using a time‑series central‑limit‑theorem (CLT)‑motivated Gaussian working approximation with long‑run‑variance dependence correction yielding Gaussian‑approximate receiver operating characteristic (ROC)/bit‑error‑rate (BER) formulas and separability design metrics. Simulations with symbol‑dependent active‑Brownian mobility and finite‑memory ISI support the proposed mechanism show empirical threshold stability over the tested gain range and indicate usefulness when mean‑domain differences are weak unreliable or intentionally suppressed.

Authors:Marco Quantschnig, Yannick Werner, Sonja Wogrin, Thomas Klatzer
Title: QGas: Interactive Gas Infrastructure Toolkit
Abstract:
Gas infrastructure datasets are essential inputs for energy system planning to support strategic decision‑making toward decarbonization. However, relevant data are typically scattered across heterogeneous sources, including geospatial datasets, image‑based infrastructure plans, and tabular data, making it complex, time‑consuming, and error‑prone to create topology‑consistent network representations with existing tools.This paper presents QGas, an interactive toolkit for visualizing, creating, and collaboratively extending georeferenced gas infrastructure datasets. QGas integrates GIS‑based geometry editing with topology‑preserving graph operations in a unified web‑based environment, enabling users to digitize infrastructure plans, edit network elements, manage attributes, and perform topology‑consistent modifications while maintaining a georeferenced representation of the system. The toolkit is implemented using a modular architecture based on Python, JavaScript, and the Leaflet mapping library. An illustrative example demonstrates its application in extending a natural gas dataset to include hydrogen and CO2 infrastructure, highlighting QGas's capability to support the preparation of consistent multi‑carrier gas infrastructure datasets for energy system planning.

Authors:Hyeongmeen Baik, Hamed Poursiami, Maryam Parsa, Jinia Roy
Title: Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
Abstract:
Always‑on converter health monitoring demands sub‑mW edge inference, a regime inaccessible to GPU‑based physics‑informed neural networks. This work separates spiking temporal processing from physics enforcement: a three‑layer leaky integrate‑and‑fire SNN estimates passive component parameters while a differentiable ODE solver provides physics‑consistent training by decoupling the ODE physics loss from the unrolled spiking loop. On an EMI‑corrupted synchronous buck converter benchmark, the SNN reduces lumped resistance error from 25.8% to 10.2% versus a feedforward baseline, within the \pm 10% manufacturing tolerance of passive components, at a projected ～270× energy reduction on neuromorphic hardware. Persistent membrane states further enable degradation tracking and event‑driven fault detection via a +5.5 percentage‑point spike‑rate jump at abrupt faults. With 93% spike sparsity, the architecture is suited for always‑on deployment on Intel Loihi 2 or BrainChip Akida.

Authors:Osasumwen Cedric Ogiesoba-Eguakun, Phani Kumar Inkollu, Rupesh Sah, Zia Rashid, Douglas Jussaume, Suman Rath
Title: Integrating AI and Simulation for Teaching Power System Dynamics: An Interactive Framework for Engineering Education
Abstract:
Artificial Intelligence (AI), especially cloud platforms and large language models (LLMs), is changing how engineering is taught by making learning more interactive and flexible. However, in electrical engineering and energy systems, students often find power system dynamics difficult to understand because the concepts are abstract, math‑heavy, and there are limited opportunities for hands‑on practice. This paper presents an AI‑based interactive learning framework that combines simulation with intelligent feedback to improve understanding and student engagement. The framework has three connected parts: an AI layer that provides explanations and guidance, a simulation layer that models system behavior, and a user layer that allows students to interact with the system in real time. These parts work together in a continuous loop where students explore how the system behaves, change parameters, and receive feedback based on the results. The paper also provides a step‑by‑step process to help educators design and apply AI‑supported learning environments, including breaking down concepts, using simulations, and assessing performance. This method helps students learn through practice and better understand how ideas from class apply to real power systems. It also provides a practical way to improve electrical engineering education and helps students get ready to use AI tools carefully and responsibly in engineering.

Authors:Abdallah Alalem Albustami, Ahmad F. Taha, Sankaran Mahadevan
Title: DAE-Aware Bayesian Inference for Joint Generator-Network Parameter Estimation
Abstract:
This paper addresses the classic problem of parameter estimation (PE) in multimachine power system models. Such models are typically described by a set of nonlinear differential‑algebraic equations (DAE), where generator physics and network power flow equations are coupled. DAE models are well established in classic power system textbooks, but parameter identification and estimation of generator inertia and damping together with network branch resistances and reactances for these models remain relatively underexplored. In contrast to prior approaches that rely on ODE approximations, this paper develops a joint Bayesian inference framework to perform PE of generator and network parameters while exploiting grid DAE models. It further combines physics‑aware statistical modeling with computationally efficient posterior sampling to make joint Bayesian calibration practical. Results on the IEEE 9‑bus system show accurate parameter recovery with well‑behaved posterior uncertainty, while a short 39‑bus study provides evidence that the framework remains effective on a materially larger joint‑estimation problem. These results are obtained without requiring overly conservative priors.

Authors:Dylan Le, Joel McCandless, Carlos Varela, Radoslav Ivanov
Title: Verification of Autonomous Systems with Optimal Controllers
Abstract:
This paper considers the problem of reachability analysis of control systems with optimal controllers, as a first step towards verifying the safety and correctness of such systems. Despite their appeal in guaranteeing task satisfaction through cost minimization, optimal controllers are often challenging to assure. In particular, as system dynamics grow in complexity, solving the resulting optimization problem may be difficult, especially given time and computation constraints on real platforms. Thus, it is essential to verify that, even if the optimal solution is not always found, such controllers still accomplish the high‑level control objective. In this paper, we focus on gradient descent algorithms and design a reachability algorithm by treating gradient descent as a separate (digital) dynamical system, embedded in the original (physical) dynamical system, with controls as part of the state. We evaluate the feasibility of the proposed method on two control systems, a two‑dimensional quadrotor and a cartpole.

Authors:Aedan Mangan, Kehan Long, Ki Myung Brian Lee, Miheer Potdar, Nikolay Atanasov, Tania K. Morimoto
Title: Contact-Aware Planning and Control of Continuum Robots in Highly Constrained Environments
Abstract:
Continuum robots are well suited for navigating confined and fragile environments, such as vascular or endoluminal anatomy, where contact with surrounding structures is often unavoidable. While controlled contact can assist motion, unfavorable contact can degrade controllability, induce kinematic singularities, or introduce safety risks. We present a contact‑aware planning approach that evaluates contact quality, penalizing hazardous interactions, while permitting benign contact. The planner produces kinematically feasible trajectories and contact‑aware Jacobians which can be used for closed‑loop control in hardware experiments. We validate the approach by testing the integrated system (planning, control, and mechanical design) on anatomical models from patient scans. The planner generates effective plans for three common anatomical environments, and, in all hardware trials, the continuum robot was able to reach the target while avoiding dangerous tip contact (100% success). Mean tracking errors were 1.9 +/‑ 0.5 mm, 1.2 +/‑ 0.1 mm, and 1.7 +/‑ 0.2 mm across the three different environments. Ablation studies showed that penalizing end‑of‑continuum‑segment (ECS) contact improved manipulability and prevented hardware failures. Overall, this work enables reliable, contact‑aware navigation in highly constrained environments.

Authors:Mengmou Li, Lijun Zhu, Masaaki Nagahara
Title: A Common Lyapunov Matrix Approach to the Exponential Stability of Augmented Primal-Dual Gradient Flow as LPV Systems
Abstract:
We show that a common Lyapunov matrix exists for the convex combination of two Hurwitz matrices if and only if the intersection of the set of strict Lyapunov matrices for one matrix and the set of non‑strict Lyapunov matrices for the other is nonempty. This simple relaxation is useful for the convergence analysis of the augmented primal‑dual gradient flow for constrained optimization problems with affine inequality constraints, which can be viewed as a polytopic linear parameter‑varying (LPV) system driven by the active‑constraint selector. Under a relaxed strong convexity condition, exponential convergence is proved for the LPV system. The analysis can further be extended to the integral quadratic constraints (IQCs) framework for LPV systems to facilitate numerical search of the convergence rate.

Authors:Taira Kaminaga, Hampei Sasahara
Title: Scalable Outer Approximation of Minkowski Sums of Matrix Ellipsoids for Data-Driven Control
Abstract:
Matrix ellipsoids provide a standard framework for representing bounded uncertainties in data‑driven control. Since noise models for sequential observations are naturally represented as the Minkowski sum of multiple matrix ellipsoids, applying existing robust control methods, which typically assume a single ellipsoidal set, requires a tight outer approximation. While techniques based on linear matrix inequalities (LMI) are applicable, their computational cost grows quadratically with the data length, limiting their scalability. This paper investigates the optimal outer approximation problem under two criteria: the sum of squared semi‑axes and the volume. We propose an LMI‑free approach by introducing a parameterized family of bounding matrix ellipsoids. Specifically, we derive an exact analytical solution for the first criterion and develop an efficient majorization‑minimization (MM) algorithm for the second. The proposed MM algorithm employs a first‑order approximation of the log‑determinant function to provide closed‑form update rules, ensuring monotonic convergence to the set of stationary points. Numerical experiments demonstrate that our method offers significantly higher computational efficiency and scalability than standard interior‑point solvers.

Authors:Mattia Mosso, George Rapakoulias, Yue Guan, Panagiotis Tsiotras
Title: Nonlinear Stochastic Density Steering via Gaussian Mixture Schrodinger Bridges and Multiple Linearizations
Abstract:
The paper studies the optimal density steering problem for nonlinear continuous‑time stochastic systems. To accurately capture nonlinear dynamics in high‑uncertainty regions that deviate significantly from a nominal linearization point, we introduce the concept of Multiple Distribution‑to‑Distribution Linearization. The proposed approach first approximates the boundary distributions using Gaussian Mixture Models (GMMs), and decomposes the original nonlinear problem into a collection of Gaussian‑to‑Gaussian Optimal Covariance Steering (OCS) subproblems between pairs of mixture components. Each elementary OCS problem is solved via local linearization around the mean trajectory connecting the corresponding initial and terminal Gaussian components. The resulting elementary policies are then combined according to their associated conditional densities. We prove that the proposed multi‑linearization approach yields tighter approximation error bounds than single‑linearization for a broad class of problems. The effectiveness of the approach is demonstrated through numerical experiments on an Earth‑to‑Mars orbit transfer scenario.

Authors:Andrii Vakhnovskyi
Title: Dual-Radio BLE-LoRa Hierarchical Mesh for Infrastructure-Free Emergency Communication
Abstract:
We present a dual‑radio hierarchical mesh architecture for infrastructure‑free emergency communication that exploits the complementary strengths of Bluetooth Low Energy (BLE) and LoRa. Nodes equipped with both an nRF52840 (BLE 5.0 Coded PHY) and an SX1262 (LoRa sub‑GHz) form local clusters via BLE advertising‑based AODV routing, while dynamically elected cluster heads bridge inter‑cluster traffic over a LoRa backbone. We derive a formal traffic offloading model showing that with locality bias beta >= 0.76, validated against search‑and‑rescue communication patterns, the architecture keeps 82‑90% of traffic on BLE, reducing LoRa energy consumption by 79% compared to LoRa‑only mesh. Analytical evaluation demonstrates 10 km+ network diameter, 250‑562 node scalability, and sub‑50 ms intra‑cluster latency on a 3.0 KB RAM footprint. To our knowledge, this is the first architecture combining BLE advertising‑based mesh routing with a multi‑hop LoRa backbone on commodity hardware.

Authors:Longchen Niu, Andrew Nasif, Gennaro Notomista
Title: Safe and Energy-Aware Multi-Robot Density Control via PDE-Constrained Optimization for Long-Duration Autonomy
Abstract:
This paper presents a novel density control framework for multi‑robot systems with spatial safety and energy sustainability guarantees. Stochastic robot motion is encoded through the Fokker‑Planck Partial Differential Equation (PDE) at the density level. Control Lyapunov and control barrier functions are integrated with PDEs to enforce target density tracking, obstacle region avoidance, and energy sufficiency over multiple charging cycles. The resulting quadratic program enables fast in‑the‑loop implementation that adjusts commands in real‑time. Multi‑robot experiment and extensive simulations were conducted to demonstrate the effectiveness of the controller under localization and motion uncertainties.

Authors:Dillon Jensen, Obi Nnorom, Grant Wilkins, Hugo Budd, Ram Rajagopal, Juan Rivas-Davila, Phil Levis
Title: EasyRider: Mitigating Power Transients in Datacenter-Scale Training Workloads
Abstract:
Large‑scale AI model training workloads use thousands of GPUs operating in tightly synchronized loops. During synchronous communication, start‑up, shut‑down, and checkpointing, GPU power consumption can swing from peak to idle within milliseconds. These large and rapid load swings endanger grid infrastructure as they induce steep power ramp rates, voltage and frequency shifts, and reactive power transients that can damage transformers, converters, and protection equipment. To solve this problem, we introduce EasyRider, a power architecture to mitigate power fluctuations at the rack level. EasyRider uses passive components and actively‑controlled auxiliary energy storage to attenuate rack power swings. A software system continually monitors the energy storage system to maximize its lifetime in the presence of frequent charge/discharge cycles. EasyRider filters rack power variations to be within grid safety requirements without requiring software modifications to AI training frameworks or wasting energy. We evaluate EasyRider on a 400VDC‑rated prototype system against published workload traces and our own GPU testbed, demonstrating its effectiveness across heterogeneous power levels and workload power profiles.

Authors:Longchen Niu, Gennaro Notomista
Title: ''It Is Much Safer to Be Sparse than Connected'': Safe Control of Robotic Swarm Density Dynamics with PDE-Optimization with State Constraints
Abstract:
This paper introduces a safety‑critical optimization‑based control strategy that leverages control Lyapunov and control barrier functions to guide the spatial density of robotic swarms governed by the Fokker‑Planck equation to a predefined target distribution. In contrast to traditional open‑loop state‑constrained optimal control strategies, the proposed approach operates in closed‑loop, and a Voronoi‑based variant further enables distributed deployments. Theoretical guarantees of safety are derived, and numerical simulations demonstrate the performance of the proposed controllers. Finally, a multi‑robot experiment showcases the real‑world applicability of the proposed controllers under localization and motion noises, illustrating how it is much easier for a sparse swarm to satisfy safety specifications than it is for a densely packed one.

Authors:David M. Fobes, Harsha Nagarajan, Manuel Garcia, Robert Ferrando, Russell Bent
Title: Load Block Modeling in Distribution Systems: Network Reconfiguration for Load Restoration
Abstract:
The distribution system restoration (DSR) problem has received considerable attention over the last decade or more. Solutions to the DSR problem identify the best set or sequence of actions to perform on a distribution circuit to restore service after a disruption. The problem is challenging from a computational perspective, with engineering constraints specific to distribution systems, such as radial operations, that are difficult to effectively model. In this paper, we revisit the model for how specific loads are shed, energized and restored‑‑and develop a formulation that more accurately models the requirements of load shedding, load energizing and restoration in distribution systems.

Authors:Max H. Cohen, Pio Ong, Pol Mestres, Aaron D. Ames
Title: Safety Filtering with an Infinite Number of Constraints
Abstract:
Control barrier functions (CBFs) provide a rigorous framework for designing controllers enforcing safety constraints. While CBF theory is well‑developed for a finite number of safety constraints, certain applications, e.g., backup CBFs, require an infinite number of constraints. Despite the practical success of CBFs, several fundamental questions remain unanswered when safe sets are defined with an infinite numbers of constraints, including: necessary and sufficient conditions for forward set invariance, the actual definition of CBFs associated with these sets, the regularity properties of the resulting controllers, and the ability to reduce a collection of infinite constraints to a finite number. This paper addresses these questions by extending CBF theory to the infinite constraint setting. We identify regularity conditions under which Nagumo's Theorem reduces to barrier‑like inequalities and when the associated CBF controllers are at least continuous. We further connect these results to optimal‑decay CBFs, bridging theoretical conditions for invariance and practical instantiations of the resulting controller. Finally, we illustrate how the developed theory addresses limitations of backup CBFs.

Authors:Joshua D. Ibrahim, Mahdi Taheri, Soon-Jo Chung, Fred Y. Hadaegh
Title: Perron-Frobenius Contractive Operator Matching for Data-Driven Reachable Fault Identification and Recovery
Abstract:
This paper focuses on data‑driven fault detection, identification, and recovery (FDIR) for nonlinear control‑affine systems under actuator faults. We create a unified framework in the space of probability densities, rather than on individual trajectories, using fault‑indexed Perron‑‑Frobenius (PF) operators to predict the evolution of state distributions under different fault profiles. By leveraging the probability‑flow representation of the Fokker‑‑Planck equation, we construct deterministic PF operators that reproduce exact stochastic marginals, define forward reachable density families, and establish certifiable 2‑Wasserstein bounds on the divergence between fault‑driven and nominal density evolutions. These provide quantitative conditions for the detectability and identifiability of various faults. The fault‑indexed operators are learned from trajectory data via flow map matching (FMM), and we demonstrate that the observable FMM residual directly bounds the approximation error of the operator in the 2‑Wasserstein metric. Additionally, we co‑train a contraction certificate that bounds the gap between the learned operator family, the actual fault‑driven density flow, and the nominal dynamics. The operator library is then used online for continuous fault parameter fitting over a continuous parameter space to generalize the learned operators to out‑of‑distribution (OOD) scenarios. To carry out the recovery control, we employ reachable density propagation and Gaussian mixture covariance steering. The proposed framework is validated on a 10‑state spacecraft attitude‑control system with four reaction wheels.

Authors:Jaime de Miguel Rodriguez, Artjom Vargunin, Brigitta Robin Raudne, David Solis Martin, Yaroslava Mykhailenko, Kaarel Oja
Title: Mapping High-Performance Regions in Battery Scheduling across Data Uncertainty, Battery Design, and Planning Horizons
Abstract:
This study presents a controlled parametric framework for analyzing energy storage planning under uncertainty in a multi‑stage model predictive control setting. The framework enables a broad and systematic exploration through parametrized generation of synthetic datasets in the context of energy price arbitrage. It facilitates the study of the joint effects of battery characteristics, signal structure, forecast uncertainty, and planning horizon on revenue performance in energy storage optimization, which are rarely considered together. The analysis is driven by two objectives. First, it characterizes how these interacting factors influence operational revenue and its sensitivity to planning horizon selection, including economic losses caused by deviations from optimal horizons. This provides guidance on expected horizon ranges and their impact on revenue and computational cost. Second, it enables a compact parametrization of the relationships between battery properties, data characteristics, forecast uncertainty, and horizon‑dependent performance, providing a basis for future modelling of optimal planning horizon length. Results show that the framework captures consistent structural dependencies across configurations and provides meaningful guidance for horizon selection under uncertainty. In particular, increasing forecast uncertainty systematically reduces the optimal planning horizon across battery types, reflecting the diminishing value of long‑term information under increasingly unreliable forecasts. Comparison with real market data shows that the parametrization reproduces the main qualitative trends of optimal horizon behavior, suggesting its potential as a lightweight surrogate for more complex simulation‑based analysis.

Authors:Neksha DeSilva
Title: Temporal Derivative Soft-Sensing and Reconstructing Solar Radiation and Heat Flux from Common Environmental Sensors
Abstract:
Modern methods of environmental monitoring are deficient in the lack of ability to take measurements of energy flows since traditional readings involve capturing parameters such as temperature, pressure, and humidity without considering their physical causes. The present research describes Differential Temporal Derivative Soft‑Sensing (DTDSS), a physics‑based approach which enables any ordinary low cost sensor array to infer estimates of the energy exchange in the environment by modeling its radiative heat fluxes. In particular, the proposed approach combines a novel paired sensor configuration along with a unique algorithmic solution called Inertial Noise Reduction or INR, that mathematically models the flow of energy in the environment by computing Global Horizontal Irradiance, or GHI, and convective heat flux. Experimental field testing has been conducted with the use of calibrated reference pyranometers supplied by the Department of Meteorology of Sri Lanka, yielding a correspondence between 8 bit embedded processor results and the reference of R2 approx. eqv. to 0.9 and RMSE approx. eqv. to 45 Watts per square meter in under 2KB RAM of a microcontroller unit.

Authors:Ryo Murakami, Yichuan Tang, Haichong K. Zhang
Title: Democratization of Real-time Multi-Spectral Photoacoustic Imaging: Open-Sourced System Architecture for OPOTEK Phocus & Verasonics Vantage Combination
Abstract:
Real‑time multi‑spectral photoacoustic imaging (RT‑mPAI) often suffers from synchronization instabilities when interfacing fast‑tuning lasers with data acquisition platforms executing on non‑real‑time operating systems. To overcome this, we establish an open‑source hardware‑software architecture tailored for the widely adopted combination of the OPOTEK Phocus lasers and Verasonics Vantage systems. By employing an independent micro‑controller for deterministic laser trigger counting alongside a decoupled client‑server data streaming framework, the proposed system circumvents OS‑induced timing deviations and local storage bottlenecks. By open‑sourcing this pipeline and cultivating a collaborative environment to share both code and ideas, we aim to lower the technical and cost barriers for RT‑mPAI, thereby democratizing access to stable RT‑mPAI research and, more ambitiously, fostering a vibrant open‑source community.

Authors:Chi Wang, David Angeli
Title: Tube-Based Robust Data-Driven Predictive Control
Abstract:
This paper presents a tractable tube‑based robust data‑driven predictive control scheme that uses only a single finite noisy input‑state trajectory of an unknown discrete‑time linear time‑invariant (LTI) system. A simplex constraint is imposed on the Hankel coefficient vector, yielding explicit polyhedral bounds on the prediction mismatch induced by bounded measurement noise. Using certified initial and terminal robust positively invariant (RPI) sets, we derive a tube‑tightened formulation whose online optimization problem is a strictly convex quadratic program (QP). The resulting controller guarantees recursive feasibility, robust satisfaction of input and state constraints, and practical input‑to‑state stability of the closed loop with respect to measurement noise. Numerical examples illustrate the effectiveness, robustness, and closed‑loop performance of the proposed method.

Authors:Anand Gokhale, Anton V. Proskurnikov, Yu Kawano, Francesco Bullo
Title: A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
Abstract:
This paper establishes a nonlinear separation principle based on contraction theory and derives sharp stability conditions for recurrent neural networks (RNNs). First, we introduce a nonlinear separation principle that guarantees global exponential stability for the interconnection of a contracting state‑feedback controller and a contracting observer, alongside parametric extensions for robustness and equilibrium tracking. Second, we derive sharp linear matrix inequality (LMI) conditions that guarantee the contractivity of both firing rate and Hopfield neural network architectures. We establish structural relationships among these certificates‑demonstrating that continuous‑time models with monotone non‑decreasing activations maximize the admissible weight space‑and extend these stability guarantees to interconnected systems and Graph RNNs. Third, we combine our separation principle and LMI framework to solve the output reference tracking problem for RNN‑modeled plants. We provide LMI synthesis methods for feedback controllers and observers, and rigorously design a low‑gain integral controller to eliminate steady‑state error. Finally, we derive an exact, unconstrained algebraic parameterization of our contraction LMIs to design highly expressive implicit neural networks, achieving competitive accuracy and parameter efficiency on standard image classification benchmarks.

Authors:Jianqiao Yu, Jia Li, Tianhua Gao
Title: Trajectory Planning for a Multi-UAV Rigid-Payload Cascaded Transportation System Based on Enhanced Tube-RRT*
Abstract:
This paper presents a two‑stage trajectory planning framework for a multi‑UAV rigid‑payload cascaded transportation system, aiming to address planning challenges in densely cluttered environments. In Stage I, an Enhanced Tube‑RRT algorithm is developed by integrating active hybrid sampling and an adaptive expansion strategy, enabling rapid generation of a safe and feasible virtual tube in environments with dense obstacles. Moreover, a trajectory smoothness cost is explicitly incorporated into the edge cost to reduce excessive turns and thereby mitigate cable‑induced oscillations. Simulation results demonstrate that the proposed Enhanced Tube‑RRT achieves a higher success rate and effective sampling rate than mixed‑sampling Tube‑RRT (STube‑RRT) and adaptive‑extension Tube‑RRT (AETube‑RRT), while producing a shorter optimal path with a smaller cumulative turning angle. In Stage II, a convex quadratic program is formulated by considering payload translational and rotational dynamics, cable tension constraints, and collision‑safety constraints, yielding a smooth, collision‑free desired payload trajectory. Finally, a centralized geometric control scheme is applied to the cascaded system to validate the effectiveness and feasibility of the proposed planning framework, offering a practical solution for payload attitude maneuvering in densely cluttered environments.

Authors:Yuchao Li, Fei Chen, Yingke Li, Chuchu Fan, Dimitri Bertsekas
Title: On-Line Policy Iteration with Trajectory-Driven Policy Generation
Abstract:
We consider deterministic finite‑horizon optimal control problems with a fixed initial state. We introduce an on‑line policy iteration method, which, starting from a given policy, however obtained, generates a sequence of cost‑improving policies and corresponding trajectories. Each policy produces a trajectory, which is used in turn to generate data for training the next policy. The method is motivated by problems that are repeatedly solved starting from the same initial state, including discrete optimization and path planning for repetitive tasks. For such problems, the method is fast enough to be used on‑line. Under a natural consistency condition, we show that the sequence of costs of the generated policies is monotonically improving for the given initial state (but not necessarily for other states). We illustrate our results with computational studies from combinatorial optimization and 3‑dimensional path planning for drones and a robot arm in the presence of obstacles. We also discuss briefly a stochastic counterpart of our algorithm. Our proposed framework combines elements of rollout and policy iteration with flexible trajectory‑based policy representations, and applies to problems involving a single as well as multiple decision makers. It also provides a principled way to train neural network‑based policies using trajectory data, while preserving monotonic cost improvement.

Authors:Timon Kopka, Sara Tamburello, Luca Oneto, Lindert van Biert, Henk Polinder, Andrea Coraddu
Title: Degradation-aware Predictive Energy Management for Fuel Cell-Battery Ship Power System with Data-driven Load Forecasting
Abstract:
Hydrogen‑based zero‑emission ships are a key element in the decarbonization of the maritime sector. To strengthen these their economic competitiveness, it is key to drive their costs to a minimum. Current literature mainly focuses on fuel consumption minimization, but there is a lack of explicit consideration of costs arising from cell degradation and optimization‑based approaches that leverage information on future load trajectories. This work aims at minimizing the operational cost of fuel cell‑battery hybrid shipboard power systems, accounting for hydrogen consumption and cell degradation as the main cost drivers. A degradation‑aware predictive energy management strategy utilizing data‑driven load forecasting is designed and showcased at the example of a virtually retrofitted harbor tug. This work shows that the real onboard measurements of the vessel can be utilized to make accurate load predictions over 15min. Results indicate that the degradation‑aware, predictive control simultaneously reduces the hydrogen consumption by up to 5.8% and the cell degradation by up to 36.4% with an aged fuel cell system when compared to a filter‑based benchmark applied to real operating data of the harbor tug. With an increased prediction horizon of 1h, further significant reductions of 3.8% and 14.0% could be shown.

Authors:Luca Claude Gino Lebon, Johan Lindberg, Claudio Altafini
Title: Minimal Input Cardinality Disturbance Decoupling of Coupled Oscillators via Output Feedback with Application to Power Networks
Abstract:
In this paper, we identify the smallest set of control input nodes and an associated output feedback law that achieves complete disturbance decoupling for a class of coupled oscillator networks. The focus is specifically on systems linearized around a stable phase‑locked synchronized state. The proposed theoretical framework is applied to the linearized swing dynamics of power grids operating near synchronization. In this context, the disturbance decoupling problem corresponds to isolating subsets of nodes from exogenous disturbances by means of batteries that can both add or withdraw active power. Numerical simulations carried out on the IEEE New England 39‑bus system show that the proposed methodology not only yields a minimal actuator placement ensuring effective disturbance rejection, but also preserves the internal stability of the closed‑loop system.

Authors:E. M. M., Kivits, Paul M. J. Van den Hof
Title: Modelling and identification of diffusively coupled linear networks with additional directed links
Abstract:
Dynamic networks consist of interconnected dynamical systems. The subsystems can be viewed as transformations of input signals into output signals, where signals flow from one system into another through interconnections. The signal flows represent directions of information flow, thus a dynamic network can be visualised by a directed graph. In contrast, natural and physical laws only impose relations between systems variables, while variables are shared among systems via interconnections. Sharing is independent of direction, and therefore a dynamic network originating from physics can be visualised by an undirected graph. Typically, dynamic networks are considered to have either directed or undirected interconnections. For both situations, network models, analytic tools, and identification algorithms have been developed. However, dynamic networks can also have both directed and undirected interconnections, for example, in physical networks equipped with digital controllers. In this work, we present mixed linear dynamic networks that contain both undirected and directed interconnections, where the nature of the interconnecting dynamics needs to be incorporated into the modelling framework, identifiability analysis, and identification procedure. For these mixed networks, we derive dynamic network models; formulate conditions for consistent identification of all dynamics in the network; and develop a tractable identification algorithm that delivers consistent estimates.

Authors:Emre Özyıldırım, Barış Yaycı, Umut Eren Akturk, Cem Tekin
Title: Multi-User mmWave Beam and Rate Adaptation via Combinatorial Satisficing Bandits
Abstract:
We study downlink beam and rate adaptation in a multi‑user mmWave MISO system where multiple base stations (BSs), each using analog beamforming from finite codebooks, serve multiple single‑antenna user equipments (UEs) with a unique beam per UE and discrete data transmission rates. BSs learn about transmission success based on ACK/NACK feedback. To encode service goals, we introduce a satisficing throughput threshold τ_r and cast joint beam and rate adaptation as a combinatorial semi‑bandit over beam‑rate tuples. Within this framework, we propose SAT‑CTS, a lightweight, threshold‑aware policy that blends conservative confidence estimates with posterior sampling, steering learning toward meeting τ_r rather than merely maximizing. Our main theoretical contribution provides the first finite‑time regret bounds for combinatorial semi‑bandits with satisficing objective: when τ_r is realizable, we upper bound the cumulative satisficing regret to the target with a time‑independent constant, and when τ_r is non‑realizable, we show that SAT‑CTS incurs only a finite expected transient outside committed CTS rounds, after which its regret is governed by the sum of the regret contributions of restarted CTS rounds, yielding an O((\log T)^2) standard regret bound. On the practical side, we evaluate the performance via cumulative satisficing regret to τ_r alongside standard regret and fairness. Experiments with time‑varying sparse multipath channels show that SAT‑CTS consistently reduces satisficing regret and maintains competitive standard regret, while achieving favorable average throughput and fairness across users, indicating that feedback‑efficient learning can equitably allocate beams and rates to meet QoS targets without channel state knowledge.

Authors:Armin Gießler, Pol Jané-Soneira, Sören Hohmann
Title: Data-driven Linear Quadratic Integral Control: A Convex Formulation and Policy Gradient Approach
Abstract:
This paper studies the data‑driven synthesis of linear quadratic integral (LQI) controllers for continuous‑time systems. The objective is to achieve optimal state‑feedback control with integral action for reference tracking using only measured data. To this end, we derive a data‑driven closed‑loop parameterization of the augmented dynamics that incorporates the integral state while relying solely on input‑state‑output measurements of the underlying system. Based on this parameterization, a data‑driven convex optimization problem is formulated whose solution yields the optimal linear quadratic regulator (LQR) feedback gain for the augmented system without explicit knowledge of the system matrices. In addition, a policy gradient flow is derived to compute the optimal controller within the space of stabilizing gains. The proposed approach enables data‑driven optimal tracking control while avoiding explicit state augmentation in the data collection phase. The effectiveness of the method is demonstrated through a numerical example involving a distributed generation unit (DGU) in a DC microgrid.

Authors:Boyu Han, Xu Du, Karl H. Johansson, Apostolos I. Rikos
Title: Mix-CALADIN: A Distributed Algorithm for Consensus Mixed-Integer Optimization
Abstract:
This paper addresses distributed consensus optimization problems with mixed‑integer variables, with a specific focus on Boolean variables. We introduce a novel distributed algorithm that extends the Consensus Augmented Lagrangian Alternating Direction Inexact Newton (CALADIN) framework by incorporating specialized techniques for handling Boolean variables without relying on local mixed‑integer solvers. Under the mild assumption of Lipschitz continuity of the objective functions, we establish rigorous convergence guarantees for both convex and nonconvex mixed‑integer programming problems. Numerical experiments demonstrate that the proposed algorithm achieves competitive performance compared to existing approaches while providing rigorous convergence guarantees.

Authors:Ertugrul Kececi, Tufan Kumbasar
Title: xFODE+: Explainable Type-2 Fuzzy Additive ODEs for Uncertainty Quantification
Abstract:
Recent advances in Deep Learning (DL) have boosted data‑driven System Identification (SysID), but reliable use requires Uncertainty Quantification (UQ) alongside accurate predictions. Although UQ‑capable models such as Fuzzy ODE (FODE) can produce Prediction Intervals (PIs), they offer limited interpretability. We introduce Explainable Type‑2 Fuzzy Additive ODEs for UQ (xFODE+), an interpretable SysID model which produces PIs alongside point predictions while retaining physically meaningful incremental states. xFODE+ implements each fuzzy additive model with Interval Type‑2 Fuzzy Logic Systems (IT2‑FLSs) and constraints membership functions to the activation of two neighboring rules, limiting overlap and keeping inference locally transparent. The type‑reduced sets produced by the IT2‑FLSs are aggregated to construct the state update together with the PIs. The model is trained in a DL framework via a composite loss that jointly optimizes prediction accuracy and PI quality. Results on benchmark SysID datasets show that xFODE+ matches FODE in PI quality and achieves comparable accuracy, while providing interpretability.

Authors:Murat Furkan Mansur, Tufan Kumbasar
Title: SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems
Abstract:
Nonlinear system identification must balance physical interpretability with model flexibility. Classical methods yield structured, control‑relevant models but rely on rigid parametric forms that often miss complex nonlinearities, whereas Neural ODEs are expressive yet largely black‑box. Physics‑Informed Neural Networks (PINNs) sit between these extremes, but inverse PINNs typically assume a known governing equation with fixed coefficients, leading to identifiability failures when the true dynamics are unknown or state‑dependent. We propose SOLIS, which models unknown dynamics via a \emphstate‑conditioned second‑order surrogate model and recasts identification as learning a Quasi‑Linear Parameter‑Varying (Quasi‑LPV) representation, recovering interpretable natural frequency, damping, and gain without presupposing a global equation. SOLIS decouples trajectory reconstruction from parameter estimation and stabilizes training with a cyclic curriculum and Local Physics Hints windowed ridge‑regression anchors that mitigate optimization collapse. Experiments on benchmarks show accurate parameter‑manifold recovery and coherent physical rollouts from sparse data, including regimes where standard inverse methods fail.

Authors:Xu Du, Boyu Han, Ivano Notarnicola, Karl H. Johansson, Apostolos I. Rikos
Title: Affine-coupled Distributed Optimization via Distributed Proximal Jacobian ADMM with Quantized Communication
Abstract:
This paper investigates distributed resource allocation optimization over directed graphs with limited communication bandwidth. We develop a novel distributed algorithm that integrates the centralized Proximal Jacobian Alternating Direction Method of Multipliers (PJ‑ADMM) with a finite‑level quantized consensus scheme, enabling nodes to cooperatively solve the optimization in a distributed fashion. Under the assumption of convex objective functions, we establish that the proposed algorithm achieves sublinear convergence to a neighborhood of the optimal solution, with the convergence accuracy explicitly bounded by the quantization level. Numerical experiments validate that the algorithm achieves competitive performance compared to existing approaches while exhibiting communication efficiency.

Authors:Armin Gießler, Pol Jané-Soneira, Sören Hohmann
Title: Towards Optimal Passive Feedback Control of LTI Systems under LQR Performance
Abstract:
We study state‑feedback design for continuous‑time LTI systems with a control input and an external input‑output pair. Our objective is to determine feedback gains that render the closed‑loop system (strictly) passive with respect to the external port while minimizing the standard LQR cost in the disturbance‑free case. The resulting constrained optimization problem is intractable due to bilinear matrix inequalities. We analyze the set of passivating gains, showing it is unbounded, possibly nonconvex, path‑connected, and contractible. We propose an indirect approach, in which the set of passivating feedback gains is inner‑approximated by a compact, convex polytope. A projected gradient flow is employed to compute a gain within this polytope that minimizes the LQR cost. Numerical examples illustrate the effectiveness of the method.

Authors:Thomas Klatzer, Yannick Werner, Sonja Wogrin
Title: Simplification Ad Absurdum? Revisiting Gas Flow Modeling for Integrated Energy System Planning
Abstract:
This paper analyzes the implications of simplified pipeline gas flow models for integrated energy system planning. A case study of an integrated power‑hydrogen expansion planning problem shows that simplifying pressure‑flow relationships and gas dynamics can lead to expansion plans that incur substantial regret when evaluated under a more realistic dynamic gas flow model ‑‑ due to suboptimal system expansion, operation, and non‑supplied hydrogen. Numerical experiments show that planning under the highly simplified transport and transport‑linepack models ‑‑ commonly used in expansion studies ‑‑ can result in regret exceeding several thousand percent and yield expansion plans that lack robustness across demand levels. Planning under steady‑state conditions partially mitigates these effects, but still leaves significant cost‑reduction potential untapped compared to dynamic planning due to neglected linepack flexibility. Developing efficient solution algorithms for the dynamic model is a promising direction for future research.

Authors:Mahsa Farjadnia, Katayoun Eshkofti, Albin Apell, Tilde Hjalmarsson, Karl Henrik Johansson, Angela Fontan, Marco Molinari
Title: Generalizability of Learning-based Occupancy Detection in Residential Buildings (extended version)
Abstract:
This paper investigates non‑intrusive occupancy detection methods for residential buildings using environmental sensor data from the KTH Live‑In Lab in Stockholm, Sweden. Three machine learning approaches, namely, logistic regression (LR), support vector machines (SVM), and long short‑term memory (LSTM) network enhanced with an attention mechanism, are evaluated in terms of predictive performance and computational complexity. The analysis considers the trade‑off between sensor availability (investment cost) and prediction accuracy in real applications, as well as the models' cross‑apartment generalizability. Hyperparameters for both the SVM and LSTM models are optimized using Bayesian optimization. All three models are evaluated on data collected from apartments not used during training, and on data generated from a calibrated digital model of the testbed. Results show that all models achieve comparable performance on the same‑apartment test data (accuracy of approximately 0.83, F1 score of approximately 0.86). When assessed on cross‑apartment data, the LSTM model demonstrates the strongest generalization capability (accuracy of 0.84, F1 score of 0.85), while LR provides a competitive, low‑complexity alternative for applications that do not require cross‑apartment generalization.

Authors:Jiwon Lee, Hugo Matias, Daniel Silvestre, Thinh T. Doan
Title: CBF-based Probabilistic Safe Navigation under Unknown Nonlinear Obstacle Dynamics
Abstract:
Safe navigation for an ego vehicle in uncertain environments characterized by dynamic obstacles with unknown nonlinear dynamics is a challenging problem of significant practical interest. Existing approaches in the literature either lack formal safety guarantees, require full model knowledge, or fail to account for the risk associated with the vehicle's exact body geometry and the temporal evolution of uncertainty between sampling instants. In this paper, we propose a data‑driven observer for the unknown obstacle dynamics that generates an alpha‑confidence set flow, which is exactly transformed into a Control Barrier Function (CBF) to enforce (1‑alpha)‑probability safety. The proposed framework accommodates nonlinear ego vehicle dynamics of arbitrary relative degree, as demonstrated through case studies involving first‑ and second‑order dynamics of an unmanned surface vehicle.

Authors:Julian Jimenez Agudelo, Paola Soto, Ayat Zaki-Hindi, Jean-Sébastien Sottet, Sébastien Faye, Nina Slamnik-Kriještorac, Johann Marquez-Barja, Miguel Camelo Botero
Title: Towards Trustworthy 6G Network Digital Twins: A Framework for Validating Counterfactual What-If Analysis in Edge Computing Resources
Abstract:
Network Digital Twins (NDTs) enable safe what‑if analysis for 6G cloud‑edge infrastructures, but adoption is often limited by fragmented workflows from telemetry to validation. We present a data‑driven NDT framework that extends 6G‑TWIN with a scalable pipeline for cloud‑edge telemetry aggregation and semantic alignment into unified data models. Our contributions include: (i) scalable cloud‑edge telemetry collection, (ii) regime‑aware feature engineering capturing the network's scaling behavior, and (iii) a validation methodology based on Sign Agreement and Directional Sensitivity. Evaluated on a Kubernetes‑managed cluster, the framework extrapolates performance to unseen high‑load regimes. Results show both Deep Neural Network (DNN) and XGBoost achieve high regression accuracy (R2 > 0.99), while the XGBoost model delivers superior directional reliability (Sa > 0.90), making the NDT a trustworthy tool for proactive resource scaling in out‑of‑distribution scenarios.

Authors:Yeongjun Jang
Title: Co-Design of Cryptographic Parameters and Delay-Aware Feedback Gain for Encrypted Control Systems
Abstract:
Encrypted control employs homomorphic encryption (HE) to protect both the computation and communication stages, making it a promising approach for secure networked control systems. Most existing results pre‑design a controller in the plaintext domain and then implement it over encrypted data. However, this can be problematic because HE induces non‑negligible communication and computation delays, which typically increase with the security level, potentially degrading control performance and even destabilizing the closed‑loop system. To address this issue, we propose a co‑design framework for cryptographic parameters and delay‑aware feedback gain. We characterize the encryption‑induced delay as a function of the cryptographic parameters and derive a sufficient condition for the existence of a stabilizing delay‑aware feedback gain, expressed as a finite set of linear matrix inequalities. This leads to a tractable outer‑inner design procedure that searches over cryptographic parameters that satisfy a desired security level and, for each such parameter, seeks a stabilizing feedback gain.

Authors:Ratnangshu Das, Negar Monir, Youssef Ait Si, Adnane Saoud, Sadegh Soudjani, Pushpak Jagtap
Title: Temporal Logic Resilience for Continuous-time Systems
Abstract:
In this paper, we present a novel framework for quantifying a lower bound on resilience in continuous‑time (non)linear systems subject to external disturbances while ensuring satisfaction of signal temporal logic specifications. Unlike robustness, which evaluates how well a system satisfies a specification under a given disturbance, resilience measures the maximum disturbance a system can tolerate from a given initial state while maintaining specification satisfaction. We first derive bounds on the perturbed trajectories and then use them to formulate a computational method based on scenario optimization to efficiently compute the maximum admissible disturbance. We validate our approach through case studies, including dc motor, temperature regulation, a nonlinear numerical example, and a vehicle collision avoidance case.

Authors:Zheng Liu
Title: Physics-Informed Machine Learning for Pouch Cell Temperature Estimation
Abstract:
Accurate temperature estimation of pouch cells with indirect liquid cooling is essential for optimizing battery thermal management systems for transportation electrification. However, it is challenging due to the computational expense of finite element simulations and the limitations of data‑driven models. This paper presents a physics‑informed machine learning (PIML) framework for the efficient and reliable estimation of steady‑state temperature profiles. The PIML approach integrates the governing heat transfer equations directly into the neural network's loss function, enabling high‑fidelity predictions with significantly faster convergence than purely data‑driven methods. The framework is evaluated on a dataset of varying cooling channel geometries. Results demonstrate that the PIML model converges more rapidly and achieves markedly higher accuracy, with a 49.1% reduction in mean squared error over the data‑driven model. Validation against independent test cases further confirms its superior performance, particularly in regions away from the cooling channels. These findings underscore the potential of PIML for surrogate modeling and design optimization in battery systems.

Authors:Tomoya Kamimura, Haruka Washiyama, Akihito Sano
Title: Model-Based Reinforcement Learning Exploits Passive Body Dynamics for High-Performance Biped Robot Locomotion
Abstract:
Embodiment is a significant keyword in recent machine learning fields. This study focused on the passive nature of the body of a biped robot to generate walking and running locomotion using model‑based deep reinforcement learning. We constructed two models in a simulator, one with passive elements (e.g., springs) and the other, which is similar to general humanoids, without passive elements. The training of the model with passive elements was highly affected by the attractor of the system. This lead that although the trajectories quickly converged to limit cycles, it took a long time to obtain large rewards. However, thanks to the attractor‑driven learning, the acquired locomotion was robust and energy‑efficient. The results revealed that robots with passive elements could efficiently acquire high‑performance locomotion by utilizing stable limit cycles generated through dynamic interaction between the body and ground. This study demonstrates the importance of implementing passive properties in the body for future embodied AI.

Authors:Zhendong Cao, Katrina G. Salvante, Ash Parameswaran, Pablo A. Nepomnaschy, Hongji Dai
Title: Design and Validation of a Low-Cost Smartphone Based Fluorescence Detection Platform Compared with Conventional Microplate Readers
Abstract:
A low cost fluorescence‑based optical system is developed for detecting the presence of certain microorganisms and molecules within a diluted sample. A specifically designed device setup compatible with conventional 96 well plates is chosen to create an ideal environment in which a smart phone camera can be used as the optical detector. In comparison with conventional microplate reading machines such as Perkin Elmer Victor Machine, the device presented in this paper is not equipped with expensive elements such as exciter filer, barrier filter and photomultiplier; instead, a phone camera is all needed to detect fluorescence within the sample. The strategy being involved is to determine the relationship between the image color of the sample in RGB color space and the molar concentration of the fluorescence specimen in that sample. This manuscript is a preprint version of work related to a publication in IEEE. The final version may differ from this manuscript.

Authors:Bin Wang, Qiang Zhang, Xiaochuan Luo, Slava Maslennikov, Mingguo Hong, Xinghao Fang, Tongxin Zheng
Title: Quantifying and Improving the Accuracy of Electromagnetic Transient-Transient Stability Hybrid Simulation
Abstract:
The increasing penetration of inverter‑based resources introduces new dynamic challenges to modern power grids, such as sub‑ and super‑synchronous oscillations and other faster dynamics. These dynamics are typically fast in nature and are difficult to accurately model and analyze using standard transient stability (TS) methods, necessitating the need for electromagnetic transient (EMT) analysis. However, EMT simulations are notoriously slow for large‑scale grids due to both equation formulations and computational limitations. To overcome this challenge, EMT‑TS hybrid simulation is often used, since it offers a balanced trade‑off between accuracy and speed, making it feasible to perform EMT analysis on large systems. One open question about EMT‑TS hybrid simulation is the accuracy of the EMT‑TS boundary or interface. This paper introduces an error index to quantify EMT‑TS hybrid interface errors, identifies conditions where the hybrid simulation approach may become inaccurate, and suggests EMT region expansions to improve the simulation accuracy. Additionally, a three‑sequence hybrid interface model is proposed to mitigate inaccuracies caused by unbalanced conditions.

Authors:Ahmed Saad Al-Karsani, Maryam Khanbaghi
Title: Quantification and Regulation of Energy Reserves for Distributed Frequency and Voltage Control of Grid-Forming Inverters
Abstract:
The introduction of Renewable Energy Sources (RES) and Distributed Energy Resources (DERs) has led to the formulation of Microgrids (MGs) and Networks of MGs (NMGs). MGs and NMGs can operate in islanded mode, transforming the grid into a more distributed system. This has led to extensive studies in the literature on distributed hierarchical control strategies. Previous works have proposed distributed secondary level frequency and voltage regulation control schemes for Battery Energy Storage System (BESS)‑based Grid‑Forming (GFM) inverters with State of Charge (SoC) balancing. However, links to tertiary level control in terms of service‑based reserves and local resource adequacy in MGs are largely unexplored. Therefore, this paper proposes a BESS energy reserves framework, to quantify reserves for hierarchical control operation. Additionally, to partially regulate the proposed energy reserves, we propose the formulation of a modified Distributed‑Averaging Proportional‑Integral (DAPI) controller with regulation energy reserve consensus. Controller Hardware‑In‑the‑Loop (CHIL) simulation is performed on an MG topologically based on the IEEE 13 bus test feeder system in MATLAB/Simulink. The proposed scheme results illustrate effective frequency and voltage regulation along with improved power and energy sharing across droop‑controlled and Virtual Synchronous Machine (VSM) controlled inverters.

Authors:Robert Mieth
Title: Integrated Investment and Policy Planning for Power Systems via Differentiable Scenario Generation
Abstract:
We formulate a method to co‑optimize power system capacity planning decisions and policy investments that shape electricity load patterns. To this end, we leverage a gradient‑based solution technique that enables the efficient solution of operation‑aware planning models. To compute gradients with respect to the conditions that define daily electricity demand profiles, we introduce and formalize the concept of differentiable scenario generation and show that generative machine learning models satisfy the mathematical requirements needed to compute consistent gradients. We demonstrate the feasibility of the proposed approach through numerical experiments using a diffusion model‑based scenario generator and a stylized generation and capacity expansion planning model.

Authors:Xiang Shi, Peng Hu
Title: Spatiotemporal Analysis of VIIRS Satellite Observations and Network Traffic During the 2025 Manitoba Wildfires
Abstract:
Climate change has intensified extreme weather and wildfire conditions globally. Canada experienced record‑breaking wildfires in 2023 and 2025, burning millions of hectares and severely impacting the Prairie provinces, with Manitoba facing its worst season in 30 years. These events highlight the urgent need to understand and mitigate escalating fire risks. While existing research largely focuses on wildfire management approaches, few studies have explored the relationship between user network traffic and wildfire activity, despite the potential of such correlations to provide valuable spatiotemporal insights into wildfire dynamics. This paper investigates the relationship between wildfire intensity and network performance during the 2025 Manitoba wildfire season, using Visible Infrared Imaging Radiometer Suite (VIIRS) satellite‑derived Fire Radiative Power data and large‑scale Speedtest measurements. We found statistically significant correlations between wildfire intensity and several network performance metrics in both the province‑wide and region‑wide case studies, as measured by Spearman's correlation coefficients (ρ) and corresponding p‑values. Throughput‑related metrics showed inverse correlations with wildfire intensity (e.g., download speed: ρ= ‑0.214, p\_value = 0.004), whereas latency‑related metrics showed positive correlations (e.g., round‑trip time latency: ρ= 0.162, p\_value = 0.0308). The findings suggest satellite fire indicators and network performance metrics together can reveal vulnerabilities during extreme environmental events and support diaster response and recovery efforts.

Authors:Aaron Jones, Marija Ilic
Title: AC-OPF Feasibility Analysis and Sensitivity-Guided Capacitor Placement in a High-PV Islanded Microgrid
Abstract:
This paper presents a comparative AC Optimal Power Flow study on a real world city scale islanded microgrid with high solar PV penetration, implemented within a Digital Twin framework. Four objective function cases economic dispatch, voltage stress exposure via PV power factor variation, then optimal load delivery, and capacitor enhanced economic dispatch as recovery options are evaluated over a 47 hour time series horizon on the same network under a shared loading scenario. Optimization sensitivities OSQ and OSV extracted from all cases are combined into a composite placement score used to rank candidate buses for shunt capacitor upgrades. A post processing planning optimization balances capacitor upgrade cost against avoided value‑of‑lost‑load, enabling direct economic comparison of infrastructure investment versus reliability penalties. Results demonstrate that sensitivity guided capacitor placement restores full load service across the horizon and provides targeted reactive support at a quantifiable cost trade off against corrective load shedding.

Authors:Saurabh Bagchi, Hyunseung Kim, Tarek Abdelzaher, Homa Alemzadeh, Somali Chaterji, Glen Chou, Yuying Duan, Fanxin Kong, Michael Lemmon, Yin Li, Mengyu Liu, Wenhao Luo, Meiyi Ma, Sibin Mohan, Ayan Mukhopadhyay, Melkior Ornik, Dimitra Panagou, Kristin Yvonne Rozier, Ivan Ruchkin, Huajie Shao, Sze Zheng Yong, Majid Zamani, Xugui Zhou
Title: Digital Guardians: The Past and The Future of Cyber-Physical Resilience
Abstract:
Resilience in cyber‑physical systems (CPS) is the fundamental ability to maintain safety and critical functionality despite adverse "perturbations," which includes security attacks, environmental disruptions, and hardware or software failures. This survey provides a comprehensive review of CPS resilience, framing the field through five interconnected themes that are required in an integrated whole to achieve real‑world resilience. The article first posits that resilience is a system‑wide property emerging from interactions between hardware, software, and human users. Second, it addresses the challenges of learning‑enabled CPS, which often operate in data‑scarce environments characterized by imbalanced or noisy data, requiring innovative solutions like synthetic data generation and foundation model adaptation. Third, the survey examines proactive measures for resilience, which include distinctive aspects of verification, testing, and redundancy. Fourth, it explores recovery mechanisms, moving beyond traditional fault models to design "just good enough" recovery strategies that prioritize safety‑critical functions during perturbations. Finally, it highlights the central role of the human, focusing on the different levels of human intervention, the necessity of trust calibration, and the requirement for explainable AI to support human‑CPS teaming. These themes are illustrated through representative application domains, primarily Connected and Autonomous Transportation Systems (CATS) and Medical CPS (MCPS). By integrating the five interconnected themes, this survey provides a systematic roadmap for achieving the resilient CPS in increasingly complex and adversarial environments.

Authors:Mohammad Mirtaba, Max H. Cohen
Title: High Order Tuners for Adaptive Safety of Robotic Systems
Abstract:
The combination of control barrier functions (CBFs) and adaptive control ‑‑ a framework referred to as adaptive safety ‑‑ has proven to be a powerful paradigm for safety‑critical control of nonlinear systems with parametric uncertainties. Yet the theoretical conditions for forward invariance within this framework are often quite conservative, and may require using large adaptation gains to achieve acceptable performance, an approach that is traditionally discouraged in adaptive control. This paper mitigates these issues via high‑order tuners, a recent class of higher‑order adaptation laws that leverages different adaptation gains at different orders of differentiation. We illustrate that these high‑order tuners decouple adaptation gain conditions from those placed on the initial conditions of the system required for set invariance. We extend these results to robotic systems whose linear‑in‑the‑parameters structure proves particularly useful for adaptive control. The efficacy of our results are illustrated via simulations.

Authors:Hideki Yoshikado, Yuta Takahashi
Title: Probabilistic Connectivity Analysis of Recursive Satellite Release for Formation Initialization
Abstract:
In the initial deployment of large‑scale distributed space systems using small satellites, achieving a reliable transition to passively stable orbits while maintaining inter‑satellite distances within effective control and communication ranges is crucial, particularly given the presence of deployment errors and uncontrolled coasting phases. This study presents a framework for designing formation initialization that provides probabilistic safety guarantees. The scope covers the initial deployment phase, from sequential release by a single carrier to commissioning, control activation, and transition to passive stabilization. Strict separation limits during initialization necessitate low release velocities to minimize relative drift before control activation. However, in the low‑velocity regime, the allowable tolerances for release velocity and angular rate errors tighten significantly to satisfy distance constraints, making hardware requirements a critical bottleneck. To address this, we model the initialization sequence as a stochastic process and derive closed‑form constraints on deployment errors and control activation intervals. These conditions ensure that inter‑satellite distances remain within the allowable separation limit with a prescribed probability. Monte Carlo simulations, configured using the error bounds and intervals derived from the proposed constraints, demonstrate that inter‑satellite distances are successfully maintained within the allowable range. The proposed framework enables the safe initialization of large‑scale distributed space systems by translating strict separation constraints into quantifiable hardware requirements.

Authors:Kristian Hengster-Movrić, Šimon Lehký, Farnaz Adib Yaghmaie
Title: Consensus and Synchronization of Multi-agent Systems over Finite Fields -- Graph Topologies
Abstract:
This paper brings cooperative protocols for multi‑agent systems with agents having a finite state‑space. Both scalar single‑integrator consensus and general LTI systems synchronization are considered. Systems having a finite state‑space describe agents with minimal memory capacity processing only a finite alphabet. Such systems are remarkably resilient to communication noise. The crucial problem, however, is to construct the admissible communication topology, which is NP‑hard. We address this by efficiently exploring the subsets of admissible matrices and propose two new algorithms to generate the topologies. Simulations validate the proposed approach.

Authors:Ratnangshu Das, Ram Padmanabhan, Melkior Ornik, Pushpak Jagtap
Title: Energetic Resilience under Temporal Logic Specifications
Abstract:
In environments with uncertainties or undesirable influences, control systems can require additional energy to achieve their task while remaining resilient to these influences. In this paper, we present an energetic resilience metric that quantifies the maximal additional energy used by a system under undesired effects, while satisfying complex specifications encoded through temporal logic. We prove that this metric satisfies properties that enable its computation even for compositions of these specifications, thus allowing considerations of sequential reachability and safety tasks. For specifications related to finite‑horizon reachability and safety, we describe how synthesizing a control input and computing this metric reduces to solving efficient quadratic programs. Two case studies on a fighter‑jet model and a planar mobile robot illustrate how the synthesized control inputs satisfy given specifications despite undesired and potentially adversarial effects. Further, we demonstrate how the energetic resilience metric varies with the initial state as well as the magnitude of undesired effects.

Authors:Zhenyu Pu, Yu Yang, Liang Yu, Xiaohong Guan
Title: End-to-End Learning-based Operation of Integrated Energy Systems for Buildings and Data Centers
Abstract:
Buildings and data centers (DCs) are energy‑intensive sectors, playing a critical role to achieve the low‑carbon and sustainable energy transition targets. To this end, integrated energy system (IES) that incorporates diverse renewables, energy generation, conversion, and storage technologies to enable coordinated multi‑energy supply have been widely investigated for both buildings and DCs. However, few works consider the two sectors jointly within IES to exploit their substantial synergistic benefits. Meanwhile, the operational optimization of IES remains challenging due to the difficulty to predict the multi‑energy demand and supply accurately. To address these gaps, this paper investigates IES for coordinated multi‑energy supply of buildings and DC, where the waste heat from DCs is recovered and reused to enhance energy efficiency. Moreover, an end‑to‑end learning‑based method is proposed for the operational optimization of IES under uncertainty. Unlike conventional predict‑then‑optimize approaches, the proposed method integrates the training of prediction models for uncertain variables with the constrained optimization of IES into a unified learning framework, guiding the training of prediction models to improve operational performance, rather than prediction accuracy, thereby mitigating the impacts of predictions errors. Case studies based on real‑world datasets show that the proposed methods improves the operational performance of IES by about 7‑9% compared to existing predict‑then‑optimize methods. In addition, coordinating buildings and DCs within IES shows substantial economic benefits. In particular, the waste heat recovery from DCs leads to approximately 10% of total energy cost reduction of the IES.

Authors:Yang Hu, Na Li
Title: Joint Identification of Linear Dynamics and Noise Covariance via Distributional Estimation
Abstract:
In this paper, we propose a novel framework for the joint identification of system dynamics and noise covariance in linear systems, under general noise distributions beyond Gaussian. Specifically, we would like to simultaneously estimate the dynamical matrix A and the noise covariance matrix \varSigma using state transition data. The formulation builds upon a novel parameterization of the state‑transition distribution, which enables more effective use of distributional "shape" information for improved identification accuracy. We introduce two practical estimators, namely the maximum likelihood estimator (MLE) and the score‑matching estimator (SME), to solve the joint dynamics‑covariance identification problem, and provide rigorous analysis of their statistical properties and sample complexity. Simulation results show that the proposed estimators outperform the ordinary least squares (OLS) baseline.

Authors:Alexandre M. V. Gouveia, Md. Umar Hashmi, Reinhilde D'hulst, Dirk Van Hertem
Title: Importance of Aggregated DER Installed Capacity in Distribution Networks
Abstract:
The increasing penetration of Distributed Energy Resources (DERs), particularly electric vehicles, heat pumps, and photovoltaic systems, is fundamentally changing power flows in Low‑Voltage (LV) distribution networks. Despite this transition, Distribution System Operators (DSOs) often lack reliable and up‑to‑date knowledge of the DER capacity connected downstream of LV substations. Limited observability, incomplete topology information, and restricted access to customer‑level data make it difficult to maintain accurate DER registries, creating uncertainty in both operational and planning processes. This paper presents aggregated DER installed capacity, estimated at LV aggregation points, as a practical and scalable approach to improving DER awareness without requiring customer‑level monitoring. We define the problem of estimating DER installed capacities from commonly available substation and feeder measurements. By linking these estimates to operational and planning needs, we discuss how knowledge of aggregated DER installed capacity enhances DER‑aware forecasting, congestion management, flexibility quantification, hosting capacity assessment, and monitoring of DER adoption.

Authors:Saeed Rahmani, Gözde Körpe, Zhenlin, Xu, Bruno Brito, Simeon Craig Calvert, Bart van Arem
Title: Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning
Abstract:
Automated driving at unsignalized intersections is challenging due to complex multi‑vehicle interactions and the need to balance safety and efficiency. Model Predictive Control (MPC) offers structured constraint handling through optimization but relies on hand‑crafted rules that often produce overly conservative behavior. Deep Reinforcement Learning (RL) learns adaptive behaviors from experience but often struggles with safety assurance and generalization to unseen environments. In this study, we present an integrated MPC‑RL framework to improve navigation performance in multi‑agent scenarios. Experiments show that MPC‑RL outperforms standalone MPC and end‑to‑end RL across three traffic‑density levels. Collectively, MPC‑RL reduces the collision rate by 21% and improves the success rate by 6.5% compared to pure MPC. We further evaluate zero‑shot transfer to a highway merging scenario without retraining. Both MPC‑based methods transfer substantially better than end‑to‑end PPO, which highlights the role of the MPC backbone in cross‑scenario robustness. The framework also shows faster loss stabilization than end‑to‑end RL during training, which indicates a reduced learning burden. These results suggest that the integrated approach can improve the balance between safety performance and efficiency in multi‑agent intersection scenarios, while the MPC component provides a strong foundation for generalization across driving environments. The implementation code is available open‑source.

Authors:Eymen Ipek
Title: Evaluating the Exp-Minus-Log Sheffer Operator for Battery Characterization
Abstract:
Odrzywolek (2026) recently introduced the Exp‑Minus‑Log (EML) operator eml (x, y) = exp(x) ‑ ln(y) and proved constructively that, paired with the constant 1, it generates the entire scientific‑calculator basis of elementary functions; in this sense EML is to continuous mathematics what NAND is to Boolean logic. We investigate whether such a uniform single‑operator representation can accelerate either the forward simulation or the parameter identification of a six‑branch RC equivalent‑circuit model (6rc ECM) of a lithium‑ion battery cell. We give the analytical EML rewrite of the discretized state‑space recursion, derive an exact operation count, and quantify the depth penalty of the master‑formula construction used for gradient‑based symbolic regression. Our analysis shows that direct EML simulation is slower than the classical exponential‑Euler scheme (a ~ 25x instruction overhead per RC branch), but EML‑based parametrization offers a structurally complete, gradient‑differentiable basis that competes favourably with non‑parametric DRT deconvolution and metaheuristic optimisation when the cardinality of RC branches is unknown a priori. We conclude with a concrete recommendation: use EML only on the parametrization side of the 6rc workflow, keeping the classical recursion at runtime.

Authors:Eymen Ipek
Title: Hardware-Efficient Neuro-Symbolic Networks with the Exp-Minus-Log Operator
Abstract:
Deep neural networks (DNNs) deliver state‑of‑the‑art accuracy on regression and classification tasks, yet two structural deficits persistently obstruct their deployment in safety‑critical, resource‑constrained settings: (i) opacity of the learned function, which precludes formal verification, and (ii) reliance on heterogeneous, library‑bound activation functions that inflate latency and silicon area on edge hardware. The recently introduced Exp‑Minus‑Log (EML) Sheffer operator, eml(x, y) = exp(x) ‑ ln(y), was shown by Odrzywolek (2026) to be sufficient ‑ together with the constant 1 ‑ to express every standard elementary function as a binary tree of identical nodes. We propose to embed EML primitives inside conventional DNN architectures, yielding a hybrid DNN‑EML model in which the trunk learns distributed representations and the head is a depth‑bounded, weight‑sparse EML tree whose snapped weights collapse to closed‑form symbolic sub‑expressions. We derive the forward equations, prove computational‑cost bounds, analyse inference and training acceleration relative to multilayer perceptrons (MLPs) and physics‑informed neural networks (PINNs), and quantify the trade‑offs for FPGA/analog deployment. We argue that the DNN‑EML pairing closes a literature gap: prior neuro‑symbolic and equation‑learner approaches (EQL, KAN, AI‑Feynman) work with heterogeneous primitive sets and do not exploit a single hardware‑realisable Sheffer element. A balanced assessment shows that EML is unlikely to accelerate training, and on commodity CPU/GPU it is also unlikely to accelerate inference; however, on a custom EML cell (FPGA logic block or analog circuit) the asymptotic latency advantage can reach an order of magnitude with simultaneous gain in interpretability and formal‑verification tractability.

Authors:Peng Xie, Abdulla Fawzy, Zhen Zhang, Amr Alanwar
Title: Data-Driven Reachability Analysis Using Matrix Perturbation Theory
Abstract:
We propose a matrix zonotope perturbation framework that leverages matrix perturbation theory to characterize how noise‑induced distortions alter the dynamics within sets of models. The framework derives interpretable Cai‑Zhang bounds for matrix zonotopes (MZs) and extends them to constrained matrix zonotopes (CMZs). Motivated by this analysis and the computational burden of CMZ‑based reachable‑set propagation, we introduce a coefficient‑space approximation in which the constrained coefficient space of the CMZ is over‑approximated by an unconstrained zonotope. Replacing CMZ‑constrained‑zonotope (CZ) products with unconstrained MZ‑zonotope multiplication yields a simpler and more scalable reachable‑set update. Experimental results demonstrate that the proposed method is substantially faster than the standard CMZ approach while producing reachable sets that are less conservative than those obtained with existing MZ‑based methods, advancing practical, accurate, and real‑time data‑driven reachability analysis.

Authors:Alessio Moreschini, Matteo Scandella
Title: Frequency Response of Nonlinear Systems: Notions, Analysis, and Graphical Representation
Abstract:
The invariance principle, through which the steady‑state behavior of nonlinear systems was introduced by Isidori and Byrnes, is leveraged in this article to bring forth a unifying characterization of the frequency response of nonlinear systems. We show that, for systems under nonlinear periodic excitations, the frequency response can still be defined as a complex‑valued function in a phasor form. However, together with suitable notions of gain and phase functions, we show the existence of another function that completes the frequency response and allows quantifying the distortion introduced by the system in the steady‑state output. This nonlinear characterization enabled the representation over input frequency and amplitude of the gain, phase, and distortion produced by the system, via a nonlinear enhancement of the Bode diagrams. This graphical representation of the frequency response is well‑suited to performance analysis of a nonlinear system and, furthermore, allows for the formulation of the loop‑shaping problem for nonlinear systems.

Authors:Peng Xie, Amr Alanwar
Title: Orthogonal Transformations for Efficient Data-Driven Reachability Analysis
Abstract:
Data‑driven reachability analysis using matrix zonotopes faces a fundamental challenge: the number of generators in the reachable set grows exponentially during propagation, while current order reduction yields overly conservative approximations in data‑driven settings. This paper introduces an orthogonal matrix‑based framework that appropriately transfers the coordinate system before reducing the generators of the reachable set, dramatically reducing reachable set volumes. By exploiting the factorized structure of data‑driven matrix zonotope generators, we develop several efficient algorithms to solve the problem. Numerical experiments demonstrate order‑of‑magnitude volume reductions compared to traditional methods, while maintaining comparable generator numbers. Our method provides a practical solution to improve precision in data‑driven safety verification.

Authors:Mohsen Jalaeian-Farimani, Mohammad-R Akbarzadeh-T, Alireza Akbarzadeh, Mostafa Ghaemi
Title: A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot
Abstract:
To date, various paradigms of soft‑Computing have been used to solve many modern problems. Among them, a self organizing combination of fuzzy systems and neural networks can make a powerful decision making system. Here, a Dynamic Growing Fuzzy Neural Controller (DGFNC) is combined with an adaptive strategy and applied to a 3PSP parallel robot position control problem. Specifically, the dynamic growing mechanism is considered in more detail. In contrast to other self‑organizing methods, DGFNC adds new rules more conservatively; hence the pruning mechanism is omitted. Instead, the adaptive strategy 'adapts' the control system to parameter variation. Furthermore, a sliding mode‑based nonlinear controller ensures system stability. The resulting general control strategy aims to achieve faster response with less computation while maintaining overall stability. Finally, the 3PSP is chosen due to its complex dynamics and the utility of such approaches in modern industrial systems. Several simulations support the merits of the proposed DGFNC strategy as applied to the 3PSP robot.

Authors:Mohammed Irshadh Ismaaeel Sathyamangalam Imran, Lasse Peters, Michael Khayyat, Stefano Arrigoni, Francesco Braghin, Laura Ferranti
Title: Homotopy-Guided Potential Games for Congestion-Aware Navigation
Abstract:
We address the multi‑agent motion planning problem where interactions, collisions, and congestion co‑exist. Conventional game‑theoretic planners capture interactions among agents but often converge to conservative, congested equilibria. Homotopy planners, on the other hand, can explore topologically distinct paths, but lack mechanisms to account for the interdependence of agents' future actions. We propose a unified framework that leverages homotopy classes as structured strategy sets within a receding‑horizon setup. At each planning stage, a deterministic homotopy planner generates topologically distinct paths for each agent, conditioned on the joint configuration. To avoid intractable growth of candidate paths, we propose a simple heuristic filtering step that selects a top‑K subset of the most suitable congestion‑free joint strategies to ensure computational tractability. These serve as initializations for a potential game that enforces homotopy‑consistent constraints and yields a generalized open‑loop Nash equilibrium (OLNE), with penalties discouraging abrupt strategy shifts in a receding‑horizon setting. Simulations with three agents demonstrate improved efficiency (faster completion) and enhanced safety (greater inter‑agent clearance, leading to reduced congestion) compared to a local baseline and NH‑ORCA that do not reason about homotopies. Hardware trials with two robots and one human demonstrate robustness to irrational behaviors, where our method adapts by switching to alternative feasible equilibria while the baseline game fails.

Authors:Yitao Yan, Shuangyu Han, Jie Bao, Biao Huang
Title: Data-Driven Probabilistic Finite $\mathcal{L}_2$-Gain Stabilization of Stochastic Linear Systems
Abstract:
In process operations, it is desirable to manage the sensitivity of the system output against external disturbance in the form of finite \mathcalL_2‑gain stabilization. This matter is, however, nonsensical for stochastic systems because the stochastic uncertainties in the control input almost always lead to an unbounded \mathcalL_2 gain from the disturbance to the output. To address this issue, this article develops a novel concept that characterizes the \mathcalL_2 gain of stochastic systems in a probabilistic way. Combined with a large data set, we formulate a data‑driven probabilistic finite \mathcalL_2‑gain stabilization design using noisy trajectory measurements and the disturbance forecast that does not necessarily agree with the actual future disturbance. The design approach consists of a data‑driven trajectory estimation algorithm, whose resulting estimation error covariance is nicely integrated into the feasibility conditions for controller synthesis, leading to a convex offline design in the form of linear matrix inequalities. The effectiveness of the proposed design, along with the additional insights provided by the approach, is illustrated via a numerical example.

Authors:Alireza Jafari, Hong-Son Nguyen, Yen-Chen Liu
Title: Empirical Prediction of Pedestrian Comfort in Mobile Robot Pedestrian Encounters
Abstract:
Mobile robots joining public spaces like sidewalks must care for pedestrian comfort. Many studies consider pedestrians' objective safety, for example, by developing collision avoidance algorithms, but not enough studies take the pedestrian's subjective safety or comfort into consideration. Quantifying comfort is a major challenge that hinders mobile robots from understanding and responding to human emotions. We empirically look into the relationship between the mobile robot‑pedestrian interaction kinematics and subjective comfort. We perform one‑on‑one experimental trials, each involving a mobile robot and a volunteer. Statistical analysis of pedestrians' reported comfort versus the kinematic variables shows moderate but significant correlations for most variables. Based on these empirical findings, we design three comfort estimators/predictors derived from the minimum distance, the minimum projected time‑to‑collision, and a composite estimator. The composite estimator employs all studied kinematic variables and reaches the highest prediction rate and classifying performance among the predictors. The composite predictor has an odds ratio of 3.67. In simple terms, when it identifies a pedestrian as comfortable, it is almost 4 times more likely that the pedestrian is comfortable rather than uncomfortable. The study provides a comfort quantifier for incorporating pedestrian feelings into path planners for more socially compliant robots.

Authors:Yitao Yan, Yu Tong, Jie Bao, Wei Wang
Title: Behavioral Systems Theory Meets Machine Learning: Control-Aware Learning of the Intrinsic Behavior from Big Data
Abstract:
The abundance of process operating data in modern industries, along with the rapid advancement of learning techniques, has led to a paradigm shift towards data‑centric analysis and control. However, integrating machine learning with control theory for big data‑driven control of nonlinear systems remains a challenging open problem. This is because the state‑based, model‑centric, and causal framework of classical control theory fundamentally contradicts the trajectory‑based, set‑theoretic, and causality‑absent rationale of big data‑based learning approaches. Using the behavioral framework, we show that dynamical systems possess an intrinsic state variable that encodes the system behavior in a bijective and causality‑free manner, and control design can be carried out entirely within the state space. This approach not only resolves the aforementioned conflict but also complements machine learning techniques well, leading to a neural network architecture that is capable of learning the behavior representation well‑suited for control design.

Authors:Patrick Schmidt, Stefan Streif
Title: Time-varying optimal control under measurement errors
Abstract:
Solving optimal control problems to determine a stabilizing controller involves a significant computational effort. Time‑varying optimal control provides a remedy by designing a tracking system, given as an ordinary differential equation, to track the solution of the optimal control problem. To improve the applicability of the method, measurement errors are considered in this paper and it is described how these errors influence a control Lyapunov function‑based decay condition. As a result of these investigations, input‑affine constraints that meet the standard formulation and that describe the set of admissible controls are obtained. The paper also derives a requirement on the necessary measurement accuracy as well as a triggering condition for taking a new measurement. The main theorem combines these results into a robustly stabilizing control algorithm, meaning that all closed‑loop trajectories starting in a vicinity around the true state converge to zero. Additionally, the tracking system ensures that the optimal control is tracked at the end of each sampling period. The effectiveness of this approach is demonstrated using a train acceleration model and the well‑known predator‑prey model.

Authors:Nana Wang, Pelin Sekercioglu, Dimos V. Dimarogonas
Title: Topology Estimation for Open Multi-Agent Systems
Abstract:
We address the problem of interaction topology identification in open multi‑agent systems (OMAS) with dynamic node sets and fast switching interactions. In such systems, new agents join and interactions change rapidly, resulting in intervals with short dwell time and rendering conventional segment‑wise estimation and clustering methods unreliable. To overcome this, we propose a projection‑based dissimilarity measure derived from a consistency property of local least‑squares operators, enabling robust mode clustering. Aggregating intervals within each cluster yields accurate topology estimates. The proposed framework offers a systematic solution for reconstructing the interaction topology of OMAS subject to fast switching. Finally, we illustrate our theoretical results via numerical simulations.

Authors:Thomas Hübner
Title: On the Design of Stochastic Electricity Auctions
Abstract:
Electricity is typically traded in day‑ahead auctions because many power system decisions, such as unit commitment, must be made in advance. However, when wind and solar generators sell power one day ahead, they face uncertainty about their actual production. In current day‑ahead auctions, this uncertainty cannot be directly communicated, leading to inefficient use of renewable energy and suboptimal system decisions. We show how this problem can be addressed using the concept of equilibrium under uncertainty from microeconomic theory. In particular, we demonstrate that electricity contracts should be conditioned not only on the time and location of delivery, but also on the state of the world (e.g., whether it will be windy or calm). This requires a precise definition of the state of the world. Since there are infinitely many possible definitions, criteria are needed to select among them. We develop such criteria and show that the resulting states correspond to solutions of an optimal partitioning problem. Finally, we illustrate how these states can be computed and interpreted using a case study of offshore wind farms in the European North Sea.

Authors:Yike Xu, Mark S. Andersland
Title: Exploiting Scheduling Flexibility via State-Based Scheduling When Guaranteeing Worst-Case Services
Abstract:
Even when providing long‑run, worst‑case guarantees to competing flows of unit‑sized tasks, a slot‑timed, constant‑capacity server's scheduler may retain significant, short‑run, scheduling flexibility. Existing worst‑case scheduling frameworks offer only limited opportunities to characterize and exploit this flexibility. We introduce a state‑based framework that overcomes these limitations. Each flow's guarantee is modeled as a worst‑case service that can be updated as tasks arrive and are served. Taking all flows' worst‑case services as a collective state, a state‑based scheduler ensures, from slot to slot, transitions between schedulable states. This constrains its scheduling flexibility to a polytope consisting of all feasible schedules that preserve schedulability. We fully characterize this polytope, enabling scheduling flexibility to be fully exploited. But, as our framework is general, full exploitation is computationally complex. To reduce complexity, we show: that when feasible schedules exist, at least one can be efficiently identified by simply maximizing the server's capacity slack; that a special class of worst‑case services, min‑plus services, can be efficiently specified and updated using the min‑plus algebra; and that efficiency can be further improved by restricting attention to a min‑plus service subclass, dual‑curve services. This last specialization turns out to be a dynamic extension of service curves that approaches near practical viability while maintaining all features essential to our framework.

Authors:Yukang Zhang, Shuqi Chai, Yuhang Zhang, Danlan Huang, Quanbo Ge
Title: Cascaded TD3-PID Hybrid Controller for Quadrotor Trajectory Tracking in Wind Disturbance Environments
Abstract:
This work presents a cascaded hybrid control framework for quadrotor trajectory tracking under nonlinear dynamics and external disturbances. In quadrotor systems, the altitude and attitude channels exhibit fast, structured dynamics that are well suited to reliable regulation, whereas horizontal‑position control is more strongly affected by coupling effects, uncertainty, and disturbances, so that neither pure feedback control nor purely learning‑based control alone is equally well suited to all channels. Accordingly, the proposed framework augments conventional proportional‑integral‑derivative (PID) stabilization for altitude and attitude control with an enhanced Twin Delayed Deep Deterministic Policy Gradient (TD3) agent incorporating a multi‑Q‑network structure, thereby improving horizontal‑position control under severe disturbances. To further strengthen disturbance rejection in altitude and attitude control, a hybrid disturbance observer (HDOB) using low‑pass and exponential moving average filtering is embedded in the control loops. The proposed TD3 enhancements are verified through ablation studies, and both numerical simulations and real‑world flight tests on the quadrotor platform demonstrate that the proposed method achieves more accurate and robust trajectory tracking under wind disturbances than baseline approaches.

Authors:Andrii Vakhnovskyi
Title: VPD-Centric Cascading Control with Neural Network Optimization for Energy-Efficient Climate Management in Controlled Environment Agriculture
Abstract:
Conventional climate control in Controlled Environment Agriculture (CEA) uses independent PID loops for temperature and humidity, creating cross‑coupling conflicts that waste 20‑40% of HVAC energy. We propose a cascading architecture that elevates Vapor Pressure Deficit (VPD) from a monitored metric to the primary outer‑loop control variable. A 7‑3‑3 neural network optimizer selects energy‑minimal temperature‑humidity setpoints along the VPD constraint surface, feeding inner PID loops that drive HVAC actuators. Lyapunov stability analysis guarantees bounded PID gains. Deployment across 30+ commercial facilities in 8 U.S. climate zones over 7+ years demonstrates 30‑38% HVAC energy reduction, 68‑73% improvement in VPD stability, and 60‑67% faster disturbance recovery compared to independent PID baselines.

Authors:Filippos N. Tzortzoglou, Pengbo Zhu, Andreas A. Malikopoulos
Title: Integrated Routing and Intersection Control for Mixed Traffic
Abstract:
The rapid development of cyber‑physical systems is driving a transition toward mixed traffic environments comprising both human‑driven and connected and automated vehicles (CAVs). This shift presents a unique opportunity to leverage the efficient operation of CAVs to improve overall network throughput. This paper introduces a hierarchical framework designed to bridge macroscopic routing optimization at the network level with microscopic vicinity control at signalized intersections. The upper layer utilizes aggregated traffic information to provide proactive routing guidance for CAVs, aiming to minimize total travel time. The lower layer leverages local vehicle states to jointly optimize traffic light phases and individual CAV trajectories, aiming to reduce intersection crossing delays and optimize energy consumption, respectively. The effectiveness of the proposed framework is validated through SUMO on the Sioux Falls benchmark network. Results demonstrate that the integration of these macroscopic and microscopic layers yields significantly better performance compared to applying either layer in isolation, significantly improving network throughput and reducing congestion.

Authors:Xi Zhang, Huqiang Cheng, Guo Chen, Huaqing Li, Liang Ran, Jinhui Hu, Tingwen Huang
Title: Optimal Decentralized Dynamic Energy Management over Asynchronous Peer-to-Peer Transactive Networks via Operator Splitting
Abstract:
Peer‑to‑peer (P2P) energy management facilitates decentralized resource allocation among prosumers, improving local hosting capacity for renewables and minimizing energy expenditures while ensuring data privacy through distributed coordination. However, conventional P2P energy management methods are confined to synchronous scheduling paradigms, creating synchronization bottlenecks that fundamentally conflict with the dynamic and decentralized nature of P2P energy management tasks. To bridge this gap, this paper focuses on resolving a class of dynamic energy management problems over asynchronous P2P (Asyn‑P2P) transactive networks. We first recast the dynamic energy management problems into a saddle‑point problem, and then propose a synchronous decentralized dynamic energy management algorithm, dubbed Syn‑DYNA,based on operator splitting theory. To eliminate the global synchronization clock in Syn‑DYNA, we introduce a random activation scheme, together with local buffers for latest state tracking, to develop an asynchronous variant of Syn‑DYNA, namely Asyn‑DYNA. Based on monotone operator theory, theoretical analysis proves a non‑asymptotic linear convergence rate for Syn‑DYNA and establishes the almost sure convergence ofAsyn‑DYNA. Numerical experiments validate effectiveness of Syn‑DYNA and Asyn‑DYNA algorithms by tackling a dynamic energy management task over P2P transactive networks.

Authors:Andrii Vakhnovskyi
Title: HierFedCEA: Hierarchical Federated Edge Learning for Privacy-Preserving Climate Control Optimization Across Heterogeneous Controlled Environment Agriculture Facilities
Abstract:
Cross‑facility knowledge transfer in Controlled Environment Agriculture (CEA) can reduce HVAC energy consumption by 30‑38% and accelerate new facility commissioning from months to days. However, facility operators refuse to share raw operational data because it encodes commercially sensitive grow recipes. We present HierFedCEA, a hierarchical federated learning framework that enables privacy‑preserving climate control optimization across heterogeneous CEA facilities. HierFedCEA decomposes the neural network PID auto‑tuning model into three tiers aligned with the physical structure of the control problem: (1) a global physics tier capturing universal thermodynamic relationships; (2) a crop‑cluster tier encoding cultivar‑specific VPD‑to‑gain mappings; and (3) a local personalization tier adapting to facility‑specific equipment dynamics. The framework applies tier‑specific differential privacy budgets and leverages the extreme compactness of the 36‑parameter PID model to achieve privacy essentially for free (excess risk < 0.15%). Simulation experiments calibrated from 7+ years of production deployment across 30+ commercial facilities in 8 U.S. climate zones demonstrate that HierFedCEA achieves 94% of centralized training performance while reducing total communication cost to under 1 MB. To the best of our knowledge, this is the first federated learning framework designed for CEA climate control.

Authors:Wenji Cao, Lu Liu, Dan Zhang, Gang Feng
Title: Distributed Resilient Fixed-Time Control for Cooperative Output Regulation of MASs over Directed Graphs under DoS Attacks
Abstract:
This paper addresses the problem of fixed‑time cooperative output regulation for linear multi‑agent systems over directed graphs under denial‑of‑service attacks. A novel distributed resilient fixed‑time controller is developed that comprises a distributed resilient fixed‑time observer taking general directed graphs into consideration, and a distributed resilient fixed‑time control law for each agent. The proposed controller neither depends on Laplacian symmetry nor requires strong connectivity and detail‑balanced condition, in contrast to existing distributed resilient fixed‑time controllers. Under the proposed controller, the regulated outputs converge to zero in a fixed time with its upper bound independent of the initial states of the multi‑agent system. Ultimately, the efficacy of the proposed controller is demonstrated via a simulation example.

Authors:Ibon Gracia, Morteza Lahijanian
Title: On the Optimality of Uncertain MDP Abstractions
Abstract:
We study the asymptotic optimality of abstraction‑based control synthesis algorithms. Specifically, we consider uncertain MDP (UMDP) abstraction, and investigate whether refinement leads to optimal results, i.e., an optimal controller and zero error bound. Additionally, we study completeness of abstraction‑refinement algorithms, i.e., that the algorithm produces near‑optimal results in finite time. The focus is on nonlinear stochastic systems with general vector fields and temporal logic specifications. We present an algorithm that abstracts the system into a UMDP and synthesizes a controller with performance guarantees via robust dynamic programming. Then, the algorithm iteratively refines the abstraction until a near‑optimality criterion is met. A thorough theoretical analysis reveals a sufficient condition, which we denote vanishing ambiguity, guaranteeing asymptotic optimality of the abstraction process and completeness of the algorithm. We show that set‑valued MDP abstractions satisfy this criterion, whereas interval MDP abstractions lack such a guarantee.

Authors:Angelo Moroncelli, Matteo Rufolo, Gunes Cagin Aydin, Asad Ali Shahid, Loris Roveda
Title: Diffusion Sequence Models for Generative In-Context Meta-Learning of Robot Dynamics
Abstract:
Accurate modeling of robot dynamics is essential for model‑based control, yet remains challenging under distributional shifts and real‑time constraints. In this work, we formulate system identification as an in‑context meta‑learning problem and compare deterministic and generative sequence models for forward dynamics prediction. We take a Transformer‑based meta‑model, as a strong deterministic baseline, and introduce to this setting two complementary diffusion‑based approaches: (i) inpainting diffusion (Diffuser), which learns the joint input‑observation distribution, and (ii) conditioned diffusion models (CNN and Transformer), which generate future observations conditioned on control inputs. Through large‑scale randomized simulations, we analyze performance across in‑distribution and out‑of‑distribution regimes, as well as computational trade‑offs relevant for control. We show that diffusion models significantly improve robustness under distribution shift, with inpainting diffusion achieving the best performance in our experiments. Finally, we demonstrate that warm‑started sampling enables diffusion models to operate within real‑time constraints, making them viable for control applications. These results highlight generative meta‑models as a promising direction for robust system identification in robotics.

Authors:Mahtab Talaei, Alex Olshevsky, Laura F. White, Ioannis Ch. Paschalidis
Title: Network Epidemic Control via Model Predictive Control: Extended Version
Abstract:
Balancing the societal costs of non‑pharmaceutical interventions with epidemic suppression requires adaptive feedback control. Rather than relying on state‑dependent operational caps, we formulate an infinite‑horizon optimal control problem for a networked SIQR model that strictly enforces suppression via a hard spectral constraint on the transmission dynamics. We derive a safety‑critical Model Predictive Control (MPC) approximation that embeds this spectral certificate stage‑wise, yielding a tunable exponential decay rate. Furthermore, we construct a terminal set ensuring recursive feasibility and a feasible continuation that decays globally, proving positive invariance directly via the physical depletion of susceptibles rather than standard quadratic Lyapunov functions. To handle prediction uncertainty, we develop a robust counterpart that replaces nominal constraints by upper‑envelope versions, recovering recursive feasibility and finite‑horizon realized decay. We conclude by validating our approaches using simulation studies that leverage public data from counties in the state of Massachusetts.

Authors:Anosh Arshad Sundhu, Aysegül Kahraman, Spyros Chatzivasileiadis
Title: Digital Twin for Real-Time Security Assessment and Flexibility Activation in the Bornholm Distribution System
Abstract:
The increasing penetration of distributed energy resources (DERs) is transforming distribution networks into actively managed systems, introducing challenges related to voltage regulation, thermal loading limits, and operational security. This paper presents the development and implementation of a real‑time Digital Twin (DT) for security assessment and coordinated flexibility activation in active distribution networks, demonstrated on the Bornholm Island system using real measurement data. The implemented DT integrates network topology and smart meter measurements to perform security assessment under normal operation and N‑1 contingencies, and to determine corrective and preventive flexibility actions using an optimization‑based approach. Results show that load variation and contingency scenarios introduce operational limit violations, primarily driven by voltage magnitude constraints. The implemented flexibility strategy effectively mitigates these violations through coordinated active and reactive power control, enhancing system security and operational efficiency. The findings highlight the potential of DT‑based approaches for reliable and flexible operation of future distribution networks.

Authors:James Dallas, Thomas Lew, John Talbot, Jonathan DeCastro, Somil Bansal, John Subosits
Title: Boundary Sampling to Learn Predictive Safety Filters via Pontryagin's Maximum Principle
Abstract:
Safety filters provide a practical approach for enforcing safety constraints in autonomous systems. While learning‑based tools scale to high‑dimensional systems, their performance depends on informative data that includes states likely to lead to constraint violation, which can be difficult to efficiently sample in complex, high‑dimensional systems. In this work, we characterize trajectories that barely avoid safety violations using the Pontryagin Maximum Principle. These boundary trajectories are used to guide data collection for learned Hamilton‑Jacobi Reachability, concentrating learning efforts near safety‑critical states to improve efficiency. The learned Control Barrier Value Function is then used directly for safety filtering. Simulations and experimental validation on a shared‑control automotive racing application demonstrate PMP sampling improves learning efficiency, yielding faster convergence, reduced failure rates, and improved safe set reconstruction, with wall times around 3ms.

Authors:Goutam Das, Takashi Tanaka
Title: Path Integral Control in Gaussian Belief Space for Partially Observed Systems
Abstract:
This paper extends path integral control (PIC) to partially observed systems by formulating the problem in Gaussian belief space. PIC relies on the diffusion being proportional to the control channel ‑‑ the so‑called matching condition ‑‑ to linearize the Hamilton‑Jacobi‑Bellman equation via the Cole‑Hopf transform; we show that this condition fails in infinite‑dimensional belief space under non‑affine observations. Restricting to Gaussian beliefs yields a finite‑dimensional approximation with deterministic covariance evolution, reducing the problem to stochastic control of the belief mean. We derive necessary and sufficient conditions for matching in this reduced space, obtain an exact Cole‑Hopf linearization with a Feynman‑Kac representation, and develop the MPPI‑Belief algorithm. Numerical experiments on a navigation task with state‑dependent observation noise demonstrate the effectiveness of MPPI‑Belief relative to certainty‑equivalent and particle‑filter‑based baselines.

Authors:Andrii Vakhnovskyi
Title: Threat Modeling and Attack Surface Analysis of IoT-Enabled Controlled Environment Agriculture Systems
Abstract:
The United States designates Food and Agriculture as one of sixteen critical infrastructure sectors, yet no mandatory cybersecurity requirements exist for agricultural operations and no formal threat model has been published for Controlled Environment Agriculture (CEA) systems. This paper presents the first comprehensive threat model for IoT‑enabled CEA, applying STRIDE analysis, MITRE ATT&CK for ICS mapping, and IEC 62443 zone‑and‑conduit decomposition to a production platform deployed across 30+ commercial facilities in 8 U.S. climate zones. We enumerate 123 unique threats across 25 data‑flow‑diagram elements spanning 15 communication protocols, 10 of which operate with zero authentication or encryption by design. We identify five novel attack classes unique to AI‑driven CEA: stealth destabilization of neural‑network‑tuned PID controllers, baseline drift poisoning of anomaly detectors, cross‑facility propagation via federated transfer learning, adversarial agronomic schedules that exploit crop biology rather than computational models, and reward poisoning of reinforcement‑learning energy optimizers. Physical impact analysis quantifies crop loss timelines from minutes (aeroponics) to days, including worker safety hazards from CO2 injection manipulation. A survey of 10 commercial CEA vendors reveals only one CVE ever issued, zero bug bounty programs, and zero IEC 62443 certifications. We propose a defense‑in‑depth countermeasure framework and recommend Security Level 2 as a minimum baseline.

Authors:Binh Nguyen, Nam T. Nguyen, Truong X. Nghiem
Title: Structure- and Stability-Preserving Learning of Port-Hamiltonian Systems
Abstract:
This paper investigates the problem of data‑driven modeling of port‑Hamiltonian systems while preserving their intrinsic Hamiltonian structure and stability properties. We propose a novel neural‑network‑based port‑Hamiltonian modeling technique that relaxes the convexity constraint commonly imposed by neural network‑based Hamiltonian approximations, thereby improving the expressiveness and generalization capability of the model. By removing this restriction, the proposed approach enables the use of more general non‑convex Hamiltonian representations to enhance modeling flexibility and accuracy. Furthermore, the proposed method incorporates information about stable equilibria into the learning process, allowing the learned model to preserve the stability of multiple isolated equilibria rather than being restricted to a single equilibrium as in conventional methods. Two numerical experiments are conducted to validate the effectiveness of the proposed approach and demonstrate its ability to achieve more accurate structure‑ and stability‑preserving learning of port‑Hamiltonian systems compared with a baseline method.

Authors:Chenyang Qiu, Mihitha Maithripala, Zongli Lin
Title: A Momentum-based Stochastic Algorithm for Linearly Constrained Nonconvex Optimization
Abstract:
This paper studies a stochastic algorithm for linearly constrained nonconvex optimization, where the objective function is smooth but only unbiased stochastic gradients with bounded variance are available. We propose a momentum‑based augmented Lagrangian method that employs a Polyak‑type gradient estimator and requires only one stochastic gradient evaluation per iteration. Under the standard stochastic oracle model and the smoothness condition of the expected objective, we establish a convergence guarantee in terms of the first‑order KKT residual of the original constrained problem. In particular, the proposed method computes an ε‑stationary solution in expectation within O(ε^‑4) stochastic gradient evaluations. Numerical experiments further show that the proposed method achieves competitive iteration complexity and improved wall‑clock efficiency compared with representative recursive‑momentum baselines.

Authors:Negin Musavi, Melkior Ornik
Title: Dynamic Regret in Time-varying MDPs with Intermittent Information
Abstract:
We study sequential decision‑making in time‑varying Markov decision processes (TVMDPs) under limited update rates, where the decision‑maker observes the system and updates its model only intermittently. Such settings arise in applications with sensing, communication, or computational constraints that preclude continuous adaptation. Our goal is to understand how the performance of an agent, which learns and plans using receding‑horizon control under these information constraints, degrades as a function of the update rate. We propose a skip‑update learning and planning framework that combines likelihood‑based estimation of time‑varying transition kernels with finite‑horizon planning and executes policies between updates using stale information. We analyze its performance via dynamic regret relative to an oracle policy with full knowledge of the dynamics and continuous observations. Our main result establishes a dynamic regret bound that explicitly quantifies the impact of intermittent updates, decomposing regret into contributions from update times and skip intervals and revealing its dependence on temporal variation, estimation uncertainty, and the duration of intervals without updates. In particular, the dominant contribution from skip intervals admits a linear dependence on the interval length and the rate of temporal variation, while its effect is mitigated by mixing‑induced contraction.

Authors:Joonkyung Kim, Yanze Zhang, Wenhao Luo, Yiwei Lyu
Title: Capability-Aware Heterogeneous Control Barrier Functions for Decentralized Multi-Robot Safe Navigation
Abstract:
Safe navigation for multi‑robot systems requires enforcing safety without sacrificing task efficiency under decentralized decision‑making. Existing decentralized methods often assume robot homogeneity, making shared safety requirements non‑uniformly interpreted across heterogeneous agents with structurally different dynamics, which could lead to avoidance obligations not physically realizable for some robots and thus cause safety violations or deadlock. In this paper, we propose Capability‑Aware Heterogeneous Control Barrier Function (CA‑HCBF), a decentralized framework for consistent safety enforcement and capability‑aware coordination in heterogeneous robot teams. We derive a canonical second‑order control‑affine representation that unifies holonomic and nonholonomic robots under acceleration‑level control via canonical transformation and backstepping, preserving forward invariance of the safe set while avoiding relative‑degree mismatch across heterogeneous dynamics. We further introduce a support‑function‑based directional capability metric that quantifies each robot's ability to follow its motion intent, deriving a pairwise responsibility allocation that distributes the safety burden proportionally to each robot's motion capability. A feasibility‑aware clipping mechanism further constrains the allocation to each agent's physically achievable range, mitigating infeasible constraint assignments common in dense decentralized CBF settings. Simulations with up to 30 heterogeneous robots and a physical multi‑robot demonstration show improved safety and task efficiency over baselines, validating real‑world applicability across robots with distinct kinematic constraints.

Authors:Tania Rifat Jahan, Donald J. Docimo
Title: A Control Co-Design Framework to Achieve Solution Feasibility in Energy System Optimization Problems
Abstract:
This work explores methods to identify energy system designs for infeasible control co‑design optimization problems. Control co‑design, or CCD, has been recognized as a powerful tool to maximize energy system capabilities through simultaneous determination of plant and controller parameters. However, due to the inherent nonlinearities, complexity, and conflicting criteria of energy systems, CCD optimization problems are susceptible to infeasibility and can lack potential solutions. While transforming the optimization problem by relaxing constraints has been developed for optimal control infeasibility challenges, solution feasibility for CCD is relatively unexplored. This paper proposes a framework to convert infeasible optimization problems into solvable forms for a class of CCD problems. The framework introduces a procedure to rank metric bounds from least likely to most likely to cause infeasibility. This provides guidance to algorithmically relax a limited number of constraints, leaving others intact. The proposed framework is applied to a CCD problem for designing a battery within a microgrid. Comparison against a baseline approach for relaxing optimization problems shows the framework requires only a reduced number of iterations to determine a solution.

Authors:Donggeon David Oh, Duy P. Nguyen, Haimin Hu, Jaime Fernández Fisac
Title: Synthesis and Deployment of Maximal Robust Control Barrier Functions through Adversarial Reinforcement Learning
Abstract:
Robust control barrier functions (CBFs) provide a principled mechanism for smooth safety enforcement under worst‑case disturbances. However, existing approaches typically rely on explicit, closed‑form structure in the dynamics (e.g., control‑affine) and uncertainty models. This has led to limited scalability and generality, with most robust CBFs certifying only conservative subsets of the maximal robust safe set. In this paper, we introduce a new robust CBF framework for general nonlinear systems under bounded uncertainty. We first show that the safety value function solving the dynamic programming Isaacs equation is a valid robust discrete‑time CBF that enforces safety on the maximal robust safe set. We then adopt the key reinforcement learning (RL) notion of quality function (or Q‑function), which removes the need for explicit dynamics by lifting the barrier certificate into state‑action space and yields a novel robust Q‑CBF constraint for safety filtering. Combined with adversarial RL, this enables the synthesis and deployment of robust Q‑CBFs on general nonlinear systems with black‑box dynamics and unknown uncertainty structure. We validate the framework on a canonical inverted pendulum benchmark and a 36‑D quadruped simulator, achieving substantially less conservative safe sets than barrier‑based baselines on the pendulum and reliable safety enforcement even under adversarial uncertainty realizations on the quadruped.

Authors:Trinh Tran, Binh Nguyen, Truong X. Nghiem
Title: HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
Abstract:
This paper presents HUANet, a constrained deep neural network architecture that unrolls the iterations of the Alternating Direction Method of Multipliers (ADMM) into a trainable neural network for solving constrained convex optimization problems. Existing end‑to‑end learning methods operate as black‑box mappings from parameters to solutions, often lacking explicit optimality principles and failing to enforce constraints. To address this limitation, we unroll ADMM and embed a hard‑constrained neural network at each iteration to accelerate the algorithm, where equality constraints are enforced via a differentiable correction stage at the network output. Furthermore, we incorporate first‑order optimality conditions as soft constraints during training to promote the convergence of the proposed unrolled algorithm. Extensive numerical experiments are conducted to validate the effectiveness of the proposed architecture for constrained optimization problems.

Authors:Isaac Remy, Caleb Chang, Karen Leung
Title: Learning Probabilistic Responsibility Allocations for Multi-Agent Interactions
Abstract:
Human behavior in interactive settings is shaped not only by individual objectives but also by shared constraints with others, such as safety. Understanding how people allocate responsibility, i.e., how much one deviates from their desired policy to accommodate others, can inform the design of socially compliant and trustworthy autonomous systems. In this work, we introduce a method for learning a probabilistic responsibility allocation model that captures the multimodal uncertainty inherent in multi‑agent interactions. Specifically, our approach leverages the latent space of a conditional variational autoencoder, combined with techniques from multi‑agent trajectory forecasting, to learn a distribution over responsibility allocations conditioned on scene and agent context. Although ground‑truth responsibility labels are unavailable, the model remains tractable by incorporating a differentiable optimization layer that maps responsibility allocations to induced controls, which are available. We evaluate our method on the INTERACTION driving dataset and demonstrate that it not only achieves strong predictive performance but also provides interpretable insights, through the lens of responsibility, into patterns of multi‑agent interaction.

Authors:Filippos N. Tzortzoglou, Logan E. Beaver, Andreas A. Malikopoulos
Title: Closed-Form Characterization of Constrained Double-Integrator Optimal Control
Abstract:
We consider the energy‑optimal control problem for double‑integrator systems subject to state and control constraints, with fixed terminal time and free terminal speed. When the constraints become active, the optimal trajectory consists of a combination of bang, unconstrained, and coast arcs, whose switching instants must be computed explicitly. In this paper, we derive closed‑form expressions for the switching times of all admissible profiles, including both constrained and unconstrained arcs, reducing the computation in each case to explicit algebraic equations. In contrast to prior work, we classify all possible combinations of arcs, including special cases, and provide the specific conditions under which each case arises. Furthermore, we prove that when the initial unconstrained trajectory violates both speed and control constraints, the optimal solution follows a predetermined bang‑affine‑coast profile, enabling direct identification of the optimal trajectory without intermediate feasibility checks.

Authors:Ding Zhang, Di Zhao, Philipp Braun, Jianqi Chen
Title: Symmetry Is Almost All You Need: Robust Stability with Uncertainty Induced by Symmetric SRG Regions
Abstract:
This paper investigates the robust stability problem of a feedback system in the presence of uncertainties induced by graphical regions in the plane where the scaled relative graphs (SRGs) reside. Our main results are developed using a novel and intuitive concept, the Davis‑Wielandt shell, together with its connection to SRGs and related variants. We first study a matrix robust nonsingularity (MRN) problem for two types of graphically induced uncertainty sets: one with prior information on θ and one without. In the former case, we show that, whenever the uncertainty‑inducing region is mirror symmetric about the θ‑axis, the separation between a specific variant of the SRG and the region provides a necessary and sufficient condition for MRN. When the region is asymmetric, the necessity generally fails. This recovers the necessity of the small gain condition, and reveals the necessity of small angle conditions and sectored‑disc conditions at the matrix level. In the latter case, we show that an additional θ‑circular connectivity property is required to obtain necessary and sufficient conditions. Building on these MRN results, we then derive sufficient conditions for robust stability of multi‑input multi‑output (MIMO) linear time‑invariant (LTI) systems under frequencywise symmetric uncertainties. In addition, connections with existing system characteristics such as disc‑boundedness are discussed and exploited to obtain state‑space characterisations for angle‑bounded and mixed gain‑angle‑bounded systems. Based on these results, we construct a θ‑angle‑gain profile of a system that provides an intuitive visualisation of its feedback robustness against conic and sectorial uncertainties.

Authors:Jianing Zhao, Zhuoting Cai, Xiang Yin
Title: Output-Feedback Safe Control of Discrete-Time Stochastic Systems with Chance Constraints
Abstract:
In this paper, we investigate safety‑critical control problem of discrete‑time stochastic systems with incomplete information, where safety constraints must be enforced using state estimates obtained from noisy measurements. We develop an output‑feedback control barrier function (CBF) framework based on an expectation‑based discrete‑time barrier condition that explicitly incorporates estimation uncertainty through the evolving belief over the state. To enable real‑time implementation, we derive deterministic sufficient conditions that conservatively enforce the expectation‑based CBF by bounding the expectation with computable functions of the belief statistics using Jensen inequalities. The resulting safety filter is formulated as a tractable optimization problem compatible with standard online controllers. Numerical simulations demonstrate that the proposed output‑feedback approach achieves fast online computation while providing reliable safety performance in the presence of process noise and measurement uncertainty.

Authors:Xu Chen, Kevin Kluge, Maximilian Basler, Lorenz Dörschel, Heike Vallery
Title: Nonlinear Stochastic Model Predictive Control with Generative Uncertainty in Homogeneous Charge Compression Ignition
Abstract:
This work addresses the challenge of ignition timing and load control in homogeneous charge compression ignition engines operating subject to uncertainty from complex combustion dynamics and external disturbances. To handle this issue, we propose a nonlinear stochastic model predictive control approach explicitly incorporating distributional information of uncertainties. Specifically, we integrate an uncertainty model learned from empirical residual data to capture realistic probabilistic characteristics and handle the nonlinear additive uncertainty propagation within the prediction horizon based on polynomial chaos expansion. Additionally, we introduce a novel cost function based on maximum mean discrepancy, enabling direct penalization of the discrepancy between predicted and desired distributions of combustion indicators. The simulation results demonstrate that our proposed method achieves over a 28 % reduction on combustion phasing variation and more than a 26 % improvement in load tracking accuracy compared to traditional nonlinear and Gaussian‑based predictive control strategies. These findings indicate the effectiveness of explicitly modeling uncertainty distributions and highlight the advantages of distribution‑level performance index in robust combustion control.

Authors:Marta Zagorowska, Lukas Ortmann, Giuseppe Belgioioso, Lars Imsland
Title: Adaptive Tuning of Online Feedback Optimization for Process Control Applications
Abstract:
Online Feedback Optimization leverages properties of optimization algorithms to develop controllers for systems with limited model availability, which is often the case in process control. The interplay between the parameters of the chosen optimization algorithm, as well as lack of direct connection to the characteristics of the underlying process make their tuning challenging. We propose a method for adaptive tuning of Online Feedback Optimization controllers based on scaled projected gradient descent by using sensitivity of the desired objective to the parameters of the algorithm. The proposed adaptive tuning method limits the operator‑tunable parameters to scalar values that represent how much the control inputs and the objective can change between iterations without requiring either additional information about the controlled system or repeated experiments. Numerical studies on a gas lift and a continuously‑stirred tank reactor processes confirm that our adaptive scheme improves closed‑loop performance of Online Feedback optimization compared to standard manual tuning methods.

Authors:Cankat Tilki, Tobias Breiten, Serkan Gugercin
Title: From Interpolation to $\mathcal{H}_2$ Optimality: Model Reduction for Infinite-Dimensional Linear Control Systems
Abstract:
We develop the interpolatory \mathcalH_2 optimal model reduction framework for linear control systems posed on infinite dimensional state, input and output spaces. Specifically, we consider linear systems formulated as controlled abstract Cauchy problems on a Banach space and approximate them via Petrov‑Galerkin projection onto finite dimensional trial and test subspaces. We show that the resulting reduced order transfer function interpolates the original at prescribed points, and we characterize precisely how the projection subspaces must be constructed to enforce this interpolation. Building on this, we develop a data‑driven realization framework ‑‑ an infinite dimensional analogue of the Loewner approach ‑‑ that recovers the system behavior directly from input‑output data without requiring access to the underlying operators. Finally, we derive \mathcalH_2 optimality conditions for the reduced model and show that the classical interpolatory characterization persists in this infinite dimensional setting: first‑order optimality requires Hermite interpolation of the transfer function at the mirror images of the reduced model's poles. Taken together, these results establish that the interpolatory \mathcalH_2 optimal model reduction theory extends naturally and completely to infinite dimensional linear control systems with infinite dimensional input and output spaces.

Authors:Saeed Rahmani, Shiva Rasouli, Daphne Cornelisse, Eugene Vinitsky, Bart van Arem, Simeon C. Calvert
Title: Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic
Abstract:
Autonomous vehicles (AVs) are now operating on public roads, which makes their testing and validation more critical than ever. Simulation offers a safe and controlled environment for evaluating AV performance in varied conditions. However, existing simulation tools mainly focus on graphical realism and rely on simple rule‑based models and therefore fail to accurately represent the complexity of driving behaviors and interactions. Artificial intelligence (AI) has shown strong potential to address these limitations; however, despite the rapid progress across AI methodologies, a comprehensive survey of their application to mixed autonomy traffic simulation remains lacking. Existing surveys either focus on simulation tools without examining the AI methods behind them, or cover ego‑centric decision‑making without addressing the broader challenge of modeling surrounding traffic. Moreover, they do not offer a unified taxonomy of AI methods covering individual behavior modeling to full scene simulation. To address these gaps, this survey provides a structured review and synthesis of AI methods for modeling AV and human driving behavior in mixed autonomy traffic simulation. We introduce a taxonomy that organizes methods into three families: agent‑level behavior models, environment‑level simulation methods, and cognitive and physics‑informed methods. The survey analyzes how existing simulation platforms fall short of the needs of mixed autonomy research and outlines directions to narrow this gap. It also provides a chronological overview of AI methods and reviews evaluation protocols and metrics, simulation tools, and datasets. By covering both traffic engineering and computer science perspectives, we aim to bridge the gap between these two communities.

Authors:Jonas Mair, Lukas Schwenkel, Matthias A. Müller, Frank Allgöwer
Title: On stability and non-averaged performance of economic MPC with terminal conditions for optimal periodic operation
Abstract:
Operation at steady state is often not optimal when optimizing over an economic cost objective. In many cases, periodic operation yields better performance. Therefore, we derive asymptotic stability guarantees of an economic model predictive control scheme with terminal conditions for systems with optimal periodic operation for a more general setup than existing methods can handle. Moreover, we establish a non‑averaged closed‑loop performance bound by defining the closed‑loop cost via a Cesàro summation instead of ordinary summation. Such a non‑averaged performance bound provides new insights for systems with periodic optimal operation.

Authors:Jovan Krajacic, Ognjen Stanojev, Mario Schweizer, Orcun Karaca, Gabriela Hug, Vladan Lazarević
Title: Grid-Forming Characterization in DC Microgrids
Abstract:
DC microgrids are converter‑based electrical networks that are increasingly being used in various applications, including data centers and industrial distribution systems. A central challenge in their operation is maintaining the DC‑bus voltage within predefined limits while ensuring overall system stability. Although a wide variety of converter control algorithms has been proposed to achieve these objectives, the literature lacks a clear and physically interpretable framework for evaluating their effectiveness and for classifying and comparing them. Moreover, the grid‑forming versus grid‑following distinction that exists in AC systems has largely been unexplored in DC microgrids. To address this gap, this paper introduces three novel impedance‑based indices that can be used to quantify the voltage‑forming and current‑forming behavior of a converter. The indices also provide a basis for defining the desired converter behavior that yields superior DC‑bus voltage regulation performance. Simulation results illustrate the application of the framework to several representative control strategies and highlight the strengths and limitations of these control algorithms.

Authors:Yu Zhou, Mengmou Li, Masaaki Nagahara
Title: Finite-Time Optimization via Scaled Gradient-Momentum Flows
Abstract:
In this paper, we develop a scaled gradient‑momentum framework for continuous‑time optimization that achieves global finite‑time convergence. A state‑dependent scaling mechanism is introduced to enable classical dynamics, such as Heavy‑Ball‑type and proportional‑integral (PI)‑type flows, to attain finite‑time convergence. We establish explicit conditions that bridge the gradient‑dominance property of the objective function and finite‑time stability of the proposed scaled dynamics. Numerical experiments validate the theoretical results.

Authors:Yangyang Luan, Muhammad Ahsan Razaq, Xiaoqun Wu, Claudio Altafini
Title: Signed DeGroot-Friedkin Dynamics with Interdependent Topics
Abstract:
This paper investigates DeGroot‑Friedkin (DF) dynamics over signed influence networks with interdependent topics. We propose a multi‑topic signed framework that combines repelling interpersonal interactions with cross‑issue self‑appraisal, examining how antagonism and topic interdependence shape the evolution of agent‑level social power. When the logic matrices (for topic interdependence) of all agents share a common dominant left eigenvector, we identify structural conditions under which the original dynamics admit an exact reduction to an explicit scalar DF map. This yields a complete classification of limiting social power configurations into pluralistic, mixed, and vertex‑dominant types. In all three cases, the dynamics are globally convergent, and in the first two the ordering induced by the interaction centrality is preserved. We further show local robustness under small heterogeneous perturbations of the logic matrices. We also clarify what changes when this common‑eigenvector structure is lost. These results extend signed social power dynamics beyond the standard nonnegative scalar setting and shed light on the robustness and scope of centrality‑based social power formation in multi‑topic signed influence systems.

Authors:Georgios Pantazis, Michelle S. Chong
Title: Data-driven Reachable Set Estimation with Tunable Adversarial and Wasserstein Distributional Guarantees
Abstract:
We study finite horizon reachable set estimation for unknown discrete‑time dynamical systems using only sampled state trajectories. Rather than treating scenario optimization as a black‑box tool, we show how it can be tailored to reachable set estimation, where one must learn a family of sets based on whole trajectories, while preserving probabilistic guarantees on future trajectory inclusion for the entire horizon. To this end, we formulate a relaxed scenario program with slack variables that yields a tunable trade‑off between reachable set size and out‑of‑sample trajectory inclusion over the horizon, thereby reducing sensitivity to outliers. Leveraging the recent results in adversarially robust scenario optimization, we then extend this formulation to account for bounded adversarial perturbations of the observed trajectories and derive a posteriori probabilistic guarantees on future trajectory inclusion. When probability distribution shifts in the Wasserstein distance occur, we obtain an explicit bound on how gracefully the theoretical probabilistic guarantees degrade. For different geometries, i.e., p‑norm balls, ellipsoids, and zonotopes, we derive tractable convex reformulations and corroborate our theoretical results in simulation.

Authors:Jan Brändle, Gabriela Hug
Title: Optimal Battery Bidding under Decision-Dependent State-of-Charge Uncertainties
Abstract:
Lithium Iron Phosphate (LFP) Battery Energy Storage Systems (BESSs) are a key enabler of the energy transition. However, they are known to exhibit significant inaccuracies in the estimation of their State of Charge (SOC). Such estimation errors can directly impact the participation of BESSs in electricity markets. In this work, we demonstrate that neglecting SOC uncertainty in battery bidding can lead to significant delivery failures, including the inability to meet promised frequency reserves. To address this risk, we investigate bidding strategies that account for SOC uncertainty. We propose three constraint‑tightening optimization approaches of increasing complexity: (i) a fixed‑margin formulation, (ii) an adaptive‑margin optimizer, and (iii) an uncertainty‑aware optimization model. The latter explicitly accounts for the decision‑dependent nature of the uncertainty. Numerical results demonstrate that while all three approaches robustify against SOC uncertainty, the uncertainty‑aware formulation outperforms the others in maximizing revenue while ensuring reliable frequency reserve provision. This highlights the significance of treating SOC uncertainty as an endogenous process within the operational strategy.

Authors:Parthib Khound
Title: Situation-Aware Feedback-Predictive Control Framework for Lane-Less Dense Traffic
Abstract:
Navigating dense, lane‑less traffic remains one of the most challenging scenarios for autonomous vehicles, especially in emerging regions where road structure and driver behavior are highly unpredictable. This paper presents a hybrid control framework tailored for such environments, integrating a 360^\circ zone‑based perception module with a dual‑layer control strategy that combines classical feedback and predictive optimization. The longitudinal feedback controller computes reference speed based on braking distance and steering dynamics, while the lateral controller tracks a virtual optimal lane derived from the spatial distribution of neighboring vehicles. The predictive planner samples control inputs over a time horizon and selects the most feasible trajectory using a multi‑term cost function. Simulation results across diverse one‑way traffic scenarios demonstrate the framework's robustness, responsiveness, and suitability for chaotic, unstructured traffic.

Authors:Xu Chen, Lorenz Dörschel
Title: Distributionally Robust Stochastic MPC under Disturbance-Affine Feedback Policies
Abstract:
This study addresses the stochastic Model Predictive Control (MPC) problem for linear time‑invariant systems subjected to unknown disturbance distributions. By leveraging the most recent disturbance data, we construct a set of distributions with similar statistical properties contained within a Wasserstein ball, thereby accounting for the worst‑case impacts on constraint satisfaction. Numerous MPC strategies, particularly tube‑based approaches, have been extensively studied under the Wasserstein ambiguity set, but these methods often introduce conservatism and can limit control performance. Unlike tube‑based approaches, we adopt a disturbance‑affine control strategy, which introduces additional control degrees of freedom. We begin by developing the Disturbance‑Affine Distributionally Robust (DA‑DR) MPC framework, subsequently reformulating the control problem into a tractable quadratic programming formulation. Furthermore, we establish the recursive feasibility and stability of the proposed MPC scheme. Finally, we present comprehensive theoretical analysis and simulation results, demonstrating the superiority of the DA‑DR MPC over tube‑based MPC in initial feasible sets, average performance, and state variance control.

Authors:Laura Boca de Giuli, Alessio La Bella, Manish Prajapat, Johannes Köhler, Anna Scampicchio, Riccardo Scattolini, Melanie Zeilinger
Title: Goal-oriented safe active learning for predictive control using Bayesian recurrent neural networks
Abstract:
A key challenge in learning‑based model predictive control (MPC) is to collect informative data online for model adaptation while ensuring safety and without penalising control performance. In this paper, we propose an online model adaptation scheme embedded within an MPC framework in which the last‑layer parameters of a recurrent neural network are recursively updated via Bayesian learning. This is achieved by means of a goal‑oriented safe active learning algorithm that alternates between an exploration phase, where the MPC actively explores system dynamics to collect informative data for model adaptation while still pursuing the main control objective, and a goal‑reaching phase, where it focuses exclusively on the main control objective. The algorithm is complemented with theoretical guarantees of (i) recursive feasibility, (ii) safety, (iii) termination of exploration in finite time, and (iv) close‑to‑optimal performance. Simulation results on a benchmark energy system demonstrate that the proposed framework achieves economic performance comparable to that of an MPC with full system knowledge, while progressively improving model accuracy and respecting operational safety constraints with high probability.

Authors:E. Javier Olucha, Valentin Preda, Amritam Das, Roland Tóth
Title: Data-driven Learning of LPV Surrogate Models of Fuel Sloshing
Abstract:
This paper aims to enhance the efficiency of validation and verification campaigns involving fuel sloshing phenomena. Our first contribution is the development of an open‑source, high‑fidelity and computationally efficient two‑dimensional smoothed‑particle hydrodynamics‑based fuel sloshing simulator that reproduces the dynamics of a spacecraft with a partially filled tank with liquid propellant. Implemented in Python using Jax, the simulator leverages GPU parallelization and supports automatic differentiation, enabling rapid generation of simulation data and system linearizations for general surrogate modelling purposes. Our second contribution is the demonstration of a practical methodology for constructing surrogate models of fuel sloshing from input‑‑output data generated by the simulator, targeting rapid simulation and model‑based control applications. The surrogate model employs a Linear Parameter‑Varying (LPV) state‑space structure with affine dependence on the scheduling variables, providing an accurate yet computationally efficient approximation of the sloshing dynamics. The capabilities of the proposed approach are demonstrated through closed‑loop simulations of a rigid spacecraft with a partially filled fuel tank for two manoeuvre profiles under zero‑gravity conditions. The identified surrogate enables simulations that are two orders of magnitude faster than the high‑fidelity model.

Authors:Rémi Buffe, Alessandro Duca, Hugo Parada
Title: Bilinear controllability for the linear KdV-Schr{ö}dinger equation
Abstract:
We study the controllability of a linear KdV‑Schrödinger equation on the one‑dimensional torus via purely imaginary bilinear controls. Considering controls spanning a suitable finite number of Fourier modes, we prove small‑time global approximate controllability in L2(T). The result holds between any pair of states with the same norm and is obtained via the saturation method by following the idea introduced in [Poz24]. We first establish small‑time controllability for phase multiplications, and then generate transport operators associated with diffeomorphisms of the torus. Finally, we combine these results to recover global approximate controllability. Note that the controllability property holds independently of the Schrödinger component of the dynamics, which may in particular be taken to vanish.

Authors:Savvas Panagi, Chrysovalantis Spanias, Petros Aristidou
Title: Enhanced Optimal Power Flow Using a Trained Neural Network Surrogate for Distribution Grid Constraints
Abstract:
The growing penetration of distributed energy resources (DERs), electric vehicles (EVs), and heat pumps (HPs) in distribution networks underscores the need for secure, computationally efficient optimal power flow (OPF) solutions. Traditional OPF formulations often suffer from scalability limitations and may rely on relaxations/approximations whose exactness is not guaranteed. This paper proposes a framework in which a trained neural network (NN) surrogate is embedded directly within the OPF as a constraint replacement. Specifically, the nonlinear power‑flow‑to‑voltage mapping is replaced by an exact mixed‑integer linear encoding of the NN (i.e., the NN input‑output map is represented without approximation), while all remaining OPF constraints are preserved. Using a realistic low‑voltage network with integrated PV, EVs, and HPs, the proposed method achieves high voltage accuracy during post‑solution AC power flow validation, with maximum deviations of less than 1.0 V in the examined test cases. The resulting NN‑OPF problems are solved to global optimality within the MILP solver tolerance, and numerical results demonstrate substantially reduced computation time compared to nonlinear OPF models, with performance competitive with SOCP‑based DistFlow formulations.

Authors:Prasad Nimantha Madusanka Ukwatta Hewage, Hao Wu
Title: Beyond Weather Correlation: A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia
Abstract:
Accurate short‑term residential energy consumption forecasting at sub‑hourly resolution is critical for smart grid management, demand response programmes, and renewable energy integration. While weather variables are widely acknowledged as key drivers of residential electricity demand, the relative merit of incorporating temporal autocorrelation ‑ the sequential memory of past consumption; over static meteorological features alone remains underexplored at fine‑grained (5‑minute) temporal resolution for Australian households. This paper presents a rigorous empirical comparison of a Multilayer Perceptron (MLP) and a Long Short‑Term Memory (LSTM) recurrent network applied to two real‑world Melbourne households: House 3 (a standard grid‑connected dwelling) and House 4 (a rooftop solar photovoltaic‑integrated household). Both models are trained on 14 months of 5‑minute interval smart meter data (March 2023‑April 2024) merged with official Bureau of Meteorology (BOM) daily weather observations, yielding over 117,000 samples per household. The LSTM, operating on 24‑step (2‑hour) sliding consumption windows, achieves coefficients of determination of R^2 = 0.883 (House 3) and R^2 = 0.865 (House 4), compared to R^2 = ‑0.055 and R^2 = 0.410 for the corresponding weather‑driven MLPs ‑ differences of 93.8 and 45.5 percentage points. These results establish that temporal autocorrelation in the consumption sequence dominates meteorological information for short‑term forecasting at 5‑minute granularity. Additionally, we demonstrate an asymmetry introduced by solar generation: for the PV‑integrated household, the MLP achieves R^2 = 0.410, revealing implicit solar forecasting from weather‑time correlations. A persistence baseline analysis and seasonal stratification contextualise model performance. We propose a hybrid weather‑augmented LSTM and federated learning extensions as directions for future work.

Authors:Shiu Mochiyama, Rikuto Kawasome
Title: Modular Drive Architecture for Software-defined Vehicles Enabled by Power-packet-based Sensorless Control
Abstract:
The transition toward software‑defined vehicles requires standardization and modularization of hardware decoupled from software, along with centralized electrical/electronic architectures. While electrified drive units, such as integrated in‑wheel drives, are expected to realize the hardware standardization and unprecedented flexibility in vehicle design, their implementation remains constrained by complex signal wiring between the module and the vehicle body and by control units decentralized across them. This paper proposes a modular drive architecture that achieves complete hardware‑software separation by leveraging the power packet dispatching system. We introduce a sensorless control method that estimates motor internal states, specifically winding current and rotor angle, solely from physical quantities measured on the vehicle side. This completely eliminates the need for physical sensors in the drive module, reducing it to a passive actuator governed by the vehicle‑side power system via a standardized packet protocol. The proposed architecture significantly reduces wiring complexity and centralizes control logic, advancing fully standardized, plug‑and‑play platforms for next‑generation electrified mobility.

Authors:Uzair Aziz Muhammad, Zheng Liu
Title: Multi-Axis Additive Manufacturing for Customized Automotive Components
Abstract:
The reproduction of automobile components through additive manufacturing presents significant geometric challenges, as many automotive parts feature complex, organically shaped surfaces that are difficult to fabricate accurately using conventional 3D printing approaches without wasteful support structures. Multi‑axis Digital Light Processing (DLP) 3D printing addresses this by orienting a robotic arm to cure resin layers at varying angles and positions, enabling the fabrication of geometries that fixed‑axis systems cannot reliably reproduce. However, this flexibility introduces a key challenge: layers printed at non‑orthogonal orientations exhibit non‑uniform thickness across their cross‑section, which traditional DLP systems cannot accommodate without subdividing the layer, increasing total layer count, print time, and the need for supporting structures. This paper introduces a variable exposure method to address this challenge. Rather than splitting a non‑uniform layer into multiple uniform ones, our approach divides each layer into sublayers and modulates the UV illumination duration for each sublayer proportionally to its local thickness. This is governed by an established cure‑depth equation relating exposure time to material penetration depth, allowing precise control over curing without additional hardware. The result is a meaningful reduction in total layer count for printed objects. Fewer layers directly translates to faster print times and a reduction in wasteful support structures. Our contribution is a practical and low‑overhead extension to existing multi‑axis DLP pipelines that improves print efficiency without sacrificing geometric accuracy, with clear applications in the rapid prototyping and reproduction of automotive components.

Authors:Onésime Hounkpe, Dena Firoozi, Shuang Gao
Title: A Decomposition Method for LQ Conditional McKean-Vlasov Control Problems with Random Coefficients
Abstract:
We propose a decomposition method for solving a general class of linear‑quadratic (LQ) McKean‑Vlasov control problems involving conditional expectations and random coefficients, where the system dynamics are driven by two independent Wiener processes. Unlike existing approaches in the literature for these problems, such as the extended stochastic maximum principle and the extended dynamic programming methods, which often involve additional technical complexities and sometimes impose restrictive conditions on control inputs, our approach decomposes the original McKean‑Vlasov control problem into two decoupled stochastic optimal control problems, one of which has a constrained admissible control set. These auxiliary problems can be solved using classical methods. We establish an equivalence between the well‑posedness and solvability of the auxiliary problems and those of the original problem, and show that the sum of the optimal controls of the auxiliary problems yields the optimal control of the original problem. Moreover, by applying a variational method, we characterize the optimal solution to the McKean‑Vlasov control problem via two decoupled sets of (non‑McKean‑Vlasov) linear forward‑backward stochastic differential equations, each corresponding to one of the auxiliary problems. Finally, we show that standard dynamic programming can also be applied to solve the resulting auxiliary problems.

Authors:Ananda Chakrabarti, Haitham H. Saleh, Indranil Nayak, Balasubramaniam Shanker, Fernando L. Teixeira, Debdipta Goswami
Title: Parametric Interpolation of Dynamic Mode Decomposition for Predicting Nonlinear Systems
Abstract:
We present parameter‑interpolated dynamic mode decomposition (piDMD), a parametric reduced‑order modeling framework that embeds known parameter‑affine structure directly into the DMD regression step. Unlike existing parametric DMD methods which interpolate modes, eigenvalues, or reduced operators and can be fragile with sparse training data or multi‑dimensional parameter spaces, piDMD learns a single parameter‑affine Koopman surrogate reduced order model (ROM) across multiple training parameter samples and predicts at unseen parameter values without retraining. We validate piDMD on fluid flow past a cylinder, electron beam oscillations in transverse magnetic fields, and virtual cathode oscillations ‑‑ the latter two being simulated using an electromagnetic particle‑in‑cell (EMPIC) method. Across all benchmarks, piDMD achieves accurate long‑horizon predictions and improved robustness over state‑of‑the‑art interpolation‑based parametric DMD baselines, with less training samples and with multi‑dimensional parameter spaces.

Authors:Ryan Matheu, John S. Baras, Calin Belta
Title: Ternary Logic Encodings of Temporal Behavior Trees with Application to Control Synthesis
Abstract:
Behavior Trees (BTs) provide designers an intuitive graphical interface to construct long‑horizon plans for autonomous systems. To ensure their correctness and safety, rigorous formal models and verification techniques are essential. Temporal BTs (TBTs) offer a promising approach by leveraging existing temporal logic formalisms to specify and verify the executions of BTs. However, this analysis is currently limited to offline post hoc analysis and trace repair. In this paper, we reformulate TBTs using a ternary‑valued Signal Temporal Logic (STL) amenable for control synthesis. Ternary logic introduces a third truth value Unknown, formally capturing cases where a trajectory has neither fully satisfied or dissatisfied a specification. We propose mixed‑integer linear encodings for partial trajectory STL and TBTs over ternary logic allowing for correct‑by‑construction control strategies for linear dynamical systems via mixed‑integer optimization. We demonstrate the utility of our framework by solving optimal control problems.

Authors:Mirsad Cosovic, Armin Teskeredzic, Antonello Monti, Dejan Vukobratovic
Title: Vectorized Gaussian Belief Propagation for Near Real-Time Fully-Distributed PMU-Based State Estimation
Abstract:
Electric power systems require accurate, scalable, distributed, and near real‑time state estimation (SE) to support reliable monitoring and control under increasingly complex operating conditions. Limited monitoring capabilities can lead to inefficient operation and, in extreme cases, large‑scale disturbances such as blackouts. To address these challenges, this paper proposes a vectorized Gaussian belief propagation (GBP) framework for phasor measurement unit‑based SE, formulated over factor graphs and specifically designed to support distributed and near real‑time monitoring. The proposed framework includes multivariate and fusion‑based GBP formulations. The multivariate formulation jointly models related state variables and their measurement relationships, while the fusion‑based formulation reduces factor graph complexity by combining multiple measurements associated with the same set of variables, resulting in a structure that more closely reflects the underlying electrical coupling of the power system. The resulting algorithms operate in a fully distributed manner at the bus level and achieve fast convergence and high estimation accuracy, often within a few iterations, as demonstrated by numerical results on systems ranging from 60 to 13659 buses, where the fusion‑based formulation achieves single‑digit millisecond iteration times on the largest test case.

Authors:Ailixier Yaermaimaiti, Jiaxin Wang, Yunjie Gu, Huanhai Xin
Title: Localization and Reshaping of Non-Minimum-Phase Zeros in Multi-Converter Systems
Abstract:
Non‑minimum‑phase (NMP) zeros in multi‑converter power systems impose bandwidth ceilings on feedback control, yet quantifying them at the system level has been impractical because commercial converters withhold their internal controller models. This paper develops a Jacobian‑based framework that decouples the NMP zeros from individual converter dynamics, proves them to be strictly real, and expresses their values as the singular values of a matrix constructed solely from the grid admittance matrix and steady‑state power injections. Because these zeros govern the peak magnitude of the complementary sensitivity function, an exponential lower bound on this peak is derived as a function of the dominant zero, establishing that as the zero approaches the origin the stability margin degrades unavoidably. To counteract this degradation, a zero reshaping strategy is proposed that ranks converter nodes by their real participation factors and identifies the optimal site for voltage droop deployment without iterative search, steering the dominant zero away from the origin and thereby suppressing the sensitivity peak.

Authors:Xinyu Zhou, Yu Mei, Faith Thomson, Christian Luedtke, Xinda Qi, Xiaobo Tan
Title: Dynamic Modeling and Robust Gait Optimization of a Compliant Worm Robot
Abstract:
Worm‑inspired robots provide an effective locomotion strategy for constrained environments by combining cyclic body deformation with alternating anchoring. For compliant robots, however, the interaction between deformable anchoring structures and the environment makes predictive modeling and deployable gait optimization challenging. This paper presents an experimentally grounded modeling and optimization framework for a compliant worm robot capable of traversing corrugated pipes. First, a hybrid dynamic locomotion model is derived, in which the robot motion is represented by continuous dynamics within a corrugation groove and discrete switching of anchoring positions between adjacent grooves. A slack‑aware actuation model is further introduced to map the commanded gait input to the realized body‑length change, and an energy model is developed based on physics and calibrated with empirical power measurement. Based on these models, a multi‑objective gait optimization problem is formulated to maximize average speed while minimizing average power. To reduce the fragility of nominal boundary‑seeking solutions, a kinematic robustness margin is introduced into the anchoring‑transition conditions, leading to a margin‑based robust gait optimization framework. Experimental results show that the proposed framework captures the dominant locomotion and energy‑consumption behavior of the robot over the tested conditions, and enables robust gait optimization for achieving speed‑power trade‑off.

Authors:Yi Ju, Lunlong Li, Jingchun Wang, Scott Moura
Title: Scalable Optimization for Mobility-Aware Coordinated Electric Vehicle Charging in Distribution Power Networks
Abstract:
Rapid growth in electric‑vehicle (EV) charging demand is placing increasing stress on distribution power networks (DPNs), whose hosting capacity is often limited and spatially uneven. Beyond demonstrating that coordination can help, this paper answers an open question that is central for planners: what is the maximal achievable benefit of EV demand flexibility in reducing overload‑driven distribution upgrades at a regional scale? Establishing such an upper bound is computationally challenging, as it entails solving and certifying near‑optimal solutions to population‑scale optimization problems with millions of variables and both spatial and temporal coupling. We introduce MAC (Mobility‑Aware Coordinated EV charging), a framework that quantifies the maximum potential of leveraging EV demand flexibility to mitigate DPN overloading risk without interrupting drivers' travel needs. (i) MAC expands feasible scheduling by coupling charging decisions over a full mobility horizon: instead of enforcing per‑session energy recovery, it only requires the EV state‑of‑charge (SOC) to remain sufficient for upcoming trips. (ii) MAC is computationally scalable via an ADMM‑based decomposition with custom subproblem solvers, and admits a decentralized interpretation in which dual variables act as locational‑temporal price signals that implement the social optimum as a competitive equilibrium. Using high‑resolution mobility trajectories and feeder hosting‑capacity data in a future‑oriented 30% EV adoption scenario for the San Francisco Bay Area, we show that MAC can dramatically reduce overload‑driven upgrade requirements relative to unmanaged charging. This paper illustrates how trajectory‑coupled flexibility and scalable, certifiable optimization can provide actionable best‑case benchmarks for DPN planning and operations.

Authors:Charis Stamouli, Anastasios Tsiamis, George J. Pappas
Title: Layered Control of Partially Observed Stochastic Systems
Abstract:
Layered control is essential for managing complexity in large‑scale systems, employing progressively coarser models at higher layers. While significant advances have been made for fully observable systems, the theoretical foundations of layered control under partial observations and stochastic noise remain underexplored. To address this gap, we propose a principled layered control framework for such settings. Given a state estimator at each layer, our approach ensures that the expected output distance between systems at successive layers remains within a priori computable bounds. This is achieved by introducing a novel notion of stochastic simulation functions for partially observed systems. For the class of linear systems with Kalman estimators, we provide a systematic construction of these functions along with the corresponding control design. We demonstrate our framework on two aerial robotic scenarios: an unmanned aerial vehicle and a hexacopter with a camera payload.

Authors:Maria G. Mendoza, Pan-Yang Su, Bryce L. Ferguson, S. Shankar Sastry
Title: Dynamic Multi-Robot Task Allocation under Uncertainty and Communication Constraints: A Game-Theoretic Approach
Abstract:
We study dynamic multi‑robot task allocation under uncertain task completion, time‑window constraints, and incomplete information. Tasks arrive online over a finite horizon and must be completed within specified deadlines, while agents operate from distributed hubs with limited sensing and communication. We model incomplete information through hub‑based sensing regions that determine task visibility and a communication graph that governs inter‑hub information exchange. Using this framework, we propose Iterative Best Response (IBR), a decentralized policy in which each agent selects the task that maximizes its marginal contribution to the locally observed welfare. We compare IBR against three baselines: Earliest Due Date first (EDD), Hungarian algorithm, and Stochastic Conflict‑Based Allocation (SCoBA), on a city‑scale package‑delivery domain with up to 100 drones and varying task arrival scenarios. Under full and sparse communication, IBR achieves competitive task‑completion performance with lower computation time.

Authors:Barron Han, Victoria Kostina, Babak Hassibi
Title: Quantized Online LQR
Abstract:
We study online linear‑quadratic regulation (LQR) with unknown dynamics under communication rate constraints. Classical networked control quantizes the plant state at every time step, requiring O(T) total bits while injecting persistent quantization noise that limits control performance. We consider a setting where the plant observes its state locally and can estimate system dynamics via ordinary least squares, while a remote controller possesses knowledge of the control cost. Rather than quantizing the raw state, the plant transmits learned dynamics estimates over a rate‑limited uplink, and the controller returns the optimal control policy so that the plant can compute actions locally using its superior state knowledge. We first prove a fundamental information‑theoretic lower bound: any scheme achieving O(T^α) regret for α\in [1/2,1) compared to the optimal infinite horizon LQR controller that knows the true system dynamics must transmit at least Ω(\log T) bits. We then design the Quantized Certainty Equivalent (QCE‑LQR) algorithm, which matches this bound. The resulting regret bound contains inflation factors Q_\mathrmslow(\varrho) and Q_\mathrmfast(\varrho) that vanish as the codebook resolution increases, smoothly recovering the unquantized baseline regret. Numerical experiments on four benchmark systems ‑‑ from a scalar unstable plant to a 24‑parameter Boeing 747 lateral model ‑‑ confirm that a variant of QCE‑LQR achieves regret comparable to an unquantized certainty equivalent controller over a horizon of T=10,000 steps.

Authors:J. Francisco Presenza, Leonardo J. Colombo, Juan I. Giribet, Ignacio Mas
Title: Angle-based Localization and Rigidity Maintenance Control for Multi-Robot Networks
Abstract:
In this work, we study angle‑based localization and rigidity maintenance control for multi‑robot networks. First, we establish the relationship between angle rigidity and bearing rigidity considering directed sensing graphs and body‑frame bearing measurements in both 2 and 3‑dimensional space. In particular, we demonstrate that a framework in \mathrmSE(d) is infinitesimally bearing rigid if and only if it is infinitesimally angle rigid and each robot obtains at least d‑1 bearing measurements (d \in \2, 3\). Building on these findings, this paper proposes a distributed angle‑based localization scheme and establishes local exponential stability under switching sensing graphs, requiring only infinitesimal angle rigidity across the visited topologies. Then, since the set of available angles strongly depends on the robots' spatial configuration due to sensing constraints, we investigate rigidity maintenance control. The angle rigidity eigenvalue is presented as a metric for the degree of rigidity. A decentralized gradient‑based controller capable of executing mission‑specific commands while maintaining a sufficient level of angle rigidity is proposed. Simulations were conducted to evaluate the scheme's effectiveness and practicality.

Authors:Younghwan Cho, Richard Sowers
Title: Koopman Representations for Non-Vanishing Time Intervals: An Optimization Approach and Sampling Effects
Abstract:
Koopman operator theory is a key tool in data assimilation of complex dynamical systems, with the potential to be applied to multimodal data. We formulate the problem of learning Koopman eigenfunctions from observations at arbitrary, possibly non‑vanishing, time intervals as an optimization problem. Analysis of the formulation reveals aliasing induced by oscillatory dynamics and the sampling pattern, making an inherent identifiability limit explicit. The analysis also uncovers phase alignment near the true Koopman frequency, which creates a steep loss valley and demands careful optimization. We further show that irregular sampling can break aliasing and lead to phase cancellation. Numerical results demonstrate the efficacy of the proposed method under large regular time intervals compared to generator extended dynamic mode decomposition, and support the idea that irregular sampling can help recover the true Koopman spectrum.

Authors:Aditya A. Krishnan, Donghoon Kim, Hokeun Kim
Title: ACT: Automated CPS Testing for Open-Source Robotic Platforms
Abstract:
Open‑source software for cyber‑physical systems (CPS) often lacks robust testing involving robotic platforms, resulting in critical errors that remain undetected. This is especially challenging when multiple modules of CPS software are developed by various open‑source contributors. To address this gap, we propose Automated CPS Testing (ACT) that performs automated, continuous testing of open‑source software with its robotic platforms, integrated with the open‑source infrastructure such as GitHub. We implement an ACT prototype and conduct a case study on an open‑source CPS with an educational robotic platform to demonstrate its capabilities.

Authors:Deeksha Prahlad, Daniel Fan, Hokeun Kim
Title: Agentic Driving Coach: Robustness and Determinism of Agentic AI-Powered Human-in-the-Loop Cyber-Physical Systems
Abstract:
Foundation models, including large language models (LLMs), are increasingly used for human‑in‑the‑loop (HITL) cyber‑physical systems (CPS) because foundation model‑based AI agents can potentially interact with both the physical environments and human users. However, the unpredictable behavior of human users and AI agents, in addition to the dynamically changing physical environments, leads to uncontrollable nondeterminism. To address this urgent challenge of enabling agentic AI‑powered HITL CPS, we propose a reactor‑model‑of‑computation (MoC)‑based approach, realized by the open‑source Lingua Franca (LF) framework. We also carry out a concrete case study using the agentic driving coach as an application of HITL CPS. By evaluating the LF‑based agentic HITL CPS, we identify practical challenges in reintroducing determinism into such agentic HITL CPS and present pathways to address them.

Authors:Iori Takaki, Ahmet Cetinkaya, Hideaki Ishii
Title: Data Poisoning Attacks on Informativity for Observability: Invariance-Based Synthesis
Abstract:
This paper studies cyber attacks against informativity‑based analysis in data‑driven control. Focusing on strong observability, we consider an adversary who post‑processes finite time‑series data by an invertible linear transformation acting on the data matrices. We show that such transformations are capable of embedding malicious states into the invariant subspace explained by the transformed dataset. We provide a constructive attack method and derive feasibility conditions that characterize when such transformations exist. Moreover, we formulate an optimization problem to obtain the minimum‑norm attack that quantifies the smallest data distortion required to destroy informativity. Numerical examples demonstrate that small and structured transformations can invalidate informativity certificates.

Authors:Gabriel Cooper, Xiaolong Liu
Title: Performance Characterization of Frequency-Selective Wireless Power Transfer Toward Scalable Untethered Magnetic Actuation
Abstract:
Frequency‑selective wireless power transfer provides a feasible route to enable independent actuation and control of multiple untethered robots in a common workspace; however, the scalability remains unquantified, particularly the maximum number of resonators that can be reliably addressed within a given frequency bandwidth. To address this, we formulate the relationship between resonator quality factor (Q‑factor) and the number of individually addressable inductor‑capacitor (LC) resonant energy harvesters within a fixed radio‑frequency (RF) spectrum, and we convert selectively activated harvested energy into mechanical motion. We theoretically proved and experimentally demonstrated that scalability depends primarily on the Q‑factor. For this proof‑of‑concept study, we define effective series resistance as a function of frequency allocating bandwidths to discrete actuators. We provide design equations for scaling untethered magnetic actuation with Q‑factor optimization. Resonator networks spanning bandwidths from 100kHz to 1MHz were analyzed to quantify how increasing the number of resonators affects independent addressability. We validated the approach experimentally by fabricating three centimeter‑scale untethered actuators that selectively trigger the motion of mechanical beams at 734kHz, 785kHz, and 855kHz. We also characterized the generated mechanical force and the activation bandwidth of each actuator, confirming that no unintended cross‑triggering occurred.

Authors:Kangyi Lu, Lan Wei, Zongcai Tan, Dandan Zhang
Title: Micro-Dexterity in Biological Micromanipulation: Embodiment, Perception, and Control
Abstract:
Microscale manipulation has advanced substantially in controlled locomotion and targeted transport, yet many biomedical applications require precise and adaptive interaction with biological micro‑objects. At these scales, manipulation is realized through three main classes of platforms: embodied microrobots that physically interact as mobile agents, field‑mediated systems that generate contactless trapping or manipulation forces, and externally actuated end‑effectors that interact through remotely driven physical tools. Unlike macroscale manipulators, these systems function in fluidic, confined, and surface‑dominated environments characterized by negligible inertia, dominant interfacial forces, and soft, heterogeneous, and fragile targets. Consequently, classical assumptions of dexterous manipulation, including rigid‑body contact, stable grasping, and rich proprioceptive feedback, become difficult to maintain. This review introduces micro‑dexterity as a framework for analyzing biological micromanipulation through the coupled roles of embodiment, perception, and control. We examine how classical manipulation primitives, including pushing, reorientation, grasping, and cooperative manipulation, are reformulated at the microscale; compare the architectures that enable them, from contact‑based micromanipulators to contactless field‑mediated systems and cooperative multi‑agent platforms; and review the perception and control strategies required for task execution. We identify the current dexterity gap between laboratory demonstrations and clinically relevant biological manipulation, and outline key challenges for future translation.

Authors:Alejandro Penacho Riveiros, Matthieu Barreau, Nicola Bastianello
Title: Detectability of Subtle Anomalies in Dynamical Systems via Log-Likelihood Ratio
Abstract:
Industrial control applications require detecting system anomalies as accurately and quickly as possible to enable prompt maintenance. In this context, it is common to consider several possible plant models, each linked to a different anomaly. The log‑likelihood ratio method can then be used to identify the most accurate model and thereby classify which anomaly, if any, has occurred. Although the method has been applied to a wide variety of systems, there is no formal analysis of what makes anomalies more or less prone to detection. In this paper, we investigate a real‑time anomaly detector based on the log‑likelihood ratio and provide a theoretical characterization of its error rate when it is applied to linear Gaussian systems. We showcase the performance of this algorithm and the characterization obtained, and demonstrate how the latter can be leveraged for observer design.

Authors:Alejandro Penacho Riveiros, Nicola Bastianello, Matthieu Barreau
Title: Model-free Anomaly Detection for Dynamical Systems with Gaussian Processes
Abstract:
In this paper we address the problem of detecting differences or anomalies in a dynamical system, based on historical data of nominal operations. This problem encompasses quality control, where newly manufactured systems are tested against desired nominal operations, and the detection of changes in the dynamics due to degradation or repairs. We propose a model free approach based on Gaussian processes (GPs). The idea is to train offline a GP based on nominal data, which is then deployed online to detect whether measurements of the system state are compatible with nominal operations or if they deviate. Detecting this deviation is made more challenging by the presence of process and measurement noise, which might obfuscate deviations in the dynamics. The detection then is based on a threshold that ensures a specific false positive rate. We showcase the promising performance of the proposed method with two systems, and highlight several interesting future research questions.

Authors:Ruixuan Zhao, Guitao Yang, Nicola Bastianello, Boli Chen
Title: Distributed State Estimation for Discrete-Time Systems With Unknown Inputs: An Optimization Approach
Abstract:
This paper proposes a novel Distributed Unknown Input Observer (DUIO) framework for state estimation in large‑scale systems subject to local unknown inputs. We consider systems where outputs are measured by a network of spatially distributed sensors and inputs are introduced through multiple dispersed channels. In this framework, each local node utilizes only its local input and output measurements to estimate the maximal locally reconstructible state. Subsequently, nodes collaboratively reconstruct the whole system state via a distributed optimization algorithm that fuses these partial estimates. We provide a rigorous analysis showing that the estimation error is bounded, with the error bound explicitly dependent on the number of communication iterations per time step and strongly convexity constant determined by the system parameters. Furthermore, to counteract curvature anisotropy induced by poor conditioned system geometry, we embed a normalization step into the distributed optimization procedure. Simulation results demonstrate the effectiveness of the proposed framework and the performance improvements yielded by the normalization procedure.

Authors:Preston T. Abadie, Donald J. Docimo
Title: A Study on the Controllability of Lithium-Ion Batteries
Abstract:
This work explores controllability and the control effort required for lithium‑ion batteries. Battery packs have become a critical technology in both personal and professional applications as a means to store large amounts of energy. Management of cells in a pack becomes increasingly difficult though, with charging and discharging operations requiring more complex strategies due to parameter variations between the cells. There are numerous studies which develop effective estimation and control schemes to reduce the impact of the imbalances present in battery packs, but the receptiveness of the individual cells to these schemes is much less explored. This paper performs a nonlinear controllability analysis for experimentally parameterized cells. A connection is shown between the condition number of a battery's controllability matrix and the amount of control effort that battery will require. This reveals that if a cell's dynamics are poorly mathematically conditioned, it will require more time or higher power to control than one that is not. The controllability condition number of each cell's model is then determined both with new and aged parameters, and a sensitivity analysis shows that the cells' conditioning is equally impacted by all parameters. This offers insight into the increased control effort required for a battery as it ages and the culprit of said increase. The results of this analysis are then used to determine the best conditioned assemblies for a batch of cells with a mix of new and second‑life parameters.

Authors:Stefan Lenz, Sotiris Michaelides, Moritz Rickert, Jonas Holtwick, Martin Henze
Title: Security Implications of 5G Communication in Industrial Systems
Abstract:
Traditionally, industrial control systems (ICS) were designed without security in mind, prioritizing availability and real‑time communication. As these systems increasingly become targets of powerful adversaries, security can no longer be neglected. Driven by flexibility and automation needs, ICS are transitioning from wired to 5G communication, introducing new attack surfaces and a less reliable communication medium, thereby exacerbating existing security challenges. Given their critical role in society, a comprehensive evaluation of their security is imperative. To this end, we introduce SWICS, a fully virtual testbed simulating an ICS in a realistic 5G environment, and study how this transition affects security under varying channel conditions. Our results show three key findings: under optimal channel conditions, industrial 5G networks can achieve resilience comparable to wired systems, while degraded channel conditions can amplify traditional attacks, threaten system stability, and undermine detection mechanisms based on predictable traffic patterns. We further demonstrate the inherent limits of securing 5G channels for ICS through eavesdropping and jamming on the open‑air interface. Our work highlights the interplay between security and 5G channel conditions, showing that traditional security controls may no longer be sufficient and motivating further research.

Authors:I. Esra Buyuktahtakin
Title: Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
Abstract:
Artificial intelligence (AI) is moving increasingly beyond prediction to support decisions in complex, uncertain, and dynamic environments. This shift creates a natural intersection with operations research and management sciences (OR/MS), which have long offered conceptual and methodological foundations for sequential decision‑making under uncertainty. At the same time, recent advances in deep learning, including feedforward neural networks, LSTMs, transformers, and deep reinforcement learning, have expanded the scope of data‑driven modeling and opened new possibilities for large‑scale decision systems. This tutorial presents an OR/MS‑centered perspective on deep learning for sequential decision‑making under uncertainty. Its central premise is that deep learning is valuable not as a replacement for optimization, but as a complement to it. Deep learning brings adaptability and scalable approximation, whereas OR/MS provides the structural rigor needed to represent constraints, recourse, and uncertainty. The tutorial reviews key decision‑making foundations, connects them to the major neural architectures in modern AI, and discusses leading approaches to integrating learning and optimization. It also highlights emerging impact in domains such as supply chains, healthcare and epidemic response, agriculture, energy, and autonomous operations. More broadly, it frames these developments as part of a wider transition from predictive AI toward decision‑capable AI and highlights the role of OR/MS in shaping the next generation of integrated learning‑‑optimization systems.

Authors:Victor Schulte, Michael Eichelbeck, Matthias Althoff
Title: To Learn or Not to Learn: A Litmus Test for Using Reinforcement Learning in Control
Abstract:
Reinforcement learning (RL) can be a powerful alternative to classical control methods when standard model‑based control is insufficient, e.g., when deriving a suitable model is intractable or impossible. In many cases, however, the choice between model‑based and RL‑based control is not obvious. Due to the high computational costs of training RL agents, RL‑based control should be limited to cases where it is expected to yield superior results compared to model‑based control. To the best of our knowledge, there exists no approach to quantify the benefit of RL‑based control that does not require RL training. In this work, we present a computationally efficient, purely simulation‑based litmus test predicting whether RL‑based control is superior to model‑based control. Our test evaluates the suitability of the given model for model‑based control by analyzing the impact of model uncertainties on the control problem. For this, we use reachset‑conformant model identification combined with simulation‑based analysis. This is followed by a learnability evaluation of the uncertainties based on correlation analysis. This two‑part analysis enables an informed decision on the suitability of RL for a control problem without training an RL agent. We apply our test to several benchmarks, demonstrating its applicability to a wide range of control problems and highlight the potential to save computational resources.

Authors:Wenqi Cai, John Abanes, Nikolaos Evangeliou, Anthony Tzes
Title: Safe Human-to-Humanoid Motion Imitation Using Control Barrier Functions
Abstract:
Ensuring operational safety is critical for human‑to‑humanoid motion imitation. This paper presents a vision‑based framework that enables a humanoid robot to imitate human movements while avoiding collisions. Human skeletal keypoints are captured by a single camera and converted into joint angles for motion retargeting. Safety is enforced through a Control Barrier Function (CBF) layer formulated as a Quadratic Program (QP), which filters imitation commands to prevent both self‑collisions and human‑robot collisions. Simulation results validate the effectiveness of the proposed framework for real‑time collision‑aware motion imitation.

Authors:Méziane Ait Ziane, Michel Zasadzinski, Cédric Join, Michel Fliess
Title: Air supply control for proton exchange membrane fuel cells without explicit modeling
Abstract:
Our objective is to study the performance and robustness of the model‑free strategy for controlling the oxygen stoichiometry of a fuel cell air supply system with a proton exchange membrane. After reviewing the literature on modeling and control of this process, the model‑free approach appears to be a good candidate because, on the one hand, it allows straightforward real‑time adaptation to track operating points and, on the other hand, it requires a low computational burden, which is attractive for industrial applications. Numerical simulations for two scenarios (constant and variable oxygen stoichiometry) with two current profiles reveal satisfactory performance of the model‑free control law. The robustness is addressed by considering significant variations in the parameters of the proton exchange membrane air supply system.

Authors:Bendegúz Györök, Roel Drenth, Chris Verhoek, Tamás Péni, Maarten Schoukens, Roland Tóth
Title: Data-driven augmentation of first-principles models under constraint-free well-posedness and stability guarantees
Abstract:
The integration of first‑principles models with learning‑based components, i.e., model augmentation, has gained increasing attention, as it offers higher model accuracy and faster convergence properties compared to black‑box approaches, while generating physically interpretable models. Recently, a unified formulation has been proposed that generalizes existing model augmentation structures, utilizing linear fractional representations (LFRs). However, several potential benefits of the approach remain underexplored. In this work, we address three key limitations. First, the added flexibility of LFRs also introduces possible algebraic loops, i.e., a problem of well‑posedness. To address this challenge, we propose a constraint‑free direct parametrization of the model structure with a well‑posedness guarantee. Second, we introduce a constraint‑free parametrization that ensures stability of the overall model augmentation structure via contraction. Third, we adopt an efficient identification pipeline capable of handling non‑smooth cost functions, such as group‑lasso regularization, which facilitates automatic model order selection and discovery of the required augmentation configuration. These contributions are demonstrated on various simulation and benchmark identification examples.

Authors:Axel Andersson, György Dán
Title: Active Bayesian Inference for Robust Control under Sensor False Data Injection Attacks
Abstract:
We present a framework for bridging the gap between sensor attack detection and recovery in cyber‑physical systems. The proposed framework models modern‑day, complex perception pipelines as bipartite graphs, which combined with anomaly detector alerts defines a Bayesian network for inferring compromised sensors. An active probing strategy exploits system nonlinearities to maximize distinguishability between attack hypotheses, while compromised sensors are selectively disabled to maintain reliable state estimation. We propose a threshold‑based probing strategy and show its effectiveness via a simplified partially observable Markov decision process (POMDP) formulation. Experiments on an inverted pendulum under single and multi‑sensor attacks show that our method significantly outperforms outlier‑robust and prediction‑based baselines, especially under prolonged attacks.

Authors:Hu Wei
Title: From Agent Loops to Structured Graphs:A Scheduler-Theoretic Framework for LLM Agent Execution
Abstract:
The dominant paradigm for building LLM based agents is the Agent Loop, an iterative cycle where a single language model decides what to do next by reading an ever growing context window. This paradigm has three structural weaknesses: implicit dependencies between steps, unbounded recovery loops, and mutable execution history that complicates debugging. We characterize the Agent Loop as a single ready unit scheduler: at any moment, at most one executable unit is active, and the choice of which unit to activate comes from opaque LLM inference rather than an inspectable policy. This perspective places Agent Loops and graph based execution engines on a single semantic continuum. We propose SGH, Structured Graph Harness, which lifts control flow from implicit context into an explicit static DAG. SGH makes three commitments: execution plans are immutable within a plan version, planning execution and recovery are separated into three layers, and recovery follows a strict escalation protocol. These choices trade some expressiveness for controllability, verifiability, and implementability. Our contributions are fourfold: a scheduler unified framework that applies classical scheduling theory to LLM agent execution and identifies challenges introduced by non deterministic LLM nodes; a trade off analysis of controllability, expressiveness, and implementability across 70 surveyed systems; a formal specification including a node state machine with termination and soundness guarantees; and an attributable experimental framework with a seven group design for future validation. This is a position paper and design proposal. We provide a theoretical framework, design analysis, and experimental protocol, not a production implementation or empirical results.

Authors:Beniamino Di Lorenzo, Gian Carlo Maffettone, Mario di Bernardo
Title: Leader-Follower Density Control of Multi-Agent Systems with Interacting Followers: Feasibility and Convergence Analysis
Abstract:
We address density control problems for large‑scale multi‑agent systems in leader‑follower settings, where a group of controllable leaders must steer a population of followers toward a desired spatial distribution. Unlike prior work, we explicitly account for follower‑follower interactions, capturing realistic behaviors such as flocking and collision avoidance. Within a macroscopic framework based on partial differential equations governing the density dynamics, we derive (i) necessary and sufficient feasibility conditions linking the target distribution to interaction strength, diffusion, and leader mass, and (ii) a feedback control law guaranteeing local stability with an explicit estimate of the basin of attraction. Our analysis reveals sharp feasibility thresholds, phase transitions beyond which no control effort can achieve the desired configuration. Numerical simulations in one‑ and two‑dimensional domains validate the theoretical results at the macroscopic level, and agent‑based simulations on finite populations confirm the practical deployability of the proposed framework.

Authors:Georgios Vasileiou, Lantian Zhang, Silun Zhang
Title: Incentive Design without Hypergradients: A Social-Gradient Method
Abstract:
Incentive design problems consider a system planner who steers self‑interested agents toward a socially optimal Nash equilibrium by issuing incentives in the presence of information asymmetry, that is, uncertainty about the agents' cost functions. A common approach formulates the problem as a Mathematical Program with Equilibrium Constraints (MPEC) and optimizes incentives using hypergradients‑the total derivatives of the planner's objective with respect to incentives. However, computing or approximating the hypergradients typically requires full or partial knowledge of equilibrium sensitivities to incentives, which is generally unavailable under information asymmetry. In this paper, we propose a hypergradient‑free incentive law, called the social‑gradient flow, for incentive design when the planner's social cost depends on the agents' joint actions. We prove that the social cost gradient is always a descent direction for the planner's objective, irrespective of the agent cost landscape. In the idealized setting where equilibrium responses are observable, the social‑gradient flow converges to the unique socially optimal incentive. When equilibria are not directly observable, the social‑gradient flow emerges as the slow‑timescale limit of a two‑timescale interaction, in which agents' strategies evolve on a faster timescale. It is established that the joint strategy‑incentive dynamics converge to the social optimum for any agent learning rule that asymptotically tracks the equilibrium. Theoretical results are also validated via numerical experiments.

Authors:Yuan Zhang, Yu Wang, Keke Huang, Zhongqi Sun, Tyrone Fernando
Title: Data-Driven Observers Design for Descriptor Systems
Abstract:
State estimation constitutes a core task in monitoring, supervision, and control of dynamic systems. This paper proposes a data‑driven framework for the design of state observers for descriptor systems. Necessary and sufficient conditions for the existence of a standard state observer are derived purely from data under mild assumptions. When the system is subject to unknown inputs, we further extend the framework to the data‑driven design method for full‑order unknown input observer (UIO). Notably, for both the standard state observer and the UIO, we establish the mathematical equivalence between the proposed data‑driven existence conditions and classical model‑based ones. Moreover, the data‑driven approach is applied to the design of extended state observers, enabling simultaneous estimation of system states and disturbances via system augmentation. Numerical simulations validate the effectiveness of the proposed methods.

Authors:Nico Holzinger, Matthias Althoff
Title: Divide and Discard: Fast Tightening of Guaranteed State Bounds for Nonlinear Systems
Abstract:
We propose a simple yet effective divide‑and‑discard (DD) approach to guaranteed state estimation for nonlinear discrete‑time systems. Our method iteratively subdivides interval enclosures of the state and propagates them forward in time using a mean‑value enclosure. The central idea is to rely on repeated refinement of simple sets rather than on more complex set representations, yielding an observer that is straightforward to implement and easy to integrate into existing frameworks. Our divide‑and‑discard strategy exploits that many sets can be discarded early and limits the number of maintained sets, resulting in low computational cost with complexity that scales only quadratically in the state dimension. The proposed method is evaluated on nonlinear benchmark problems previously used to compare guaranteed observers, where it outperforms state‑of‑the‑art approaches in terms of both computational efficiency and enclosure tightness.

Authors:Sidhanta Mohanty, Shaunak Sen
Title: Block Diagram Analysis of a Design Principle for Amplitude-Frequency Profiles in Biological Oscillations
Abstract:
An important design principle for biological oscillators divides the oscillators into two classes: fixed frequency, variable amplitude and fixed amplitude, variable frequency. Because of the interplay of nonlinearity and feedback, both positive and negative, analytical investigations of this design principle are primarily based on numerical simulations of ordinary differential equations. To enhance the qualitative and quantitative characterization, we adapted and developed a block diagram modeling framework. We showed how the observed amplitude‑frequency characteristics could be obtained from the block diagram models. We obtained constraints on the positive feedback and negative feedback strengths for the oscillations to exist. These results should contribute to a systems and control perspective on oscillations in biology and related contexts.

Authors:Huisheng Wang, H. Vicky Zhao
Title: Mechanism Design for Investment Regulation under Herding
Abstract:
Herding, where investors imitate others' decisions rather than relying on their own analysis, is a prevalent phenomenon in financial markets. Excessive herding distorts rational decisions, amplifies volatility, and can be exploited by manipulators to harm the market. Traditional regulatory tools, such as information disclosure and transaction restrictions, are often imprecise and lack theoretical guarantees for effectiveness. This calls for a quantitative approach to regulating herding. We propose a regulator‑leader‑follower trilateral game framework based on optimal control theory to study the complex dynamics among them. The leader makes rational decisions, the follower maximizes utility while aligning with the leader's decisions, whereas the regulator designs a mechanism to maximize social welfare and minimize regulatory cost. We derive the follower's decisions and the regulator's mechanisms, theoretically analyze the impact of regulation on decisions, and investigate effective mechanisms to improve social welfare.

Authors:Tao Zhang, Zhiguo Hao, Songhao Yang, Hongyue Ma, Baohui Zhang
Title: An Argument-Principle Based Stability Assessment Method for Grey-Box DFIG Systems
Abstract:
Considerable efforts have been made to analyze the small‑signal stability of doubly fed induction generator (DFIG) systems. However, commercial confidentiality and frequency coupling make the DFIG system a grey‑box multiple‑input‑multiple‑output (MIMO) system with highly challenging stability analysis. This paper proposes an Argument‑principle based stability assessment method to analyze the stability of the grey‑box DFIG system. The frequency sweeping technique is first used to acquire the MIMO model of the black‑box device, as well as the determinant of the system's return difference matrix. Then a stability criterion based on the determinant trajectory is presented. This criterion applies to the stability analysis of grey‑box MIMO systems without detailed system models. Further, acritical‑pole estimation method with trajectory information is developed to assess the dominant mode of the target system. The simulation and hardware‑in‑loop experiment results demonstrate the effectiveness of the proposed method. Finally, some concerns about this method, such as model selection, estimation errors and application potential, are thoroughly analyzed and clarified.

Authors:Mahdieh Zaker, Andrii Mironchenko, Amy Nejati, Abolfazl Lavaei
Title: Data-Driven Global Stabilization of Unknown Infinite Networks
Abstract:
This paper develops a direct data‑driven framework for infinite networks with unknown nonlinear polynomial subsystems, enabling the synthesis of controllers that ensure the entire network is uniformly globally asymptotically stable (UGAS). To address scalability challenges arising from high dimensionality, we develop a data‑driven approach to construct an input‑to‑state stable (ISS) Lyapunov function and its corresponding controller for each unknown subsystem using only a single set of noise‑corrupted input‑state trajectories collected from that subsystem. Once each subsystem admits a data‑driven ISS Lyapunov function, we leverage a compositional small‑gain framework for infinite‑dimensional spaces to construct a global control Lyapunov function and its associated controller, thereby ensuring UGAS of the entire infinite network. The effectiveness of the proposed data‑driven approach is demonstrated through three case studies, including infinite networks of spacecraft, Lorenz chaotic systems, and an academic example with a state‑dependent control input matrix.

Authors:Meng Yuan, Tianyou Chai
Title: Contouring Error Bounded Control for Biaxial Systems with Structural Flexibility and Input Delay
Abstract:
Precision contouring control is crucial in industrial machining processes, particularly for applications such as laser and water jet cutting, where contouring accuracy directly determines product quality. This paper presents a novel control strategy for biaxial machines featuring position‑dependent flexibility and input delays, ensuring that the end‑effector accurately traverses the desired contour within specified contouring error bounds and system constraints. To capture the rotation dynamics for systems with mechanical vibration, we introduce a high‑fidelity model and explicitly consider the input delay with augmented system states. The controller design is based on the model predictive control scheme to enforce system states staying in robust control invariant sets defined by the reference model and switched linear time‑invariant control‑oriented models. The proposed algorithm is not restricted to a specific shape of the curve that is being traversed. The effectiveness of the proposed control algorithm is demonstrated in an experimental environment with discretizations and input delay. The results show that a bounded contouring error can be achieved by the proposed method in a performance degradation environment with a low commissioning effort.

Authors:Aron Brenner, Deepjyoti Deka, Line Roald, Saurabh Amin
Title: Strategic Spatial Load Shifting and Market Efficiency
Abstract:
Large, spatially flexible electricity consumers such as data centers can reallocate demand across locations, influencing dispatch and prices in wholesale electricity markets. While flexible load is often assumed to improve system efficiency, this intuition typically relies on price‑taking behavior. We study price‑anticipatory spatial load shifting by modeling a large flexible consumer as a Stackelberg leader interacting with DC optimal power flow (DC‑OPF) based market clearing. We show that decentralized, cost‑minimizing load shifting need not align with system operating cost minimization, and that misalignment arises at boundaries between DC‑OPF operating regimes, where small changes in load can induce discrete changes in marginal generators or congestion patterns. We evaluate strategic load shifting on the 73‑bus RTS‑GMLC test system, where findings indicate reductions in system operating cost in most hours, but misalignment in a subset of cases that are driven by redispatch at merit‑order discontinuities. We find that these outcomes are primarily redistributive relative to a price‑taking benchmark, reducing generator profits while lowering electricity procurement costs for both flexible and inflexible consumers, even in cases where total system operating costs increase.

Authors:Biswarup Mukherjee
Title: A Two-Stage Optimization Framework for Validating Electric Vehicle Charging Infrastructure under Grid Constraints
Abstract:
This paper proposes a two‑stage optimization framework to evaluate whether cost‑optimal electric vehicle (EV) charging infrastructure translates into effective operation under distribution grid constraints. The proposed approach explicitly links infrastructure planning with grid‑constrained charging operation through a consistent optimal power flow (OPF) formulation applied in both stages. The framework is formulated as a mixed‑integer program (MIP) and evaluated across different fleet sizes, demonstrating its scalability and applicability to realistic planning scenarios. The model incorporates heterogeneous charging technologies, including fast and slow chargers with both single‑port and multi‑port configurations. The results show a fundamental trade‑off between cost optimality and service performance. Infrastructure configurations that minimize capital investment tend to spatially concentrate charging resources, resulting in lower achieved state‑of‑charge (SOC) and higher unmet energy demand. In contrast, uniformly distributed deployments of the same infrastructure significantly improve the spatial availability of charging and operational performance, reducing energy shortfall by up to 74%. Our findings reveal that cost‑optimal planning alone is insufficient to guarantee satisfactory system performance. Effective EV charging infrastructure design must jointly consider cost optimality, spatial distribution of charging resources, and grid constraints. Sensitivity analysis with respect to battery capacity further highlights the nonlinear scaling of infrastructure requirements.

Authors:Zihan Li, Ziming Wang, Xin Wang
Title: On Switched Event-triggered Full State-constrained Formation Control for Multi-vehicle Systems
Abstract:
Vehicular formation control is an important component of intelligent transportation systems (ITSs). In practical implementations, the controller design needs to satisfy multiple state constraints, including inter‑vehicle spacing and vehicle speed. When system states approach the constraint boundaries, control singularity and excessive control effort may arise, which limits the practical applicability of existing methods. To address this problem, this paper investigates a class of nonlinear vehicular formation systems for autonomous vehicles (AVs) with uncertain dynamics and develops a switched event‑triggered control framework. A smooth nonlinear mapping is first introduced to transform the constrained state space into an unconstrained one, thereby avoiding singularity near the constraint boundaries. A radial basis function neural network (RBFNN) is then employed to approximate the unknown nonlinear dynamics online, based on which an adaptive controller is constructed via the backstepping technique. In addition, a switched event‑triggered mechanism (SETM) is designed to increase the control update frequency during the transient stage and reduce the communication burden during the steady‑state stage. Lyapunov‑based analysis proves that all signals in the closed‑loop system remain uniformly bounded and that Zeno behavior is excluded. Simulation results verify that the proposed method achieves stable platoon formation under prescribed state constraints while significantly reducing communication updates.

Authors:Zheng Liu
Title: Generative Design for Direct-to-Chip Liquid Cooling for Data Centers
Abstract:
Rapid growth in artificial intelligence (AI) workloads is driving up data center power densities, increasing the need for advanced thermal management. Direct‑to‑chip liquid cooling can remove heat efficiently at the source, but many cold plate channel layouts remain heuristic and are not optimized for the strongly non‑uniform temperature distribution of modern heterogeneous packages. This work presents a generative design framework for synthesizing cooling channel geometries for the NVIDIA GB200 Grace Blackwell Superchip. A physics‑based finite‑difference thermal model provides rapid steady‑state temperature predictions and supplies spatial thermal feedback to a constrained reaction‑diffusion process that generates novel channel topologies while enforcing inlet/outlet and component constraints. By iterating channel generation and thermal evaluation in a closed loop, the method naturally redistributes cooling capacity toward high‑power regions and suppresses hot‑spot formation. Compared with a baseline parallel channel design, the resulting channels achieve more than a 5 degree Celsius reduction in average temperature and over 35 degree Celsius reduction in maximum temperature. Overall, the results demonstrate that coupling generative algorithms with lightweight physics‑based modeling can significantly enhance direct‑to‑chip liquid cooling performance, supporting more sustainable scaling of AI computing.

Authors:Joppe De Jonghe, Konstantin Usevich, Philippe Dreesen, Mariya Ishteva
Title: Tensor-based Multi-layer Decoupling
Abstract:
The decoupling of multivariate functions is a powerful modeling paradigm for learning multivariate input‑output relations from data. For the single‑layer case, established CPD‑based methods are available, but the multi‑layer case remained largely unexplored. This work introduces a tensor‑based framework for multi‑layer decoupling, which is based on ParaTuck‑type tensor decompositions and constrained optimization. We provide theoretical justification behind the considered tensor decompositions and parameterizations. Furthermore, we formulate a structured coupled matrix‑tensor factorization that incorporates both Jacobian and function evaluations, together with a bilevel optimization approach for adaptively balancing first‑ and zeroth‑order information. The feasibility of the proposed methodology is illustrated on synthetic systems, a nonlinear system identification benchmark and neural network compression.

Authors:Buxin She, Brian Chen, Luanzheng Guo, Fangxing Li
Title: PFAgent: A Tractable and Self-Evolving Power-Flow Agent for Interactive Grid Analysis
Abstract:
Power system simulation workflows remain expert‑intensive. Engineers must translate study intents into code or API calls, execute analyses, and interpret outputs. To automate this workflow, this paper presents PFAgent, a tractable and self‑evolving power‑flow agent for interactive grid analysis. PFAgent integrates four key capabilities: i) a tractable and interactive architecture for intent parsing, knowledge retrieval, tool execution, and structured reporting; ii) a self‑evolution mechanism combining verification‑driven refinement and human‑in‑the‑loop feedback; iii) an AI‑assisted evaluation and debugging loop that leverages conversational context, generated code, and execution errors for iterative fixing; and iv) an evaluation framework covering task success, convergence validity, numerical consistency, and explanation quality. Verification on IEEE benchmark systems shows that PFAgent can automate case change, analyze voltage violations, perform N‑1 contingency analysis, generate plots and concise summaries, and return reproducible results with transparent execution logs. The proposed framework highlights a shift from conventional simulation tools to interactive, tractable, and self‑evolving agents for power system analysis.

Authors:Farzaneh Barat, Sara Wilson, Huijeong Kim, Huazhen Fang
Title: System Identification of Lithium-Ion Battery Equivalent Circuit Models Using Ensemble Kalman Inversion
Abstract:
System identification remains an intriguing challenge for lithium‑ion batteries, as many models are nonlinear, exhibit multi‑physics coupling, and involve a large number of parameters. In this paper, we address this challenge using the ensemble Kalman inversion (EnKI) method for battery system identification. EnKI performs maximum a posteriori parameter estimation through successive local Gaussian approximations, enabling an iterative and incremental search for unknown parameters. The search combines Monte Carlo sampling with Kalman‑type updates to evolve an ensemble of samples, thereby offering empirical stability and the ability to handle strongly nonlinear models. We validate the proposed approach on two equivalent circuit models with coupled electro‑thermal dynamics, through both simulation and experiments. The results demonstrate that the proposed approach achieves accurate parameter estimation with rapid iterative convergence, and it shows strong potential for application to other battery models.

Authors:Hongbin Ni, Ozgur B. Akan
Title: A Control-Referenced Tri-Channel OECT Receiver for Hybrid Molecular Communication Toward Brain Organoid Interfaces
Abstract:
Brain organoid interfaces that seek neuromodulator readout benefit from chemical receivers with molecular specificity and tolerance to drift. This paper presents a receiver‑centric theoretical study of a control‑referenced tri‑channel organic electrochemical transistor (OECT) receiver with dopamine‑ and serotonin‑selective pixels alongside a hydrogel‑matched control pixel. The Ag/AgCl electrode provides the electrochemical gate reference, whereas the control pixel is used only as a matched reference for common‑mode drift and other low‑frequency baseline fluctuations during amplitude decisions. We couple finite‑duration release, restricted diffusion with clearance, aptamer binding, OECT transduction, and correlated thermal, flicker, and drift noise, and we evaluate MoSK, CSK‑4, and a 2‑bit Hybrid detector on the same front‑end by Monte Carlo simulation. At r=45 micrometers, control referencing mainly benefits the Hybrid amplitude branch, reducing Hybrid SER from 3.71× 10^‑2 to 1.09× 10^‑2 at N_m=1.40× 10^4 molecules/symbol while barely changing the MoSK component. In calibrated no‑ISI front‑end benchmarks, Hybrid+CTRL reaches an LoD of 11866 molecules/symbol at 45 micrometers and remains below CSK‑4+CTRL over much of the medium‑to‑long‑distance range studied. The reported SER and LoD values are scenario‑based receiver forecasts, whereas the more transferable result is the regime‑dependent rule for when matched control referencing benefits Hybrid amplitude decoding.

Authors:Rahman Khorramfar, Aron Brenner, Lara Booth, Ana Rivera, Ruaridh Macdonald, Priya Donti, Saurabh Amin
Title: Optimization Under Uncertainty for Energy Infrastructure Planning: A Synthesis of Methods, Tools, and Open Challenges
Abstract:
Energy infrastructure planning under uncertainty has become increasingly complex as electrification, interdependence between energy carriers, decarbonization, and extreme weather events reshape long‑term investment decisions. This paper surveys recent advances at the intersection of generation and transmission expansion, and optimization under uncertainty, with a focus on stochastic programming, robust optimization, and distributionally robust optimization. We then categorize modeling needs along the axes of modeling fidelity, uncertainty characterization, and solution methods to identify dominant modeling features and trace research gaps. We further examine emerging directions at the interface of optimization and machine learning, including surrogate modeling, learning uncertainty sets, probabilistic forecasting, and synthetic scenarios, and discuss how these tools can be embedded within infrastructure planning models.

Authors:Subir Majumder, Minlan Yu, Le Xie
Title: Workload composition smooths aggregate power demand while sustaining short-horizon ramps in AI data centers
Abstract:
Artificial intelligence (AI) is driving rapid growth in electricity demand, yet the grid‑facing power dynamics of AI data centers remain poorly understood. Here we show that, in shared‑GPU systems, the composition of batch and inference workloads decouples aggregate power variability from short‑horizon ramping. As the inference share rises, variability becomes U‑shaped, whereas ramping becomes hump‑shaped, particularly under higher loading. The magnitude and turning points of these patterns also depend on system loading. Using a trace‑calibrated framework linking workload arrivals, queueing, scheduling, and GPU power, we show that the underlying mechanism is asymmetric. At intermediate workload mixes, queued batch jobs fill capacity left idle by fluctuating inference demand, reducing aggregate power variability. However, short‑horizon ramping remains elevated because inference‑side fluctuations propagate more directly into realized power. AI data centers should therefore be understood as dynamic systems whose workload composition shapes their grid impact.

Authors:Matthew D. Kvalheim
Title: Stabilizability of first-order dynamics in second-order systems
Abstract:
We study whether second‑order systems can be made to behave like prescribed first‑order dynamical systems through feedback control. More precisely, we study whether prescribed vector fields on compact smooth manifolds, viewed geometrically as sections of the tangent bundle, can be asymptotically stabilized in a strong sense by second‑order control systems on the base manifold. Our class of second‑order systems includes most Lagrangian systems, and we obtain both positive and negative results. The positive result asserts that, for fully actuated systems, the section corresponding to any smooth vector field can be made globally exponentially stable, normally hyperbolic, and more. In particular, not only does each closed‑loop solution asymptotically have the prescribed velocities, but it also converges to a trajectory of the first‑order dynamics generated by the prescribed vector field at an exponential rate. Thus, the closed‑loop second‑order system asymptotically reproduces the prescribed first‑order dynamics. In contrast, the negative result asserts that, for underactuated systems on manifolds with nonzero Euler characteristic, sections corresponding to "almost all" smooth vector fields cannot even be locally asymptotically stabilized. This includes, in particular, all vector fields with only isolated zeros. An example shows that the Euler characteristic assumption is necessary for the negative result.

Authors:Soham Chatterjee, Aman Batra, Vivek Natarajan
Title: Motion planning and approximate controllability of a moving cantilever beam with a tip-mass
Abstract:
Consider a non‑uniform Euler‑Bernoulli beam with a tip‑mass at one end and a cantilever joint at the other end. The cantilever joint is not fixed and can itself be moved along an axis perpendicular to the beam. The position of the cantilever joint is the control input to the beam. The dynamics of the beam is governed by a coupled PDE‑ODE model with boundary input. On a natural state‑space, there exists a unique state trajectory for this beam model for every initial state and each twice continuously differentiable control input which is compatible with the initial state. In this paper, we study the motion planning problem of transferring the beam model from an initial state to a final state over a prescribed time‑interval and then employ the results obtained to establish the approximate controllability of this model. We address these problems by extending and applying the generating functions approach to flatness‑based control to the beam model. We prove that the transfer described above is feasible if the initial and final states belong to a certain set, which also contains the steady‑states of the beam model. We then establish that this set contains all the eigenfunctions of the beam model, which form a Riesz basis for the state‑space, and thereby conclude the approximate controllability of the beam model over all time intervals. We illustrate our theoretical results on motion planning using simulations and experiments.

Authors:Wen Fan, Dandan Zhang
Title: i-Tac: Inverse Design of 3D-Printed Tactile Elastomers with Scalable and Tunable Optical and Mechanical Properties
Abstract:
Elastomers are central to vision‑based tactile sensors (VBTSs), where they transduce external contact into observable deformation. Different VBTS architectures, however, require distinct optical and mechanical properties, particularly transparency and hardness. Conventional elastomer design relies on a forward, trial‑and‑error optimisation process from material preparation to property evaluation, which is inefficient and offers limited property scalability and target tunability. In this work, we present i‑Tac, an inverse design pipeline for tailoring 3D‑printed tactile elastomers with target optical and mechanical properties. Inspired by the composite structure of the human dermis, i‑Tac exploits multi‑material PolyJet additive manufacturing with three complementary resins. A mixture design methodology is employed to characterise the printed elastomers and establish response surface models (ReSMs) that map material compositions to functional properties, thereby defining a scalable property space. Based on user‑defined targets, a desirability‑function‑based multi‑objective optimisation is then performed to identify feasible composition regions and derive an optimal operating window for fabrication. This enables elastomers with desired properties to be manufactured in a single iteration, thereby achieving efficient target tunability. Experimental results validate the proposed i‑Tac framework in terms of both property scalability and inverse design performance, showing that i‑Tac can effectively tailor elastomer transparency and hardness while reducing the iterative burden of conventional forward design. By fabricating physical sensor samples from both commercial and custom designs, the proposed framework further demonstrates the potential of inverse‑designed, monolithically manufactured elastomers for customisable VBTS fabrication.

Authors:Youssef Ait Si, Ratnangshu Das, Negar Monir, Sadegh Soudjani, Pushpak Jagtap, Adnane Saoud
Title: Resilient and Effort-Optimal Controller Synthesis under Temporal Logic Specifications
Abstract:
In this paper, we consider the notions of effort and resilience of a dynamical control system defined by the maximum disturbance the system can withstand while satisfying given finite temporal logic specifications. Given a dynamical system and a specification, the objective is to synthesize the controller such that the system satisfies the specification while maximizing its resilience, taking into account input constraints. In addition, we introduce a new metric, called the effort metric, which characterizes the minimal input bound necessary to satisfy a given specification for a perturbed system. The problem for both metrics is formulated as a robust optimization program where the objective is to compute the maximum resilience for the system with input constraints or the minimal effort while simultaneously synthesizing the corresponding controller parameters. Moreover, we study the trade‑off between resilience and effort, where we seek to maximize resilience and minimize the control effort. For linear systems and linear controllers, exact solutions are provided for the class of time‑varying polytopic specifications for the closed‑loop and open‑loop systems. For the case of nonlinear systems, nonlinear controllers, and more general specifications, we leverage tools from the scenario optimization approach, offering a probabilistic guarantee of the solution as well as computational feasibility. Different case studies are presented to illustrate the theoretical results.

Authors:Jingliang Duan, Jie Li, Yinsong Ma, Liye Tang, Guofa Li, Liping Zhang, Shengbo Eben Li, Lin Zhao
Title: On the Optimization Landscape of Observer-based Dynamic Linear Quadratic Control
Abstract:
Understanding the optimization landscape of linear quadratic regulation (LQR) problems is fundamental to the design of efficient reinforcement learning solutions. Recent work has made significant progress in characterizing the landscape of static output‑feedback control and linear quadratic Gaussian (LQG) control. For LQG, much of the analysis leverages the separation principle, which allows the controller and estimator to be designed independently. However, this simplification breaks down when the gradients with respect to the estimator and controller parameters are inherently coupled, leading to a more intricate analysis. This paper investigates the optimization landscape of observer‑based dynamic output‑feedback control of LQR problems. We derive the optimal observer‑controller pair in settings where transient quadratic performance cannot be neglected. Our analysis reveals that, in general, the combination of the standard LQR controller and the observer that minimizes the trace of the accumulated estimation error covariance does not correspond to a stationary point of the overall closed‑loop performance objective. Moreover, we derive a pair of discrete‑time Sylvester equations with symmetric structure, both involving the same set of matrix elements, that characterize the stationary point of the observer‑based dynamic LQR problem. These equations offer analytical insight into the structure of the optimality conditions and provide a foundation for developing numerical policy gradient methods aimed at learning complex controllers that rely on reconstructed state information.

Authors:Yiyang Liu, Xianwei Li, Shaoyuan Li
Title: Distributed Observers with Dynamic Event-Triggered Communication
Abstract:
This paper studies the problem of distributed state estimation of linear time‑invariant (LTI) systems under event‑triggered communication. For event‑triggering mechanisms, the existence of positive minimum inter‑event times (MIETs) is an essential property for ensuring practicality. It is widely recognized that dynamic event‑triggering mechanisms can effectively reduce redundant communication. However, for distributed observers, it remains unclear whether dynamic event‑triggering mechanisms can ensure positive MIETs. This paper proposes a dynamic event‑triggered distributed observer. By introducing new comparison functions, it is proven that the dynamic event‑triggered distributed observer can guarantee strictly positive MIETs and ensure the exponential convergence of the estimation error. Moreover, most existing works on event‑triggered distributed observers only consider node‑based event‑triggering mechanisms, while both node‑based and edge‑based dynamic event‑triggering mechanisms are constructed in this paper. Numerical examples are provided to illustrate the effectiveness of the proposed results.

Authors:Domagoj Herceg, Duarte Antunes
Title: Distributionally Robust PAC-Bayesian Control
Abstract:
We present a distributionally robust PAC‑Bayesian framework for certifying the performance of learning‑based finite‑horizon controllers. While existing PAC‑Bayes control literature typically assumes bounded losses and matching training and deployment distributions, we explicitly address unbounded losses and environmental distribution shifts (the sim‑to‑real gap). We achieve this by drawing on two modern lines of research, namely the PAC‑Bayes generalization theory and distributionally robust optimization via the type‑1 Wasserstein distance. By leveraging the System Level Synthesis (SLS) reparametrization, we derive a sub‑Gaussian loss proxy and a bound on the performance loss due to distribution shift. Both are tied directly to the operator norm of the closed‑loop map. For linear time‑invariant systems, this yields a computationally tractable optimization‑based framework together with high‑probability safety certificates for deployment in real‑world environments that differ from those used in training.

Authors:Chang Su, Ming Li, Zhanglin Shangguan, Zhaojian Wang, Bo Yang
Title: Real-Time Coordinated Operation of Off-Grid Wind Powered Multi-Electrolyzer Systems Considering Thermal Dynamics and HTO Safety
Abstract:
Coordinated operation of alkaline water electrolysis (AWE) systems with multiple electrolyzers under fluctuating renewable power input is challenging due to varying power availability and dynamic safety constraints. Moreover, the conventional separation between optimization and control may result in inconsistent decisions across timescales. To address these issues, this paper proposes a two‑layer coordinated operation method integrating feedback optimization (FO) with a projection‑based safety layer. The FO layer generates real‑time reference inputs to improve renewable energy utilization, while the safety layer corrects these inputs to ensure compliance with operational and safety constraints. To explicitly address the safety constraints arising from the inertial dynamics of AWE systems, discrete‑time control barrier function theory is incorporated into the safety layer, thereby enhancing safety assurance and online computational tractability. Theoretical analysis establishes the feasibility and effectiveness of the proposed method. Case studies based on annual wind generation data show that the proposed method achieves high energy utilization, maintains safe operation, and demonstrates online applicability, scalability, and robustness.

Authors:Noha Hassan, Xavier Fernando, Halim Yanikomeroglu
Title: Quantum Graph Neural Networks for Double-Sided Reconfigurable Intelligent Surface Optimization
Abstract:
As a key enabler for sixth‑generation (6G) wireless communications, reconfigurable intelligent surfaces (RISs) provide the flexibility to control signal strength. Nevertheless, optimizing hundreds of elements is computationally expensive. To overcome this challenge, we present a quantum framework (QGCN) to jointly optimize the physical and electromagnetic response of a double‑sided RIS design that incorporates discrete phase shifts and inter‑element coupling. The core contribution is the adaptive activation or deactivation of elements, allowing a virtual spacing mechanism using PIN diode switches. We then solve a multi‑objective problem that maximizes the minimum user data rate subject to constraints on aperture length and mutual coupling between active elements. Experimental results on IBM Quantum's 127‑qubit ibm_kyiv superconducting processor demonstrate that the proposed QGCN algorithm reduces both per‑iteration computational complexity and memory requirements compared to existing approaches. Also, the QGCN outperforms classical graph neural networks (GNN) on an equivalent graph topology by an additional +0.38 bps/Hz. This advantage is increasing with increasing array sizes.

Authors:Jihyeon Yun, Abdullah Yasin Etcibasi, Ming Shi, C. Emre Koksal
Title: A Queueing-Theoretic Framework for Dynamic Attack Surfaces: Data-Integrated Risk Analysis and Adaptive Defense
Abstract:
We develop a queueing‑theoretic framework to model the temporal evolution of cyber‑attack surfaces, where the number of active vulnerabilities is represented as the backlog of a queue. Vulnerabilities arrive as they are discovered or created, and leave the system when they are patched or successfully exploited. Building on this model, we study how automation affects attack and defense dynamics by introducing an AI amplification factor that scales arrival, exploit, and patching rates. Our analysis shows that even symmetric automation can increase the rate of successful exploits. We validate the model using vulnerability data collected from an open source software supply chain and show that it closely matches real‑world attack surface dynamics. Empirical results reveal heavy‑tailed patching times, which we prove induce long‑range dependence in vulnerability backlog and help explain persistent cyber risk. Utilizing our queueing abstraction for the attack surface, we develop a systematic approach for cyber risk mitigation. We formulate the dynamic defense problem as a constrained Markov decision process with resource‑budget and switching‑cost constraints, and develop a reinforcement learning (RL) algorithm that achieves provably near‑optimal regret. Numerical experiments validate the approach and demonstrate that our adaptive RL‑based defense policies significantly reduce successful exploits and mitigate heavy‑tail queue events. Using trace‑driven experiments on the ARVO dataset, we show that the proposed RL‑based defense policy reduces the average number of active vulnerabilities in a software supply chain by over 90% compared to existing defense practices, without increasing the overall maintenance budget. Our results allow defenders to quantify cumulative exposure risk under long‑range dependent attack dynamics and to design adaptive defense strategies with provable efficiency.

Authors:Yeong-Ung Kim, Hyo-Sung Ahn
Title: Distributed Optimization with Coupled Constraints over Time-Varying Digraph
Abstract:
In this paper, we develop a distributed algorithm for solving a class of distributed convex optimization problems where the local objective functions can be a general nonsmooth function, and all equalities and inequalities are network‑wide coupled. This type of problem arises from many areas, such as economic dispatch, network utility maximization, and demand response. Integrating the decomposition by right hand side allocation and primal‑dual methods, the proposed algorithm is able to handle the distributed optimization over networks with time‑varying directed graph in fully distributed fashion. This algorithm does not require the communication of sensitive information, such as primal variables, for privacy issues. Further, we show that the proposed algorithm is guaranteed to achieve an O(1/k) rate of convergence in terms of optimality based on duality analysis under the condition that local objective functions are strongly convex but not necessarily differentiable, and the subdifferential of local inequalities is bounded. We simulate the proposed algorithm to demonstrate its remarkable performance.

Authors:Chencheng Zhang, Hao Yang, Bin Jiang, Shaoxuan Cui
Title: Stability and Robustness of Tensor-Coupled Flow-Conservation Dynamical Systems on Hypergraphs
Abstract:
This paper develops an entropy‑based stability and robustness framework for nonlinear hypergraph dynamics with conservation and flow balance. We consider generator‑form systems on the simplex whose state‑dependent transition rates capture higher‑order (tensor) interactions among nodes. Under a tensor generalized detailed‑balance (TGDB) condition, we show that the system admits a unique equilibrium and an entropy Lyapunov function ensuring global asymptotic stability. The Jacobian restricted to the tangent subspace of the simplex is Hurwitz, and its spectral gap determines the exponential convergence rate. Building on this structure, we derive first‑order sensitivity bounds of the equilibrium under perturbations of the coupling tensor and establish a local input‑to‑state stability (ISS) estimate with respect to external inputs. The results reveal a quantitative link between the spectral gap and the system's robustness margin: larger spectral gaps imply smaller equilibrium shifts and faster recovery under structural or parametric perturbations. Numerical experiments on tensor‑coupled flow models confirm the theoretical predictions and illustrate how the proposed entropy‑dissipating framework unifies stability and robustness analysis for conservative higher‑order network systems.

Authors:Zunnan Xu, Zhaoxia Jing, Zhanhua Pan
Title: A Dual-Positive Monotone Parameterization for Multi-Segment Bids and a Validity Assessment Framework for Reinforcement Learning Agent-based Simulation of Electricity Markets
Abstract:
Reinforcement learning agent‑based simulation (RL‑ABS) has become an important tool for electricity market mechanism analysis and evaluation. In the modeling of monotone, bounded, multi‑segment stepwise bids, existing methods typically let the policy network first output an unconstrained action and then convert it into a feasible bid curve satisfying monotonicity and boundedness through post‑processing mappings such as sorting, clipping, or projection. However, such post‑processing mappings often fail to satisfy continuous differentiability, injectivity, and invertibility at boundaries or kinks, thereby causing gradient distortion and leading to spurious convergence in simulation results. Meanwhile, most existing studies conduct mechanism analysis and evaluation mainly on the basis of training‑curve convergence, without rigorously assessing the distance between the simulation outcomes and Nash equilibrium, which severely undermines the credibility of the results. To address these issues, this paper proposes...

Authors:Saikat Chowdhury, Mona Ghassemi
Title: Performance Enhancement of MVDC Aircraft Cables Using Micro-Multilayer Insulation Under Low-Pressure Conditions
Abstract:
The development of medium‑voltage direct current (MVDC) cable systems for wide‑body all‑electric aircraft (AEA) requires insulation technologies capable of operating reliably under reduced‑pressure environments. Conventional underground cable insulation, designed for atmospheric conditions, exhibits degraded partial discharge (PD) and dielectric performance at low pressure, limiting its applicability to aerospace systems. This work presents a controlled experimental comparison between a conventional single‑layer extruded insulation system and a micro‑multilayer multifunctional electrical insulation (MMEI) architecture, in which all cable components are kept identical except for the insulation. The MMEI system is implemented with only 10% of the baseline insulation thickness to evaluate the effectiveness of insulation architecture in enhancing performance. PD characteristics and dielectric strength are experimentally evaluated under DC voltage at atmospheric pressure and 18.8 kPa. Results show that the MMEI‑based cable exhibits higher PD inception voltage (PDIV) and maintains a detectable PD extinction voltage (PDEV) under reduced pressure, unlike the conventional cable. Furthermore, despite its significantly reduced thickness, the MMEI system demonstrates a substantial increase in dielectric breakdown strength, withstanding voltages exceeding 20 kV compared to below 5 kV for the conventional design under low‑pressure conditions. These findings demonstrate that insulation architecture, rather than thickness alone, governs performance in MVDC aerospace cables. The results highlight the potential of MMEI systems to enable lighter, more compact, and higher‑performance cable designs for future electrified aviation platforms.

Authors:Kanghong Shi, Diyako Dadkhah, Ian R. Petersen, S. O. Reza Moheimani
Title: Digital Control of Negative Imaginary Systems Using Discrete-Time Multi-HIGS: Application to a Dual-Stage MEMS Force Sensor
Abstract:
In this paper, we propose a digital control approach for multi‑input multi‑output negative imaginary (NI) systems using discrete‑time hybrid integrator‑gain systems (HIGS) controllers. We show the NI property of the bimodal and trimodal discrete‑time HIGS, as well as the parallel combinations of them, which are referred to as the multi‑HIGS. Also, we demonstrate that linear NI systems can be asymptotically stabilized using discrete‑time HIGS in digital control. We apply discrete‑time bimodal and trimodal multi‑HIGS controllers to a two‑input two‑output dual‑stage force sensor with lightly damped resonant modes. To validate the theoretical findings, the closed‑loop performance is evaluated in both time and frequency domains. Experimental results show that the discrete‑time multi‑HIGS effectively suppresses resonances while preserving favorable phase characteristics, which highlights its potential as a robust nonlinear NI controller for the digital control of NI systems.

Authors:Keivan Faghih Niresi, Christian Møller Jensen, Carsten Skovmose Kallesøe, Rafael Wisniewski, Olga Fink
Title: Virtual Smart Metering in District Heating Networks via Heterogeneous Spatial-Temporal Graph Neural Networks
Abstract:
Intelligent operation of thermal energy networks aims to improve energy efficiency, reliability, and operational flexibility through data‑driven control, predictive optimization, and early fault detection. Achieving these goals relies on sufficient observability, requiring continuous and well‑distributed monitoring of thermal and hydraulic states. However, district heating systems are typically sparsely instrumented and frequently affected by sensor faults, limiting monitoring. Virtual sensing offers a cost‑effective means to enhance observability, yet its development and validation remain limited in practice. Existing data‑driven methods generally assume dense synchronized data, while analytical models rely on simplified hydraulic and thermal assumptions that may not adequately capture the behavior of heterogeneous network topologies. Consequently, modeling the coupled nonlinear dependencies between pressure, flow, and temperature under realistic operating conditions remains challenging. In addition, the lack of publicly available benchmark datasets hinders systematic comparison of virtual sensing approaches. To address these challenges, we propose a heterogeneous spatial‑temporal graph neural network (HSTGNN) for constructing virtual smart heat meters. The model incorporates the functional relationships inherent in district heating networks and employs dedicated branches to learn graph structures and temporal dynamics for flow, temperature, and pressure measurements, thereby enabling the joint modeling of cross‑variable and spatial correlations. To support further research, we introduce a controlled laboratory dataset collected at the Aalborg Smart Water Infrastructure Laboratory, providing synchronized high‑resolution measurements representative of real operating conditions. Extensive experiments demonstrate that the proposed approach significantly outperforms existing baselines.

Authors:Henrik Johansson, Qianli Xing, Nathaniel Taylor, Xiongfei Wang
Title: Analysis and Enhancement of Incremental-Quantity-Based Distance Protection With Grid-Forming Inverters
Abstract:
Grid‑forming (GFM) inverters are expected in future inverter‑dominated grids. In such grids, time‑domain protection schemes, for example those based on instantaneous incremental quantities (IQs), are being advocated as potential solutions to the challenges faced by traditional phasor‑based protection schemes, due to their ability to process nonlinear data. However, IQ‑based protection uses the superposition principle; thus, linearity is still assumed in their application, while GFM inverters are nonlinear sources during faults. This paper proposes an analytical model to study the impact of GFM inverters on the relay‑measured IQs. The model is validated with PSCAD/EMTDC simulations, and is used to investigate the interoperability of time‑domain IQ‑based distance protection with GFM inverters employing different current limiters. Results show that time‑domain IQ‑based distance protection demonstrates superior dependability for close‑in faults compared to that of quadrilateral distance protection with GFM inverters, and it has the possibility to be secure for external faults when quadrilateral distance protection overreaches; however, tuning of its settings is hard to generalize for various sources and faults. Taking the observed interoperability issues into account, a trip criterion for dependable and secure time‑domain IQ‑based distance protection is proposed, which facilitates easy‑to‑tune and general settings for applications with GFM inverters.

Authors:Franco Bagnoli, Sara Dridi, Bassem Sellami, Amira Mouakher, Samira El Yacoubi
Title: General control of linear cellular automata
Abstract:
In mathematics and engineering, control theory is concerned with the analysis of dynamical systems through the application of suitable control inputs. One of the prominent problems in control theory is controllability which concerns the ability to determine whether there exists a control input that can steer a dynamical system from an initial state to a desired final state within a finite time horizon. There is a general theory for controlling linear or linearizable system, but it cannot be applied to discrete systems like cellular automata, which is the problem of that we address in this paper. We develop a general theory for linear (and affine) cellular automata, and apply it to examples of one‑dimensional and two‑dimensional Boolean cases. We introduce the concept of controllability matrix and show that controllability holds if and only if the controllability matrix is invertible.

Authors:Franco Bagnoli, Bassem Sellami, Amira Mouakher, Samira El Yacoubi
Title: Control of Cellular Automata by Moving Agents with Reinforcement Learning
Abstract:
In this exploratory paper we introduce the problem of cognitive agents that learn how to modify their environment according to local sensing to reach a global goal. We concentrate on discrete dynamics (cellular automata) on a two‑dimensional system. We show that agents may learn how to approximate their goal when the environment is passive, while this task becomes impossible if the environment follows an active dynamics.

Authors:Tianyi Zhang, Wenhan Cao, Shengbo Eben Li
Title: Natural Gradient Gaussian Approximation Filter with Positive Definiteness Guarantee
Abstract:
Popular Bayes filters often apply linearization techniques, such as Taylor expansion or stochastic linear regression, to enable the use of the Kalman filter structure, but this can lead to large errors in strongly nonlinear systems. The recently proposed NANO filter addresses this issue by interpreting the prediction and update steps of Bayesian filtering as two distinct optimization problems and solving them through moment matching and natural gradient descent, thereby avoiding model linearization errors. However, the natural gradient update in NANO can occasionally diverge because the posterior covariance in its iteration may lose positive definiteness. Our analysis shows that the posterior covariance is the sum of the inverse prior covariance and the expected Hessian of the log‑likelihood function, and that the indefiniteness of the latter term is the root cause of update failure. To address this issue, we propose two remedies. The first approximates the log‑likelihood Hessian using the Gauss‑Newton method, representing it as the self‑adjoint product of the Jacobian of the normalized measurement residual, which is guaranteed to be positive semi‑definite. The second reformulates the covariance update as an exponential‑form update of the Cholesky factor and reconstructs the covariance via its Gram matrix, which ensures positive definiteness. Experiments on three classical nonlinear systems demonstrate that the proposed NANO filter with guaranteed positive definiteness outperforms popular members of the Kalman filter family and original NANO filter.

Authors:Tianxiao Sun, Noah Schwarzkopf
Title: A simulation-optimization approach for fractional, profitability-oriented inventory control under service-level type constraints
Abstract:
Managing stock efficiently remains a core issue in modern logistics, where companies must reconcile cost efficiency with dependable service despite unpredictable market conditions. Conventional models often overlook the direct connection between investment in inventory and overall financial performance. This study introduces a data‑driven decision framework that combines stochastic simulations with a profit‑oriented optimization routine to enhance decision‑making under uncertainty. The simulation stage generates performance estimates across multiple operating scenarios, providing realistic data on expenditures, revenues, and service reliability. These outcomes inform a fractional optimization process that searches for policies yielding the highest financial returns while maintaining required availability levels. The algorithm iteratively refines parameter values through feedback between simulated outcomes and optimization results, ensuring adaptability to dynamic enterprise systems. Computational experiments using representative business settings confirm that this approach improves both service consistency and financial yield. Overall, the framework demonstrates a practical, data‑driven path for firms seeking to align operational responsiveness with sustainable profitability.

Authors:Qinjuan Wang, Shan Yang, Yongli Zhu
Title: Agentic Application in Power Grid Static Analysis: Automatic Code Generation and Error Correction
Abstract:
This paper introduces an LLM agent that automates power grid static analysis by converting natural language into MATPOWER scripts. The framework utilizes DeepSeek‑OCR to build an enhanced vector database from MATPOWER manuals. To ensure reliability, it devises a three‑tier error‑correction system: a static pre‑check, a dynamic feedback loop, and a semantic validator. Operating via the Model Context Protocol, the tool enables asynchronous execution and automatically debugging in MATLAB. Experimental results demonstrate that the system achieves a 82.38% accuracy regarding the code fidelity, effectively eliminating hallucinations even in complex analysis tasks.

Authors:Mohamed-Amine Lahmeri, Ata Khalili, Yujiao Liu, Anke Schmeink, Robert Schober
Title: Deep Reinforcement Learning for Cognitive Time-Division Joint SAR and Secure Communications
Abstract:
Synthetic aperture radar (SAR) imaging can be exploited to enhance wireless communication performance through high‑precision environmental awareness. However, integrating sensing and communication functionalities in such wideband systems remains challenging, motivating the development of a joint SAR and communication (JSARC) framework. We propose a dynamic time‑division JSARC (TD‑JSARC) framework for secure aerial communications that is relevant for critical scenarios, such as surveillance or post‑disaster communication, where conventional localization of mobile adversaries often fails. In particular, we consider a secure downlink communication scenario where an aerial base station (ABS) serves a ground user (UE) in the presence of a ground‑moving eavesdropper. To detect and track the eavesdropper, the ABS uses cognitive SAR along‑track interferometry (ATI) to estimate its position and velocity. Based on these estimates, the ABS applies adaptive beamforming and artificial‑noise jamming to enhance secrecy. To this end, we jointly optimize the time and power allocation to maximize the worst‑case secrecy rate, while satisfying both SAR and communication constraints. Using the estimated eavesdropper trajectory, we formulate the problem as a Markov decision process (MDP) and solve it via deep reinforcement learning (DRL). Simulation results show that the proposed learning‑based approach outperforms both learning and non‑learning baseline schemes employing equal‑aperture and random time allocation. The proposed method also generalizes well to previously unseen eavesdropper motion patterns.

Authors:Xinwei Liu, Vladimir Dvorkin
Title: Online Electricity Pricing from Frequency Measurements
Abstract:
Frequency dynamics in power systems reflect active power imbalance in real time, thereby providing an instantaneous signal to inform electricity pricing. However, existing real‑time markets operate on much slower timescales and fail to exploit this signal. In this letter, we develop integrated market‑‑frequency dynamics that enable online pricing directly from frequency measurements. Representing the real‑time market as a dynamic price‑discovery process, and integrating this process with the grid frequency dynamics, we derive an explicit price formation mechanism from frequency measurements. This mechanism manifests as a distributed PID‑like controller for each generator, where frequency response is driven and remunerated by electricity prices derived solely from local frequency measurements.

Authors:Shiva Poudel, Thiagarajan Ramachandran, Orestis Vasios, Andrew P. Reiman
Title: Agentic Workflows for Resolving Conflict Over Shared Resources: A Power Grid Application
Abstract:
The increasing use of LLM‑based agents to support decision‑making and control across diverse domains motivates the need for systematic deconfliction of their proposed actions. We present a deconfliction framework for coordinating multiple agents that formally encapsulate individual applications, each proposing potentially conflicting actions over shared resources. Conflicts are resolved through three deconfliction modes: bilateral negotiation, structured mediation, and procedural (deterministic) deconfliction. We define design principles for large language model‑based client agents, including a chain‑of‑thought style reasoning process, and introduce an iterative weighted‑consensus mechanism that does not require the applications themselves to solve optimization problems. The framework is domain agnostic and supports both numeric and non‑numeric decisions. Its performance is demonstrated on a power distribution use case with conflicting advanced distribution management system applications for cost optimization and resilience, coordinating diesel generators and battery energy storage systems.

Authors:Sachith Wijesooriya, Sandun S. Kuruppu
Title: Variable Dead-Time Based Novel Soft-Start Method for Dual Active Bridge Converters
Abstract:
Effective startup control is critical for the safe and reliable operation of Dual Active Bridge (DAB) converters. Unlike traditional soft‑start techniques that rely solely on phase‑shift control or fixed dead‑time settings, the proposed approach gradually reduces the dead time from a value close to one switching period to the hardware‑defined minimum. This enables a smooth buildup of the secondary‑side voltage while effectively minimizing voltage overshoot and suppressing inrush current during startup. As a result, the leakage inductor current rises in a controlled manner, ensuring safe and predictable startup behavior. Simulation results demonstrate that conventional startup methods lead to severe voltage overshoot and high inrush currents, whereas the proposed method achieves a gradual voltage rise with well‑regulated current profiles. Experimental validation using a 15 kW hardware platform confirms the effectiveness and robustness of the approach under different operating conditions. The proposed technique is simple, hardware‑friendly, easily implementable on standard microcontrollers, and applicable to nth ‑ order DAB architecture, making it a versatile solution for enhancing the reliability and safety of DAB converters in practical applications.

Authors:Udit Halder, Nicolas Echeverria Zambrano, Xincheng Li
Title: Kinematics of continuum planar grasping
Abstract:
This paper presents an analytical framework to study the geometry arising when a soft continuum arm grasps a planar object. Both the arm centerline and the object boundary are modeled as smooth curves. The grasping problem is formulated as a kinematic boundary following problem, in which the object boundary acts as the arm's 'shadow curve'. This formulation leads to a set of reduced kinematic equations expressed in terms of relative geometric shape variables, with the arm curvature serving as the control input. An optimal control problem is formulated to determine feasible arm shapes that achieve optimal grasping configurations, and its solution is obtained using Pontryagin's Maximum Principle. Based on the resulting optimal grasp kinematics, a class of continuum grasp quality metrics is proposed using the algebraic properties of the associated continuum grasp map. Feedback control aspects in the dynamic setting are also discussed. The proposed methodology is illustrated through systematic numerical simulations.

Authors:Xincheng Li, Tengyue Liu, Udit Halder
Title: On Feedback Speed Control for a Planar Tracking
Abstract:
This paper investigates a planar tracking problem between a leader and follower agent. We propose a novel feedback speed control law, paired with a constant bearing steering strategy, to maintain an abreast formation between the two agents. We prove that the proposed control yields asymptotic stability of the closed‑loop system when the steering of the leader is known. For the case when the leader's steering is unavailable to the follower, we show that the system is still input‑to‑state stable with respect to the leader's steering viewed as an input. Furthermore, we demonstrate that if the leader's steering is periodic, the follower will asymptotically converge to a periodic orbit with the same period. We validate these results through numerical simulations and experimental implementations on mobile robots. Finally, we demonstrate the scalability of the proposed approach by extending the two‑agent control law to an N‑agent chain network, illustrating its implications for directional information propagation in biological and engineered flocks.

Authors:Bhaskar Varma, Ying Shuai Quan, Karl D. von Ellenrieder, Paolo Falcone
Title: Decentralized Opinion-Integrated Decision making at Unsignalized Intersections via Signed Networks
Abstract:
In this letter, we consider the problem of decentralized decision making among connected autonomous vehicles at unsignalized intersections, where existing centralized approaches do not scale gracefully under mixed maneuver intentions and coordinator failure. We propose a closed‑loop opinion‑dynamic decision model for intersection coordination, where vehicles exchange intent through dual signed networks: a conflict topology based communication network and a commitment‑driven belief network that enable cooperation without a centralized coordinator. Continuous opinion states modulate velocity optimizer weights prior to commitment; a closed‑form predictive feasibility gate then freezes each vehicle's decision into a GO or YIELD commitment, which propagates back through the belief network to pre‑condition neighbor behavior ahead of physical conflicts. Crossing order emerges from geometric feasibility and arrival priority without the use of joint optimization or a solver. The approach is validated across three scenarios spanning fully competitive, merge, and mixed conflict topologies. The results demonstrate collision‑free coordination and lower last‑vehicle exit times compared to first come first served (FCFS) in all conflict non‑trivial configurations.

Authors:Carl R. Richardson, Jichen Zhang, Ethan King, Ján Drgoňa
Title: Stability Enhanced Gaussian Process Variational Autoencoders
Abstract:
A novel stability‑enhanced Gaussian process variational autoencoder (SEGP‑VAE) is proposed for indirectly training a low‑dimensional linear time invariant (LTI) system, using high‑dimensional video data. The mean and covariance function of the novel SEGP prior are derived from the definition of an LTI system, enabling the SEGP to capture the indirectly observed latent process using a combined probabilistic and interpretable physical model. The search space of LTI parameters is restricted to the set of semi‑contracting systems via a complete and unconstrained parametrisation. As a result, the SEGP‑VAE can be trained using unconstrained optimisation algorithms. Furthermore, this parametrisation prevents numerical issues caused by the presence of a non‑Hurwitz state matrix. A case study applies SEGP‑VAE to a dataset containing videos of spiralling particles. This highlights the benefits of the approach and the application‑specific design choices that enabled accurate latent state predictions.

Authors:Tianyu Zhou, Zihao Liang, Zehui Lu, Shaoshuai Mou
Title: Online Intention Prediction via Control-Informed Learning
Abstract:
This paper presents an online intention prediction framework for estimating the goal state of autonomous systems in real time, even when intention is time‑varying, and system dynamics or objectives include unknown parameters. The problem is formulated as an inverse optimal control / inverse reinforcement learning task, with the intention treated as a parameter in the objective. A shifting horizon strategy discounts outdated information, while online control‑informed learning enables efficient gradient computation and online parameter updates. Simulations under varying noise levels and hardware experiments on a quadrotor drone demonstrate that the proposed approach achieves accurate, adaptive intention prediction in complex environments.

Authors:Sami Leon Noel Aziz Hanna, Nicolas Hoischen, Sandra Hirche, Armin Lederer
Title: On the Existence of Quadratic Control Lyapunov Functions for Koopman-Operator based Bilinear Systems
Abstract:
Koopman operator‑based methods enable data‑driven bilinear representations of unknown nonlinear control systems. Accurate representations often demand significantly higher dimensions than the original system, making control design challenging. Control Lyapunov Functions (CLFs) are widely used for controller synthesis, with quadratic CLF candidates being the most common due to their simplicity. Yet, we show that this class is highly restrictive, especially when the state dimension is large: under mild conditions, their existence implies stabilizability of the bilinear system by a constant input ‑‑ that is, the control remains fixed over time. We establish this result by formulating a quadratically constrained quadratic program (QCQP) that exactly characterizes valid CLFs. Since QCQPs are NP‑hard, we propose a convex semidefinite relaxation that offers a sufficient validity condition. For single‑input systems, we prove that a quadratic CLF requires constant control stabilizability, and empirically demonstrate that this extends to high‑dimensional multi‑input systems in many cases.

Authors:Veronica Centorrino, Rawan Hoteit, Efe C. Balta, John Lygeros
Title: A Unified Control-Theoretic Framework for Saddle-Point Dynamics in Constrained Optimization
Abstract:
This paper studies equality‑constrained minimization problems through the lens of feedback control. We introduce a unified control‑theoretic framework by showing that a PID feedback law acting on the dual variable induces the PID saddle‑point flow (PID‑SPF), a broad class of saddle‑point dynamics associated with the augmented Lagrangian. This framework recovers several classical primal‑dual flows as special cases. We prove that the equilibria of the proposed flow coincide with the stationary points of the original problem. Our analysis reveals how the feedback gains affect the optimization: integral action enforces constraint satisfaction, proportional action introduces the augmented Lagrangian structure, and derivative action modifies the geometry of the primal dynamics by inducing a state‑dependent Riemannian metric. Moreover, for convex problems with affine constraints, we establish global exponential convergence by leveraging contraction theory for all admissible PID gains, providing in the process explicit bounds on the convergence rate. Finally, we validate our theoretical results on numerical examples including an application to bilevel optimization.

Authors:Riccardo Morselli, Davide Tebaldi, Roberto Zanasi
Title: Discrete-Time Model of a Two-Speed PowerShift suitable for Real-Time Control and Simulation
Abstract:
In this paper, a new discrete‑time approach to model the clutches engagement/disengagement in a two‑speed powershift is proposed. The core idea is the development of a model for the computation of the exact torque needed to achieve the clutches engagement, including both the cases of single clutch engagement and of simultaneous clutch engagement (full lock condition). Based on this, the control logic for the clutches engagement and disengagement phases is also developed. The advantages in terms of real‑time applicability with respect to the continuous‑time version are shown through extensive simulation results.

Authors:Taulant Kerci, Federico Milano
Title: Frequency Quality Metrics based on Second-Order Derivative and Autocorrelation
Abstract:
This industry‑oriented paper originates from the observation that current frequency quality metrics utilized by transmission system operators (TSOs) fail to fully capture the dynamic behavior of the grid frequency. Motivated by this gap, the paper proposes novel frequency quality metrics based on second‑order dynamics and stochastic autocorrelation. Using real‑world data with 0.1 s and 1 s resolution from the Irish, Great Britain and Nordic systems and running dynamic stochastic simulations, the paper shows that the proposed metrics bring new and counterintuitive insights in terms of how good or poor the frequency quality of power grids is beyond current well‑known metrics. In particular, the paper shows that a power system may show good frequency quality using standard metrics and poor frequency quality using the proposed metrics. Overall, the paper contributes to improve the understanding of frequency quality.

Authors:Elias Milios, Felix Berkel, Felix Gruber, Melanie N. Zeilinger, Kim P. Wabersich
Title: Efficient Uniform Feasible Set Sampling for Approximate Linear MPC
Abstract:
Model Predictive Control (MPC) offers safe and near‑optimal control but suffers from high computational costs. Approximate MPC (AMPC) mitigates this by learning a cheaper surrogate policy, typically by training a neural network on state‑MPC input pairs. Generating training data is a major bottleneck, requiring solving the MPC for numerous states sampled from its feasible set. Since this feasible set is implicitly defined and unknown, efficient sampling is nontrivial but crucial. We propose the linear MPC Hit‑and‑Run (LMPC‑HR) sampler for linear MPC with polyhedral constraints. We identify the feasible set boundaries along search directions, a crucial step within HR, by formulating the problem as a convex linear program, replacing expensive iterative searches with a single optimization step. A numerical study demonstrates that LMPC‑HR achieves an order of magnitude reduction in computation time for generating uniformly distributed samples from the feasible set compared to naive baselines.

Authors:Yixuan Zhu, Bo Zhang, Yinkang Gao, Haoyuan Ren, Cheng Tang, Caixu Zhao, Lei Gong, Teng Wang, Wenqi Lou, Xi Li
Title: Scheduling Cause-Effect Chains without Timing Anomalies in End-to-End Latency
Abstract:
In real‑time systems, both individual task execution and data propagation must meet strict timing constraints. Cause‑effect (CE) chains are widely used to analyze such behaviors by end‑to‑end latency. However, timing anomalies (TAs) can distort it, where a local reduction in execution times leads to an increase in the overall end‑to‑end latency. As a result, precisely analyzing the upper bounds of the latency becomes challenging, and such systems typically exhibit larger upper bounds than TA‑eliminated systems. Existing studies either eliminate TAs by completely sacrificing average latency to simplify analysis or, despite adopting complex safe analysis methods, do not eliminate TAs effectively, still having high latencies. To address this issue, we identify two basic causes of TAs in end‑to‑end latency. Based on these causes, we propose the first treatment that eliminates TAs in the latency with negligible average latency loss using Deterministic Data Flow (DDF). We further formally prove its TA‑free property. Therefore, we can get a precise upper bound for latency when all jobs execute with their worst‑case execution times. Experimental results show that it effectively reduces the maximum end‑to‑end latency, the average latency, and latency jitter compared with the state‑of‑the‑art (SOTA) method.

Authors:Pelin Sekercioglu, Nana Wang, Angela Fontan, Dimos V. Dimarogonas
Title: Topology Identification of Dynamical Signed Graphs
Abstract:
We propose an adaptive control protocol for identifying the topology of dynamical networks interconnected over undirected graphs with cooperative and antagonistic interactions. The signed network is modeled using a repelling Laplacian. Topology identification relies on an edge‑based formulation of the network and adaptive control protocols through the design of a persistently excited auxiliary network. Our approach guarantees the simultaneous identification and synchronization of the unknown signed network and establishes uniform semiglobal practical asymptotic stability of the estimation errors. Numerical simulations validate our theoretical results.

Authors:Thierry Schwaller, Feiran Zhao, Florian Dörfler
Title: A Bayesian Perspective on the Data-Driven LQR
Abstract:
The data‑driven linear quadratic regulator (ddLQR) is a widely studied control method for unknown dynamical systems with disturbance. Existing approaches, both indirect, i.e., those that identify a model followed by model‑based design, and direct, which bypasses the identification step, often rely on the certainty‑equivalence principle and therefore do not explicitly account for model uncertainty. In this paper, we propose a Bayesian formulation for both indirect and direct ddLQR that incorporates posterior uncertainty into the control design. The resulting expected cost decomposes into a certainty‑equivalence term and a variance‑dependent term, providing a principled interpretation of regularization. We further show that the indirect and direct formulations are equivalent under this perspective. The resulting direct method admits a tractable semidefinite program whose size is independent of the data length. Numerical simulations demonstrate improved optimality gap and closed‑loop stability, particularly in low‑data regimes.

Authors:Kavita Shekhawat, Nandan K Sinha
Title: A Study of the Circular Pursuit Dynamics using Bifurcation Theoretic Computational Approach
Abstract:
A circular pursuit guidance problem involving pursuer‑target engagement is studied in this paper using a bifurcation theory based numerical approach. While target is modeled as a point mass moving around in a circle with certain velocity, pursuer dynamics is driven by the relative position and orientation with respect to the target. A planar case is currently considered. A mathematical model representing the engagement scenario is derived and two cases are presented, one without and the other with a basic model for pursuer speed dynamics accounting for limitations imposed by available force. Analytical and simulation results are presented to elucidate the novel approach. Advantages of using this approach for arriving at laws for pursuer‑target engagement are highlighted.

Authors:Zhanglin Shangguan, Wei Xiao, Qi Li, Bo Yang, Xinping Guan
Title: Synthesizing Safety in Infinite-Horizon Optimal Control for Disturbed High-Relative-Degree Systems via Barrier-Regulating Auxiliary Variables
Abstract:
Optimal stabilization of safety‑critical nonlinear systems requires balancing long‑term performance and strict safety constraints. Existing quadratic‑programming‑based control barrier function (CBF) safety filters are point‑wise and may exhibit myopic behavior and local trapping when the safeguarding action conflicts with the nominal optimal control. This paper develops a safety‑aware infinite‑horizon optimal control framework by embedding a barrier‑Lyapunov function (BLF)‑based safeguarding action into the system dynamics and introducing a barrier‑regulating auxiliary variable, thereby reformulating the original constrained problem as an unconstrained one on an extended state space. To mitigate local trapping, we introduce an adaptive alignment‑conditioned tangential excitation orthogonal to the safety direction, with activation adaptively modulated by the degree of directional alignment between the nominal and safeguarding controllers, and incorporate it as an admissible \mathcalL2 disturbance in an H\infty formulation. For high‑relative‑degree systems under disturbances, we further augment the recursive high‑order safe‑set construction with barrier compensation terms to obtain a high‑order BLF and formulate an adversarial disturbance attenuation problem, which is approximately solved via safe‑exploration‑enhanced online critic learning. Simulations demonstrate reduced local trapping, improved safety‑‑performance trade‑offs, and safe operation under disturbances.

Authors:Subhrajit Sinha
Title: Linear Systems as Representations of Time Groups
Abstract:
In this paper, we develop a representation‑theoretic formulation of discrete‑time linear systems. We show that such systems are naturally viewed as representations of time groups acting on vector spaces, thereby endowing the state space with a canonical algebraic structure. This perspective provides a unified framework for linear systems over different fields, in which familiar structural properties arise from the underlying representation. In particular, invariant decompositions of the state space correspond to invariant subrepresentations, while the distinctions between real, complex, and finite‑field systems emerge from the algebraic properties of the base field and the time group. We further show that linear systems over finite fields naturally correspond to representations of finite cyclic time groups, leading to module structures over polynomial quotient rings. This provides a systematic alternative to spectral analysis in settings where eigenvalue‑based methods are not the most natural organizing language.

Authors:Mohamad H. Kazma, Ahmad F. Taha
Title: Connections Between Determinantal Point Processes and Gramians in Control
Abstract:
Determinantal point processes (DPPs) are probability models over subsets of a ground set that favor diverse selections while suppressing redundancy. That is, they tend to assign higher likelihood to collections whose elements complement one another instead of repeating the same information. For example, in recommendation systems, a DPP prefers showing users several relevant items that differ in content or style, rather than many near‑duplicates of essentially the same item. Although DPPs have been studied extensively in machine learning, random matrix theory, and popularized through components of YouTube's search recommendation system, they have not been considered in the context of dynamic systems; time domain analysis is not a feature of DPPs. This paper establishes interesting connections between DPPs and control theory. By showing that the observability (controllability) Gramian parameterized by sensor (control) node subsets is a DPP, we provide a probabilistic and spectral perspective on sensor (actuator) selection for linear dynamic systems. This notion of probability here does not represent stochastic uncertainty in the system dynamics; it instead represents a likelihood measure over sensor (actuator) configurations induced by the Gramian. To that end, we derive an effective observable rank condition, characterize the balance between individual node contributions and diversity, and establish node inclusion monotonicity and negative dependence properties. Finally, we show that this formulation recovers classical greedy optimization guarantees and admits a maximum a posteriori interpretation of the sensor/actuator node selection problem. Numerical case studies on three network topologies corroborate the theoretical results.

Authors:Chuanghong Weng, Ehsan Nekouei
Title: Optimal Privacy-Aware Co-Design of Quantizer and Controller in Networked Control Systems
Abstract:
This paper investigates the optimal privacy‑aware networked control problem, in which the dynamical system affected by a private input process sends its measurement to a remote controller after stochastic quantization. An adversary seeks to infer private system inputs from quantization results and control outputs. The optimal privacy‑aware quantizer and controller are obtained by solving a stochastic control problem with mutual information regularization, where the mutual information measures the privacy leakage through the quantizer and controller. We first derive the coupled Bellman equations for the optimal quantizer and controller using the dynamic programming decomposition method. We then analyze the structural properties of the solution, showing that the optimal controller is deterministic, while the optimal quantizer regulates the adversary's belief in a closed‑loop manner to enhance privacy. To enable numerical optimization, the quantizer and controller are jointly parameterized and then updated via policy gradient methods, and a binary classification approach is used to approximate privacy leakage. Finally, we validate the effectiveness of the proposed approach through numerical experiments on a building control system.

Authors:Shaoze Li, Bohang Fang, Cong Chen
Title: Risk-Aware Allocation of Transmission Capacity for AI Data Centers
Abstract:
Rapid growth in AI‑driven data center loads is creating significant challenges for transmission grid interconnection. This paper proposes robust and risk‑aware frameworks to quantify transmission capacity as firm and flexible capacities. We efficiently solve the robust optimization problem to determine firm capacity when minimizing unserved data center demand. Building upon this, we introduce a risk‑aware allocation for flexible capacity, showing that tolerating a minimal probability of service interruption and blackout can unlock substantial flexible capacity of transmission networks and accelerate data center interconnection. To efficiently allocate scarce transmission capacities among competing data centers, we adopt the simultaneous ascending auction, characterizing products by capacity, risk level, and location. Under additive or symmetric concave valuation functions, the auction converges to a competitive equilibrium and achieves efficient allocation.

Authors:Ella C. Davidson, Lorenzo Zino, Ming Cao, Mengbin Ye
Title: Modelling the coevolution of opinion dynamics and decision making in social dilemmas
Abstract:
This paper proposes a mathematical model for the coevolution of actions and opinions for a population facing a social dilemma. In particular, we assume each person participates in a Public Goods Game (PGG), with their action being to cooperate or defect, and holds an opinion about which action they prefer. We propose a payoff function that combines the PGG with the Friedkin‑‑Johnsen model from opinion dynamics to form a coevolutionary game. According to a discrete‑time process, players asynchronously update their actions and opinions, aiming to maximise their individual payoff for the coevolutionary game using myopic best‑response. We study the equilibria and provide conditions for the existence of the all‑defection and all‑cooperation consensus equilibria. We also establish conditions for global convergence to the all‑defection equilibrium.

Authors:Kazuya Echigo, David E. J. van Wijk, Pol Mestres, Ersin Daş, Joel W. Burdick, Aaron D. Ames
Title: Probabilistic Control Barrier Functions for Systems with State Estimation Uncertainty using Sub-Gaussian Concentration
Abstract:
Safety‑critical control systems, such as spacecraft performing proximity operations, must provide formal safety guarantees despite stochastic uncertainties from state estimation and unmodeled dynamics. Although Control Barrier Functions (CBFs) have been extended to stochastic systems, existing approaches typically face a trade‑off between the tightness of probabilistic guarantees and computational tractability. This paper presents a particle‑based probabilistic CBF framework that overcomes this limitation by exploiting the sub‑Gaussian structure of the barrier function increment under Gaussian uncertainties. We establish that Gaussian uncertainties propagating through Lipschitz‑continuous control‑affine dynamics preserve sub‑Gaussianity of the barrier function increment, with explicit tail bounds. Leveraging this structure, we derive finite‑sample bounds on the approximation error between particle‑based Conditional Value at Risk (CVaR) estimates and ground‑truth probabilistic constraints; applying this yields a tractable optimization problem formulation with finite‑sample safety certificates. We show through numerical experiments how the proposed approach provides tight yet provably valid probabilistic safety guarantees.

Authors:Yashodhan D. Hakke, Almuatazbellah M. Boker, Lamine Mili, Michael von Spakovsky, Hoda Eldardiry
Title: Alleviating Community Fear in Disasters via Multi-Agent Actor-Critic Reinforcement Learning
Abstract:
During disasters, cascading failures across power grids, communication networks, and social behavior amplify community fear and undermine cooperation. Existing cyber‑physical‑social (CPS) models simulate these coupled dynamics but lack mechanisms for active intervention. We extend the CPS resilience model of Valinejad and Mili (2023) with control channels for three agencies, communication, power, and emergency management, and formulate the resulting system as a three‑player non‑zero‑sum differential game solved via online actor‑critic reinforcement learning. Simulations based on Hurricane Harvey data show 70% mean fear reduction with improved infrastructure recovery; cross‑validation in the case of Hurricane Irma (without refitting) achieves 50% fear reduction, confirming generalizability.

Authors:Arslan Ahmad, Ian Dobson, Anne Kimber
Title: Quantifying the resilience benefits of undergrounding a circuit with utility data
Abstract:
We leverage historical outage data to quantify the resilience benefits of undergrounding a circuit. The historical performance of the overhead circuit is compared to the performance if the circuit had been undergrounded in the past. The number of outages, customers affected, outage duration, and customer hours lost are used as metrics to quantify the benefits of undergrounding. Results show 75% and 78% reductions in customer hours lost per year for two selected circuits, as well as a significant reduction in the average number of outages and customers affected per year, highlighting the advantages of undergrounding. The benefits of investments that result in 10% faster outage restoration are also calculated by rerunning history with the faster restoration included.

Authors:Kaushik Lakshmiramanan, Vineeta Nair, Ching-Yi Lin, Sheng-Yu Peng, Sahil Shah
Title: An Asynchronous Delta Modulator for Spike Encoding in Event-Driven Brain-Machine Interface
Abstract:
This paper presents the design and implementation of an asynchronous delta modulator as a spike encoder for event‑driven neural recording in a 65nm CMOS process. The proposed neuromorphic front‑end converts analog signals into discrete, asynchronous ON and OFF spikes, effectively compressing continuous biopotentials into spike trains compatible with spiking neural networks (SNNs). Its asynchronous operation enables seamless integration with neuromorphic architectures for real‑time decoding in closed‑loop brain‑machine interfaces (BMIs). Measurement results from silicon demonstrate an energy consumption of 60.73 nJ/spike, an F1‑score of 80% compared to a behavioral model of the asynchronous delta modulator, and a compact pixel area of 73.45 um × 73.64 um.

Authors:Julian Quick, Marcus Binder Nilsen, Andreas Bechmann, Tran Nguyen Le, Pierre-Elouan Mikael Rethore
Title: Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control
Abstract:
Plant‑level control is an emerging wind energy technology that presents opportunities and challenges. By controlling turbines in a coordinated manner via a central controller, it is possible to achieve greater wind power plant efficiency. However, there is a risk that measurement errors will confound the process, or even that hackers will alter the telemetry signals received by the central controller. This paper presents a framework for developing a safe plant controller by training it with an adversarial agent designed to confound it. This necessitates training the adversary to confound the controller, creating a sort of circular logic or "Arms Race." This paper examines three broad training approaches for co‑training the protagonist and adversary, finding that an Arms Race approach yields the best results. These initial results indicate that the Arms Race adversarial training reduced worst‑case performance degradation from 39% power loss to 7.9% power gain relative to a baseline operational strategy.

Authors:Qi Zhao, Shaoxuan Cui, Baolin Zhang, Junwei Du, Yuanshi Zheng
Title: Invariance of Competition Outcomes in Hypergraph Competitive Dynamics
Abstract:
Winner‑take‑all (WTA)‑‑type selection is a fundamental mechanism in networked competition, yet its dependence on higher‑order interactions remains insufficiently understood. We study a Lotka‑‑Volterra competitive dynamics on higher‑order networks, where classical pairwise inhibition is augmented by multi‑way interaction terms induced by hyperedges of uniform hypergraphs. The proposed model shows multiple competitive outcomes, including WTA, winner‑share‑all (WSA), and variant winner‑take‑all (VWTA). The existence, uniqueness and stability of equilibria are rigorously proved through mathematical analysis, which relies on classical stability theory and recent advances in tensor algebra. We show that the eventual selection outcome is relatively insensitive to the hyperedge order and the specific higher‑order coupling structure, and is instead determined by a small set of interpretable scalar parameters, such as the ratio between self‑inhibition and lateral‑inhibition and the external inputs. Numerical experiments support the theory by showing that higher‑order interactions affect convergence and steady states, yet yield the similar outcome taxonomy (WTA/WSA/VWTA) as in standard graphs. These results provide a network‑scientific explanation of the robustness of WTA‑type outcomes under complex group interactions and offer principled guidance for designing selection mechanisms on higher‑order networks.

Authors:Shaoxuan Cui, Qi Zhao, Guanlin Li, Hildeberto Jardon Kojakhmetov, Ming Cao
Title: Linear Feedback Controller for Homogeneous Polynomial Systems
Abstract:
This paper studies stabilization and its corresponding closed‑loop region‑of‑attraction (ROA) for homogeneous polynomial dynamical systems whose nonlinear term admits an orthogonally decomposable (ODECO) tensor representation. While recent tensor‑based results provide explicit solutions and sharp global characterizations for open‑loop ODECO systems, closed‑loop synthesis and computable ROA estimates are still often dominated by local linearization or Lyapunov/SOS (sum of squares) methods, which can be conservative and computationally demanding. We propose a structure‑preserving linear feedback design that shares the ODECO eigenbasis of the system's tensor, thereby enabling closed‑form trajectory expressions, explicit convergence/escape thresholds, and sharp ROA characterizations. Under mild conditions, we further derive robustness/ISS‑type bounds for bounded disturbances. Numerical examples validate the theoretical results.

Authors:Hamed Faghihian, Parisa Ansari Bonab, Arman Sargolzaei
Title: An Energy-Efficient Lyapunov-Based Cooperative Adaptive Cruise Controller for Electric Vehicles
Abstract:
As electric vehicles (EVs) are increasingly adopted as platforms for connected and automated vehicles (CAVs), enhancing their energy efficiency becomes critical. With the emergence of vehicle‑to‑vehicle (V2V) communication, cooperative adaptive cruise control (CACC) offers improved traffic flow, safety, and energy efficiency by enabling real‑time coordination among EVs. However, conventional CACC algorithms neglected acceleration and regenerative braking dynamics in their implementation. To address this gap, this paper proposes a third‑order dynamic model for EVs which has been derived from real‑world experimental data. We also propose a novel, practical, and energy‑efficient Lyapunov‑based CACC controller explicitly designed for EV platoons. The proposed controller is requiring lower control gains while ensuring string stability and energy efficiency. To validate its effectiveness, we conduct both simulation and experimental environments, demonstrating that our approach reduces velocity fluctuations, maintains string stability at lower headway times, and improves energy efficiency of the CACC platoon by up to 38.5% compared to a baseline CACC.

Authors:Robert H. Moldenhauer, Karl Worthmann, Romain Postoyan, Dragan Nešić, Mathieu Granzotto
Title: Discounted MPC and infinite-horizon optimal control under plant-model mismatch: Stability and suboptimality
Abstract:
We study closed‑loop stability and suboptimality for MPC and infinite‑horizon optimal control solved using a surrogate model that differs from the real plant. We employ a unified framework based on quadratic costs to analyze both finite‑ and infinite‑horizon problems, encompassing discounted and undiscounted scenarios alike. Plant‑model mismatch bounds proportional to states and controls are assumed, under which the origin remains an equilibrium. Under continuity of the model and cost‑controllability, exponential stability of the closed loop can be guaranteed. Furthermore, we give a suboptimality bound for the closed‑loop cost recovering the optimal cost of the surrogate. The results reveal a tradeoff between horizon length, discounting and plant‑model mismatch. The robustness guarantees are uniform over the horizon length, meaning that larger horizons do not require successively smaller plant‑model mismatch.

Authors:Kooktae Lee
Title: Density-Driven Optimal Control: Convergence Guarantees for Stochastic LTI Multi-Agent Systems
Abstract:
This paper addresses the decentralized non‑uniform area coverage problem for multi‑agent systems, a critical task in missions with high spatial priority and resource constraints. While existing density‑based methods often rely on computationally heavy Eulerian PDE solvers or heuristic planning, we propose Stochastic Density‑Driven Optimal Control (D^2OC). This is a rigorous Lagrangian framework that bridges the gap between individual agent dynamics and collective distribution matching. By formulating a stochastic MPC‑like problem that minimizes the Wasserstein distance as a running cost, our approach ensures that the time‑averaged empirical distribution converges to a non‑parametric target density under stochastic LTI dynamics. A key contribution is the formal convergence guarantee established via reachability analysis, providing a bounded tracking error even in the presence of process and measurement noise. Numerical results verify that Stochastic D^2OC achieves robust, decentralized coverage while outperforming previous heuristic methods in optimality and consistency.

Authors:Oleksii Molodchyk, Omid Mokhtari, Samuel Chevalier, Mads R. Almassalkhi, Timm Faulwasser
Title: Data-Driven Power Flow for Radial Distribution Networks with Sparse Real-Time Data
Abstract:
Real‑time control of distribution networks requires accurate information about the system state. In practice, however, such information is difficult to obtain because real‑time measurements are available only at a limited number of locations. This paper proposes a novel data‑driven power flow (DDPF) framework for balanced radial distribution networks. The proposed algorithm combines the behavioral approach with the DistFlow model and leverages offline historical data to solve power flow problems using only a limited set of real‑time measurements. To design DDPF under sparse measurement conditions, we develop a sensor placement problem based on optimal network reductions. This allows us to determine sensor locations subject to a predefined sensor budget and to explicitly account for the radial nature of distribution networks. Unlike approaches that rely on full observability, the proposed framework is designed for practical distribution grids with sparse measurement availability. This enables data‑driven power flow for real‑time operation while reducing the number of required sensors. On several test cases, the proposed DDPF algorithm could demonstrate accurate voltage magnitude predictions, with a maximum error less than 0.001 p.u., with as little as 25% of total locations equipped with sensors.

Authors:Peihu Duan, Jiabao He, Yuezu Lv, Guanghui Wen
Title: Data-Driven Moving Horizon Estimators for Linear Systems with Sample Complexity Analysis
Abstract:
This paper investigates the state estimation problem for linear systems subject to Gaussian noise, where the model parameters are unknown. By formulating and solving an optimization problem that incorporates both offline and online system data, a novel data‑driven moving horizon estimator (DDMHE) is designed. We prove that the expected 2‑norm of the estimation error of the proposed DDMHE is ultimately bounded. Further, we establish an explicit relationship between the system noise covariances and the estimation error of the proposed DDMHE. Moreover, through a sample complexity analysis, we show how the length of the offline data affects the estimation error of the proposed DDMHE. We also quantify the performance gap between the proposed DDMHE using noisy data and the traditional moving horizon estimator with known system matrices. Finally, the theoretical results are validated through numerical simulations.

Authors:Ram Padmanabhan, Melkior Ornik
Title: Finite-time Reachability for Constrained, Partially Uncontrolled Nonlinear Systems
Abstract:
This paper presents a technique to drive the state of a constrained nonlinear system to a specified target state in finite time, when the system suffers a partial loss in control authority. Our technique builds on a recent method to control constrained nonlinear systems by building a simple, linear driftless approximation at the initial state. We construct a partition of the finite time horizon into successively smaller intervals, and design controlled inputs based on the approximate dynamics in each partition. Under conditions that bound the length of the time horizon, we prove that these inputs result in bounded error from the target state in the original nonlinear system. As successive partitions of the time horizon become shorter, the error reduces to zero despite the effect of uncontrolled inputs. A simulation example on the model of a fighter jet demonstrates that the designed sequence of controlled inputs achieves the target state despite the system suffering a loss of control authority over one of its inputs.

Authors:Matthias Pirlet, Adrien Bolland, Alexandre Huynen, Quentin Louveaux, Gilles Louppe, Damien Ernst
Title: Bayesian Inference for Estimating Generation Costs in Electricity Markets
Abstract:
Estimating generation costs from observed electricity market data is essential for market simulation, strategic bidding, and system planning. To that end, we model the relationship between generation costs and production schedules with a latent variable model. Estimating generation costs from observed schedules is then formulated as Bayesian inference. A prior distribution encodes an initial belief on parameters, and the inference consists of updating the belief with the posterior distribution given observations. We use balanced neural posterior estimation (BNPE) to learn this posterior. Validation on the IEEE RTS‑96 test system shows that marginal costs are recovered with narrow credible intervals, while start‑up costs remain largely unidentifiable from schedules alone. The method is benchmarked against an inverse‑optimization algorithm that exhibits larger parameter errors without uncertainty quantification.

Authors:Ada Yildirim, Bryce L. Ferguson
Title: Stability and Sensitivity Analysis for Objective Misspecifications Among Model Predictive Game Controllers
Abstract:
Model‑based multi‑agent control requires agents to possess a model of the behavior of others to make strategic decisions. Solution concepts from game theory are often used to model the emergent collective behavior of self‑interested agents and have found active use in multi‑agent control design. Model predictive games are a class of controllers in which an agent iteratively solves a finite‑horizon game to predict the behavior of a multi‑agent system and synthesize their own control action. When multiple agents implement these types of controllers, there may exist misspecifications in the respective game models embedded in their controllers, stemming from inaccurate estimates or conjectures of other agents' objectives. This paper analyzes the resulting prediction misalignments and their effects on the system's behavior. We provide criteria for the stability of multi‑agent dynamic systems with heterogeneous model predictive game controllers, and quantify the sensitivity of the equilibria to individual agents' game parameters.

Authors:Alberto Mingoia, Matthias Pezzutto, Fernando S Barbosa, David Umsonst
Title: Bandwidth reduction methods for packetized MPC over lossy networks
Abstract:
We study the design of an offloaded model predictive control (MPC) operating over a lossy communication channel. We introduce a controller design that utilizes two complementary bandwidth‑reduction methods. The first method is a multi‑horizon MPC formulation that decreases the number of optimization variables, and therefore the size of transmitted input trajectories. The second method is a communication‑rate reduction mechanism that lowers the frequency of packet transmissions. We derive theoretical guarantees on recursive feasibility and constraint satisfaction under minimal assumptions on packet loss, and we establish reference‑tracking performance for the rate‑reduction strategy. The proposed methods are validated using a hardware‑in‑the‑loop setup with a real 5G network, demonstrating simultaneous improvements in bandwidth efficiency and computational load.

Authors:Debarpita Banerjee, Sumana Ghosh, Snigdha Das, Shilpa Budhkar, Rana Pratap Sircar
Title: FORSLICE: An Automated Formal Framework for Efficient PRB-Allocation towards Slicing Multiple Network Services
Abstract:
Network slicing is a modern 5G technology that provides efficient network experience for diverse use cases. It is a technique for partitioning a single physical network infrastructure into multiple virtual networks, called slices, each equipped for specific services and requirements. In this work, we particularly deal with radio access network (RAN) slicing and resource allocation to RAN slices. In 5G, physical resource blocks (PRBs) being the fundamental units of radio resources, our main focus is to allocate PRBs to the slices efficiently. While addressing a spectrum of needs for multiple services or the same services with multi‑priorities, we need to ensure two vital system properties: i) fairness to every service type (i.e., providing the required resources and a desired range of throughput) even after prioritizing a particular service type, and ii) PRB‑optimality or minimizing the unused PRBs in slices. These serve as the core performance evaluation metrics for PRB‑allocation in our work. We adopt the 3‑layered hierarchical PRB‑partitioning technique for allocating PRBs to network slices. The case‑specific, AI‑based solution of the state‑of‑the‑art method lacks sufficient correctness to ensure consistent system performance. To achieve guaranteed correctness and completeness, we leverage formal methods and propose the first approach for a fair and optimal PRB distribution to RAN slices. We formally model the PRB‑allocation problem as a 3‑layered framework, FORSLICE, specifically by employing satisfiability modulo theories. Next, we apply formal verification to ensure that the desired system properties: fairness and PRB‑optimality, are satisfied by the model. The proposed method offers an efficient, versatile and automated approach compatible with all 3‑layered hierarchical network structure configurations, yielding significant system property improvements compared to the baseline.

Authors:Stephen Ampleman, Dennice F. Gayme
Title: From Cut-In to Rated: Multi-Region Floating Offshore Wind Farm Control for Secondary Frequency Regulation
Abstract:
This paper describes a multi‑region control framework for floating offshore wind farms. Specifically, we propose a novel generator torque controller that regulates rotor speed in Region 2, corresponding to wind speeds between the cut‑in and rated values. In Region 3 (wind speeds at or above rated but below cut‑out speed) we employ a PI‑LQR for collective blade pitch. Control blending across the transitional wind speeds (Region 2.5) employs a sigmoid weighting function applied to the control variables. Two modeling paradigms are proposed for farm‑level power tracking with rotor speed regularization: a nonlinear model predictive controller (NL‑MPC) with a dynamic wake model, and a reduced order model predictive controller based on linear parameter varying turbine models with a time delay representation of wake advection (LPVTD‑MPC). These approaches are evaluated over three wind inlet conditions using the PJM ancillary service certification criteria for participation in a secondary frequency regulation market. Results show that both approaches achieve scores of at least 89.9% for the three different testing scenarios, which are well above the qualification threshold of 75%. However, the LPVTD‑MPC approach solves the problem in under half the time versus NL‑MPC but with slightly larger fluctuations in farm‑level power output, highlighting the trade‑off between performance and computational tractability. The control framework is among the first to address multi‑region wind turbine dynamics together with market driven power tracking objectives for floating offshore wind farms. Such multi‑region control becomes increasingly necessary in the floating turbine setting where large (region spanning) wind speed variations are common due to wave induced platform pitching.

Authors:Alexandre Sanfelici Bazanella, Tristão Garcia
Title: The restrictive conditions to solve LTI Systems by Ordinary Differential Equations
Abstract:
Ordinary differential equations (ODE's) are a cornerstone of systems and control theory. Accordingly, they are standard material in undergraduate programs in engineering and there is abundant didactic literature about this topic. Yet, the solution methods and formulas prescribed in this didactic literature are unclear about the assumptions behind their derivation and thus about the limits of their applicability. Specifically, smoothness of the input is rarely discussed, even though it is a critical property to define the character of the solutions and the validity of the methods and formulas prescribed. On the other hand, the relationships with the state space representation (SSR) of linear systems is absent from this same literature and only marginally discussed in more advanced texts. In this paper we detail these gaps left behind in the didactic literature, then we provide a formal delimitation of the boundaries of the standard solutions and methods for linear ODE's. Our analysis relies on some key properties of state space representations, so we establish the formal connections between ODEs and SSR's, defining an equivalence between the two that is absent in the literature and is of conceptual interest by itself.

Authors:Thanana Nuchkrua, Sudchai Boonto, Xiaoqi Liu
Title: Cognitive Flexibility as a Latent Structural Operator for Bayesian State Estimation
Abstract:
Deep stochastic state‑space models enable Bayesian filtering in nonlinear, partially observed systems but typically assume a fixed latent structure. When this assumption is violated, parameter adaptation alone may result in persistent belief inconsistency. We introduce \emphCognitive Flexibility (CF) as a representation‑level operator that selects latent structures online via an innovation‑based predictive score, while preserving the Bayesian filtering recursion. Structural mismatch is formalized as irreducible predictive inconsistency under fixed structure. The resulting belief‑‑structure recursion is shown to be well posed, to exhibit a structural descent property, and to admit finite switching, with reduction to standard Bayesian filtering under correct specification. Experiments on latent‑dynamics mismatch, observation‑structure shifts, and well‑specified regimes confirm that CF improves predictive accuracy under a mismatch while remaining non‑intrusive when the model is correctly specified.

Authors:Elisabeth Vogel, Peter Langendörfer
Title: Resilience as a Dynamical Property of Risk Trajectories in CPSoS
Abstract:
Resilience in cyber‑physical systems of systems (CPSoS) is often assessed using static indices or point‑in‑time metrics that do not adequately account for the temporal evolution of risk following a disruption. This paper formalizes resilience as a functional of the risk trajectory by modelling risk as a dynamic state variable. It is analytically shown that key resilience properties are structurally determined by maximum deviation (peak) and effective damping, and that cumulative risk exposure depends on their ratio. A simplified energy‑dependent system illustrates the resulting differences in peak magnitude, recovery dynamics, and cumulative impact. The proposed approach links resilience assessment to stability properties of dynamic systems and provides a system‑theoretically consistent foundation for the analysis of time‑dependent resilience in CPSoS.

Authors:Alessandro Melis, Soulaimane Berkane, Tarek Hamel
Title: Complementary Filtering on SO(3) for Attitude Estimation with Scalar Measurements
Abstract:
Attitude estimation using scalar measurements, corresponding to partial vectorial observations, arises naturally when inertial vectors are not fully observed but only measured along specific body‑frame vectors. Such measurements arise in problems involving incomplete vector measurements or attitude constraints derived from heterogeneous sensor information. Building on the classical complementary filter on SO(3), we propose an observer with a modified innovation term tailored to this scalar‑output structure. The main result shows that almost‑global asymptotic stability is recovered, under suitable persistence of excitation conditions, when at least three inertial vectors are measured along a common body‑frame vector, which is consistent with the three‑dimensional structure of SO(3). For two‑scalar configurations ‑ corresponding either to one inertial vector measured along two body‑frame vectors, or to two inertial vectors measured along a common body‑frame vector ‑ we further derive sufficient conditions guaranteeing convergence within a reduced basin of attraction. Different examples and numerical results demonstrate the effectiveness of the proposed scalar‑based complementary filter for attitude estimation in challenging scenarios involving reduced sensing and/or novel sensing modalities.

Authors:Enno Breukelman, Takumi Shinohara, Joowon Lee, Henrik Sandberg
Title: Data-Driven Unknown Input Reconstruction for MIMO Systems with Convergence Guarantees
Abstract:
In this paper, we consider data‑driven reconstruction of unknown inputs to linear time‑invariant (LTI) multiple‑input multiple‑output (MIMO) systems. We propose a novel autoregressive estimator based on a constrained least‑squares formulation over Hankel matrices, splitting the problem into an output‑consistency constraint and an input‑history‑matching objective. Our method relies on previously recorded input‑output data to represent the system, but does not require knowledge of the true input to initialize the algorithm. We show that the proposed estimator is strictly stable if and only if all the invariant zeros of the trajectory‑generating system lie strictly inside the unit circle, which can be verified purely from input and output data. This mirrors existing results from model‑based input reconstruction and closes the gap between model‑based and data‑driven settings. Lastly, we provide numerical examples to demonstrate the theoretical results.

Authors:Amirhossein Afsharrad, Amirhesam Abedsoltan, Ahmadreza Moradipari, Sanjay Lall
Title: On-Policy Distillation of Language Models for Autonomous Vehicle Motion Planning
Abstract:
Large language models (LLMs) have recently demonstrated strong potential for autonomous vehicle motion planning by reformulating trajectory prediction as a language generation problem. However, deploying capable LLMs in resource‑constrained onboard systems remains a fundamental challenge. In this paper, we study how to effectively transfer motion planning knowledge from a large teacher LLM to a smaller, more deployable student model. We build on the GPT‑Driver framework, which represents driving scenes as language prompts and generates waypoint trajectories with chain‑of‑thought reasoning, and investigate two student training paradigms: (i) on‑policy generalized knowledge distillation (GKD), which trains the student on its own self‑generated outputs using dense token‑level feedback from the teacher, and (ii) a dense‑feedback reinforcement learning (RL) baseline that uses the teacher's log‑probabilities as per‑token reward signals in a policy gradient framework. Experiments on the nuScenes benchmark show that GKD substantially outperforms the RL baseline and closely approaches teacher‑level performance despite a 5× reduction in model size. These results highlight the practical value of on‑policy distillation as a principled and effective approach to deploying LLM‑based planners in autonomous driving systems.

Authors:S. Betancur Giraldo, J. Mårtensson, M. Barreau
Title: Second Order Physics-Informed Learning of Road Density using Probe Vehicles
Abstract:
We propose a Physics Informed Learning framework for reconstructing traffic density from sparse trajectory data. The approach combines a second‑order Aw‑Rascle and Zhang model with a first‑order training stage to estimate the equilibrium velocity. The method is evaluated in both equilibrium and transient traffic regimes using SUMO simulations. Results show that while learning the equilibrium velocity improves reconstruction under steady state conditions, it becomes unstable in transient regimes due to the breakdown of the equilibrium assumption. In contrast, the second‑order model consistently provides more accurate and robust reconstructions than first‑order approaches, particularly in nonequilibrium conditions.

Authors:Riccardo Ramaschi, Mario Paolone, Sonia Leva
Title: A Game-Theoretic Decentralized Real-Time Control of Electric Vehicle Charging Stations - Part II: Numerical Simulations
Abstract:
In the first part of this two‑part paper a game‑theoretic decentralized real‑time control is proposed in the context of Electric Vehicle (EV) Charging Station (CS). This method, relying on a Stackelberg Game‑based Alternating Direction of Multipliers (SG‑ADMM), intends to steer the EVs' individual objectives towards the CS optimum by means of an incentive design mechanism, while controlling the EV power dispatch in a distributed manner. We integrate SG‑ADMM in a hierachical multi‑layered Energy Management System (EMS) as the real‑time control algorithm, formulating the two‑layer approach so that the SG leader (i.e., the CS), holding commitment power, trades off the available power with the incentives to the EVs, and the SG followers (i.e., the EVs) optimizes their charging curve in response to the leader decision. In this second part, we demonstrate the applicability of SG‑ADMM as a incentive design mechanism inside an EVCS EMS, testing it in a large‑scale EVCS. We benchmark this method with a decentralized (ADMM‑based), a centralized and a uncontrolled approach, showing that our method exploits EV‑level flexibility in a cost‑effective, fair and computationally efficient manner.

Authors:Riccardo Ramaschi, Mario Paolone, Sonia Leva
Title: A Game-Theoretic Decentralized Real-Time Control of Electric Vehicle Charging Stations - Part I: Incentive Design
Abstract:
Large‑scale Electric Vehicle (EV) Charging Station (CS) may be too large to be dispatched in real‑time via a centralized approach. While a decentralized approach may be a viable solution, the lack of incentives could impair the alignment of EVs' individual objectives with the controller's optimum. In this work, we integrate a decentralized algorithm into a hierarchical three‑layer Energy Management System (EMS), where it operates as the real‑time control layer and incorporates an incentive design mechanism. A centralized approach is proposed for the dispatch plan definition and for the intra‑day refinement, while a decentralized game‑theoretic approach is proposed for the real time control. We employ a Stackelberg Game‑based Alternating Direction Method of Multipliers (SG‑ADMM) to simultaneously design an incentive mechanism while managing the EV control in a distributed manner, while framing the leadership‑followership relation between the EVCS and the EVs as a non‑cooperative game where the leader has commitment power. Part I of this two‑part paper deals with the SG‑ADMM approach description, literature review and integration in the abovementioned hierarchical EMS, focusing on the modifications needed for the proposed application.

Authors:Chieh Tsai, Muhammad Junayed Hasan Zahed, Salim Hariri, Hossein Rastgoftar
Title: Learning over Forward-Invariant Policy Classes: Reinforcement Learning without Safety Concerns
Abstract:
This paper proposes a safe reinforcement learning (RL) framework based on forward‑invariance‑induced action‑space design. The control problem is cast as a Markov decision process, but instead of relying on runtime shielding or penalty‑based constraints, safety is embedded directly into the action representation. Specifically, we construct a finite admissible action set in which each discrete action corresponds to a stabilizing feedback law that preserves forward invariance of a prescribed safe state set. Consequently, the RL agent optimizes policies over a safe‑by‑construction policy class. We validate the framework on a quadcopter hover‑regulation problem under disturbance. Simulation results show that the learned policy improves closed‑loop performance and switching efficiency, while all evaluated policies remain safety‑preserving. The proposed formulation decouples safety assurance from performance optimization and provides a promising foundation for safe learning in nonlinear systems.

Authors:Xiaojing Chen, Haiqi Yu, Wei Ni, Dusit Niyato, Ruichen Zhang, Xin Wang, Shunqing Zhang, Shugong Xu
Title: Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey
Abstract:
The rapid emergence of Large Language Models (LLMs) has catalyzed Agentic artificial intelligence (AI), autonomous systems integrating perception, reasoning, and action into closed‑loop pipelines for continuous adaptation. While unlocking transformative applications in mobile edge computing, autonomous systems, and next‑generation wireless networks, this paradigm creates fundamental energy challenges through iterative inference and persistent data exchange. Unlike traditional AI where bottlenecks are computational Floating Point Operations (FLOPs), Agentic AI faces compounding computational and communication energy costs. In this survey, we propose an energy accounting framework identifying computational and communication costs across the Perception‑Reasoning‑Action cycle. We establish a unified taxonomy spanning model simplification, computation control, input and attention optimization, and hardware‑aware inference. We explore cross‑layer co‑design strategies jointly optimizing model parameters, wireless transmissions, and edge resources. Finally, we identify open challenges of federated green learning, carbon‑aware agency, 6th generation mobile communication (6G)‑native Agentic AI, and self‑sustaining systems, providing a roadmap for scalable autonomous intelligence.

Authors:Kevin Riehl, Lea Künstler, Ying-Chuan Ni, Anastasia Psarou, Shaimaa K. El-Baklish, Anastasios Kouvelas, Michail A. Makridis
Title: Distributive Perimetral Queue Balancing Mechanisms: Towards Equitable Urban Traffic Gating and Fair Perimeter Control
Abstract:
Perimeter control is an effective urban traffic management strategy that regulates inflow to congested urban regions using aggregate network dynamics. While existing approaches primarily optimize system‑level efficiency, such as total travel time or network throughput, they often overlook equity considerations, leading to uneven delay distributions across entry points. This work integrates fairness objectives into perimeter control design through explicit queue balancing mechanisms.A large‑scale, microscopic case study of the Financial District in the San Francisco urban network is used to evaluate both performance and implementation challenges. The results demonstrate conventional perimeter control not only reduces total and internal delays but can also improve fairness metrics (Harsanyian, Rawlsian, Utilitarian, Egalitarian). Building on this observation, queue balancing strategies match conventional performance while yielding measurable fairness improvements, especially in heterogeneous demand scenarios, where congestion is unevenly distributed across entry points. The proposed framework contributes toward equitable control design for emerging intelligent transportation systems and higher user acceptance for those.

Authors:Hanmin Cai, Federica Bellizio, Yi Guo, Gabriele Humbert, Mina Montazeri, Julie Rousseau, Matthias Brandes, Arnab Chatterjee, Andrea Gattiglio, Leandro von Krannichfeldt, Emmanouil Thrampoulidis, Varsha N. Behrunani, Goran Strbac, Philipp Heer
Title: Towards socio-techno-economic power systems with demand-side flexibility
Abstract:
Harnessing the demand‑side flexibility in building and mobility sectors can help to better integrate renewable energy into power systems and reduce global CO2 emissions. Enabling this sector coupling can be achieved with advances in energy management, business models, control technologies, and power grids. The study of demand‑side flexibility extends beyond engineering, spanning social science, economics, and power and control systems, which present both challenges and opportunities to researchers and engineers in these fields. This Review outlines recent trends and studies in social, economic, and technological advancements in power systems that leverage demand‑side flexibility. We first provide a concept of a socio‑techno‑economic system with an abstraction of end‑users, building and mobility sectors, control systems, electricity markets, and power grids. We discuss the interconnections between these elements, highlighting the importance of bidirectional flows of information and coordinated decision‑making. We then emphasize that fully realizing demand‑side flexibility necessitates deep integration across stakeholders and systems, moving beyond siloed approaches. Finally, we discuss the future directions in renewable‑based power systems and control engineering to address key challenges from both research and practitioners' perspectives. A holistic approach for identifying, measuring, and utilizing demand‑side flexibility is key to successfully maximizing its multi‑stakeholder benefits but requires further transdisciplinary collaboration and commercially viable solutions for broader implementation.

Authors:Tong Duy Son, Zhihao Liu, Piero Brigida, Yerlan Akhmetov, Gurudevan Devarajan, Kai Liu, Ajinkya Bhave
Title: Automotive Engineering-Centric Agentic AI Workflow Framework
Abstract:
Engineering workflows such as design optimization, simulation‑based diagnosis, control tuning, and model‑based systems engineering (MBSE) are iterative, constraint‑driven, and shaped by prior decisions. Yet many AI methods still treat these activities as isolated tasks rather than as parts of a broader workflow. This paper presents Agentic Engineering Intelligence (AEI), an industrial vision framework that models engineering workflows as constrained, history‑aware sequential decision processes in which AI agents support engineer‑supervised interventions over engineering toolchains. AEI links an offline phase for engineering data processing and workflow‑memory construction with an online phase for workflow‑state estimation, retrieval, and decision support. A control‑theoretic interpretation is also possible, in which engineering objectives act as reference signals, agents act as workflow controllers, and toolchains provide feedback for intervention selection. Representative automotive use cases in suspension design, reinforcement learning tuning, multimodal engineering knowledge reuse, aerodynamic exploration, and MBSE show how diverse workflows can be expressed within a common formulation. Overall, the paper positions engineering AI as a problem of process‑level intelligence and outlines a practical roadmap for future empirical validation in industrial settings.

Authors:Tong Duy Son, Kohta Sugiura, Marc Brughmans, Andrey Hense, Zhihao Liu, Amirthalakshmi Veeraraghavan, Ajinkya Bhave, Jay Masters, Paolo di Carlo, Theo Geluk
Title: Toward Generalizable Graph Learning for 3D Engineering AI: Explainable Workflows for CAE Mode Shape Classification and CFD Field Prediction
Abstract:
Automotive engineering development increasingly relies on heterogeneous 3D data, including finite element (FE) models, body‑in‑white (BiW) representations, CAD geometry, and CFD meshes. At the same time, engineering teams face growing pressure to shorten development cycles, improve performance and accelerate innovation. Although artificial intelligence (AI) is increasingly explored in this domain, many current methods remain task‑specific, difficult to interpret, and hard to reuse across development stages. This paper presents a practical graph learning framework for 3D engineering AI, in which heterogeneous engineering assets are converted into physics‑aware graph representations and processed by Graph Neural Networks (GNNs). The framework is designed to support both classification and prediction tasks. The framework is validated on two automotive applications: CAE vibration mode shape classification and CFD aerodynamic field prediction. For CAE vibration mode classification, a region‑aware BiW graph supports explainable mode classification across vehicle and FE variants under label scarcity. For CFD aerodynamic field prediction, a physics‑informed surrogate predicts pressure and wall shear stress (WSS) across aerodynamic body shape variants, while symmetry preserving down sampling retains accuracy with lower computational cost. The framework also outlines data generation guidance that can help engineers identify which additional simulations or labels are valuable to collect next. These results demonstrate a practical and reusable engineering AI workflow for more trustworthy CAE and CFD decision support.

Authors:Leyou Zhou, Mucheng Li, Xiaojie Shi, Meng Zhan, Juanjuan Wang, Dan Wu
Title: Differences in Small-Signal Stability Boundaries Between Aggregated and Granular DFIG Models
Abstract:
Broadband oscillations in wind farms have been widely reported in recent years. Past studies have examined various types of oscillations in wind farms, relating small‑signal stability to control settings, operating conditions, and electrical parameters. However, most analyses are performed on aggregated single‑unit models, which may deviate from the true behavior, leading to misleading stability assessments. To investigate how aggregation affects stability conclusions, this paper develops detailed single‑, two‑, and three‑unit doubly‑fed induction generator (DFIG) models and their aggregated counterparts. Then, a D‑decomposition‑related ray‑extrapolation method is proposed to characterize the small‑signal stability region of nonlinear DFIG models in the parameter space, delineating stability boundaries under numerous parameter combinations. The study reveals that aggregated models stability regions within the parameter planes of control settings and operating conditions differ from those of granular models in terms of basic shape, critical modes, and evolution patterns, posing a risk of misjudging stability margins.

Authors:Zhewei Wang, Emrah Akyol, Marcos M. Vasconcelos
Title: Learning to Coordinate over Networks with Bounded Rationality
Abstract:
Network coordination games are widely used to model collaboration among interconnected agents, with applications across diverse domains including economics, robotics, and cyber‑security. We consider networks of bounded‑rational agents who interact through binary stag hunt games, a canonical game theoretic model for distributed collaborative tasks. Herein, the agents update their actions using logit response functions, yielding the Log‑Linear Learning (LLL) algorithm. While convergence of LLL to a risk‑dominant Nash equilibrium requires unbounded rationality, we consider regimes in which rationality is strictly bounded. We first show that the stationary probability of states corresponding to perfect coordination is monotone increasing in the rationality parameter β. For K‑regular networks, we prove that the stationary probability of a perfectly coordinated action profile is monotone in the connectivity degree K, and we provide an upper bound on the minimum rationality required to achieve a desired level of coordination. For irregular networks, we show that the stationary probability of perfectly coordinated action profiles increases with the number of edges in the graph. We show that, for a large class of networks, the partition function of the Gibbs measure is well approximated by the moment generating function of Gaussian random variable. This approximation allows us to optimize degree distributions and establishes that the optimal network ‑ i.e., the one that maximizes the stationary probability of coordinated action profiles ‑ is K‑regular. Consequently, our results indicate that networks of uniformly bounded‑rational agents achieve the most reliable coordination when connectivity is evenly distributed among agents.

Authors:Lida Lamakani, Efstratios N. Pistikopoulos
Title: On Linear Critical-Region Boundaries in Continuous-Time Multiparametric Optimal Control
Abstract:
When an optimal control problem is solved for all possible initial conditions at once, the initial‑state space splits into critical regions, each carrying a closed‑form control law that can be evaluated online without solving any optimization. This is the multiparametric approach to explicit control. In the continuous‑time setting, the boundaries between these regions are determined by extrema of Lagrange multipliers and constraint functions along the optimal trajectory. Whether a boundary is a hyperplane, computable analytically, or a curved manifold that requires numerical methods has a direct effect on how the partition is built. We show that a boundary is a hyperplane if and only if the relevant extremum is attained at either the initial time or the terminal time, regardless of the initial condition. The reason is that the costate is a linear function of the initial state at any fixed time, so when the extremum is tied to a fixed endpoint, the boundary condition is linear and the boundary normal follows directly from two matrix exponentials and a linear solve. When the extremum occurs at a time that shifts with the initial condition, such as a switching time or an interior stationary point, the boundary is generally curved. We demonstrate the result on a third‑order system, obtaining the complete three‑dimensional critical‑region partition analytically for the first time in this problem class. A comparison with a discrete‑time formulation shows how sharply the region count grows under discretization, while the continuous‑time partition remains unchanged.

Authors:Zaid Ghazal, Hadiza Yusuf, Khouloud Gaaloul
Title: Towards Counterfactual Explanation and Assertion Inference for CPS Debugging
Abstract:
Verification and validation of cyber‑physical systems (CPS) via large‑scale simulation often surface failures that are hard to interpret, especially when triggered by interactions between continuous and discrete behaviors at specific events or times. Existing debugging techniques can localize anomalies to specific model components, but they provide little insight into the input‑signal values and timing conditions that trigger violations, or the minimal, precisely timed changes that could have prevented the failure. In this article, we introduce DeCaF, a counterfactual‑guided explanation and assertion‑based characterization framework for CPS debugging. Given a failing test input, DeCaF generates counterfactual changes to the input signals that transform the test from failing to passing. These changes are designed to be minimal, necessary, and sufficient to precisely restore correctness. Then, it infers assertions as logical predicates over inputs that generalize recovery conditions in an interpretable form engineers can reason about, without requiring access to internal model details. Our approach combines three counterfactual generators with two causal models, and infers success assertions. Across three CPS case studies, DeCaF achieves its best success rate with KD‑Tree Nearest Neighbors combined with M5 model tree, while Genetic Algorithm combined with Random Forest provides the strongest balance between success and causal precision.

Authors:Zhe Li, Ilias Mitrai
Title: Learning interpretable and stable dynamical models via mixed-integer Lyapunov-constrained optimization
Abstract:
In this paper, we consider the data‑driven discovery of stable dynamical models with a single equilibrium. The proposed approach uses a basis‑function parameterization of the differential equations and the associated Lyapunov function. This modeling approach enables the discovery of both the dynamical model and a Lyapunov function in an interpretable form. The Lyapunov conditions for stability are enforced as constraints on the training data. The resulting learning task is a mixed‑integer quadratically constrained optimization problem that can be solved to optimality using current state‑of‑the‑art global optimization solvers. Application to two case studies shows that the proposed approach can discover the true model of the system and the associated Lyapunov function. Moreover, in the presence of noise, the model learned with the proposed approach achieves higher predictive accuracy than models learned with baselines that do not consider Lyapunov‑related constraints.

Authors:Ralph Sabbagh, Asmaa Eldesoukey, Mahmoud Abdelgalil, Tryphon T. Georgiou
Title: On the Isospectral Nature of Minimum-Shear Covariance Control
Abstract:
We revisit Brockett's attention in the context of bilinear gradient flow of an ensemble, and explore an alternative formalism that aims to reduce shear by minimizing the conditioning number of the dynamics; equivalently, we minimize the range of the eigenvalues of the dynamics. Remarkably, the evolution is isospectral, and this property is inherited by the coupled nonlinear dynamics of the control problem from a Lax isospectral flow.

Authors:Andrii Vakhnovskyi
Title: IOGRUCloud: A Scalable AI-Driven IoT Platform for Climate Control in Controlled Environment Agriculture
Abstract:
Controlled Environment Agriculture (CEA) demands precise, adaptive climate management across distributed infrastructure. This paper presents IOGRUCloud, a scalable three‑tier IoT platform that integrates AI‑driven control with edge computing for automated greenhouse climate regulation. The system architecture separates field‑level sensing and actuation (L1), facility‑level coordination (L2), and cloud‑level optimization (L3‑L4), enabling progressive autonomy from rule‑based to fully autonomous operation. A Vapor Pressure Deficit (VPD) cascading control loop governs temperature and humidity with GRU‑enhanced PID tuning, reducing manual calibration effort by 73%. Deployed across 14 production greenhouses totaling 47,000 m2, the platform demonstrates 23% reduction in energy consumption and 31% improvement in climate stability versus baseline. The system handles 2.3M daily sensor events with 99.7% uptime. We release the architecture specification and deployment results to support reproducibility in smart agriculture research.

Authors:Peter Amorese, Morteza Lahijanian
Title: Learning Markov Processes as Sum-of-Square Forms for Analytical Belief Propagation
Abstract:
Harnessing the predictive capability of Markov process models requires propagating probability density functions (beliefs) through the model. For many existing models however, belief propagation is analytically infeasible, requiring approximation or sampling to generate predictions. This paper proposes a functional modeling framework leveraging sparse Sum‑of‑Squares (SoS) forms for valid (conditional) density estimation. We study the theoretical restrictions of modeling conditional densities using the SoS form, and propose a novel functional form for addressing such limitations. The proposed architecture enables generalized simultaneous learning of basis functions and coefficients, while preserving analytical belief propagation. In addition, we propose a training method that allows for exact adherence to the normalization and non‑negativity constraints. Our results show that the proposed method achieves accuracy comparable to state‑of‑the‑art approaches while requiring significantly less memory in low‑dimensional spaces, and it further scales to 12D systems when existing methods fail beyond 2D.

Authors:Monika Tomar, Takashi Tanaka
Title: Linearly Solvable Continuous-Time General-Sum Stochastic Differential Games
Abstract:
This paper introduces a class of continuous‑time, finite‑player stochastic general‑sum differential games that admit solutions through an exact linear PDE system. We formulate a distribution planning game utilizing the cross‑log‑likelihood ratio to naturally model multi‑agent spatial conflicts, such as congestion avoidance. By applying a generalized multivariate Cole‑Hopf transformation, we decouple the associated non‑linear Hamilton‑Jacobi‑Bellman (HJB) equations into a system of linear partial differential equations. This reduction enables the efficient, grid‑free computation of feedback Nash equilibrium strategies via the Feynman‑Kac path integral method, effectively overcoming the curse of dimensionality.

Authors:Gricel Vázquez, Calum Imrie, Sepeedeh Shahbeigi, Nawshin Mannan Proma, Tian Gan, Victoria J Hodge, John Molloy, Simos Gerasimou
Title: Formally Guaranteed Control Adaptation for ODD-Resilient Autonomous Systems
Abstract:
Ensuring reliable performance in situations outside the Operational Design Domain (ODD) remains a primary challenge in devising resilient autonomous systems. We explore this challenge by introducing an approach for adapting probabilistic system models to handle out‑of‑ODD scenarios while, in parallel, providing quantitative guarantees. Our approach dynamically extends the coverage of existing system situation capabilities, supporting the verification and adaptation of the system's behaviour under unanticipated situations. Preliminary results demonstrate that our approach effectively increases system reliability by adapting its behaviour and providing formal guarantees even under unforeseen out‑of‑ODD situations.

Authors:Roberto Vercellino, Jared Willard, Gustavo Campos, Weslley da Silva Pereira, Olivia Hull, Matthew Selensky, Juliane Mueller
Title: Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning
Abstract:
The rapid growth of generative artificial intelligence (AI) has introduced unprecedented computational demands, driving significant increases in the energy footprint of data centers. However, existing power consumption data is largely proprietary and reported at varying resolutions, creating challenges for estimating whole‑facility energy use and planning infrastructure. In this work, we present a methodology that bridges this gap by linking high‑resolution workload power measurements to whole‑facility energy demand. Using NLR's high‑performance computing data center equipped with NVIDIA H100 GPUs, we measure power consumption of AI workloads at 0.1‑second resolution for AI training, fine‑tuning and inference jobs. Workloads are characterized using MLCommons benchmarks for model training and fine‑tuning, and vLLM benchmarks for inference, enabling reproducible and standardized workload profiling. The dataset of power consumption profiles is made publicly available. These power profiles are then scaled to the whole‑facility‑level using a bottom‑up, event‑driven, data center energy model. The resulting whole‑facility energy profiles capture realistic temporal fluctuations driven by AI workloads and user‑behavior, and can be used to inform infrastructure planning for grid connection, on‑site energy generation, and distributed microgrids.

Authors:Yurun Gan, Ziyu Song, Jing Yang, Zheng Lin, Jianuo Zhang, Tongtong Gu, Haitao Ding, Nan Xu, Por Lip Yee, Wei Ni, Jun Luo
Title: Dual-Envelope Constrained Nonlinear MPC for Distributed Drive Electric Vehicles Drifting Under Bounded Steering and Direct Yaw-Moment Control
Abstract:
Distributed drive electric vehicles offer superior yaw moment control for autonomous drifting in extreme maneuvers. Conventional drift analysis constructs stability boundaries from open loop equilibria points and assumes a fixed envelope structure. However, coupling among control inputs reshapes the phase plane and shifts saddle point location, which can invalidate open loop envelopes when used for closed loop drifting. To address this issue, a saddle point coordinate model is established in this paper by combining a nonlinear tire model with the handling diagram and explicitly accounting for road adhesion coefficient, longitudinal velocity, front wheel steering angle, and additional yaw moment. Based on saddle point properties, an extended dual envelope framework is constructed in the phase plane of slip angle and yaw rate. Using the convergence tendency of state points toward saddle points under bounded control inputs, the outer envelope defines a recoverable set under constraints on front wheel steering angle and additional yaw moment. The inner envelope characterizes the non‑drifting stability region associated with unsaturated tire forces. Finally, a nonlinear model predictive control (NMPC) controller is developed using the extended dual envelope constraint. Hardware‑in‑the‑loop experiments show that, compared with NMPC without envelope constraints, the proposed method enables smoother convergence toward the drift saddle point, reduces the steady‑state tracking errors of vehicle speed, sideslip angle, and yaw rate by 33.07%, 71.18%, and 31.27%, respectively, and decreases the peak tracking error by 63.66% under road‑friction mismatch.

Authors:Alessandro Baldini, Riccardo Felicetti, Alessandro Freddi, Andrea Monteriù
Title: Active Propeller Fault Detection and Isolation in Multirotors Via Vibration Model
Abstract:
In rotary‑wing aircraft, rotating blades are exposed to collisions and subsequent damage. The detection and isolation of blade damage constitute the first step in fault mitigation; however, they are particularly challenging when considerable input redundancy is available, as in the case of multirotors. In this article, we propose an active model‑based approach that deliberately perturbs the control inputs to isolate blade faults in multirotor vehicles. By exploiting a model that captures the vibrations caused by blade damage, the isolation method relies solely on vibration data from the onboard inertial measurement unit. The strategy is tested in simulation using an octarotor platform, and both time‑domain and frequency‑domain features are analyzed. Several accuracy‑related metrics of the technique are evaluated on a set of 9600 simulations and compared with the most relevant variables.

Authors:Lorenzo Giordano, Josep M. Olm, Mario di Bernardo
Title: Complex-Valued Kuramoto Networks: A Unified Control-Theoretic Framework
Abstract:
Synchronization in networks of coupled oscillators is classically studied via the Kuramoto model, whose intrinsic nonlinearity limits analytical tractability and complicates control design. Complex‑valued extensions circumvent this by embedding phase dynamics into a higher‑dimensional linear state space, where regulating complex‑state moduli to a common value recovers Kuramoto phase behavior. Existing approaches to address this problem correspond, within a unified control framework, to state‑feedback and hybrid reset‑based strategies, each with performance constraints. We propose two switched control designs that overcome these limitations: a switched feedforward law ensuring exact phase correspondence at all times, and a feedforward plus sliding‑mode law achieving finite‑time convergence without spectral gain tuning. Additionally, we present a non‑autonomous complex‑valued MIMO sliding‑mode controller that enforces phase locking at a prescribed frequency in finite time, independent of natural frequencies and coupling strengths. Simulations confirm improved transient response, steady‑state accuracy, and robustness, including synchronization of heterogeneous networks where the classical real‑valued Kuramoto model fails.

Authors:Ezzat Elokda, Ruiting Wang, Karl H. Johansson, Angela Fontan
Title: Flexible Electric Vehicle Charging with Karma
Abstract:
Motivated by the need to develop fair and efficient schemes to facilitate the electrification of transport, this paper proposes a non‑monetary karma economy for flexible Electric Vehicle (EV) charging, managing the intertemporal allocation of limited power capacity. We consider a charging facility with limited capacity that must schedule arriving EVs to charge in real‑time. For this purpose, the facility adopts online karma auctions, in which each EV user is endowed with non‑tradable karma tokens, places a karma bid in each time interval it is present in the facility, and capacity is allocated to the highest bidders, who must pay their bids. These payments are subsequently redistributed to the users to form a closed, indefinitely sustainable economy. The main contribution is to extend previous karma Dynamic Population Game (DPG) formulations to this setting which features novel State of Charge (SOC) dynamics and private trip deadlines in addition to urgency. A Stationary Nash Equilibrium (SNE) of the EV charging karma economy is guaranteed to exist, and it is demonstrated to provide pronounced benefits with respect to benchmark scheduling schemes as it balances between meeting deadlines and prioritizing high urgency.

Authors:Emmanuel Junior Wafo Wembe, Adnane Saoud
Title: A Trajectory-Based Approach to Controlled Invariance and Recursively Feasible MPC
Abstract:
In this paper, we revisit the computation of controlled invariant sets for linear discrete‑time systems through a trajectory‑based viewpoint. We begin by introducing the notion of convex feasible points, which provides a new characterization of controlled invariance using finitely long state trajectories. We further show that combining this notion with the classical backward fixed‑point algorithm allows for the computation of the maximal controlled invariant set. Building on these results, we propose a model predictive control (MPC) scheme that guarantees recursive feasibility without relying on precomputed terminal sets. Finally, we formulate the search for convex feasible points as an optimization problem, yielding a practical computational method for constructing controlled invariant sets. The effectiveness of the approach is illustrated through numerical examples.

Authors:Victor Moznon Baeza, Symeon Chatzinotas
Title: From 6G Scenarios and Requirements to Design Drivers: Insights from 3GPP Release 20
Abstract:
The definition of sixth‑generation (6G) systems is being shaped by early standardization efforts, including the 3GPP TR 38.914 (Release 20) study on scenarios and requirements. This study introduces a comprehensive set of deployment environments, service classes, and performance targets that will guide the evolution toward IMT‑2030. This article provides a design‑oriented interpretation of these definitions, bridging the gap between standardized scenarios and system design. We first organize 6G deployment scenarios and emerging services into a unified framework. We then identify key design drivers derived from the 3GPP requirements, including terrestrial‑non‑terrestrial integration, GNSS‑free operation, AI‑native networking, and joint communication and sensing. Finally, we discuss the implications of these drivers on 6G architecture and highlight open challenges for future standardization and research.

Authors:Ziyao Zhou, Tiancheng Cao, Chen Shen, Jiaqi Zhang, Yuting Liu, Hen-Wei Huang
Title: Multiprotocol Wireless Timer Synchronization for IoT Systems
Abstract:
Accurate time synchronization is essential for Internet of Things (IoT) systems, where multiple distributed nodes must share a common time base for coordinated sensing and data fusion. However, conventional synchronization approaches suffer from nondeterministic transmission latency, limited precision, or restricted bidirectional functionality. This paper presents a protocol‑independent wireless timer synchronization method that exploits radio timeslots to transmit precisely timestamped beacons in a proprietary radio mode. By decoupling synchronization from upper‑layer packet retransmissions and leveraging hardware‑timed radio events, the proposed approach significantly reduces scheduling uncertainty and achieves nanosecond‑level synchronization accuracy. Comprehensive experiments evaluate the impacts of synchronization frequency, RSSI, BLE connection interval, and throughput on synchronization performance. The results demonstrate that an optimal synchronization frequency of 1000 Hz yields an approximately 20 ns delay in the absence of communication stack activity while maintaining sub‑500 ns accuracy under most realistic BLE traffic conditions. Furthermore, larger connection intervals, lower application throughput, and higher RSSI consistently improve synchronization quality by reducing radio resource contention and packet loss. The proposed scheme provides a general and high‑precision synchronization solution suitable for resource‑constrained IoT systems.

Authors:Ziyao Zhou, Chen Shen, Sicong Shen, Hen-Wei Huang
Title: Enhanced ShockBurst for Ultra Low-Power On-Demand Sensing
Abstract:
On demand sensing is emerging as a key paradigm in Internet of Things (IoT) systems, where devices remain in low power states and transmit data only upon event triggers. Such an operation requires wireless communication schemes that provide low latency, minimal wake up overhead, and high energy efficiency. However, widely adopted protocols such as Bluetooth Low Energy (BLE) rely on connection oriented mechanisms that incur non negligible latency and energy overhead during sleep wake transitions, limiting their effectiveness for event driven sensing. In this work, Nordic Semiconductor's proprietary Enhanced ShockBurst (ESB) protocol is investigated as an alternative communication scheme for low power on demand IoT systems. A systematic experimental comparison between ESB and BLE is presented on the same hardware platform, evaluating packet level latency, transmission energy, achievable throughput, wake up overhead under duty cycled operation, and bidirectional communication characteristics. Results show that ESB achieves a packet latency of 0.68 ms for a 244 byte payload, reduces per packet transmission time and energy by nearly 2x, increases maximum throughput by approximately 2x, and lowers wake up time and energy by up to 10x compared with BLE. To demonstrate system level impact, an implantable loop recorder prototype with FIFO triggered electrocardiogram transmission is implemented. The ESB based system enables rapid event driven communication with a minimum communication power of 0.5 mW and reduces total system power consumption by approximately 60 percent relative to BLE. These results highlight the limitations of connection oriented protocols for on demand sensing and establish ESB as a lightweight and effective communication alternative for energy constrained IoT applications, including biomedical implants and event driven monitoring systems.

Authors:Alan Veliz-Cuba, Zeyu Wang
Title: A modular approach to achieve multistationarity using AND-gates
Abstract:
Systems of differential equations have been used to model biological systems such as gene and neural networks. A problem of particular interest is to understand the number of stable steady states. Here we propose conjunctive networks (systems of differential equations equations created using AND gates) to achieve any desired number of stable steady states. Our approach uses combinatorial tools to predict the number of stable steady states from the structure of the wiring diagram. Furthermore, AND gates have been successfully engineered by experimentalists for gene networks, so our results provide a modular approach to design gene networks that achieve arbitrary number of phenotypes.

Authors:Qintao Du, Ran Li, Weiyi Lv, Huan Zhou, Moduo Yu, Jianzhe Liu
Title: Decision-focused Conservation Voltage Reduction to Consider the Cascading Impact of Forecast Errors
Abstract:
Conservation Voltage Reduction (CVR) relies on the effective coordination of slow‑acting devices, such as OLTCs and CBs, and fast‑acting devices, such as SVGs and PV inverters, typically implemented through a hierarchical multi‑stage Volt‑Var Control (VVC) spanning day‑ahead scheduling, intra‑day dispatch, and real‑time control. However, existing sequential methods fail to account for the cas‑cading impact of forecast errors on multi‑stage decision‑making. This oversight results in suboptimal day‑ahead schedules for OLTCs and CBs that hinder the ef‑fective coordination with fast‑acting SVGs and inverters, inevitably driving a trade‑off between real‑time voltage security and CVR efficiency. To improve the Pareto front of this trade‑off, this paper proposes a novel bi‑level multi‑timescale forecasting (Bi‑MTF) framework for multi‑stage VVC optimization. By integrating the downstream multi‑stage VVC optimization into the upstream forecasting mod‑els training, the decision‑focused forecasting models are able to learn the trade‑offs across temporal horizons. To solve the computationally challenging bi‑level for‑mulation, a modified sensitivity‑driven integer L‑shaped method is developed. It utilizes a hybrid gradient feedback mechanism that integrates numerical sensitivity analysis for discrete variables with analytical dual information for continuous fore‑cast parameters to ensure tractability. Numerical results on a modified IEEE 33‑bus system demonstrate that the proposed approach yields superior energy savings and operational safety compared to conventional MSE‑based sequential paradigms. Specifically, as the capacity of fast‑acting devices increases, the energy savings of the proposed method rise from 2.74% to 3.41%, which is far superior to the 1.50% to 1.76% achieved by conventional MSE‑based sequential paradigms.

Authors:Christian Gatke, Julian D. Schiller, Matthias A. Müller
Title: Small-gain analysis of exponential incremental input/output-to-state stability for large-scale distributed systems
Abstract:
We provide a detectability analysis for nonlinear large‑scale distributed systems in the sense of exponential incremental input/output‑to‑state stability (i‑IOSS). In particular, we prove that the overall system is exponentially i‑IOSS if each subsystem is i‑IOSS, with interconnections treated as external inputs, and a suitable small‑gain condition holds. The analysis is extended to a Lyapunov characterization, resulting in a different quantitative outcome regarding the small‑gain condition, which is further analyzed within this work. Moreover, we derive linear matrix inequality conditions posed solely on the local subsystems and their interconnections, which guarantee exponential i‑IOSS of the overall distributed system. The results are illustrated on a numerical example.

Authors:Muhammad Zakwan, Vaibhav Gupta, Alireza Karimi, Efe C. Balta, Giancarlo Ferrari-Trecate
Title: Controller Design for Structured State-space Models via Contraction Theory
Abstract:
This paper presents an indirect data‑driven output feedback controller synthesis for nonlinear systems, leveraging Structured State‑space Models (SSMs) as surrogate models. SSMs have emerged as a compelling alternative in modelling time‑series data and dynamical systems. They can capture long‑term dependencies while maintaining linear computational complexity with respect to the sequence length, in comparison to the quadratic complexity of Transformer‑based architectures. The contributions of this work are threefold. We provide the first analysis of controllability and observability of SSMs, which leads to scalable control design via Linear Matrix Inequalities (LMIs) that leverage contraction theory. Moreover, a separation principle for SSMs is established, enabling the independent design of observers and state‑feedback controllers while preserving the exponential stability of the closed‑loop system. The effectiveness of the proposed framework is demonstrated through a numerical example, showcasing nonlinear system identification and the synthesis of an output feedback controller.

Authors:Botao Zhu, Xianbin Wang
Title: Trust-as-a-Service: Task-Specific Orchestration for Effective Task Completion via Model Context Protocol-Aided Agentic AI
Abstract:
As future tasks in networked systems are increasingly relying on collaborative execution among distributed devices, trust has become an essential tool for securing both reliable collaborators and task‑specific resources. However, the diverse requirements of different tasks, the limited information of task owners on others, and the complex relationships among networked devices pose significant challenges to achieving timely and accurate trust evaluation of potential collaborators for meeting task‑specific needs. To address these challenges, this paper proposes Trust‑as‑a‑Service (TaaS), a novel paradigm that encapsulates complex trust mechanisms into a unified, system‑wide service. This paradigm enables efficient utilization of distributed trust‑related data, need‑driven trust evaluation service provision, and task‑specific collaborator organization. To realize TaaS, we develop an agentic AI‑based framework as the enabling platform by leveraging the Model Context Protocol (MCP). The central server‑side agent autonomously performs trust‑related operations in accordance with specific task requirements, delivering the trust assessment service to all task owners through a unified interface. Meanwhile, all device‑side agents expose their capabilities and resources via MCP servers, allowing devices to be dynamically discovered, evaluated, engaged, and released, thereby forming task‑specific collaborative units. Experimental results demonstrate that the proposed TaaS achieves 100% collaborator selection accuracy, along with high reliability and resource‑efficient task completion.

Authors:Mohammad Almomani, Ahmed Alkhonain, Venkataramana Ajjarapu
Title: TSO-DSO Coordinated Reactive Power Dispatch for Smart Inverters with Multiple Control Modes Real-Time Implementation
Abstract:
This paper presents TSO‑DSO coordinated reactive power dispatch, with a focus on real‑time implementation. A sensitivity‑aware, mixed‑integer linear programming (MILP) formulation is developed to model the IEEE 1547‑compliant droop‑based control modes Volt VAR (VV), Volt Watt (VW), and Watt VAR (WV) of smart inverters. The algorithm employs a hierarchical optimization strategy using Special Ordered Sets (SOS1) to enhance computational efficiency and supports limited measurement scenarios through Recursive Least Squares (RLS) estimation. The proposed method is tested on the IEEE 13‑bus and 123‑bus distribution networks, which are connected to a 9‑bus transmission system. Results demonstrate the feasibility and effectiveness of the real‑time dispatch framework in improving voltage regulation and minimizing power curtailment.

Authors:Mohammad Almomani, Muhammad Sarwar, Venkataramana Ajjarapu
Title: Trajectory-Based Nonlinear Indices for Real-Time Monitoring and Quantification of Short-Term Voltage Stability
Abstract:
Existing short term voltage stability (STVS) methods typically address either voltage oscillations or delayed voltage recovery; however, the coexistence of both phenomena has not been adequately covered in the literature. Moreover, existing real‑time STVS assessment methods often provide only binary stability classifications. This paper proposes novel indices that enable early detection and quantify the degree of stability. The proposed method decomposes post‑fault voltage trajectories using Empirical Mode Decomposition (EMD) into residual and oscillatory components. It then employs Lyapunov Exponents (LEs) to characterize the dynamic behavior of each component and evaluates the stability degree using Kullback Leibler (KL) divergence by comparing the LEs of each component with those of a predefined critical signal. The proposed indices assess oscillatory stability significantly faster than the traditional LE method applied directly to the original signal. Specifically, they detect stability within 0.6 seconds after a fault, compared to approximately 10 seconds for the conventional LE approach. In addition, the delayed‑recovery index can identify generator trips caused by over‑excitation limits within 3 seconds, well before the actual trip occurs at approximately 20 seconds, thereby providing operators and controllers sufficient time to take preventive actions. Furthermore, thresholds are derived to distinguish between stable and unstable cases, offering a graded measure of the stability margin. Simulation studies on the Nordic test system under varying load conditions demonstrate the effectiveness of the proposed indices.

Authors:Lantian Zhang, Bo Wahlberg, Silun Zhang
Title: Stochastic Adaptive Control for Systems with Nonlinear Parameterization: Almost Sure Stability and Tracking
Abstract:
This paper concerns the adaptive control problem for a class of nonlinear stochastic systems in which the state update is given by a nonlinear function of linear dynamics plus additive stochastic noise. Such systems arise in a wide range of applications, including recurrent neural networks, social dynamics, and signal processing. Despite their importance, adaptive control for these systems remains relatively unexplored in the literature. This gap is primarily due to the inherently nonconvex dependence of the system dynamics on unknown parameters, which significantly complicates both controller design and analysis. To address these challenges, we propose an online nonlinear weighted least‑squares (WLS)‑based parameter estimation algorithm and establish the global strong consistency of the resulting parameter estimates. In contrast to most existing results, our consistency analysis does not rely on restrictive assumptions such as persistent excitation conditions of the trajectory data, making it applicable to stochastic adaptive control settings. Building on the proposed estimator, we further develop an adaptive control algorithm with an attenuating excitation signal that can effectively combine adaptive estimation and feedback control. Finally, we are able to show that the resulting closed‑loop system is globally stable and that the system trajectory can track, in a long‑run average sense, the reference trajectory generated with the true system parameters. The proposed methods and theoretical results are finally validated through simulations in two nonlinear interaction network applications.

Authors:Shijie Pan, Zaint A. Alexakis, Charalambos Konstantinou
Title: When Market Prices Drive the Load: Modeling, Grid-Security Analysis, and Mitigation of Data Center Workload Scheduling
Abstract:
Data centers (DCs) are emerging as large, geographically distributed, controllable loads whose participation in electricity markets can significantly affect grid operation, especially when cloud platforms shift workloads across sites to exploit energy‑arbitrage opportunities. This paper analyzes and seeks to mitigate the grid impacts of geographically distributed multi‑site DCs under exogenous electricity prices. It develops a detailed job‑level scheduling framework for market‑driven DCs, formulated as a mixed‑integer model that preserves execution logic and captures a unified set of implementable control actions. It also incorporates service‑side quality‑of‑service (QoS) constraints and penalty terms to improve fidelity. Case studies on a modified IEEE 14‑bus system, complemented by a more realistic network based on Travis County, Texas, show that purely price‑driven scheduling improves economic performance, but also increases voltage‑security risk and congestion exposure by inducing localized demand concentration and sharp site‑level load variation. To mitigate these effects, this work introduces load‑redistribution policies that curb extreme load shifting and support grid operators in managing such conditions.

Authors:Shaoxuan Cui, Lingfei Wang, Hildeberto Jardon-Kojakhmetov, Karl Henrik Johansson, Ming Cao
Title: Markov Chains and Random Walks with Memory on Hypergraphs: A Tensor-Based Approach
Abstract:
Many complex systems exhibit interactions that depend not only on pairwise connections, but also group structures and memory effects. To capture such effects, we develop a unified tensor framework for modeling higher‑order Markov chains with memory. Our formulation introduces an even‑order paired tensor that links folded and unfolded dynamics and characterizes their steady states and convergence. We further show that a Markov chain with memory can be approximated by a low‑dimensional nonlinear tensor‑based system and then provide a full system analysis. As an application, we define random walks on hypergraphs where memory naturally arises from the hyperedge structure, providing new tools for analyzing higher‑order networks with time‑dependent effects.

Authors:Hang Zou, Yuzhi Yang, Lina Bariah, Yu Tian, Yuhuan Lu, Bohao Wang, Anis Bara, Brahim Mefgouda, Hao Liu, Yiwei Tao, Sergy Petrov, Salma Cheour, Nassim Sehad, Sumudu Samarakoon, Chongwen Huang, Samson Lasaulce, Mehdi Bennis, Mérouane Debbah
Title: Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G
Abstract:
The integration of machine learning tools into telecom networks, has led to two prevailing paradigms, namely, language‑based systems, such as Large Language Models (LLMs), and physics‑based systems, such as Digital Twins (DTs). While LLM‑based approaches enable flexible interaction and automation, they lack explicit representations of network dynamics. DTs, in contrast, offer a high‑fidelity network simulation, but remain scenario‑specific and are not designed for learning or decision‑making under uncertainty. This gap becomes critical for 6G systems, where decisions must take into account the evolving network states, uncertainty, and the cascading effects of control actions across multiple layers. In this article, we introduce the Telecom World Model~(TWM) concept, an architecture for learned, action‑conditioned, uncertainty‑aware modeling of telecom system dynamics. We decompose the problem into two interacting worlds, a controllable system world consisting of operator‑configurable settings and an external world that captures propagation, mobility, traffic, and failures. We propose a three‑layer architecture, comprising a field world model for spatial environment prediction, a control/dynamics world model for action‑conditioned Key Performance Indicator (KPI) trajectory prediction, and a telecom foundation model layer for intent translation and orchestration. We showcase a comparative analysis between existing paradigms, which demonstrates that TWM jointly provides telecom state grounding, fast action‑conditioned roll‑outs, calibrated uncertainty, multi‑timescale dynamics, model‑based planning, and LLM‑integrated guardrails. Furthermore, we present a proof‑of‑concept on network slicing to validate the proposed architecture, showing that the full three‑layer pipeline outperforms single‑world baselines and accurately predicts KPI trajectories.

Authors:Xinyuan Qiu, Ruohan Wang, Siyuan Liu, Sofie Haesaert
Title: Compressing Correct-by-Design Synthesis for Stochastic Homogeneous Multi-Agent Systems with Counting LTL
Abstract:
Correct‑by‑design synthesis provides a principled framework for establishing formal safety guarantees for stochastic multi‑agent systems (MAS). However, conventional approaches based on finite abstractions often incur prohibitive computational costs as the number of agents and the complexity of temporal logic specifications increase. In this work, we study homogeneous stochastic MAS under counting linear temporal logic (cLTL) specifications, and show that the corresponding satisfaction probability admits a structured tensor decomposition via leveraging deterministic finite automata (DFA). Building on this structure, we develop a dual‑tree‑based value iteration framework that reduces redundant computation in the process of dynamic programming. Numerical results demonstrate the proposed approach's effectiveness and scalability for complex specifications and large‑scale MAS.

Authors:Ahmad Amine, Nick-Marios T. Kokolakis, Ugo Rosolia, Truong X. Nghiem, Rahul Mangharam
Title: Failure-Aware Iterative Learning of State-Control Invariant Sets
Abstract:
In this paper, we address the problem of computing maximal state‑control invariant sets using failing trajectories. We introduce the concept of state‑control invariance, which extends control invariance from the state space to the joint state‑control space. The maximal state‑control invariant (MSCI) set simultaneously encodes the maximal control invariant set (MCI) and, for each state in the MCI, the set of control inputs that preserve invariance. We prove that the state projection of the MSCI is the MCI and the state‑dependent sections of the MSCI are the admissible invariance‑preserving inputs. Building on this framework, we develop a Failure‑Aware Iterative Learning (FAIL) algorithm for deterministic linear time invariant systems with polytopic constraints. The algorithm iteratively updates a constraint set in the state‑control space by learning predecessor halfspaces from one‑step failing state‑input pairs, without knowing the dynamics. For each failure, FAIL learns the violated halfspaces of the predecessor of the constraint set by a regression on failing trajectories. We prove that the learned constraint set converges monotonically to the MSCI. Numerical experiments on a double integrator system validate the proposed approach.

Authors:Tejaswi K. C., Taeyoung Lee
Title: Uncertainty Propagation in Stochastic Hybrid Systems with Dimension-Varying Resets
Abstract:
This paper studies probability density evolution for stochastic hybrid systems with reset maps that change the dimension of the continuous state across modes. Existing Frobenius‑‑Perron formulations typically represent reset‑induced probability transfer through boundary conditions, which is insufficient when resets map guard sets into the interior or onto lower‑dimensional subsets of another mode. We develop a weak‑form formulation in which reset‑induced transfer is represented by the pushforward of probability flux across the guard, yielding a unified description for such systems. The proposed framework naturally captures both cases: when the reset decreases dimension, the transferred probability appears as an interior source density, whereas when the reset increases dimension, it generally appears as a singular source supported on a lower‑dimensional subset. The approach is illustrated using a stochastic hybrid model in which two particles merge into one and later split back into two, demonstrating how dimension‑changing resets lead to source terms beyond classical boundary‑condition‑based formulations.

Authors:Xinyi Zhao, Prasanna Raut, Chaoyue Zhao, Alexandre Moreira
Title: A Markov Decision Process Framework for Enhancing Power System Resilience during Wildfires under Decision-Dependent Uncertainty
Abstract:
Wildfires pose an increasing threat to the safety and reliability of power systems, particularly in distribution networks located in fire‑prone regions. To mitigate ignition risk from electrical infrastructure, utilities often employ safety power shutoffs, which proactively de‑energize high‑risk lines during hazardous weather and restore them once conditions improve. While this strategy can result in temporary load loss, it helps prevent equipment damage and wildfire ignition development in the system. In this paper, we develop a state‑based decision‑making framework to optimize such switching actions over time, with the goal of minimizing total operational costs throughout a wildfire event. The model represents network topologies as Markov states, with transitions influenced by both exogenous weather conditions and endogenous power flow dynamics. To address the computational challenges posed by the large state and action spaces, we propose an approximate dynamic programming algorithm based on post‑decision states. The effectiveness and scalability of the proposed approach are demonstrated through case studies on 54‑bus and 138‑bus distribution systems, showcasing its potential for enhancing wildfire resilience across different grid configurations.

Authors:Shida Jiang, Shengyu Tao, Vincent Molina, Junzhe Shi, Scott Moura
Title: Model-Agnostic Energy Throughput Control for Range and Lifetime Extension of Electric Vehicles via Cell-Level Inverters
Abstract:
A conventional electric vehicle (EV) powertrain relies on a centralized high‑voltage DC‑AC inverter, thereby limiting cell‑level control and potentially reducing overall driving range and battery lifetime. This paper studies an H‑bridge‑based cell‑level inverter topology that performs power conversion at the cell level, enabling independent control of individual cells and expanding the design space for battery management. Leveraging these additional degrees of freedom, we propose a model‑agnostic energy‑throughput control strategy that extends EV range while improving battery‑pack lifetime. Because usable energy (and thus driving range) and lifetime are governed by the cells with the lowest state‑of‑charge (SOC) and state‑of‑health (SOH), respectively, the proposed controller preferentially routes energy throughput to healthier cells. Specifically, during charging, it permits cell SOCs to diverge to promote SOH equalization; during discharging, it rebalances SOC to maximize usable capacity under per‑cell constraints. The proposed SOC‑SOH‑aware control strategy is evaluated on two aging models representing lithium manganese oxide and lithium iron phosphate chemistries, using a Tesla Model 3 charge‑discharge profile across 14 different parameter settings. Simulations show a 7‑38% improvement in lifetime relative to a conventional SOC‑only balancing baseline. More broadly, the results suggest a software‑defined pathway to extend EV pack life through routine charging, with minimal reliance on specific degradation models or discharge profiles.

Authors:Maickol Fernandez-Obando, Luis G. Leon-Vega, Leonardo Cardinale-Villalobos, Christopher Vega-Sanchez, Luis D. Murillo-Soto
Title: Design and Implementation of a Multi-Sensor DAQ System for Comparative Photovoltaic Performance Analysis
Abstract:
The rigorous analysis of specialized physical processes often demands custom data acquisition architectures that offer flexibility and precision beyond the capabilities of general‑purpose commercial loggers. This paper presents the design and implementation of a robust data acquisition system (DAQ) for a comparative analysis of the performance of two photovoltaic panels with two different cooling systems. The system integrates a custom PCB design for 20 thermistors, dual high‑precision INA228 current/voltage sensors, environmental monitoring equipment, and a Raspberry Pi 4‑based acquisition platform. The software architecture implements autonomous operation with enhanced fault recovery, dual storage redundancy (local CSV and InfluxDB), cloud synchronization via Google Drive, and real‑time visualization through Grafana dashboards. Field deployment demonstrated system reliability, including automatic recovery from power interruptions, a 1‑minute sampling rate, remote monitoring capabilities, and continuous operation during a 5 AM to 6 PM daily window. The modular hardware and software architecture enables simultaneous monitoring of two photovoltaic panels for research on direct performance comparison under identical environmental conditions.

Authors:Yanxi Zhang, Mingwu Yao, Qinghai Yang, Muyu Mei
Title: Network-Wide PAoI Guarantee in CF-mMIMO Networks with S&C Coexistence: A Unified Framework for Spatial Partitioning Toward xURLLC
Abstract:
As a key capability of 6G, sensing‑communication (S&C) coexistence over distributed infrastructure is expected to support next‑generation ultra‑reliable and low‑latency communication (xURLLC) applications, which demand both robust connectivity and real‑time environmental awareness. This paper investigates network‑wide information freshness in large‑scale cell‑free massive multiple‑input multiple‑output (CF‑mMIMO) with S&C coexistence. A challenge arises from the spatial partitioning of access points (APs) into S&C roles: allocating more APs to sensing improves update generation, whereas allocating more APs to communication enhances reliable short‑packet delivery. To address this, we develop a unified analytical framework by combining stochastic geometry and stochastic network calculus (SNC) to characterize the peak age of information (PAoI) violation probability (PAVP). Specifically, we derive the moment generating functions (MGFs) of sensory packet inter‑arrival and service times, accounting for the joint stochastic spatial distribution of APs and users, imperfect channel state information (CSI), and finite blocklength coding (FBC). This facilitates the derivation of a tractable upper bound on the PAVP, which is minimized to determine the optimal AP partitioning. The derived bound accurately captures the performance trend and yields a minimizing partition factor that closely matches simulations. Therefore, the framework provides an efficient and low‑complexity tool for network‑wide PAoI guarantee and coexistence‑oriented design in CF‑mMIMO networks toward xURLLC.

Authors:Xiaobo Zeng, Liangcai Chen, Pan Liu, Ruonan Deng
Title: SSBI-Free Direct Detection via Phase Diverse of Residual Optical Carrier Enabled by Finite Extinction Ratio IQ Modulator for Datacenter Interconnections
Abstract:
Cost‑effective, low‑complexity and spectrally efficient interconnection can offer fundamental guiding law for future datacenter. In this work, we demonstrate a cost‑efficient SSBI‑free direct detection for datacenter interconnection, leveraging the phase diversity of residual optical carrier caused by finite‑extinction ratio (ER) IQ modulators, combining the device cost‑effective IQ modulator with finite‑ER and efficient SSBI‑free phase‑diverse direct detection receiver. Specifically, the proposed solution transforms the inherent limitation of finite‑ER of cost‑effective IQ modulator into the residual optical carrier advantage of SSBI‑free direct detection systems, eliminating SSBI without additional hardware and control complexity. A digital pre‑distortion and offset correction algorithms, and a PD‑thermal‑noise constrained SSBI‑free direct detection and signal recovery algorithms are derived and implemented. Comprehensive simulations are conducted. A Global‑SNR gain of 1.78 dB and 400 Gb/s data rate are achieved in 100‑km SSMF transmission when (ER_i, ER_o)= (7 dB, 25 dB) of IQ modulator. The proposed solution enables low‑complexity, cost‑effective, and spectrally‑efficient interconnects for next‑generation datacenters.

Authors:Xingyu Zhao, Junbo Zhao
Title: Dynamic Modeling of Data-Center Power Delivery for Power System Resonance Analysis
Abstract:
The rapid proliferation of data centers is reshaping modern power system dynamics. Unlike legacy industrial loads, data centers have power‑electronic interfaces whose multi‑timescale dynamics can interact strongly with the grid, inducing oscillatory behavior. However, analytical models that are grid‑integratable for revealing the underlying resonance mechanisms remain largely unexplored. To fill this research gap, this paper derives an explicit, component‑informed dynamic model of data‑center power‑delivery chains, which preserves component‑level fidelity and captures inter‑stage control interactions. This model is formulated as a time‑invariant representation in the positive‑sequence domain, enabling seamless integration with the phasor (or RMS) domain power‑system dynamic models. The analytical derivation reveals how realistic server‑load fluctuations at specific frequencies can excite coupled control modes, thereby inducing oscillation amplification and propagation in power grids with heterogeneous dynamic resources, including synchronous machines and grid‑forming/following inverters. Case studies on test systems with some realistic data center data demonstrate the effectiveness of the proposed solutions.

Authors:Siddharth Singh, Soumee Guha, Qing Chang, Scott Acton
Title: Train-Small Deploy-Large: Leveraging Diffusion-Based Multi-Robot Planning
Abstract:
Learning based multi‑robot path planning methods struggle to scale or generalize to changes, particularly variations in the number of robots during deployment. Most existing methods are trained on a fixed number of robots and may tolerate a reduced number during testing, but typically fail when the number increases. Additionally, training such methods for a larger number of agents can be both time consuming and computationally expensive. However, analytical methods can struggle to scale computationally or handle dynamic changes in the environment. In this work, we propose to leverage a diffusion model based planner capable of handling dynamically varying number of agents. Our approach is trained on a limited number of agents and generalizes effectively to larger numbers of agents during deployment. Results show that integrating a single shared diffusion model based planner with dedicated inter‑agent attention computation and temporal convolution enables a train small deploy‑large paradigm with good accuracy. We validate our method across multiple scenarios and compare the performance with existing multi‑agent reinforcement learning techniques and heuristic control based methods.

Authors:Yangjun Zeng, Yiwei Qiu, Xiaocong Sun, Jie Zhu, Jiarong Li, Shi Chen, Buxiang Zhou, Kaigui Xie
Title: Hot Standby in Ammonia Synthesis Reshapes Market Equilibrium in Renewable P2A Systems: A Potential Game Approach
Abstract:
Integrating renewable generation, hydrogen production, and renewable ammonia (RA) synthesis into power‑to‑ammonia (P2A) systems creates interactions across electricity and hydrogen markets. Limited operational flexibility, however, places RA at a disadvantage at the Nash equilibrium (NE). Recent advances in ammonia synthesis reactor design enable hot standby (HSB) operation, improving flexibility but introducing integer decision variables that complicate market equilibrium analysis. To address this challenge, we develop a potential game model and derive a convergent ε‑approximate equilibrium via an iterative best‑response approach. Case studies show that HSB reduces RA's reliance on hydrogen purchases and increases its profit by 20.14%. More importantly, HSB shifts the market equilibrium toward a more mutually beneficial outcome.

Authors:Fiona Majeau, Jose Daniel Lara, Eduardo Corona, Bri-Mathias Hodge
Title: DAE Index Reduction for Electromagnetic Transient Models
Abstract:
Electromagnetic transient (EMT) models are index‑2 differential‑algebraic equations when they include certain topologies and are formulated with modified nodal analysis. Such systems are difficult to numerically integrate, a challenge that is currently addressed by applying model approximations or reformulating with index‑reduction algorithms. These algorithms exist in general‑purpose software tools, but their reliance on symbolic representation makes them computationally prohibitive for large network‑wide EMT models. This paper derives and presents two modular index‑reduced subsystem models that allow EMT models to be integrated with standard solvers, without approximations or symbolic algorithms. Both subsystems include a transformer, one isolated and one machine‑coupled. We measure the computational performance of constructing EMT models with up to 1152 buses using the custom subsystem models and the symbolic algorithms. The custom approach reduces memory usage and runtime of model construction by several orders of magnitude compared to the general approach, shifting the bottleneck from construction to integration.

Authors:Guofeng Zhang, Ian R. Petersen
Title: Coherent feedback $H^\infty$ control of quantum linear systems
Abstract:
The purpose of this paper is to investigate the coherent feedback H^\infty control problem for linear quantum systems. A key contribution is a simplified design methodology that guarantees closed‑loop stability and a prescribed level of disturbance attenuation. It is shown that for general linear quantum systems, a physically realizable quantum controller can be obtained by solving at most four Lyapunov equations. In the passive case, a necessary and sufficient condition is provided in terms of two uncoupled pairs of Lyapunov equations. These results represent a significant simplification over the standard approach, which requires solving two coupled algebraic Riccati equations. The effectiveness of the proposed method is demonstrated through two typical quantum optical devices: an empty optical cavity and a degenerate parametric amplifier. These results provide a computationally efficient procedure for the robust and optimal control of quantum optical and optomechanical systems.

Authors:Umair Zulfiqar
Title: $LDL^\top$ Factorization-based Generalized Low-rank ADI Algorithm for Solving Large-scale Algebraic Riccati Equations
Abstract:
The low‑rank alternating direction implicit (ADI) method is an efficient and effective solver for large‑scale standard continuous‑time algebraic Riccati equations that admit low‑rank solutions. However, the existing low‑rank ADI algorithm for Riccati equations (RADI) cannot be directly applied to general‑form Riccati equations. This paper introduces a generalized RADI algorithm based on an LDL^\top factorization, which efficiently handles the general Riccati equations arising in important applications like state estimation and controller design. An efficient implementation is presented that avoids the Sherman‑Morrison‑Woodbury formula and instead uses a low‑rank Cholesky factor ADI method as the base algorithm to compute low‑rank factors of general‑form Riccati equations. Sample MATLAB‑based implementations of the proposed algorithm are also provided. An approach for automatically and efficiently generating ADI shifts is discussed. Numerical examples solving several Riccati equations of orders ranging from 10^6 to 10^7 accurately and efficiently are presented, demonstrating the effectiveness of the proposed algorithm.

Authors:Hossein Papi, Muzaffar Qureshi, Kyle Volle, Rushikesh Kamalapurkar
Title: Decentralized Scalar Field Mapping using Gaussian Process
Abstract:
Decentralized Gaussian process (GP) methods offer a scalable framework for multi‑agent scalar‑field estimation by replacing a centralized global model with multiple local models maintained by individual agents. A team of agents operates through overlapping domains; neighboring agents generally produce inconsistent distributions over shared regions. This paper investigates whether these inter‑agent posterior discrepancies can be systematically exploited to improve team‑level predictive performance and answers this question positively through a novel decentralized intersection data‑sharing and assimilation protocol. Specifically, each agent constructs neighbor‑specific packets from its local GP together with the geometry of the overlap between subdomains and selectively assimilates information received from neighboring agents to improve consistency of its posterior over the shared regions. The proposed architecture preserves locality in both computation and communication, supports decentralized neighbor‑to‑neighbor data assimilation, and allows local GP models to evolve cooperatively across the network without requiring the exchange full packet exchange or centralized inference.

Authors:Md Umar Hashmi, Harsha Nagarajan, Dirk Van Hertem1
Title: Multi-Region Optimal Energy Storage Arbitrage
Abstract:
The increasing interconnection of power systems through AC and DC links enables energy storage units to access multiple electricity markets yet most existing arbitrage models remain limited to singlemarket participation This gap restricts understanding of the economic value and operational constraints associated with crossborder storage operation To address this an optimal multiregion energy storage arbitrage model is developed for a gridscale battery located at one end of an interconnector linking two distinct dayahead markets The formulation incorporates battery capacity and ramping limits converter and interconnector losses and marketspecific buying and selling prices Using disjunctive linearization of nonlinear terms this work exactly reformulates the multiregion energy arbitrage optimization as a mixedinteger linear programming problem The proposed formulation ensures that the battery either charges or discharges from all participating energy markets simultaneously at any given time Case studies using eight years of BelgianUK price data demonstrate that multiregion participation can increase arbitrage revenue by more than 40% compared to local energy arbitrage operation only while also highlighting the negative impact of interconnector congestion on achievable gains The results indicate that crossborder market access substantially enhances storage profitability while considering the cycle of battery and that the proposed formulation provides a computationally efficient framework for evaluating and operating storage assets in interconnected power systems Finally a pseudoefficiency term is introduced to improve battery utilization by discarding less profitable charging and discharging battery cycles

Authors:Asmaa Eldesoukey, Yongxin Chen, Abhishek Halder
Title: A Generalized Sinkhorn Algorithm for Mean-Field Schrödinger Bridge
Abstract:
The mean‑field Schrödinger bridge (MFSB) problem concerns designing a minimum‑effort controller that guides a diffusion process with nonlocal interaction to reach a given distribution from another by a fixed deadline. Unlike the standard Schrödinger bridge, the dynamical constraint for MFSB is the mean‑field limit of a population of interacting agents with controls. It serves as a natural model for large‑scale multi‑agent systems. The MFSB is computationally challenging because the nonlocal interaction makes the problem nonconvex. We propose a generalization of the Hopf‑Cole transform for MFSB and, building on it, design a Sinkhorn‑type recursive algorithm to solve the associated system of integro‑PDEs. Under mild assumptions on the interaction potential, we discuss convergence guarantees for the proposed algorithm. We present numerical examples with repulsive and attractive interactions to illustrate the theoretical contributions.

Authors:V. Cerone, S. M. Fosson, S. Pirrera, A. Re, D. Regruto
Title: Feedback control of Lagrange multipliers for non-smooth constrained optimization
Abstract:
In this work, we develop a control‑theoretic framework for constrained optimization problems with composite objective functions including non‑differentiable terms. Building on the proximal augmented Lagrangian formulation, we construct a plant whose equilibria correspond to the stationary points of the optimization problem. Within this framework, we propose two control strategies ‑ a static controller and a dynamic controller ‑ leading to two novel optimization algorithms. We provide a theoretical analysis, establishing global exponential convergence under strong convexity assumptions. Finally, we demonstrate the effectiveness of the proposed methods through numerical experiments, benchmarking their performance against state‑of‑the‑art approaches.

Authors:Anil Sapkota, Omer San
Title: Hyperfastrl: Hypernetwork-based reinforcement learning for unified control of parametric chaotic PDEs
Abstract:
Spatiotemporal chaos in fluid systems exhibits severe parametric sensitivity, rendering classical adjoint‑based optimal control intractable because each operating regime requires recomputing the control law. We address this bottleneck with hyperFastRL, a parameter‑conditioned reinforcement learning framework that leverages Hypernetworks to shift from tuning isolated controllers per‑regime to learning a unified parametric control manifold. By mapping a physical forcing parameter μ directly to the weights of a spatial feedback policy, the architecture cleanly decouples parametric adaptation from spatial boundary stabilization. To overcome the extreme variance inherent to chaotic reward landscapes, we deploy a pessimistic distributional value estimation over a massively parallel environment ensemble. We evaluate three Hypernetwork functional forms, ranging from residual MLPs to periodic Fourier and Kolmogorov‑Arnold (KAN) representations, on the Kuramoto‑Sivashinsky equation under varying spatial forcing. All forms achieve robust stabilization. KAN yields the most consistent energy‑cascade suppression and tracking across unseen parametrizations, while Fourier networks exhibit worse extrapolation variability. Furthermore, leveraging high‑throughput parallelization allows us to intentionally trade a fraction of peak asymptotic reward for a 37% reduction in training wall‑clock time, identifying an optimal operating regime for practical deployment in complex, parameter‑varying chaotic PDEs.

Authors:Ali Umut Kaypak, Prashanth Krishnamurthy, Farshad Khorrami
Title: A Control Barrier Function-Constrained Model Predictive Control Framework for Safe Reinforcement Learning
Abstract:
Ensuring safety under unknown and stochastic dynamics remains a significant challenge in reinforcement learning (RL). In this paper, we propose a model predictive control (MPC)‑based safe RL framework, called Probabilistic Ensembles with CBF‑constrained Trajectory Sampling (PECTS), to address this challenge. PECTS jointly learns stochastic system dynamics with probabilistic neural networks (PNNs) and control barrier functions (CBFs) with Lipschitz‑bounded neural networks. Safety is enforced by incorporating learned CBF constraints into the MPC formulation while accounting for the model stochasticity. This enables probabilistic safety under model uncertainty. To solve the resulting MPC problem, we utilize a sampling‑based optimizer together with a safe trajectory sampling method that discards unsafe trajectories based on the learned system model and CBF. We validate PECTS in various simulation studies, where it outperforms baseline methods.

Authors:Pranjal Sharma, Zirui Xu, Vasileios Tzoumas
Title: Asynchronous Distributed Bandit Submodular Maximization under Heterogeneous Communication Delays
Abstract:
We study asynchronous distributed decision‑making for scalable multi‑agent bandit submodular maximization. We are motivated by distributed information‑gathering tasks in unknown environments and under heterogeneous inter‑agent communication delays. To enable scalability despite limited communication delays, existing approaches restrict each agent to coordinate only with its one‑hop neighbors. But these approaches assume homogeneous communication delays among the agents and a synchronous global clock. In practice, however, delays are heterogeneous, and agents operate with mismatched local clocks. That is, each agent does not receive information from all neighbors at the same time, compromising decision‑making. In this paper, we provide an asynchronous coordination algorithm to overcome the challenges. We establish a provable approximation guarantee against the optimal synchronized centralized solution, where the suboptimality gap explicitly depends on communication delays and clock mismatches. The bounds also depend on the topology of each neighborhood, capturing the effect of distributed decision‑making via one‑hop‑neighborhood messages only. We validate the approach through numerical simulations on multi‑camera area monitoring.

Authors:Vakhtang Chulukhadze, Kristi Nguyen, Eric Stolt, Kilian Shambaugh, Weston Braun, Tzu-Hsuan Hsu, Osama Jameel, Juan Rivas-Davila, Ruochen Lu
Title: Spurious-Free Lithium Niobate Bulk Acoustic Wave Resonator with Grounded-Ring Electrode
Abstract:
High‑performance piezoelectric resonators are promising energy storage elements for piezoelectric power conversion due to their compact footprint and low loss at frequencies where conventional magnetic components become bulky and inefficient. However, their practical use is often limited by the trade‑off between a high electromechanical coupling coefficient (k^2) for wide‑band operation and the emergence of spurious acoustic modes that limit the resonators' inductive bandwidth. This work reports a spurious‑free thickness‑extensional (TE)‑mode bulk acoustic wave (BAW) resonator in single‑crystal lithium niobate (LN) based on a grounded‑ring electrode architecture. The proposed structure is analyzed through simulation and experimentally validated using electrical characterization and laser Doppler vibrometry (LDV). The results show that the grounded ring modifies the effective boundary conditions of the acoustic device, enabling a piston‑like modal response that suppresses lateral spurious modes across the inductive band. The demonstrated device operates at 10.14 MHz and achieves an electromechanical coupling coefficient of 29.6%, a maximum in‑band Bode quality factor (Q_Bode) of 5230, and a figure of merit (FoM, Qk^2) of 1548. These results establish the grounded‑ring TE‑mode LN BAW resonator as a practical platform for piezoelectric power conversion and a broader design approach for realizing high‑performance spurious‑free acoustic resonators.

Authors:Sewedo Todowede
Title: Probabilistic Frequency Hazard Analysis: Adapting the Seismic Hazard Framework to Power System Frequency Exceedance Risk
Abstract:
The declining synchronous inertia in power systems undergoing the energy transition increases the sensitivity of system frequency to generation and interconnector disturbances, making accurate frequency risk quantification increasingly important. Existing methods for frequency risk assessment, while valuable, lack formal uncertainty quantification, continuous hazard curves, and source‑level disaggregation. This paper introduces Probabilistic Frequency Hazard Analysis (PFHA), a framework that adapts the mathematical architecture of Probabilistic Seismic Hazard Analysis (PSHA), the standard methodology in earthquake engineering, to power system frequency exceedance risk. The PFHA hazard integral computes annual exceedance rates by integrating over all combinations of loss sources, disturbance sizes, and system operating states through a frequency response prediction equation with calibrated aleatory variability. The framework is implemented with a 51‑source catalogue constructed from operational data, empirical loss distributions from settlement‑period generation records, Bayesian occurrence rate estimation, a dual analytical and physics‑based frequency response prediction architecture, and a 324‑path logic tree for epistemic uncertainty quantification. Application to the Great Britain power system using four years of operational data demonstrates agreement with the independently developed Frequency Risk and Control Report to within a factor of 1.5 at 49.2 Hz, while also quantifying the risk reduction from Dynamic Containment and Low‑Frequency Demand Disconnection controls. To the author's knowledge, this is the first published explicit PSHA‑style hazard‑integral formulation for bulk power‑system frequency exceedance risk.

Authors:Gael Luna, Tyler Summers
Title: Augmented Graphs of Convex Sets and the Traveling Salesman Problem
Abstract:
We present a trajectory optimization algorithm for the traveling salesman problem (TSP) in graphs of convex sets (GCS). Our framework uses an augmented graph of convex sets to encode the TSP specification and solve it exactly as a shortest path problem in GCS. We establish a precise relationship between the landmark Bellman‑Held‑Karp algorithm and the augmented graph of convex sets with a TSP specification. Additionally, we present a branch and bound heuristic that uses minimum 1‑trees to obtain certifiably optimal or near optimal solutions and scales to problems far larger than the exact framework can handle. To assess and certify performance, we explore several alternative lower bounds.

Authors:Vineet Jagadeesan Nair
Title: Multiobjective optimization-based design and dispatch of islanded, hybrid microgrids for remote, off-grid communities in sub-Saharan Africa
Abstract:
Reliable, affordable electricity remains inaccessible to over 600 million people in sub‑Saharan Africa (SSA), where islanded hybrid microgrids combining renewable generation, battery storage, and diesel backup offer a viable electrification pathway. This paper presents a multiobjective, multiperiod optimization framework for the design, sizing, and dispatch of such systems, with a case study for a remote community in Kenya. System sizing is optimized over a one‑year horizon and dispatch over a representative day, both at hourly resolution. The formulation jointly minimizes lifecycle levelized cost of energy (LCOE), emissions, lost load, and dumped energy, while maximizing renewable penetration. Seven optimization algorithms are benchmarked; particle swarm optimization (PSO) achieves the best trade‑off between runtime (63 s) and solution quality (normalized objective 0.146) and is used for subsequent analyses. The optimal configuration of solar PV, wind, lithium‑ion battery storage, and diesel backup achieves a normalized LCOE of 0.46 USD per kWh with over 94 percent renewable penetration, outperforming alternatives. Pareto fronts highlight trade‑offs between cost, emissions, and reliability, showing that cost‑only optimization yields inferior outcomes. Sensitivity analyses identify fuel prices and discount rates as the most influential parameters in SSA contexts. A break‑even distance analysis shows microgrids are economically competitive with grid extension at the study site. The dispatch model produces day‑ahead schedules that are robust to short‑term uncertainty, though extended wind lulls increase diesel reliance. This work fills a critical gap by providing a comprehensive multiobjective design and dispatch framework tailored to SSA resource, economic, and operational conditions.

Authors:Marton Laszlo Ambrus, Stuart Hillmansen, Zhongbei Tian
Title: Algorithmic Energy Management in Constrained Railway Traction Networks: A Systematic Review
Abstract:
The decarbonisation of heavy‑duty railway networks requires maximising the capacity of existing electrical infrastructure. Integrating heavy freight alongside fast passenger services exposes the hard physical limits of conventional alternating current traction networks, causing severe localised power quality degradation, phase unbalance, and low‑voltage behaviour that triggers protective substation tripping. Because upgrading physical hardware is highly capital‑intensive, software‑based Energy Management Strategies (EMS) offer a potentially viable alternative for preventing these power capacity challenges. This systematic review synthesises the literature on algorithmic energy management for grid‑constrained multi‑train AC railway networks, classifying the reviewed studies along three axes: algorithm family, operational scope, and constraint coupling. The review documents three consistent findings across the included studies. First, single‑train trajectory optimisation, however mathematically refined, cannot represent the coupled electrical interactions that increasingly define network capacity on mixed‑traffic networks. Second, while multi‑train Train‑Track‑Power (TTP) simulations correctly capture these interactions, the algorithm families currently used to solve them face well‑documented trade‑offs between computational tractability and constraint flexibility. Third, the literature increasingly identifies a gap between mathematically optimal speed profiles and operationally executable ones, particularly for networks operated by human drivers rather than Automatic Train Operation systems. The review delineates where current methods succeed, where they fail, and which directions the literature has identified as open.

Authors:Trivikram Satharasi, Tochukwu E. Ogri, Muzaffar Qureshi, Kyle Volle, Rushikesh Kamalapurkar
Title: Adaptive Control with Sparse Identification of Nonlinear Dynamics
Abstract:
This paper develops a sparsity‑promoting integral concurrent learning (SP‑ICL) adaptation law for a linearly parametrized uncertain nonlinear control‑affine system. The unknown parameters are learned using ICL with sparsity‑promoting \ell_1 regularization. The use of \ell_1 regularization for sparsity promotion is common in system identification and machine learning; however, unlike existing approaches, this paper develops an online parameter update law that integrates the regularization penalty with ICL via sliding modes. Using the SP‑ICL update law, we show via non‑smooth Lyapunov analysis that the trajectories of the closed‑loop system are ultimately bounded. Simulations verify the effectiveness of the sparsity penalty in the SP‑ICL update law on recovering sparse dynamics during trajectory tracking.

Authors:Adam Hallmark, Pan Zhao
Title: Improving INDI for Input Nonaffine Systems via Learning-Based Nonlinear Control Allocation
Abstract:
This paper first demonstrates that applying standard incremental nonlinear dynamic inversion (INDI) with incremental control allocation (ICA) to input nonaffine systems relies on an untenable linear approximation of the actuator model. It then shows that avoiding this issue, while retaining the static control allocation paradigm, generally requires solving a nonlinear programming (NLP) problem. To address the associated online computational challenges, the paper subsequently presents a supervised learning‑based approach. Numerical experiments on an example problem validate the identified limitations of standard INDI + ICA for input nonaffine systems, while also demonstrating that the proposed learning‑based method provides an effective and computationally tractable alternative.

Authors:Pengyang Wu, Jing Shuang Li
Title: An Evolutionary Algorithm for Actuator-Sensor-Communication Co-Design in Distributed Control
Abstract:
This paper studies the co‑design of actuators, sensors, and communication in the distributed setting, where a networked plant is partitioned into subsystems each equipped with a sub‑controller interacting with other sub‑controllers. The objective is to jointly minimize control cost (measured by LQ cost) and material cost (measured by the number of actuators, sensors, and communication links used). We approach this using an evolutionary algorithm to selectively prune a baseline dense LQR controller. We provide convergence and stability analyses for this algorithm. For unstable plants, controller pruning is more likely to induce instability; we provide an algorithm modification to address this. The proposed methods is validated in simulations. One key result is that co‑design of a 98‑state swing equation model can be done on a standard laptop in seconds; the co‑design outperforms naive controller pruning by over 50%.

Authors:Lukas-Benedikt Fiechtner, Jose Blanchet
Title: Distributionally Robust Regret Optimal LQR with Common Stage-Law Ambiguity
Abstract:
We study, to our knowledge, the first tractable multistage ex‑ante distributionally robust regret optimization (DRRO) formulation for stochastic control. We consider finite‑horizon LQR under common stage‑law ambiguity: disturbances are independent across time but share an unknown stage law whose mean and covariance lie in a Gelbrich ball around nominal parameters. Unlike the single‑stage quadratic case, the nominal certainty‑equivalent (CE) controller is generally not regret‑optimal, because reuse of the stage law makes past disturbances informative for future decisions. Despite the general NP‑hardness of DRRO, we show that over linear disturbance‑feedback policies the resulting multistage DRRO‑LQR problem admits an exact semidefinite programming reformulation. The optimal controller is the nominal certainty‑equivalent LQR law plus a strictly causal empirical‑mean correction. We also characterize worst‑case distributions and show that those for the DRRO‑optimal policy are nonunique. Numerical results show that, relative to the corresponding DRO controller under the same ambiguity set, DRRO is often substantially less conservative while preserving the intended regret guarantee, and that its correction coefficients empirically approach the certainty‑equivalent feedforward coefficient.

Authors:Chen Song, Angela Fontan, Rong Su, Julien M. Hendrickx, Vladimir Cvetkovic, Karl H. Johansson
Title: On the Convergence of an Opinion-Action Coevolution Model with Bounded Confidence
Abstract:
This paper presents a theoretical convergence analysis for an opinion‑action coevolution model that integrates the opinion updating rule of the Hegselmann‑Krause model with a utility‑based decision‑making mechanism. The model is reformulated into an augmented state‑space representation, where the state matrix induces a time‑varying social interaction digraph. The convergence analysis is grounded on two existing theoretical findings that establish convergence for the Hegselmann‑Krause type of models and containment control systems with multiple stationary leaders, respectively. Results indicate that, if the structure of the interaction digraph stabilizes within finite time, the model either converges to consensus, where all agents' opinions and actions reach an identical state, or exhibits clustering, where some opinion nodes act as stationary leaders while the remaining nodes approach the convex hull formed by the leaders. Numerical simulations are then provided to validate the theoretical results.

Authors:Haoyu Yin, Xudong Chen, Bruno Sinopoli
Title: On Permanence of Conservative Replicator Dynamics with Four Strategies
Abstract:
In this paper, we study four‑strategy conservative replicator dynamics induced by constant payoff matrices. We establish necessary and sufficient conditions for permanence to occur by associating the payoff matrix with its digraph, revealing exactly five distinct digraph classes governing the global behavior. We further show that, whenever the dynamics is permanent, every non‑equilibrium trajectory in the relative interior of the simplex is a Lyapunov‑stable periodic orbit. Together with the classification of the boundary phase portraits, these results provide a complete characterization of the global dynamics in the four‑strategy case with permanence.

Authors:Alex Zongo, Peng Wei
Title: eVTOL Aircraft Energy Overhead Estimation under Conflict Resolution in High-Density Airspaces
Abstract:
Electric vertical takeoff and landing (eVTOL) aircraft operating in high‑density urban airspace must maintain safe separation through tactical conflict resolution, yet the energy cost of such maneuvers has not been systematically quantified. This paper investigates how conflict‑resolution maneuvers under the Modified Voltage Potential (MVP) algorithm affect eVTOL energy consumption. Using a physics‑based power model integrated within a traffic simulation, we analyze approximately 71,767 en route sections within a sector, across traffic densities of 10‑60 simultaneous aircraft. The main finding is that MVP‑based deconfliction is energy‑efficient: median energy overhead remains below 1.5% across all density levels, and the majority of en route flights within the sector incur negligible penalty. However, the distribution exhibits pronounced right‑skewness, with tail cases reaching 44% overhead at the highest densities due to sustained multi‑aircraft conflicts. The 95th percentile ranges from 3.84% to 5.3%, suggesting that a 4‑5% reserve margin accommodates the vast majority of tactical deconfliction scenarios. To support operational planning, we develop a machine learning model that estimates energy overhead at mission initiation. Because conflict outcomes depend on future traffic interactions that cannot be known in advance, the model provides both point estimates and uncertainty bounds. These bounds are conservative; actual outcomes fall within the predicted range more often than the stated confidence level, making them suitable for safety‑critical reserve planning. Together, these results validate MVP's suitability for energy‑constrained eVTOL operations and provide quantitative guidance for reserve energy determination in Advanced Air Mobility.

Authors:Yunxiao Ren, Dingguo Liang, Yuezu Lv, Zhisheng Duan
Title: Coalitional Zero-Sum Games for ${H_{\infty}}$ Leader-Following Consensus Control
Abstract:
This paper investigates the leader‑following consensus problem for a class of multi‑agent systems subject to adversarial attack‑like external inputs. To address this, we formulate the robust leader‑following control problem as a global coalitional min‑max zero‑sum game using differential game theory. Specifically, the agents' control inputs form a coalition to minimize a global cost function, while the attacks form an opposing coalition to maximize it. Notably, when these external adversarial attacks manifest as disturbances, the designed game‑theoretic control policy systematically yields a robust H_\infty control law. Addressing this problem inherently requires solving a high‑dimensional generalized algebraic Riccati equation (GARE), which poses significant challenges for distributed computation and controller implementation. To overcome these challenges, we propose a two‑fold approach. First, a decentralized computational strategy is devised to decompose the high‑dimensional GARE into multiple uniform, lower‑dimensional GAREs. Second, a dynamic average consensus‑based decoupling algorithm is developed to resolve the inherent coupling structure of the robust control law, thereby facilitating its distributed implementation. Finally, numerical simulations on the formation control of multi‑vehicle systems with feedback‑linearized dynamics are conducted to validate the effectiveness of the proposed algorithms.

Authors:Michele Mascherpa, Victor Molnö, Carsten Skovmose Kallesøe, Johan Karlsson
Title: A proximal approach to the Schrödinger bridge problem with incomplete information and application to contamination tracking in water networks
Abstract:
In this work, we study a discrete Schrödinger bridge problem with partial marginal observations. A main difficulty compared to the classical Schrödinger bridge formulation is that our problem is not strictly convex and standard Sinkhorn‑type methods cannot be directly applied. To address this issue, we propose a scalable computational method based on an entropic proximal scheme. Furthermore, we develop a framework for this problem that includes duality results, characterization of the optimal solutions, and an observability condition that determines when the optimal solution is unique. We validate the method on the problem of estimating contamination in a water distribution network, where the partial marginals correspond to measured pollutant concentrations at the sensor locations. The experiments were conducted on a laboratory‑scale water distribution network.

Authors:Zahra Hashemi, Dipankar Maity
Title: Linear Reformulation of Event-Triggered LQG Control under Unreliable Communication
Abstract:
We consider event‑triggered linear‑quadratic Gaussian (LQG) control when sensor updates are transmitted over an i.i.d. packet‑erasure channel. Although the optimal controller in a standard LQG setup is available in closed form, choosing when to transmit remains computationally and analytically difficult because packet drops randomize packet delivery and couple scheduling decisions with the estimation‑error dynamics, making direct dynamic‑programming solutions impractical. By certainty equivalence, the co‑design problem becomes choosing a binary send/skip sequence that balances control performance and communication cost. We derive a closed‑form expansion of the error covariance as precomputable Gramian terms scaled by a survival factor that depends only on the number of transmission attempts on each interval. This converts the problem into an unconstrained binary program that we linearize exactly via running attempt counters and a one‑hot encoding, yielding a compact MILP well suited to receding‑horizon implementation. On the linearized Boeing‑747 benchmark, a model predictive control (MPC) scheduler lowers cost while attempting far fewer transmissions than a one‑shot baseline across channel success rates.

Authors:Daniel M. Cherenson, Dimitra Panagou
Title: Staggered Integral Online Conformal Prediction for Safe Dynamics Adaptation with Multi-Step Coverage Guarantees
Abstract:
Safety‑critical control of uncertain, adaptive systems often relies on conservative, worst‑case uncertainty bounds that limit closed‑loop performance. Online conformal prediction is a powerful data‑driven method for quantifying uncertainty when truth values of predicted outputs are revealed online; however, for systems that adapt the dynamics without measurements of the state derivatives, standard online conformal prediction is insufficient to quantify the model uncertainty. We propose Staggered Integral Online Conformal Prediction (SI‑OCP), an algorithm utilizing an integral score function to quantify the lumped effect of disturbance and learning error. This approach provides long‑run coverage guarantees, resulting in long‑run safety when synthesized with safety‑critical controllers, including robust tube model predictive control. Finally, we validate the proposed approach through a numerical simulation of an all‑layer deep neural network (DNN) adaptive quadcopter using robust tube MPC, highlighting the applicability of our method to complex learning parameterizations and control strategies.

Authors:Guangyi Liu, Vivek Pandey, Christoforos Somarakis, Nader Motee
Title: Incremental Risk Assessment for Cascading Failures in Large-Scale Multi-Agent Systems
Abstract:
We develop a framework for studying and quantifying the risk of cascading failures in time‑delay consensus networks, motivated by a team of agents attempting temporal rendezvous under stochastic disturbances and communication delays. To assess how failures at one or multiple agents amplify the risk of deviation across the network, we employ the Average Value‑at‑Risk as a systemic measure of cascading uncertainty. Closed‑form expressions reveal explicit dependencies of the risk of cascading failure on the Laplacian spectrum, communication delay, and noise statistics. We further establish fundamental lower bounds that characterize the best‑achievable network performance under time‑delay constraints. These bounds serve as feasibility certificates for assessing whether a desired safety or performance goal can be achieved without exhaustive search across all possible topologies. In addition, we develop an efficient single‑step update law that enables scalable propagation of conditional risk as new failures are detected. Analytical and numerical studies demonstrate significant computational savings and confirm the tightness of the theoretical limits across diverse network configurations.

Authors:Alberto Piccina, Massimiliano Bertoni, Angelo Cenedese, Giulia Michieletto
Title: Force Polytope-Based Cant-Angle Selection for Tilting Hexarotor UAVs
Abstract:
From a maneuverability perspective, the main advantage of tilting multirotor UAVs lies in the dynamic variability of the feasible executable wrench, which represents a key asset for physical interaction tasks. Accordingly, cant‑angle selection should be optimized to ensure high performance while avoiding abrupt variations and preserving real‑world feasibility. In this context, this work proposes a lightweight control framework for star‑shaped interdependent cant‑tilting hexarotor UAVs performing interaction tasks. The method uses an offline‑computed look‑up table of zero‑moment force polytopes to identify feasible cant angles for a desired control force and select the optimal one by balancing efficiency and smoothness. The framework is integrated with a geometric full‑pose controller and validated through Monte Carlo simulations in MATLAB/Simulink and compared against a baseline strategy. The results show a significant reduction in computation time, together with improved pose‑tracking performance and competitive actuation efficiency. A final physics‑based simulation of a complete wall inspection task in Simscape further confirms the feasibility of the proposed strategy in interacting scenarios.

Authors:Ian J. Willebeek-LeMair, Craig A. Woolsey
Title: Practical Universal Tracking With Pivoted Unidirectional Actuation
Abstract:
This paper addresses the problem of tracking control for robotic vehicles equipped with pivoted unidirectional actuators. Starting from a baseline robust controller that assumes unconstrained inputs, we redesign the control law to be compatible with the pivoted actuator. This is accomplished by driving the output of the pivoted actuator to a ball centered at the target input value. The guarantees for the baseline controller are recovered in a practical sense. The theory is illustrated with simulation examples.

Authors:Georgios Vasileiou, Lantian Zhang, Silun Zhang
Title: Adaptive Incentive Design with Regret Minimization
Abstract:
Incentive design constitutes a foundational paradigm for influencing the behavior of strategic agents, wherein a system planner (principal) publicly commits to an incentive mechanism designed to align individual objectives with collective social welfare. This paper introduces the Regret‑Minimizing Adaptive Incentive Design (RAID) problem, which aims to synthesize incentive laws under information asymmetry and achieve asymptotically minimal regret compared to an oracle with full information. To this end, we develop the RAID algorithm, which employs a switching policy alternating between probing (exploration) and estimate‑based incentivization (exploitation). The associated type estimator relies only on a weaker excitation condition required for strong consistency in least squares estimation, substantially relaxing the persistence‑of‑excitation assumptions previously used in adaptive incentive design. In addition, we establish the strong consistency of the proposed type estimator and prove that the incentive obtained asymptotically minimizes the planner's average regret almost surely. Numerical experiments illustrate the convergence rate of the proposed methodology.

Authors:Yun Jeong Yang, Jin Gyu Lee
Title: A note on input signal generators: A relaxation of Willems' fundamental lemma in the SISO case
Abstract:
We provide a practical relaxation of Willems' fundamental lemma for discrete‑time linear time‑invariant (single‑input‑single‑output) systems. Instead of maintaining conventional Willems' persistency of excitation condition in the behavioral theory, we reformulate the problem in terms of signal generators, hence going back to the dynamical systems theory. We discuss the relationship between the persistency of excitation order and the dimension of the signal generator. Furthermore, we identify a necessary and sufficient condition on the signal generator that can generate informative input‑‑output data for almost all systems and initial conditions. This even includes inputs outside the class originally suggested by Willems' fundamental lemma, for example, sinusoidal sequences with fewer frequencies. Finally, the signal generator perspective allows a natural extension to continuous‑time systems.

Authors:Fabian Raisch, Timo Germann, J. Nathan Kutz, Christoph Goebel, Benjamin Tischler
Title: Transfer Learning for Neural Parameter Estimation applied to Building RC Models
Abstract:
Parameter estimation for dynamical systems remains challenging due to non‑convexity and sensitivity to initial parameter guesses. Recent deep learning approaches enable accurate and fast parameter estimation but do not exploit transferable knowledge across systems. To address this, we introduce a transfer‑learning‑based neural parameter estimation framework based on a pretraining‑fine‑tuning paradigm. This approach improves accuracy and eliminates the need for an initial parameter guess. We apply this framework to building RC thermal models, evaluating it against a Genetic Algorithm and a from‑scratch neural baseline across eight simulated buildings, one real‑world building, two RC model configurations, and four training data lengths. Results demonstrate an 18.6‑24.0% performance improvement with only 12 days of training data and up to 49.4% with 72 days. Beyond buildings, the proposed method represents a new paradigm for parameter estimation in dynamical systems.

Authors:Aihui Liu, Magnus Jansson
Title: Local Sensitivity Analysis for Kernel-Regularized ARX Predictors in Data-Driven Predictive Control
Abstract:
We study local sensitivity of structured ARX‑based data‑driven predictive control. Although predictor estimation is linear in the ARX parameters, the lifted multi‑step predictor used in MPC depends on them implicitly, which complicates both uncertainty propagation and task‑aware regularization. We derive a local first‑order linearization of this implicit predictor map. The resulting Jacobian yields both an approximate control‑relevant prediction uncertainty term and a task‑dependent sensitivity metric for shaping kernel regularization. Numerical results show that the proposed analysis is most useful in weak‑excitation regimes, where baseline SS regularization already provides substantial robustness gains and the proposed sensitivity shaping yields a further smaller improvement.

Authors:Dongzhe Zheng, Wenjie Mei
Title: A Posteriori Second-Order Guarantees for Bolza Problems via Collocation
Abstract:
Direct collocation for Bolza optimal control yields discrete Karush‑Kuhn‑Tucker (KKT) points, while practical solvers expose only discrete quantities such as primal‑dual iterates, reduced Hessians, and Jacobians. This creates a gap between continuous second‑order optimality theory and what can be certified from solver output. We develop an a posteriori certification framework that bridges this gap. Starting from a discrete KKT solution, we reconstruct piecewise polynomial state, control, and costate trajectories, evaluate residuals of the dynamics, boundary, and stationarity conditions, and derive a computable lower bound for the continuous second variation. The bound is expressed as the discrete reduced curvature minus explicit residual‑dependent correction terms. A positive bound yields a sufficient certificate for continuous second‑order sufficiency and provides quantitative information relevant to local growth and trust‑region sizing. The constants entering the certification inequality are conservatively estimable from reconstructed discrete data. The resulting test is operationally verifiable from collocation outputs and naturally supports adaptive mesh refinement through residual decomposition. We also outline an extension to path inequalities with isolated transversal switches.

Authors:Wenyuan Wu, Peng Xie, Zhen Zhang, Yanliang Huang, Karl H. Johansson, Amr Alanwar
Title: From Points to Sets: Set-Based Safety Verification in the Latent Space
Abstract:
We extend latent representation methods for safety control design to set‑valued states. Recent work has shown that barrier functions designed in a learned latent space can transfer safety guarantees back to the original system, but these methods evaluate certificates at single state points, ignoring state uncertainty. A fixed safety margin can partially address this but cannot adapt to the anisotropic and time‑varying nature of the uncertainty gap across different safety constraints. We instead represent the system state as a zonotope, propagate it through the encoder to obtain a latent zonotope, and evaluate certificates over the worst case of the entire set. On a 16‑dimensional quadrotor suspended‑load gate passage task, set‑valued evaluation achieves 5/5 collision‑free passages, compared to 1/5 for point‑based evaluation and 2/5 for a fixed‑margin baseline. Set evaluation reports safety in 44.4% of per‑head evaluations versus 48.5% for point‑based, and this greater conservatism detects 4.1% blind spots where point evaluation falsely certifies safety, enabling earlier corrective control. The safety gap between point and set evaluation varies up to 12× across certificate heads, explaining why no single fixed margin suffices and confirming the need for per‑head, per‑timestep adaptation, which set evaluation provides by construction.

Authors:Jannis Lübsen, Annika Eichler
Title: Robust Nonlinear System Identification in Reproducing Kernel Hilbert Spaces via Scenario Optimization
Abstract:
This paper proposes a method for constructing one‑step prediction tubes for nonlinear systems using reproducing kernel Hilbert spaces. We approximate a bounded reproducing kernel Hilbert space (RKHS) hypothesis set by a finite‑dimensional subspace using bounds based on n‑widths and a greedy algorithm for basis reduction. For kernels whose native spaces are norm‑equivalent to Sobolev spaces, we derive how the required basis size scales with kernel smoothness and input dimension. This finite‑dimensional representation enables the use of convex scenario optimization to obtain violation guarantees for the learned predictor without requiring an a priori bound on the true system's RKHS norm or Lipschitz constant. The method is demonstrated on an obstacle‑avoidance task. We also discuss the main limitations of the current analysis, including dimensional scaling and dependence on i.i.d. data.

Authors:Yangye Jiang, Jiachen Wang, Daofei Li
Title: Physics-Informed Neural Optimal Control for Precision Immobilization Technique in Emergency Scenarios
Abstract:
Precision Immobilization Technique (PIT) is a potentially effective intervention maneuver for emergency out‑of‑control vehicle, but its automation is challenged by highly nonlinear collision dynamics, strict safety constraints, and real‑time computation requirements. This work presents a PIT‑oriented neural optimal‑control framework built around PicoPINN (Planning‑Informed Compact Physics‑Informed Neural Network), a compact physics‑informed surrogate obtained through knowledge distillation, hierarchical parameter clustering, and relation‑matrix‑based parameter reconstruction. A hierarchical neural‑OCP (Optimal Control Problem) architecture is then developed, in which an upper virtual decision layer generates PIT decision packages under scenario constraints and a lower coupled‑MPC (Model Predictive Control) layer executes interaction‑aware control. To evaluate the framework, we construct a PIT Scenario Dataset and conduct surrogate‑model comparison, planning‑structure ablation, and multi‑fidelity assessment from simulation to scaled by‑wire vehicle tests. In simulation, adding the upper planning layer improves PIT success rate from 63.8% to 76.7%, and PicoPINN reduces the original PINN parameter count from 8965 to 812 and achieves the smallest average heading error among the learned surrogates (0.112 rad). Scaled vehicle experiments are further used as evidence of control feasibility, with 3 of 4 low‑speed controllable‑contact PIT trials achieving successful yaw reversal.

Authors:Ioannis Stefanakos, Roisin Bradley, Radu Calinescu, Beverley Townsend, Tianyuan Wang, Jihong Zhu
Title: Hazard Management in Robot-Assisted Mammography Support
Abstract:
Robotic and embodied‑AI systems have the potential to improve accessibility and quality of care in clinical settings, but their deployment in close physical contact with vulnerable patients introduces significant safety risks. This paper presents a hazard management methodology for MammoBot, an assistive robotic system designed to support patients during X‑ray mammography. To ensure safety from early development stages, we combine stakeholder‑guided process modelling with Software Hazard Analysis and Resolution in Design (SHARD) and System‑Theoretic Process Analysis (STPA). The robot‑assisted workflow is defined collaboratively with clinicians, roboticists, and patient representatives to capture key human‑robot interactions. SHARD is applied to identify technical and procedural deviations, while STPA is used to analyse unsafe control actions arising from user interaction. The results show that many hazards arise not from component failures, but from timing mismatches, premature actions, and misinterpretation of system state. These hazards are translated into refined and additional safety requirements that constrain system behaviour and reduce reliance on correct human timing or interpretation alone. The work demonstrates a structured and traceable approach to safety‑driven design with potential applicability to assistive robotic systems in clinical environments.

Authors:Melvyn Tyloo
Title: Network Reconstruction in Consensus Algorithms with Hidden Agents
Abstract:
Reconstructing the parameters that encode the influence between model variables based on time‑series measurements represents an outstanding question in the theory of complex network‑coupled systems. Here, we propose a solution to this problem for a class of noisy leader‑follower consensus algorithm, where one has access to measurements only from the followers but not from the leaders. Leveraging the directed Laplacian coupling of such systems, we present an autoregressive expansion of the observed dynamics which can be truncated at different orders, depending on the memory of the leaders. When their memory is short, this allows one to correctly reconstruct the full dynamical matrix with hidden leader agents, provided some additional assumption on the system to lift the degeneracy in the reconstruction. We illustrate and check the theory using numerical simulations for the cases of both a single and multiple hidden leaders.

Authors:Bart Wolleswinkel, Riccardo Ferrari
Title: Quantifying Control Performance Loss for a Least Significant Bits Authentication Scheme
Abstract:
Industrial control systems (ICSs) often consist of many legacy devices, which were designed without security requirements in mind. With the increase in cyberattacks targeting critical infrastructure, there is a growing urgency to develop legacy‑compatible security solutions tailored to the specific needs and constraints of real‑time control systems. We propose a least significant bits (LSBs) coding scheme providing message authentication and integrity, which is compatible with legacy devices and never compromises availability. The scheme comes with provable security guarantees, and we provide a simple yet effective method to deal with synchronization issues due to packet dropouts. Furthermore, we quantify the control performance loss for both a fixed‑point and floating‑point quantization architecture when using the proposed coding scheme. We demonstrate its effectiveness in detecting cyberattacks, as well as the impact on control performance, on a hydro power turbine control system.

Authors:Elizaveta Semenyakina, Ivan Snegirev, Mariya Lezina, Miguel Altamirano Cabrera, Safina Gulyamova, Dzmitry Tsetserukou
Title: GraspSense: Physically Grounded Grasp and Grip Planning for a Dexterous Robotic Hand via Language-Guided Perception and Force Maps
Abstract:
Dexterous robotic manipulation requires more than geometrically valid grasps: it demands physically grounded contact strategies that account for the spatially non‑uniform mechanical properties of the object. However, existing grasp planners typically treat the surface as structurally homogeneous, even though contact in a weak region can damage the object despite a geometrically perfect grasp. We present a pipeline for grasp selection and force regulation in a five‑fingered robotic hand, based on a map of locally admissible contact loads. From an operator command, the system identifies the target object, reconstructs its 3D geometry using SAM3D, and imports the model into Isaac Sim. A physics‑informed geometric analysis then computes a force map that encodes the maximum lateral contact force admissible at each surface location without deformation. Grasp candidates are filtered by geometric validity and task‑goal consistency. When multiple candidates are comparable under classical metrics, they are re‑ranked using a force‑map‑aware criterion that favors grasps with contacts in mechanically admissible regions. An impedance controller scales the stiffness of each finger according to the locally admissible force at the contact point, enabling safe and reliable grasp execution. Validation on paper, plastic, and glass cups shows that the proposed approach consistently selects structurally stronger contact regions and keeps grip forces within safe bounds. In this way, the work reframes dexterous manipulation from a purely geometric problem into a physically grounded joint planning problem of grasp selection and grip execution for future humanoid systems.

Authors:Amirhossein Samii, Dimitrios Angelopoulos, Nikolaos Bekiaris-Liberis
Title: Predictor-Feedback CACC for Vehicular Platoons with Actuation and Communication Delays Based on a Multiple-Predecessor-Following CTH Nominal Strategy
Abstract:
We develop a predictor‑feedback cooperative adaptive cruise control (CACC) design relying on a multiple‑predecessor‑following (MPF) topology‑based nominal delay‑free CACC law. We consider vehicular platoons with heterogeneous vehicles, whose dynamics are described by a third‑order linear system subject to actuation delay, along with vehicle‑to‑vehicle (V2V) communication delay. The design achieves individual vehicle stability, string stability, and zero, steady‑state speed/spacing tracking errors, for any value of the actuation delay. The proofs of individual vehicle stability, string stability, and regulation rely on employment of an input‑output approach on the frequency domain, capitalizing on the delay‑compensating property of the design, which enables as to derive explicit string stability conditions on control and vehicle models parameters. The theoretical guarantees of string stability and the respective conditions on parameters are illustrated also numerically. We present consistent simulation results, for a ten‑vehicle platoon, illustrating the potential of the design in traffic throughput improvement, as compared with a predictor‑feedback CACC design in which, each ego vehicle's controller utilizes information only from a single preceding vehicle. We also present simulation results in a realistic scenario in which the leading vehicle's trajectory is obtained from NGSIM data.

Authors:Jesus Bautista, Enric Morella, Lili Wang, Hector Garcia de Marina
Title: Leaderless Collective Motion in Affine Formation Control over the Complex Plane
Abstract:
We propose a method for the collective maneuvering of affine formations in the plane by modifying the original weights of the Laplacian matrix used to achieve static formations of robot swarms. Specifically, the resulting collective motion is characterized as a time‑varying affine transformation of a reference configuration, or shape. Unlike the traditional leader‑follower strategy, our leaderless scheme allows agents to maintain distinct and possibly time‑varying velocities, enabling a broader range of collective motions, including all the linear combinations of translations, rotations, scaling and shearing of a reference shape. Our analysis provides the analytic solution governing the resulting collective motion, explicitly designing the eigenvectors and eigenvalues that define this motion as a function of the modified weights in the new Laplacian matrix. To facilitate a more tractable analysis and design of affine formations in 2D, we propose the use of complex numbers to represent all relevant information. Simulations with up to 20 agents validate the theoretical results.

Authors:Renzi Wang, Panagiotis Patrinos, Alberto Bemporad
Title: Parametric Nonconvex Optimization via Convex Surrogates
Abstract:
This paper presents a novel learning‑based approach to construct a surrogate problem that approximates a given parametric nonconvex optimization problem. The surrogate function is designed to be the minimum of a finite set of functions, given by the composition of convex and monotonic terms, so that the surrogate problem can be solved directly through parallel convex optimization. As a proof of concept, numerical experiments on a nonconvex path tracking problem confirm the approximation quality of the proposed method.

Authors:Yicheng Lin, Bingxian Wu, Nan Bai, Yunxiao Ren, Zhongkui Li, Zhisheng Duan
Title: Optimality Robustness in Koopman-Based Control
Abstract:
The Koopman operator enables simplified representations for nonlinear systems in data‑driven optimal control, but the accompanying uncertainties inevitably induce deviations in the optimal controller and associated value function. This raises a distinct and fundamental question on optimality robustness, specifically, how uncertainties affect the optimal solution itself. To address this problem, we adopt a unified analysis‑to‑design perspective for systematically quantifying and improving optimality robustness. At the analysis level, we derive explicit upper bounds on the deviations of both the value function and the optimal controller, where uncertainties from multiple sources are systematically integrated into a unified norm‑bounded representation. At the design level, we develop a robustness‑aware optimal control methodology that provably reduces such optimality deviations, thereby enhancing robustness while explicitly revealing a quantitative trade‑off between nominal optimality and robustness. As for practical implementation aspect, we further propose a tractable policy iteration algorithm, whose well‑posedness and convergence are established via vanishing viscosity regularization and elliptic partial differential equation (PDE) techniques. Numerical examples validate the theoretical findings and demonstrate the effectiveness of proposed methodology.

Authors:Tao Zhang, Songhao Yang, Zhiguo Hao, Hongyue Ma, Baohui Zhang
Title: An Additional Resonance Damping Control for Grey-Box D-PMSG Wind Farm Integrated Weak Grid
Abstract:
Considerable efforts have been made to address the resonance issue of the Direct‑drive Permanent Magnet Synchronous Generator (D‑PMSG) wind farm integrated power systems. However, the D‑PMSG controller structure and parameters are concealed because of commercial secrecy, thus the target system exhibits grey‑box characteristics. The existing resonance damping methods are either unavailable for grey‑box systems or economically infeasible, which makes resonance damping of grey‑box systems extremely challenging. To address this issue, this paper proposes an Additional Resonance Damping Control (ARDC) specfically for the grey‑box D‑PMSG system. This strategy is achieved by incorporating an additional control loop outside the D‑PMSG controller. Firstly, the external impedance characteristics are obtained by the frequency sweeping technique ofline and then the key parameter of the additional control loop is determined by the Bode‑diagram‑based method under the worst stability scenario. Once the resonance occurs, the external impedance of the black‑box D‑PMSG is reshaped online to increase the magnitude stability margin of the system, thus providing effective resonance damping. The ARDC's effectiveness is finally verfied in the simulation and controller‑hardware‑in‑the‑loop experiment under various operating conditions.

Authors:Eder Baron-Prada, Julius P. J. Krebbekx, Adolfo Anta, Florian Dörfler
Title: Scaled Graph Containment for Feedback Stability: Soft-Hard Equivalence and Conic Regions
Abstract:
Scaled graphs (SGs) offer a geometric framework for feedback stability analysis. This paper develops containment conditions for SGs within multiplier‑defined regions, addressing both circular and conic geometries. For circular regions, we show that soft and hard SG containment are equivalent whenever the associated multiplier is positive‑negative. This enables hard stability certification from soft computations alone, bypassing both the positive semidefinite storage constraint and the homotopy condition of existing methods. Numerical experiments on systems with up to 300 states demonstrate computational savings of 15‑44 % for the circular containment framework. We further characterize which conic regions are hyperbolically convex, a condition our frequency‑domain certificate requires, and demonstrate that such regions provide tighter SG bounds than circles whenever the operator SG is nonsymmetric.

Authors:Kaito Ito, Alexandre Proutiere
Title: Optimal Centered Active Excitation in Linear System Identification
Abstract:
We propose an active learning algorithm for linear system identification with optimal centered noise excitation. Notably, our algorithm, based on ordinary least squares and semidefinite programming, attains the minimal sample complexity while allowing for efficient computation of an estimate of a system matrix. More specifically, we first establish lower bounds of the sample complexity for any active learning algorithm to attain the prescribed accuracy and confidence levels. Next, we derive a sample complexity upper bound of the proposed algorithm, which matches the lower bound for any algorithm up to universal factors. Our tight bounds are easy to interpret and explicitly show their dependence on the system parameters such as the state dimension.

Authors:Rui Huang, Zhiqian Cai, Siyu Tang, Pengxuan Wei, Lidong Li, Xin Chen, Wenhan Cao, Zhenyu Zhang, Lin Zhao
Title: MARS-Dragonfly: Agile and Robust Flight Control of Modular Aerial Robot Systems
Abstract:
Modular Aerial Robot Systems (MARS) comprise multiple drone units with reconfigurable connected formations, providing high adaptability to diverse mission scenarios, fault conditions, and payload capacities. However, existing control algorithms for MARS rely on simplified quasi‑static models and rule‑based allocation, which generate discontinuous and unbounded motor commands. This leads to attitude error accumulation as the number of drone units scales, ultimately causing severe oscillations during docking, separation, and waypoint tracking. To address these limitations, we first design a compact mechanical system that enables passive docking, detection‑free passive locking, and magnetic‑assisted separation using a single micro servo. Second, we introduce a force‑torque‑equivalent and polytope‑constraint virtual quadrotor that explicitly models feasible wrench sets. Together, these abstractions capture the full MARS dynamics and enable existing quadrotor controllers to be applied across different configurations. We further optimize the yaw angle that maximizes control authority to enhance agility. Third, building on this abstraction, we design a two‑stage predictive‑allocation pipeline: a constrained predictive tracker computes virtual inputs while respecting force/torque bounds, and a dynamic allocator maps these inputs to individual modules with balanced objectives to produce smooth, trackable motor commands. Simulations across over 10 configurations and real‑world experiments demonstrate stable docking, locking, and separation, as well as effective control performance. To our knowledge, this is the first real‑world demonstration of MARS achieving agile flight and transport with 40 deg peak pitch while maintaining an average position error of 0.0896 m. The video is available at: https://youtu.be/yqjccrIpz5o

Authors:Tinko Sebastian Bartels, Ruixiang Wu, Xinyu Lu, Yikai Lu, Fanzeng Xia, Haoxiang Yang, Yue Chen, Tongxin Li
Title: Bridging Natural Language and Microgrid Dynamics: A Context-Aware Simulator and Dataset
Abstract:
Addressing the critical need for intelligent, context‑aware energy management in renewable systems, we introduce the OpenCEM Simulator and Dataset: the first open‑source digital twin explicitly designed to integrate rich, unstructured contextual information with quantitative renewable energy dynamics. Traditional energy management relies heavily on numerical time series, thereby neglecting the significant predictive power embedded in human‑generated context (e.g., event schedules, system logs, user intentions). OpenCEM bridges this gap by offering a unique platform comprising both a meticulously aligned, language‑rich dataset from a real‑world PV‑and‑battery microgrid installation and a modular simulator capable of natively processing this multi‑modal context. The OpenCEM Simulator provides a high‑fidelity environment for developing and validating novel control algorithms and prediction models, particularly those leveraging Large Language Models. We detail its component‑based architecture, hybrid data‑driven and physics‑based modelling capabilities, and demonstrate its utility through practical examples, including context‑aware load forecasting and the implementation of online optimal battery charging control strategies. By making this platform publicly available, OpenCEM aims to accelerate research into the next generation of intelligent, sustainable, and truly context‑aware energy systems.

Authors:Yize Chen, Xiaogui Zheng
Title: To Defer or To Shift? The Role of AI Data Center Flexibility on Grid Interconnection
Abstract:
The integration of AI data centers into power grid represents one of the most emerging and complex challenges for the energy systems. As computational demand scales at an unprecedented rate, the traditional grid planning study's paradigm of treating data centers as rigid, inflexible loads is becoming economically, mathematically and operationally untenable. This work tries to understand and address the large load interconnection bottleneck by modeling and evaluating AI load flexibility. By examining data center's temporal and spatial shifting capabilities within a grid capacity expansion framework, we build a quantitative grid planning model, and evaluate their impacts on additional generation, operational costs, and network congestion. Numerical study reveals interesting observations, as AI data center flexibility are not felt consistently, and increasing flexibility does not necessarily translate to less generation capacity required. Depending on data center's locations, flexibility range, and grid load conditions, flexible AI load can help reduce grid investment and operational costs by 3‑21%. Our work also indicate that longer deferral time of AI compute has diminishing returns for offloading grid electricity dispatch pressure.

Authors:Songhao Yang, Yubo Zhang, Zhiguo Hao, Zexuan Lin, Baohui Zhang
Title: CT Saturation Detection and Compensation: A Hybrid Physical Model- and Data-Driven Method
Abstract:
Current transformer (CT) saturation is one of the dominant causes of relay protection devices' malfunctions, which pose a threat to the safe operation of the power system. To address this problem, we propose a hybrid physical model‑ and data‑driven method. The method firstly detects the CT saturation and then compensates it to reproduce the real waveform. Considering the multi‑factor and strong nonlinearity of CT saturation, a data‑driven model, namely the Fully Convolutional Network (FCN), is built to detect the operation status of CT. As for the compensation, a physical model of short‑circuit current is used for its conciseness and universality. Through tactfully integrating the data model and the physical model, the proposed method is endowed with two major merits: the arduous adjustment of universal thresholds and parameters in existing methods is avoided, and the deficiency in generalization and interpretability of the data‑driven method is assuaged. Simulation and experimental results verify the effectiveness of the proposed method. Furthermore, its application potential to future protection is explored.

Authors:Yihui Wang, Sheng-Yu Peng, Sahil Shah
Title: An Ultra-Low-Power Synthesizable Asynchronous AER Encoder for Neuromorphic Edge Devices
Abstract:
This paper presents a fully synthesizable, treebased Address‑Event Representation (AER) encoder designed for scalable neuromorphic computing systems. To achieve high throughput while maintaining strict compatibility with commercial EDA workflows, the asynchronous design employs a bundled‑data protocol within a semi‑decoupled micropipeline. The architecture replaces traditional transparent latches with standard edge‑triggered flip‑flops, enabling digital synthesis and place‑and‑route (PnR) using Cadence toolkits. A cross‑coupled NAND‑based random‑priority arbiter is embedded within the encoder of each tree node to resolve event collisions efficiently. An 8‑event AER prototype is fabricated in 65 nm CMOS technology utilizing a purely digital standard‑cell flow. Post‑fabrication silicon measurements validate the design, demonstrating a peak throughput of 33 MEvent/s and an average event latency of 50 ns, equating to a propagation delay of 17 ns/(event‑bit). The design consumes only 435 fJ per encoded event.

Authors:Shinkyu Park, Behrouz Touri, Marcos M. Vasconcelos
Title: Strategic Delay and Coordination Efficiency in Global Games
Abstract:
We investigate a coordination model for a two‑stage collective decision‑making problem within the framework of global games. The agents observe noisy signals of a shared random variable, referred to as the fundamental, which determines the underlying payoff. Based on these signals, the agents decide whether to participate in a collective action now or to delay. An agent who delays acquires additional information by observing the identities of agents who have chosen to participate in the first stage. This informational advantage, however, comes at the cost of a discounted payoff if coordination ultimately succeeds. Within this decision‑making framework, we analyze how the option to delay can enhance collective outcomes. We show that this intertemporal trade‑off between information acquisition and payoff reduction can improve coordination and increase the efficiency of collective decision‑making.

Authors:Nour Al Dandachly, Shuang Gao, Roland Malhamé
Title: Price-Coordinated Mean Field Games with State Augmentation for Decentralized Battery Charging
Abstract:
This paper addresses the decentralized coordinated charging problem for a large population of battery storage agents (e.g. residential batteries, electrical vehicles, charging station batteries) using Mean Field Game (MFG). Agents are assumed to have affine dynamics and are coupled through a price that is continuous and monotonically increasing with respect to the difference between the average charging power and the grid's desired average charging power. An important modeling feature of the proposed framework is the state augmentation, that is, the charging power is treated as a state variable and its rate of change (i.e. the ramp rate) as the control input. The resulting MFG equilibrium is characterized by two nonlinearly coupled forward‑backward differential equations. The existence and uniqueness of the MFG equilibrium is established for any continuous and monotonically increasing nonlinear price function without additional restrictions on the time horizon. Moreover, in the special case where the price is affine in the average charging power, we further simplify the characterization of the MFG equilibrium strategy via two separate Riccati equations, both of which admit unique positive semi‑definite solutions without additional assumptions.

Authors:Joe Moeller, Aaron D. Ames
Title: Hybrid Systems as Coalgebras: Lyapunov Morphisms for Zeno Stability
Abstract:
Hybrid dynamical systems exhibit a diverse array of stability phenomena, each currently addressed by separate Lyapunov‑like results. We show that these results are all instances of a single theorem: a Lyapunov function is a morphism from a hybrid system into a simple stable target system σ, and different stability notions such as Lyapunov stability, asymptotic stability, exponential stability, and Zeno stability correspond to different choices of σ. This unification is achieved by expressing hybrid systems as coalgebras of an endofunctor \mathcal H on a category \mathsfChart that naturally blends continuous and discrete dynamics. Instantiating a general categorical Lyapunov theorem for coalgebras to this setting results in new Lypaunov‑like conditions for the stability of Zeno equilibria and the existence of Zeno behavior in hybrid systems.

Authors:Yu Xing, Aneesh Raghavan, Michael T. Schaub, Karl H. Johansson
Title: Approximate Simulation-Based Verification of Compatibility of the Friedkin-Johnsen Model with Binary Observations
Abstract:
We consider a verification problem for opinion dynamics based on binary observations. The opinion dynamics is governed by a Friedkin‑Johnsen (FJ) model, where only a sequence of binary outputs is available instead of the agents' continuous opinions. At every time‑step we observe a binarized output for each agent depending on whether the opinion exceeds a fixed threshold. The objective is to verify whether an FJ model with a given set of stubbornness parameters and initial opinions can generate the observed binary outputs up to a small error. The FJ model is formulated as a transition system, and an approximate simulation relation of two transition systems is defined in terms of the proximity of their opinion trajectories and output sequences. We then construct a finite set of abstract FJ models by simplifying the influence matrix and discretizing the stubbornness parameters and the initial opinions. It is shown that the abstraction approximately simulates any concrete FJ model with continuous parameters and initial opinions, and is itself approximately simulated by some concrete FJ model. These results ensure that consistency verification can be performed over the finite abstraction. Specifically, by checking whether an abstract model satisfies the observation constraints, we can conclude whether the corresponding family of concrete FJ models is consistent with the binary observations. Finally, numerical experiments are presented to illustrate the proposed verification framework.

Authors:Zhexian Li, Ketan Savla
Title: FNO$^{\angle θ}$: Extended Fourier neural operator for learning state and optimal control of distributed parameter systems
Abstract:
We propose an extended Fourier neural operator (FNO) architecture for learning state and linear quadratic additive optimal control of systems governed by partial differential equations. Using the Ehrenpreis‑Palamodov fundamental principle, we show that any state and optimal control of linear PDEs with constant coefficients can be represented as an integral in the complex domain. The integrand of this representation involves the same exponential term as in the inverse Fourier transform, where the latter is used to represent the convolution operator in FNO layer. Motivated by this observation, we modify the FNO layer by extending the frequency variable in the inverse Fourier transform from the real to complex domain to capture the integral representation from the fundamental principle. We illustrate the performance of FNO in learning state and optimal control for the nonlinear Burgers' equation, showing order of magnitude improvements in training errors and more accurate predictions of non‑periodic boundary values over FNO.

Authors:Nishanth Venkatesh, Andreas A. Malikopoulos
Title: Cross-fitted Proximal Learning for Model-Based Reinforcement Learning
Abstract:
Model‑based reinforcement learning is attractive for sequential decision‑making because it explicitly estimates reward and transition models and then supports planning through simulated rollouts. In offline settings with hidden confounding, however, models learned directly from observational data may be biased. This challenge is especially pronounced in partially observable systems, where latent factors may jointly affect actions, rewards, and future observations. Recent work has shown that policy evaluation in such confounded partially observable Markov decision processes (POMDPs) can be reduced to estimating reward‑emission and observation‑transition bridge functions satisfying conditional moment restrictions (CMRs). In this paper, we study the statistical estimation of these bridge functions. We formulate bridge learning as a CMR problem with nuisance objects given by a conditional mean embedding and a conditional density. We then develop a K‑fold cross‑fitted extension of the existing two‑stage bridge estimator. The proposed procedure preserves the original bridge‑based identification strategy while using the available data more efficiently than a single sample split. We also derive an oracle‑comparator bound for the cross‑fitted estimator and decompose the resulting error into a Stage I term induced by nuisance estimation and a Stage II term induced by empirical averaging.

Authors:Owen Shen, Hung-po Chao, Haihao Lu, Patrick Jaillet
Title: End-to-End Learning of Correlated Operating Reserve Requirements in Security-Constrained Economic Dispatch
Abstract:
Operating reserve requirements in security‑constrained economic dispatch (SCED) depend strongly on the assumed correlation structure of renewable forecast errors, yet that structure is usually specified exogenously rather than learned for the dispatch task itself. This paper formulates correlated reserve‑set design as an end‑to‑end trainable robust optimization problem: choose the ellipsoidal uncertainty‑set shape to minimize robust dispatch cost subject to a target coverage requirement. By profiling the coverage constraint into a shape‑dependent radius, the original bilevel problem becomes a single‑stage differentiable objective, and KKT/dual information from the SCED solve provides task gradients without differentiating through the solver. For unknown distributions, a four‑way train/tune/calibrate/test split combines a smoothed quantile‑sensitivity estimator for training with split conformal calibration for deployment, yielding finite‑sample marginal coverage under exchangeability and a consistent gradient estimator for the smoothed objective. The same task gradient can also be passed upstream to context‑dependent encoders, which we report as a secondary extension. The framework is evaluated on the IEEE~118‑bus system with a coupled SCED formulation that includes inter‑zone transfer constraints. The learned static ellipsoid reduces dispatch cost by about 4.8% relative to the Sample Covariance baseline while maintaining empirical coverage above the target level.

Authors:Arkadeep Saha, Pieter van Goor, Ravi Banavar
Title: Synchronous Observer Design for Landmark-Inertial SLAM with Magnetometer and Intermittent GNSS Measurements
Abstract:
In Landmark‑Inertial Simultaneous Localisation and Mapping (LI‑SLAM), the positions of landmarks in the environment and the robot's pose relative to these landmarks are estimated using landmark position measurements, and measurements from the Inertial Measurement Unit (IMU). However, the robot and landmark positions in the inertial frame, and the yaw of the robot, are not observable in LI‑SLAM. This paper proposes a nonlinear observer for LI‑SLAM that overcomes the observability constraints with the addition of intermittent GNSS position and magnetometer measurements. The full‑state error dynamics of the proposed observer is shown to be both almost‑globally asymptotically stable and locally exponentially stable, and this is validated using simulations.

Authors:Vishnudatta Thota, Anastasia Bizyaeva
Title: Constraint-Induced Redistribution of Social Influence in Nonlinear Opinion Dynamics
Abstract:
We study how intrinsic hard constraints on the decision dynamics of social agents shape collective decisions on multiple alternatives in a heterogeneous group. Such constraints may arise due to structural and behavioral limitations, such as adherence to belief systems in social networks or hardware limitations in autonomous networks. In this work, agent constraints are encoded as projections in a multi‑alternative nonlinear opinion dynamics framework. We prove that projections induce an invariant subspace on which the constraints are always satisfied and study the dynamics of networked opinions on this subspace. We then show that heterogeneous pairwise alignments between individuals' constraint vectors generate an effective weighted social graph on the invariant subspace, even when agents exchange opinions over an unweighted communication graph in practice. With analysis and simulation studies, we illustrate how the effective constraint‑induced weighted graph reshapes the centrality of agents in the decision process and the group's sensitivity to distributed inputs.

Authors:Lan Sang, Chinmay Maheshwari
Title: Nash Approximation Gap in Truncated Infinite-horizon Partially Observable Markov Games
Abstract:
Partially Observable Markov Games (POMGs) provide a general framework for modeling multi‑agent sequential decision‑making under asymmetric information. A common approach is to reformulate a POMG as a fully observable Markov game over belief states, where the state is the conditional distribution of the system state and agents' private information given common information, and actions correspond to mappings (prescriptions) from private information to actions. However, this reformulation is intractable in infinite‑horizon settings, as both the belief state and action spaces grow with the accumulation of information over time. We propose a finite‑memory truncation framework that approximates infinite‑horizon POMGs by a finite‑state, finite‑action Markov game, where agents condition decisions only on finite windows of common and private information. Under suitable filter stability (forgetting) conditions, we show that any Nash equilibrium of the truncated game is an \varepsilon‑Nash equilibrium of the original POMG, where \varepsilon \to 0 as the truncation length increases.

Authors:Varun Madabushi, Akash Harapanahalli, Samuel Coogan, Maegan Tucker
Title: Differentiable Invariant Sets for Hybrid Limit Cycles with Application to Legged Robots
Abstract:
For hybrid systems exhibiting periodic behavior, analyzing the invariant set containing the limit cycle is a natural way to study the robustness of the closed‑loop system. However, computing these sets can be computationally expensive, especially when applied to contact‑rich cyber‑physical systems such as legged robots. In this work, we extend existing methods for overapproximating reachable sets of continuous systems using parametric embeddings to compute a forward‑invariant set around the nominal trajectory of a simplified model of a bipedal robot. Our three‑step approach (i) computes an overapproximating reachable set around the nominal continuous flow, (ii) catalogs intersections with the guard surface, and (iii) passes these intersections through the reset map. If the overapproximated reachable set after one step is a strict subset of the initial set, we formally verify a forward invariant set for this hybrid periodic orbit. We verify this condition on the bipedal walker model numerically using immrax, a JAX‑based library for parametric reachable set computation, and use it within a bi‑level optimization framework to design a tracking controller that maximizes the size of the invariant set.

Authors:Varun Madabushi, Elizabeth Dietrich, Hanna Krasowski, Maegan Tucker
Title: Finite-Step Invariant Sets for Hybrid Systems with Probabilistic Guarantees
Abstract:
Poincare return maps are a fundamental tool for analyzing periodic orbits in hybrid dynamical systems, including legged locomotion, power electronics, and other cyber‑physical systems with switching behavior. The Poincare return map captures the evolution of the hybrid system on a guard surface, reducing the stability analysis of a periodic orbit to that of a discrete‑time system. While linearization provides local stability information, assessing robustness to disturbances requires identifying invariant sets of the state space under the return dynamics. However, computing such invariant sets is computationally difficult, especially when system dynamics are only available through forward simulation. In this work, we propose an algorithmic framework leveraging sampling‑based optimization to compute a finite‑step invariant ellipsoid around a nominal periodic orbit using sampled evaluations of the return map. The resulting solution is accompanied by probabilistic guarantees on finite‑step invariance satisfying a user‑defined accuracy threshold. We demonstrate the approach on two low‑dimensional systems and a compass‑gait walking model.

Authors:Mohammadreza Rostami, Shahriar Talebi, Solmaz S. Kia
Title: Scalar Federated Learning for Linear Quadratic Regulator
Abstract:
We propose ScalarFedLQR, a communication‑efficient federated algorithm for model‑free learning of a common policy in linear quadratic regulator (LQR) control of heterogeneous agents. The method builds on a decomposed projected gradient mechanism, in which each agent communicates only a scalar projection of a local zeroth‑order gradient estimate. The server aggregates these scalar messages to reconstruct a global descent direction, reducing per‑agent uplink communication from O(d) to O(1), independent of the policy dimension. Crucially, the projection‑induced approximation error diminishes as the number of participating agents increases, yielding a favorable scaling law: larger fleets enable more accurate gradient recovery, admit larger stepsizes, and achieve faster linear convergence despite high dimensionality. Under standard regularity conditions, all iterates remain stabilizing and the average LQR cost decreases linearly fast. Numerical results demonstrate performance comparable to full‑gradient federated LQR with substantially reduced communication.

Authors:Larsen Bier, Shahriar Talebi
Title: Learning Kalman Policy for Singular Unknown Covariances via Riemannian Regularization
Abstract:
Kalman filtering is a cornerstone of estimation theory, yet learning the optimal filter under unknown and potentially singular noise covariances remains a fundamental challenge. In this paper, we revisit this problem through the lens of control‑‑estimation duality and data‑driven policy optimization, formulating the learning of the steady‑state Kalman gain as a stochastic policy optimization problem directly from measurement data. Our key contribution is a Riemannian regularization that reshapes the optimization landscape, restoring structural properties such as coercivity and gradient dominance. This geometric perspective enables the effective use of first‑order methods under significantly relaxed conditions, including unknown and rank‑deficient noise covariances. Building on this framework, we develop a computationally efficient algorithm with a data‑driven gradient oracle, enabling scalable stochastic implementations. We further establish non‑asymptotic convergence and error guarantees enabled by the Riemannian regularization, quantifying the impact of bias and variance in gradient estimates and demonstrating favorable scaling with problem dimension. Numerical results corroborate the effectiveness of the proposed approach and robustness to the choice of stepsize in challenging singular estimation regimes.

Authors:Georges Bastin, Jean-Michel Coron, Amaury Hayat
Title: Global boundary stabilization of 1d systems of scalar conservation laws
Abstract:
We study a system of several one‑dimensional scalar conservation laws coupled through boundary feedback conditions that combine physical boundary constraints with static feedback control laws. Our first contribution establishes the well‑posedness of the system in the space of L^\infty entropy solutions. Our second contribution provides a set of sufficient dissipative conditions on the boundary coupling that ensure global exponential stability in the L^1 and L^\infty norms.

Authors:Ankit Hemant Lade, Sai Krishna Jasti, Nikhil Sinha, Indar Kumar, Akanksha Tiwari
Title: PCA-Driven Adaptive Sensor Triage for Edge AI Inference
Abstract:
Multi‑channel sensor networks in industrial IoT often exceed available bandwidth. We propose PCA‑Triage, a streaming algorithm that converts incremental PCA loadings into proportional per‑channel sampling rates under a bandwidth budget. PCA‑Triage runs in O(wdk) time with zero trainable parameters (0.67 ms per decision). We evaluate on 7 benchmarks (8‑‑82 channels) against 9 baselines. PCA‑Triage is the best unsupervised method on 3 of 6 datasets at 50% bandwidth, winning 5 of 6 against every baseline with large effect sizes (r = 0.71‑‑0.91). On TEP, it achieves F1 = 0.961 +/‑ 0.001 ‑‑ within 0.1% of full‑data performance ‑‑ while maintaining F1 > 0.90 at 30% budget. Targeted extensions push F1 to 0.970. The algorithm is robust to packet loss and sensor noise (3.7‑‑4.8% degradation under combined worst‑case).

Authors:Arthur N. Montanari, Francesco Bullo, Dmitry Krotov, Adilson E. Motter
Title: Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization
Abstract:
Recent advances at the intersection of control theory, neuroscience, and machine learning have revealed novel mechanisms by which dynamical systems perform computation. These advances encompass a wide range of conceptual, mathematical, and computational ideas, with applications for model learning and training, memory retrieval, data‑driven control, and optimization. This tutorial focuses on neuro‑inspired approaches to computation that aim to improve scalability, robustness, and energy efficiency across such tasks, bridging the gap between artificial and biological systems. Particular emphasis is placed on energy‑based dynamical models that encode information through gradient flows and energy landscapes. We begin by reviewing classical formulations, such as continuous‑time Hopfield networks and Boltzmann machines, and then extend the framework to modern developments. These include dense associative memory models for high‑capacity storage, oscillator‑based networks for large‑scale optimization, and proximal‑descent dynamics for composite and constrained reconstruction. The tutorial demonstrates how control‑theoretic principles can guide the design of next‑generation neurocomputing systems, steering the discussion beyond conventional feedforward and backpropagation‑based approaches to artificial intelligence.

Authors:Justin Curry, Alberto Speranzon
Title: Stratifying Reinforcement Learning with Signal Temporal Logic
Abstract:
In this paper, we develop a stratification‑based semantics for Signal Temporal Logic (STL) in which each atomic predicate is interpreted as a membership test in a stratified space. This perspective reveals a novel correspondence principle between stratification theory and STL, showing that most STL formulas can be viewed as inducing a stratification of space‑time. The significance of this interpretation is twofold. First, it offers a fresh theoretical framework for analyzing the structure of the embedding space generated by deep reinforcement learning (DRL) and relates it to the geometry of the ambient decision space. Second, it provides a principled framework that both enables the reuse of existing high‑dimensional analysis tools and motivates the creation of novel computational techniques. To ground the theory, we (1) illustrate the role of stratification theory in Minigrid games and (2) apply numerical techniques to the latent embeddings of a DRL agent playing such a game where the robustness of STL formulas is used as the reward. In the process, we propose computationally efficient signatures that, based on preliminary evidence, appear promising for uncovering the stratification structure of such embedding spaces.

Authors:Peng Xie, Zhen Zhang, Rolf Findeisen, Amr Alanwar
Title: Bridging Data-Driven Reachability Analysis and Statistical Estimation via Constrained Matrix Convex Generators
Abstract:
Data‑driven reachability analysis enables safety verification when first‑principles models are unavailable. This requires constructing sets of system models consistent with measured trajectories and noise assumptions. Existing approaches rely on zonotopic or box‑based approximations, which do not fit the geometry of common noise distributions such as Gaussian disturbances and can lead to significant conservatism, especially in high‑dimensional settings. This paper builds on ellipsotope‑based representations to introduce mixed‑norm uncertainty sets for data‑driven reachability. The highest‑density region defines the exact minimum‑volume noise confidence set, while Constrained Convex Generators (CCG) and their matrix counterpart (CMCG) provide compatible geometric representations at the noise and parameter level. We show that the resulting CMCG coincides with the maximum‑likelihood confidence ellipsoid for Gaussian disturbances, while remaining strictly tighter than constrained matrix zonotopes for mixed bounded‑Gaussian noise. For non‑convex noise distributions such as Gaussian mixtures, a minimum‑volume enclosing ellipsoid provides a tractable convex surrogate. We further prove containment of the CMCG times CCG product and bound the conservatism of the Gaussian‑Gaussian interaction. Numerical examples demonstrate substantially tighter reachable sets compared to box‑based approximations of Gaussian disturbances. These results enable less conservative safety verification and improve the accuracy of uncertainty‑aware control design.

Authors:Bernard T. Agyeman, Zhe Li, Ilias Mitrai, Prodromos Daoutidis
Title: Feasibility-Aware Imitation Learning for Benders Decomposition
Abstract:
Mixed‑integer optimization problems arise in a wide range of control applications. Benders decomposition is a widely used algorithm for solving such problems by decomposing them into a mixed‑integer master problem and a continuous subproblem. A key computational bottleneck is the repeated solution of increasingly complex master problems across iterations. In this paper, we propose a feasibility‑aware imitation learning framework that predicts the values of the integer variables of the master problem at each iteration while accounting for feasibility with respect to constraints governing admissible integer assignments and the accumulated Benders feasibility cuts. The agent is trained using a two‑stage procedure that combines behavioral cloning with a feasibility‑based logit adjustment to bias predictions toward assignments that satisfy the evolving cut set. The agent is deployed within an agent‑based Benders decomposition framework that combines explicit feasibility checks with a time‑limited solver computation of a valid lower bound. The proposed approach retains finite convergence properties, as the lower bound is certified at each iteration. Application to a prototypical case study shows that the proposed method improves solution time relative to existing imitation learning approaches for accelerating Benders decomposition, while preserving solution accuracy.

Authors:Brooks A. Butler, Xiao Tan, Aaron D. Ames, Magnus Egerstedt
Title: Collaborative Altruistic Safety in Coupled Multi-Agent Systems
Abstract:
This paper presents a novel framework for ensuring safety in dynamically coupled multi‑agent systems through collaborative control. Drawing inspiration from ecological models of altruism, we develop collaborative control barrier functions that allow agents to cooperatively enforce individual safety constraints under coupling dynamics. We introduce an altruistic safety condition based on the so‑called Hamilton's rule, enabling agents to trade off their own safety to support higher‑priority neighbors. By incorporating these conditions into a distributed optimization framework, we demonstrate increased feasibility and robustness in maintaining system‑wide safety. The effectiveness of the proposed approach is illustrated through simulation in a simplified formation control scenario.

Authors:Peng Xie, Davide M. Raimondo, Rolf Findeisen, Amr Alanwar
Title: Data-Driven Reachability Analysis with Optimal Input Design
Abstract:
This paper addresses the conservatism in data‑driven reachability analysis for discrete‑time linear systems subject to bounded process noise, where the system matrices are unknown and only input‑‑state trajectory data are available. Building on the constrained matrix zonotope (CMZ) framework, two complementary strategies are proposed to reduce conservatism in reachable‑set over‑approximations. First, the standard Moore‑‑Penrose pseudoinverse is replaced with a row‑norm‑minimizing right inverse computed via a second‑order cone program (SOCP), which directly reduces the size of the resulting model set, yielding tighter generators and less conservative reachable sets. Second, an online A‑optimal input design strategy is introduced to improve the informativeness of the collected data and the conditioning of the resulting model set, thereby reducing uncertainty. The proposed framework extends naturally to piecewise affine systems through mode‑dependent data partitioning. Numerical results on a five‑dimensional stable LTI system and a two‑dimensional piecewise affine system demonstrate that combining designed inputs with the row‑norm right inverse significantly reduces conservatism compared to a baseline using random inputs and the pseudoinverse, leading to tighter reachable sets for safety verification.

Authors:Jan-Felix Klein, Yongkuk Jeong, Erik Flores-García, Magnus Wiktorsson
Title: Toward Self-Organizing Production Logistics: A Multi-Agent Approach
Abstract:
Production logistics (PL) is increasingly exposed to variability, dynamic interdependencies, and operational disturbances that challenge conventional centralized planning and control. These characteristics are particularly pronounced in circular production systems, but are increasingly relevant across PL more generally. This paper addresses this challenge through the concept of Self‑Organizing Production Logistics (SOPL) using the Design Science Research Methodology (DSRM) as a structuring framework. The paper identifies key technological and systemic drivers motivating SOPL, including autonomous logistics resources, distributed AI‑based decision‑making, and increasing operational uncertainty in circular production. Based on these drivers, system‑level objectives and design requirements for SOPL are derived. Building on these requirements, an initial multi‑agent architecture is proposed that combines embodied and non‑embodied agents, event‑driven coordination, semantic knowledge structures, and digital twins. In addition, a three‑phase demonstration roadmap is presented, ranging from an initial laboratory demonstrator toward increasingly distributed and adaptive SOPL systems. The Phase I demonstrator serves as an experimental setup for investigating disturbance handling, human involvement, and supervisory coordination in an order‑driven kitting and supply scenario. Overall, the paper contributes a conceptual foundation for the design, implementation, and experimental evaluation of SOPL systems.

Authors:Kalyan Cherukuri, Lav R. Varshney
Title: Hallucination Basins: A Dynamic Framework for Understanding and Controlling LLM Hallucinations
Abstract:
Large language models (LLMs) hallucinate: they produce fluent outputs that are factually incorrect. We present a geometric dynamical systems framework in which hallucinations arise from task‑dependent basin structure in latent space. Using autoregressive hidden‑state trajectories across multiple open‑source models and benchmarks, we find that separability is strongly task‑dependent rather than universal: factoid settings can show clearer basin separation, whereas summarization and misconception‑heavy settings are typically less stable and often overlap. We formalize this behavior with task‑complexity and multi‑basin theorems, characterize basin emergence in L‑layer transformers, and show that geometry‑aware steering can reduce hallucination probability without retraining.

Authors:Natsuki Katayama, Alexandre Mauroy, Yoshihiko Susuki
Title: Global Linearization of Parameterized Nonlinear Systems with Stable Equilibrium Point Using the Koopman Operator
Abstract:
The Koopman operator framework enables global analysis of nonlinear systems through its inherent linearity. This study aims to clarify spectral properties of the Koopman operators for nonlinear systems with control inputs. To this end, we treat the inputs as parameters throughout this paper. We then introduce the Koopman operator for a parameterized dynamical system with a globally exponentially stable equilibrium point and analyze how eigenfunctions of the operator depend on the parameter. As a main result, we obtain a global linearization, which enables one to transform the nonlinear system into a finite‑dimensional linear system, and we show that it depends continuously on the parameter. Subsequently, for a control‑affine system, we investigate a condition under which the transformation providing a global bilinearization does not depend on the parameter. This provides the condition under which the global bilinearization for the control‑affine system is independent of the parameter.

Authors:Mahendra Kheti, Debapratim Ghosh, Soumya P. Dash
Title: Compact Reconfigurable Intelligent Surface with Phase-Gradient Coded Beam Steering and Controlled Substrate Loss
Abstract:
This paper presents a 1‑bit reconfigurable intelligent surface (RIS) fabricated using a three‑layer structure. It employs a manual layer stackup incorporating an optimal air gap to reduce the effective dielectric losses while using a low‑cost FR4 substrate. The new design of the unit cells of the proposed RIS is outlined, with each unit cell featuring a PIN‑diode‑based, compact, simplified biasing network that simplifies the control circuit while maintaining distinct \boldsymbol0^\circ/180^\circ \pm 20^\circ phase states between ON/OFF conditions. The designed RIS is in the form of a \boldsymbol10×10 array with a compact size of \boldsymbol2.9λ_g × 2.9λ_g. Additionally, a phase‑gradient coding scheme is presented and utilized that achieves measured beam steering up to \boldsymbol\pm30^\circ in both anechoic and noisy environments. Controlled and driven by an Arduino‑cum‑digital interface, the proposed RIS exhibits measured reflected wave gain enhancement of about 9\,dB over an incident wave angular range of \boldsymbol\pm 30^\circ. Furthermore, the design is also experimentally validated by transmitting quadrature phase‑shift keying‑modulated symbols via the RIS‑assisted wireless channel. The proposed RIS works for the range 3.38‑‑3.67\,GHz (8.3%), and is suitable for deployment for the 5G n78 \mboxband (3.5\,GHz).

Authors:Filipe Marques Barbosa, Johan Löfberg
Title: Stochastic Model Predictive Control with Online Risk Allocation and Feedback Gain Selection
Abstract:
Stochastic Model Predictive Control addresses uncertainties by incorporating chance constraints that provide probabilistic guarantees of constraint satisfaction. However, simultaneously optimizing over the risk allocation and the feedback policies leads to intractable nonconvex problems. This is due to (i) products of functions involving the feedback law and risk allocation in the deterministic counterpart of the chance constraints, and (ii) the presence of the nonconvex Gaussian quantile (probit) function. Existing methods rely on two‑stage optimization, which is nonconvex. To address this, we derive disjunctive convex chance constraints and select the feedback law from a set of precomputed candidates. The inherited compositions of the probit function are replaced with power‑ and exponential‑cone representable approximations. The main advantage is that the problem can be formulated as a mixed‑integer conic optimization problem and efficiently solved with off‑the‑shelf software. Moreover, the proposed formulations apply to general chance constraints with products of exclusive disjunctive and Gaussian variables. The proposed approaches are validated with a path‑planning application.

Authors:Martin Kristjansen, Kim Guldstrand Larsen, Marius Mikučionis, Christian Schilling
Title: Safe and Near-Optimal Gate Control: A Case Study from the Danish West Coast
Abstract:
Ringkoebing Fjord is an inland water basin on the Danish west coast separated from the North Sea by a set of gates used to control the amount of water entering and leaving the fjord. Currently, human operators decide when and how many gates to open or close for controlling the fjord's water level, with the goal to satisfy a range of conflicting safety and performance requirements such as keeping the water level in a target range, allowing maritime traffic, and enabling fish migration. Uppaal Stratego. We then use this digital twin along with forecasts of the sea level and the wind speed to learn a gate controller in an online fashion. We evaluate the learned controllers under different sea‑level scenarios, representing normal tidal behavior, high waters, and low waters. Our evaluation demonstrates that, unlike a baseline controller, the learned controllers satisfy the safety requirements, while performing similarly regarding the other requirements.

Authors:Eric Lubat, Pierre-Emmanuel Hladik, Yoann Mateu, Rémi Sauvère
Title: Modelling and Analysis of Supply Chains using Product Time Petri Nets
Abstract:
Supply chains involve geographically distributed manufacturing and assembly sites that must be coordinated under strict timing and resource constraints. While many existing approaches rely on Colored Petri Nets to model material flows, this work focuses on the temporal feasibility of supply chain processes. We propose a modular modelling approach based on Product Time Petri Nets (PTPNs), where each subsystem is represented independently and the global behaviour emerges through synchronised transition labels. A key feature of the model is the explicit representation of the supply chain manager as a critical shared and mobile resource, whose availability directly impacts system feasibility. We analyse how timing constraints and managerial capacity influence the system behaviour, identifying configurations that lead to successful executions, timeouts, or timelocks induced by incompatible timing constraints. This approach enables systematic what‑if analysis of supply chain coordination policies and demonstrates the relevance of PTPNs for modelling and analysing synchronised timed systems.

Authors:Ji-Hong Li
Title: PCT-Based Trajectory Tracking for Underactuated Marine Vessels
Abstract:
This paper investigates the trajectory tracking problem of underactuated marine vessels within a polar coordinate framework. By introducing two polar coordinate transformations (PCTs), the original two‑input‑three‑output second‑order tracking model expressed in the Cartesian frame is reduced to a two‑input‑two‑output feedback system. However, the resulting model does not necessarily satisfy the strict‑feedback condition required by conventional backstepping approaches. To circumvent potential singularities arising in the controller design, a novel concept termed exponential modification of orientation (EMO) is proposed. While the PCTs yield substantial structural simplification, they also introduce inherent limitations, most notably singularities associated with angular coordinates. Addressing these singularities constitutes another key focus of this paper. Numerical simulation results are presented to demonstrate the effectiveness of the proposed control strategy.

Authors:Aboozar Heydaribeni, Hamzeh Beyranvand, Sahar Eslami
Title: DRL-Based Phase Optimization for O-RIS in Dual-Hop Hard-Switching FSO/RIS-aided RF and UWOC Systems
Abstract:
This paper presents a dual‑hop hybrid framework that integrates a free‑space optical (FSO)/RIS‑aided radio frequency (RF) link operating under a hard‑switching protocol as the first hop, and an optical reconfigurable intelligent surface (O‑RIS)‑assisted underwater wireless optical communication (UWOC) link as the second hop. To capture realistic underwater dynamics, the Oceanic Turbulence Optical Power Spectrum (OTOPS) is employed for accurate turbulence modeling. For efficient O‑RIS phase control, deep reinforcement learning (DRL) algorithms, specifically the Deep Deterministic Policy Gradient (DDPG) and Twin Delayed DDPG (TD3), have been developed to optimize the phase shifts of O‑RIS elements. Simulation results demonstrate that the proposed system substantially improves outage probability and channel capacity, with TD3 achieving superior robustness and adaptability. These findings highlight the DRL‑enabled O‑RIS as a promising approach for achieving reliable and high‑capacity 6G cross‑domain UWOC networks.

Authors:Augustinos D. Saravanos, Isin M. Balci, Arshiya Taj Abdul, Efstathios Bakolas, Evangelos A. Theodorou
Title: Distributed Covariance Steering via Non-Convex ADMM for Large-Scale Multi-Agent Systems
Abstract:
This paper studies the problem of steering large‑scale multi‑agent stochastic linear systems between Gaussian distributions under probabilistic collision avoidance constraints. We introduce a family of distributed covariance steering (DCS) methods based on the Alternating Direction Method of Multipliers (ADMM), each offering different trade‑offs between conservatism and computational efficiency. The first method, Full‑Covariance‑Consensus (FCC)‑DCS, enforces consensus over both the means and covariances of neighboring agents, yielding the least conservative safe solutions. The second approach, Partial‑Covariance‑Consensus (PCC)‑DCS, leverages the insight that safety can be maintained by exchanging only partial covariance information, reducing computational demands. The third method, Mean‑Consensus (MC)‑DCS, provides the most scalable alternative by requiring consensus only on mean states. Furthermore, we establish novel convergence guarantees for distributed ADMM with iteratively linearized non‑convex constraints, covering a broad class of consensus optimization problems. This analysis proves convergence to stationary points for PCC‑DCS and MC‑DCS, while the convergence of FCC‑DCS follows from standard ADMM theory. Simulations in 2D and 3D multi‑agent environments verify safety, illustrate the trade‑offs between methods, and demonstrate scalability to thousands of agents.

Authors:Qiang Ji, Lin Cheng, Yue Zhou, Ning Qi, Kaidi Huang, Jianzhong Wu, Ming Cheng
Title: A Process-Aware Demand Response Evaluation Framework for Hydrogen-Integrated Zero-Carbon Steel Plants Coupled with Methanol Production
Abstract:
High penetration of renewables (RES) and the retirement of thermal units aggravate flexibility scarcity in power systems. Hydrogen‑based low‑carbon steel production systems possess substantial demand response (DR) potential. This paper proposes a process‑aware DR evaluation framework for hydrogen‑integrated zero‑carbon steel plants coupled with methanol production (H2‑DRI‑EAF‑MeOH). First, a novel H2‑DRI‑EAF‑MeOH architecture is introduced to eliminate residual emissions via methanol synthesis. Integrated energy‑material flows are formulated to reflect coupling interactions governing DR potential. Second, to capture electric arc furnace (EAF) operational constraints while preserving tractability, an operating feasible region model is developed and validated using field data from a pure hydrogen direct reduced iron and EAF plant, yielding a 4.1% average relative error. Third, a process‑aware DR potential evaluation model is formulated, incorporating a nonlinear asymmetric penalty and an adaptive rolling mechanism to reflect operators' aversion to process deviations and avoid myopic scheduling. Finally, dual‑side evaluation metrics are established to quantify grid‑side delivered DR capacity and ramping risks, making load‑side unit‑level regulation behaviors observable. Case studies show the proposed framework achieves an average effective delivered DR capacity of 178.3 MW, improves RES‑load matching from 0.257 to 0.587, and reduces costs by 15.68% compared to the baseline. Furthermore, the exponential asymmetric penalty mitigates extreme tail risks of process deviations. Ultimately, this work provides a theoretical foundation for leveraging RES‑steel‑chemical synergies to mitigate flexibility scarcity.

Authors:Lorenzo Fici, Dalim Wahby, Alvaro Detailleur, Matthieu Barreau, Guillaume Ducard
Title: Region of Attraction Estimation for Linear Quadratic Regulator, Linear and Robust Model Predictive Control on a Two-Wheeled Inverted Pendulum
Abstract:
Nonlinear underactuated systems such as two‑wheeled inverted pendulums (TWIPs) exhibit a limited region of attraction (RoA), which defines the set of initial conditions from which the closed‑loop system converges to the equilibrium. The RoA of nonlinear and constrained systems is generally nonconvex and analytically intractable, requiring numerical or approximate estimation methods. This work investigates the estimation of the RoA for a TWIP stabilized under three model‑based control strategies: saturated linear quadratic regulator (LQR), linear model predictive control (MPC), and constraint tightening MPC (CTMPC). We first derive a Lyapunov‑based invariant set that provides a certified inner approximation of the RoA. Since this analytical bound is highly conservative, a Monte Carlo‑based estimation procedure is then employed to obtain a more representative approximation of the RoA, capturing how the controllers behave beyond the analytically guaranteed region. The proposed methodology combines analytical guarantees with data‑driven estimation, providing both a formally certified inner bound and an empirical characterization of the RoA, offering a practical way to evaluate controller performance without relying solely on conservative analytical bounds or purely empirical simulation.

Authors:Haoxin Lin, Junjie Zhou, Daheng Xu, Yang Yu
Title: ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller
Abstract:
Braking system, the key module to ensure the safety and steer‑ability of current vehicles, relies on extensive manual calibration during production. Reducing labor and time consumption while maintaining the Vehicle Braking Controller (VBC) performance greatly benefits the vehicle industry. Model‑based methods in offline reinforcement learning, which facilitate policy exploration within a data‑driven dynamics model, offer a promising solution for addressing real‑world control tasks. This work proposes ReinVBC, which applies an offline model‑based reinforcement learning approach to deal with the vehicle braking control problem. We introduce useful engineering designs into the paradigm of model learning and utilization to obtain a reliable vehicle dynamics model and a capable braking policy. Several results demonstrate the capability of our method in real‑world vehicle braking and its potential to replace the production‑grade anti‑lock braking system.

Authors:Young-ho Cho, Min-Seung Ko, Hao Zhu
Title: LACE-S: Toward Sensitivity-consistent Locational Average Carbon Emissions via Neural Representation
Abstract:
Carbon‑aware grid optimization relies on accurate locational emission metrics to effectively guide demand‑side decarbonization tasks such as spatial load shifting. However, existing metrics are only valid around limited operating regions and unfortunately cannot generalize the emission patterns beyond these regions. When these metrics are used to signal carbon‑sensitive resources, they could paradoxically increase system‑wide emissions. This work seeks to develop a sensitivity‑consistent metric for locational average carbon emissions (LACE‑S) using a neural representation approach. To ensure physical validity, the neural model enforces total emission balance through an explicit projection layer while matching marginal emission sensitivities across the entire loading region. Jacobian‑based regularization is further introduced to capture the underlying partition of load buses with closely aligned generator responses. Moreover, we present a scalable zonal aggregation strategy, ZACE‑S, to reduce the model complexity by mapping nodal inputs to predefined market zones. Numerical tests on the IEEE 30‑bus system have verified the performance improvements of LACE‑S in matching total emissions and their sensitivities over the non‑regularized design. Crucially, while spatial load shifting driven by existing metrics often increases the post‑shift emissions, the proposed LACE‑S metric has led to a reliable reduction of system‑wide emissions, demonstrating its excellent consistency with the global emission patterns.

Authors:Narim Jeong, Donghwan Lee
Title: Finite-Time Analysis of Q-Value Iteration for General-Sum Stackelberg Games
Abstract:
Reinforcement learning has been successful both empirically and theoretically in single‑agent settings, but extending these results to multi‑agent reinforcement learning in general‑sum Markov games remains challenging. This paper studies the convergence of Stackelberg Q‑value iteration in two‑player general‑sum Markov games from a control‑theoretic perspective. We introduce a relaxed policy condition tailored to the Stackelberg setting and model the learning dynamics as a switching system. By constructing upper and lower comparison systems, we establish finite‑time error bounds for the Q‑functions and characterize their convergence properties. Our results provide a novel control‑theoretic perspective on Stackelberg learning. Moreover, to the best of the authors' knowledge, this paper offers the first finite‑time convergence guarantees for Q‑value iteration in general‑sum Markov games under Stackelberg interactions.

Authors:Konstantinos Spalas
Title: Evaluating Future Air Traffic Management Security
Abstract:
The L‑Band Digital Aviation Communication System (LDACS) aims to modernize communications between the aircraft and the tower. Besides digitizing this type of communication, the contributors also focus on protecting them against cyberattacks. There are several proposals regarding LDACS security, and a recent one suggests the use of physical unclonable functions (PUFs) for the authentication module. This work demonstrates this PUF‑based authentication mechanism along with its potential vulnerabilities. Sophisticated models are able to predict PUFs, and, on the other hand, quantum computers are capable of threatening current cryptography, consisting factors that jeopardize the authentication mechanism giving the ability to perform impersonation attacks. In addition, aging is a characteristic that affects the stability of PUFs, which may cause instability issues, rendering the system unavailable. In this context, this work proposes the well‑established Public Key Infrastructure (PKI), as an alternative solution.

Authors:Maria G. Mendoza, Victoria Marie Tuck, Chinmay Maheshwari, Shankar Sastry
Title: Decentralized Ergodic Coverage Control in Unknown Time-Varying Environments
Abstract:
A key challenge in disaster response is maintaining situational awareness of an evolving landscape, which requires balancing exploration of unobserved regions with sustained monitoring of changing Regions of Interest (ROIs). Unmanned Aerial Vehicles (UAVs) have emerged as an effective response tool, particularly in applications like environmental monitoring and search‑and‑rescue, due to their ability to provide aerial coverage, withstand hazardous conditions, and navigate quickly and flexibly. However, efficient and adaptable multi‑robot coverage with limited sensing in disaster settings and evolving time‑varying information maps remains a significant challenge, necessitating better methods for UAVs to continuously adapt their trajectories in response to changes. In this paper, we propose a decentralized multi‑agent coverage framework that serves as a high‑level planning strategy for adaptive coverage in unknown, time‑varying environments under partial observability. Each agent computes an adaptive ergodic policy, implemented via a Markov‑chain transition model, that tracks a continuously updated belief over the underlying importance map. Gaussian Processes are used to perform those online belief updates. The resulting policy drives agents to spend time in ROIs proportional to their estimated importance, while preserving sufficient exploration to detect and adapt to time‑varying environmental changes. Unlike existing approaches that assume known importance maps, require centralized coordination, or assume a static environment, our framework addresses the combined challenges of unknown, time‑varying distributions in a more realistic decentralized and partially observable setting. We compare against alternative coverage strategies and analyze our method's response to simulated disaster evolution, highlighting its improved adaptability and transient performance in dynamic scenarios.

Authors:Thomas Beckers, Leonardo Colombo
Title: Data-Driven Boundary Control of Distributed Port-Hamiltonian Systems
Abstract:
Distributed Port‑Hamiltonian (dPHS) theory provides a powerful framework for modeling physical systems governed by partial differential equations and has enabled a broad class of boundary control methodologies. Their effectiveness, however, relies heavily on the availability of accurate system models, which may be difficult to obtain in the presence of nonlinear and partially unknown dynamics. To address this challenge, we combine Gaussian Process distributed Port‑Hamiltonian system (GP‑dPHS) learning with boundary control by interconnection. The GP‑dPHS model is used to infer the unknown Hamiltonian structure from data, while its posterior uncertainty is incorporated into an energy‑based robustness analysis. This yields probabilistic conditions under which the closed‑loop trajectories remain bounded despite model mismatch. The method is illustrated on a simulated shallow water system.

Authors:Shuang Gao, Peter E. Caines
Title: Transmission Neural Networks: Inhibitory and Excitatory Connections
Abstract:
This paper extends the Transmission Neural Network model proposed by Gao and Caines in [1]‑[3] to incorporate inhibitory connections and neurotransmitter populations. The extended network model contains binary neuronal states, transmission dynamics, and inhibitory and excitatory connections. Under technical assumptions, we establish the characterization of the firing probabilities of neurons, and show that such a characterization considering inhibitions can be equivalently represented by a neural network where each neuron has a continuous state of dimension 2. Moreover, we incorporated neurotransmitter populations into the modeling and establish the limit network model when the number of neurotransmitters at all synaptic connections go to infinity. Finally, sufficient conditions for stability and contraction properties of the limit network model are established.

Authors:Shima Sadat Mousavi, Max H. Cohen, Pol Mestres, Aaron D. Ames
Title: Structure, Feasibility, and Explicit Safety Filters for Linear Systems
Abstract:
Safety filters based on control barrier functions (CBFs) and high‑order control barrier functions (HOCBFs) are often implemented through quadratic programs (QPs). In general, especially in the presence of multiple constraints, feasibility is difficult to certify before solving the QP and may be lost as the state evolves. This paper addresses this issue for linear time‑invariant (LTI) systems with affine safety constraints. Exploiting the resulting geometry of the constraint normals, and considering both unbounded and bounded inputs, we characterize feasibility for several structured classes of constraints. For certain such cases, we also derive closed‑form safety filters. These explicit filters avoid online optimization and provide a simple alternative to QP‑based implementations. Numerical examples illustrate the results.

Authors:Shima Sadat Mousavi, Pol Mestres, Aaron D. Ames
Title: Stability Margins of CBF-QP Safety Filters: Analysis and Synthesis
Abstract:
Control barrier function (CBF)‑QP safety filters enforce safety by minimally modifying a nominal controller. While prior work has mainly addressed robustness of safety under uncertainty, robustness of the resulting closed‑loop \emphstability is much less understood. This issue is important because once the safety filter becomes active, it modifies the nominal dynamics and can reduce stability margins or even destabilize the system, despite preserving safety. For linear systems with a single affine safety constraint, we show that the active‑mode dynamics admit an exact scalar loop representation, leading to a classical robust‑control interpretation in terms of gain, phase, and delay margins. This viewpoint yields exact stability‑margin characterizations and tractable linear matrix inequality (LMI)‑based certificates and synthesis conditions for controllers with certified robustness guarantees. Numerical examples illustrate the proposed analysis and the enlargement of certified stability margins for safety‑filtered systems.

Authors:Aniruddh G. Puranic, Sebastian Schirmer, John S. Baras, Calin Belta
Title: Learning from Imperfect Demonstrations via Temporal Behavior Tree-Guided Trajectory Repair
Abstract:
Learning robot control policies from demonstrations is a powerful paradigm, yet real‑world data is often suboptimal, noisy, or otherwise imperfect, posing significant challenges for imitation and reinforcement learning. In this work, we present a formal framework that leverages Temporal Behavior Trees (TBT), an extension of Signal Temporal Logic (STL) with Behavior Tree semantics, to repair suboptimal trajectories prior to their use in downstream policy learning. Given demonstrations that violate a TBT specification, a model‑based repair algorithm corrects trajectory segments to satisfy the formal constraints, yielding a dataset that is both logically consistent and interpretable. The repaired trajectories are then used to extract potential functions that shape the reward signal for reinforcement learning, guiding the agent toward task‑consistent regions of the state space without requiring knowledge of the agent's kinematic model. We demonstrate the effectiveness of this framework on discrete grid‑world navigation and continuous single and multi‑agent reach‑avoid tasks, highlighting its potential for data‑efficient robot learning in settings where high‑quality demonstrations cannot be assumed.

Authors:Avishka Herath, Chanula Luckshan, Lochana Katugaha, Udara Mendis, Kithmin Wickremasinghe
Title: Area Optimization of Open-Source Low-Power INA in 130nm CMOS using Hybrid Mixed-Variable PSO
Abstract:
As open‑source silicon initiatives democratize access to integrated circuit development using multi‑project environments, silicon area has become a premium resource. However, minimizing this layout area traditionally forces designers to compromise on core performance specifications. To address this challenge, this paper presents an open‑source framework based on a hybrid mixed‑variable particle swarm optimization algorithm and the gm/ID methodology to minimize the layout area of complex analog circuits while meeting design requirements. The framework's efficacy is demonstrated by designing a low‑power instrumentation amplifier that achieves a 90.33% reduction in gate area over existing implementations.

Authors:Hrishav Das, Melkior Ornik
Title: Input Matrix Optimization for Desired Reachable Set Warping of Linear Systems
Abstract:
Shaping the reachable set of a dynamical system is a fundamental challenge in control design, with direct implications for both performance and safety. This paper considers the problem of selecting the optimal input matrix for a linear system that maximizes warping of the reachable set along a direction of interest. The main result establishes that under certain assumptions on the dynamics, the problem reduces to a finite number of linear optimization problems. When these assumptions are relaxed, we show heuristically that the same approach yields good results. The results are validated on two systems: a linearized ADMIRE fighter jet model and a damped oscillator with complex eigenvalues. The paper concludes with a discussion of future directions for reachable set warping research.

Authors:Ziqing Guo, Xiaobing Shen, Ruth V. Sabariego
Title: A Multi-Scale ResNet-augmented Fourier Neural Operator Framework for High-Frequency Sequence-to-Sequence Prediction of Magnetic Hysteresis
Abstract:
Accurate modeling of magnetic hysteresis is essential for high‑fidelity power electronics device simulations. The transient hysteresis phenomena such as the ringing effect and the minor loops are the bottleneck for the accurate hysteresis modeling and the core losses estimation. To capture the hysteresis loops with both the macro structure and the micro transient details, in this paper, we propose the multi‑scale ResNet augmented Fourier Neural Operator (Res‑FNO). The framework employs a hybrid input structure that combines sequential time‑series data with scalar material labels through specialized feature engineering. Specifically, the time derivative of magnetic flux density (\fracdBdt) is incorporated as a critical physical feature to enhance the model sensitivity to high‑frequency oscillations and minor loop triggers. The proposed architecture synergizes global spectral modeling with localized refinement by integrating a multi‑scale ResNet path in parallel with the FNO blocks. This design allows the global operator path to capture the underlying physical evolution while the local refinement path, compensates for spectral bias and reconstructs fine‑grained temporal details. Extensive experimental validation across diverse magnetic materials from 79 to Material 3C90 demonstrates the strong generalization capability of the proposed Res‑FNO, proving its robust ability to model complex ringing effects and minor loops in realistic power electronic applications.

Authors:Jochen Lorenz Cremer
Title: Assessing Maintenance of Medium Voltage Cable Networks Under Time-Varying Loading
Abstract:
The electrification and ongoing energy transition lead to systematic changes in electricity loading and variability in power systems. Distribution systems were designed for regular operating patterns, assuming constant low loading. Now, operators need to assess whether their assets can withstand more, as well as time‑varying loading. Operating the system at or near its ampacity potentially accelerates thermal ageing, so the question arises: 'how much can one operate at the limits while keeping maintenance and failures low?' This paper introduces a novel approach that derives a time‑varying Weibull approximation of failure rates using thermal models and provides a shortcut method to quantify maintenance implications under time‑varying loading for heterogeneous MV cable populations. The case studies investigate a dataset from Denmark and the Oberrhein Medium Voltage (MV) system in Germany, studying ageing assets and the interplay with loading, and replacement paradigms of two different cable insulation types. The studies demonstrate that a small fraction of 25% of old, low‑quality cables leads to 82% of failures, and 1.4% of the time of highest loading can cause 46% of cable ageing. The case studies also demonstrate that maintenance needs may be between 10‑300 times higher under future loading conditions associated with the energy transition, specifically in networks that have older PILC cables. This paper provides a new tool for operators to plan maintenance under more realistic, future operating conditions.

Authors:Zhenshuai Liu, Yitong Li, Zirui Wang, Jiashuo Gu, Yao Qin, Jinjun Liu
Title: Ideally-Smooth Transition between Grid-Forming and Grid-Following Inverters based on State Mapping Method
Abstract:
There has been widespread global increasing use of renewable energy sources, which are usually connected to the electricity grids via power electronic inverters. Traditionally, these inverter‑based resources operate in either grid‑forming (GFM) or grid‑following (GFL) mode. But more recently, the need of switching between these two modes are glowingly required because of the complex operation scenarios of systems such as source‑side limitations, grid‑side services, fault disturbances, etc. However, due to the differences between GFM and GFL modes, a direct switching between them would lead to large oscillations or even instability of inverters. Therefore, in this paper, a method called state mapping method for analyzing the switching transient and designing the switching control is proposed. Based on this method, an ideally‑smooth transition between GFM and GFL can be achieved. The effectiveness of the proposed method is verified by both the theoretical analysis and experiment tests.

Authors:Mohamed Afouene Melki, Mohammad Shehab, Mohamed-Slim Alouini
Title: Multi-AUV Trajectory Learning for Sustainable Underwater IoT with Acoustic Energy Transfer
Abstract:
The Internet of Underwater Things (IoUT) supports ocean sensing and offshore monitoring but requires coordinated mobility and energy‑aware communication to sustain long‑term operation. This letter proposes a multi‑AUV framework that jointly addresses trajectory control and acoustic communication for sustainable IoUT operation. The problem is formulated as a Markov decision process that integrates continuous AUV kinematics, propulsion‑aware energy consumption, acoustic energy transfer feasibility, and Age of Information (AoI) regulation. A centralized deep reinforcement learning policy based on Proximal Policy Optimization (PPO) is developed to coordinate multiple AUVs under docking and safety constraints. The proposed approach is evaluated against structured heuristic baselines and demonstrates significant reductions in average AoI while improving fairness and data collection efficiency. Results show that cooperative multi‑AUV control provides scalable performance gains as the network size increases.

Authors:Bohan Cui, Ziyue Ma, Alessandro Giua, Xiang Yin
Title: Opacity Enforcing Supervisory Control with a Priori Unknown Supervisors
Abstract:
We investigate the enforcement of opacity in discrete‑event systems via supervisory control. A system is said to be opaque if a passive intruder can never unambiguously infer whether the system is in a secret state through its observations. In this context, the intruder's knowledge about the supervisor plays a critical role in both problem formulation and solvability. Existing studies typically assume that the policy of the supervisor is either fully unknown to the intruder or fully known a priori, the latter leading to severe technical challenges and unresolved problems under incomparable observations. This paper investigates opacity supervisory control under a new intermediate information setting, which we refer to as the a priori unknown supervisor setting. In this setting, the supervisor's internal realization is not publicly available, but the intruder can partially infer its behavior by eavesdropping on the control decisions issued online during system execution. We formalize the intruder's information‑flow under both observation‑triggered and decision‑triggered decision‑issuance mechanisms and define the corresponding notions of opacity. We provide sound and complete algorithms for synthesizing opacity‑enforcing supervisors without imposing any restrictions on the observable or controllable event sets. By constructing an information‑state structure that embeds the supervisor's estimate of the intruder's belief, the synthesis problem is reduced to a safety game. Finally, we show that, under strictly finer intruder observations, the proposed setting coincides with the standard a priori known supervisor model.

Authors:Bohan Cui, Jianing Zhao, Yu Chen, Alessandro Abate, Marta Kwiatkowska, Xiang Yin
Title: Certificates Synthesis for A Class of Observational Properties in Stochastic Systems: A Unified Approach
Abstract:
In this paper, we investigate the probabilistic formal verification of stochastic dynamical systems over continuous state spaces. Motivated by problems in state estimation and information‑flow security, we introduce the notion of observational properties, which characterize the inferences an external observer can draw from system outputs. These properties are formulated as probabilistic hyperproperties based on HyperLTL over finite traces, yielding a unified framework that subsumes several existing notions studied separately in the literature. We reduce the verification problem to reachability analysis over an augmented structure that integrates the system dynamics with an automaton representation of the specification. Building on this construction, we develop stochastic barrier certificates that provide probabilistic guarantees for property satisfaction while avoiding explicit state‑space discretization. The effectiveness of the proposed framework is demonstrated through a case study.

Authors:Fatiha Hamdi, Abdelhafid Zeroual, Fouzi Harrou
Title: Extended Hybrid Timed Petri Nets with Semi-Supervised Anomaly Detection for Switched Systems, Modelling and Fault Detection
Abstract:
Hybrid physical systems combine continuous and discrete dynamics, which can be simultaneously affected by faults. Conventional fault detection methods often treat these dynamics separately, limiting their ability to capture interacting fault patterns. This paper proposes a unified fault detection framework for hybrid dynamical systems by integrating an Extended Timed Continuous Petri Net (ETCPN) model with semi‑supervised anomaly detection. The proposed ETCPN extends existing Petri net formalisms by introducing marking‑dependent flow functions, enabling intrinsic coupling between discrete and continuous dynamics. Based on this structure, a mode‑dependent hybrid observer is designed, whose stability under arbitrary switching is ensured via Linear Matrix Inequalities (LMIs), solved offline to determine observer gains. The observer generates residuals that reflect discrepancies between the estimated and measured outputs. These residuals are processed using semi‑supervised methods, including One‑Class SVM (OC‑SVM), Support Vector Data Description (SVDD), and Elliptic Envelope (EE), trained exclusively on normal data to avoid reliance on labeled faults. The framework is validated through simulations involving discrete faults, continuous faults, and hybrid faults. Results demonstrate high detection accuracy, fast convergence, and robust performance, with OC‑SVM and SVDD providing the best trade‑off between detection rate and false alarms. The framework is computationally efficient for real‑time deployment, as the main complexity is confined to the offline LMI design phase.

Authors:Satoshi Nakano, Masahiro Suzuki, Misa Ohashi, Noboru Chikami, Shusuke Otabe
Title: Periodic Event-Triggered Explicit Reference Governor for Constrained Attitude Control on SO(3)
Abstract:
This letter addresses the constrained attitude control problem for rigid bodies directly on the special orthogonal group SO(3), avoiding singularities associated with parameterizations such as Euler angles. We propose a novel Periodic Event‑Triggered Explicit Reference Governor (PET‑ERG) that enforces input saturation and geometric pointing constraints without relying on online optimization. A key feature is a periodic event‑triggered supervisory update: the auxiliary reference is updated only at sampled instants when a robust safety condition is met, thereby avoiding continuous‑time reference updates and enabling a rigorous stability analysis of the cascade system on the manifold. Through this structured approach, we rigorously establish the asymptotic stability and exponential convergence of the closed‑loop system for almost all initial configurations. Numerical simulations validate the effectiveness of the proposed control architecture and demonstrate constraint satisfaction and convergence properties.

Authors:Kun Cao, Lihua Xie
Title: Element-based Formation Control: a Unified Perspective from Continuum Mechanics
Abstract:
This paper establishes a unified element‑based framework for formation control by introducing the concept of the deformation gradient from continuum mechanics. Unlike traditional methods that rely on geometric constraints defined on graph edges, we model the formation as a discrete elastic body composed of simplicial elements. By defining a generalized distortion energy based on the local deformation gradient tensor, we derive a family of distributed control laws that can enforce various geometric invariances, including translation, rotation, scaling, and affine transformations. The convergence properties and the features of the proposed controllers are analyzed in detail. Theoretically, we show that the proposed framework serves as a bridge between existing rigidity‑based and Laplacian‑based approaches. Specifically, we show that rigidity‑based controllers are mathematically equivalent to minimizing specific projections of the deformation energy tensor. Furthermore, we establish a rigorous link between the proposed energy minimization and Laplacian‑based formation control. Numerical simulations in 2D and 3D validate the effectiveness and the unified nature of the proposed framework.

Authors:Satoshi Nakano, Emanuele Garone, Gennaro Notomista
Title: Optimization-Free Constrained Control with Guaranteed Recursive Feasibility: A CBF-Based Reference Governor Approach
Abstract:
This letter presents a constrained control framework that integrates Explicit Reference Governors (ERG) with Control Barrier Functions (CBF) to ensure recursive feasibility without online optimization. We formulate the reference update as a virtual control input for an augmented system, governed by a smooth barrier function constructed from the softmin aggregation of Dynamic Safety Margins (DSMs). Unlike standard CBF formulations, the proposed method guarantees the feasibility of safety constraints by design, exploiting the forward invariance properties of the underlying Lyapunov level sets. This allows for the derivation of an explicit, closed‑form reference update law that strictly enforces safety while minimizing deviation from a nominal reference trajectory. Theoretical results confirm asymptotic convergence, and numerical simulations demonstrate that the proposed method achieves performance comparable to traditional ERG frameworks.

Authors:Raktim Bhattacharya, Felix Biertümpfel
Title: Robust $\Hinf$ Observer Design via Finsler's Lemma and IQCs
Abstract:
This paper develops a Finsler‑based LMI for robust \Hinf observer design with integral quadratic constraints (IQCs) and block‑structured uncertainty. By introducing a slack variable that relaxes the coupling between the Lyapunov matrix, the observer gain, and the IQC multiplier, the formulation addresses two limitations of the standard block‑diagonal approach: the LMI requirement \HePA \prec 0 (which fails for marginally stable dynamics), and a multiplier‑‑Lyapunov trade‑off that causes infeasibility for wide uncertainty ranges. For marginally stable dynamics, artificial damping in the design model balances certified versus actual performance. The framework is demonstrated on quaternion attitude estimation with angular velocity uncertainty and mass‑spring‑damper state estimation with uncertain physical parameters.

Authors:Moh Kamalul Wafi, Yurid E. Nugraha, Bambang L. Widjiantoro, Katherin Indriawati
Title: Cooperative $\mathcal{H}_\infty$ Fault-Tolerant Tracking with ISS Guarantees for Networked Systems with Sensor Faults
Abstract:
This paper develops a cooperative fault‑tolerant tracking framework for heterogeneous networked linear systems subject to sensor faults and external disturbances. Each unit employs an augmented \mathcalH_\infty observer that jointly reconstructs the system state and unknown sensor fault, providing disturbance‑attenuated estimation guarantees. An inner state‑feedback gain is synthesized through convex \mathcalH_\infty Linear Matrix Inequalities (LMIs) to ensure robust closed‑loop stabilization and disturbance rejection, while an outer distributed integral action eliminates steady‑state tracking offsets and enables cooperative tracking of a setpoint source. The resulting cooperative error dynamics are shown to satisfy an Input‑to‑State Stability (ISS) property with respect to disturbances and residual estimation uncertainty, and converge exponentially to zero in the disturbance‑free case. Furthermore, vanishing cooperative error guarantees network‑wide consensus tracking of the desired setpoint. Numerical studies on heterogeneous DC‑motor networks with star, cyclic, and path communication topologies demonstrate accurate state and fault estimation, robust cooperative tracking, and resilience against disturbances and time‑varying sensor faults. The proposed framework provides a scalable and robust coordination strategy for interconnected systems operating under sensing imperfections and uncertain environments.

Authors:Moh Kamalul Wafi, Ahmad Ataka, Yul Y. Nazaruddin, Bayu Jayawardhana
Title: Distributed Nonlinear Control of Networked Two-Wheeled Robots under Adversarial Interactions
Abstract:
This paper studies distributed trajectory tracking for networks of nonholonomic mobile robots under adversarial information exchange. An exact global input‑‑output feedback linearization scheme is developed to regulate planar position outputs, yielding linear error dynamics without prescribing internal state trajectories. To mitigate corrupted neighbor information, a resilient desired‑signal construction is proposed that combines local redundancy with trusted in‑neighbor signals, without requiring adversary detection or isolation. When sufficient redundancy is available, the method suppresses adversarial influence and recovers nominal tracking performance. If redundancy conditions are violated, adversarial effects enter as bounded disturbances and the tracking error remains ultimately bounded. Simulation results on star, cyclic, and path topologies validate the analysis and demonstrate the superior resilience of cyclic networks due to distributed information propagation.

Authors:Aditya Kudre, Heng-Sheng Chang, Prashant G. Mehta
Title: Duality Theory for Non-Markovian Linear Gaussian Models
Abstract:
This work develops a duality theory for partially observed linear Gaussian models in discrete time. The state process evolves according to a causal but non‑Markovian (or higher‑order Gauss‑Markov) structure, captured by a lower‑triangular transition operator, which is related to transformer, with T as the context length. The main contributions are: (i) a dual control system for the linear Gaussian model, formulated as a backward difference equation (B Δ E); (ii) a duality principle establishing that a specific linear‑quadratic optimal control problem for the B Δ E is dual to the filtering problem for the partially observed model; and (iii) an explicit optimal control formula yielding a novel (transformer‑like) linear predictor, referred to as the dual filter, whose computational complexity scales linearly in the time horizon T, in contrast to the O(T^3) cost of classical smoothing and Wiener‑Hopf approaches.

Authors:Miroslav Krstic, Iasson Karafyllis, Luke Bhan, Carina Veil
Title: Lotka-Sharpe Neural Operators for Control of Population PDEs
Abstract:
Age‑structured predator‑prey integro‑partial differential equations provide models of interacting populations in ecology, epidemiology, and biotechnology. A key challenge in feedback design for these systems is the scalar ζ, defined implicitly by the Lotka‑Sharpe nonlinear integral condition, as a mapping from fertility and mortality rates to ζ. To solve this challenge with operator learning, we first prove that the Lotka‑Sharpe operator is Lipschitz continuous, guaranteeing the existence of arbitrarily accurate neural operator approximations over a compact set of fertility and mortality functions. We then show that the resulting approximate feedback law preserves semi‑global practical asymptotic stability under propagation of the operator approximation error through various other nonlinear operators, all the way through to the control input. In the numerical results, not only do we learn ``once‑and‑for‑all'' the canonical Lotka‑Sharpe (LS) operator, and thus make it available for future uses in control of other age‑structured population interconnections, but we demonstrate the online usage of the neural LS operator under estimation of the fertility and mortality functions.

Authors:Clinton Enwerem, John S. Baras, Calin Belta
Title: Risk-Constrained Belief-Space Optimization for Safe Control under Latent Uncertainty
Abstract:
Many safety‑critical control systems must operate under latent uncertainty that sensors cannot directly resolve at decision time. Such uncertainty, arising from unknown physical properties, exogenous disturbances, or unobserved environment geometry, influences dynamics, task feasibility, and safety margins. Standard methods optimize expected performance and offer limited protection against rare but severe outcomes, while robust formulations treat uncertainty conservatively without exploiting its probabilistic structure. We consider partially observed dynamical systems whose dynamics, costs, and safety constraints depend on a latent parameter maintained as a belief distribution, and propose a risk‑sensitive belief‑space Model Predictive Path Integral (MPPI) control framework that plans under this belief while enforcing a Conditional Value‑at‑Risk (CVaR) constraint on a trajectory safety margin over the receding horizon. The resulting controller optimizes a risk‑regularized performance objective while explicitly constraining the tail risk of safety violations induced by latent parameter variability. We establish three properties of the resulting risk‑constrained controller: (1) the CVaR constraint implies a probabilistic safety guarantee, (2) the controller recovers the risk‑neutral optimum as the risk weight in the objective tends to zero, and (3) a union‑bound argument extends the per‑horizon guarantee to cumulative safety over repeated solves. In physics‑based simulations of a vision‑guided dexterous stowing task in which a grasped object must be inserted into an occupied slot with pose uncertainty exceeding prescribed lateral clearance requirements, our method achieves 82% success with zero contact violations at high risk aversion, compared to 55% and 50% for a risk‑neutral configuration and a chance‑constrained baseline, both of which incur nonzero exterior contact forces.

Authors:Anton Hinneck
Title: Location-Invariant Assessment of Flexibility Potential under Distribution System Reconfiguration
Abstract:
The growing integration of renewable and decentralized generation increases the need for flexibility in distribution systems. This flexibility, typically represented in a PQ capability curve, is constrained by network limits and topology. Distribution system reconfiguration (DSR) introduces additional degrees of freedom through switching actions. This paper proposes an AC‑constrained methodology to assess flexibility under network reconfiguration, explicitly considering radial operation. The impact of topology changes on PQ capability curves, which serve as a measure of flexibility potential, is analyzed. To that end, a novel measure called location‑invariant flexibility potential (LI‑FP) is introduced. Results show that reconfiguration can significantly influence and improve operational flexibility. The approach presented enables transparency for system operators, facilitating improved coordination of flexibility providers.

Authors:Takeyuki Iwasaki, Yutaka Hori
Title: Bounding Transient Moments for a Class of Stochastic Reaction Networks Using Kolmogorov's Backward Equation
Abstract:
Stochastic chemical reaction networks (SRNs) in cellular systems are commonly modeled as continuous‑time Markov chains (CTMCs) describing the dynamics of molecular copy numbers. The exact evaluation of transient copy number statistics is, however, often hindered by a non‑closed hierarchy of moment equations. In this paper, we propose a method for computing theoretically guaranteed upper and lower bounds on transient moments based on the Kolmogorov's backward equation, which provides a dual representation of the CME, the governing equation for the probability distribution of the CTMC. This dual formulation avoids the moment closure problem by shifting the source of infinite dimensionality to the dependence on the initial state. We show that, this dual formulation, combined with the monotonicity of the CTMC generator, leads to a finite‑dimensional linear time‑invariant system that provides bounds on transient moments. The resulting system enables efficient evaluation of moment bounds across multiple initial conditions by simple inner‑product operations without recomputing the bounding system. Further, for certain classes of SRNs, the bounding ODEs admit explicit construction from the reaction model, providing a systematic and constructive framework for computing provable bounds.

Authors:Tomoki Sadatoshi, Antonis Papachristodoulou, Yutaka Hori
Title: Acceleration of Moment Bound Optimization for Stochastic Chemical Reactions Using Reaction-wise Sparsity of Moment Equations
Abstract:
Moment dynamics in stochastic chemical kinetics often involve an infinite chain of coupled equations, where lower‑order moments depend on higher‑order ones, making them analytically intractable. Moment bounding via semidefinite programming provides guaranteed upper and lower bounds on stationary moments. However, this formulation suffers from the rapidly growing size of semidefinite constraints due to the combinatorial growth of moments with the number of molecular species. In this paper, we propose a sparsity‑exploiting matrix decomposition method for semidefinite constraints in stationary moment bounding problems to reduce the computational cost of the resulting semidefinite programs. Specifically, we characterize the sparsity structure of moment equations, where each reaction involves only a subset of variables determined by its reactants, and exploit this structure to decompose the semidefinite constraints into smaller ones. We demonstrate that the resulting formulation reduces the computational cost of the optimization problem while providing practically useful bounds.

Authors:Nikolaos D. Tantaroudas, Jake Hollins, Konstantinos Agathos, Evangelos Papatheou
Title: Nonlinear Model Updating of Aerospace Structures via Taylor-Series Reduced-Order Models
Abstract:
Finite element model updating is a mature discipline for linear structures, yet its extension to nonlinear regimes remains an open challenge. This paper presents a methodology that combines nonlinear model order reduction (NMOR) based on Taylor‑series expansion of the equations of motion with the projection‑basis adaptation scheme recently proposed by Hollins et al. [2026] for linear model updating. The structural equations of motion, augmented with proportional (Rayleigh) damping and polynomial stiffness nonlinearity, are recast as a first‑order autonomous system whose Jacobian possesses complex eigenvectors forming a biorthogonal basis. Taylor operators of second and third order are derived for the nonlinear internal forces and projected onto the reduced eigenvector basis, yielding a low‑dimensional nonlinear reduced‑order model (ROM). The Cayley transform, generalised from the real orthogonal to the complex unitary group, parametrises the adaptation of the projection basis so that the ROM mode shapes optimally correlate with experimental measurements. The resulting nonlinear model‑updating framework is applied to a representative wingbox panel model. Numerical studies demonstrate that the proposed approach captures amplitude‑dependent natural frequencies and modal assurance criterion(MAC) values that a purely linear updating scheme cannot reproduce, while recovering the underlying stiffness parameters with improved accuracy.

Authors:Ranjeet Kumbhar, Rajmeet Singh, Appaso M Gadade, Ashish Singla, Irfan Hussain
Title: A Novel Hybrid PID-LQR Controller for Sit-To-Stand Assistance Using a CAD-Integrated Simscape Multibody Lower Limb Exoskeleton
Abstract:
Precise control of lower limb exoskeletons during sit‑to‑stand (STS) transitions remains a central challenge in rehabilitation robotics owing to the highly nonlinear, time‑varying dynamics of the human‑exoskeleton system and the stringent trajectory tracking requirements imposed by clinical safety. This paper presents the systematic design, simulation, and comparative evaluation of three control strategies: a classical Proportional‑Integral‑Derivative (PID) controller, a Linear Quadratic Regulator (LQR), and a novel Hybrid PID‑LQR controller applied to a bilateral lower limb exoskeleton performing the sit‑to‑stand transition. A high‑fidelity, physics‑based dynamic model of the exoskeleton is constructed by importing a SolidWorks CAD assembly directly into the MATLAB/Simulink Simscape Multibody environment, preserving accurate geometric and inertial properties of all links. Physiologically representative reference joint trajectories for the hip, knee, and ankle joints are generated using OpenSim musculoskeletal simulation and decomposed into three biomechanical phases: flexion‑momentum (0‑33%), momentum‑transfer (34‑66%), and extension (67‑100%). The proposed Hybrid PID‑LQR controller combines the optimal transient response of LQR with the integral disturbance rejection of PID through a tuned blending coefficient alpha = 0.65. Simulation results demonstrate that the Hybrid PID‑LQR achieves RMSE reductions of 72.3% and 70.4% over PID at the hip and knee joints, respectively, reduces settling time by over 90% relative to PID across all joints, and limits overshoot to 2.39%‑6.10%, confirming its superiority over both baseline strategies across all evaluated performance metrics and demonstrating strong translational potential for clinical assistive exoskeleton deployment.

Authors:Yangjun Zeng, Huayan Geng, Yiwei Qiu, Xiuli Sun, Liuchao Xu, Jiarong Li, Shi Chen, Buxiang Zhou, Kaigui Xie
Title: Carbon-Driven Incentive Mechanism for Renewable Power-to-Ammonia Production in Coupled Carbon and Ammonia Markets
Abstract:
Renewable power‑to‑ammonia (ReP2A) production offers a promising pathway to decarbonize the power, transport and, chemical sectors, yet its competitiveness remains limited by high costs and fragmented carbon‑policy frameworks. In particular, a unified mechanism that links ReP2A producers with fossil‑based gray ammonia (GA) competitors in carbon and ammonia markets, while coordinating incentives among renewable generation, hydrogen production, and ammonia synthesis stakeholders in the ReP2A process chain, is still lacking. To address this gap, this paper proposes a hierarchical carbon‑driven incentive mechanism (PCIM) that integrates carbon policy with multi‑energy market interactions. A two‑layer trading framework is developed, where ReP2A and GA compete in carbon allowance (CA) and ammonia markets (outer layer), while electricity and hydrogen transactions coordinate the ReP2A chain (inner layer). The resulting interactions are modeled as a hierarchical equilibrium, where the inner layer is reformulated as a tractable equivalent optimization problem, and the outer layer is solved as a mixed‑integer linear program (MILP) derived from Karush‑Kuhn‑Tucker conditions. Based on equilibrium analysis, the carbon‑related revenue of ReP2A is quantified, and a CA allocation mechanism (PCAM) is proposed to ensure individually rationality among stakeholders. Results show that the proposed mechanism reduces carbon emissions by 12.9% with only a 1.8% decrease in sector‑wide revenue. Moreover, carbon pricing under the proposed framework redistributes profits between green and gray ammonia without reducing total welfare, and the PCAM further enhances stakeholders' willingness to participate in ReP2A production.

Authors:Meng Yuan, Tinghui Yan, Zhezhuang Xu
Title: Reinforcement Learning-Based Energy Management for Industrial Park with Heterogeneous Batteries under Demand Response
Abstract:
The integration of photovoltaic (PV) systems, stationary energy storage systems (ESSs), and electric vehicles (EVs) alongside demand response (DR) programmes in industrial parks presents opportunities to reduce costs and improve renewable energy utilisation. Coordinating these resources is challenging because office and production zones have distinct operational objectives, and battery ageing costs are often ignored. This paper proposes a DR‑based energy management framework that jointly optimises grid interaction costs, thermal comfort, EV departure state‑of‑charge requirements, carbon emissions, and battery ageing. We model heterogeneous load characteristics using a dynamic energy distribution ratio and incorporate dispatch‑level ageing models for both ESS and EV batteries. The problem is formulated as a Markov decision process (MDP) and solved with a deep deterministic policy gradient (DDPG) algorithm. High‑fidelity simulations using data from a practical industrial park in China show the framework maintains indoor comfort while significantly reducing total operating costs, yielding savings of 44.58% and 40.68% compared with a rule‑based DR strategy and a conventional time‑of‑use arbitrage approach, respectively.

Authors:Zirui Wang, Yitong Li, Quanchi Wu, Jinjun Liu
Title: Hybrid Voltage-Current Control of Grid-Forming and Grid-Following Inverters
Abstract:
Grid‑connected inverters are required to operate stably under a wide range of grid conditions. However, conventional grid‑following (GFL) control may suffer from instability under weak‑grid conditions, while grid‑forming (GFM) control may exhibit unstable oscillations under strong‑grid conditions. To address these issues, a hybrid voltage‑current control method is proposed in this article. A voltage control is introduced on the d‑axis, while a current control is adopted on the q‑axis, enabling the inverter to exhibit voltage‑source characteristics on the d‑axis and current‑source characteristics on the q‑axis. In this way, the proposed control integrates the characteristics of both conventional GFL and GFM control. A full‑order model is established to analyze the port characteristics and small‑signal stability of the systems. Finally, the effectiveness of the proposed control strategy is validated through simulations and experiments on a 1.5 kW inverter experimental platform. The results show that the proposed control maintains stable operation under different grid conditions with varying short‑circuit ratios (SCRs).

Authors:Mohammad Merati, H. M. Sabbir Ahmad, Wenchao Li, David Castañón
Title: Multi-Robot Multi-Queue Control via Exhaustive Assignment Actor-Critic Learning
Abstract:
We study online task allocation for multi‑robot, multi‑queue systems with asymmetric stochastic arrivals and switching delays. We formulate the problem in discrete time: each location can host at most one robot per slot, servicing a task consumes one slot, switching between locations incurs a one‑slot travel delay, and arrivals at locations are independent Bernoulli processes with heterogeneous rates. Building on our previous structural result that optimal policies are of exhaustive type, we formulate a discounted‑cost Markov decision process and develop an exhaustive‑assignment actor‑critic policy architecture that enforces exhaustive service by construction and learns only the next‑queue allocation for idle robots. Unlike the exhaustive‑serve‑longest (ESL) queue rule, whose optimality is known only under symmetry, the proposed policy adapts to asymmetry in arrival rates. Across different server‑location ratios, loads, and asymmetric arrival profiles, the proposed policy consistently achieves lower discounted holding cost and smaller mean queue length than the ESL baseline, while remaining near‑optimal on instances where an optimal benchmark is available. These results show that structure‑aware actor‑critic methods provide an effective approach for real‑time multi‑robot scheduling.

Authors:Liudong Chen, Hyemi Kim, Adam N. Elmachtoub, Bolun Xu
Title: Fair Aggregation in Virtual Power Plants
Abstract:
A virtual power plant (VPP) is operated by an aggregator that acts as a market intermediary, aggregating consumers to participate in wholesale power markets. By setting incentive prices, the aggregator induces consumers to sell energy and profits by providing this aggregated energy to the market. This supply is enabled by consumers' flexibility to adjust electricity consumption in response to market conditions. However, heterogeneity in flexibility means that profit‑maximizing VPP pricing can create inequalities in participation and benefit allocation across consumers. In this paper, we develop a fairness‑aware pricing framework to analyze how different fairness notions reshape system performance, measured by consumer Nash welfare, total consumer utility, and social welfare. We consider three fairness criteria: energy fairness, which ensures equitable energy provision; price fairness, which ensures similar incentive prices; and utility fairness, which ensures comparable levels of consumer utility. We model the aggregator‑consumer interaction as a Stackelberg game and derive consumers' optimal responses to incentive prices. Using a stylized model, we show that profit‑only pricing systematically disadvantages less flexible consumers. We further show that energy fairness can either improve or worsen all performance measures, and gains across most measures arise only at moderate fairness levels. Surprisingly, price fairness never benefits less flexible consumers, even when it reduces price disparities. By contrast, utility fairness protects less flexible consumers without benefiting more flexible ones. We validate our findings using data from an experiment in Norway under a tiered pricing scheme. Our results provide regulators and VPP operators with a systematic map linking fairness definitions and enforcement levels to operational and welfare outcomes.

Authors:Pio Ong, David E. J. van Wijk, Massimiliano de Sa, Joel W. Burdick, Aaron D. Ames
Title: SafeSpace: Aggregating Safe Sets from Backup Control Barrier Functions under Input Constraints
Abstract:
Control barrier functions (CBFs) provide a principled framework for enforcing safety in control systems ‑‑ yet the certified safe operating region in practice is often conservative, especially under input bounds. In many applications, multiple smaller safe sets can be certified independently, e.g., around distinct equilibria with different stabilizing controllers. This paper proposes a framework for uniting such regions into a single certified safe set using \emphcombinatorial CBFs. We refine the combinatorial CBF framework by introducing an auxiliary variable that enables logical compositions of individual CBFs. In the proposed framework, we show that such compositions yield a \emphgeneralized combinatorial CBF under a condition termed \emphconjunctive compatibility. Building on this result, we extend the framework to enable the aggregation of multiple implicit safe sets generated by the backup CBF framework. We show that the resulting CBF‑based quadratic program yields a continuous safety filter over the aggregated safe region. The approach is demonstrated on two spacecraft safety problems, safe attitude control and safe station keeping, where multiple certified safe regions are combined to expand the operational envelope.

Authors:Xin Mao, Joshua Pickard, Can Chen
Title: Data-Driven Tensor Decomposition Identification of Homogeneous Polynomial Dynamical Systems
Abstract:
Homogeneous polynomial dynamical systems (HPDSs), which can be equivalently represented by tensors, are essential for modeling higher‑order networked systems, including ecological networks, chemical reactions, and multi‑agent robotic systems. However, identifying such systems from data is challenging due to the rapid growth in the number of parameters with increasing system dimension and polynomial degree. In this article, we adopt compact and scalable representations of HPDSs leveraging low‑rank tensor decompositions, including tensor train, hierarchical Tucker, and canonical polyadic decompositions. These representations exploit the intrinsic multilinear structure of HPDSs and substantially reduce the dimensionality of the parameter space. Rather than identifying the full dynamic tensor, we develop a data‑driven framework that directly learns the underlying factor tensors or matrices in the associated decompositions from time‑series data. The resulting identification problem is solved using alternating least‑squares algorithms tailored to each tensor decomposition, achieving both accuracy and computational efficiency. We further analyze the robustness of the proposed framework in the presence of measurement noise and characterize data informativity. Finally, we demonstrate the effectiveness of our framework with numerical examples.

Authors:Omar M. Sleem, Sahand Kiani, Constantino M. Lagoa
Title: RAIN-FIT: Learning of Fitting Surfaces and Noise Distribution from Large Data Sets
Abstract:
This paper proposes a method for estimating a surface that contains a given set of points from noisy measurements. More precisely, by assuming that the surface is described by the zero set of a function in the span of a given set of features and a parametric description of the distribution of the noise, a computationally efficient method is described that estimates both the surface and the noise distribution parameters. In the provided examples, polynomial and sinusoidal basis functions were used. However, any chosen basis that satisfies the outlined conditions mentioned in the paper can be approximated as a combination of trigonometric, exponential, and/or polynomial terms, making the presented approach highly generalizable. The proposed algorithm exhibits linear computational complexity in the number of samples. Our approach requires no hyperparameter tuning or data preprocessing and effectively handles data in dimensions beyond 2D and 3D. The theoretical results demonstrating the convergence of the proposed algorithm have been provided. To highlight the performance of the proposed method, comprehensive numerical results are conducted, evaluating our method against state‑of‑the‑art algorithms, including Poisson Reconstruction and the Neural Network‑based Encoder‑X, on 2D and 3D shapes. The results demonstrate the superiority of our method under the same conditions.

Authors:Addie McCurdy, Isabel Collins, Emily Jensen
Title: Conditions for Complete Decentralization of the Linear Quadratic Regulator
Abstract:
An unconstrained optimal control policy is completely decentralized if computing actuation for each subsystem only requires information directly available to its own subcontroller. Parameters that admit a completely decentralized optimal controller have been characterized in a variety of systems, but attempts to physically explain the phenomenon have been limited. As a step toward a general characterization of complete decentralization, this paper presents conditions for complete decentralization of Linear Quadratic Regulators for several simple cases and physically interprets these conditions with illustrative examples. These simple cases are then leveraged to characterize complete decentralization of more complex systems.

Authors:Rijad Alisic, Saurabh Amin
Title: How Sensor Attacks Transfer Across Lie Groups
Abstract:
Sensor spoofing analysis in cyber‑physical systems is predominantly confined to linear state spaces, where attack transferability is trivial. On Lie groups, however, the noncommutativity of the dynamics can distort certain sensor attacks, exposing nominally stealthy attacks during complex maneuvers. We present a geometric framework characterizing when a sensor attack can transfer across operating conditions, preserving both its physical impact and stealthiness. We prove that successful transfer requires the attack to commute with the nominal dynamics (a Lie bracket condition), which isolates transferable attacks to an invariant subspace, while attacks outside this subspace identifiably alter residuals. For small deviations from ideal transferable attacks, our decomposition theorem reveals a fundamental asymmetry: the flow's Adjoint action amplifies the physical impact of the bracket‑violating component. Furthermore, although the attack perturbs the innovation linearly, the accumulated error drift undergoes distortion via the Adjoint action. Finally, we demonstrate how turning maneuvers on a Dubins unicycle collapse the transferable subspace to a single direction, verifying that imperfect attacks remain within theoretical detection bounds.

Authors:Ahmed Mesfer Alkhudaydi, Bai Cui
Title: A Wirtinger Power Flow Jacobian Singularity Condition for Voltage Stability in Converter-Rich Power Systems
Abstract:
The progression of modern power systems towards converter‑rich operations calls for new models and analytics in steady‑state voltage stability assessment. The classic modeling assumption of the generators as stiff voltage sources no longer holds. Instead, the voltage‑ and current‑limited behaviors of converters need to be considered. In this paper, we develop a Wirtinger derivative‑based formulation for the power flow Jacobian and derive an explicit sufficient condition for its singularity. Compared to existing works, we extend the explicit sufficient singularity condition to incorporate all bus types instead of only slack and PQ types. We prove that the singularity of the alternative Jacobian coincides with that of the conventional one. A bus‑wise voltage stability index, denoted C_\mathrmW, is derived from diagonal dominance conditions. The condition \min_i C_W,i being greater than one certifies the nonsingularity of the Jacobian and provides a fast, non‑iterative stability margin. Case studies in standard IEEE test systems show that the proposed index yields less conservative and more localized assessments than classical indices such as the L‑index, the K_\mathrmR index, and the SCR index.

Authors:Samuel G. Gessow, Pio Ong, Aaron D. Ames, Brett T. Lopez
Title: High-Order Matrix Control Barrier Functions: Well-Posedness and Feasibility via Matrix Relative Degree
Abstract:
Control barrier functions (CBFs) provide an effective framework for enforcing safety in dynamical systems with scalar constraints. However, many safety constraints are more naturally expressed as matrix‑valued conditions, such as positive definiteness or eigenvalue bounds ‑ scalar formulations introduce potential nonsmoothness that complicates analysis. Matrix control barrier functions (MCBFs) address this limitation by directly enforcing matrix‑valued safety constraints. Yet for constraints where the control input does not appear in the first derivative, high‑order formulations are required. While such extensions are well understood in the scalar case, they remain largely unexplored in the matrix case. This paper develops high‑order matrix control barrier functions (HOMCBFs) and establishes conditions ensuring well‑posedness and feasibility of the associated constraints, enabling enforcement of matrix‑valued safety constraints for systems with high‑order dynamics. We further show that, using an optimal‑decay HOMCBF formulation, forward invariance can be ensured while requiring control only over the minimum eigenspace. The framework is demonstrated on a localization safety problem by enforcing positive definiteness of the information matrix for a double integrator system with a nonlinear measurement model.

Authors:David Sewell, Xingjian Li, Stepan Tretiakov, Krishna Kumar, David Fridovich-Keil
Title: Neural Operators for Multi-Task Control and Adaptation
Abstract:
Neural operator methods have emerged as powerful tools for learning mappings between infinite‑dimensional function spaces, yet their potential in optimal control remains largely unexplored. We focus on multi‑task control problems, whose solution is a mapping from task description (e.g., cost or dynamics functions) to optimal control law (e.g., feedback policy). We approximate these solution operators using a permutation‑invariant neural operator architecture. Across a range of parametric optimal control environments and a locomotion benchmark, a single operator trained via behavioral cloning accurately approximates the solution operator and generalizes to unseen tasks, out‑of‑distribution settings, and varying amounts of task observations. We further show that the branch‑trunk structure of our neural operator architecture enables efficient and flexible adaptation to new tasks. We develop structured adaptation strategies ranging from lightweight updates to full‑network fine‑tuning, achieving strong performance across different data and compute settings. Finally, we introduce meta‑trained operator variants that optimize the initialization for few‑shot adaptation. These methods enable rapid task adaptation with limited data and consistently outperform a popular meta‑learning baseline. Together, our results demonstrate that neural operators provide a unified and efficient framework for multi‑task control and adaptation.

Authors:Sribalaji C. Anand, George J. Pappas
Title: Adversarial Robustness of Deep State Space Models for Forecasting
Abstract:
State‑space model (SSM) for time‑series forecasting have demonstrated strong empirical performance on benchmark datasets, yet their robustness under adversarial perturbations is poorly understood. We address this gap through a control‑theoretic lens, focusing on the recently proposed Spacetime SSM forecaster. We first establish that the decoder‑only Spacetime architecture can represent the optimal Kalman predictor when the underlying data‑generating process is autoregressive ‑ a property no other SSM possesses. Building on this, we formulate robust forecaster design as a Stackelberg game against worst‑case stealthy adversaries constrained by a detection budget, and solve it via adversarial training. We derive closed‑form bounds on adversarial forecasting error that expose how open‑loop instability, closed‑loop instability, and decoder state dimension each amplify vulnerability ‑ offering actionable principles towards robust forecaster design. Finally, we show that even adversaries with no access to the forecaster can nonetheless construct effective attacks by exploiting the model's locally linear input‑output behavior, bypassing gradient computations entirely. Experiments on the Monash benchmark datasets highlight that model‑free attacks, without any gradient computation, can cause at least 33% more error than projected gradient descent with a small step size.

Authors:Max F. Crisafulli, Andrew R. Teel
Title: Two-Timescale Asymptotic Simulations of Hybrid Inclusions with Applications to Stochastic Hybrid Optimization
Abstract:
Convergence properties of model‑free two‑timescale asymptotic simulations of singularly perturbed hybrid inclusions are developed. A hybrid inclusion combines constrained differential and difference inclusions to capture continuous (flow) and discrete (jump) dynamics, respectively. Sufficient conditions are established under which sequences of iterates and step sizes constitute a two‑timescale asymptotic simulation of such a system, with limiting behavior characterized via weakly invariant and internally chain‑transitive sets of an associated boundary layer and reduced system. To illustrate the applicability of these results, conditions are given under which a two‑timescale stochastic approximation of a hybrid optimization algorithm asymptotically recovers the behavior of its deterministic counterpart.

Authors:Jianqiang Ding, Nishant Jayesh Bhave, Shankar A. Deka
Title: Reach-Avoid Model Predictive Control with Guaranteed Recursive Feasibility via Input Constrained Backstepping
Abstract:
This letter proposes a novel sampled‑data model predictive control framework for continuous control‑affine nonlinear systems that provides rigorous reach‑avoid and recursive feasibility guarantees under physical constraints. By propagating both input and output constraints through backstepping process, we present a constructive approach to synthesize a reach‑avoid invariant set that complies with control input limits. Using this reach‑avoid set as a terminal set, we prove that the proposed sampled‑data MPC framework recursively admits feasible control inputs that safely steer the continuous system into the target set under fast sampling conditions. Numerical results demonstrate the efficacy of the proposed approach.

Authors:Yana Lishkova, Pio Ong, Sander Tonkens, Sylvia Herbert, Aaron D. Ames
Title: Steering with Contingencies: Combinatorial Stabilization and Reach-Avoid Filters
Abstract:
In applications such as autonomous landing and navigation, it is often desirable to steer toward a target while retaining the ability to divert to at least r (out of p) alternative sites if conditions change. In this work, we formalize this combinatorial contingency requirement and develop tractable control filters for enforcement. Combinatorial stabilization requires asymptotic stability of a selected equilibrium while ensuring the trajectory remains within the safe region of attraction of at least r‑out‑of‑p candidates. To enforce this requirement, we use control Lyapunov functions (CLFs) to construct regions of attraction, which are combined combinatorially within an optimization‑based filter. Combinatorial targeting extends this framework to finite‑horizon problems using Hamilton‑Jacobi backward reach‑avoid sets, accommodating shrinking reachable regions due to finite horizons or resource depletion. In both formulations, the resulting combinatorial stability filter and combinatorial reach‑avoid filter require only p+1 constraints, preventing combinatorial blow‑up and enabling safe real‑time switching between targets. The framework is demonstrated on two examples where the filters ensure steering with contingency and enable safe diversion.

Authors:Tristan Thomas, Sachin Shivakumar, Javad Mohammadpour Velni
Title: Impulse-to-Peak-Output Norm Optimal State-Feedback Control of Linear PDEs
Abstract:
Impulse‑to‑peak response (I2P) analysis for state‑space ordinary differential equation (ODE) systems is a well‑studied classical problem. However, the techniques employed for I2P optimal control of ODEs have not been extended to partial differential equation (PDE) systems due to the lack of a universal transfer function and state‑space representation. Recently, however, partial integral equation (PIE) representation was proposed as the desired state‑space representation of a PDE, and Lyapunov stability theory was used to solve various control problems, such as stability and optimal H_\infty control. In this work, we utilize this PIE framework, and associated Lyapunov techniques, to formulate the I2P response analysis problem as a solvable convex optimization and obtain provable bounds for the I2P‑norm of linear PDEs. Moreover, by establishing strong duality between primal and dual formulations of the optimization problem, we develop a constructive method for I2P optimal state‑feedback control of PDEs and demonstrate the effectiveness of the method on various examples.

Authors:Dennis Marquis, Mazen Farhood
Title: Hypernetwork-Conditioned Reinforcement Learning for Robust Control of Fixed-Wing Aircraft under Actuator Failures
Abstract:
This paper presents a reinforcement learning‑based path‑following controller for a fixed‑wing small uncrewed aircraft system (sUAS) that is robust to certain actuator failures. The controller is conditioned on a parameterization of actuator faults using hypernetwork‑based adaptation. We consider parameter‑efficient formulations based on Feature‑wise Linear Modulation (FiLM) and Low‑Rank Adaptation (LoRA), trained using proximal policy optimization. We demonstrate that hypernetwork‑conditioned policies can improve robustness compared to standard multilayer perceptron policies. In particular, hypernetwork‑conditioned policies generalize effectively to time‑varying actuator failure modes not encountered during training. The approach is validated through high‑fidelity simulations, using a realistic six‑degree‑of‑freedom fixed‑wing aircraft model.

Authors:Xiaowen Ma, Onur Ayan, Yunpu Ma, Xueli An
Title: Customized User Plane Processing via Code Generating AI Agents for Next Generation Mobile Networks
Abstract:
Generative AI is envisioned to have a crucial impact on next generation mobile networking, making the sixth generation (6G) system considerably more autonomous, flexible, and adaptive than its predecessors. By leveraging their natural language processing and code generation capabilities, AI agents enable novel interactions and services between networks and vertical applications. A particularly promising and interesting use case is the customization of connectivity services for vertical applications by generating new customized processing blocks based on text‑based service requests. More specifically, AI agents are able to generate code for a new function block that handles user plane traffic, allowing it to inspect and decode a protocol data unit (PDU) and perform specified actions as requested by the application. In this study, we investigate the code generation problem for generating such customized processing blocks on‑demand. We evaluate various factors affecting the accuracy of the code generation process in this context, including model selection, prompt design, and the provision of a code template for the agent to utilize. Our findings indicate that AI agents are capable of generating such blocks with the desired behavior on‑demand under suitable conditions. We believe that exploring the code generation for network‑specific tasks is a very interesting problem for 6G and beyond, enabling networks to achieve a new level of customization by generating new capabilities on‑demand.

Authors:Chenghao Huang, Jiarong Fan, Weiqing Wang, Hao Wang
Title: Safe Decentralized Operation of EV Virtual Power Plant with Limited Network Visibility via Multi-Agent Reinforcement Learning
Abstract:
As power systems advance toward net‑zero targets, behind‑the‑meter renewables are driving rapid growth in distributed energy resources (DERs). Virtual power plants (VPPs) increasingly coordinate these resources to support power distribution network (PDN) operation, with EV charging stations (EVCSs) emerging as a key asset due to their strong impact on local voltages. However, in practice, VPPs must make operational decisions with only partial visibility of PDN states, relying on limited, aggregated information shared by the distribution system operator. This work proposes a safety‑enhanced VPP framework for coordinating multiple EVCSs under such realistic information constraints to ensure voltage security while maintaining economic operation. We develop Transformer‑assisted Lagrangian Multi‑Agent Proximal Policy Optimization (TL‑MAPPO), in which EVCS agents learn decentralized charging policies via centralized training with Lagrangian regularization to enforce voltage and demand‑satisfaction constraints. A transformer‑based embedding layer deployed on each EVCS agent captures temporal correlations among prices, loads, and charging demand to improve decision quality. Experiments on a realistic 33‑bus PDN show that the proposed framework reduces voltage violations by approximately 45% and operational costs by approximately 10% compared to representative multi‑agent DRL baselines, highlighting its potential for practical VPP deployment.

Authors:Qixu Wang, Patrick McNamee, Zahra Nili Ahmadabadi
Title: Logarithmic Barrier Functions for Practically Safe Extremum Seeking Control
Abstract:
This paper presents a methodology for Practically Safe Extremum Seeking (PSfES), designed to optimize unknown objective functions while strictly enforcing safety constraints via a Logarithmic Barrier Function (LBF). Unlike traditional safety‑filtered approaches that may induce chattering, the proposed method augments the cost function with an LBF, creating a repulsive potential that penalizes proximity to the safety boundary. We employ averaging theory to analyze the closed‑loop dynamics. A key contribution of this work is the rigorous proof of practical safety for the original system. We establish that the system trajectories remain confined within a safety margin, ensuring forward invariance of the safe set for a sufficiently fast dither signal. Furthermore, our stability analysis shows that the model‑free ESC achieves local practical convergence to the modified minimizer strictly within the safe set, through the sequential tuning of small parameters. The theoretical results are validated through numerical simulations.

Authors:Ege Yuceel, Teodor Tchalakov, Sayan Mitra
Title: Minimal Information Control Invariance via Vector Quantization
Abstract:
Safety‑critical autonomous systems must satisfy hard state constraints under tight computational and sensing budgets, yet learning‑based controllers are often far more complex than safe operation requires. To formalize this gap, we study how many distinct control signals are needed to render a compact set forward invariant under sampled‑data control, connecting the question to the information‑theoretic notion of invariance entropy. We propose a vector‑quantized autoencoder that jointly learns a state‑space partition and a finite control codebook, and develop an iterative forward certification algorithm that uses Lipschitz‑based reachable‑set enclosures and sum‑of‑squares programming. On a 12‑dimensional nonlinear quadrotor model, the learned controller achieves a 157× reduction in codebook size over a uniform grid baseline while preserving invariance, and we empirically characterize the minimum sensing resolution compatible with safe operation.

Authors:Mohammad Ashraf Hossain Sadi, Soham Ghosh, Siby Plathottam, Mohd. Hasan Ali
Title: Distributed Snitch Digital Twin-Based Anomaly Detection for Smart Voltage Source Converter-Enabled Wind Power Systems
Abstract:
Existing cyberattack detection methods for smart grids such as Artificial Neural Networks (ANNs) and Deep Reinforcement Learning (DRL) often suffer from limited adaptability, delayed response, and inadequate coordination in distributed energy systems. These techniques may struggle to detect stealthy or coordinated attacks, especially under communication delays or system uncertainties. This paper proposes a novel Snitch Digital Twin (Snitch‑DT) architecture for cyber‑physical anomaly detection in grid‑connected wind farms using Smart Voltage Source Converters (VSCs). Each wind generator is equipped with a local Snitch‑DT that compares real‑time operational data with high‑fidelity digital models and generates trust scores for measured signals. These trust scores are coordinated across nodes to detect distributed or stealthy cyberattacks. The performance of the Snitch‑DT system is benchmarked against previously published Artificial Neural Network (ANN) and Deep Reinforcement Learning (DRL)‑based detection frameworks. Simulation results using an IEEE 39‑bus wind‑integrated test system demonstrate improved attack detection accuracy, faster response time, and higher robustness under various cyberattack scenarios.

Authors:Balthazar Donon, Geoffroy Jamgotchian, Hugo Kulesza, Louis Wehenkel
Title: Self-Supervised Graph Neural Networks for Full-Scale Tertiary Voltage Control
Abstract:
A growing portion of operators workload is dedicated to Tertiary Voltage Control (TVC), namely the regulation of voltages by means of adjusting a series of setpoints and connection status. TVC may be framed as a Mixed Integer Non Linear Program, but state‑of‑the‑art optimization methods scale poorly to large systems, making them impractical for real‑scale and real‑time decision support. Observing that TVC does not require any optimality guarantee, we frame it as an Amortized Optimization problem, addressed by the self‑supervised training of a Graph Neural Network (GNN) to minimize voltage violations. As a first step, we consider the specific use case of post‑processing the forecasting pipeline used by the French TSO, where the trained GNN would serve as a TVC proxy. After being trained on one year of full‑scale HV‑EHV French power grid day‑ahead forecasts, our model manages to significantly reduce the average number of voltage violations.

Authors:Sibo Tian, Xiao Liang, Sara Behdad, Minghui Zheng
Title: Redefining End-of-Life: Intelligent Automation for Electronics Remanufacturing Systems
Abstract:
Remanufacturing is fundamentally more challenging than traditional manufacturing due to the significant uncertainty, variability, and incompleteness inherent in end‑of‑life (EoL) products. At the same time, it has become increasingly essential and urgent for facilitating a circular economy, driven by the growing volume of discarded electronic products and the escalating scarcity of critical materials. In this paper, we review the existing literature and examine the key challenges as well as emerging opportunities in intelligent automation for EoL electronics remanufacturing, providing a comprehensive overview of how robotics, control, and artificial intelligence (AI) can jointly enable scalable, safe, and intelligent remanufacturing systems. This paper starts with the definition, scope, and motivation of remanufacturing within the context of a circular economy, highlighting its societal and environmental significance. Then it delves into intelligent automation approaches for disassembly, inspection, sorting, and component reprocessing in this domain, covering advanced methods for multimodal perception, decision‑making under uncertainty, flexible planning algorithms, and force‑aware manipulation. The paper further reviews several emerging techniques, including large foundation models, human‑in‑the‑loop integration, and digital twins that have the potential to support future research in this area. By integrating these topics, we aim to illustrate how next‑generation remanufacturing systems can achieve robust, adaptable, and efficient operation in the face of complex real‑world challenges.

Authors:Huu-Thinh Do, Ionela Prodan
Title: On ANN-enhanced positive invariance for nonlinear flat systems
Abstract:
The concept of positively invariant (PI) sets has proven effective in the formal verification of stability and safety properties for autonomous systems. However, the characterization of such sets is challenging for nonlinear systems in general, especially in the presence of constraints. In this work, we show that, for a class of feedback linearizable systems, called differentially flat systems, a PI set can be derived by leveraging a neural network approximation of the linearizing mapping. More specifically, for the class of flat systems, there exists a linearizing variable transformation that converts the nonlinear system into linear controllable dynamics, albeit at the cost of distorting the constraint set. We show that by approximating the distorted set using a rectified linear unit neural network, we can derive a PI set inside the admissible domain through its set‑theoretic description. This offline characterization enables the synthesis of various efficient online control strategies, with different complexities and performances. Numerical simulations are provided to demonstrate the validity of the proposed framework.

Authors:Luca Mayer, Frank Woittennek
Title: On observer forms for hyperbolic PDEs with boundary dynamics
Abstract:
A hyperbolic observer canonical form (HOCF) for linear hyperbolic PDEs with boundary dynamics is presented. The transformation to the HOCF is based on a general procedure that uses so‑called observability coordinates as an intermediate step. These coordinates are defined from an input‑output relation given by a neutral functional differential equation (FDE), which, in the autonomous case, reduces to an autonomous FDE for the output. The HOCF coordinates are directly linked to this FDE, while the state transformation between the original coordinates and the observability coordinates is obtained by restricting the observability map to the interval corresponding to the maximal time shift appearing in the FDE. The proposed approach is illustrated on a string‑mass‑spring example.

Authors:Sharan Srinivasan, Vijay Gupta, Harsha Honnappa
Title: The Variational Approach in Filtering and Correlated Noise
Abstract:
The variational formulation of nonlinear filtering due to Mitter and Newton characterizes the filtering distribution as the unique minimizer of a free energy functional involving the relative entropy with respect to the prior and an expected energy. This formulation rests on an absolute continuity condition between the joint path measure and a product reference measure. We prove that this condition necessarily fails whenever the signal and observation diffusions share a common noise source. Specifically we show that the joint and product measures are mutually singular, so no choice of reference measure can salvage the formulation. We then introduce a conditional variational principle that replaces the prior with a reference measure that preserves the noise correlation structure. This generalization recovers the Mitter‑‑Newton formulation as a special case when the noises are independent, and yields an explicit free energy characterization of the filter in the linear correlated‑noise setting.

Authors:Elizabeth Dietrich, Hanna Krasowski, Murat Arcak
Title: Probably Approximately Correct (PAC) Guarantees for Data-Driven Reachability Analysis: A Theoretical and Empirical Comparison
Abstract:
Reachability analysis evaluates system safety, by identifying the set of states a system may evolve within over a finite time horizon. In contrast to model‑based reachability analysis, data‑driven reachability analysis estimates reachable sets and derives probabilistic guarantees directly from data. Several popular techniques for validating reachable sets ‑‑ conformal prediction, scenario optimization, and the holdout method ‑‑ admit similar Probably Approximately Correct (PAC) guarantees. We establish a formal connection between these PAC bounds and present an empirical case study on reachable sets to illustrate the computational and sample trade‑offs associated with these methods. We argue that despite the formal relationship between these techniques, subtle differences arise in both the interpretation of guarantees and the parameterization. As a result, these methods are not generally interchangeable. We conclude with practical advice on the usage of these methods.

Authors:Elizabeth Dietrich, Hanna Krasowski, Vegard Flovik, Murat Arcak
Title: Importance Sampling for Statistical Certification of Viable Initial Sets
Abstract:
We study the problem of statistically certifying viable initial sets (VISs) ‑‑ sets of initial conditions whose trajectories satisfy a given control specification. While VISs can be obtained from model‑based methods, these methods typically rely on simplified models. We propose a simulation‑based framework to certify VISs by estimating the probability of specification violations under a high‑fidelity or black‑box model. Since detecting these violations may be challenging due to their scarcity, we propose a sample‑efficient framework that leverages importance sampling to target high‑risk regions. We derive an empirical Bernstein inequality for weighted random variables, enabling finite‑sample guarantees for importance sampling estimators. We demonstrate the effectiveness of the proposed approach on two systems and show improved convergence of the resulting bounds on an Adaptive Cruise Control benchmark.

Authors:Carlos Moreno-Blazquez, Filiberto Fele, Teodoro Alamo
Title: Accelerated kriging interpolation for real-time grid frequency forecasting
Abstract:
The integration of renewable energy sources and distributed generation in the power system calls for fast and reliable predictions of grid dynamics to achieve efficient control and ensure stability. In this work, we present a novel nonparametric data‑driven prediction algorithm based on kriging interpolation, which exploits the problem's numerical structure to achieve the required computational efficiency for fast real‑time forecasting. Our results enable accurate frequency prediction directly from measurements, achieving sub‑second computation times. We validate our findings on a simulated distribution grid case study.

Authors:Michael C. Fu
Title: Augmenting Automatic Differentiation for a Single-Server Queue via the Leibniz Integral Rule
Abstract:
New recursive estimators for computing higher‑order derivatives of mean queueing time from a single sample path of a first‑come, first‑served single‑server queue are presented, derived using the well‑known Lindley equation and applying the Leibniz integral rule of differential calculus. Illustrative examples are provided.

Authors:Shuyue Li, Miguel López-Benítez, Eng Gee Lim, Fei Ma, Qian Dong, Mengze Cao, Limin Yu, Xiaohui Qin
Title: An Asynchronous Two-Speed Kalman Filter for Real-Time UUV Cooperative Navigation Under Acoustic Delays
Abstract:
In GNSS‑denied underwater environments, individual unmanned underwater vehicles (UUVs) suffer from unbounded dead‑reckoning drift, making collaborative navigation crucial for accurate state estimation. However, the severe communication delay inherent in underwater acoustic channels poses serious challenges to real‑time state estimation. Traditional filters, such as Extended Kalman Filters (EKF) or Unscented Kalman Filters (UKF), usually block the main control loop while waiting for delayed data, or completely discard Out‑of‑Sequence Measurements (OOSM), resulting in serious drift. To address this, we propose an Asynchronous Two‑Speed Kalman Filter (TSKF) enhanced by a novel projection mechanism, which we term Variational History Distillation (VHD). The proposed architecture decouples the estimation process into two parallel threads: a fast‑rate thread that utilizes Gaussian Process (GP) compensated dead reckoning to guarantee high‑frequency real‑time control, and a slow‑rate thread dedicated to processing asynchronously delayed collaborative information. By introducing a finite‑length State Buffer, the algorithm applies delayed measurements (t‑T) to their corresponding historical states, and utilizes a VHD‑based projection to fast‑forward the correction to the current time without computationally heavy recalculations. Simulation results demonstrate that the proposed TSKF maintains trajectory Root Mean Square Error (RMSE) comparable to computationally intensive batch‑optimization methods under severe delays (up to 30 s). Executing in sub‑millisecond time, it significantly outperforms standard EKF/UKF. The results demonstrate an effective control, communication, and computing (3C) co‑design that significantly enhances the resilience of autonomous marine automation systems.

Authors:Dechuan Liu, Ruigang Wang, Ian R. Manchester
Title: Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning
Abstract:
This paper presents a learning‑based approach for all‑pairs motion planning, where the initial and goal states are allowed to be arbitrary points in a safe set. We construct smooth goal‑conditioned neural ordinary differential equations (neural ODEs) via bi‑Lipschitz diffeomorphisms. Theoretical results show that the proposed model can provide guarantees of global exponential stability and safety (safe set forward invariance) regardless of goal location. Moreover, explicit bounds on convergence rate, tracking error, and vector field magnitude are established. Our approach admits a tractable learning implementation using bi‑Lipschitz neural networks and can incorporate demonstration data. We illustrate the effectiveness of the proposed method on a 2D corridor navigation task.

Authors:Haejoon Lee, Dimitra Panagou
Title: Fully Byzantine-Resilient Distributed Multi-Agent Q-Learning
Abstract:
We study Byzantine‑resilient distributed multi‑agent reinforcement learning (MARL), where agents must collaboratively learn optimal value functions over a compromised communication network. Existing resilient MARL approaches typically guarantee almost sure convergence only to near‑optimal value functions, or require restrictive assumptions to ensure convergence to optimal solution. As a result, agents may fail to learn the optimal policies under these methods. To address this, we propose a novel distributed Q‑learning algorithm, under which all agents' value functions converge almost surely to the optimal value functions despite Byzantine edge attacks. The key idea is a redundancy‑based filtering mechanism that leverages two‑hop neighbor information to validate incoming messages, while preserving bidirectional information flow. We then introduce a new topological condition for the convergence of our algorithm, present a systematic method to construct such networks, and prove that this condition can be verified in polynomial time. We validate our results through simulations, showing that our method converges to the optimal solutions, whereas prior methods fail under Byzantine edge attacks.

Authors:Ting Bai, Xinfeng Ru, Shaoyuan Li, Andreas A. Malikopoulos
Title: Rollout-Based Charging Scheduling for Electric Truck Fleets in Large Transportation Networks
Abstract:
In this paper, we investigate the charging scheduling optimization problem for large electric truck fleets operating with dedicated charging infrastructure. A central coordinator jointly determines the charging sequence and power allocation of each truck to minimize the total operational cost of the fleet. The problem is inherently combinatorial and nonlinear due to the coupling between discrete sequencing decisions and continuous charging control, rendering exact optimization intractable for real‑time implementation. To address this challenge, we propose a rollout‑based dynamic programming framework built upon an inner‑outer two‑layer structure, which decouples ordering decisions from the schedule optimization, thus enabling efficient policy evaluation and approximation. The proposed method achieves near‑optimal solutions with polynomial‑time complexity and adapts to dynamic arrivals and time‑varying electricity prices. Simulation studies show that the rollout‑based approach significantly outperforms conventional heuristics with high computational efficiency, demonstrating its effectiveness and practical applicability for real‑time charging management in large‑scale transportation networks.

Authors:Mengmou Li, Yu Zhou, Xun Shen, Masaaki Nagahara
Title: A Canonical Structure for Constructing Projected First-Order Algorithms With Delayed Feedback
Abstract:
This work introduces a canonical structure for a broad class of unconstrained first‑order algorithms that admit a Lur'e representation, including systems with relative degree greater than one, e.g., systems with delayed gradient feedback. The proposed canonical structure is obtained through a simple linear transformation. It enables a direct extension from unconstrained optimization algorithms to set‑constrained ones through projection in a Lyapunov‑induced norm. The resulting projected algorithms attain the optimal solution while preserving the convergence rates of their unconstrained counterparts.

Authors:Minhyuk Jang, Jungjin Lee, Astghik Hakobyan, Naira Hovakimyan, Insoon Yang
Title: Residual-Aware Distributionally Robust EKF: Absorbing Linearization Mismatch via Wasserstein Ambiguity
Abstract:
The extended Kalman filter (EKF) is a cornerstone of nonlinear state estimation, yet its performance is fundamentally limited by noise‑model mismatch and linearization errors. We develop a residual‑aware distributionally robust EKF that addresses both challenges within a unified Wasserstein distributionally robust state estimation framework. The key idea is to treat linearization residuals as uncertainty and absorb them into an effective uncertainty model captured by a stage‑wise ambiguity set, enabling noise‑model mismatch and approximation errors to be handled within a single formulation. This approach yields a computable effective radius along with deterministic upper bounds on the prior and posterior mean‑squared errors of the true nonlinear estimation error. The resulting filter admits a tractable semidefinite programming reformulation while preserving the recursive structure of the classical EKF. Simulations on coordinated‑turn target tracking and uncertainty‑aware robot navigation demonstrate improved estimation accuracy and safety compared to standard EKF baselines under model mismatch and nonlinear effects.

Authors:Kazumune Hashimoto, Shunki Kimura, Kazunobu Serizawa, Junya Ikemoto, Yulong Gao, Kai Cai
Title: Data-Driven Synthesis of Probabilistic Controlled Invariant Sets for Linear MDPs
Abstract:
We study data‑driven computation of probabilistic controlled invariant sets (PCIS) for safety‑critical reinforcement learning under unknown dynamics. Assuming a linear MDP model, we use regularized least squares and self‑normalized confidence bounds to construct a conservative estimate of the states from which the system can be kept inside a prescribed safe region over an \(N\)‑step horizon, together with the corresponding set‑valued safe action map. This construction is obtained through a backward recursion and can be interpreted as a conservative approximation of the \(N\)‑step safety predecessor operator. When the associated conservative‑inclusion event holds, a conservative fixed point of the approximate recursion can be certified as an \((N,ε)\)‑PCIS with confidence at least \(η\). For continuous state spaces, we introduce a lattice abstraction and a Lipschitz‑based discretization error bound to obtain a tractable approximation scheme. Finally, we use the resulting conservative fixed‑point approximation as a runtime candidate PCIS in a practical shielding architecture with iterative updates, and illustrate the approach on a numerical experiment.

Authors:Bingcong Zhang, Yihang Lyv, Lianbo Ma, Yushi He, Pengfei Wei, Xingchi Liu, Jinhua Li, Jianchang Zhao, Lizhi Pan
Title: A Rapid Instrument Exchange System for Humanoid Robots in Minimally Invasive Surgery
Abstract:
Humanoid robot technologies have demonstrated immense potential for minimally invasive surgery (MIS). Unlike dedicated multi‑arm surgical platforms, the inherent dual‑arm configuration of humanoid robots necessitates an efficient instrument exchange capability to perform complex procedures, mimicking the natural workflow where surgeons manually switch instruments. To address this, this paper proposes an immersive teleoperated rapid instrument exchange system. The system utilizes a low‑latency mechanism based on single‑axis compliant docking and environmental constraint release. Integrated with real‑time first‑person view (FPV) perception via a head‑mounted display (HMD), this framework significantly reduces operational complexity and cognitive load during the docking process. Comparative evaluations between experts and novices demonstrate high operational robustness and a rapidly converging learning curve; novice performance in instrument attachment and detachment improved substantially after brief training. While long‑distance spatial alignment still presents challenges in time cost and collaborative stability, this study successfully validates the technical feasibility of humanoid robots executing stable instrument exchanges within constrained clinical environments.

Authors:Minh Nguyen, Jingqi Li, Gechen Qu, Claire J. Tomlin
Title: Inverse Safety Filtering: Inferring Constraints from Safety Filters for Decentralized Coordination
Abstract:
Safe multi‑agent coordination in uncertain environments can benefit from learning constraints from other agents. Implicitly communicating safety constraints through actions is a promising approach, allowing agents to coordinate and maintain safety without expensive communication channels. This paper introduces an online method to infer constraints from observing the safety‑filtered actions of other agents. We approach the problem by using safety filters to ensure forward safety and exploit their structure to work backwards and infer constraints. We provide sufficient conditions under which we can infer these constraints and prove that our inference method converges. This constraint inference procedure is coupled with a decentralized planning method that ensures safety when the constraint activation distance is sufficiently large. We then empirically validate our method with Monte Carlo simulations and hardware experiments with quadruped robots.

Authors:Seonghoon Yoo, Seulhyun Kwon, Kawon Han, Elaheh Ataeebojd, Mehdi Rasti, Joonhyuk Kang
Title: Robust Beamforming Design for Coherent Distributed ISAC with Statistical RCS and Phase Synchronization Uncertainty
Abstract:
Distributed integrated sensing and communication (D‑ISAC) enables multiple spatially distributed nodes to cooperatively perform sensing and communication. However, achieving coherent cooperation across distributed nodes is challenging due to practical impairments. In particular, residual phase synchronization errors result in imperfect channel state information (CSI), while angle‑of‑arrival (AoA) uncertainties induce radar cross‑section (RCS) variations. These impairments jointly degrade target detection performance in D‑ISAC systems. To address these challenges jointly, this paper proposes a robust beamforming design for coherent D‑ISAC systems. Multiple distributed nodes coordinated by a central unit (CU) jointly perform joint transmission coordinated multipoint (JT‑CoMP) communication and multi‑input multi‑output (MIMO) radar sensing to detect a target while serving multiple user equipments (UEs). We formulate a robust beamforming problem that maximizes the expected Kullback‑Leibler divergence (KLD) under statistical RCS variations while satisfying system power and per‑user minimum signal‑to‑interference‑plus‑noise ratio (SINR) constraints under imperfect CSI to ensure the communication quality of service (QoS). The problem is solved using semidefinite relaxation (SDR) and successive convex approximation (SCA), and numerical results show that the proposed method achieves up to 3 dB signal‑to‑clutter‑plus‑noise ratio (SCNR) gain over the conventional beamforming schemes for target detection while maintaining the required communication QoS.

Authors:Zhen Zhang, M. Umar B. Niazi, Michelle S. Chong, Karl H. Johansson, Amr Alanwar
Title: Data-Driven Nonconvex Reachability Analysis using Exact Set Propagation
Abstract:
This paper studies deterministic data‑driven reachability analysis for dynamical systems with unknown dynamics and nonconvex reachable sets. Existing deterministic data‑driven approaches typically employ zonotopic set representations, for which the multiplication between a zonotopic model set and a zonotopic state set cannot be represented algebraically exactly, thereby necessitating over‑approximation steps in reachable‑set propagation. To remove this structural source of conservatism, we introduce constrained polynomial matrix zonotopes (CPMZs) to represent data‑consistent model sets, and show that the multiplication between a CPMZ model set and a constrained polynomial zonotope (CPZ) state set admits an algebraically exact CPZ representation. This property enables set propagation entirely within the CPZ representation, thereby avoiding propagation‑induced over‑approximation and even retaining the ability to represent nonconvex reachable sets. Moreover, we develop set‑theoretic results that enable the intersection of data‑consistent model sets as new data become available, yielding the proposed online refinement scheme that progressively tightens the data‑consistent model set and, in turn, the resulting reachable set. Beyond linear systems, we extend the proposed framework to polynomial dynamics and develop additional set‑theoretic results that enable both model‑based and data‑driven reachability analysis within the same algebraic representation. By deriving algebraically exact CPZ representations for monomials and their compositions, reachable‑set propagation can be carried out directly at the set level without resorting to interval arithmetic or relaxation‑based bounding techniques. Numerical examples for both linear and polynomial systems demonstrate a significant reduction in conservatism compared to state‑of‑the‑art deterministic data‑driven reachability methods.

Authors:Amir Sajadi, Barry Mather, Bri-Mathias Hodge
Title: Synchronous Condensers: Enhancing Stability in Power Systems with Grid-Following Inverters
Abstract:
Large‑scale integration of inverter‑based resources into power grids worldwide is challenging their stability and security. This paper takes a closer look at synchronous condensers as a solution to mitigate stability challenges caused by the preponderance of grid‑following inverters. It finds that while they are not grid‑forming assets themselves, they could enhance grid stability. Throughout this paper, different facets of power system stability and their underlying phenomena are discussed. In addition, instances of instability and mitigation strategies using synchronous condenser are demonstrated using electromagnetic transient simulations. The analysis in this paper highlights the underlying mechanism by which synchronous condensers enhance angular stability, frequency response, and voltage stability. Moreover, it underscores the criticality of their choice of location by demonstrating the destabilizing behavior that could be initiated by the interactions of synchronous condensers.

Authors:Shanting Wang, Weihao Sun, Andreas A. Malikopoulos
Title: An Online Learning Approach for Two-Player Zero-Sum Linear Quadratic Games
Abstract:
In this paper, we present an online learning approach for two‑player zero‑sum linear quadratic games with unknown dynamics. We develop a framework combining regularized least squares model estimation, high probability confidence sets, and surrogate model selection to maintain a regular model for policy updates. We apply a shrinkage step at each episode to identify a surrogate model in the region where the generalized algebraic Riccati equation admits a stabilizing saddle point solution. We then establish regret analysis on algorithm convergence, followed by a numerical example to illustrate the convergence performance and verify the regret analysis.

Authors:Satyaprajna Sahoo, Anamitra Pal
Title: Wildfire Risk-Informed Preventive-Corrective Decision Making under Renewable Uncertainty
Abstract:
The increasing frequency and intensity of wildfires poses severe threats to the secure and stable operation of power grids, particularly one that is interspersed with renewable generation. Unlike conventional contingencies, wildfires affect multiple assets, leading to cascading outages and rapid degradation of system operability and stability. At the same time, the usual precursors of large wildfires, namely dry and windy conditions, are known with high confidence at least a day in advance. Thus, a coordinated decision‑making scheme employing both day‑ahead and real‑time information has a significant potential to mitigate dynamic wildfire risks in renewable‑rich power systems. Such a scheme is developed in this paper through a novel stochastic preventive‑corrective cut‑set and stability‑constrained unit commitment and optimal power flow formulation that also accounts for the variability of renewable generation. The results obtained using a reduced 240‑bus system of the US Western Interconnection demonstrate that the proposed approach increases the resilience of power systems across multiple levels of wildfire risks while maintaining economic viability.

Authors:Abin Mathew, Zhitong He, Lingxi Li, Yaobin Chen
Title: Dynamic Risk Generation for Autonomous Driving: Naturalistic Reconstruction of Vehicle-E-Scooter Interactions
Abstract:
The increasing, high‑risk interactions between vehicles and vulnerable micromobility users, such as e‑scooter riders, challenge vehicular safety functions and Automated Driving (AD) techniques, often resulting in severe consequences due to the dynamic uncertainty of e‑scooter motion. Despite advances in data‑driven AD methods, traffic data addressing the e‑scooter interaction problem, particularly for safety‑critical moments, remains underdeveloped. This paper proposes a pipeline that utilizes collected on‑road traffic data and creates configurable synthetic interactions for validating vehicle motion planning algorithms. A Social Force Model (SFM) is applied to offer more dynamic and potentially risky movements for the e‑scooter, thereby testing the functionality and reliability of the vehicle collision avoidance systems. A case study based on a real‑world interaction scenario was conducted to verify the practicality and effectiveness of the established simulator. Simulation experiments successfully demonstrate the capability of extending the target scenario to more critical interactions that may result in a potential collision.

Authors:David Grasev, Miguel A. Mendez
Title: Nonlinear System Identification of Variable-Pitch Propellers Using a Wiener Model
Abstract:
This work presents the system identification of a variable‑pitch propeller (VPP) powertrain, encompassing the full actuation chain from PWM signals to thrust generation, with the aim of developing compact models suitable for real‑time digital twinning and control applications. The identification is grounded in experimental data covering both static and dynamic responses of the system. The proposed model takes the form of a Wiener‑like architecture, where the PWM inputs are first processed through linear first‑order dynamics describing the motor and pitch actuation, and the resulting states are then mapped via a static nonlinear relation to the generated thrust. This structure naturally arises under the assumptions that the electronic actuation operates on a much faster time scale than the mechanical response, and that the contribution of the aerodynamically induced torque is negligible in the tested regime. The resulting parsimonious representation is shown to reproduce the measured dynamics with good accuracy while remaining interpretable and computationally light, thereby providing a practical basis for integration in control‑oriented digital twin frameworks.

Authors:Pengbo Zhu, Meng Xu, Andreas A. Malikopoulos, Nikolas Geroliminis
Title: Cooperative Detour Planning for Dual-Task Drone Fleets
Abstract:
As Urban air mobility scales, commercial drone fleets offer a compelling, yet underexplored opportunity to function as mobile sensor networks for real‑time urban traffic monitoring. In this paper, we propose a decentralized framework that enables drone fleets to simultaneously execute delivery tasks and observe network traffic conditions. We model the urban environment with dynamic information values associated with road segments, which accumulate traffic condition uncertainty over time and are reset upon drone visitation. This problem is formulated as a mixed‑integer linear programming problem where drones maximize the traffic information reward while respecting the maximum detour for each delivery and the battery budget of each drone. Unlike centralized approaches that are computationally heavy for large fleets, our method focuses on dynamic local clustering. When drones enter communication range, they exchange their belief in traffic status and transition from isolated path planning to a local joint optimization mode, resolving coupled constraints to obtain replanned paths for each drone, respectively. Simulation results built on the real city network of Barcelona, Spain, demonstrate that, compared to a shortest‑path policy that ignores the traffic monitoring task, our proposed method better utilizes the battery and detour budget to explore the city area and obtain adequate traffic information; and, thanks to its decentralized manner, this ``meet‑and‑merge" strategy achieves near‑global optimality in network coverage with significantly reduced computation overhead compared to the centralized baseline.

Authors:Shobhit Singhal, Lesia Mitridati, Licio Romao
Title: Truthful Production Uncertainty in Electricity Markets: A Two-Stage Mechanism
Abstract:
Renewable power sources have low marginal pro‑duction costs, but may result in high balancing costs due to the inherent production uncertainty. Current day‑ahead markets elicit only point production profiles and neglect the degree of uncertainty associated with each generating asset, preventing the market operator from accounting for balancing costs in day‑ahead dispatch and ancillary service procurement. This increases total system costs and undermines market efficiency, especially in renewable‑heavy power systems. To address this, we propose a new market clearing paradigm based on a two‑stage mechanism, where producers report their production forecast distribution in the day‑ahead stage, followed by the realized production in the real‑time stage. By extending the Vickery‑Clarke‑Groves (VCG) payments to the two‑stage setting, we show appealing properties in terms of incentive compatibility and individual rationality. An electricity market case study validates the theoretical claims, and illustrates the effectiveness of the proposed mechanism to reduce system costs.

Authors:Alberto Padoan
Title: Scaled Relative Graphs in Normed Spaces
Abstract:
The paper extends the Scaled Relative Graph (SRG) framework of Ryu, Hannah, and Yin from Hilbert spaces to normed spaces. Our extension replaces the inner product with a regular pairing, whose asymmetry gives rise to directional angles and, in turn, directional SRGs. Directional SRGs are shown to provide geometric containment tests certifying key operator properties, including contraction and monotonicity. Calculus rules for SRGs under scaling, inversion, addition, and composition are also derived. The theory is illustrated by numerical examples, including a graphical contraction certificate for Bellman operators.

Authors:Taekyung Kim, Aswin D. Menon, Akshunn Trivedi, Dimitra Panagou
Title: Backup-Based Safety Filters: A Comparative Review of Backup CBF, Model Predictive Shielding, and gatekeeper
Abstract:
This paper revisits three backup‑based safety filters ‑‑ Backup Control Barrier Functions (Backup CBF), Model Predictive Shielding (MPS), and gatekeeper ‑‑ through a unified comparative framework. Using a common safety‑filter abstraction and shared notation, we make explicit both their common backup‑policy structure and their key algorithmic differences. We compare the three methods through their filter‑inactive sets, i.e., the states where the nominal policy is left unchanged. In particular, we show that MPS is a special case of gatekeeper, and we further relate gatekeeper to the interior of the Backup CBF inactive set within the implicit safe set. This unified view also highlights a key source of conservatism in backup‑based safety filters: safety is often evaluated through the feasibility of a backup maneuver, rather than through the nominal policy's continued safe execution. The paper is intended as a compact tutorial and review that clarifies the theoretical connections and differences among these methods.

Authors:Bader Alabdulrazzaq, Bri-Mathias Hodge
Title: Selective State-Space Models for Koopman-based Data-driven Distribution System State Estimation
Abstract:
Distribution System State Estimation (DSSE) plays an increasingly‑important role in modern power grids due to the integration of distributed energy resources (DERs). The inherent characteristics of distribution systems make classical estimation methods struggle, and recent advancements in data‑driven learning methods, although promising, exhibit systematic failure in generalization and scalability that limits their applicability. In this work, we propose MambaDSSE, a model‑free data‑driven framework that incorporates Koopman‑theoretic probabilistic filtering with a selective state‑space model that learn to infer the underlying time‑varying behavior of the system from data. We evaluate the model across a variety of test systems and scenarios, and demonstrate that the proposed method outperforms machine learning baselines on scalability, resilience to DER penetration levels, and robustness to data sampling rate irregularities. We further highlight the Mamba‑based SSM's ability to capture long range dependencies from data, improving performance on the DSSE task.

Authors:Weiting Feng, Federico Renda, Yunjie Yang, Francesco Giorgio-Serchi
Title: A virtual-variable-length method for robust inverse kinematics of multi-segment continuum robots
Abstract:
This paper proposes a new, robust method to solve the inverse kinematics (IK) of multi‑segment continuum manipulators. Conventional Jacobian‑based solvers, especially when initialized from neutral/rest configurations, often exhibit slow convergence and, in certain conditions, may fail to converge (deadlock). The Virtual‑Variable‑Length (VVL) method proposed here introduces fictitious variations of segments' length during the solution iteration, conferring virtual axial degrees of freedom that alleviate adverse behaviors and constraints, thus enabling or accelerating convergence. Comprehensive numerical experiments were conducted to compare the VVL method against benchmark Jacobian‑based and Damped Least Square IK solvers. Across more than 1.8× 10^6 randomized trials covering manipulators with two to seven segments, the proposed approach achieved up to a 20% increase in convergence success rate over the benchmark and a 40‑80% reduction in average iteration count under equivalent accuracy thresholds (10^‑4‑10^‑8). While deadlocks are not restricted to workspace boundaries and may occur at arbitrary poses, our empirical study identifies boundary‑proximal configurations as a frequent cause of failed convergence and the VVL method mitigates such occurrences over a statistical sample of test cases.

Authors:Sohrab Rezaei, Xiaomo Wang, Sijia Geng
Title: Data-Driven Koopman Predictive Control for Frequency Regulation of Power Systems using Black-Box IBRs
Abstract:
Model uncertainty of inverter‑based resources (IBRs) presents significant challenges for power system control and stability. This work studies secondary frequency regulation in inverter‑based power systems using a Data‑driven Koopman Predictive Control (DKPC) framework. The method employs Koopman theory to lift the nonlinear system dynamics into a higher‑dimensional space where they can be approximated as linear. Based on Willems' fundamental lemma, a behavioral model is constructed directly from lifted input‑output data. A receding‑horizon predictive control formulation is then provided that operates entirely using observed data, without requiring a parametric model, while satisfying explicit constraints on the control input and system output. The proposed approach is particularly suited for IBRs with complex or uncertain dynamics. Numerical results demonstrate its effectiveness for frequency control as benchmarked against the Data‑enabled Predictive Control (DeePC). The trade‑off between tracking performance and control effort is illustrated through tuning of the weighting parameters.

Authors:Xingyu Ren, Michael C. Fu, Steven I. Marcus
Title: Sensitivity analysis for stopping criteria with application to organ transplantations
Abstract:
We consider a stopping problem and its application to the decision‑making process regarding the optimal timing of organ transplantation for individual patients. At each decision period, the patient state is inspected and a decision is made whether to transplant. If the organ is transplanted, the process terminates; otherwise, the process continues until a transplant happens or the patient dies. Under suitable conditions, we show that there exists a control limit optimal policy. We propose a smoothed perturbation analysis (SPA) estimator for the gradient of the total expected discounted reward with respect to the control limit. Moreover, we show that the SPA estimator is asymptotically unbiased.

Authors:Xingyu Ren, Michael C. Fu, Steven I. Marcus
Title: Stochastic Control for Organ Donations: A Review
Abstract:
We review the literature on individual patient organ acceptance decision making by presenting a Markov Decision Process (MDP) model to formulate the organ acceptance decision process as a stochastic control problem. Under the umbrella of the MDP framework, we classify and summarize the major research streams and contributions. In particular, we focus on control limit‑type policies, which are shown to be optimal under certain conditions and easy to implement in practice. Finally, we briefly discuss open problems and directions for future research.

Authors:Anath Bandhu Das, Pinaki Pal
Title: A unified framework for synchronization optimization in directed multiplex networks
Abstract:
The multiplex network paradigm has been instrumental in revealing many unexpected phenomena and dynamical regimes in complex interacting systems. Nevertheless, most of the current research focuses on undirected multiplex structures, whereas real‑world systems predominantly involve directed interactions. Here, we present an analytical framework for attaining optimal synchronization in directed multiplex networks composed of phase oscillators, considering both frustrated and non‑frustrated regimes. A multiplex synchrony alignment function (MSAF) is introduced for this purpose, whose formulation integrates structural properties and dynamical characteristics of the individual directed layers. Using this function, we derive two classes of frequency distributions: one that yields perfect synchronization at a prescribed coupling strength in the presence of phase‑lag, and another that optimizes synchronization over a broad range of coupling strengths. Numerical simulations on various directed duplex topologies demonstrate that both frequency sets substantially outperform conventional distributions. We also explore network optimization through a directed link rewiring strategy aimed at minimizing the MSAF, along with a swapping algorithm for optimally assigning fixed frequencies on both layers of a given directed duplex network. Examination of synchrony‑optimized directed networks uncovers three notable correlations: a positive relationship between frequency and out‑degree, a negative correlation between neighboring frequencies, and an anti‑correlation between mirror node frequencies across directed layers.

Authors:Jiping Luo, Nikolaos Pappas
Title: Computing the Exact Pareto Front in Average-Cost Multi-Objective Markov Decision Processes
Abstract:
Many communication and control problems are cast as multi‑objective Markov decision processes (MOMDPs). The complete solution to an MOMDP is the Pareto front. Much of the literature approximates this front via scalarization into single‑objective MDPs. Recent work has begun to characterize the full front in discounted or simple bi‑objective settings by exploiting its geometry. In this work, we characterize the exact front in average‑cost MOMDPs. We show that the front is a continuous, piecewise‑linear surface lying on the boundary of a convex polytope. Each vertex corresponds to a deterministic policy, and adjacent vertices differ in exactly one state. Each edge is realized as a convex combination of the policies at its endpoints, with the mixing coefficient given in closed form. We apply these results to a remote state estimation problem, where each vertex on the front corresponds to a threshold policy. The exact Pareto front and solutions to certain non‑convex MDPs can be obtained without explicitly solving any MDP.

Authors:Parth R. Brahmbhatt, Hari S. Ganesh, Styliani Avraamidou
Title: Explicit Distributed MPC: Reducing Computation and Communication Load by Exploiting Facet Properties
Abstract:
Classical Distributed Model Predictive Control (DiMPC) requires multiple iterations to achieve convergence, leading to high computational and communication burdens. This work focuses on the improvement of an iteration‑free distributed MPC methodology that minimizes computational effort and communication load. The aforementioned methodology leverages multiparametric programming to compute explicit control laws offline for each subsystem, enabling real‑time control without iterative data exchanges between subsystems. Extending our previous work on iteration‑free DiMPC, here we introduce a FAcet‑based Critical region Exploration Technique for iteration‑free DiMPC (FACET‑DiMPC) that further reduces computational complexity by leveraging facet properties to do targeted critical region exploration. Simulation results demonstrate that the developed method achieves comparable control performance to centralized methods, while significantly reducing communication overhead and computation time. In particular, the proposed methodology offers substantial efficiency gains in terms of the average computation time reduction of 98% compared to classic iterative DiMPC methods and 42% compared to iteration‑free DiMPC methods, making it well‑suited for real‑time control applications with tight latency and computation constraints.

Authors:Zhen Zhang, Peng Xie, Wenyuan Wu, Yanliang Huang, Amr Alanwar
Title: Transformer-Enhanced Data-Driven Output Reachability with Conformal Coverage Guarantees
Abstract:
This paper considers output reachability analysis for linear time‑invariant systems with unknown state‑space matrices and unknown observation map, given only noisy input‑output measurements. The Cayley‑‑Hamilton theorem is applied to eliminate the latent state algebraically, producing an autoregressive input‑output model whose parameter uncertainty is enclosed in a matrix zonotope. Set‑valued propagation of this model yields output reachable sets with deterministic containment guarantees under a bounded aggregated residual assumption. The conservatism inherent in the lifted matrix‑zonotope product is then mitigated by a decoder‑only Transformer trained on labels obtained through directional contraction of the formal envelope via an exterior non‑reachability certificate. Split conformal prediction restores distribution‑free coverage at both per‑step and trajectory levels without access to the true reachable‑set hull. The framework is validated on a five‑dimensional system with multiple unknown observation matrices.

Authors:Vineet Jagadeesan Nair, Morteza Vahid-Ghavidel, Anuradha M. Annaswamy
Title: Dynamic resource coordination can increase grid hosting capacity to support more renewables, storage, and electrified load growth
Abstract:
We show that dynamic coordination of distributed energy resources (DERs) can increase the capacity of low‑ and medium‑voltage grids, improve reliability and power quality, and reduce solar curtailment. We develop three approaches to compute hosting capacity on a representative distribution grid with realistic scenarios. A deterministic iterative method provides insight into how dynamic operation and DER interactions enhance capacity and affect power flows, demonstrating clear gains over static methods even with low‑to‑moderate levels of storage and flexible demand. A stochastic programming approach jointly optimizes DER siting and sizing, showing that nodal colocation and complementary effects expand the feasible region of solar, heat pump, and battery penetrations by over 22X. This enables up to 200% solar, 100% battery, and 90% heat pump penetration. Batteries emerge as the most critical technology, followed by heat pumps and electric vehicles. A Monte Carlo‑based extension shows that uncertainty significantly impacts hosting capacity and grid metrics, with 46% higher volatility under dynamic operation.

Authors:Zhen Zhang, Ahmad Hafez, Peng Xie, Yanliang Huang, Wenyuan Wu, Amr Alanwar
Title: Transformer-Accelerated Interpolated Data-Driven Reachability Analysis from Noisy Data
Abstract:
Data‑driven reachability analysis provides guaranteed outer approximations of reachable sets from input‑state measurements, yet each propagation step requires a matrix‑zonotope multiplication whose cost grows with the horizon length, limiting scalability. We observe that data‑driven propagation is inherently step‑size sensitive, in the sense that set‑valued operators at different discretization resolutions yield non‑equivalent reachable sets at the same physical time, a property absent in model‑based propagation. Exploiting this multi‑resolution structure, we propose Interpolated Reachability Analysis (IRA), which computes a sparse chain of coarse anchor sets sequentially and reconstructs fine‑resolution intermediate sets in parallel across coarse intervals. We derive a fully data‑driven coarse‑noise over‑approximation that removes the need for continuous‑time system knowledge, prove deterministic outer‑approximation guarantees for all interpolated sets, and establish conditional tightness relative to the fine‑resolution chain. To replace the remaining matrix‑zonotope multiplications in the fine phase, we further develop Transformer‑Accelerated IRA (TA‑IRA), where an encoder‑decoder Transformer is calibrated via split conformal prediction to provide finite‑sample pointwise and path‑wise coverage certificates. Numerical experiments on a five‑dimensional linear system confirm the theoretical guarantees and demonstrate significant computational savings.

Authors:Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
Title: Safe Control of Feedback-Interconnected Systems via Singular Perturbations
Abstract:
Control Barrier Functions (CBFs) have emerged as a powerful tool in the design of safety‑critical controllers for nonlinear systems. In modern applications, complex systems often involve the feedback interconnection of subsystems evolving at different timescales, e.g., two parts from different physical domains (e.g., the electrical and mechanical parts of robotic systems) or a physical plant and an (optimization or control) algorithm. In these scenarios, safety constraints often involve only a portion of the overall system. Inspired by singular perturbations for stability analysis, we develop a formal procedure to lift a safety certificate designed on a reduced‑order model to the overall feedback‑interconnected system. Specifically, we show that under a sufficient timescale separation between slow and fast dynamics, a composite CBF can be designed to certify the forward invariance of the safe set for the interconnected system. As a result, the online safety filter only needs to be solved for the lower‑dimensional, reduced‑order model. We numerically test the proposed approach on: (i) a robotic arm with joint motor dynamics, and (ii) a physical plant driven by an optimization algorithm.

Authors:Liang Wu, Yunhong Che, Wallace Gian Yion Tan, Efstathios Iliakis, Richard D. Braatz, Ján Drgoňa
Title: Fixed-time-stable ODE Representation of Lasso
Abstract:
Lasso problems arise in many areas, including signal processing, machine learning, and control, and are closely connected to sparse coding mechanisms observed in neuroscience. A continuous‑time ordinary differential equation (ODE) representation of the Lasso problem not only enables its solution on analog computers but also provides a framework for interpreting neurophysiological phenomena. This article proposes a fixed‑time‑stable ODE representation of the Lasso problem by first transforming it into a smooth nonnegative quadratic program (QP) and then designing a projection‑free Newton‑based ODE representation of the Lasso problem by first transforming it into a smooth nonnegative quadratic program (QP) and then designing a projection‑free Newton‑based fixed‑time‑stable ODE system for solving the corresponding Karush‑Kuhn‑Tucker (KKT) conditions. Moreover, the settling time of the ODE is independent of the problem data and can be arbitrarily prescribed. Numerical experiments verify that the trajectory reaches the optimal solution within the prescribed time.

Authors:Xiaofei Song, Kerstin Eder, Jonathan Lawry, R. Eddie Wilson
Title: Systematic Analyses of Reinforcement Learning Controllers in Signalized Urban Corridors
Abstract:
In this work, we extend our systematic capacity region perspective to multi‑junction traffic networks, focussing on the special case of an urban corridor network. In particular, we train and evaluate centralized, fully decentralized, and parameter‑sharing decentralized RL controllers, and compare their capacity regions and ATTs together with a classical baseline MaxPressure controller. Further, we show how the parametersharing controller may be generalised to be deployed on a larger network than it was originally trained on. In this setting, we show some initial findings that suggest that even though the junctions are not formally coordinated, traffic may self organise into `green waves'.

Authors:Alessandro Dimauro, Davide Tebaldi, Fabio Pini, Luigi Biagiotti, Francesco Leali
Title: Integrated Identification of Collaborative Robots for Robot Assisted 3D Printing Processes
Abstract:
In recent years, the integration of additive manufacturing (AM) and industrial robotics has opened new perspectives for the production of complex components, particularly in the automotive sector. Robot‑assisted additive manufacturing processes overcome the dimensional and kinematic limitations of traditional Cartesian systems, enabling non‑planar deposition and greater geometric flexibility. However, the increasing dynamic complexity of robotic manipulators introduces challenges related to precision, control, and error prediction. This work proposes a model‑based approach equipped with an integrated identification procedure of the system's parameters, including the robot, the actuators and the controllers. We show that the integrated modeling procedure allows to obtain a reliable dynamic model even in the presence of sensory and programming limitations typical of collaborative robots. The manipulator's dynamic model is identified through an integrated five step methodology: starting with geometric and inertial analysis, followed by friction and controller parameters identification, all the way to the remaining parameters identification. The proposed procedure intrinsically ensures the physical consistency of the identified parameters. The identification approach is validated on a real world case study involving a 6‑Degrees‑Of‑Freedom (DoFs) collaborative robot used in a thermoplastic extrusion process. The very good matching between the experimental results given by actual robot and those given by the identified model shows the potential enhancement of precision, control, and error prediction in Robot Assisted 3D Printing Processes.

Authors:Alexander Medvedev, Anton V. Proskurnikov
Title: Output Corridor Impulsive Control of First-order Continuous System with Non-local Attractivity Analysis
Abstract:
This paper addresses the design of an impulsive controller for a continuous scalar time‑invariant linear plant that constitutes the simplest conceivable model of chemical kinetics. The model is ubiquitous in process control as well as pharmacometrics and readily generalizes to systems of Wiener structure. Given the impulsive nature of the feedback, the control problem formulation is particularly suited to discrete dosing applications in engineering and medicine, where both doses and inter‑dose intervals are manipulated. Since the feedback controller acts at discrete time instants and employs both amplitude and frequency modulation, whereas the plant is continuous, the closed‑loop system exhibits hybrid dynamics featuring complex nonlinear phenomena. The problem of confining the plant output to a predefined corridor of values is considered. The method at the heart of the proposed approach is to design a stable periodic solution, called a 1‑cycle, whose one‑dimensional orbit coincides with the predefined corridor. Conditions ensuring local and global attractivity of the 1‑cycle are established. As a numerical illustration of the proposed approach, the problem of intravenous paracetamol dosing is considered.

Authors:Ricardo Gutierrez, Jesse B. Hoagg
Title: Receding-Horizon Nonlinear Optimal Control With Safety Constraints Using Constrained Approximate Dynamic Programming
Abstract:
We present a receding‑horizon optimal control for nonlinear continuous‑time systems subject to state constraints. The cost is a quadratic finite‑horizon integral. The key enabling technique is a new constrained approximate dynamic programming (C‑ADP) approach for finite‑horizon nonlinear optimal control with constraints that are affine in the control. The C‑ADP approach is intuitive because it uses a quadratic approximation of the cost‑to‑go function at each backward step. This method yields a sequence of analytic closed‑form optimal control functions, which have identical structure and where parameters are obtained from 2 Riccati‑like difference equations. This C‑ADP method is well suited for real‑time implementation. Thus, we use the C‑ADP approach in combination with control barrier functions to obtain a continuous‑time receding‑horizon optimal control that is farsighted in the sense that it optimizes the integral cost subject to state constraints along the entire prediction horizon. Lastly, receding‑horizon C‑ADP control is demonstrated in simulation of a nonholonomic ground robot subject to velocity and no‑collision constraints. We compare performance with 3 other approaches.

Authors:Yubo Zhang, Songhao Yang, Zhiguo Hao, Baohui Zhang
Title: Model-Free Fast Frequency Support of Wind Farms for Tracking Optimal Frequency Trajectory
Abstract:
The fast frequency support (FFS) towards frequency trajectory optimization provides a system view for the frequency regulation of wind farms (WFs). However, the existing frequency trajectory optimization‑based FFS generally relies on the accurate governor dynamics model of synchronous generators (SGs), which aggrandizes the difficulty of controller implementation. In this paper, a proportional‑integral (PI) based FFS of WFs is designed for tracking the optimal frequency trajectory, which gets rid of the dependence on the governor model. Firstly, the prototypical PI‑based FFS of WFs is proposed and its feasibility for tracking the optimal frequency trajectory is analyzed and demonstrated. Then, based on the "frequency‑RoCoF" form of the optimal frequency trajectory, a more practical PI controller is constructed, avoiding the time dependence of the prototypical PI controller. Besides, an adaptive gain associated with PI parameters is designed for multi‑WF coordination. Finally, the validity of the proposed method is verified in both the single‑WF system and the multi‑WF system.

Authors:Jonathan Shelby
Title: Architectural Implications of the UK Cyber Security and Resilience Bill
Abstract:
The UK Cyber Security and Resilience (CS&R) Bill represents the most significant reform of UK cyber legislation since the Network and Information Systems (NIS) Regulations 2018. While existing analysis has addressed the Bill's regulatory requirements, there is a critical gap in guidance on the architectural implications for organisations that must achieve and demonstrate compliance. This paper argues that the CS&R Bill's provisions (expanded scope to managed service providers (MSPs), data centres, and critical suppliers; mandatory 24/72‑hour dual incident reporting; supply chain security duties; and Secretary of State powers of direction‑), collectively constitute an architectural forcing function that renders perimeter‑centric and point‑solution security postures structurally non‑compliant. We present a systematic mapping of the Bill's key provisions to specific architectural requirements, demonstrate that Zero Trust Architecture (ZTA) provides the most coherent technical foundation for meeting these obligations, and propose a reference architecture and maturity‑based adoption pathway for CISOs and security architects. The paper further addresses the cross‑regulatory challenge facing UK financial services firms operating under simultaneous CS&R, DORA, and NIS2 obligations, and maps the architectural framework against the NCSC Cyber Assessment Framework v4.0. This work extends a companion practitioner guide to the Bill by translating regulatory analysis into actionable architectural strategy. Keywords: Cyber Security and Resilience Bill, Zero Trust Architecture, Security Architecture, Critical National Infrastructure, NIS Regulations, DORA, Supply Chain Security, NCSC CAF v4.0

Authors:Zexuan Lin, Songhao Yang, Yubo Zhang, Zhiguo Hao, Baohui Zhang
Title: A Data-Aided Power Transformer Differential Protection without Inrush Blocking Module
Abstract:
When a slightly faulty transformer closes without load, the current waveform presents the coexistence of inrush and fault current. At this time, the inrush blocking module will block the relay, which may delay the removal of the slight fault and lead to more serious faults. To address this problem, this paper proposes a data‑aided power transformer differential protection without inrush blocking module. The key to eliminating the negative influence of inrush current is to extract the fundamental component from the non‑inrush part of the current waveform, which corresponds to the unsaturation period of the transformer core. Firstly, a data‑aided module, namely an Attention module embedded Fully Convolutional Network (A‑FCN), is built to distinguish the inrush and non‑inrush parts of the current waveform. Then, a physical model of the current waveform is built for the non‑inrush part, and the fundamental component is extracted by the nonlinear least square (NLS) algorithm. The proposed method can avoid the block of differential protections when inrush current occurs, which improves the sensitivity and rapidity of the relay, especially in the case of a weak internal fault hidden in inrush current. Finally, simulation and experimental data verify the effectiveness and generalization of the proposed method.

Authors:Songhao Yang, Ruixin Shen, Jin Shu, Tao Zhang, Yujun Li, Baohui Zhang, Zhiguo Hao
Title: PLL Based Sub-/Super-synchronous Resonance Damping Controller for D-PMSG Wind Farm Integrated Power Systems
Abstract:
Existing sub‑/super‑synchronous (SSO) suppression methods for the direct‑drive permanent magnet synchronous generators (D‑PMSG) integrated power systems are mainly achieved by external devices or sub‑synchronous resonance damping controller (SSRDC) at the converters, facing challenges of considerable control costs, complex parameters tuning, or inadaptability to various operating conditions. To address these problems, this paper proposes an adaptive SSRDC based on the phase‑locked loop (PLL) for D‑PMSG integrated power systems. Firstly, the PLL parameter is found critical to SSO suppression by a comprehensive sensitivity analysis on the dominant poles of the impedance closed‑loop transfer function. Motivated by this finding, this paper then designs a PLL‑based SSRDC, which features a simple structure, easy parameter tuning, and flexible adaptability to various operating modes. The simplicity in structure is guaranteed by the avoidance of phase compensation. Benefiting from the simple structure, only one key parameter needs to be tuned. Moreover, two principles of parameter tuning are proposed to enhance the efficiency, robustness, and adaptability of the proposed SSRDC. The controller‑hardware‑in‑the‑loop (CHIL) tests verify the validity of the proposed SSRDC under various operating conditions. Finally, some concerns about this method such as frequency estimation, computational efficiency and potential impacts on PLL are thoroughly analyzed and clarified.

Authors:Jake Welde, Pieter van Goor
Title: A Weak Notion of Symmetry for Dynamical Systems
Abstract:
Many nonlinear dynamical systems exhibit symmetry, affording substantial benefits for control design, observer architecture, and data‑driven control. While the classical notion of group invariance enables a cascade decomposition of the system into highly structured subsystems, it demands very rigid structure in the original system. Conversely, much more general notions (e.g., partial symmetry) have been shown to be sufficient for obtaining less‑structured decompositions. In this work, we propose a middle ground termed "weak invariance", studying diffeomorphisms (resp., vector fields) that are group invariant up to a diffeomorphism of (resp., vector field on) the symmetry group. Remarkably, we prove that weak invariance implies that this diffeomorphism of (resp., vector field on) the symmetry group must be an automorphism (resp., group linear). Additionally, we demonstrate that a vector field is weakly invariant if and only if its flow is weakly invariant, where the associated group linear vector field generates the associated automorphisms. Finally, we show that weakly invariant systems admit a cascade decomposition in which the dynamics are group affine along the orbits. Weak invariance thus generalizes both classical invariance and the important class of group affine dynamical systems on Lie groups, laying a foundation for new methods of symmetry‑informed control and observer design.

Authors:Antonio Franchi
Title: Global Geometry of Orthogonal Foliations of Signed-Quadratic Systems
Abstract:
This work formalizes the differential topology of redundancy resolution for systems governed by signed‑quadratic actuation maps. By analyzing the minimally redundant case, the global topology of the continuous fiber bundle defining the nonlinear actuation null‑space is established. The distribution orthogonal to these fibers is proven to be globally integrable and governed by an exact logarithmic potential field. This field foliates the actuator space, inducing a structural stratification of all orthants into transverse layers whose combinatorial sizes follow a strictly binomial progression. Within these layers, adjacent orthants are continuously connected via lower‑dimensional strata termed reciprocal hinges, while the layers themselves are separated by boundary hyperplanes, or portals, that act as global sections of the fibers. This partition formally distinguishes extremal and transitional layers, which exhibit fundamentally distinct fiber topologies and foliation properties. Exploiting this geometric framework, we prove that the orthogonal manifolds within the extremal orthants form a global diffeomorphism to the entire unbounded task space. This establishes the theoretical existence of globally smooth right‑inverses that permanently confine the system to a single orthant, guaranteeing the absolute avoidance of kinematic singularities. While motivated by the physical actuation maps of multirotor and marine vehicles, the results provide a strictly foundational topological classification of signed‑quadratic surjective systems.

Authors:Athanasios Gkelias, Felix T. A. Burt, Kin K. Leung
Title: Quantum Networking Fundamentals: From Physical Protocols to Network Engineering
Abstract:
The realization of the Quantum Internet promises transformative capabilities in secure communication, distributed quantum computing, and high‑precision metrology. However, transitioning from laboratory experiments to a scalable, multi‑tenant network utility introduces deep orchestration challenges. Current development is often siloed within physics communities, prioritizing hardware, while the classical networking community lacks architectural models to manage fragile quantum resources. This tutorial bridges this divide by providing a network‑centric view of quantum networking. We dismantle idealized assumptions in current simulators to address the "simulation‑reality gap," recasting them as explicit control‑plane constraints. To bridge this gap, we establish Software‑Defined Quantum Networking (SDQN) as a prerequisite for scale, prioritizing a symbiotic, dual‑plane architecture where classical control dictates quantum data flow. Specifically, we synthesize reference models for SDQN and the Quantum Network Operating System (QNOS) for hardware abstraction, and adapt a Quantum Network Utility Maximization (Q‑NUM) framework as a unifying mathematical lens for engineers to reason about trade‑offs between entanglement routing, scheduling, and fidelity. Furthermore, we analyze Distributed Quantum AI (DQAI) over imperfect networks as a case study, illustrating how physical constraints such as probabilistic stragglers and decoherence dictate application‑layer viability. Ultimately, this tutorial equips network engineers with the tools required to transition quantum networking from a bespoke physics experiment into a programmable, multi‑tenant global infrastructure.

Authors:Timo de Groot, Tom Oomen, W. P. M. H. Heemels, Sebastiaan van den Eijnden
Title: Scaled Relative Graphs and Dynamic Integral Quadratic Constraints: Connections and Computations for Nonlinear Systems
Abstract:
Scaled relative graphs (SRGs) enable graphical analysis and design of nonlinear systems. In this paper, we present a systematic approach for computing both soft and hard SRGs of nonlinear systems using dynamic integral quadratic constraints (IQCs). These constraints are exploited via application of the S‑procedure to compute tractable SRG overbounds. In particular, we show that the multipliers associated with the IQCs define regions in the complex plane. Soft SRG computations are formulated through frequency‑domain conditions, while hard SRGs are obtained via hard factorizations of multipliers and linear matrix inequalities. The overbounds are used to derive an SRG‑based feedback stability result for Lur'e‑type systems, providing a new graphical interpretation of classical IQC stability results with dynamic multipliers.

Authors:Yiran Ma, Jerome Le Ny, Zhichao Chen, Zhihuan Song
Title: Towards Intrinsically Calibrated Uncertainty Quantification in Industrial Data-Driven Models via Diffusion Sampler
Abstract:
In modern process industries, data‑driven models are important tools for real‑time monitoring when key performance indicators are difficult to measure directly. While accurate predictions are essential, reliable uncertainty quantification (UQ) is equally critical for safety, reliability, and decision‑making, but remains a major challenge in current data‑driven approaches. In this work, we introduce a diffusion‑based posterior sampling framework that inherently produces well‑calibrated predictive uncertainty via faithful posterior sampling, eliminating the need for post‑hoc calibration. In extensive evaluations on synthetic distributions, the Raman‑based phenylacetic acid soft sensor benchmark, and a real ammonia synthesis case study, our method achieves practical improvements over existing UQ techniques in both uncertainty calibration and predictive accuracy. These results highlight diffusion samplers as a principled and scalable paradigm for advancing uncertainty‑aware modeling in industrial applications.

Authors:Pantelis Dogoulis, Maxime Cordy
Title: Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Grids
Abstract:
Topology control for power grid operation is a challenging sequential decision making problem because the action space grows combinatorially with the size of the grid and action evaluation through simulation is computationally expensive. We propose a physics‑informed Reinforcement Learning framework that combines semi‑Markov control with a Gibbs prior, that encodes the system's physics, over the action space. The decision is only taken when the grid enters a hazardous regime, while a graph neural network surrogate predicts the post action overload risk of feasible topology actions. These predictions are used to construct a physics‑informed Gibbs prior that both selects a small state‑dependent candidate set and reweights policy logits before action selection. In this way, our method reduces exploration difficulty and online simulation cost while preserving the flexibility of a learned policy. We evaluate the approach in three realistic benchmark environments of increasing difficulty. Across all settings, the proposed method achieves a strong balance between control quality and computational efficiency: it matches oracle‑level performance while being approximately 6× faster on the first benchmark, reaches 94.6% of oracle reward with roughly 200× lower decision time on the second one, and on the most challenging benchmark improves over a PPO baseline by up to 255% in reward and 284% in survived steps while remaining about 2.5× faster than a strong specialized engineering baseline. These results show that our method provides an effective mechanism for topology control in power grids.

Authors:Seyed Soroush Karimi Madahi, Kenneth Bruninx, Bert Claessens, Chris Develder
Title: Neural Network-Assisted Model Predictive Control for Implicit Balancing
Abstract:
In Europe, balance responsible parties can deliberately take out‑of‑balance positions to support transmission system operators (TSOs) in maintaining grid stability and earn profit, a practice called implicit balancing. Model predictive control (MPC) is widely adopted as an effective approach for implicit balancing. The balancing market model accuracy in MPC is critical to decision quality. Previous studies modeled this market using either (i) a convex market clearing approximation, ignoring proactive manual actions by TSOs and the market sub‑quarter‑hour dynamics, or (ii) machine learning methods, which cannot be directly integrated into MPC. To address these shortcomings, we propose a data‑driven balancing market model integrated into MPC using an input convex neural network to ensure convexity while capturing uncertainties. To keep the core network computationally efficient, we incorporate attention‑based input gating mechanisms to remove irrelevant data. Evaluating on Belgian data shows that the proposed model both improves MPC decisions and reduces computational time.

Authors:Johannes Böhm, Eric Schöneberg, Kevin Schmidt, Wolfgang Fischer, Daniel Görges
Title: Cooperative Adaptive Cruise Control with Variable Time Headway for Graceful Degradation under Fluctuating Network Quality of Service
Abstract:
This paper proposes a dynamic distance adaptation for Cooperative Adaptive Cruise Control (CACC) under time‑varying network conditions. When the Quality of Service (QoS) drops below a level required to maintain desired inter‑vehicle distances, an online adaptation of the reference distances, reflected by a change of the time headway factor, becomes necessary. We present a control design algorithm realizing a graceful degradation, for which a distance control to a virtual preceding vehicle is introduced. Furthermore, the Integral Quadratic Constraints (IQC) framework is applied to guarantee robust stability of the time‑varying system. The concept is validated in simulation and experimentally using small‑scale test vehicles.

Authors:Gianni Cario, Valentino Carriuolo, Alessandro Casavola, Gianfranco Gagliardi, Marco Lupia, Franco Angelo Torchiaro
Title: Set-Theoretic Receding Horizon Control for Obstacle Avoidance and Overtaking in Autonomous Highway Driving
Abstract:
This article addresses obstacle avoidance motion planning for autonomous vehicles, specifically focusing on highway overtaking maneuvers. The control design challenge is handled by considering a mathematical vehicle model that captures both lateral and longitudinal dynamics. Unlike existing numerical optimization methods that suffer from significant online computational overhead, this work extends the state‑of‑the‑art by leveraging a fast set‑theoretic ellipsoidal Model Predictive Control (Fast‑MPC) technique. While originally restricted to stabilization tasks, the proposed framework is successfully adapted to handle motion planning for vehicles modeled as uncertain polytopic discrete‑time linear systems. The control action is computed online via a set‑membership evaluation against a structured sequence of nested inner ellipsoidal approximations of the exact one‑step ahead controllable set within a receding horizon framework. A six‑degrees‑of‑freedom (6‑DOF) nonlinear model characterizes the vehicle dynamics, while a polytopic embedding approximates the nonlinearities within a linear framework with parameter uncertainties. Finally, to assess performance and real‑time feasibility, comparative co‑simulations against a baseline Non‑Linear MPC (NLMPC) were conducted. Using the high‑fidelity CARLA 3D simulator, results demonstrate that the proposed approach seamlessly rejects dynamic traffic disturbances while reducing online computational time by over 90% compared to standard optimization‑based approaches.

Authors:Weiqi Meng, Hongyi Li, Bai Cui
Title: Day-Ahead Offering for Virtual Power Plants: A Stochastic Linear Programming Reformulation and Projected Subgradient Method
Abstract:
Virtual power plants (VPPs) are an emerging paradigm that aggregates distributed energy resources (DERs) for coordinated participation in power systems, including bidding as a single dispatchable entity in the wholesale market. In this paper, we address a critical operational challenge for VPPs: the day‑ahead offering problem under highly intermittent and uncertain DER outputs and market prices. The day‑ahead offering problem determines the price‑quantity pairs submitted by VPPs while balancing profit opportunities against operational uncertainties. First, we formulate the problem as a scenario‑based two‑stage stochastic adaptive robust optimization problem, where the uncertainty of the locational marginal prices follows a Markov process and DER uncertainty is characterized by static uncertainty sets. Then, motivated by the outer approximation principle of the column‑and‑constraint generation (CC&G) algorithm, we propose a novel inner approximation‑based projected subgradient method. By exploiting the problem structure, we propose two novel approaches to improve computational tractability. First, we show that under mild modeling assumptions, the robust second‑stage problem can be equivalently reformulated as a linear program (LP) with a nested resource allocation structure that is amenable to an efficient greedy algorithm. Furthermore, motivated by the computational efficiency of solving the reformulated primal second‑stage problem and the isotonic structure of the first‑stage feasible region, we propose an efficient projected subgradient algorithm to solve the overall stochastic LP problem. Extensive computational experiments using real‑world data demonstrate that the overall projected subgradient descent method achieves about two orders of magnitude speedup over CC&G while maintaining solution quality.

Authors:David Grasev
Title: Koopman-Based Nonlinear Identification and Adaptive Control of a Turbofan Engine
Abstract:
This paper investigates Koopman operator‑based approaches for multivariable control of a two‑spool turbofan engine. A physics‑based component‑level model is developed to generate training data and validate the controllers. A meta‑heuristic extended dynamic mode decomposition is developed, with a cost function designed to accurately capture both spool‑speed dynamics and the engine pressure ratio (EPR), enabling the construction of a single Koopman model suitable for multiple control objectives. Using the identified time‑varying Koopman model, two controllers are developed: an adaptive Koopman‑based model predictive controller (AKMPC) with a disturbance observer and a Koopman‑based feedback linearization controller (K‑FBLC), which serves as a benchmark. The controllers are evaluated for two control strategies, namely configurations of spool speeds and EPR, under both sea‑level and varying flight conditions. The results demonstrate that the proposed identification approach enables accurate predictions of both spool speeds and EPR, allowing the Koopman model to be reused flexibly across different control formulations. While both control strategies achieve comparable performance in steady conditions, the AKMPC exhibits superior robustness compared with the K‑FBLC under varying flight conditions due to its ability to compensate for model mismatch. Moreover, the EPR control strategy improves the thrust response. The study highlights the applicability of Koopman‑based control and demonstrates the advantages of the AKMPC‑based framework for robust turbofan engine control.

Authors:Ahmet Sacid Sümer, Ebubekir Memişoğlu, Hüseyin Arslan
Title: Phase-Shifted Pilot Design for NOMA-Empowered Uplink ISAC Systems
Abstract:
The deployment of multiple transmitters (TXs) in integrated sensing and communication (ISAC) networks necessitates efficient resource sharing to overcome the limitations of orthogonal allocation. While conventional interleaved (CI) pilots combined with non‑orthogonal multiple access (NOMA) improve spectral efficiency (SE), they inherently compromise sensing resolution due to spectral sparsity, rendering the CI nulling (CIN) extension a strictly limited remedy. This paper proposes a phase‑shifted (PS) pilot design and its novel PS nulling (PSN) variant to integrate a communication TX (CTX) over the PS‑ISAC framework. The PSN variant strategically punctures sensing signals at CTX pilot locations to preserve initial channel estimates, enabling a dense data overlay. To resolve the resulting multi‑TX interference, joint iterative interference cancellation (IIC) is adapted for non‑nulling configurations and sequential IIC is adapted for nulling variants, optimizing for both detection robustness and convergence speed. Simulation results across varying STX densities and modulation orders demonstrate that the phase‑shifted frameworks maintain sensing integrity while explicitly reducing receiver‑side computational complexities by 18.8% and 21.0% against their respective interleaved baselines.

Authors:Hyeonyeong Jang, Jin Gyu Lee
Title: Steady-state response assignment for a given disturbance and reference: Sylvester equation rather than regulator equations
Abstract:
Conventionally, the concept of moment has been primarily employed in model order reduction to approximate system by matching the moment, which is merely the specific set of steady‑state responses. In this paper, we propose a novel design framework that extends this concept from "moment matching" for approximation to "moment assignment" for the active control of steady‑state. The key observation is that the closed‑loop moment of an interconnected linear system can be decomposed into the open‑loop moment and a term linearly parameterized by the moment of the compensator. Based on this observation, we provide necessary and sufficient conditions for the assignability of desired moment and a canonical form of the dynamic compensator, followed by constructive synthesis procedure of compensator. This covers both output regulation and closed‑loop interpolation, and further suggests using only the Sylvester equation, rather than regulator equations.

Authors:Moh Kamalul Wafi, Arthur C. B. de Oliveira, Eduardo D. Sontag
Title: When is cumulative dose response monotonic? Analysis of incoherent feedforward motifs
Abstract:
We study the monotonicity of the cumulative dose response (cDR) for a class of incoherent feedforward motifs (IFFM) systems with linear intermediate dynamics and nonlinear output dynamics. While the instantaneous dose response (DR) may be nonmonotone with respect to the input, the cDR can still be monotone. To analyze this phenomenon, we derive an integral representation of the sensitivity of cDR with respect to the input and establish general sufficient conditions for both monotonicity and non‑monotonicity. These results reduce the problem to verifying qualitative sign properties along system trajectories. We apply this framework to four canonical IFFM systems and obtain a complete characterization of their behavior. In particular, IFFM1 and IFFM3 exhibit monotone cDR despite potentially non‑monotone DR, while IFFM2 is monotone already at the level of DR, which implies monotonicity of cDR. In contrast, IFFM4 violates these conditions, leading to a loss of monotonicity. Numerical simulations indicate that these properties persist beyond the structured initial conditions used in the analysis. Overall, our results provide a unified framework for understanding how network structure governs monotonicity in cumulative input‑output responses.

Authors:Dimitrios Moustroufis, Panagiotis Tsiotras
Title: Data-Driven Covariance Steering with Output Feedback
Abstract:
This paper addresses the problem of output‑feedback covariance steering for stochastic, discrete‑time, linear, time‑invariant systems without knowledge of the system model. We employ a controllable, non‑minimal state representation constructed from past inputs and outputs and convert the problem to one in state‑feedback form. In this representation, the induced disturbance becomes temporally correlated, which requires explicit propagation of the cross‑covariance between the state and disturbance processes. To handle the lack of a system model, we leverage persistently exciting data collected offline and formulate the mean and covariance steering problems using an indirect and a direct approach, respectively. The indirect formulation requires an estimate of the mean dynamics model, while the direct formulation relies on an estimate of the noise realization in the collected data. To this end, we present an estimation method suitable to handle temporally correlated noise, enabling consistent identification of both components. Using a convex relaxation, we convert the covariance steering problem to a semidefinite program that can be solved efficiently. We conduct numerical simulations to evaluate the performance of the developed framework.

Authors:Viet-Anh Le, Renukanandan Tumu, Rahul Mangharam
Title: Toward Single-Step MPPI via Differentiable Predictive Control
Abstract:
Model predictive path integral (MPPI) is a sampling‑based method for solving complex model predictive control (MPC) problems, but its real‑time implementation faces two key challenges: the computational cost and sample requirements grow with the prediction horizon, and manually tuning the sampling covariance requires balancing exploration and noise. To address these issues, we propose Step‑MPPI, a framework that learns a sampling distribution for efficient single‑step lookahead MPPI implementation. Specifically, we use a neural network to parameterize the MPPI proposal distribution at each time step, and train it in a self‑supervised manner over a long horizon using the MPC cost, constraint penalties, and a maximum‑entropy regularization term. By embedding long‑horizon objectives into training the neural distribution policy, Step‑MPPI achieves the foresight of a multi‑step optimizer with the millisecond‑level latency of single‑step lookahead. We demonstrate the efficiency of Step‑MPPI across multiple challenging tasks in which MPPI suffers from high dimensionality and/or long control horizons.

Authors:Chenjin Wang, Zheng Yan, Yanmin Zhou, Runjie Shen, Bin He
Title: MorphoGuard: A Morphology-Based Whole-Body Interactive Motion Controller
Abstract:
Whole‑body control (WBC) has demonstrated significant advantages in complex interactive movements of high‑dimensional robotic systems. However, when a robot is required to handle dynamic multi‑contact combinations along a single kinematic chain‑such as pushing open a door with its elbow while grasping an object‑it faces major obstacles in terms of complex contact representation and joint configuration coupling. To address this, we propose a new control approach that explicitly manages arbitrary contact combinations, aiming to endow robots with whole‑body interactive capabilities. We develop a morphology‑constrained WBC network (MorphoGuard)‑which is trained on a self‑constructed dual‑arm physical and simulation platform. A series of model recommendation experiments are designed to systematically investigate the impact of backbone architecture, fusion strategy, and model scale on network performance. To evaluate the control performance, we adopt a multi‑object interaction task as the benchmark, requiring the model to simultaneously manipulate multiple target objects to specified positions. Experimental results show that the proposed method achieves a contact point management error of approximately 1 cm, demonstrating its effectiveness in whole‑body interactive control.

Authors:Julian Martinez, Kooktae Lee
Title: Feedforward Density-Driven Optimal Control for Tracking Time-Varying Distributions with Guaranteed Stability
Abstract:
This paper addresses the spatiotemporal mismatch in multi‑agent distribution tracking within time‑varying environments. While recent advancements in Density‑Driven Optimal Control (D^2OC) have enabled finite‑time distribution matching using Optimal Transport theory, existing formulations primarily assume a stationary reference density. In dynamic scenarios, such as tracking evolving wildfires or moving plumes, this assumption leads to a structural tracking lag where the agent configuration inevitably falls behind the shifting reference flow. To resolve this, we propose a feedforward‑augmented D^2OC framework that explicitly incorporates the reference velocity field, modeled via the continuity equation, into the control law. We provide a formal mathematical quantification of the induced tracking lag and analytically prove that the proposed predictive mechanism effectively reduces the cumulative tracking error. Furthermore, an analytical ultimate bound for the local Wasserstein distance is established under discretization errors and transport jitter. Theoretical analysis and numerical results demonstrate that our approach significantly mitigates tracking latency, ensuring robust and high‑fidelity tracking performance in rapidly changing environments.

Authors:Thomas Banker, Nathan P. Lawrence, Ali Mesbah
Title: Soft MPCritic: Amortized Model Predictive Value Iteration
Abstract:
Reinforcement learning (RL) and model predictive control (MPC) offer complementary strengths, yet combining them at scale remains computationally challenging. We propose soft MPCritic, an RL‑MPC framework that learns in (soft) value space while using sample‑based planning for both online control and value target generation. soft MPCritic instantiates MPC through model predictive path integral control (MPPI) and trains a terminal Q‑function with fitted value iteration, aligning the learned value function with the planner and implicitly extending the effective planning horizon. We introduce an amortized warm‑start strategy that recycles planned open‑loop action sequences from online observations when computing batched MPPI‑based value targets. This makes soft MPCritic computationally practical, while preserving solution quality. soft MPCritic plans in a scenario‑based fashion with an ensemble of dynamic models trained for next‑step prediction accuracy. Together, these ingredients enable soft MPCritic to learn effectively through robust, short‑horizon planning on classic and complex control tasks. These results establish soft MPCritic as a practical and scalable blueprint for synthesizing MPC policies in settings where policy extraction and direct, long‑horizon planning may fail.

Authors:Dhruv Shah, Jorge Cortes
Title: Koopman Subspace Pruning in Reproducing Kernel Hilbert Spaces via Principal Vectors
Abstract:
Data‑driven approximations of the infinite‑dimensional Koopman operator rely on finite‑dimensional projections, where the predictive accuracy of the resulting models hinges heavily on the invariance of the chosen subspace. Subspace pruning systematically discards geometrically misaligned directions to enhance this invariance proximity, which formally corresponds to the largest principal angle between the subspace and its image under the operator. Yet, existing techniques are largely restricted to Euclidean settings. To bridge this gap, this paper presents an approach for computing principal angles and vectors to enable Koopman subspace pruning within a Reproducing Kernel Hilbert Space (RKHS) geometry. We first outline an exact computational routine, which is subsequently scaled for large datasets using randomized Nystrom approximations. Based on these foundations, we introduce the Kernel‑SPV and Approximate Kernel‑SPV algorithms for targeted subspace refinement via principal vectors. Simulation results validate our approach.

Authors:Victor Hugo Pereira Rodrigues, Tiago Roux Oliveira, Miroslav Krstić, Frank Allgöwer
Title: Discrete-Time Event-Triggered Extremum Seeking
Abstract:
This paper proposes a discrete‑time event‑triggered extremum seeking control scheme for real‑time optimization of nonlinear systems. Unlike conventional discrete‑time implementations relying on periodic updates, the proposed approach updates the control input only when a state‑dependent triggering condition is satisfied, reducing unnecessary actuation and communication. The resulting closed‑loop system combines extremum seeking with an event‑triggering mechanism that adaptively determines the input update instants. Using discrete‑time averaging and Lyapunov analysis, we establish practical convergence of the trajectories to a neighborhood of the unknown extremum point and show exponential stability of the associated average dynamics. The proposed method preserves the optimization capability of classical extremum seeking while significantly reducing the number of input updates. Simulation results illustrate the effectiveness of the approach for resource‑aware real‑time optimization.

Authors:Yi Lok Lo, Longhao Qian, Hugh H. T. Liu
Title: Neural Robust Control on Lie Groups Using Contraction Methods (Extended Version)
Abstract:
In this paper, we propose a learning framework for synthesizing a robust controller for dynamical systems evolving on a Lie group. A robust control contraction metric (RCCM) and a neural feedback controller are jointly trained to enforce contraction conditions on the Lie group manifold. Sufficient conditions are derived for the existence of such an RCCM and neural controller, ensuring that the geometric constraints imposed by the manifold structure are respected while establishing a disturbance‑dependent tube that bounds the output trajectories. As a case study, a feedback controller for a quadrotor is designed using the proposed framework. Its performance is evaluated using numerical simulations and compared with a geometric controller.

Authors:Georgiy A. Bondar, Abigail Eisenklam, Yifan Cai, Robert Gifford, Tushar Sial, Linh Thi Xuan Phan, Abhishek Halder
Title: Generative Profiling for Soft Real-Time Systems and its Applications to Resource Allocation
Abstract:
Modern real‑time systems require accurate characterization of task timing behavior to ensure predictable performance, particularly on complex hardware architectures. Existing methods, such as worst‑case execution time analysis, often fail to capture the fine‑grained timing behaviors of a task under varying resource contexts (e.g., an allocation of cache, memory bandwidth, and CPU frequency), which is necessary to achieve efficient resource utilization. In this paper, we introduce a novel generative profiling approach that synthesizes context‑dependent, fine‑grained timing profiles for real‑time tasks, including those for unmeasured resource allocations. Our approach leverages a nonparametric, conditional multi‑marginal Schrödinger Bridge (MSB) formulation to generate accurate execution profiles for unseen resource contexts, with maximum likelihood guarantees. We demonstrate the efficiency and effectiveness of our approach through real‑world benchmarks, and showcase its practical utility in a representative case study of adaptive multicore resource allocation for real‑time systems.

Authors:Daran Xu, Amirhossein Taghvaei
Title: Causal Optimal Coupling for Gaussian Input-Output Distributional Data
Abstract:
We study the problem of identifying an optimal coupling between input‑output distributional data generated by a causal dynamical system. The coupling is required to satisfy prescribed marginal distributions and a causality constraint reflecting the temporal structure of the system. We formulate this problem as a Schr"odinger Bridge, which seeks the coupling closest ‑ in Kullback‑Leibler divergence ‑ to a given prior while enforcing both marginal and causality constraints. For the case of Gaussian marginals and general time‑dependent quadratic cost functions, we derive a fully tractable characterization of the Sinkhorn iterations that converges to the optimal solution. Beyond its theoretical contribution, the proposed framework provides a principled foundation for applying causal optimal transport methods to system identification from distributional data.

Authors:Zishun Liu, Liqian Ma, Hongzhe Yu, Yongxin Chen
Title: Concentration of Stochastic System Trajectories with Time-varying Contraction Conditions
Abstract:
We establish two concentration inequalities for nonlinear stochastic system under time‑varying contraction conditions. The key to our approach is an energy function termed Averaged Moment Generating Function (AMGF). By combining it with incremental stability analysis, we develop a concentration inequality that bounds the deviation between the stochastic system state and its deterministic counterpart. As this inequality is restricted to single time instance, we further combine AMGF with martingale‑based methods to derive a concentration inequality that bounds the fluctuation of the entire stochastic trajectory. Additionally, by synthesizing the two results, we significantly improve the trajectory‑level concentration inequality for strongly contractive systems. Given the probability level 1‑δ, the derived inequalities ensure an \mO(\sqrt\log(1/δ)) bound on the deviation of stochastic trajectories, which is tight under our assumptions. Our results are exemplified through a case study on stochastic safe control.

Authors:Yiting Chen, Pol Mestres, Emiliano Dall'Anese, Jorge Cortés
Title: Safe Policy Optimization via Control Barrier Function-based Safety Filters
Abstract:
Control barrier function (CBF)‑based safety filters provide a systematic way to enforce state constraints, but they can significantly alter the closed‑loop dynamics induced by a nominal, stabilizing controller. In particular, the resulting safety‑filtered system may exhibit undesirable behaviors including limit cycles, unbounded trajectories, and undesired equilibria. This paper develops a policy optimization framework to maximally enhance the stability properties of safety‑filtered controllers. Focusing on linear systems with linear nominal controllers, we jointly parameterize the nominal feedback gain and safety‑filter components, and optimize them using trajectory‑based objectives computed from closed‑loop rollouts. To ensure that the nominal controller remains stabilizing throughout training, we encode Lyapunov‑based stability conditions as smooth scalar constraints and enforce them using robust safe gradient flow. This guarantees feasibility of the stability constraints along the optimization iterates and therefore avoids instability during training. Numerical experiments on obstacle‑avoidance problems show that the proposed approach can remove asymptotically stable undesired equilibria and improve convergence behavior while maintaining forward invariance of the safe set.

Authors:Xiuzhen Ye, Wentao Tang
Title: Dissipativity Analysis of Nonlinear Systems: A Linear--Radial Kernel-based Approach
Abstract:
Estimating the dissipativity of nonlinear systems from empirical data is useful for the analysis and control of nonlinear systems, especially when an accurate model is unavailable. Based on a Koopman operator model of the nonlinear system on a reproducing kernel Hilbert space (RKHS), the storage function and supply rate functions are expressed as kernel quadratic forms, through which the dissipative inequality is expressed as a linear operator inequality. The RKHS is specified by a linear‑‑radial kernel, which inherently encode the information of equilibrium point, thus ensuring that all functions in the RKHS are locally at least linear around the origin and that kernel quadratic forms are locally at least quadratic, which expressively generalize conventional quadratic forms including sum‑of‑squares polynomials. Based on the kernel matrices of the sampled data, the dissipativity estimation can be posed as a finite‑dimensional convex optimization problem, and a statistical learning bound can be derived on the kernel quadratic form for the probabilistic approximate correctness of dissipativity estimation.

Authors:Alessandro Riccardi, Thom Badings, Luca Laurenti, Alessandro Abate, Bart De Schutter
Title: Temporal Logic Control of Nonlinear Stochastic Systems with Online Performance Optimization
Abstract:
The deployment of autonomous systems in safety‑critical environments requires control policies that guarantee satisfaction of complex control specifications. These systems are commonly modeled as nonlinear discrete‑time stochastic systems. A~popular approach to computing a policy that provably satisfies a complex control specification is to construct a finite‑state abstraction, often represented as a Markov decision process (MDP) with intervals of transition probabilities, i.e., an interval MDP (IMDP). However, existing abstraction techniques compute a \emphsingle policy, thus leaving no room for online cost or performance optimization, e.g., of energy consumption. To overcome this limitation, we propose a novel IMDP abstraction technique that yields a \emphset of policies, each of which satisfies the control specification with a certain minimum probability. We can thus use any online control algorithm to search through this set of verified policies while retaining the guaranteed satisfaction probability of the entire policy set. In particular, we employ model predictive control (MPC) to minimize a desired cost function that is independent of the control specification considered in the abstraction. Our experiments demonstrate that our approach yields better control performance than state‑of‑the‑art single‑policy abstraction techniques, with a small degradation of the guarantees.

Authors:Cédric Join, Luis Almeida, Michel Fliess
Title: Sterile mosquito release via intelligent proportional controllers
Abstract:
The Sterile Insect Technique (SIT) against insect pests and insect vectors consists of releasing males that have been previously sterilized in order to reduce or eliminate a specific wild population. We study this complex control question via model‑free control, ultra‑local models, and intelligent proportional controllers that have already proven their effectiveness in various fields. They permit addressing, perhaps for the first time, the essential sampling question. Computer simulations are displayed and discussed.

Authors:Bhuban Dhamala, Mona Ghassemi
Title: A Comprehensive Test System for Transmission Expansion Planning: Modeling N-1 Contingencies and Multi-Loading Scenarios
Abstract:
This paper presents a high‑voltage test system designed specifically for transmission expansion planning (TEP) and explores multiple TEP studies using this test system. The network incorporates long transmission lines, lines are accurately modeled, and line parameters are calculated using the equivalent π circuit model for long transmission lines to account for the distributed nature of line parameters. The paper provides detailed load flow analyses for both normal and all contingency conditions for three different loading conditions (peak load, dominant load, and light load), demonstrating that the proposed test system offers technically feasible load flow solutions at these loading scenarios. As the real power system is subject to various loading scenarios and should be effectively operable under all conditions, this test system accurately replicates the properties of real power systems. Furthermore, this paper presents multiple TEP cases to supply the load at a new location. TEP cases are conducted with different numbers of transmission line connections, and each case is underscored by its respective maximum capacity satisfying all technical requirements for normal and all single contingencies under three different scenarios. The cost of TEP for each case is calculated and compared in terms of the average cost per MW of power delivered to the new bus.

Authors:Rui Xu, Shanyin Tong, Xuan Di
Title: Risk Control of Traffic Flow Through Chance Constraints and Large Deviation Approximation
Abstract:
Existing macroscopic traffic control methods often struggle to strictly regulate rare, safety‑critical extreme events under stochastic disturbances. In this paper, we develop a rare chance‑constrained optimal control framework for autonomous traffic management. To efficiently enforce these probabilistic safety specifications, we exploit a large deviation theory (LDT) based approximation method, which converts the original highly non‑convex, sampling‑heavy optimization problem into a tractable deterministic nonlinear programming problem. In addition, the proposed LDT‑based reformulation exhibits superior computational scalability, as it maintains a constant computational burden regardless of the target violation probability level, effectively bypassing the extreme scaling bottlenecks of traditional sampling‑based methods. The effectiveness of the proposed framework in achieving precise near‑target probability control and superior computational efficiency over risk‑averse baselines is illustrated through extensive numerical simulations across diverse traffic risk measures.

Authors:Cameron Khanpour, Daniel Turizo, Samuel Talkington
Title: Making Every Bit Count for $A$-Optimal State Estimation
Abstract:
We study the problem of controlling how a limited communication bandwidth budget is allocated across heterogeneously quantized sensor measurements. The performance criterion is the trace of the error covariance matrix of the linear minimum mean square error (LMMSE) state estimator, i.e., an A‑optimal design criterion. Minimizing this criterion with a bit budget constraint yields a nonconvex optimization problem. We derive a formula that reduces each evaluation of the gradient to a single Cholesky factorization. This enables efficient optimization by both a projection‑free Frank‑Wolfe method (with a computable convergence certificate) and an interior point method with L‑BFGS Hessian approximation over the problem's continuous relaxation. A largest remainder rounding procedure recovers integer bit allocations with a bound on the quality of the rounded solution. Numerical experiments in IEEE power grid test cases with up to 300 buses compare both solvers and demonstrate that the analytic gradient is the key computational enabler for both methods. Additionally, the heterogeneous bit allocation is compared to standard uniform bit allocation on the 500 bus IEEE power grid test case.

Authors:Neelay Junnarkar, Peter Seiler, Murat Arcak
Title: Polynomial Constraints for Robustness Analysis of Nonlinear Systems
Abstract:
This paper presents a framework for abstracting uncertain or non‑polynomial components of dynamical systems using polynomial constraints. This enables the application of polynomial‑based analysis tools, such as sum‑of‑squares programming, to a broader class of non‑polynomial systems. A numerical method for constructing these constraints is proposed. The relationship between polynomial constraints and existing integral quadratic constraints (IQCs) is investigated, providing transformations of IQCs into polynomial constraints. The effectiveness of polynomial constraints in characterizing nonlinearities is validated via numerical examples to compute inner estimates of the region of attraction for two systems.

Authors:Neelay Junnarkar, Yasin Sonmez, Murat Arcak
Title: Learning Neural Network Controllers with Certified Robust Performance via Adversarial Training
Abstract:
Neural network (NN) controllers achieve strong empirical performance on nonlinear dynamical systems, yet deploying them in safety‑critical settings requires robustness to disturbances and uncertainty. We present a method for jointly synthesizing NN controllers and dissipativity certificates that formally guarantee robust closed‑loop performance using adversarial training, in which we use counterexamples to the robust dissipativity condition to guide training. Verification is done post‑training using alpha,beta‑CROWN, a branch‑and‑bound‑based method that enables direct analysis of the nonlinear dynamical system. The proposed method uses quadratic constraints (QCs) only for characterization of non‑parametric uncertainties. The method is tested in numerical experiments on maximizing the volume of the set on which a system is certified to be robustly dissipative. Our method certifies regions up to 78 times larger than the region certified by a linear matrix inequality‑based approach that we derive for comparison.

Authors:Abdullah Tokmak, Toni Karvonen, Thomas B. Schön, Dominik Baumann
Title: Safe learning-based control via function-based uncertainty quantification
Abstract:
Uncertainty quantification is essential when deploying learning‑based control methods in safety‑critical systems. This is commonly realized by constructing uncertainty tubes that enclose the unknown function of interest, e.g., the reward and constraint functions or the underlying dynamics model, with high probability. However, existing approaches for uncertainty quantification typically rely on restrictive assumptions on the unknown function, such as known bounds on functional norms or Lipschitz constants, and struggle with discontinuities. In this paper, we model the unknown function as a random function from which independent and identically distributed realizations can be generated, and construct uncertainty tubes via the scenario approach that hold with high probability and rely solely on the sampled realizations. We integrate these uncertainty tubes into a safe Bayesian optimization algorithm, which we then use to safely tune control parameters on a real Furuta pendulum.

Authors:Amir Modares, Bahare Kiumarsi, Hamidreza Modares
Title: Data-based Low-conservative Nonlinear Safe Control Learning
Abstract:
This paper develops a data‑driven safe control framework for nonlinear discrete‑time systems with parametric uncertainty and additive disturbances. The proposed approach constructs a data‑consistent closed‑loop representation that enables controller synthesis and safety certification directly from data. Unlike existing methods that treat unmodeled nonlinearities as global worst‑case uncertainties using Lipschitz bounds, the proposed approach embeds nonlinear terms directly into the invariance conditions via a geometry‑aware difference‑of‑convex formulation. This enables facet‑ and direction‑specific convexification, avoiding both nonlinearity cancellation and the excessive conservatism induced by uniform global bounds. We further propose a vertex‑dependent controller construction that enforces convexity and contractivity conditions locally on the active facets associated with each vertex, thereby enlarging the class of certifiable invariant sets. For systems subject to additive disturbances, disturbance effects are embedded directly into the verification conditions through optimized, geometry‑dependent bounds, rather than via uniform margin inflation, yielding less conservative robust safety guarantees. As a result, the proposed methods can certify substantially larger safe sets, naturally accommodate joint state and input constraints, and provide data‑driven safety guarantees. The simulation results show a significant improvement in both nonlinearity tolerance and the size of the certified safe set.

Authors:Alexey Iskakov
Title: Spectral Decomposition of Discrete-Time Controllability Gramian and Its Inverse via System Eigenvalues
Abstract:
This paper develops a closed‑form spectral decomposition framework for the Gramian matrices of discrete‑time linear dynamical systems. The main results provide explicit decompositions of the discrete‑time controllability Gramian and its inverse in terms of the eigenvalues of the dynamics matrix, yielding a mode‑resolved representation of these matrices. In contrast to the more common use of aggregate Gramian characteristics, such as eigenvalues, singular values, determinants, and trace‑based metrics, the proposed approach describes the internal structure of the Gramian itself through contributions associated with individual modes and their pairwise combinations. The framework is extended further to the solution of the discrete‑time Lyapunov difference equation, placing the obtained formulas in a broader context relevant to the analysis and computation of time‑varying and nonlinear systems. In addition, the decomposition is generalized to systems whose dynamics matrix has multiple eigenvalues, enabling a closed‑form estimation of the effects of resonant interactions between eigenmodes. The proposed results provide a structural tool for the analysis of controllability, observability and stability in discrete‑time systems and complement existing Gramian‑based methods used in model reduction, estimation, actuator and sensor selection, and energy‑aware control. Beyond their theoretical interest, the derived decompositions may support the development of improved computational procedures and more informative performance criteria for a range of discrete‑time control problems.

Authors:George Rapakoulias, Fengjiao Liu, Panagiotis Tsiotras
Title: Schrodinger Bridges and Density Steering Problems for Gaussian Mixtures Models in Discrete-Time
Abstract:
In this work, we revisit the discrete‑time Schrödinger Bridge (SB) and Density Steering (DS) problems for Gaussian mixture model (GMM) boundary distributions. Building on the existing literature, we construct a set of feasible Markovian policies that transport the initial distribution to the final distribution, and are expressed as mixtures of elementary component‑to‑component optimal policies. We then study the policy optimization within this feasible set in the context of discrete‑time SBs and density‑steering problems, respectively. We show that for minimum‑effort density‑steering problems, the proposed policy achieves the same control cost as existing approaches in the literature. For discrete‑time SB problems, the proposed policy yields a cost smaller than or equal to that in the literature, resulting in a less conservative approximation. Finally, we study the continuous‑time limit of our proposed discrete‑time approach and show that it agrees with recently proposed approximations to the continuous‑time SB for GMM boundary distributions. We illustrate this new result through two numerical examples.

Authors:Carl R Richardson, Declan S Jagt, Matthew M Peet, Antonis Papachristodoulou
Title: A Distributed SOS Program For Local Stability Analysis of Polynomial PDEs in the PIE Representation
Abstract:
It has recently been shown that the evolution of a state, described by a Partial Differential Equation (PDE), can be more conveniently represented as the evolution of the state's highest spatial derivative (the ``fundamental state''), which lies in L_2 and has no boundary conditions (BCs) or continuity constraints. For linear PDEs, this yields a Partial Integral Equation (PIE) parametrized by Partial Integral (PI) operators mapping the fundamental state to the PDE state. In this paper, we show that for polynomial PDEs, the dynamics of the fundamental state can instead be compactly expressed as a distributed polynomial in the fundamental state, parametrized by a new tensor algebra of PI operators acting on the tensor product of the fundamental state. We further define a SOS parametrization of the distributed polynomial and use this to construct a distributed SOS program, for testing local stability of polynomial PDEs.

Authors:Tanmay Mishra, Mads R Almassalkhi
Title: Maximizing Power Flexibility of Hybrid Energy Systems for Capacity Market
Abstract:
Hybrid Energy Systems (HES), integrating generation sources, energy storage, and controllable loads, are well‑positioned to provide real‑time grid flexibility. However, quantifying this maximum flexibility is challenging due to renewable generation uncertainty and the complexity of power allocation across multiple assets in real time. This paper presents a rule‑based framework for characterizing HES flexibility and systematically allocating power among its constituent assets. The flexibility envelope defines the dynamic power boundary within which the HES can inject or absorb power without violating operational constraints. Shaped in real time by capacity bids, available solar generation, and power allocation protocol, it enables reliable and predictable HES participation in regulation markets. Depending on the operational objective, the framework supports both symmetric and asymmetric flexibility cases. Further, the proposed power‑allocation rule is benchmarked against an optimal dispatch, providing a performance reference under realistic conditions. Finally, state of charge drift correction control is presented to ensure sustained battery operation and system reliability. This work, therefore, offers a rigorous and practical framework for integrating HES into capacity markets through effective flexibility characterization.

Authors:Weihao Sun, Gehui Xu, Alessio Moreschini, Thomas Parisini, Andreas A. Malikopoulos
Title: A Functional Learning Approach for Team-Optimal Traffic Coordination
Abstract:
In this paper, we develop a kernel‑based policy iteration functional learning framework for computing team‑optimal strategies in traffic coordination problems. We consider a multi‑agent discrete‑time linear system with a cost function that combines quadratic regulation terms and nonlinear safety penalties. Building on the Hilbert space formulation of offline receding‑horizon policy iteration, we seek approximate solutions within a reproducing kernel Hilbert space, where the policy improvement step is implemented via a discrete Fréchet derivative. We further study the model‑free receding‑horizon scenario, where the system dynamics are estimated using recursive least squares, followed by updating the policy using rolling online data. The proposed method is tested in signal‑free intersection scenarios via both model‑based and model‑free simulations and validated in SUMO.

Authors:Armel Koulong, Ali Pakniyat
Title: Tube-Based Safety for Anticipative Tracking in Multi-Agent Systems
Abstract:
A tube‑based safety framework is presented for robust anticipative tracking in nonlinear Brunovsky multi‑agent systems subject to bounded disturbances. The architecture establishes robust safety certificates for a feedforward‑augmented ancillary control policy. By rendering the state‑deviation dynamics independent of the agents' internal nonlinearities, the formulation strictly circumvents the restrictive Lipschitz‑bound feasibility conditions otherwise required for robust stabilization. Consequently, this structure admits an explicit, closed‑form robust positively invariant (RPI) tube radius that systematically attenuates the exponential control barrier function (eCBF) tightening margins, thereby mitigating constraint conservatism while preserving formal forward invariance. Within the distributed model predictive control (MPC) layer, mapping the local tube radii through the communication graph yields a closed‑form global formation error bound formulated via the minimum singular value of the augmented Laplacian. Robust inter‑agent safety is enforced with minimal communication overhead, requiring only a single scalar broadcast per neighbor at initialization. Numerical simulations confirm the framework's efficacy in safely navigating heterogeneous formations through cluttered environments.

Authors:Avalpreet Singh Brar, Rong Su, Jaskaranveer Kaur, Gioele Zardini
Title: Mean-Field Control of Adherence in Participation-Coupled Vehicle Rebalancing Systems
Abstract:
Human driver participation is a critical source of uncertainty in Mobility‑on‑Demand (MoD) rebalancing. Drivers follow platform recommendations probabilistically, and their willingness to comply evolves with experienced outcomes. This creates a closed‑loop feedback in which stronger recommendations increase participation, participation increases congestion, congestion lowers allocation success, and realized allocations update adherence beliefs. We propose a microscopic stochastic model that couples (i) belief‑driven participation, (ii) Poisson demand, (iii) uniform matching, and (iv) Beta‑‑Bernoulli belief updates. Under a large‑population closure, we derive a deterministic mean‑field recursion for the population adherence state under platform actuation. For i.i.d. Poisson demand and constant recommendation intensity, we prove global well‑posedness and invariance of the recursion, establish equilibrium existence, provide uniqueness conditions, and show global convergence in the regime where platform recommendations are no weaker than baseline participation. We then define steady‑state adherence and throughput, characterize the induced performance frontier, and show that adherence and throughput cannot, in general, be simultaneously maximized under uniform time‑invariant actuation. This yields a throughput‑maximization problem with an adherence floor. Exploiting the monotone frontier structure, we show the optimal uniform time‑invariant policy is the maximal feasible recommendation intensity and provide an efficient bisection‑based algorithm.

Authors:Efstathios Iliakis, Wallace Gian Yion Tan, Liang Wu, Jan Drgona, Richard D. Braatz
Title: Polynomial Parametric Koopman Operators for Stochastic MPC
Abstract:
This paper develops a parametric Koopman operator framework for Stochastic Model Predictive Control (SMPC), where the Koopman operator is parametrized by Polynomial Chaos Expansions (PCEs). The model is learned from data using the Extended Dynamic Mode Decomposition ‑‑ Dictionary Learning (EDMD‑DL) method, which preserves the convex least‑squares structure for the PCE coefficients of the EDMD matrix. Unlike conventional stochastic Galerkin projection approaches, we derive a condensed deterministic reformulation of the SMPC problem whose dimension scales only with the control horizon and input dimension, and is independent of both the lifted state dimension and the number of retained PCE terms. Our framework, therefore, enables efficient nonlinear SMPC problems with expectation and second‑order moment constraints with standard convex optimization solvers. Numerical examples demonstrate the efficacy of our framework for uncertainty‑aware SMPC of nonlinear systems.

Authors:Kai Kang, Feng Liu
Title: Dispatch-Embedded Long-Term Tail Risk Assessment and Mitigation via CVaR for Renewable Power Systems
Abstract:
Renewable energy (RE) generation exhibits pronounced seasonality and variability, and neglecting these features can lead to significant underestimation of long‑term power system risks in power supply. While long‑term dispatch strategies are essential for evaluating and mitigating tail risks, they are often excluded from existing models due to their complexity. This paper proposes a long‑term tail risk assessment and mitigation framework for renewable power systems, explicitly embedding dispatch strategies. A representative scenario generation method is designed, combining multi‑timescale Copula modeling to capture RE's long‑range variability and correlation. Building on these scenarios, an evolution‑based risk assessment model is established, where Conditional Value‑at‑Risk (CVaR) is employed as a robust metric to quantify tail risks. Finally, a controlled evolution‑based risk mitigation scheme is introduced to refine long‑term dispatch strategies for mitigating tail risks. Case studies on a modified IEEE‑39 bus system incorporating real‑world data substantiate the efficacy of the proposed method.

Authors:András Sasfi, Jaap Eising, Florian Dörfler
Title: Soft projections for robust data-driven control
Abstract:
We consider data‑based predictive control based on behavioral systems theory. In the linear setting this means that a system is described as a subspace of trajectories, and predictive control can be formulated using a projection onto the intersection of this behavior and a constraint set. Instead of learning the model, or subspace, we focus on determining this projection from data. Motivated by the use of regularization in data‑enabled predictive control (DeePC), we introduce the use of soft projections, which approximate the true projector onto the behavior from noisy data. In the simplest case, these are equivalent to known regularized DeePC schemes, but they exhibit a number of benefits. First, we provide a bound on the approximation error consisting of a bias and a variance term that can be traded‑off by the regularization weight. The derived bound is independent of the true system order, highlighting the benefit of soft projections compared to low‑dimensional subspace estimates. Moreover, soft projections allow for intuitive generalizations, one of which we show has superior performance on a case study. Finally, we provide update formulas for soft projectors enabling the efficient adaptation of the proposed data‑driven control methods in the case of streaming data.

Authors:Joschua Wüthrich, Romir Damle, Giona Fieni, Melanie N. Zeilinger, Christopher H. Onder, Andrea Carron
Title: Bridging RL and MPC for mixed-integer optimal control with application to Formula 1 race strategies
Abstract:
We propose a hybrid reinforcement learning (RL) and model predictive control (MPC) framework for mixed‑integer optimal control, where discrete variables enter the cost and dynamics but not the constraints. Existing hierarchical approaches use RL only for the discrete action space, leaving continuous optimization to MPC. Unlike these methods, we train the RL agent on the full hybrid action space, ensuring consistency with the cost of the underlying Markov decision process. During deployment, the RL actor is rolled out over the prediction horizon to parametrize an integer‑free nonlinear MPC through the discrete action sequence and provide a continuous warm‑start. The learned critic serves as a terminal cost to capture long‑term performance. We prove recursive feasibility, and validate the framework on a Formula 1 race strategy problem. The hybrid method achieves near‑optimal performance relative to an offline mixed‑integer nonlinear program benchmark, outperforming a standalone RL agent. Moreover, the hybrid scheme enables adaptation to unseen disturbances through modular MPC extensions at zero retraining cost.

Authors:Shreyas Bharadwaj, Bamdev Mishra, Cyrus Mostajeran, Alberto Padoan, Jeremy Coulson, Ravi Banavar
Title: Min-Max Grassmannian Optimization for Online Subspace Tracking
Abstract:
This paper discusses robustness guarantees for online tracking of time‑varying subspaces from noisy data. Building on recent work in optimization over a Grassmannian manifold, we introduce a new approach for robust subspace tracking by modeling data uncertainty in a Grassmannian ball. The robust subspace tracking problem is cast into a min‑max optimization framework, for which we derive a closed‑form solution for the worst‑case subspace, enabling a geometric robustness adjustment that is both analytically tractable and computationally efficient, unlike iterative convex relaxations. The resulting algorithm, GeRoST (Geometrically Robust Subspace Tracking), is validated on two case studies: tracking a linear time‑varying system and online foreground‑background separation in video.

Authors:Jingyuan Zhou, Yuexuan Wang, Kaidi Yang
Title: Neural Vector Lyapunov-Razumikhin Certificates for Delayed Interconnected Systems
Abstract:
Ensuring scalable input‑to‑state stability (sISS) is critical for the safety and reliability of large‑scale interconnected systems, especially in the presence of communication delays. While learning‑based controllers can achieve strong empirical performance, their black‑box nature makes it difficult to provide formal and scalable stability guarantees. To address this gap, we propose a framework to synthesize and verify neural vector Lyapunov‑Razumikhin certificates for discrete‑time delayed interconnected systems. Our contributions are three‑fold. First, we establish a sufficient condition for discrete‑time sISS via vector Lyapunov‑Razumikhin functions, which enables certification for large‑scale delayed interconnected systems. Second, we develop a scalable synthesis and verification framework that learns the neural certificates and verifies the certificates on reachability‑constrained delay domains with scalability analysis. Third, we validate our approach on mixed‑autonomy platoons, drone formations, and microgrids against multiple baselines, showing improved verification efficiency with competitive control performance.

Authors:Mehrdad Salimnejad, Anthony Ephremides, Marios Kountouris, Nikolaos Pappas
Title: Optimal Sampling and Actuation Policies of a Markov Source over a Wireless Channel
Abstract:
This paper studies efficient data management and timely information dissemination for real‑time monitoring of an N‑state Markov process, enabling accurate state estimation and reliable actuation decisions. First, we analyze the Age of Incorrect Information (AoII) and derive closed‑form expressions for its time average under several scheduling policies, including randomized stationary, change‑aware randomized stationary, semantics‑aware randomized stationary, and threshold‑aware randomized stationary policies. We then formulate and solve constrained optimization problems to minimize the average AoII under a time‑averaged sampling action constraint, and compare the resulting optimal sampling and transmission policies to identify the conditions under which each policy is most effective. We further show that directly using reconstructed states for actuation can degrade system performance, especially when the receiver is uncertain about the state estimate or when actuation is costly. To address this issue, we introduce a cost function, termed the Cost of Actions under Uncertainty (CoAU), which determines when the actuator should take correct actions and avoid incorrect ones when the receiver is uncertain about the reconstructed source state. We propose a randomized actuation policy and derive a closed‑form expression for the probability of taking no incorrect action. Finally, we formulate an optimization problem to find the optimal randomized actuation policy that maximizes this probability. The results show that the resulting policy substantially reduces incorrect actuator actions.

Authors:Julian Geis, Michael Lindner, Tom Brown
Title: Managing the Mismatch: The Role of Flexibility on the Path to a Carbon-Neutral Energy System
Abstract:
A rapid expansion of system flexibility is essential to integrate increasing shares of renewable energy into future energy systems. However, flexibility needs and technology‑specific contributions to flexibility remain poorly quantified in energy system modelling. Existing methods are not widely applied, leaving key questions unanswered: which flexibility technologies are critical for climate neutrality, and what are the cost implications of alternative deployment strategies? To address this gap, we apply a correlation‑based flexibility metric to a high‑resolution, sector‑coupled model of the German energy system, covering its transformation towards climate neutrality. For our default scenario, we find that daily flexibility needs increase by a factor of 3.7 between 2025 and 2045, driven primarily by the expansion of solar PV. By 2045, stationary batteries provide 38% of daily flexibility, while flexible electric vehicle charging contributes 30%. Systems with constrained flexibility increase system costs by 6.9%, electricity prices by 14 EUR/MWh and trigger 47% higher hydrogen and e‑fuel imports compared to an unconstrained system in 2045. In contrast, scenarios with high shares of flexible electric vehicle charging, vehicle‑to‑grid, and industrial demand‑side management achieve system cost reductions of 3.3%, while also reducing import dependence. Higher flexibility also reduces electricity price ranges, decreases average electricity prices by 3 EUR/MWh, and reduces backup capacity by 22% (22 GW). Overall, our results highlight the decisive role of specific flexibility technologies in achieving cost‑efficient and energy‑secure climate‑neutral energy systems, providing quantitative guidance for policy and investment decisions.

Authors:Weijie Xia, James Ciyu Qin, Edgar Mauricio Salazar Duque, Hongjin Du, Peter Palensky, Giovanni Sansavini, Pedro P. Vergara
Title: Analytical Probabilistic Power Flow Approximation Using Invertible Neural Networks
Abstract:
Probabilistic power flow (PPF) is essential for quantifying operational uncertainty in modern distribution systems with high penetration of renewable generation and flexible loads. Conventional PPF methods primarily rely on Monte Carlo (MC) based power flow (PF) simulations or simplified analytical approximations. While MC approaches are computationally intensive and demand substantial data storage, analytical approximations often compromise accuracy. In this paper, we propose a novel analytical PPF framework that eliminates the dependence on MC‑based PF simulations and, in principle, enables an approximation of the analytical form of arbitrary voltage distributions. The core idea is to learn an explicit and invertible mapping between stochastic power injections and system voltages using invertible neural networks (INNs). By leveraging the Change of Variable Theorem, the proposed framework facilitates direct approximation of the analytical form of voltage probability distributions without repeated PF computations. Extensive numerical studies demonstrate that the proposed framework achieves state‑of‑the‑art performance both as a high‑accuracy PF solver and as an efficient analytical PPF estimator.

Authors:Carlos J. G. Rojas, Esteban Lage Cano, Leyla Özkan
Title: Explicit MPC for Parameter Dependent Linear Systems
Abstract:
This paper presents two explicit Model Predictive Control formulations for linear systems parameterized in terms of design variables. Such parameter dependent behavior commonly arises from operating point dependent linearization of nonlinear systems as well as from variations in mechanical, electrical, or thermal properties associated with material selection in the design of the process or system components. In contrast to explicit MPC approaches that treat design parameter variations and dependencies as disturbances, the proposed methods incorporate the parameters directly into the system matrices in an affine manner. However, explicitly incorporating these dependencies significantly increases the complexity of explicit MPC formulations due to resulting nonlinear terms involving decision variables and parameters. We address this complexity by proposing two approximation methods. Both methods are applied to two examples, and their performances are compared with respect to the exact eMPC implementation.

Authors:Juan Pablo Bertucci, Mauro Salazar, Theo Hofman
Title: Battery Electric Truck Infrastructure Co-design via Joint Optimization and Agent-based Simulation
Abstract:
As zero‑emission zones emerge in European cities, fleet operators are shifting to electric vehicles. To maintain their current operations, a clear understanding of the charging infrastructure required and its relationship to existing power grid limitations is needed. This study presents an optimization frame‑work for jointly designing charging infrastructure and schedules within a logistics distribution network, validated through agent‑based simulations. We formulate the problem as a mixed‑integer linear program and develop an agent‑based model to evaluate various designs and operations under stochastic conditions. Our experiments compare rule‑based and optimized strategies in a case study of the Netherlands. Results show that current commercial solutions suffice for middle‑mile logistics, with central co‑design yielding average cost reductions of 5.2% to 6.4% and an average 20.1% decrease in total installed power. While rule‑based control effectively manages charging operations and mitigates delays, optimizing charge scheduling significantly reduces queuing times (99%), charging costs (13.5%), and time spent near capacity (10.9%). Our optimization‑simulation framework paves the way for combining optimized infrastructure planning and realistic fleet operations in digital‑twin environments.

Authors:Maitreyee Dutta, Jiayu Chen, Masakazu Koike, Yuichiro Yano, Yuko Hanado, Takayuki Ishizaki
Title: Optimal GNSS Time Tracking for Long-term Stable Time Realisation in Synchronised Atomic Clocks
Abstract:
In this manuscript, we propose a novel optimal Global Navigation Satellite System (GNSS) time tracking algorithm to collectively steer an ensemble consisting of synchronising miniature atomic clocks towards standard GNSS time. The synchronising miniature atomic clocks generate a common synchronised time which has good short term performance but its accuracy and precision, which is measured by Allan variance, deteriorates in the long run. So, a supervisor designs and periodically broadcasts the proposed GNSS time tracking control to the ensemble miniature atomic clocks that steer the average of ensemble towards the average of GNSS receivers, which are receivers of GNSS time. The tracking control is constructed using a Kalman filter estimation process that estimates the difference in average of GNSS receivers and average of ensemble clocks by using relative clock readings between GNSS receivers and their adjacent ensemble clock. Under the influence of the periodically received tracking control, the stabilised ensemble clocks have better long term accuracy and precision over long averaging periods. Since the tracking control is designed to solely influence the average of the ensemble, the tracking process does not interfere with the synchronisation process and vice versa. The feedback matrix associated with the tracking control is obtained from an optimisation problem that minimises steady‑state Allan variance. Numerical results are provided to show the efficacy of the proposed algorithm for enhancing long term performance.

Authors:Declan S. Jagt, Matthew M. Peet
Title: Verifying Well-Posedness of Linear PDEs using Convex Optimization
Abstract:
Ensuring that a PDE model is well‑posed is a necessary precursor to any form of analysis, control, or numerical simulation. Although the Lumer‑Phillips theorem provides necessary and sufficient conditions for well‑posedness of dissipative PDEs, these conditions must hold only on the domain of the PDE ‑‑ a proper subspace of L_2 ‑‑ which can make them difficult to verify in practice. In this paper, we show how the Lumer‑Phillips conditions for PDEs can be tested more conveniently using the equivalent Partial Integral Equation (PIE) representation. This representation introduces a fundamental state in the Hilbert space L_2 and provides a bijection between this state space and the PDE domain. Using this bijection, we reformulate the Lumer‑Phillips conditions as operator inequalities on L_2. We show how these inequalities can be tested using convex optimization methods, establishing a least upper bound on the exponential growth rate of solutions. We demonstrate the effectiveness of the proposed approach by verifying well‑posedness for several classical examples of parabolic and hyperbolic PDEs.

Authors:Hossam Farag, Cedomir Stefanovic
Title: Toward Efficient Deployment and Synchronization in Digital Twins-Empowered Networks
Abstract:
Digital twins (DTs) are envisioned as a key enabler of the cyber‑physical continuum in future wireless networks. However, efficient deployment and synchronization of DTs in dynamic multi‑access edge computing (MEC) environments remains challenging due to time‑varying communication and computational resources. This paper investigates the joint optimization of DT deployment and synchronization in dynamic MEC environments. A deep reinforcement learning (DRL) framework is proposed for adaptive DT placement and association to minimize interaction latency between physical and digital entities. To ensure semantic freshness, an update scheduling policy is further designed to minimize the long‑term weighted sum of the Age of Changed Information (AoCI) and the update cost. A relative policy iteration algorithm with a threshold‑based structure is developed to derive the optimal policy. Simulation results show that the proposed methods achieve lower latency, enhanced information freshness, and reduced system cost compared with benchmark schemes

Authors:Tao Li, Chen Shen
Title: Typical Scenarios Generation Method Considering System-level Characteristics of Power System
Abstract:
This paper proposes a method for generating typical scenarios based on system‑level macroscopic characteristics of power system and considering its stability properties. First, considering uncertainties such as renewable energy generation in power‑electronics‑dominated power systems, multidimensional scaling is used to construct an electrical coordinate system. Based on this, system‑level characteristics of the distribution of physical quantities, such as power generation and load, are characterized. Furthermore, a method for generating typical scenarios based on the system's system‑level characteristics and stability properties is proposed. For the obtained joint probability distribution of system‑level characteristics, weighted Mahalanobis distance can be used to predict the stability properties of random scenarios. Finally, the typicality and representativeness of the scenarios generated by the proposed method with respect to stability properties are verified on the CSEE benchmark case, and stability prediction for random scenarios is achieved using a probabilistic testing method.

Authors:Felix Brändle, Frank Allgöwer
Title: Robust IMMPC: An Offset-free MPC for Rejecting Unknown Disturbances
Abstract:
Output regulation is the problem of finding a control input to asymptotically track reference trajectories and reject disturbances. This can be addressed by using the internal model principle to embed a model of the disturbance in the controller. In this work, we present a Model Predictive Control scheme to achieve offset‑free control. To do so, we extend Internal Model MPC to general bounded disturbances that must not be generated by the disturbance model. We show recursive feasibility, constraint satisfaction, and provide convergence conditions for the optimal reachable output. The proposed controller is validated on a four‑tank system.

Authors:Felix Brändle, Nicolas Chatzikiriakos, Andrea Iannelli, Frank Allgöwer
Title: Beyond Bounded Noise: Stochastic Set-Membership Estimation for Nonlinear Systems
Abstract:
In this paper, we derive a novel procedure for set‑membership estimation of dynamical systems affected by stochastic noise with unbounded support. Employing a bound on the sample covariance matrix, we are able to provide a finite‑ sample uncertainty set containing the true system parameters with high probability. Our approach can be natively applied to a wide class of nonlinear systems affected by sub‑Gaussian noise. Our analysis provides conditions under which the proposed uncertainty set converges to the true system parameters and establishes an upper bound on the convergence rate. The proposed uncertainty set can be used directly for robust controller synthesis with probabilistic stability and performance guarantees. Concluding numerical examples demonstrate the advantages of the proposed formulation over established approaches.

Authors:Simone Garatti, Lucrezia Manieri, Alessandro Falsone, Algo Carè, Marco C. Campi, Maria Prandini
Title: Scenario theory for multi-criteria data-driven decision making
Abstract:
The scenario approach provides a powerful data‑driven framework for designing solutions under uncertainty with rigorous probabilistic robustness guarantees. Existing theory, however, primarily addresses assessing robustness with respect to a single appropriateness criterion for the solution based on a dataset, whereas many practical applications ‑ including multi‑agent decision problems ‑ require the simultaneous consideration of multiple criteria and the assessment of their robustness based on multiple datasets, one per criterion. This paper develops a general scenario theory for multi‑criteria data‑driven decision making. A central innovation lies in the collective treatment of the risks associated with violations of individual criteria, which yields substantially more accurate robustness certificates than those derived from a naive application of standard results. In turn, this approach enables a sharper quantification of the robustness level with which all criteria are simultaneously satisfied. The proposed framework applies broadly to multi‑criteria data‑driven decision problems, providing a principled, scalable, and theoretically grounded methodology for design under uncertainty.

Authors:Dominik Beňo, Patrik Valábek, Martin Hromčík, Martin Klaučo
Title: Star-Tracker-Constrained Attitude MPC for CubeSats
Abstract:
This paper presents an online linear model predictive control (MPC) framework for slew maneuvers that maintains star‑tracker availability during ground‑target tracking. The nonlinear rigid‑body dynamics and geometric exclusion constraints are analytically linearized about the current state estimate at each control step, yielding a time‑varying linear MPC formulation cast as a standard quadratic program (QP). This structure is compatible with established aerospace flight‑software practices and offers a computational profile with lower online complexity than comparable nonlinear MPC schemes. The controller incorporates angular‑rate, actuator, and star‑tracker exclusion constraints over a receding horizon. Performance is assessed in high‑fidelity nonlinear model‑in‑the‑loop simulations using NASA's "42" spacecraft dynamics simulator, including a Monte Carlo campaign over varying target geometries and inertia perturbations.

Authors:Onel Luis Alcaraz López
Title: Sequential Monte Carlo for Network Resilience Assessment and Control
Abstract:
Resilience is emerging as a key requirement for next‑generation wireless communication systems, requiring the ability to assess and control rare, path‑dependent failure events arising from sequential degradation and delayed recovery. In this work, we develop a sequential Monte Carlo (SMC) framework for resilience assessment and control in networked systems. Resilience failures are formulated as staged, path‑dependent events and represented through a reaction‑coordinate‑based decomposition that captures the progression toward non‑recovery. Building on this structure, we propose a multilevel splitting approach with fixed, semantically interpretable levels and a budget‑adaptive population control mechanism that dynamically allocates computational effort under a fixed total simulation cost. The framework is further extended to incorporate mitigation policies by leveraging SMC checkpoints for policy evaluation, comparison, and state‑contingent selection via simulation‑based lookahead. A delay‑critical wireless network use case is considered to demonstrate the approach. Numerical results show that the proposed SMC method significantly outperforms standard Monte Carlo in estimating rare non‑recovery probabilities and enables effective policy‑driven recovery under varying system conditions. The results highlight the potential of SMC as a practical tool for resilience‑oriented analysis and control in future communication systems.

Authors:Branislav Daráš, Patrik Valábek, Martin Klaučo
Title: DeePC vs. Koopman MPC for Pasteurization: A Comparative Study
Abstract:
Data‑driven predictive control methods can provide the constraint handling and optimization of model predictive control (MPC) without first‑principles models. Two such methods differ in how they replace the model: Data‑enabled predictive control (DeePC) uses behavioral systems theory to predict directly from input‑‑output trajectories via Hankel matrices, while Koopman‑based MPC (KMPC) learns a lifted linear state‑space representation from data. Both methods are well studied on their own, but head‑to‑head comparisons on multivariable process control problems are few. This paper compares them on a pasteurization unit with three manipulated inputs and three measured outputs, using a neural‑network‑based digital twin as the plant simulator. Both controllers share identical prediction horizons, cost weights, and constraints, so that differences in closed‑loop behavior reflect the choice of predictive representation. Results show that both methods achieve feasible constrained control with comparable tracking error, but with a clear trade‑off: KMPC tracks more tightly under the chosen cost, while DeePC produces substantially smoother input trajectories. These results help practitioners choose between the two approaches for thermal processing applications.

Authors:Kyle Poe, Uday Kiran Reddy Tadipatri, Benjamin D. Haeffele, Rene Vidal
Title: BLISS: Global Blind Identification of Linear Systems with Sparse Inputs
Abstract:
Linear system identification and sparse dictionary learning can both be seen as structured matrix factorization problems. However, these two problems have historically been studied in isolation by the systems theory and machine learning communities. Although linear system identification enjoys a mature theory when inputs are known, blind linear system identification remains poorly understood beyond restrictive settings. In contrast, complete sparse dictionary learning has recently benefited from strong global identifiability results and scalable nonconvex algorithms. In this work, we bridge these two areas by showing that under a sparse input assumption, fully observed blind system identification becomes a generalization of complete dictionary learning. This connection allows us to develop global identifiability guarantees for blind system identification, by leveraging techniques from the complete dictionary learning literature. We further show empirically that a principled application of the alternating direction method of multipliers can globally recover the ground‑truth system from a single trajectory, provided sufficient samples and input sparsity.

Authors:Rodolfo Verdin, Hugo Moreno, Mark W. Spong, Gerardo Flores
Title: The QuadSoft: Design, Construction, and Experimental Validation of a Soft and Actuated Quadrotor
Abstract:
This paper presents QuadSoft, a novel fully actuated quadrotor equipped with continuous‑curvature, tendon‑driven soft robotic arms. The design combines a semi‑rigid central frame with flexible arms, enabling controlled structural reconfiguration during flight without altering the propeller layout. Unlike existing soft aerial platforms that rely on discrete bending joints, QuadSoft utilizes a continuum deformation approach to modulate arm curvature, actively adjusting its thrust vector and aerodynamic characteristics. We characterize the geometric mapping between servomotor input and the resulting constant curvature, validating it experimentally. Outdoor flight tests demonstrate stable take‑off, hover, directional maneuvers, and landing, confirming that controlled arm bending can generate horizontal displacement while preserving altitude. Measurements of pitch, roll, and curvature angles show that the platform follows intended actuation patterns with minimal attitude deviations. These results demonstrate that QuadSoft preserves the baseline stability of rigid quadrotors while enabling morphology‑driven maneuverability, all under the standard PX4 autopilot without retuning. Beyond a proof of concept, this work establishes a distinctive outdoor validation of a tendon‑driven continuum morphing quadrotor, opening a new research avenue toward adaptive aerial systems that combine the safety and versatility of soft robotics with the performance of conventional UAVs.

Authors:Simon Kuang, Xinfan Lin
Title: Incremental stability in $p=1$ and $p=\infty$: classification and synthesis
Abstract:
All Lipschitz dynamics with the weak infinitesimal contraction (WIC) property can be expressed as a Lipschitz nonlinear system in proportional negative feedback ‑‑ this statement, a ``structure theorem,'' is true in the p=1 and p=\infty norms. Equivalently, a Lipschitz vector field is WIC if and only if it can be written as a scalar decay plus a Lipschitz‑bounded residual. We put this theorem to use using neural networks to approximate Lipschitz functions. This results in a map from unconstrained parameters to the set of WIC vector fields, enabling standard gradient‑based training with no projections or penalty terms. Because the induced 1‑ and \infty‑norms of a matrix reduce to row or column sums, Lipschitz certification costs only O(d^2) operations ‑‑ the same order as a forward pass and appreciably cheaper than eigenvalue or semidefinite methods for the 2‑norm. Numerical experiments on a planar flow‑fitting task and a four‑node opinion network demonstrate that the parameterization (re‑)constructs contracting dynamics from trajectory data. In a discussion of the expressiveness of non‑Euclidean contraction, we prove that the set of 2× 2 systems that contract in a weighted 1‑ or \infty‑norm is characterized by an eigenvalue cone, a strict subset of the Hurwitz region that quantifies the cost of moving away from the Euclidean norm.

Authors:Jiayi Yao, Cong Chen, Baosen Zhang
Title: Competition and Cooperation of LLM Agents in Games
Abstract:
Large language model (LLM) agents are increasingly deployed in competitive multi‑agent settings, raising fundamental questions about whether they converge to equilibria and how their strategic behavior can be characterized. In this paper, we study LLM agent interactions in two standard games: a network resource allocation game and a Cournot competition game. Rather than converging to Nash equilibria, we find that LLM agents tend to cooperate when given multi‑round prompts and non‑zero‑sum context. Chain‑of‑thought analysis reveals that fairness reasoning is central to this behavior. We propose an analytical framework that captures the dynamics of LLM agent reasoning across rounds and explains these experimental findings.

Authors:Mingjie Bi
Title: CASCADE: Cascaded Scoped Communication for Multi-Agent Re-planning in Disrupted Industrial Environments
Abstract:
Industrial disruption replanning demands multi‑agent coordination under strict latency and communication budgets, where disruptions propagate through tightly coupled physical dependencies and rapidly invalidate baseline schedules and commitments. Existing coordination schemes often treat communication as either effectively free (broadcast‑style escalation) or fixed in advance (hand‑tuned neighborhoods), both of which are brittle once the disruption footprint extends beyond a local region. We present \CASCADE, a budgeted replanning mechanism that makes communication scope explicit and auditable rather than fixed or implicit. Each agent maintains an explicit knowledge base, solves role‑conditioned local decision problems to revise commitments, and coordinates through lightweight contract primitives whose footprint expands only when local validation indicates that the current scope is insufficient. This design separates a unified agent substrate (Knowledge Base / Decision Manager / Communication Manager) from a scoped interaction layer that controls who is contacted, how far coordination propagates, and when escalation is triggered under explicit budgets. We evaluate \CASCADE on disrupted manufacturing and supply‑chain settings using unified diagnostics intended to test a mechanism‑design claim ‑‑ whether explicit scope control yields useful quality‑latency‑communication trade‑offs and improved robustness under uncertainty ‑‑ rather than to provide a complete algorithmic ranking.

Authors:Amirhossein Dezhboro, Fateme Maleki, Arman Adibi, Erfan Amini, Jose E. Ramirez-Marquez
Title: Convergence of Byzantine-Resilient Gradient Tracking via Probabilistic Edge Dropout
Abstract:
We study distributed optimization over networks with Byzantine agents that may send arbitrary adversarial messages. We propose \emphGradient Tracking with Probabilistic Edge Dropout (GT‑PD), a stochastic gradient tracking method that preserves the convergence properties of gradient tracking under adversarial communication. GT‑PD combines two complementary defense layers: a universal self‑centered projection that clips each incoming message to a ball of radius τ around the receiving agent, and a fully decentralized probabilistic dropout rule driven by a dual‑metric trust score in the decision and tracking channels. This design bounds adversarial perturbations while preserving the doubly stochastic mixing structure, a property often lost under robust aggregation in decentralized settings. Under complete Byzantine isolation (p_b=0), GT‑PD converges linearly to a neighborhood determined solely by stochastic gradient variance. For partial isolation (p_b>0), we introduce \emphGradient Tracking with Probabilistic Edge Dropout and Leaky Integration (GT‑PD‑L), which uses a leaky integrator to control the accumulation of tracking errors caused by persistent perturbations and achieves linear convergence to a bounded neighborhood determined by the stochastic variance and the clipping‑to‑leak ratio. We further show that under two‑tier dropout with p_h=1, isolating Byzantine agents introduces no additional variance into the honest consensus dynamics. Experiments on MNIST under Sign Flip, ALIE, and Inner Product Manipulation attacks show that GT‑PD‑L outperforms coordinate‑wise trimmed mean by up to 4.3 percentage points under stealth attacks.

Authors:Hossein Gholampour, Logan E. Beaver
Title: Reachability-Aware Time Scaling for Path Tracking
Abstract:
This paper studies tracking of collision‑free waypoint paths produced by an offline planner for a planar double‑integrator system with bounded speed and acceleration. Because sampling‑based planners must route around obstacles, the resulting waypoint paths can contain sharp turns and high‑curvature regions, so one‑step reachability under acceleration limits becomes critical even when the path geometry is collision‑free. We build on a pure‑pursuit‑style, reachability‑guided quadratic‑program (QP) tracker with a one‑step acceleration margin. Offline, we evaluate this margin along a spline fitted to the waypoint path and update a scalar speed‑scaling profile so that the required one‑step acceleration remains below the available bound. Online, the same look‑ahead tracking structure is used to track the scaled reference.

Authors:Shiyu Cheng, Luyao Niu, Bhaskar Ramasubramanian, Andrew Clark, Radha Poovendran
Title: Distributed Safety-Critical Control of Multi-Agent Systems with Time-Varying Communication Topologies
Abstract:
Coordinating multiple autonomous agents to reach a target region while avoiding collisions and maintaining communication connectivity is a core problem in multi‑agent systems. In practice, agents have a limited communication range. Thus, network links can appear and disappear as agents move, making the topology state‑dependent and time‑varying. Existing distributed solutions to multi‑agent reach‑avoid problems typically assume a fixed communication topology, and thus are not applicable when encountering discontinuities raised by time‑varying topologies. This paper presents a distributed optimization‑based control framework that addresses these challenges through two complementary mechanisms. First, we introduce a truncation function that converts the time‑varying communication graph into a smoothly state‑dependent one, ensuring that constraints remain continuous as communication links are created or removed. Second, we employ auxiliary mismatch variables with two‑time‑scale dynamics to decouple globally coupled state‑dependent constraints, yielding a singular perturbation system that each agent can solve using only local information and neighbor communication. Through singular perturbation analysis, we prove that the distributed controller guarantees collision avoidance, connectivity preservation, and convergence to the target region. We validate the proposed framework through numerical simulations involving multi‑agent navigation with obstacles and time‑varying communication topologies.

Authors:Tan Chin Hong, Chung-Han Hsieh
Title: Dynamic Weight Optimization for Double Linear Policy: A Stochastic Model Predictive Control Approach
Abstract:
The Double Linear Policy (DLP) framework guarantees a Robust Positive Expectation (RPE) under optimized constant‑weight designs or admissible prespecified time‑varying policies. However, the sequential optimization of these time‑varying weights remains an open challenge. To address this gap, we propose a Stochastic Model Predictive Control (SMPC) framework. We formulate weight selection as a receding‑horizon optimal control problem that explicitly maximizes risk‑adjusted returns while enforcing survivability and predicted positive expectation constraints. Notably, an analytical gradient is derived for the non‑convex objective function, enabling efficient optimization via the L‑BFGS‑B algorithm. Empirical results demonstrate that this dynamic, closed‑loop approach improves risk‑adjusted performance and drawdown control relative to constant‑weight and prescribed time‑varying DLP baselines.

Authors:Sanchita Ghosh, Tanushree Roy
Title: Explainable Functional Relation Discovery for Battery State-of-Health Using Kolmogorov-Arnold Network
Abstract:
Battery health management is heavily dependent on reliable State‑of‑Health (SoH) estimation to ensure battery safety with maximized energy utilization. Although SoH estimation can effectively track battery degradation, it requires continuous battery data acquisition. In addition, model‑based SoH estimation methods rely on accurate battery model knowledge, whereas data‑driven approaches often suffer from limited interpretability. In contrast, analytical characterization of SoH will offer a direct and tractable handle on battery performance degradation, while also establishing a foundation for further analytical studies toward effective battery health management. Thus, in this work, we propose a Kolmogorov Arnold Network (KAN)‑based data‑driven pipeline to establish a functional relationship for SoH degradation using battery temperature data. Specifically, we learn long‑term battery thermal dynamics and battery heat generation via learnable activation functions of our KAN model. We utilize the learned mapping to obtain an explicit functional relationship between SoH degradation and cycle number. The proposed pipeline was validated using real‑world data, yielding a closed‑form analytical formula of SoH degradation with high accuracy.

Authors:Shihao Li, Jiachen Li, Jiamin Xu, Dongmei Chen
Title: Behavioral Score Diffusion: Model-Free Trajectory Planning via Kernel-Based Score Estimation from Data
Abstract:
Diffusion‑based trajectory optimization has emerged as a powerful planning paradigm, but existing methods require either learned score networks trained on large datasets or analytical dynamics models for score computation. We introduce \emphBehavioral Score Diffusion (BSD), a training‑free and model‑free trajectory planner that computes the diffusion score function directly from a library of trajectory data via kernel‑weighted estimation. At each denoising step, BSD retrieves relevant trajectories using a triple‑kernel weighting scheme ‑‑ diffusion proximity, state context, and goal relevance ‑‑ and computes a Nadaraya‑Watson estimate of the denoised trajectory. The diffusion noise schedule naturally controls kernel bandwidths, creating a multi‑scale nonparametric regression: broad averaging of global behavioral patterns at high noise, fine‑grained local interpolation at low noise. This coarse‑to‑fine structure handles nonlinear dynamics without linearization or parametric assumptions. Safety is preserved by applying shielded rollout on kernel‑estimated state trajectories, identical to existing model‑based approaches. We evaluate BSD on four robotic systems of increasing complexity (3D‑‑6D state spaces) in a parking scenario. BSD with fixed bandwidth achieves 98.5% of the model‑based baseline's average reward across systems while requiring no dynamics model, using only 1,000 pre‑collected trajectories. BSD substantially outperforms nearest‑neighbor retrieval (18‑‑63% improvement), confirming that the diffusion denoising mechanism is essential for effective data‑driven planning.

Authors:Shihao Li, Jiachen Li, Dongmei Chen
Title: Gradient-Based Data Valuation Improves Curriculum Learning for Game-Theoretic Motion Planning
Abstract:
We demonstrate that gradient‑based data valuation produces curriculum orderings that significantly outperform metadata‑based heuristics for training game‑theoretic motion planners. Specifically, we apply TracIn gradient‑similarity scoring to GameFormer on the nuPlan benchmark and construct a curriculum that weights training scenarios by their estimated contribution to validation loss reduction. Across three random seeds, the TracIn‑weighted curriculum achieves a mean planning ADE of 1.704\pm0.029\,m, significantly outperforming the metadata‑based interaction‑difficulty curriculum (1.822\pm0.014\,m; paired t‑test p=0.021, Cohen's d_z=3.88) while exhibiting lower variance than the uniform baseline (1.772\pm0.134\,m). Our analysis reveals that TracIn scores and scenario metadata are nearly orthogonal (Spearman ρ=‑0.014), indicating that gradient‑based valuation captures training dynamics invisible to hand‑crafted features. We further show that gradient‑based curriculum weighting succeeds where hard data selection fails: TracIn‑curated 20% subsets degrade performance by 2×, whereas full‑data curriculum weighting with the same scores yields the best results. These findings establish gradient‑based data valuation as a practical tool for improving sample efficiency in game‑theoretic planning.

Authors:Jiachen Li, Shihao Li, Dongmei Chen
Title: Data-Attributed Adaptive Control Barrier Functions: Safety-Certified Training Data Curation via Influence Analysis
Abstract:
Learning‑based adaptation of Control Barrier Function (CBF) parameters offers a promising path toward safe autonomous navigation that balances conservatism with performance. Yet the accuracy of the underlying safety predictor is ultimately constrained by training data quality, and no prior work has formally characterized how prediction errors propagate through the adaptive pipeline to degrade closed‑loop safety guarantees. We introduce Data‑Attributed Adaptive CBF (DA‑CBF), a framework that integrates TracIn‑based data attribution into adaptive CBF learning. Our theoretical contributions are fourfold: (i) corrected two‑sided bounds relating the safety‑loss surrogate to the CBF constraint margin; (ii) a safety margin preservation theorem showing that prediction error induces quantifiable margin degradation and, via a smooth parameter selector, yields a genuine closed‑loop forward invariance guarantee not conditioned on a fixed trajectory; (iii) a CBF‑QP constraint perturbation bound that links prediction accuracy directly to recursive feasibility; and (iv) a principled leave‑one‑out justification for influence‑based data curation under explicit smoothness assumptions. On a DynamicUnicycle2D benchmark, DA‑CBF reduces prediction RMSE by 35.6%, expands the certified safe operating set by 39%, and achieves collision‑free navigation in a 16‑obstacle environment where the uncurated baseline incurs 3 collisions.

Authors:Qiang Ji, Lin Cheng, Kaidi Huang, Junxin Lv, Yue Zhou, Zeng Liang
Title: Demand response potential evaluation of a zero carbon hydrogen metallurgy system considering shaft furnace's flexibility
Abstract:
The increasing penetration of intermittent renewable energy sources and the retirement of thermal units have widened the power system flexibility gap. Industrial demand response (DR) driven by real‑time pricing is widely regarded as a viable solution. In this paper, we propose a framework to quantify the DR potential of a zero‑carbon hydrogen metallurgy system (ZCHMS) considering shaft furnace's flexibility. First, we model the shaft furnace as a constrained flexible load and validate the model via simulation, achieving a root mean square error of 4.48% of the rated load. Second, we formulate a DR potential evaluation method that determines baseline and DR‑based production scheduling schemes by minimizing operating cost subject to production orders. Finally, the numerical results show that compared with the baseline, DR‑based ZCHMS reduces operating cost by 6.6%, incentivizing demand‑side management in ironmaking and strengthening power‑ironmaking synergies.

Authors:Sahand Kiani, Constantino M. Lagoa
Title: Willems' Fundamental Lemma with Large Noisy Fragmented Dataset
Abstract:
Willems' Fundamental Lemma enables parameterizing all trajectories generated by a Linear Time‑Invariant (LTI) system directly from data. However, this lemma relies on the assumption of noiseless measurements. In this paper, we provide an approach that enables the applicability of Willems' Fundamental Lemma with a large noisy‑input, noisy‑output fragmented dataset, without requiring prior knowledge of the noise distribution. We introduce a computationally tractable and lightweight algorithm that, despite processing a large dataset, executes in the order of seconds to estimate the invariants of the underlying system, which is obscured by noise. The simulation results demonstrate the effectiveness of the proposed method.

Authors:Shuo Liu, Wei Xiao, Christos G. Cassandras, Calin A. Belta
Title: Event-Triggered Adaptive Taylor-Lagrange Control for Safety-Critical Systems
Abstract:
This paper studies safety‑critical control for nonlinear systems under sampled‑data implementations of the controller. The recently proposed Taylor‑‑Lagrange Control (TLC) method provides rigorous safety guarantees but relies on a fixed discretization‑related parameter, which can lead to infeasibility or unsafety in the presence of input constraints and inter‑sampling effects. To address these limitations, we propose an adaptive Taylor‑‑Lagrange Control (aTLC) framework with an event‑triggered implementation, where the discretization‑related parameter defines the discretization time scale and is selected online as state‑dependent rather than fixed. This enables the controller to dynamically balance feasibility and safety by adjusting the effective time scale of the Taylor expansion. The resulting controller is implemented as a sequence of Quadratic Programs (QPs) with input constraints. We further introduce a selection rule to choose the discretization‑related parameter from a finite candidate set, favoring feasible inputs and improved safety. Simulation results on an adaptive cruise control (ACC) problem demonstrate that the proposed approach improves feasibility, guarantees safety, and achieves smoother control actions compared to TLC while requiring a single automatically tuned parameter.

Authors:Tomoya Kamimura, Yuya Oshita, Mau Adachi, Yuichi Ambe, Akihito Sano, Naomi Wada, Fumitoshi Matsuno, Shinya Aoi
Title: Phase Relationship between Spinal Motion and Limb Support Determines High-speed Running Performance in a Cheetah Model with Asymmetric Spinal Stiffness
Abstract:
Cheetahs are characterized by large spinal flexion and extension during high‑speed running, yet the dynamical role of the phase relationship between spinal motion and limb support remains unclear. We aimed to clarify how this phase relationship affects running performance, focusing on the effect of asymmetric spinal stiffness. Using a simple planar cheetah model with asymmetric torsional spinal stiffness, we numerically searched for periodic bounding solutions over a range of stiffness parameters and compared their ground reaction forces, horizontal velocities, and stability. We obtained both cheetah‑like solutions, in which the spine extends after hindlimb liftoff and flexes after forelimb liftoff, and non‑cheetah‑like solutions, in which the spine flexes after hindlimb liftoff and extends after forelimb liftoff. Under asymmetric spinal stiffness, cheetah‑like solutions reduced ground reaction forces while maintaining horizontal velocity more effectively than non‑cheetah‑like solutions. The phase relationship between spinal motion and stance timing is a key determinant of high‑speed running performance. These findings provide a dynamical understanding of cheetah locomotion and suggest design principles for spined legged robots.

Authors:Zhiquan Zhang, Melkior Ornik
Title: Hierarchical Motion Planning and Control under Unknown Nonlinear Dynamics via Predicted Reachability
Abstract:
Autonomous motion planning under unknown nonlinear dynamics requires learning system properties while navigating toward a target. In this work, we develop a hierarchical planning‑control framework that enables online motion synthesis with limited prior system knowledge. The state space is partitioned into polytopes and approximates the unknown nonlinear system using a piecewise‑affine (PWA) model. The local affine models are identified once the agent enters the corresponding polytopes. To reduce computational complexity, we introduce a non‑uniform adaptive state space partition strategy that refines the partition only in task‑relevant regions. The resulting PWA system is abstracted into a directed weighted graph, whose edge existence is incrementally verified using reach control theory and predictive reachability conditions. Certified edges are weighted using provable time‑to‑reach bounds, while uncertain edges are assigned information‑theoretic weights to guide exploration. The graph is updated online as new data becomes available, and high‑level planning is performed by graph search, while low‑level affine feedback controllers are synthesized to execute the plan. Furthermore, the conditions of classical reach control theory are often difficult to satisfy in underactuated settings. We therefore introduce relaxed reachability conditions to extend the framework to such systems. Simulations demonstrate effective exploration‑exploitation trade‑offs with formal reachability guarantees.

Authors:Mohammadreza Kamaldar
Title: Nonlinear Moving-Horizon Estimation Using State- and Control-Dependent Models
Abstract:
This paper presents a state‑ and control‑dependent moving‑horizon estimation (SCD‑MHE) algorithm for nonlinear discrete‑time systems. Within this framework, a pseudo‑linear representation of nonlinear dynamics is leveraged utilizing state‑ and control‑dependent coefficients, where the solution to a moving‑horizon estimation problem is iteratively refined. At each discrete time step, a quadratic program is executed over a sliding window of historical measurements. Moreover, system matrices are consecutively updated based upon prior iterates to capture nonlinear regimes. In contrast to the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), nonlinearities and bounds are accommodated within a structured optimization framework, thereby circumventing the reliance on local Jacobian matrices. Furthermore, theoretical analysis is presented to establish the convergence of the iterative sequence, and bounded estimation errors are mathematically guaranteed under uniform observability conditions. Finally, comparative numerical experiments utilizing a quadrotor vertical kinematics system demonstrate that the SCD‑MHE achieves superior estimation accuracy relative to the EKF, the UKF, and a fully nonlinear moving‑horizon estimator, while reducing per‑step computational latency by over an order of magnitude.

Authors:Mohamed Serry, S. Sivaranjani, Jun Liu
Title: Set-Based Value Function Characterization and Neural Approximation of Stabilization Domains for Input-Constrained Discrete-Time Systems
Abstract:
Analyzing nonlinear systems with stabilizable controlled invariant sets (CISs) requires accurate estimation of their domains of stabilization (DOS) together with associated stabilizing controllers. Despite extensive research, estimating DOSs for general nonlinear systems remains challenging due to fundamental theoretical and computational limitations. In this paper, we propose a novel framework for estimating DOSs for controlled input‑constrained discrete‑time systems. The DOS is characterized via newly introduced value functions defined on metric spaces of compact sets. We establish the fundamental properties of these value functions and derive the associated Bellman‑type (Zubov‑type) functional equations. Building on this characterization, we develop a physics‑informed neural network (NN) framework that learns the value functions by embedding the derived functional equations directly into the training process. The proposed methodology is demonstrated through two numerical examples, illustrating its ability to accurately estimate DOSs and synthesize stabilizing controllers from the learned value functions.

Authors:Yujia Li, Alexandre Moreira, Miguel Heleno
Title: From Net Load Modifiers to Firm Capacity: The Role of Distributed Energy Resources in Resource Adequacy
Abstract:
Distributed energy resources (DERs) such as rooftop solar, battery storage, and demand response offer substantial potential for power system reliability, yet integrating them into resource adequacy (RA) frameworks as firm capacity contributors remains difficult across jurisdictions. Existing analyses often treat these barriers as isolated technical problems at individual stages of the RA participation process, overlooking the cross‑stage dependencies that prevent reforms at one stage from producing scalable participation. This paper introduces a four‑gate compliance pathway (entry and classification, metering and verification, accreditation, and enforcement), preceded by an upstream forecasting layer, as a unified lens for tracing where DER capacity value is lost at the institutional interfaces between these stages. Using a document‑grounded comparative synthesis of tariff provisions, compliance protocols, and regulatory documents across five jurisdictions spanning U.S. capacity markets and European capacity remuneration mechanisms, we show that these barriers persist despite substantial variation in market design and regulatory structure, indicating that the problem is structural rather than jurisdiction‑specific. We identify three cross‑stage coupling mechanisms that explain why gate‑level reforms have repeatedly failed to scale DER participation, and derive coordination principles for end‑to‑end compliance redesign. The central finding is that compliance architecture, rather than DER technology itself, is the binding constraint on translating DER capability into firm RA contributions.

Authors:Yanliang Huang, Peng Xie, Zhen Zhang, Wenyuan Wu, Zhuoqi Zeng, Amr Alanwar
Title: Certified Set Convergence for Piecewise Affine Systems via Neural Lyapunov Functions
Abstract:
Safety‑critical control of piecewise affine (PWA) systems under bounded additive disturbances requires guarantees not for individual states but for entire state sets simultaneously: a single control action must steer every state in the set toward a target, even as sets crossing mode boundaries split and evolve under distinct affine dynamics. Certifying such set convergence via neural Lyapunov functions couples the Lipschitz constants of the value function and the policy, yet certified bounds for expressive networks exceed true values by orders of magnitude, creating a certification barrier. We resolve this through a three‑stage pipeline that decouples verification from the policy. A value function from Hamilton‑Jacobi backward reachability, trained via reinforcement learning, is the Lyapunov candidate. A permutation‑invariant Deep Sets controller, distilled via regret minimization, produces a common action. Verification propagates zonotopes through the value network, yielding verified Lyapunov upper bounds over entire sets without bounding the policy Lipschitz constant. On four benchmarks up to dimension six, including systems with per‑mode operator norms exceeding unity, the framework certifies set convergence with positive margin on every system. A spectrally constrained local certificate completes the terminal guarantee, and the set‑actor is the only tested method to achieve full strict set containment, at constant‑time online cost.

Authors:Yanliang Huang, Peng Xie, Wenyuan Wu, Zhuoqi Zeng, Amr Alanwar
Title: Data-Driven Reachability Analysis via Diffusion Models with PAC Guarantees
Abstract:
We present a data‑driven framework for reachability analysis of nonlinear dynamical systems that requires no explicit model. A denoising diffusion probabilistic model learns the time‑evolving state distribution of a dynamical system from trajectory data alone. The predicted reachable set takes the form of a sublevel set of a nonconformity score derived from the reconstruction error, with the threshold calibrated via the Learn Then Test procedure so that the probability of excluding a reachable state is bounded with high probability. Experiments on three nonlinear systems, a forced Duffing oscillator, a planar quadrotor, and a high‑dimensional reaction‑diffusion system, confirm that the empirical miss rate remains below the Probably Approximately Correct (PAC) bound while scaling to state dimensions beyond the reach of classical grid‑based and polynomial methods.

Authors:Simone Betteti, Luca Laurenti
Title: Hybrid Energy-Based Models for Physical AI: Provably Stable Identification of Port-Hamiltonian Dynamics
Abstract:
Energy‑based models (EBMs) implement inference as gradient descent on a learned Lyapunov function, yielding interpretable, structure‑preserving alternatives to black‑box neural ODEs and aligning naturally with physical AI. Yet their use in system identification remains limited, and existing architectures lack formal stability guarantees that globally preclude unstable modes. We address this gap by introducing an EBM framework for system identification with stable, dissipative, absorbing invariant dynamics. Unlike classical global Lyapunov stability, absorbing invariance expands the class of stability‑preserving architectures, enabling more flexible and expressive EBMs. We extend EBM theory to nonsmooth activations by establishing negative energy dissipation via Clarke derivatives and deriving new conditions for radial unboundedness, exposing a stability‑expressivity tradeoff in standard EBMs. To overcome this, we introduce a hybrid architecture with a dynamical visible layer and static hidden layers, prove absorbing invariance under mild assumptions, and show that these guarantees extend to port‑Hamiltonian EBMs. Experiments on metric‑deformed multi‑well and ring systems validate the approach, showcasing how our hybrid EBM architecture combines expressivity with sound and provable safety guarantees by design.

Authors:William Clark, Leonardo Colombo, Anthony Bloch
Title: Dissipation-assisted stabilization of periodic orbits via actuated exterior impacts in hybrid mechanical systems with symmetry
Abstract:
Impulsive mechanical systems exhibit discontinuous jumps in their state, and when such jumps are triggered by spatial events, the geometry of the impact surface carries information about the controllability of the hybrid dynamics. For mechanical systems defined on principal G‑bundles, two qualitatively distinct types of impacts arise: interior impacts, associated with events on the shape space, and exterior impacts, associated with events on the fibers. A key distinction is that interior impacts preserve the mechanical connection, whereas exterior impacts generally do not. In this paper, we exploit this distinction by allowing actuation through exterior impacts. We study the pendulum‑on‑a‑cart system, derive controlled reset laws induced by moving‑wall impacts, and analyze the resulting periodic motions. Our results show that reset action alone does not provide a convincing stabilizing regime, whereas the addition of dissipation in the continuous flow yields exponentially stable periodic behavior for suitable feedback gains.

Authors:Xuanyu Liang, Ahmed Al-Tahmeesschi, Swarna Chetty, Cicek Cavdar, Berk Canberk, Hamed Ahmadi
Title: Scalable machine learning-based approaches for energy saving in densely deployed Open RAN
Abstract:
Densely deployed base stations are responsible for the majority of the energy consumed in Radio access network (RAN). While these deployments are crucial to deliver the required data rate in busy hours of the day, the network can save energy by switching some of them to sleep mode and maintain the coverage and quality of service with the other ones. Benefiting from the flexibility provided by the Open RAN in embedding machine learning (ML) in network operations, in this work we propose Deep Reinforcement Learning (DRL)‑based energy saving solutions. Firstly we propose 3 different DRL‑based methods in the form of xApps which control the Active/Sleep mode of up to 6 radio units (RUs) from Near Real time RAN Intelligent Controller (RIC). We also propose a further scalable federated DRL‑based solution with an aggregator as an rApp in None Real time RIC and local agents as xApps. Our simulation results present the convergence of the proposed methods. We also compare the performance of our federated DRL across three layouts spanning 6‑‑24 RUs and 500‑‑1000\,m regions, including a composite multi‑region scenario. The results show that our proposed federated TD3 algorithm achieves up to 43.75% faster convergence, more than 50% network energy saving and 37. 4% lower training energy versus centralized baselines, while maintaining the quality of service and improving the robustness of the policy.

Authors:Ravish Gupta, Saket Kumar
Title: Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis of Emerging Labor Market Disruption
Abstract:
This paper extends the Acemoglu‑Restrepo task exposure framework to address the labor market effects of agentic artificial intelligence systems: autonomous AI agents capable of completing entire occupational workflows rather than discrete tasks. Unlike prior automation technologies that substitute for individual subtasks, agentic AI systems execute end‑to‑end workflows involving multi‑step reasoning, tool invocation, and autonomous decision‑making, substantially expanding occupational displacement risk beyond what existing task‑level analyses capture. We introduce the Agentic Task Exposure (ATE) score, a composite measure computed algorithmically from ONET task data using calibrated adoption parameters‑‑not a regression estimate‑‑incorporating AI capability scores, workflow coverage factors, and logistic adoption velocity. Applying the ATE framework across five major US technology regions (Seattle‑Tacoma, San Francisco Bay Area, Austin, New York, and Boston) over a 2025‑2030 horizon, we find that 93.2% of the 236 analyzed occupations across six information‑intensive SOC groups (financial, legal, healthcare, healthcare support, sales, and administrative/clerical) cross the moderate‑risk threshold (ATE >= 0.35) in Tier 1 regions by 2030, with credit analysts, judges, and sustainability specialists reaching ATE scores of 0.43‑0.47. We simultaneously identify seventeen emerging occupational categories benefiting from reinstatement effects, concentrated in human‑AI collaboration, AI governance, and domain‑specific AI operations roles. Our findings carry implications for workforce transition policy, regional economic planning, and the temporal dynamics of labor market adjustment

Authors:Yitao Bai, Thinh T. Doan, Justin Romberg
Title: Finite-Time Analysis of Projected Two-Time-Scale Stochastic Approximation
Abstract:
We study the finite‑time convergence of projected linear two‑time‑scale stochastic approximation with constant step sizes and Polyak‑‑Ruppert averaging. We establish an explicit mean‑square error bound, decomposing it into two interpretable components, an approximation error determined by the constrained subspace and a statistical error decaying at a sublinear rate, with constants expressed through restricted stability margins and a coupling invertibility condition. These constants cleanly separate the effect of subspace choice (approximation errors) from the effect of the averaging horizon (statistical errors). We illustrate our theoretical results through a number of numerical experiments on both synthetic and reinforcement learning problems.

Authors:Ethan Cantor, Yinyin Ge, Hongxing Ye, Jie Li
Title: Advanced Capacity Accreditation of Future Energy System Resources with Deep Uncertainties
Abstract:
The electric power sector has seen an increased penetration of renewable energy sources (RESs) that could strain the system reliability due to their inherent uncertainties in availability and controllability. Effective load carrying capability (ELCC) is widely used to quantify the reliability contributions of these RESs. However, existing ELCC methods can over‑ or under‑estimate their contributions and often neglect or simplify other critical factors such as transmission constraints and evolving climate trends, leading to inaccurate capacity credit (CC) allocations and inefficient reliability procurement in capacity markets. To address these limitations, this paper proposes TRACED (TRansmission And Climate Enhanced Delta) ‑‑ an advanced capacity accreditation approach that integrates transmission constraints and climate‑adjusted system conditions into a Delta ELCC evaluation. Case studies on a modified IEEE‑118 bus system with high RES and energy storage penetrations demonstrate that TRACED produces portfolio‑consistent CC allocations by capturing resource interactions and avoiding the double‑counting of shared reliability benefits inherent in marginal ELCC, which may otherwise lead to under‑procurement of reliability resources. Results further demonstrate that transmission congestion and evolving climate trends have mutual impacts on CC allocation, justifying their necessary integration into TRACED.

Authors:Alireza Naderi Akhormeh, Ahmad Hafez, Abdulla Fawzy, Amr Alanwar
Title: Data-Driven Reachability of Nonlinear Lipschitz Systems via Koopman Operator Embeddings
Abstract:
Data‑driven safety verification of robotic systems often relies on zonotopic reachability analysis due to its scalability and computational efficiency. However, for nonlinear systems, these methods can become overly conservative, especially over long prediction horizons and under measurement noise. We propose a data‑driven reachability framework based on the Koopman operator and zonotopic set representations that lifts the nonlinear system into a finite‑dimensional, linear, state‑input‑dependent model. Reachable sets are then computed in the lifted space and projected back to the original state space to obtain guaranteed over‑approximations of the true dynamics. The proposed method reduces conservatism while preserving formal safety guarantees, and we prove that the resulting reachable sets over‑approximate the true reachable sets. Numerical simulations and real‑world experiments on an autonomous vehicle show that the proposed approach yields substantially tighter reachable set over‑approximations than both model‑based and linear data‑driven methods, particularly over long horizons.

Authors:Balark Tiwari, Nishan Khadka, Nicholas Capps, Andre Bos, Douglas Meredith, John Bernardin, Edward C. Kinzel, Robert G. Landers
Title: Temperature Control of Digital Glass Forming Processes
Abstract:
Digital Glass Forming (DGF) is a new manufacturing process for low‑batch glass fabrication. The work zone temperature in DGF processes must be maintained in the glass's working range to ensure good fabrication. If the temperature is too low, the filament will not wet to the substrate or previously deposited material and, if the temperature is too high, the filament may disengage from the substrate or previously deposited material, or it may partially vaporize. In this work, a real‑time temperature control system capable of synchronizing process parameter, thermal camera, and visual camera data for the DGF process is introduced. A process parameter map for a scan velocity of 0.5 mm/s is constructed, as is a data‑driven dynamic temperature process model. A digital controller is designed to regulate the work zone temperature. The temperature controller is a closed loop tracking controller that adjusts the commanded laser power to regulate the measured temperature. Two sets of experiments are conducted to analyze the controller performance. In the first set of experiments, single tracks on a substrate are fabricated with constant laser power and with the closed loop temperature controller. It is seen that the closed loop controller is able to extend the process parameter map into regions where using a constant laser power will result in a failed build. In the second set of experiments, walls are fabricated. Using constant laser power results in a failed build (i.e., material vaporization at the corners and the filament prematurely detaching from the substrate) as the temperature process dynamics change with layer and at the corners. The closed loop controller successfully fabricated the wall without vaporization at the corners and premature filament detachment as the controller adjusts the laser power to account for the changing temperature process dynamics.

Authors:Anand Gokhale, Anton V. Proskurnikov, Yu Kawano, Francesco Bullo
Title: Contracting Neural Networks: Sharp LMI Conditions with Applications to Integral Control and Deep Learning
Abstract:
This paper studies contractivity of firing‑rate and Hopfield recurrent neural networks. We derive sharp LMI conditions on the synaptic matrices that characterize contractivity of both architectures, for activation functions that are either non‑expansive or monotone non‑expansive, in both continuous and discrete time. We establish structural relationships among these conditions, including connections to Schur diagonal stability and the recovery of optimal contraction rates for symmetric synaptic matrices. We demonstrate the utility of these results through two applications. First, we develop an LMI‑based design procedure for low‑gain integral controllers enabling reference tracking in contracting firing rate networks. Second, we provide an exact parameterization of weight matrices that guarantee contraction and use it to improve the expressivity of Implicit Neural Networks, achieving competitive performance on image classification benchmarks with fewer parameters.

Authors:Michael Chertkov
Title: Temporal Memory for Resource-Constrained Agents: Continual Learning via Stochastic Compress-Add-Smooth
Abstract:
An agent that operates sequentially must incorporate new experience without forgetting old experience, under a fixed memory budget. We propose a framework in which memory is not a parameter vector but a stochastic process: a Bridge Diffusion on a replay interval [0,1], whose terminal marginal encodes the present and whose intermediate marginals encode the past. New experience is incorporated via a three‑step \emphCompress‑‑Add‑‑Smooth (CAS) recursion. We test the framework on the class of models with marginal probability densities modeled via Gaussian mixtures of fixed number of components~K in d dimensions; temporal complexity is controlled by a fixed number~L of piecewise‑linear protocol segments whose nodes store Gaussian‑mixture states. The entire recursion costs O(LKd^2) flops per day ‑‑ no backpropagation, no stored data, no neural networks ‑‑ making it viable for controller‑light hardware. Forgetting in this framework arises not from parameter interference but from lossy temporal compression: the re‑approximation of a finer protocol by a coarser one under a fixed segment budget. We find that the retention half‑life scales linearly as a_1/2\approx c\,L with a constant c>1 that depends on the dynamics but not on the mixture complexity~K, the dimension~d, or the geometry of the target family. The constant~c admits an information‑theoretic interpretation analogous to the Shannon channel capacity. The stochastic process underlying the bridge provides temporally coherent ``movie'' replay ‑‑ compressed narratives of the agent's history, demonstrated visually on an MNIST latent‑space illustration. The framework provides a fully analytical ``Ising model'' of continual learning in which the mechanism, rate, and form of forgetting can be studied with mathematical precision.

Authors:Hyun Jong Yang, Howon Lee, Kyuhong Shim, Jeongho Kwak, Hyunsoo Kim, Donghoon Kim, Khoa Anh Ngo, Sehyun Ryu, Jaehyun Choi, Youbin Kim, Chanjun Moon, Michael Ryoo, Byonghyo Shim
Title: Advancing Multi-Robot Networks via MLLM-Driven Sensing, Communication, and Computation: A Comprehensive Survey
Abstract:
Imagine advanced humanoid robots, powered by multimodal large language models (MLLMs), coordinating missions across industries like warehouse logistics, manufacturing, and safety rescue. While individual robots show local autonomy, realistic tasks demand coordination among multiple agents sharing vast streams of sensor data. Communication is indispensable, yet transmitting comprehensive data can overwhelm networks, especially when a system‑level orchestrator or cloud‑based MLLM fuses multimodal inputs for route planning or anomaly detection. These tasks are often initiated by high‑level natural language instructions. This intent serves as a filter for resource optimization: by understanding the goal via MLLMs, the system can selectively activate relevant sensing modalities, dynamically allocate bandwidth, and determine computation placement. Thus, R2X is fundamentally an intent‑to‑resource orchestration problem where sensing, communication, and computation are jointly optimized to maximize task‑level success under resource constraints. This survey examines how integrated design paves the way for multi‑robot coordination under MLLM guidance. We review state‑of‑the‑art sensing modalities, communication strategies, and computing approaches, highlighting how reasoning is split between on‑device models and powerful edge/cloud servers. We present four end‑to‑end demonstrations (sense ‑> communicate ‑> compute ‑> act): (i) digital‑twin warehouse navigation with predictive link context, (ii) mobility‑driven proactive MCS control, (iii) a FollowMe robot with a semantic‑sensing switch, and (iv) real‑hardware open‑vocabulary trash sorting via edge‑assisted MLLM grounding. We emphasize system‑level metrics ‑‑ payload, latency, and success ‑‑ to show why R2X orchestration outperforms purely on‑device baselines.

Authors:Haowen Xu, Xin Chen
Title: Model-Free Coordinated Optimization of IBR Controllers for Enhanced Grid-Level Transient Dynamic Performance
Abstract:
With the increasing penetration of inverter‑based resources (IBRs) in power grids, system‑level coordinated optimization of IBR controllers has become increasingly important for maintaining overall system stability. Unlike most existing methods that rely on simplified or linearized dynamic models and focus on small‑signal stability or isolated tuning of individual facilities, this paper proposes a novel simulation‑based, model‑free framework for the coordinated optimization of IBR control parameters to enhance grid transient dynamic performance. The framework uses a high‑fidelity power system simulator to accurately evaluate grid transient dynamic responses, and a projected multi‑point zeroth‑order optimization algorithm with adaptive moment estimation, termed PMZO‑Adam, is proposed to solve the problem in a model‑free manner, thus eliminating the need for explicit mathematical models of complex nonlinear system dynamics. The proposed framework enables direct optimization of grid transient dynamic behavior and system‑wide coordinated tuning of IBR controllers. Extensive simulations demonstrate the effectiveness of the proposed approach in optimizing IBR control parameters to improve grid transient frequency response under large disturbances.

Authors:Ingyu Jang, Leila J. Bridgeman
Title: Consensus-Based Multi-Objective Controller Synthesis
Abstract:
Despite longstanding interest, controller synthesis remains challenging for networks of heterogeneous, nonlinear agents. Moreover, the requirements for computational scalability and information privacy have become increasingly critical. This paper introduces a dissipativity‑based distributed controller synthesis framework for networks with heterogeneous agents and diverse performance objectives, leveraging the Network Dissipativity Theorem and iterative convex overbounding. Our approach enables the synthesis of controllers in a distributed way by achieving a network‑wide consensus on agents' dissipativity variables while keeping sensitive subsystem information locally. The proposed framework is applied to full‑state feedback controller synthesis.

Authors:Marios Impraimakis, Feiyu Zhou, Andrew Plummer
Title: An Information-Theoretic Method for Dynamic System Identification With Output-Only Damping Estimation
Abstract:
The system identification capabilities of a novel information‑theoretic method are examined here. Specifically, this work uses information‑theoretic metrics and vibration‑based measurements to enhance damping estimation accuracy in mechanical systems. The method refers to a key limitation in system identification, signal processing, monitoring, and alert systems. These systems integrate various components, including sensors, data acquisition devices, and alert mechanisms. They are designed to operate in an environment to calculate key parameters such as peak accelerations and duration of high acceleration values. The current operational modal identification methods, though, suffer from limitations related to obtaining poor damping estimates due to their empirical nature. This has a significant impact on alert warning systems. This occurs when their duration is misestimated; specifically, when using the vibration amplitudes as an indicator of danger alerts for monitoring systems in damage or anomaly detection scenarios. To this end, approaches based on the Shannon entropy and the Kullback‑Leibler divergence concept are proposed. The primary objective is to monitor the vibration levels in near real‑time and provide immediate alerts when predefined thresholds are exceeded. In considering the proposed approach, both new real‑world data from the multi‑axis simulation table at the University of Bath, as well as the benchmark International Association for Structural Control‑American Society of Civil Engineers (IASC‑ASCE) structural health monitoring problem are considered. Importantly, the approach is shown to select the optimal model, which accurately captures the correct alert duration, providing a powerful tool for system identification and monitoring.

Authors:Hans Riess, Yujun Huang, Matthew Klawonn, Gioele Zardini, Matthew Hale
Title: Quantale-Enriched Co-Design: Toward a Framework for Quantitative Heterogeneous System Design
Abstract:
Monotone co‑design enables compositional engineering design by modeling components through feasibility relations between required resources and provided functionalities. However, its standard boolean formulation cannot natively represent quantitative criteria such as cost, confidence, or implementation choice. In practice, these quantities are often introduced through ad hoc scalarization or by augmenting the resource space, which obscures system structure and increases computational burden. We address this limitation by developing a quantale‑enriched theory of co‑design. We model resources and functionalities as quantale‑enriched categories and design problems as quantale‑enriched profunctors, thereby lifting co‑design from boolean feasibility to general quantitative evaluation. We show that the fundamental operations of series, parallel, and feedback composition remain valid over arbitrary commutative quantales. We further introduce heterogeneous composition through change‑of‑base maps between quantales, enabling different subsystems to be evaluated in different local semantics and then composed in a common framework. The resulting theory unifies feasibility‑, cost‑, confidence‑, and implementation‑aware co‑design within one compositional formalism. Numerical examples on a target‑tracking system and a UAV delivery problem demonstrate the framework and highlight how native quantitative enrichment can avoid the architectural and computational drawbacks of boolean‑only formulations.

Authors:Yi Zhang, Zixing Wang, Fulvio Forni
Title: Passive iFIR filters for data-driven velocity control in robotics
Abstract:
We present a passive, data‑driven velocity control method for nonlinear robotic manipulators that achieves better tracking performance than optimized PID with comparable design complexity. Using only three minutes of probing data, a VRFT‑based design identifies passive iFIR controllers that (i) preserve closed‑loop stability via passivity constraints and (ii) outperform a VRFT‑tuned PID baseline on the Franka Research 3 robot in both joint‑space and Cartesian‑space velocity control, achieving up to a 74.5% reduction in tracking error for the Cartesian velocity tracking experiment with the most demanding reference model. When the robot end‑effector dynamics change, the controller can be re‑learned from new data, regaining nominal performance. This study bridges learning‑based control and stability‑guaranteed design: passive iFIR learns from data while retaining passivity‑based stability guarantees, unlike many learning‑based approaches.

Authors:Bukunmi G. Odunlami, Marcos Netto, Hai Lin
Title: Salted Fisher Information for Hybrid Systems
Abstract:
Discrete events alter how parameter influence propagates in hybrid systems. Prevailing Fisher information formulations assume that sensitivities evolve smoothly according to continuous‑time variational equations and therefore neglect the sensitivity updates induced by discrete events. This paper derives a Fisher information matrix formulation compatible with hybrid systems. To do so, we use the saltation matrix, which encodes the first order transformation of sensitivities induced by discrete events. The resulting formulation is referred to as the salted Fisher information matrix (SFIM). The proposed framework unifies continuous information accumulation during flows with discrete updates at event times. We further establish that hybrid persistence of excitation provides a sufficient condition for positive definiteness of the SFIM. Examples are provided to demonstrate the merit of the proposed approach, including a three bus generator wind turbine differential algebraic power system

Authors:Victor G. Lopez, Malte Heinrich, Matthias A. Müller
Title: An Output Feedback Q-learning Algorithm for Optimal Control of Nonlinear Systems with Koopman Linear Embedding
Abstract:
In the reinforcement learning literature, strong theoretical guarantees have been obtained for algorithms applicable to LTI systems. However, in the nonlinear case only weaker results have been obtained for algorithms that mostly rely on the use of function approximation strategies like, for example, neural networks. In this paper, we study the applicability of a known output‑feedback Q‑learning algorithm to the class of nonlinear systems that admit a Koopman linear embedding. This algorithm uses only input‑output data, and no knowledge of either the system model or the Koopman lifting functions is required. Moreover, no function approximation techniques are used, and the same theoretical guarantees as for LTI systems are preserved. Furthermore, we analyze the performance of the algorithm when the Koopman linear embedding is only an approximation of the real nonlinear system. A simulation example verifies the applicability of this method.

Authors:Juan Pablo Bertucci, Theo Hofman, Mauro Salazar
Title: Simultaneous Optimization of Electric Ferry Operations and Charging Infrastructure
Abstract:
Electrification of marine transport is a promising solution to reduce sector greenhouse gas emissions and operational costs. However, the large upfront cost of electric vessels and the required charging infrastructure can be a barrier to the development of this technology. Optimization algorithms that jointly design the charging infrastructure and the operation of electric vessels can help to reduce these costs and make these projects viable. In this paper, we present a mixed‑integer linear programming optimization framework that jointly schedules ferry operations, charging infrastructure and ship battery size. We analyze our algorithms with the case of the China Zorrilla, the largest electric ferry in the world, which will operate between Buenos Aires and Colonia del Sacramento in 2025. We find that the joint system and operations design can reduce the total costs by 7.8% compared to a scenario with fixed power limits and no port energy management system.

Authors:Severin Beger, Yuling Chen, Sandra Hirche
Title: Where to Put Safety? Control Barrier Function Placement in Networked Control Systems
Abstract:
Ensuring safe behavior is critical for modern autonomous cyber‑physical systems. Control barrier functions (CBFs) are widely used to enforce safety in autonomous systems, yet their placement within networked control architectures remains largely unexplored. In this work, we investigate where to enforce safety in a networked control system in which a remote model predictive controller (MPC) communicates with the plant over a delayed network. We compare two safety strategies: i) a local myopic CBF filter applied at the plant and ii) predictive CBF constraints embedded in the remote MPC. For both architectures, we derive state‑dependent disturbance tolerance bounds and show that safety placement induces a fundamental trade‑off: local CBFs provide higher disturbance tolerance due to access to fresh state measurements, whereas MPC‑CBF enables improved performance through anticipatory behavior, but yields stricter admissible disturbance levels. Motivated by this insight, we propose a combined architecture that integrates predictive and local safety mechanisms. The theoretical findings are illustrated in simulations on a planar three‑degree‑of‑freedom robot performing a collision‑avoidance task.

Authors:Haris Gacanin
Title: AI-Programmable Wireless Connectivity: Challenges and Research Directions Toward Interactive and Immersive Industry
Abstract:
This vision paper addresses the research challenges of integrating traditional signal processing with Artificial Intelligence (AI) to enable energy‑efficient, programmable, and scalable wireless connectivity infrastructures. While prior studies have primarily focused on high‑level concepts, such as the potential role of Large Language Model (LLM) in 6G systems, this work advances the discussion by emphasizing integration challenges and research opportunities at the system level. Specifically, this paper examines the role of compact AI models, including Tiny and Real‑time Machine Learning (ML), in enhancing wireless connectivity while adhering to strict constraints on computing resources, adaptability, and reliability. Application examples are provided to illustrate practical considerations and highlight how AI‑driven signal processing can support next‑generation wireless networks. By combining classical signal processing with lightweight AI methods, this paper outlines a pathway toward efficient and adaptive connectivity solutions for 6G and beyond.

Authors:Hendrik Vater, Oliver Schweins, Lukas Hölsch, Wilhelm Kirchgässner, Till Piepenbrock, Oliver Wallscheid
Title: RHINO-MAG: Recursive H-Field Inference based on Observed Magnetic Flux under Dynamic Excitation
Abstract:
Driven by the MagNet Challenge 2025 (MC2), increased research interest is directed towards modeling transient magnetic fields within ferrite material. An accurate time‑resolved and temperature‑aware H‑field prediction is essential for optimizing magnetic components in applications with quasi‑stationary / non‑stationary excitation waveforms. Within the scope of this investigation, a selection of model structures with varying degrees of physically motivated structure are compared. Based on a Pareto investigation, a rather black‑box gated recurrent unit (GRU) model structure with a graceful initialization setup is found to offer the most attractive model size vs. model accuracy trade‑off, while the physics‑inspired models performed worse. For a GRU‑based model with only 325 parameters, a sequence relative error of 8.02 % and a normalized energy relative error of 1.07 % averaged across five different materials are achieved on unseen test data. With this excellent parameter efficiency, the proposed model won the first place in the performance category of the MC2.

Authors:Yahia Salaheldin Shaaban, Abdelrahman Sayed Sayed, M. Umar B. Niazi, Karl Henrik Johansson
Title: HyperKKL: Learning KKL Observers for Non-Autonomous Nonlinear Systems via Hypernetwork-Based Input Conditioning
Abstract:
Kazantzis‑Kravaris/Luenberger (KKL) observers are a class of state observers for nonlinear systems that rely on an injective map to transform the nonlinear dynamics into a stable quasi‑linear latent space, from where the state estimate is obtained in the original coordinates via a left inverse of the transformation map. Current learning‑based methods for these maps are designed exclusively for autonomous systems and do not generalize well to controlled or non‑autonomous systems. In this paper, we propose two learning‑based designs of neural KKL observers for non‑autonomous systems whose dynamics are influenced by exogenous inputs. To this end, a hypernetwork‑based framework (HyperKKL) is proposed with two input‑conditioning strategies. First, an augmented observer approach (HyperKKL_obs) adds input‑dependent corrections to the latent observer dynamics while retaining static transformation maps. Second, a dynamic observer approach (HyperKKL_dyn) employs a hypernetwork to generate encoder and decoder weights that are input‑dependent, yielding time‑varying transformation maps. We derive a theoretical worst‑case bound on the state estimation error. Numerical evaluations on four nonlinear benchmark systems show that input conditioning yields consistent improvements in estimation accuracy over static autonomous maps, with an average symmetric mean absolute percentage error (SMAPE) reduction of 29% across all non‑zero input regimes.

Authors:Hyeonho Noh, Jonggyu Jang
Title: α-Fair Multistatic ISAC Beamforming for Multi-User MIMO-OFDM Systems via Riemannian Optimization
Abstract:
This paper proposes an α‑fair multistatic integrated sensing and communication (ISAC) framework for multi‑user multi‑input multi‑output (MIMO)‑orthogonal frequency division multiplexing (OFDM) systems, where communication users act as passive bistatic receivers to enable multistatic sensing. Unlike existing works that optimize aggregate sensing metrics and thus favor geometrically advantageous targets, we minimize the α‑fairness utility over per‑target Cramér‑‑Rao lower bounds (CRLBs) subject to per‑user minimum data rate and transmit power constraints. The resulting non‑convex problem is solved via the Riemannian conjugate gradient (RCG) method with a smooth penalty reformulation. Simulation results validate the effectiveness of the proposed scheme in achieving a favorable sensing fairness‑‑communication trade‑off.

Authors:Yu Liu
Title: Load Scheduling for Pulse Charging to Flatten Aggregate Power Demand
Abstract:
Pulse charging can be used to boost up charging speed for lithium‑ion batteries and delay battery capacity fading by periodically pausing the current during charging. However, this technique introduces intermittence for current and may thus challenge the electric stability of charger as well as its energy supply source. To deal with this challenge, a coordination method for multiple loads simultaneously being charged has been proposed in this paper. The method exploits the off‑time intervals of pulse current to charge other loads. By properly grouping and coordinating the charging loads, the fluctuation and amplitude of the charging current can be mitigated. To optimally schedule all charging loads, mathematical models are formulated to help find out the best scheduling scheme for the loads. Two scenarios have been considered as well as two mathematical models have been proposed and elucidated in the paper. In one scenario all loads are charged using PCs with the same frequency, while in the other scenario PCs with various frequencies are considered. In addition, a procedure of scheduling the charging process considering power limit is developed. The proposed method has been applied to and quantitatively evaluated in two application scenarios. Compared to randomly charging, both fluctuation and amplitude of the total current for multiple loads simultaneously being charged have been mitigated after properly scheduled. Using the proposed method, the merits of pulse charging for batteries can be utilized while the stability issue can be alleviated.

Authors:Soumyodipta Nath, Pranav Tiwari, Ravi Prakash
Title: SafeDMPs: Integrating Formal Safety with DMPs for Adaptive HRI
Abstract:
Robots operating in human‑centric environments must be both robust to disturbances and provably safe from collisions. Achieving these properties simultaneously and efficiently remains a central challenge. While Dynamic Movement Primitives (DMPs) offer inherent stability and generalization from single demonstrations, they lack formal safety guarantees. Conversely, formal methods like Control Barrier Functions (CBFs) provide provable safety but often rely on computationally expensive, real‑time optimization, hindering their use in high‑frequency control. This paper introduces SafeDMPs, a novel framework that resolves this trade‑off. We integrate the closed‑form efficiency and dynamic robustness of DMPs with a provably safe, non‑optimization‑based control law derived from Spatio‑Temporal Tubes (STTs). This synergy allows us to generate motions that are not only robust to perturbations and adaptable to new goals, but also guaranteed to avoid static and dynamic obstacles. Our approach achieves a closed‑form solution for a problem that traditionally requires online optimization. Experimental results on a 7‑DOF robot manipulator demonstrate that SafeDMPs is orders of magnitude faster and more accurate than optimization‑based baselines, making it an ideal solution for real‑time, safe, and collaborative robotics.

Authors:Jonas Ohnemus, Alexandre Didier, Ahmed Aboudonia, Andrea Carron, Melanie N. Zeilinger
Title: Distributed Predictive Control Barrier Functions: Towards Scalable Safety Certification in Modular Multi-Agent Systems
Abstract:
We consider safety‑critical multi‑agent systems with distributed control architectures and potentially varying network topologies. While learning‑based distributed control enables scalability and high performance, a lack of formal safety guarantees in the face of unforeseen disturbances and unsafe network topology changes may lead to system failure. To address this challenge, we introduce structured control barrier functions (s‑CBFs) as a multi‑agent safety framework. The s‑CBFs are augmented to a distributed predictive control barrier function (D‑PCBF), a predictive, optimization‑based safety layer that uses model predictions to guarantee recoverable safety at all times. The proposed approach enables a permissive yet formal plug‑and‑play protocol, allowing agents to join or leave the network while ensuring safety recovery if a change in network topology requires temporarily unsafe behavior. We validate the formulation through simulations and real‑time experiments of a miniature race‑car platoon.

Authors:E. Javier Olucha, Valentin Preda, Amritam Das, Roland Tóth
Title: Learning Surrogate LPV State-Space Models with Uncertainty Quantification
Abstract:
The Linear Parameter‑Varying (LPV) framework enables the construction of surrogate models of complex nonlinear and high‑dimensional systems, facilitating efficient stability and performance analysis together with controller design. Despite significant advances in data‑driven LPV modelling, existing approaches do not quantify the uncertainty of the obtained LPV models. Consequently, assessing model reliability for analysis and control or detecting operation outside the training regime requires extensive validation and user expertise. This paper proposes a Bayesian approach for the joint estimation of LPV state‑space models together with their scheduling, providing a characterization of model uncertainty and confidence bounds on the predicted model response directly from input‑output data. Both aleatoric uncertainty due to measurement noise and epistemic uncertainty arising from limited training data and structural bias are considered. The resulting model preserves the LPV structure required for controller synthesis while enabling computationally efficient simulation and uncertainty propagation. The approach is demonstrated on the surrogate modelling of a two‑dimensional nonlinear interconnection of mass‑spring‑damper systems.

Authors:Liang Xu, Tao Liu, Zhiyun Lin
Title: Cooperative Control of Parallel Actuators for Linear Robust Output Regulation of Uncertain Linear Minimum-phase Plants
Abstract:
This paper investigates the robust output regulation problem for an uncertain linear minimum‑phase plant with cooperative parallel operation of multiple actuators. Building on the internal model approach, we first propose a dynamic output feedback control law to solve the robust output regulation problem with a single actuator. Then, we construct a distributed dynamic output feedback control law that is nearly independent of the number of actuators and incorporates coupling terms to address the linear robust output regulation problem with cooperative parallel operation of multiple actuators over undirected communication networks. We reveal the connection in the design of parameters between the dynamic output feedback control law under single actuator operation and the distributed dynamic output feedback control law under cooperative parallel operation with multiple actuators. Moreover, we remove the existing assumption that the actuator dynamics must be Hurwitz stable, thereby enabling the incorporation of unstable actuators in our framework. Finally, two numerical examples are provided to validate the effectiveness of the proposed control laws.

Authors:Yunqi Wang, Xinghuo Yu, Mahdi Jalili
Title: GeoDistNet: An Open-Source Tool for Synthetic Distribution Network Generation
Abstract:
Distribution‑level studies increasingly require feeder models that are both electrically usable and structurally representative of practical service areas. However, detailed utility feeder data are rarely accessible, while benchmark systems often fail to capture the geographic organization of real urban and suburban networks. This paper presents GeoDistNet, an open‑source tool for synthetic distribution network generation from publicly available geographic information. Starting from map‑derived spatial data, the proposed workflow constructs a candidate graph, synthesizes feeder‑compatible radial topology through a mixed‑integer formulation, assigns representative electrical parameters and loads, and exports the resulting network for power‑flow analysis. A Melbourne case study shows that the generated feeder remains geographically interpretable, topologically structured, and directly usable in \textttpandapower under multiple loading levels. GeoDistNet therefore provides a reproducible workflow for bridging publicly accessible GIS data and simulation‑ready distribution feeder models when detailed utility networks are unavailable.

Authors:Shuyue Li, Miguel López-Benítez, Eng Gee Lim, Fei Ma, Qian Dong, Mengze Cao, Limin Yu, Xiaohui Qin
Title: Communication Outage-Resistant UUV State Estimation: A Variational History Distillation Approach
Abstract:
The reliable operation of Unmanned Underwater Vehicle (UUV) clusters is highly dependent on continuous acoustic communication. However, this communication method is highly susceptible to intermittent interruptions. When communication outages occur, standard state estimators such as the Unscented Kalman Filter (UKF) will be forced to make open‑loop predictions. If the environment contains unmodeled dynamic factors, such as unknown ocean currents, this estimation error will grow rapidly, which may eventually lead to mission failure. To address this critical issue, this paper proposes a Variational History Distillation (VHD) approach. VHD regards trajectory prediction as an approximate Bayesian reasoning process, which links a standard motion model based on physics with a pattern extracted directly from the past trajectory of the UUV. This is achieved by synthesizing ``virtual measurements'' distilled from historical trajectories. Recognizing that the reliability of extrapolated historical trends degrades over extended prediction horizons, an adaptive confidence mechanism is introduced. This mechanism allows the filter to gradually reduce the trust of virtual measurements as the communication outage time is extended. Extensive Monte Carlo simulations in a high‑fidelity environment demonstrate that the proposed method achieves a 91% reduction in prediction Root Mean Square Error (RMSE), reducing the error from approximately 170 m to 15 m during a 40‑second communication outage. These results demonstrate that VHD can maintain robust state estimation performance even under complete communication loss.

Authors:Teruki Kato, Koshi Oishi, Seigo Ito
Title: Model Predictive Path Integral PID Control for Learning-Based Path Following
Abstract:
Classical proportional‑‑integral‑‑derivative (PID) control is widely employed in industrial applications; however, achieving higher performance often motivates the adoption of model predictive control (MPC). Although gradient‑based methods are the standard for real‑time optimization, sampling‑based approaches have recently gained attention. In particular, model predictive path integral (MPPI) control enables gradient‑free optimization and accommodates non‑differentiable models and objective functions. However, directly sampling control input sequences may yield discontinuous inputs and increase the optimization dimensionality in proportion to the prediction horizon. This study proposes MPPI‑‑PID control, which applies MPPI to optimize PID gains at each control step, thereby replacing direct high‑dimensional input‑sequence optimization with low‑dimensional gain‑space optimization. This formulation enhances sample efficiency and yields smoother inputs via the PID structure. We also provide theoretical insights, including an information‑theoretic interpretation that unifies MPPI and MPPI‑‑PID, an analysis of the effect of optimization dimensionality on sample efficiency, and a characterization of input continuity induced by the PID structure. The proposed method is evaluated on the learning‑based path following of a mini forklift using a residual‑learning dynamics model that integrates a physical model with a neural network. System identification is performed with real driving data. Numerical path‑following experiments demonstrate that MPPI‑‑PID improves tracking performance compared with fixed‑gain PID and achieves performance comparable to conventional MPPI while significantly reducing input increments. Furthermore, the proposed method maintains favorable performance even with substantially fewer samples, demonstrating its improved sample efficiency.

Authors:Simon Schmidt, Nicole Gehring, Abdurrahman Irscheid
Title: Flatness-based control of a Timoshenko beam
Abstract:
The paper presents an approach to flatness‑based control design for hyperbolic multi‑input systems, building upon the hyperbolic controller form (HCF). The transformation into HCF yields a simplified system representation that considerably facilitates the design of state feedback controllers for trajectory tracking. The proposed concept is demonstrated for a Timoshenko beam and validated through numerical simulations, demonstrating trajectory tracking and closed‑loop stability.

Authors:Philipp Reis, Jacqueline Henle, Stefan Otten, Eric Sax
Title: From Big Data to Fast Data: Towards High-Quality Datasets for Machine Learning Applications from Closed-Loop Data Collection
Abstract:
The increasing capabilities of machine learning models, such as vision‑language and multimodal language models, are placing growing demands on data in automotive systems engineering, making the quality and relevance of collected data enablers for the development and validation of such systems. Traditional Big Data approaches focus on large‑scale data collection and offline processing, while Smart Data approaches improve data selection strategies but still rely on centralized and offline post‑processing. This paper introduces the concept of Fast Data for automotive systems engineering. The approach shifts data selection and recording onto the vehicle as the data source. By enabling real‑time, context‑aware decisions on whether and which data should be recorded, data collection can be directly aligned with data quality objectives and collection strategies within a closed‑loop. This results in datasets with higher relevance, improved coverage of critical scenarios, and increased information density, while at the same time reducing irrelevant data and associated costs. The proposed approach provides a structured foundation for designing data collection strategies that are aligned with the needs of modern machine learning algorithms. It supports efficient data acquisition and contributes to scalable and cost‑effective ML development processes in automotive systems engineering.

Authors:Sampath Kumar Mulagaleti, Alberto Bemporad
Title: Dual MPC for quasi-Linear Parameter Varying systems
Abstract:
We present a dual Model Predictive Control (MPC) framework for the simultaneous identification and control of quasi‑Linear Parameter Varying (qLPV) systems. The framework is composed of an online estimator for the states and parameters of the qLPV system, and a controller that leverages the estimated model to compute inputs with a dual purpose: tracking a reference output while actively exciting the system to enhance parameter estimation. The core of this approach is a robust tube‑based MPC scheme that exploits recent developments in polytopic geometry to guarantee recursive feasibility and stability in spite of model uncertainty. The effectiveness of the framework in achieving improved tracking performance while identifying a model of the system is demonstrated through a numerical example.

Authors:Yihan Liu, Meiqi Tian, Teng Yan, Bingzhuo Zhong
Title: Communication-Aware Synthesis of Safety Controller for Networked Control Systems
Abstract:
Networked control systems (NCS) are widely used in safety‑critical applications, but they are often analyzed under the assumption of ideal communication channels. This work focuses on the synthesis of safety controllers for discrete‑time linear systems affected by unknown disturbances operating in imperfect communication channels. The proposed method guarantees safety by constructing ellipsoidal robust safety invariant (RSI) sets and verifying their invariance through linear matrix inequalities (LMI), which are formulated and solved as semi‑definite programming (SDP). In particular, our framework simultaneously considers controller synthesis and communication errors without requiring explicit modeling of the communication channel. A case study on cruise control problem demonstrates that the proposed controller ensures safety in the presence of unexpected disturbances and multiple communication imperfections simultaneously.

Authors:Weiheng Hua, Changyu Hao
Title: ARC: Alignment-based RPM Estimation with Curvature-adaptive Tracking
Abstract:
Tacho‑less rotational speed estimation is critical for vibration‑based prognostics and health management (PHM) of rotating machinery, yet traditional methods‑‑such as time‑domain periodicity, cepstrum, and harmonic comb matching‑‑struggle under noise, non‑stationarity, and inharmonic interference. Probabilistic tracking offers a principled way to fuse multiple estimators, but a major challenge is that heterogeneous estimators produce evidence on incompatible axes and scales. We address this with ARC (Alignment‑based RPM Estimation with Curvature‑adaptive Tracking) by unifying the observation representation. Each estimator outputs a one‑dimensional evidence curve on its native axis, which is mapped onto a shared RPM grid and converted into a comparable grid‑based log‑likelihood via robust standardization and a Gibbs‑form energy shaping. Standard recursive filtering with fixed‑variance motion priors can fail under multi‑modal or ambiguous evidence. To overcome this, ARC introduces a curvature‑informed, state‑dependent motion prior, where the transition variance is derived from the local discrete Hessian of the previous log‑posterior. This design enforces smooth tracking around confident modes while preserving competing hypotheses, such as octave alternatives. Experiments on synthetic stress tests and real vibration‑table data demonstrate stable, physically plausible trajectories with interpretable uncertainty, and ablations confirm that these gains arise from uncertainty‑aware temporal propagation rather than per‑frame peak selection or ad hoc rules.

Authors:Shiva Shakeri, Péter Baranyi, Mehran Mesbahi
Title: Receding-Horizon Policy Gradient for Polytopic Controller Synthesis
Abstract:
We propose the Polytopic Receding‑Horizon Policy Gradient (P‑RHPG) algorithm for synthesizing Parallel Distributed Compensation (PDC) controllers via Tensor Product (TP) model transformation. Standard LMI‑based PDC synthesis grows increasingly conservative as model fidelity improves; P‑RHPG instead solves a finite‑horizon integrated cost via backward‑stage decomposition. The key result is that each stage subproblem is a strongly convex quadratic in the vertex gains, a consequence of the linear independence of the HOSVD weighting functions, guaranteeing a unique global minimizer and linear convergence of gradient descent from any initialization. With zero terminal cost, the optimal cost increases monotonically to a finite limit and the gain sequence remains bounded; terminal costs satisfying a mild Lyapunov condition yield non‑increasing convergence. Experiments on an aeroelastic wing benchmark confirm convergence to a unique infinite‑horizon optimum across all tested terminal cost choices and near‑optimal performance relative to the pointwise Riccati lower bound.

Authors:Osasumwen Cedric Ogiesoba-Eguakun, Kaveh Ashenayi, Suman Rath
Title: Real-Time Surrogate Modeling for Fast Transient Prediction in Inverter-Based Microgrids Using CNN and LightGBM
Abstract:
Real‑time monitoring of inverter‑based microgrids is essential for stability, fault response, and operational decision‑making. However, electromagnetic transient (EMT) simulations, required to capture fast inverter dynamics, are computationally intensive and unsuitable for real‑time applications. This paper presents a data‑driven surrogate modeling framework for fast prediction of microgrid behavior using convolutional neural networks (CNN) and Light Gradient Boosting Machine (LightGBM). The models are trained on a high‑fidelity EMT digital twin dataset of a microgrid with ten distributed generators under eleven operating and disturbance scenarios, including faults, noise, and communication delays. A sliding‑window method is applied to predict important system variables, including voltage magnitude, frequency, total active power, and voltage dip. The results show that model performance changes depending on the type of variable being predicted. The CNN demonstrates high accuracy for time‑dependent signals such as voltage, with an R^2 value of 0.84, whereas LightGBM shows better performance for structured and disturbance‑related variables, achieving an R^2 of 0.999 for frequency and 0.75 for voltage dip. A combined CNN+LightGBM model delivers stable performance across all variables. Beyond accuracy, the surrogate models also provide major improvements in computational efficiency. LightGBM achieves more than 1000× speedup and runs faster than real time, while the hybrid model achieves over 500× speedup with near real‑time performance. These findings show that data‑driven surrogate models can effectively represent microgrid dynamics. They also support real‑time and faster‑than‑real‑time predictions. As a result, they are well‑suited for applications such as monitoring, fault analysis, and control in inverter‑based power systems.

Authors:Igor G. Vladimirov, Ian R. Petersen, Guodong Shi
Title: Pointwise and dynamic programming control synthesis for finite-level open quantum memory systems
Abstract:
This paper is concerned with finite‑level quantum memory systems for retaining initial dynamic variables in the presence of external quantum noise. The system variables have an algebraic structure, similar to that of the Pauli matrices, and their Heisenberg picture evolution is governed by a quasilinear quantum stochastic differential equation. The latter involves a Hamiltonian whose parameters depend affinely on a classical control signal in the form of a deterministic function of time. The memory performance is quantified by a mean‑square deviation of quantum system variables of interest from their initial conditions. We relate this functional to a matrix‑valued state of an auxiliary classical control‑affine dynamical system. This leads to a pointwise control design where the control signal minimises the time‑derivative of the mean‑square deviation with an additional quadratic penalty on the control. In an alternative finite‑horizon setting with a terminal‑integral cost functional, we apply dynamic programming and obtain a quadratically nonlinear Hamilton‑Jacobi‑Bellman equation, for which a solution is outlined in the form of a recursively computed asymptotic expansion.

Authors:Ian Xul Belaustegui, Himani Sinhmar, Ling-Wei Kong, Andrew Michael Hein, Naomi Ehrich Leonard
Title: A Continuous-Time and State-Space Relaxation of the Linear Threshold Model with Nonlinear Opinion Dynamics
Abstract:
The Linear Threshold Model (LTM) is widely used to study the propagation of collective behaviors as complex contagions. However, its dependence on discrete states and timesteps restricts its ability to capture the multiple time‑scales inherent in decision‑making, as well as the effects of subthreshold signaling. To address these limitations, we introduce a continuous‑time and state‑space relaxation of the LTM based on the Nonlinear Opinion Dynamics (NOD) framework. By replacing the discontinuous step‑function thresholds of the LTM with the smooth bifurcations of the NOD model, we map discrete cascade processes to the continuous flow of a dynamical system. We prove that, under appropriate parameter choices, activation in the discrete LTM guarantees activation in the continuous NOD relaxation for any given seed set. We establish computable conditions for equivalence: by sufficiently bounding the social coupling parameter, the continuous NOD cascades exactly recover the cascades of the discrete LTM. We then illustrate how this NOD relaxation provides a richer analytical framework than the LTM, allowing for the exploration of cascades driven by strictly subthreshold inputs and the role of temporally distributed signals.

Authors:Luke Bhan, Peter Quawas, Miroslav Krstic, Yuanyuan Shi
Title: Sampling-Horizon Neural Operator Predictors for Nonlinear Control under Delayed Inputs
Abstract:
Modern control systems frequently operate under input delays and sampled state measurements. A common delay‑compensation strategy is predictor feedback; however, practical implementations require solving an implicit ODE online, resulting in intractable computational cost. Moreover, predictor formulations typically assume continuously available state measurements, whereas in practice measurements may be sampled, irregular, or temporarily missing due to hardware faults. In this work, we develop two neural‑operator predictor‑feedback designs for nonlinear systems with delayed inputs and sampled measurements. In the first design, we introduce a sampling‑horizon prediction operator that maps the current measurement and input history to the predicted state trajectory over the next sampling interval. In the second design, the neural operator approximates only the delay‑compensating predictor, which is then composed with the closed‑loop flow between measurements. The first approach requires uniform sampling but yields residual bounds that scale directly with the operator approximation error. In contrast, the second accommodates non‑uniform, but bounded sampling schedules at the cost of amplified approximation error, revealing a practical tradeoff between sampling flexibility and approximation sensitivity for the control engineer. For both schemes, we establish semi‑global practical stability with explicit neural operator error‑dependent bounds. Numerical experiments on a 6‑link nonlinear robotic manipulator demonstrate accurate tracking and substantial computational speedup of 25× over a baseline approach.

Authors:Luke Bhan, Miroslav Krstic, Yuanyuan Shi
Title: Predictor-Based Output-Feedback Control of Linear Systems with Time-Varying Input and Measurement Delays via Neural-Approximated Prediction Horizons
Abstract:
Due to simplicity and strong stability guarantees, predictor feedback methods have stood as a popular approach for time delay systems since the 1950s. For time‑varying delays, however, implementation requires computing a prediction horizon defined by the inverse of the delay function, which is rarely available in closed form and must be approximated. In this work, we formulate the inverse delay mapping as an operator learning problem and study predictor feedback under approximation of the prediction horizon. We propose two approaches: (i) a numerical method based on time integration of an equivalent ODE, and (ii) a data‑driven method using neural operators to learn the inverse mapping. We show that both approaches achieve arbitrary approximation accuracy over compact sets, with complementary trade‑offs in computational cost and scalability. Building on these approximations, we then develop an output‑feedback predictor design for systems with delays in both the input and the measurement. We prove that the resulting closed‑loop system is globally exponentially stable when the prediction horizon is approximated with sufficiently small error. Lastly, numerical experiments validate the proposed methods and illustrate their trade‑offs between accuracy and computational efficiency.

Authors:Gabriele Marini, Alessandro Colombo, Andrea Lanubile, William A. Paxton, Simona Onori
Title: Design of an embedded hardware platform for cell-level diagnostics in commercial battery modules
Abstract:
While battery aging is commonly studied at the cell‑level, evaluating aging and performance within battery modules remains a critical challenge. Testing cells within fully assembled modules requires hardware solutions to access cell‑level information without compromising module integrity. In this paper, we design and develop a hardware testing platform to monitor and control the internal cells of battery modules contained in the Audi e‑tron battery pack. The testing is performed across all 36 modules of the pack. The platform integrates voltage sensors, balancing circuitry, and a micro‑controller to enable safe, simultaneous cell screening without disassembling the modules. Using the proposed testing platform, cell voltage imbalances within each module are constrained to a defined reference value, and cell signals can be safely accessed, enabling accurate and non‑invasive cell‑level state‑of‑health assessments. On a broader scale, our solution allows for the quantification of internal heterogeneity within modules, providing valuable insights for both first‑ and second‑life applications and supporting efficient battery pack maintenance and repurposing.

Authors:Yubo Cai, Yujun Huang, Meshal Alharbi, Gioele Zardini
Title: Scalable Co-Design via Linear Design Problems: Compositional Theory and Algorithms
Abstract:
Designing complex engineered systems requires managing tightly coupled trade‑offs between subsystem capabilities and resource requirements. Monotone co‑design provides a compositional language for such problems, but its generality does not by itself reveal which problem classes admit exact and scalable computation. This paper isolates such a class by introducing Linear Design Problems (LDPs): design problems whose feasible functionality‑‑resource relations are polyhedra over Euclidean posets. We show that queries on LDPs reduce exactly to Multi‑Objective Linear Programs (MOLPs), thereby connecting monotone co‑design semantics with polyhedral multiobjective optimization. We further prove that LDPs are closed under the fundamental co‑design interconnections, implying that any interconnection of linear components induces a system‑level LDP. To compute the resulting feasible sets, we develop two complementary constructions: a monolithic lifted formulation that preserves block‑angular sparsity, and a compositional formulation that incrementally eliminates internal variables through polyhedral projection. Beyond the exact linear setting, we show that convex co‑design resource queries admit arbitrarily accurate polyhedral outer approximations, with recession‑cone error identically zero for standard nonnegative resource cones. Numerical studies on synthetic series‑chain benchmarks, a gripper, and a rover co‑design validate the theory.

Authors:Leonardo Colombo, Álvaro Rodríguez Abella, Alexandre Anahory Simoes, Anthony Bloch
Title: Stable Walking for Bipedal Locomotion under Foot-Slip via Virtual Nonholonomic Constraints
Abstract:
Foot slip is a major source of instability in bipedal locomotion on low‑friction or uncertain terrain. Standard control approaches typically assume no‑slip contact and therefore degrade when slip occurs. We propose a control framework that explicitly incorporates slip into the locomotion model through virtual nonholonomic constraints, which regulate the tangential stance‑foot velocity while remaining compatible with the virtual holonomic constraints used to generate the walking gait. The resulting closed‑loop system is formulated as a hybrid dynamical system with continuous swing dynamics and discrete impact events. A nonlinear feedback law enforces both classes of constraints and yields a slip‑compatible hybrid zero dynamics manifold for the reduced‑order locomotion dynamics. Stability of periodic walking gaits is characterized through the associated Poincaré map, and numerical results illustrate stabilization under slip conditions.

Authors:Dhruv Shah, Jorge Cortes
Title: A Unified Algebraic Framework for Subspace Pruning in Koopman Operator Approximation via Principal Vectors
Abstract:
Finite‑dimensional approximations of the Koopman operator rely critically on identifying nearly invariant subspaces. This invariance proximity can be rigorously quantified via the principal angles between a candidate subspace and its image under the operator. To systematically minimize this error, we propose an algebraic framework for subspace pruning utilizing principal vectors. We establish the equivalence of this approach to existing consistency‑based methods while providing a foundation for broader generalizations. To ensure scalability, we introduce an efficient numerical update scheme based on rank‑one modifications, reducing the computational complexity of tracking principal angles by an order of magnitude. Finally, we demonstrate the effectiveness of our framework through numerical simulations.

Authors:Chengyang Gu, Yuxin Pan, Hui Xiong, Yize Chen
Title: A Pontryagin Method of Model-based Reinforcement Learning via Hamiltonian Actor-Critic
Abstract:
Model‑based reinforcement learning (MBRL) improves sample efficiency by leveraging learned dynamics models for policy optimization. However, the effectiveness of methods such as actor‑critic is often limited by compounding model errors, which degrade long‑horizon value estimation. Existing approaches, such as Model‑Based Value Expansion (MVE), partially mitigate this issue through multi‑step rollouts, but remain sensitive to rollout horizon selection and residual model bias. Motivated by the Pontryagin Maximum Principle (PMP), we propose Hamiltonian Actor‑Critic (HAC), a model‑based approach that eliminates explicit value function learning by directly optimizing a Hamiltonian defined over the learned dynamics and reward for deterministic systems. By avoiding value approximation, HAC reduces sensitivity to model errors while admitting convergence guarantees. Extensive experiments on continuous control benchmarks, in both online and offline RL settings, demonstrate that HAC outperforms model‑free and MVE‑based baselines in control performance, convergence speed, and robustness to distributional shift, including out‑of‑distribution (OOD) scenarios. In offline settings with limited data, HAC matches or exceeds state‑of‑the‑art methods, highlighting its strong sample efficiency.

Authors:Kartik Loya, Phanindra Tallapragada
Title: Koopman Operator Framework for Modeling and Control of Off-Road Vehicle on Deformable Terrain
Abstract:
This work presents a hybrid physics‑informed and data‑driven modeling framework for predictive control of autonomous off‑road vehicles operating on deformable terrain. Traditional high‑fidelity terramechanics models are often too computationally demanding to be directly used in control design. Modern Koopman operator methods can be used to represent the complex terramechanics and vehicle dynamics in a linear form. We develop a framework whereby a Koopman linear system can be constructed using data from simulations of a vehicle moving on deformable terrain. For vehicle simulations, the deformable‑terrain terramechanics are modeled using Bekker‑Wong theory, and the vehicle is represented as a simplified five‑degree‑of‑freedom (5‑DOF) system. The Koopman operators are identified from large simulation datasets for sandy loam and clay using a recursive subspace identification method, where Grassmannian distance is used to prioritize informative data segments during training. The advantage of this approach is that the Koopman operator learned from simulations can be updated with data from the physical system in a seamless manner, making this a hybrid physics‑informed and data‑driven approach. Prediction results demonstrate stable short‑horizon accuracy and robustness under mild terrain‑height variations. When embedded in a constrained MPC, the learned predictor enables stable closed‑loop tracking of aggressive maneuvers while satisfying steering and torque limits.

Authors:Nane Zimmermann, Lukas P. Wagner, Luca von Rönn, Florian Strobel, Paul Hüttmann, Felix Gehlhoff
Title: From Energy Transition Pathways to Measurement Requirements: A Scenario-Based Study of Low-Voltage Grids
Abstract:
Increasing penetration of electric vehicles, heat pumps, and rooftop photovoltaics is creating thermal and voltage stress in low‑voltage distribution grids. This work links three German energy transition pathways (2025‑2045) with state estimation performance requirements, evaluated on two SimBench reference networks across three equipment quality levels (good, medium, poor) and three VDE Forum Netztechnik/Netzbetrieb (VDE FNN) measurement constellations that differ in the availability of transformer and feeder‑level instrumentation. Congestion is caused exclusively by transformer overloading and voltage‑band violations. No individual line exceeds its thermal rating. Equipment quality is the primary factor: under good equipment, congestion remains nearly absent through 2045 (1/26 scenarios), under medium equipment it emerges from 2035 (10/26), under poor equipment from 2025 (25/26), reaching 208 % peak transformer loading. Without transformer instrumentation, voltage estimation errors remain at 6‑35% regardless of smart meter penetration. Adding a single transformer measurement reduces errors by a factor of 3 to 24, achieving median errors below 1.1% under poor equipment. Per‑feeder measurements achieve comparable accuracy and outperform the transformer‑only configuration under poor equipment in rural networks (0.8% vs. 1.1%). In urban networks under poor and medium equipment, transformer and feeder‑level instrumentation meet the VDE FNN voltage accuracy target without requiring customer‑side sensors. These findings motivate prioritizing transformer instrumentation as an effective first step for grid observability and supplementing the current consumption‑driven metering rollout with risk‑based deployment criteria linked to local congestion exposure.

Authors:Marcell Bartos, Bruce D. Lee, Lenart Treven, Andreas Krause, Florian Dörfler, Melanie N. Zeilinger
Title: Optimistic Online LQR via Intrinsic Rewards
Abstract:
Optimism in the face of uncertainty is a popular approach to balance exploration and exploitation in reinforcement learning. Here, we consider the online linear quadratic regulator (LQR) problem, i.e., to learn the LQR corresponding to an unknown linear dynamical system by adapting the control policy online based on closed‑loop data collected during operation. In this work, we propose Intrinsic Rewards LQR (IR‑LQR), an optimistic online LQR algorithm that applies the idea of intrinsic rewards originating from reinforcement learning and the concept of variance regularization to promote uncertainty‑driven exploration. IR‑LQR retains the structure of a standard LQR synthesis problem by only modifying the cost function, resulting in an intuitively pleasing, simple, computationally cheap, and efficient algorithm. This is in contrast to existing optimistic online LQR formulations that rely on more complicated iterative search algorithms or solve computationally demanding optimization problems. We show that IR‑LQR achieves the optimal worst‑case regret rate of \sqrtT, and compare it to various state‑of‑the‑art online LQR algorithms via numerical experiments carried out on an aircraft pitch angle control and an unmanned aerial vehicle example.

Authors:J. de Curtò, Adrianne Schneider, Ricardo Yanez, María Begara, Álvaro Rodríguez, Javier López, Martina Fraga, Ignacio Gómez, Arman Akdag, Sumit Kulkarni, Siddhant Nair, Kiyan Govender, Eian Wratchford, Eli Lynskey, Seamus Dunlap, Cooper Nervick, Nicolas Tête, Rocío Fernández, Pablo González, Elena Municio, I. de Zarzà
Title: A Computational Framework for Cross-Domain Mission Design and Onboard Cognitive Decision Support
Abstract:
The design of distributed autonomous systems for operation beyond reliable ground contact presents a fundamental tension: as round‑trip communication latency grows, the set of decisions delegable to ground operators shrinks. This paper establishes a unified computational methodology for quantifying and comparing this constraint across seven heterogeneous mission architectures, spanning Earth low‑orbit surveillance constellations, Mars orbital navigation systems, autonomous underwater mine‑clearing swarms, deep‑space inter‑satellite link networks, and outer‑planet in‑situ buoy platforms. We introduce the Autonomy Necessity Score, a log‑domain latency metric mapping each system continuously from the ground‑dependent to the fully‑autonomous regime, grounded in nine independently validated computational studies covering Walker spherical‑cap coverage mechanics, infrared Neyman‑Pearson detection, Extended Kalman Filter hypersonic tracking, cross‑mission RF and acoustic link budgets spanning seven orders of magnitude in range, Monte Carlo science‑yield sensitivity for TDMA inter‑satellite protocols, cross‑architecture power budget sizing, distributed magnetic‑signature formation emulation, and Arrhenius‑corrected cryogenic swarm reliability. Building on this foundation, we evaluate an LLM‑based Autonomous Mission Decision Support layer in which three foundation models (Llama‑3.3‑70B, DeepSeek‑V3, and Qwen3‑A22B) are queried live via the Nebius AI Studio API across ten structured anomaly scenarios derived directly from the preceding analyses. The best‑performing model achieves 80% decision accuracy against physics‑grounded ground truth, with all 180 inference calls completing within a 2 s latency budget consistent with radiation‑hardened edge deployment, establishing the viability of foundation models as an onboard cognitive layer for high‑ANS missions.

Authors:Tushar Sial, Abhishek Halder
Title: Symmetrizing Bregman Divergence on the Cone of Positive Definite Matrices: Which Mean to Use and Why
Abstract:
This work uncovers variational principles behind symmetrizing the Bregman divergences induced by generic mirror maps over the cone of positive definite matrices. We show that computing the canonical means for this symmetrization can be posed as minimizing the desired symmetrized divergences over a set of mean functionals defined axiomatically to satisfy certain properties. For the forward symmetrization, we prove that the arithmetic mean over the primal space is canonical for any mirror map over the positive definite cone. For the reverse symmetrization, we show that the canonical mean is the arithmetic mean over the dual space, pulled back to the primal space. Applying this result to three common mirror maps used in practice, we show that the canonical means for reverse symmetrization, in those cases, turn out to be the arithmetic, log‑Euclidean and harmonic means. Our results improve understanding of existing symmetrization practices in the literature, and can be seen as a navigational chart to help decide which mean to use when.

Authors:Guillem Pascual, Sonia Martínez
Title: Input-to-State Stability of Gradient Flows in Distributional Space
Abstract:
This paper proposes a new notion of distributional Input‑to‑State Stability (dISS) for dynamic systems evolving in probability spaces over a domain. Unlike other norm‑based ISS concepts, we rely on the Wasserstein metric, which captures more precisely the effects of the disturbances on atomic and non‑atomic measures. We show how dISS unifies both ISS and Noise to State Stability (NSS) over compact domains for particle dynamics, while extending the classical notions to sets of probability distributions. We then apply the dISS framework to study the robustness of various Wasserstein gradient flows with respect to perturbations. In particular, we establish dISS for gradient flows defined by a class of l‑smooth and λ‑convex functionals subject to bounded disturbances, such as those induced by entropy in optimal transport. Further, we study the dISS robustness of the large‑scale algorithms when using Kernel and sample‑based approximations. This results into a characterization of the error incurred when using a finite number of agents, which can guide the selection of the swarm size to achieve a mean‑field objective with prescribed accuracy and stability guarantees.

Authors:Alex Zongo, Filippos Fotiadis, Ufuk Topcu, Peng Wei
Title: Robust Multi-Agent Reinforcement Learning for Small UAS Separation Assurance under GPS Degradation and Spoofing
Abstract:
We address robust separation assurance for small Unmanned Aircraft Systems (sUAS) under GPS degradation and spoofing via Multi‑Agent Reinforcement Learning (MARL). In cooperative surveillance, each aircraft (or agent) broadcasts its GPS‑derived position; when such position broadcasts are corrupted, the entire observed air traffic state becomes unreliable. We cast this state observation corruption as a zero‑sum game between the agents and an adversary: with probability R, the adversary perturbs the observed state to maximally degrade each agent's safety performance. We derive a closed‑form expression for this adversarial perturbation, bypassing adversarial training entirely and enabling linear‑time evaluation in the state dimension. We show that this expression approximates the true worst‑case adversarial perturbation with second‑order accuracy. We further bound the safety performance gap between clean and corrupted observations, showing that it degrades at most linearly with the corruption probability under Kullback‑Leibler regularization. Finally, we integrate the closed‑form adversarial policy into a MARL policy gradient algorithm to obtain a robust counter‑policy for the agents. In a high‑density sUAS simulation, we observe near‑zero collision rates under corruption levels up to 35%, outperforming a baseline policy trained without adversarial perturbations.

Authors:Gavin Glenn, Emma J. Reid
Title: H Infinity Minimal Destabilizing Feedback for Vulnerability Analysis and Attack Design of Nonlinear Systems
Abstract:
The robust stability problem involves designing a controlled system which remains stable in the presence of modeling uncertainty. In this context, results known as small gain theorems are used to quantify the maximum amount of uncertainty for which stability is guaranteed. These notions inform the design of numerous control systems, including critical infrastructure components such as power grids, gas pipelines, and water systems. However, these same concepts can be used by an adversary to design a malicious feedback attack, of minimal size, to drive the closed‑loop system to instability. In this paper, we first present a detailed review of the results in robust control which allow for the construction of minimal destabilizers. These minimally sized attacks merely push the system to the stability boundary, which we demonstrate do not necessarily destabilize nonlinear systems even when the linearization is destabilized. Our main result leverages linear perturbation theory to explicitly prove, in the state space context, that internal destabilization is guaranteed for a broad class of nonlinear systems when the gain of these attacks is slightly increased.

Authors:Qin He, Jing Shuang Li
Title: Associative Memory System via Threshold Linear Networks
Abstract:
Humans learn and form memories in stochastic environments. Auto‑associative memory systems model these processes by storing patterns and later recovering them from corrupted versions. Here, memories are learned by associating each pattern with an attractor in a latent space. After learning, when (possibly corrupted) patterns are presented to the system, latent dynamics facilitate retrieval of the appropriate uncorrupted pattern. In this work, we propose a novel online auto‑associative memory system. In contrast to existing works, our system supports sequential memory formation and provides formal guarantees of robust memory retrieval via region‑of‑attraction analysis. We use a threshold‑linear network as latent space dynamics in combination with an encoder, decoder, and controller. We show in simulation that the memory system successfully reconstructs patterns from corrupted inputs.

Authors:Astghik Hakobyan, Amaras Nazarians, Aditya Gahlawat, Naira Hovakimyan, Ilya Kolmanovsky
Title: Distributionally Robust Planning with $\mathcal{L}_1$ Adaptive Control
Abstract:
Safe operation of autonomous systems requires robustness to both model uncertainty and uncertainty in the environment. We propose DRP‑\mathcalL_1AC, a hierarchical framework for stochastic nonlinear systems that integrates distributionally robust model predictive control (DR‑MPC) with \mathcalL_1‑adaptive control. The key idea is to use the \mathcalL_1‑adaptive controller's online distributional certificates that bound the Wasserstein distance between nominal and true state distributions, thereby certifying the ambiguity sets used for planning without requiring distribution samples. Environmental uncertainty is captured via data‑driven ambiguity sets constructed from finite samples. These are incorporated into a DR‑MPC planner enforcing distributionally robust chance constraints over a receding horizon. Using Wasserstein duality, the resulting problem admits tractable reformulations and a sample‑based implementation. We show theoretically and via numerical experimentation that our framework ensures certifiable safety in the presence of simultaneous system and environmental uncertainties.

Authors:Yaozhi Du, Jing Shuang Li
Title: Sparse State-Space Realizations of Linear Controllers
Abstract:
This paper provides a novel approach for finding sparse state‑space realizations of linear systems (e.g., controllers). Sparse controllers are commonly used in distributed control, where a controller is synthesized with some sparsity penalty. Here, motivated by a modeling problem in sensorimotor neuroscience, we study a complementary question: given a linear time‑invariant system (e.g., controller) in transfer function form and a desired sparsity pattern, can we find a suitably sparse state‑space realization for the transfer function? This problem is highly nonconvex, but we propose an exact method to solve it. We show that the problem reduces to finding an appropriate similarity transform from the modal realization, which in turn reduces to solving a system of multivariate polynomial equations. Finally, we leverage tools from algebraic geometry (namely, the Gröbner basis) to solve this problem exactly. We provide algorithms to find real‑ and complex‑valued sparse realizations and demonstrate their efficacy on several examples.

Authors:Aclécio J. Santos, Jean C. Pereira, Guilherme V. Raffo
Title: Constrained Optimization on Matrix Lie Groups via Interior-Point Method
Abstract:
This paper proposes an interior‑point framework for constrained optimization problems whose decision variables evolve on matrix Lie groups. The proposed method, termed the Matrix Lie Group Interior‑Point Method (MLG‑IPM), operates directly on the group structure using a minimal Lie algebra parametrization, avoiding redundant matrix representations and eliminating explicit dependence on Riemannian metrics. A primal‑dual formulation is developed in which the Newton system is constructed through sensitivity and curvature matrices. Also, multiplicative updates are performed via the exponential map, ensuring intrinsic feasibility with respect to the group structure while maintaining strict positivity of slack and dual variables through a barrier strategy. A local analysis establishes quadratic convergence under standard regularity assumptions and characterizes the behavior under inexact Newton steps. Statistical comparisons against Riemannian Interior‑Point Methods, specifically for optimization problems defined over the Special Orthogonal Group SO(n) and Special Linear Group SL(n), demonstrate that the proposed approach achieves higher success rates, fewer iterations, and superior numerical accuracy. Furthermore, its robustness under perturbations suggests that this method serves as a consistent and reliable alternative for structured manifold optimization.

Authors:Zidi Tao, A. Agung Julius, John T Wen
Title: Alertness Optimization for Shift Workers Using a Physiology-based Mathematical Model
Abstract:
Sleep is vital for maintaining cognitive function, facilitating metabolic waste removal, and supporting memory consolidation. However, modern societal demands, particularly shift work, often disrupt natural sleep patterns. This can induce excessive sleepiness among shift workers in critical sectors such as healthcare and transportation and increase the risk of accidents. The primary contributors to this issue are misalignments of circadian rhythms and enforced sleep‑wake schedules. Regulating circadian rhythms that are tied to alertness can be regarded as a control problem with control inputs in the form of light and sleep schedules. In this paper, we address the problem of optimizing alertness by optimizing light and sleep schedules to improve the cognitive performance of shift workers. A key tool in our approach is a mathematical model that relates the control input variables (sleep and lighting schedules) to the dynamics of the circadian clock and sleep. In the sleep and circadian modeling literature, the newer physiology‑based model shows better accuracy in predicting the alertness of shift workers than the phenomenology‑based model, but the dynamics of physiological‑based model have differential equations with different time scales, which pose challenges in optimization. To overcome the challenge, we propose a hybrid version of the PR model by applying singular perturbation techniques to reduce the system to a non‑stiff, differentiable hybrid system. This reformulation facilitates the application of the calculus of variation and the gradient descent method to find the optimal light and sleep schedules that maximize the subjective alertness of shift worker. Our approach is validated through numerical simulations, and the simulation results demonstrate improved alertness compared to other existing schedules.

Authors:Mohamed Elgouhary, Amr S. El-Wakeel
Title: Dynamic Lookahead Distance via Reinforcement Learning-Based Pure Pursuit for Autonomous Racing
Abstract:
Pure Pursuit (PP) is a widely used path‑tracking algorithm in autonomous vehicles due to its simplicity and real‑time performance. However, its effectiveness is sensitive to the choice of lookahead distance: shorter values improve cornering but can cause instability on straights, while longer values improve smoothness but reduce accuracy in curves. We propose a hybrid control framework that integrates Proximal Policy Optimization (PPO) with the classical Pure Pursuit controller to adjust the lookahead distance dynamically during racing. The PPO agent maps vehicle speed and multi‑horizon curvature features to an online lookahead command. It is trained using Stable‑Baselines3 in the F1TENTH Gym simulator with a KL penalty and learning‑rate decay for stability, then deployed in a ROS2 environment to guide the controller. Experiments in simulation compare the proposed method against both fixed‑lookahead Pure Pursuit and an adaptive Pure Pursuit baseline. Additional real‑car experiments compare the learned controller against a fixed‑lookahead Pure Pursuit controller. Results show that the learned policy improves lap‑time performance and repeated lap completion on unseen tracks, while also transferring zero‑shot to hardware. The learned controller adapts the lookahead by increasing it on straights and reducing it in curves, demonstrating effectiveness in augmenting a classical controller by online adaptation of a single interpretable parameter. On unseen tracks, the proposed method achieved 33.16 s on Montreal and 46.05 s on Yas Marina, while tolerating more aggressive speed‑profile scaling than the baselines and achieving the best lap times among the tested settings. Initial real‑car experiments further support sim‑to‑real transfer on a 1:10‑scale autonomous racing platform

Authors:David Laranjinho, Daniel Silvestre
Title: Fault-Tolerant MPC Control for Trajectory Tracking
Abstract:
An MPC controller uses a model of the dynamical system to plan an optimal control strategy for a finite horizon, which makes its performance intrinsically tied to the quality of the model. When faults occur, the compromised model will degrade the performance of the MPC with this impact being dependent on the designed cost function. In this paper, we aim to devise a strategy that combines active fault identification while driving the system towards the desired trajectory. The explored approaches make use of an exact formulation of the problem in terms of set‑based propagation resorting to Constrained Convex Generators (CCGs) and a suboptimal version that resorts to the SVD decomposition to achieve the active fault isolation in order to adapt the model in runtime.

Authors:Yu-Han Huang, Argyrios Gerogiannis, Subhonmesh Bose, Venugopal V. Veeravalli
Title: Learning Where to Look: UCB-Driven Controlled Sensing for Quickest Change Detection
Abstract:
We study the multichannel quickest change detection problem with bandit feedback and controlled sensing, in which an agent sequentially selects one of the data streams to observe at each time‑step and aims to detect an unknown change as quickly as possible while controlling false alarms. Assuming known pre‑ and post‑change distributions and allowing an arbitrary subset of streams to be affected by the change, we propose two novel and computationally efficient detection procedures inspired by the Upper Confidence Bound (UCB) multi‑armed bandit algorithm. Our methods adaptively concentrate sensing on the most informative streams while preserving false‑alarm guarantees. We show that both procedures achieve first‑order asymptotic optimality in detection delay under standard false‑alarm constraints. We also extend the UCB‑driven controlled sensing approach to the setting where the pre‑ and post‑change distributions are unknown, except for a mean‑shift in at least one of the channels at the change‑point. This setting is particularly relevant to the problem of learning in piecewise stationary environments. Finally, extensive simulations on synthetic benchmarks show that our methods consistently outperform existing state‑of‑the‑art approaches while offering substantial computational savings.

Authors:Luke Rickard, Paola Falugi, Eric C. Kerrigan
Title: Coalition Formation with Limited Information Sharing for Local Energy Management
Abstract:
Distributed energy systems with prosumers require new methods for coordinating energy exchange among agents. Coalitional control provides a framework in which agents form groups to cooperatively reduce costs; however, existing bottom‑up coalition‑formation methods typically require full information sharing, raising privacy concerns and imposing significant computational overhead. In this work, we propose a limited information coalition‑formation algorithm that requires only limited aggregate information exchange among agents. By constructing an upper bound on the value of candidate coalitions, we eliminate the need to solve optimisation problems for each potential merge, significantly reducing computational complexity while limiting information exchange. We prove that the proposed method guarantees cost no greater than that of decentralised operation. Coalition strategies are optimised using a distributed approach based on the Alternating Direction Method of Multipliers (ADMM), further limiting information sharing within coalitions. We embed the framework within a model predictive control scheme and evaluate it on real‑world data, demonstrating improved economic performance over decentralised control with substantially lower computational cost than full‑information approaches.

Authors:Yu Han, Aaron Ceross
Title: Measuring Cross-Jurisdictional Transfer of Medical Device Risk Concepts with Explainable AI
Abstract:
Medical device regulators in the United States(FDA), China (NMPA), and Europe (EU MDR) all use the language of risk, but classify devices through structurally different mechanisms. Whether these apparently shared concepts carry transferable classificatory signal across jurisdictions remains unclear. We test this by reframing explainable AI as an empirical probe of cross‑jurisdictional regulatory overlap. Using 141,942 device records, we derive seven EU MDR risk factors, including implantability, invasiveness, and duration of use, and evaluate their contribution across a three‑by‑three transfer matrix. Under a symmetric extraction pipeline designed to remove jurisdiction‑specific advantages, factor contribution is negligible in all jurisdictions, indicating that clean cross‑jurisdictional signal is at most marginal. Under jurisdiction specific pipelines, a modest gain appears only in the EU MDR‑to‑NMPA direction, but sensitivity analyses show that this effect is weak, context‑dependent, and partly confounded by extraction and representation choices. Reverse direction probes show strong asymmetry: FDA‑derived factors do not transfer meaningfully in any direction, and NMPA‑derived factors do not carry signal back to EU MDR. Zero‑shot transfer further fails on EU MDR Class I, consistent with a mismatch between residual and positional class definitions. Overall, cross‑jurisdictional transfer is sparse, asymmetric, and weak. Shared regulatory vocabulary does not, under this operationalisation, translate into strong portable classification logic. The findings challenge a common assumption in cross‑jurisdictional regulatory AI and show how explainable AI can be used to measure, rather than assume, regulatory overlap.

Authors:Samira Abdelrahman, Hossam Farag
Title: Intelligent Radio Resource Slicing for 6G In-Body Subnetworks
Abstract:
6G In‑body Subnetworks (IBSs) represent a key enabler for supporting standalone eXtended Reality (XR) applications. IBSs are expected to operate as an underlay to existing cellular networks, giving rise to coexistence challenges when sharing radio resources with other cellular users, such as enhanced Mobile Broadband (eMBB) users. Such resource allocation problem is highly dynamic and inherently non‑convex due to heterogeneous service demands and fluctuating channel conditions. In this paper, we propose an intelligent radio resource slicing strategy based on the Soft Actor‑Critic (SAC) deep reinforcement learning algorithm. The proposed SAC‑based slicing method addresses the coexistence challenge between IBSs and eMBB users by optimizing a refined reward function that explicitly incorporates XR cross‑modal delay alignment to ensure immersive experience while preserving eMBB service guarantees. Extensive system‑level simulations are performed under realistic network conditions and the results demonstrate that the proposed method can enhance user experience by 12‑85% under different network densities compared to baseline methods while maintaining the target data rate for eMBB users.

Authors:Songhao Yang, Zhiguo Hao, Baohui Zhang, Masahide Hojo
Title: An Accurate and Fast Start-up Scheme for Power System Real-time Emergency Control
Abstract:
With the development of PMUs in power systems, the response‑based real‑time emergency control becomes a promising way to prevent power outages when power systems are subjected to large disturbances. The first step in the emergency control is to start up accurately and fast when needed. To this end, this paper proposes a well‑qualified start‑up scheme for the power system real‑time emergency control. Three key technologies are proposed to ensure the effectiveness of the scheme. They are an instability index, a Critical Machines (CMs) identification algorithm and a two‑layer Single Machine Infinite Bus (SMIB) equivalence framework. The concave‑convex area based instability index shows good accuracy and high reliability, which is used to identify the transient instability of the system. The CMs identification algorithm can track the changes of CMs and form the proper SMIB system at each moment. The new two‑layer SMIB equivalence framework, compared with conventional ones, can significantly reduce the communication burden and improve the computation efficiency. The simulations in two test power systems show that the scheme can identify the transient instability accurately and fast to restore the system to stability after the emergency control. Besides, the proposed method is robust to measurement errors, which enhances its practicality.

Authors:Yubo Zhang, Zhiguo Hao, Songhao Yang, Baohui Zhang
Title: A System-View Optimal Additional Active Power Control of Wind Turbines for Grid Frequency Support
Abstract:
Additional active power control (AAPC) of wind turbines (WTs) is essential to improve the transient frequency stability of low‑inertia power systems. Most of the existing research has focused on imitating the frequency response of the synchronous generator (SG), known as virtual inertia control (VIC), but are such control laws optimal for the power systems? Inspired by this question, this paper proposes an optimal AAPC of WTs to maximize the frequency nadir post a major power deficit. By decoupling the WT response and the frequency dynamics, the optimal frequency trajectory is solved based on the trajectory model, and its universality is strictly proven. Then the optimal AAPC of WTs is constructed reversely based on the average system frequency (ASF) model with the optimal frequency trajectory as the desired control results. The proposed method can significantly improve the system frequency nadir. Meanwhile, the event insensitivity makes it can be deployed based on the on‑line rolling update under a hypothetic disturbance, avoiding the heavy post‑event computational burden. Finally, simulation results in a two‑machine power system and the IEEE 39 bus power system verify the effectiveness of the optimal AAPC of WTs.

Authors:Konstantinos Bountrogiannis, Anthony Ephremides, Panagiotis Tsakalides, George Tzagkarakis
Title: Age of Incorrect Information for Generic Discrete-Time Markov Sources
Abstract:
This work introduces a framework for analyzing the Age of Incorrect Information (AoII) in a real‑time monitoring system with a generic discrete‑time Markov source. We study a noisy communication system employing a hybrid automatic repeat request (HARQ) protocol, subject to a transmission rate constraint. The optimization problem is formulated as a constrained Markov decision process (CMDP), and it is shown that there exists an optimal policy that is a randomized mixture of two stationary policies. To overcome the intractability of computing the optimal stationary policies, we develop a multiple‑threshold policy class where thresholds depend on the source, the receiver, and the packet count. By establishing a Markov renewal structure induced by threshold policies, we derive closed‑form expressions for the long‑term average AoII and transmission rate. The proposed policy is constructed via a relative value iteration algorithm that leverages the threshold structure to skip computations, combined with a bisection search to satisfy the rate constraint. To accommodate scenarios requiring lower computational complexity, we adapt the same technique to produce a simpler single‑threshold policy that trades optimality for efficiency. Numerical experiments exhibit that both thresholdbased policies outperform periodic scheduling, with the multiplethreshold approach matching the performance of the globally optimal policy.

Authors:Ján Boldocký, Cary Faulkner, Elad Michael, Martin Gulan, Aaron Tuor, Ján Drgoňa
Title: Data Center Chiller Plant Optimization via Mixed-Integer Nonlinear Differentiable Predictive Control
Abstract:
We present a computationally tractable framework for real‑time predictive control of multi‑chiller plants that involve both discrete and continuous control decisions coupled through nonlinear dynamics, resulting in a mixed‑integer optimal control problem. To address this challenge, we extend Differentiable Predictive Control (DPC) ‑‑ a self‑supervised, model‑based learning methodology for approximately solving parametric optimal control problems ‑‑ to accommodate mixed‑integer control policies. We benchmark the proposed framework against a state‑of‑the‑art Model Predictive Control (MPC) solver and a fast heuristic Rule‑Based Controller (RBC). Simulation results demonstrate that our approach achieves significant energy savings over the RBC while maintaining orders‑of‑magnitude faster computation times than MPC, offering a scalable and practical alternative to conventional combinatorial mixed‑integer control formulations.

Authors:Denis Fatkhiev, João dos Reis Frazão, Alireza H. Derkani, Kadir Gümüş, Menno van den Hout, Aaron Albores-Mejia, Chigo Okonkwo
Title: Compact Continuous-Variable Quantum Key Distribution System Employing Monolithically Integrated Silicon Photonic Transceiver
Abstract:
We demonstrate the first CV‑QKD system featuring a custom‑designed monolithic silicon photonic dual‑polarisation transceiver. Leveraging PS‑64‑QAM, we achieved 1.9 Mbit/s secret key rate across 25 km of standard single‑mode fibre, highlighting the potential of electronic‑photonic integration for practical QKD.

Authors:Wytze de Vries, Erik van den Eshof, Jorn van Kampen, Mauro Salazar
Title: Competitor-aware Race Management for Electric Endurance Racing
Abstract:
Electric endurance racing is characterized by severe energy constraints and strong aerodynamic interactions. Determining race‑winning policies therefore becomes a fundamentally multi‑agent, game‑theoretic problem. These policies must jointly govern low‑level driver inputs as well as high‑level strategic decisions, including energy management and charging. This paper proposes a bi‑level framework for competitor‑aware race management that combines game‑theoretic optimal control with reinforcement learning. At the lower level, a multi‑agent game‑theoretic optimal control problem is solved to capture aerodynamic effects and asymmetric collision‑avoidance constraints inspired by motorsport rules. Using this single‑lap problem as the environment, reinforcement learning agents are trained to allocate battery energy and schedule pit stops over an entire race. The framework is demonstrated in a two‑agent, 45‑lap simulated race. The results show that effective exploitation of aerodynamic interactions is decisive for race outcome, with strategies that prioritize finishing position differing fundamentally from single‑agent, minimum‑time approaches.

Authors:Wenqi Cai, Kyriakos G. Vamvoudakis, Sébastien Gros, Anthony Tzes
Title: Cost-Matching Model Predictive Control for Efficient Reinforcement Learning in Humanoid Locomotion
Abstract:
In this paper, we propose a cost‑matching approach for optimal humanoid locomotion within a Model Predictive Control (MPC)‑based Reinforcement Learning (RL) framework. A parameterized MPC formulation with centroidal dynamics is trained to approximate the action‑value function obtained from high‑fidelity closed‑loop data. Specifically, the MPC cost‑to‑go is evaluated along recorded state‑action trajectories, and the parameters are updated to minimize the discrepancy between MPC‑predicted values and measured returns. This formulation enables efficient gradient‑based learning while avoiding the computational burden of repeatedly solving the MPC problem during training. The proposed method is validated in simulation using a commercial humanoid platform. Results demonstrate improved locomotion performance and robustness to model mismatch and external disturbances compared with manually tuned baselines.

Authors:Michela Boffi, Jessica Leoni, Fabrizio Leonforte, Mara Tanelli, Paolo Oliaro
Title: An Optimal Battery-Free Approach for Emission Reduction by Storing Solar Surplus in Building Thermal Mass
Abstract:
Decarbonization in buildings calls for advanced control strategies that coordinate on‑site renewables, grid electricity, and thermal demand. Literature approaches typically rely on demand side management strategies or on active energy storage, like batteries. However, the first solution often neglects carbon‑aware objectives, and could lead to grid overload issues, while batteries entail environmental, end‑of‑life, and cost concerns. To overcome these limitations, we propose an optimal, carbon‑aware optimization strategy that exploits the building's thermal mass as a passive storage, avoiding dedicated batteries. Specifically, when a surplus of renewable energy is available, our strategy computes the optimal share of surplus to store by temporarily adjusting the indoor temperature setpoint within comfort bounds. Thus, by explicitly accounting for forecasts of building energy consumption, solar production, and time‑varying grid carbon intensity, our strategy enables emissions‑aware load shifting while maintaining comfort. We evaluate the approach by simulating three TRNSYS models of the same system with different thermal mass. In all cases, the results show consistent reductions in grid electricity consumption with respect to a baseline that does not leverage surplus renewable generation. These findings highlight the potential of thermal‑mass‑based control for building decarbonization.

Authors:T. de Groot, W. P. M. H. Heemels, S. J. A. M. van den Eijnden
Title: Analysis and Design of Reset Control Systems via Base Linear Scaled Graphs
Abstract:
In this letter, we prove that under mild conditions, the scaled graph of a reset control system is bounded by the scaled graph of its underlying base linear system, i.e., the system without resets. Building on this new insight, we establish that the negative feedback interconnection of a linear time‑invariant plant and a reset controller is stable, if the scaled graphs of the underlying base linear components are strictly separated. This result simplifies reset system analysis, as stability conditions reduce to verifying properties of linear time‑invariant systems. We exploit this result to develop a systematic approach for reset control system design. Our framework also accommodates reset systems with time‑regularization, which were not addressed in the context of scaled graphs before.

Authors:Mahmoud Zamani, Guosong Yang
Title: Input-to-state stabilization of linear systems under data-rate constraints
Abstract:
We study feedback stabilization of continuous‑time linear systems under finite data‑rate constraints in the presence of unknown disturbances. A communication and control strategy based on sampled and quantized state measurements is proposed, where the quantization range is dynamically adjusted using reachable‑set propagation and disturbance estimates derived from quantization parameters. The strategy alternates between stabilizing and searching stages to handle escapes from the quantization range and employs an additional quantization symbol to ensure robustness near the equilibrium. It guarantees input‑to‑state stability (ISS), improving upon existing results that yield only practical ISS or lack explicit data‑rate conditions. Simulation results illustrate the effectiveness of the strategy.

Authors:Akash Harapanahalli, Samuel Coogan, Alexander Davydov
Title: Learning Certified Neural Network Controllers Using Contraction and Interval Analysis
Abstract:
We present a novel framework that jointly trains a neural network controller and a neural Riemannian metric with rigorous closed‑loop contraction guarantees using formal bound propagation. Directly bounding the symmetric Riemannian contraction linear matrix inequality causes unnecessary overconservativeness due to poor dependency management. Instead, we analyze an asymmetric matrix function G, where 2^n GPU‑parallelized corner checks of its interval hull verify that an entire interval subset X is a contraction region in a single shot. This eliminates the sample complexity problems encountered with previous Lipschitz‑based guarantees. Additionally, for control‑affine systems under a Killing field assumption, our method produces an explicit tracking controller capable of exponentially stabilizing any dynamically feasible trajectory using just two forward inferences of the learned policy. Using JAX and \textttimmrax for linear bound propagation, we apply this approach to a full 10‑state quadrotor model. In under 10 minutes of post‑JIT training, we simultaneously learn a control policy π, a neural contraction metric Θ, and a verified 10‑dimensional contraction region X.

Authors:Xiucheng Wang, Junxi Huang, Conghao Zhou, Xuemin Shen, Nan Cheng
Title: Physics-informed line-of-sight learning for scalable deterministic channel modeling
Abstract:
Deterministic channel modeling maps a physical environment to its site‑specific electromagnetic response. Ray tracing produces complete multi‑dimensional channel information but remains prohibitively expensive for area‑wide deployment. We identify line‑of‑sight (LoS) region determination as the dominant bottleneck. To address this, we propose D^2LoS, a physics‑informed neural network that reformulates dense pixel‑level LoS prediction into sparse vertex‑level visibility classification and projection point regression, avoiding the spectral bias at sharp boundaries. A geometric post‑processing step enforces hard physical constraints, yielding exact piecewise‑linear boundaries. Because LoS computation depends only on building geometry, cross‑band channel information is obtained by updating material parameters without retraining. We also construct RayVerse‑100, a ray‑level dataset spanning 100 urban scenarios with per‑ray complex gain, angle, delay, and geometric trajectory. Evaluated against rigorous ray tracing ground truth, D^2LoS achieves 3.28~dB mean absolute error in received power, 4.65^\circ angular spread error, and 20.64~ns delay spread error, while accelerating visibility computation by over 25×.

Authors:Kainat Yasmeen, Shobha Sundar Ram, Debidas Kundu
Title: Radar Cross Section Characterization of Quantized Reconfigurable Intelligent Surfaces
Abstract:
We present a radar sensing framework based on a low‑complexity, quantized reconfigurable intelligent surface (RIS) that enables programmable manipulation of electromagnetic wavefronts for enhanced detection in non‑specular and shadowed regions. We develop closed‑form expressions for the scattered field and radar cross section (RCS) of phase‑quantized RIS apertures based on aperture field theory, accurately capturing the effects of quantized phase, periodicity, and grating lobes on radar detection performance. The theory enables us to analyze the RIS's RCS along both the forward and backward paths from the radar to the target. The theory is benchmarked against full‑wave electromagnetic simulations incorporating realistic unit‑cell amplitude and phase responses. To validate practical feasibility, a [16×10] 1‑bit RIS operating at 5.5 GHz is fabricated and experimentally characterized inside an anechoic chamber. Measurements of steering angles, beam‑squint errors, and peak‑to‑specular ratios of the RCS patterns exhibit strong agreement with analytical and simulated results. Further experiments demonstrate that the RIS can redirect the beam in a non‑specular direction and recover micro‑Doppler signatures that remain undetectable with a conventional radar deployment.

Authors:Too Matsuo, Yuki Nishimura, Kenta Hoshino, Daisuke Tabuchi
Title: Stochastic Safety-critical Control Compensating Safety Probability for Marine Vessel Tracking
Abstract:
A marine vessel is a nonlinear system subject to irregular disturbances such as wind and waves, which cause tracking errors between the nominal and actual trajectories. In this study, a nonlinear vessel maneuvering model that includes a tracking controller is formulated and then controlled using a linear approximation around the nominal trajectory. The resulting stochastic linearized system is analyzed using a stochastic zeroing control barrier function (ZCBF). A stochastic safety compensator is designed to ensure probabilistic safety, and its effectiveness is verified through numerical simulations.

Authors:Ruixing Ren, Minqi Tao, Junhui Zhao, Qiuping Li, Xiaoke Sun
Title: Adaptive Multi-Dimensional Coordinated Comprehensive Routing Scheme for IoV
Abstract:
The characteristics of high‑speed node movement and dynamic topology changes pose great challenges to the design of internet of vehicles (IoV) routing protocols. Existing schemes suffer from common problems such as insufficient adaptability and lack of global consideration, making it difficult to achieve a globally optimal balance between routing reliability, real‑time performance and transmission efficiency. This paper proposes an adaptive multi‑dimensional coordinated comprehensive routing scheme for IoV environments. A complete IoV system model including network topology, communication links, hierarchical congestion and transmission delay is first constructed, the routing problem is abstracted into a single‑objective optimization model with multiple constraints, and a single‑hop link comprehensive routing metric integrating link reliability, node local load, network global congestion and link stability is defined. Second, an intelligent transmission switching mechanism is designed: candidate nodes are screened through dual criteria of connectivity and progressiveness, a dual decision‑making of primary and backup paths and a threshold switching strategy are introduced to avoid link interruption and congestion, and an adaptive update function is constructed to dynamically adjust weight coefficients and switching thresholds to adapt to changes in network status. Simulation results show that the proposed scheme can effectively adapt to the high dynamic topology and network congestion characteristics of IoV, perform excellently in key indicators such as routing interruption times, packet delivery rate and end‑to‑end delay, and its comprehensive performance is significantly superior to traditional routing schemes.

Authors:Taichi Arimura, Yuki Nishimura, Taichi Ikezaki, Daisuke Tabuchi
Title: Collision Avoidance Control for a Two-wheeled Vehicle under Stochastic Vibration using an Almost Sure Control Barrier Function
Abstract:
In recent years, many control problems of autonomous mobile robots have been developed. In particular, the robots are required to be safe; that is, they need to be controlled to avoid colliding with people or objects while traveling. In addition, since safety should be ensured even under irregular disturbances, the control for safety is required to be effective for stochastic systems. In this study, we design an almost sure safety‑critical control law, which ensures safety with probability one, for a two‑wheeled vehicle based on the stochastic control barrier function approach. In the procedure, we also consider a system model using the relative distance measured by a 2D LiDAR. The validity of the proposed control scheme is confirmed by experiments of a collision avoidance problem for a two‑wheeled vehicle under vibration.

Authors:Andrew W. Singletary, Max H. Cohen, Tamas G. Molnar, Aaron D. Ames
Title: Safety Guardrails in the Sky: Realizing Control Barrier Functions on the VISTA F-16 Jet
Abstract:
The advancement of autonomous systems ‑‑ from legged robots to self‑driving vehicles and aircraft ‑‑ necessitates executing increasingly high‑performance and dynamic motions without ever putting the system or its environment in harm's way. In this paper, we introduce Guardrails ‑‑ a novel runtime assurance mechanism that guarantees dynamic safety for autonomous systems, allowing them to safely evolve on the edge of their operational domains. Rooted in the theory of control barrier functions, Guardrails offers a control strategy that carefully blends commands from a human or AI operator with safe control actions to guarantee safe behavior. To demonstrate its capabilities, we implemented Guardrails on an F‑16 fighter jet and conducted flight tests where Guardrails supervised a human pilot to enforce g‑limits, altitude bounds, geofence constraints, and combinations thereof. Throughout extensive flight testing, Guardrails successfully ensured safety, keeping the pilot in control when safe to do so and minimally modifying unsafe pilot inputs otherwise.

Authors:Sungyong Chung, Yanlin Zhang, Nachuan Li, Dana Monzer, Alireza Talebpour
Title: Data is All You Need: Markov Chain Car-Following (MC-CF) Model
Abstract:
Car‑following behavior is fundamental to traffic flow theory, yet traditional models often fail to capture the stochasticity of naturalistic driving. This paper introduces a new car‑following modeling category called the empirical probabilistic paradigm, which bypasses conventional parametric assumptions. Within this paradigm, we propose the Markov Chain Car‑Following (MC‑CF) model, which represents state transitions as a Markov process and predicts behavior by randomly sampling accelerations from empirical distributions within discretized state bins. Evaluation of the MC‑CF model trained on the Waymo Open Motion Dataset (WOMD) demonstrates that its variants significantly outperform physics‑based models including IDM, Gipps, FVDM, and SIDM in both one‑step and open‑loop trajectory prediction accuracy. Statistical analysis of transition probabilities confirms that the model‑generated trajectories are indistinguishable from real‑world behavior, successfully reproducing the probabilistic structure of naturalistic driving across all interaction types. Zero‑shot generalization on the Naturalistic Phoenix (PHX) dataset further confirms the model's robustness. Finally, microscopic ring road simulations validate the framework's scalability. By incrementally integrating unconstrained free‑flow trajectories and high‑speed freeway data (TGSIM) alongside a conservative inference strategy, the model drastically reduces collisions, achieving zero crashes in multiple equilibrium and shockwave scenarios, while successfully reproducing naturalistic and stochastic shockwave propagation. Overall, the proposed MC‑CF model provides a robust, scalable, and calibration‑free foundation for high‑fidelity stochastic traffic modeling, uniquely suited for the data‑rich future of intelligent transportation.

Authors:Yuda Li, Xiang Yin
Title: On the Computation of Backward Reachable Sets for Max-Plus Linear Systems with Disturbances
Abstract:
This paper investigates one‑step backward reachability for uncertain max‑plus linear systems with additive disturbances. Given a target set, the problem is to compute the set of states from which there exists an admissible control input such that, for all admissible disturbances, the successor state remains in the target set. This problem is closely related to safety analysis and is challenging due to the high computational complexity of existing approaches. To address this issue, we develop a computational framework based on tropical polyhedra. We assume that the target set, the control set, and the disturbance set are all represented as tropical polyhedra, and study the structural properties of the associated backward operators. In particular, we show that these operators preserve the tropical‑polyhedral structure, which enables the constructive computation of reachable sets within the same framework. The proposed approach provides an effective geometric and algebraic tool for reachability analysis of uncertain max‑plus linear systems. Illustrative examples are included to demonstrate the proposed method.

Authors:Yunda Yan, Chenxi Tao, Jinya Su, Cunjia Liu, Shihua Li
Title: MPC as a Copilot: A Predictive Filter Framework with Safety and Stability Guarantees
Abstract:
Ensuring both safety and stability remains a fundamental challenge in learning‑based control, where goal‑oriented policies often neglect system constraints and closed‑loop state convergence. To address this limitation, this paper introduces the Predictive Safety‑‑Stability Filter (PS2F), a unified predictive filter framework that guarantees constraint satisfaction and asymptotic stability within a single architecture. The PS2F framework comprises two cascaded optimal control problems: a nominal model predictive control (MPC) layer that serves solely as a copilot, implicitly defining a Lyapunov function and generating safety‑ and stability‑certified predicted trajectories, and a secondary filtering layer that adjusts external command to remain within a provably safe and stable region. This cascaded structure enables PS2F to inherit the theoretical guarantees of nominal MPC while accommodating goal‑oriented external commands. Rigorous analysis establishes recursive feasibility and asymptotic stability of the closed‑loop system without introducing additional conservatism beyond that associated with the nominal MPC. Furthermore, a time‑varying parameterisation allows PS2F to transition smoothly between safety‑prioritised and stability‑oriented operation modes, providing a principled mechanism for balancing exploration and exploitation. The effectiveness of the proposed framework is demonstrated through comparative numerical experiments.

Authors:Artun Sel, Mehmet Koruturk, Erdi Sayar
Title: Estimation of Regions of Attraction for Nonlinear Systems via Coordinate-Transformed TS Models
Abstract:
This paper presents a novel method for estimating larger Region of Attractions (ROAs) for continuous‑time nonlinear systems modeled via the Takagi‑Sugeno (TS) framework. While classical approaches rely on a single TS representation derived from the original nonlinear system to compute an ROA using Lyapunov‑based analysis, the proposed method enhances this process through a systematic coordinate transformation strategy. Specifically, we construct multiple TS models, each obtained from the original nonlinear system under a distinct linear coordinate transformation. Each transformed system yields a local ROA estimate, and the overall ROA is taken as the union of these individual estimates. This strategy leverages the variability introduced by the transformations to reduce conservatism and expand the certified stable region. Numerical examples demonstrate that this approach consistently provides larger ROAs compared to conventional single‑model TS‑based techniques, highlighting its effectiveness and potential for improved nonlinear stability analysis.

Authors:Abdullah Y. Etcibasi, C. Emre Koksal, Eylem Ekici
Title: Separation is Optimal for LQR under Intermittent Feedback
Abstract:
In this work, we first prove that the separation principle holds for communication‑constrained LQR problems under i.i.d. zero‑mean disturbances with a symmetric distribution. We then solve the dynamic programming problem and show that the optimal scheduling policy is a symmetric threshold rule on the accumulated disturbance since the most recent update, while the optimal controller is a discounted linear feedback law independent of the scheduling policy.

Authors:Yiru Ji, Constance Crozier, Matthew Liska
Title: A Sensitivity Analysis of Flexibility from GPU-Heavy Data Centers
Abstract:
The rapid growth of GPU‑heavy data centers has significantly increased electricity demand and creating challenges for grid stability. Our paper investigates the extent to which an energy‑aware job scheduling algorithm can provide flexibility in GPU‑heavy data centers. Compared with the traditional first‑in first‑out (FIFO) baseline, we show that more efficient job scheduling not only increases profit, but also brings latent power flexibility during peak price period. This flexibility is achieved by moving lower energy jobs, preferentially executing jobs with lower GPU utilization and smaller node requirements, when the electricity price is high. We demonstrate that data centers with lower queue length and higher variance in job characteristics such as job GPU utilization and job size, offer the greatest flexibility potential. Finally we show that data center flexibility is highly price sensitive, a 7% demand reduction is achieved with a small incentive, but unrealistically high prices are required to achieve a 33% reduction.

Authors:John Slane, Adam Mate
Title: Impact of Inverter-Based Resources on the Protection of the Electrical Grid
Abstract:
In recent years, the contribution of renewable energy resources to the electrical grid has increased drastically; the most common of these are photovoltaic solar panels and wind turbines. These resources rely on inverters to interface with the grid, which do not inherently exhibit the same fault characteristics as synchronous generators. Consistently, they can strain grid reliability and security, cause increased number of blackouts, and, in some cases, allow relatively minor faults to turn into cascading failures. Solar and wind energy provide benefits and can support grid stability; however, several challenges and gaps in understanding must be explored and addressed before this can be realized. This paper provides a comprehensive literature review of grid codes, modeling techniques, and tools, as well as current methods for responding to various faults. It also presents an overview of the industry's state as it relates to grid fault response in the presence of inverter‑based resources.

Authors:Zirui Xu, Vasileios Tzoumas
Title: Distributed Online Submodular Maximization under Communication Delays: A Simultaneous Decision-Making Approach
Abstract:
We provide a distributed online algorithm for multi‑agent submodular maximization under communication delays. We are motivated by the future distributed information‑gathering tasks in unknown and dynamic environments, where utility functions naturally exhibit the diminishing‑returns property, i.e., submodularity. Existing approaches for online submodular maximization either rely on sequential multi‑hop communication, resulting in prohibitive delays and restrictive connectivity assumptions, or restrict each agent's coordination to its one‑hop neighborhood only, thereby limiting the coordination performance. To address the issue, we provide the Distributed Online Greedy (DOG) algorithm, which integrates tools from adversarial bandit learning with delayed feedback to enable simultaneous decision‑making across arbitrary network topologies. We provide the approximation performance of DOG against an optimal solution, capturing the suboptimality cost due to decentralization as a function of the network structure. Our analyses further reveal a trade‑off between coordination performance and convergence time, determined by the magnitude of communication delays. By this trade‑off, DOG spans the spectrum between the state‑of‑the‑art fully centralized online coordination approach [1] and fully decentralized one‑hop coordination approach [2].

Authors:Tao Chen, Andreu Cecilia, Lei Wang, Daniele Astolfi, Zhitao Liu
Title: A Nonlinear Incremental Approach for Replay Attack Detection
Abstract:
Replay attacks comprise replaying previously recorded sensor measurements and injecting malicious signals into a physical plant, causing great damage to cyber‑physical systems. Replay attack detection has been widely studied for linear systems, whereas limited research has been reported for nonlinear cases. In this paper, the replay attack is studied in the context of a nonlinear plant controlled by an observer‑based output feedback controller. We first analyze replay attack detection using an innovation‑based detector and reveal that this detector alone may fail to detect such attacks. Consequently, we turn to a watermark‑based design framework to improve the detection. In the proposed framework, the effects of the watermark on attack detection and closed‑loop system performance loss are quantified by two indices, which exploit the incremental gains of nonlinear systems. To balance the detection performance and control system performance loss, an explicit optimization problem is formulated. Moreover, to achieve a better balance, we generalize the proposed watermark design framework to co‑design the watermark, controller and observer. Numerical simulations are presented to validate the proposed frameworks.

Authors:Panagiotis Kounatidis, Andreas A. Malikopoulos
Title: Safety-Constrained Optimal Control for Unknown System Dynamics
Abstract:
In this paper, we present a framework for solving continuous optimal control problems when the true system dynamics are approximated through an imperfect model. We derive a control strategy by applying Pontryagin's Minimum Principle to the model‑based Hamiltonian functional, which includes an additional penalty term that captures the deviation between the model and the true system. We then derive conditions under which this model‑based strategy coincides with the optimal control strategy for the true system under mild convexity assumptions. We demonstrate the framework on a real robotic testbed for the cruise control application with safety distance constraints.

Authors:Alexey Pavlov, Michael Ruderman
Title: Adaptive differentiating filter: case study of PID feedback control
Abstract:
This paper presents an adaptive causal discrete‑time filter for derivative estimation, exemplified by its use in estimating relative velocity in a mechatronic application. The filter is based on a constrained least squares estimator with window adaptation. It demonstrates low sensitivity to low‑amplitude measurement noise, while preserving a wide bandwidth for large‑amplitude changes in the process signal. Favorable performance properties of the filter are discussed and demonstrated in a practical case study of PID feedback controller and compared experimentally to a standard linear low‑pass filter‑based differentiator and a robust sliding‑mode based homogeneous differentiator.

Authors:Shakib Mustavee, Arvind Singh, Shaurya Agarwal
Title: Time-varying System Identification of Bedform Dynamics Using Modal Decomposition
Abstract:
Measuring sediment transport in riverbeds has long been a challenging research problem in geomorphology and river engineering. Traditional approaches rely on direct measurements using sediment samplers. Although such measurements are often considered ground truth, they are intrusive, labor‑intensive, and prone to large variability. As an alternative, sediment flux can be inferred indirectly from the kinematics of migrating bedforms and temporal changes in bathymetry. While such approaches are helpful, bedform dynamics are nonlinear and multiscale, making it difficult to determine the contributions of different scales to the overall sediment flux. Fourier decomposition has been applied to examine bedform scaling, but it treats spatial and temporal variability separately. In this work, we introduce Dynamic Mode Decomposition (DMD) as a data‑driven framework for analyzing riverbed evolution. By incorporating this representation into the Exner equation, we establish a link between modal dynamics and net sediment flux. This formulation provides a surrogate measure for scale‑dependent sediment transport, enabling new insights into multiscale bedform‑driven sediment flux in fluvial channels.

Authors:Rishi Rani, Massimo Franceschetti
Title: Fundamental Limits of Man-in-the-Middle Attack Detection in Model-Free Reinforcement Learning
Abstract:
We consider the problem of learning‑based man‑in‑the‑middle (MITM) attacks in cyber‑physical systems (CPS), and extend our previously proposed Bellman Deviation Detection (BDD) framework for model‑free reinforcement learning (RL). We refine the standard MDP attack model by allowing the reward function to depend on both the current and subsequent states, thereby capturing reward variations induced by errors in the adversary's transition estimate. We also derive an optimal system‑identification strategy for the adversary that minimizes detectable value deviations. Further, we prove that the agent's asymptotic learning time required to secure the system scales linearly with the adversary's learning time, and that this matches the optimal lower bound. Hence, the proposed detection scheme is order‑optimal in detection efficiency. Finally, we extend the framework to asynchronous and intermittent attack scenarios, where reliable detection is preserved.

Authors:Yuqi Ping, Huahao Ding, Tianhao Liang, Longyu Zhou, Guangyu Lei, Xinglin Chen, Junwei Wu, Jieyu Zhou, Tingting Zhang
Title: LLM-Enabled Low-Altitude UAV Natural Language Navigation via Signal Temporal Logic Specification Translation and Repair
Abstract:
Natural language (NL) navigation for low‑altitude unmanned aerial vehicles (UAVs) offers an intelligent and convenient solution for low‑altitude aerial services by enabling an intuitive interface for non‑expert operators. However, deploying this capability in urban environments necessitates the precise grounding of underspecified instructions into safety‑critical, dynamically feasible motion plans subject to spatiotemporal constraints. To address this challenge, we propose a unified framework that translates NL instructions into Signal Temporal Logic (STL) specifications and subsequently synthesizes trajectories via mixed‑integer linear programming (MILP). Specifically, to generate executable STL formulas from free‑form NL, we develop a reasoning‑enhanced large language model (LLM) leveraging chain‑of‑thought (CoT) supervision and group‑relative policy optimization (GRPO), which ensures high syntactic validity and semantic consistency. Furthermore, to resolve infeasibilities induced by stringent logical or spatial requirements, we introduce a specification repair mechanism. This module combines MILP‑based diagnosis with LLM‑guided semantic reasoning to selectively relax task constraints while strictly enforcing safety guarantees. Extensive simulations and real‑world flight experiments demonstrate that the proposed closed‑loop framework significantly improves NL‑to‑STL translation robustness, enabling safe, interpretable, and adaptable UAV navigation in complex scenarios.

Authors:Anh Tung Nguyen, Andreas Hertzberg, André MH Teixeira
Title: Centrality-Based Security Allocation in Networked Control Systems
Abstract:
This paper addresses the security allocation problem within networked control systems, which consist of multiple interconnected control systems under the influence of two opposing agents: a defender and a malicious adversary. The adversary aims to maximize the worst‑case attack impact on system performance while remaining undetected by launching stealthy data injection attacks on one or several interconnected control systems. Conversely, the defender's objective is to allocate security resources to detect and mitigate these worst‑case attacks. A novel centrality‑based approach is proposed to guide the allocation of security resources to the most connected or influential subsystems within the network. The methodology involves comparing the worst‑case attack impact for both the optimal and centrality‑based security allocation solutions. The results demonstrate that the centrality measure approach enables significantly faster allocation of security resources with acceptable levels of performance loss compared to the optimal solution, making it suitable for large‑scale networks. The proposed method is validated through numerical examples using Erdos‑Renyi graphs.

Authors:Thomas Beckers, Anthony Bloch, Leonardo Colombo
Title: Structure-Preserving Learning of Nonholonomic Dynamics
Abstract:
Data‑driven modeling is playing an increasing role in robotics and control, yet standard learning methods typically ignore the geometric structure of nonholonomic systems. As a consequence, the learned dynamics may violate the nonholonomic constraints and produce physically inconsistent motions. In this paper, we introduce a structure‑preserving Gaussian process (GP) framework for learning nonholonomic dynamics. Our main ingredient is a nonholonomic matrix‑valued kernel that incorporates the constraint distribution directly into the GP prior. This construction ensures that the learned vector field satisfies the nonholonomic constraints for all inputs. We show that the proposed kernel is positive semidefinite, characterize its associated reproducing kernel Hilbert space as a space of admissible vector fields, and prove that the resulting estimator admits a coordinate representation adapted to the constraint distribution. We also establish the consistency of the learned model. Numerical simulations on a vertical rolling disk illustrate the effectiveness of the proposed approach.

Authors:Qian Yang, Miaomiao Wang, Abdelhamid Tayebi
Title: MPC-Based Trajectory Tracking for a Quadrotor UAV with Uniform Semi-Global Asymptotic Stability Guarantees
Abstract:
This paper proposes a model predictive trajectory tracking approach for quadrotors subject to input constraints. Our proposed approach relies on a hierarchical control strategy with an outer‑loop feedback generating the required thrust and desired attitude and an inner‑loop feedback regulating the actual attitude to the desired one. For the outer‑loop translational dynamics, the generation of the virtual control input is formulated as a constrained model predictive control problem with time‑varying input constraints and a control strategy, endowed with uniform global asymptotic stability guarantees, is proposed. For the inner‑loop rotational dynamics, a hybrid geometric controller is adopted, achieving semi‑global exponential tracking of the desired attitude. Finally, we prove that the overall cascaded system is semi‑globally asymptotically stable. Simulation results illustrate the effectiveness of the proposed approach.

Authors:Siying Huang, Yifen Mu, Ge Chen
Title: Decentralized MARL for Coarse Correlated Equilibrium in Aggregative Markov Games
Abstract:
This paper studies the problem of decentralized learning of Coarse Correlated Equilibrium (CCE) in aggregative Markov games (AMGs), where each agent's instantaneous reward depends only on its own action and an aggregate quantity. Existing CCE learning algorithms for general Markov games are not designed to leverage the aggregative structure, and research on decentralized CCE learning for AMGs remains limited. We propose an adaptive stage‑based V‑learning algorithm that exploits the aggregative structure under a fully decentralized information setting. Based on the two‑timescale idea, the algorithm partitions learning into stages and adjusts stage lengths based on the variability of aggregate signals, while using no‑regret updates within each stage. We prove the algorithm achieves an epsilon‑approximate CCE in O(S Amax T5 / epsilon2) episodes, avoiding the curse of multiagents which commonly arises in MARL. Numerical results verify the theoretical findings, and the decentralized, model‑free design enables easy extension to large‑scale multi‑agent scenarios.

Authors:Ahmed G. Ghallab, Ian R. Petersen
Title: Velocity-Free Horizontal Position Control of Quadrotor Aircraft via Nonlinear Negative Imaginary Systems Theory
Abstract:
This paper presents a velocity‑free position control strategy for quadrotor unmanned aerial vehicles based on nonlinear negative imaginary (NNI) systems theory. Unlike conventional position control schemes that require velocity measurements or estimation, the proposed approach achieves asymptotic stability using only position feedback. We establish that the quadrotor horizontal position subsystem, when augmented with proportional feedback, exhibits the NNI property with respect to appropriately defined horizontal thrust inputs. A strictly negative imaginary integral resonant controller is then designed for the outer loop, and robust asymptotic stability is guaranteed through satisfaction of explicit sector‑bound conditions relating controller and plant parameters. The theoretical framework accommodates model uncertainties and external disturbances while eliminating the need for velocity sensors. Simulation results validate the theoretical predictions and demonstrate effective position tracking performance.

Authors:Masih Haseli, Jorge Cortés, Joel W. Burdick
Title: Control Forward-Backward Consistency: Quantifying the Accuracy of Koopman Control Family Models
Abstract:
This paper extends the forward‑backward consistency index, originally introduced in Koopman modeling of systems without input, to the setting of control systems, providing a closed‑form computable measure of accuracy for data‑driven models associated with the Koopman Control Family (KCF). Building on a forward‑backward regression perspective, we introduce the control forward‑backward consistency matrix and demonstrate that it possesses several favorable properties. Our main result establishes that the relative root‑mean‑square error of KCF function predictors is strictly bounded by the square root of the control consistency index, defined as the maximum eigenvalue of the consistency matrix. This provides a sharp, closed‑form computable error bound for finite‑dimensional KCF models. We further specialize this bound to the widely used lifted linear and bilinear models. We also discuss how the control consistency index can be incorporated into optimization‑based modeling and illustrate the methodology via simulations.

Authors:Hanghang Cui, Arash Khalatbarisoltani, Jie Han, Wenxue Liu, Muhammad Saeed, Xiaosong Hu
Title: Driving Condition-Aware Multi-Agent Integrated Power and Thermal Management for Hybrid Electric Vehicles
Abstract:
Effective co‑optimization of energy management strategy (EMS) and thermal management (TM) is crucial for optimizing fuel efficiency in hybrid electric vehicles (HEVs). Driving conditions significantly influence the performance of both EMS and TM in HEVs. This study presents a novel driving condition‑aware integrated thermal and energy management (ITEM) framework. In this context, after analyzing and segmenting driving data into micro‑trips, two primary features (average speed and maximum acceleration) are measured. Using the K‑means approach, the micro‑trips are clustered into three main groups. Finally, a deep neural network is employed to develop a real‑time driving recognition model. An ITEM is then developed based on multi‑agent deep reinforcement learning (DRL), leveraging the proposed real‑time driving recognition model. The primary objectives are to improve the fuel economy and reduce TM power consumption while maintaining a pleasant cabin temperature for passengers. Our simulation results illustrate the effectiveness of the suggested framework and the positive impact of recognizing driving conditions on ITEM, improving fuel economy by 16.14% and reducing TM power consumption by 8.22% compared to the benchmark strategy.

Authors:Takashi Hikihara
Title: Communication-Induced Bifurcation and Collective Dynamics in Power Packet Networks: A Thermodynamic Approach to Information-Constrained Energy Grids
Abstract:
This paper investigates the nonlinear dynamics and phase transitions in power packet network connected with routers, conceptualized as macroscopic information‑ratchets. In the emerging paradigm of cyber‑physical energy systems, the interplay between stochastic energy fluctuations and the thermodynamic cost of control information defines fundamental operational limits. We first formulate the dynamics of a single router using a Langevin framework, incorporating an exponential cost function for information acquisition. Our analysis reveals a discontinuous (first‑order) phase transition, where the system adopts a strategic abandon of regulation as noise intensity exceeds a critical threshold D_c. This transition represents a fundamental information‑barrier inherent to autonomous energy management. Here, we extend this model to network configurations, where multiple routers are linked through diffusive coupling, sharing energy between them. We demonstrate that the network topology and coupling strength significantly extend the bifurcation points, with collective resilient behaviors against local fluctuations. These results provide a rigorous mathematical basis for the design of future complex communication‑energy network, suggesting that the stability of proposed systems is governed by the synergistic balance between physical energy flow and the thermodynamics of information exchange. It will serve to design future complex communication‑energy networks, including internal energy management for autonomous robots.

Authors:Shafayeth Jamil, Rehan Kapadia
Title: Interpretable Physics Extraction from Data for Linear Dynamical Systems using Lie Generator Networks
Abstract:
When the system is linear, why should learning be nonlinear? Linear dynamical systems, the analytical backbone of control theory, signal processing and circuit analysis, have exact closed‑form solutions via the state transition matrix. Yet when system parameters must be inferred from data, recent neural approaches offer flexibility at the cost of physical guarantees: Neural ODEs provide flexible trajectory approximation but may violate physical invariants, while energy preserving architectures do not natively represent dissipation essential to real‑world systems. We introduce Lie Generator Networks (LGN), which learn a structured generator A and compute trajectories directly via matrix exponentiation. This shift from integration to exponentiation preserves structure by construction. By parameterizing A = S ‑ D (skew‑symmetric minus positive diagonal), stability and dissipation emerge from the underlying architecture and are not introduced during training via the loss function. LGN provides a unified framework for linear conservative, dissipative, and time‑varying systems. On a 100‑dimensional stable RLC ladder, standard derivative‑based least‑squares system identification can yield unstable eigenvalues. The unconstrained LGN yields stable but physically incorrect spectra, whereas LGN‑SD recovers all 100 eigenvalues with over two orders of magnitude lower mean eigenvalue error than unconstrained alternatives. Critically, these eigenvalues reveal poles, natural frequencies, and damping ratios which are interpretable physics that black‑box networks do not provide.

Authors:Mohammad Javad Najafirad, Shirantha Welikala
Title: Dissipativity-Based Distributed Control and Communication Topology Co-Design for Nonlinear DC Microgrids
Abstract:
This paper presents a dissipativity‑based distributed droop‑free control and communication topology co‑design framework for voltage regulation and current sharing in DC microgrids (MGs), where constant‑power loads (CPLs) and voltage‑source converter (VSC) input saturation introduce significant nonlinearities. In particular, CPLs introduce an inherently destabilizing nonlinearity, while VSC input saturation imposes hard amplitude constraints on applicable control input at each distributed generator (DG), collectively making the DC MG control system design extremely challenging. To this end, the DC MG is modeled as a networked system of DGs, transmission lines, and loads coupled through a static interconnection matrix. Each DG is equipped with a local PI‑based controller with an anti‑windup compensator and a distributed consensus‑based global controller, from which a nonlinear networked error dynamics model is derived. The CPL nonlinearity is characterized via sector‑boundedness with the S‑procedure applied directly to yield tight LMI conditions, while the VSC input saturation is handled via a dead‑zone decomposition and sector‑boundedness, with both nonlinearities simultaneously absorbed into the dissipativity analysis. Both nonlinearities are simultaneously absorbed into the dissipativity analysis using the S‑procedure. Subsequently, local controller gains and passivity indices, and distributed controller gains and the communication topology are co‑designed by solving a sequence of local and global Linear Matrix Inequality (LMI) problems, enabling a one‑shot co‑design process that avoids iterative procedures. The effectiveness of the proposed framework is validated through simulation of an islanded DC MG under multiple operating scenarios, demonstrating robust performance superior to conventional control approaches.

Authors:Mayur Sawant, Abdelhamid Tayebi
Title: Dynamic Constrained Stabilization on the n-sphere
Abstract:
We consider the constrained stabilization problem of second‑order systems evolving on the n‑sphere. We propose a control strategy with a constraint proximity‑based dynamic damping mechanism that ensures safe and almost global asymptotic stabilization of the target point in the presence of star‑shaped constraints on the n‑sphere. It is also shown that the proposed approach can be used to deal with the constrained rigid‑body attitude stabilization. The effectiveness of the proposed approach is demonstrated through simulation results on the 2‑sphere in the presence of star‑shaped constraint sets.

Authors:Spencer Schutz, Charlott Vallon, Francesco Borrelli
Title: Safe Adaptive-Sampling Control via Robust M-Step Hold Model Predictive Control
Abstract:
In adaptive‑sampling control, the control frequency can be adjusted during task execution. Ensuring that these changes do not jeopardize the safety of the system being controlled requires attention. We introduce robust M‑step hold model predictive control (MPC) to address this. Our formulation provides robust constraint satisfaction for an uncertain discrete‑time system model with a fixed sampling time subject to an adaptable multi‑step input hold (referred to as M‑step hold). We show how to ensure recursive feasibility of the MPC utilizing M‑step hold extensions of robust invariant sets, and demonstrate how to enable safe adaptive‑sampling control via the online selection of M. We evaluate the utility of the robust M‑step hold MPC formulation in a cruise control example.

Authors:Afreen Islam
Title: Learning swarm behaviour from a flock of homing pigeons using inverse optimal control
Abstract:
In this work, Global Position System (GPS) data from a flock of homing pigeons are analysed. The flocking behaviour of the considered homing pigeons is formulated as a swarm optimal trajectory tracking control problem. The swarm problem in this work is modeled with the idea that one or two pigeons at the forefront lead the flock. Each follower pigeon is assumed to follow a leader pigeon immediately ahead of themselves, instead of directly following the leaders at the forefront of the flock. The trajectory of each follower pigeon is assumed to be a solution of an optimal trajectory tracking control problem. An optimal control problem framework is created for each follower pigeon. An important aspect of an optimal control problem is the cost function. A minimum principle based method for multiple flight data is proposed, which can help in learning the unknown weights of the cost function of the optimal trajectory tracking control problem for each follower pigeon, from flight trajectories' information obtained from GPS data.

Authors:Hussein Naser, Hashim A. Hashim, Mojtaba Ahmadi
Title: Quaternion-based Unscented Kalman Filter for Robust Wrench Estimation of Human-UAV Physical Interaction
Abstract:
This paper introduces an advanced Quaternion‑based Unscented Kalman Filter (QUKF) for real‑time, robust estimation of system states and external wrenches in assistive aerial payload transportation systems that engage in direct physical interaction. Unlike conventional filtering techniques, the proposed approach employs a unit‑quaternion representation to inherently avoid singularities and ensure globally consistent, drift‑free estimation of the platform's pose and interaction wrenches. A rigorous quaternion‑based dynamic model is formulated to capture coupled translational and rotational dynamics under interaction forces. Building on this model, a comprehensive QUKF framework is established for state prediction, measurement updates, and external wrench estimation. The proposed formulation fully preserves the nonlinear characteristics of rotational motion, enabling more accurate and numerically stable estimation during physical interaction compared to linearized filtering schemes. Extensive simulations validate the effectiveness of the QUKF, showing significant improvements over the Extended Kalman Filter (EKF). Specifically, the QUKF achieved a 79.41% reduction in Root Mean Squared Error (RMSE) for torque estimation, with average RMSE improvements of 79% and 56%, for position and angular rates, respectively. These findings demonstrate enhanced robustness to measurement noise and modeling uncertainties, providing a reliable foundation for safe, stable, and responsive human‑UAV physical interaction in cooperative payload transportation tasks.

Authors:Alejandro Carrasco, Mariko Storey-Matsutani, Victor Rodriguez-Fernandez, Richard Linares
Title: GUIDE: Guided Updates for In-context Decision Evolution in LLM-Driven Spacecraft Operations
Abstract:
Large language models (LLMs) have been proposed as supervisory agents for spacecraft operations, but existing approaches rely on static prompting and do not improve across repeated executions. We introduce \textscGUIDE, a non‑parametric policy improvement framework that enables cross‑episode adaptation without weight updates by evolving a structured, state‑conditioned playbook of natural‑language decision rules. A lightweight acting model performs real‑time control, while offline reflection updates the playbook from prior trajectories. Evaluated on an adversarial orbital interception task in the Kerbal Space Program Differential Games environment, GUIDE's evolution consistently outperforms static baselines. Results indicate that context evolution in LLM agents functions as policy search over structured decision rules in real‑time closed‑loop spacecraft interaction.

Authors:Takuya Sasatani, Alanson P. Sample, Yoshihiro Kawahara
Title: Reconfiguring room-scale magnetoquasistatic wireless power transfer with hierarchical resonators
Abstract:
Magnetoquasistatic wireless power transfer can deliver substantial power to mobile devices over near‑field links. Room‑scale implementations, such as quasistatic cavity resonators, extend this capability over large enclosed volumes, but their efficiency drops sharply for centimeter‑scale or misoriented receivers because the magnetic field is spatially broad and weakly coupled to small coils. Here, we introduce hierarchical resonators that act as selectively activated relays within a room‑scale quasistatic cavity resonator, capturing the ambient magnetic field and re‑emitting it to concentrate flux at a target receiver. This architecture reconfigures the wireless power environment on demand and enables localized energy delivery to miniature devices. Experimentally, the hierarchical link improves power transfer efficiency by more than two orders of magnitude relative to direct room‑scale transfer and delivers up to 500 mW of DC power to a 15 mm receiver. We further demonstrate selective multi‑relay operation and field reorientation for furniture‑embedded charging scenarios. These results establish a scalable route to reconfigurable wireless power delivery for miniature and batteryless devices in room‑scale environments.

Authors:Zili Wang, Hao Yang, Xiangxiang Wang, Bin Jiang, Long Wang, Marios M. Polycarpou
Title: Prospect Theoretic Approach to Pursuit-evasion Differential Games with Risk Aversion and Probability Sensitivity
Abstract:
This paper considers for the first time pursuit‑evasion (PE) differential games with irrational perceptions of both pursuer and evader on probabilistic characteristics of environmental uncertainty. Firstly, the irrational perceptions of risk aversion and probability sensitivity are modeled and incorporated within a Bayesian PE differential game framework by using Cumulative Prospect Theory (CPT) approach; Secondly, several sufficient conditions of capturability are established in terms of system dynamics and irrational parameters; Finally, the existence of CPT‑Nash equilibria is rigorously analyzed by invoking Brouwer's fixed‑point theorem. The new results reveal that irrational behaviors benefit the pursuer in some cases and the evader in others. Certain captures that are unachievable under rational behaviors can be achieved under irrational ones. By bridging irrational behavioral theory with game‑theoretic control, this framework establishes a rigorous theoretical foundation for practical control engineering within complex human‑machine systems.

Authors:Mohamed Elgouhary, Amr S. El-Wakeel
Title: Robust Global-Local Behavior Arbitration via Continuous Command Fusion Under LiDAR Errors
Abstract:
Modular autonomous driving systems must coordinate global progress objectives with local safety‑driven reactions under imperfect sensing and strict real‑time constraints. This paper presents a ROS2‑native arbitration module that continuously fuses the outputs of two unchanged and interpretable controllers: a global reference‑tracking controller based on Pure Pursuit and a reactive LiDAR‑based Gap Follow controller. At each control step, both controllers propose Ackermann commands, and a PPO‑trained policy predicts a continuous gate from a compact feature observation to produce a single fused drive command, augmented with practical safety checks. For comparison under identical ROS topic inputs and control rate, we implement a lightweight sampling‑based predictive baseline. Robustness is evaluated using a ROS2 impairment protocol that injects LiDAR noise, delay, and dropout, and additionally sweeps forward‑cone false short‑range outliers. In a repeatable close‑proximity passing scenario, we report safe success and failure rates together with per‑step end‑to‑end controller runtime as sensing stress increases. The study is intended as a command‑level robustness evaluation in a modular ROS2 setting, not as a replacement for planning‑level interaction reasoning.

Authors:Vinay Kathiriya, Saurabh Kumar, Shashi Ranjan Kumar
Title: Path-Following Guidance for Unmanned Aerial Vehicle with Bounded Lateral Acceleration
Abstract:
This paper addresses the three‑dimensional path‑following guidance problem for unmanned aerial vehicles under explicit actuator constraints. Unlike conventional approaches that assume unbounded control inputs or handle saturation heuristically, the proposed method incorporates bounded lateral acceleration directly into the guidance design. A nonlinear guidance framework is developed employing a nested saturation‑based control technique. The proposed guidance strategy guarantees bounded control inputs while ensuring exponential convergence of cross‑track errors to zero. The formulation is applicable to general smooth paths and is systematically extended from planar to three‑dimensional scenarios using a path‑tangent coordinate framework. Rigorous stability analysis based on Lyapunov theory establishes convergence and feasibility properties of the closed‑loop system. Numerical simulations on representative paths, including straight‑line, circular, and sinusoidal paths, demonstrate that the proposed method achieves superior tracking performance, reduced control effort, and robustness against disturbances compared to existing guidance laws. The simplicity of the design and its compatibility with practical actuator limits make it suitable for real‑world UAV applications.

Authors:Yujia Li, Alexandre Moreira, Miguel Heleno
Title: Time Window-Based Netload Range Cost Curves for Coordinated Transmission and Distribution Planning Under Uncertainty
Abstract:
Mechanisms to coordinate transmission and distribution planning should be regulatory compliant and keep the spheres of DSO and TSO decisions separate, without requiring disclosure of proprietary data or unrealistic computationally expensive T&D co‑simulations. The concept of Netload Range Cost Curves (NRCC) has been recently proposed as simple non‑invasive form of coordinating T&D investments under distribution netload uncertainty. This paper extends the NRCC concept to accommodate the temporal dimension of the T&D planning process. We propose to compute a hierarchy of certified temporal interface products that represent the different levels of flexibility that distribution networks can provide transmission grids with at the planning stage. The first product (P1) maps distribution investment into scenario‑robust, per‑window service envelopes within which any TSO service call (to modify load within specified bounds) is guaranteed distribution‑network‑feasible. The second product (P2) adds lexicographic rebound minimization, preserving P1‑optimal service capacity while certifying post‑service recovery under three governance variants with qualitatively distinct rebound‑budget responses. In our numerical results, based on a real distribution feeder, we compare the performance of our proposed time‑window‑based flexibility products to an atemporal product (P0) that offers a static bound on the aggregate distribution grid netload across all time periods. Our results demonstrate the superiority of our proposed products in properly valuing the benefits of incremental investments in storage to allow for temporal flexibility.

Authors:Lintao Ye, Ankang Zhang, Ming Chi, Bin Du, Jianghai Hu
Title: Online Learning of Kalman Filtering: From Output to State Estimation
Abstract:
In this paper, we study the problem of learning Kalman filtering with unknown system model in partially observed linear dynamical systems. We propose a unified algorithmic framework based on online optimization that can be used to solve both the output estimation and state estimation scenarios. By exploring the properties of the estimation error cost functions, such as conditionally strong convexity, we show that our algorithm achieves a \log T‑regret in the horizon length T for the output estimation scenario. More importantly, we tackle the more challenging scenario of learning Kalman filtering for state estimation, which is an open problem in the literature. We first characterize a fundamental limitation of the problem, demonstrating the impossibility of any algorithm to achieve sublinear regret in T. By further introducing a random query scheme into our algorithm, we show that a \sqrtT‑regret is achievable when rendering the algorithm limited query access to more informative measurements of the system state in practice. Our algorithm and regret readily capture the trade‑off between the number of queries and the achieved regret, and shed light on online learning problems with limited observations. We validate the performance of our algorithms using numerical examples.

Authors:Zhendong Cao, Hongji Dai, Zhida Li, Ash Parameswaran
Title: Quantitative measurements of biological/chemical concentrations using smartphone cameras
Abstract:
This paper presents a smartphone‑based imaging system capable of quantifying the concentration of an assortment of biological/chemical assay samples. The main objective is to construct an image database which characterizes the relationship between color information and concentrations of the biological/chemical assay sample. For this aim, a designated optical setup combined with image processing and data analyzing techniques was implemented. A series of experiments conducted on selected assays, including fluorescein, RNA Mango, homogenized milk and yeast have demonstrated that the proposed system estimates the concentration of fluorescent materials and colloidal mixtures comparable to currently used commercial and laboratory instruments. Furthermore, by utilizing the camera and computational power of smartphones, eventual development can be directed toward extremely compact, inexpensive and portable analysis and diagnostic systems which will allow experiments and tests to be conducted in remote or impoverished areas.

Authors:Heling Zhang, Siqi Du, Roy Dong
Title: A Controllability Perspective on Steering Follow-the-Regularized-Leader Learners in Games
Abstract:
Follow‑the‑regularized‑leader (FTRL) algorithms have become popular in the context of games, providing easy‑to‑implement methods for each agent, as well as theoretical guarantees that the strategies of all agents will converge to some equilibrium concept (provided that all agents follow the appropriate dynamics). However, with these methods, each agent ignores the coupling in the game, and treats their payoff vectors as exogenously given. In this paper, we take the perspective of one agent (the controller) deciding their mixed strategies in a finite game, while one or more other agents update their mixed strategies according to continuous‑time FTRL. Viewing the learners' dynamics as a nonlinear control system evolving on the relative interior of a simplex or product of simplices, we ask when the controller can steer the learners to a target state, using only its own mixed strategy and without modifying the game's payoff structure. For the two‑player case we provide a necessary and sufficient criterion for controllability based on the existence of a fully mixed neutralizing controller strategy and a rank condition on the projected payoff map. For multi‑learner interactions we give two sufficient controllability conditions, one based on uniform neutralization and one based on a periodic‑drift hypothesis together with a Lie‑algebra rank condition. We illustrate these results on canonical examples such as Rock‑Paper‑Scissors and a construction related to Brockett's integrator.

Authors:Mrdjan Jankovic
Title: Proprioceptive feedback paradigm for safe and resilient motion control
Abstract:
Proprioception is a human sense that provides feedback from muscles and joints about body position and motion. This key capability keeps us upright, moving, and responding quickly to slips or stumbles. In this paper we discuss a proprioception‑like feature (machine proprioceptive feedback ‑ MPF) for motion control systems. An unexpected response of one actuator, or one agent in a multi‑agent system, is compensated by other actuators/agents through fast feedback loops that react only to the unexpected portion. The paper appropriates the predictor‑corrector mechanism of decentralized, multi‑agent controllers as "proprioceptive feedback" for centrally controlled ones. It analyzes a nature and degree of impairment that can be managed and offers two options, full‑ MPF and split‑MPF, with different wiring architectures as well as different stability and safety properties. Multi‑vehicle interchange lane‑swap traffic simulations confirm the analytical results.

Authors:Ike Griss Salas, Ethan King
Title: Data-driven discovery and control of multistable nonlinear systems and hysteresis via structured Neural ODEs
Abstract:
Many engineered physical processes exhibit nonlinear but asymptotically stable dynamics that converge to a finite set of equilibria determined by control inputs. Identifying such systems from data is challenging: stable dynamics provide limited excitation and model discovery is often non‑unique. We propose a minimally structured Neural Ordinary Differential Equation (NODE) architecture that enforces trajectory stability and provides a tractable parameterization for multistable systems, by learning a vector field in the form F(x,u) = f(x)\,(x ‑ g(x,u)), where f(x) < 0 elementwise ensures contraction and g(x,u) determines the multi‑attractor locations. Across several nonlinear benchmarks, the proposed structure is efficient on short time horizon training, captures multiple basins of attraction, and enables efficient gradient‑based feedback control through the implicit equilibrium map g.

Authors:Zhonggang Li, Geert Leus, Raj Thilak Rajan
Title: Multicluster Design and Control of Large-Scale Affine Formations
Abstract:
Conventional affine formation control (AFC) empowers a network of agents with flexible but collective motions ‑ a potential which has not yet been exploited for large‑scale swarms. One of the key bottlenecks lies in the design of an interaction graph, characterized by the Laplacian‑like stress matrix. Efficient and scalable design solutions often yield suboptimal solutions on various performance metrics, e.g., convergence speed and communication cost, to name a few. The current state‑of‑the‑art algorithms for finding optimal solutions are computationally expensive and therefore not scalable. In this work, we propose a more efficient optimal design for any generic configuration, with the potential to further reduce complexity for a large class of nongeneric rotationally symmetric configurations. Furthermore, we introduce a multicluster control framework that offers an additional scalability improvement, enabling not only collective affine motions as in conventional AFC but also partially independent motions naturally desired for large‑scale swarms. The overall design is compatible with a swarm size of several hundred agents with fast formation convergence, as compared to up to only a few dozen agents by existing methods. Experimentally, we benchmark the performance of our algorithm compared with several state‑of‑the‑art solutions and demonstrate the capabilities of our proposed control strategies.

Authors:Xiao Tan, Rahal Nanayakkara, Paulo Tabuada, Aaron D. Ames
Title: A Duality-Based Optimization Formulation of Safe Control Design with State Uncertainties
Abstract:
State estimation uncertainty is prevalent in real‑world applications, hindering the application of safety‑critical control. Existing methods address this by strengthening a Control Barrier Function (CBF) condition either to handle actuation errors induced by state uncertainty, or to enforce stricter, more conservative sufficient conditions. In this work, we take a more direct approach and formulate a robust safety filter by analyzing the image of the set of all possible states under the CBF dynamics. We first prove that convexifying this image set does not change the set of possible inputs. Then, by leveraging duality, we propose an equivalent and tractable reformulation for cases where this convex hull can be expressed as a polytope or ellipsoid. Simulation results show the approach in this paper to be less conservative than existing alternatives.

Authors:Youssef Ahmed, Arnob Ghosh, Chih-Chun Wang, Ness B. Shroff
Title: Beyond Freshness and Semantics: A Coupon-Collector Framework for Effective Status Updates
Abstract:
For status update systems operating over unreliable energy‑constrained wireless channels, we address Weaver's long‑standing Level‑C question: do my packets actually improve the plant's behavior? Each fresh sample carries a stochastic expiration time ‑‑ governed by the plant's instability dynamics ‑‑ after which the information becomes useless for control. Casting the problem as a coupon‑collector variant with expiring coupons, we (i) formulate a two‑dimensional average‑reward MDP, (ii) prove that the optimal schedule is doubly thresholded in the receiver's freshness timer and the sender's stored lifetime, (iii) derive a closed‑form policy for deterministic lifetimes, and (iv) design a Structure‑Aware Q‑learning algorithm (SAQ) that learns the optimal policy without knowing the channel success probability or lifetime distribution. Simulations validate our theoretical predictions: SAQ matches optimal Value Iteration performance while converging significantly faster than baseline Q‑learning, and expiration‑aware scheduling achieves up to 50% higher reward than age‑based baselines by adapting transmissions to state‑dependent urgency ‑‑ thereby delivering Level‑C effectiveness under tight resource constraints.

Authors:Prajakta Surve, Shaunak D. Bopardikar, Daigo Shishika, Dipankar Maity, Michael Dorothy
Title: Optimal Hiding with Partial Information of the Seeker's Route
Abstract:
We consider a hide‑and‑seek game between a Hider and a Seeker over a finite set of locations. The Hider chooses one location to conceal a stationary treasure, while the Seeker visits the locations sequentially along a route. As the search progresses, the Hider observes a prefix of the Seeker's route. After observing this information, the Hider has the option to relocate the treasure at most once to another unvisited location by paying a switching cost. We study two seeker models. In the first, the Seeker is unaware of the fact that the Hider can relocate. In the second, the Seeker select its route while accounting for the possibility that the Hider observes its path and reallocates. For the restricted case, we define the value‑of‑information created by the reveal and derive upper bounds in terms of the switching cost using a worst‑case evaluation over routes. We also show that seeker awareness reduces the game value, with the difference between the restricted and feedback models bounded by the entry‑wise gap between the corresponding payoff matrices. Numerical examples show how this benefit decreases as the switching cost increases and as the reveal occurs later along the route.

Authors:Farong Wang, Sai Swaminathan, Fei Liu
Title: Surface-Constrained Offline Warping with Contact-Aware Online Pose Projection for Safe Robotic Trajectory Execution
Abstract:
Robotic manipulation tasks that require repeated tool motion along curved surfaces frequently arise in surface finishing, inspection, and guided interaction. In practice, nominal motion primitives are often designed independently of the deployment surface and later reused across varying geometries. Directly tiling such primitives onto nonplanar surfaces introduces geometric inconsistencies, leading to interpenetration, orientation discontinuities, and cumulative drift over repeated cycles. We present a two‑stage framework that separates geometric embedding from execution‑level regulation. An offline surface‑constrained warping operator embeds a nominal periodic primitive onto curved surfaces through asymmetric diffeomorphic deformation of dual‑track waypoints and axis‑consistent orientation completion, producing a surface‑adapted reference trajectory. An online contact‑aware projection operator then enforces bounded deviation relative to this reference using FSR‑driven disturbance adaptation and a conic orientation safety constraint. Experiments across multiple analytic surface families and real‑robot validation on a sinusoidal surface demonstrate improved geometric continuity, reduced large orientation jumps, and robust contact maintenance compared with direct tiling. These results show that decoupling offline geometric remapping from lightweight online projection enables stable and repeatable surface‑embedded trajectory execution under sensor‑lite feedbacks.

Authors:Barak Diker, Itzik Klein
Title: Neural Aided Adaptive Innovation-Based Invariant Kalman Filter
Abstract:
Autonomous platforms require accurate positioning to complete their tasks. To this end, a Kalman filter‑based algorithms, such as the extended Kalman filter or invariant Kalman filter, utilizing inertial and external sensor fusion are applied. To cope with real‑world scenarios, adaptive noise estimation methods have been developed primarily for classical Euclidean formulations. However, these methods remain largely unexplored in the tangent Lie space, despite it provides a principled geometric framework with favorable error dynamics on Lie groups. To fill this gap, we combine invariant filtering theory with neural‑aided adaptive noise estimation in real‑world settings. To this end, we derive a novel theoretical extension of classical innovation‑based process noise adaptation formulated directly within the Lie‑group framework. We further propose a lightweight neural network that estimates the process noise covariance parameters directly from raw inertial data. Trained entirely in a sim2real framework via domain adaptation, the network captures motion‑dependent and sensor‑dependent noise characteristics without requiring labeled real‑world data. To examine our proposed neural‑aided adaptive invariant Kalman filter, we focus on the challenging real‑world scenario of autonomous underwater navigation. Experimental results demonstrate superior performance compared to existing methods in terms of position root mean square error. These results validate our sim2real pipeline and further confirm that geometric invariance significantly enhances learning‑based adaptation and that adaptive noise estimation in the tangent Lie space offers a powerful mechanism for improving navigation accuracy in nonlinear autonomous platforms.

Authors:Erling W. Eriksen, Magnus M. Nygård, Niklas Erdmann, Heine N. Riise
Title: Deep Learning Multi-Horizon Irradiance Nowcasting: A Comparative Evaluation of Three Methods for Leveraging Sky Images
Abstract:
We investigate three distinct methods of incorporating all‑sky imager (ASI) images into deep learning (DL) irradiance nowcasting. The first method relies on a convolutional neural network (CNN) to extract features directly from raw RGB images. The second method uses state‑of‑the‑art algorithms to engineer 2D feature maps informed by domain knowledge, e.g., cloud segmentation, the cloud motion vector, solar position, and cloud base height. These feature maps are then passed to a CNN to extract compound features. The final method relies on aggregating the engineered 2D feature maps into time‑series input. Each of the three methods were then used as part of a DL model trained on a high‑frequency, 29‑day dataset to generate multi‑horizon forecasts of global horizontal irradiance up to 15 minutes ahead. The models were then evaluated using root mean squared error and skill score on 7 selected days of data. Aggregated engineered ASI features as model input yielded superior forecasting performance, demonstrating that integration of ASI images into DL nowcasting models is possible without complex spatially‑ordered DL‑architectures and inputs, underscoring opportunities for alternative image processing methods as well as the potential for improved spatial DL feature processing methods.

Authors:Phillip Kingston, Nicholas Johnston
Title: Physicochemical-Neural Fusion for Semi-Closed-Circuit Respiratory Autonomy in Extreme Environments
Abstract:
This paper introduces Galactic Bioware's Life Support System, a semi‑closed‑circuit breathing apparatus designed for integration into a positive‑pressure firefighting suit and governed by an AI control system. The breathing loop incorporates a soda lime CO2 scrubber, a silica gel dehumidifier, and pure O2 replenishment with finite consumables. One‑way exhaust valves maintain positive pressure while creating a semi‑closed system in which outward venting gradually depletes the gas inventory. Part I develops the physicochemical foundations from first principles, including state‑consistent thermochemistry, stoichiometric capacity limits, adsorption isotherms, and oxygen‑management constraints arising from both fire safety and toxicity. Part II introduces an AI control architecture that fuses three sensor tiers, external environmental sensing, internal suit atmosphere sensing (with triple‑redundant O2 cells and median voting), and firefighter biometrics. The controller combines receding‑horizon model‑predictive control (MPC) with a learned metabolic model and a reinforcement learning (RL) policy advisor, with all candidate actuator commands passing through a final control‑barrier‑function safety filter before reaching the hardware. This architecture is intended to optimize performance under unknown mission duration and exertion profiles. In this paper we introduce an 18‑state, 3‑control nonlinear state‑space formulation using only sensors viable in structural firefighting, with triple‑redundant O2 sensing and median voting. Finally, we introduce an MPC framework with a dynamic resource scarcity multiplier, an RL policy advisor for warm‑starting, and a final control‑barrier‑function safety filter through which all actuator commands must pass, demonstrating 18‑34% endurance improvement in simulation over PID baselines while maintaining tighter physiological and fire‑safety margins.

Authors:Ashutosh Soni, Peizhong Ju, Atilla Eryilmaz, Ness B. Shroff
Title: An LP-based Sampling Policy for Multi-Armed Bandits with Side-Observations and Stochastic Availability
Abstract:
We study the stochastic multi‑armed bandit (MAB) problem where an underlying network structure enables side‑observations across related actions. We use a bipartite graph to link actions to a set of unknowns, such that selecting an action reveals observations for all the unknowns it is connected to. While previous works rely on the assumption that all actions are permanently accessible, we investigate the more practical setting of stochastic availability, where the set of feasible actions (the "activation set") varies dynamically in each round. This framework models real‑world systems with both structural dependencies and volatility, such as social networks where users provide side‑information about their peers' preferences, yet are not always online to be queried. To address this challenge, we propose UCB‑LP‑A, a novel policy that leverages a Linear Programming (LP) approach to optimize exploration‑exploitation trade‑offs under stochastic availability. Unlike standard network bandit algorithms that assume constant access, UCB‑LP‑A computes an optimal sampling distribution over the realizable activation sets, ensuring that the necessary observations are gathered using only the currently active arms. We derive a theoretical upper bound on the regret of our policy, characterizing the impact of both the network structure and the activation probabilities. Finally, we demonstrate through numerical simulations that UCB‑LP‑A significantly outperforms existing heuristics that ignore either the side‑information or the availability constraints.

Authors:Takuya Sasatani, Yoshihiro Kawahara
Title: Patched-Wall Quasistatic Cavity Resonators for 3-D Wireless Power Transfer
Abstract:
Traditional wireless power transfer (WPT) systems are largely limited to 1‑D charging pads or 2‑D charging surfaces and therefore do not support a truly ubiquitous device‑powering experience. Although room‑scale WPT based on multimode quasistatic cavity resonance (QSCR) has demonstrated full‑volume coverage by leveraging multiple resonant modes, existing high‑coverage implementations require obstructive internal conductive structures, such as a central pole. This letter presents a new structure, termed the patched‑wall QSCR, that eliminates such internal obstructions while preserving full‑volume coverage. By using conductive wall segments interconnected by capacitors, the proposed structure supports two complementary resonant modes that cover both the peripheral and central regions without obstructions within the charging volume. Electromagnetic simulations show that, by selectively exciting these two resonant modes, the proposed structure achieves a minimum power‑transfer efficiency of 48.1% across the evaluated 54 m^3 charging volume while preserving an unobstructed interior space.

Authors:Bogdan Gheorghe, Amr Alanwar, Florin Stoican
Title: Inclusion conditions for the Constrained Polynomial Zonotopic case
Abstract:
Set operations are well understood for convex sets but become considerably more challenging in the non‑convex case due to the loss of structural properties in their representation. Constrained polynomial zonotopes (CPZs) offer an effective compromise, as they can capture complex, typically non‑convex geometries while maintaining an algebraic structure suitable for further manipulation. Building on this, we propose novel nonlinear encodings that provide sufficient conditions for testing inclusion between two CPZs and adapt them for seamless integration within optimization frameworks.

Authors:Chi Ho Leung, Philip E. Paré
Title: Port-Transversal Barriers: Graph-Theoretic Safety for Port-Hamiltonian Systems
Abstract:
We study port‑Hamiltonian systems with energy functions that split into local storage terms. From the interconnection and dissipation structure, we construct a graph on the energy compartments. From this graph, we show that the shortest‑path distance from a constrained compartment to the nearest actuated one gives a lower bound on the relative degree of the corresponding safety constraint. We also show that no smooth static feedback can reduce it when no path exists. When the relative degree exceeds one and the immediate graph neighbors of the constrained compartment is connected to at least one input port, we reshape the constraint by subtracting their shifted local storages, producing a candidate barrier function of relative degree one. We then identify sufficient regularity conditions that recover CBF feasibility under bounded inputs. We validate the framework on an LC ladder network, where the enforceability of a capacitor charge constraint depends only on the input topology.

Authors:Camilla Celli, Andrea Bini, Valerio Novelli, Alessandro Filippeschi, Francesco Porcini, Antonio Frisoli
Title: Optimal Prioritized Dissipation and Closed-Form Damping Limitation under Actuator Constraints for Haptic Interfaces
Abstract:
In haptics, guaranteeing stability is essential to ensure safe interaction with remote or virtual environments. One of the most relevant methods at the state‑of‑the‑art is the Time Domain Passivity Approach (TDPA). However, its high conservatism leads to a significant degradation of transparency. Moreover, the stabilizing action may conflict with the device's physical limitations. State‑of‑the‑art solutions have attempted to address these actuator limits, but they still fail to account simultaneously for the power limits of each actuator while maximizing transparency. This work proposes a new damping limitation method based on prioritized dissipation actions. It prioritizes an optimal dissipation direction that minimizes actuator load, while any excess dissipation is allocated to the orthogonal hyperplane. The solution provides a closed‑form formulation and is robust in multi‑DoF scenarios, even in the presence of actuator and motion anisotropies. The method is experimentally validated using a parallel haptic interface interacting with a virtual environment and tested under different operating conditions.

Authors:Ajith Anil Meera, Wouter Kouw
Title: Curvature-aware Expected Free Energy as an Acquisition Function for Bayesian Optimization
Abstract:
We propose an Expected Free Energy‑based acquisition function for Bayesian optimization to solve the joint learning and optimization problem, i.e., optimize and learn the underlying function simultaneously. We show that, under specific assumptions, Expected Free Energy reduces to Upper Confidence Bound, Lower Confidence Bound, and Expected Information Gain. We prove that Expected Free Energy has unbiased convergence guarantees for concave functions. Using the results from these derivations, we introduce a curvature‑aware update law for Expected Free Energy and show its proof of concept using a system identification problem on a Van der Pol oscillator. Through rigorous simulation experiments, we show that our adaptive Expected Free Energy‑based acquisition function outperforms state‑of‑the‑art acquisition functions with the least final simple regret and error in learning the Gaussian process.

Authors:Bingfang Li, Songhao Yang, Pu Cheng, Zhiguo Hao
Title: Transient Stability of GFL Converters Subjected to Switching of Droop-Controlled GFM Converters
Abstract:
Integrating grid‑forming converters (GFMCs) into grid‑following converter (GFLC)‑dominated power systems enhances the grid strength, but GFMCs' current‑limiting characteristic triggers dynamic switching between constant voltage control (CVC) and current limit control (CLC). This switching feature poses critical transient stability risks to GFLCs, requiring urgent investigation. This paper first develops a mathematical model for this switched system. Then, it derives switching conditions for droop‑controlled GFMCs, which are separately GFMC angle‑dependent and GFLC angle‑dependent. On this basis, the stability boundaries of GFLC within each subsystem are analyzed, and the impact of GFMC switching arising from GFLC angle oscillation is investigated. The findings reveal that the switched system's stability boundary coincides with that of the CLC subsystem. To enhance GFLC's transient stability and ensure GFMC converges to the CVC mode, this paper introduces a virtual fixed d‑axis control (VFDC) strategy. Compared with existing methods, this method achieves decoupling and self‑stabilization using only local state variables from individual converters. The conclusions are validated through simulations and Controller Hardware‑in‑the‑Loop tests.

Authors:Moritz Landwehr, Patrick Hoher, Johannes Reuter
Title: Aging States Estimation and Monitoring Strategies of Li-Ion Batteries Using Incremental Capacity Analysis and Gaussian Process Regression
Abstract:
Existing approaches for battery health forecasting often rely on extensive cycling histories and continuously monitored cells. In contrast, many real‑world scenarios provide only sparse information, e.g. a single diagnostic cycle. In our study, we investigate state of health (SoH)‑ and remaining useful life (RUL) estimation of previously unseen lithium‑ion cells, relying on cycling data from begin of life (BOL) to end of life (EOL) of multiple similar cells by using the publicly available Oxford battery aging dataset. The estimator applies incremental capacity analysis (ICA)‑based feature extraction in combination with data‑efficient regression methods. Particular emphasis is placed on a multi‑model Gaussian process regression ensemble approach (GPRn), which also provides uncertainty quantification. Due to a rather cell invariant behaviour, the mapping of ICA features to SoH estimation is highly precise and points out a normalized mean absolute error (NMAE) of 1.3%. The more cell variant mapping to RUL estimation is challenging, reflecting in a NMAE of 5.3%. Using the estimation results, a RUL monitoring strategy is derived. The objective is to safely operate a battery cell from BOL to EOL by only taking sparse diagnostic measurements. On average, only four diagnostic measurements are required during a cell's lifetime of 3300 to 5000 cycles.

Authors:Irfan Qaisar, Kailai Sun, Qingshan Jia, Qianchuan Zhao
Title: Experimental study on surveillance video-based indoor occupancy measurement with occupant-centric control
Abstract:
Accurate occupancy information is essential for closed‑loop occupant‑centric control (OCC) in smart buildings. However, existing vision‑based occupancy measurement methods often struggle to provide stable and accurate measurements in real indoor environments, and their implications for downstream HVAC control remain insufficiently studied. To achieve Net Zero emissions by 2050, this paper presents an experimental study of large language models (LLMs)‑enhanced vision‑based indoor occupancy measurement and its impact on OCC‑enabled HVAC operation. Detection‑only, tracking‑based, and LLM‑based refinement pipelines are compared under identical conditions using real surveillance data collected from a research laboratory in China, with frame‑level manual ground‑truth annotations. Results show that tracking‑based methods improve temporal stability over detection‑only measurement, while LLM‑based refinement further improves occupancy measurement performance and reduces false unoccupied prediction. The best‑performing pipeline, YOLOv8+DeepSeek, achieves an accuracy of 0.8824 and an F1‑score of 0.9320. This pipeline is then integrated into an HVAC supervisory model predictive control framework in OpenStudio‑EnergyPlus. Experimental results demonstrate that the proposed framework can support more efficient OCC operation, achieving a substantial HVAC energy‑saving potential of 17.94%. These findings provide an effective methodology and practical foundation for future research in AI‑enhanced smart building operations.

Authors:Leilei Cui, Richard D. Braatz
Title: LQR for Systems with Probabilistic Parametric Uncertainties: A Gradient Method
Abstract:
A gradient‑based method is proposed for solving the linear quadratic regulator (LQR) problem for linear systems with nonlinear dependence on time‑invariant probabilistic parametric uncertainties. The approach explicitly accounts for model uncertainty and ensures robust performance. By leveraging polynomial chaos theory (PCT) in conjunction with policy optimization techniques, the original stochastic system is lifted into a high‑dimensional linear time‑invariant (LTI) system with structured state‑feedback control. A first‑order gradient descent algorithm is then developed to directly optimize the structured feedback gain and iteratively minimize the LQR cost. We rigorously establish linear convergence of the gradient descent algorithm and show that the PCT‑based approximation error decays algebraically at a rate O(N^‑p) for any positive integer p, where N denotes the order of the polynomials. Numerical examples demonstrate that the proposed method achieves significantly higher computational efficiency than conventional bilinear matrix inequality (BMI)‑based approaches.

Authors:Dianyu Zhong, Tian Xing, Kailai Sun, Xu Yang, Heye Huang, Irfan Qaisar, Tinggang Jia, Shaobo Wang, Qianchuan Zhao
Title: Hierarchical Control Framework Integrating LLMs with RL for Decarbonized HVAC Operation
Abstract:
Heating, ventilation, and air conditioning (HVAC) systems account for a substantial share of building energy consumption. Environmental uncertainty and dynamic occupancy behavior bring challenges in decarbonized HVAC control. Reinforcement learning (RL) can optimize long‑horizon comfort‑energy trade‑offs but suffers from exponential action‑space growth and inefficient exploration in multi‑zone buildings. Large language models (LLMs) can encode semantic context and operational knowledge, yet when used alone they lack reliable closed‑loop numerical optimization and may result in less reliable comfort‑energy trade‑offs. To address these limitations, we propose a hierarchical control framework in which a fine‑tuned LLM, trained on historical building operation data, generates state‑dependent feasible action masks that prune the combinatorial joint action space into operationally plausible subsets. A masked value‑based RL agent then performs constrained optimization within this reduced space, improving exploration efficiency and training stability. Evaluated in a high‑fidelity simulator calibrated with real‑world sensor and occupancy data from a 7‑zone office building, the proposed method achieves a mean PPD of 7.30%, corresponding to reductions of 39.1% relative to DQN, the best vanilla RL baseline in comfort, and 53.1% relative to the best vanilla LLM baseline, while reducing daily HVAC energy use to 140.90~kWh, lower than all vanilla RL baselines. The results suggest that LLM‑guided action masking is a promising pathway toward efficient multi‑zone HVAC control.

Authors:Yimeng Sun, Zhuoyuan Wang, Xiaole Zhang, Heng Ping, Jintang Xue, Paul Bogdan, Yorie Nakahira
Title: Fractional Risk Analysis of Stochastic Systems with Jumps and Memory
Abstract:
Accurate risk assessment is essential for safety‑critical autonomous and control systems under uncertainty. In many real‑world settings, stochastic dynamics exhibit asymmetric jumps and long‑range memory, making long‑term risk probabilities difficult to estimate across varying system dynamics, initial conditions, and time horizons. Existing sampling‑based methods are computationally expensive due to repeated long‑horizon simulations to capture rare events, while existing partial differential equation (PDE)‑based formulations are largely limited to Gaussian or symmetric jump dynamics and typically treat memory effects in isolation. In this paper, we address these challenges by deriving a space‑ and time‑fractional PDE that characterizes long‑term safety and recovery probabilities for stochastic systems with both asymmetric Levy jumps and memory. This unified formulation captures nonlocal spatial effects and temporal memory within a single framework and enables the joint evaluation of risk across initial states and horizons. We show that the proposed PDE accurately characterizes long‑term risk and reveals behaviors that differ fundamentally from systems without jumps or memory and from standard non‑fractional PDEs. Building on this characterization, we further demonstrate how physics‑informed learning can efficiently solve the fractional PDEs, enabling accurate risk prediction across diverse configurations and strong generalization to out‑of‑distribution dynamics.

Authors:Gagan Acharya, Erfan Nozari
Title: Passivity-Based Control of Electrographic Seizures in a Neural Mass Model of Epilepsy
Abstract:
Recent advances in neurotechnologies and decades of scientific and clinical research have made closed‑loop electrical neuromodulation one of the most promising avenues for the treatment of drug‑resistant epilepsy (DRE), a condition that affects over 15 million individuals globally. Yet, with the existing clinical state of the art, only 18% of patients with DRE who undergo closed‑loop neuromodulation become seizure‑free. In a recent study, we demonstrated that a simple proportional feedback policy based on the framework of passivity‑based control (PBC) can significantly outperform the clinical state of the art. However, this study was purely numerical and lacked rigorous mathematical analysis. The present study addresses this gap and provides the first rigorous analysis of PBC for the closed‑loop control of epileptic seizures. Using the celebrated Epileptor neural mass model of epilepsy, we analytically demonstrate that (i) seizure dynamics are, in their standard form, neither passive nor passivatable, (ii) epileptic dynamics, despite their lack of passivity, can be stabilized by sufficiently strong passive feedback, and (iii) seizure dynamics can be passivated via proper output redesign. To our knowledge, our results provide the first rigorous passivity‑based analysis of epileptic seizure dynamics, as well as a theoretically‑grounded framework for sensor placement and feedback design for a new form of closed‑loop neuromodulation with the potential to transform seizure management in DRE.

Authors:Joshua D. Ibrahim, Mahdi Taheri, Soon-Jo Chung, Fred Y. Hadaegh
Title: Data-Driven Probabilistic Fault Detection and Identification via Density Flow Matching
Abstract:
Fault detection and identification (FDI) is critical for maintaining the safety and reliability of systems subject to actuator and sensor faults. In this paper, the problem of FDI for nonlinear control‑affine systems under simultaneous actuator and sensor faults is studied. We model fault signatures through the evolution of the probability density flow along the trajectory and characterize detectability using the 2‑Wasserstein metric. In order to introduce quantifiable guarantees for fault detectability based on system parameters and fault magnitudes, we derive upper bounds on the distributional separation between nominal and faulty dynamics. The latter is achieved through a stochastic contraction analysis of probability distributions in the 2‑Wasserstein metric. A data‑driven FDI method is developed by means of a conditional flow‑matching scheme that learns neural vector fields governing density propagation under different fault profiles. To generalize the data‑driven FDI method across continuous fault magnitudes, Gaussian bridge interpolation and Feature‑wise Linear Modulation (FiLM) conditioning are incorporated. The effectiveness of our proposed method is illustrated on a spacecraft attitude control system, and its performance is compared with an augmented Extended Kalman Filter (EKF) baseline. The results confirm that trajectory‑based distributional analysis provides improved discrimination between fault scenarios and enables reliable data‑driven FDI with a lower false alarm rate compared with the augmented EKF.

Authors:Keith Paarporn, Jason R. Marden
Title: Move Over, Prisoner's Dilemma: Colonel Blotto has arrived
Abstract:
The Prisoner's Dilemma, zero‑sum games, LQR team problems, and differential games have shaped game theory in controls for decades, but the field's most pressing adversarial challenges demand a richer framework, and its name is Colonel Blotto. Strategic adversarial constraints represent a fundamental consideration in control systems, from cybersecurity defense to infrastructure protection. Colonel Blotto games, despite their direct relevance to such applications, remain underutilized in the controls community relative to other game‑theoretic approaches. This article aims to close that gap for the controls community. Indeed, theoretical advances within the last two decades have spurred a resurgence of interest and enabled their applications across several domains. In this article, we introduce the Colonel Blotto framework, survey key analytical and computational results, and demonstrate how problems spanning cybersecurity, network defense, and multi‑agent systems fit naturally within this structure. Three research directions are examined in depth: interdependent contest objectives that capture networked vulnerabilities, alternate winning rules that model partial rewards and structural asymmetries, and multi‑agent competitive environments involving coalition formation and strategic concessions. Taken together, these directions reveal a framework that is both practically deployable and rich enough to capture the strategic complexity inherent in adversarial resource allocation.

Authors:Jaidev Gill, Jing Shuang Li
Title: Firing Rate Neural Network Implementations of Model Predictive Control
Abstract:
Human and animal brains perform planning to enable complex movements and behaviors. This process can be effectively described using model predictive control (MPC); that is, brains can be thought of as implementing some version of MPC. How is this done? In this work, we translate model predictive controllers into firing rate neural networks, offering insights into the nonlinear neural dynamics that underpin planning. This is done by first applying the projected gradient method to the dual problem, then generating alternative networks through factorization and contraction analysis. This allows us to explore many biologically plausible implementations of MPC. We present a series of numerical simulations to study different neural networks performing MPC to balance an inverted pendulum on a cart (i.e., balancing a stick on a hand). We illustrate that sparse neural networks can effectively implement MPC; this observation aligns with the sparse nature of the brain.

Authors:Lekshmi P, Neha Karanjkar
Title: On Integrating Resilience and Human Oversight into LLM-Assisted Modeling Workflows for Digital Twins
Abstract:
LLM‑assisted modeling holds the potential to rapidly build executable Digital Twins of complex systems from only coarse descriptions and sensor data. However, resilience to LLM hallucination, human oversight, and real‑time model adaptability remain challenging and often mutually conflicting requirements. We present three critical design principles for integrating resilience and oversight into such workflows, derived from insights gained through our work on FactoryFlow ‑ an open‑source LLM‑assisted framework for building simulation‑based Digital Twins of manufacturing systems. First, orthogonalize structural modeling and parameter fitting. Structural descriptions (components, interconnections) are LLM‑translated from coarse natural language to an intermediate representation (IR) with human visualization and validation, which is algorithmically converted to the final model. Parameter inference, in contrast, operates continuously on sensor data streams with expert‑tunable controls. Second, restrict the model IR to interconnections of parameterized, pre‑validated library components rather than monolithic simulation code, enabling interpretability and error‑resilience. Third, and most important, is to use a density‑preserving IR. When IR descriptions expand dramatically from compact inputs hallucination errors accumulate proportionally. We present the case for Python as a density‑preserving IR : loops express regularity compactly, classes capture hierarchy and composition, and the result remains highly readable while exploiting LLMs strong code generation capabilities. A key contribution is detailed characterization of LLM‑induced errors across model descriptions of varying detail and complexity, revealing how IR choice critically impacts error rates. These insights provide actionable guidance for building resilient and transparent LLM‑assisted simulation automation workflows.

Authors:Hao Tu, Jackson Fogelquist, Iman Askari, Xinfan Lin, Yebin Wang, Shiguang Deng, Huazhen Fang
Title: Accelerating Bayesian Optimization for Nonlinear State-Space System Identification with Application to Lithium-Ion Batteries
Abstract:
This paper studies system identification for nonlinear state‑space models, a problem that arises across many fields yet remains challenging in practice. Focusing on maximum likelihood estimation, we employ Bayesian optimization (BayesOpt) to address this problem by leveraging its derivative‑free global search capability enabled by surrogate modeling of the likelihood function. Despite these advantages, standard BayesOpt often suffers from slow convergence, high computational cost, and practical difficulty in attaining global optima under limited computational budgets, especially for high‑dimensional nonlinear models with many unknown parameters. To overcome these limitations, we propose an accelerated BayesOpt framework that integrates BayesOpt with the Nelder‑‑Mead method. Heuristics‑based, the Nelder‑‑Mead method provides fast local search, thereby assisting BayesOpt when the surrogate model lacks fidelity or when over‑exploration occurs in broad parameter spaces. The proposed framework incorporates a principled strategy to coordinate the two methods, effectively combining their complementary strengths. The resulting hybrid approach significantly improves both convergence speed and computational efficiency while maintaining strong global search performance. In addition, we leverage an implicit particle filtering method to enable accurate and efficient likelihood evaluation. We validate the proposed framework on the identification of the BattX model for lithium‑ion batteries, which features ten state dimensions, 18 unknown parameters, and strong nonlinearity. Both simulation and experimental results demonstrate the effectiveness of the proposed approach as well as its advantages over alternative methods.

Authors:Andreas Oliveira, Arthur C. B. de Oliveira, Mario Sznaier, Eduardo Sontag
Title: On incremental and semi-global exponential stability of gradient flows satisfying generalized Łojasiewicz inequalities
Abstract:
The Łojasiewicz inequality characterizes objective‑value convergence along gradient flows and, in special cases, yields exponential decay of the cost. However, such results do not directly give rates of convergence in the state. In this paper, we use contraction theory to derive state‑space guarantees for gradient systems satisfying generalized Łojasiewicz inequalities. We first show that, when the objective has a unique strongly convex minimizer, the generalized Łojasiewicz inequality implies semi‑global exponential stability; on arbitrary compact subsets, this yields exponential stability. We then give two curvature‑based sufficient conditions, together with constraints on the Łojasiewicz rate, under which the nonconvex gradient flow is globally incrementally exponentially stable.

Authors:Rayan Mazouz, Luca Laurenti, Morteza Lahijanian
Title: Time-Varying Reach-Avoid Control Certificates for Stochastic Systems
Abstract:
Reach‑avoid analysis is fundamental to reasoning about the safety and goal‑reaching behavior of dynamical systems, and serves as a foundation for specifying and verifying more complex control objectives. This paper introduces a reach‑avoid certificate framework for discrete‑time, continuous‑space stochastic systems over both finite‑ and infinite‑horizon settings. We propose two formulations: time‑varying and time‑invariant certificates. We also show how these certificates can be synthesized using sum‑of‑squares (SOS) optimization, providing a convex formulation for verifying a given controller. Furthermore, we present an SOS‑based method for the joint synthesis of an optimal feedback controller and its corresponding reach‑avoid certificate, enabling the maximization of the probability of reaching the target set while avoiding unsafe regions. Case studies and benchmark results demonstrate the efficacy of the proposed framework in certifying and controlling stochastic systems with continuous state and action spaces.

Authors:Fabian Schneider, Christian Wölfel
Title: Parameter-interval estimation for cooperative reactive sputtering processes
Abstract:
Reactive sputtering is a plasma‑based technique to deposit a thin film on a substrate. This contribution presents a novel parameter‑interval estimation method for a well‑established model that describes the uncertain and nonlinear reactive sputtering process behaviour. Building on a proposed monotonicity‑based model classification, the method guarantees that all parameterizations within the parameter interval yield output trajectories and static characteristics consistent with the enclosure induced by the parameter interval. Correctness and practical applicability of the new method are demonstrated by an experimental validation, which also reveals inherent structural limitations of the well‑established process model for state‑estimation tasks.

Authors:Daniele Ravasio, Claudia Sbardi, Marcello Farina, Andrea Ballarino
Title: Physics-informed structured learning of a class of recurrent neural networks with guaranteed properties
Abstract:
This paper proposes a physics‑informed learning framework for a class of recurrent neural networks tailored to large‑scale and networked systems. The approach aims to learn control‑oriented models that preserve the structural and stability properties of the plant. The learning algorithm is formulated as a convex optimisation problem, allowing the inclusion of linear matrix inequality constraints to enforce desired system features. Furthermore, when the plant exhibits structural modularity, the resulting optimisation problem can be parallelised, requiring communication only among neighbouring subsystems. Simulation results show the effectiveness of the proposed approach.

Authors:Jinshui Zhang, Zane Mannings, Chris Dittmer, Angel V Peterchev, Stefan M Goetz
Title: Four-Transistor Four-Diode (4T4D) Series/Parallel Chopper Module for Auto-Balancing STATCOM and Low Control and Development Complexity
Abstract:
Static synchronous compensators (STATCOMs) manage reactive power compensation in modern power grids and have become essential for the integration of renewable energy sources such as wind farms. Cascaded H bridges have become the preferred topology for high‑power STATCOMs, but balancing module capacitor voltages remains a persistent challenge. Conventional solutions equip every module with a voltage sensor ‑‑ a component that is costly, temperature‑sensitive, and prone to aging‑related failures. Recent parallel‑capable module topologies can balance voltage through switched‑capacitor operation. The latest developments reduced the sensor requirement from one per module to one per arm. However, these implementations require twice as many individual transistors compared to series‑only topologies. We present a STATCOM solution based on the four‑transistor four‑diode (4T4D) series\,/\,parallel chopper cell. This topology achieves bidirectional parallelization with only four transistors per module ‑‑ exactly as many as a conventional full bridge. Furthermore, we propose a dual‑loop control strategy that fully eliminates module voltage sensors by inferring voltage levels from the modulation index. This scheme also improves output quality by regulating the modulation depth. We validated our proposal through simulation and experiments. We built a prototype to interface the grid. The prototype further passed robustness tests with step change, current direction reversal, and grid disturbance. This work demonstrates the first modular STATCOM implementation that combines minimum transistor count with complete elimination of module voltage sensors.

Authors:Hamid Reza Hashempour, Mina Khadem, Eduard A. Jorswieck
Title: DRL-Based Spectrum Sharing for RIS-Aided Local High-Quality Wireless Networks
Abstract:
This paper investigates a smart spectrum‑sharing framework for reconfigurable intelligent surface (RIS)‑aided local high‑quality wireless networks (LHQWNs) within a mobile network operator (MNO) ecosystem. Although RISs are often considered potentially harmful due to interference, this work shows that properly controlled RISs can enhance the quality of service (QoS). The proposed system enables temporary spectrum access for multiple vertical service providers (VSPs) by dynamically allocating radio resources according to traffic demand. The spectrum is divided into dedicated subchannels assigned to individual VSPs and reusable subchannels shared among multiple VSPs, while RIS is employed to improve propagation conditions. We formulate a multi‑VSP utility maximization problem that jointly optimizes subchannel assignment, transmit power, and RIS phase configuration while accounting for spectrum access costs, RIS leasing costs, and QoS constraints. The resulting mixed‑integer non‑linear program (MINLP) is intractable using conventional optimization methods. To address this challenge, the problem is modeled as a Markov decision process (MDP) and solved using deep reinforcement learning (DRL). Specifically, deep deterministic policy gradient (DDPG) and soft actor‑critic (SAC) algorithms are developed and compared. Simulation results show that SAC outperforms DDPG in convergence speed, stability, and achievable utility, reaching up to 96% of the exhaustive search benchmark and demonstrating the potential of RIS to improve overall utility in multi‑VSP scenarios.

Authors:Paolo Guida, Didier Barradas-Bautista
Title: Real-time control of multiphase processes with learned operators
Abstract:
Multiphase flows frequently occur naturally and in manufactured devices. Controlling such phenomena is extremely challenging due to the strongly non‑linear dynamics, rapid phase transitions, and the limited spatial and temporal resolution of available sensors, which can lead to significant inaccuracies in predicting and managing these flows. In most cases, numerical models are the only way to access high spatial and temporal resolution data to an extent that allows for fine control. While embedding numerical models in control algorithms could enable fine control of multiphase processes, the significant computational burden currently limits their practical application. This work proposes a surrogate‑assisted model predictive control (MPC) framework for regulating multiphase processes using learned operators. A Fourier Neural Operator (FNO) is trained to forecast the spatiotemporal evolution of a phase‑indicator field (the volume fraction) over a finite horizon from a short history of recent states and a candidate actuation signal. The neural operator surrogate is then iteratively called during the optimisation process to identify the optimal control variable. To illustrate the approach, we solve an optimal control problem (OCP) on a two‑phase Eulerian bubble column. Here, the controller tracks piecewise‑constant liquid level setpoints by adjusting the gas flow rate introduced into the system. The results we obtained indicate that field‑level forecasting with FNOs are well suited for closed‑loop optimization since they have relatively low evaluation cost. The latter provide a practical route toward MPC for fast multiphase unit operations and a foundation for future extensions to partial observability and physics‑informed operator learning.

Authors:Bingfang Li, Songhao Yang, Guosong Wang, Yiwen Hu, Xu Zhang, Zhiguo Hao, Dongxu Chang, Baohui Zhang
Title: Entire Period Transient Stability of Synchronous Generators Considering LVRT Switching of Nearby Renewable Energy Sources
Abstract:
In scenarios where synchronous generators (SGs) and grid‑following renewable energy sources (GFLR) are co‑located, existing research, which mainly focuses on the first‑swing stability of SGs, often overlooks ongoing dynamic interactions between GFLRs and SGs throughout the entire rotor swing period. To address this gap, this study first reveals that the angle oscillations of SG can cause periodic grid voltage fluctuations, potentially triggering low‑voltage ride‑through (LVRT) control switching of GFLR repeatedly. Then, the periodic energy changes of SGs under "circular" and "rectangular" LVRT limits are analyzed. The results indicate that circular limits are detrimental to SG's first‑swing stability, while rectangular limits and their slow recovery strategies can lead to SG's multi‑swing instability. Conservative stability criteria are also proposed for these phenomena. Furthermore, an additional controller based on feedback linearization is introduced to enhance the entire period transient stability of SG by adjusting the post‑fault GFLR output current. Finally, the efficacy of the analysis is validated through electromagnetic transient simulations and controller hardware‑in‑the‑loop (CHIL) tests.

Authors:Iasson Karafyllis, Dionysios Theodosis, Miroslav Krstic
Title: Global Stability Analysis of the Age-Structured Chemostat With Substrate Dynamics
Abstract:
In this paper we study the stability properties of the equilibrium point for an age‑structured chemostat model with renewal boundary condition and coupled substrate dynamics under constant dilution rate. This is a complex infinite‑dimensional feedback system. It has two feedback loops, both nonlinear. A positive static loop due to reproduction at the age‑zero boundary of the PDE, counteracted and dominated by a negative dynamic loop with the substrate dynamics. The derivation of explicit sufficient conditions that guarantee global stability estimates is carried out by using an appropriate Lyapunov functional. The constructed Lyapunov functional guarantees global exponential decay estimates and uniform global asymptotic stability with respect to a measure related to the Lyapunov functional. From a biological perspective, stability arises because reproduction is constrained by substrate availability, while dilution, mortality, and substrate depletion suppress transient increases in biomass before age‑structure effects can amplify them. The obtained results are applied to a chemostat model from the literature, where the derived stability condition is compared with existing results that are based on (necessarily local) linearization methods.

Authors:Georg Kordowich, Julian Oelhaf, Siming Bayer, Andreas Maier, Matthias Kereit, Johann Jaeger
Title: Feature Selection for Fault Prediction in Distribution Systems
Abstract:
While conventional power system protection isolates faulty components only after a fault has occurred, fault prediction approaches try to detect faults before they can cause significant damage. Although initial studies have demonstrated successful proofs of concept, development is hindered by scarce field data and ineffective feature selection. To address these limitations, this paper proposes a surrogate task that uses simulation data for feature selection. This task exhibits a strong correlation (r = 0.92) with real‑world fault prediction performance. We generate a large dataset containing 20000 simulations with 34 event classes and diverse grid configurations. From 1556 candidate features, we identify 374 optimal features. A case study on three substations demonstrates the effectiveness of the selected features, achieving an F1‑score of 0.80 and outperforming baseline approaches that use frequency‑domain and wavelet‑based features.

Authors:Davide Tebaldi, Niccolò Paradisi, Fabio Pini, Luigi Biagiotti
Title: A Minimum-Energy Control Approach for Redundant Mobile Manipulators in Physical Human-Robot Interaction Applications
Abstract:
Research on mobile manipulation systems that physically interact with humans has expanded rapidly in recent years, opening the way to tasks which could not be performed using fixed‑base manipulators. Within this context, developing suitable control methodologies is essential since mobile manipulators introduce additional degrees of freedom, making the design of control approaches more challenging and more prone to performance optimization. This paper proposes a control approach for a mobile manipulator, composed of a mobile base equipped with a robotic arm mounted on the top, with the objective of minimizing the overall kinetic energy stored in the whole‑body mobile manipulator in physical human‑robot interaction applications. The approach is experimentally tested with reference to a peg‑in‑hole task, and the results demonstrate that the proposed approach reduces the overall kinetic energy stored in the whole‑body robotic system and improves the system performance compared with the benchmark method.

Authors:Jurrien Keulen, Bayu Jayawardhana, Arjan van der Schaft
Title: On Port-Hamiltonian Formulation of Hysteretic Energy Storage Elements: The Backlash Case
Abstract:
This paper presents a port‑Hamiltonian formulation of hysteretic energy storage elements. First, we revisit the passivity property of backlash‑driven storage elements by presenting a family of storage functions associated to the dissipativity property of such elements. We explicitly derive the corresponding available storage and required supply functions `a la Willems [1], and show the interlacing property of the aforementioned family of storage functions sandwiched between the available storage and required supply functions. Second, using the proposed family of storage functions, we present a port‑Hamiltonian formulation of hysteretic inductors as prototypical storage elements in port‑Hamiltonian systems. In particular, we show how a Hamiltonian function can be chosen from the family of storage functions and how the hysteretic elements can be expressed as port‑Hamiltonian system with feedthrough term, where the feedthrough term represents energy dissipation. Correspondingly, we illustrate its applicability in describing an RLC circuit (in parallel and in series) containing a hysteretic inductor element.

Authors:Bingfang Li, Songhao Yang, Qinglan Wang, Xu Zhang, Huan Xie, Chuan Qin, Zhiguo Hao
Title: Dominant Transient Stability of the Co-located PLL-Based Grid-Following Renewable Plant and Synchronous Condenser Systems
Abstract:
Deploying synchronous condensers (SynCons) near grid‑following renewable energy sources (GFLRs) is an effective and increasingly adopted strategy for grid support. However, the potential transient instability risks in such configurations remain an open research question. This study investigates the mechanism of dominant synchronization instability source transition upon SynCon integration and proposes a straightforward approach to enhance system stability by leveraging their interactive characteristics. Firstly, a dual‑timescale decoupling model is established, partitioning the system into a fast subsystem representing phase‑locked loop (PLL) dynamics and a slow subsystem characterizing SynCon rotor dynamics. The study then examines the influence of SynCons on the transient stability of nearby PLLs and their own inherent stability. The study shows that SynCon's voltage‑source characteristics and its time‑scale separation from PLL dynamics can significantly enhance the PLL's stability boundary and mitigate non‑coherent coupling effects among multiple GFLRs. However, the dominant instability source shifts from the fast‑time‑scale PLL to the slow‑time‑scale SynCon after SynCon integration. Crucially, this paper demonstrates that the damping effect of PLL control can also be transferred from the fast to the slow time scale, allowing well‑tuned PLL damping to suppress SynCon rotor acceleration. Consequently, by utilizing SynCon's inherent support capability and a simple PLL damping loop, the transient stability of the co‑located system can be significantly enhanced. These conclusions are validated using a converter controller‑based Hardware‑in‑the‑Loop (CHIL) platform.

Authors:Songhao Yang, Bingfang Li, Zhiguo Hao, Yiwen Hu, Huan Xie, Tianqi Zhao, Baohui Zhang
Title: Multi-Swing Transient Stability of Synchronous Generators and IBR Combined Generation Systems
Abstract:
In traditional views, the build‑up of accelerating energy during faults can cause the well‑known first‑swing angle instability in synchronous generators (SGs). Interestingly, this letter presents a new insight that the accumulation of decelerating energy due to the low voltage ride‑through (LVRT) and recovery control of grid‑following inverter‑based resources (GFL‑IBRs), might also result in transient angle instability in SGs. The transient energy accumulated during angle‑decreasing swing transforms into the acceleration energy of the subsequent swing, hence such phenomena often manifest as multi‑swing instability. Both theoretical analysis and simulation support these findings.

Authors:Shumpei Nishida, Kunihisa Okano
Title: Distributed Event-Triggered Consensus Control of Discrete-Time Linear Multi-Agent Systems under LQ Performance Constraints
Abstract:
This paper proposes a distributed event‑triggered control method that not only guarantees consensus of multi‑agent systems but also satisfies a given LQ performance constraint. Taking the standard distributed control scheme with all‑time communication as a baseline, we consider the problem of designing an event‑triggered communication rule such that the resulting LQ cost satisfies a performance constraint with respect to the baseline cost while consensus is achieved. The main difficulty is that the performance requirement is global, whereas triggering decisions are made locally and asynchronously by individual agents, which cannot directly evaluate the global performance degradation. To address this issue, we decompose allowable degradation across agents and design a triggering rule that uses only locally available information to satisfy the given LQ performance constraint. For general linear agents on an undirected graph, we derive a sufficient condition that guarantees both consensus and the prescribed performance level. We also develop a tractable offline design method for the triggering parameters. Numerical examples illustrate the effectiveness of the proposed method.

Authors:Haruki Kawase, Taiga Sugawara, A. Daniel Carnerero
Title: Dissimilarity-Based Persistent Coverage Control of Multi-Robot Systems for Improving Solar Irradiance Prediction Accuracy in Solar Thermal Power Plants
Abstract:
Accurate forecasting of future solar irradiance is essential for the effective control of solar thermal power plants. Although various kriging‑based methods have been proposed to address the prediction problem, these methods typically do not provide an appropriate sampling strategy to dynamically position mobile sensors for optimizing prediction accuracy in real time, which is critical for achieving accurate forecasts with a minimal number of sensors. This paper introduces a dissimilarity map derived from a kriging model and proposes a persistent coverage control algorithm that effectively guides agents toward regions where additional observations are required to improve prediction performance. By means of experiments using mobile robots, the proposed approach was shown to obtain more accurate predictions than the considered baselines under various emulated irradiance fields.

Authors:Behrad Samari, Henrik Sandberg, Karl H. Johansson, Abolfazl Lavaei
Title: From Noisy Data to Hierarchical Control: A Model-Order-Reduction Framework
Abstract:
This paper develops a direct data‑driven framework for constructing reduced‑order models (ROMs) of discrete‑time linear dynamical systems with unknown dynamics and process disturbances. The proposed scheme enables controller synthesis on the ROM and its refinement to the original system by an interface function designed using noisy data. To achieve this, the notion of simulation functions (SFs) is employed to establish a formal relation between the original system and its ROM, yielding a quantitative bound on the mismatch between their output trajectories. To construct such relations and interface functions, we rely on data collected from the unknown system. In particular, using noise‑corrupted input‑state data gathered along a single trajectory of the system, and without identifying the original dynamics, we propose data‑dependent conditions, cast as a semidefinite program, for the simultaneous construction of ROMs, SFs, and interface functions. Through a case study, we demonstrate that data‑driven controller synthesis on the ROM, combined with controller refinement via the interface function, enables the enforcement of complex specifications beyond stability.

Authors:Ebonye Smith, Sampada Deglurkar, Jingqi Li, Gechen Qu, Claire J. Tomlin
Title: From Global to Local: Hierarchical Probabilistic Verification for Reachability Learning
Abstract:
Hamilton‑Jacobi (HJ) reachability provides formal safety guarantees for nonlinear systems. However, it becomes computationally intractable in high‑dimensional settings, motivating learning‑based approximations that may introduce unsafe errors or overly optimistic safe sets. In this work, we propose a hierarchical probabilistic verification framework for reachability learning that bridges offline global certification and online local refinement. We first construct a coarse safe set using scenario optimization, providing an efficient global probabilistic certificate. We then introduce an online local refinement module that expands the certified safe set near its boundary by solving a sequence of convex programs, recovering regions excluded by the global verification. This refinement reduces conservatism while focusing computation on critical regions of the state space. We provide probabilistic safety guarantees for both the global and locally refined sets. Integrated with a switching mechanism between a learned reachability policy and a model‑based controller, the proposed framework improves success rates in goal‑reaching tasks with safety constraints, as demonstrated in simulation experiments of two drones racing to a goal with complex safety constraints.

Authors:Hiroyuki Tetsuka, Minoru Hirano
Title: Wireless bioelectronic control architectures for biohybrid robotic systems
Abstract:
Wireless bioelectronic interfaces are increasingly used to control tissue‑engineered biohybrid robotic systems. However, a unifying engineering framework linking device design to system‑level control remains underdeveloped. Here, we propose that wireless control in biohybrid robotics can be formulated as a coupled co‑design problem of integrating signal delivery, spatial selectivity, scalability, and interface stability. We analyze three representative control strategies, wireless electrical stimulation, wireless optoelectronic stimulation, and neuromuscular integration, which operates within a distinct regime with characteristic trade‑offs. Across these modalities, the tissue‑device interface emerges as a key constraint, governing the interplay between electromagnetic coupling, circuit performance, and biomechanical response. Based on this framework, we outline practical design principles spanning electromagnetic field distribution, circuit architecture, and actuator mechanics. We further propose a transition from open‑loop stimulation to closed‑loop biohybrid autonomy enabled by organoid‑integrated bioelectronics and bidirectional microelectrode interfaces. This work establishes a system‑level perspective on wireless bioelectronic control and provides design guidelines for developing stable, scalable, and autonomous biohybrid robotic systems.

Authors:Sampada Deglurkar, Ebonye Smith, Jingqi Li, Claire J. Tomlin
Title: Active Calibration of Reachable Sets Using Approximate Pick-to-Learn
Abstract:
Reachability computations that rely on learned or estimated models require calibration in order to uphold confidence about their guarantees. Calibration generally involves sampling scenarios inside the reachable set. However, producing reasonable probabilistic guarantees may require many samples, which can be costly. To remedy this, we propose that calibration of reachable sets be performed using active learning strategies. In order to produce a probabilistic guarantee on the active learning, we adapt the Pick‑to‑Learn algorithm, which produces generalization bounds for standard supervised learning, to the active learning setting. Our method, Approximate Pick‑to‑Learn, treats the process of choosing data samples as maximizing an approximate error function. We can then use conformal prediction to ensure that the approximate error is close to the true model error. We demonstrate our technique for a simulated drone racing example in which learning is used to provide an initial guess of the reachable tube. Our method requires fewer samples to calibrate the model and provides more accurate sets than the baselines. We simultaneously provide tight generalization bounds.

Authors:Jared Miller, Carsten Scherer, Fabian Jakob, Andrea Iannelli
Title: Structure, Analysis, and Synthesis of First-Order Algorithms
Abstract:
Optimization algorithms can be interpreted through the lens of dynamical systems as the interconnection of linear systems and a set of subgradient nonlinearities. This dynamical systems formulation allows for the analysis and synthesis of optimization algorithms by solving robust control problems. In this work, we use the celebrated internal model principle in control theory to structurally factorize convergent composite optimization algorithms into suitable network‑dependent internal models and core subcontrollers. As the key benefit, we reveal that this permits us to synthesize optimization algorithms even if information is transmitted over networks featuring dynamical phenomena such as time delays, channel memory, or crosstalk. Design of these algorithms is achieved under bisection in the exponential convergence rate either through a nonconvex local search or by alternation of convex semidefinite programs. We demonstrate factorization of existing optimization algorithms and the automated synthesis of new optimization algorithms in the networked setting.

Authors:Pawan Kumar, Hokeun Kim
Title: Cyber-Physical System Design Space Exploration for Affordable Precision Agriculture
Abstract:
Precision agriculture promises higher yields and sustainability, but adoption is slowed by the high cost of cyber‑physical systems (CPS) and the lack of systematic design methods. We present a cost‑aware design space exploration (DSE) framework for multimodal drone‑rover platforms to integrate budget, energy, sensing, payload, computation, and communication constraints. Using integer linear programming (ILP) with SAT‑based verification, our approach trades off among cost, coverage, and payload while ensuring constraint compliance and a multitude of alternatives. We conduct case studies on smaller and larger‑sized farms to show that our method consistently achieves full coverage within budget while maximizing payload efficiency, outperforming state‑of‑the‑art CPS DSE approaches.

Authors:Sounak Dutta, Fin Amin, Sushil Panda, Jonathan Rabe, Yuejiang Wen, Paul Franzon
Title: Can an Actor-Critic Optimization Framework Improve Analog Design Optimization?
Abstract:
Analog design often slows down because even small changes to device sizes or biases require expensive simulation cycles, and high‑quality solutions typically occupy only a narrow part of a very large search space. While existing optimizers reduce some of this burden, they largely operate without the kind of judgment designers use when deciding where to search next. This paper presents an actor‑critic optimization framework (ACOF) for analog sizing that brings that form of guidance into the loop. Rather than treating optimization as a purely black‑box search problem, ACOF separates the roles of proposal and evaluation: an actor suggests promising regions of the design space, while a critic reviews those choices, enforces design legality, and redirects the search when progress is hampered. This structure preserves compatibility with standard simulator‑based flows while making the search process more deliberate, stable, and interpretable. Across our test circuits, ACOF improves the top‑10 figure of merit by an average of 38.9% over the strongest competing baseline and reduces regret by an average of 24.7%, with peak gains of 70.5% in FoM and 42.2% lower regret on individual circuits. By combining iterative reasoning with simulation‑driven search, the framework offers a more transparent path toward automated analog sizing across challenging design spaces.

Authors:Melwin Xavier, Melveena Jolly, Vaisakh M A, Midhun Xavier
Title: IndustriConnect: MCP Adapters and Mock-First Evaluation for AI-Assisted Industrial Operations
Abstract:
AI assistants can decompose multi‑step workflows, but they do not natively speak industrial protocols such as Modbus, MQTT/Sparkplug B, or OPC UA, so this paper presents INDUSTRICONNECT, a prototype suite of Model Context Protocol (MCP) adapters that expose industrial operations as schema‑discoverable AI tools while preserving protocol‑specific connectivity and safety controls; the system uses a common response envelope and a mock‑first workflow so adapter behavior can be exercised locally before connecting to plant equipment, and a deterministic benchmark covering normal, fault‑injected, stress, and recovery scenarios evaluates the flagship adapters, comprising 870 runs (480 normal, 210 fault‑injected, 120 stress, 60 recovery trials) and 2820 tool calls across 7 fault scenarios and 12 stress scenarios, where the normal suite achieved full success, the fault suite confirmed structured error handling with adapter‑level uint16 range validation, the stress suite identified concurrency boundaries, and same‑session recovery after endpoint restart is demonstrated for all three protocols, with results providing evidence spanning adapter correctness, concurrency behavior, and structured error handling for AI‑assisted industrial operations.

Authors:Abdullah Bahamdan, Emma Pajak, John D. Hedengren, Antonio del Rio Chanona
Title: Sketch2Simulation: Automating Flowsheet Generation via Multi Agent Large Language Models
Abstract:
Converting process sketches into executable simulation models remains a major bottleneck in process systems engineering, requiring substantial manual effort and simulator‑specific expertise. Recent advances in generative AI have improved both engineering‑diagram interpretation and LLM‑assisted flowsheet generation, but these remain largely disconnected: diagram‑understanding methods often stop at extracted graphs, while text‑to‑simulation workflows assume structured inputs rather than raw visual artifacts. To bridge this gap, we present an end‑to‑end multi‑agent large language model system that converts process diagrams directly into executable Aspen HYSYS flowsheets. The framework decomposes the task into three coordinated layers: diagram parsing and interpretation, simulation model synthesis, and multi‑level validation. Specialized agents handle visual interpretation, graph‑based intermediate representation construction, code generation for the HYSYS COM interface, execution, and structural verification. We evaluate the framework on four chemical engineering case studies of increasing complexity, from a simple desalting process to an industrial aromatic production flowsheet with multiple recycle loops. The system produces executable HYSYS models in all cases, achieving complete structural fidelity on the two simpler cases and strong performance on the more complex ones, with connection consistency above 0.93 and stream consistency above 0.96. These results demonstrate a viable end‑to‑end sketch‑to‑simulation workflow while highlighting remaining challenges in dense recycle structures, implicit diagram semantics, and simulator‑interface constraints.

Authors:Anton Ponomarev, Lutz Gröll, Veit Hagenmeyer
Title: Period-aware asymptotic gain with application to a periodically forced synchronization circuit
Abstract:
The classical asymptotic gain (AG) is a concept known from the input‑to‑state stability theory. Given a uniform input bound, AG estimates the asymptotic bound of the output. Sometimes, however, more information is known about the input than just a bound. In this paper we consider the case of a periodic input. Under the assumption that the system converges to a periodic solution, we introduce a new gain, called period‑aware asymptotic gain (PAG), which employs periodicity to enable a sharper asymptotic estimation of the output. Since the PAG can distinguish between short‑period ("high‑frequency") and long‑period ("low‑frequency") signals, it is able to rigorously quantify such properties as bandwidth, resonant behavior, and high‑frequency damping. We discuss how the PAG can be computed and illustrate it with a numerical example from the field of power electronics.

Authors:Qijun Liao, Jue Yang
Title: Response-Aware Risk-Constrained Control Barrier Function With Application to Vehicles
Abstract:
This paper proposes a unified control framework based on Response‑Aware Risk‑Constrained Control Barrier Function for dynamic safety boundary control of vehicles. Addressing the problem of physical model parameter mismatch, the framework constructs an uncertainty propagation model that fuses nominal dynamics priors with direct vehicle body responses. Utilizing simplified single‑track dynamics to provide a baseline direction for control gradients and covering model deviations through statistical analysis of body response signals, the framework eliminates the dependence on accurate online estimation of road surface adhesion coefficients. By introducing Conditional Value at Risk (CVaR) theory, the framework reformulates traditional deterministic safety constraints into probabilistic constraints on the tail risk of barrier function derivatives. Combined with a Bayesian online learning mechanism based on inverse Wishart priors, it identifies environmental noise covariance in real‑time, adaptively tuning safety margins to reduce performance loss under prior parameter mismatch. Finally, based on Control Lyapunov Function (CLF), a unified Second‑Order Cone Programming (SOCP) controller is constructed. Theoretical analysis establishes convergence of Sequential Convex Programming to local Karush‑Kuhn‑Tucker points and provides per‑step probabilistic safety bounds. High‑fidelity dynamics simulations demonstrate that under extreme conditions, the method not only eliminates the output divergence phenomenon of traditional methods but also achieves Pareto improvement in both safety and tracking performance. For the chosen risk level, the per‑step safety violation probability is theoretically bounded by approximately 2%, validated through high‑fidelity simulations showing zero boundary violations across all tested scenarios.

Authors:Adam K. Kiss, Ersin Das, Tamas G. Molnar, Aaron D. Ames
Title: Integral Control Barrier Functions with Input Delay: Prediction, Feasibility, and Robustness
Abstract:
Time delays in feedback control loops can cause controllers to respond too late, and with excessively large corrective actions, leading to unsafe behavior (violation of state constraints) and controller infeasibility (violation of input constraints). To address this problem, we develop a safety‑critical control framework for nonlinear systems with input delay using dynamically defined (integral) controllers. Building on the concept of Integral Control Barrier Functions (ICBFs), we concurrently address two fundamental challenges: compensating the effect of delays, while ensuring feasibility when state and input constraints are imposed jointly. To this end, we embed predictor feedback into a dynamically defined control law to compensate for delays, with the predicted state evolving according to delay‑free dynamics. Then, utilizing ICBFs, we formulate a quadratic program for safe control design. For systems subject to simultaneous state and input constraints, we derive a closed‑form feasibility condition for the resulting controller, yielding a compatible ICBF pair that guarantees forward invariance under delay. We also address robustness to prediction errors (e.g., caused by delay uncertainty) using tunable robust ICBFs. Our approach is validated on an adaptive cruise control example with actuation delay.

Authors:Kevin Jamsahar, Adrian Wiltz, Maria Charitidou, Dimos V. Dimarogonas
Title: A Modular Platooning and Vehicle Coordination Simulator for Research and Education
Abstract:
This work presents a modular, Python‑based simulator that simplifies the evaluation of novel vehicle control and coordination algorithms in complex traffic scenarios while keeping the implementation overhead low. It allows researchers to focus primarily on developing the control and coordination strategies themselves, while the simulator manages the setup of complex road networks, vehicle configuration, execution of the simulation and the generation of video visualizations of the results. It is thereby also well‑suited to support control education by allowing instructors to create interactive exercises providing students with direct visual feedback. Thanks to its modular architecture, the simulator remains easily customizable and extensible, lowering the barrier for conducting advanced simulation studies in vehicle and traffic control research.

Authors:Ingyu Jang, Leila J. Bridgeman
Title: Communication-Aware Dissipative Output Feedback Control
Abstract:
Communication‑aware control is essential to reduce costs and complexity in large‑scale networks. This work proposes a method to design dissipativity‑augmented output feedback controllers with reduced online communication. The contributions of this work are three fold: a generalized well‑posedness condition for the controller network, a convex relaxation for the constraints that infer stability of a network from dissapativity of its agents, and a synthesis algorithm integrating the Network Dissipativity Theorm, alternating direction method of multipliers, and iterative convex overbounding. The proposed approach yields a sparsely interconnected controller that is both robust and applicable to networks with heterogeneous nonlinear agents. The efficiency of these methods is demonstrated on heterogeneous networks with uncertain and unstable agents, and is compared to standard \cH_\infty control.

Authors:Mihaela-Larisa Clement, Mónika Farsang, Agnes Poks, Johannes Edelmann, Manfred Plöchl, Radu Grosu, Ezio Bartocci
Title: Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling
Abstract:
The practical deployment of nonlinear model predictive control (NMPC) is often limited by online computation: solving a nonlinear program at high control rates can be expensive on embedded hardware, especially when models are complex or horizons are long. Learning‑based NMPC approximations shift this computation offline but typically demand large expert datasets and costly training. We propose Sequential‑AMPC, a sequential neural policy that generates MPC candidate control sequences by sharing parameters across the prediction horizon. For deployment, we wrap the policy in a safety‑augmented online evaluation and fallback mechanism, yielding Safe Sequential‑AMPC. Compared to a naive feedforward policy baseline across several benchmarks, Sequential‑AMPC requires substantially fewer expert MPC rollouts and yields candidate sequences with higher feasibility rates and improved closed‑loop safety. On high‑dimensional systems, it also exhibits better learning dynamics and performance in fewer epochs while maintaining stable validation improvement where the feedforward baseline can stagnate.

Authors:Mahyar Fazlyab, Sina Sharifi, Jiarui Wang
Title: Model Predictive Path Integral Control as Preconditioned Gradient Descent
Abstract:
Model Predictive Path Integral (MPPI) control is a widely used sampling‑based method for trajectory optimization, yet its convergence properties remain only partially understood. This paper provides a direct convergence analysis using variational optimization. By lifting constrained trajectory optimization to a Kullback‑Leibler (KL) regularized problem over decision distributions, we derive a reduced free‑energy objective defined over a parametric sampling family. For general parametric families, we derive gradient and Hessian representations of this reduced objective and analyze preconditioned gradient descent on the sampling‑distribution parameters. In the fixed‑covariance Gaussian case, the classical MPPI update is recovered exactly as a unit‑step preconditioned gradient update. We prove descent and stationarity guarantees for the exact expectation‑based iteration when the Hessian of the reduced objective is bounded in the metric induced by the preconditioner. For the Gaussian family, we further show that the preconditioned Hessian is governed by the covariance of the Gibbs‑tilted distribution relative to the covariance of the sampling distribution, yielding a covariance‑dependent sufficient condition for the descent of exact unit‑step MPPI. Numerical experiments illustrate the theory and the effect of key hyperparameters.

Authors:Samuel Filgueira da Silva, Mehmet Fatih Ozkan, Faissal El Idrissi, Marcello Canova
Title: Conformalized Transfer Learning for Li-ion Battery State of Health Forecasting under Manufacturing and Usage Variability
Abstract:
Accurate forecasting of state‑of‑health (SOH) is essential for ensuring safe and reliable operation of lithium‑ion cells. However, existing models calibrated on laboratory tests at specific conditions often fail to generalize to new cells that differ due to small manufacturing variations or operate under different conditions. To address this challenge, an uncertainty‑aware transfer learning framework is proposed, combining a Long Short‑Term Memory (LSTM) model with domain adaptation via Maximum Mean Discrepancy (MMD) and uncertainty quantification through Conformal Prediction (CP). The LSTM model is trained on a virtual battery dataset designed to capture real‑world variability in electrode manufacturing and operating conditions. MMD aligns latent feature distributions between simulated and target domains to mitigate domain shift, while CP provides calibrated, distribution‑free prediction intervals. This framework improves both the generalization and trustworthiness of SOH forecasts across heterogeneous cells.

Authors:Tao Tan, Rui Xie, Meng Yang, Yue Chen
Title: Robust Optimal Operation of Virtual Power Plants Under Decision-Dependent Uncertainty of Price Elasticity
Abstract:
The rapid deployment of distributed energy resources (DERs) is one of the essential efforts to mitigate global climate change. However, a vast number of small‑scale DERs are difficult to manage individually, motivating the introduction of virtual power plants (VPPs). A VPP operator coordinates a group of DERs by setting suitable prices, and aggregates them for interaction with the power grid. In this context, optimal pricing plays a critical role in VPP operation. This paper proposes a robust optimal operation model for VPPs that considers uncertainty in the price elasticity of demand. Specifically, the demand elasticity is found to be influenced by the pricing decision, giving rise to decision‑dependent uncertainty (DDU). An improved column‑and‑constraint (C&CG) algorithm, together with tailored transformation and reformulation techniques, is developed to solve the robust model with DDU efficiently. Case studies based on actual electricity consumption data of London households demonstrate the effectiveness of the proposed model and algorithm.

Authors:Martina Alutto, Lorenzo Zino, Karl H. Johansson, Angela Fontan
Title: On a Co-evolving Opinion-Leadership Model in Social Networks
Abstract:
Leadership in social groups is often a dynamic characteristic that emerges from interactions and opinion exchange. Empirical evidence suggests that individuals with strong opinions tend to gain influence, at the same time maintaining alignment with the social context is crucial for sustained leadership. Motivated by the social psychology literature that supports these empirical observations, we propose a novel dynamical system in which opinions and leadership co‑evolve within a social network. Our model extends the Friedkin‑Johnsen framework by making susceptibility to peer influence time‑dependent, turning it into the leadership variable. Leadership strengthens when an agent holds strong yet socially aligned opinions, and declines when such alignment is lost, capturing the trade‑off between conviction and social acceptance. After illustrating the emergent behavior of this complex system, we formally analyze the coupled dynamics, establishing sufficient conditions for convergence to a non‑trivial equilibrium, and examining two time‑scale separation regimes reflecting scenarios where opinion and leadership evolve at different speeds.

Authors:Jan Åslund
Title: Graph-Theoretic Analysis of Residual Generation Under Computational Constraints
Abstract:
A unified structural framework is presented for model‑based fault diagnosis that explicitly incorporates both fault locations and constraints imposed by the residual generation methodology. Building on the concepts of proper and minimal structurally overdetermined (PSO/MSO) sets and Test Equation Supports (TES/MTES), the framework introduces testable PSO sets, Residual Generation (RG) sets, irreducible fault signatures (IFS), and Irreducible RG (IRG) sets to characterize which submodels are suitable for residual generation under given computational restrictions. An operator M^ is defined to extract, from any model, the largest testable PSO subset consistent with a specified residual generation method. Using this operator, an algorithm is developed to compute all RG sets, and it is shown that irreducible fault signature sets form the join‑irreducible elements of a join‑semilattice of sets and fully capture the multiple‑fault isolability properties in the method‑constrained setting. The approach is exemplified on a semi‑explicit linear DAE model, where low structural differential index can be used to define M^. The results demonstrate that the proposed framework generalizes MTES‑based analysis to residual generation scenarios with explicit computational limitations.

Authors:Liuxun Xue, Shu Sun, Ruifeng Gao, Xiaoqian Yi
Title: Spatial Correlation, Non-Stationarity, and Degrees of Freedom of Holographic Curvature-Reconfigurable Apertures
Abstract:
Low‑altitude wireless platforms increasingly require lightweight, conformal, and densely sampled antenna array apertures with high array gain and spatial selectivity. However, when deployed on nonplanar surfaces, curvature alters the array manifold, local visibility, and propagation support, potentially invalidating spatial‑stationarity assumptions. In this paper, we investigate a holographic curvature‑reconfigurable aperture (HoloCuRA), modeled as a curvature‑controllable holographic surface, and develop a visibility‑aware spatial characterization framework for its low‑altitude applications. Specifically, the framework jointly quantifies array‑domain spatial non‑stationarity (SnS), and spatial degrees of freedom (DoF) in line‑of‑sight, 3GPP non‑line‑of‑sight, and isotropic‑scattering propagation environments. For SnS, a novel Power‑balanced, Visibility‑aware Correlation‑Matrix Distance (PoVi‑CMD) and a two‑stage subarray‑screening procedure are introduced. For DoF, the Rényi‑2 effective rank is adopted, and tractable spatial‑correlation expressions under isotropic scattering are developed for efficient DoF analysis. Furthermore, a realizable antenna port mode is introduced to connect SnS with DoF. Numerical results reveal that curvature and propagation support are the primary determinants of both SnS and DoF in HoloCuRA: array domain SnS determines whether subarray statistics can be treated as locally consistent, whereas DoF limits the global spatial modes. The findings provide useful guidance for low‑altitude antenna‑system design.

Authors:Guihlerme Daubt, Adrian Redder
Title: C-STEP: Continuous Space-Time Empowerment for Physics-informed Safe Reinforcement Learning of Mobile Agents
Abstract:
Safe navigation in complex environments remains a central challenge for reinforcement learning (RL) in robotics. This paper introduces Continuous Space‑Time Empowerment for Physics‑informed (C‑STEP) safe RL, a novel measure of agent‑centric safety tailored to deterministic, continuous domains. This measure can be used to design physics‑informed intrinsic rewards by augmenting positive navigation reward functions. The reward incorporates the agents internal states (e.g., initial velocity) and forward dynamics to differentiate safe from risky behavior. By integrating C‑STEP with navigation rewards, we obtain an intrinsic reward function that jointly optimizes task completion and collision avoidance. Numerical results demonstrate fewer collisions, reduced proximity to obstacles, and only marginal increases in travel time. Overall, C‑STEP offers an interpretable, physics‑informed approach to reward shaping in RL, contributing to safety for agentic mobile robotic systems.

Authors:Lukas Theiner, Maik Pfefferkorn, Yongpeng Zhao, Sebastian Hirt, Rolf Findeisen
Title: Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Models
Abstract:
Tuning control policies manually to meet high‑level objectives is often time‑consuming. Bayesian optimization provides a data‑efficient framework for automating this process using numerical evaluations of an objective function. However, many systems, particularly those involving humans, require optimization based on subjective criteria. Preferential Bayesian optimization addresses this by learning from pairwise comparisons instead of quantitative measurements, but relying solely on preference data can be inefficient. We propose a multi‑fidelity, multi‑modal Bayesian optimization framework that integrates low‑fidelity numerical data with high‑fidelity human preferences. Our approach employs Gaussian process surrogate models with both hierarchical, autoregressive and non‑hierarchical, coregionalization‑based structures, enabling efficient learning from mixed‑modality data. We illustrate the framework by tuning an autonomous vehicle's trajectory planner, showing that combining numerical and preference data significantly reduces the need for experiments involving the human decision maker while effectively adapting driving style to individual preferences.

Authors:Chungeng Tian, Fenghua He, Ning Hao
Title: Equivariant Filter Transformations for Consistent and Efficient Visual--Inertial Navigation
Abstract:
This paper presents an equivariant filter (EqF) transformation approach for visual‑‑inertial navigation. By establishing analytical links between EqFs with different symmetries, the proposed approach enables systematic consistency design and efficient implementation. First, we formalize the mapping from the global system state to the local error‑state and prove that it induces a nonsingular linear transformation between the error‑states of any two EqFs. Second, we derive transformation laws for the associated linearized error‑state systems and unobservable subspaces. These results yield a general consistency design principle: for any unobservable system, a consistent EqF with a state‑independent unobservable subspace can be synthesized by transforming the local coordinate chart, thereby avoiding ad hoc symmetry analysis. Third, to mitigate the computational burden arising from the non‑block‑diagonal Jacobians required for consistency, we propose two efficient implementation strategies. These strategies exploit the Jacobians of a simpler EqF with block‑diagonal structure to accelerate covariance operations while preserving consistency. Extensive Monte Carlo simulations and real‑world experiments validate the proposed approach in terms of both accuracy and runtime.

Authors:Thomas Conway
Title: A Low Cost Discrete Digital Isolator Circuit
Abstract:
This work presents a fully discrete, low cost digital isolator requiring no specialized ICs and implemented entirely with general purpose transistors and a two layer PCB embedded air core transformer. The design avoids vendor lock in and long term component obsolescence risks, while providing >1 kV isolation, ~200 ns propagation delay, and validated NRZ data rates of 1 Mbps. A modified dual oscillator architecture enables inherent hardware lockout suitable for half bridge gate driver applications. Measured performance and PCB layout guidelines are provided.

Authors:J. J. H. van Gemert, V. Breschi, D. R. Yntema, K. J. Keesman, M. Lazar
Title: The impact of sensor placement on graph-neural-network-based leakage detection
Abstract:
Sensor placement for leakage detection in water distribution networks is an important and practical challenge for water utilities. Recent work has shown that graph neural networks can estimate and predict pressures and detect leaks, but their performance strongly depends on the available sensor measurements and configurations. In this paper, we investigate how sensor placement influences the performance of GNN‑based leakage detection. We propose a novel PageRank‑Centrality‑based sensor placement method and demonstrate that it substantially impacts reconstruction, prediction, and leakage detection on the EPANET Net1.

Authors:Hanbyel Cho, Sang-Hun Kim, Jeonguk Kang, Donghan Koo
Title: SafeFlow: Real-Time Text-Driven Humanoid Whole-Body Control via Physics-Guided Rectified Flow and Selective Safety Gating
Abstract:
Recent advances in real‑time interactive text‑driven motion generation have enabled humanoids to perform diverse behaviors. However, kinematics‑only generators often exhibit physical hallucinations, producing motion trajectories that are physically infeasible to track with a downstream motion tracking controller or unsafe for real‑world deployment. These failures often arise from the lack of explicit physics‑aware objectives for real‑robot execution and become more severe under out‑of‑distribution (OOD) user inputs. Hence, we propose SafeFlow, a text‑driven humanoid whole‑body control framework that combines physics‑guided motion generation with a 3‑Stage Safety Gate driven by explicit risk indicators. SafeFlow adopts a two‑level architecture. At the high level, we generate motion trajectories using Physics‑Guided Rectified Flow Matching in a VAE latent space to improve real‑robot executability, and further accelerate sampling via Reflow to reduce the number of function evaluations (NFE) for real‑time control. The 3‑Stage Safety Gate enables selective execution by detecting semantic OOD prompts using a Mahalanobis score in text‑embedding space, filtering unstable generations via a directional sensitivity discrepancy metric, and enforcing final hard kinematic constraints such as joint and velocity limits before passing the generated trajectory to a low‑level motion tracking controller. Extensive experiments on the Unitree G1 demonstrate that SafeFlow outperforms prior diffusion‑based methods in success rate, physical compliance, and inference speed, while maintaining diverse expressiveness.

Authors:Riwa Karam, Alexander A. Nguyen, Ruoyu Lin, David R. Martin, Diana Morales, Brooks A. Butler, Magnus Egerstedt
Title: Collaboration in Multi-Robot Systems: Taxonomy and Survey over Frameworks for Collaboration
Abstract:
Collaboration is a central theme in multi‑robot systems as tasks and demands increasingly require capabilities that go beyond what any one individual robot possesses. Yet, despite extensive work on cooperative control and coordinated behaviors, the terminology surrounding collective multi‑robot interaction remains inconsistent across research communities. In particular, cooperation, coordination, and collaboration are often treated interchangeably, without clearly articulating the differences among them. To address this gap, we propose definitions that distinguish and relate cooperation, coordination, and collaboration in multi‑robot systems, highlighting the support of new capabilities in collaborative behaviors, and illustrate these concepts through representative examples. Building on this taxonomy, different frameworks for collaboration are reviewed, and technical challenges and promising future research directions are identified for collaborative multi‑robot systems.

Authors:Gustave Bainier, Antoine Chaillet, Rodolphe Sepulchre, Alessio Franci
Title: State-space fading memory
Abstract:
The fading‑memory (FM) property captures the progressive loss of influence of past inputs on a system's current output and has originally been formalized by Boyd and Chua in an operator‑theoretic framework. Despite its importance for systems approximation, reservoir computing, and recurrent neural networks, its connection with state‑space notions of nonlinear stability, especially incremental ones, remains understudied. This paper introduces a state‑space definition of FM. In state‑space, FM can be interpreted as an extension of incremental input‑to‑output stability (δIOS) that explicitly incorporates a memory kernel upper‑bounding the decay of past input differences. It is also closely related to Boyd and Chua's FM definition, with the sole difference of requiring uniform, instead of general, continuity of the memory functional with respect to an input‑fading norm. We demonstrate that incremental input‑to‑state stability (δISS) implies FM semi‑globally for time‑invariant systems under an equibounded input assumption. Notably, Boyd and Chua's approximation theorems apply to δISS state‑space models. As a closing application, we show that, under mild assumptions, the state‑space model of current‑driven memristors possess the FM property.

Authors:Bon Choe, Minhee Kang, Heejin Ahn
Title: High-Density Automated Valet Parking with Relocation-Free Sequential Operations
Abstract:
In this paper, we present DROP, high‑Density Relocation‑free sequential OPerations in automated valet parking. DROP addresses the challenges in high‑density parking & vehicle retrieval without relocations. Each challenge is handled by jointly providing area‑efficient layouts and relocation‑free parking & exit sequences, considering accessibility with relocation‑free sequential operations. To generate such sequences, relocation‑free constraints are formulated as explicit logical conditions expressed in boolean variables. Recursive search strategies are employed to derive the logical conditions and enumerate relocation‑free sequences under sequential constraints. We demonstrate the effectiveness of our framework through extensive simulations, showing its potential to significantly improve area utilization with relocation‑free constraints. We also examine its viability on an application problem with prescribed operational order. The results from all experiments are available at: https://drop‑park.github.io.

Authors:Harun Tolasa, Volkan Patoglu
Title: Human-in-the-Loop Pareto Optimization: Trade-off Characterization for Assist-as-Needed Training and Performance Evaluation
Abstract:
During human motor skill training and physical rehabilitation, there is an inherent trade‑off between task difficulty and user performance. Characterizing this trade‑off is crucial for evaluating user performance, designing assist‑as‑needed (AAN) protocols, and assessing the efficacy of training protocols. In this study, we propose a novel human‑in‑the‑loop (HiL) Pareto optimization approach to characterize the trade‑off between task performance and the perceived challenge level of motor learning or rehabilitation tasks. We adapt Bayesian multi‑criteria optimization to systematically and efficiently perform HiL Pareto characterizations. Our HiL optimization employs a hybrid model that measures performance with a quantitative metric, while the perceived challenge level is captured with a qualitative metric. We demonstrate the feasibility of the proposed HiL Pareto characterization through a user study. Furthermore, we present the utility of the framework through three use cases in the context of a manual skill training task with haptic feedback. First, we demonstrate how the characterized trade‑off can be used to design a sample AAN training protocol for a motor learning task and to evaluate the group‑level efficacy of the proposed AAN protocol relative to a baseline adaptive assistance protocol. Second, we demonstrate that individual‑level comparisons of the trade‑offs characterized before and after the training session enable fair evaluation of training progress under different assistance levels. This evaluation method is more general than standard performance evaluations, as it can provide insights even when users cannot perform the task without assistance. Third, we show that the characterized trade‑offs also enable fair performance comparisons among different users, as they capture the best possible performance of each user under all feasible assistance levels.

Authors:Xinyi Yi, Ioannis Lestas
Title: Data-driven online control for real-time optimal economic dispatch and temperature regulation in district heating systems
Abstract:
District heating systems (DHSs) require coordinated economic dispatch and temperature regulation under uncertain operating conditions. Existing DHS operation strategies often rely on disturbance forecasts and nominal models, so their economic and thermal performance may degrade when predictive information or model knowledge is inaccurate. This paper develops a data‑driven online control framework for DHS operation by embedding steady‑state economic optimality conditions into the temperature dynamics, so that the closed‑loop system converges to the economically optimal operating point without relying on disturbance forecasts. Based on this formulation, we develop a Data‑Enabled Policy Optimization (DeePO)‑based online learning controller and incorporate Adaptive Moment Estimation (ADAM) to improve closed‑loop performance. We further establish convergence and performance guarantees for the resulting closed‑loop system. Simulations on an industrial‑park DHS in Northern China show that the proposed method achieves stable near‑optimal operation and strong empirical robustness to both static and time‑varying model mismatch under practical disturbance conditions.

Authors:Praveen Kumar Ranjan, Abhinav Sinha, Yongcan Cao
Title: Engagement-Zone-Aware Input-Constrained Guidance for Safe Target Interception in Contested Environments
Abstract:
We address target interception in contested environments in the presence of multiple defenders whose interception capability is limited by finite ranges. Conventional methods typically impose conservative stand‑off constraints based on maximum engagement distance and neglect the interceptors' actuator limitations. Instead, we formulate safety constraints using defender‑induced engagement zones. To account for actuator limits, the vehicle model is augmented with input saturation dynamics. A time‑varying safe‑set tightening parameter is introduced to compensate for transient constraint violations induced by actuator dynamics. To ensure scalable safety enforcement in multi‑defender scenarios, a smooth aggregate safety function is constructed using a log‑sum‑exp operator combining individual threat measures associated with each defender's capability. A smooth switching guidance strategy is then developed to coordinate interception and safety objectives. The attacker pursues the target when sufficiently distant from threat boundaries and progressively activates evasive motion as the EZ boundaries are approached. The resulting controller relies only on relative measurements and does not require knowledge of defender control inputs, thus facilitating a fully distributed and scalable implementation. Rigorous analysis provides sufficient conditions guaranteeing target interception, practical safety with respect to all defender engagement zones, and satisfaction of actuator bounds. An input‑constrained guidance law based on conservative stand‑off distance is also developed to quantify the conservatism of maximum‑range‑based safety formulations. Simulations with stationary and maneuvering defenders demonstrate that the proposed formulation yields shorter interception paths and reduced interception time compared with conventional methods while maintaining safety throughout the engagement.

Authors:Sungjoo Chung, Ying Zhang
Title: Utilizing Adversarial Training for Robust Voltage Control: An Adaptive Deep Reinforcement Learning Method
Abstract:
Adversarial training is a defense method that trains machine learning models on intentionally perturbed attack inputs, so they learn to be robust against adversarial examples. This paper develops a robust voltage control framework for distribution networks with high penetration of distributed energy resources (DERs). Conventional voltage control methods are vulnerable to strategic cyber attacks, as they typically consider only random or black‑box perturbations. To address this, we formulate white‑box adversarial attacks using Projected Gradient Descent (PGD) and train a deep reinforcement learning (DRL) agent adversarially. The resulting policy adapts in real time to high‑impact, strategically optimized perturbations. Simulations on DER‑rich networks show that the approach maintains voltage stability and operational efficiency under realistic attack scenarios, highlighting the effectiveness of gradient‑based adversarial DRL in enhancing robustness and adaptability in modern distribution system control.

Authors:Subhankar Banerjee, Stavros Mitrolaris, Sennur Ulukus
Title: Index-Based Scheduling for a Resource-Constrained Quantum Switch
Abstract:
We consider a quantum switch with a finite number of quantum memory registers that aims to serve multipartite entanglement requests among N users. We propose scheduling policies that aim to optimize the average number of requests served per unit time by efficiently utilizing the switch's available memory. To measure the performance of the scheduling policies, we employ the newly introduced metric of age of entanglement establishment (AoEE). We formulate the scheduling problem in a restless multi‑armed bandit (RMAB) framework. We show that the scheduling of entanglement requests is indexable. Subsequently, we find a closed‑form expression of the Whittle index for all possible request‑age pairs. By modeling the Whittle index of each request as its reward and its cardinality as its cost, we formulate the memory‑constrained scheduling problem as a 0‑1 knapsack problem and solve it via dynamic programming. Furthermore, we consider two low‑complexity sequential greedy policies that leverage two different modified Whittle indices.

Authors:Moon Hwan Lee, Mohd. Afzal Khan, Akm Ashiquzzaman, Eunbin Lee, Jonghun Lee, Euiheon Chung, Hyuk-Sang Kwon, Jae Youn Hwang
Title: Bridging the numerical-physical gap in acoustic holography via end-to-end differentiable structural optimization
Abstract:
Acoustic holography provides a practical means of flexibly controlling acoustic wavefronts. However, high‑fidelity shaping of acoustic fields remains constrained by the numerical‑physical gap inherent in conventional phase‑only designs. These approaches realize a two‑dimensional phase‑delay profile as a three‑dimensional thickness‑varying lens, while neglecting wave‑matter interactions arising from the lens structure. Here, we introduce an end‑to‑end, physics‑aware differentiable structural optimization framework that directly incorporates three‑dimensional lens geometries into the acoustic simulation and optimization loop. Using a novel differentiable relaxation, termed Differentiable Hologram Lens Approximation (DHLA), the lens geometry is treated as a differentiable design variable, ensuring intrinsic consistency between numerical design and physical realization. The resulting Thickness‑Only Acoustic Holograms (TOAHs) significantly outperform state‑of‑the‑art phase‑only acoustic holograms (POAHs) in field reconstruction fidelity and precision under complex conditions. We further demonstrate the application of the framework to spatially selective neuromodulation in a neuropathic pain mouse model, highlighting its potential for non‑invasive transcranial neuromodulation. In summary, by reconciling numerical design with physical realization, this work establishes a robust strategy for high‑fidelity acoustic wavefront shaping in complex environments.

Authors:Anne Somalwar, Bruce D. Lee, George J. Pappas, Nikolai Matni
Title: Statistical Efficiency of Single- and Multi-step Models for Forecasting and Control
Abstract:
Compounding error, where small prediction mistakes accumulate over time, presents a major challenge in learning‑based control. A common remedy is to train multi‑step predictors directly instead of rolling out single‑step models. However, it is unclear when the benefits of multi‑step predictors outweigh the difficulty of learning a more complex model. We provide the first quantitative analysis of this trade‑off for linear dynamical systems. We study three predictor classes: (i) single step models, (ii) multi‑step models, and (iii) single step models trained with multi‑step losses. We show that when the model class is well‑specified and accurately captures the system dynamics, single‑step models achieve the lowest asymptotic prediction error. On the other hand, when the model class is misspecified due to partial observability, direct multi‑step predictors can significantly reduce bias and improve accuracy. We provide theoretical and empirical evidence that these trade‑offs persist when predictors are used in closed‑loop control.

Authors:Sumukha Udupa, Jie Fu
Title: Information-Driven Active Perception for k-step Predictive Safety Monitoring
Abstract:
This work studies the synthesis of active perception policies for predictive safety monitoring in partially observable stochastic systems. Operating under strict sensing and communication budgets, the proposed monitor dynamically schedules sensor queries to maximize information gain about the safety of future states. The underlying stochastic dynamics are captured by a labeled hidden Markov model (HMM), with safety requirements defined by a deterministic finite automaton (DFA). To enable active information acquisition, we introduce minimizing k‑step Shannon conditional entropy of the safety of future states as a planning objective, under the constraint of a limited sensor query budget. Using observable operators, we derive an efficient algorithm to compute the k‑step conditional entropy and analyze key properties of the conditional entropy gradient with respect to policy parameters. We validate the effectiveness of the method for predictive safety monitoring through a dynamic congestion game example.

Authors:Antoine Martinez, Balthazar Donon, Louis Wehenkel, Efthymios Karangelos
Title: Self-Supervised Graph Neural Networks for Optimal Substation Reconfiguration
Abstract:
Changing the transmission system topology is an efficient and costless lever to reduce congestion or increase exchange capacities. The problem of finding the optimal switch states within substations is called Optimal Substation Reconfiguration (OSR), and may be framed as a Mixed Integer Linear Program (MILP). Current state‑of‑the‑art optimization techniques come with prohibitive computing times, making them impractical for real‑time decision‑making. Meanwhile, deep learning offers a promising perspective with drastically smaller computing times, at the price of an expensive training phase and the absence of optimality guarantees. In this work, we frame OSR as an Amortized Optimization problem, where a Graph Neural Network (GNN) model ‑‑ our data being graphs ‑‑ is trained in a self‑supervised way to improve the objective function. We apply our approach to the maximization of the exchange capacity between two areas of a small‑scale 12‑substations system. Once trained, our GNN model improves the exchange capacity by 10.2% on average compared to the all connected configuration, while a classical MILP solver reaches an average improvement of 15.2% with orders‑of‑magnitude larger computing times.

Authors:Ann Mary Toms, Xingpeng Li
Title: WAKE-NET: 3D-Wake-Aware Turbine Layout and Cabling Optimization Framework of Multi-Hub-Height Wind Farms for Grid-Scale and Industrial Power Systems
Abstract:
The global transition towards renewable energy has accelerated the deployment of utility‑scale wind farms, increasing the need for accurate performance and economic assessments. Although wind energy offers substantial potential for carbon emission reduction, investment decisions are highly sensitive to predicted annual energy production and economic profitability. Conventionally wind farm analyses often estimate turbine power output based solely on incoming wind conditions, neglecting wake interactions between turbines. These wake effects can significantly reduce downstream turbine performance, leading to overestimation of energy yield and financial returns. This study proposes WAKE‑NET a wake‑aware optimization framework that incorporates both turbine layout optimization and hub height diversification across turbines of varying capacities. Unlike traditional approaches that assume a uniform hub height or ignore wake dynamics, the proposed methodology accounts for wake‑induced power losses in its framework. Results indicate that the benchmark model that neglects wake effects can overestimate annual profits, while the use of multiple hub heights reduces wake overlap and associated power losses. Overall, the findings demonstrate that wake‑aware design and hub height diversity improve energy yield accuracy and economic viability, offering a valuable guidance for wind farm developers and investors seeking to invest in renewable energy systems.

Authors:Arbel Yaniv, Kilian Golinski, Christoph Goebel
Title: Robust and Interpretable Graph Neural Networks for Power Systems State Estimation
Abstract:
This study analyzes Graph Neural Networks (GNNs) for distribution system state estimation (DSSE) by employing an interpretable Graph Neural Additive Network (GNAN) and by utilizing an edge‑conditioned message‑passing mechanism. The architectures are benchmarked against the standard Graph Attention Network (GAT) architecture. Multiple SimBench grids with topology changes and various measurement penetration rates were used to evaluate performance. Empirically, GNAN trails GAT in accuracy but serves as a useful probe for graph learning when accompanied with the proposed edge attention mechanism. Together, they demonstrate that incorporating information from distant nodes could improve learning depending on the grid topology and available data. This study advances the state‑of‑the‑art understanding of learning on graphs for the state estimation task and contributes toward reliable GNN‑based DSSE prediction technologies.

Authors:Hernan Haimovich, Jose L. Mancilla-Aguilar
Title: Time-Delay Systems with Discrete and Distributed delays: Discontinuous Initial Conditions and Reachability Sets
Abstract:
Time‑invariant finite‑dimensional systems, under reasonable continuity assumptions, exhibit the property that if solutions exist for all future times, the set of vectors reachable from a bounded set of initial conditions over bounded time intervals is also bounded. This property can be summarized as follows: forward completeness implies bounded reachability sets. By contrast, this property does not necessarily hold for infinite‑dimensional systems in general, and time‑delay systems in particular. Sufficient conditions for this property to hold that can be directly tested on the function defining the system dynamics are only known in the case of systems with pointwise (or discrete) delays. This paper develops novel sufficient conditions for the boundedness of the reachability sets of time‑delay systems involving mixed pointwise and distributed delays. Broad classes of systems satisfying these conditions are identified.

Authors:Federico Rosato, Lorenzo Nespoli, Vasco Medici
Title: Underdetermined Library-aided Impedance Estimation with Terminal Smart Meter Data
Abstract:
Smart meters provide relevant information for impedance identification, but they lack global phase alignment and internal network nodes are often unobserved. A few methods for this setting were developed, but they have requirements on data correlation and/or network topology. In this paper, we offer a unifying view of data‑ and structure‑driven identifiability issues, and use this groundwork to propose a method for underdetermined impedance identification. The method can handle intrinsically ambiguous topologies and data; its output is not forcedly a single estimate, but instead a collection of data‑compatible impedance assignments. It uses a library of plausible commercial cable types as a prior to refine the solutions, and we show how it can support topology identification workflows built around known georeferenced joints without degree guarantees. The method depends on a small number of non‑sensitive parameters and achieves high identification performance on a sizeable benchmark case even with low‑size injection/voltage datasets. We identify key steps that can be accelerated via GPU‑based parallelization. Finally, we assess the tolerance of the identification to noisy input.

Authors:Chuhao Qin, Lukas Esterle, Evangelos Pournaras
Title: Privacy-Aware Smart Cameras: View Coverage via Socially Responsible Coordination
Abstract:
Coordination of view coverage via privacy‑aware smart cameras is key to a more socially responsible urban intelligence. Rather than maximizing view coverage at any cost or over relying on expensive cryptographic techniques, we address how cameras can coordinate to legitimately monitor public spaces while excluding privacy‑sensitive regions by design. This article proposes a decentralized framework in which interactive smart cameras coordinate to autonomously select their orientation via collective learning, while eliminating privacy violations via soft and hard constraint satisfaction. The approach scales to hundreds up to thousands of cameras without any centralized control. Experimental evidence shows 18.42% higher coverage efficiency and 85.53% lower privacy violation than baselines and other state‑of‑the‑art approaches. This significant advance further unravels practical guidelines for operators and policymakers: how the field of view, spatial placement, and budget of cameras operating by ethically‑aligned artificial intelligence jointly influence coverage efficiency and privacy protection in large‑scale and sensitive urban environments.

Authors:Álvaro Belmonte-Baeza, José Luis Ramón, Leonard Felicetti, Miguel Cazorla, Jorge Pomares
Title: Path Planning and Reinforcement Learning-Driven Control of On-Orbit Free-Flying Multi-Arm Robots
Abstract:
This paper presents a hybrid approach that integrates trajectory optimization (TO) and reinforcement learning (RL) for motion planning and control of free‑flying multi‑arm robots in on‑orbit servicing scenarios. The proposed system integrates TO for generating feasible, efficient paths while accounting for dynamic and kinematic constraints, and RL for adaptive trajectory tracking under uncertainties. The multi‑arm robot design, equipped with thrusters for precise body control, enables redundancy and stability in complex space operations. TO optimizes arm motions and thruster forces, reducing reliance on the arms for stabilization and enhancing maneuverability. RL further refines this by leveraging model‑free control to adapt to dynamic interactions and disturbances. The experimental results validated through comprehensive simulations demonstrate the effectiveness and robustness of the proposed hybrid approach. Two case studies are explored: surface motion with initial contact and a free‑floating scenario requiring surface approximation. In both cases, the hybrid method outperforms traditional strategies. In particular, the thrusters notably enhance motion smoothness, safety, and operational efficiency. The RL policy effectively tracks TO‑generated trajectories, handling high‑dimensional action spaces and dynamic mismatches. This integration of TO and RL combines the strengths of precise, task‑specific planning with robust adaptability, ensuring high performance in the uncertain and dynamic conditions characteristic of space environments. By addressing challenges such as motion coupling, environmental disturbances, and dynamic control requirements, this framework establishes a strong foundation for advancing the autonomy and effectiveness of space robotic systems.

Authors:Antonio Spallone, Davide Fiore, Fabrizio Cartenì, Mario di Bernardo
Title: Feedback Control of a Recirculating Bioreactor with Electrophoretic Removal of Inhibitory Extracellular DNA
Abstract:
Extracellular DNA accumulation in recirculating bioprocesses inhibits microbial growth and reduces productivity. We consider a continuous bioreactor with a recirculating loop and an electrophoretic filtration unit for selective DNA removal, and develop a feedback control framework combining online state and parameter estimation via an Unscented Kalman Filter with two control strategies: an adaptive Model Predictive Controller that jointly optimizes dilution rate and filtration activation, and a simpler bang‑‑bang filtration policy with lookup‑table dilution rate selection. Closed‑loop simulations under nominal and perturbed conditions show that the MPC strategy achieves significantly higher cumulative profit while keeping DNA concentration below the inhibition threshold.

Authors:Hiroshi Okajima
Title: Stable Inversion of Discrete-Time Linear Periodically Time-Varying Systems via Cyclic Reformulation
Abstract:
Stable inverse systems for periodically time‑varying plants are essential for feedforward control and iterative learning control of multirate and periodic systems, yet existing approaches either require complex‑valued Floquet factors and noncausal processing or operate on a block time scale via lifting. This paper proposes a systematic method for constructing stable inverse systems for discrete‑time linear periodically time‑varying (LPTV) systems that avoids these limitations. The proposed approach proceeds in three steps: (i) cyclic reformulation transforms the LPTV system into an equivalent LTI representation; (ii) the inverse of the resulting LTI system is constructed using standard LTI inversion theory; and (iii) the periodically time‑varying inverse matrices are recovered from the block structure of the cycled inverse through parameter extraction. For the fundamental case of relative degree zero, where the output depends directly on the current input, the inverse system is obtained as an explicit closed‑form time‑varying matrix expression. For systems with periodic relative degree r >= 1, the r‑step‑delayed inverse is similarly obtained in explicit closed form via the periodic Markov parameters. The stability of the resulting inverse system is characterized by the transmission zeros of the cycled plant, generalizing the minimum phase condition from the LTI case. Numerical examples for both relative degree zero and higher relative degree systems confirm the validity of the stability conditions and demonstrate the effectiveness of the proposed framework, including exact input reconstruction via causal real‑valued inverse systems.

Authors:Xiao Zhang, Min Meng, Changxi Li, Ka-Fai Cedric Yiu
Title: Optimal Control of Switched Systems Governed by Logical Switching Dynamics
Abstract:
This paper investigates the optimal co‑design of logical and continuous controls for switched linear systems governed by controlled logical switching dynamics. Unlike traditional switched systems with arbitrary or state‑dependent switching, the switching signals here are generated by an internal logical dynamical system and explicitly integrated into the control synthesis. By leveraging the semi‑tensor product (STP) of matrices, we embed the coupled logical and continuous dynamics into a unified algebraic state‑space representation, transforming the co‑design problem into a tractable linear‑quadratic framework. We derive Riccati‑type backward recursions for both deterministic and stochastic logical dynamics, which yield optimal state‑feedback laws for continuous control alongside value‑function‑based, state‑dependent decision rules for logical switching. To mitigate the combinatorial explosion inherent in logical decision‑making, a hierarchical algorithm is developed to decouple offline precomputation from efficient online execution. Numerical simulations demonstrate the efficacy of the proposed framework.

Authors:Juan A. Martinez-Velasco, Pau Casals-Torrens, Ricard Bosch-Tous, Alexandre Serrano-Fontova
Title: Power System Studies Using Open-Access Software
Abstract:
The use of open‑access software is an option that can be considered by those interested in power system studies. In addition, the combination of two or more of these tools can expand the capabilities and the fields of application of each tool. This paper proposes the implementation of a flexible and powerful simulation environment based on R/Rstudio for carrying out power system studies. Several simple case studies are presented aimed at showing how the combination of either EMTP/ATP or OpenDSS with R/RStudio can expand the capabilities of each of these tools for performing either steady‑state or transient power system studies. Basically, the proposed environment uses RStudio as control center from which each simulation tool (e.g., R, ATP, OpenDSS) can be run. Some procedures for generating information that must be exchanged between RStudio and ATP or RStudio and OpenDSS have been implemented. Such exchanges are bidirectional: ATP and OpenDSS produce simulation results that can be read by RStudio (text files in the case of ATP, comma separated value (CSV) and text files in the case of OpenDSS), while RStudio capabilities are used to generate files that are embedded into the input file to be read by either ATP or OpenDSS. This late option can be used to change either the configuration or some parameters of the test system under study. Finally, one very interesting option illustrated in this paper is the possibility of using machine learning algorithms to predict the performance of the test system.

Authors:Milad Banitalebi Dehkordi, Manas Mejari, Dario Piga
Title: Rao-Blackwellized Stein Gradient Descent for Joint State-Parameter Estimation
Abstract:
We present a filtering framework for online joint state estimation and parameter identification in nonlinear, time‑varying systems. The algorithm uses Rao‑Blackwellization technique to infer joint state‑parameter posteriors efficiently. In particular, conditional state distributions are computed analytically via Kalman filtering, while model parameters including process and measurement noise covariances are approximated using particle‑based Stein Variational Gradient Descent (SVGD), enabling stable real‑time inference. We prove a theoretical consistency result by bounding the impact of the SVGD approximated parameter posterior on state estimates, relating the divergence between the true and approximate parameter posteriors to the total variation distance between the resulting state marginals. Performance of the proposed filter is validated on two case studies: a bioreactor with Haldane kinetics and a neural‑network‑augmented dynamic system. The latter demonstrates the filter's capacity for online neural network training within a dynamical model, showcasing its potential for fully adaptive, data‑driven system identification.

Authors:Haozhen Cheng, Hüseyin K. Çakmak, Veit Hagenmeyer
Title: JanusBM: A Dual-Fidelity Multi-Zone White-Box Building Modeling Framework
Abstract:
Accurate building energy models are crucial for analyzing sector‑coupled energy systems, where buildings interact with electrified heating, energy storage, and advanced control across various scenarios. High‑fidelity (HiFi) white‑box models that resolve hydronic distribution and emitter dynamics can capture short‑term transients, yet their numerical stiffness and computational burden limit long‑term simulations and large‑scale scenario exploration. Conversely, reduced‑order low‑fidelity (LoFi) representations enable rapid annual assessments but may fail to capture the hydronic‑ and control‑induced dynamics that govern transient and peak behavior. This paper proposes a dual‑fidelity, multi‑zone white‑box building modeling framework, which is called JanusBM, built on a novel topology‑driven modeling tool RoomFlex6D, coupling a HiFi hydronic model and a LoFi ideal‑load surrogate that removes explicit hydronic states in Modelica. To ensure applicability and physical consistency across time scales, we introduce a two‑stage hybrid validation and calibration pipeline that uses complementary data: the IEA EBC Annex 60 benchmark for energy‑scale validation and time‑series measurements from real‑world experimental buildings for hydronic dynamics‑scale calibration. Results show that the generated LoFi models achieve a high degree of consistency with Annex 60 benchmark on the energy scale, and the proposed calibration workflow robustly improves loop‑level return water temperature transients and zone‑level temperature dynamics. Moreover, the LoFi model achieves orders‑of‑magnitude faster simulations suited to annual energy analyses, whereas the HiFi model becomes necessary when the required heat differs from the actual delivered heat due to distribution and control limitations, especially in transient and peak‑oriented assessments.

Authors:E. D. Gomez Anccas, E. A. MacPherson, J. Tegeler, K. Röbert, M. Fischer, C. A. Hans, D. Schulz
Title: Experimental Characterisation of Distributed Reactive Power Sharing under Communication-Induced Stress in Parallel Grid-Forming Inverters
Abstract:
Synchronisation of parallel grid‑forming inverters is crucial for stable operation of future power systems. This includes accurate and robust reactive power sharing under realistic operating conditions such as impedance mismatch and communication constraints. In this work, reactive power sharing by virtue of a distributed control law is investigated under line impedance mismatch. Furthermore, robustness and transient behaviour of the proposed approach are experimentally evaluated under communication‑induced stressors including a fixed 3% packet loss and communication delays ranging from 50 ms to 100 ms, artificially introduced through a software‑defined overlay. The study is conducted in a low‑voltage laboratory‑scale microgrid comprising two parallel grid‑forming inverters, an AC load, and a grid‑following battery system acting as a reactive power injector. The results show reactive power sharing convergence up to 90 ms communication delay, with a stability boundary between 90 ms and 100 ms, which decreases with increasing integral gain.

Authors:David Ohlin, Anders Rantzer, Emma Tegling
Title: Positive Observers Revisited
Abstract:
The paper shows that positive linear systems can be stabilized using positive Luenberger‑type observers. This is achieved by structuring the observer as monotonically converging upper and lower bounds on the state. Analysis of the closed‑loop properties under linear observer feedback gives conditions that cover a larger class than previous observer designs. The results are applied to nonpositive systems by enforcing positivity of the dynamics using feedback from the upper bound observer. The setting is expanded to include stochastic noise, giving conditions for convergence in expectation using feedback from positive observers.

Authors:Evagoras Makridis, Themistoklis Charalambous
Title: Cooperative Bandit Learning in Directed Networks with Arm-Access Constraints
Abstract:
Sequential decision‑making under uncertainty often involves multiple agents learning which actions (arms) yield the highest rewards through repeated interaction with a stochastic environment. This setting is commonly modeled by cooperative multi‑agent multi‑armed bandit problems, where agents explore and share information without centralized coordination. In many realistic systems, agents have heterogeneous capabilities that limit their access to subsets of arms and communicate over asymmetric networks represented by directed graphs. In this work, we study multi‑agent multi‑armed bandit problems with partial arm access, where agents explore and exploit only the arms available to them while exchanging information with neighbors. We propose a distributed consensus‑based upper confidence bound (UCB) algorithm that accounts for both the arm accessibility structure and network asymmetry. Our approach employs a mass‑preserving information mixing mechanism, ensuring that reward estimates remain unbiased across the network despite accessibility constraints and asymmetric information flow. Under standard stochastic assumptions, we establish logarithmic regret for every agent, with explicit dependence on network mixing properties and arm accessibility constraints. These results quantify how heterogeneous arm access and directed communication shape cooperative learning performance.

Authors:Kaoru Teranishi
Title: Secure Two-Party Matrix Multiplication from Lattices and Its Application to Encrypted Control
Abstract:
In this study, we propose a two‑party computation protocol for approximate matrix multiplication of fixed‑point numbers. The proposed protocol is provably secure under standard lattice‑based cryptographic assumptions and enables matrix multiplication at a desired approximation level within a single round of communication. We demonstrate the feasibility of the protocol by applying it to the secure implementation of a linear control law. Our evaluation reveals that the client achieves lower online computational complexity compared to the original controller computation, while ensuring the privacy of controller inputs, outputs, and parameters. Furthermore, a numerical example confirms that the proposed method maintains sufficient precision of control inputs even in the presence of approximation and quantization errors.

Authors:Kunal Garg
Title: Equivalence of Finite- and Fixed-time Stability to Asymptotic Stability
Abstract:
In this paper, we present new results on finite‑ and fixed‑time convergence for dynamical systems using LaSalle‑like invariance principles. In particular, we provide first and second‑order non‑smooth Lyapunov‑like results for finite‑ and fixed‑time convergence, thereby relaxing the requirement of existence a differentiable, positive definite Lyapunov function. Based on these findings, we show that a dynamical system whose equilibrium point is globally asymptotically stable can be modified through scaling so that the resulting dynamical system has a fixed‑time stable equilibrium point. The results in this paper expand our understanding of various convergence rates and strengthen the hypothesis that all the convergence rates are interconnected through a suitable transformation.

Authors:Fengyu Lin, Miaomiao Wang, Housheng Su, Abdelhamid Tayebi
Title: Distributed Hybrid Feedback for Global Pose Synchronization of Multiple Rigid Body Systems on $SE(3)$
Abstract:
This paper investigates the problem of pose synchronization for multiple rigid body systems evolving on the matrix Lie group \SE(3). We propose a distributed hybrid feedback control scheme with global asymptotic stability guarantees using relative pose and group velocity measurements. The key idea consists of constructing a new potential function on \SE(3) × \mathbbR with a generalized non‑diagonal weighting matrix, and a set of auxiliary scalar variables with continuous‑discrete hybrid dynamics. Based on the new potential function and the auxiliary scalar variables, a geometric distributed hybrid feedback designed directly on \SE(3) is proposed to achieve global pose synchronization. Numerical simulation results are presented to illustrate the performance of the proposed distributed hybrid control scheme.

Authors:Jiachen Li, Shihao Li, Jian Chu, Wei Li, Dongmei Chen
Title: Fleet-Level Battery-Health-Aware Scheduling for Autonomous Mobile Robots
Abstract:
Autonomous mobile robot fleets must coordinate task allocation and charging under limited shared resources, yet most battery aware planning methods address only a single robot. This paper extends degradation cost aware task planning to a multi robot setting by jointly optimizing task assignment, service sequencing, optional charging decisions, charging mode selection, and charger access while balancing degradation across the fleet. The formulation relies on reduced form degradation proxies grounded in the empirical battery aging literature, capturing both charging mode dependent wear and idle state of charge dependent aging; the bilinear idle aging term is linearized through a disaggregated piecewise McCormick formulation. Tight big M values derived from instance data strengthen the LP relaxation. To manage scalability, we propose a hierarchical matheuristic in which a fleet level master problem coordinates assignments, routes, and charger usage, while robot level subproblems whose integer part decomposes into trivially small independent partition selection problems compute route conditioned degradation schedules. Systematic experiments compare the proposed method against three baselines: a rule based nearest available dispatcher, an energy aware formulation that enforces battery feasibility without modeling degradation, and a charger unaware formulation that accounts for degradation but ignores shared charger capacity limits.

Authors:Guangpu Wu, Shibei Xue, Yuting Zhu, Guofeng Zhang, Ian R. Petersen
Title: Optimal filtering for a giant cavity in waveguide QED systems
Abstract:
In waveguide quantum electrodynamics (QED) systems, a giant cavity can be engineered to interact with quantum fields by multiple distant coupling points so that its non‑Markovian dynamics are quite different from traditional quantum optical cavity systems. Towards feedback control this system, this paper designs an optimal filter for the giant cavity systems to estimate its state evolution under continuous quantum measurements. Firstly, the Langevin equation in the Heisenberg picture are derived, which is a linear continuous‑time system with both states and inputs delays resulting from the unconventional distant couplings. Compared to existing modeling approaches, this formulation effectively preserves the nonlocal coupling and multiple delay dynamic characteristics inherent in the original system. In particular, the presence of coupling and propagation delays leads to noncommutativity among the system operators at different times, which prevents the direct application of existing quantum filtering methods. To address this issue, an optimal filter is designed, in which the delayed‑state covariance matrices are computed. By iteratively evaluating the delayed‑state covariance over successive time intervals, the resulting optimal filter can be implemented in an interval‑wise backward recursion algorithm. Finally, numerical simulations are conducted to evaluate the tracking performance of the proposed optimal filter for the giant cavity. By comparing between the evolutions of Wigner functions of coherent and cat states and the filter, the effectiveness of the optimal filter is validated.

Authors:Bo Wang, Miroslav Krstic
Title: Universal Formula Families for Safe Stabilization of Single-Input Nonlinear Systems
Abstract:
We develop an optimization‑free framework for safe stabilization of single‑input control‑affine nonlinear systems with a given control Lyapunov function (CLF) and a given control barrier function (CBF), where the desired equilibrium lies in the interior of the safe set. An explicit compatibility condition is derived that is necessary and sufficient for the pointwise simultaneous satisfaction of the CLF and CBF inequalities. When this condition holds, two closed‑form continuous state‑feedback laws are constructed from the Lie‑derivative data of the CLF and CBF via standard universal stabilizer formulas, yielding asymptotic stabilization of the origin and forward invariance of the interior of the safe set, without online quadratic programming. The two laws belong to broader families parametrized by a free nondecreasing function, providing additional design flexibility. When the compatibility condition fails, a safety‑prioritizing modification preserves forward invariance and drives the state toward the safe‑set boundary until a compatible region is reached, whereupon continuity at the origin and asymptotic stabilization are recovered. The framework produces families of explicit constructive alternatives to CLF‑CBF quadratic programming for scalar‑input nonlinear systems.

Authors:Yingjie Mi, Zihao Ren, Lei Wang, Daniel E. Quevedo, Guodong Shi
Title: Explicit Model Predictive Control with Quantum Encryption
Abstract:
This paper studies quantum‑encrypted explicit MPC for constrained discrete‑time linear systems in a cloud‑based architecture. A finite‑horizon quadratic MPC problem is solved offline to obtain a piecewise‑affine controller. Shared quantum keys generated from Bell pairs and protected by quantum key distribution are used to encrypt the online control evaluation between the sensor and actuator. Based on this architecture, we develop a lightweight encrypted explicit MPC protocol, prove exact recovery of the plaintext control action, and characterize its computational efficiency. Numerical results demonstrate lower online complexity than classical encrypted MPC, while security is discussed in terms of confidentiality of plant data and control inputs.

Authors:Anna Stuhlmacher, Panupong Srisuthankul, Johanna L. Mathieu, Peter Seiler
Title: A Model Predictive Control Approach to Dual-Axis Agrivoltaic Panel Tracking
Abstract:
Agrivoltaic systems‑‑photovoltaic (PV) panels installed above agricultural land‑‑have emerged as a promising dual‑use solution to address competing land demands for food and energy production. In this paper, we propose a model predictive control (MPC) approach to dual‑axis agrivoltaic panel tracking control that dynamically adjusts panel positions in real time to maximize power production and crop yield given solar irradiance and ambient temperature measurements. We apply convex relaxations and shading factor approximations to reformulate the MPC optimization problem as a convex second‑order cone program that determines the PV panel position adjustments away from the sun‑tracking trajectory. Through case studies, we demonstrate our approach, exploring the Pareto front between i) an approach that maximizes power production without considering crop needs and ii) crop yield with no agrivoltaics. We also conduct a case study exploring the impact of forecast error on MPC performance. We find that dynamically adjusting agrivoltaic panel position helps us actively manage the trade‑offs between power production and crop yield, and that active panel control enables the agrivoltaic system to achieve land equivalent ratio values of up to 1.897.

Authors:Zhiyi Zhou, Ján Drgoňa, Yury Dvorkin
Title: L2O-CCG: Adversarial Learning with Set Generalization for Adaptive Robust Optimization
Abstract:
The adversarial subproblem in two‑stage adaptive robust optimization (ARO), which identifies the worst‑case uncertainty realization, is a major computational bottleneck. This difficulty is exacerbated when the recourse value function is non‑concave and the uncertainty set shifts across applications. Existing approaches typically exploit specific structural assumptions on the value function or the uncertainty set geometry to reformulate this subproblem, but degrade when these assumptions are violated or the geometry changes at deployment. To address this challenge, we propose L2O‑CCG, a bi‑level framework that enables the integration of structure‑aware adversarial solvers within the constraint‑and‑column generation (CCG) algorithm. As one instantiation, we develop a generalizable adversarial learning method, which replaces solver‑based adversarial search with a learned proximal gradient optimizer that can generalize across uncertainty set geometries without retraining. Here, an inner‑level neural network approximates the recourse value function from offline data, while an outer‑level pre‑trained mapping generates iteration‑dependent step sizes for a proximal gradient scheme. We also establish out‑of‑distribution convergence bounds under uncertainty set parameter shifts, showing how the trajectory deviation of the learned optimizer is bounded by the uncertainty set shift. We illustrate performance of the L2O‑CCG method on a building HVAC management task.

Authors:Yihui Mao, Tian Tan, Xuehui Shen, Warren E. Dixon, Rushikesh Kamalapurkar
Title: Parallel OctoMapping: A Scalable Framework for Enhanced Path Planning in Autonomous Navigation
Abstract:
Mapping is essential in robotics and autonomous systems because it provides the spatial foundation for path planning. Efficient mapping enables planning algorithms to generate reliable paths while ensuring safety and adapting in real time to complex environments. Fixed‑resolution mapping methods often produce overly conservative obstacle representations that lead to suboptimal paths or planning failures in cluttered scenes. To address this issue, we introduce Parallel OctoMapping (POMP), an efficient OctoMap‑based mapping technique that maximizes available free space and supports multi‑threaded computation. To the best of our knowledge, POMP is the first method that, at a fixed occupancy‑grid resolution, refines the representation of free space while preserving map fidelity and compatibility with existing search‑based planners. It can therefore be integrated into existing planning pipelines, yielding higher pathfinding success rates and shorter path lengths, especially in cluttered environments, while substantially improving computational efficiency.

Authors:Danilo Saccani, Luca Furieri, Giancarlo Ferrari-Trecate
Title: Stability-Preserving Online Adaptation of Neural Closed-loop Maps
Abstract:
The growing complexity of modern control tasks calls for controllers that can react online as objectives and disturbances change, while preserving closed‑loop stability. Recent approaches for improving the performance of nonlinear systems while preserving closed‑loop stability rely on time‑invariant recurrent neural‑network controllers, but offer no principled way to update the controller during operation. Most importantly, switching from one stabilizing policy to another can itself destabilize the closed‑loop. We address this problem by introducing a stability‑preserving update mechanism for nonlinear, neural‑network‑based controllers. Each controller is modeled as a causal operator with bounded \ell_p‑gain, and we derive gain‑based conditions under which the controller may be updated online. These conditions yield two practical update schemes, time‑scheduled and state‑triggered, that guarantee the closed‑loop remains \ell_p‑stable after any number of updates. Our analysis further shows that stability is decoupled from controller optimality, allowing approximate or early‑stopped controller synthesis. We demonstrate the approach on nonlinear systems with time‑varying objectives and disturbances, and show consistent performance improvements over static and naive online baselines while guaranteeing stability.

Authors:Chi Wang, David Angeli
Title: Data-Driven Synthesis of Robust Positively Invariant Sets from Noisy Data
Abstract:
This paper develops a method to construct robust positively invariant (RPI) tube sets from finite noisy input‑state data of an unknown linear time‑invariant (LTI) system, yielding tubes that can be directly embedded in tube‑based robust data‑driven predictive control. Data‑consistency uncertainty sets are constructed under process/measurement noise with polytopic/ellipsoidal bounds. In the measurement‑noise case, we provide a deterministic and data‑consistent procedure to certify the induced residual bound from data. Based on these sets, a robustly stabilizing state‑feedback gain is certified via a common quadratic contraction, which in turn enables constructive polyhedral/ellipsoidal RPI tube computation. Numerical examples quantify the conservatism induced by noisy data and the employed certification step.

Authors:Anni Li, Yingqing Chen, Christos G. Cassandras, Wei Xiao
Title: Finite-time Convergent Control Barrier Functions with Feasibility Guarantees
Abstract:
This paper studies the problem of finite‑time convergence to a prescribed safe set for nonlinear systems whose initial states violate the safety constraints. Existing Control Lyapunov‑Barrier Functions (CLBFs) can enforce recovery to the safe set but may suffer from the issue of chattering and they do not explicitly consider control bounds. To address these limitations, we propose a new Control Barrier Function (CBF) formulation that guarantees finite‑time convergence to the safe set while ensuring feasibility under control constraints. Specifically, we strengthen the initially violated safety constraint by introducing a parameter which enables the exploitation of the asymptotic property of a CBF to converge to the safe set in finite time. Furthermore, the conditions for the existence of such a CBF under control bounds to achieve finite‑time convergence are derived via reachability analysis and constraint comparison, providing a systematic approach for parameter design. A case study on 2D obstacle avoidance is presented to demonstrate the effectiveness and advantages of the proposed method.

Authors:Seyed Amir Mansouri, Kenneth Bruninx
Title: A Portfolio-Level Optimization Framework for Coordinated Market Participation and Operational Scheduling of Hydrogen-Centric Companies
Abstract:
The vision of electrolytic hydrogen as a clean energy vector prompts the emergence of hydrogen‑centric companies that must simultaneously engage in electricity, hydrogen, and green certificate markets while operating complex, geographically distributed asset portfolios. This paper proposes a portfolio‑level optimization framework tailored for the integrated operational scheduling and market participation of such companies. The model co‑optimizes asset scheduling and market decisions across multiple sites, incorporating spatial distribution, technical constraints, and company‑level policy requirements. It supports participation in the electricity market, physical and virtual Power Purchase Agreements (PPAs), bundled and unbundled hydrogen markets, and green certificate transactions. The model is applied to three operational scenarios to evaluate the economic and operational impacts of different compliance strategies. Results show that centralized, portfolio‑level control unlocks the full flexibility of geographically distributed assets, enabling a 2.42‑fold increase in hydrogen production and a 9.4% reduction in daily operational costs, while satisfying all company policy constraints.

Authors:Shridhar Velhal, Avijit Banerjee, George Nikolakopoulos
Title: Route-Phasing-Split-Encoded Genetic Algorithm for Multi-Satellite On-Orbit Servicing Mission Planning
Abstract:
This article addresses multi‑servicer on‑orbit servicing mission planning in geosynchronous Earth orbit, where routing decisions are tightly coupled with time‑dependent orbital phasing and strict propellant and mission‑duration constraints. We propose a Route‑Phasing‑Split Genetic Algorithm (RPS‑GA) that simultaneously optimizes target sequencing, discrete phasing rotation decisions (i.e., the number of phasing revolutions/waiting cycles), and route partitioning across multiple servicing spacecrafts (SSCs). An RPS triplet chromosome encodes route order, phasing rotations, and route splits in a unified structure, enabling split‑aware recombination without disrupting feasible multi‑servicer route blocks. Feasibility is enforced through a constraint‑aware fitness function that ranks feasible solutions based on total ΔV, while penalizing propellant and mission duration violations, using aggregate and imbalance penalties. This formulation discourages the concentration of violations on a single servicing spacecraft (SSC). Once a feasible best solution is identified, it is preserved as feasible in subsequent generations, thereby enhancing convergence stability. The framework incorporates split‑aware crossover, mutation and a regret‑based Large Neighborhood Search for local intensification. Experiments on representative GEO servicing scenarios demonstrate that RPS‑GA produces feasible multi‑servicer plans with substantially improved fuel efficiency, reducing total ΔV by 24.5%, (from 1956.36 \ m/s to 1476.32\ m/s ) compared with a state‑of‑the‑art LNS‑AGA baseline.

Authors:Prashant Solanki, Jasper Van Beers, Coen De Visser
Title: From Singleton Obstacles to Clutter: Translation Invariant Compositional Avoid Sets
Abstract:
This paper studies obstacle avoidance under translation invariant dynamics using an avoid‑side travel cost Hamilton Jacobi formulation. For running costs that are zero outside an obstacle and strictly negative inside it, we prove that the value function is non‑positive everywhere, equals zero exactly outside the avoid set, and is strictly negative exactly on it. Under translation invariance, this yields a reuse principle: the value of any translated obstacle is obtained by translating a single template value function. We show that the pointwise minimum of translated template values exactly characterizes the union of the translated single‑obstacle avoid sets and provides a conservative inner certificate of unavoidable collision in clutter. To reduce conservatism, we introduce a blockwise composition framework in which subsets of obstacles are merged and solved jointly. This yields a hierarchy of conservative certificates from singleton reuse to the exact clutter value, together with monotonicity under block merging and an exactness criterion based on the existence of a common clutter avoiding control. The framework is illustrated on a Dubins car example in a repeated clutter field.

Authors:Afsoon Alidadi Shamsabadi, Cosmas Mwaba, Thomas Nugent, Jie Gao, Pablo Madoery, Halim Yanikomeroglu, Subhadeep Pal
Title: DQN Based Joint UAV Trajectory and Association Planning in NTN Assisted Networks
Abstract:
Advanced Air Mobility (AAM) has emerged as a key pillar of next‑generation transportation systems, encompassing a wide range of uncrewed aerial vehicle (UAV) applications. To enable AAM, maintaining reliable and efficient communication links between UAVs and control centers is essential. At the same time, the highly dynamic nature of wireless networks, combined with the limited onboard energy of UAVs, makes efficient trajectory planning and network association crucial. Existing terrestrial networks often fail to provide ubiquitous coverage due to frequent handovers and coverage gaps. To address these challenges, geostationary Earth orbit (GEO) satellites offer a promising complementary solution for extending UAV connectivity beyond terrestrial boundaries. This work proposes an integrated GEO terrestrial network architecture to ensure seamless UAV connectivity. Leveraging artificial intelligence (AI), a deep Q network (DQN) based algorithm is developed for joint UAV trajectory and association planning (JUTAP), aiming to minimize energy consumption, handover frequency, and disconnectivity. Simulation results validate the effectiveness of the proposed algorithm within the integrated GEO terrestrial framework.

Authors:Isabelle Krauss, Victor G. Lopez, Matthias A. Müller
Title: Sample-based detectability and moving horizon state estimation of continuous-time systems
Abstract:
In this paper we propose a detectability condition for nonlinear continuous‑time systems with irregular/infrequent output measurements, namely a sample‑based version of incremental integral input/output‑to‑state stability (i‑iIOSS). We provide a sufficient condition for an i‑iIOSS system to be sample‑based i‑iIOSS. This condition is also exploited to analyze the relationship between sample‑based i‑iIOSS and sample‑based observability for linear systems, such that previously established sampling strategies for linear systems can be used to guarantee sample‑based i‑iIOSS. Furthermore, we present a sample‑based moving horizon estimation scheme, for which robust stability can be shown. Finally, we illustrate the applicability of the proposed estimation scheme through a biomedical simulation example.

Authors:Kaipeng Xu, Zhuo Zhi, Ruixuan Zhao, Keyue Jiang
Title: End-to-End Differentiable Predictive Control with Guaranteed Constraint Satisfaction and feasibility for Building Demand Response
Abstract:
The high energy consumption of buildings presents a critical need for advanced control strategies like Demand Response (DR). Differentiable Predictive Control (DPC) has emerged as a promising method for learning explicit control policies, yet conventional DPC frameworks are hindered by three key limitations: the use of simplistic dynamics models with limited expressiveness, a decoupled training paradigm that fails to optimize for closed‑loop performance, and a lack of practical safety guarantees under realistic assumptions. To address these shortcomings, this paper proposes a novel End‑to‑End Differentiable Predictive Control (E2E‑DPC) framework. Our approach utilizes an Encoder‑Only Transformer to model the complex system dynamics and employs a unified, performance‑oriented loss to jointly train the model and the control policy. Crucially, we introduce an online tube‑based constraint tightening method that provides theoretical guarantees for recursive feasibility and constraint satisfaction without requiring complex offline computation of terminal sets. The framework is validated in a high‑fidelity EnergyPlus simulation, controlling a multi‑zone building for a DR task. The results demonstrate that the proposed method with guarantees achieves near‑perfect constraint satisfaction ‑ a reduction of over 99% in violations compared to the baseline ‑ at the cost of only a minor increase in electricity expenditure. This work provides a deployable, performance‑driven control solution for building energy management and establishes a new pathway for developing verifiable learning‑based control systems under milder assumptions.

Authors:Kaipeng Xu, Zhuo Zhi, Keyue Jiang
Title: Input Convex Encoder-Only Transformer for Fast and Gradient-Stable MPC in Building Demand Response
Abstract:
Learning‑based Model Predictive Control (MPC) has emerged as a powerful strategy for building demand response. However, its practical deployment is often hindered by the non‑convex optimization problems induced by standard neural network models. These problems lead to long solver times and suboptimal solutions, making real‑time control over long horizons challenging. While Input Convex Neural Networks (ICNNs), such as Input‑Convex Long Short‑Term Memorys (IC‑LSTMs), are developed to address the convexity issue, their recurrent architectures suffer from high computational cost and gradient instability as the prediction horizon increases. To overcome these limitations, this paper introduces the Input‑Convex Encoder‑only Transformer (IC‑EoT), a novel architecture that synergizes the parallel processing capabilities of the Transformer with the guaranteed tractability of input convexity. The IC‑EoT was developed and evaluated in a high‑fidelity co‑simulation framework using the Energym Python library to interface with the EnergyPlus building simulator, and compared against its recurrent convex counterpart (IC‑LSTM) and standard non‑convex models. The results demonstrate that the IC‑EoT is structurally immune to the gradient instability that affects recurrent ICNNs while maintaining comparable predictive accuracy. More critically, it substantially reduces MPC solver times; this speed advantage grows with the prediction horizon, with the IC‑EoT proving 2.7 to 8.3 times faster than the IC‑LSTM across horizons spanning from one to eight hours. This leap in computational efficiency makes the IC‑EoT a robust and practical solution, enabling effective, real‑time MPC for building energy management under realistic horizon decision‑making scenarios.

Authors:Ehimare Okoyomon, Christoph Goebel
Title: BOOST-RPF: Boosted Sequential Trees for Radial Power Flow
Abstract:
Accurate power flow analysis is critical for modern distribution systems, yet classical solvers face scalability issues, and current machine learning models often struggle with generalization. We introduce BOOST‑RPF, a novel method that reformulates voltage prediction from a global graph regression task into a sequential path‑based learning problem. By decomposing radial networks into root‑to‑leaf paths, we leverage gradient‑boosted decision trees (XGBoost) to model local voltage‑drop regularities. We evaluate three architectural variants: Absolute Voltage, Parent Residual, and Physics‑Informed Residual. This approach aligns the model architecture with the recursive physics of power flow, ensuring size‑agnostic application and superior out‑of‑distribution robustness. Benchmarked against the Kerber Dorfnetz grid and the ENGAGE suite, BOOST‑RPF achieves state‑of‑the‑art results with its Parent Residual variant which consistently outperforms both analytical and neural baselines in standard accuracy and generalization tasks. While global Multi‑Layer Perceptrons (MLPs) and Graph Neural Networks (GNNs) often suffer from performance degradation under topological shifts, BOOST‑RPF maintains high precision across unseen feeders. Furthermore, the framework displays linear O(N) computational scaling and significantly increased sample efficiency through per‑edge supervision, offering a scalable and generalizable alternative for real‑time distribution system operator (DSO) applications.

Authors:Ying Shuai Quan, Paolo Falcone, Jonas Sjöberg
Title: Interaction-Aware Predictive Environmental Control Barrier Function for Emergency Lane Change
Abstract:
Safety‑critical motion planning in mixed traffic remains challenging for autonomous vehicles, especially when it involves interactions between the ego vehicle (EV) and surrounding vehicles (SVs). In dense traffic, the feasibility of a lane change depends strongly on how SVs respond to the EV motion. This paper presents an interaction‑aware safety framework that incorporates such interactions into a control barrier function (CBF)‑based safety assessment. The proposed method predicts near‑future vehicle positions over a finite horizon, thereby capturing reactive SV behavior and embedding it into the CBF‑based safety constraint. To address uncertainty in the SV response model, a robust extension is developed by treating the model mismatch as a bounded disturbance and incorporating an online uncertainty estimate into the barrier condition. Compared with classical environmental CBF methods that neglect SV reactions, the proposed approach provides a less conservative and more informative safety representation for interactive traffic scenarios, while improving robustness to uncertainty in the modeled SV behavior.

Authors:German Svistunov, Matteo Bernabe, David Lopez-Perez
Title: Performance Analysis of Tri-Sector Reflector Antennas for HAPS-Based Cellular Networks
Abstract:
The increasing demand for ubiquitous, highcapacity mobile connectivity has driven cellular systems to explore beyond‑terrestrial deployments. In this paper, we present a system‑level performance evaluation of fifth‑generation (5G) non‑terrestrial network (NTN) enabled by high‑altitude platform station (HAPS)‑based base stations (BSs) equipped with tri‑sectoral reflector antennas against fourth‑generation (4G) terrestrial network (TN) and 5G TN deployments in a multicell dense urban environment. Using the simulation results comprising the average effective downlink signal‑to‑interference‑plus‑noise ratio (SINR) and the average user throughput, along with the subsequent interference analysis, we demonstrate that the reflector‑based HAPS architecture is primarily constrained by inter‑cell interference, while the combination of reflector configuration and deployment altitude represents a key design parameter.

Authors:Seungyeop Lee, Jong-Han Kim
Title: Collision-Free Velocity Scheduling for Multi-Agent Systems on Predefined Routes via Inexact-Projection ADMM
Abstract:
In structured multi‑agent transportation systems, agents often must follow predefined routes, making spatial rerouting undesirable or impossible. This paper addresses route‑constrained multi‑agent coordination by optimizing waypoint passage times while preserving each agent's assigned waypoint order and nominal route assignment. A differentiable surrogate trajectory model maps waypoint timings to smooth position profiles and captures first‑order tracking lag, enabling pairwise safety to be encoded through distance‑based penalties evaluated on a dense temporal grid spanning the mission horizon. The resulting nonlinear and nonconvex velocity‑scheduling problem is solved using an inexact‑projection Alternating Direction Method of Multipliers (ADMM) algorithm that combines structured timing updates with gradient‑based collision‑correction steps and avoids explicit integer sequencing variables. Numerical experiments on random‑crossing, bottleneck, and graph‑based network scenarios show that the proposed method computes feasible and time‑efficient schedules across a range of congestion levels and yields shorter mission completion times than a representative hierarchical baseline in the tested bottleneck cases.

Authors:Jesper B. Christensen, Ciaran Bench, Spencer A. Thomas, Hüsnü Aslan, David Balslev-Harder, Nadia A. S. Smith, Alessandra Manzin
Title: Ctrl-A: Control-Driven Online Data Augmentation
Abstract:
We introduce ControlAugment (Ctrl‑A), an automated data augmentation algorithm for image‑vision tasks, which incorporates principles from control theory for online adjustment of augmentation strength distributions during model training. Ctrl‑A eliminates the need for initialization of individual augmentation strengths. Instead, augmentation strength distributions are dynamically, and individually, adapted during training based on a control‑loop architecture and what we define as relative operation response curves. Using an operation‑dependent update procedure provides Ctrl‑A with the potential to suppress augmentation styles that negatively impact model performance, alleviating the need for manually engineering augmentation policies for new image‑vision tasks. Experiments on the CIFAR‑10, CIFAR‑100, and SVHN‑core benchmark datasets using the common WideResNet‑28‑10 architecture demonstrate that Ctrl‑A is highly competitive with existing state‑of‑the‑art data augmentation strategies.

Authors:Turki Bin Mohaya, Peter Seiler
Title: Partial Attention in Deep Reinforcement Learning for Safe Multi-Agent Control
Abstract:
Attention mechanisms excel at learning sequential patterns by discriminating data based on relevance and importance. This provides state‑of‑the‑art performance in advanced generative artificial intelligence models. This paper applies this concept of an attention mechanism for multi‑agent safe control. We specifically consider the design of a neural network to control autonomous vehicles in a highway merging scenario. The environment is modeled as a Decentralized Partially Observable Markov Decision Process (Dec‑POMDP). Within a QMIX framework, we include partial attention for each autonomous vehicle, thus allowing each ego vehicle to focus on the most relevant neighboring vehicles. Moreover, we propose a comprehensive reward signal that considers the global objectives of the environment (e.g., safety and vehicle flow) and the individual interests of each agent. Simulations are conducted in the Simulation of Urban Mobility (SUMO). The results show better performance compared to other driving algorithms in terms of safety, driving speed, and reward.

Authors:Longyu Zhou, Supeng Leng, Tianhao Liang, Jianping Yao
Title: LSAI: A Large Small AI Model Codesign Framework for Agentic Robot Scenarios
Abstract:
The development of Artificial Intelligence (AI) has enabled agentic robots an appealing paradigm for various applications, such as research and rescue in complex environment. In this context, the next wireless communication technology facilitates robot cooperation for efficient environment sensing and exploration. However, traditional AI solutions cannot always provide reasonable resource utilization decisions, which makes it challenging to achieve both accurate and low‑latency research and rescue. To address this issue, we propose a, LSAI, a large small AI model codesign framework to achieve highly accurate and real‑time robot cooperation with deep interaction between large AI model and small AI model. We first propose an attention‑based model aggregation for LAI construction. It can assist agentic robots in accurately sensing physical environments. Next, we design an adaptive model splitting and update algorithm to enable the robots to perform accurate path planning for high‑efficiency environment sensing with low energy consumption. Finally, we demonstrate the effectiveness of our proposed LSAI framework. The simulation results indicate that our solution achieves sensing accuracy of up to 20.4% while reducing sensing cooperation latency by an average of 17.9% compared to traditional AI solutions.

Authors:Mogens Plessen
Title: Simple Trajectory Smoothing for UAV Reference Path Planning Based on Decoupling, Spatial Modeling and Linear Programming
Abstract:
A method for trajectory smoothing for UAV reference path planning is presented. It is derived based on the dynamics of a Dubins airplane model, and involves a decoupling step, spatial modeling and linear programming. The decoupling step enables algebraic control laws for flight‑path angle and speed control. Only for roll angle control an optimization step is applied, involving the solution of a small linear program. Two variations are discussed. They differ by reference centerline tracking and the introduction of a path shaping constraint. The benefit of natural dimensionality reduction for spatial modeling is discussed. The simplicity of the overall method is highlighted. An extension to aerobatic flight is outlined, which comes at the cost of a model approximation, however at the gain of maintaining the general model structure. An extension of the method to tractor path planning along 3D terrain is discussed. The method is validated in simulations.

Authors:Daniyaer Paizulamu, Lin Cheng, Ning Qi, Zhengmao Li, Nikos D. Hatziargyriou
Title: Full Timescale Hierarchical MPC-MTIP Framework for Hybrid Energy Storage Management in Low-Carbon Industrial Microgrid
Abstract:
Uncertainties in balancing generation and load in low‑carbon industrial microgrids (IMGs) make hybrid energy storage systems (HESS) crucial for their stable and economic operation. Existing model predictive control (MPC) techniques typically enforce periodic state of charge (SOC) constraints to maintain long term stability. However, these hard constraints compromise dispatch flexibility near the end of the prediction horizon, preventing sufficient energy release during critical peaks and leading to optimization infeasibility. This paper eliminates the periodic SOC constraints of individual storage units and proposes a novel full‑timescale hierarchical MPC scheduling framework. Specifically, comprehensive physical and cost models are established for the HESS composed of flywheel, battery, compressed‑air, and hydrogen‑methanol energy storage. The control problem is decoupled into a hierarchical MPC architecture. Furthermore, a novel adaptive feedback mechanism based on micro trajectory inverse projection (MTIP) is embedded into the scheduling process, accurately mapping the high frequency dynamic buffering capabilities of lower tier storages into the upper decision space to generate dynamic boundaries. Experiments using 14 consecutive months of second‑level data from a real‑world IMG validate the effectiveness of the proposed method, demonstrating its significant superiority over existing approaches. By effectively preventing limit violations and deadlocks in lower‑tier storages under extreme fluctuations, it achieves a 97.4% net load smoothing rate and a 62.2% comprehensive cycle efficiency.

Authors:Evanns Morales-Cuadrado, Long Kiu Chung, Shreyas Kousik, Samuel Coogan
Title: RTD-RAX: Fast, Safe Trajectory Planning for Systems under Unknown Disturbances
Abstract:
Reachability‑based Trajectory Design (RTD) is a provably safe, real‑time trajectory planning framework that combines offline reachable‑set computation with online trajectory optimization. However, standard RTD implementations suffer from two key limitations: conservatism induced by worst‑case reachable‑set overapproximations, and an inability to account for real‑time disturbances during execution. This paper presents RTD‑RAX, a runtime‑assurance extension of RTD that utilizes a non‑conservative RTD formulation to rapidly generate goal‑directed candidate trajectories, and utilizes mixed monotone reachability for fast, disturbance‑aware online safety certification. When proposed trajectories fail safety certification under real‑time uncertainty, a repair procedure finds nearby safe trajectories that preserve progress toward the goal while guaranteeing safety under real‑time disturbances.

Authors:Che Chen, Lanhua Li, Shimin Gong, Yu Zhao, Yuming Fang, Dusit Niyato
Title: Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications
Abstract:
In this paper, we employ multiple UAVs to accelerate data transmissions from ground users (GUs) to a remote base station (BS) via the UAVs' relay communications. The UAVs' intermittent information exchanges typically result in delays in acquiring the complete system state and hinder their effective collaboration. To maximize the overall throughput, we first propose a delay‑tolerant multi‑agent deep reinforcement learning (MADRL) algorithm that integrates a delay‑penalized reward to encourage information sharing among UAVs, while jointly optimizing the UAVs' trajectory planning, network formation, and transmission control strategies. Additionally, considering information loss due to unreliable channel conditions, we further propose a spatio‑temporal attention based prediction approach to recover the lost information and enhance each UAV's awareness of the network state. These two designs are envisioned to enhance the network capacity in UAV‑assisted wireless networks with limited communications. The simulation results reveal that our new approach achieves over 50% reduction in information delay and 75% throughput gain compared to the conventional MADRL. Interestingly, it is shown that improving the UAVs' information sharing will not sacrifice the network capacity. Instead, it significantly improves the learning performance and throughput simultaneously. It is also effective in reducing the need for UAVs' information exchange and thus fostering practical deployment of MADRL in UAV‑assisted wireless networks.

Authors:Koki Hirano, Hiroyasu Tsukamoto
Title: Conformal Koopman for Embedded Nonlinear Control with Statistical Robustness: Theory and Real-World Validation
Abstract:
We propose a fully data‑driven, Koopman‑based framework for statistically robust control of discrete‑time nonlinear systems with linear embeddings. Establishing a connection between the Koopman operator and contraction theory, it offers distribution‑free probabilistic bounds on the state tracking error under Koopman modeling uncertainty. Conformal prediction is employed here to rigorously derive a bound on the state‑dependent modeling uncertainty throughout the trajectory, ensuring safety and robustness without assuming a specific error prediction structure or distribution. Unlike prior approaches that merely combine conformal prediction with Koopman‑based control in an open‑loop setting, our method establishes a closed‑loop control architecture with formal guarantees that explicitly account for both forward and inverse modeling errors. Also, by expressing the tracking error bound in terms of the control parameters and the modeling errors, our framework offers a quantitative means to formally enhance the performance of arbitrary Koopman‑based control. We validate our method both in numerical simulations with the Dubins car and in real‑world experiments with a highly nonlinear flapping‑wing drone. The results demonstrate that our method indeed provides formal safety guarantees while maintaining accurate tracking performance under Koopman modeling uncertainty.

Authors:Jiachen Li, Soovadeep Bakshi, Jian Chu, Shihao Li, Dongmei Chen
Title: Auction-Based Task Allocation with Energy-Conscientious Trajectory Optimization for AMR Fleets
Abstract:
This paper presents a hierarchical two‑stage framework for multi‑robot task allocation and trajectory optimization in asymmetric task spaces: (1) a sequential auction allocates tasks using closed‑form bid functions, and (2) each robot independently solves an optimal control problem for energy‑minimal trajectories with a physics‑based battery model, followed by a collision avoidance refinement step using pairwise proximity penalties. Event‑triggered warm‑start rescheduling with bounded trigger frequency handles robot faults, priority arrivals, and energy deviations. Across 505 scenarios with 2‑20 robots and up to 100 tasks on three factory layouts, both energy‑ and distance‑based auction variants achieve 11.8% average energy savings over nearest‑task allocation, with rescheduling latency under 10 ms. The central finding is that bid‑metric performance is regime‑dependent: in uniform workspaces, distance bids outperform energy bids by 3.5% (p < 0.05, Wilcoxon) because a 15.7% closed‑form approximation error degrades bid ranking accuracy to 87%; however, when workspace friction heterogeneity is sufficient (r < 0.85 energy‑distance correlation), a zone‑aware energy bid outperforms distance bids by 2‑2.4%. These results provide practitioner guidance: use distance bids in near‑uniform terrain and energy‑aware bids when friction variation is significant.

Authors:Jiachen Li, Shihao Li, Soovadeep Bakshi, Jiamin Xu, Dongmei Chen
Title: IF-CPS: Influence Functions for Cyber-Physical Systems -- A Unified Framework for Diagnosis, Curation, and Safety Attribution
Abstract:
Neural network controllers trained via behavior cloning are increasingly deployed in cyber‑physical systems (CPS), yet practitioners lack tools to trace controller failures back to training data. Existing data attribution methods assume i.i.d.\ data and standard loss targets, ignoring CPS‑specific properties: closed‑loop dynamics, safety constraints, and temporal trajectory structure. We propose IF‑CPS, a modular influence function framework with three CPS‑adapted variants: safety influence (attributing constraint violations), trajectory influence (temporal discounting over trajectories), and propagated influence (tracing effects through plant dynamics). We evaluate IF‑CPS on six benchmarks across diagnosis, curation, and safety attribution tasks. IF‑CPS improves over standard influence functions in the majority of settings, achieving AUROC 1.00 in Pendulum (5‑10% poisoning), 0.92 vs.\ 0.50 in HVAC (10%), and the strongest constraint‑boundary correlation (Spearman ρ= 0.55 in Pendulum).

Authors:Jiachen Li, Shihao Li, Soovadeep Bakshi, Jiamin Xu, Dongmei Chen
Title: Stochastic Trajectory Influence Functions for LQR: Joint Sensitivity Through Dynamics and Noise Covariance
Abstract:
Model‑based controllers learned from data have the biases and noise of their training trajectories, making it important to know which trajectories help or hurt closed‑loop performance. Influence functions, widely used in machine learning for data attribution, approximate this effect through first‑order parameter‑shift surrogates, avoiding costly retraining. Applying them to stochastic LQR, however, is nontrivial because the cost depends on the learned dynamics through the Riccati equation, and the process‑noise covariance is estimated from the same residuals. We develop a three‑level influence hierarchy that accounts for both channels.

Authors:Qirui Zheng, Dan Wu, Franz-Erich Wolter, Sijia Geng
Title: Evaluating Power Flow Manifold from Local Data around a Single Operating Point via Geodesics
Abstract:
The widespread adoption of renewable energy poses a challenge in maintaining a feasible operating point in highly variable scenarios. This paper demonstrates that, within a feasible region of a power system that meets practical stability requirements, the power flow equations define a smooth bijection between nodal voltage phasors (angle and magnitude) and nodal active/reactive power injections. Based on this theoretical foundation, this paper proposes a data‑based power flow evaluation method that can imply the associated power flow manifold from a limited number of data points around a single operating point. Using techniques from differential geometry and analytic functions, we represent geodesic curves in the associated power flow manifold as analytic functions at the initial point. Then, a special algebraic structure of the power flow problem is revealed and applied to reduce the computation of all higher‑order partial derivatives to that of the first‑order ones. Integrating these techniques yields the proposed data‑based evaluation method, suggesting that a small number of local measurements around a single operating point is sufficient to imply the entire associated power flow manifold. Numerical cases with arbitrary directional variations are tested, certifying the efficacy of the proposed method.

Authors:Junyi Ji, Derek Gloudemans, Gergely Zachár, William Barbour, Jonathan Sprinkle, Benedetto Piccoli, Daniel B. Work
Title: Emission reduction potential of freeway stop-and-go wave smoothing
Abstract:
Real‑world potential of stop‑and‑go wave smoothing at scale remains largely unquantified. Smoothing freeway traffic waves requires creating a gap so the wave can dissipate, but the gap suggested is often too large and impractical. We propose a counterfactual wave smoothing benchmark that reconstructs a smooth and feasible trajectory from each empirical trajectory by solving a quadratic program with fixed boundary conditions and a maximum allowable gap constraint. We estimate the emission reduction potential from trajectories using the MOVES model. Applying the framework to nine weeks of weekday peak traffic data, featuring rich day‑to‑day stop‑and‑go wave dynamics, from the I‑24 MOTION testbed, we find meaningful reduction potential under a 0.1‑mile maximum gap: average CO2 reductions of 7.92% to 12.04% across lanes, with concurrent reductions of 14.30% to 28.91% CO, 23.15% to 29.42% HC, and 24.37% to 30.98% NOx. Our analysis also quantifies the trade‑off between maximum allowable gap opening and emissions benefits.

Authors:Zongqi Hu, Weiqi Meng, Bai Cui
Title: Unified Sensitivity-Based Heuristic for Optimal Line Switching and Substation Reconfiguration
Abstract:
Optimal transmission switching (OTS) determines which transmission lines to remove from service to minimize dispatch costs. Unlike topology design, it alters the operational status of operating lines. Sensitivity‑based methods, as advanced optimization techniques, select lines whose outage yields a significant cost reduction. However, these methods overlook bus splitting, an effective congestion management strategy that our work incorporates to achieve improved economic gains. In this work, we formulate an optimal transmission reconfiguration (OTR) problem that incorporates both line switching and bus splitting. We develop a novel approach to quantify the sensitivity of the OTR objective to line switching and bus splitting, establish connections between the proposed sensitivity framework and existing heuristic metrics, prove the equivalence between bus splitting and a generalized line switching to enable unified treatment, and provide a simpler derivation of Bus Split Distribution Factor (BSDF). Simulations on nine IEEE test systems spanning 118 to 13,659 buses demonstrate the high effectiveness of our proposed sensitivity method. They also demonstrate that incorporating bus splitting into transmission reconfiguration achieves greater cost savings than line switching alone. The results confirm the economic advantage of this comprehensive approach to transmission system operation.

Authors:Régulo E. Ávila-Martínez, Luis Rouco, Javier Renedo, Lukas Sigrist, Aurelio Garcia-Cerrada
Title: Active-power control strategies in grid-forming power converters to improve transient stability in power systems with 100% converter-based generation
Abstract:
Grid‑forming voltage source converters (GFM‑VSCs) play a crucial role in the stability of power systems with large amounts of converter‑based generation. Transient stability (angle stability under large disturbances) is a critical limiting factor in stressed power systems. Previous studies have proposed control strategies in GFM‑VSCs to improve transient stability. These approaches typically rely on suitable current‑limiting algorithms, voltage/reactive‑power and active‑power supplementary control strategies. This paper investigates and compares the effectiveness of three active‑power control strategies in GFM‑VSCs to enhance transient stability in power systems with 100 % converter‑based generation: (i) a wide‑area control strategy (TSP‑WACS) using the centre of inertia (COI) frequency, (ii) a local transient damping method (TSP‑TDM), and (iii) a novel local control strategy (TSP‑L) proposed in this work. All strategies were implemented and assessed using short‑circuit simulations on Kundur two‑area test system with 100 % GFM‑VSC generators, demonstrating critical clearing time (CCT) improvement. The TSP‑WACS strategy achieves the best performance but requires a communication infrastructure, while TSP‑L strategy offers a simple‑but‑robust alternative using local measurements, only.

Authors:Arno Strouwen, Bart M. Nicolaï, Peter Goos
Title: Adaptive and robust experimental design for linear dynamical models using Kalman filter
Abstract:
Current experimental design techniques for dynamical systems often only incorporate measurement noise, while dynamical systems also involve process noise. To construct experimental designs we need to quantify their information content. The Fisher information matrix is a popular tool to do so. Calculating the Fisher information matrix for linear dynamical systems with both process and measurement noise involves estimating the uncertain dynamical states using a Kalman filter. The Fisher information matrix, however, depends on the true but unknown model parameters. In this paper we combine two methods to solve this issue and develop a robust experimental design methodology. First, Bayesian experimental design averages the Fisher information matrix over a prior distribution of possible model parameter values. Second, adaptive experimental design allows for this information to be updated as measurements are being gathered. This updated information is then used to adapt the remainder of the design.

Authors:Prasannaa Kumar D., Gulshan Kumar, Jay Dhariwal, Seshan Srirangarajan
Title: Design and Development of Low-Cost Datalogger for Indoor and Outdoor Air Quality Monitoring
Abstract:
The rising demand for low‑cost air quality monitors stems from increased public awareness and interest within the research community. These monitors play a pivotal role in empowering citizens and scientists to comprehend spatiotemporal variations in air quality parameters, aiding in the formulation of effective mitigation policies. The primary challenge lies in the diverse array of application scenarios these monitors encounter. The developed data logging device is exceptionally well‑suited for air quality monitoring applications, offering exceptional versatility by seamlessly operating on a range of power sources, including solar energy, batteries, and direct electrical supply. The integration of a built‑in battery charger enhances its applicability for deployment in regions with solar power or intermittent electricity availability. To ensure strong network connectivity, the advanced datalogger seamlessly integrates with WiFi, Bluetooth, and LoRaWAN networks. A notable feature is its adaptable MCU system, enabling users to swap the MCU based on specific connectivity, power, and computational requirements. Importantly, the system carefully identifies key parameters crucial for both indoor and outdoor air quality assessment, customizing sensor selection accordingly. Furthermore, optimization efforts have prioritized energy efficiency, enabling the system to function with minimal power consumption while maintaining data integrity. Additional I2C and UART ports facilitate the monitoring of supplementary parameters.

Authors:Tahmin Mahmud
Title: High-Endurance UCAV Propulsion System: A 1-D CNN-Based Real-Time Fault Classification for Tactical-Grade IPMSM Drive
Abstract:
High‑performance propulsion for mission‑critical applications demands unprecedented reliability and real‑time fault resilience. Conventional diagnostic methods (signal‑based analysis and standard ML models) are essential for stator/rotor fault detection but suffer from high latency and poor generalization across variable speeds. This paper proposes a 1‑D Convolutional Neural Network (CNN) framework for real‑time fault classification in the HPDM‑350 interior permanent magnet synchronous motor (IPMSM). The proposed architecture extracts discriminative features directly from high‑frequency current and speed signals, enabling sub‑millisecond inference on embedded controllers. Compared to state‑of‑the‑art long short term memory (LSTM) and classical ML approaches, the 1‑D CNN achieves a superior weighted F1‑score of 0.9834. Validated through high‑fidelity magnetic‑domain MATLAB/Simscape models, the method demonstrates robust performance across a +‑2700 RPM envelope, providing a lightweight solution for mission‑critical electric propulsion systems.

Authors:Tahmin Mahmud
Title: Physics-Infused Neural MPC of a DC-DC Boost Converter with Adaptive Transient Recovery and Enhanced Dynamic Stability
Abstract:
DC‑DC boost converters require advanced control to ensure efficiency and stability under varying loads. Traditional model predictive control (MPC) and data‑driven neural network methods face challenges such as high complexity and limited physical constraint enforcement. This paper proposes a hybrid physics‑informed neural network (PINN) combined with finite control set MPC (FCS‑MPC) for boost converters. The PINN embeds physical laws into neural training, providing accurate state predictions, while FCS‑MPC ensures constraint satisfaction and multi‑objective optimization. The method features adaptive transient recovery, explicit duty‑ratio control, and enhanced dynamic stability. Experimental results on a commercial boost module demonstrate improved transient response, reduced voltage ripple, and robust operation across conduction modes. The proposed framework offers a computationally efficient, physically consistent solution for real‑time control in power electronics.

Authors:Flemming Holtorf, Sungho Shin
Title: Approximate Dynamic Programming for Degradation-aware Market Participation of Battery Energy Storage Systems: Bridging Market and Degradation Timescales
Abstract:
We present an approximate dynamic programming framework for designing degradation‑aware market participation policies for battery energy storage systems. The approach employs a tailored value function approximation that reduces the state space to state of charge and battery health, while performing dynamic programming along a pseudo‑time axis encoded by state of health. This formulation enables an offline/online computation split that separates long‑term degradation dynamics (months to years) from short‑term market dynamics (seconds to minutes) ‑‑ a timescale mismatch that renders conventional predictive control and dynamic programming approaches computationally intractable. The main computational effort occurs offline, where the value function is approximated via coarse‑grained backward induction along the health dimension. Online decisions then reduce to a real‑time tractable one‑step predictive control problem guided by the precomputed value function. This decoupling allows the integration of high‑fidelity physics‑informed degradation models without sacrificing real‑time feasibility. Backtests on historical market data show that the resulting policy outperforms several benchmark strategies with optimized hyperparameters.

Authors:M. Hossein Abedinzadeh, Emrah Akyol
Title: Multidimensional Opinion Dynamics with Confirmation Bias: A Multi-Layer Framework
Abstract:
We study multidimensional opinion dynamics under confirmation bias in social networks. Each agent holds a vector of correlated opinions across multiple topic layers. Peer interaction is modeled through a static, informationally symmetric social channel, while external information enters through a dynamic, informationally asymmetric source channel. Source influence is described by nonnegative state‑dependent functions of agent‑‑source opinion mismatch, which captures confirmation bias without hard thresholds. For general Lipschitz source‑influence functions, we give sufficient conditions under which the dynamics are contractive and converge to a unique steady state independent of the initial condition. For affine confirmation‑bias functions, we show that the steady state can be computed through a finite sign‑consistency search and identify a regime in which it admits a closed form. For broader classes of bounded nonlinear source‑influence functions, we derive explicit lower and upper bounds on the fixed point. Numerical examples and a study on a real‑world adolescent lifestyle network illustrate the role of multidimensional coupling and show that source‑design conclusions can change qualitatively when confirmation bias is ignored.

Authors:Shuo Liu, Liang Wu, Dawei Zhang, Jan Drgona, Calin. A. Belta
Title: Koopman-Based Linear MPC for Safe Control using Control Barrier Functions
Abstract:
This paper proposes a Koopman‑based linear model predictive control (LMPC) framework for safety‑critical control of nonlinear discrete‑time systems. Existing MPC formulations based on discrete‑time control barrier functions (DCBFs) enforce safety through barrier constraints but typically result in computationally demanding nonlinear programming. To address this challenge, we construct a DCBF‑augmented dynamical system and employ Koopman operator theory to lift the nonlinear dynamics into a higher‑dimensional space where both the system dynamics and the barrier function admit a linear predictor representation. This enables the transformation of the nonlinear safety‑constrained MPC problem into a quadratic program (QP). To improve feasibility while preserving safety, a relaxation mechanism with slack variables is introduced for the barrier constraints. The resulting approach combines the modeling capability of Koopman operators with the computational efficiency of QP. Numerical simulations on a navigation task for a robot with nonlinear dynamics demonstrate that the proposed framework achieves safe trajectory generation and efficient real‑time control.

Authors:Birva Sevak, Shrenik Jadhav, Van-Hai Bui
Title: Physics-Informed Graph Neural Jump ODEs for Cascading Failure Prediction in Power Grids
Abstract:
Cascading failures in power grids pose severe risks to infrastructure reliability, yet real‑time prediction of their progression remains an open challenge. Physics‑based simulators require minutes to hours per scenario, while existing graph neural network approaches treat cascading failures as static classification tasks, ignoring temporal evolution and physical laws. This paper proposes Physics‑Informed Graph Neural Jump ODEs (PI‑GN‑JODE), combining an edge‑conditioned graph neural network encoder, a Neural ODE for continuous power redistribution, a jump process handler for discrete relay trips, and Kirchhoff‑based physics regularization. The model simultaneously predicts edge and node failure probabilities, severity classification, and demand not served, while an autoregressive extension enables round‑by‑round temporal cascade prediction. Evaluated on the IEEE 24‑bus and 118‑bus systems with 20,000 scenarios each, PI‑GN‑JODE achieves a Precision‑‑Recall Area Under the Curve of 0.991 for edge failure detection, 0.973 for node failure detection, and a coefficient of determination of 0.951 for demand‑not‑served regression on the 118‑bus system, outperforming a standard graph convolutional network baseline (0.948, 0.925, and 0.912, respectively). Ablation studies reveal that the four components function synergistically, with the physics‑informed loss alone contributing +9.2 points to demand‑not‑served regression. Performance improves when scaling to larger grids, and the architecture achieves the highest balanced accuracy (0.996) on the PowerGraph benchmark using data from a different simulation framework.

Authors:Hongyu Yi, Chenbei Lu, Jing Yu
Title: Achieving $\widetilde{O}(1/ε)$ Sample Complexity for Bilinear Systems Identification under Bounded Noises
Abstract:
This paper studies finite‑sample set‑membership identification for discrete‑time bilinear systems under bounded symmetric log‑concave disturbances. Compared with existing finite‑sample results for linear systems and related analyses under stronger noise assumptions, we consider the more challenging bilinear setting with trajectory‑dependent regressors and allow marginally stable dynamics with polynomial mean‑square state growth. Under these conditions, we prove that the diameter of the feasible parameter set shrinks with sample complexity \widetildeO(1/ε). Simulation supports the theory and illustrates the advantage of the proposed estimator for uncertainty quantification.

Authors:P Sangeerth, Abolfazl Lavaei, Pushpak Jagtap
Title: Towards Certified Sim-to-Real Transfer via Stochastic Simulation-Gap Functions
Abstract:
This paper introduces the notion of stochastic simulation‑gap function, which formally quantifies the gap between an approximate mathematical model and a high‑fidelity stochastic simulator. Since controllers designed for the mathematical model may fail in practice due to unmodeled gaps, the stochastic simulation‑gap function enables the simulator to be interpreted as the nominal model with bounded state‑ and input‑dependent disturbances. We propose a data‑driven approach and establish a formal guarantee on the quantification of this gap. Leveraging the stochastic simulation‑gap function, we design a controller for the mathematical model that ensures the desired specification is satisfied in the high‑fidelity simulator with high confidence, taking a step toward bridging the sim‑to‑real gap. We demonstrate the effectiveness of the proposed method using a TurtleBot model and a pendulum system in stochastic simulators.

Authors:Yanyong Mao, Johanna L. Mathieu, Vladimir Dvorkin
Title: Online Feedback Optimization of Energy Storage to Smooth Data Center Grid Impacts
Abstract:
The growing electricity demand of AI data centers introduces significant voltage variability in power networks, affecting not only their own operation but also the experience of all users sharing the network. To smooth data center impacts on power networks, we develop an online feedback optimization approach that controls distributed battery energy storage systems to mitigate voltage issues induced by data center operations. The controller adjusts the active and reactive power setpoints of distributed battery systems in response to voltage measurements, with a two‑fold objective: managing voltage to minimize the magnitude of constraint violations and smoothing voltage profiles. Control performance is evaluated in a high‑fidelity simulation environment that integrates a three‑phase distribution feeder and a detailed battery system model, and benchmarked against a local control approach with similar objectives but without optimality guarantees and constraint enforcement. We show that the proposed controller delivers consistent voltage regulation in the long term, while the local control approach pursues the objectives more aggressively but quickly hits the storage limits.

Authors:Mustafa Mohammed Hasan Alkalsh, Adam Samorzewski, Adrian Kliks
Title: Sustainable Load Balancing for Wireless Networks With Renewable Energy Sources
Abstract:
Future wireless networks powered by renewable energy sources and storage systems (e.g., batteries) require energy‑aware mechanisms to ensure stability in critical and high‑demand scenarios. These include large‑scale user gatherings, especially during evening hours when solar generation is unavailable, and days with poor wind conditions that limit the effectiveness of wind‑based energy harvesting. Maintaining network performance under such constraints, while preserving stored energy, remains a key challenge. This work proposes an enhanced Proactive‑Reactive Load Balancing algorithm that integrates energy conditions into mobility management. By leveraging standardized mobility events, the algorithm optimizes traffic distribution and energy utilization (avoiding complete drainage of stored energy), thereby preventing service degradation. Simulations show improved energy sustainability and network performance under congestion and limited solar availability.

Authors:Bowen Li, Edwin K. P. Chong, Ali Pezeshki
Title: Performance Guarantees for Data-Driven Sequential Decision-Making
Abstract:
The solutions to many sequential decision‑making problems are characterized by dynamic programming and Bellman's principle of optimality. However, due to the inherent complexity of solving Bellman's equation exactly, there has been significant interest in developing various approximate dynamic programming (ADP) schemes to obtain near‑optimal solutions. A fundamental question that arises is: how close are the objective values produced by ADP schemes relative to the true optimal objective values? In this paper, we develop a general framework that provides performance guarantees for ADP schemes in the form of ratio bounds. Specifically, we show that the objective value under an ADP scheme is at least a computable fraction of the optimal value. We further demonstrate the applicability of our theoretical framework through two applications: data‑driven robot path planning and multi‑agent sensor coverage.

Authors:James R. Baxter, Bogdan I. Epureanu, Paramsothy Jayakumar, Tulga Ersal
Title: High-Speed, All-Terrain Autonomy: Ensuring Safety at the Limits of Mobility
Abstract:
A novel local trajectory planner, capable of controlling an autonomous off‑road vehicle on rugged terrain at high‑speed is presented. Autonomous vehicles are currently unable to safely operate off‑road at high‑speed, as current approaches either fail to predict and mitigate rollovers induced by rough terrain or are not real‑time feasible. To address this challenge, a novel model predictive control (MPC) formulation is developed for local trajectory planning. A new dynamics model for off‑road vehicles on rough, non‑planar terrain is derived and used for prediction. Extreme mobility, including tire liftoff without rollover, is safely enabled through a new energy‑based constraint. The formulation is analytically shown to mitigate rollover types ignored by many state‑of‑the‑art methods, and real‑time feasibility is achieved through parallelized GPGPU computation. The planner's ability to provide safe, extreme trajectories is studied through both simulated trials and full‑scale physical experiments. The results demonstrate fewer rollovers and more successes compared to a state‑of‑the‑art baseline across several challenging scenarios that push the vehicle to its mobility limits.

Authors:Jose A. Solano-Castellanos, Hassan Haes Alhelou, Ali T. Al- Awami, Mohannad Alkhraijah, Anuradha M. Annaswamy
Title: A Control Architecture for Fast Frequency Regulation with Increasing Penetration of Inverter Based Resources
Abstract:
This paper addresses frequency regulation under operational constraints in interconnected power systems with high penetration of inverter‑based renewable generation. A two‑layer control architecture is proposed that combines optimized droop and Virtual Synchronous Machine (VSM) primary control with a Model Predictive Control (MPC) secondary layer operating at realistic control‑room update rates. Unlike recent proposed approaches, the proposed framework integrates MPC within existing grid control structures, enabling constraint‑aware coordination. A reduced‑order frequency response model is systematically derived from a high‑fidelity grid model using Hankel singular values, and a reduced‑order Kalman‑Bucy observer enables state and disturbance estimation using only measurable outputs. Validation using representative data from the Kingdom of Saudi Arabia demonstrates effective frequency regulation under realistic operating conditions.

Authors:Le Fang, Wangkun Xu, Fei Teng
Title: Flow-based Polynomial Chaos Expansion for Uncertainty Quantification in Power System Dynamic Simulation
Abstract:
The large‑scale integration of renewable energy sources introduces significant operational uncertainty into power systems. Although Polynomial Chaos Expansion (PCE) provides an efficient tool for uncertainty quantification (UQ) in power system dynamics, its accuracy depends critically on the faithful representation of input uncertainty, an assumption that is oftern violated in practice due to correlated, non‑Gaussian, and otherwise complex data distributions. In contrast to purely data‑driven surrogates that often overlook rigorous input distribution modelling, this paper introduces flow‑based PCE, a unified framework that couples expressive input modelling with efficient uncertainty propagation. Specifically, normalising flows are employed to learn an invertible transport map from a simple base distribution to the empirical joint distribution of uncertain inputs, and this map is then integrated directly into the PCE construction. In addition, the Map Smoothness Index (MSI) is introduced as a new metric to quantify the quality of the learned map, and smoother transformations are shown to yield more accurate PCE surrogates. The proposed Flow‑based PCE framework is validated on benchmark dynamic models, including the IEEE 14‑bus system and the Great Britain transmission system, under a range of uncertainty scenarios.

Authors:Islam M. Tanash, Nuria Gonzalez-Prelcic, Risto Wichman
Title: Performance Analysis of LEO-Terrestrial Systems in Presence of Doppler Effect
Abstract:
In this paper, we present a novel stochastic geometry‑based approach to analyze the effect of residual Doppler shift on orthogonal frequency‑division multiple access (OFDMA) systems in low earth orbit (LEO) satellite‑terrestrial networks. Focusing on multiuser systems employing common Doppler compensation, we analytically formulate the coverage probability by explicitly capturing the loss of OFDMA subcarrier orthogonality caused by geometry‑induced residual Doppler through inter‑carrier interference. The analysis accounts for the spatial distribution of ground terminals within the serving satellite's cell and is validated through extensive Monte‑Carlo simulations for both S‑band and Ka‑band settings. The results demonstrate the high accuracy of both the Doppler shift approximation and the derived coverage probability expression, while also highlighting the significant impact of residual Doppler shift, even after compensation, emphasizing the necessity of considering this effect in the design of future satellite networks.

Authors:Hannah Berin-Costain, Harry Wang, Kirsten Morris, Jun Liu
Title: Verifiable Error Bounds for Physics-Informed Neural KKL Observers
Abstract:
This paper proposes a computable state‑estimation error bound for learning‑based Kazantzis‑‑Kravaris/Luenberger (KKL) observers. Recent work learns the KKL transformation map with a physics‑informed neural network (PINN) and a corresponding left‑inverse map with a conventional neural network. However, no computable state‑estimation error bounds are currently available for this approach. We derive a state‑estimation error bound that depends only on quantities that can be certified over a prescribed region using neural network verification. We further extend the result to bounded additive measurement noise and demonstrate the guarantees on nonlinear benchmark systems.

Authors:Saba Rafiei, Samuel Chevalier
Title: Activate the Dual Cones: A Tight Reformulation of Conic ACOPF Constraints
Abstract:
By exploiting the observed tightness of dual rotated second‑order cone (RSOC) constraints, this paper transforms the dual of a conic ACOPF relaxation into an equivalent, non‑conic problem where dual constraints are implicitly enforced through eliminated dual RSOC variables. To accomplish this, we apply the RSOC‑based Jabr relaxation of ACOPF, pose its dual, and then show that all dual RSOC constraints must be tight (i.e., active) at optimality. We then construct a reduced dual maximization problem with only non‑negativity constraints, avoiding the explicit RSOC inequality constraints. Numerical experiments confirm that the tight formulation recovers the same dual objective values as a mature conic solver (e.g., MOSEK via PowerModels) on various PGLib benchmark test systems (ranging from 3‑ to 1354‑buses). The proposed formulation has useful performance benefits, compared with its conic counterpart, and it allows us to define a bounding function which provides a guaranteed lower bound on system cost. While this paper focuses on demonstrating the correctness and validity of the proposed structural simplification, it lays the groundwork for future GPU‑accelerated first‑order optimization methods which can exploit the unconstrained nature of the proposed formulation.

Authors:Ata Poyraz Turna, Asrin Efe Yorulmaz, Tamer Başar
Title: Meta-Learning for Repeated Bayesian Persuasion
Abstract:
Classical Bayesian persuasion studies how a sender influences receivers through carefully designed signaling policies within a single strategic interaction. In many real‑world environments, such interactions are repeated across multiple games, creating opportunities to exploit structural similarity across tasks. In this work, we introduce Meta‑Persuasion algorithms, establishing the first line of theoretical results for both full‑feedback and bandit‑feedback settings in the Online Bayesian Persuasion (OBP) and Markov Persuasion Process (MPP) frameworks. We show that our proposed meta‑persuasion algorithms achieve provably sharper regret rates under natural notions of task similarity, improving upon the best‑known convergence rates for both OBP and MPP. At the same time, they recover the standard single‑game guarantees when the sequence of games is picked arbitrarily. Finally, we complement our theoretical analysis with numerical experiments that highlight our regret improvements and the benefits of meta‑learning in repeated persuasion environments.

Authors:Leonardo Pedroso, W. P. M. H. Heemels, Pedro Batista
Title: A Unified Family-optimal Solution to Covariance Intersection Problems with Semidefinite Programming
Abstract:
Covariance intersection (CI) methods provide a principled approach to fusing estimates with unknown cross‑correlations by minimizing a worst‑case measure of uncertainty that is consistent with the available information. This paper introduces a generalized CI framework, called overlapping covariance intersection (OCI), which unifies several existing CI formulations within a single optimization‑based framework. This unification enables the characterization of family‑optimal solutions for multiple CI variants, including standard CI and split covariance intersection (SCI), as solutions to a semidefinite program, for which efficient off‑the‑shelf solvers are available. When specialized to the corresponding settings, the proposed family‑optimal solutions recover the state‑of‑the‑art family‑optimal solutions previously reported for CI and SCI. The resulting formulation facilitates the systematic design and real‑time implementation of CI‑based fusion methods in large‑scale distributed estimation problems, such as cooperative localization.

Authors:Lila Perkins, Baosen Zhang
Title: Resource Allocation in Electricity Markets with Budget Constrained Customers
Abstract:
In electricity markets, customers are increasingly constrained by their budgets. A budget constraint for a user is an upper bound on the price multiplied by the quantity. However, since prices are determined by the market equilibrium, the budget constrained welfare maximization problem is difficult to define rigorously and to work with. In this letter, we show that a natural dual‑ascent algorithm converges to a unique competitive equilibrium under budget constraints. Further, this budget‑constrained equilibrium is exactly the solution of a convex welfare maximization problem in which each user's utility is replaced by a modified utility that splices the original utility with a logarithmic function where the budget binds. We also provide an explicit piecewise construction of this modified utility and demonstrate the results on examples with quadratic and square root utility functions.

Authors:Islam Guven, Mehmet Parlak
Title: JCAS-MARL: Joint Communication and Sensing UAV Networks via Resource-Constrained Multi-Agent Reinforcement Learning
Abstract:
Multi‑UAV networks are increasingly deployed for large‑scale inspection and monitoring missions, where operational performance depends on the coordination of sensing reliability, communication quality, and energy constraints. In particular, the rapid increase in overflowing waste bins and illegal dumping sites has created a need for efficient detection of waste hotspots. In this work, we introduce JCAS‑MARL, a resource‑aware multi‑agent reinforcement learning (MARL) framework for joint communication and sensing (JCAS)‑enabled UAV networks. Within this framework, multiple UAVs operate in a shared environment where each agent jointly controls its trajectory and the resource allocation of an OFDM waveform used simultaneously for sensing and communication. Battery consumption, charging behavior, and associated CO_2 emissions are incorporated into the system state to model realistic operational constraints. Information sharing occurs over a dynamic communication graph determined by UAV positions and wireless channel conditions. Waste hotspot detection requires consensus among multiple UAVs to improve reliability. Using this environment, we investigate how MARL policies exploit the sensing‑communication‑energy trade‑off in JCAS‑enabled UAV networks. Simulation results demonstrate that adaptive pilot‑density control learned by the agents can outperform static configurations, particularly in scenarios where sensing accuracy and communication connectivity vary across the environment.

Authors:Chiran Cherian, Simone Servadio
Title: Polynomial Updates for the Unscented Kalman Filter
Abstract:
Most nonlinear filters used in spacecraft navigation are based on a linear approximation of the optimal minimum mean square error estimator. The Unscented Kalman Filter (UKF) handles nonlinear dynamics through a sigma‑point transform, but the resulting state estimate remains a linear function of the measurement. This paper proposes a polynomial approximation of the optimal Bayesian update, leading to a Polynomial Unscented Kalman Filter that retains the structure of the standard UKF but enriches the measurement update with higher‑order (polynomial) terms. To compute the moments required by this polynomial estimator, we employ a Conjugate Unscented Transformation (CUT), which accurately captures higher‑order central moments of the state and measurement. Numerical examples, including Clohessy‑Wiltshire and Circular Restricted 3‑Body dynamics with non‑Gaussian measurement noise, illustrate that the resulting polynomial‑CUT filters improve both state estimation accuracy and covariance consistency when compared with their linear counterparts.

Authors:Tianshu Zhang, Huan Sun
Title: SciNav: A General Agent Framework for Scientific Coding Tasks
Abstract:
Autonomous science agents built on large language models (LLMs) are increasingly used to generate hypotheses, design experiments, and produce reports. However, prior work mainly targets open‑ended scientific problems with subjective outputs that are difficult to evaluate. Scientific coding benchmarks, by contrast, provide executable outputs for objective assessment. Existing approaches remain engineering‑driven pipelines, revealing the need for structured, end‑to‑end science agent frameworks for scientific coding tasks. We address this gap by focusing on scientific coding tasks, where evaluation can be made rigorously, and introducing an agent framework SciNav (Scientific Navigator) that enables more effective solution exploration. Our framework is designed to operate under constrained search budgets, moving beyond reliance on pre‑defined success metrics and prolonged search cycles. Inspired by findings that comparative judgments often reveal finer‑grained quality differences and therefore provide greater discriminative power than absolute scoring, our framework leverages pairwise relative judgments within a tree search process to select top‑K promising solution branches, prune low‑potential ones, and progressively narrow down the solution candidates on the selected branches guided by relative comparisons. We demonstrate our agent's effectiveness across different types of tasks on two benchmarks. Experiments show that SciNav significantly outperforms direct prompting and prior agents like OpenHands and Self‑Debug across different base models, task types, and difficulty levels, and exceeds different frontier comparators such as random selection and LLM absolute scoring. These results confirm the strength of our agent design and highlight the effectiveness of relative judgment‑guided top‑K search for high‑quality scientific coding, marking a step toward more practical science agents.

Authors:Yanxin Si, Bayu Jayawardhana, J. Nathan Kutz, Yunpeng Zhu, Liangliang Cheng
Title: Experimental Modal Analysis for engineering structures via time-delay Dynamic Mode Decomposition with Control
Abstract:
Experimental Modal Analysis (EMA) has been widely used to identify structural dynamic properties, including natural frequencies, damping ratios, and mode shapes, for structural integrity assessment. The Poly‑reference Least Squares Complex Frequency (pLSCF) method is one of the most widely adopted approaches for EMA because of its strong ability to separate closely spaced modes and its robustness to measurement noise. However, pLSCF‑based EMA is generally limited to low‑dimensional cases with a small number of measurement points, as its computational cost increases rapidly for high‑dimensional or continuous structural measurements, particularly with increasing model order. To overcome this limitation, this paper develops a high‑dimensional EMA framework based on Dynamic Mode Decomposition with control (DMDc), a powerful data‑driven technique originally developed in fluid dynamics, for modal identification under high‑dimensional measurement scenarios. Specifically, the relationship between pLSCF and time‑delay DMDc is clarified through the discrete state‑space representation of the auto‑regressive with exogenous inputs (ARX) model for linear systems. By showing that both methods describe the same physical dynamics of the structure, this study provides a physics‑based rationale for applying time‑delay DMDc to EMA. The capability and advantages of time‑delay DMDc for modal parameter identification in both low‑ and high‑dimensional measurements are validated through numerical simulations of a 6‑DOF system and experiments on a cantilever beam using a digital camera. The results demonstrate that time‑delay DMDc enables robust and reliable modal parameter identification, effectively addressing high‑dimensional EMA problems that are difficult for conventional pLSCF and highlighting its potential for real‑world structural dynamics applications.

Authors:Liangshun Wu, Jianbo Du, Junsuo Qu
Title: Joint Trajectory, RIS, and Computation Offloading Optimization via Decentralized Model-Based PPO in Urban Multi-UAV Mobile Edge Computing
Abstract:
Efficient computation offloading in multi‑UAV edge networks becomes particularly challenging in dense urban areas, where line‑of‑sight (LoS) links are frequently blocked and user demand varies rapidly. Reconfigurable intelligent surfaces (RISs) can mitigate blockage by creating controllable reflected links, but realizing their potential requires tightly coupled decisions on UAV trajectories, offloading schedules, and RIS phase configurations. This joint optimization is hard to solve in practice because multiple UAVs must coordinate under limited information exchange, and purely model‑free multi‑agent reinforcement learning (MARL) often learns too slowly in highly dynamic environments. To address these challenges, we propose a decentralized model‑based MARL framework. Each UAV optimizes mobility and offloading using observations from several hop neighbors, and submits an RIS phase proposal that is aggregated by a lightweight RIS controller. To boost sample efficiency and stability, agents learn local dynamics models and perform short horizon branched rollouts for proximal policy optimization (PPO) updates. Simulations show near centralized performance with improved throughput and energy efficiency at scale.

Authors:Anqi Dong, Yongxin Chen, Karl H. Johansson, Johan Karlsson
Title: Learning Sampled-data Control for Swarms via MeanFlow
Abstract:
Steering large‑scale swarms with only limited control updates is often needed due to communication or computational constraints, yet most learning‑based approaches do not account for this and instead model instantaneous velocity fields. As a result, the natural object for decision making is a finite‑window control quantity rather than an infinitesimal one. To address this gap, we consider the recent machine learning framework MeanFlow and generalize it to the setting with general linear dynamic systems. This results in a new sampled‑data learning framework that operates directly in control space and that can be applied for swarm steering. To this end, we learn the finite‑horizon coefficient that parameterizes the minimum‑energy control applied over each interval, and derive a differential identity that connects this quantity to a local bridge‑induced supervision signal. This identity leads to a simple stop‑gradient regression objective, allowing the interval coefficient field to be learned efficiently from bridge samples. The learned policy is deployed through sampled‑data updates, guaranteeing that the resulting controller exactly respects the prescribed linear time‑invariant dynamics and actuation channel. The resulting method enables few‑step swarm steering at scale, while remaining consistent with the finite‑window actuation structure of the underlying control system.

Authors:Mandana Mohammadi Looey, Amrita Basak, Satadru Dey
Title: Robust Linear Quadratic Optimal Control of Cementitious Material Extrusion
Abstract:
Extrusion‑based 3D printing of cementitious materials enables fabrication of complex structures, however it is highly sensitive to disturbances, material property variations, and process uncertainties that decrease flow stability and dimensional fidelity. To address these challenges, this study proposes a robust linear quadratic optimal control framework for regulating material extrusion in cementitious direct ink writing systems. The printer is modeled using two coupled subsystems: an actuation system representing nozzle flow dynamics and a printing system describing the printed strand flow on the build plate. A hybrid control architecture combining sliding mode control for disturbance rejection with linear quadratic optimal feedback for energy‑efficient tracking is developed to ensure robustness and optimality. In simulation case studies, the control architecture guarantees acceptable convergence of nozzle and strand flow tracking errors under bounded disturbances.

Authors:H. Sinan Bank, Daniel R. Herber, Thomas H. Bradley
Title: Design-OS: A Specification-Driven Framework for Engineering System Design with a Control-Systems Design Case
Abstract:
Engineering system design ‑‑ whether mechatronic, control, or embedded ‑‑ often proceeds in an ad hoc manner, with requirements left implicit and traceability from intent to parameters largely absent. Existing specification‑driven and systematic design methods mostly target software, and AI‑assisted tools tend to enter the workflow at solution generation rather than at problem framing. Human‑‑AI collaboration in the design of physical systems remains underexplored. This paper presents Design‑OS, a lightweight, specification‑driven workflow for engineering system design organized in five stages: concept definition, literature survey, conceptual design, requirements definition, and design definition. Specifications serve as the shared contract between human designers and AI agents; each stage produces structured artifacts that maintain traceability and support agent‑augmented execution. We position Design‑OS relative to requirements‑driven design, systematic design frameworks, and AI‑assisted design pipelines, and demonstrate it on a control systems design case using two rotary inverted pendulum platforms ‑‑ an open‑source SimpleFOC reaction wheel and a commercial Quanser Furuta pendulum ‑‑ showing how the same specification‑driven workflow accommodates fundamentally different implementations. A blank template and the full design‑case artifacts are shared in a public repository to support reproducibility and reuse. The workflow makes the design process visible and auditable, and extends specification‑driven orchestration of AI from software to physical engineering system design.

Authors:Marc Seidel, Martina Maggio, Frank Allgöwer
Title: A Controller Synthesis Framework for Weakly-Hard Control Systems
Abstract:
Deadline misses are more common in real‑world systems than one may expect. The weakly‑hard task model has become a standard abstraction to describe and analyze how often these misses occur, and has been especially used in control applications. Most existing control approaches check whether a controller manages to stabilize the system it controls when its implementation occasionally misses deadlines. However, they usually do not incorporate deadline‑overrun knowledge during the controller synthesis process. In this paper, we present a framework that explicitly integrates weakly‑hard constraints into the control design. Our method supports various overrun handling strategies and guarantees stability and performance under weakly‑hard constraints. We validate the synthesized controllers on a Furuta pendulum, a representative control benchmark. The results show that constraint‑aware controllers significantly outperform traditional designs, demonstrating the benefits of proactive and informed synthesis for overrun‑aware real‑time control.

Authors:Ruixuan Zhao, Guitao Yang, James Fleming, Boli Chen
Title: Distributed State Estimation for Discrete-time LTI Systems: the Design Trilemma and a Novel Framework
Abstract:
With the advancement of IoT technologies and the rapid expansion of cyber‑physical systems, there is increasing interest in distributed state estimation, where multiple sensors collaboratively monitor large‑scale dynamic systems. Compared with its continuous‑time counterpart, a discrete‑time distributed observer faces greater challenges, as it cannot exploit high‑gain mechanisms or instantaneous communication. Existing approaches depend on three tightly coupled factors: (i) system observability, (ii) communication frequency and dimension of the exchanged information, and (iii) network connectivity. However, the interdependence among these factors remains underexplored. This paper identifies a fundamental trilemma among these factors and introduces a general design framework that balances them through an iterative semidefinite programming approach. As such, the proposed method mitigates the restrictive assumptions present in existing works. The effectiveness and generality of the proposed approach are demonstrated through a simulation example.

Authors:Ravish Gupta, Saket Kumar, Shreeya Sharma, Maulik Dang, Abhishek Aggarwal
Title: An Agentic Multi-Agent Architecture for Cybersecurity Risk Management
Abstract:
Getting a real cybersecurity risk assessment for a small organization is expensive ‑‑ a NIST CSF‑aligned engagement runs 15,000 on the low end, takes weeks, and depends on practitioners who are genuinely scarce. Most small companies skip it entirely. We built a six‑agent AI system where each agent handles one analytical stage: profiling the organization, mapping assets, analyzing threats, evaluating controls, scoring risks, and generating recommendations. Agents share a persistent context that grows as the assessment proceeds, so later agents build on what earlier ones concluded ‑‑ the mechanism that distinguishes this from standard sequential agent pipelines. We tested it on a 15‑person HIPAA‑covered healthcare company and compared outputs to independent assessments by three CISSP practitioners ‑‑ the system agreed with them 85% of the time on severity classifications, covered 92% of identified risks, and finished in under 15 minutes. We then ran 30 repeated single‑agent assessments across five synthetic but sector‑realistic organizational profiles in healthcare, fintech, manufacturing, retail, and SaaS, comparing a general‑purpose Mistral‑7B against a domain fine‑tuned model. Both completed every run. The fine‑tuned model flagged threats the baseline could not see at all: PHI exposure in healthcare, OT/IIoT vulnerabilities in manufacturing, platform‑specific risks in retail. The full multi‑agent pipeline, however, failed every one of 30 attempts on a Tesla T4 with its 4,096‑token default context window ‑‑ context capacity, not model quality, turned out to be the binding constraint.

Authors:Ipek Kuvvetli, Christofer Sundström, Sogol Kharrazi, Erik Frisk
Title: Grid-Constrained Smart Charging of Large EV Fleets: Comparative Study of Sequential DP and a Full Fleet Solver
Abstract:
This paper presents a comparative optimization framework for smart charging of electrified vehicle fleets. Using heuristic sequential dynamic programming (SeqDP), the framework minimizes electricity costs while adhering to constraints related to the power grid, charging infrastructure, vehicle availability, and simple considerations of battery aging. Based on real‑world operational data, the model incorporates discrete energy states, time‑varying tariffs, and state‑of‑charge (SoC) targets to deliver a scalable and cost‑effective solution. Classical DP approach suffers from exponential computational complexity as the problem size increases. This becomes particularly problematic when conducting monthly‑scale analyses aimed at minimizing peak power demand across all vehicles. The extended time horizon, coupled with multi‑state decision‑making, renders exact optimization impractical at larger scales. To address this, a heuristic method is employed to enable systematic aggregation and tractable computation for the Non‑Linear Programming (NLP) problem. Rather than seeking a globally optimal solution, this study focuses on a time‑efficient smart charging strategy that aims to minimize energy cost while flattening the overall power profile. In this context, a sequential heuristic DP approach is proposed. Its performance is evaluated against a full‑fleet solver using Gurobi, a widely used commercial solver in both academia and industry. The proposed algorithm achieves a reduction of the overall cost and peak power by more than 90% compared to uncontrolled schedules. Its relative cost remains within 9% of the optimal values obtained from the full‑fleet solver, and its relative peak‑power deviation stays below 15% for larger fleets.

Authors:Andreas Katsanikakis, Nikolaos Bekiaris-Liberis, Delphine Bresch-Pietri
Title: Predictor-Feedback Stabilization of Linear Switched Systems with State-Dependent Switching and Input Delay
Abstract:
We develop a predictor‑feedback control design for a class of linear systems with state‑dependent switching. The main ingredient of our design is a novel construction of an exact predictor state. Such a construction is possible as for a given, state‑dependent switching rule, an implementable formula for the predictor state can be derived in a way analogous to the case of nonlinear systems with input delay. We establish uniform exponential stability of the corresponding closed‑loop system via a novel construction of multiple Lyapunov functionals, relying on a backstepping transformation that we introduce. We validate our design in simulation considering a switching rule motivated by communication networks.

Authors:Francisco Rego
Title: Steady State Distributed Kalman Filter
Abstract:
This paper addresses the synthesis of an optimal fixed‑gain distributed observer for discrete‑time linear systems over wireless sensor networks. The proposed approach targets the steady‑state estimation regime and computes fixed observer gains offline from the asymptotic error covariance of the global distributed BLUE estimator. Each node then runs a local observer that exchanges only state estimates with its neighbors, without propagating error covariances or performing online information fusion. Under collective observability and strong network connectivity, the resulting distributed observer achieves optimal asymptotic performance among fixed‑gain schemes. In comparison with covariance intersection‑based methods, the proposed design yields strictly lower steady state estimation error covariance while requiring minimal communication. Numerical simulations illustrate the effectiveness of the approach and its advantages in terms of accuracy and implementation simplicity.

Authors:Rudra Prakash, S. Janardhanan, Shaunak Sen
Title: Computational Complexity Analysis of Interval Methods in Solving Uncertain Nonlinear Systems
Abstract:
This paper analyzes the computational complexity of validated interval methods for uncertain nonlinear systems and steady‑state enclosure. Interval analysis produces guaranteed enclosures that account for uncertainty and round‑off, but its adoption is often limited by computational cost in high dimensions. We develop an algorithm‑level worst‑case framework that makes explicit the dependence on the problem dimension n, the initial search region size \mathrmVol(X_0), the target tolerance \varepsilon, and the costs of validated primitives (inclusion‑function evaluation, Jacobian evaluation, and interval linear algebra). Within this framework, we derive worst‑case time and space bounds for interval bisection, subdivision+filter, interval constraint propagation, interval Newton, and interval Krawczyk, and identify dominant cost drivers. We also show that the computation of the determinant and inverse of interval matrices via naive Laplace expansion exhibits factorial growth with increasing matrix dimension, motivating specialized interval linear algebra. We complement the worst‑case bounds with computational results on two application‑motivated biochemical steady‑state models (a Hill‑type regulatory network and an enzyme‑saturation‑based winner‑take‑all circuit) in dimensions n\in\2,5,10\, including instances that process millions of boxes. The resulting analysis and experiments support the practical design of validated solvers for uncertainty‑aware steady‑state screening tasks such as robust operating‑point certification and multistability assessment.

Authors:Joshua Pickard, Xin Mao, Can Chen
Title: Structural Controllability of Large-Scale Hypergraphs
Abstract:
Controlling real‑world networked systems, including ecological, biomedical, and engineered networks that exhibit higher‑order interactions, remains challenging due to inherent nonlinearities and large system scales. Despite extensive studies on graph controllability, the controllability properties of hypergraphs remain largely underdeveloped. Existing results focus primarily on exact controllability, which is often impractical for large‑scale hypergraphs. In this article, we develop a structural controllability framework for hypergraphs by modeling hypergraph dynamics as polynomial dynamical systems. In particular, we extend classical notions of accessibility and dilation from linear graph‑based systems to polynomial hypergraph dynamics and establish a hypergraph‑based criterion under which the topology guarantees satisfaction of classical Lie‑algebraic and Kalman‑type rank conditions for almost all parameter choices. We further derive a topology‑based lower bound on the minimum number of driver nodes required for structural controllability and leverage this bound to design a scalable driver node selection algorithm combining dilation‑aware initialization via maximum matching with greedy accessibility expansion. We demonstrate the effectiveness and scalability of the proposed framework through numerical experiments on hypergraphs with tens to thousands of nodes and higher‑order interactions.

Authors:Patrick Malcolm, Klaus Bogenberger
Title: On the Capacity of Future Lane-Free Urban Infrastructure
Abstract:
In this paper, the potential capacity and spatial efficiency of future autonomous lane‑free traffic in urban environments are explored using a combination of analytical and simulation‑based approaches. For lane‑free roadways, a simple analytical approach is employed, which shows not only that lane‑free traffic offers a higher capacity than lane‑based traffic for the same street width, but also that the relationship between capacity and street width is continuous under lane‑free traffic. To test the potential capacity and properties of lane‑free signal‑free intersections (automated intersection management), two approaches were simulated and compared, including a novel approach which we call OptWULF. This approach uses a multi‑agent conflict‑based search approach with a low‑level planner that uses a combination of optimization and simple window‑based reservation. With these simulations, we confirm the continuous relationship between capacity and street width for intersection scenarios. We also show that OptWULF results in an even utilization of the entire drivable area of the street and intersection area. Furthermore, we show that OptWULF is capable of handling asymmetric demand patterns without any substantial loss in capacity compared to symmetric demand patterns.

Authors:Ondrej Straka, Felipe Giraldo-Grueso, Renato Zanetti
Title: Learning Adaptive Parameter Policies for Nonlinear Bayesian Filtering
Abstract:
For many nonlinear Bayesian state estimation problems, the posterior recursion is not analytically tractable, leading to algorithms that are influenced by numerical approximation errors. These algorithms depend on parameters that affect the approximation's accuracy and computational cost. The parameters include, for example, the number of particles, scaling parameters, and the number of iterations in iterative computations. Typically, these parameters are fixed or adjusted heuristically, although the approximation accuracy can change over time with the local degree of nonlinearity and uncertainty. The approximation errors introduced at a time step propagate through subsequent updates, affecting the accuracy, consistency, and robustness of future estimates. This paper presents adaptive parameter selection in nonlinear Bayesian filtering as a sequential decision‑making problem, where parameters influence not only the immediate estimation outcome but also the future estimates. The decision‑making problem is addressed using reinforcement learning to learn adaptive parameter policies for nonlinear Bayesian filters. Experiments with the unscented Kalman filter and stochastic integration filter demonstrate that the learned policies improve both estimate quality and consistency.

Authors:Nikolas Sofos, Federico Milano
Title: Complex Frequency as Generalized Eigenvalue
Abstract:
This paper shows that the concept of complex frequency, originally introduced to characterize the dynamics of signals with complex values, constitutes a generalization of eigenvalues when applied to the states of linear time‑invariant (LTI) systems. Starting from the definition of geometric frequency, which provides a geometrical interpretation of frequency in electric circuits that admits a natural decomposition into symmetric and antisymmetric components associated with amplitude variation and rotational motion, respectively, we show that complex frequency arises as its restriction to the two‑dimensional Euclidean plane. For LTI systems, it is shown that the complex frequencies computed from the system's states subject to a non‑isometric transformation, coincide with the original system's eigenvalues. This equivalence is demonstrated for diagonalizable systems of any order. The paper provides a unified geometric interpretation of eigenvalues, bridging classical linear system theory with differential geometry of curves. The paper also highlights that this equivalence does not generally hold for nonlinear systems. On the other hand, the geometric frequency of the system can always be defined, providing a geometrical interpretation of the system flow. A variety of examples based on linear and nonlinear circuits illustrate the proposed framework.

Authors:Inkyu Jang, Chams E. Mballo, Claire J. Tomlin, H. Jin Kim
Title: A Spectral Perspective on Stochastic Control Barrier Functions
Abstract:
Stochastic control barrier functions (SCBFs) provide a safety‑critical control framework for systems subject to stochastic disturbances by bounding the probability of remaining within a safe set. However, synthesizing a valid SCBF that explicitly reflects the true safety probability of the system, which is the most natural measure of safety, remains a challenge. This paper addresses this issue by adopting a spectral perspective, utilizing the linear operator that governs the evolution of the closed‑loop system's safety probability. We find that the dominant eigenpair of this Koopman‑like operator encodes fundamental safety information of the stochastic system. The dominant eigenfunction is a natural and valid SCBF, with values that explicitly quantify the relative long‑term safety of the state, while the dominant eigenvalue indicates the global rate at which the safety probability decays. A practical synthesis algorithm is proposed, termed power‑policy iteration, which jointly computes the dominant eigenpair and an optimized backup policy. The method is validated using simulation experiments on safety‑critical dynamics models.

Authors:Franek Stark, Jakob Middelberg, Shubham Vyas
Title: Mixed-Integer vs. Continuous Model Predictive Control for Binary Thrusters: A Comparative Study
Abstract:
Binary on/off thrusters are commonly used for spacecraft attitude and position control during proximity operations. However, their discrete nature poses challenges for conventional continuous control methods. The control of these discrete actuators is either explicitly formulated as a mixed‑integer optimization problem or handled in a two‑layer approach, where a continuous controller's output is converted to binary commands using analog‑to digital modulation techniques such as Delta‑Sigma‑modulation. This paper provides the first systematic comparison between these two paradigms for binary thruster control, contrasting continuous Model Predictive Control (MPC) with Delta‑Sigma modulation against direct Mixed‑Integer MPC (MIMPC) approaches. Furthermore, we propose a new variant of MPC for binary actuated systems, which is informed using the state of the Delta‑Sigma Modulator. The two variations for the continuous MPC along with the MIMPC are evaluated through extensive simulations using ESA's REACSA platform. Results demonstrate that while all approaches perform similarly in high‑thrust regimes, MIMPC achieves superior fuel efficiency in low‑thrust conditions. Continuous MPC with modulation shows instabilities at higher thrust levels, while binary informed MPC, which incorporates modulator dynamics, improves robustness and reduces the efficiency gap to the MIMPC. It can be seen from the simulated and real‑system experiments that MIMPC offers complete stability and fuel efficiency benefits, particularly for resource‑constrained missions, while continuous control methods remain attractive for computationally limited applications.

Authors:Henrik Krauss, Johann Licher, Naoya Takeishi, Annika Raatz, Takehisa Yairi
Title: Accurate Open-Loop Control of a Soft Continuum Robot Through Visually Learned Latent Representations
Abstract:
This work addresses open‑loop control of a soft continuum robot (SCR) from video‑learned latent dynamics. Visual Oscillator Networks (VONs) from previous work are used, that provide mechanistically interpretable 2D oscillator latents through an attention broadcast decoder (ABCD). Open‑loop, single‑shooting optimal control is performed in latent space to track image‑specified waypoints without camera feedback. An interactive SCR live simulator enables design of static, dynamic, and extrapolated targets and maps them to model‑specific latent waypoints. On a two‑segment pneumatic SCR, Koopman, MLP, and oscillator dynamics, each with and without ABCD, are evaluated on setpoint and dynamic trajectories. ABCD‑based models consistently reduce image‑space tracking error. The VON and ABCD‑based Koopman models attains the lowest MSEs. Using an ablation study, we demonstrate that several architecture choices and training settings contribute to the open‑loop control performance. Simulation stress tests further confirm static holding, stable extrapolated equilibria, and plausible relaxation to the rest state. To the best of our knowledge, this is the first demonstration that interpretable, video‑learned latent dynamics enable reliable long‑horizon open‑loop control of an SCR.

Authors:Siddharth Chandak, Anuj Yadav, Ayfer Ozgur, Nicholas Bambos
Title: Heavy-Tailed and Long-Range Dependent Noise in Stochastic Approximation: A Finite-Time Analysis
Abstract:
Stochastic approximation (SA) is a fundamental iterative framework with broad applications in reinforcement learning and optimization. Classical analyses typically rely on martingale difference or Markov noise with bounded second moments, but many practical settings, including finance and communications, frequently encounter heavy‑tailed and long‑range dependent (LRD) noise. In this work, we study SA for finding the root of a strongly monotone operator under these non‑classical noise models. We establish the first finite‑time moment bounds in both settings, providing explicit convergence rates that quantify the impact of heavy tails and temporal dependence. Our analysis employs a noise‑averaging argument that regularizes the impact of noise without modifying the iteration. Finally, we apply our general framework to stochastic gradient descent (SGD) and gradient play, and corroborate our finite‑time analysis through numerical experiments.

Authors:Ken Nakamura, Masahiko Ueda
Title: On the existence of fair zero-determinant strategies in the periodic prisoner's dilemma game
Abstract:
Repeated games are a framework for investigating long‑term interdependence of multi‑agent systems. In repeated games, zero‑determinant (ZD) strategies attract much attention in evolutionary game theory, since they can unilaterally control payoffs. Especially, fair ZD strategies unilaterally equalize the payoff of the focal player and the average payoff of the opponents, and they were found in several games including the social dilemma games. Although the existence condition of ZD strategies in repeated games was specified, its extension to stochastic games is almost unclear. Stochastic games are an extension of repeated games, where a state of an environment exists, and the state changes to another one according to an action profile of players. Because of the transition of an environmental state, the existence condition of ZD strategies in stochastic games is more complicated than that in repeated games. Here, we investigate the existence condition of fair ZD strategies in the periodic prisoner's dilemma game, which is one of the simplest stochastic games. We show that fair ZD strategies do not necessarily exist in the periodic prisoner's dilemma game, in contrast to the repeated prisoner's dilemma game. Furthermore, we also prove that the Tit‑for‑Tat strategy, which imitates the opponent's action, is not necessarily a fair ZD strategy in the periodic prisoner's dilemma game, whereas the Tit‑for‑Tat strategy is always a fair ZD strategy in the repeated prisoner's dilemma game. Our results highlight difference between ZD strategies in the periodic prisoner's dilemma game and ones in the standard repeated prisoner's dilemma game.

Authors:Vrushabh Zinage, Narek Harutyunyan, Eric Verheyden, Fred Y. Hadaegh, Soon-Jo Chung
Title: ContractionPPO: Certified Reinforcement Learning via Differentiable Contraction Layers
Abstract:
Legged locomotion in unstructured environments demands not only high‑performance control policies but also formal guarantees to ensure robustness under perturbations. Control methods often require carefully designed reference trajectories, which are challenging to construct in high‑dimensional, contact‑rich systems such as quadruped robots. In contrast, Reinforcement Learning (RL) directly learns policies that implicitly generate motion, and uniquely benefits from access to privileged information, such as full state and dynamics during training, that is not available at deployment. We present ContractionPPO, a framework for certified robust planning and control of legged robots by augmenting Proximal Policy Optimization (PPO) RL with a state‑dependent contraction metric layer. This approach enables the policy to maximize performance while simultaneously producing a contraction metric that certifies incremental exponential stability of the simulated closed‑loop system. The metric is parameterized as a Lipschitz neural network and trained jointly with the policy, either in parallel or as an auxiliary head of the PPO backbone. While the contraction metric is not deployed during real‑world execution, we derive upper bounds on the worst‑case contraction rate and show that these bounds ensure the learned contraction metric generalizes from simulation to real‑world deployment. Our hardware experiments on quadruped locomotion demonstrate that ContractionPPO enables robust, certifiably stable control even under strong external perturbations.

Authors:Qiping Lai, Yi Shen, Chen Shen
Title: Grid-following and Grid-forming Switching Control for Grid-connected Inverters Considering Small-signal Security Region
Abstract:
In high‑penetration renewable power systems with complex and highly variable operating scenarios, grid‑connected inverters (GCIs) may transition between different control modes to adapt to diverse grid conditions. Among these, the switching between grid‑following (GFL) and grid‑forming (GFM) control modes is particularly critical. Nevertheless, safe and robust GFL‑GFM switching control strategies for GCIs remain largely unexplored. To overcome this challenge, this paper establishes a full‑order small‑signal state‑space model for the GFL‑GFM switched system, precisely reflecting all internal circuit and control dynamics. Subsequently, the small‑signal security region (SSSR) of the switched system is defined and characterized, followed by an in‑depth investigation into the multi‑parameter impacts on the SSSRs and internal stability margin distributions (ISMDs). Furthermore, a novel comprehensive stability index (CSI) is proposed by integrating the stability margin, parameter sensitivity, and boundary distance. Based on this CSI, a multi‑objective adaptive GFL‑GFM switching control strategy is designed to guarantee the dynamic security and robustness of the system. Finally, the proposed SSSR analysis method for the GFL‑GFM switched system and the designed CSI‑based switching control mechanism are validated through electromagnetic transient (EMT) simulations.

Authors:Xingyu Feng, Chang Sun, Yuzhu Wang, Zhangbing Zhou, Chengwen Luo, Zhuangzhuang Chen, Xiaomin Ouyang, Huanqi Yang
Title: PowerLens: Taming LLM Agents for Safe and Personalized Mobile Power Management
Abstract:
Battery life remains a critical challenge for mobile devices, yet existing power management mechanisms rely on static rules or coarse‑grained heuristics that ignore user activities and personal preferences. We present PowerLens, a system that tames the reasoning power of Large Language Models (LLMs) for safe and personalized mobile power management on Android devices. The key idea is that LLMs' commonsense reasoning can bridge the semantic gap between user activities and system parameters, enabling zero‑shot, context‑aware policy generation that adapts to individual preferences through implicit feedback. PowerLens employs a multi‑agent architecture that recognizes user context from UI semantics and generates holistic power policies across 18 device parameters. A PDL‑based constraint framework verifies every action before execution, while a two‑tier memory system learns individualized preferences from implicit user overrides through confidence‑based distillation, requiring no explicit configuration and converging within 3‑‑5 days. Extensive experiments on a rooted Android device show that PowerLens achieves 81.7% action accuracy and 38.8% energy saving over stock Android, outperforming rule‑based and LLM‑based baselines, with high user satisfaction, fast preference convergence, and strong safety guarantees, with the system itself consuming only 0.5% of daily battery capacity.

Authors:Najme Ebrahimi, Haoling Li, Gun Suer, Kin Chung Fong, Leonardo Ranzani
Title: Direct Digital-to-Physical Synthesis: From mmWave Transmitter to Qubit Control
Abstract:
The increasing demand for high‑speed wireless connectivity and scalable quantum information processing has driven parallel advancements in millimeter‑wave (MMW) communication transmitters and cryogenic qubit controllers. Despite serving different applications, both systems rely on the precise generation of radio frequency (RF) waveforms with stringent requirements on spectral purity, timing, and amplitude control. Recent architecture eliminates conventional methods by embedding digital signal generation and processing directly into the RF path, transforming digital bits into physical waveforms for either electromagnetic transmission or quantum state control. This article presents a unified analysis of direct‑digital modulation techniques across both domains, showing the synergy and similarities between these two domains. The article also focuses on four core architectures: Cartesian I/Q, Polar, RF‑ Digital‑to‑Analog Converter (DAC), and harmonic/subharmonic modulation across both domains. We analyze their respective trade‑offs in energy efficiency, signal integrity, waveform synthesis, error mitigations, and highlight how architectural innovations in one domain can accelerate progress in the other

Authors:Jun Liu
Title: Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton-Jacobi-Bellman Equations
Abstract:
Many core problems in nonlinear systems analysis and control can be recast as solving partial differential equations (PDEs) such as Lyapunov and Hamilton‑Jacobi‑Bellman (HJB) equations. Physics‑informed neural networks (PINNs) have emerged as a promising mesh‑free approach for approximating their solutions, but in most existing works there is no rigorous guarantee that a small PDE residual implies a small solution error. This paper develops verifiable error bounds for approximate solutions of Lyapunov and HJB equations, with particular emphasis on PINN‑based approximations. For both the Lyapunov and HJB PDEs, we show that a verifiable residual bound yields relative error bounds with respect to the true solutions as well as computable a posteriori estimates in terms of the approximate solutions. For the HJB equation, this also yields certified upper and lower bounds on the optimal value function on compact sublevel sets and quantifies the optimality gap of the induced feedback policy. We further show that one‑sided residual bounds already imply that the approximation itself defines a valid Lyapunov or control Lyapunov function. We illustrate the results with numerical examples.

Authors:Arthur C. B. de Oliveira, Ruigang Wang, Ian R. Manchester, Eduardo D. Sontag
Title: Remarks on Lipschitz-Minimal Interpolation: Generalization Bounds and Neural Network Implementation
Abstract:
This note establishes a theoretical framework for finding (potentially overparameterized) approximations of a function on a compact set with a‑priori bounds for the generalization error. The approximation method considered is to choose, among all functions that (approximately) interpolate a given data set, one with a minimal Lipschitz constant. The paper establishes rigorous generalization bounds over practically relevant classes of approximators, including deep neural networks. It also presents a neural network implementation based on Lipschitz‑bounded network layers and an augmented Lagrangian method. The results are illustrated for a problem of learning the dynamics of an input‑to‑state stable system with certified bounds on simulation error.

Authors:Richard Schubert, Marvin Loba, Alexander Blödel, David Klüner, Alexandru Kampmann, Steven Peters
Title: Coordinating Stakeholders in the Consideration of Performance Indicators and Respective Interface Requirements for Automated Vehicles
Abstract:
This paper presents a process for coordinating stakeholders in their consideration of performance indicators and respective interface requirements for automated vehicles. These performance indicators are obtained and processed based on the system's self‑perception and enable the realization of self‑aware and self‑adaptive vehicles. This is necessary to allow SAE Level 4 vehicles to handle external disturbances as well as internal degradations and failures at runtime. Without such a systematic process for stakeholder coordination, architectural decisions on realizing self‑perception become untraceable and effective communication between stakeholders may be compromised. Our process‑oriented approach includes necessary ingredients, steps, and artifacts that explicitly address stakeholder communication, traceability, and knowledge transfer through clear documentation. Our approach is based on the experience gained from applying the process in the autotech.agil project, from which we further present lessons learned, identified gaps, and steps for future work.

Authors:Mandana Mohammadi Looey, Amrita Basak, Satadru Dey
Title: Real-Time Regulation of Direct Ink Writing Using Model Reference Adaptive Control
Abstract:
Direct Ink Writing (DIW) has gained attention for its potential to reduce printing time and material waste. However, maintaining precise geometry and consistent print quality remains challenging under dynamically varying operating conditions. This paper presents a control‑focused approach using a model reference adaptive control (MRAC) strategy based on a reduced‑order model (ROM) of extrusion‑based 3D printing for a candidate cementitious material system. The proposed controller actively compensates for uncertainties and disturbances by adjusting process parameters in real time, with the objective of minimizing reference‑tracking errors. Stability and convergence are rigorously verified via Lyapunov analysis, demonstrating that tracking errors asymptotically approach zero. Performance evaluation under realistic simulation scenarios confirms the effectiveness of the adaptive control framework in maintaining accurate and robust extrusion behavior.

Authors:Tanmay Dokania, Yashwanth Kumar Nakka
Title: Exact and Approximate Convex Reformulation of Linear Stochastic Optimal Control with Chance Constraints
Abstract:
In this paper, we present an equivalent convex optimization formulation for discrete‑time stochastic linear systems subject to linear chance constraints, alongside a tight convex relaxation for quadratic chance constraints. By lifting the state vector to encode moment information explicitly, the formulation captures linear chance constraints on states and controls across multiple time steps exactly, without conservatism, yielding strict improvements in both feasibility and optimality. For quadratic chance constraints, we derive convex approximations that are provably less conservative than existing methods. We validate the framework on minimum‑snap trajectory generation for a quadrotor, demonstrating that the proposed approach remains feasible at noise levels an order of magnitude beyond the operating range of prior formulations.

Authors:Jihoon Suh, Yeongjun Jang, Junsoo Kim, Takashi Tanaka
Title: Variational Encrypted Model Predictive Control
Abstract:
We develop a variational encrypted model predictive control (VEMPC) protocol whose online execution relies only on encrypted polynomial operations. The proposed approach reformulates the MPC problem into a sampling‑based estimator, in which the computation of the quadratic cost is naturally handled by tilting the sampling distribution, thus reducing online encrypted computation. The resulting protocol requires no additional communication rounds or intermediate decryption, and scales efficiently through two complementary levels of parallelism. We analyze the effect of encryption‑induced errors on optimality, and simulation results demonstrate the practical applicability of the proposed method.

Authors:Lucas Souza e Silva, Luis Rodrigues
Title: String stable platoons of all-electric aircraft with operating costs and airspace complexity trade-off
Abstract:
This paper formulates an optimal control framework for computing cruise airspeeds in predecessor‑follower platoons of all‑electric aircraft that balance operational cost and airspace complexity. To quantify controller workload and coordination effort, a novel pairwise dynamic workload (PDW) function is developed. Within this framework, the optimal airspeed solution is derived for all‑electric aircraft under longitudinal wind disturbances. Moreover, an analytical suboptimal solution for heterogeneous platoons with nonlinear aircraft dynamics is determined, for which a general sufficient condition for string stability is formally established. The methodology is validated through case studies of all‑electric aircraft operating in air corridors that are suitable for low‑altitude advanced/urban air mobility (AAM/UAM) applications. Results show that the suboptimal solution closely approximates the optimal, while ensuring safe separations, maintaining string stability, and reducing operational cost and airspace complexity. These findings support the development of sustainable and more autonomous air traffic procedures that will enable the implementation of emerging air transportation technologies, such as AAM/UAM, and their integration to the air traffic system environment.

Authors:Veronica Sanz
Title: Operational tracking loss in nonautonomous second-order oscillator networks
Abstract:
We study when a network of coupled oscillators with inertia ceases to follow a time‑dependent driving protocol coherently, using a simplified graph‑based model motivated by inverter‑dominated energy systems. We show that this loss of tracking is diagnosed most clearly in the frequency dynamics, rather than in phase‑based observables. Concretely, a tracking ratio built from the frequency‑disagreement observable E_ω(t) and normalized by the instantaneous second‑order modal decay rate yields a robust protocol‑dependent freeze‑out time whose relative dispersion decreases with system size. Graph topology matters substantially: the resulting freeze‑out time is only partly captured by the algebraic connectivity λ_2, while additional structural descriptors, particularly Fiedler‑mode localization and low‑spectrum structure, improve the explanation of graph‑to‑graph variation. By contrast, phase‑sector observables develop strong non‑monotonic and underdamped structure, so simple diagonal low‑mode relaxation closures are not quantitatively reliable in the same regime. These results identify the frequency sector as the natural operational sector for nonautonomous tracking loss in second‑order oscillator networks and clarify both the usefulness and the limits of reduced spectral descriptions in this setting.

Authors:Giuseppe C. Calafiore
Title: Bridging Conformal Prediction and Scenario Optimization: Discarded Constraints and Modular Risk Allocation
Abstract:
Scenario optimization and conformal prediction share a common goal, that is, turning finite samples into safety margins. Yet, different terminology often obscures the connection between their respective guarantees. This paper revisits that connection directly from a systems‑and‑control viewpoint. Building on the recent conformal/scenario bridge of \citetOSullivanRomaoMargellos2026, we extend the forward direction to feasible sample‑and‑discard scenario algorithms. Specifically, if the final decision is determined by a stable subset of the retained sampled constraints, the classical mean violation law admits a direct exchangeability‑based derivation. In this view, discarded samples naturally appear as admissible exceptions. We also introduce a simple modular composition rule that combines several blockwise calibration certificates into a single joint guarantee. This rule proves particularly useful in multi‑output prediction and finite‑horizon control, where engineers must distribute risk across coordinates, constraints, or prediction steps. Finally, we provide numerical illustrations using a calibrated multi‑step tube around an identified predictor. These examples compare alternative stage‑wise risk allocations and highlight the resulting performance and safety trade‑offs in a standard constraint‑tightening problem.

Authors:Danilo Saccani, Haoming Shen, Luca Furieri, Giancarlo Ferrari-Trecate
Title: Safety-Aware Performance Boosting for Constrained Nonlinear Systems
Abstract:
We study a control architecture for nonlinear constrained systems that integrates a performance‑boosting (PB) controller with a scheduled Predictive Safety Filter (PSF). The PSF acts as a pre‑stabilizing base controller that enforces state and input constraints. The PB controller, parameterized as a causal operator, influences the PSF in two ways: it proposes a performance input to be filtered, and it provides a scheduling signal to adjust the filter's Lyapunov‑decrease rate. We prove two main results: (i) Stability by design: any controller adhering to this parametrization maintains closed‑loop stability of the pre‑stabilized system and inherits PSF safety. (ii) Trajectory‑set expansion: the architecture strictly expands the set of safe, stable trajectories achievable by controllers combined with conventional PSFs, which rely on a pre‑defined Lyapunov decrease rate to ensure stability. This scheduling allows the PB controller to safely execute complex behaviors, such as transient detours, that are provably unattainable by standard PSF formulations. We demonstrate this expanded capability on a constrained inverted pendulum task with a moving obstacle.

Authors:Huaide Jiang, Yash Chaudhary, Yuping Wang, Zehao Wang, Raghav Sharma, Manan Mehta, Yang Zhou, Lichao Sun, Zhiwen Fan, Zhengzhong Tu, Jiachen Li
Title: NavTrust: Benchmarking Trustworthiness for Embodied Navigation
Abstract:
There are two major categories of embodied navigation: Vision‑Language Navigation (VLN), where agents navigate by following natural language instructions; and Object‑Goal Navigation (OGN), where agents navigate to a specified target object. However, existing work primarily evaluates model performance under nominal conditions, overlooking the potential corruptions that arise in real‑world settings. To address this gap, we present NavTrust, a unified benchmark that systematically corrupts input modalities, including RGB, depth, and instructions, in realistic scenarios and evaluates their impact on navigation performance. To our best knowledge, NavTrust is the first benchmark that exposes embodied navigation agents to diverse RGB‑Depth corruptions and instruction variations in a unified framework. Our extensive evaluation of seven state‑of‑the‑art approaches reveals substantial performance degradation under realistic corruptions, which highlights critical robustness gaps and provides a roadmap toward more trustworthy embodied navigation systems. Furthermore, we systematically evaluate four distinct mitigation strategies to enhance robustness against RGB‑Depth and instructions corruptions. Our base models include Uni‑NaVid and ETPNav. We deployed them on a real mobile robot and observed improved robustness to corruptions. The project website is: https://navtrust.github.io.

Authors:Huiwen Yan, Mushuang Liu
Title: Markov Potential Game and Multi-Agent Reinforcement Learning for Autonomous Driving
Abstract:
Autonomous driving (AD) requires safe and reliable decision‑making among interacting agents, e.g., vehicles, bicycles, and pedestrians. Multi‑agent reinforcement learning (MARL) modeled by Markov games (MGs) provides a suitable framework to characterize such agents' interactions during decision‑making. Nash equilibria (NEs) are often the desired solution in an MG. However, it is typically challenging to compute an NE in general‑sum games, unless the game is a Markov potential game (MPG), which ensures the NE attainability under a few learning algorithms such as gradient play. However, it has been an open question how to construct an MPG and whether these construction rules are suitable for AD applications. In this paper, we provide sufficient conditions under which an MG is an MPG and show that these conditions can accommodate general driving objectives for autonomous vehicles (AVs) using highway forced merge scenarios as illustrative examples. A parameter‑sharing neural network (NN) structure is designed to enable decentralized policy execution. The trained driving policy from MPGs is evaluated in both simulated and naturalistic traffic datasets. Comparative studies with single‑agent RL and with human drivers whose behaviors are recorded in the traffic datasets are reported, respectively.

Authors:Muhammad Hamza Ali, Amritanshu Pandey
Title: Tutorial: Grid-Following Inverter for Electrical Power Grid
Abstract:
The growing use of inverter‑based resources in modern power systems has made grid‑following inverters a central topic in power‑system modeling, control, and simulation. Despite their widespread deployment, introductory material that explains grid‑following inverter operation from first principles and connects control design to time‑domain simulation remains limited. To address this need, this tutorial presents a circuit‑theoretic introduction to the modeling and simulation of a grid‑ following inverter connected to an electrical power grid. We describe the inverter synchronization with the grid (PLL), power control, and current control structure and show how these elements can be represented within an electromagnetic transient (EMT) simulation framework using companion model‑based formulations similar to those used in circuit simulators such as SPICE and Cadence. In this tutorial, we use the grid‑following inverter as the primary example to illustrate how its governing equations, control loops, and network interface can be formulated and simulated from first principles. By the end of the document, readers should gain a clear introductory understanding of how to model and simulate a grid‑following inverter in an EMT platform.

Authors:Yingqing Chen, Christos G. Cassandras, Wei Xiao, Anni Li
Title: Exact-Time Safety Recovery using Time-Varying Control Barrier Functions with Optimal Barrier Tracking
Abstract:
This paper is motivated by controllers developed for autonomous vehicles which occasionally result into conditions where safety is no longer guaranteed. We develop an exact‑time safety recovery framework for any control‑affine nonlinear system when its state is outside a safe region using time‑varying Control Barrier Functions (CBFs) with optimal barrier tracking. Unlike conventional formulations that provide only conservative upper bounds on recovery time convergence, the proposed approach guarantees recovery to the safe set at a prescribed time. The key mechanism is an active barrier tracking condition that forces the barrier function to follow exactly a designer‑specified recovery trajectory. This transforms safety recovery into a trajectory design problem. The recovery trajectory is parameterized and optimized to achieve optimal performance while preserving feasibility under input constraints, avoiding the aggressive corrective actions typically induced by conventional finite‑time formulations. The safety recovery framework is applied to the roundabout traffic coordination problem for Connected and Automated Vehicles (CAVs), where any initially violated safe merging constraint is replaced by an exact‑time recovery barrier constraint to ensure safety guarantee restoration before CAV conflict points are reached. Simulation results demonstrate improved feasibility and performance.

Authors:Alfredo P. Vega-Leal, Jose L. Mora
Title: Assessment of Analog Time Multiplexing in SDM Digital to Analog Converters
Abstract:
Analog multiplexing for sigma delta modulated Digital to Analog Converters has been recently proposed as a means of achieving robustness. This preprint analyses said scheme via simulations. The main limitation introduced by the proposed architecture comes from mismatch in the DACs gain, which can drastically impact performances. A new technique of dynamic elements matching is proposed here to overcome this problem.

Authors:Amine Othmane, Maria Camila Bustos Vivas, Johannes Steuer, Jana Hutter
Title: Heart Artifact Removal in Electrohysterography Measurements Using Algebraic Differentiators
Abstract:
Electrohysterography (EHG) enables non‑invasive monitoring of uterine contractions but can be contaminated by electrocardiogram (ECG) artifacts. This work presents an ECG removal method using algebraic differentiators, a control‑theoretic tool for model‑free derivative estimation, that preserves signal shape outside the detected cardiac pulse locations. The differentiator parameters are designed to simultaneously suppress slow physiological artifacts and powerline interference while maximizing output signal‑to‑noise ratio. Cross‑channel clustering distinguishes cardiac pulses from localized artifacts, enabling accurate pulse subtraction without auxiliary ECG references. Implemented as a causal FIR filter, the method is validated on multichannel EHG recordings from female and male subjects and compared to the template subtraction method.

Authors:Nikolaos D. Tantaroudas
Title: On the Minimum Number of Control Laws for Nonlinear Systems with Input-Output Linearisation Singularities
Abstract:
This paper addresses the fundamental question of determining the minimum number of distinct control laws required for global controllability of nonlinear systems that exhibit singularities in their feedback linearising controllers. We introduce and rigorously prove the (k+1)‑Controller Lemma, which establishes that for an nth order single‑input single‑output nonlinear system with a singularity manifold parameterised by k algebraically independent conditions, exactly k+1 distinct control laws are necessary and sufficient for complete state‑space coverage. The sufficiency proof is constructive, employing the approximate linearisation methodology together with transversality arguments from differential topology. The necessity proof proceeds by contradiction, using the Implicit Function Theorem, a dimension‑counting argument and structural constraints inherent to the approximate linearisation framework. The result is validated through exhaustive analysis of the ball‑and‑beam system, a fourth‑order mechanical system that exhibits a two‑parameter singularity at the third output derivative.

Authors:Peter Stadler, Alexander Meinert, Niklas Baldauf, Alen Turnwald
Title: Lightweight Model Predictive Control for Spacecraft Rendezvous Attitude Synchronization
Abstract:
This work introduces two lightweight model predictive control (MPC) approaches for attitude tracking with reaction wheels during spacecraft rendezvous synchronization. Both approaches are based on a novel attitude deviation formulation, which enables the use of inherently linear constraints on angular velocity. We develop a single‑loop and a dual‑loop MPC; the latter embeds a stabilizing feedback controller within the inner loop, yielding a linear time‑invariant system. Both controllers are implemented with CasADi ‑ including automatic code generation ‑ evaluated across various solvers, and validated within the Basilisk astrodynamics simulation framework. The experimental results demonstrate improved tracking accuracy alongside reductions in computational effort and memory consumption. Finally, embedded delivery to an ARM Cortex‑M7 ‑ representative of commercial off‑the‑shelf devices used in New Space platforms ‑ confirms the real‑time feasibility of these approaches and highlights their suitability for onboard attitude control in resource‑constrained spacecraft rendezvous missions.

Authors:Alexander Meinert, Niklas Baldauf, Peter Stadler, Alen Turnwald
Title: Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations
Abstract:
This paper presents a safety‑guaranteed, runtime‑efficient imitation learning framework for spacecraft close proximity control. We leverage Control Barrier Functions (CBFs) for safety certificates and Control Lyapunov Functions (CLFs) for stability as unified design principles across data generation, training, and deployment. First, a nonlinear Model Predictive Control (NMPC) expert enforces CBF constraints to provide safe reference trajectories. Second, we train a neural policy with a novel CBF‑CLF‑informed loss and DAgger‑like rollouts with curriculum weighting, promoting data‑efficiency and reducing future safety filter interventions. Third, at deployment a lightweight one‑step CBF‑CLF quadratic program minimally adjusts the learned control input to satisfy hard safety constraints while encouraging stability. We validate the approach for ESA‑compliant close proximity operations, including fly‑around with a spherical keep‑out zone and final approach inside a conical approach corridor, using the Basilisk high‑fidelity simulator with nonlinear dynamics and perturbations. Numerical experiments indicate stable convergence to decision points and strict adherence to safety under the filter, with task performance comparable to the NMPC expert while significantly reducing online computation. A runtime analysis demonstrates real‑time feasibility on a commercial off‑the‑shelf processor, supporting onboard deployment for safety‑critical on‑orbit servicing.

Authors:Damyon Kim, Yuichi Honjo, Tatsuya Iizuka, Naomi Okubo, Naoto Endo, Hiroshi Matsubara, Yoshihiro Kawahara, Naoto Morita, Takuya Sasatani
Title: A Passive Elastic-Folding Mechanism for Stackable Airdrop Sensors
Abstract:
Air‑dispersed sensor networks deployed from aerial robotic systems (e.g., UAVs) provide a low‑cost approach to wide‑area environmental monitoring. However, existing methods often rely on active actuators for mid‑air shape or trajectory control, increasing both power consumption and system cost. Here, we introduce a passive elastic‑folding hinge mechanism that transforms sensors from a flat, stackable form into a three‑dimensional structure upon release. Hinges are fabricated by laminating commercial sheet materials with rigid printed circuit boards (PCBs) and programming fold angles through a single oven‑heating step, enabling scalable production without specialized equipment. Our geometric model links laminate geometry, hinge mechanics, and resulting fold angle, providing a predictive design methodology for target configurations. Laboratory tests confirmed fold angles between 10 degrees and 100 degrees, with a standard deviation of 4 degrees and high repeatability. Field trials further demonstrated reliable data collection and LoRa transmission during dispersion, while the Horizontal Wind Model (HWM)‑based trajectory simulations indicated strong potential for wide‑area sensing exceeding 10 km.

Authors:Xiucheng Wang, Yue Zhang, Nan Cheng
Title: BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding
Abstract:
Integrating large language models (LLMs) into wireless communication optimization is a promising yet challenging direction. Existing approaches either use LLMs as black‑box solvers or code generators, tightly coupling them with numerical computation. However, LLMs lack the precision required for physical‑layer optimization, and the scarcity of wireless training data makes domain‑specific fine‑tuning impractical. We propose BeamAgent, an LLM‑aided MIMO beamforming framework that explicitly decouples semantic intent parsing from numerical optimization. The LLM serves solely as a semantic translator that converts natural language descriptions into structured spatial constraints. A dedicated gradient‑based optimizer then jointly solves the discrete base station site selection and continuous precoding design through an alternating optimization algorithm. A scene‑aware prompt enables grounded spatial reasoning without fine‑tuning, and a multi‑round interaction mechanism with dual‑layer intent classification ensures robust constraint verification. A penalty‑based loss function enforces dark‑zone power constraints while releasing optimization degrees of freedom for bright‑zone gain maximization. Experiments on a ray‑tracing‑based urban MIMO scenario show that BeamAgent achieves a bright‑zone power of 84.0\,dB, outperforming exhaustive zero‑forcing by 7.1 dB under the same dark‑zone constraint. The end‑to‑end system reaches within 3.3 dB of the expert upper bound, with the full optimization completing in under 2 s on a laptop.

Authors:Xiucheng Wang, Zhenye Chen, Nan Cheng
Title: Learn for Variation: Variationally Guided AAV Trajectory Learning in Differentiable Environments
Abstract:
Autonomous aerial vehicles (AAVs) empower sixth‑generation (6G) Internet‑of‑Things (IoT) networks through mobility‑driven data collection. However, conventional reward‑driven reinforcement learning for AAV trajectory planning suffers from severe credit assignment issues and training instability, because sparse scalar rewards fail to capture the long‑term and nonlinear effects of sequential movements. To address these challenges, this paper proposes Learn for Variation (L4V), a gradient‑informed trajectory learning framework that replaces high‑variance scalar reward signals with dense and analytically grounded policy gradients. Particularly, the coupled evolution of AAV kinematics, distance‑dependent channel gains, and per‑user data‑collection progress is first unrolled into an end‑to‑end differentiable computational graph. Backpropagation through time then serves as a discrete adjoint solver, which propagates exact sensitivities from the cumulative mission objective to every control action and policy parameter. These structured gradients are used to train a deterministic neural policy with temporal smoothness regularization and gradient clipping. Extensive simulations demonstrate that L4V consistently outperforms representative baselines, including a genetic algorithm, DQN, A2C, and DDPG, in mission completion time, average transmission rate, and training cost

Authors:Meysam Masoudi, Milad Ganjalizadeh, Tahar Zanouda, Pal Frenger
Title: Holistic Energy Performance Management: Enablers, Capabilities, and Features
Abstract:
Energy consumption is a significant concern for mobile network operators, and to enable further network energy improvements it is also an important target when developing the emerging 6G standard. In this paper we show that, despite the existence of many energy‑saving features in 5G new radio (NR) networks, activating them in isolation yields only suboptimal savings and often compromises other network key performance indicators (KPIs) such as coverage or latency. We first introduce a compact taxonomy that distinguishes hardware capabilities from higher‑layer features. Features fall into two classes: (i) signaling and scheduling mechanisms that create idle windows, and (ii) features that utilize those windows to save energy. We then present a feature orchestrator as a logical node to coordinate between features to maximize the gain. Using a 3GPP‑aligned simulator with product‑realistic parameters, we show that coordinating lean NR, scheduling, and advanced sleep modes significantly reduces gNodeB (gNB) energy consumption with negligible throughput loss, compared to the uncoordinated scenario. We conclude by outlining open issues in observability, system dynamics, coordination, and intelligent automation for energy performance management.

Authors:Jiguang Yu, Nicholas Brendle, Joel T. Johnson, David Starobinski
Title: Physics-grounded Mechanism Design for Spectrum Sharing between Passive and Active Users
Abstract:
We propose a physics‑grounded mechanism design for dynamic spectrum sharing that bridges the gap between radiometric retrieval constraints and economic incentives. We formulate the active and passive users coexistence problem as a Vickrey‑Clarke‑Groves (VCG) auctions mechanism, where the radiometer dynamically procures ``quiet'' time‑frequency tiles from active users based on the marginal reduction in retrieval error variance. This approach ensures allocative efficiency and dominant‑strategy incentive compatibility (DSIC). To overcome the computational intractability of exact VCG on large grids, we derive an approximation algorithm by using the monotone submodularity induced by the radiometer equation. AMSR‑2‑based simulations show that the approach avoids high‑cost tiles by aggregating low‑cost spectrum across time and frequency. In an interference‑trap case study, the proposed framework reduces procurement costs by about 60% over a fixed‑band baseline while satisfying accuracy targets.

Authors:Lorenzo Zino, Mengbin Ye, Brian D. O. Anderson
Title: Assessing performance tradeoffs in hierarchical organizations using a diffusive coupling model
Abstract:
We study a continuous‑time dynamical system of nodes diffusively coupled over a hierarchical network to examine the efficiency and performance tradeoffs that organizations, teams, and command and control units face while achieving coordination and sharing information across layers. Specifically, after defining a network structure that captures real‑world features of hierarchical organizations, we use linear systems theory and perturbation theory to characterize the rate of convergence to a consensus state, and how effectively information can propagate through the network, depending on the breadth of the organization and the strength of inter‑layer communication. Interestingly, our analytical insights highlight a fundamental performance tradeoff. Namely, networks that favor fast coordination will have decreased ability to share information that is generated in the lower layers of the organization and is to be passed up the hierarchy. Numerical results validate and extend our theoretical results.

Authors:Cinzia Tomaselli, Gian Carlo Maffettone, Samy Wu Fung, Levon Nurbekyan, Mario di Bernardo
Title: Mean-field control barrier functions for stochastic multi-agent systems
Abstract:
Many applications involving multi‑agent systems require fulfilling safety constraints. Control barrier functions offer a systematic framework to enforce forward invariance of safety sets. Recent work extended this paradigm to mean‑field scenarios, where the number of agents is large enough to make density‑space descriptions a reasonable workaround for the curse of dimensionality. However, an open gap in the recent literature concerns the development of mean‑field control barrier functions for Fokker‑Planck (advection‑diffusion) equations. In this work, we address this gap, enabling safe mean‑field control of agents with stochastic microscopic dynamics. We provide bounded stability guarantees under safety corrections and corroborate our results through numerical simulations in two representative scenarios, coverage and shepherding control of multi‑agent systems.

Authors:Haotian Lu, Jincong Lu, Sachin Sachdeva, Sheldon X. -D. Tan
Title: WarPGNN: A Parametric Thermal Warpage Analysis Framework with Physics-aware Graph Neural Network
Abstract:
With the advent of system‑in‑package (SiP) chiplet‑based design and heterogeneous 2.5D/3D integration, thermal‑induced warpage has become a critical reliability concern. While conventional numerical approaches can deliver highly accurate results, they often incur prohib‑ itively high computational costs, limiting their scalability for complex chiplet‑package systems. In this paper, we present WarPGNN, an ef‑ ficient and accurate parametric thermal warpage analysis framework powered by Graph Neural Networks (GNNs). By operating directly on graphs constructed from the floorplans, WarPGNN enables fast warpage‑aware floorplan exploration and exhibits strong transfer‑ ability across diverse package configurations. Our method first en‑ codes multi‑die floorplans into reduced Transitive Closure Graphs (rTCGs), then a Graph Convolution Network (GCN)‑based encoder extracts hierarchical structural features, followed by a U‑Net inspired decoder that reconstructs warpage maps from graph feature embed‑ dings. Furthermore, to address the long‑tailed pattern of warpage data distribution, we developed a physics‑informed loss and revised a message‑passing encoder based on Graph Isomorphic Network (GIN) that further enhance learning performance for extreme cases and expressiveness of graph embeddings. Numerical results show that WarPGNN achieves more than 205.91x speedup compared with the 2‑D efficient FEM‑based method and over 119766.64x acceleration with 3‑D FEM method COMSOL, respectively, while maintaining comparable accuracy at only 1.26% full‑scale normalized RMSE and 2.21% warpage value error. Compared with recent DeepONet‑based model, our method achieved comparable prediction accuracy and in‑ ference speedup with 3.4x lower training time. In addition, WarPGNN demonstrates remarkable transferability on unseen datasets with up to 3.69% normalized RMSE and similar runtime.

Authors:Khushiyant
Title: HEP Statistical Inference for UAV Fault Detection: CLs, LRT, and SBI Applied to Blade Damage
Abstract:
This paper transfers three statistical methods from particle physics to multirotor propeller fault detection: the likelihood ratio test (LRT) for binary detection, the CLs modified frequentist method for false alarm rate control, and sequential neural posterior estimation (SNPE) for quantitative fault characterization. Operating on spectral features tied to rotor harmonic physics, the system returns three outputs: binary detection, controlled false alarm rates, and calibrated posteriors over fault severity and motor location. On UAV‑FD, a hexarotor dataset of 18 real flights with 5% and 10% blade damage, leave‑one‑flight‑out cross‑validation gives AUC 0.862 +/‑ 0.007 (95% CI: 0.849‑‑0.876), outperforming CUSUM (0.708 +/‑ 0.010), autoencoder (0.753 +/‑ 0.009), and LSTM autoencoder (0.551). At 5% false alarm rate the system detects 93% of significant and 81% of subtle blade damage. On PADRE, a quadrotor platform, AUC reaches 0.986 after refitting only the generative models. SNPE gives a full posterior over fault severity (90% credible interval coverage 92‑‑100%, MAE 0.012), so the output includes uncertainty rather than just a point estimate or fault flag. Per‑flight sequential detection achieves 100% fault detection with 94% overall accuracy.

Authors:Vincent Pacelli, Evangelos A. Theodorou
Title: Fundamental Limits for Sensor-Based Control via the Gibbs Variational Principle
Abstract:
Fundamental limits on the performance of feedback controllers are essential for benchmarking algorithms, guiding sensor selection, and certifying task feasibility ‑‑ yet few general‑purpose tools exist for computing them. Existing information‑theoretic approaches overestimate the information a sensor must provide by evaluating it against the uncontrolled system, producing bounds that degrade precisely when feedback is most valuable. We derive a lower bound on the minimum expected cost of any causal feedback controller under partial observations by applying the Gibbs variational principle to the joint path measure over states and observations. The bound applies to nonlinear, nonholonomic, and hybrid dynamics with unbounded costs and admits a self‑consistent refinement: any good controller concentrates the state, which limits the information the sensor can extract, which tightens the bound. The resulting fixed‑point equation has a unique solution computable by bisection, and we provide conditions under which the free energy minimization is provably convex, yielding a certifiably correct numerical bound. On a scalar LQG problem the self‑consistent bound captures over 80% of the known optimal cost at moderate sensor noise, and on a nonlinear Dubins car tracking problem it remains informative across all noise levels where a bound using the uncontrolled state distribution is vacuous.

Authors:David E. J. van Wijk, Dohyun Lee, Ersin Das, Tamas G. Molnar, Aaron D. Ames, Joel W. Burdick
Title: Generalizations of Backup Control Barrier Functions: Expansion and Adaptation for Input-Bounded Safety-Critical Control
Abstract:
Guaranteeing the safety of nonlinear systems with bounded inputs remains a key challenge in safe autonomy. Backup control barrier functions (bCBFs) provide a powerful mechanism for constructing controlled invariant sets by propagating trajectories under a pre‑verified backup controller to a forward invariant backup set. While effective, the standard bCBF method utilizes the same backup controller for both set expansion and safety certification, which can restrict the expanded safe set and lead to conservative dynamic behavior. In this study, we generalize the bCBF framework by separating the set‑expanding controller from the verified backup controller, thereby enabling a broader class of expansion strategies while preserving formal safety guarantees. We establish sufficient conditions for forward invariance of the resulting implicit safe set and show how the generalized construction recovers existing bCBF methods as special cases. Moreover, we extend the proposed framework to parameterized controller families, enabling online adaptation of the expansion controller while maintaining safety guarantees in the presence of input bounds.

Authors:Kaan T. Gun, Xiaozhe Wang, Danial Jafarigiv
Title: Deceiving Flexibility: A Stealthy False Data Injection Model in Vehicle-to-Grid Coordination
Abstract:
Electric vehicles (EVs) in Vehicle‑to‑Grid (V2G) systems act as distributed energy resources that support grid stability. Centralized coordination such as the extended State Space Model (eSSM) enhances scalability and estimation efficiency but may introduce new cyber‑attack surfaces. This paper presents a stealthy False Data Injection Attack (FDIA) targeting eSSM‑based V2G coordination. Unlike prior studies that assume attackers can disrupt physical charging or discharging processes, we consider an adversary who compromises only a subset of EVs, and limiting their influence to the manipulation of reported State of Charge (SoC) and power measurements. By doing so, the attacker can deceive the operator's perception of fleet flexibility while remaining consistent with model‑based expectations, thus evading anomaly detection. Numerical simulations show that the proposed stealthy FDIA can deteriorate grid frequency stability even without direct access to control infrastructure. These findings highlight the need for enhanced detection and mitigation mechanisms tailored to aggregated V2G frameworks

Authors:Massimiliano de Sa, Aaron D. Ames
Title: Topological Obstructions to the Existence of Control Barrier Functions
Abstract:
In 1983, Brockett developed a topological necessary condition for the existence of continuous, asymptotically stabilizing control laws. Building upon recent work on necessary conditions for set stabilization, we develop Brockett‑like necessary conditions for the existence of control barrier functions (CBFs). By leveraging the unique geometry of CBF safe sets, we provide simple and self‑contained derivations of necessary conditions for the existence of CBFs and their safe, continuous controllers. We demonstrate the application of these conditions to instructive examples and kinematic nonholonomic systems, and discuss their relationship to Brockett's necessary condition.

Authors:Janani S K, Kushagra Gupta, Ufuk Topcu, David Fridovich-Keil
Title: Interleaved Information Structures in Dynamic Games: A General Framework with Application to the Linear-Quadratic Case
Abstract:
A fundamental problem in noncooperative dynamic game theory is the computation of Nash equilibria under different information structures, which specify the information available to each agent during decision‑making. Prior work has extensively studied equilibrium solutions for two canonical information structures: feedback, where agents observe the current state at each time, and open‑loop, where agents only observe the initial state. However, these paradigms are often too restrictive to capture realistic settings exhibiting interleaved information structures, in which each agent observes only a subset of other agents at every timestep. To date, there is no systematic framework for modeling and solving dynamic games under arbitrary interleaved information structures. To this end, we make two main contributions. First, we introduce a method to model deterministic dynamic games with arbitrary interleaved information structures as Mathematical Program Networks (MPNs), where the network structure encodes the informational dependencies between agents. Second, for linear‑quadratic (LQ) dynamic games, we leverage the MPN formulation to develop a systematic procedure for deriving Riccati‑like equations that characterize Nash equilibria. Finally, we illustrate our approach through an example involving three agents exhibiting a cyclic information structure.

Authors:Yeongjun Jang, Kaoru Teranishi, Junsoo Kim
Title: A Distributionally Robust Optimal Control Approach for Differentially Private Dynamical Systems
Abstract:
In this paper, we develop a distributionally robust optimal control approach for differentially private dynamical systems, enabling a plant to securely outsource control computation to an untrusted remote server. We consider a plant that ensures differential privacy of its state trajectory by injecting calibrated noise into its output measurements. Unlike prior works, we assume that the server only has access to an ambiguity set consisting of admissible noise distributions, rather than the exact distribution. To account for this uncertainty, the server formulates a distributionally robust optimal control problem to minimize the worst‑case expected cost over all admissible noise distributions. However, the formulated problem is computationally intractable due to the nonconvexity of the ambiguity set. To overcome this, we relax it into a convex Kullback‑‑Leibler divergence ball, so that the reformulated problem admits a tractable closed‑form solution.

Authors:Ruixuan Zhao, Guitao Yang, Thomas Parisini, Boli Chen
Title: Distributed Unknown Input Observer Design: A Geometric Approach
Abstract:
We present a geometric approach to designing distributed unknown input observers (DUIOs) for linear time‑invariant systems, where measurements are distributed across nodes and each node is influenced by \emphunknown inputs through distinct channels. The proposed distributed estimation scheme consists of a network of observers, each tasked with reconstructing the entire system state despite having access only to local input‑output signals that are individually insufficient for full state observation. Unlike existing methods that impose stringent rank conditions on the input and output matrices at each node, our approach leverages the (C,A)‑invariant (conditioned invariant) subspace at each node from a geometric perspective. This enables the design of DUIOs in both continuous‑ and discrete‑time settings under relaxed conditions, for which we establish sufficiency and necessity. The effectiveness of our methodology is demonstrated through extensive simulations, including a practical case study on a power grid system.

Authors:Eric Cramer, Laura M. Heiser, Young Hwan Chang
Title: Trajectory Landscapes for Therapeutic Strategy Design in Agent-Based Tumor Microenvironment Models
Abstract:
Multiplex tissue imaging (MTI) enables high‑ dimensional, spatially resolved measurements of the tumor microenvironment (TME), but most clinical datasets are tempo‑ rally undersampled and longitudinally limited, restricting direct inference of underlying spatiotemporal dynamics and effective intervention timing. Agent‑based models (ABMs) provide mech‑ anistic, stochastic simulators of TME evolution; yet their high‑ dimensional state space and uncertain parameterization make direct control design challenging. This work presents a reduced‑ order, simulation‑driven framework for therapeutic strategy design using ABM‑derived trajectory ensembles. Starting from a nominal ABM, we systematically perturb biologically plausible parameters to generate a set of simulated trajectories and construct a low‑dimensional trajectory landscape describing TME evolution. From time series of spatial summary statistics extracted from the simulations, we learn a probabilistic Markov State Model (MSM) that captures metastable states and the transitions between them. To connect simulation dynamics with clinical observations, we map patient MTI snapshots onto the landscape and assess concordance with observed spatial phenotypes and clinical outcomes. We further show that conditioning the MSM on dominant governing parameters yields group‑specific transition models to formulate a finite‑horizon Markov Decision Process (MDP) for treatment scheduling. The resulting framework enables simulation‑grounded therapeutic policy design for partially observed biological systems without requiring longitudinal patient measurements.

Authors:Sara Pohland, Xenofon Foukas, Ganesh Ananthanarayanan, Andrey Kolobov, Sanjeev Mehrotra, Bozidar Radunovic, Ankit Verma
Title: Offload or Overload: A Platform Measurement Study of Mobile Robotic Manipulation Workloads
Abstract:
Mobile robotic manipulation‑‑the ability of robots to navigate spaces and interact with objects‑‑is a core capability of physical AI. Foundation models have led to breakthroughs in their performance, but at a significant computational cost. We present the first measurement study of mobile robotic manipulation workloads across onboard, edge, and cloud GPU platforms. We find that the full workload stack is infeasible to run on smaller onboard GPUs, while larger onboard GPUs drain robot batteries several hours faster. Offloading alleviates these constraints but introduces its own challenges, as additional network latency degrades task accuracy, and the bandwidth requirement makes naive cloud offloading impractical. Finally, we quantify opportunities and pitfalls of sharing compute across robot fleets. We believe our measurement study will be crucial to designing inference systems for mobile robots.

Authors:Gökçen Devlet Şen, Juan E. Machado, Gülay Öke Günel, Johannes Schiffer
Title: Delay-Robust Primal-Dual Dynamics for Distributed Optimization
Abstract:
Continuous‑time primal‑dual gradient dynamics (PDGD) is an ubiquitous approach for dynamically solving constrained distributed optimization problems. Yet, the distributed nature of the dynamics makes it prone to communication uncertainties, especially time delays. To mitigate this effect, we propose a delay‑robust continuous‑time PDGD. The dynamics is obtained by augmenting the standard PDGD with an auxiliary state coupled through a gain matrix, while preserving the optimal solution. Then, we present sufficient tuning conditions for this gain matrix in the form of linear matrix inequalities, which ensure uniform asymptotic stability in the presence of bounded, time‑varying delays. The criterion is derived via the Lyapunov‑Krasovskii method. A numerical example illustrates the improved delay robustness of our approach compared to the standard PDGD under large, time‑varying delays.

Authors:Hassan Abdelraouf, Vijay Gupta, Jeff S. Shamma
Title: Convergence of Payoff-Based Higher-Order Replicator Dynamics in Contractive Games
Abstract:
We study the convergence properties of a payoff‑based higher‑order version of replicator dynamics, a widely studied model in evolutionary dynamics and game‑theoretic learning, in contractive games. Recent work has introduced a control‑theoretic perspective for analyzing the convergence of learning dynamics through passivity theory, leading to a classification of learning dynamics based on the passivity notion they satisfy, such as \textdelta‑passivity, equilibrium‑independent passivity, and incremental passivity. We leverage this framework for the study of higher‑order replicator dynamics for contractive games, which form the complement of passive learning dynamics. Standard replicator dynamics can be represented as a cascade interconnection between an integrator and the softmax mapping. Payoff‑based higher‑order replicator dynamics include a linear time‑invariant (LTI) system in parallel with the existing integrator. First, we show that if this added system is strictly passive and asymptotically stable, then the resulting learning dynamics converge locally to the Nash equilibrium in contractive games. Second, we establish global convergence properties using incremental stability analysis for the special case of symmetric matrix contractive games.

Authors:Steven Li, Luis Rodrigues
Title: Minimum Energy Cruise of All-Electric Aircraft with Applications to Advanced Air Mobility
Abstract:
Electrified propulsion is expected to play an important role in the sustainable development of Advanced Air Mobility (AAM). However, the limited energy density of batteries motivates the need to minimize energy consumption during flight. This paper studies the minimum total energy problem for an all‑electric aircraft in steady cruise flight. The problem is formulated as an optimal control problem in which the cruise airspeed and final cruise time are optimization variables. The battery supply voltage is modeled as an affine function of the battery charge. Pontryagin's Minimum Principle is used to derive the necessary and sufficient conditions for optimality, from which closed‑form expressions for the optimal cruise airspeed and optimal final cruise time are obtained. Additional analytical conditions are derived that determine when all‑electric operation is feasible, one of which is that sufficient electric charge must be available. Numerical simulations based on the BETA Technologies CX300 all‑electric aircraft and a representative AAM scenario illustrate how the aircraft weight, cruising altitude, electrical system efficiency, and initial battery charge influence the optimal airspeed and the feasibility of all‑electric cruise.

Authors:Sriram Gopalakrishnan
Title: Don't Vibe Code, Do Skele-Code: Interactive No-Code Notebooks for Subject Matter Experts to Build Lower-Cost Agentic Workflows
Abstract:
Skele‑Code is a natural‑language and graph‑based interface for building workflows with AI agents, designed especially for less or non‑technical users. It supports incremental, interactive notebook‑style development, and each step is converted to code with a required set of functions and behavior to enable incremental building of workflows. Agents are invoked only for code generation and error recovery, not orchestration or task execution. This agent‑supported, but code‑first approach to workflows, along with the context‑engineering used in Skele‑Code, can help reduce token costs compared to the multi‑agent system approach to executing workflows. Skele‑Code produces modular, easily extensible, and shareable workflows. The generated workflows can also be used as skills by agents, or as steps in other workflows.

Authors:Leonardo Pedroso, Andrea Agazzi, W. P. M. H. Heemels, Mauro Salazar
Title: Token Economy for Fair and Efficient Dynamic Resource Allocation in Congestion Games
Abstract:
Self‑interested behavior in sharing economies often leads to inefficient aggregate outcomes compared to a centrally coordinated allocation, ultimately harming users. Yet, centralized coordination removes individual decision power. This issue can be addressed by designing rules that align individual preferences with system‑level objectives. Unfortunately, rules based on conventional monetary mechanisms introduce unfairness by discriminating among users based on their wealth. To solve this problem, in this paper, we propose a token‑based mechanism for congestion games that achieves efficient and fair dynamic resource allocation. Specifically, we model the token economy as a continuous‑time dynamic game with finitely many boundedly rational agents, explicitly capturing their evolutionary policy‑revision dynamics. We derive a mean‑field approximation of the finite‑population game and establish strong approximation guarantees between the mean‑field and the finite‑population games. This approximation enables the design of integer tolls in closed form that provably steer the aggregate dynamics toward an optimal efficient and fair allocation from any initial condition.

Authors:Luís Martins, Carlos Cardeira, Paulo Oliveira
Title: Robust Global Position and Heading Tracking on SE(3) via Saturated Hybrid Feedback
Abstract:
This letter presents a novel control solution to the robust global position and heading tracking problem for underactuated vehicles, equipped with single‑axis thrust and full torque actuation, operating under strict, user‑defined actuation limits. The architecture features a saturated position tracking controller augmented with two first‑order filters. This formulation ensures the boundedness of the first and second derivatives, yielding less conservative bounds and systematically generating bounded attitude references whose limits are easily tuned via design parameters. To track these dynamic references, the inner loop comprises a saturated, modified Rodrigues parameter (MRP)‑based controller paired with a hybrid dynamic path‑lifting mechanism. This approach allows the attitude tracking law to be designed on a covering space of the configuration manifold. By leveraging a stability equivalence framework, the methodology establishes that the resulting interconnected system achieves robust global asymptotic and semi‑global exponential tracking on SE(3), while complying with user‑defined input saturation bounds. Numerical simulations validate the proposed solution.

Authors:Mark M. Bailey
Title: Quotient Geometry and Persistence-Stable Metrics for Swarm Configurations
Abstract:
Swarm and constellation reconfiguration can be viewed as motion of an unordered point configuration in an ambient space. Here, we provide persistence‑stable, symmetry‑invariant geometric representations for comparing and monitoring multi‑agent configuration data. We introduce a quotient formation space \mathcalS_n(M,G)=M^n/(G× S_n) and a formation matching metric d_M,G obtained by optimizing a worst‑case assignment error over ambient symmetries g\in G and relabelings σ\in S_n. This metric is a structured, physically interpretable relaxation of Gromov‑‑Hausdorff distance: the induced inter‑agent metric spaces satisfy d_\mathrmGH(X_x,X_y)\le d_M,G([x],[y]). Composing this bound with stability of Vietoris‑‑Rips persistence yields d_B(Φ_k([x]),Φ_k([y]))\le d_M,G([x],[y]), providing persistence‑stable signatures for reconfiguration monitoring. We analyze the metric geometry of (\mathcalS_n(M,G),d_M,G): under compactness/completeness assumptions on M and compact G it is compact/complete and the metric induces the quotient topology; if M is geodesic then the quotient is geodesic and exhibits stratified singularities along collision and symmetry strata, relating it to classical configuration spaces. We study expressivity of the signatures, identifying symmetry‑mismatch and persistence‑compression mechanisms for non‑injectivity. Finally, in a phase‑circle model we prove a conditional inverse theorem: under semicircle support and a gap‑labeling margin, the H_0 signature is locally bi‑Lipschitz to d_M,G up to an explicit factor, yielding two‑sided control. Examples on \mathbbS^2 and \mathbbT^m illustrate satellite‑constellation and formation settings.

Authors:Cheng Kang, Xinye Chen, Daniel Novak, Xujing Yao
Title: Using Laplace Transform To Optimize the Hallucination of Generation Models
Abstract:
To explore the feasibility of avoiding the confident error (or hallucination) of generation models (GMs), we formalise the system of GMs as a class of stochastic dynamical systems through the lens of control theory. Numerous factors can be attributed to the hallucination of the learning process of GMs, utilising knowledge of control theory allows us to analyse their system functions and system responses. Due to the high complexity of GMs when using various optimization methods, we cannot figure out their solution of Laplace transform, but from a macroscopic perspective, simulating the source response provides a virtual way to address the hallucination of GMs. We also find that the training progress is consistent with the corresponding system response, which offers us a useful way to develop a better optimization component. Finally, the hallucination problem of GMs is fundamentally optimized by using Laplace transform analysis.

Authors:Eliza Cohn, Ning Qi, Upmanu Lall, Bolun Xu
Title: Real-time Coordination of Cascaded Hydropower under Decision-Dependent Uncertainty
Abstract:
This study proposes a real‑time control framework for cascaded hydropower systems that incorporates decision‑dependent uncertainty (DDU) to capture the coupling of streamflow uncertainties across the reservoirs. The framework jointly models exogenous forecast errors and endogenous uncertainty propagation, explicitly characterizing the dependence between upstream releases and downstream inflow variability through a heteroskedastic variance model conditioned on past errors, variance, and control actions. We formulate a joint chance‑constrained optimization problem to ensure reliable system operation under uncertainty and develop a tractable supporting hyperplane algorithm that enables explicit and adaptive risk allocation under DDU. We establish the convergence of the proposed method and show its risk allocation behavior under steady‑state conditions. A randomized case study based on Columbia River data demonstrates that incorporating DDU reduces the constraint violations by up to 7.0% and increases total generation by up to 0.5% relative to decision‑independent uncertainty (DIU). Sensitivity analyses of the dry‑season streamflow conditions further highlight the value of adaptive risk allocation for resilient and risk‑aware hydropower operations.

Authors:Marijn Ruiter, Miguel Aguiar, Jake Rap, Karl H. Johansson, Amritam Das
Title: RHYME-XT: A Neural Operator for Spatiotemporal Control Systems
Abstract:
We propose RHYME‑XT, an operator‑learning framework for surrogate modeling of spatiotemporal control systems governed by input‑affine nonlinear partial integro‑differential equations (PIDEs) with localized rhythmic behavior. RHYME‑XT uses a Galerkin projection to approximate the infinite‑dimensional PIDE on a learned finite‑dimensional subspace with spatial basis functions parameterized by a neural network. This yields a projected system of ODEs driven by projected inputs. Instead of integrating this non‑autonomous system, we directly learn its flow map using an architecture for learning flow functions, avoiding costly computations while obtaining a continuous‑time and discretization‑invariant representation. Experiments on a neural field PIDE show that RHYME‑XT outperforms a state‑of‑the‑art neural operator and is able to transfer knowledge effectively across models trained on different datasets, through a fine‑tuning process.

Authors:Victor M. Preciado
Title: Koopman Generator Decomposition for Port-Hamiltonian System
Abstract:
We study how the vector‑field structure of nonlinear port‑Hamiltonian systems is reflected in the infinitesimal Koopman generator. The generator admits a natural bracket decomposition into a conservative interconnection‑bracket derivation, a dissipative metric‑bracket derivation, and an input‑port derivation. The conservative component is formally skew‑adjoint on a test space whenever the conservative flow preserves the reference measure and the relevant boundary terms vanish. The dissipative component is not claimed to be a positive operator on arbitrary observables; rather, the positive semidefinite object is the metric bracket [f,f]_R=\nabla f^\intercal\mathbfR\nabla f\ge 0, which yields the exact port‑Hamiltonian energy balance for the Hamiltonian observable: \[ \mathcalK_\mathbfu\mathcalH =‑[\mathcalH,\mathcalH]_R +\mathbfy^\intercal\mathbfu \le \mathbfy^\intercal\mathbfu. \] We use these bracket identities to motivate finite‑dimensional weak Galerkin and data‑driven lifted models: when the Galerkin measure is conservative for the Hamiltonian interconnection flow and boundary terms vanish, the conservative contribution is skew in the Galerkin mass metric, while the dissipative bracket induces a positive semidefinite Dirichlet matrix. These identities motivate structure‑preserving lifted port‑Hamiltonian surrogates that are passive and support damping injection in the lifted coordinates, while distinguishing exact bracket identities, projection residuals, finite‑data estimation error, and the residual and injectivity assumptions needed to transfer lifted conclusions back to the original nonlinear state.

Authors:Petros Ellinas, Indrajit Chaudhuri, Johanna Vorwerk, Spyros Chatzivasileiadis
Title: Verification and Validation of Physics-Informed Surrogate Component Models for Dynamic Power-System Simulation
Abstract:
Physics‑informed machine learning surrogates are increasingly explored to accelerate dynamic simulation of generators, converters, and other power grid components. The key question, however, is not only whether a surrogate matches a stand‑alone component model on average, but whether it remains accurate after insertion into a differential‑algebraic simulator, where the surrogate outputs enter the algebraic equations coupling the component to the rest of the system. This paper formulates that in‑simulator use as a verification and validation (V\&V) problem. A finite‑horizon bound is derived that links allowable component‑output error to algebraic‑coupling sensitivity, dynamic error amplification, and the simulation horizon. Two complementary settings are then studied: model‑based verification against a reference component solver, and data‑based validation through conformal calibration of the component‑output variables exchanged with the simulator. The framework is general, but the case study focuses on physics‑informed neural‑network surrogates of second‑, fourth‑, and sixth‑order synchronous‑machine models. Results show that good stand‑alone surrogate accuracy does not by itself guarantee accurate in‑simulator behavior, that the largest discrepancies concentrate in stressed operating regions, and that small equation residuals do not necessarily imply small state‑trajectory errors.

Authors:Siyuan Li, Chengyuan Liu, Wen-Hua Chen
Title: An HMDP-MPC Decision-making Framework with Adaptive Safety Margins and Hysteresis for Autonomous Driving
Abstract:
This paper presents a unified decision‑making framework that integrates Hybrid Markov Decision Processes (HMDPs) with Model Predictive Control (MPC), augmented by velocity‑dependent safety margins and a prediction‑aware hysteresis mechanism. Both the ego and surrounding vehicles are modeled as HMDPs, allowing discrete maneuver transition and kinematic evolution to be jointly considered within the MPC optimization. Safety margins derived from the Intelligent Driver Model (IDM) adapt to traffic context but vary with speed, which can cause oscillatory decisions and velocity fluctuations. To mitigate this, we propose a frozen‑release hysteresis mechanism with distinct trigger and release thresholds, effectively enlarging the reaction buffer and suppressing oscillations. Decision continuity is further safeguarded by a two‑layer recovery scheme: a global bounded relaxation tied to IDM margins and a deterministic fallback policy. The framework is evaluated through a case study, an ablation against a no‑hysteresis baseline, and largescale randomized experiments across 18 traffic settings. Across 8,050 trials, it achieves a collision rate of only 0.05%, with 98.77% of decisions resolved by nominal MPC and minimal reliance on relaxation or fallback. These results demonstrate the robustness and adaptability of the proposed decision‑making framework in heterogeneous traffic conditions.

Authors:Yunxiang Ma, Yibo Wang, Zhongmei Li, Chao Shang
Title: Data-Driven Predictive Control for Stochastic Descriptor Systems: An Innovation-Based Approach Handling Non-Causal Dependencies
Abstract:
Descriptor systems arise naturally in real‑world applications governed by algebraic constraints, such as power networks, robotics and chemical processes. When a descriptor model contains a nontrivial nilpotent block, the discrete‑time input‑‑output map may be improper: the current output depends on future inputs and, in the stochastic case, on future noise terms. This letter proposes a data‑driven predictive control framework for stochastic descriptor systems that handles these non‑causal dependencies without explicitly identifying system matrices. The key idea is to split fast subsystem into noise‑driven and input‑driven parts, and then combine the former with the slow subsystem such that an innovation‑driven Kalman filter can be appropriately defined to reformulate the stochastic descriptor system into an innovation‑driven form. Based on this, a new behavioral system representation is derived, which inspires a data‑driven innovation‑based multi‑step output predictor and a practical Inno‑DeePC algorithm that enables data‑driven predictive control design without known system matrices while implicitly handling algebraic constraints. Numerical experiments on a DC microgrid demonstrate the effectiveness of the proposed approach.

Authors:Yingqing Chen, Anni Li, Christos G. Cassandras, Homayoun Hamedmoghadam, Fabian Wirth, Robert Shorten
Title: Robust Dynamic Pricing and Admission Control with Fairness Guarantees
Abstract:
Dynamic pricing is commonly used to regulate congestion in shared service systems. This paper is motivated by the fact that in the presence of users with varying price sensitivity (responsiveness), conventional monotonic pricing can lead to unfair outcomes by disproportionately excluding price‑elastic users, particularly under high or uncertain demand. We therefore develop a fairness‑oriented mechanism under demand uncertainty. The paper's contributions are twofold. First, we show that when fairness is imposed as a hard state constraint, the optimal (revenue maximizing) pricing policy is generally non‑monotonic in demand. This structural result departs fundamentally from standard surge pricing rules and reveals that price reduction under heavy load may be necessary to maintain equitable access. Second, we address the problem that price elasticity among heterogeneous users is unobservable. To solve it, we develop a robust dynamic pricing and admission control framework that enforces capacity and fairness constraints for all user type distributions consistent with aggregate measurements. By integrating integral High Order Control Barrier Functions (iHOCBFs) with a robust optimization framework under uncertain user‑type distribution, we obtain a controller that guarantees forward invariance of safety and fairness constraints while optimizing revenue. Numerical experiments demonstrate improved fairness and revenue performance relative to monotonic surge pricing policies.

Authors:Bogdan Gheorghe, Daniel Ioan, Cristian Flutur, Ionela Prodan, Florin Stoican
Title: On maximal positive invariant set computation for rank-deficient linear systems
Abstract:
The maximal positively invariant (MPI) set is obtained through a backward reachability procedure involving the iterative computation and intersection of predecessor sets under state and input constraints. However, standard static feedback synthesis may place some of the closed‑loop eigenvalues at zero, leading to rank‑deficient dynamics. This affects the MPI computation by inducing projections onto lower‑dimensional subspaces during intermediate steps. By exploiting the Schur decomposition, we explicitly address this singular case and propose a robust algorithm that computes the MPI set in both polyhedral and constrained‑zonotope representations.

Authors:Tijana Devaja, Milica Petkovic, Sokol Kosta, Dejan Vukobratovic, Cedomir Stefanovic
Title: Physical Layer Security in Finite Blocklength Massive IoT with Randomly Located Eavesdroppers
Abstract:
This paper analyzes the physical layer security performance of massive uplink Internet of Things (IoT) networks operating under the finite blocklength (FBL) regime. IoT devices and base stations (BS) are modeled using a stochastic geometry approach, while an eavesdropper is placed at a random location around the transmitting device. This system model captures security risks common in dense IoT deployments. Analytical expressions for the secure success probability, secrecy outage probability and secrecy throughput are derived to characterize how stochastic interference, fading and eavesdropper spatial uncertainty interact with FBL constraints in short packet uplink transmissions. Numerical results illustrate key system behavior under different network and channel conditions.

Authors:Kirill Kuroptev, Florian Steinke, Efthymios Karangelos
Title: Defending the power grid by segmenting the EV charging cyber infrastructure
Abstract:
This paper examines defending the power grid against load‑altering attacks using electric vehicle charging. It proposes to preventively segment the cyber infrastructure that charging station operators (CSOs) use to communicate with and control their charging stations, thereby limiting the impact of successful cyber‑attacks. Using real German charging station data and a reconstructed transmission grid model, a threat analysis shows that without segmentation, the successful hack of just two CSOs can overload two transmission grid branches, exceeding the N‑1 security margin and necessitating defense measures. A novel defense design problem is then formulated that minimizes the number of imposed segmentations while bounding the number of branch overloads under worst‑case attacks. The resulting IP‑MILP bi‑level problem can be solved with an exact column and constraint generation algorithm and with heuristics for fast computation on large‑scale instances. For the near‑real‑world Germany case, the applicability of the heuristics is demonstrated and validated under relevant load and dispatch scenarios. It is found that the simple scheme of segmenting CSOs evenly by their installed capacity leads to only 23% more segments compared to the heuristic optimization result, suggesting potential relevance as a regulatory measure.

Authors:Siyuan Li, Chengyuan Liu, Wen-Hua Chen
Title: Hierarchical Decision-Making under Uncertainty: A Hybrid MDP and Chance-Constrained MPC Approach
Abstract:
This paper presents a hierarchical decision‑making framework for autonomous systems operating under uncertainty, demonstrated through autonomous driving as a representative application. Surrounding agents are modeled using Hybrid Markov Decision Processes (HMDPs) that jointly capture maneuver‑level and dynamic‑level uncertainties, enabling the multi‑modal environmental prediction. The ego agent is modeled using a separate HMDP and integrated into a Model Predictive Control (MPC) framework that unifies maneuver selection with dynamic feasibility within a single optimization. A set of joint chance constraints serves as the bridge between environmental prediction and optimization, incorporating multi‑modal environment predictions into the MPC formulation and ensuring safety across all plausible interaction scenarios. The proposed framework provides theoretical guarantees on recursive feasibility and asymptotic stability, and its benefits in terms of safety and efficiency are validated through comprehensive evaluations in highway and urban environments, together with comparisons against a rule‑based baseline.

Authors:Lars Bartels, Amon Lahr, Andrea Carron, Melanie N. Zeilinger
Title: Real-Time Online Learning for Model Predictive Control using a Spatio-Temporal Gaussian Process Approximation
Abstract:
Learning‑based model predictive control (MPC) can enhance control performance by correcting for model inaccuracies, enabling more precise state trajectory predictions than traditional MPC. A common approach is to model unknown residual dynamics as a Gaussian process (GP), which leverages data and also provides an estimate of the associated uncertainty. However, the high computational cost of online learning poses a major challenge for real‑time GP‑MPC applications. This work presents an efficient implementation of an approximate spatio‑temporal GP model, offering online learning at constant computational complexity. It is optimized for GP‑MPC, where it enables improved control performance by learning more accurate system dynamics online in real‑time, even for time‑varying systems. The performance of the proposed method is demonstrated by simulations and hardware experiments in the exemplary application of autonomous miniature racing.

Authors:Sinan Ibrahim, Grégoire Ouerdane, Hadi Salloum, Henni Ouerdane, Stefan Streif, Pavel Osinenko
Title: Benchmarking Reinforcement Learning via Stochastic Converse Optimality: Generating Systems with Known Optimal Policies
Abstract:
The objective comparison of Reinforcement Learning (RL) algorithms is notoriously complex as outcomes and benchmarking of performances of different RL approaches are critically sensitive to environmental design, reward structures, and stochasticity inherent in both algorithmic learning and environmental dynamics. To manage this complexity, we introduce a rigorous benchmarking framework by extending converse optimality to discrete‑time, control‑affine, nonlinear systems with noise. Our framework provides necessary and sufficient conditions, under which a prescribed value function and policy are optimal for constructed systems, enabling the systematic generation of benchmark families via homotopy variations and randomized parameters. We validate it by automatically constructing diverse environments, demonstrating our framework's capacity for a controlled and comprehensive evaluation across algorithms. By assessing standard methods against a ground‑truth optimum, our work delivers a reproducible foundation for precise and rigorous RL benchmarking.

Authors:Zhe Pan, Qi Xu, Ruixiang Wang, Zhenghao Jin, Jianzhao Geng
Title: An Extended T-A Formulation Based on Potential-Chain Recursion for Electromagnetic Modeling of Parallel-Wound No-Insulation HTS Coils
Abstract:
Parallel‑wound no‑insulation (PW‑NI) high‑temperature superconducting (HTS) coils significantly reduce charging delay while maintaining excellent self‑protection capability, demonstrating great potential for high‑field applications. Existing models that couple the T‑A formulation with equivalent circuits have demonstrated high accuracy in electromagnetic analysis of PW‑NI coils. However, eliminating the computational overhead caused by frequent variable mapping and data exchange between electromagnetic and circuit modules is important for improving computational efficiency, particularly in long‑duration transient simulations of large‑scale magnets. To address this issue, an extended T‑A formulation based on potential‑chain recursion, termed PCR‑TA, is proposed. By directly embedding inter‑tape current sharing and radial current bypass behaviors into the finite‑element framework, this method computes the transient electromagnetic response of PW‑NI coils without requiring an explicit equivalent circuit model. Building upon it, a multi‑scale approach is further developed for large‑scale PW‑NI coils. The validity of the proposed method and its multi‑scale extension is verified through comparisons with experimental measurements and field‑circuit coupled modeling results. Comparative analyses demonstrate that the PCR‑TA method achieves a speedup of approximately 2.4 over the field‑circuit coupled method, whereas its multi‑scale extension further increases this speedup to roughly 5.8. Furthermore, the PCR‑TA method is extended to model the continuous transition of PW‑NI coils from power‑supply charging to closed‑loop operation. This work provides an efficient method and tool for the electromagnetic modeling of PW‑NI coils under both driven and closed‑loop operating conditions.

Authors:Norihisa Namura, Hiroya Nakao
Title: Optimal Control for Steady Circulation of a Diffusion Process via Spectral Decomposition of Fokker-Planck Equation
Abstract:
We present a formulation of an optimal control problem for a two‑dimensional diffusion process governed by a Fokker‑Planck equation to achieve a nonequilibrium steady state with a desired circulation while accelerating convergence toward the stationary distribution. To achieve the control objective, we introduce costs for both the probability density function and flux rotation to the objective functional. We formulate the optimal control problem through dimensionality reduction of the Fokker‑Planck equation via eigenfunction expansion, which requires a low‑computational cost. We demonstrate that the proposed optimal control achieves the desired circulation while accelerating convergence to the stationary distribution through numerical simulations.

Authors:Wasif H. Syed, Juan E. Machado, Hans Würfel, Ekrem Hanli, Johannes Schiffer
Title: Distributed Adaptive Control for DC Power Distribution in Hybrid-Electric Aircraft: Design and Experimental Validation
Abstract:
To reduce CO2 emissions and tackle increasing fuel costs, the aviation industry is swiftly moving towards the electrification of aircraft. From the viewpoint of systems and control, a key challenge brought by this transition corresponds to the management and safe operation of the propulsion system's onboard electrical power distribution network. In this work, for a series‑hybrid‑electric propulsion system, we propose a distributed adaptive controller for regulating the voltage of a DC bus that energizes the electricity‑based propulsion system. The proposed controller ‑‑ whose design is based on principles of back‑stepping, adaptive, and passivity‑based control techniques ‑‑ also enables the proportional sharing of the electric load among multiple converter‑interfaced sources, which reduces the likelihood of over‑stressing individual sources. Compared to existing control strategies, our method ensures stable, convergent, and accurate voltage regulation and load‑sharing even if the effects of power lines of unknown resistances and inductances are considered. The performance of the proposed control scheme is experimentally validated and compared to state‑of‑the‑art controllers in a power hardware‑in‑the‑loop (PHIL) environment.

Authors:Xiucheng Wang, Yuhao Pan, Nan Cheng
Title: A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awarenes
Abstract:
The integration of artificial intelligence into next‑generation wireless networks necessitates the accurate construction of radio maps (RMs) as a foundational prerequisite for electromagnetic digital twins. A RM provides the digital representation of the wireless propagation environment, mapping complex geographical and topological boundary conditions to critical spatial‑spectral metrics that range from received signal strength to full channel state information matrices. This tutorial presents a comprehensive survey of learning‑based RM construction, systematically addressing three intertwined dimensions: data, paradigms, and physics‑awareness. From the data perspective, we review physical measurement campaigns, ray tracing simulation engines, and publicly available benchmark datasets, identifying their respective strengths and fundamental limitations. From the paradigm perspective, we establish a core taxonomy that categorizes RM construction into source‑aware forward prediction and source‑agnostic inverse reconstruction, and examine five principal neural architecture families spanning convolutional neural networks, vision transformers, graph neural networks, generative adversarial networks, and diffusion models. We further survey optics‑inspired methods adapted from neural radiance fields and 3D Gaussian splatting for continuous wireless radiation field modeling. From the physics‑awareness perspective, we introduce a three‑level integration framework encompassing data‑level feature engineering, loss‑level partial differential equation regularization, and architecture‑level structural isomorphism. Open challenges including foundation model development, physical hallucination detection, and amortized inference for real‑time deployment are discussed to outline future research directions.

Authors:Emmanuel O. Badmus, Amritanshu Pandey
Title: PowerDAG: Reliable Agentic AI System for Automating Distribution Grid Analysis
Abstract:
This paper introduces PowerDAG, an agentic AI system for automating complex distribution‑grid analysis. We address the reliability challenges of state‑of‑the‑art agentic systems in automating complex engineering workflows by introducing two innovative active mechanisms: adaptive retrieval, which uses a similarity‑decay cutoff algorithm to dynamically select the most relevant annotated exemplars as context, and just‑in‑time (JIT) supervision, which actively intercepts and corrects tool‑usage violations during execution. On a benchmark of unseen distribution grid analysis queries, PowerDAG achieves a 100% success rate with GPT‑5.2 and 94.4‑‑96.7% with smaller open‑source models, outperforming base ReAct (41‑88%), LangChain (30‑90%), and CrewAI (9‑41%) baselines by margins of 6‑50 percentage points.

Authors:Jinyu Miao, Pu Zhang, Rujun Yan, Yifei He, Bowei Zhang, Zheng Fu, Ke Wang, Qi Song, Kun Jiang, Mengmeng Yang, Diange Yang
Title: Physics-informed Deep Mixture-of-Koopmans Vehicle Dynamics Model with Dual-branch Encoder for Distributed Electric-drive Trucks
Abstract:
Advanced autonomous driving systems require accurate vehicle dynamics modeling. However, identifying a precise dynamics model remains challenging due to strong nonlinearities and the coupled longitudinal and lateral dynamic characteristics. Previous research has employed physics‑based analytical models or neural networks to construct vehicle dynamics representations. Nevertheless, these approaches often struggle to simultaneously achieve satisfactory performance in terms of system identification efficiency, modeling accuracy, and compatibility with linear control strategies. In this paper, we propose a fully data‑driven dynamics modeling method tailored for complex distributed electric‑drive trucks (DETs), leveraging Koopman operator theory to represent highly nonlinear dynamics in a lifted linear embedding space. To achieve high‑precision modeling, we first propose a novel dual‑branch encoder which encodes dynamic states and provides a powerful basis for the proposed Koopman‑based methods entitled KODE. A physics‑informed supervision mechanism, grounded in the geometric consistency of temporal vehicle motion, is incorporated into the training process to facilitate effective learning of both the encoder and the Koopman operator. Furthermore, to accommodate the diverse driving patterns of DETs, we extend the vanilla Koopman operator to a mixture‑of‑Koopman operator framework, enhancing modeling capability. Simulations conducted in a high‑fidelity TruckSim environment and real‑world experiments demonstrate that the proposed approach achieves state‑of‑the‑art performance in long‑term dynamics state estimation.

Authors:Puskar Neupane, Bai Cui
Title: A Cycle-Based Solvability Condition for Real Power Flow Equations
Abstract:
Certifying power flow solvability is important for reliable power system operations under volatile operating conditions, but solving power flow equations repeatedly can be costly and may encounter convergence issues. In this paper, we develop an explicit cycle‑based solvability condition for the lossless real power flow equations on meshed networks. We decompose every feasible nodal balance solution into a particular flow plus a cycle flow correction vector. The power flow problem is then reduced to enforcing edge‑wise feasibility and cycle consistency. We show that the cycle consistency function is strongly monotone, and is the gradient of a strongly convex energy function. By exploiting these properties, we derive an explicit condition on the existence and uniqueness of power flow solution with bounded angle difference. The resulting condition is invariant under the choice of cycle basis and can be verified through simple algebraic computations. Numerical results on standard test systems show that the proposed condition is significantly less conservative than existing sufficient conditions and closely approximates true loading limits.

Authors:Lei Xie, Peihui Lin, Naiyu Wang, Paolo Gardoni
Title: Real-Time, Crowdsourcing-Enhanced Forecasting of Building Functionality During Urban Floods
Abstract:
Urban flood emergency response increasingly relies on infrastructure impact forecasts rather than hazard variables alone. However, real‑time predictions are unreliable due to biased rainfall, incomplete flood knowledge, and sparse observations. Conventional open‑loop forecasting propagates impacts without adjusting the system state, causing errors during critical decisions. This study presents CRAF (Crowdsourcing‑Enhanced Real‑Time Awareness and Forecasting), a physics‑informed, closed‑loop framework that converts sparse human‑sensed evidence into rolling, decision‑grade impact forecasts. By coupling physics‑based simulation learning with crowdsourced observations, CRAF infers system conditions from incomplete data and propagates them forward to produce multi‑step, real‑time predictions of zone‑level building functionality loss without online retraining. This closed‑loop design supports continuous state correction and forward prediction under weakly structured data with low‑latency operation. Offline evaluation demonstrates stable generalization across diverse storm scenarios. In operational deployment during Typhoon Haikui (2023) in Fuzhou, China, CRAF reduces 1‑3 hour‑ahead forecast errors by 84‑95% relative to fixed rainfall‑driven forecasting and by 73‑80% relative to updated rainfall‑driven forecasting, while limiting computation to 10 minutes per update cycle. These results show that impact‑state alignment‑rather than hazard refinement alone‑is essential for reliable real‑time decision support, providing a pathway toward operational digital twins for resilient urban infrastructure systems.

Authors:Jayanth Bhargav, Zirui Xu, Vasileios Tzoumas, Mahsa Ghasemi, Shreyas Sundaram
Title: Distributed Equilibrium-Seeking in Target Coverage Games via Self-Configurable Networks under Limited Communication
Abstract:
We study a target coverage problem in which a team of sensing agents, operating under limited communication, must collaboratively monitor targets that may be adaptively repositioned by an attacker. We model this interaction as a zero‑sum game between the sensing team (known as the defender) and the attacker. However, computing an exact Nash equilibrium (NE) for this game is computationally prohibitive as the action space of the defender grows exponentially with the number of sensors and their possible orientations. Exploiting the submodularity property of the game's utility function, we propose a distributed framework that enables agents to self‑configure their communication neighborhoods under bandwidth constraints and collaboratively maximize the target coverage. We establish theoretical guarantees showing that the resulting sensing strategies converge to an approximate NE of the game. To our knowledge, this is the first distributed, communication‑aware approach that scales effectively for games with combinatorial action spaces while explicitly incorporating communication constraints. To this end, we leverage the distributed bandit‑submodular optimization framework and the notion of Value of Coordination that were introduced in [1]. Through simulations, we show that our approach attains near‑optimal game value and higher target coverage compared to baselines.

Authors:Panuganti Chirag Sai, Gandholi Sarat, R. Raghunatha Sarma, Venkata Kalyan Tavva, Naveen M
Title: ReLMXEL: Adaptive RL-Based Memory Controller with Explainable Energy and Latency Optimization
Abstract:
Reducing latency and energy consumption is critical to improving the efficiency of memory systems in modern computing. This work introduces ReLMXEL (Reinforcement Learning for Memory Controller with Explainable Energy and Latency Optimization), a explainable multi‑agent online reinforcement learning framework that dynamically optimizes memory controller parameters using reward decomposition. ReLMXEL operates within the memory controller, leveraging detailed memory behavior metrics to guide decision‑making. Experimental evaluations across diverse workloads demonstrate consistent performance gains over baseline configurations, with refinements driven by workload‑specific memory access behaviour. By incorporating explainability into the learning process, ReLMXEL not only enhances performance but also increases the transparency of control decisions, paving the way for more accountable and adaptive memory system designs.

Authors:Martin Jouve-Genty, Han Su, Sota Sato, Jie An, Zhenya Zhang, Ichiro Hasuo
Title: STLts-Div: Diversified Trace Synthesis from STL Specifications Using MILP (Extended Version)
Abstract:
Modern cyber‑physical systems are complex, and requirements are often written in Signal Temporal Logic (STL). Writing the right STL is difficult in practice; engineers benefit from concrete executions that illustrate what a specification actually admits. Trace synthesis addresses this need, but a single witness rarely suffices to understand intent or explore edge cases ‑ diverse satisfying behaviors are far more informative. We introduce diversified trace synthesis: the automatic generation of sets of behaviorally diverse traces that satisfy a given STL formula. Building on a MILP encoding of STL and system model, we formalize three complementary diversification objectives ‑ Boolean distance, random Boolean distance, and value distance ‑ all captured by an objective function and solved iteratively. We implement these ideas in STLts‑Div, a lightweight Python tool that integrates with Gurobi.

Authors:Pieter van Goor, Chiara Gabellieri, Antonio Franchi
Title: The Geometry of Coordinated Trajectories for Non-stop Flying Carriers Holding a Cable-Suspended Load
Abstract:
This work considers the problem of using multiple aerial carriers to hold a cable‑suspended load while remaining in periodic motion at all times. Using a novel differential geometric perspective, it is shown that the problem may be recast as that of finding an immersion of the unit circle into the smooth manifold of admissible configurations. Additionally, this manifold is shown to be path connected under a mild assumption on the attachment points of the carriers to the load. Based on these ideas, a family of simple linear solutions to the original problems is presented that overcomes the constraints of alternative solutions previously proposed in the literature. Simulation results demonstrate the flexibility of the theory in identifying suitable solutions.

Authors:Tianyu Qiu, Filippos Fotiadis, Xinjie Liu, Christian Ellis, Jesse Milzman, Wesley Suttle, Ufuk Topcu, David Fridovich-Keil
Title: Linear-Quadratic Gaussian Games with Distributed Sparse Estimation
Abstract:
Linear‑quadratic Gaussian games provide a framework for modeling strategic interactions in multi‑agent systems, where agents must estimate system states from noisy observations while also making decisions to optimize a quadratic cost. However, these formulations usually require agents to utilize the full set of available observations when forming their state estimates, which can be unrealistic in large‑scale or resource‑constrained settings. In this paper, we consider linear‑quadratic Gaussian games with sparse interagent observations. To enforce sparsity in the estimation stage, we design a distributed estimator that balances estimation effectiveness with interagent measurement sparsity via a group lasso problem, while agents implement feedback Nash strategies based on their state estimates. We provide sufficient conditions under which the sparse estimator is guaranteed to trigger a corrective reset to the optimal estimation gain, ensuring that estimation quality does not degrade beyond a level determined by the regularization parameters. Simulations on a formation game show that the proposed approach yields a significant reduction in communication resources consumed while only minimally affecting the nominal equilibrium trajectories.

Authors:Zhiyuan Fan, Tianyi Lin, Bolun Xu
Title: Voluntary Renewable Programs: Optimal Pricing and Revenue Allocation
Abstract:
This paper develops a multi‑period optimization framework to design a voluntary renewable program (VRP) for an electric utility company, aiming to maximize total renewable energy deployments. In the business model of VRP, the utility must ensure it generates renewable energy up to the total amount of contract during each market episode (i.e., a year), while all the revenue collected from the VRP must either be used to invest in procuring renewable capacities or to maintain the current renewable fleet and infrastructure. We thus formulate the problem as an optimal pricing problem coupled with revenue allocation and renewable deployment decisions. We model the demand function of voluntary renewable contracts as an exponential decay function based on survey data. We analytically derive the optimal pricing policy of the VRP as a function of the current grid carbon intensity. We prove that a myopic policy is conditionally optimal, which maximizes renewable capacity in each period, attains the long‑run optimum due to the utility's revenue‑neutral constraint. We show different binding conditions and marginal values of decision variables correspond to different phases of the energy transition, and that the utility should strategically design its revenue‑sharing decisions, balancing investments in renewable expansion and subsidizing existing renewable fleets. Finally, we show that voluntary renewable programs can only extend renewable penetration but cannot achieve net‑zero emissions or a fully renewable grid. This pricing‑allocation‑expansion framework highlights both the potential and limitations of voluntary renewable demand, providing analytical insight into optimal policy design and the qualitative shifts occurring during the energy transition process.

Authors:Maurizio Clemente, Marcello Canova
Title: Quadratic Surrogate Attractor for Particle Swarm Optimization
Abstract:
This paper presents a particle swarm optimization algorithm that leverages surrogate modeling to replace the conventional global best solution with the minimum of an n‑dimensional quadratic form, providing a better‑conditioned dynamic attractor for the swarm. This refined convergence target, informed by the local landscape, enhances global convergence behavior and increases robustness against premature convergence and noise, while incurring only minimal computational overhead. The surrogate‑augmented approach is evaluated against the standard algorithm through a numerical study on a set of benchmark optimization functions that exhibit diverse landscapes. To ensure statistical significance, 400 independent runs are conducted for each function and algorithm, and the results are analyzed based on their statistical characteristics and corresponding distributions. The quadratic surrogate attractor consistently outperforms the conventional algorithm across all tested functions. The improvement is particularly pronounced for quasi‑convex functions, where the surrogate model can exploit the underlying convex‑like structure of the landscape.

Authors:Sheryl Paul, Leslie Cruz Juarez, Jyotirmoy V. Deshmukh, Ketan Savla
Title: On Online Control of Opinion Dynamics
Abstract:
Networked multi‑agent dynamical systems have been used to model how individual opinions evolve over time due to the opinions of other agents in the network. Particularly, such a model has been used to study how a planning agent can be used to steer opinions in a desired direction through repeated, budgeted interventions. In this paper, we consider the problem where individuals' susceptibilities to external influences are unknown. We propose an online algorithm that alternates between estimating this susceptibility parameter, and using the current estimate to drive the opinion to a desired target. We provide conditions that guarantee stability and convergence to the desired target opinion when the planning agent faces budgetary or temporal constraints. Our analysis shows that the key advantage of estimating the susceptibility parameter is that it helps achieve near‑optimal convergence to the target opinion given a finite amount of intervention rounds, and, for a given intervention budget, quantifies how close the opinion can get to the desired target.

Authors:Sahel Vahedi Noori, Bin Hu, Geir Dullerud, Peter Seiler
Title: Integral Quadratic Constraints for Repeated ReLU
Abstract:
This paper presents a new dynamic integral quadratic constraint (IQC) for the repeated Rectified Linear Unit (ReLU). These dynamic IQCs can be used to analyze stability and induced \ell_2‑gain performance of discrete‑time, recurrent neural networks (RNNs) with ReLU activation functions. These analysis conditions can be incorporated into learning‑based controller synthesis methods, which currently rely on static IQCs. We show that our proposed dynamic IQCs for repeated ReLU form a superset of the dynamic IQCs for repeated, slope‑restricted nonlinearities. We also prove that the \ell_2‑gain bounds are nonincreasing with respect to the horizon used in the dynamic IQC filter. A numerical example using a simple (academic) RNN shows that our proposed IQCs lead to less conservative bounds than existing IQCs.

Authors:Lauren Streitmatter, Trager Joswig-Jones, Baosen Zhang
Title: Convexity and Optimal Online Control of Grid-Interfacing Converters with Current Limits
Abstract:
Converter‑based generators and loads are growing in prevalence on power grids across the globe. The rise of these resources necessitates controllers that handle the power electronic devices' strict current limits without jeopardizing stability or overly constraining behavior. Existing controllers often employ complex, cascaded control loop architecture to saturate currents, but these controllers are challenging to tune properly and can destabilize following large disturbances. In this paper, we extend previous analysis to prove the feasible output region of a grid‑connected converter is convex regardless of filter topology. We then formulate a convex optimal control problem from which we derive a projected gradient descent‑based controller with convergence guarantees. This approach drives the converter toward optimality in real‑time and differs from conventional control strategies that regulate converter outputs around predefined references regardless of surrounding grid conditions. Simulation results demonstrate safe and stabilizing behavior of the proposed controller, in both the single‑converter‑infinite‑bus systems and multi‑converter networks.

Authors:MD Abul Kashem Niloy, Adam Hallmark, Yikun Cheng, Pan Zhao
Title: Neural-NPV Control: Learning Parameter-Dependent Controllers and Lyapunov Functions with Neural Networks
Abstract:
Nonlinear parameter‑varying (NPV) systems are a class of nonlinear systems whose dynamics explicitly depend on time‑varying external parameters, making them suitable for modeling real‑world systems with dynamics variations. Traditional synthesis methods for NPV systems, such as sum‑of‑squares (SOS) optimization, are only applicable to control‑affine systems, face scalability challenges and often lead to conservative results due to structural restrictions. To address these limitations, we propose Neural‑NPV, a two‑stage learning‑based framework that leverages neural networks to jointly synthesize a PD controller and a PD Lyapunov function for an NPV system under input constraints. In the first stage, we utilize a computationally cheap, gradient‑based counterexample‑guided procedure to synthesize an approximately valid PD Lyapunov function and a PD controller. In the second stage, a level‑set guided refinement is then conducted to obtain a valid Lyapunov function and controller while maximizing the robust region of attraction (R‑ROA). We demonstrate the advantages of Neural‑NPV in terms of applicability, performance, and scalability compared to SOS‑based methods through numerical experiments involving an simple inverted pendulum with one scheduling parameter and a quadrotor system with three scheduling parameters.

Authors:Laszlo Gacsi, Adam K. Kiss, Ersin Das, Tamas G. Molnar
Title: Enforcing Mixed State-Input Constraints with Multiple Backup Control Barrier Functions: A Projection-based Approach
Abstract:
Ensuring the safety of control systems often requires the satisfaction of constraints on states (such as position or velocity), control inputs (such as force), and a mixture of states and inputs (such as power that depends on both velocity and force). This paper presents a safety‑critical control framework for enforcing mixed state‑input constraints through a generalization of backup control barrier functions (backup CBFs). First, we extend the backup CBF approach to maintain multiple decoupled state and input constraints using a single backup set‑backup controller pair. Second, we address mixed state‑input constraints by converting them into state constraints using a projection from the state‑input space to the state space along the backup controller. In the special case of decoupled state and input constraints, the proposed method simplifies the synthesis of backup CBFs by eliminating the need for saturating backup control laws. Finally, we demonstrate the efficacy of the proposed method on an inverted pendulum example, where constraints on the angle (state), torque (input), and power (mixture of state and input) are satisfied simultaneously.

Authors:Amin Taghieh, SangWoo Park
Title: Stability Guarantees for Data-Driven Predictive Control of Nonlinear Systems via Approximate Koopman Embeddings
Abstract:
Data‑driven model predictive control based on Willems' fundamental lemma has proven effective for linear systems, but extending stability guarantees to nonlinear systems remains an open challenge. In this paper, we establish conditions under which data‑driven MPC, applied directly to input‑output data from a nonlinear system, yields practical exponential stability. The key insight is that the existence of an approximate Koopman linear embedding certifies that the nonlinear data can be interpreted as noisy data from a linear time‑invariant system, enabling the application of existing robust stability theories. Crucially, the Koopman embedding serves only as a theoretical certificate; the controller itself operates on raw nonlinear data without knowledge of the lifting functions. We further show that the proportional structure of the embedding residual can be exploited to obtain an ultimate bound that depends only on the irreducible offset, rather than the worst‑case embedding error. The framework is demonstrated on a synchronous generator connected to an infinite bus, for which we construct an explicit physics‑informed embedding with error bounds.

Authors:Mahdis Rabbani, Navid Mojahed, Shima Nazari
Title: Asymmetric Nash Seeking via Best Response Maps: Global Linear Convergence and Robustness to Inexact Reaction Models
Abstract:
Nash equilibria provide a principled framework for modeling interactions in multi‑agent decision‑making and control. However, many equilibrium‑seeking methods implicitly assume that each agent has access to the other agents' objectives and constraints, an assumption that is often unrealistic in practice. This letter studies a class of asymmetric‑information two‑player constrained games with decoupled feasible sets, in which Player 1 knows its own objective and constraints while Player 2 is available only through a best‑response map. For this class of games, we propose an asymmetric projected gradient descent‑best response iteration that does not require full mutual knowledge of both players' optimization problems. Under suitable regularity conditions, we establish the existence and uniqueness of the Nash equilibrium and prove global linear convergence of the proposed iteration when the best‑response map is exact. Recognizing that best‑response maps are often learned or estimated, we further analyze the inexact case and show that, when the approximation error is uniformly bounded by \varepsilon, the iterates enter an explicit O(\varepsilon) neighborhood of the true Nash equilibrium. Numerical results on a benchmark game corroborate the predicted convergence behavior and error scaling.

Authors:Kasidit Muenprasitivej, Derya Aksaray
Title: Contingency-Aware Planning via Certified Neural Hamilton-Jacobi Reachability
Abstract:
Hamilton‑Jacobi (HJ) reachability provides formal safety guarantees for dynamical systems, but solving high‑dimensional HJ partial differential equations limits its use in real‑time planning. This paper presents a contingency‑aware multi‑goal navigation framework that integrates learning‑based reachability with sampling‑based planning in unknown environments. We use Fourier Neural Operator (FNO) to approximate the solution operator of the Hamilton‑Jacobi‑Isaacs variational inequality under varying obstacle configurations. We first provide a theoretical under‑approximation guarantee on the safe backward reach‑avoid set, which enables formal safety certification of the learned reachable sets. Then, we integrate the certified reachable sets with an incremental multi‑goal planner, which enforces reachable‑set constraints and a recovery policy that guarantees finite‑time return to a safe region. Overall, we demonstrate that the proposed framework achieves asymptotically optimal navigation with provable contingency behavior, and validate its performance through real‑time deployment on KUKA's youBot in Webots simulation.

Authors:Pablo Krupa, Alberto Bemporad
Title: Learning generalized Nash equilibria from pairwise preferences
Abstract:
Generalized Nash Equilibrium Problems (GNEPs) arise in many applications, including non‑cooperative multi‑agent control problems. Although many methods exist for finding generalized Nash equilibria, most of them rely on assuming knowledge of the objective functions or being able to query the best responses of the agents. We present a method for learning solutions of GNEPs only based on querying agents for their preference between two alternative decisions. We use the collected preference data to learn a GNEP whose equilibrium approximates a GNE of the underlying (unknown) problem. Preference queries are selected using an active‑learning strategy that balances exploration of the decision space and exploitation of the learned GNEP. We present numerical results on game‑theoretic linear quadratic regulation problems, as well as on other literature GNEP examples, showing the effectiveness of the proposed method.

Authors:Darshan Gadginmath, Ahmed Allibhoy, Fabio Pasqualetti
Title: Constricting Tubes for Prescribed-Time Safe Control
Abstract:
We propose a constricting Control Barrier Function (CBF) framework for prescribed‑time control of control‑affine systems with input constraints. Given a system starting outside a target safe set, we construct a time‑varying safety tube that shrinks from a relaxed set containing the initial condition to the target set at a user‑specified deadline. Any controller rendering this tube forward invariant guarantees prescribed‑time recovery by construction. The constriction schedule is bounded and tunable by design, in contrast to prescribed‑time methods where control effort diverges near the deadline. Feasibility under input constraints reduces to a single verifiable condition on the constriction rate, yielding a closed‑form minimum recovery time as a function of control authority and initial violation. The framework imposes a single affine constraint per timestep regardless of state dimension, scaling to settings where grid‑based reachability methods are intractable. We validate on a 16‑dimensional multi‑agent system and a unicycle reach‑avoid problem, demonstrating prescribed‑time recovery with bounded control effort.

Authors:Jiahua Hu, Hai L. Vu, Wynita Griggs, Hao Wang
Title: Impacts of Electric Vehicle Charging Regimes and Infrastructure Deployments on System Performance: An Agent-Based Study
Abstract:
The rapid growth of electric vehicles (EVs) requires more effective charging infrastructure planning. Infrastructure layout not only determines deployment cost, but also reshapes charging behavior and influences overall system performance. In addition, destination charging and en‑route charging represent distinct charging regimes associated with different power requirements, which may lead to substantially different infrastructure deployment outcomes. This study applies an agent‑based modeling framework to generate trajectory‑level latent public charging demand under three charging regimes based on a synthetic representation of the Melbourne (Australia) metropolitan area. Two deployment strategies, an optimization‑based approach and a utilization‑refined approach, are evaluated across different infrastructure layouts. Results show that utilization‑refined deployments reduce total system cost, accounting for both infrastructure deployment cost and user generalized charging cost, with the most significant improvement observed under the combined charging regime. In particular, a more effective allocation of AC slow chargers reshapes destination charging behavior, which in turn reduces unnecessary reliance on en‑route charging and lowers detour costs associated with en‑route charging. This interaction highlights the behavioral linkage between destination and en‑route charging regimes and demonstrates the importance of accounting for user response and multiple charging regimes in charging infrastructure planning.

Authors:Mohsen Sahraei Ardakani, Hong Wan, Rui Song
Title: Entropy-Aware Task Offloading in Mobile Edge Computing
Abstract:
Mobile Edge Computing (MEC) technology has been introduced to enable could computing at the edge of the network in order to help resource limited mobile devices with time sensitive data processing tasks. In this paradigm, mobile devices can offload their computationally heavy tasks to more efficient nearby MEC servers via wireless communication. Consequently, the main focus of researches on the subject has been on development of efficient offloading schemes, leaving the privacy of mobile user out. While the Blockchain technology is used as the trust mechanism for secured sharing of the data, the privacy issues induced from wireless communication, namely, usage pattern and location privacy are the centerpiece of this work. The effects of these privacy concerns on the task offloading Markov Decision Process (MDP) is addressed and the MDP is solved using a Deep Recurrent Q‑Netwrok (DRQN). The Numerical simulations are presented to show the effectiveness of the proposed method.

Authors:Elsa Bou Gebrael, Majd Olleik, Sebastian Zwickl-Bernhard
Title: Market Power and Distributed Solar Integration in Microgrids under Limited Regulation
Abstract:
Decentralized electricity systems increasingly emerge where centralized grids fail to provide reliable supply. In such settings, privately operated neighborhood microgrids, often based on diesel generators, exhibit significant market power, limited regulatory oversight, and high environmental externalities. In parallel, households increasingly deploy off‑grid solar photovoltaic (PV) systems to gain control over electricity supply. However, these systems suffer from curtailed excess generation during peak solar hours and unreliable access at other times. While prior studies have optimized microgrids in low‑reliability grid contexts from a techno‑economic perspective, they largely neglect the market power exerted by monopolistic private generators. This paper addresses this gap by developing a bi‑level game‑theoretic model that enables household‑generated electricity to be fed into the microgrid while explicitly accounting for the market power of a neighborhood diesel generator company (DGC). The regulator sets price and feed‑in‑tariff caps to maximize household economic surplus (HES), while the DGC acts as a profit‑maximizing agent controlling access and supply. The model is illustrated using high‑resolution empirical data from Lebanon. Results show that: (i) price and feed‑in‑tariff caps substantially increase HES and consistently induce significant household PV feed‑in to the microgrid; (ii) higher DGC budgets or greater PV‑owner penetration lead to pronounced gains in HES; and (iii) the renewable energy share reaches 60% under base conditions and approaches 100% at sufficiently high budgets or PV‑owner penetration levels, compared to 0% under the status quo.

Authors:Lida Lamakani, Efstratios N. Pistikopoulos
Title: Multiparametric continuous-time optimal control via Pontryagin's Maximum Principle: explicit solutions and comparisons with discrete-time formulations
Abstract:
Model predictive control offers a powerful framework for managing constrained systems, but its repeated online optimization can become computationally prohibitive. Multiparametric programming addresses this challenge by precomputing optimal solutions offline, enabling real‑time control through simple function evaluation. While extensively developed for discrete‑time systems, this approach suffers from combinatorial growth in solution complexity as discretization is refined. This paper presents a systematic continuous‑time multiparametric framework for linear‑quadratic optimal control that directly solves Pontryagin's optimality conditions without discretization artifacts. Through two illustrative examples, we demonstrate that continuous‑time formulations yield solutions with substantially fewer critical regions than their discrete‑time counterparts. Beyond this reduction in partition complexity, the continuous‑time approach provides deeper insight into system dynamics by explicitly identifying switching times and eliminating discretization artifacts that obscure the true structure of optimal control policies. Knowledge of the switching structure also accelerates online optimization methods by providing analytical information about the solution topology. Clear step‑by‑step algorithms are provided for identifying switching structures, computing parametric switching times, and constructing critical regions, making the continuous‑time framework accessible for practical implementation.

Authors:Ramy Rashad
Title: The Port-Hamiltonian Structure of Vehicle Manipulator Systems
Abstract:
This paper presents a port‑Hamiltonian formulation of vehicle‑manipulator systems (VMS), a broad class of robotic systems including aerial manipulators, underwater manipulators, space robots, and omnidirectional mobile manipulators. Unlike existing Lagrangian formulations that obscure the underlying energetic structure, the proposed port‑Hamiltonian formulation explicitly reveals the energy flow and conservation properties of these complex mechanical systems. We derive the port‑Hamiltonian dynamics from first principles using Hamiltonian reduction theory. Two complementary formulations are presented: a standard form that directly exposes the energy structure, and an inertially‑decoupled form that leverages the principal bundle structure of the VMS configuration space and is particularly suitable for control design and numerical simulation. The coordinate‑free geometric approach we follow avoids singularities associated with local parameterizations of the base orientation. We rigorously establish the mathematical equivalence between our port‑Hamiltonian formulations and existing reduced Euler‑Lagrange and Boltzmann‑Hamel equations found in the robotics and geometric mechanics literature.

Authors:Chidozie Ezeakunne, Jose E. Tabarez, Reeju Pokharel, Anup Pandey
Title: PowerModelsGAT-AI: Physics-Informed Graph Attention for Multi-System Power Flow with Continual Learning
Abstract:
Solving the alternating current power flow equations in real time is essential for secure grid operation, yet classical Newton‑Raphson solvers can be slow under stressed conditions. Existing graph neural networks for power flow are typically trained on a single system and often degrade on different systems. We present PowerModelsGAT‑AI, a physics‑informed graph attention network that predicts bus voltages and generator injections. The model uses bus‑type‑aware masking to handle different bus types and balances multiple loss terms, including a power‑mismatch penalty, using learned weights. We evaluate the model on 14 benchmark systems (4 to 6,470 buses) and train a unified model on 13 of these under N‑2 (two‑branch outage) conditions, achieving an average normalized mean absolute error of 0.89% for voltage magnitudes and R^2 > 0.99 for voltage angles. We also show continual learning: when adapting a base model to a new 1,354‑bus system, standard fine‑tuning causes severe forgetting with error increases exceeding 1000% on base systems, while our experience replay and elastic weight consolidation strategy keeps error increases below 2% and in some cases improves base‑system performance. Interpretability analysis shows that learned attention weights correlate with physical branch parameters (susceptance: r = 0.38; thermal limits: r = 0.22), and feature importance analysis supports that the model captures established power flow relationships.

Authors:Liraz Mudrik, Isaac Kaminer, Sean Kragelund, Abram H. Clark
Title: Prescribed-Time Distributed Generalized Nash Equilibrium Seeking
Abstract:
This paper proposes the first fully distributed algorithm for finding the Generalized Nash Equilibrium (GNE) of a non‑cooperative game with shared coupling constraints and general cost coupling at a user‑prescribed finite time T. As a foundation, a centralized gradient‑based prescribed‑time convergence result is established for the GNE problem, extending the optimization Lyapunov function framework to gradient dynamics, the only known realization among existing alternatives that naturally decomposes into per‑agent computations. Building on this, a fully distributed architecture is designed in which each agent concurrently runs three coupled dynamics: a prescribed‑time distributed state observer, a gradient‑based optimization law, and a dual consensus mechanism that enforces the shared‑multiplier requirement of the variational GNE, thus guaranteeing convergence to the same solution as the centralized case. The simultaneous operation of these layers creates bidirectional perturbations between consensus and optimization, which are resolved through gain synchronization that matches the temporal singularities of the optimization and consensus layers, ensuring all error components vanish exactly at T. The Fischer‑Burmeister reformulation renders the algorithm projection‑free and guarantees constraint satisfaction at the deadline. Numerical simulations on a Nash‑Cournot game and a time‑critical sensor coverage problem validate the approach.

Authors:Navid Mojahed, Mahdis Rabbani, Shima Nazari
Title: Koopman Lifted Finite Memory Identification via Truncated Grunwald Letnikov Kernels
Abstract:
We propose a data‑driven linear modeling framework for controlled nonlinear hereditary systems that combines Koopman lifting with a truncated Grunwald‑Letnikov memory term. The key idea is to model nonlinear state dependence through a lifted observable representation while imposing history dependence directly in the lifted coordinates through fixed fractional‑difference weights. This preserves linearity in the lifted state‑transition and input matrices, yielding a memory‑compensated regression that can be identified from input‑state data by least squares and extending standard Koopman‑based identification beyond the Markovian setting. We further derive an equivalent augmented Markovian realization by stacking a finite window of lifted states, thereby rewriting the finite‑memory recursion as a standard discrete‑time linear state‑space model. Numerical experiments on a nonlinear hereditary benchmark with a non‑Grunwald‑Letnikov Prony‑series ground‑truth kernel demonstrate improved multi‑step open‑loop prediction accuracy relative to memoryless Koopman and non‑lifted state‑space baselines.

Authors:Jello Zhou, Vudtiwat Ngampruetikorn, David J. Schwab
Title: Stochastic Resetting Accelerates Policy Convergence in Reinforcement Learning
Abstract:
Stochastic resetting, where a dynamical process is intermittently returned to a fixed reference state, has emerged as a powerful mechanism for optimizing first‑passage properties. Existing theory largely treats static, non‑learning processes. Here we ask how stochastic resetting interacts with reinforcement learning, where the underlying dynamics adapt through experience. In tabular grid environments, we find that resetting accelerates policy convergence even when it does not reduce the search time of a purely diffusive agent, indicating a novel mechanism beyond classical first‑passage optimization. In a continuous control task with neural‑network‑based value approximation, we show that random resetting improves deep reinforcement learning when exploration is difficult and rewards are sparse. Unlike temporal discounting, resetting preserves the optimal policy while accelerating convergence by truncating long, uninformative trajectories to enhance value propagation. Our results establish stochastic resetting as a simple, tunable mechanism for accelerating learning, translating a canonical phenomenon of statistical mechanics into an optimization principle for reinforcement learning.

Authors:Arslan Ahmad, Ian Dobson
Title: Typical models of the distribution system restoration process
Abstract:
Accurate probabilistic modeling of the power system restoration process is essential for resilience planning, operational decision‑making, and realistic simulation of resilience events. In this work, we develop data‑driven probabilistic models of the restoration process using outage data from four distribution utilities. We decompose restoration into three components: normalized restore time progression, total restoration duration, and the time to first restore. The Beta distribution provides the best‑pooled fit for restore time progression, and the Uniform distribution is a defensible, parsimonious approximation for many events. Total duration is modeled as a heteroskedastic Lognormal process that scales superlinearly with event size. The time to first restore is well described by a Gamma model for moderate and large events. Together, these models provide an end‑to‑end stochastic model for Monte Carlo simulation, probabilistic duration forecasting, and resilience planning that moves beyond summary statistics, enabling uncertainty‑aware decision support grounded in utility data.

Authors:Arslan Ahmad, Ian Dobson
Title: Measuring outage resilience in a distribution system with the number of outages in large events
Abstract:
We develop LENORI, a Large Event Number of Outages Resilience Index measuring distribution system resilience with the number of forced line outages observed in large extreme events. LENORI is calculated from standard utility outage data. The statistical accuracy of LENORI is ensured by taking the logarithm of the outage data. A related Average Large Event Number of Outages metric ALENO is also developed, and both metrics are applied to a distribution system to quantify the power grid strength relative to the extreme events stressing the grid. The metrics can be used to track resilience and quantify the contributions of various types of hazards to the overall resilience.

Authors:Lea Bold, Irene Schimperna, Karl Worthmann, Johannes Köhler
Title: Exponential stability of data-driven nonlinear MPC based on input/output models
Abstract:
We consider nonlinear model predictive control (MPC) schemes without stabilizing terminal conditions, where the model used in the optimization step is generated based on input‑output data only. We establish exponential stability for sufficiently long prediction horizons assuming exponential stabilizability and a proportional error bound. Moreover, we verify the imposed condition on the approximation using kernel interpolation and demonstrate the practical applicability to nonlinear systems by numerical simulations.

Authors:Leonardo Pedroso, Pedro Batista, W. P. M. H. Heemels
Title: Overlapping Covariance Intersection: Fusion with Partial Structural Knowledge of Correlation from Multiple Sources
Abstract:
Emerging large‑scale engineering systems rely on distributed fusion for situational awareness, where agents combine noisy local sensor measurements with exchanged information to obtain fused estimates. However, at the sheer scale of these systems, tracking cross‑correlations becomes infeasible, preventing the use of optimal filters. Covariance intersection (CI) methods address fusion problems with unknown correlations by minimizing worst‑case uncertainty based on available information. Existing CI extensions exploit limited correlation knowledge but cannot incorporate structural knowledge of correlation from multiple sources, which naturally arises in distributed fusion problems. This paper introduces Overlapping Covariance Intersection (OCI), a generalized CI framework that accommodates this novel information structure. We formalize the OCI problem and establish necessary and sufficient conditions for feasibility. We show that a family‑optimal solution can be computed efficiently via semidefinite programming, enabling real‑time implementation. The proposed tools enable improved fusion performance for large‑scale systems while retaining robustness to unknown correlations.

Authors:Theofania Karampela, Ryne Beeson
Title: A Variational Pseudo-Observation Guided Nudged Particle Filter
Abstract:
Nonlinear filtering with standard PF methods requires mitigative techniques to quell weight degeneracy, such as resampling. This is especially true in high‑dimensional systems with sparse observations. Unfortunately, such techniques are also fragile when applied to systems with exceedingly rare events. Nonlinear systems with these properties can be assimilated effectively with a control‑based PF method known as the nPF, but this method has a high computational cost burden. In this work, we aim to retain this strength of the nudged method while reducing the computational cost by introducing a variational method into the algorithm that acts as a continuous pseudo‑observation path. By maintaining a PF representation, the resulting algorithm continues to capture an approximation of the filtering distribution, while reducing computational runtime and improving robustness to the "rare" event of switching phases. Preliminary testing of the new approach is demonstrated on a stochastic variant of the nonlinear and chaotic L63 model, which is used as a surrogate for mimicking "rare" events. The new approach helps to overcome difficulties in applying the nPF for realistic problems and performs favorably with respect to a standard PF with a higher number of particles.

Authors:Davide Salzano, Gian Carlo Maffettone, Mario di Bernardo
Title: Robust multi-scale leader-follower control of large multi-agent systems
Abstract:
In many multi‑agent systems of practical interest, such as traffic networks or crowd evacuation, control actions cannot be exerted on all agents. Instead, controllable leaders must indirectly steer uncontrolled followers through local interactions. Existing results address either leader‑follower density control of simple, unperturbed multi‑agent systems or robust density control of a single directly actuated population, but not their combination. We bridge this gap by deriving a coupled continuum description for leaders and followers subject to unknown bounded perturbations, and designing a macroscopic feedback law that guarantees global asymptotic convergence of the followers' density to a desired distribution. The coupled stability of the leader‑follower system is analyzed via singular perturbation theory, and an explicit lower bound on the leader‑to‑follower mass ratio required for feasibility is derived. Numerical simulations on heterogeneous biased random walkers validate our theoretical findings.

Authors:Ruslan Zakirzyanov
Title: Bio-inspired metaheuristic optimization for hierarchical architecture design of industrial control systems
Abstract:
Automated process control systems (APCS) are widely used in modern industrial enterprises. They address three key objectives: ensuring the required quality of manufactured products, ensuring process safety for people and the environment, and reducing capital and operating costs. At large industrial enterprises, APCSs are typically geographically distributed and characterized by a large number of monitored parameters. Such systems often consist of several subsystems built using various technical means and serving different functional purposes. APCSs usually have a hierarchical structure consisting of several levels, where each level hosts commercially available technical devices with predetermined characteristics. This article examines the engineering problem of selecting an optimal software and hardware structure for a distributed process control system applied to a continuous process in the chemical industry. A formal formulation of the optimization problem is presented, in which the hierarchical structure of the system is represented as an acyclic graph. Optimization criteria and constraints are defined. A solution method based on a metaheuristic ant colony optimization algorithm, widely used for this class of problems, is proposed. A brief overview of the developed software tool used to solve a number of numerical examples is provided. The experimental results are discussed, along with parameter selection and possible algorithm modifications aimed at improving solution quality. Information on the verification of the control system implemented using the selected software and hardware structure is presented, and directions for further research are outlined.

Authors:Alessio Rimoldi, Carlo Cenedese, Alberto Padoan, Florian Dörfler, John Lygeros
Title: Data-driven generalized perimeter control: Zürich case study
Abstract:
Urban traffic congestion is a key challenge for the development of modern cities, requiring advanced control techniques to optimize existing infrastructures usage. Despite the extensive availability of data, modeling such complex systems remains an expensive and time consuming step when designing model‑based control approaches. On the other hand, machine learning approaches require simulations to bootstrap models, or are unable to deal with the sparse nature of traffic data and enforce hard constraints. We propose a novel formulation of traffic dynamics based on behavioral systems theory and apply data‑enabled predictive control to steer traffic dynamics via dynamic traffic light control. A high‑fidelity simulation of the city of Zürich, the largest closed‑loop microscopic simulation of urban traffic in the literature to the best of our knowledge, is used to validate the performance of the proposed method in terms of total travel time and CO2 emissions.

Authors:Ardavan Rahimian
Title: A Baseline Mobility-Aware IRS-Assisted Uplink Framework With Energy-Detection-Based Channel Allocation
Abstract:
This paper develops a self‑contained framework for studying a mobility‑aware intelligent reflecting surface (IRS)‑assisted multi‑node uplink under simplified but explicit modeling assumptions. The considered system combines direct and IRS‑assisted narrowband propagation, geometric IRS phase control with finite‑bit phase quantization, adaptive IRS‑user focusing based on inverse‑rate priority weights, and sequential channel allocation guided by energy detection. The analytical development is restricted to a physics‑based two‑hop cascaded path‑loss formulation with appropriate scaling, an expectation‑level reflected‑power characterization under the stated independence assumptions, and the exact chi‑square threshold for energy detection, together with its large‑sample Gaussian approximation. A MATLAB implementation is used to generate a sample run, which is interpreted as a numerical example. This work is intended as a consistent, practically‑aligned baseline to support future extensions involving richer mobility models or more advanced scheduling policies.

Authors:Souvik Das, Siddhartha Ganguly
Title: Residual-based attack detection in cyber-physical systems: an optimal transport viewpoint
Abstract:
This letter presents an optimal‑transport (OT)‑driven, distributionally robust attack detection algorithm, OT‑DETECT, for cyber‑physical systems (CPS) modeled as partially observed linear stochastic systems. The underlying detection problem is formulated as a minmax optimization problem using 1‑Wasserstein ambiguity sets constructed from observer residuals under both the nominal (attack‑free) and attacked regimes, and show that the minmax detection problem can be reduced to a finite‑dimensional linear program for computing the worst‑case distribution (WCD). Off‑support residuals are handled via a kernel‑smoothed score function that drives a CUSUM procedure for sequential detection. We also establish a non‑asymptotic tail bound on the false‑positive error of the CUSUM statistic under the nominal (attack‑free) condition, under mild assumptions. Numerical illustrations are provided to evaluate the robustness properties of OT‑DETECT.

Authors:Han Zhou, Haojie Chang, David Widen
Title: Deep Learning-Driven Black-Box Doherty Power Amplifier with Pixelated Output Combiner and Extended Efficiency Range
Abstract:
This article presents a deep learning‑driven inverse design methodology for Doherty power amplifiers (PA) with multi‑port pixelated output combiner networks. A deep convolutional neural network (CNN) is developed and trained as an electromagnetic (EM) surrogate model to accurately and rapidly predict the S‑parameters of pixelated passive networks. By leveraging the CNN‑based surrogate model within a blackbox Doherty framework and a genetic algorithm (GA)‑based optimizer, we effectively synthesize complex Doherty combiners that enable an extended back‑off efficiency range using fully symmetrical devices. As a proof of concept, we designed and fabricated two Doherty PA prototypes incorporating three‑port pixelated combiners, implemented with GaN HEMT transistors. In measurements, both prototypes demonstrate a maximum drain efficiency exceeding 74% and deliver an output power surpassing 44.1 dBm at 2.75 GHz. Furthermore, a measured drain efficiency above 52% is maintained at the 9‑dB back‑off power level for both prototypes at the same frequency. To evaluate linearity and efficiency under realistic signal conditions, both prototypes are tested using a 20‑MHz 5G new radio (NR)‑like waveform exhibiting a peak‑to‑average power ratio (PAPR) of 9.0 dB. After applying digital predistortion (DPD), each design achieves an average power added efficiency (PAE) above 51%, while maintaining an adjacent channel leakage ratio (ACLR) better than ‑60.8 dBc.

Authors:Shokoufeh Naderi, Maude Blondin, Sébastien Roy
Title: Consensus in Multi-Agent Systems with Uniform and Nonuniform Communication Delays
Abstract:
This paper analyzes consensus in multi‑agent systems under uniform and nonuniform communication delays, a key challenge in distributed coordination with applications to robotic swarms. It investigates the convergence of a consensus algorithm accounting for delays across communication links in a connected, undirected graph. Novel convergence results are derived using Rouché's theorem and Lyapunov‑based stability analysis. The system is shown to reach consensus at a steady‑state value given by a weighted average determined by the delay distribution, with stability ensured under explicit parameter bounds. Both uniform and nonuniform delay scenarios are analyzed, and the corresponding convergence values are explicitly derived. The theoretical results are validated through simulations, which explore the impact of delay heterogeneity on consensus outcomes. Furthermore, the algorithm is implemented and experimentally tested on a swarm of QBOT3 ground robots to solve the rendezvous problem, demonstrating the agents' ability to converge to a common location despite realistic communication constraints, thus confirming the algorithm's robustness and practical applicability. The results provide guidelines for designing consensus protocols that tolerate communication delays, offer insights into the relationship between network delays and coordination performance, and demonstrate their applicability to distributed robotic systems.

Authors:Lauren Ervin, Harish Bezawada, Vishesh Vikas
Title: When Rolling Gets Weird: A Curved-Link Tensegrity Robot for Non-Intuitive Behavior
Abstract:
Conventional mobile tensegrity robots constructed with straight links offer mobility at the cost of locomotion speed. While spherical robots provide highly effective rolling behavior, they often lack the stability required for navigating unstructured terrain common in many space exploration environments. This research presents a solution with a semi‑circular, curved‑link tensegrity robot that strikes a balance between efficient rolling locomotion and controlled stability, enabled by discontinuities present at the arc endpoints. Building upon an existing geometric static modeling framework [1], this work presents the system design of an improved Tensegrity eXploratory Robot 2 (TeXploR2). Internal shifting masses instantaneously roll along each curved‑link, dynamically altering the two points of contact with the ground plane. Simulations of quasistatic, piecewise continuous locomotion sequences reveal new insights into the positional displacement between inertial and body frames. Non‑intuitive rolling behaviors are identified and experimentally validated using a tetherless prototype, demonstrating successful dynamic locomotion. A preliminary impact test highlights the tensegrity structure's inherent shock absorption capabilities and conformability. Future work will focus on finalizing a dynamic model that is experimentally validated with extended testing in real‑world environments as well as further refinement of the prototype to incorporate additional curved‑links and subsequent ground contact points for increased controllability.

Authors:Amon Lahr, Anna Scampicchio, Johannes Köhler, Melanie N. Zeilinger
Title: Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function
Abstract:
Non‑conservative uncertainty bounds are essential for making reliable predictions about latent functions from noisy data, and thus, a key enabler for safe learning‑based control. In this domain, kernel methods such as Gaussian process regression are established techniques, thanks to their inherent uncertainty quantification mechanism. Still, existing bounds either pose strong assumptions on the underlying noise distribution, are conservative, do not directly apply in the multi‑output case, or are difficult to integrate into downstream tasks. This paper addresses these limitations by presenting a tight, deterministic bound for multi‑output functions in Reproducing Kernel Hilbert Spaces (RKHSs) subject to bounded noise. It is obtained through an unconstrained, duality‑based formulation, which shares the same structure as classic Gaussian process confidence bounds, and can thus be straightforwardly integrated into downstream optimization pipelines. We show that the proposed bound generalizes existing results and illustrate its application using an example inspired by quadrotor dynamics learning.

Authors:Linghao Zhang, Haitao Zhao, Bo Xu, Hongbo Zhu, Xianbin Wang
Title: Agentic AI for SAGIN Resource Management_Semantic Awareness, Orchestration, and Optimization
Abstract:
Space‑air‑ground integrated networks (SAGIN) promise ubiquitous 6G connectivity but face significant resource management challenges due to heterogeneous infrastructure, dynamic topologies, and stringent quality‑of‑service (QoS) requirements. Conventional model‑driven approaches struggle with scalability and adaptability in such complex environments. This paper presents an agentic artificial intelligence (AI) framework for autonomous SAGIN resource management by embedding large language model (LLM)‑based agents into a Monitor‑Analyze‑Plan‑ Execute‑Knowledge (MAPE‑K) control plane. The framework incorporates three specialized agents, namely semantic resource perceivers, intent‑driven orchestrators, and adaptive learners, that collaborate through natural language reasoning to bridge the gap between operator intents and network execution. A key innovation is the hierarchical agent‑reinforcement learning (RL) collaboration mechanism, wherein LLM‑based orchestrators dynamically shape reward functions for RL agents based on semantic network conditions. Validation through UAV‑assisted AIGC service orchestration in energy‑constrained scenarios demonstrates that LLM‑driven reward shaping achieves 14% energy reduction and the lowest average service latency among all compared methods. This agentic paradigm offers a scalable pathway toward adaptive, AI‑native 6G networks, capable of autonomously interpreting intents and adapting to dynamic environments.

Authors:Qi Wei, Jianfeng Tao, Hongyu Nie, Wangtao Tan
Title: Early-Terminable Energy-Safe Iterative Coupling for Parallel Simulation of Port-Hamiltonian Systems
Abstract:
Parallel simulation and control of large‑scale robotic systems often rely on partitioned time stepping, yet finite‑iteration coupling can inject spurious energy by violating power consistency‑‑even when each subsystem is passive. This letter proposes a novel energy‑safe, early‑terminable iterative coupling for port‑Hamiltonian subsystems by embedding a Douglas‑‑Rachford (DR) splitting scheme in scattering (wave) coordinates. The lossless interconnection is enforced as an orthogonal constraint in the wave domain, while each subsystem contributes a discrete‑time scattering port map induced by its one‑step integrator. Under a discrete passivity condition on the subsystem time steps and a mild impedance‑tuning condition, we prove an augmented‑storage inequality certifying discrete passivity of the coupled macro‑step for any finite inner‑iteration budget, with the remaining mismatch captured by an explicit residual. As the inner budget increases, the partitioned update converges to the monolithic discrete‑time update induced by the same integrators, yielding a principled, adaptive accuracy‑‑compute trade‑off, supporting energy‑consistent real‑time parallel simulation under varying computational budgets. Experiments on a coupled‑oscillator benchmark validate the passivity certificates at numerical roundoff (on the order of 10e‑14 in double precision) and show that the reported RMS state error decays monotonically with increasing inner‑iteration budgets, consistent with the hard‑coupling limit.

Authors:Qi Wei, Jianfeng Tao, Haoyang Tan, Hongyu Nie
Title: Featurized Occupation Measures for Structured Global Search in Numerical Optimal Control
Abstract:
Numerical optimal control has long been split between globally structured but dimensionally intractable Hamilton‑‑Jacobi‑‑Bellman (HJB) methods and scalable but local trajectory optimization. We introduce Featurized Occupation Measures (FOM), a finite‑dimensional primal‑‑dual interface for coupling numerical optimal control solvers with explicit HJB subsolutions: the certificate guides the primal search, while primal residuals tighten the certificate in a primal‑dual language. Two realizations are developed. The explicit realization uses finite weak‑form Liouville tests, and the implicit realization couples rollout‑based search with sampled primal‑‑dual residuals. Both are proved asymptotically consistent with the exact occupation‑measure linear program under refinement, separating primal expressiveness from dual accuracy in the limit. The framework also gives structural conditions under which HJB‑type certificates avoid full state‑space representation. For factor graphs induced by compatible passivity‑based interconnections, blockwise HJB inequalities assemble into globally feasible OM‑dual certificates, and the decomposition is preserved under blockwise approximation. The curse of dimensionality is then shifted from state space to interconnection topology. Approximate certificates remain reusable under time shifts and bounded model perturbations, with explicit degradation bounds. On a static obstacle‑avoidance benchmark, certificates of increasing tightness guide a sample‑based optimizer toward global optima, confirming that even a coarse certificate carries useful global information.

Authors:Lanhao Zhao, Yangzhou Chen
Title: Decentralized design of leader-following consensus protocols for asymmetric matrix-weighted heterogeneous multiagent systems
Abstract:
This paper investigates a decentralized design approach of leader‑following consensus protocols for heterogeneous multiagent systems under a fixed communication topology with a directed spanning tree (DST) and asymmetric weight matrix. First, a control protocol using only the information of the neighbor on the DST of each agent is designed, which is called the consensus protocol with minimal communication links. Particularly, the DST‑based linear transformation method is used to transform the consensus problem into a partial variable stability problem of a corresponding system, and a decentralized design method is proposed to find the gain matrices in the protocols. Next, the decentralized design approach is extended to the protocols using all neighbor information in the original communication topology with the help of the matrix diagonally dominant method. Some numerical simulations are given to illustrate the theoretical results.

Authors:Yangzhou Chen, Lanhao Zhao
Title: Decentralized design of consensus protocols with minimal communication links based on directed spanning tree
Abstract:
This paper proposes a decentralized design approach of consensus protocols of multi‑agent systems via a directed‑spanning‑tree(DST)‑based linear transformation and the corresponding minimal communication links. First, the consensus problem of multi‑agent systems is transformed into the decentralized output stabilization problem by constructing a linear transformation based on a DST of the communication topology, and thus a necessary and sufficient consensus criterion in terms of decentralized fixed mode is derived. Next, a new distributed protocol is designed by using only the neighbors information on the DST, which is a fully decentralized design approach. Finally, some numerical examples are given to verify the results attained.

Authors:Arno Strouwen, Sebastian Micluţa-Câmpeanu
Title: Deep Adaptive Model-Based Design of Experiments
Abstract:
Model‑based design of experiments (MBDOE) is essential for efficient parameter estimation in nonlinear dynamical systems. However, conventional adaptive MBDOE requires costly posterior inference and design optimization between each experimental step, precluding real‑time applications. We address this by combining Deep Adaptive Design (DAD), which amortizes sequential design into a neural network policy trained offline, with differentiable mechanistic models. For dynamical systems with known governing equations but uncertain parameters, we extend sequential contrastive training objectives to handle nuisance parameters and propose a transformer‑based policy architecture that respects the temporal structure of dynamical systems. We demonstrate the approach on four systems of increasing complexity: a fed‑batch bioreactor with Monod kinetics, a Haldane bioreactor with uncertain substrate inhibition, a two‑compartment pharmacokinetic model with nuisance clearance parameters, and a DC motor for real‑time deployment.

Authors:Milad Alipour Shahraki, Laurent Lessard
Title: Near-Optimal Constrained Feedback Control of Nonlinear Systems via Approximate HJB and Control Barrier Functions
Abstract:
This paper presents a two‑stage framework for constrained near‑optimal feedback control of input‑affine nonlinear systems. An approximate value function for the unconstrained control problem is computed offline by solving the Hamilton‑‑Jacobi‑‑Bellman equation. Online, a quadratic program is solved that minimizes the associated approximate Hamiltonian subject to safety constraints imposed via control barrier functions. Our proposed architecture decouples performance from constraint enforcement, allowing constraints to be modified online without recomputing the value function. Validation on a linear 2‑state 1D hovercraft and a nonlinear 9‑state spacecraft attitude control problem demonstrates near‑optimal performance relative to open‑loop optimal control benchmarks and superior performance compared to control Lyapunov function‑based controllers.

Authors:Tianyu Han, Guangwei Wang, Bo Wang
Title: Eliminating Persistent Boundary Residence via Matrosov-Type Auxiliary Functions
Abstract:
Control barrier functions enforce safety by guaranteeing forward invariance of an admissible set. Under standard (non‑strict) barrier conditions, however, forward invariance alone does not prevent trajectories from remaining on the boundary of the safe set for arbitrarily long time intervals, potentially leading to boundary sticking or deadlock phenomena. This paper studies the elimination of persistent boundary residence under forward‑invariant barrier conditions. Inspired by Matrosov‑type arguments, we introduce an auxiliary function framework that preserves forward invariance while excluding infinite‑time residence within boundary layers. Sufficient conditions are established under which any trajectory can only remain in a prescribed neighborhood of the boundary for finite time, thereby restoring boundary‑level liveness without altering forward invariance. The proposed construction does not rely on singular barrier formulations or controller‑specific modifications, and can be incorporated into standard safety‑critical control architectures. Numerical examples illustrate the removal of boundary sticking behaviors while maintaining safety across representative systems.

Authors:Solomon Goldgraber Casspi, Daniel Zelazo
Title: The Geometry of Transmission Zeros in Distance-Based Formations
Abstract:
This letter presents a geometric input‑output analysis of distance‑based formation control, focusing on the phenomenon of steady‑state signal blocking between actuator and sensor pairs. We characterize steady‑state multivariable transmission zeros, where fully excited rigid‑body and deformational modes destructively interfere at the measured output. By analyzing the DC gain transfer matrix of the linearized closed‑loop dynamics, we prove that for connected, flexible frameworks, structural transmission zeros are strictly non‑generic; the configuration‑dependent cross‑coupling required to induce them occupies a proper algebraic set of measure zero. However, because extracting actionable sensor‑placement rules from these complex algebraic varieties is analytically intractable, we restrict our focus to infinitesimally rigid formations. For these baselines, we prove that the absence of internal flexes forces the zero‑transmission condition to collapse into an explicit affine hyperplane defined by the actuator and the global formation geometry, which we term the spatial locus of transmission zeros. Finally, we introduce the global transmission polygon‑‑a convex polytope constructed from the intersection of these loci. This construct provides a direct geometric synthesis rule for robust sensor allocation, guaranteeing full‑rank steady‑state transmission against arbitrary single‑node excitations.

Authors:Guido Cavraro, Andrey Bernstein, Emiliano Dall'Anese
Title: Demand Response Under Stochastic, Price-Dependent User Behavior
Abstract:
This paper focuses on price‑based residential demand response implemented through dynamic adjustments of electricity prices during DR events. It extends existing DR models to a stochastic framework in which customer response is represented by price‑dependent random variables, leveraging models and tools from the theory of stochastic optimization with decision‑dependent distributions. The inherent epistemic uncertainty in the customers' responses renders open‑loop, model‑based DR strategies impractical. To address this challenge, the paper proposes to employ stochastic, feedback‑based pricing strategies to compensate for estimation errors and uncertainty in customer response. The paper then establishes theoretical results demonstrating the stability and near‑optimality of the proposed approach and validates its effectiveness through numerical simulations.

Authors:Jungbae Chun, Sengiyumva Kisole, Matthew M. Peet, Peter Seiler
Title: Time-Transformation-Based Analysis of Systems with Periodic Delay via Perturbative Expansion
Abstract:
It is difficult to analyze the stability of systems with time‑varying delays. One approach is to construct a time‑transformation that converts the system into a form with a constant delay but with a time‑varying scalar appearing in the system matrices. The stability of this transformed system can then be analyzed using methods to bound the effect of the time‑varying scalar. One issue is that this transformation is non‑unique and requires the solution of an Abel equation. A specific time‑transformation typically must be computed numerically. We address this issue by computing an explicit, although approximate, time‑transformation for systems where the delay has a constant plus small periodic term. We use a perturbative expansion to construct our explicit solutions. We provide a simple numerical example to illustrate the approach. We also demonstrate the use of this time‑transformation to analyze stability of the system with this class of periodic delays.

Authors:Sengiyumva Kisole, Jungbae Chun, Peter Seiler, Matthew M. Peet
Title: Parameterization of Seed Functions for Equivalent Representations of Time-Varying Delay Systems
Abstract:
Abel's classic transformation shows that any well‑posed system with time‑varying delay is equivalent to a parameter‑varying system with fixed delay. The existence of such a parameter‑varying constant delay representation then simplifies the problems of stability analysis and optimal control. Unfortunately, the method for construction of such transformations has been ad‑hoc ‑‑ requiring an iterative time‑stepping approach to constructing the transformation beginning with a seed function subject to boundary‑value constraints. Moreover, a poor choice of seed function often results in a constant delay representation with large time‑variations in system parameters ‑‑ obviating the benefits of such a representation. In this paper, we show how the set of all feasible seed functions can be parameterized using a basis for L_2. This parameterization is then used to search for seed functions for which the corresponding time‑transformation results in smaller parameter variation. The parameterization of admissible seed functions is illustrated with numerical examples that contrast how well‑chosen and poorly chosen seed functions affect the boundedness of a time transformation.

Authors:Shaked Regev, Eve Tsybina, Slaven Peles
Title: Fast Relax-and-Round Unit Commitment with Economic Horizons
Abstract:
We expand our novel computational method for unit commitment (UC) to include long‑horizon planning. We introduce a fast novel algorithm to commit hydro‑generators, provably accurately. We solve problems with thousands of generators at 5 minute market intervals. We show that our method can solve interconnect size UC problems in approximately 1 minute on a commodity hardware and that an increased planning horizon leads to sizable operational cost savings (our objective). This scale is infeasible for current state‑of‑the‑art tools. We attain this runtime improvement by introducing a heuristic tailored for UC problems. Our method can be implemented using existing continuous optimization solvers and adapted for different applications. Combined, the two algorithms would allow an operator operating large systems with hydro units to make horizon‑aware economic decisions.

Authors:Anchita Dey, Shubhendu Bhasin
Title: Adaptive Tube MPC: Beyond a Common Quadratically Stabilizing Feedback Gain
Abstract:
This paper proposes an adaptive tube framework for model predictive control (MPC) of discrete‑time linear time‑invariant systems subject to parametric uncertainty and additive disturbances. In contrast to conventional tube‑based MPC schemes that employ fixed tube geometry and constraint tightening designed for worst‑case uncertainty, the proposed approach incorporates online parameter learning to progressively refine the parametric uncertainty set and update the parameter estimates. These updates are used to adapt the components of the MPC optimization problem, including the prediction model, feedback gain, terminal set, and tube cross‑sections. As the uncertainty set contracts, the required amount of constraint tightening reduces and the tube shrinks accordingly, yielding less conservative control actions. Recursive feasibility, robust constraint satisfaction, and closed‑loop stability are formally established. Furthermore, the framework does not require the existence of a common quadratically stabilizing linear feedback gain for the entire parametric uncertainty set, thereby relaxing a standard assumption in existing tube‑based MPC formulations. Numerical examples illustrate the effectiveness of the proposed approach.

Authors:Goutam Das, Michael Dorothy, Kyle Volle, Daigo Shishika
Title: Game-Theory-Assisted Reinforcement Learning for Border Defense: Early Termination based on Analytical Solutions
Abstract:
Game theory provides the gold standard for analyzing adversarial engagements, offering strong optimality guarantees. However, these guarantees often become brittle when assumptions such as perfect information are violated. Reinforcement learning (RL), by contrast, is adaptive but can be sample‑inefficient in large, complex domains. This paper introduces a hybrid approach that leverages game‑theoretic insights to improve RL training efficiency. We study a border defense game with limited perceptual range, where defender performance depends on both search and pursuit strategies, making classical differential game solutions inapplicable. Our method employs the Apollonius Circle (AC) to compute equilibrium in the post‑detection phase, enabling early termination of RL episodes without learning pursuit dynamics. This allows RL to concentrate on learning search strategies while guaranteeing optimal continuation after detection. Across single‑ and multi‑defender settings, this early termination method yields 10‑20% higher rewards, faster convergence, and more efficient search trajectories. Extensive experiments validate these findings and demonstrate the overall effectiveness of our approach.

Authors:Taulant Kerci, Angel Vaca, Andrew Groom, Julia Matevosyan, Federico Milano
Title: Rethinking Frequency Control in Power Systems
Abstract:
Frequency control in power systems is implemented in a hierarchical structure traditionally known as primary frequency control (PFC), secondary frequency control (SFC) and tertiary control reserve (TCR) and, some jurisdictions, include time error control (TEC) as well. This hierarchical structure has been designed around a century ago based on timescales separation, that is, approximately an order of magnitude difference between each control structure. This paper argues, based on real‑world observations as well as detailed dynamic simulations on a model of the All‑Island power system (AIPS) of Ireland, that this frequency control structure is not necessary in current and future converter‑dominated power grids. The paper proposes to redesign this structure by removing the SFC and TCR and rely on PFC and a real‑time energy market. The PFC is responsible for addressing fast power imbalances in timescales of tens of ms to few minutes (e.g., 100 ms to 5 minutes) while the real‑time energy market is responsible for addressing longer imbalances in timescales of minutes to hours (e.g., 5 minutes to 1 hour). TEC, on the other hand, is considered as optional.

Authors:Maryam Salamatmoghadasi, Metin Ozturk, Halim Yanikomeroglu
Title: Two-Phase Cell Switching in 6G vHetNets: Sleeping-Cell Load Estimation and Renewable-Aware Switching Toward NES
Abstract:
This paper proposes a two phase framework to improve the sustainability in vertical heterogeneous networks that integrate various types of base stations~(BSs), including terrestrial macro BSs~(MBSs), small BSs~(SBSs), and a high altitude platform station super MBS (HAPS SMBS). In Phase I, we address the critical and often overlooked challenge of estimating the traffic load of sleeping SBSs, a prerequisite for practical cell switching, by introducing three methods with varying data dependencies: (i) a distance based estimator (no historical data), (ii) a multi level clustering (MLC) estimator (limited historical data), and (iii) a long short term memory~(LSTM) based temporal predictor (full historical data). In Phase II, we incorporate the most accurate estimation results from Phase I into a renewable energy aware cell switching strategy, explicitly modeling solar powered SBSs in three operational scenarios that reflect realistic hybrid grid renewable deployments. This flexible design allows the framework to adapt switching strategies based on renewable availability and storage conditions, making it more practical and robust for real world networks. Using a real call detail record dataset from Milan, simulation results show that the LSTM method achieves a mean absolute percentage error (MAPE) below 1% in Phase I, while in Phase II, the threshold based solar integration scenario achieves up to 23% network energy saving (NES) relative to conventional cell switching. Overall, the proposed framework bridges the gap between theoretical cell switching models and practical, sustainable 6G radio access network~(RAN) operation, enabling significant energy saving without compromising quality of service.

Authors:Steven Nguyen, Jorge Cortés, Boris Kramer
Title: Reachability Analysis for Design Optimization
Abstract:
We present an approach to approximate reachable sets for linear systems with bounded L‑infinity controls in finite time. Our first approach investigates the boundaries of these sets and reveals an exact characterization for single‑input, planar systems with real, distinct eigenvalues. The second approach leverages convergence of the Lp‑norms to L‑infinity and uses Lp‑norm reachable sets as an approximation of the L‑infinity‑norm reachable sets. Our optimal control results yield insights that make computational approximations of the Lp‑norm reachable sets more tractable, and yield exact characterizations for L‑infinity with the previous assumptions on the system. As an example, we incorporate our reachability analysis into the design optimization of a highly‑maneuverable aircraft. Introducing constraints based on reachability allow us to factor physical limitations to desired flight maneuvers into the design process.

Authors:Francesco Ceccanti, Aldo Bischi, Marco Antonelli, Andrea Baccioli
Title: Solar Daylighting to Offset LED Lighting in Vertical Farming: A Techno-Economic Study of Light Pipes
Abstract:
Vertical farming is a controlled‑environment agriculture (CEA) approach in which crops are grown in stacked layers under regulated climate and lighting, enabling predictable production but requiring high electricity input. This study quantifies the techno‑economic impact of roof‑mounted daylighting in a three‑tier container vertical farm using a light‑pipe (LP) system that delivers sunlight to the upper tier. The optical chain, comprising a straight duct and a tilting aluminum‑coated mirror within a rotating dome, was modelled in Tonatiuh to estimate crop‑level photon delivery and solar gains. These outputs were coupled with a transient AGRI‑Energy model to perform year‑round simulations for Dubai. Tier‑3 strategies were compared against a fully LED benchmark, including daylight‑only operation, on/off supplementation, PWM dimming, UV‑IR filtering, variable‑transmittance control, and simple glazing. Ray‑tracing predicted an overall LP optical efficiency of 45%‑75%, depending on solar position, quantifying the fraction of incident daylight at the collector aperture delivered to the target growing zone. Daylight‑only operation reduced the total three‑tier yield by 17% and was not economically viable despite 27‑29% electricity savings. Hybrid daylight‑LED strategies preserved benchmark yield while reducing electricity use. PWM dimming combined with UV‑IR filtering achieved the lowest specific electricity energy consumption (6.32 kWh/kg), 14% below the benchmark. Overall, viability remains CAPEX‑limited because achievable electricity savings are insufficient to offset the added investment and thus improves mainly under high electricity and carbon‑price contexts, although the LP system delivers a 15‑38% lower light cost than an optical‑fiber reference under identical incident daylight.

Authors:Liraz Mudrik, Isaac Kaminer, Sean Kragelund, Abram H. Clark
Title: Saddle Point Evasion via Curvature-Regularized Gradient Dynamics
Abstract:
Nonconvex optimization underlies many modern machine learning and control tasks, where saddle points pose the dominant obstacle to reliable convergence in high‑dimensional settings. Escaping these saddle points deterministically using continuous‑time optimization remains an open challenge: gradient descent is blind to curvature, stochastic perturbation methods lack deterministic guarantees, and Newton‑type approaches suffer from Hessian singularity. Adopting the perspective of viewing optimization algorithms as dynamical systems, we present Curvature‑Regularized Gradient Dynamics (CRGD), which augments the objective with a smooth penalty on the negative Hessian eigenvalues, yielding an augmented cost that serves as an optimization Lyapunov function with user‑selectable convergence rates to second‑order stationary points. Numerical experiments confirm that CRGD converges to second‑order stationary points, even in regimes where gradient descent fails.

Authors:Mingyuan Yan, Trager Joswig-Jones, Baosen Zhang, Yize Chen, Wenqi Cui
Title: Switching-Reference Voltage Control for Distribution Systems with AI-Training Data Centers
Abstract:
Large‑scale AI training workloads in modern data centers exhibit rapid and periodic power fluctuations, which may induce significant voltage deviations in power distribution systems. Existing voltage regulation methods, such as droop control, are primarily designed for slowly varying loads and may therefore be ineffective in mitigating these fast fluctuations. In addition, repeated control actions can incur substantial cost. To address this challenge, this paper proposes a decentralized switching‑reference voltage control framework that exploits the structured behavior of AI training workloads. We establish conditions for voltage convergence and characterize an effective reference design that aligns with the two dominant operating levels of the AI training workload. The switching rule for voltage references is implemented solely using local voltage measurements, enabling simple local implementation while significantly reducing control effort. Simulation studies demonstrate that the proposed method substantially reduces both voltage deviations and reactive control effort, while remaining compatible with internal data center control strategies without requiring extensive coordination.

Authors:Anton Kolonin, Vladimir Krykov
Title: Computational Concept of the Psyche
Abstract:
This article presents an overview of approaches to modeling the human psyche in the context of constructing an artificial one. Based on this overview, a concept of cognitive architecture is proposed, in which the psyche is viewed as the operating system of a living or artificial subject, comprising a space of states, including the state of needs that determine the meaning of a subject's being in relation to stimuli from the external world, and intelligence as a decision‑making system regarding actions in this world to satisfy these needs. Based on this concept, a computational formalization is proposed for creating artificial general intelligence systems for an agent through experiential learning in a state space that includes agent's needs, taking into account their biological or existential significance for the intelligent agent, along with agent's sensations and actions. Thus, the problem of constructing artificial general intelligence is formalized as a system for making optimal decisions in the space of specific agent needs under conditions of uncertainty, maximizing success in achieving goals, minimizing existential risks, and maximizing energy efficiency. A minimal experimental implementation of the model is presented.

Authors:Jay Sarkar, Vamsi Pavan Rayaprolu, Abhijeet Bhalerao
Title: Co-Design of Memory-Storage Systems for Workload Awareness with Interpretable Models
Abstract:
Solid‑state storage architectures based on NAND or emerging memory devices (SSD), are fundamentally architected and optimized for both reliability and performance. Achieving these simultaneous goals requires co‑design of memory components with firmware‑architected Error Management (EM) algorithms for density‑ and performance‑scaled memory technologies. We describe a Machine Learning (ML) for systems methodology and modeling for co‑designing the EM subsystem together with the natural variance inherent to scaled silicon process of memory components underlying SSD technology. The modeling analyzes NAND memory components and EM algorithms interacting with comprehensive suite of synthetic (stress‑focused and JEDEC) and emulation (YCSB and similar) workloads across Flash Translation abstraction layers, by leveraging a statistically interpretable and intuitively explainable ML algorithm. The generalizable co‑design framework evaluates several thousand datacenter SSDs spanning multiple generations of memory and storage technology. Consequently, the modeling framework enables continuous, holistic, data‑driven design towards generational architectural advancements. We additionally demonstrate that the framework enables Representation Learning of the EM‑workload domain for enhancement of the architectural design‑space across broad spectrum of workloads.

Authors:Ivan Stetsenko
Title: Lore: Repurposing Git Commit Messages as a Structured Knowledge Protocol for AI Coding Agents
Abstract:
As AI coding agents become both primary producers and consumers of source code, the software industry faces an accelerating loss of institutional knowledge. Each commit captures a code diff but discards the reasoning behind it ‑ the constraints, rejected alternatives, and forward‑looking context that shaped the decision. I term this discarded reasoning the Decision Shadow. This paper proposes Lore, a lightweight protocol that restructures commit messages ‑ using native git trailers ‑ into self‑contained decision records carrying constraints, rejected alternatives, agent directives, and verification metadata. Lore requires no infrastructure beyond git, is queryable via a standalone CLI tool, and is discoverable by any agent capable of running shell commands. The paper formalizes the protocol, compares it against five competing approaches, stress‑tests it against its strongest objections, and outlines an empirical validation path.

Authors:Muhammad Sharjeel Javaid, Gabriel Covarrubias Maureira, Ambuj Gupta, Debraj Bhattacharjee, Jianli Gao, Balarko Chaudhuri, Mark O'Malley
Title: Spatial Characterization of Sub-Synchronous Oscillations Using Black-Box IBR Models
Abstract:
Power systems with high penetration of inverter‑based resources (IBRs) are prone to sub‑synchronous oscillations (SSO). The opaqueness of vendor‑specific IBR models limits the ability to predict the severity and the spread of SSO. This paper demonstrates that black‑box IBR models estimated through frequency‑domain identification techniques, along with dynamic network model can replicate the actual oscillatory behavior. The estimated IBR models are validated against actual IBR models in a closed‑loop multi‑IBR test system through modal analysis by comparing closed‑loop eigenvalues, and participation factors. Furthermore, using output‑observable right eigenvectors, spatial heatmaps are developed to visualize the spread and severity of dominant SSO modes. The case studies on the 11‑bus and 39‑bus test systems confirm that even with the estimated IBR models, the regions susceptible to SSO can be identified in IBR‑dominated power systems.

Authors:Timo Jakumeit, Bastian Heinlein, Vukašin Spasojević, Vahid Jamali, Robert Schober, Maximilian Schäfer
Title: Matched Filter-Based Molecule Source Localization in Advection-Diffusion-Driven Pipe Networks with Known Topology
Abstract:
Synthetic molecular communication (MC) has emerged as a powerful framework for modeling, analyzing, and designing communication systems where information is encoded into properties of molecules. Among the envisioned applications of MC is the localization of molecule sources in pipe networks (PNs) like the human cardiovascular system (CVS), sewage networks (SNs), and industrial plants. While existing algorithms mostly focus on simplified scenarios, in this paper, we propose the first framework for source localization in complex PNs with known topology, by leveraging the mixture of inverse Gaussians for hemodynamic transport (MIGHT) model as a closed‑form representation for advection‑diffusion‑driven MC in PNs. We propose a matched filter (MF)‑based approach to identify molecule sources under realistic conditions such as unknown release times, random numbers of released molecules, sensor noise, and limited sensor sampling rate. We apply the algorithm to localize a source of viral markers in a real‑world SN and show that the proposed scheme outperforms randomly guessing sources even at low signal‑to‑noise ratios (SNRs) at the sensor and achieves error‑free localization under favorable conditions, i.e., high SNRs and sampling rates. Furthermore, by identifying clusters of frequently confused sources, reliable cluster‑level localization is possible at substantially lower SNRs and sampling rates.

Authors:Kang Tong, Christian Grussler, Michelle S. Chong
Title: Unimodal self-oscillations and their sign-symmetry for discrete-time relay feedback systems with dead zone
Abstract:
This paper characterizes self‑oscillations in discrete‑time linear time‑invariant (LTI) relay feedback systems with nonnegative dead zone. Specifically, we aim to establish existence criteria for unimodal self‑oscillations, defined as periodic solutions where the output exhibits a single‑peaked period. Assuming that the linear part of system is stable, with a strictly monotonically decreasing impulse response on its infinite support, we propose a novel analytical framework based on the theory of total positivity to address this problem. We demonstrate that unimodal self‑oscillations subject to mild variation‑based constraints exist only if the number of positive and negative values of the system's loop gain coincides within a given strictly positive period, i.e., the self‑oscillation is sign‑symmetric. Building upon these findings, we derive conditions for the existence of such self‑oscillations, establish tight bounds on their periods, and address the question of their uniqueness.

Authors:Huayan Geng, Yangjun Zeng, Yiwei Qiu
Title: Mitigating Renewable-Induced Risks for Green and Conventional Ammonia Producers through Coordinated Production and Futures Trading
Abstract:
Renewable power‑to‑ammonia (ReP2A), which uses hydrogen produced from renewable electricity as feedstock, is a promising pathway for decarbonizing the energy, transportation, and chemical sectors. However, variability in renewable generation causes fluctuations in hydrogen supply and ammonia production, leading to revenue instability for both ReP2A producers and conventional fossil‑based gray ammonia (GA) producers in the market. Existing studies mainly rely on engineering measures, such as production scheduling, to manage this risk, but their effectiveness is constrained by physical system limits. To address this challenge, this paper proposes a financial instrument termed \emphrenewable ammonia futures and integrates it with production decisions to hedge ammonia output risk. Production and trading models are developed for both ReP2A and GA producers, with conditional value‑at‑risk (CVaR) used to represent risk preferences under uncertainty. A game‑theoretic framework is established in which the two producers interact in coupled ammonia spot and futures markets, and a Nash bargaining mechanism coordinates their production and trading strategies. Case studies based on a real‑world system show that introducing renewable ammonia futures increases the CVaR utilities of ReP2A and GA producers by 5.103% and 10.14%, respectively, improving profit stability under renewable uncertainty. Sensitivity analysis further confirms the effectiveness of the mechanism under different levels of renewable variability and capacity configurations.

Authors:Andrii Mironchenko, Fabian Wirth, Antoine Chaillet, Lucas Brivadis
Title: A superposition approach for the ISS Lyapunov-Krasovskii theorem with pointwise dissipation
Abstract:
We show that the existence of a Lyapunov‑Krasovskii functional (LKF) with pointwise dissipation (i.e. dissipation in terms of the current solution norm) suffices for input‑to‑state stability, provided that uniform global stability can also be ensured using the same LKF. To this end, we develop a stability theory, in which the behavior of solutions is not assessed through the classical norm but rather through a specific LKF, which may provide significantly tighter estimates. We discuss the advantages of our approach by means of an example.

Authors:Pouya Samanipour, Hasan A. Poonawala
Title: ReLU Barrier Functions for Nonlinear Systems with Constrained Control: A Union of Invariant Sets Approach
Abstract:
Certifying safety for nonlinear systems with polytopic input constraints is challenging because CBF synthesis must ensure control admissibility under saturation. We propose an approximation‑‑verification pipeline that performs convex barrier synthesis on piecewise‑affine (PWA) surrogates and certifies safety for the original nonlinear system via facet‑wise verification. To reduce conservatism while preserving tractability, we use a two‑slope Leaky ReLU surrogate for the extended class‑\mathcalK function α(\cdot) and combine multiple certificates using a Union of Invariant Sets (UIS). Counterexamples are handled through local uncertainty updates. Simulations on pendulum and cart‑pole systems with input saturation show larger certified invariant sets than linear‑α designs with tractable computation time.

Authors:Dinesh Patra, Prajakta Surve, Ashish R. Hota, Shaunak D. Bopardikar
Title: Encirclement Guaranteed Finite-Time Capture against Unknown Evader Strategies
Abstract:
We consider a pursuit‑evasion scenario involving a group of pursuers and a single evader in a two‑dimensional unbounded environment. The pursuers aim to capture the evader in finite time while ensuring the evader remains enclosed within the convex hull of their positions until capture, without knowledge of the evader's heading angle. Prior works have addressed the problem of encirclement and capture separately in different contexts. In this paper, we present a class of strategies for the pursuers that guarantee capture in finite time while maintaining encirclement, irrespective of the evader's strategy. Furthermore, we derive an upper bound on the time to capture. Numerical results highlight the effectiveness of the proposed framework against a range of evader strategies.

Authors:Alma Lago
Title: Mechanistic Foundations of Goal-Directed Control
Abstract:
Mechanistic interpretability has transformed the analysis of transformer circuits by decomposing model behavior into competing algorithms, identifying phase transitions during training, and deriving closed‑form predictions for when and why strategies shift. However, this program has remained largely confined to sequence‑prediction architectures, leaving embodied control systems without comparable mechanistic accounts. Here we extend this framework to sensorimotor‑cognitive development, using infant motor learning as a model system. We show that foundational inductive biases give rise to causal control circuits, with learned gating mechanisms converging toward theoretically motivated uncertainty thresholds. The resulting dynamics reveal a clean phase transition in the arbitration gate whose commitment behavior is well described by a closed‑form exponential moving‑average surrogate. We identify context window k as the critical parameter governing circuit formation: below a minimum threshold (k\leq4) the arbitration mechanism cannot form; above it (k\geq8), gate confidence scales asymptotically as log k. A two‑dimensional phase diagram further reveals task‑demand‑dependent route arbitration consistent with the prediction that prospective execution becomes advantageous only when prediction error remains within the task tolerance window. Together, these results provide a mechanistic account of how reactive and prospective control strategies emerge and compete during learning. More broadly, this work sharpens mechanistic accounts of cognitive development and provides principled guidance for the design of interpretable embodied agents.

Authors:Runze Lin, Ziqi Zhuo, Junghui Chen, Lei Xie, Hongye Su
Title: Iterative Learning Control-Informed Reinforcement Learning for Batch Process Control
Abstract:
A significant limitation of Deep Reinforcement Learning (DRL) is the stochastic uncertainty in actions generated during exploration‑exploitation, which poses substantial safety risks during both training and deployment. In industrial process control, the lack of formal stability and convergence guarantees further inhibits adoption of DRL methods by practitioners. Conversely, Iterative Learning Control (ILC) represents a well‑established autonomous control methodology for repetitive systems, particularly in batch process optimization. ILC achieves desired control performance through iterative refinement of control laws, either between consecutive batches or within individual batches, to compensate for both repetitive and non‑repetitive disturbances. This study introduces an Iterative Learning Control‑Informed Reinforcement Learning (IL‑CIRL) framework for training DRL controllers in dual‑layer batch‑to‑batch and within‑batch control architectures for batch processes. The proposed method incorporates Kalman filter‑based state estimation within the iterative learning structure to guide DRL agents toward control policies that satisfy operational constraints and ensure stability guarantees. This approach enables the systematic design of DRL controllers for batch processes operating under multiple disturbance conditions.

Authors:Mario di Bernardo
Title: Multi-Scale Control of Large Agent Populations: From Density Dynamics to Individual Actuation
Abstract:
We review a body of recent work by the author and collaborators on controlling the spatial organisation of large agent populations across multiple scales. A central theme is the systematic bridging of microscopic agent‑level dynamics and macroscopic density descriptions, enabling control design at the most natural level of abstraction and subsequent translation across scales. We show how this multi‑scale perspective provides a unified approach to both \emphdirect control, where every agent is actuated, and \emphindirect control, where few leaders or herders steer a larger uncontrolled population. The review covers continuification‑based control with robustness under limited sensing and decentralised implementation via distributed density estimation; leader‑‑follower density regulation with dual‑feedback stability guarantees and bio‑inspired plasticity; optimal‑transport methods for coverage control and macro‑to‑micro discretisation; nonreciprocal field theory for collective decision‑making; mean‑field control barrier functions for population‑level safety; and hierarchical reinforcement learning for settings where closed‑form solutions are intractable. Together, these results demonstrate the breadth and versatility of a multi‑scale control framework that integrates analytical methods, learning, and physics‑inspired approaches for large agent populations.

Authors:Renato Quartullo, Andrea Garulli, Mirko Leomanni
Title: Data-Driven Robust Predictive Control with Interval Matrix Uncertainty Propagation
Abstract:
This paper presents a new data‑driven robust predictive control law, for linear systems affected by unknown‑but‑bounded process disturbances. A sequence of input‑state data is used to construct a suitable uncertainty representation based on interval matrices. Then, the effect of uncertainty along the prediction horizon is bounded through an operator leveraging matrix zonotopes. This yields a tube that is exploited within a variable‑horizon optimal control problem, to guarantee robust satisfaction of state and input constraints. The resulting data‑driven predictive control scheme is proven to be recursively feasible and practically stable. A numerical example shows that the proposed approach compares favorably to existing methods based on zonotopic tubes.

Authors:Patricia Nöther, Lars Watermann, Johann Reger
Title: Chattering Reduction for a Second-Order Actuator via Dynamic Sliding Manifolds
Abstract:
We analyze actuator chattering in a scalar integrator system subject to second‑order actuator dynamics with an unknown time constant and first‑order sliding‑mode control, using both a conventional static sliding manifold and a dynamic sliding manifold. Using the harmonic balance method, we prove that it is possible to adjust the parameters of the dynamic sliding manifold for the specified system class so as to reduce the amplitude of the chattering in comparison to the static manifold. We illustrate our results with a simulation example. This contribution serves as a proof of concept to motivate further investigations in chattering reduction via dynamic sliding manifolds.

Authors:Xinhui Rong, Girish N. Nair
Title: A System-Theoretic Approach to Hawkes Process Identification with Guaranteed Positivity and Stability
Abstract:
The Hawkes process models self‑exciting event streams, requiring a strictly non‑negative and stable stochastic intensity. Standard identification methods enforce these properties using non‑negative causal bases, yielding conservative parameter constraints and severely ill‑conditioned least‑squares Gram matrices at higher model orders. To overcome this, we introduce a system‑theoretic identification framework utilizing the sign‑indefinite orthonormal Laguerre basis, which guarantees a well‑conditioned asymptotic Gram matrix independent of model order. We formulate a constrained least‑squares problem enforcing the necessary and sufficient conditions for positivity and stability. By constructing the empirical Gram matrix via a Lyapunov equation and representing the constraints through a sum‑of‑squares trace equivalence, the proposed estimator is efficiently computed via semidefinite programming.

Authors:Amos Alwala, Yuchen Hu, Gabriel da Silva Lima, Wallace Moreira Bessa
Title: Intelligent Control of Differential Drive Robots Subject to Unmodeled Dynamics with EKF-based State Estimation
Abstract:
Reliable control and state estimation of differential drive robots (DDR) operating in dynamic and uncertain environments remains a challenge, particularly when system dynamics are partially unknown and sensor measurements are prone to degradation. This work introduces a unified control and state estimation framework that combines a Lyapunov‑based nonlinear controller and Adaptive Neural Networks (ANN) with Extended Kalman Filter (EKF)‑based multi‑sensor fusion. The proposed controller leverages the universal approximation property of neural networks to model unknown nonlinearities in real time. An online adaptation scheme updates the weights of the radial basis function (RBF), the architecture chosen for the ANN. The learned dynamics are integrated into a feedback linearization (FBL) control law, for which theoretical guarantees of closed‑loop stability and asymptotic convergence in a trajectory‑tracking task are established through a Lyapunov‑like stability analysis. To ensure robust state estimation, the EKF fuses inertial measurement unit (IMU) and odometry from monocular, 2D‑LiDAR and wheel encoders. The fused state estimate drives the intelligent controller, ensuring consistent performance even under drift, wheel slip, sensor noise and failure. Gazebo simulations and real‑world experiments are done using DDR, demonstrating the effectiveness of the approach in terms of improved velocity tracking performance with reduction in linear and angular velocity errors up to 53.91% and 29.0% in comparison to the baseline FBL.

Authors:Turki Bin Mohaya, Maitham F. AL-Sunni, John M. Dolan, Peter Seiler
Title: Transformers As Generalizable Optimal Controllers
Abstract:
We study whether optimal state‑feedback laws for a family of heterogeneous Multiple‑Input, Multiple‑Output (MIMO) Linear Time‑Invariant (LTI) systems can be captured by a single learned controller. We train one transformer policy on LQR‑generated trajectories from systems with different state and input dimensions, using a shared representation with standardization, padding, dimension encoding, and masked loss. The policy maps recent state history to control actions without requiring plant matrices at inference time. Across a broad set of systems, it achieves empirically small sub‑optimality relative to Linear Quadratic Regulator (LQR), remains stabilizing under moderate parameter perturbations, and benefits from lightweight fine‑tuning on unseen systems. These results support transformer policies as practical approximators of near‑optimal feedback laws over structured linear‑system families.

Authors:Joshua Pilipovsky
Title: Free Final Time Adaptive Mesh Covariance Steering via Sequential Convex Programming
Abstract:
In this paper we develop a sequential convex programming (SCP) framework for free‑final‑time covariance steering of nonlinear stochastic differential equations (SDEs) subject to both additive and multiplicative diffusion. We cast the free‑final‑time objective through a time‑normalization and introduce per‑interval time‑dilation variables that induce an adaptive discretization mesh, enabling the simultaneous optimization of the control policy and the temporal grid. A central difficulty is that, under multiplicative noise, accurate covariance propagation within SCP requires retaining the first‑order diffusion linearization and its coupling with time dilation. We therefore derive the exact local linear stochastic model (preserving the multiplicative structure) and introduce a tractable discretization that maintains the associated diffusion terms, after which each SCP subproblem is solved via conic/semidefinite covariance‑steering relaxations with terminal moment constraints and state/control chance constraints. Numerical experiments on a nonlinear double‑integrator with drag and velocity‑dependent diffusion validate free‑final‑time minimization through adaptive time allocation and improved covariance accuracy relative to frozen‑diffusion linearizations.

Authors:Yoshiki Yamamoto, Maina Sogabe, Shunichi Hirahara, Toshiki Kaisaki, Tetsuro Miyazaki, Kenji Kawashima
Title: Surgical Robot, Path Planning, Joint Space, Riemannian Manifolds
Abstract:
Robotic surgery for minimally invasive surgery can reduce the surgeon's workload by autonomously guiding robotic forceps. Movement of the robot is restricted around a fixed insertion port. The robot often encounters angle limitations during operation. Also, the surface of the abdominal cavity is non‑concave, making it computationally expensive to find the desired path.In this work, to solve these problems, we propose a method for path planning in joint space by transforming the position into a Riemannian manifold. An edge cost function is defined to search for a desired path in the joint space and reduce the range of motion of the joints. We found that the organ is mostly non‑concave, making it easy to find the optimal path using gradient descent method. Experimental results demonstrated that the proposed method reduces the range of joint angle movement compared to calculations in position space.

Authors:Haoyuan Sun, Ali Jadbabaie
Title: Online Learning for Supervisory Switching Control
Abstract:
We study supervisory switching control for partially‑observed linear dynamical systems. The objective is to identify and deploy the best controller for the unknown system by periodically selecting among a collection of N candidate controllers, some of which may destabilize the underlying system. While classical estimator‑based supervisory control guarantees asymptotic stability, it lacks quantitative finite‑time performance bounds. Conversely, current non‑asymptotic methods in both online learning and system identification require restrictive assumptions that are incompatible in a control setting, such as system stability, which preclude testing potentially unstable controllers. To bridge this gap, we propose a novel, non‑asymptotic analysis of supervisory control that adapts multi‑armed bandit algorithms to a control‑theoretic setting. The proposed data‑driven algorithm evaluates candidate controllers via scoring criteria that leverage system observability to isolate the effects of state history, enabling both detection of destabilizing controllers and accurate system identification. We present two algorithmic variants with dimension‑free, finite‑time guarantees, where each identifies the most suitable controller in \mathcalO(N \log N) steps, while simultaneously achieving finite L_2‑gain with respect to system disturbances.

Authors:Saad Alqithami
Title: EcoFair-CH-MARL: Scalable Constrained Hierarchical Multi-Agent RL with Real-Time Emission Budgets and Fairness Guarantees
Abstract:
Global decarbonisation targets and tightening market pressures demand maritime logistics solutions that are simultaneously efficient, sustainable, and equitable. We introduce EcoFair‑CH‑MARL, a constrained hierarchical multi‑agent reinforcement learning framework that unifies three innovations: (i) a primal‑dual budget layer that provably bounds cumulative emissions under stochastic weather and demand; (ii) a fairness‑aware reward transformer with dynamically scheduled penalties that enforces max‑min cost equity across heterogeneous fleets; and (iii) a two‑tier policy architecture that decouples strategic routing from real‑time vessel control, enabling linear scaling in agent count. New theoretical results establish O(\sqrtT) regret for both constraint violations and fairness loss. Experiments on a high‑fidelity maritime digital twin (16 ports, 50 vessels) driven by automatic identification system traces, plus an energy‑grid case study, show up to 15% lower emissions, 12% higher through‑put, and a 45% fair‑cost improvement over state‑of‑the‑art hierarchical and constrained MARL baselines. In addition, EcoFair‑CH‑MARL achieves stronger equity (lower Gini and higher min‑max welfare) than fairness‑specific MARL baselines (e.g., SOTO, FEN), and its modular design is compatible with both policy‑ and value‑based learners. EcoFair‑CH‑MARL therefore advances the feasibility of large‑scale, regulation‑compliant, and socially responsible multi‑agent coordination in safety‑critical domains.

Authors:Jack Cline, Christian Macaranas, Siavash Farzan
Title: Progress-Based Fault Detection and Health-Aware Task Allocation for Heterogeneous Multi-Robot Systems
Abstract:
We present a progress‑based fault detection module and its integration with dynamic task allocation for heterogeneous robot teams. The detector monitors a normalized task‑completion signal with a lightweight Kalman filter (KF) and a normalized innovation squared (NIS) test, augmented with a low‑rate stall gate, an uncertainty gate, and debounce logic. Health estimates influence the allocator via health‑weighted costs and health‑dependent masks; reallocation is event‑triggered and regularized with an \ell_1 assignment‑change penalty to limit reassignment churn while preserving feasibility through slack variables. The detector has constant per‑robot update cost, and the allocation remains a convex quadratic program (QP). Experiments on a common team‑task setup evaluate measurement‑noise increases, velocity‑slip biases, communication dropouts, and task abandonment. The results show timely detection in the noise and bias cases, maintained task completion with limited reassignment, and the expected observability delays under communication dropouts.

Authors:Gaurav Pandey, Gregory Stevens, Henry Liu
Title: Functional Safety Analysis for Infrastructure-Enabled Depot Autonomy System
Abstract:
This paper presents the functional safety analysis for an Infrastructure‑Enabled Depot Autonomy (IX‑DA) system. The IX‑DA system automates the marshalling of delivery vehicles within a controlled depot environment, navigating connected autonomous vehicles (CAVs) between drop‑off zones, service stations (washing, calibration, charging, loading), and pick‑up zones without human intervention. We describe the system architecture comprising three principal subsystems ‑‑ the connected autonomous vehicle, the infrastructure sensing and compute layer, and the human operator interface ‑‑ and derive their functional requirements. Using ISO 26262‑compliant Hazard Analysis and Risk Assessment (HARA) methodology, we identify eight hazardous events, evaluate them across different operating scenarios, and assign Automotive Safety Integrity Levels~(ASILs) ranging from Quality Management (QM) to ASIL C. Six safety goals are derived and allocated to vehicle and infrastructure subsystems. The analysis demonstrates that high‑speed uncontrolled operation imposes the most demanding safety requirements (ASIL C), while controlled low‑speed operation reduces most goals to QM, offering a practical pathway for phased deployment.

Authors:Meysam Masoudi, Tahar Zanouda, Milad Ganjalizadeh, Cicek Cavdar
Title: Collective Grid: Privacy-Preserved Multi-Operator Energy Sharing Optimization via Federated Energy Prediction
Abstract:
Electricity consumption in mobile networks is increasing with the continued 5G expansion, rising data traffic, and more complex infrastructures. However, energy management is often handled independently by each mobile network operator (MNO), leading to limited coordination and missed opportunities for collective efficiency gains. To address this gap, we propose a privacy‑preserving framework for automated energy infrastructure sharing among co‑located MNOs. Our framework consists of three modules: (i) a federated learning‑based privacy‑preserving site energy consumption forecasting module, (ii) an orchestration module in which a mixed‑integer linear program is solved to schedule energy purchases from the grid, utilization of renewable sources, and shared battery charging or discharging, based on real‑time prices, forecasts, and battery state, and (iii) an energy source selection module which handles the selection of cost‑effective power sources and storage actions based on predicted demand across MNOs for the next control window. Using data from operational networks, our experiments confirm that the proposed solution substantially reduces operational costs and outperforms non‑sharing baselines, with gains that increase as network density rises in 5G‑and‑beyond deployments.

Authors:Yongkang Su, Sei Zhen Khong, Lanlan Su
Title: Consensus in Plug-and-Play Heterogeneous Dynamical Networks: A Passivity Compensation Approach
Abstract:
This paper investigates output consensus in heterogeneous dynamical networks within a plug‑and‑play framework. The networks are interconnected through nonlinear diffusive couplings and operate in the presence of measurement and communication noise. Focusing on systems that are input feedforward passive (IFP), we propose a passivity‑compensation approach that exploits the surplus passivity of coupling links to locally offset shortages of passivity at the nodes. This mechanism enables subnetworks to be interconnected without requiring global reanalysis, thereby preserving modularity. Specifically, we derive locally verifiable interface conditions, expressed in terms of passivity indices and coupling gains, to guarantee that consensus properties of individual subnetworks are preserved when forming larger networks.

Authors:Avik Kar, Siddharth Chandak, Rahul Singh, Eric Moulines, Shalabh Bhatnagar, Nicholas Bambos
Title: High-Probability Bounds for SGD under the Polyak-Lojasiewicz Condition with Markovian Noise
Abstract:
We present the first uniform‑in‑time high‑probability bound for SGD under the PL condition, where the gradient noise contains both Markovian and martingale difference components. This significantly broadens the scope of finite‑time guarantees, as the PL condition arises in many machine learning and deep learning models while Markovian noise naturally arises in decentralized optimization and online system identification problems. We further allow the magnitude of noise to grow with the function value, enabling the analysis of many practical sampling strategies. In addition to the high‑probability guarantee, we establish a matching 1/k decay rate for the expected suboptimality. Our proof technique relies on the Poisson equation to handle the Markovian noise and a probabilistic induction argument to address the lack of almost‑sure bounds on the objective. Finally, we demonstrate the applicability of our framework by analyzing three practical optimization problems: token‑based decentralized linear regression, supervised learning with subsampling for privacy amplification, and online system identification.

Authors:Waldemar Bauer, Jerzy Baranowski
Title: Bayesian and Classical Feature Ranking for Interpretable BLDC Fault Diagnosis
Abstract:
This paper compares Bayesian and classical feature ranking methods for interpretable fault diagnosis of brushless DC (BLDC) motors. Two Bayesian approaches, spike‑and‑slab and ARD logistic ranking, are evaluated against three classical baselines on a public BLDC benchmark in binary and multiclass settings using current‑based, rotational‑speed‑based, and combined feature sets. The strongest overall results are obtained for the combined representation. In binary classification, ReliefF achieves the highest balanced accuracy of 0.923, while ARD logistic and spike‑and‑slab remain very close at 0.919 and 0.920 with much smaller subsets (k=5). In multiclass classification, ARD logistic performs best for the combined variant with balanced accuracy 0.914, followed closely by LASSO (0.913) and spike‑and‑slab (0.912). The results show that Bayesian ranking is particularly competitive for current‑only and combined descriptors, while ReliefF remains especially effective for speed‑based ranking. Because the benchmark consists of short segmented observations from a limited number of experimental conditions, the findings are interpreted primarily as benchmark‑specific evidence rather than strong claims of fault generalization.

Authors:Songming Ping, Shaoyue Wen, Junhong Chen, Wen Fan, Lan Wei, Dandan Zhang
Title: Surgi-HDTMR: Closing the Sensorimotor Loop in Bimanual Microsurgery via Haptics, Digital Twin, and Mixed Reality
Abstract:
Robotic microsurgery demands precise bimanual control, intuitive interaction, and informative force feedback. However, most training platforms for robotic microsurgery lack immersive 3D interaction and high‑fidelity haptics. Here, we present Surgi‑HDTMR, a mixed‑reality (MR) and digital‑twin (DT) training system that couples bimanual haptic teleoperation with a benchtop microsurgical robotic platform, and 3D‑printed phantoms. A metrically co‑registered, time‑synchronized DT aligns in‑situ MR guidance with the physical workspace and drives a depth‑adaptive haptic model that renders contact, puncture, and tissue‑retraction forces. In a within‑subjects study of simulated cortical navigation and tumor resection, Surgi‑HDTMR shortened task time, reduced harmful contacts and collisions, and improved perceptual accuracy relative to non‑haptic and non‑adaptive baselines. These results suggest that tightly coupling MR overlays with a synchronized DT, together with depth‑adaptive haptics, can accelerate skill acquisition and improve safety in robot‑assisted microsurgery, pointing toward next‑generation surgical training.

Authors:Eric Lupascu, Xiao Li, Benjamin Schäfer
Title: Predicting power grid frequency dynamics with invertible Koopman-based architectures
Abstract:
The system frequency is a critical measure of power system stability and understanding, and modeling it are key to ensure reliable power system operations. Koopman‑based autoencoders are effective at approximating complex nonlinear data patterns, with potential applications in the frequency dynamics of power systems. However, their non‑invertibility can result in a distorted latent representation, leading to significant prediction errors. Invertible neural networks (INNs) in combination with the Koopman operator framework provide a promising approach to address these limitations. In this study, we analyze different INN architectures and train them on simulation datasets. We further apply extensions to the networks to address inherent limitations of INNs and evaluate their impact. We find that coupling‑layer INNs achieve the best performance when used in isolation. In addition, we demonstrate that hybrid approaches can improve the performance when combined with suitable INNs, while reducing the generalization capabilities in combination with disadvantageous architectures. Overall, our results provide a clearer overview of how architectural choices influence INN performance, offering guidance for selecting and designing INNs for modeling power system frequency dynamics.

Authors:Lukas Flad, Mark Leyer, Felix Sebastian Nitz, Tobias Krawutschke
Title: A Comprehensive Survey of Redundancy Systems with a Focus on Triple Modular Redundancy (TMR)
Abstract:
Despite its maturity, the field of fault‑tolerant redundancy suffers from significant terminological fragmentation, where functionally equivalent methods are frequently described under disparate names across academic and industrial domains. This survey addresses this ambiguity by providing a structured and comprehensive analysis of redundancy techniques, with a primary focus on Triple Modular Redundancy (TMR). A unified taxonomy is established to classify redundancy strategies into Spatial, Temporal, and Mixed categories, alongside the introduction of a novel five‑class framework for voter architectures. Key findings synthesize practical tradeoffs, contrasting high‑reliability spatial TMR for safety‑critical applications against resource‑efficient temporal methods for constrained systems. Furthermore, the shift toward Mixed and Adaptive TMR (e.g., Approximate Triple Modular Redundancy (ATMR), X‑Rel) for dynamic and error‑tolerant applications, such as Artificial Intelligence (AI) acceleration, is explored. This work identifies critical research gaps, including the threat of Multi‑Bit Upsets (MBUs) in sub‑28nm technologies, the scarcity of public‑domain data on proprietary high‑integrity systems, and the absence of high‑level toolchains for dynamic reconfiguration. Finally, suggestions are offered for future research directions, emphasizing the need for terminological standardization, MBU‑resilient design methodologies, and the development of open‑source tools for adaptive fault tolerance.

Authors:Shiming Fang, Xilin Li, Changzhi Wu, Kaiyan Yu
Title: DRCC-LPVMPC: Robust Data-Driven Control for Autonomous Driving and Obstacle Avoidance
Abstract:
Safety in obstacle avoidance is critical for autonomous driving. While model predictive control (MPC) is widely used, simplified prediction models such as linearized or single‑track vehicle models introduce discrepancies between predicted and actual behavior that can compromise safety. This paper proposes a distributionally robust chance‑constrained linear parameter‑varying MPC (DRCC‑LPVMPC) framework that explicitly accounts for such discrepancies. The single‑track vehicle dynamics are represented in a quasi‑linear parameter‑varying (quasi‑LPV) form, with model mismatches treated as additive uncertainties of unknown distribution. By constructing chance constraints from finite sampled data and employing a Wasserstein ambiguity set, the proposed method avoids restrictive assumptions on boundedness or Gaussian distributions. The resulting DRCC problem is reformulated as tractable convex constraints and solved in real time using a quadratic programming solver. Recursive feasibility of the approach is formally established. Simulation and real‑world experiments demonstrate that DRCC‑LPVMPC maintains safer obstacle clearance and more reliable tracking than conventional nonlinear MPC and LPVMPC controllers under significant uncertainties.

Authors:Johannes Autenrieb, Peter A. Fisher, Anuradha Annaswamy
Title: Robust Safety Filters for Lipschitz-Bounded Adaptive Closed-Loop Systems with Structured Uncertainties
Abstract:
Adaptive control provides closed‑loop stability and reference tracking for uncertain dynamical systems through online parameter adaptation. These properties alone, however, do not ensure safety in the sense of forward invariance of state constraints, particularly during transient phases of adaptation. Control barrier function (CBF)‑based safety filters have been proposed to address this limitation, but existing approaches often rely on conservative constraint tightening or static safety margins within quadratic program formulations. This paper proposes a reference‑based adaptive safety framework for systems with structured parametric uncertainty that explicitly accounts for transient plant‑reference mismatch. Safety is enforced at the reference level using a barrier‑function‑based filter, while adaptive control drives the plant to track the safety‑certified reference. By exploiting Lipschitz bounds on the closed‑loop error dynamics, a robust CBF condition is derived and reformulated as a convex second‑order cone program (SOCP). The resulting approach reduces conservatism while preserving formal guarantees of forward invariance, stability, and tracking.

Authors:Jialin Lin, Dandan Zhang
Title: DexterousMag: A Reconfigurable Electromagnetic Actuation System for Miniature Helical Robot
Abstract:
Despite the promise of magnetically actuated miniature helical robots for minimally invasive interventions, state‑of‑the‑art electromagnetic actuation systems are often space‑inefficient and geometrically fixed. These constraints hinder clinical translation and, moreover, prevent task‑adaptive trade‑offs among workspace coverage, energy distribution, and field/gradient capability. We present DexterousMag, a robot‑arm‑assisted three‑coil electromagnetic actuation system that enables continuous geometric reconfiguration of a compact coil group, thereby redistributing magnetic‑field and gradient capability for task‑adaptive operation. The reconfiguration is realized by a parallel mechanism that exposes a single geometric DOF of the coil group, conveniently parameterized by the polar angle. Using an FEM‑based modeling pipeline, we precompute actuation and gradient libraries and quantify the resulting trade‑offs under current limits: configurations that favor depth reach expand the feasible region but reduce peak field/gradient, whereas configurations that favor near‑surface capability concentrate stronger fields/gradients and support lifting. We validate these trade‑offs on representative tasks (deep translation, planar tracking, and 3D lifting) and further demonstrate a proof‑of‑concept online geometry scheduling scheme for combined tasks, benchmarked against fixed‑geometry settings. Overall, DexterousMag establishes continuous geometric reconfiguration as an operational mechanism for enlarging the practical envelope of miniature helical robot actuation while improving energy efficiency and safety.

Authors:Yongchen Wang, Kangyi Lu, Lan Wei, Dandan Zhang
Title: Context-Aware Adaptive Shared Control for Magnetically-Driven Bimanual Dexterous Micromanipulation
Abstract:
Magnetically actuated robots provide a promising untethered platform for navigation in confined environments, enabling biological studies and targeted micro‑delivery. However, dexterous manipulation in complex structures remains challenging. While single‑arm magnetic actuation suffices for simple transport, steering through tortuous or bifurcating channels demands coordinated control of multiple magnetic sources to generate the torques required for precise rotation and directional guidance. Bimanual teleoperation enables such dexterous steering but imposes high cognitive demands, as operators must handle the nonlinear dynamics of magnetic actuation while coordinating two robotic manipulators. To address these limitations, we propose Bi‑CAST, a context‑aware adaptive shared control framework for bimanual magnetic micromanipulation. A multimodal network fuses spatio‑temporal visual features, spatial risk metrics, and historical states to continuously adjust the control authority of each manipulator in real time. In parallel, a bidirectional haptic interface integrates force‑based intent recognition with risk‑aware guidance, enabling force feedback to provide a continuous channel for dynamic human‑machine authority negotiation. We validate the framework through user studies with eight participants performing three navigation tasks of increasing complexity in a vascular phantom. Compared with fixed authority and discrete switching baselines, Bi‑CAST achieves up to 76.6% reduction in collisions, 25.9% improvement in trajectory smoothness, and 44.4% lower NASA‑TLX workload, while delivering the fastest task completion times.

Authors:Jun Xie, Yuan-Hua Ni, Yiqin Yang, Bo Xu
Title: Data-Enabled Policy and Value Iteration for Continuous-Time Linear Quadratic Output Feedback Control
Abstract:
This paper proposes efficient policy iteration and value iteration algorithms for the continuous‑time linear quadratic regulator problem with unmeasurable states and unknown system dynamics, from the perspective of direct data‑driven control. Specifically, by re‑examining the data characteristics of input‑output filtered vectors and introducing QR decomposition, an improved substitute state construction method is presented that further eliminates redundant information, ensures a full row rank data matrix, and enables a complete parameterized representation of the feedback controller. Furthermore, the original problem is transformed into an equivalent linear quadratic regulator problem defined on the substitute state with a known input matrix, verifying the stabilizability and detectability of the transformed system. Consequently, model‑free policy iteration and value iteration algorithms are designed that fully exploit the full row rank substitute state data matrix. The proposed algorithms offer distinct advantages: they avoid the need for prior knowledge of the system order or the calculation of signal derivatives and integrals; the iterative equations can be solved directly without relying on the traditional least‑squares paradigm, guaranteeing feasibility in both single‑output and multi‑output settings; and they demonstrate superior numerical stability, reduced data demand, and higher computational efficiency. Moreover, the heuristic results regarding trajectory generation for continuous‑time systems are discussed, circumventing potential failure modes associated with existing approaches.

Authors:Waldemar Bauer, Jerzy Baranowski
Title: Low-Data Predictive Maintenance of Railway Station Doors and Elevators Using Bayesian Proxy Flow Modeling
Abstract:
This paper proposes a low‑data predictive maintenance framework for automatic doors and elevators in a railway station building. The method is intended for assets without direct condition monitoring, where only aggregate passenger traffic information and expert knowledge about movement patterns are available. Passenger flows are modeled on a reduced station graph using a Bayesian formulation with uncertain totals and routing shares. The inferred flows are converted into approximate operating‑cycle loads for doors and elevators through simple stochastic proxy relations. These loads are combined with uncertain age‑ and cycle‑based maintenance thresholds to estimate the probability that predefined maintenance conditions have been reached. A cost‑aware scheduling model is then used to align maintenance activities while accounting for service costs, disruption, delay penalties, and grouping opportunities within each asset class. The framework is illustrated on a simulated case study reflecting a real station layout. The results show that proxy operational data can support maintenance scheduling with low incremental implementation cost and can improve alignment relative to a calendar‑based policy.

Authors:Zhangcheng Qiang, Stuart Hands, Kerry Taylor, Subbu Sethuvenkatraman, Daniel Hugo, Pouya Ghiasnezhad Omran, Madhawa Perera, Armin Haller
Title: A Systematic Comparison and Evaluation of Building Ontologies for Deploying Data-Driven Analytics in Smart Buildings
Abstract:
Ontologies play a critical role in data exchange, information integration, and knowledge sharing across diverse smart building applications. Yet, semantic differences between the prevailing building ontologies hamper their purpose of bringing data interoperability and restrict the ability to reuse building ontologies in real‑world applications. In this paper, we propose and adopt a framework to conduct a systematic comparison and evaluation of four popular building ontologies (Brick Schema, RealEstateCore, Project Haystack and Google's Digital Buildings) from both axiomatic design and assertions in a use case, namely the Terminological Box (TBox) evaluation and the Assertion Box (ABox) evaluation. In the TBox evaluation, we use the SQuaRE‑based Ontology Quality Evaluation (OQuaRE) Framework and concede that Project Haystack and Brick Schema are more compact with respect to the ontology axiomatic design. In the ABox evaluation, we apply an empirical study with sample building data that suggests that Brick Schema and RealEstateCore have greater completeness and expressiveness in capturing the main concepts and relations within the building domain. The results implicitly indicate that there is no universal building ontology for integrating Linked Building Data (LBD). We also discuss ontology compatibility and investigate building ontology design patterns (ODPs) to support ontology matching, alignment, and harmonisation.

Authors:Aashi Shrinate, Twinkle Tripathy, Laxmidhar Behera
Title: Topological Conditions for Echo Chamber Formation under the FJ model: A Cluster Consensus-based Approach
Abstract:
The Friedkin‑Johnsen (FJ) model is a popular opinion dynamics model that explains the disagreement that can occur even among closely interacting individuals. Cluster consensus is a special type of disagreement, where agents in a network split into subgroups such that those within a subgroup agree and those in different subgroups disagree. In large‑scale social networks, users often distribute into echo chambers (i.e. groups of users with aligned views) while discussing contested issues such as electoral politics, social norms, etc. Additionally, they are exposed only to opinions and news sources that align with their existing beliefs. Hence, the interaction network plays a key role in the formation of an echo chamber. Since cluster consensus can represent echo chambers in a social network, we examine the conditions for cluster consensus under the FJ model with the objective of determining the properties of the interaction network that lead to echo chamber formation. We present topology‑based necessary and sufficient conditions for cluster consensus under the FJ model, regardless of the edge weights in the network and stubbornness values (which are difficult to estimate parameters in a social network). A major advantage of the proposed results is that they are applicable to arbitrary digraphs. Moreover, using the proposed conditions, we explain the emergence of bow‑tie structures which are often observed in real‑world echo chambers. Finally, we also develop a computationally feasible methodology to verify the proposed conditions for cluster consensus.

Authors:Giuseppe C. Calafiore
Title: Geometry-Aware Set-Membership Multilateration: Directional Bounds and Anchor Selection
Abstract:
In this paper, we study anchor selection for range‑based localization under unknown‑but‑bounded measurement errors. We start from the convex localization set \X=\Xd\cap\Hset recently introduced in \citeCalafioreSIAM, where \Xd is a polyhedron obtained from pairwise differences of squared‑range equations between the unknown location x and the anchors, and \Hset is the intersection of upper‑range hyperspheres. Our first goal is \emphoffline design: we derive geometry‑only E‑ and D‑type scores from the centered scatter matrix S(A)=AQ_mA\tran, where A collects the anchor coordinates and Q_m=I_m‑\frac1m\one\one\tran is the centering projector, showing that λ_\min(S(A)) controls worst‑direction and diameter surrogates for the polyhedral certificate \Xd, while \det S(A) controls principal‑axis volume surrogates. Our second goal is \emphonline uncertainty assessment for a selected subset of anchors: exploiting the special structure \X=\Xd\cap\Hset, we derive a simplex‑aggregated enclosing ball for \Hset and an exact support‑function formula for \Hset, which lead to finite hybrid bounds for the actual localization set \X, even when the polyhedral certificate deteriorates. Numerical experiments are performed in two dimensions, showing that geometry‑based subset selection is close to an oracle combinatorial search, that the D‑score slightly dominates the E‑score for the area‑oriented metric considered here, and that the new \Hset‑aware certificates track the realized size of the selected localization set closely.

Authors:Alex Nguyen-Le, Nikolai Matni
Title: On Globally Optimal Stochastic Policy Gradient Methods for Domain Randomized LQR Synthesis
Abstract:
Domain randomization is a simple, effective, and flexible scheme for obtaining robust feedback policies aimed at reducing the sim‑to‑real gap due to model mismatch. While domain randomization methods have yielded impressive demonstrations in the robotics‑learning literature, general and theoretically motivated principles for designing optimization schemes that effectively leverage the randomization are largely unexplored. We address this gap by considering a stochastic policy gradient descent method for the domain randomized linear‑quadratic regulator synthesis problem, a situation simple enough to provide theoretical guarantees. In particular, we demonstrate that stochastic gradients obtained by repeatedly sampling new systems at each gradient step converge to global optima with appropriate hyperparameters choices, and yield better controllers with lower variability in the final controllers when compared to approaches that do not resample. Sampling is often a quick and cheap operation, so computing policy gradients with newly sampled systems at each iteration is preferable to evaluating gradients on a fixed set of systems.

Authors:Stefano De Carli, Davide Previtali, Mirko Mazzoleni, Fabio Previdi
Title: Chaos-Free Networks are Stable Recurrent Neural Networks
Abstract:
Gated Recurrent Neural Networks (RNNs) are widely used for nonlinear system identification due to their high accuracy, although they often exhibit complex, chaotic dynamics that are difficult to analyze. This paper investigates the system‑theoretic properties of the Chaos‑Free Network (CFN), an architecture originally proposed to eliminate the chaotic behavior found in standard gated RNNs. First, we formally prove that the CFN satisfies Input‑to‑State Stability (ISS) by design. However, we demonstrate that ensuring Incremental ISS (delta‑ISS) still requires specific parametric constraints on the CFN architecture. Then, to address this, we introduce the Decoupled‑Gate Network (DGN), a novel structural variant of the CFN that removes internal state connections in the gating mechanisms. Finally, we prove that the DGN unconditionally satisfies the delta‑ISS property, providing an incrementally stable architecture for identifying nonlinear dynamical systems without requiring complex network training modifications. Numerical results confirm that the DGN maintains the modeling capabilities of standard architectures while adhering to these rigorous stability guarantees.

Authors:Hongliang Li, Herschel C. Pangborn, Ilya Kovalenko
Title: Energy-Aware Integrated Proactive Maintenance Planning and Production Scheduling
Abstract:
Demand‑side energy management, such as the real‑time pricing (RTP) program, offers manufacturers opportunities to reduce energy costs by shifting production to low‑price hours. However, this strategy is challenging to implement when machine degradation is considered, as degraded machines have decreased processing capacity and increased energy consumption. Proactive maintenance (PM) can restore machine health but requires production downtime, creating a challenging trade‑off: scheduling maintenance during low‑price periods sacrifices energy savings opportunities, while deferring maintenance leads to capacity losses and higher energy consumption. To address this challenge, we propose a hierarchical bi‑level control framework that jointly optimizes PM planning and runtime production scheduling, considering the machine degradation. A higher‑level optimization, with the lower‑level model predictive control (MPC) embedded as a sub‑problem, determines PM plans that minimize total operational costs under day‑ahead RTP. At runtime, the lower‑level MPC executes closed‑loop production scheduling to minimize energy costs under realized RTP, meeting delivery targets. Simulation results from a lithium‑ion battery pack assembly line case study demonstrate that the framework strategically shifts PM away from bottlenecks and high‑price hours, meeting daily production targets while reducing energy costs.

Authors:Gokul Puthumanaillam, Melkior Ornik
Title: Amortizing Trajectory Diffusion with Keyed Drift Fields
Abstract:
Diffusion‑based trajectory planners can synthesize rich, multimodal action sequences for offline reinforcement learning, but their iterative denoising incurs substantial inference‑time cost, making closed‑loop planning slow under tight compute budgets. We study the problem of achieving diffusion‑like trajectory planning behavior with one‑step inference, while retaining the ability to sample diverse candidate plans and condition on the current state in a receding‑horizon control loop. Our key observation is that conditional trajectory generation fails under naïve distribution‑matching objectives when the similarity measure used to align generated trajectories with the dataset is dominated by unconstrained future dimensions. In practice, this causes attraction toward average trajectories, collapses action diversity, and yields near‑static behavior. Our key insight is that conditional generative planning requires a conditioning‑aware notion of neighborhood: trajectory updates should be computed using distances in a compact key space that reflects the condition, while still applying updates in the full trajectory space. Building on this, we introduce Keyed Drifting Policies (KDP), a one‑step trajectory generator trained with a drift‑field objective that attracts generated trajectories toward condition‑matched dataset windows and repels them from nearby generated samples, using a stop‑gradient drifted target to amortize iterative refinement into training. At inference, the resulting policy produces a full trajectory window in a single forward pass. Across standard RL benchmarks and real‑time hardware deployments, KDP achieves strong performance with one‑step inference and substantially lower planning latency than diffusion sampling. Project website, code and videos: https://keyed‑drifting.github.io/

Authors:Yujun Huang, Gioele Zardini
Title: Distributional Uncertainty and Adaptive Decision-Making in System Co-design
Abstract:
Complex engineered systems require coordinated design choices across heterogeneous components under multiple conflicting objectives and uncertain specifications. Monotone co‑design provides a compositional framework for such problems by modeling each subsystem as a design problem: a feasible relation between provided functionalities and required resources in partially ordered sets. Existing uncertain co‑design models rely on interval bounds, which support worst‑case reasoning but cannot represent probabilistic risk or multi‑stage adaptive decisions. We develop a distributional extension of co‑design that models uncertain design outcomes as distributions over design problems and supports adaptive decision processes through Markov‑kernel re‑parameterizations. Using quasi‑measurable and quasi‑universal spaces, we show that the standard co‑design interconnection operations remain compositional under this richer notion of uncertainty. We further introduce queries and observations that extract probabilistic design trade‑offs, including feasibility probabilities, confidence bounds, and distributions of minimal required resources. A task‑driven unmanned aerial vehicle case study illustrates how the framework captures risk‑sensitive and information‑dependent design choices that interval‑based models cannot express.

Authors:Zongyan Zhang, Chao Shen, Xu Wan, Jie Song, Mingyang Sun
Title: LLM-Guided Safe Reinforcement Learning for Energy System Topology Reconfiguration
Abstract:
The increasing penetration of renewable generation and the growing variability of electrified demand introduce substantial operational uncertainty to modern power systems. Topology reconfiguration is widely recognized as an effective and economical means to enhance grid resilience. Due to the coexistence of AC power‑flow constraints and discrete switching decisions, topology reconfiguration in large‑scale systems leads to a highly nonlinear and nonconvex optimization problem, making traditional methods computationally prohibitive. Consequently, several studies have explored reinforcement learning‑based approaches to improve scalability and operational efficiency. However, its practical implementation is challenged by the high‑dimensional combinatorial action space and the need to ensure safety during learning‑based decision‑making. To address these challenges, this paper presents a safe and intelligent topology control framework that integrates Large Language Models (LLMs) with a Safety Soft Actor‑Critic (Safety‑SAC) architecture. Operational voltage and thermal limits are reformulated into smooth safety‑cost signals, enabling risk‑aware policy optimization within a constrained Markov decision process. A knowledge‑based Safety‑LLM module is further introduced to refine unsafe or suboptimal transitions through domain knowledge and state‑informed reasoning, thus guiding the learning agent toward safer and more effective switching actions. Experiments on the IEEE 36‑bus and 118‑bus Grid2Op benchmarks show that the proposed method consistently improves reward, survival time, and safety metrics, achieving higher reward, longer survival, and lower safety cost compared with SAC, ACE, and their safety‑enhanced variants. These results demonstrate the potential of combining LLM‑based reasoning with safe reinforcement learning to achieve scalable and reliable grid topology control.

Authors:Leo Seugnet, Shuang Gao
Title: Discrete-time linear quadratic stochastic control with equality-constrained inputs: Application to energy demand response
Abstract:
We investigate the discrete‑time stochastic linear quadratic control problem for a population of cooperative agents under the hard equality constraint on total control inputs, motivated by demand response in renewable energy systems. We establish the optimal solution that respects hard equality constraints for systems with additive noise in the dynamics. The optimal control law is derived using dynamic programming and Karush‑Kuhn‑Tucker (KKT) conditions, and the resulting control solution depends on a discrete‑time Riccati‑like recursive equation. Application examples of coordinating the charging of a network of residential batteries to absorb excess solar power generation are demonstrated, and the proposed control is shown to achieve exact power tracking while considering individual State‑of‑Charge (SoC) objectives

Authors:Chayan Kumar Paul, Bhabani Shankar Dey, Indra Narayan Kar
Title: Safety in Admittance Control using Reference Trajectory Shaping
Abstract:
This paper presents a switched model reference admittance control framework to achieve safe and compliant human‑robot collaboration through reference trajectory shaping. The proposed method generates variable admittance parameters according to task compliance and task‑space safety requirements. Additionally, a disturbance bound is incorporated to enhance robustness against disturbances. Safety guarantees are explicitly established by integrating invariance control, ensuring that the reference trajectory remains within the admissible region. Stability of the switched system is analyzed using a common quadratic Lyapunov function, which confirms asymptotic convergence of the tracking error. The effectiveness of the approach is demonstrated through simulations on a two link manipulator and comparisons with existing methods are also presented. Furthermore, real time implementation on a single link manipulator validates the practical feasibility of the controller, highlighting its ability to achieve both compliance and safety in physical interaction scenarios.

Authors:Eder Baron-Prada, Adolfo Anta, Florian Dörfler
Title: On the Impact of Operating Points on Small-Signal Stability: Decentralized Stability Sets via Scaled Relative Graphs
Abstract:
This paper presents a decentralized frequency‑domain framework to characterize the influence of the operating point on the small‑signal stability of converter‑dominated power systems. The approach builds on Scaled Relative Graph (SRG) analysis, extended here to address Linear Parameter‑Varying (LPV) systems. By exploiting the affine dependence of converter admittances on their steady‑state operating points, the centralized small‑signal stability assessment of the grid is decomposed into decentralized, frequency‑wise geometric tests. Each converter can independently evaluate its feasible stability region, expressed as a set of linear inequalities in its parameter space. The framework provides closed‑form geometric characterizations applicable to both grid‑following (GFL) and grid‑forming (GFM) converters, and validation results confirm its effectiveness.

Authors:Arnab Pal, Suman Singha Roy, Asim Kumar Naskar
Title: Fully Distributed Adaptive Consensus Approach for Economic Dispatch Problem
Abstract:
This research presents a novel approach to solving the economic load dispatch (ELD) problem in smart grid systems by leveraging a multi‑agent distributed consensus strategy. The core idea revolves around achieving agreement among generators on their incremental cost values, thereby enabling an optimal allocation of power generation. To enhance convergence and robustness, the study introduces an adaptive coupling weight mechanism within a fully decentralized consensus framework, carefully designed with appropriate initial settings for incremental costs. The proposed distributed control protocol is versatile it functions effectively in both constrained and unconstrained generator capacity scenarios. Importantly, the methodology ensures that total power generation continuously matches dynamic load demands throughout the dispatch process, maintaining system‑wide balance. To accommodate fluctuating and time varying load profiles, a dummy node is incorporated into the network architecture, acting as a flexible proxy for real time demand changes. The resilience of the method is further evaluated under communication disruptions, specifically by analyzing generator link failures through a switching network topology. Stability of the system is rigorously established using a Lyapunov‑based analysis, assuming an undirected and connected communication graph among agents. To validate the practical efficacy of the proposed technique, comprehensive simulations are conducted on the IEEE 30 bus test system within the MATLAB environment, confirming its accuracy, adaptability, and computational efficiency in realistic smart grid conditions.

Authors:Xuping Hou, Xiaofeng Zong, Yong He
Title: Fully distributed consensus control for stochastic multi-agent systems under undirected and directed topologies
Abstract:
This work aims to address the design of fully distributed control protocols for stochastic consensus, and, for the first time, establishes the existence and uniqueness of solutions for the path‑dependent and highly nonlinear closed‑loop systems under both undirected and directed topologies, bridging a critical gap in the literature. For the case of directed graphs, a unified fully distributed control protocol is designed for the first time to guarantee mean square and almost sure consensus of stochastic multi‑agent systems under directed graphs. Moreover, an enhanced fully distributed protocol with additional tunable parameters designed for undirected graphs is proposed, which guarantees stochastic consensus while achieving superior convergence speed. Additionally, our work provides explicit exponential estimates for the corresponding convergence rates of stochastic consensus, elucidating the relationship between the exponential convergence rate and the system parameters. Simulations validate the theoretical results.

Authors:Tianmu Niu, Xiaoqun Wu
Title: Non-trivial consensus on directed signed matrix-weighted networks with compound measurement noises and time-varying topologies
Abstract:
This paper studies non‑trivial consensus‑‑a relatively novel and unexplored convergence behavior‑‑on directed signed matrix‑weighted networks subject to both additive and multiplicative measurement noises under time‑varying topologies. Building upon grounded matrix‑weighted Laplacian properties, a stochastic dynamic model is established that simultaneously captures inter‑dimensional cooperative and antagonistic interactions, compound measurement noises and time‑varying network structures. Based on stochastic differential equations theory, protocols that guarantee mean square and almost sure non‑trivial consensus are proposed. Specifically, for any predetermined non‑trivial consensus state, all agents are proven to converge toward this non‑zero value in the mean‑square and almost‑sure senses. The design of control gain function in our protocols highlights a balanced consideration of the cumulative effect over time, the asymptotic decay property and the finite energy corresponding to measurement noises. Notably, the conditions on time‑varying topologies in our protocols only require boundedness of elements in edge weight matrices, which facilitate the practicality of concept "time‑varying topology" in matrix‑weighted network consensus algorithms. Furthermore, the proposed protocols operate under milder connectivity conditions and no requirements on structural (un)balance properties. The work in this paper demonstrates that groups with both cooperative and antagonistic inter‑dimensional interactions can achieve consensus even in the presence of compound measurement noises and time‑varying topologies, challenging the conventional belief that consensus is attainable only in fully cooperative settings.

Authors:Daniel Shen, Marija Ilic, John Parsons
Title: Peak-Load Pricing and Investment Cost Recovery with Duration-Limited Storage
Abstract:
Energy storage shifts energy from off‑peak periods to on‑peak periods. Unlike conventional generation, storage is duration‑limited: the stored energy capacity constrains the duration over which it can supply power. To understand how these constraints affect optimal pricing and investment decisions, we extend the classic two‑period peak‑load pricing model to include duration‑limited storage. By adopting assumptions typical of solar‑dominated systems, we link on‑ and off‑peak prices to storage investment costs, round‑trip efficiency, and the duration of the peak period. The bulk of the scarcity premium from on‑peak prices is associated with the fixed costs of storage as opposed to variable costs stemming from round‑trip efficiency losses. Unlike conventional generators, the binding duration constraints lead storage to recover energy capacity costs on a per‑peak‑event basis instead of amortizing these costs over total peak hours. A numerical example illustrates the implications for equilibrium prices and capacity investment.

Authors:John-Paolo Casasanta, John W. Simpson-Porco
Title: A Lyapunov Characterization of Robust D-Stability with Application to Decentralized Integral Control of LTI Systems
Abstract:
The concept of matrix D‑stability plays an important role in applications, ranging from economic and biological system models to decentralized control. Here we provide necessary and sufficient Lyapunov‑type conditions for the robust (block) D‑stability property. We leverage this characterization as part of a novel Lyapunov analysis of decentralized integral control for MIMO LTI systems, providing sufficient conditions guaranteeing stability under low‑gain and under arbitrary connection and disconnection of individual control loops.

Authors:Kunal Garg, Xi Yu
Title: Hybrid topology control: a dynamic leader-based distributed edge-addition and deletion mechanism
Abstract:
Coordinated operations of multi‑robot systems (MRS) require agents to maintain communication connections to accomplish team objectives. However, maintaining the connections imposes costs in terms of restricted robot mobility, resulting in suboptimal team performance. In this work, we consider a realistic MRS framework in which agents are subject to unknown dynamical disturbances and experience communication delays. Most existing works on connectivity maintenance use consensus‑based frameworks for graph reconfiguration, where decision‑making time scales with the number of nodes and requires multiple rounds of communication, making them ineffective under communication delays. To address this, we propose a novel leader‑based decision‑making algorithm that uses a central node for efficient real‑time reconfiguration, reducing decision‑making time to depend on the graph diameter rather than the number of nodes and requiring only one round of information transfer through the network. We propose a novel method for estimating robot locations within the MRS that actively accounts for unknown disturbances and the communication delays. Using these position estimates, the central node selects a set of edges to delete while allowing the formation of new edges, aiming to keep the diameter of the new graph within a threshold. We provide numerous simulation results to showcase the efficacy of the proposed method.

Authors:Hongchao Zhang, Mohamad H. Kazma, Meiyi Ma, Taylor T. Johnson, Ahmad F. Taha
Title: Verification and Forward Invariance of Control Barrier Functions for Differential-Algebraic Systems
Abstract:
Differential‑algebraic equations (DAEs) arise in power networks, chemical processes, and multibody systems, where algebraic constraints encode physical conservation laws. The safety of such systems is critical, yet safe control is challenging because algebraic constraints restrict allowable state trajectories. Control barrier functions (CBFs) provide computationally efficient safety filters for ordinary differential equation (ODE) systems. However, existing CBF methods are not directly applicable to DAEs due to potential conflicts between the CBF condition and the constraint manifold. This paper introduces DAE‑aware CBFs that incorporate the differential‑algebraic structure through projected vector fields. We derive conditions that ensure forward invariance of safe sets while preserving algebraic constraints and extend the framework to higher‑index DAEs. A systematic verification framework is developed, establishing necessary and sufficient conditions for geometric correctness and feasibility of DAE‑aware CBFs. For polynomial systems, sum‑of‑squares certificates are provided, while for nonpolynomial and neural network candidates, satisfiability modulo theories are used for falsification. The approach is validated on wind turbine and flexible‑link manipulator systems.

Authors:Wenchao Wu, Shutong Chen, Wenjie Liu, Zhibo Pang, Yansha Deng, Robert Schober
Title: Safety-aware Goal-oriented Semantic Sensing, Communication, and Control for Robotics
Abstract:
Wirelessly‑connected robotic systems empower robots with real‑time intelligence by leveraging remote computing resources for decision‑making. However, the data exchange between robots and edge servers often overwhelms communication links, introducing latency that degrades task performance. To tackle this, goal‑oriented semantic communication (GSC) has been introduced for wirelessly‑connected robotic systems to extract and transmit only goal‑relevant semantic representations. While this improves task effectiveness, it generally overlooks practical safety requirements. Meanwhile, existing robotics research often treats safety primarily as a control‑level problem, without systematically considering safety across sensing, communication, and control in a closed‑loop manner. To bridge this gap, we investigate how to enable safety‑aware goal‑oriented semantic (SA‑GS) sensing, communication, and control co‑design in wirelessly‑connected robotic systems, aiming to maximize the robotic task effectiveness subject to practical safety requirements. We first introduce an architecture for wirelessly‑connected robotic systems and representative use cases. We then summarize general safety requirements and effectiveness metrics across the use cases. Next, we systematically analyze the unique safety and effectiveness challenges in sensing, communication, and control. Based on these, we further present potential SA‑GS research directions. Finally, an Unmanned Aerial Vehicle (UAV) target tracking case study validates that one of the presented SA‑GS research directions, i.e., semantic‑based C\&C packet execution, could significantly improve safety rate and tracking success rate by more than 2 times and 4.5 times, respectively.

Authors:Kushal Khemani, Anjum Nazir Qureshi
Title: AI-Driven Predictive Maintenance with Environmental Context Integration for Connected Vehicles: Simulation, Benchmarking, and Field Validation
Abstract:
Predictive maintenance for connected vehicles offers the potential to reduce unexpected breakdowns and improve fleet reliability, but most existing systems rely exclusively on internal diagnostic signals and are validated on simulated or industrial benchmark data. This paper presents a contextual data fusion framework integrating vehicle‑internal sensor streams with external environmental signals ‑‑ road quality, weather, traffic density, and driver behaviour ‑‑ acquired via V2X communication and third‑party APIs, with inference at the vehicle edge. The framework is evaluated across four layers. A feature group ablation study on a physics‑informed synthetic dataset shows contextual features contribute a 2.6‑point F1 improvement; removing all context reduces macro F1 from 0.855 to 0.807. On the AI4I 2020 benchmark (10,000 samples), LightGBM achieves AUC‑ROC 0.973 under 5‑fold stratified cross‑validation with SMOTE confined to training folds. A noise sensitivity analysis shows macro F1 remains above 0.88 at low noise and degrades to 0.74 at high noise. Most critically, the pipeline is validated on real‑world telemetry from five vehicles across three countries (India, Germany, Brazil), comprising 992 trips and 11 evaluable service events identified from component wear resets in the trip logs. Across six wear‑driven events spanning four vehicles, the model achieves 100% detection with mean MAE of 12.2 days. A fine‑tuning ablation shows the base synthetic model already achieves 6/6 binary detection; per‑vehicle adaptation reduces wear‑driven MAE from 25.9 to 12.2 days. SHAP analysis confirms contextual and interaction features rank among the top 15 predictors. Edge‑based inference reduces estimated latency from 3.5 seconds to under 1.0 second relative to cloud‑only processing.

Authors:David Costa, Francesco Cerrito, Massimo Canale, Carlo Novara
Title: Unifying Decision Making and Trajectory Planning in Automated Driving through Time-Varying Potential Fields
Abstract:
This paper proposes a unified decision making and local trajectory planning framework based on Time‑Varying Artificial Potential Fields (TVAPFs). The TVAPF explicitly models the predicted motion via bounded uncertainty of dynamic obstacles over the planning horizon, using information from perception and V2X sources when available. TVAPFs are embedded into a finite horizon optimal control problem that jointly selects the driving maneuver and computes a feasible, collision free trajectory. The effectiveness and real‑time suitability of the approach are demonstrated through a simulation test in a multi‑actor scenario with real road topology, highlighting the advantages of the unified TVAPF‑based formulation.

Authors:Ognjen Stanojev, Pol Jane Soneira, Gösta Stomberg, Mario Schweizer
Title: EMT and RMS Modeling of Thyristor Rectifiers for Stability Analysis of Converter-Based Systems
Abstract:
Thyristor rectifiers are a well‑established and cost‑effective solution for controlled high‑power rectification, commonly used for hydrogen electrolysis and HVDC transmission. However, small‑signal modeling and analysis of thyristor rectifiers remain challenging due to their line‑commutated operation and nonlinear switching dynamics. This paper first revisits conventional RMS‑based modeling of thyristor rectifiers and subsequently proposes a novel nonlinear state‑space EMT model in the dq domain that can be linearized for small‑signal analysis. The proposed model accurately captures all the relevant dynamic phenomena, including PLL dynamics, the commutation process, and switching delays. It is derived in polar coordinates, offering novel insights into the impact of the PLL and commutation angle on the thyristor rectifier dynamics. We verify the RMS and EMT models against a detailed switching model and demonstrate their applicability through small‑signal stability analysis of a modified IEEE 39‑bus test system that incorporates thyristor rectifier‑interfaced hydrogen electrolyzers, synchronous generators, and grid‑forming converters.

Authors:Gabriele Gualandi, Alessandro V. Papadopoulos
Title: From Passive Monitoring to Active Defence: Resilient Control of Manipulators Under Cyberattacks
Abstract:
Cyber‑physical robotic systems are vulnerable to false data injection attacks (FDIAs), in which an adversary corrupts sensor signals while evading residual‑based passive anomaly detectors such as the chi‑squared test. Such stealthy attacks can induce substantial end‑effector deviations without triggering alarms. This paper studies the resilience of redundant manipulators to stealthy FDIAs and advances the architecture from passive monitoring to active defence. We formulate a closed‑loop model comprising a feedback‑linearized manipulator, a steady‑state Kalman filter, and a chi‑squared‑based anomaly detector. Building on this passive monitoring layer, we propose an active control‑level defence that attenuates the control input through a monotone function of an anomaly score generated by a novel actuation‑projected, measurement‑free state predictor. The proposed design provides probabilistic guarantees on nominal actuation loss and preserves closed‑loop stability. From the attacker perspective, we derive a convex QCQP for computing one‑step optimal stealthy attacks. Simulations on a 6‑DOF planar manipulator show that the proposed defence significantly reduces attack‑induced end‑effector deviation while preserving nominal task performance in the absence of attacks.

Authors:Silvia Mura, Chiara Cavigliano, Anna Marcucci, Pietro Savazzi, Anna Vizziello, Maurizio Magarini
Title: A Physics-Based Digital Human Twin for Galvanic-Coupling Wearable Communication Links
Abstract:
This paper presents a systematic characterization of wearable galvanic coupling (GC) channels under narrowband and wideband operation. A physics‑consistent digital human twin maps anatomical properties, propagation geometry, and electrode‑skin interfaces into complex transfer functions directly usable for communication analysis. Attenuation, phase delay, and group delay are evaluated for longitudinal and radial configurations, and dispersion‑induced variability is quantified through attenuation ripple and delay standard deviation metrics versus bandwidth. Results confirm electro‑quasistatic, weakly dispersive behavior over 10 kHz‑1 MHz. Attenuation is primarily geometry‑driven, whereas amplitude ripple and delay variability increase with bandwidth, tightening equalization and synchronization constraints. Interface conditioning (gel and foam) significantly improves amplitude and phase stability, while propagation geometry governs link budget and baseline delay. Overall, the framework quantitatively links tissue electromagnetics to waveform distortion, enabling informed trade‑offs among bandwidth, interface design, and transceiver complexity in wearable GC systems.

Authors:You Wu, Zhenguo Wang, Naiyu Wang
Title: From AI Weather Prediction to Infrastructure Resilience: A Correction-Downscaling Framework for Tropical Cyclone Impacts
Abstract:
This paper addresses a missing capability in infrastructure resilience: turning fast, global AI weather forecasts into asset‑scale, actionable risk. We introduce the AI‑based Correction‑Downscaling Framework (ACDF), which transforms coarse AI weather prediction (AIWP) into 500‑m, unbiased wind fields and transmission tower/line failure probabilities for tropical cyclones. ACDF separates storm‑scale bias correction from terrain‑aware downscaling, preventing error propagation while restoring sub‑kilometer variability that governs structural loading. Tested on 11 typhoons affecting Zhejiang, China under leave‑one‑storm‑out evaluation, ACDF reduces station‑scale wind‑speed MAE by 38.8% versus Pangu‑Weather, matches observation‑assimilated mesoscale analyses, yet runs in 25 s per 12‑h cycle on a single GPU. In the Typhoon Hagupit case, ACDF reproduced observed high‑wind tails, isolated a coastal high‑risk corridor, and flagged the line that failed, demonstrating actionable guidance at tower and line scales. ACDF provides an end‑to‑end pathway from AI global forecasts to operational, impact‑based early warning for critical infrastructure.

Authors:Pawel Marczewski, Paulina Superczynska, Jakub Bernat, Szymon Szczesny
Title: Reinforcement Learning for Elliptical Cylinder Motion Control Tasks
Abstract:
The control of devices with limited input always bring attention to solve by research due to its difficulty and non‑trival solution. For instance, the inverted pendulum is benchmarking problem in control theory and machine learning. In this work, we are focused on the elliptical cylinder and its motion under limited torque. The inspiration of the problem is from untethered magnetic devices, which due to distance have to operate with limited input torque. In this work, the main goal is to define the control problem of elliptic cylinder with limited input torque and solve it by Reinforcement Learning. As a classical baseline, we evaluate a two‑stage controller composed of an energy‑shaping swing‑up law and a local Linear Quadratic Regulator (LQR) stabilizer around the target equilibrium. The swing‑up controller increases the system's mechanical energy to drive the state toward a neighborhood of the desired equilibrium, a linearization of the nonlinear model yields an LQR that regulates the angle and angular‑rate states to the target orientation with bounded input. This swing‑up + LQR policy is a strong, interpretable reference for underactuated system and serves a point of comparison to the learned policy under identical limits and parameters. The solution shows that the learning is possible however, the different cases like stabilization in upward position or rotating of half turn are very difficult for increasing mass or ellipses with a strongly unequal perimeter ratio.

Authors:Nicole Gehring
Title: On the strict-feedback form of hyperbolic distributed-parameter systems
Abstract:
The paper is concerned with the strict‑feedback form of hyperbolic distributed‑parameter systems. Such a system structure is well known to be the basis for the recursive backstepping control design for nonlinear ODEs and is also reflected in the Volterra integral transformation used in the backstepping‑based stabilization of parabolic PDEs. Although such integral transformations also proved very helpful in deriving state feedback controllers for hyperbolic PDEs, they are not necessarily related to a strict‑feedback form. Therefore, the paper looks at structural properties of hyperbolic systems in the context of controllability. By combining and extending existing backstepping results, exactly controllable heterodirectional hyperbolic PDEs as well as PDE‑ODE systems are mapped into strict‑feedback form. While stabilization is not the objective in this paper, the obtained system structure is the basis for a recursive backstepping design and provides new insights into coupling structures of distributed‑parameter systems that allow for a simple control design. In that sense, the paper aims to take backstepping for PDEs back to its ODE origin.

Authors:Yoshiyuki Ohmura, Yasuo Kuniyoshi
Title: Dual-Laws Model for a theory of artificial consciousness
Abstract:
Objectively verifying the generative mechanism of consciousness is extremely difficult because of its subjective nature. As long as theories of consciousness focus solely on its generative mechanism, developing a theory remains challenging. We believe that broadening the theoretical scope and enhancing theoretical unification are necessary to establish a theory of consciousness. This study proposes seven questions that theories of consciousness should address: phenomena, self, causation, state, function, contents, and universality. The questions were designed to examine the functional aspects of consciousness and its applicability to system design. Next, we will examine how our proposed Dual‑Laws Model (DLM) can address these questions. Based on our theory, we anticipate two unique features of a conscious system: autonomy in constructing its own goals and cognitive decoupling from external stimuli. We contend that systems with these capabilities differ fundamentally from machines that merely follow human instructions. This makes a design theory that enables high moral behavior indispensable.

Authors:Ankur Kamboj, Rajiv Ranganathan, Xiaobo Tan, Vaibhav Srivastava
Title: Skill-informed Data-driven Haptic Nudges for High-dimensional Human Motor Learning
Abstract:
In this work, we propose a data‑driven framework to design optimal haptic nudge feedback leveraging the learner's estimated skill to address the challenge of learning a novel motor task in a high‑dimensional, redundant motor space. A nudge is a series of vibrotactile feedback delivered to the learner to encourage motor movements that aid in task completion. We first model the stochastic dynamics of human motor learning under haptic nudges using an Input‑Output Hidden Markov Model (IOHMM), which explicitly decouples latent skill evolution from observable performance measures. Leveraging this predictive model, we formulate the haptic nudge feedback design problem as a Partially Observable Markov Decision Process (POMDP). This allows us to derive an optimal nudging policy that minimizes long‑term performance cost and implicitly guides the learner toward superior skill states. We validate our approach through a human participant study (N=30) involving a high‑dimensional motor task rendered through a hand exoskeleton. Results demonstrate that participants trained with the POMDP‑derived policy exhibit significantly accelerated movement efficiency and endpoint accuracy compared to groups receiving heuristic‑based feedback or no feedback. Furthermore, synergy analysis reveals that the POMDP group discovers efficient low‑dimensional motor representations more rapidly.

Authors:Buddhika Nettasinghe, Geethu Joseph
Title: As Language Models Scale, Low-order Linear Depth Dynamics Emerge
Abstract:
Large language models are often viewed as high‑dimensional nonlinear systems and treated as black boxes. Here, we show that transformer depth dynamics admit accurate low‑order linear surrogates within context. Across tasks including toxicity, irony, hate speech and sentiment, a 32‑dimensional linear surrogate reproduces the layerwise sensitivity profile of GPT‑2‑large with near‑perfect agreement, capturing how the final output shifts under additive injections at each layer. We then uncover a surprising scaling principle: for a fixed‑order linear surrogate, agreement with the full model improves monotonically with model size across the GPT‑2 family. This linear surrogate also enables principled multi‑layer interventions that require less energy than standard heuristic schedules when applied to the full model. Together, our results reveal that as language models scale, low‑order linear depth dynamics emerge within contexts, offering a systems‑theoretic foundation for analyzing and controlling them.

Authors:Hao Zhang, Jack Hirschman, Randy Lemons, Nicole R. Neveu, Joseph Robinson, Auralee L. Edelen, Tor O. Raubenheimer, Dan Wang, Ji Qiang, Sergio Carbajo
Title: Physics-Guided Inverse Design of Optical Waveforms for Nonlinear Electromagnetic Dynamics
Abstract:
Structured optical waveforms are emerging as powerful control fields for the next generation of complex photonic and electromagnetic systems, where the temporal structure of light can determine the ultimate performance of scientific instruments. However, identifying optimal optical drive fields in strongly nonlinear regimes remains challenging because the mapping between optical inputs and system response is high‑dimensional and typically accessible only through computationally expensive simulations. Here, we present a physics‑guided deep learning framework for the inverse design of optical temporal waveforms. By training a light‑weighted surrogate model on simulations, the method enables gradient‑based synthesis of optical profiles that compensate nonlinear field distortions in driven particle‑field systems. As a representative application, we apply the approach to the generation of electron beams used in advanced photon and particle sources. The learned optical waveform actively suppresses extrinsic emittance growth by more than 52% compared with conventional Gaussian operation and by approximately 9% relative to the theoretical flattop limit in simulation. We further demonstrate experimental feasibility by synthesizing the predicted waveform using a programmable pulse‑shaping platform; incorporating the measured optical profile into beamline simulations yields a 31% reduction in the extrinsic emittance contribution. Beyond accelerator applications, this work establishes a general way for physics‑guided inverse design of optical control fields, enabling structured light to approach fundamental performance limits in nonlinear photonic and high‑frequency electromagnetic systems.

Authors:Xin Yu, Wei Lin
Title: Compensation of Input/Output Delays for Retarded Systems by Sequential Predictors: A Lyapunov-Halanay Method
Abstract:
This paper presents a Lyapunov‑Halanay method to study global asymptotic stabilization (GAS) of nonlinear retarded systems subject to large constant delays in input/output ‑ a challenging problem due to their inherent destabilizing effects. Under the conditions of global Lipschitz continuity (GLC) and global exponential stabilizability (GES) of the retarded system without input delay, a state feedback controller is designed based on sequential predictors to make the closed‑loop retarded system GAS. Moreover, if the retarded system with no output delay permits a global exponential observer, a dynamic output compensator is also constructed based on sequential predictors, achieving GAS of the corresponding closed‑loop retarded system with input/output delays. The predictor based state and output feedback stabilization results are then extended to a broader class of nonlinear retarded systems with input/output delays, which may not be GES but satisfy global asymptotic stabilizability/observability and suitable ISS conditions. As an application, a pendulum system with delays in the state, input and output is used to illustrate the effectiveness of the proposed state and output feedback control strategies based on sequential predictors.

Authors:Everest Bloomer, Irem Didin, Ching-Yi Lin, Sahil Shah
Title: Ising-ReRAM: A Low Power Ising Machine ReRAM Crossbar for NP Problems
Abstract:
Computational workloads are growing exponentially, driving power consumption to unsustainable levels. Efficiently distributing large‑scale networks is an NP‑Complete problem equivalent to Boolean satisfiability (SAT), making it one of the core challenges in modern computation. To address this, physics and device inspired methods such as Ising systems have been explored for solving SAT more efficiently. In this work, we implement an Ising model equivalence of the 3‑SAT problem using a ReRAM crossbar fabricated in the Skywater 130 nm CMOS process. Our ReRAM‑based algorithm achieves 91.0% accuracy in matrix representation across iterative reprogramming cycles. Additionally, we establish a foundational energy profile by measuring the energy costs of small sub‑matrix structures within the problem space, demonstrating under linear growth trajectory for combining sub‑matrices into larger problems. These results demonstrate a promising platform for developing scalable architectures to accelerate NP‑Complete problem solving.

Authors:Melih Özcan, Umut Orguner, Ozgur S. Oguz
Title: Push, Press, Slide: Mode-Aware Planar Contact Manipulation via Reduced-Order Models
Abstract:
Non‑prehensile planar manipulation, including pushing and press‑and‑slide, is critical for diverse robotic tasks, but notoriously challenging due to hybrid contact mechanics, under‑actuation, and asymmetric friction limits that traditionally necessitate computationally expensive iterative control. In this paper, we propose a mode‑aware framework for planar manipulation with one or two robotic arms based on contact topology selection and reduced‑order kinematic modeling. Our core insight is that complex wrench‑twist limit surface mechanics can be abstracted into a discrete library of physically intuitive models. We systematically map various single‑arm and bimanual contact topologies to simple non‑holonomic formulations, e.g. unicycle for simplified press‑and‑slide motion. By anchoring trajectory generation to these reduced‑order models, our framework computes the required object wrench and distributes feasible, friction‑bounded contact forces via a direct algebraic allocator. We incorporate manipulator kinematics to ensure long‑horizon feasibility and demonstrate our fast, optimization‑free approach in simulation across diverse single‑arm and bimanual manipulation tasks. Supplementary videos and additional information are available at: https://sites.google.com/view/pushpressslide

Authors:Duncan Eddy, Esen Yel, Emma Passmore, Niles Egan, Grayson Armour, Dylan M. Asmar, Mykel J. Kochenderfer
Title: Optimizing Task Completion Time Updates Using POMDPs
Abstract:
Managing announced task completion times is a fundamental control problem in project management. While extensive research exists on estimating task durations and task scheduling, the problem of when and how to update completion times communicated to stakeholders remains understudied. Organizations must balance announcement accuracy against the costs of frequent timeline updates, which can erode stakeholder trust and trigger costly replanning. Despite the prevalence of this problem, current approaches rely on static predictions or ad‑hoc policies that fail to account for the sequential nature of announcement management. In this paper, we formulate the task announcement problem as a Partially Observable Markov Decision Process (POMDP) where the control policy must decide when to update announced completion times based on noisy observations of true task completion. Since most state variables (current time and previous announcements) are fully observable, we leverage the Mixed Observability MDP (MOMDP) framework to enable more efficient policy optimization. Our reward structure captures the dual costs of announcement errors and update frequency, enabling synthesis of optimal announcement control policies. Using off‑the‑shelf solvers, we generate policies that act as feedback controllers, adaptively managing announcements based on belief state evolution. Simulation results demonstrate significant improvements in both accuracy and announcement stability compared to baseline strategies, achieving up to 75% reduction in unnecessary updates while maintaining or improving prediction accuracy.

Authors:Yanliang Huang, Zhen Zhang, Peng Xie, Zhuoqi Zeng, Amr Alanwar
Title: Conformalized Data-Driven Reachability Analysis with PAC Guarantees
Abstract:
Data‑driven reachability analysis computes over‑approximations of reachable sets directly from noisy data. Existing deterministic methods require either known noise bounds or system‑specific structural parameters such as Lipschitz constants. We propose Conformalized Data‑Driven Reachability (CDDR), a framework that provides Probably Approximately Correct (PAC) coverage guarantees through the Learn Then Test (LTT) calibration procedure, requiring only that calibration and test trajectories be independently and identically distributed. CDDR is developed for three settings: linear time‑invariant (LTI) systems with unknown process noise distributions, LTI systems with bounded measurement noise, and general nonlinear systems including non‑Lipschitz dynamics. Experiments on a 5‑dimensional LTI system under Gaussian and heavy‑tailed Student‑t noise and on a 2‑dimensional non‑Lipschitz system with fractional damping demonstrate that CDDR achieves valid coverage where deterministic methods do not provide formal guarantees. Under anisotropic noise, a normalized score function reduces the reachable set volume while preserving the PAC guarantee.

Authors:Christian Doh Dinga, Francesco Lombardi, Roald Arkesteijn, Arjan van Voorden, Sander van Rijn, Laurens James de Vries, Milos Cvetkovic
Title: Technology configurations for decarbonizing residential heat supply through district heating and implications for the electricity network
Abstract:
District heating networks (DHNs) have significant potential to decarbonize residential heating and accelerate the energy transition. However, designing carbon‑neutral DHNs requires balancing several objectives, including economic costs, social acceptance, long‑term uncertainties, and grid‑integration challenges from electrification. By combining modeling‑to‑generate‑alternatives with power flow simulation techniques, we develop a decision‑support method for designing carbon‑neutral DHNs that are cost‑effective, socially acceptable, robust to future risks, and impose minimal impacts on the electricity grid. Applying our method to a Dutch case, we find substantial diversity in how carbon‑neutral DHNs can be designed. The flexibility in technology choice, sizing, and location enables accommodating different real‑world needs and achieving high electrification levels without increasing grid loading. For instance, intelligently located heat pumps and thermal storage can limit grid stress even when renewable baseload heat sources and green‑fuel boilers are scarce. Using our method, planners can explore diverse carbon‑neutral DHN designs and identify the design that best balances stakeholders' preferences.

Authors:Samuel Mallick, Laura Boca de de Giuli, Alessio La Bella, Azita Dabiri, Bart De Schutter, Riccardo Scattolini
Title: Integrated Online Monitoring and Adaption of Process Model Predictive Controllers
Abstract:
This paper addresses the design of an event‑triggered, data‑based, and performance‑oriented adaption method for model predictive control (MPC). The performance of such a strategy strongly depends on the accuracy of the prediction model, which may require online adaption to prevent performance degradation under changing operating conditions. Unlike existing methods that continuously update model and control parameters from data, potentially leading to catastrophic forgetting and unnecessary control modifications, we propose a novel approach based on statistical monitoring of closed‑loop performance indicators. This framework enables the detection of performance degradation, and, when required, controller adaption is performed via reinforcement learning and identification techniques. The proposed strategy is validated on a high‑fidelity simulation of a district heating system benchmark.

Authors:Anqi Dong, Anzhi Sheng, Xin Mao, Can Chen
Title: Maximum-Entropy Random Walks on Hypergraphs
Abstract:
Random walks are fundamental tools for analyzing complex networked systems, including social networks, biological systems, and communication infrastructures. While classical random walks focus on pairwise interactions, many real‑world systems exhibit higher‑order interactions naturally modeled by hypergraphs. Existing random walk models on hypergraphs often focus on undirected structures or do not incorporate entropy‑based inference, limiting their ability to capture directional flows, uncertainty, or information diffusion in complex systems. In this article, we develop a maximum‑entropy random walk framework on directed hypergraphs with two interaction mechanisms: broadcasting where a pivot node activates multiple receiver nodes and merging where multiple pivot nodes jointly influence a receiver node. We infer a transition kernel via a Kullback‑‑Leibler divergence projection onto constraints enforcing stochasticity and stationarity. The resulting optimality conditions yield a multiplicative scaling form, implemented using Sinkhorn‑‑Schrödinger‑type iterations with tensor contractions. We further analyze ergodicity, including projected linear kernels for broadcasting and tensor spectral criteria for polynomial dynamics in merging. The effectiveness of our framework is demonstrated with both synthetic and real‑world examples.

Authors:Nivand Khosravi, Niusha Khosravi, Mohammad Bozorg, Masoud S. Bahraini
Title: Decentralized Cooperative Localization for Multi-Robot Systems with Asynchronous Sensor Fusion
Abstract:
Decentralized cooperative localization (DCL) is a promising approach for nonholonomic mobile robots operating in GPS‑denied environments with limited communication infrastructure. This paper presents a DCL framework in which each robot performs localization locally using an Extended Kalman Filter, while sharing measurement information during update stages only when communication links are available and companion robots are successfully detected by LiDAR. The framework preserves cross‑correlation consistency among robot state estimates while handling asynchronous sensor data with heterogeneous sampling rates and accommodating accelerations during dynamic maneuvers. Unlike methods that require pre‑aligned coordinate systems, the proposed approach allows robots to initialize with arbitrary reference‑frame orientations and achieves automatic alignment through transformation matrices in both the prediction and update stages. To improve robustness in feature‑sparse environments, we introduce a dual‑landmark evaluation framework that exploits both static environmental features and mobile robots as dynamic landmarks. The proposed framework enables reliable detection and feature extraction during sharp turns, while prediction accuracy is improved through information sharing from mutual observations. Experimental results in both Gazebo simulation and real‑world basement environments show that DCL outperforms centralized cooperative localization (CCL), achieving a 34% reduction in RMSE, while the dual‑landmark variant yields an improvement of 56%. These results demonstrate the applicability of DCL to challenging domains such as enclosed spaces, underwater environments, and feature‑sparse terrains where conventional localization methods are ineffective.

Authors:Francesca Marafini, Giacomo Zini, Alberto Barontini, Nuno Mendes, Alice Cicirello, Michele Betti, Gianni Bartoli
Title: Numerical benchmark for damage identification in Structural Health Monitoring
Abstract:
The availability of a dataset for validation and verification purposes of novel data‑driven strategies and/or hybrid physics‑data approaches is currently one of the most pressing challenges in the engineering field. Data ownership, security, access and metadata handiness are currently hindering advances across many fields, particularly in Structural Health Monitoring (SHM) applications. This paper presents a simulated SHM dataset, comprised of dynamic and static measurements (i.e., acceleration and displacement), and includes the conceptual framework designed to generate it. The simulated measurements were generated to incorporate the effects of Environmental and Operational Variations (EOVs), different types of damage, measurement noise and sensor faults and malfunctions, in order to account for scenarios that may occur during real acquisitions. A fixed‑fixed steel beam structure was chosen as reference for the numerical benchmark. The simulated monitoring was operated under the assumptions of a Single Degree of Freedom (SDOF) for generating acceleration records and of the Euler‑Bernoulli beam for the simulated displacement measurements. The generation process involved the use of parallel computation, which is detailed within the provided open‑source code. The generated data is also available open‑source, thus ensuring reproducibility, repeatability and accessibility for further research. The comprehensive description of data types, formats, and collection methodologies makes this dataset a valuable resource for researchers aiming to develop or refine SHM techniques, fostering advancements in the field through accessible, high‑quality synthetic data.

Authors:Julius Wanner, Hoang-Vu Phan, Charbel Toumieh, Dario Floreano
Title: Flight through Narrow Gaps with Morphing-Wing Drones
Abstract:
The size of a narrow gap traversable by a fixed‑wing drone is limited by its wingspan. Inspired by birds, here, we enable the traversal of a gap of sub‑wingspan width and height using a morphing‑wing drone capable of temporarily sweeping in its wings mid‑flight. This maneuver poses control challenges due to sudden lift loss during gap‑passage at low flight speeds and the need for precisely timed wing‑sweep actuation ahead of the gap. To address these challenges, we first develop an aerodynamic model for general wing‑sweep morphing drone flight including low flight speeds and post‑stall angles of attack. We integrate longitudinal drone dynamics into an optimal reference trajectory generation and Nonlinear Model Predictive Control framework with runtime adaptive costs and constraints. Validated on a 130 g wing‑sweep‑morphing drone, our method achieves an average altitude error of 5 cm during narrow‑gap passage at forward speeds between 5 and 7 m/s, whilst enforcing fully swept wings near the gap across variable threshold distances. Trajectory analysis shows that the drone can compensate for lift loss during gap‑passage by accelerating and pitching upwards ahead of the gap to an extent that differs between reference trajectory optimization objectives. We show that our strategy also allows for accurate gap passage on hardware whilst maintaining a constant forward flight speed reference and near‑constant altitude.

Authors:Tommaso Grigoletto, Alain Sarlette, Francesco Ticozzi, Lorenza Viola
Title: Approximate Reduced Lindblad Dynamics via Algebraic and Adiabatic Methods
Abstract:
We present an algebraic framework for approximate model reduction of Markovian open quantum dynamics that guarantees complete positivity and trace preservation by construction. First, we show that projecting a Lindblad generator on its center manifold ‑‑ the space spanned by eigenoperators with purely imaginary eigenvalue ‑‑ yields an asymptotically exact reduced quantum dynamical semigroup whose dynamics is unitary, with exponentially decaying transient error controlled by the generator's spectral gap. Second, for analytic perturbations of a Lindblad generator with a tractable center manifold, we propose a perturbative reduction that keeps the reduced space fixed at the unperturbed center manifold. The resulting generator is shown to remain a valid Lindbladian for arbitrary perturbation strengths, and explicit finite‑time error bounds, that quantify leakage from the unperturbed center sector, are provided. We further clarify the connection to adiabatic elimination methods, by both showing how the algebraic reduction can be directly related to a first‑order adiabatic‑elimination and by providing sufficient conditions under which the latter method can be applied while preserving complete positivity. We showcase the usefulness of our techniques in dissipative many‑body quantum systems exhibiting non‑stationary long‑time dynamics.

Authors:Rui Xie, Jun Wang, Jiaxu Duan, Chao Ma, Yunhui Liu, Yue Chen
Title: Robust Parametric Microgrid Dispatch Under Endogenous Uncertainty of Operation- and Temperature-Dependent Battery Degradation
Abstract:
Batteries play a critical role in microgrid energy management by ensuring power balance, enhancing renewable utilization, and reducing operational costs. However, battery degradation poses a significant challenge, particularly under extreme temperatures. This paper investigates the optimal trade‑off between battery degradation and operational costs in microgrid dispatch to find a robust cost‑effective strategy from a full life‑cycle perspective. A key challenge arises from the endogenous uncertainty (or decision‑dependent uncertainty, DDU) of battery degradation: Dispatch decisions influence the probability distribution of battery degradation, while in turn degradation changes battery operation model and thus affects dispatch. In this paper, we first develop an XGBoost‑based probabilistic degradation model trained on experimental data across varying temperature conditions. We then formulate a parametric model predictive control (MPC) framework for microgrid dispatch, where the weight parameters of the battery degradation penalty terms are tuned through long‑term simulation of degradation and dispatch interactions. Case studies validate the effectiveness of the proposed approach.

Authors:Yiliu He, Haiwang Zhong, Grant Ruan, Yan Xu, Chongqing Kang
Title: Emergency-Aware and Frequency-Constrained HVDC Planning for A Multi-Area Asynchronously Interconnected Grid
Abstract:
High‑voltage direct current (HVDC) technology has played a crucial role for long‑distance transmission of renewable power generation. However, the integration of large‑capacity HVDC lines introduces significant frequency security challenges during HVDC fault emergencies. This paper proposes an emergency‑aware and frequency‑constrained HVDC planning method to optimize the capacity of inter‑area HVDC tie‑lines in a multi‑area asynchronously interconnected grid. Firstly, a coordinated emergency frequency control scheme is proposed to allocate the emergency control resources during HVDC faults. Then, an enhanced system frequency response model integrating event‑driven emergency frequency control is developed and a weighted oblique decision tree approach is employed to extract frequency nadir security constraints. The proposed planning model considers all potential HVDC fault emergencies while treating candidate HVDC capacities as decision variables. Simulation results demonstrate superior performance in balancing economic efficiency with frequency security requirements, providing a practical solution for inter‑area HVDC planning.

Authors:Scott Angus, Jethro Browell, David Greenwood, Matthew Deakin
Title: Risk-Based Dynamic Thermal Rating in Distribution Transformers via Probabilistic Forecasting
Abstract:
Low voltage (LV) distribution transformers face accelerating demand growth while replacement lead times and costs continue to rise, making improved utilisation of existing assets essential. Static and conservative protection devices (PDs) in distribution transformers are inflexible and limit the available headroom of the transformer. This paper presents a probabilistic framework for dynamically forecasting optimal thermal protection settings. The proposed approach directly predicts the day‑ahead scale factor which maximises the dynamic thermal rating of the transformer from historical load, temperature, and metadata using clustered quantile regression models trained on 644 UK LV transformers. Probabilistic forecasting quantifies overheating risk directly through the prediction percentile, enabling risk‑informed operational decisions. Results show a 10‑‑12% additional capacity gain compared to static settings, with hotspot temperature risk matching the selected percentile, including under realistic temperature forecast errors. These results demonstrate a practical approach for distribution network operators to take advantage of PDs with adaptive settings to maximise capacity and manage risk on operational time scales.

Authors:Pieter Pas, Kristoffer Fink Løwenstein, Daniele Bernardini, Panagiotis Patrinos
Title: Exploiting Parallelism in a QPALM-based Solver for Optimal Control
Abstract:
We discuss the opportunities for parallelization in the recently proposed QPALM‑OCP algorithm, a solver tailored to quadratic programs arising in optimal control. A significant part of the computational work can be carried out independently for the different stages in the optimal control problem. We exploit this specific structure to apply parallelization and vectorization techniques in an optimized C++ implementation of the method. Results for optimal control benchmark problems and comparisons to the original QPALM method are provided.

Authors:Qi Dai, Beixiong Zheng, Yanhua Tan, Weidong Mei, Shiqi Gong, Jie Tang, Chengwen Xing
Title: Rotatable Antenna Enabled Covert Communication
Abstract:
Unlike conventional fixed‑antenna architectures, rotatable antenna (RA) has shown great potential in enhancing wireless communication performance by exploiting additional spatial degrees of freedom (DoFs) in a cost‑effective manner. In this letter, we propose a novel RA‑enabled covert communication system, where an RA array‑based transmitter (Alice) sends covert information to a legitimate user (Bob) in the presence of multiple wardens (Willies). To maximize the covert rate, we optimize the transmit beamforming vector and the rotational angles of individual RAs, subject to the constraints on covertness, transmit power, and antenna rotational range. To address the non‑convex formulated problem, we decompose it into two subproblems and propose an efficient alternating optimization (AO) algorithm to solve the two subproblems iteratively, where the second‑order cone programming (SOCP) method and successive convex approximation (SCA) approach are applied separately. Simulation results demonstrate that the proposed RA‑enabled covert communication system can provide significantly superior covertness performance to other benchmark schemes.

Authors:Qijun Liao, Jue Yang, Yiting Kang, Xinxin Zhao, Yong Zhang, Mingan Zhao
Title: Hybrid Energy-Aware Reward Shaping: A Unified Lightweight Physics-Guided Methodology for Policy Optimization
Abstract:
Deep reinforcement learning excels in continuous control but often requires extensive exploration, while physics‑based models demand complete equations and suffer cubic complexity. This study proposes Hybrid Energy‑Aware Reward Shaping (H‑EARS), unifying potential‑based reward shaping with energy‑aware action regularization. H‑EARS constrains action magnitude while balancing task‑specific and energy‑based potentials via functional decomposition, achieving linear complexity O(n) by capturing dominant energy components without full dynamics. We establish a theoretical foundation including: (1) functional independence for separate task/energy optimization; (2) energy‑based convergence acceleration; (3) convergence guarantees under function approximation; and (4) approximate potential error bounds. Lyapunov stability connections are analyzed as heuristic guides. Experiments across baselines show improved convergence, stability, and energy efficiency. Vehicle simulations validate applicability in safety‑critical domains under extreme conditions. Results confirm that integrating lightweight physics priors enhances model‑free RL without complete system models, enabling transfer from lab research to industrial applications.

Authors:Zhirun Li, Derek Hollenbeck, Ruikun Wu, Michelle Sherman, Sihua Shao, Xiang Sun, Mostafa Hassanalian
Title: Multi-Agent Reinforcement Learning for UAV-Based Chemical Plume Source Localization
Abstract:
Undocumented orphaned wells pose significant health and environmental risks to nearby communities by releasing toxic gases and contaminating water sources, with methane emissions being a primary concern. Traditional survey methods such as magnetometry often fail to detect older wells effectively. In contrast, aerial in‑situ sensing using unmanned aerial vehicles (UAVs) offers a promising alternative for methane emission detection and source localization. This study presents a robust and efficient framework based on a multi‑agent deep reinforcement learning (MARL) algorithm for the chemical plume source localization (CPSL) problem. The proposed approach leverages virtual anchor nodes to coordinate UAV navigation, enabling collaborative sensing of gas concentrations and wind velocities through onboard and shared measurements. Source identification is achieved by analyzing the historical trajectory of anchor node placements within the plume. Comparative evaluations against the fluxotaxis method demonstrate that the MARL framework achieves superior performance in both localization accuracy and operational efficiency.

Authors:Yuhao Zhang, Xiangru Xu
Title: Forward and Backward Reachability Analysis of Closed-loop Recurrent Neural Networks via Hybrid Zonotopes
Abstract:
Recurrent neural networks (RNNs) are widely employed to model complex dynamical systems due to their hidden‑state structure, which inherently captures temporal dependencies. This work presents a hybrid zonotope‑based approach for computing exact forward and backward reachable sets of closed‑loop RNN systems with ReLU activation functions. The method formulates state‑pair sets to compute reachable sets as hybrid zonotopes without requiring unrolling. To improve scalability, a tunable relaxation scheme is proposed that ranks unstable ReLU units across all layers using a triangle‑area score and selectively applies convex relaxations within a fixed binary limit in the hybrid zonotopes. This scheme enables an explicit tradeoff between computational complexity and approximation accuracy, with exact reachability as a special case. In addition, a sufficient condition is derived to certify the safety of closed‑loop RNN systems. Numerical examples demonstrate the effectiveness of the proposed approach.

Authors:Rui Wang, Kaitao Meng, Deshi Li, Liang Xu
Title: ISAC-Enabled Multi-UAV Collaborative Target Sensing for Low-Altitude Economy
Abstract:
Integrated sensing and communication (ISAC) has attracted growing research interests to facilitate the large‑scale development of the low‑altitude economy (LAE). However, the high dynamics of low‑altitude targets may overwhelm fixed ISAC systems, particularly at the edge of their coverage or in blind zones. Driven by high flexibility, unmanned aerial vehicle (UAV)‑assisted ISAC can provide more freedom of design to enhance communication and sensing abilities. In this paper, we propose an ISAC‑enabled multi‑UAV dynamic collaborative target sensing scheme, where UAVs can dynamically adjust their flight and resource allocation for cooperative sensing of mobile target through communicating with the terrestrial cellular network with ISAC signals. To achieve the precise sensing of the dynamic target, the posterior Cramer‑Rao bound (PCRB) for the target state is derived. Subsequently, the PCRB minimization problem is formulated by jointly optimizing the UAV‑BS association, UAVs' trajectories and bandwidth allocation, subject to the communication requirements for the UAVs. However, the problem is challenging since it involves non‑convex and implicit objective function with coupled optimization variables. For a fast implementation of sensing and tracking, we propose a low‑complexity iterative algorithm that can efficiently obtain a sub‑optimal solution to the problem. Specifically, the UAV‑BS association is first determined by the communication‑optimal solution. Then the UAVs' trajectories and bandwidth allocation are alternatively optimized based on the descent direction search algorithm. Finally, numerical results are provided to validate the superiority of our proposed designs as compared to various benchmarks.

Authors:Zehua Zou, Yiran Ma, Yulong Zhang, Zhengnan Li, Zeyu Yang, Jinhao Xie, Xiaoyu Jiang, Zhichao Chen
Title: Slack More, Predict Better: Proximal Relaxation for Probabilistic Latent Variable Model-based Soft Sensors
Abstract:
Nonlinear Probabilistic Latent Variable Models (NPLVMs) are a cornerstone of soft sensor modeling due to their capacity for uncertainty delineation. However, conventional NPLVMs are trained using amortized variational inference, where neural networks parameterize the variational posterior. While facilitating model implementation, this parameterization converts the distributional optimization problem within an infinite‑dimensional function space to parameter optimization within a finite‑dimensional parameter space, which introduces an approximation error gap, thereby degrading soft sensor modeling accuracy. To alleviate this issue, we introduce KProxNPLVM, a novel NPLVM that pivots to relaxing the objective itself and improving the NPLVM's performance. Specifically, we first prove the approximation error induced by the conventional approach. Based on this, we design the Wasserstein distance as the proximal operator to relax the learning objective, yielding a new variational inference strategy derived from solving this relaxed optimization problem. Based on this foundation, we provide a rigorous derivation of KProxNPLVM's optimization implementation, prove the convergence of our algorithm can finally sidestep the approximation error, and propose the KProxNPLVM by summarizing the abovementioned content. Finally, extensive experiments on synthetic and real‑world industrial datasets are conducted to demonstrate the efficacy of the proposed KProxNPLVM.

Authors:Hossein Mohammadi, Seyed Bagher Hashemi Natanzi, Ramak Nassiri, Jamshid Hassanpour, Bo Tang, Vuk Marojevic
Title: SliceFed: Federated Constrained Multi-Agent DRL for Dynamic Spectrum Slicing in 6G
Abstract:
Dynamic spectrum slicing is a critical enabler for 6G Radio Access Networks (RANs), allowing the coexistence of heterogeneous services. However, optimizing resource allocation in dense, interference‑limited deployments remains challenging due to non‑stationary channel dynamics, strict Quality‑of‑Service (QoS) requirements, and the need for data privacy. In this paper, we propose SliceFed, a novel Federated Constrained Multi‑Agent Deep Reinforcement Learning (F‑MADRL) framework. SliceFed formulates the slicing problem as a Constrained Markov Decision Process (CMDP) where autonomous gNB agents maximize spectral efficiency while explicitly satisfying inter‑cell interference budgets and hard ultra‑reliable low‑latency communication (URLLC) latency deadlines. We employ a Lagrangian primal‑dual approach integrated with Proximal Policy Optimization (PPO) to enforce constraints, while Federated Averaging enables collaborative learning without exchanging raw local data. Extensive simulations in a dense multi‑cell environment demonstrate that SliceFed converges to a stable, safety‑aware policy. Unlike heuristic and unconstrained baselines, SliceFed achieves nearly 100% satisfaction of 1~ms URLLC latency deadlines and exhibits superior robustness to traffic load variations, verifying its potential for reliable and scalable 6G spectrum management.

Authors:Yu Kawano, Francesco Bullo
Title: Contractivity of Multi-Stage Runge-Kutta Dynamics
Abstract:
Many control, optimization, and learning algorithms rely on discretizations of continuous‑time contracting systems, where preservation of contractivity under numerical integration is key for stability, robustness, and reliable fixed‑point computation. In this paper, we establish conditions under which multi‑stage Runge‑Kutta methods preserve strong contractivity when discretizing infinitesimally contractive continuous‑time systems. For explicit Runge‑Kutta methods, preservation conditions are derived by bounding Lipschitz constants of the associated composite stage mappings, leading to coefficient‑dependent criteria. For implicit methods, the algebraic structure of the stage equations enables explicit conditions on the Runge‑Kutta coefficients that guarantee preservation of strong contractivity. In the implicit case, these results extend classical guarantees, typically limited to weak contractivity in the Euclidean metric, to strong contractivity with respect to the \ell_1‑, \ell_2‑, and \ell_\infty‑norms. In addition, we study well‑definedness of implicit methods through an auxiliary continuous‑time system associated with the stage equations. We show that strong infinitesimal contractivity of this auxiliary system is sufficient to guarantee unique solvability of the stage equations. This analysis generalizes standard well‑definedness conditions and provides a dynamic implementation approach that avoids direct solution of the implicit algebraic equations.

Authors:Niusha Khosravi, Rodrigo Ventura, Meysam Basiri
Title: Distributed Kalman--Consensus Filtering with Adaptive Uncertainty Weighting for Multi-Object Tracking in Mobile Robot Networks
Abstract:
This paper presents an implementation and evaluation of a Distributed Kalman‑‑Consensus Filter (DKCF) for Multi‑Object Tracking (MOT) in mobile robot networks operating under partial observability and heterogeneous localization uncertainty. A key challenge in such systems is the fusion of information from agents with differing localization quality, where frame misalignment can lead to inconsistent estimates, track duplication, and ghost tracks. To address this issue, we build upon the MOTLEE framework and retain its frame‑alignment methodology, which uses consistently tracked dynamic objects as transient landmarks to improve relative pose estimates between robots. On top of this framework, we propose an uncertainty‑aware adaptive consensus weighting mechanism that dynamically adjusts the influence of neighbor information based on the covariance of the transmitted estimates, thereby reducing the impact of unreliable data during distributed fusion. Local tracking is performed using a Kalman Filter (KF) with a Constant Velocity Model (CVM) and Global Nearest Neighbor (GNN) data association. simulation results demonstrate that adaptive weighting effectively protects local estimates from inconsistent data, yielding a MOTA improvement of 0.09 for agents suffering from localization drift, although system performance remains constrained by communication latency.

Authors:Luis Mora, Yann Le Gorrec, Hector Ramirez, Denis Matignon
Title: Conduction-Diffusion in N-Dimensional settings as irreversible port-Hamiltonian systems
Abstract:
This work extends previous 1D irreversible port‑Hamiltonian system (IPHS) formulations to boundary‑controlled ND distributed parameter systems describing conduction‑diffusion fluid phenomena. Within a unified and thermodynamically consistent framework, we show that conduction and diffusion can be represented through a single coherent structure that preserves global energy balance and ensures a correct characterization of entropy production. The resulting formulation provides a foundation for the systematic modeling and control of complex multi‑physical processes governed by coupled transport mechanisms in N dimensions. In the longer term, this framework opens the door to structure‑preserving numerical schemes capable of enforcing thermodynamic principles directly at the discretized level.

Authors:Lai Wei, Andrew McDonald, Vaibhav Srivastava
Title: Multi-Robot Multitask Gaussian Process Estimation and Coverage
Abstract:
Coverage control is essential for the optimal deployment of agents to monitor or cover areas with sensory demands. While traditional coverage involves single‑task robots, increasing autonomy now enables multitask operations. This paper introduces a novel multitask coverage problem and addresses it for both the cases of known and unknown sensory demands. For known demands, we design a federated multitask coverage algorithm and establish its convergence properties. For unknown demands, we employ a multitask Gaussian Process (GP) framework to learn sensory demand functions and integrate it with the multitask coverage algorithm to develop an adaptive algorithm. We introduce a novel notion of multitask coverage regret that compares the performance of the adaptive algorithm against an oracle with prior knowledge of the demand functions. We establish that our algorithm achieves sublinear cumulative regret, and numerically illustrate its performance.

Authors:Ahlam Ouardi, Arijit Sarkar, Hector Ramirez, Yann Le Gorrec
Title: Irreversible Port-Hamiltonian Formulations for 1-Dimensional fluid systems
Abstract:
The Irreversible Port‑Hamiltonian Systems (IPHS) framework is extended to the modelling of non‑isentropic fluids with viscous dissipation in the Eulerian description. Building on earlier IPHS formulations for diffusion‑driven and non‑convective distributed systems, it is shown that convective transport can be consistently encompassed by the framework by modifying the underlying differential operators. After revisiting the constitutive relations of non‑isentropic fluids in both Eulerian and Lagrangian coordinates, it is demonstrate how these systems fit within an extended IPHS formulation. Furthermore, an extended parametrisation of the boundary port variables which ensures that the first and second laws of Thermodynamics are fulfilled allows to define a general class of boundary controlled IPHS.

Authors:Ruslan Zakirzyanov
Title: Metaheuristic algorithm parameters selection for building an optimal hierarchical structure of a control system: a case study
Abstract:
Metaheuristic algorithms are currently widely used to solve a variety of optimization problems across various industries. This article discusses the application of a metaheuristic algorithm to optimize the hierarchical architecture of an industrial distributed control system. The success of the algorithm depends largely on the choice of starting conditions and algorithm parameters. We examine the impact of parameter selection on the convergence of a modified ant colony algorithm and provide recommendations for tuning the algorithm to achieve optimal results for a specific industrial problem. The findings presented in this article can also be applied to other combinatorial optimization problems.

Authors:Jiurun Song, Xiao Liang, Minghui Zheng
Title: TATIC: Task-Aware Temporal Learning for Human Intent Inference from Physical Corrections in Human-Robot Collaboration
Abstract:
In human‑robot collaboration (HRC), robots must adapt online to dynamic task constraints and evolving human intent. While physical corrections provide a natural, low‑latency channel for operators to convey motion‑level adjustments, extracting task‑level semantic intent from such brief interactions remains challenging. Existing foundation‑model‑based approaches primarily rely on vision and language inputs and lack mechanisms to interpret physical feedback. Meanwhile, traditional physical human‑robot interaction (pHRI) methods leverage physical corrections for trajectory guidance but struggle to infer task‑level semantics. To bridge this gap, we propose TATIC, a unified framework that utilizes torque‑based contact force estimation and a task‑aware Temporal Convolutional Network (TCN) to jointly infer discrete task‑level intent and estimate continuous motion‑level parameters from brief physical corrections. Task‑aligned feature canonicalization ensures robust generalization across diverse layouts, while an intent‑driven adaptation scheme translates inferred human intent into robot motion adaptations. Experiments achieve a 0.904 Macro‑F1 score in intent recognition and demonstrate successful hardware validation in collaborative disassembly (see experimental video at https://youtu.be/xF8A52qwEc8).

Authors:Yichang Feng, Xiao Liang, Minghui Zheng
Title: DRAFTO: Decoupled Reduced-space and Adaptive Feasibility-repair Trajectory Optimization for Robotic Manipulators
Abstract:
This paper introduces a new algorithm for trajectory optimization, Decoupled Reduced‑space and Adaptive Feasibility‑repair Trajectory Optimization (DRAFTO). It first constructs a constrained objective that accounts for smoothness, safety, joint limits, and task requirements. Then, it optimizes the coefficients, which are the coordinates of a set of basis functions for trajectory parameterization. To reduce the number of repeated constrained optimizations while handling joint‑limit feasibility, the optimization is decoupled into a reduced‑space Gauss‑Newton (GN) descent for the main iterations and constrained quadratic programming for initialization and terminal feasibility repair. The two‑phase acceptance rule with a non‑monotone policy is applied to the GN model, which uses a hinge‑squared penalty for inequality constraints, to ensure globalizability. The results of our benchmark tests against optimization‑based planners, such as CHOMP, TrajOpt, GPMP2, and FACTO, and sampling‑based planners, such as RRT‑Connect, RRT, and PRM, validate the high efficiency and reliability across diverse scenarios and tasks. The experiment involving grabbing an object from a drawer further demonstrates the potential for implementation in complex manipulation tasks. The supplemental video is available at https://youtu.be/XisFI37YyTQ.

Authors:Seetharami Seelam, Jerry M. Chow, Antonio Córcoles, Sarah Sheldon, Tushar Mittal, Abhinav Kandala, Sean Dague, Ian Hincks, Hiroshi Horii, Blake Johnson, Michael Le, Hani Jamjoom, Jay M. Gambetta
Title: Reference Architecture of a Quantum-Centric Supercomputer
Abstract:
Quantum computers have demonstrated utility in simulating quantum systems beyond brute‑force classical approaches. As the community builds on these demonstrations to explore using quantum computing for applied research, algorithms and workflows have emerged that require leveraging both quantum computers and classical high‑performance computing (HPC) systems to scale applications, especially in chemistry and materials, beyond what either system can simulate alone. Today, these disparate systems operate in isolation, forcing users to manually orchestrate workloads, coordinate job scheduling, and transfer data between systems ‑‑ a cumbersome process that hinders productivity and severely limits rapid algorithmic exploration. These challenges motivate the need for flexible and high‑performance Quantum‑Centric Supercomputing (QCSC) systems that integrate Quantum Processing Units (QPUs), Graphics Processing Units (GPUs), and Central Processing Units (CPUs) to accelerate discovery of such algorithms across applications. These systems will be co‑designed across quantum and classical HPC infrastructure, middleware, and application layers to accelerate the adoption of quantum computing for solving critical computational problems. We envision QCSC evolution through three distinct phases: (1) quantum systems as specialized compute offload engines within existing HPC complexes; (2) heterogeneous quantum and classical HPC systems coupled through advanced middleware, enabling seamless execution of hybrid quantum‑classical algorithms; and (3) fully co‑designed heterogeneous quantum‑HPC systems for hybrid computational workflows. This article presents a reference architecture and roadmap for these QCSC systems.

Authors:Mohammad Itani, Manuel Schaller, Karl Worthmann, Timm Faulwasser
Title: Towards Polynomial Immersion of Port-Hamiltonian Systems
Abstract:
Port‑Hamiltonian (pH) systems offer a highly structured and energy‑based modular framework for control systems. Many pH systems exhibit non‑polynomial non‑linearities. We consider the problem of immersing such systems into a higher‑dimensional polynomial representation. We prove that, along system trajectories, important features of the non‑polynomial pH system are preserved such as the internal interconnection geometry, the energy balance relation with passivity supply rate, as well as energy dissipation. We illustrate how the lifted system enables the design of stabilizing feedback laws by combining sum‑of‑squares optimization with concepts from passivity‑based control. We draw upon several examples to illustrate our findings.

Authors:Sergi Foix, Jaume Oriol, Carme Torras, Júlia Borràs
Title: A gripper for flap separation and opening of sealed bags
Abstract:
Separating thin, flexible layers that must be individually grasped is a common but challenging manipulation primitive for most off‑the‑shelf grippers. A prominent example arises in clinical settings: the opening of sterile flat pouches for the preparation of the operating room, where the first step is to separate and grasp the flaps. We present a novel gripper design and opening strategy that enables reliable flap separation and robust seal opening. This capability addresses a high‑volume repetitive hospital procedure in which nurses manually open up to 240 bags per shift, a physically demanding task linked to musculoskeletal injuries. Our design combines an active dented‑roller fingertip with compliant fingers that exploit environmental constraints to robustly grasp thin flexible flaps. Experiments demonstrate that the proposed gripper reliably grasps and separates sealed bag flaps and other thin‑layered materials from the hospital, the most sensitive variable affecting performance being the normal force applied. When two copies of the gripper grasp both flaps, the system withstands the forces needed to open the seals robustly. To our knowledge, this is one of the first demonstrations of robotic assistance to automate this repetitive, low‑value, but critical hospital task.

Authors:Clement Wong, Amalie Trewartha, Steven B. Torrisi, Alexandre L. S. Filipowicz
Title: The potential and viability of V2G for California BEV drivers
Abstract:
Vehicle‑to‑Grid (V2G) adoption is hindered by uncertainties regarding its effects on battery lifetime and vehicle usability. These uncertainties are compounded by limited insight into real‑world vehicle usage. Here, we leverage real‑world Californian BEV usage data to design and evaluate a user‑centric V2G strategy. We identified four clustered driver profiles for V2G assessment, ranging from "Daily Chargers" to "Public Chargers". We show that V2G participation is most feasible for "Daily Chargers," and that the effects on battery lifetime depend on calendar aging sensitivity. For batteries with low sensitivity, V2G participation increases capacity loss for all drivers. However, for batteries with high sensitivity, V2G participation can lead to negligible changes in capacity or even improved capacity retention, particularly for drivers who tend to keep their batteries at high states of charge. Our findings enable stakeholders to better assess the potential and viability of V2G adoption.

Authors:Yuzhang Peng, Wei Wang, Jiaqi Yan, Mengze Yu
Title: Distributed Safety Critical Control among Uncontrollable Agents Using Reconstructed Control Barrier Functions
Abstract:
This paper investigates the distributed safety critical control for multi‑agent systems (MASs) in the presence of uncontrollable agents with uncertain behaviors. To ensure system safety, the control barrier function (CBF) is employed in this paper. However, a key challenge is that the CBF constraints are coupled when MASs perform collaborative tasks, which depend on information from multiple agents and impede the design of a fully distributed safe control scheme. To overcome this, a novel reconstructed CBF approach is proposed. In this method, the coupled CBF is reconstructed by leveraging state estimates of other agents obtained from a distributed adaptive observer. Furthermore, a prescribed performance adaptive parameter is designed to modify this reconstruction, ensuring that satisfying the reconstructed CBF constraint is sufficient to meet the original coupled one. Based on the reconstructed CBF, we design a safety‑critical quadratic programming (QP) controller and prove that the proposed distributed control scheme rigorously guarantees the safety of the MAS, even in the uncertain dynamic environments involving uncontrollable agents. The effectiveness of the proposed method is illustrated through a simulation.

Authors:Surya Malladi, Nima Monshizadeh
Title: Distributed Stability Certification and Control from Local Data
Abstract:
Most data‑driven analysis and control methods rely on centralized access to system measurements. In contrast, we consider a setting in which the measurements are distributed across multiple agents and raw data are not shared. Each agent has access only to locally held samples, possibly as little as a single measurement, and agents exchange only locally computed signals. Consequently, no individual agent possesses sufficient information to identify the entire system or synthesize a controller independently. To address this limitation, we develop distributed dynamical algorithms that enable the agents to collectively compute global system certificates from local data. Two problems are addressed. First, for stable linear time‑invariant (LTI) systems, the agents compute a Lyapunov certificate by solving the Lyapunov equation in a fully distributed manner. Second, for general LTI systems, they compute the stabilizing solution of the algebraic Riccati equation and hence the optimal linear‑quadratic regulator (LQR). An initially proposed scheme guarantees practical convergence, while a subsequent augmented PI‑type algorithm achieves exact convergence to the desired solution. We further establish robustness of the resulting LQR controller to uncertainty and measurement noise. The approach is illustrated through distributed Lyapunov certification of a quadruple‑tank process and distributed LQR design for helicopter dynamics.

Authors:Mohamad Alkadamani, Amir Ghasemi, Halim Yanikomeroglu
Title: Towards Intelligent Spectrum Management: Spectrum Demand Estimation Using Graph Neural Networks
Abstract:
The growing demand for wireless connectivity, combined with limited spectrum resources, calls for more efficient spectrum management. Spectrum sharing is a promising approach; however, regulators need accurate methods to characterize demand dynamics and guide allocation decisions. This paper builds and validates a spectrum demand proxy from public deployment records and uses a graph attention network in a hierarchical, multi‑resolution setup (HR‑GAT) to estimate spectrum demand at fine spatial scales. The model captures both neighborhood effects and cross‑scale patterns, reducing spatial autocorrelation and improving generalization. Evaluated across five Canadian cities and against eight competitive baselines, HR‑GAT reduces median RMSE by roughly 21% relative to the best alternative and lowers residual spatial bias. The resulting demand maps are regulator‑accessible and support spectrum sharing and spectrum allocation in wireless networks.

Authors:Mohamad Alkadamani, Colin Brown, Halim Yanikomeroglu
Title: AI-Enhanced Spatial Cellular Traffic Demand Prediction with Contextual Clustering and Error Correction for 5G/6G Planning
Abstract:
Accurate spatial prediction of cellular traffic demand is essential for 5G NR capacity planning, network densification, and data‑driven 6G planning. Although machine learning can fuse heterogeneous geospatial and socio‑economic layers to estimate fine‑grained demand maps, spatial autocorrelation can cause neighborhood leakage under naive train/test splits, inflating accuracy and weakening planning reliability. This paper presents an AI‑driven framework that reduces leakage and improves spatial generalization via a context‑aware two‑stage splitting strategy with residual spatial error correction. Experiments using crowdsourced usage indicators across five major Canadian cities show consistent mean absolute error (MAE) reductions relative to location‑only clustering, supporting more reliable bandwidth provisioning and evidence‑based spectrum planning and sharing assessments.

Authors:Ali Eslami, Jiangbo Yu
Title: A Control-Theoretic Foundation for Agentic Systems
Abstract:
This paper develops a control‑theoretic framework for analyzing agentic systems embedded within feedback control loops, where an AI agent may adapt controller parameters, select among control strategies, invoke external tools, reconfigure decision architectures, and modify control objectives during operation. These capabilities are formalized by interpreting agency as hierarchical runtime decision authority over elements of the control architecture, leading to an augmented closed‑loop representation in which physical states, internal memory, tool outputs, interaction signals, and design variables evolve as a coupled dynamical system. A five‑level hierarchy of agency is defined, ranging from fixed control laws to runtime synthesis of control architectures and objectives. The analysis shows that increasing agency introduces interacting dynamical mechanisms such as time‑varying adaptation, endogenous switching, decision‑induced delays, and structural reconfiguration. The framework is developed in both nonlinear and linear settings, providing explicit design constraints for AI‑enabled control systems in safety‑critical applications.

Authors:Abram H. Clark, Liraz Mudrik, Colton Kawamura, Nathan C. Redder, João P. Hespanha, Isaac Kaminer
Title: Scaling and Trade-offs in Multi-agent Autonomous Systems
Abstract:
Designing autonomous drone swarms is hampered by a vast design space spanning platform, algorithmic, and numerical‑strength choices. We perform large‑scale agent‑based simulations in three canonical scenarios: swarm‑on‑swarm battle, cooperative area search with attrition, and pursuit of scattering targets. We demonstrate how dimensional‑analysis and data‑scaling can be leveraged to collapse performance data onto scaling functions that are mathematically simple, yet counterintuitive and therefore difficult to predict a priori. These scaling laws reveal success‑failure boundaries, including sharp break points which we show can be framed as an ``effective swarm size.'' Additionally, we show how this technique can be used to quantify trade‑offs between agent count and platform parameters such as velocity, sensing or weapon range, and attrition rate. Furthermore, we show the benefits of embedding an optimal path planning loop within this framework, which can qualitatively improve the scaling laws that govern the outcome. The methods we demonstrate are highly flexible and would enable rapid, budget‑aware sizing and algorithm selection for large autonomous swarms.

Authors:Yilin Zou, Zhong Zhang, Maxime Robic, Fanghua Jiang
Title: Parallel-in-Time Nonlinear Optimal Control via GPU-native Sequential Convex Programming
Abstract:
Real‑time trajectory optimization for nonlinear constrained autonomous systems is critical and typically performed by CPU‑based sequential solvers. Specifically, reliance on global sparse linear algebra or the serial nature of dynamic programming algorithms restricts the utilization of massively parallel computing architectures like GPUs. To bridge this gap, we introduce a fully GPU‑native trajectory optimization framework that combines sequential convex programming with a consensus‑based alternating direction method of multipliers. By applying a temporal splitting strategy, our algorithm decouples the optimization horizon into independent, per‑node subproblems that execute massively in parallel. The entire process runs fully on the GPU, eliminating costly memory transfers and large‑scale sparse factorizations. This architecture naturally scales to multi‑trajectory optimization. We validate the solver on a quadrotor agile flight task and a Mars powered descent problem using an on‑board edge computing platform. Benchmarks reveal a sustained 4x throughput speedup and a 51% reduction in energy consumption over a heavily optimized 12‑core CPU baseline. Crucially, the framework saturates the hardware, maintaining over 96% active GPU utilization to achieve planning rates exceeding 100 Hz. Furthermore, we demonstrate the solver's extensibility to robust Model Predictive Control by jointly optimizing dynamically coupled scenarios under stochastic disturbances, enabling scalable and safe autonomy.

Authors:Shriram Hari, M Venkata Sai Nikhil, R Prasanth Kumar
Title: Dynamic Modeling and Attitude Control of a Reaction-Wheel-Based Low-Gravity Bipedal Hopper
Abstract:
Planetary bodies characterized by low gravitational acceleration, such as the Moon and near‑Earth asteroids, impose unique locomotion constraints due to diminished contact forces and extended airborne intervals. Among traversal strategies, hopping locomotion offers high energy efficiency but is prone to mid‑flight attitude instability caused by asymmetric thrust generation and uneven terrain interactions. This paper presents an underactuated bipedal hopping robot that employs an internal reaction wheel to regulate body posture during the ballistic flight phase. The system is modeled as a gyrostat, enabling analysis of the dynamic coupling between torso rotation and reaction wheel momentum. The locomotion cycle comprises three phases: a leg‑driven propulsive jump, mid‑air attitude stabilization via an active momentum exchange controller, and a shock‑absorbing landing. A reduced‑order model is developed to capture the critical coupling between torso rotation and reaction wheel dynamics. The proposed framework is evaluated in MuJoCo‑based simulations under lunar gravity conditions (g = 1.625 m/s^2). Results demonstrate that activation of the reaction wheel controller reduces peak mid‑air angular deviation by more than 65% and constrains landing attitude error to within 3.5 degrees at touchdown. Additionally, actuator saturation per hop cycle is reduced, ensuring sufficient control authority. Overall, the approach significantly mitigates in‑flight attitude excursions and enables consistent upright landings, providing a practical and control‑efficient solution for locomotion on irregular extraterrestrial terrains.

Authors:Giulio Fattore, Maria Elena Valcher, Rui Gao, Guang-Hong Yang
Title: Distributed State Estimation of Discrete-Time LTI Systems via Jordan Canonical Representation
Abstract:
In this paper, we address the problem of distributed state estimation for a discrete‑time, linear time‑invariant system. Building on the framework proposed in [2], we exploit the Jordan canonical form of the system matrix to develop a distributed estimation scheme that ensures the asymptotic convergence of the local state estimates to the true system state. The proposed approach relies on the idea that each node reconstructs the components of the system state that are detectable for it through a local Luenberger observer, while employing a consensus‑based strategy to estimate the undetectable components. Necessary and sufficient conditions for the existence of a distributed observer that guarantees asymptotic estimation accuracy are derived. Compared with the previous work [2], the proposed design offers greater flexibility in the selection of the coupling gains and leads to a less restrictive set of conditions for solvability.

Authors:Mehmet Eren Uluçınar, Özgün Ersoy, Berk Ciloglu, Metin Ozturk, Ali Gorcin
Title: Propagation and Rate-Aware Cell Switching Optimization in HAPS-Assisted Wireless Networks
Abstract:
Cell switching is a promising approach for improving energy efficiency in wireless networks; however, existing studies largely rely on simplified models and energy‑centric formulations that overlook key performance‑limiting factors. This paper revisits the cell switching concept by redefining its modeling assumptions and mathematical formulation, explicitly incorporating realistic propagation effects such as building entry loss (BEL) and atmospheric losses relevant to non‑terrestrial networks (NTN), particularly high‑altitude platform station (HAPS). Beyond proposing a new cell switching strategy, the conventional energy‑focused problem is reformulated as a multi‑objective optimization framework that jointly minimizes power consumption, unconnected users, and data rate degradation. Through this reformulation, the proposed methods ensure that energy‑efficient operation is achieved without compromising user connectivity and data rate performance, thereby inherently supporting sustainability objectives for sixth‑generation (6G) networks. To solve this reformulated problem, two complementary approaches are employed: the weighted sum method (WSM), which enables flexible and adaptive weighting mechanism, and the ε‑constraint‑inspired method (εCM), which converts connectivity and rate‑related objectives into constraints within the conventional energy‑focused problem. Moreover, unlike prior work relying only on simulations, this study combines system‑level simulations with Sionna‑OpenAirInterface (OAI) based emulation on a smaller network to validate the proposed cell switching concept under realistic conditions. The results show that, compared to the conventional approach, WSM reduces rate degradation for up to 70% for high‑loss indoor users and eliminates the 44% drop for low‑loss indoor users.

Authors:James Li, Philip H. W. Leong, Thomas Chaffey
Title: Quantization Robustness of Monotone Operator Equilibrium Networks
Abstract:
Monotone operator equilibrium networks are implicit‑layer models whose output is the unique equilibrium of a monotone operator, guaranteeing existence, uniqueness, and convergence. When deployed on low‑precision hardware, weights are quantized, potentially destroying these guarantees. We analyze weight quantization as a spectral perturbation of the underlying monotone inclusion. Convergence of the quantized solver is guaranteed whenever the spectral‑norm weight perturbation is smaller than the monotonicity margin; the displacement between quantized and full‑precision equilibria is bounded in terms of the perturbation size and margin; and a condition number characterizing the ratio of the operator norm to the margin links quantization precision to forward error. MNIST experiments confirm a phase transition at the predicted threshold: three‑ and four‑bit post‑training quantization diverge, while five‑bit and above converge. The backward‑pass guarantee enables quantization‑aware training, which recovers provable convergence at four bits.

Authors:Fangsheng Qian, Shuhan Chen, Wei Wei, Jiashuai Xu, Kai Yang, Junyan Zheng, Zijun Ren, Xingyu Liu, Yansong Yang
Title: Suppressing Acoustomigration and Temperature Rise for High-power Robust Acoustics
Abstract:
High‑frequency acoustic wave transducers, vibrating at gigahertz (GHz), favored for their compact size, are not only dominating the front‑end of mobile handsets but are also expanding into various interdisciplinary fields, including quantum acoustics, acoustic‑optics, acoustic‑fluids, acoustoelectric, and sustainable power conversion systems. However, like strong vibration can "shake off" substances and produce heat, a long‑standing bottleneck has been the ability to harness acoustics under high‑power vibration loads, while simultaneously suppressing temperature rise, especially for IDT‑based surface acoustic wave (SAW) systems. Here, we proposed a layered acoustic wave (LAW) platform, utilizing a quasi‑infinite multifunctional top layer, that redefines mechanical and thermal boundary conditions to overcome three fundamental challenges in high‑power acoustic wave vibration: self‑heating, thermal instability, and acoustomigration. By simply leveraging a simplified, thick single‑material overlayer to achieve electro‑thermo‑mechanical co‑design, this acoustic platform moves beyond prior substrate‑focused thermal management in SAW technology. It demonstrates, for the first time from the top boundary, simultaneous redistribution of the von Mises stress field and the creation of an efficient vertical thermal dissipation path. The LAW transducer, vibrating at over 2 GHz, achieves a 70% reduction in temperature rise under identical power loads, a first‑order temperature coefficient of frequency (TCF) of ‑13 ppm/C with minimal dispersion, and an unprecedented threshold power density of 45.61 dBm/mm2 ‑ over one order‑of‑magnitude higher than that of state‑of‑the‑art thin‑film surface acoustic wave (TF‑SAW) counterparts at the same wavelength.

Authors:Kai Chin Lim, Khay Wai See
Title: World Model for Battery Degradation Prediction Under Non-Stationary Aging
Abstract:
Degradation prognosis for lithium‑ion cells requires forecasting the state‑of‑health (SOH) trajectory over future cycles. Existing data‑driven approaches can produce trajectory outputs through direct regression, but lack a mechanism to propagate degradation dynamics forward in time. This paper formulates battery degradation prognosis as a world model problem, encoding raw voltage, current, and temperature time‑series from each cycle into a latent state and propagating it forward via a learned dynamics transition to produce a future trajectory spanning 80 cycles. To investigate whether electrochemical knowledge improves the learned dynamics, a Single Particle Model (SPM) constraint is incorporated into the training loss. Three configurations are evaluated on the Severson LiFePO4 (LFP) dataset of 138 cells. Iterative rollout halves the trajectory forecast error compared to direct regression from the same encoder. The SPM constraint improves prediction at the degradation knee where the resistance to SOH relationship is most applicable, without changing aggregate accuracy.

Authors:Sangmim Song, Sarath Kodagoda, Marc Carmichael, Karthick Thiyagarajan
Title: Overcoming Visual Clutter in Vision Language Action Models via Concept-Gated Visual Distillation
Abstract:
Vision‑Language‑Action (VLA) models demonstrate impressive zero‑shot generalization but frequently suffer from a "Precision‑Reasoning Gap" in cluttered environments. This failure is driven by background‑induced feature dilution, where high‑frequency semantic noise corrupts the geometric grounding required for precise manipulation. To bridge this gap, we propose Concept‑Gated Visual Distillation (CGVD), a training‑free, model‑agnostic inference framework that stabilizes VLA policies. CGVD operates by parsing instructions into safe and distractor sets, utilizing a two‑layer target refinement process‑‑combining cross‑validation and spatial disambiguation‑‑to explicitly penalize false positives and isolate genuine manipulation targets. We then process the scene via Fourier‑based inpainting, generating a clean observation that actively suppresses semantic distractors while preserving critical spatial geometry and visual proprioception. Extensive evaluations in highly cluttered manipulation tasks demonstrate that CGVD prevents performance collapse. In environments with dense semantic distractors, our method significantly outperforms state‑of‑the‑art baselines, achieving a 77.5% success rate compared to the baseline's 43.0%. By enforcing strict attribute adherence, CGVD establishes inference‑time visual distillation as a critical prerequisite for robust robotic manipulation in the clutter.

Authors:Wuping Xin
Title: Simulation-in-the-Reasoning (SiR): A Conceptual Framework for Empirically Grounded AI in Autonomous Transportation
Abstract:
Large Language Models (LLMs) have advanced reasoning through techniques like Chain‑of‑Thought (CoT). However, their reasoning largely re‑mains textual and hypothetical, lacking empirical grounding in complex, dynamic domains like transportation. This paper introduces Simulation‑in‑the‑Reasoning (SiR), a novel conceptual framework that embeds domain‑specific simulators directly into the LLM reasoning loop. By treating intermediate reasoning steps as executable simulation experiments, SiR transforms LLM reasoning from narrative plausibility into a falsifiable, hypothesis‑simulate‑analyze workflow. We discuss applications, where LLM can formulate Intelligent Transport System (ITS) strategy hypotheses, invoke a traffic simulator via the Model Context Protocol (MCP), evaluate results under different demand patterns, and refine strategies through verification and aggregation. While implementing the framework is part of our ongoing work, this paper primarily establishes the conceptual foundation, discusses design considerations like API granularity, and outlines the vision of SiR as a cornerstone for interactive transportation digital twins. We argue that SiR represents a critical step towards trustworthy, empirically‑validated AI for autonomous transportation systems.

Authors:Yeongjun Jang, Hamin Chang, Heein Park, Hyeonyeong Jang, Takashi Tanaka, Hyungbo Shim
Title: Inverse Learning-Based Output Feedback Control of Nonlinear Systems with Verifiable Guarantees
Abstract:
In this paper, we present a data‑driven output feedback controller for nonlinear systems that achieves practical output regulation, using noise‑free input/output measurement data. The proposed controller is based on (i) an inverse model of the system identified via kernel interpolation, which maps a desired output and the current state to the corresponding desired control input; and (ii) a data‑driven reference selection framework that actively chooses a suitable desired output from the dataset which has been used for the identification. We establish a verifiable sufficient condition on the dataset under which the proposed controller guarantees practical output regulation. Numerical simulations demonstrate the effectiveness of the proposed controller, with additional evaluations in the presence of output measurement noise to assess its robustness empirically.

Authors:Yuma Shida, Yuji Ito
Title: Avoiding Semi-Infinite Programming in Distributionally Robust Control Based on Mean-Variance Metrics
Abstract:
Conventional stochastic control methods have several limitations. They focus on optimizing the average performance and, in some cases, performance variability; however, their problem settings still require an explicit specification of the probability distributions that determine the system's stochastic behavior. Distributionally robust control (DRC) methods have recently been developed to address these challenges. However, many DRC approaches involve handling infinitely many inequalities. For instance, DRC problems based on the Wasserstein distance are commonly obtained by solving semi‑infinite programming (SIP) problems. Our proposed method eliminates the need for SIP when solving discrete‑time, discounted, distributionally robust optimal control problems. By introducing a penalty term based on a specific distributional distance, we establish upper bounds, and under appropriate conditions, demonstrate the equivalence between distributionally robust optimization problems and mean‑variance minimization problems. This reformulation reduces the original DRC problem to a discounted mean‑variance cost optimization problem. In linear‑quadratic regulator settings, the corresponding control laws are obtained by solving the Riccati equation. Numerical experiments demonstrate that the theoretical maximum value of the discounted cumulative cost for the proposed method is lower than that for the conventional method.

Authors:Simone Mariano, Paolo Frasca
Title: Optimal Control Synthesis of Closed-Loop Recommendation Systems over Social Networks
Abstract:
This paper addresses the problem of designing recommendation systems for social networks and e‑commerce platforms from a control‑theoretic perspective. We treat the design of recommendation systems as a state‑feedback infinite‑horizon optimal control problem with a performance index that (i) rewards alignment and engagement, (ii) penalizes polarization and large deviations from an uncontrolled baseline, and (iii) regularizes exposure across neighboring users. The recommendation entries are fed to the platform users, who are assumed to follow a networked, multi‑topic, continuous‑time opinion dynamics. We show that the designed control yields a stabilizing recommendation system under simple algebraic spectral conditions on the weights that encode the platform's preference for engagement, stability of preferences, polarization, and cross‑user diversity. Conversely, we show that when ill‑posed weights are selected in the optimal control problem (namely, when engagement is excessively rewarded), the closed‑loop system can exhibit destabilizing, pathological behaviors that conflict with the design objectives.

Authors:Osasumwen Cedric Ogiesoba-Eguakun, Kaveh Ashenayi, Suman Rath
Title: High-Fidelity Digital Twin Dataset Generation for Inverter-Based Microgrids Under Multi-Scenario Disturbances
Abstract:
Public power‑system datasets often lack electromagnetic transient (EMT) waveforms, inverter control dynamics, and diverse disturbance coverage, which limits their usefulness for training surrogate models and studying cyber‑physical behavior in inverter‑based microgrids. This paper presents a high‑fidelity digital twin dataset generated from a MATLAB/Simulink EMT model of a low‑voltage AC microgrid with ten inverter‑based distributed generators. The dataset records synchronized three‑phase PCC voltages and currents, per‑DG active power, reactive power, and frequency, together with embedded scenario labels, producing 38 aligned channels sampled at Δt = 2~μs over T = 1~s (N = 500,001 samples) per scenario. Eleven operating and disturbance scenarios are included: normal operation, load step, voltage sag (temporary three‑phase fault), load ramp, frequency ramp, DG trip, tie‑line trip, reactive power step, single‑line‑to‑ground faults, measurement noise injection, and communication delay. To ensure numerical stability without altering sequence length, invalid samples (NaN, Inf, and extreme outliers) are repaired using linear interpolation. Each scenario is further validated using system‑level evidence from mean frequency, PCC voltage magnitude, total active power, voltage unbalance, and zero‑sequence current to confirm physical observability and correct timing. The resulting dataset provides a consistent, labeled EMT benchmark for surrogate modeling, disturbance classification, robustness testing under noise and delay, and cyber‑physical resilience analysis in inverter‑dominated microgrids. The dataset and processing scripts will be released upon acceptance

Authors:Michael Epp, Fabio Molinari, Jörg Raisch
Title: Over-the-Air Consensus-based Formation Control of Heterogeneous Agents: Communication-Rate and Geometry-Aware Convergence Guarantees
Abstract:
This paper investigates the formation control problem of heterogeneous, autonomous agents that communicate over a wireless multiple access channel. Instead of avoiding interference through orthogonal node‑to‑node transmissions, we exploit the superposition property of the wireless channel to compute, at each receiver, normalized convex combinations of simultaneously broadcast neighbor signals. At every communication instant, agents update their reference positions from these aggregates, and track the references in continuous time between updates. The only assumption on the agent dynamics is that each agent tracks constant reference positions exponentially, which accommodates a broad class of platforms. Under this assumption, we analyze the resulting jump‑flow system under time‑varying communication graphs and unknown channel coefficients. We derive a communication‑rate based sufficient condition that guarantees convergence to a prescribed formation. We then provide a geometry‑aware refinement showing how favorable tracking transients can relax the required condition. Simulations with unicycle agents illustrate the theoretical results and demonstrate a substantial reduction in the number of required orthogonal transmissions compared to interference‑avoiding node‑to‑node communication protocols.

Authors:D. Bluedorn, A. Badawy, B. E. Saunders, D. Roettgen, A. Abdelkefi
Title: A neural operator for predicting vibration frequency response curves from limited data
Abstract:
In the design of engineered components, rigorous vibration testing is essential for performance validation and identification of resonant frequencies and amplitudes encountered during operation. Performing this evaluation numerically via machine learning has great potential to accelerate design iteration and make testing workflows more efficient. However, dynamical systems are conventionally difficult to solve via machine learning methods without using physics‑based regularizing loss functions. To properly perform this forecasting task, a structure that has an inspectable physical obedience can be devised without the use of regularizing terms from first principles. The method employed in this work is a neural operator integrated with an implicit numerical scheme. This architecture enables operators to learn of the underlying state‑space dynamics from limited data, allowing generalization to untested driving frequencies and initial conditions. This network can infer the system's global frequency response by training on a small set of input conditions. As a foundational proof of concept, this investigation verifies the machine learning algorithm with a linear, single‑degree‑of‑freedom system, demonstrating implicit obedience of dynamics. This approach demonstrates 99.87% accuracy in predicting the Frequency Response Curve (FRC), forecasting the frequency and amplitude of linear resonance training on 7% of the bandwidth of the solution. By training machine learning models to internalize physics information rather than trajectory, better generalization accuracy can be realized, vastly improving the timeframe for vibration studies on engineered components.

Authors:Yiwei Dong, Wenqi Cui, Han Xu, Adam Wierman, Steven Low
Title: Data-Driven Successive Linearization for Optimal Voltage Control
Abstract:
Power distribution systems are increasingly exposed to large voltage fluctuations driven by intermittent renewable generation and time varying loads (e.g., electric vehicles and storage). To address this challenge, a number of advanced controllers have been proposed for voltage regulation. However, these controllers typically rely on fixed linear approximations of voltage dynamics. As a result, the solutions may become infeasible when applied to the actual voltage behavior governed by nonlinear power flow equations, particularly under heavy power injection from distributed energy resources. This paper proposes a data‑driven successive linearization approach for voltage control under nonlinear power flow constraints. By leveraging the fact that the deviation between the nonlinear power flow solution and its linearization is bounded by the distance from the operating point, we perform data‑driven linearization around the most recent operating point. Convergence of the proposed method to a neighborhood of KKT points is established by exploiting the convexity of the objective function and structural properties of the nonlinear constraints. Case studies show that the proposed approach achieves fast convergence and adapts quickly to changes in net load.

Authors:Moriba Kemessia Jah
Title: The Epistemic Support-Point Filter: Jaynesian Maximum Entropy Meets Popperian Falsification
Abstract:
This paper proves that the Epistemic Support‑Point Filter (ESPF) is the unique optimal recursive estimator within the class of epistemically admissible evidence‑only filters. Where Bayesian filters minimize mean squared error and are driven toward an assumed truth, the ESPF minimizes maximum entropy and surfaces what has not been proven impossible ‑‑ a fundamentally different epistemic commitment with fundamentally different failure modes. Two results locate this theorem within the broader landscape of estimation theory. The first is a unification: the ESPF's optimality criterion is the log‑geometric mean of the alpha‑cut volume family in the Holder mean hierarchy. The Popperian minimax bound and the Kalman MMSE criterion occupy the p=+inf and p=0 positions on the same curve. Possibility and probability are not competing frameworks: they are the same ignorance functional evaluated under different alpha‑cut geometries. The Kalman filter is the Gaussian specialization of the ESPF's optimality criterion, not a separate invention. The second result is a diagnostic: numerical validation over a 2‑day, 877‑step Smolyak Level‑3 orbital tracking run shows that possibilistic stress manifests through necessity saturation and surprisal escalation rather than MVEE sign change ‑‑ a direct consequence of the Holder ordering, not an empirical observation. Three lemmas establish the result: the Possibilistic Entropy Lemma decomposes the ignorance functional; the Possibilistic Cramer‑Rao Bound limits entropy reduction per measurement; the Evidence‑Optimality Lemma proves minimum‑q selection is the unique minimizer and that any rule incorporating prior possibility risks race‑to‑bottom bias.

Authors:Ruihan Xu, Jiajin Li, Yiping Lu
Title: On the Width Scaling of Neural Optimizers Under Matrix Operator Norms I: Row/Column Normalization and Hyperparameter Transfer
Abstract:
A central question in modern deep learning is how to design optimizers whose behavior remains stable as the network width w increases. We address this question by interpreting several widely used neural‑network optimizers, including \textrmAdamW and \textrmMuon, as instances of steepest descent under matrix operator norms. This perspective links optimizer geometry with the Lipschitz structure of the network forward map, and enables width‑independent control of both Lipschitz and smoothness constants. However, steepest‑descent rules induced by standard p \to q operator norms lack layerwise composability and therefore cannot provide width‑independent bounds in deep architectures. We overcome this limitation by introducing a family of mean‑normalized operator norms, denoted \pmean \to \qmean, that admit layerwise composability, yield width‑independent smoothness bounds, and give rise to practical optimizers such as \emphrescaled \textrmAdamW, row normalization, and column normalization. The resulting learning rate width‑aware scaling rules recover μP scaling~\citeyang2021tensor as a special case and provide a principled mechanism for cross‑width learning‑rate transfer across a broad class of optimizers. We further show that \textrmMuon can suffer an \mathcalO(\sqrtw) worst‑case growth in the smoothness constant, whereas a new family of row‑normalized optimizers we propose achieves width‑independent smoothness guarantees. Based on the observations, we propose MOGA (Matrix Operator Geometry Aware), a width‑aware optimizer based only on row/column‑wise normalization that enables stable learning‑rate transfer across model widths. Large‑scale pre‑training on GPT‑2 and LLaMA shows that MOGA, especially with row normalization, is competitive with Muon while being notably faster in large‑token and low‑loss regimes.

Authors:Mohamad Alkadamani, Amir Ghasemi, Halim Yanikomeroglu
Title: Towards Flexible Spectrum Access: Data-Driven Insights into Spectrum Demand
Abstract:
In the diverse landscape of 6G networks, where wireless connectivity demands surge and spectrum resources remain limited, flexible spectrum access becomes paramount. The success of crafting such schemes hinges on our ability to accurately characterize spectrum demand patterns across space and time. This paper presents a data‑driven methodology for estimating spectrum demand variations over space and identifying key drivers of these variations in the mobile broadband landscape. By leveraging geospatial analytics and machine learning, the methodology is applied to a case study in Canada to estimate spectrum demand dynamics in urban regions. Our proposed model captures 70% of the variability in spectrum demand when trained on one urban area and tested on another. These insights empower regulators to navigate the complexities of 6G networks and devise effective policies to meet future network demands.

Authors:Juana M. Martínez-Heredia, Adrián Portos, Marcel Štěpánek, Francisco Colodro
Title: Emergency Locator Transmitters in the Era of More Electric Aircraft: A Comprehensive Review of Energy, Integration and Safety Challenges
Abstract:
The progressive electrification of aircraft systems under the more electric aircraft (MEA) paradigm is reshaping the design and qualification constraints of safety‑critical avionics. Emergency locator transmitters (ELTs), which are essential for post‑accident localization and search and rescue (SAR) operations, have evolved from legacy 121.5/243 MHz beacons to digitally encoded 406 MHz systems, typically retaining 121.5 MHz as a homing signal in combined units. In parallel, the modernization of the Cospas‑Sarsat infrastructure, especially MEOSAR, together with multi‑constellation global navigation satellite system (GNSS) integration and second‑generation beacon capabilities, is reducing detection latency and enabling richer distress messaging. However, MEA platforms impose stricter constraints on available power, thermal management, wiring density, and electromagnetic compatibility (EMC). As a result, ELT performance increasingly depends not only on the device itself, but also on its installation conditions and on the aircraft's overall electrical environment. This review summarizes the ELT architectures and activation/operational cycles, outlines key technological milestones, and consolidates the main integration challenges for MEA, with emphasis on energy autonomy, battery qualification frameworks, EMC and installation practices, and survivability‑driven failure modes (e.g., antenna/feedline damage, mounting, and post‑impact shielding). Finally, emerging trends include ELT for distress tracking (DT), energy‑based designs, advanced health monitoring, and certification‑ready pathways for next‑generation SAR services are discussed, highlighting research directions that can deliver demonstrable, certifiable gains in reliability, energy efficiency, and robust integration for future electrified aircraft.

Authors:Colin Brown, Mohamad Alkadamani, Halim Yanikomeroglu
Title: AI-Enabled Data-driven Intelligence for Spectrum Demand Estimation
Abstract:
Accurately forecasting spectrum demand is a key component for efficient spectrum resource allocation and management. With the rapid growth in demand for wireless services, mobile network operators and regulators face increasing challenges in ensuring adequate spectrum availability. This paper presents a data‑driven approach leveraging artificial intelligence (AI) and machine learning (ML) to estimate and manage spectrum demand. The approach uses multiple proxies of spectrum demand, drawing from site license data and derived from crowdsourced data. These proxies are validated against real‑world mobile network traffic data to ensure reliability, achieving an R^2 value of 0.89 for an enhanced proxy. The proposed ML models are tested and validated across five major Canadian cities, demonstrating their generalizability and robustness. These contributions assist spectrum regulators in dynamic spectrum planning, enabling better resource allocation and policy adjustments to meet future network demands.

Authors:Syed Izzat Ullah, Jose Baca
Title: NanoBench: A Multi-Task Benchmark Dataset for Nano-Quadrotor System Identification, Control, and State Estimation
Abstract:
Existing aerial‑robotics benchmarks target vehicles from hundreds of grams to several kilograms and typically expose only high‑level state data. They omit the actuator‑level signals required to study nano‑scale quadrotors, where low‑Reynolds number aerodynamics, coreless DC motor nonlinearities, and severe computational constraints invalidate models and controllers developed for larger vehicles. We introduce NanoBench, an open‑source multi‑task benchmark collected on the commercially available Crazyflie 2.1 nano‑quadrotor (takeoff weight 27 g) in a Vicon motion capture arena. The dataset contains over 170 flight trajectories spanning hover, multi‑frequency excitation, standard tracking, and aggressive maneuvers across multiple speed regimes. Each trajectory provides synchronized Vicon ground truth, raw IMU data, onboard extended Kalman filter estimates, PID controller internals, and motor PWM commands at 100 Hz, alongside battery telemetry at 10 Hz, aligned with sub‑0.5 ms consistency. NanoBench defines standardized evaluation protocols, train/test splits, and open‑source baselines for three tasks: nonlinear system identification, closed‑loop controller benchmarking, and onboard state estimation assessment. To our knowledge, it is the first public dataset to jointly provide actuator commands, controller internals, and estimator outputs with millimeter‑accurate ground truth on a commercially available nano‑scale aerial platform.

Authors:Mihitha Maithripala, Chenyang Qiu, Zongli Lin
Title: Dynamic Average Consensus with Privacy Guarantees and Its Application to Battery Energy Storage Systems
Abstract:
A privacy‑preserving dynamic average consensus (DAC) algorithm is proposed that achieves consensus while preventing external eavesdroppers from inferring the reference signals and their derivatives. During the initialization phase, each agent generates a set of sinusoidal signals with randomly selected frequencies and exchanges them with its neighboring agents to construct a masking signal. Each agent masks its reference signals using this composite masking signal before executing the DAC update rule. It is shown that the developed scheme preserves the convergence properties of the conventional DAC framework while preventing information leakage to external eavesdroppers. Furthermore, the developed algorithm is applied to state‑of‑charge (SoC) balancing in a networked battery energy storage system to demonstrate its practical applicability. Simulation results validate the theoretical findings.

Authors:Tolga O. Atalay, Alireza Famili, Amirreza Ghafoori, Angelos Stavrou
Title: A Survey on Cloud-Based 6G Deployments: Current Solutions, Future Directions and Open Challenges
Abstract:
The next generation of cellular networks is designed to provide ubiquitous connectivity to a wide range of devices. As Telecommunication Service Providers (TSPs) increasingly collaborate with public cloud providers to deploy 5G and beyond networks, a fundamental shift is underway, from hardware‑bound Physical Network Functions (PNFs) to cloud‑native, containerized deployments managed through platforms like Kubernetes. While this transition promises greater scalability, flexibility, and cost efficiency, it also introduces a complex set of technical and operational challenges that must be thoroughly understood before large‑scale cellular deployments can take place in cloud environments. In this survey, we present a structured taxonomy that categorizes the design space of cloud‑based cellular deployments across four dimensions: deployment architecture, resource management and orchestration, multi‑tenancy and isolation, and economic and ownership models. Using this taxonomy as a foundation, we critically analyze six key investigation areas, security and privacy, scalability and elasticity, performance and latency, cost optimization, resilience and fault management, and compliance and sovereignty, examining each through a cloud‑native lens. To benchmark the state of industry adoption, we examine the deployment strategies of leading Infrastructure‑as‑a‑Service (IaaS) providers, namely Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Finally, we identify emerging trends such as AI‑driven orchestration, quantum‑safe protocols for virtualized network functions, and serverless networking for 6G, while articulating the open challenges that remain in realizing robust, scalable cloud‑based cellular networks.

Authors:Abin Francis, Nikhil Kumar, Prince Philip
Title: Field Free Novel Architecture for Spintronic Flash Analog to Digital Converter
Abstract:
A 3 bit Analog to Digital Converter (ADC) is designed using perpendicular Spin Orbit Torque Magnetic Tunnel Junction (SOT MTJ). A sampled analog input signal is transmitted as a spin orbit torque current (Iin) to a perpendicular SOT MTJ, and deterministic switching is supported by the Voltage Controlled Magnetic Anisotropy (VCMA) and Spin Transfer Torque (STT) switching methods. Analog to digital conversion is done by comparing input signal with varied critical current of SOT MTJs. The critical current of each is SOT MTJ governed by varying widths of Heavy Metal (HM). In the 3 bit ADC, there are two sets of 7 SOT MTJs for quantizing input value, a conversion set and dummy set for comparing the change in resistance state. As input signal passed through conversion set SOT MTJs switches from Parallel (P) to AntiParallel (AP) state if the input signal exceeds its critical current. The conversion set change in state is converted to thermometer codes by StrongARM latch comparator by comparing the resistance with dummy set SOT MTJs, where all the in P state or low resistance. A novel architecture is proposed for increasing speed of throughput, by utilizing the dummy set of as a conversion set and conversion set as dummy set, thus eliminating the reset step from analog to digital conversion. And by improving SOT‑MTJ and timing blocks a field free spin flash ADC has a power consumption of 476 uW with a conversion rate of 304.1 MHz is produced.

Authors:Mohamad Alkadamani, Halim Yanikomeroglu, Amir Ghasemi
Title: A Graph-Based Approach to Spectrum Demand Prediction Using Hierarchical Attention Networks
Abstract:
The surge in wireless connectivity demand, coupled with the finite nature of spectrum resources, compels the development of efficient spectrum management approaches. Spectrum sharing presents a promising avenue, although it demands precise characterization of spectrum demand for informed policy‑making. This paper introduces HR‑GAT, a hierarchical resolution graph attention network model, designed to predict spectrum demand using geospatial data. HR‑GAT adeptly handles complex spatial demand patterns and resolves issues of spatial autocorrelation that usually challenge standard machine learning models, often resulting in poor generalization. Tested across five major Canadian cities, HR‑GAT improves predictive accuracy of spectrum demand by 21% over eight baseline models, underscoring its superior performance and reliability.

Authors:Yixuan Yu, Rajni K. Bansal, Yan Jiang, Pengcheng You
Title: Learning-Augmented Primal-Dual Control Design for Secondary Frequency Regulation
Abstract:
Frequency stability is fundamental to the secure operation of power systems. With growing uncertainty and volatility introduced by renewable generation, secondary frequency regulation must now deliver enhanced performance not only in the steady state but also during transients. This paper presents a systematic framework to embed learning in the design of a primal‑dual controller that provides provable (potentially exponential) stability and steady‑state optimality, while simultaneously improving key transient metrics, including frequency nadir and control effort, in a data‑driven manner. In particular, we employ the primal‑dual dynamics of an optimization problem that encodes steady‑state objectives to realize secondary frequency control with asymptotic stability guarantee. To augment transient performance of the controller via learning, a change of variables on control inputs, which will be deployed by neural networks, is proposed such that under mild conditions, stability and steady‑state optimality are preserved. It further allows us to define a learning goal that accounts for the exponential convergence rate, frequency nadir and accumulated control effort, and use sample trajectories to enhance these metrics. Simulation results validate the theories and demonstrate superior transient performance of the learning‑augmented primal‑dual controller.

Authors:Elisabetta Marini, Silvia Mura, Marco Hernandez, Matti Hamalainen, Maurizio Magarini
Title: Experimental Characterization of Biological Tissue Dielectric Properties through THz Time-Domain Spectroscopy
Abstract:
Terahertz (THz) radiation provides a non‑ionizing, highly sensitive probe of the dielectric properties of biological tissues. In this study, we present a comprehensive experimental characterization of dielectric properties using pork skin tissue, a widely used surrogate for human tissue, as a biological sample. Measurements are conducted employing THz time‑domain spectroscopy in the 0.1‑11 THz frequency range with photoconductive antennas for both signal generation and detection. Frequency‑dependent refractive indices, absorption, and complex permittivity are extracted from transmitted time‑domain signals. Our results confirm strong absorption and low transmittance at low THz frequencies due to water content, while highlighting frequency‑dependent dispersion and narrowband transmission features at higher frequencies. This work provides one of the first extended‑frequency datasets of biological tissue dielectric properties, supporting realistic channel modeling for the design and development of intra‑body nanosensor networks in the THz band.

Authors:Nivand Khosravi, Meysam Basiri, Rodrigo Ventura
Title: Lightweight 3D LiDAR-Based UAV Tracking: An Adaptive Extended Kalman Filtering Approach
Abstract:
Accurate relative positioning is crucial for swarm aerial robotics, enabling coordinated flight and collision avoidance. Although vision‑based tracking has been extensively studied, 3D LiDAR‑based methods remain underutilized despite their robustness under varying lighting conditions. Existing systems often rely on bulky, power‑intensive sensors, making them impractical for small UAVs with strict payload and energy constraints. This paper presents a lightweight LiDAR‑based UAV tracking system incorporating an Adaptive Extended Kalman Filter (AEKF) framework. Our approach effectively addresses the challenges posed by sparse, noisy, and nonuniform point cloud data generated by non‑repetitive scanning 3D LiDARs, ensuring reliable tracking while remaining suitable for small drones with strict payload constraints. Unlike conventional filtering techniques, the proposed method dynamically adjusts the noise covariance matrices using innovation and residual statistics, thereby enhancing tracking accuracy under real‑world conditions. Additionally, a recovery mechanism ensures continuity of tracking during temporary detection failures caused by scattered LiDAR returns or occlusions. Experimental validation was performed using a Livox Mid‑360 LiDAR mounted on a DJI F550 UAV in real‑world flight scenarios. The proposed method demonstrated robust UAV tracking performance under sparse LiDAR returns and intermittent detections, consistently outperforming both standard Kalman filtering and particle filtering approaches during aggressive maneuvers. These results confirm that the framework enables reliable relative positioning in GPS‑denied environments without the need for multi‑sensor arrays or external infrastructure.

Authors:André Urbano, Pablo Lanillos, Sander Keemink
Title: Efficient and robust control with spikes that constrain free energy
Abstract:
Animal brains exhibit remarkable efficiency in perception and action, while being robust to both external and internal perturbations. The means by which brains accomplish this remains, for now, poorly understood, hindering our understanding of animal and human cognition, as well as our own implementation of efficient algorithms for control of dynamical systems.A potential candidate for a robust mechanism of state estimation and action computation is the free energy principle, but existing implementations of this principle have largely relied on conventional, biologically implausible approaches without spikes. We propose a novel, efficient, and robust spiking control framework with realistic biological characteristics. The resulting networks function as free energy constrainers, in which neurons only fire if they reduce the free energy of their internal representation. The networks offer efficient operation through highly sparse activity while matching performance with other similar spiking frameworks, and have high resilience against both external (e.g. sensory noise or collisions) and internal perturbations (e.g. synaptic noise and delays or neuron silencing) that such a network would be faced with when deployed by either an organism or an engineer. Overall, our work provides a novel mathematical account for spiking control through constraining free energy, providing both better insight into how brain networks might leverage their spiking substrate and a new route for implementing efficient control algorithms in neuromorphic hardware.

Authors:Arnim Kargl, Mario Hermle, Zhiqiang Zhang, Yanmin Li, Dainan Zhao, Yong Cui, Peter Eberhard
Title: Embedded Model Predictive Control for EMS-type Maglev Vehicles
Abstract:
Current developments of high‑speed magnetic levitation technology using the principle of the electromagnet suspension (EMS) focus on reaching vehicle speeds of more than 600 km/h. With increasing vehicle speeds, however, updated control algorithms need to be investigated to reliably stabilize the system and meet the demands in terms of ride comfort. This article examines the modern and popular approach of model predictive control and its application to the magnetic levitation control system. Investigated key aspects are the parameterization of the model predictive controller and its implementation on embedded, resource constrained hardware. The results reveal that model predictive control is capable to robustly stabilize the highly nonlinear and constrained system even at very high speed. Furthermore, processor‑in‑the‑loop studies are carried out to validate the designed control algorithms on a microcontroller.

Authors:Bing Zhu, Xiaozhuoer Yuan, Zewei Zheng, Zongyu Zuo
Title: Constrained finite-time stabilization by model predictive control: an infinite control horizon framework
Abstract:
Existing results on finite‑time model predictive control (MPC) often rely on terminal equality constraint, switching inside one‑step region, or terminal cost with short control horizon, leading to limited initial feasibility. This paper proposes an infinite‑horizon Model Predictive Control (MPC) framework for the constrained finite‑time stabilization of discrete‑time systems, overcoming limitations found in existing finite‑time MPC results. The proposed framework is built upon a terminal cost strategy, but expands it by replacing the short‑horizon terminal cost with the sum of stage costs over an infinite control horizon. This design choice significantly enlarges the initial feasibility region and avoids the need for terminal equality constraints or switching strategies during implementation. It is proved that the proposed finite‑time MPC guarantees finite‑time stabilization performance once the state trajectory enters the predefined terminal set. The infinite‑horizon finite‑time MPC is shown to be equivalently implementable as a finite‑horizon MPC with a terminal cost, thereby ensuring computational tractability. The proposed finite‑time MPC is systematically extended and shown to be applicable to both constrained multi‑input linear systems and a class of constrained nonlinear systems that are feedback linearizable.

Authors:Simon Brandt, Paul Haider, Walter Senn, Federico Benitez, Mihai A. Petrovici
Title: A Variational Latent Equilibrium for Learning in Neuronal Circuits
Abstract:
Brains remain unrivaled in their ability to recognize and generate complex spatiotemporal patterns. While AI is able to reproduce some of these capabilities, deep learning algorithms remain largely at odds with our current understanding of brain circuitry and dynamics. This is prominently the case for backpropagation through time (BPTT), the go‑to algorithm for learning complex temporal dependencies. In this work we propose a general formalism to approximate BPTT in a controlled, biologically plausible manner. Our approach builds on, unifies and extends several previous approaches to local, time‑continuous, phase‑free spatiotemporal credit assignment based on principles of energy conservation and extremal action. Our starting point is a prospective energy function of neuronal states, from which we calculate real‑time error dynamics for time‑continuous neuronal networks. In the general case, this provides a simple and straightforward derivation of the adjoint method result for neuronal networks, the time‑continuous equivalent to BPTT. With a few modifications, we can turn this into a fully local (in space and time) set of equations for neuron and synapse dynamics. Our theory provides a rigorous framework for spatiotemporal deep learning in the brain, while simultaneously suggesting a blueprint for physical circuits capable of carrying out these computations. These results reframe and extend the recently proposed Generalized Latent Equilibrium (GLE) model.

Authors:Hui Yin, Xiang Li, Yifan Liu, Weijia Yao
Title: Vector-field guided constraint-following control for path following of uncertain mechanical systems
Abstract:
This note proposes a general control approach, called vector‑field guided constraint‑following control, to solve the dynamics control problem of geometric path‑following for a class of uncertain mechanical systems. More specifically, it operates at the dynamics level and can handle both fully‑actuated and underactuated mechanical systems, heterogeneous (possibly fast) time‑varying uncertainties with unknown bounds, and geometric desired paths that may be self‑intersecting. Simulations are conducted to demonstrate the effectiveness of the approach.

Authors:Ichiro Toyoshima, Pierre-Louis Poirion, Tomohide Yamazaki, Kota Yaguchi, Masayuki Kubota, Ryota Mizutani, Akiko Takeda
Title: Fairness in Robust Unit Commitment Problem Considering Suppression of Renewable Energy
Abstract:
Power company operators make power generation plans one day in advance, in what is known as the Unit Commitment (UC) problem. UC is exposed to uncertainties, such as unknown electricity load and disturbances caused by renewable energy sources, especially PVs. In previous research, we proposed the Renewable Energy Robust Optimization Problem (RE‑RP), which solves these uncertainties by considering suppression. In this paper, we propose a new model called RE‑RP with fairness (RE‑RPfair), which aims to achieve fair allocation among PVs allocation. This model is an expansion of the original RE‑RP, and we prove its effectiveness through simulation. To measure the degree of fairness, we use the Gini Index, which is well‑known in social science.

Authors:Rodrigo Bernal, Taulant Kerci, Federico Milano
Title: System-wide Dynamic Performance Metric for IBR-based Power Networks
Abstract:
In power networks based on Inverter‑Based Resources (IBRs), fast controllers cause frequency and voltage dynamics to overlap. Thus, it becomes critical to assess the overall dynamic performance of such networks through a combined system‑wide metric. This letter presents a unified metric designed to evaluate dynamic performance in such cases. The proposed metric consists of a weighted sum of local voltage phasor variations at each bus, where the weights are the complex powers injected at the buses. The proposed metric is further decomposed into device‑driven and network‑driven components, enabling a more comprehensive assessment of grid dynamics. A case study based on a modified version of the IEEE 39‑bus system is presented, in which synchronous machines are replaced by inverter‑based resources. A sensitivity analysis of the R/X ratio is utilized to evaluate the metric in conventional grids, as well as in those characterized by strong voltage‑frequency coupling with complex power flows.

Authors:Weijiang Zheng, Jiayi Huang, Bing Zhu
Title: Distributionally robust two-stage model predictive control: adaptive constraint tightening with stability guarantee
Abstract:
Model Predictive Control (MPC) is widely recognized for its ability to explicitly handle system constraints. In practice, system states are often affected by disturbances with unknown distributions. While robust MPC guarantees constraint satisfaction under worst‑case scenarios, it tends to be overly conservative. Stochastic MPC balances conservatism and performance but relies on precise knowledge of the disturbance distribution, which is often unavailable. To address this challenge, this paper introduces Distributionally Robust Optimization (DRO) into the MPC framework and proposes a novel Two‑Stage Distributionally Robust MPC (TSDR‑MPC) scheme. The key innovation lies in formulating constraint violation penalties as a second‑stage optimization problem, which, combined with the first‑stage quadratic cost, constitutes a two‑stage distributionally robust program. This structure enables adaptive constraint tightening against disturbances with unknown time‑varying means and covariances. Utilizing a Wasserstein ambiguity set, we derive a tractable reformulation via strong duality and develop a cutting‑plane algorithm that converges in a finite number of iterations, suitable for real‑time implementation. To ensure closed‑loop stability even under non‑zero mean disturbances, we introduce a terminal constraint applied solely to the nominal system; this constraint is proportional to the current state and independent of distributional uncertainty, thus preserving overall feasibility. We provide rigorous theoretical guarantees, including recursive feasibility, finite‑time algorithm termination, and an asymptotic performance bound on the average closed‑loop cost. Numerical simulations validate the adaptability and robustness of the proposed framework under various disturbance scenarios.

Authors:Hieu Trinh, Phan Thanh Nam, Tyrone Fernando
Title: Existence and Design of Functional Observers for Time-Delay Systems with Delayed Output Measurements
Abstract:
This paper investigates the problem of functional state estimation for linear time‑delay systems in which the delay affecting the state evolution differs from the delay affecting the output measurements. While existing observer designs typically assume instantaneous output availability, practical systems often exhibit measurement delays that are distinct from and not aligned with the intrinsic state delay. We explicitly distinguish between the state delay τ and the measurement delay h and address the problem of estimating a desired functional z(t)=Fx(t) under such mismatched delay conditions. Three functional observer structures are proposed to accommodate different delay configurations, each capable of realizing functional observers of different orders. This flexibility is important since a functional observer whose order equals the number of estimated functionals may not always exist. For each structure, algebraic existence conditions are established together with constructive synthesis procedures. A functional augmentation framework is developed to derive verifiable rank‑based conditions for observers of various orders. In addition, the notion of generalized functionals, defined over an augmented delayed state vector, is introduced to provide greater flexibility in satisfying observer existence conditions and facilitating systematic design. Numerical examples illustrate the proposed theory.

Authors:Julius P. J. Krebbekx, Roland Tóth, Amritam Das, Thomas Chaffey
Title: Amplitude Dependent Bode Diagrams via Scaled Relative Graphs
Abstract:
Scaled Relative Graphs (SRGs) provide an intuitive graphical frequency‑domain method for the analysis of Nonlinear (NL) systems, generalizing the Nyquist diagram. In this paper, we develop a method for computing L_2‑gain bounds for Lur'e systems over bounded frequency and amplitude ranges. We do this by restricting the input space of the SRG both in frequency and energy content, and combining with methods from Sobolev theory. The resulting gain bounds over restricted sets of inputs are less conservative than bounds computed over all of L_2, and yield three‑dimensional NL generalization of the Bode diagram, plotting L_2‑gain as function of both input frequency and energy content. In the zero‑energy limit, the Linear Time‑Invariant (LTI) Bode diagram is recovered, and at the infinite‑energy zero‑frequency limit, we recover the L_2‑gain. The effectiveness of our method is demonstrated on an example that resembles Phase‑Locked Loop dynamics.

Authors:Eymen Ipek, Oliver Korak, Georg Gsellmann, Andrey Golubkov
Title: Safe or Slow? The Illusion of Thermal Stability Under Reduced-Velocity Nail Intrusion
Abstract:
This study investigates the effects of nail penetration speed on the safety outcomes of large‑format automotive lithium‑ion pouch cells. Through six controlled tests varying the speed of nail insertion, we observed that lower penetration speeds did not induce thermal runaway; instead, the cells exhibited self‑discharge while the nail remained embedded. These findings suggest that penetration speed is a critical factor in the onset of thermal runaway, providing valuable insights for the development of safer battery systems and more effective safety testing protocols.

Authors:Yuri Shimane, Purnanand Elango, Avishai Weiss
Title: Optimization-Based Formation Flight on Libration Point Orbits
Abstract:
A model predictive control (MPC) framework is developed for station‑keeping in spacecraft formation flight along libration point orbits. At each control period, the MPC policy solves a multi‑vehicle optimal control problem (MVOCP) that tracks a reference trajectory, while enforcing path constraints on the relative motion of the formation. The control policy makes use of a limited set of control nodes consistent with operational constraints that allow only a small number of maneuver opportunities per revolution. To promote recursive feasibility, path constraints are progressively tightened across the prediction horizon. An isoperimetric reformulation of the constraints is used to prevent inter‑sample violations. The resulting MVOCP is a nonconvex program, which is solved via sequential convex programming. The proposed approach is evaluated in a high‑fidelity ephemeris model under realistic uncertainties for a formation along the near‑rectilinear halo orbit (NRHO), and subject to path constraints on interspacecraft separation and relative Sun phase angle. The results demonstrate maintenance of a spacecraft formation that satisfies the path constraints with cumulative propellant consumption comparable to that of existing methods

Authors:Wuping Xin
Title: Differentiable Stochastic Traffic Dynamics: Physics-Informed Generative Modelling in Transportation
Abstract:
Macroscopic traffic flow is stochastic, but the physics‑informed deep learning methods currently used in transportation literature embed deterministic PDEs and produce point‑valued outputs; the stochasticity of the governing dynamics plays no role in the learned representation. This work develops a framework in which the physics constraint itself is distributional and directly derived from stochastic traffic‑flow dynamics. Starting from an Ito‑type Lighthill‑Whitham‑Richards model with Brownian forcing, we derive a one‑point forward equation for the marginal traffic density at each spatial location. The spatial coupling induced by the conservation law appears as an explicit conditional drift term, which makes the closure requirement transparent. Based on this formulation, we derive an equivalent deterministic Probability Flow ODE that is pointwise evaluable and differentiable once a closure is specified. Incorporating this as a physics constraint, we then propose a score network with an advection‑closure module, trainable by denoising score matching together with a Fokker‑Planck residual loss. The resulting model targets a data‑conditioned density distribution, from which point estimates, credible intervals, and congestion‑risk measures can be computed. The framework provides a basis for distributional traffic‑state estimation and for stochastic fundamental‑diagram analysis in a physics‑informed generative setting.

Authors:Sobhan Badakhshan, Roshni Anna Jacob, Ali Mahboub Rad, Chao Pan, Yaoyu Li, Jie Zhang
Title: Dynamic Stability Assessment of Grid-Connected Data Centers Powered by Small Modular Reactors
Abstract:
The accelerating growth of computational demand in modern data centers has further heightened the need for power infrastructures that are highly reliable, environmentally sustainable, and capable of supporting grid stability. Small Modular Reactors (SMRs) as a clean source of energy are particularly attractive for next‑generation hyperscale data centers with significant electrical and cooling demands. This paper presents a comprehensive dynamic modeling and stability analysis of a grid‑connected Integrated Energy System (IES) designed for data center applications. The proposed IES integrates an SMR and a battery energy storage system to jointly supply electricity for computational and cooling load while providing stability support to the main grid. A coupled computational‑thermal load model is developed to capture the real‑time power demand of the data center, incorporating CPU utilization, cooling efficiency, and ambient temperature effects. The integrated SMR‑powered data center model is implemented in PSSE and tested on the IEEE 118‑bus system under various fault scenarios. Simulation results demonstrate that the IES substantially enhances voltage and frequency stability compared to a conventionally grid‑connected data center, minimizing disturbance‑induced deviations and improving post‑fault recovery.

Authors:Rongxiang Zeng, Yongqi Dong
Title: Latent World Models for Automated Driving: A Unified Taxonomy, Evaluation Framework, and Open Challenges
Abstract:
Emerging generative world models and vision‑language‑action (VLA) systems are rapidly reshaping automated driving by enabling scalable simulation, long‑horizon forecasting, and capability‑rich decision making. Across these directions, latent representations serve as the central computational substrate: they compress high‑dimensional multi‑sensor observations, enable temporally coherent rollouts, and provide interfaces for planning, reasoning, and controllable generation. This paper proposes a unifying latent‑space framework that synthesizes recent progress in world models for automated driving. The framework organizes the design space by the target and form of latent representations (latent worlds, latent actions, latent generators; continuous states, discrete tokens, and hybrids) and by structural priors for geometry, topology, and semantics. Building on this taxonomy, the paper articulates five cross‑cutting internal mechanics (i.e, structural isomorphism, long‑horizon temporal stability, semantic and reasoning alignment, value‑aligned objectives and post‑training, as well as adaptive computation and deliberation) and connects these design choices to robustness, generalization, and deployability. The work also proposes concrete evaluation prescriptions, including a closed‑loop metric suite and a resource‑aware deliberation cost, designed to reduce the open‑loop / closed‑loop mismatch. Finally, the paper identifies actionable research directions toward advancing latent world model for decision‑ready, verifiable, and resource‑efficient automated driving.

Authors:Bowen Yi, Leyan Fang, Romeo Ortega
Title: On the solvability of parameter estimation-based observers for nonlinear systems
Abstract:
Parameter estimation‑based observer (PEBO) is a recently developed constructive tool to design state observers for nonlinear systems. It reformulates the state estimation problem as one of online parameter identification, effectively addressing many open estimation challenges in practical applications. The feasibility of a PEBO design relies on two fundamental properties: transformability and identifiability. The former pertains to the existence of an injective solution to a suitable partial differential equation, whereas the latter characterizes the uniqueness of the parameterization induced by the resulting nonlinear regression model. In this paper, we analyze the existence of PEBOs for general nonlinear systems by studying these two properties in detail and by providing sufficient conditions under which they hold.

Authors:Samuel I. Akinwande, Sydney M. Katz, Mykel J. Kochenderfer, Clark Barrett
Title: The FABRIC Strategy for Verifying Neural Feedback Systems
Abstract:
Forward reachability analysis is a dominant approach for verifying reach‑avoid specifications in neural feedback systems, i.e., dynamical systems controlled by neural networks, and a number of directions have been proposed and studied. In contrast, far less attention has been given to backward reachability analysis for these systems, in part because of the limited scalability of known techniques. In this work, we begin to address this gap by introducing new algorithms for computing both over‑ and underapproximations of backward reachable sets for nonlinear neural feedback systems. We also describe and implement an integration of these backward reachability techniques with existing ones for forward analysis. We call the resulting algorithm Forward and Backward Reachability Integration for Certification (FaBRIC). We evaluate our algorithms on a representative set of benchmarks and show that they significantly outperform the prior state of the art.

Authors:Hanzhi Yu, Hasan Farooq, Julien Forgeat, Shruti Bothe, Kristijonas Cyras, Md Moin Uddin Chowdhury, Mingzhe Chen
Title: Optimizing Reinforcement Learning Training over Digital Twin Enabled Multi-fidelity Networks
Abstract:
In this paper, we investigate a novel digital network twin (DNT) assisted deep learning (DL) model training framework. In particular, we consider a physical network where a base station (BS) uses several antennas to serve multiple mobile users, and a DNT that is a virtual representation of the physical network. The BS must adjust its antenna tilt angles to optimize the data rates of all users. Due to user mobility, the BS may not be able to accurately track network dynamics such as wireless channels and user mobilities. Hence, a reinforcement learning (RL) approach is used to dynamically adjust the antenna tilt angles. To train the RL, we can use data collected from the physical network and the DNT. The data collected from the physical network is more accurate but incurs more communication overhead compared to the data collected from the DNT. Therefore, it is necessary to determine the ratio of data collected from the physical network and the DNT to improve the training of the RL model. We formulate this problem as an optimization problem whose goal is to jointly optimize the tilt angle adjustment policy and the data collection strategy, aiming to maximize the data rates of all users while constraining the time delay introduced by collecting data from the physical network. To solve this problem, we propose a hierarchical RL framework that integrates robust adversarial loss and proximal policy optimization (PPO). Simulation results show that our proposed method reduces the physical network data collection delay by up to 28.01% and 1x compared to a hierarchical RL that uses vanilla PPO as the first level RL, and the baseline that uses robust‑RL at the first level and selects the data collection ratio randomly.

Authors:Xiaojing Zhang, Jun Chen, Guanghui Wang
Title: Feedback Does Not Increase the Capacity of Approximately Memoryless Surjective POST Channels
Abstract:
We study a class of finite‑state channels, known as POST channels, in which the previous channel output serves as the current state. A POST channel is deemed approximately memoryless when the state‑dependent transition matrices are sufficiently close to one another. For this family of channels, under a surjectivity condition on the associated memoryless reference channel, we show that the feedback capacity coincides with the non‑feedback capacity. Consequently, for almost all approximately memoryless POST channels whose input alphabet size is no smaller than the output alphabet size, feedback provides no capacity gain. This result extends Shannon's classical theorem on discrete memoryless channels and demonstrates that the phenomenon holds well beyond the strictly memoryless case.

Authors:Seyedreza Rezaei, Junjie Kang, Amaldev Haridevan, Jinjun Shan
Title: SEP-NMPC: Safety Enhanced Passivity-Based Nonlinear Model Predictive Control for a UAV Slung Payload System
Abstract:
Model Predictive Control (MPC) is widely adopted for agile multirotor vehicles, yet achieving both stability and obstacle‑free flight is particularly challenging when a payload is suspended beneath the airframe. This paper introduces a Safety Enhanced Passivity‑Based Nonlinear MPC (SEP‑NMPC) that provides formal guarantees of stability and safety for a quadrotor transporting a slung payload through cluttered environments. Stability is enforced by embedding a strict passivity inequality, which is derived from a shaped energy storage function with adaptive damping, directly into the NMPC. This formulation dissipates excess energy and ensures asymptotic convergence despite payload swings. Safety is guaranteed through high‑order control barrier functions (HOCBFs) that render user‑defined clearance sets forward‑invariant, obliging both the quadrotor and the swinging payload to maintain separation while interacting with static and dynamic obstacles. The optimization remains quadratic‑program compatible and is solved online at each sampling time without gain scheduling or heuristic switching. Extensive simulations and real‑world experiments confirm stable payload transport, collision‑free trajectories, and real‑time feasibility across all tested scenarios. The SEP‑NMPC framework therefore unifies passivity‑based closed‑loop stability with HOCBF‑based safety guarantees for UAV slung‑payload transportation.

Authors:Leila Amanzadeh, Taizoon Chunawala, Randall T. Fawcett, Alexander Leonessa, Kaveh Akbari Hamed
Title: Predictive Control with Indirect Adaptive Laws for Payload Transportation by Quadrupedal Robots
Abstract:
This paper formally develops a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots, integrating a model predictive control (MPC) algorithm with a gradient‑descent‑based adaptive updating law. At the framework's high level, an indirect adaptive law estimates the unknown parameters of the reduced‑order (template) locomotion model under varying payloads. These estimated parameters feed into an MPC algorithm for real‑time trajectory planning, incorporating a convex stability criterion within the MPC constraints to ensure the stability of the template model's estimation error. The optimal reduced‑order trajectories generated by the high‑level adaptive MPC (AMPC) are then passed to a low‑level nonlinear whole‑body controller (WBC) for tracking. Extensive numerical investigations validate the framework's capabilities, showcasing the robot's proficiency in transporting unmodeled, unknown static payloads up to 109% in experiments on flat terrains and 91% on rough experimental terrains. The robot also successfully manages dynamic payloads with 73% of its mass on rough terrains. Performance comparisons with a normal MPC and an L1 MPC indicate a significant improvement. Furthermore, comprehensive hardware experiments conducted in indoor and outdoor environments confirm the method's efficacy on rough terrains despite uncertainties such as payload variations, push disturbances, and obstacles.

Authors:Juana M. Martínez-Heredia, José L. Mora
Title: Neural Network Tuning of FSMPC for Drives
Abstract:
This preprint presents a neural network tuner for the finite state model predictive control of an induction motor. The tuner deals with the parameters of the controllers in the speed loop and in the stator current loop. The results are assessed using a five phase machine in an experimental setup. Data for the neural network training is obtained from the experiments using step tests.

Authors:Mohammed Cherifi
Title: Autonomous Edge-Deployed AI Agents for Electric Vehicle Charging Infrastructure Management
Abstract:
Public EV charging infrastructure suffers from significant failure rates ‑‑ with field studies reporting up to 27.5% of DC fast chargers non‑functional ‑‑ and multi‑day mean time to resolution, imposing billions in annual economic burden. Cloud‑centric architectures cannot achieve the latency, reliability, and bandwidth characteristics required for autonomous operation. We present Auralink SDC (Software‑Defined Charging), an architecture deploying domain‑specialized AI agents at the network edge for autonomous charging infrastructure management. Key contributions include: (1) Confidence‑Calibrated Autonomous Resolution (CCAR), enabling autonomous remediation with formal false‑positive bounds; (2) Adaptive Retrieval‑Augmented Reasoning (ARA), combining dense and sparse retrieval with dynamic context allocation; (3) Auralink Edge Runtime, achieving sub‑50ms TTFT on commodity hardware under PREEMPT_RT constraints; and (4) Hierarchical Multi‑Agent Orchestration (HMAO). Implementation uses AuralinkLM models fine‑tuned via QLoRA on a domain corpus spanning OCPP 1.6/2.0.1, ISO 15118, and operational incident histories. Evaluation on 18,000 labeled incidents in a controlled environment establishes 78% autonomous incident resolution, 87.6% diagnostic accuracy, and 28‑48ms TTFT latency (P50). This work presents architecture and implementation patterns for edge‑deployed industrial AI systems with safety‑critical constraints.

Authors:Young-ho Cho, Mohamad Chehade, Fatima Al-Janahi, Sol Lim, Javad Mohammadi, Hao Zhu
Title: Carbon-aware Market Participation for Building Energy Management Systems
Abstract:
Tackling climate change requires the rapid and deep decarbonization of electric power systems. While energy management systems (EMSs) play a central role in this transition, conventional EMSs focus mainly on economic efficiency and often overlook the environmental impact of operational decisions. To address this gap, this paper proposes a unified, real‑time building‑level carbon‑aware EMS (CAEMS) capable of simultaneously co‑optimizing grid imports, energy storage, and flexible demand within a single integrated framework. We formulate a mixed‑integer linear program (MILP) model that directly integrates time‑varying marginal carbon intensity signals into the EMS objective for coordinated participation in both day‑ahead (DA) and real‑time (RT) markets. To relax the unrealistic assumption of perfect foresight, we incorporate a model predictive control (MPC) extension driven by a Transformer‑based forecaster that jointly predicts electricity prices and carbon intensity. The proposed CAEMS is validated using real‑world data from the PJM electricity market. Simulation results demonstrate that modest carbon prices can achieve a significant 22.5% reduction in emissions with only a 1.7% increase in cost.

Authors:Joonwon Choi, Kartik Anand Pant, Karthik Nune, Inseok Hwang
Title: Reachability-based Temporal Logic Verification for Reliable LLM-guided Human-Autonomy Teaming
Abstract:
We propose a reachability‑based framework for reliable LLM‑guided human‑autonomy teaming (HAT) using signal temporal logic (STL). In the proposed framework, LLM is leveraged as a translator that transfers natural language commands given by a human operator into corresponding STL specifications or vice versa. An STL feasibility filter (SFF) is proposed to check the feasibility of the generated STL. The SFF first decomposes the complex and nested LLM translation into a set of simpler subformulas for parallelization and informative feedback generation. The reachability analysis method is then applied to verify if each subformula is feasible for a target dynamical system: if feasible, perform mission planning, otherwise, reject it. The proposed SFF can identify infeasible subformulas, more than simply providing the boolean verification results for the whole STL, thereby facilitating the feedback generation of LLM to request modification of the command to the human. Consequently, the proposed framework can allow more reliable HAT by enabling safe and informative communication between the human operator and the autonomous agent. Our experiments demonstrate that the proposed framework can successfully filter out infeasible subformulas and generate informative feedback based on such information.

Authors:Mario Zanon
Title: Rethinking Strict Dissipativity for Economic MPC
Abstract:
Stability of economic model predictive control can be proven under the assumption that a strict dissipativity condition holds. This assumption has a clear interpretation in terms of the so‑called rotated stage cost, which must have its minimum at the optimal steady state. However, contrary to dissipativity, for strict dissipativity the storage function cannot be immediately related to the value function of an optimal control problem formulated with the economic stage cost. We propose the novel concept of two‑storage strict dissipativity, which requires two storage functions to satisfy dissipativity and be separated by a positive definite function. This new condition can be immediately related to optimal control by means of value functions and might be easier to verify than strict dissipativity. Furthermore, we prove that two‑storage strict dissipativity is sufficient and necessary for asymptotic stability, it is related to strict dissipativity, and also to alternative approaches relying on the so‑called cost‑to‑travel. Finally, we discuss commonly used and new terminal cost designs that guarantee asymptotic stability in the finite‑horizon case.

Authors:Mirko Mizzoni, Pieter van Goor, Barbara Bazzana, Antonio Franchi
Title: Input Dexterity and Output Negotiation in Feedback-Linearizable Nonlinear Systems
Abstract:
We introduce a task‑relative taxonomy of actuator inputs for nonlinear systems within the input‑output feedback‑linearization framework. Given a flat output specifying the task, inputs are classified as essential, redundant, or dexterity: essential inputs are required for exact linearization, redundant inputs can be removed without effect, and dexterity inputs can be deactivated while preserving exact linearization of a reduced task. We show that a subset is dexterity if and only if, under a suitable dynamic prolongation, it can appear as additional output channels (flat‑input complement) on a common validity set. Whenever a family of systems obtained by (de)activating dexterity inputs admits a common prolongation, the family can be interpreted as a single prolonged system endowed with different output selections. This enables a unified linearizing controller that negotiates between full and reduced tasks without transients on shared outputs under compatibility and dwell‑time conditions. Simulations on a fully actuated aerial platform illustrate graceful task downgrades from six‑dimensional pose tracking as lateral‑force channels are deactivated.

Authors:Shaoze Li, Justin S. Kim, Cong Chen
Title: Behavioral Generative Agents for Power Dispatch and Auction
Abstract:
This paper presents positive initial evidence that generative agents can relax the rigidity of traditional mathematical models for human decision‑making in power dispatch and auction settings. We design two proof‑of‑concept energy experiments with generative agents powered by a large language model (LLM). First, we construct a home battery management testbed with stochastic electricity prices and blackout interventions, and benchmark LLM decisions against dynamic programming. By incorporating an in‑context learning (ICL) module, we show that behavioral patterns discovered by a stronger reasoning model can be transferred to a smaller LLM via example‑based prompting, leading agents to prioritize post‑blackout energy reserves over short‑term profit. Second, we study LLM agents in simultaneous ascending auctions (SAA) for power network access, comparing their behavior with an optimization benchmark, the straightforward bidding strategy. By designing ICL prompts with rule‑based, myopic, and strategic objectives, we find that structured prompting combined with ICL enables LLM agents to both reproduce economically rational strategies and exhibit systematic behavioral deviations. Overall, these results suggest that LLM‑powered agents provide a flexible and expressive testbed for modeling human decision‑making in power system applications.

Authors:Shreya Das, Kundan Kumar, Muhammad Iqbal, Outi Savolainen, Dominik Baumann, Laura Ruotsalainen, Simo Särkkä
Title: Integrating Lagrangian Neural Networks into the Dyna Framework for Reinforcement Learning
Abstract:
Model‑based reinforcement learning (MBRL) is sample‑efficient but depends on the accuracy of the learned dynamics, which are often modeled using black‑box methods that do not adhere to physical laws. Those methods tend to produce inaccurate predictions when presented with data that differ from the original training set. In this work, we employ Lagrangian neural networks (LNNs), which enforce an underlying Lagrangian structure to train the model within a Dyna‑based MBRL framework. Furthermore, we train the LNN using stochastic gradient‑based and state‑estimation‑based optimizers to learn the network's weights. The state‑estimation‑based method converges faster than the stochastic gradient‑based method during neural network training. Simulation results are provided to illustrate the effectiveness of the proposed LNN‑based Dyna framework for MBRL.

Authors:Andrei Starodubov, Yaqub Aris Prabowo, Andreas Hadjipieris, Ioannis Kyriakides, Roberto Galeazzi
Title: Adaptive Entropy-Driven Sensor Selection in a Camera-LiDAR Particle Filter for Single-Vessel Tracking
Abstract:
Robust single‑vessel tracking from fixed coastal platforms is hindered by modality‑specific degradations: cameras suffer from illumination and visual clutter, while LiDAR performance drops with range and intermittent returns. We present a heterogeneous multi‑sensor fusion particle‑filter tracker that incorporates an information‑gain (entropy‑reduction) adaptive sensing policy to select the most informative configuration at each fusion time bin. The approach is validated in a real maritime deployment at the CMMI Smart Marina Testbed (Ayia Napa Marina, Cyprus), using a shore‑mounted 3D LiDAR and an elevated fixed camera to track a rigid inflatable boat with onboard GNSS ground truth. We compare LiDAR‑only, camera‑only, all‑sensors, and adaptive configurations. Results show LiDAR dominates near‑field accuracy, the camera sustains longer‑range coverage when LiDAR becomes unavailable, and the adaptive policy achieves a favorable accuracy‑continuity trade‑off by switching modalities based on information gain. By avoiding continuous multi‑stream processing, the adaptive configuration provides a practical baseline for resilient and resource‑aware maritime surveillance.

Authors:Xi Mo
Title: IronEngine: Towards General AI Assistant
Abstract:
This paper presents IronEngine, a general AI assistant platform organized around a unified orchestration core that connects a desktop user interface, REST and WebSocket APIs, Python clients, local and cloud model backends, persistent memory, task scheduling, reusable skills, 24‑category tool execution, MCP‑compatible extensibility, and hardware‑facing integration. IronEngine introduces a three‑phase pipeline ‑‑ Discussion (Planner‑‑Reviewer collaboration), Model Switch (VRAM‑aware transition), and Execution (tool‑augmented action loop) ‑‑ that separates planning quality from execution capability. The system features a hierarchical memory architecture with multi‑level consolidation, a vectorized skill repository backed by ChromaDB, an adaptive model management layer supporting 92 model profiles with VRAM‑aware context budgeting, and an intelligent tool routing system with 130+ alias normalization and automatic error correction. We present experimental results on file operation benchmarks achieving 100% task completion with a mean total time of 1541 seconds across four heterogeneous tasks, and provide detailed comparisons with representative AI assistant systems including ChatGPT, Claude Desktop, Cursor, Windsurf, and open‑source agent frameworks. Without disclosing proprietary prompts or core algorithms, this paper analyzes the platform's architectural decomposition, subsystem design, experimental performance, safety boundaries, and comparative engineering advantages. The resulting study positions IronEngine as a system‑oriented foundation for general‑purpose personal assistants, automation frameworks, and future human‑centered agent platforms.

Authors:Yao Qin, Yitong Li, Wei Wang, Shaoze Zhou, Zheng Wei, Jinjun Liu
Title: Eigenvalue Patterns and Participation Analysis of Symmetric Renewable Energy Power Systems
Abstract:
State‑space analysis is widely employed for examining power system dynamics but faces challenges in large‑scale power systems integrated with numerous inverter‑based resources (IBRs), where the significant increase of system states complicates modal analysis. Notably, renewable energy power systems often consist of multiple homogeneous generation units. This uniformity, termed symmetry in this paper, can facilitate the system stability analysis. Eigenvalue patterns and participation factors in three types of symmetric renewable energy power systems are investigated, including ideally‑, quasi‑, and group‑symmetric systems. An ideally‑symmetric (quasi‑symmetric) system comprises a group of identical (similar) subsystems connected to an external grid. A system containing multiple such groups is termed group‑symmetric. In these symmetric systems, two types of modes are defined to characterize different interactions: inner‑group modes, which describe the interactions among subsystems within a single group, and group‑grid modes, which describe the interactions between the groups and the external grid. A new concept termed group participation factor is also proposed to extend the use of conventional participation factors for repeated and close modes. In addition, the invariance properties of the inner‑group modes and group‑grid modes are discussed. The findings provide insights for stability analysis and targeted optimization in power systems. Theoretical advances are validated through numerical results and electromagnetic transient (EMT) simulations on example power systems of varied types and scales.

Authors:Hang Nguyen, Koen Kok, Trung Thai Tran, Phuong H. Nguyen
Title: The coordination between TSO and DSO in the context of energy transition - A review
Abstract:
Nowadays, energy transition is ongoing in many countries, aiming to reduce dependence on fossil fuels and CO2 emissions. Besides the positive impacts on the environment, this transition brings technical challenges to the system operators, such as the intricacies of energy system integration, diminishing uncertainty, and incentivizing customers with advanced transaction models. The coordination between the Transmission system operator (TSO) and the Distribution system operator (DSO) is one of the most important aspects of encountering these obstacles. This coordination enhances the utilization of flexibility from Distributed energy resources (DERs) by incentivizing the market parties with better willingness to pay schemes. This paper provides an overview of the coordination schemes (CS), their classification, assessment of the current situation and the challenges associated with applying these schemes in practical context. The main purpose is to investigate the most effective way for TSO/DSOs to use the flexibility resource to maintain the balancing of the entire system while ensuring no congestion occurs in the network. A broad range of possible coordination schemes along with exploiting flexibility services is presented and the pros and cons are analyzed. Additionally, the study presents a general scenario that describes the interaction between the operators and the third party in providing service to the balancing market, considering cases with and without coordination.

Authors:Poulomee Ghosh, Shubhendu Bhasin
Title: Adaptive Tracking Control of Euler-Lagrange Systems with Time-Varying State and Input Constraints
Abstract:
This paper presents an adaptive control framework for Euler‑Lagrange (E‑L) systems that enforces user‑defined time‑varying state and input constraints in the presence of parametric uncertainties and bounded disturbances. The proposed design integrates a time‑varying barrier Lyapunov Function (TVBLF) with a saturated control law to guarantee constraint satisfaction without resorting to real‑time optimization. A key contribution is the development of an offline, verifiable feasibility condition that certifies the existence of a feasible control policy for any prescribed pair of time‑varying state and input envelopes. Additionally, we prove boundedness of all closed‑loop signals. Real‑time experiments conducted on a 2‑DoF helicopter model validate the efficacy and practical viability of the proposed method.

Authors:Kamal Adekola, Laurens de Vries, Kenneth Bruninx
Title: Coupling Europe's Capacity Markets
Abstract:
European Member States are increasingly introducing national capacity mechanisms (CMs) to manage growing adequacy risks. However, isolated national CMs are inefficient in highly interconnected electricity systems, such as the European system. While progress has been made in facilitating cross‑border participation by generation capacity in CMs, existing arrangements are prone to under‑ or over‑investment and do not properly value the contribution of interconnection capacity to Member States' adequacy targets. In this paper, we propose a novel conceptual design for a coupled European capacity market that utilises the logic of flow‑based market coupling. In a comparative analysis of different market design scenarios in an illustrative multi‑zone case study, using a bespoke long‑run equilibrium problem, we show that the proposed flow‑based coupling of capacity markets reduces system costs by harnessing available capacity in neighbouring market zones while ensuring deliverability with respect to network constraints in all scarcity situations.

Authors:Yiming Wang, Mihindukulasooriya Sheral Crescent Tissera, Haihong Yu, Kai Jie Ethan Foo, Sean Yeo Keyuan, Ankit Srivastava, Hao An
Title: Augmented Model Predictive Control: A Balance between Satellite Agility and Computation Complexity
Abstract:
Agile earth observation satellites employ multiple actuators to enable flexible and responsive imaging capabilities. While significant advancements in actuator technology have enhanced satellites' torque and momentum, relatively little attention has been given to control strategies specifically tailored to improve satellite agility. This paper provides a comparative analysis of different Model Predictive Control (MPC) formulations and introduces an augmented‑MPC method that effectively balances agility requirements with hardware implementation constraints. The proposed method achieves the high‑performance characteristics of nonlinear MPC while preserving the computational simplicity of linear MPC. Numerical simulations and physical experiments are conducted to validate the effectiveness and feasibility of the proposed approach.

Authors:Philipp Schitz, Johann C. Dauer, Paolo Mercorelli
Title: Trajectory Tracking Control Design for Autonomous Helicopters with Guaranteed Error Bounds
Abstract:
This paper presents a systematic framework for computing formally guaranteed trajectory tracking error bounds for autonomous helicopters based on Robust Positive Invariant (RPI) sets. The approach focuses on establishing a closed‑loop translational error dynamics which is cast into polytopic linear parameter‑varying form with bounded additive and state‑dependent disturbances. Ellipsoidal RPI sets are computed, yielding explicit position error bounds suitable as certified buffer zones in upper‑level trajectory planning. Three controller architectures are compared with respect to the conservatism of their error bounds and tracking performance. Simulation results on a nonlinear helicopter model demonstrate that all architectures respect the derived bounds, while highlighting trade‑offs between dynamical fidelity and conservatism in invariant set computation.

Authors:Jiaqi Hu, Karl H. Johansson, Apostolos I. Rikos
Title: Distributed Coordination Algorithms with Efficient Communication for Open Multi-Agent Systems with Dynamic Communication Links and Processing Delays
Abstract:
In this paper we focus on the distributed quantized average consensus problem in open multi‑agent systems consisting of dynamic directed communication links among active nodes. We propose three communication‑efficient distributed algorithms designed for different scenarios. Our first algorithm solves the quantized averaging problem over the currently active node set under finite network openness (i.e., when the active set eventually stabilizes). Our second algorithm extends the aforementioned approach for the case where nodes suffer from arbitrary bounded processing delays. Our third algorithm operates over indefinitely open multi‑agent networks with dynamic communication links (i.e., with continuous node arrivals and departures), computing the average that incorporates both active and historically active nodes. We analyze our algorithms' operation, establish their correctness, and present novel necessary and sufficient topological conditions ensuring their finite‑time convergence. Numerical simulations on distributed sensor fusion for environmental monitoring demonstrate fast finite‑time convergence and robustness across varying network sizes, departure/arrival rates, and processing delays. Finally, it is shown that our proposed algorithms compare favorably to algorithms in the existing literature.

Authors:Antonio Franchi
Title: Aero-Promptness: Drag-Aware Aerodynamic Manipulability for Propeller-driven Vehicles
Abstract:
This work introduces the Drag‑Aware Aerodynamic Manipulability (DAAM), a geometric framework for control allocation in redundant multirotors. By equipping the propeller spin‑rate space with a Riemannian metric based on the remaining symmetric acceleration capacity of each motor, the formulation explicitly accounts for motor torque limits and aerodynamic drag. Mapping this metric through the nonlinear thrust law to the generalized force space yields a state‑dependent manipulability volume. The log‑determinant of this volume acts as a natural barrier function, strictly penalizing drag‑induced saturation and low‑spin thrust loss. Optimizing this volume along the allocation fibers provides a redundancy resolution strategy inherently invariant to arbitrary coordinate scaling in the generalized‑force space. Analytically, we prove that the resulting optimal allocations locally form smooth embedded manifolds, and we geometrically characterize the global jump discontinuities that inevitably arise from physical actuator limits and spin‑rate sign transitions.

Authors:Yang Yang, Chenggang Cui, Xitong Niu, Jiaming Liu, Chuanlin Zhang
Title: Model-Free DRL Control for Power Inverters: From Policy Learning to Real-Time Implementation via Knowledge Distillation
Abstract:
In response to the trade‑off between control performance and computational burden hindering the deployment of Deep Reinforcement Learning (DRL) in power inverters, this paper presents a novel model‑free control framework leveraging policy distillation. To handle the convergence instability and steady‑state errors inherent in model‑free agents, an error energy‑guided hybrid reward mechanism is established to theoretically constrain the exploration space. More specifically, an adaptive importance weighting mechanism is integrated into the distillation architecture to amplify the significance of fluctuation regions, ensuring high‑quality transfer of transient control logic by mitigating the observational bias dominated by steady‑state data. This approach efficiently compresses the heavy DRL policy into a lightweight neural network, retaining the desired control performance while overcoming the computational bottleneck during deployment. The proposed method is validated through a hardware‑based kilowatt‑level experimental platform. Experimental comparison results with traditional methods demonstrate that the proposed technique reduces inference time to the microsecond level and achieves superior transient response speed and parameter robustness.

Authors:Xu Zhang, Fen Wu
Title: Robust control synthesis for uncertain linear systems with input saturation using mixed IQCs
Abstract:
This paper develops a robust control synthesis method for uncertain linear systems with input saturation in the framework of integral quadratic constraints (IQCs). The system is reformulated as a linear fractional representation (LFR) that captures both dead‑zone nonlinearity and time‑varying uncertainties. By combining mixed IQC‑based dissipation inequalities with quadratic Lyapunov functions, sufficient conditions for robust stabilization are established. Compared with conventional approaches based on a single static sector condition for the dead‑zone nonlinearity, the proposed method yields improved \mathcalL_2‑gain performance through the use of scaled mixed IQCs. For systems subject to time‑varying structured uncertainties, a new scaled bounded real lemma is further developed based on the IQC characterization. The resulting \mathcalH_\infty synthesis conditions are expressed as linear matrix inequalities (LMIs), which are numerically tractable in all decision variables, including the scaling factors in the IQC multipliers. The proposed method is validated using a second‑order uncertain system in linear fractional form, and its superiority over an anti‑windup design is further illustrated by a cart‑pendulum example.

Authors:Dilermando Almeida, Juliano Negri, Guilherme Lazzarini, Thiago H. Segreto, Ranulfo Bezerra, Ricardo V. Godoy, Marcelo Becker
Title: Viewpoint-Agnostic Grasp Pipeline using VLM and Partial Observations
Abstract:
Robust grasping in cluttered, unstructured environments remains challenging for mobile legged manipulators due to occlusions that lead to partial observations, unreliable depth estimates, and the need for collision‑free, execution‑feasible approaches. In this paper we present an end‑to‑end pipeline for language‑guided grasping that bridges open‑vocabulary target selection to safe grasp execution on a real robot. Given a natural‑language command, the system grounds the target in RGB using open‑vocabulary detection and promptable instance segmentation, extracts an object‑centric point cloud from RGB‑D, and improves geometric reliability under occlusion via back‑projected depth compensation and two‑stage point cloud completion. We then generate and collision‑filter 6‑DoF grasp candidates and select an executable grasp using safety‑oriented heuristics that account for reachability, approach feasibility, and clearance. We evaluate the method on a quadruped robot with an arm in two cluttered tabletop scenarios, using paired trials against a view‑dependent baseline. The proposed approach achieves a 90% overall success rate (9/10) against 30% (3/10) for the baseline, demonstrating substantially improved robustness to occlusions and partial observations in clutter.

Authors:Torsten Djurhuus, Viktor Krozer
Title: A Novel Phase-Noise Module for the QUCS Circuit Simulator. Part II : Noise Analysis
Abstract:
The paper documents the implementation of a novel phase‑noise analysis module within the open‑source QUCS circuit simulator environment. The underlying algorithm is based on a rigorous, unified time‑domain methodology of (coupled) oscillator noise‑response, recently proposed by the authors. The theoretical approach used to develop this model is entirely unconstrained by any empirical and/or phenomenological modelling techniques, such as e.g. LTI and LTV theory, and this differentiates it from all prior proposals on this topic. The paper introduces important, and previously unpublished, extensions to this framework, in the form of novel unified closed‑form expressions for both the amplitude and phase‑amplitude correlation response of a general coupled oscillating circuit perturbed by noise. The research discussed herein has many important scientific and industrial applications w.r.t. predicting, synthesizing and optimizing the performance of noise‑perturbed free‑running and coupled autonomous circuits operating under large‑signal steady‑state conditions. These timing circuits are ubiquitous in all modern communication and remote‑sensing systems and the developed simulation tools will prove to have great impact in various areas of industrial circuit design. This paper represents second part of a two‑part series with the first part discussing the implementation of the underlying steady‑state analysis module. The open‑source simulator, discussed and developed herein, applies advanced state‑of‑the‑art stochastic modelling techniques, in‑order to produce noise simulation tools with capabilities and scope which, in many areas, exceed what is found in the commercial EDAs currently on the market.

Authors:Arash Khalatbarisoltani, Amin Mahmoudi, Jie Han, Muhammad Saeed, Wenxue Liu, Jinwen Li, Solmaz Kahourzade, Amirmehdi Yazdani, Xiaosong Hu
Title: Leveraging Quantum Annealing for Large-Scale Household Energy Scheduling with Hydrogen Storage
Abstract:
Hydrogen integration into microgrids facilitates the absorption of intermittencies from renewable energy resources. However, significant challenges remain due to complex optimization problems, particularly in large‑scale applications involving multiple fuel cells (FCs) and electrolyzers (ELs) with numerous binary decision variables. This paper presents a hierarchical quantum annealing (QA) model predictive control‑based power allocation framework aimed at accelerating these optimization problems. First, in a day‑ahead stage, the framework determines the startup and shutdown of the FCs and ELs. The short‑term stage then refines the output power of the FCs and the hydrogen generation rate of the ELs. The feasibility is evaluated through a case study consisting of multiple households in Australia. Our findings demonstrate that while the traditional optimization approach performs satisfactorily in scenarios with a small number of households, the QA approach becomes more appropriate and effectively solves the problem within an acceptable range as the number of connected households increases.

Authors:Masoud Salmani Arani, Reza Shahidi, Lihong Zhang
Title: A Curved Monopole Antenna for HF Radar with Enhanced Gain and Bandwidth
Abstract:
This paper presents the design and simulation of a new curved monopole antenna optimized for skywave HF radar applications, with a systematic investigation of the effects of curvature and fixed‑section length on antenna performance. The proposed design achieves improved impedance matching, broader bandwidth, and enhanced realized gain compared to a conventional quarter‑wavelength monopole at 15 MHz. Parametric analysis shows that fully bending the monopole degrades performance, whereas introducing a straight section and carefully optimizing the curvature enables a 18.5% gain increase and a 400 kHz bandwidth expansion. The single‑element design is further extended to a 12‑element linear array with 0.45λ spacing (where λ is the wavelength), demonstrating stable embedded‑element behavior and improved low‑to‑ moderate elevation gain for skywave over‑the‑horizon radar operation. At θ = 30°, the proposed array achieves 14.04 dBi compared to 13.11 dBi for the reference array, corresponding to 24% gain enhancement, which is significant in high‑power HF radar systems. These results confirm that the proposed curved monopole antenna provides a compact, broadband, and scalable solution for next‑generation HF radar arrays.

Authors:Arash Khalatbarisoltani, Amin Mahmoudi, Jie Han, Muhammad Saeed, Wenxue Liu, Jinwen Li, Solmaz Kahourzade, Amirmehdi Yazdani, Xiaosong Hu
Title: Temperature-Aware Scheduling of LLM Inference in Large-Scale Geo-Distributed Edge Data Centers with Distributed Optimization
Abstract:
The environmental impact of Large Language Models (LLMs) on data centers hosting these models is becoming a significant concern. While many efforts have focused on reducing the substantial training overhead of LLMs, carbon and water consumption during the inference phase can often surpass the costs associated with their training. The cooling systems of data centers are crucial in this context, but they are frequently modeled with a location‑independent efficiency term. However, their energy efficiency is highly influenced by ambient temperature, which can vary significantly across different geographical locations. Leveraging this temperature diversity can help reduce total cooling energy costs and improve the performance of edge data centers. To address these critical sustainability issues related to LLMs, this study proposes a temperature‑aware approach that co‑optimizes LLM energy costs, carbon emissions, time‑to‑first token, and water consumption. The approach employs a distributed optimization algorithm based on an alternating direction method of multipliers, aimed at enhancing the sustainability of LLM hosting across geo‑distributed edge data centers in Australia. Our method demonstrates reductions in cooling energy consumption and improves overall cost efficiency for geo‑distributed cloud environments.

Authors:Kursad Metehan Gul, Selahattin Burak Sarsilmaz
Title: Robust Cooperative Output Regulation of Discrete-Time Heterogeneous Multi-Agent Systems
Abstract:
This article considers robust cooperative output regulation of discrete‑time uncertain heterogeneous (in dimension) multi‑agent systems (MASs). We show that the solvability of this problem with an internal model‑based distributed control law reduces to the existence of a structured control gain that makes the nominal closed‑loop system matrix of the MAS Schur. Accordingly, this article focuses on global and agent‑wise local sufficient conditions for the existence and design of such a structured control gain. Based on a structured Lyapunov inequality, we present a convexification that yields a linear matrix inequality (LMI), whose feasibility is a global sufficient condition for the existence and design. Considering the individual nominal dynamics of each agent, the existence is also ensured if each agent solves a structure‑free control problem. Its convexification yields LMIs that allow each agent to separately design its structure‑free control gain. Lastly, we study the relationships between the sets of control gains emerging from both global and local perspectives.

Authors:Ming Li, Jin Chen, Dimos V. Dimarogonas
Title: Tunable Input-to-State Safety with Input Constraints
Abstract:
Tunable input‑to‑state safety (TISSf) generalizes the input‑to‑state safety (ISSf) framework by incorporating a tuning function that regulates safety conservatism while preserving robustness against perturbations. Despite its flexibility, the TISSf tuning function is often designed without explicitly incorporating actuator limits, which can lead to incompatibility with input constraints. To address this gap, this paper proposes a framework that integrates general compact input constraints into tuning function synthesis. Leveraging a geometric perspective, we characterize the TISSf condition as a state‑dependent half‑space constraint and derive a verifiable certificate for input compatibility using support functions. This characterization transforms the compatibility requirement into a design constraint on the tuning function, yielding a prescriptive lower bound that defines an admissible family of tunings under input constraints. These results are specialized to norm‑bounded, polyhedral, and box constraints, yielding tractable control design conditions. We show that these conditions, combined with tuning function monotonicity, guarantee input compatibility and recursive feasibility of the resulting quadratic program (QP)‑based safety filter. Furthermore, an offline parameter selection procedure using a covering‑based sampling strategy ensures compatibility across the entire safe set via a linear program (LP). A connected cruise control (CCC) application demonstrates robust safety under TISSf while enforcing input constraints by design.

Authors:Yuanlong Chen, Bowen Zhu, Bing Xia, Yichuan Wang
Title: A Lightweight MPC Bidding Framework for Brand Auction Ads
Abstract:
Brand advertising plays a critical role in building long‑term consumer awareness and loyalty, making it a key objective for advertisers across digital platforms. Although real‑time bidding has been extensively studied, there is limited literature on algorithms specifically tailored for brand auction ads that fully leverage their unique characteristics. In this paper, we propose a lightweight Model Predictive Control (MPC) framework designed for brand advertising campaigns, exploiting the inherent attributes of brand ads ‑‑ such as stable user engagement patterns and fast feedback loops ‑‑ to simplify modeling and improve efficiency. Our approach utilizes online isotonic regression to construct monotonic bid‑to‑spend and bid‑to‑conversion models directly from streaming data, eliminating the need for complex machine learning models. The algorithm operates fully online with low computational overhead, making it highly practical for real‑world deployment. Simulation results demonstrate that our approach significantly improves spend efficiency and cost control compared to baseline strategies, providing a scalable and easily implementable solution for modern brand advertising platforms.

Authors:Yuchen Qi, Ye Guo, Yinliang Xu
Title: VB-NET: A physics-constrained gray-box deep learning framework for modeling air conditioning systems as virtual batteries
Abstract:
The increasing penetration of renewable energy necessitates unlocking demand‑side flexibility. While air conditioning (AC) systems offer significant thermal inertia, existing physical and data‑driven models struggle with parameter acquisition, interpretability, and data scarcity. This paper proposes VB‑NET, a physics‑constrained gray‑box deep learning framework that transforms complex AC thermodynamics into a standardized Virtual Battery (VB) model. We first mathematically prove the isomorphic equivalence between the AC and VB models. Subsequently, VB‑NET is designed to strictly enforces physical laws by decoupling shared meteorological drivers from private building thermal fingerprints and embedding a differentiable physics layer. Experimental results demonstrate that VB‑NET significantly outperforms conventional black‑box models in state of charge tracking while successfully recovering underlying thermodynamic laws to yield physically consistent parameters. Furthermore, utilizing multi‑task learning and terminal sensitivity modulation, VB‑NET overcomes the cold‑start dilemma, achieving high‑precision modeling for new AC units using only 2% to 6% of historical data. Ultimately, this study provides an interpretable and data‑efficient pathway for aggregating decentralized AC resources for grid regulation.

Authors:Luigi Romano, Ole Morten Aamo, Jan Åslund, Erik Frisk
Title: Inverse-dynamics observer design for a linear single-track vehicle model with distributed tire dynamics
Abstract:
Accurate estimation of the vehicle's sideslip angle and tire forces is essential for enhancing safety and handling performances in unknown driving scenarios. To this end, the present paper proposes an innovative observer that combines a linear single‑track model with a distributed representation of the tires and information collected from standard sensors. In particular, by adopting a comprehensive representation of the tires in terms of hyperbolic partial differential equations (PDEs), the proposed estimation strategy exploits dynamical inversion to reconstruct the lumped and distributed vehicle states solely from yaw rate and lateral acceleration measurements. Simulation results demonstrate the effectiveness of the observer in estimating the sideslip angle and tire forces even in the presence of noise and model uncertainties.

Authors:Fen Wu
Title: IQC-Based Output-Feedback Control of LPV Systems with Time-Varying Input Delays
Abstract:
Input delays are a common source of performance degradation and instability in control systems. This paper addresses the \mathcalH_\infty output‑feedback control problem for LPV systems with time‑varying input delays under the integral quadratic constraint (IQC) framework. By integrating parameter‑dependent Lyapunov functions with dynamic IQC multipliers, we derive convex, delay‑dependent synthesis conditions formulated as parameter‑dependent LMIs, enabled by the proposed exact‑memory controller structure. An explicit controller reconstruction formula is provided to recover the LPV controller from the LMI solution, avoiding the need to specify the functional form of the parameter‑dependent controller gains. While the synthesis problem for memoryless control is inherently non‑convex, the proposed approach demonstrates significant performance improvement, reduced conservatism, and computational efficiency for standard output‑feedback design. Numerical examples illustrate the effectiveness and broad applicability of the method to LPV systems with time‑varying input delays.

Authors:Yi Tian, Kaiqing Zhang, Russ Tedrake, Suvrit Sra
Title: Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part II
Abstract:
We study the problem of state representation learning for control from partial and potentially high‑dimensional observations. We approach this problem via cost‑driven state representation learning, in which we learn a dynamical model in a latent state space by predicting cumulative costs. In particular, we establish finite‑sample guarantees on finding a near‑optimal representation function and a near‑optimal controller using the learned latent model for infinite‑horizon time‑invariant Linear Quadratic Gaussian (LQG) control. We study two approaches to cost‑driven representation learning, which differ in whether the transition function of the latent state is learned explicitly or implicitly. The first approach has also been investigated in Part I of this work, for finite‑horizon time‑varying LQG control. The second approach closely resembles MuZero, a recent breakthrough in empirical reinforcement learning, in that it learns latent dynamics implicitly by predicting cumulative costs. A key technical contribution of this Part II is to prove persistency of excitation for a new stochastic process that arises from the analysis of quadratic regression in our approach, and may be of independent interest.

Authors:Kenechi Omeke, Attai Abubakar, Michael Mollel, Lei Zhang, Qammer H. Abbasi, Muhammad Ali Imran
Title: Machine Learning for the Internet of Underwater Things: From Fundamentals to Implementation
Abstract:
The Internet of Underwater Things (IoUT) is becoming a critical infrastructure for ocean observation, marine resource management, and climate science. Its development is hindered by severe acoustic attenuation, propagation delays far exceeding those of terrestrial wireless systems, strict energy constraints, and dynamic topologies shaped by ocean currents. Machine learning (ML) has emerged as a key enabler for addressing these limitations, offering data driven mechanisms that enhance performance across all layers of underwater wireless sensor networks. This tutorial survey synthesises ML methodologies supervised, unsupervised, reinforcement, and deep learning specifically contextualised for underwater communication environments. It outlines the algorithmic principles of each paradigm and examines the conditions under which particular approaches deliver superior performance. A layer wise analysis highlights physical layer gains in localisation and channel estimation, MAC layer adaptations that improve channel utilisation, network layer routing strategies that extend operational lifetime, and transport layer mechanisms capable of reducing packet loss by up to 91 percent. At the application layer, ML enables substantial data compression and object detection accuracies reaching 92 percent. Drawing on 300 studies from 2012 to 2025, the survey documents energy efficiency gains of 7 to 29 times, throughput improvements over traditional protocols, and cross layer optimisation benefits of up to 42 percent. It also identifies persistent barriers, including limited datasets, computational constraints, and the gap between theoretical models and real world deployment. The survey concludes with emerging research directions and a technology roadmap supporting ML adoption in operational underwater networks.

Authors:Chih-Fan Pai, Xu Shang, Jiachen Qian, Yang Zheng
Title: Online Tracking with Predictions for Nonlinear Systems with Koopman Linear Embedding
Abstract:
We study the problem of online tracking in unknown nonlinear dynamical systems, where only short‑horizon predictions of future target states are available. This setting arises in practical scenarios where full future information and exact system dynamics are unavailable. We focus on a class of nonlinear systems that admit a Koopman linear embedding, enabling the dynamics to evolve linearly in a lifted space. Exploiting this structure, we analyze a model‑free predictive tracking algorithm based on Willems' fundamental lemma, which imposes dynamic constraints using only past data within a receding‑horizon control framework. We show that, for Koopman‑linearizable systems, the cumulative cost and dynamic regret of the nonlinear tracking problem coincide with those of the lifted linear counterpart. Moreover, we prove that the dynamic regret of our algorithm decays exponentially with the prediction horizon, as validated by numerical experiments.

Authors:Jingzehua Xu, Guanwen Xie, Jiwei Tang, Shuai Zhang, Xiaofan Li
Title: Underwater Embodied Intelligence for Autonomous Robots: A Constraint-Coupled Perspective on Planning, Control, and Deployment
Abstract:
Autonomous underwater robots are increasingly deployed for environmental monitoring, infrastructure inspection, subsea resource exploration, and long‑horizon exploration. Yet, despite rapid advances in learning‑based planning and control, reliable autonomy in real ocean environments remains fundamentally constrained by tightly coupled physical limits. Hydrodynamic uncertainty, partial observability, bandwidth‑limited communication, and energy scarcity are not independent challenges; they interact within the closed perception‑planning‑control loop and often amplify one another over time. This Review develops a constraint‑coupled perspective on underwater embodied intelligence, arguing that planning and control must be understood within tightly coupled sensing, communication, coordination, and resource constraints in real ocean environments. We synthesize recent progress in reinforcement learning, belief‑aware planning, hybrid control, multi‑robot coordination, and foundation‑model integration through this embodied perspective. Across representative application domains, we show how environmental monitoring, inspection, exploration, and cooperative missions expose distinct stress profiles of cross‑layer coupling. To unify these observations, we introduce a cross‑layer failure taxonomy spanning epistemic, dynamic, and coordination breakdowns, and analyze how errors cascade across autonomy layers under uncertainty. Building on this structure, we outline research directions toward physics‑grounded world models, certifiable learning‑enabled control, communication‑aware coordination, and deployment‑aware system design. By internalizing constraint coupling rather than treating it as an external disturbance, underwater embodied intelligence may evolve from performance‑driven adaptation toward resilient, scalable, and verifiable autonomy under real ocean conditions.

Authors:Haiyuan Li, Yulei Wu, Dimitra Simeonidou
Title: Multi-Agentic AI for Conflict-Aware rApp Policy Orchestration in Open RAN
Abstract:
Open Radio Access Network (RAN) enables flexible, AI‑driven control of mobile networks through disaggregated, multi‑vendor components. In this architecture, xApps handle real‑time functions, whereas rApps in the non‑real‑time controller generate strategic policies. However, current rApp development remains largely manual, brittle, and poorly scalable as xApp diversity proliferates. In this work, we propose a Multi‑Agentic AI framework to automate rApp policy generation and orchestration. The architecture integrates three specialized large language model (LLM)‑based agents, Perception, Reasoning, and Refinement, supported by retrieval‑augmented generation (RAG) and memory‑based analogical reasoning. These agents collectively analyze potential conflicts, synthesize intent‑aligned control pipelines, and incrementally refine deployment decisions. Experiments across diverse deployment scenarios demonstrate that the proposed system achieves over 70% improvement in deployment accuracy and 95% reduction in reasoning cost compared to baseline methods, while maintaining zero‑shot generalization to unseen intents. These results establish a scalable and conflict‑aware solution for fully autonomous, zero‑touch rApp orchestration in Open RAN.

Authors:Alessandra Parisio
Title: Towards Network-Aware Operation of Integrated Energy Systems: A Comprehensive Review
Abstract:
Integrated Energy Systems (IES) are systems of interconnected electricity, gas, heating, and cooling networks, where the carriers interact and depend on one another. Beyond these core vectors, IES may also incorporate additional infrastructures, such as hydrogen, transportation and water networks, whenever sector coupling or cross‑vector exchanges are relevant. Although modern cities already function as multi‑energy systems, these networks are still planned and operated in isolation, which leads to inefficiencies and unused flexibility. As distributed energy resources (DERs) grow, local coupling among electricity, heating, and gas networks becomes stronger, so coordinated operation across carriers and infrastructures is essential. IES can improve efficiency, flexibility, and renewable integration, yet operation is challenging because of complex interdependencies, non‑convex behaviors, and multi‑scale dynamics of the energy networks. A key point that the literature often overlooks is the explicit role of network constraints and topology, which shape feasible operating regions, affect scalability, and determine how uncertainty and formal guarantees can be addressed. This review provides a first comprehensive analysis of network‑aware modeling, optimization, and control methods for IES. We identify methodological limitations related to tractability, feasibility guarantees, and scalability. Building on these insights, we outline research directions that include distributed optimization with theoretical guarantees and control approaches informed by operational data. The review offers a foundation for scalable, network‑aware operational frameworks for future low‑carbon energy systems.

Authors:Jingbo Wang, Roshni Anna Jacob, Harshal D. Kaushik, Jie Zhang
Title: Reinforcement Learning for Vehicle-to-Grid Voltage Regulation: Single-Hub to Multi-Hub Coordination with Battery-Aware Constraints
Abstract:
This paper presents a Vehicle‑to‑Grid (V2G) coordination framework using reinforcement learning (RL). An intelligent control strategy based on the soft actor‑critic algorithm is developed for voltage regulation through single and multi‑hub charging systems while respecting realistic fleet constraints. A two‑phase training approach integrates stability‑focused learning with battery‑aware deployment to ensure practical feasibility. Simulation studies on the IEEE 34‑bus system validate the framework against a standard Volt‑Var/Volt‑Watt droop controller. Results indicate that the RL agent achieves performance comparable to the baseline control strategy in nominal scenarios. Under aggressive overloading, it provides robust voltage recovery (within 10% of the baseline) while prioritizing fleet availability and state‑of‑charge preservation, demonstrating the viability of constraint‑aware learning for critical grid services.

Authors:Soulaimane Berkane
Title: Tutorial on Aided Inertial Navigation Systems: A Modern Treatment Using Lie-Group Theoretical Methods
Abstract:
This tutorial presents a control‑oriented introduction to aided inertial navigation systems using a Lie‑group formulation centered on the extended Special Euclidean group SE_2(3). The focus is on developing a clear and implementation‑oriented geometric framework for fusing inertial measurements with aiding information, while making the role of invariance and symmetry explicit. Recent extensions, including higher‑order state representations, synchronous observer designs, and equivariant filtering methods, are discussed as natural continuations of the same underlying principles. The goal is to provide readers with a coherent system‑theoretic perspective that supports both understanding and practical use of modern aided inertial navigation methods.

Authors:Ziyi Zhou, Pengyuan Shu, Ruize Cao, Yuntian Zhao, Ye Zhao
Title: ACLM: ADMM-Based Distributed Model Predictive Control for Collaborative Loco-Manipulation
Abstract:
Collaborative transportation of heavy payloads via loco‑manipulation is a challenging yet essential capability for legged robots operating in complex, unstructured environments. Centralized planning methods, e.g., holistic trajectory optimization, capture dynamic coupling among robots and payloads but scale poorly with system size, limiting real‑time applicability. In contrast, hierarchical and fully decentralized approaches often neglect force and dynamic interactions, leading to conservative behavior. This study proposes an Alternating Direction Method of Multipliers (ADMM)‑based distributed model predictive control framework for collaborative loco‑manipulation with a team of quadruped robots with manipulators. By exploiting the payload‑induced coupling structure, the global optimal control problem is decomposed into parallel individual‑robot‑level subproblems with consensus constraints. The distributed planner operates in a receding‑horizon fashion and achieves fast convergence, requiring only a few ADMM iterations per planning cycle. A wrench‑aware whole‑body controller executes the planned trajectories, tracking both motion and interaction wrenches. Extensive simulations with up to four robots demonstrate scalability, real‑time performance, and robustness to model uncertainty.

Authors:Rihan Aaron D'Silva, Hiroyasu Tsukamoto
Title: Statistical Contraction for Chance-Constrained Trajectory Optimization of Non-Gaussian Stochastic Systems
Abstract:
This paper presents novel method for distribution‑free robust trajectory optimization and control of discrete‑time, nonlinear, and non‑Gaussian stochastic systems, with closed‑loop guarantees on chance constraint satisfaction. Our framework employs conformal inference to generate coverage‑based confidence sets for the closed‑loop dynamics around arbitrary reference trajectories, by constructing a joint nonconformity score to quantify both the validity of contraction (i.e., incremental stability) conditions and the impact of external stochastic disturbance on the closed‑loop dynamics, without any distributional assumptions. Via appropriate constraint tightening, chance constraints can be reformulated into tractable, statistically valid deterministic constraints on the reference trajectories. This enables a formal pathway to leverage and validate learning‑based motion planners and controllers, such as those with neural contraction metrics, in safety‑critical real‑world applications. Notably, our statistical guarantees are non‑diverging and can be computed with finite samples of the underlying uncertainty, without overly conservative structural priors. We demonstrate our approach in motion planning problems for designing safe, dynamically feasible trajectories in both numerical simulation and hardware experiments.

Authors:Hochul Hwang, Soowan Yang, Anh N. H. Nguyen, Parth Goel, Krisha Adhikari, Sunghoon I. Lee, Joydeep Biswas, Nicholas A. Giudice, Donghyun Kim
Title: GuideTWSI: A Diverse Tactile Walking Surface Indicator Dataset from Synthetic and Real-World Images for Blind and Low-Vision Navigation
Abstract:
Tactile Walking Surface Indicators (TWSIs) are safety‑critical landmarks that blind and low‑vision (BLV) pedestrians use to locate crossings and hazard zones. From our observation sessions with BLV guide dog handlers, trainers, and an O&M specialist, we confirmed the critical importance of reliable and accurate TWSI segmentation for navigation assistance of BLV individuals. Achieving such reliability requires large‑scale annotated data. However, TWSIs are severely underrepresented in existing urban perception datasets, and even existing dedicated paving datasets are limited: they lack robot‑relevant viewpoints (e.g., egocentric or top‑down) and are geographically biased toward East Asian directional bars ‑ raised parallel strips used for continuous guidance along sidewalks. This narrow focus overlooks truncated domes ‑ rows of round bumps used primarily in North America and Europe as detectable warnings at curbs, crossings, and platform edges. As a result, models trained only on bar‑centric data struggle to generalize to dome‑based warnings, leading to missed detections and false stops in safety‑critical environments.

Authors:Ishrat Jahan Eliza, Xuan Huang, Aashish Panta, Alper Sahistan, Zhimin Li, Amy A. Gooch, Valerio Pascucci
Title: Animating Petascale Time-varying Data on Commodity Hardware with LLM-assisted Scripting
Abstract:
Scientists face significant visualization challenges as time‑varying datasets grow in speed and volume, often requiring specialized infrastructure and expertise to handle massive datasets. Petascale climate models generated in NASA laboratories require a dedicated group of graphics and media experts and access to high‑performance computing resources. Scientists may need to share scientific results with the community iteratively and quickly. However, the time‑consuming trial‑and‑error process incurs significant data transfer overhead and far exceeds the time and resources allocated for typical post‑analysis visualization tasks, disrupting the production workflow. Our paper introduces a user‑friendly framework for creating 3D animations of petascale, time‑varying data on a commodity workstation. Our contributions: (i) Generalized Animation Descriptor (GAD) with a keyframe‑based adaptable abstraction for animation, (ii) efficient data access from cloud‑hosted repositories to reduce data management overhead, (iii) tailored rendering system, and (iv) an LLM‑assisted conversational interface as a scripting module to allow domain scientists with no visualization expertise to create animations of their region of interest. We demonstrate the framework's effectiveness with two case studies: first, by generating animations in which sampling criteria are specified based on prior knowledge, and second, by generating AI‑assisted animations in which sampling parameters are derived from natural‑language user prompts. In all cases, we use large‑scale NASA climate‑oceanographic datasets that exceed 1PB in size yet achieve a fast turnaround time of 1 minute to 2 hours. Users can generate a rough draft of the animation within minutes, then seamlessly incorporate as much high‑resolution data as needed for the final version.

Authors:Biswas Rudra Jyoti Arka, Md Zahidul Islam, Yuzhang Lin, Vinod M. Vokkarane, Junbo Zhao
Title: Communication Network-Aware Missing Data Recovery for Enhanced Distribution Grid Visibility
Abstract:
Power distribution systems increasingly rely on dense sensor networks for real‑time monitoring, yet unreliable communication links and equipment malfunctions often result in missing or incomplete measurement sets at the operating center, requiring accurate data recovery techniques. Most existing approaches operate solely on the available measurements and overlook the role of the communication network that delivers sensor data, leading to large, spatially correlated losses when multiple sensors share failing communication links. This paper proposes a communication‑aware framework that integrates routing constraints with low‑rank matrix completion to improve data recovery accuracy under communication failures. Sensors are grouped into balanced clusters, and routing paths are designed to limit intracluster sensors sharing a common communication path, preventing complete data loss within any cluster. The remaining measurements for each cluster are then recovered using an optimal singular value thresholding (OSVT) method. Simulation results on the IEEE standard test feeder with real‑world data demonstrate that the proposed framework significantly improves recovery accuracy compared to communication‑agnostic, measurement‑only methods.

Authors:Roshni Anna Jacob, Prithvi Poddar, Jaidev Goel, Souma Chowdhury, Yulia R. Gel, Jie Zhang
Title: Topology-Aware Reinforcement Learning over Graphs for Resilient Power Distribution Networks
Abstract:
Extreme weather events and cyberattacks can cause component failures and disrupt the operation of power distribution networks (DNs), during which reconfiguration and load shedding are often adopted for resilience enhancement. This study introduces a topology‑aware graph reinforcement learning (RL) framework for outage management that embeds higher‑order topological features of the DN into a graph‑based RL model, enabling reconfiguration and load shedding to maximize energy supply while maintaining operational stability. Results on the modified IEEE 123‑bus feeder across 300 diverse outage scenarios demonstrate that incorporating the topological data analysis (TDA) tool, persistence homology (PH), yields 9‑18% higher cumulative rewards, up to 6% increase in power delivery, and 6‑8% fewer voltage violations compared to a baseline graph‑RL model. These findings highlight the potential of integrating RL with TDA to enable self‑healing in DNs, facilitating fast, adaptive, and automated restoration.

Authors:Nanhong Liu, Jingyi Yan, Mucun Sun, Jie Zhang
Title: A SISA-based Machine Unlearning Framework for Power Transformer Inter-Turn Short-Circuit Fault Localization
Abstract:
In practical data‑driven applications on electrical equipment fault diagnosis, training data can be poisoned by sensor failures, which can severely degrade the performance of machine learning (ML) models. However, once the ML model has been trained, removing the influence of such harmful data is challenging, as full retraining is both computationally intensive and time‑consuming. To address this challenge, this paper proposes a SISA (Sharded, Isolated, Sliced, and Aggregated)‑based machine unlearning (MU) framework for power transformer inter‑turn short‑circuit fault (ITSCF) localization. The SISA method partitions the training data into shards and slices, ensuring that the influence of each data point is isolated within specific constituent models through independent training. When poisoned data are detected, only the affected shards are retrained, avoiding retraining the entire model from scratch. Experiments on simulated ITSCF conditions demonstrate that the proposed framework achieves almost identical diagnostic accuracy to full retraining, while reducing retraining time significantly.

Authors:Eric Foss, Andrew Tai, Carlo Bosio, Mark W. Mueller
Title: Energy-Efficient Collaborative Transport of Tether-Suspended Payloads via Rotating Equilibrium
Abstract:
Collaborative aerial transportation of tethered payloads is fundamentally limited by space, power, and weight constraints. Conventional approaches rely on static equilibrium conditions, where each vehicle tilts to generate the forces that ensure they maintain a formation geometry that avoids aerodynamic interactions and collision. This horizontal thrust component represents a significant energy penalty compared to the ideal case in which each vehicle produces purely vertical thrust to lift the payload. Operating in tighter tether configurations can minimize this effect, but at the cost of either having to fly the vehicles in closer proximity, which risks collision, or significantly increasing the length of the tether, which increases complexity and reduces potential use‑cases. We propose operating the tether‑suspended flying system at a rotating equilibrium. By maintaining steady circular motion, centrifugal forces provide the necessary horizontal tether tension, allowing each quadrotor to generate purely vertical thrust and thus reducing the total force (and power) required compared to an equilibrium where the thrusts are not vertical. It also allows for a wider range of tether configurations to be used without sacrificing efficiency. Results demonstrate that rotating equilibria can reduce power consumption relative to static lifting by up to 20%, making collaborative aerial solutions more practically relevant.

Authors:Taekyung Kim
Title: Is Your Safe Controller Actually Safe? A Critical Review of CBF Tautologies and Hidden Assumptions
Abstract:
This tutorial provides a critical review of the practical application of Control Barrier Functions (CBFs) in robotic safety. While the theoretical foundations of CBFs are well‑established, I identify a recurring gap between the mathematical assumption of a safe controller's existence and its constructive realization in systems with input constraints. I highlight the distinction between candidate and valid CBFs by analyzing the interplay of system dynamics, actuation limits, and class‑K functions. I further show that some purported demonstrations of safe robot policies or controllers are limited to passively safe systems, such as single integrators or kinematic manipulators, where safety is already inherited from the underlying physics and even naive geometric hard constraints suffice to prevent collisions. By revisiting simple low‑dimensional examples, I show when CBF formulations provide valid safety guarantees and when they fail due to common misuses. I then provide practical guidelines for constructing realizable safety arguments for systems without such passive safety. The goal of this tutorial is to bridge the gap between theoretical guarantees and actual implementation, supported by an open‑source interactive web demonstration that visualizes these concepts intuitively.

Authors:Kaustav Goswami, Sean Peisert, Venkatesh Akella, Jason Lowe-Power
Title: Space-Control: Process-Level Isolation for Sharing CXL-based Disaggregated Memory
Abstract:
Memory disaggregation via CXL enables multi‑host resource sharing. However, existing CXL sharing mechanisms enforce coarse‑grained, host‑level permissions only, leaving isolation to the operating system. Today, virtual memory enables process‑level isolation on a host and CXL enables host‑level isolation. This creates a critical security gap: the absence of process‑level memory isolation in shared disaggregated memory. We present Space‑Control, an architectural abstraction that introduces a cross‑host identity primitive to enforce confidentiality and integrity. We decouple authorization from the untrusted OS using a hardware‑rooted validation engine (SPACE) to establish immutable process identity and a Permission Checker at the memory egress point for fine‑grained permission validation. Our design supports 127 concurrent processes across 255 hosts with only 1.56% storage overhead. Cycle‑level evaluation using gem5 + SST shows that Space‑Control incurs a minimal 3.3% performance penalty with a modest 16 KiB cache, providing a practical and scalable foundation for secure, process‑level memory disaggregation.

Authors:Marco Iorio, Mohammad Golgol, Anamitra Pal
Title: Impact of Work Schedule Flexibility on EV Hosting Capacity: Insights from Analyzing Field Data
Abstract:
Uncoordinated electric vehicle (EV) charging is altering residential load patterns and pushing distribution transformers to operate beyond their limits. These outcomes can be offset by exploiting the flexibility in work schedules (hybrid, remote vs. in‑person) of EV owners, particularly when combined with rooftop photovoltaic (PV) generation. However, this phenomenon has not been explored in‑depth yet. This paper addresses this research gap by introducing weekly work schedule‑aware robust and chance‑constrained optimization formulations for EV charging coordination to determine a transformer's EV hosting capacity. The results obtained using data from a residential feeder in Arizona indicate that an intelligent combination of work schedule flexibility with PV generation can help power utilities effectively manage changing grid demands.

Authors:Hussein N. Naser, Hashim A. Hashim, Mojtaba Ahmadi
Title: Adaptive Gain Nonlinear Observer for External Wrench Estimation in Human-UAV Physical Interaction
Abstract:
This paper presents an Adaptive Gain Nonlinear Observer (AGNO) for estimating the external interaction wrench (forces and torques) in human‑UAV physical interaction for assistive payload transportation. The proposed AGNO uses the full nonlinear dynamic model to achieve an accurate and robust wrench estimation without relying on dedicated force‑torque sensors. A key feature of this approach is the explicit consideration of the non‑constant inertia matrix, which is essential for aerial systems with asymmetric mass distribution or shifting payloads. A comprehensive dynamic model of a cooperative transportation system composed of two quadrotors and a shared payload is derived, and the stability of the observer is rigorously established using Lyapunov‑based analysis. Simulation results validate the effectiveness of the proposed observer in enabling intuitive and safe human‑UAV interaction. Comparative evaluations demonstrate that the proposed AGNO outperforms an Extended Kalman Filter (EKF) in terms of estimation root mean square errors (RMSE), particularly for torque estimation under nonlinear interaction conditions. This approach reduces system weight and cost by eliminating additional sensing hardware, enhancing practical feasibility.

Authors:Bojan Derajić, Sebastian Bernhard, Wolfgang Hönig
Title: CN-CBF: Composite Neural Control Barrier Function for Safe Robot Navigation in Dynamic Environments
Abstract:
Safe navigation of autonomous robots remains one of the core challenges in the field, especially in dynamic and uncertain environments. One of the prevalent approaches is safety filtering based on control barrier functions (CBFs), which are easy to deploy but difficult to design. Motivated by the shortcomings of existing learning‑ and model‑based methods, we propose a simple yet effective neural CBF design method for safe robot navigation in dynamic environments. We employ the idea of a composite CBF, where multiple neural CBFs are combined into a single CBF. The individual CBFs are trained via the Hamilton‑Jacobi reachability framework to approximate the optimal safe set for single moving obstacles. Additionally, we use the residual neural architecture, which guarantees that the estimated safe set does not intersect with the corresponding failure set. The method is extensively evaluated in simulation experiments for a ground robot and a quadrotor, comparing it against several baseline methods. The results show improved success rates of up to 18% compared to the best baseline, without increasing the conservativeness of the motion. Also, the method is demonstrated in hardware experiments for both types of robots.

Authors:Marjorie Hoegen, René Glebke, M. Sahnawaz Alam, Alessandro David, Juan Navarro Arenas, Nikolaus Wirtz, Mario Albanese, Daniele Carta, Felix Motzoi, Antonello Monti, Carsten Schuck, Andrea Benigni, Klaus Wehrle, Ferdinanda Ponci
Title: Quantum Technologies and Edge Devices in Electrical Grids: Opportunities, Challenges, and Future Directions
Abstract:
In modern power systems, edge devices serve as local hubs that collect data, perform on‑site computing, sense electrical parameters, execute control actions, and communicate with neighboring edge devices as part of the larger grid. However, as the number of monitored nodes and control loops grows, traditional edge devices face serious limits. They can become overloaded by complex signal processing and decision tasks, causing delays and higher energy use. Standard sensors hit a noise floor that prevents them from detecting miniature changes, making it harder to spot early signs of faults or instability. Meanwhile, conventional communication links struggle with bandwidth limits, security risks, and rising encryption demands, which together slow down and weaken the transfer of critical grid information. Quantum technologies have the potential to overcome these challenges. Quantum computers can deliver exponential speed‑ups for optimization and machine‑learning tasks that ordinary processors cannot handle. Quantum sensors can sense signals with atomic precision, giving edge devices a more precise view of grid dynamics. Quantum communication techniques, including quantum key distribution, offer methods to achieve information‑theoretic security and ensure that information arrives quickly and without tampering. We explore how quantum technologies can be integrated into edge devices, highlighting both opportunities and challenges.

Authors:Edward Morgan, Nenyi K Dadson, Corina Barbalata
Title: Uncertainty-Aware Adaptive Dynamics For Underwater Vehicle-Manipulator Robots
Abstract:
Accurate and adaptive dynamic models are critical for underwater vehicle‑manipulator systems where hydrodynamic effects induce time‑varying parameters. This paper introduces a novel uncertainty‑aware adaptive dynamics model framework that remains linear in lumped vehicle and manipulator parameters, and embeds convex physical consistency constraints during online estimation. Moving horizon estimation is used to stack horizon regressors, enforce realizable inertia, damping, friction, and hydrostatics, and quantify uncertainty from parameter evolution. Experiments on a BlueROV2 Heavy with a 4‑DOF manipulator demonstrate rapid convergence and calibrated predictions. Manipulator fits achieve R2 = 0.88 to 0.98 with slopes near unity, while vehicle surge, heave, and roll are reproduced with good fidelity under stronger coupling and noise. Median solver time is approximately 0.023 s per update, confirming online feasibility. A comparison against a fixed parameter model shows consistent reductions in MAE and RMSE across degrees of freedom. Results indicate physically plausible parameters and confidence intervals with near 100% coverage, enabling reliable feedforward control and simulation in underwater environments.

Authors:Krishan Kumar Tiwari, Giuseppe Caire
Title: Codebook Design and Baseband Precoding for Pragmatic Array-Fed RIS Hybrid Multiuser MIMO
Abstract:
In our previous work [2], we introduced a hardware‑ and power‑efficient architecture for hybrid digital‑analog (HDA) multiuser MIMO (MU‑MIMO) based on stacking identical basic modules. Each module consists of a small active multi‑antenna feeder (AMAF) placed in the near field of a larger reflective intelligent surface (RIS). Each AMAF is driven by one RF chain and conveys one spatial stream, achieving a multiplexing gain of K with K stacked modules. While [2] focused on module design and efficiency compared to active arrays, performance was evaluated only under pure line‑of‑sight (LOS) conditions. This work extends our approach in several ways. First, we propose a simple, pragmatic method for designing phase‑only flat‑top beams for the AMAF‑RIS module, enabling wide angular coverage with low ripple and sidelobes. This design supports hierarchical beamforming codebooks for efficient beam acquisition. Second, we evaluate MU‑MIMO performance under realistic mmWave multipath channels including both LOS and non‑LOS (NLOS) components modeled using a 3D von Mises‑Fisher distribution. We propose a low‑complexity HDA MU‑MIMO framework with: user‑beam association via standard beam acquisition; dynamic user grouping (one user per beam); effective baseband MIMO channel estimation using 3GPP‑compliant pilots; and downlink transmission with zero‑forcing precoding under per‑antenna power constraints. Results show high spectral efficiency and multiplexing gain while preserving hardware simplicity and power efficiency. Crucially, the approach is fully compliant with 3GPP 5GNR beam acquisition and sounding reference signaling mechanisms.

Authors:Yifan Xie, Julian Berberich, Frank Allgöwer
Title: Adaptive Data-Driven Min-Max MPC for Linear Time-Varying Systems
Abstract:
In this paper, we propose an adaptive data‑driven min‑max model predictive control (MPC) scheme for discrete‑time linear time‑varying (LTV) systems. We assume that prior knowledge of the system dynamics and bounds on the variations are known, and that the states are measured online. Starting from an initial state‑feedback gain derived from prior knowledge, the algorithm updates the state‑feedback gain using online input‑state data. To this end, a semidefinite program (SDP) is solved to minimize an upper bound on the infinite‑horizon optimal cost and to derive a corresponding state‑feedback gain. We prove that the resulting closed‑loop system is exponentially stabilized and satisfies the constraints. Further, we extend the proposed scheme to LTV systems with process noise. The resulting closed‑loop system is shown to be robustly stabilized to a robust positive invariant (RPI) set. Finally, the proposed methods are demonstrated by numerical simulations.

Authors:Ömür Arslan, Nikolay Atanasov
Title: Control Barrier Corridors: From Safety Functions to Safe Sets
Abstract:
Safe autonomy is a critical requirement and a key enabler for robots to operate safely in unstructured complex environments. Control barrier functions and safe motion corridors are two widely used but technically distinct safety methods, functional and geometric, respectively, for safe motion planning and control. Control barrier functions are applied to the safety filtering of control inputs to limit the decay rate of system safety, whereas safe motion corridors are geometrically constructed to define a local safe zone around the system state for use in motion optimization and reference‑governor design. This paper introduces a new notion of control barrier corridors, which unifies these two approaches by converting control barrier functions into local safe goal regions for reference goal selection in feedback control systems. We show, with examples on fully actuated systems, kinematic unicycles, and linear output regulation systems, that individual state safety can be extended locally over control barrier corridors for convex barrier functions, provided the control convergence rate matches the barrier decay rate, highlighting a trade‑off between safety and reactiveness. Such safe control barrier corridors enable safely reachable persistent goal selection over continuously changing barrier corridors during system motion, which we demonstrate for verifiably safe and persistent path following in autonomous exploration of unknown environments.

Authors:Mohammadhossein Ghahramani, Mengchu Zhou
Title: CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing
Abstract:
Accurate fault detection in high‑dimensional industrial environments remains a major challenge due to the inherent complexity, noise, and redundancy in sensor data. This paper introduces CLAIRE, i.e., a hybrid end‑to‑end learning framework that integrates unsupervised deep representation learning with supervised classification for intelligent quality control in smart manufacturing systems. It employs an optimized deep autoencoder to transform raw input into a compact latent space, effectively capturing the intrinsic data structure while suppressing irrelevant or noisy features. The learned representations are then fed into a downstream classifier to perform binary fault prediction. Experimental results on a high‑dimensional dataset demonstrate that CLAIRE significantly outperforms conventional classifiers trained directly on raw features. Moreover, the framework incorporates a post hoc phase, using a game‑theory‑based interpretability technique, to analyze the latent space and identify the most informative input features contributing to fault predictions. The proposed framework highlights the potential of integrating explainable AI with feature‑aware regularization for robust fault detection. The modular and interpretable nature of the proposed framework makes it highly adaptable, offering promising applications in other domains characterized by complex, high‑dimensional data, such as healthcare, finance, and environmental monitoring.

Authors:Yaojun Li, Yulong Yang, Christine Allen-Blanchette
Title: Frequency-Separable Hamiltonian Neural Network for Multi-Timescale Dynamics
Abstract:
While Hamiltonian mechanics provides a powerful inductive bias for neural networks modeling dynamical systems, Hamiltonian Neural Networks and their variants often fail to capture complex temporal dynamics spanning multiple timescales. This limitation is commonly linked to the spectral bias of deep neural networks, which favors learning low‑frequency, slow‑varying dynamics. Prior approaches have sought to address this issue through symplectic integration schemes that enforce energy conservation or by incorporating geometric constraints to impose structure on the configuration‑space. However, such methods either remain limited in their ability to fully capture multiscale dynamics or require substantial domain specific assumptions. In this work, we exploit the observation that Hamiltonian functions admit decompositions into explicit fast and slow modes and can be reconstructed from these components. We introduce the Frequency‑Separable Hamiltonian Neural Network (FS‑HNN), which parameterizes the system Hamiltonian using multiple networks, each governed by Hamiltonian dynamics and trained on data sampled at distinct timescales. We further extend this framework to partial differential equations by learning a state‑ and boundary‑conditioned symplectic operators. Empirically, we show that FS‑HNN improves long‑horizon extrapolation performance on challenging dynamical systems and generalizes across a broad range of ODE and PDE problems.

Authors:Simon Arberet, Riqiang Gao, Martin Kraus, Florin C. Ghesu, Wilko Verbakel, Mamadou Diallo, Anthony Magliari, Venkatesan Karuppusamy, Sushil Beriwal, REQUITE Consortium, Ali Kamen, Dorin Comaniciu
Title: AI End-to-End Radiation Treatment Planning Under One Second
Abstract:
Artificial intelligence‑based radiation therapy (RT) planning has the potential to reduce planning time and inter‑planner variability, improving efficiency and consistency in clinical workflows. Most existing automated approaches rely on multiple dose evaluations and corrections, resulting in plan generation times of several minutes. We introduce AIRT (Artificial Intelligence‑based Radiotherapy), an end‑to‑end deep‑learning framework that directly infers deliverable treatment plans from CT images and structure contours. AIRT generates single‑arc VMAT prostate plans, from imaging and anatomical inputs to leaf sequencing, in under one second on a single Nvidia A100 GPU. The framework includes a differentiable dose feedback, an adversarial fluence map shaping, and a plan generation augmentation to improve plan quality and robustness. The model was trained on more than 10,000 intact prostate cases. Non‑inferiority to RapidPlan Eclipse was demonstrated across target coverage and OAR sparing metrics. Target homogeneity (HI = 0.10 \pm 0.01) and OAR sparing were similar to reference plans when evaluated using AcurosXB. These results represent a significant step toward ultra‑fast standardized RT planning and a streamlined clinical workflow.

Authors:Vittorio Franzese
Title: Star-based Navigation in the Outer Solar System
Abstract:
This paper investigates an autonomous navigation method for spacecraft operating in the outer solar system, up to 250 AU from the Sun, using the parallactic shifts of nearby stars. These measurements enable estimation of the spacecraft trajectory while distant stars provide attitude information through conventional star‑pattern matching. Stellar observation models are developed, accounting for delta light‑time, parallax, and aberration effects. Navigation performance is assessed using two approaches: (1) a least‑squares estimator using simultaneous multi‑star measurements, and (2) a Kalman filter processing sequential single‑star observations along deep‑space trajectories. Monte Carlo simulations on trajectories representative of Voyager 1, Voyager 2, Pioneer 10, Pioneer 11, and New Horizons missions show sub‑AU position accuracies at 250 AU, and velocity accuracies better than 0.00004 AU/day, under realistic spacecraft and instrumentation uncertainties. These values correspond to relative errors below 0.4% in position and velocity with respect to the reference trajectories. Although less precise than radiometric tracking, this performance can support navigation in the outer solar system without reliance on Earth. When ground‑based navigation remains necessary, this approach can be employed during long cruising phases, lowering the number of ground contacts. The method additionally shows potential for future missions venturing farther from the Sun.

Authors:Yuchong Zhang, Yi Cao, Xianghui Cao
Title: A Dual-AoI-based Approach for Optimal Transmission Scheduling in Wireless Monitoring Systems with Random Data Arrivals
Abstract:
In Internet of Things (IoTs), the freshness of system status information is crucial for real‑time monitoring and decision‑making. This paper studies the transmission scheduling problem in wireless monitoring systems, where information freshness ‑‑ typically quantified by the Age of Information (AoI) ‑‑ is heavily constrained by limited channel resources and influenced by factors such as the randomness of data arrivals and unreliable wireless channel. Such randomness leads to asynchronous AoI evolution at local sensors and the monitoring center, rendering conventional scheduling policies that rely solely on the monitoring center's AoI inefficient. To this end, we propose a dual‑AoI model that captures asynchronous AoI dynamics and formulate the problem as minimizing a long‑term time‑average AoI function. We develop a scheduling policy based on Markov decision process (MDP) to solve the problem, and analyze the existence and monotonicity of a deterministic stationary optimal policy. Moreover, we derive a low‑complexity scheduling policy which exhibits a channel‑state‑dependent threshold structure. In addition, we establish a necessary and sufficient condition for the stability of the AoI objective. Simulation results demonstrate that the proposed policy outperforms existing approaches.

Authors:Heston Roberts, Pronoy Sarker, Sm Ashikul Islam, Min Gyu Kim
Title: Improved hopping control on slopes for small robots using spring mass modeling
Abstract:
Hopping robots often lose balance on slopes because the tilted ground creates unwanted rotation at landing. This work analyzes that effect using a simple spring mass model and identifies how slope induced impulses destabilize the robot. To address this, we introduce two straightforward fixes, adjusting the bodys touchdown angle based on the slope and applying a small corrective torque before takeoff. Together, these steps effectively cancel the unwanted rotation caused by inclined terrain, allowing the robot to land smoothly and maintain stable hopping even on steep slopes. Moreover, the proposed method remains simple enough to implement on low cost robotic platforms without requiring complex sensing or computation. By combining this analytical model with minimal control actions, this approach provides a practical path toward reliable hopping on uneven terrain. The results from simulation confirm that even small slope aware adjustments can dramatically improve landing stability, making the technique suitable for future autonomous field robots that must navigate natural environments such as hills, rubble, and irregular outdoor landscapes.

Authors:Yoshihiko Susuki, Natsuki Katayama, Alexandre Mauroy, Igor Mezić
Title: On Koopman Resolvents and Frequency Response of Nonlinear Systems
Abstract:
This paper proposes a novel formulation of frequency response for nonlinear systems in the Koopman operator framework. This framework is a promising direction for the analysis and synthesis of systems with nonlinear dynamics based on (linear) Koopman operators. We show that the frequency response of a nonlinear plant is derived through the Laplace transform of the output of the plant, which is a generalization of the classical approach to LTI plants and is guided by the resolvent theory of Koopman operators. The response is a complex‑valued function of the driving angular frequency, allowing one to draw the so‑called Bode plots, which display the gain and phase characteristics. Sufficient conditions for the existence of the frequency response are presented for three classes of dynamics.

Authors:Johannes Autenrieb, Mark Spiller, Hyo-Sang Shin, Namhoon Cho
Title: Combinatorial Safety-Critical Coordination of Multi-Agent Systems via Mixed-Integer Responsibility Allocation and Control Barrier Functions
Abstract:
This paper presents a hybrid safety‑critical coordination architecture for multi‑agent systems operating in dense environments. While control barrier functions (CBFs) provide formal safety guarantees, decentralized implementations typically rely on ego‑centric safety filtering and may lead to redundant constraint enforcement and conservative collective behavior. To address this limitation, we introduce a combinatorial coordination layer formulated as a mixed‑integer linear program (MILP) that assigns collision‑avoidance responsibilities among agents. By explicitly distributing enforcement tasks, redundant reactions are eliminated and computational complexity is reduced. Each agent subsequently solves a reduced local quadratic program enforcing only its assigned constraints.

Authors:Mohamad H. Kazma, Ahmad F. Taha
Title: Exploring Uncertainty Propagation in Coupled Hydrologic and Hydrodynamic Systems via Distribution-Agnostic State Space Analysis
Abstract:
Accurate overland runoff and infiltration predictions are critical for effective water resources management, in particular for urban flood management. However, the inherent uncertainty in rainfall patterns, soil properties, and initial conditions makes reliable flood forecasting a challenging task. This paper presents a framework for quantifying the impact of these uncertainties on hydrologic and hydrodynamic simulations via a state space approach based on a differential algebraic equation (DAE) formulation that couples surface and subsurface constraints with the governing dynamics. Under this formulation, the complex interactions between overland flow and infiltration dynamics are captured in realtime. To account for uncertainty in inputs and parameters, the proposed framework quantifies and propagates these uncertainties through the DAE model formulation under partial measurements. The effectiveness of the approach is demonstrated through a series of numerical experiments on synthetic and real world catchments, highlighting its ability to provide probabilistic estimates of watershed state conditions while accounting for uncertainty. An important aspect of the proposed methods is that they are distribution‑agnostic, i.e., they only require covariances of uncertainty and not specific types of distributions. The proposed framework is further validated against Monte Carlo (MC) ensemble simulations while providing probabilistic state estimates for measured and unmeasured watershed states under partial gauging.

Authors:Richard Campos, Erica Fischer, Eduardo Cotilla-Sanchez
Title: Electrical Power Network Modeling Framework for Wildfire Risk and Resilience Analysis
Abstract:
The increasing intensity and frequency of wildfires are causing significant economic and societal impacts on communities through direct effects on the built environment, particularly critical infrastructure. Electrical systems can both initiate wild‑fires (grid‑to‑fire) and be damaged by wildfire exposure (fire‑to‑grid). Therefore, resilient electric systems can both limit ignitions and be hardened such that they are more robust to fire demands. Researchers have investigated wildfire mitigation strategies using traditional transmission and distribution electrical test‑system models. However, these test cases may not accurately represent realistic electrical system configurations or fuel landscapes, nor capture community impacts, particularly the social and economic effects of mitigation strategies. A wildfire‑aware modeling framework enables researchers to develop test cases that benchmark resilience and mitigation strategies while reducing reliance on overly simplistic assumptions about wildfire effects on electrical systems and communities. This study proposes a modeling framework for wildfire‑electrical system research by analyzing recent literature and identifying key dimensions as well as gaps within these dimensions. In particular, the framework considers how fire in the wildland‑urban interface propagates in space and time, how hazard‑infrastructure interactions (e.g., wind and fire) cause system‑ and component‑level damage, and how wildfire‑related power outages affect communities.

Authors:Abhinav Sharma, Zijun He, Danjue Chen
Title: Introducing the transitional autonomous vehicle lane-changing dataset: Empirical Experiments
Abstract:
Transitional autonomous vehicles (tAVs), which operate beyond SAE Level 1‑2 automation but short of full autonomy, are increasingly sharing the road with human‑driven vehicles (HDVs). As these systems interact during complex maneuvers such as lane changes, new patterns may emerge with implications for traffic stability and safety. Assessing these dynamics, particularly during mandatory lane changes, requires high‑resolution trajectory data, yet datasets capturing tAV lane‑changing behavior are scarce. This study introduces the North Carolina Transitional Autonomous Vehicle Lane‑Changing (NC‑tALC) Dataset, a high‑fidelity trajectory dataset designed to characterize tAV interactions during lane‑changing maneuvers. The dataset includes two controlled experimental series. In the first, tAV lane‑changing experiments, a tAV executes lane changes in the presence of adaptive cruise control (ACC) equipped target vehicles, enabling analysis of lane‑changing execution. In the second, tAV responding experiments, two tAVs act as followers and respond to cut‑in maneuvers initiated by another tAV, enabling analysis of follower response dynamics. The dataset contains 152 trials (72 lane‑changing and 80 responding trials) sampled at 20 Hz with centimeter‑level RTK‑GPS accuracy. The NC‑tALC dataset provides a rigorous empirical foundation for evaluating tAV decision‑making and interaction dynamics in controlled mandatory lane‑changing scenarios.

Authors:Jiachen Qian, Yang Zheng
Title: Regret Guarantees for Model-Free Cooperative Filtering under Asynchronous Observations
Abstract:
Predicting the output of a dynamical system from streaming data is fundamental to real‑time feedback control and decision‑making. We first derive an autoregressive representation that relates future local outputs to asynchronous past outputs. Building on this structure, we propose an online least‑squares algorithm to learn this autoregressive model for real‑time prediction. We then establish a regret bound of O(log^3 N) relative to the optimal model‑based predictor, which holds for marginally stable systems. Moreover, we provide a sufficient condition characterized via a symplectic matrix, under which the proposed cooperative online learning method provably outperforms the optimal model‑based predictor that relies solely on local observations. From a technical standpoint, our analysis exploits the orthogonality of the innovation process under asynchronous data structure and the persistent excitation of the Gram matrix despite delay‑induced asymmetries. Overall, these results offer both theoretical guarantees and practical algorithms for model‑free cooperative prediction with asynchronous observations, thereby enriching the theory of online learning for dynamical systems.

Authors:Max Eland, Jeyan Thiyagalingam, Dinesh Pamunuwa, Roshan Weerasekera
Title: NL2GDS: LLM-aided interface for Open Source Chip Design
Abstract:
The growing complexity of hardware design and the widening gap between high‑level specifications and register‑transfer level (RTL) implementation hinder rapid prototyping and system design. We introduce NL2GDS (Natural Language to Layout), a novel framework that leverages large language models (LLMs) to translate natural language hardware descriptions into synthesizable RTL and complete GDSII layouts via the open‑source OpenLane ASIC flow. NL2GDS employs a modular pipeline that captures informal design intent, generates HDL using multiple LLM engines and verifies them, and orchestrates automated synthesis and layout. Evaluations on ISCAS'85 and ISCAS'89 benchmark designs demonstrate up to 36% area reduction, 35% delay reduction, and 70% power savings compared to baseline designs, highlighting its potential to democratize ASIC design and accelerate hardware innovation.

Authors:Logeshwaran Vijayan
Title: Near-Optimal Low-Complexity MIMO Detection via Structured Reduced-Search Enumeration
Abstract:
Maximum‑likelihood (ML) detection in high‑order MIMO systems is computationally prohibitive due to exponential complexity in the number of transmit layers and constellation size. In this white paper, we demonstrate that for practical MIMO dimensions (up to 8x8) and modulation orders, near‑ML hard‑decision performance can be achieved using a structured reduced‑search strategy with complexity linear in constellation size. Extensive simulations over i.i.d. Rayleigh fading channels show that list sizes of 3|X| for 3x3, 4|X| for 4x4, and 8|X| for 8x8 systems closely match full ML performance, even under high channel condition numbers, |X| being the constellation size. In addition, we provide a trellis based interpretation of the method. We further discuss implications for soft LLR generation and FEC interaction.

Authors:Wenjian Hao, Yuxuan Fang, Zehui Lu, Shaoshuai Mou
Title: Accelerating Sampling-Based Control via Learned Linear Koopman Dynamics
Abstract:
This paper presents an efficient model predictive path integral (MPPI) control framework for systems with complex nonlinear dynamics. To improve the computational efficiency of classic MPPI while preserving control performance, we replace the nonlinear dynamics used for trajectory propagation with a learned linear deep Koopman operator (DKO) model, enabling faster rollout and more efficient trajectory sampling. The DKO dynamics are learned directly from interaction data, eliminating the need for analytical system models. The resulting controller, termed MPPI‑DK, is evaluated in simulation on pendulum balancing and surface vehicle navigation tasks, and validated on hardware through reference‑tracking experiments on a quadruped robot. Experimental results demonstrate that MPPI‑DK achieves control performance close to MPPI with true dynamics while substantially reducing computational cost, enabling efficient real‑time control on robotic platforms.

Authors:Liraz Mudrik, Yaakov Oshman
Title: Comprehensive Approach to Directly Addressing Estimation Delays in Stochastic Guidance
Abstract:
In realistic pursuit‑evasion scenarios, abrupt target maneuvers generate unavoidable periods of elevated uncertainty that result in estimation delays. Such delays can degrade interception performance to the point of causing a miss. Existing delayed‑information guidance laws fail to provide a complete remedy, as they typically assume constant and known delays. Moreover, in practice they are fed by filtered estimates, contrary to these laws' foundational assumptions. We present an overarching strategy for tracking and interception that explicitly accounts for time‑varying estimation delays. We first devise a guidance law that incorporates two time‑varying delays, thereby generalizing prior deterministic formulations. This law is driven by a particle‑based fixed‑lag smoother that provides it with appropriately delayed state estimates. Furthermore, using semi‑Markov modeling of the target's maneuvers, the delays are estimated in real‑time, enabling adaptive adjustment of the guidance inputs during engagement. The resulting framework consistently conjoins estimation, delay modeling, and guidance. Its effectiveness and superior robustness over existing delayed‑information guidance laws are demonstrated via an extensive Monte Carlo study.

Authors:Chengdong Wu, Sven Kirchner, Nils Purschke, Axel Torschmied, Norbert Kroth, Yinglei Song, André Schamschurko, Erik Leo Haß, Kuo-Yi Chao, Yi Zhang, Nenad Petrovic, Alois C. Knoll
Title: From Code to Road: A Vehicle-in-the-Loop and Digital Twin-Based Framework for Central Car Server Testing in Autonomous Driving
Abstract:
Simulation is one of the most essential parts in the development stage of automotive software. However, purely virtual simulations often struggle to accurately capture all real‑world factors due to limitations in modeling. To address this challenge, this work presents a test framework for automotive software on the centralized E/E architecture, which is a central car server in our case, based on Vehicle‑in‑the‑Loop (ViL) and digital twin technology. The framework couples a physical test vehicle on a dynamometer test bench with its synchronized virtual counterpart in a simulation environment. Our approach provides a safe, reproducible, realistic, and cost‑effective platform for validating autonomous driving algorithms with a centralized architecture. This test method eliminates the need to test individual physical ECUs and their communication protocols separately. In contrast to traditional ViL methods, the proposed framework runs the full autonomous driving software directly on the vehicle hardware after the simulation process, eliminating flashing and intermediate layers while enabling seamless virtual‑physical integration and accurately reflecting centralized E/E behavior. In addition, incorporating mixed testing in both simulated and physical environments reduces the need for full hardware integration during the early stages of automotive development. Experimental case studies demonstrate the effectiveness of the framework in different test scenarios. These findings highlight the potential to reduce development and integration efforts for testing autonomous driving pipelines in the future.

Authors:Saray Bakker, Martin Schonger, Tobias Löw, Javier Alonso-Mora, Sylvain Calinon
Title: Curve-Induced Dynamical Systems on Riemannian Manifolds and Lie Groups
Abstract:
Deploying robots in household environments requires safe, adaptable, and interpretable behaviors that respect the geometric structure of tasks. Often represented on Lie groups and Riemannian manifolds, this includes poses on SE(3) or symmetric positive definite matrices encoding stiffness or damping matrices. In this context, dynamical system‑based approaches offer a natural framework for generating such behavior, providing stability and convergence while remaining responsive to changes in the environment. We introduce Curve‑induced Dynamical systems on Smooth Manifolds (CDSM), a real‑time framework for constructing dynamical systems directly on Riemannian manifolds and Lie groups. The proposed approach constructs a nominal curve on the manifold, and generates a dynamical system which combines a tangential component that drives motion along the curve and a normal component that attracts the state toward the curve. We provide a stability analysis of the resulting dynamical system and validate the method quantitatively. On an S2 benchmark, CDSM demonstrates improved trajectory accuracy, reduced path deviation, and faster generation and query times compared to state‑of‑the‑art methods. Finally, we demonstrate the practical applicability of the framework on both a robotic manipulator, where poses on SE(3) and damping matrices on SPD(n) are adapted online, and a mobile manipulator.

Authors:Talitha Nauta, Richard Pates
Title: Computing Scaled Relative Graphs of Discrete-time LTI Systems from Data
Abstract:
Graphical methods for system analysis have played a central role in control theory. A recently emerging tool in this field is the Scaled Relative Graph (SRG). In this paper, we further extend its applicability by showing how the SRG of discrete‑time linear‑time‑invariant (LTI) systems can be computed exactly from its state‑space representation using linear matrix inequalities. We additionally propose a fully data‑driven approach where we demonstrate how to compute the SRG exclusively from input‑output data. Furthermore, we introduce a robust version of the SRG, which can be computed from noisy data trajectories and contains the SRG of the actual system.

Authors:Saurabh Vaishampayan, Maryam Kamgarpour
Title: Uncertainty and Autarky: Cooperative Game Theory for Stable Local Energy Market Partitioning
Abstract:
Local energy markets empower prosumers to form coalitions for energy trading. However, the optimal partitioning of the distribution grid into such coalitions remains unclear, especially in constrained grids with stochastic production and consumption. This analysis must take into account the interests of both the grid operator and the constituent prosumers. In this work, we present a cooperative game theoretic framework to study distribution grid partitioning into local energy market coalitions under uncertain prosumption and grid constraints. We formulate the optimal stable partitioning problem to balance the interests of the grid operator with that of prosumers. Under deterministic load and generation, we show that the largest market coalition is the optimal stable partition. For the case of stochastic loads and generation, we provide an algorithm to evaluate the optimal stable partition. Numerical experiments are performed on benchmark and real world distribution grids. Our results help in understanding how uncertainty affects local energy market partitioning decisions in constrained distribution grids.

Authors:Ram Milan Kumar Verma, Shashi Ranjan Kumar, Hemendra Arya
Title: Trajectory Tracking for Uncrewed Surface Vessels with Input Saturation and Dynamic Motion Constraints
Abstract:
This work addresses the problem of constrained motion control of the uncrewed surface vessels. The constraints are imposed on states/inputs of the vehicles due to the physical limitations, mission requirements, and safety considerations. We develop a nonlinear feedback controller utilizing log‑type Barrier Lyapunov Functions to enforce static and dynamic motion constraints. The proposed scheme uniquely addresses asymmetric constraints on position and heading alongside symmetric constraints on surge, sway, and yaw rates. Additionally, a smooth input saturation model is incorporated in the design to guarantee stability even under actuator bounds, which, if unaccounted for, can lead to severe performance degradation and poor tracking. Rigorous Lyapunov stability analysis shows that the closed‑loop system remains stable and that all state variables remain within their prescribed bounds at all times, provided the initial conditions also lie within those bounds. Numerical simulations demonstrate the effectiveness of the proposed strategies for surface vessels without violating the motion and actuator constraints.

Authors:Menno van Zutphen, Giannis Delimpaltadakis, Duarte J. Antunes
Title: Formal Entropy-Regularized Control of Stochastic Systems
Abstract:
Analyzing and controlling system entropy is a powerful tool for regulating predictability of control systems. Applications benefiting from such approaches range from reinforcement learning and data security to human‑robot collaboration. In continuous‑state stochastic systems, accurate entropy analysis and control remains a challenge. In recent years, finite‑state abstractions of continuous systems have enabled control synthesis with formal performance guarantees on objectives such as stage costs. However, these results do not extend to entropy‑based performance measures. We solve this problem by first obtaining bounds on the entropy of system discretizations using traditional formal‑abstractions results, and then obtaining an additional bound on the difference between the entropy of a continuous distribution and that of its discretization. The resulting theory enables formal entropy‑aware controller synthesis that trades predictability against control performance while preserving formal guarantees for the original continuous system. More specifically, we focus on minimizing the linear combination of the KL divergence of the system trajectory distribution to uniform ‑‑ our system entropy metric ‑‑ and a generic cumulative cost. We note that the bound we derive on the difference between the KL divergence to uniform of a given continuous distribution and its discretization can also be relevant in more general information‑theoretic contexts. A set of case studies illustrates the effectiveness of the method.

Authors:Kazumune Hashimoto, Kazunobu Serizawa, Masako Kishida
Title: Receding-Horizon Maximum-Likelihood Estimation of Neural-ODE Dynamics and Thresholds from Event Cameras
Abstract:
Event cameras emit asynchronous brightness‑change events where each pixel triggers an event when the last event exceeds a threshold, yielding a history‑dependent measurement model. We address online maximum‑likelihood identification of continuous‑time dynamics from such streams. The latent state follows a Neural ODE and is mapped to predicted log‑intensity through a differentiable state‑to‑image model. We model events with a history‑dependent marked point process whose conditional intensity is a smooth surrogate of contrast‑threshold triggering, treating the contrast threshold as an unknown parameter. The resulting log‑likelihood consists of an event term and a compensator integral. We propose a receding‑horizon estimator that performs a few gradient steps per update on a receding horizon window. For streaming evaluation, we store two scalars per pixel (last‑event time and estimated log‑intensity at that time) and approximate the compensator via Monte Carlo pixel subsampling. Synthetic experiments demonstrate joint recovery of dynamics parameters and the contrast threshold, and characterize accuracy‑‑latency trade‑offs with respect to the window length.

Authors:Xinyu Qiao, Yongyang Xiong, Yu Han, Keyou You
Title: A Unified Hybrid Control Architecture for Multi-DOF Robotic Manipulators
Abstract:
Multi‑degree‑of‑freedom (DOF) robotic manipulators exhibit strongly nonlinear, high‑dimensional, and coupled dynamics, posing significant challenges for controller design. To address these issues, this work proposes a unified hybrid control architecture that integrates model predictive control (MPC) with feedback regulation, together with a stability analysis of the proposed scheme. The proposed approach mitigates the optimization difficulty associated with high‑dimensional nonlinear systems and enhances overall control performance. Furthermore, a hardware implementation scheme based on machine learning (ML) is proposed to achieve high computational efficiency while maintaining control accuracy. Finally, simulation and hardware experiments under external disturbances validate the proposed architecture, demonstrating its superior performance, hardware feasibility, and generalization capability for multi‑DOF manipulation tasks.

Authors:Yan Tong, Qin Wang, Sihao Chen, Xue Hu, Zhaoyuan Wu
Title: Design of Grid Forming Multi Timescale Coordinated Control Strategies for Dynamic Virtual Power Plants
Abstract:
As the penetration level of distributed energy resources (DERs) continues to rise, traditional frequency and voltage support from synchronous machines declines. This weakens grid stability and increases the need for fast and adaptive control in a dynamic manner, especially in weak grids. However, most virtual power plants (VPPs) rely on static aggregation and plan based resource allocation strategies. These methods overlook differences in device response times and limit flexibility for ancillary services. To address this issue, we propose a dynamic virtual power plant (DVPP) that coordinates heterogeneous resources across multiple time scales using grid forming control. We first contrast grid following and grid forming converters: grid following designs rely on a phase locked loop which can undermine stability in weak grids, whereas our DVPP applies virtual synchronous generator control at the aggregate level to provide effective inertia and damping. Then, we introduce a dynamic participation factor framework that measures each device s contribution through the frequency active power and voltage reactive power loops. Exploiting device heterogeneity, we adopt a banded allocation strategy: slow resources manage steady state and low frequency regulation; intermediate resources smooth transitions; and fast resources deliver rapid response and high frequency damping. Comparative simulations demonstrate that this coordinated, timescale aware approach enhances stability and ancillary service performance compared to conventional VPPs.

Authors:Zehao Wang, Yuxuan Tang, Han Zhang, Jingchuan Wang, Weidong Chen
Title: U-OBCA: Uncertainty-Aware Optimization-Based Collision Avoidance via Wasserstein Distributionally Robust Chance Constraints
Abstract:
Uncertainties arising from localization error, trajectory prediction errors of the moving obstacles and environmental disturbances pose significant challenges to robot's safe navigation. Existing uncertainty‑aware planners often approximate polygon‑shaped robots and obstacles using simple geometric primitives such as circles or ellipses. Though computationally convenient, these approximations substantially shrink the feasible space, leading to overly conservative trajectories and even planning failure in narrow environments. In addition, many such methods rely on specific assumptions about noise distributions, which may not hold in practice and thus limit their performance guarantees. To address these limitations, we extend the Optimization‑Based Collision Avoidance (OBCA) framework to an uncertainty‑aware formulation, termed \emphU‑OBCA. The proposed method explicitly accounts for the collision risk between polygon‑shaped robots and obstacles by formulating OBCA‑based chance constraints, and hence avoiding geometric simplifications and reducing unnecessary conservatism. These probabilistic constraints are further tightened into deterministic nonlinear constraints under mild distributional assumptions, which can be solved efficiently by standard numerical optimization solvers. The proposed approach is validated through theoretical analysis, numerical simulations and real‑world experiments. The results demonstrate that U‑OBCA significantly mitigates the conservatism in trajectory planning and achieves higher navigation efficiency compared to existing baseline methods, particularly in narrow and cluttered environments.

Authors:Shuaichen Yan, Xiao Hu, Jiayang Sun, Zeyuan Yang, Shipeng Li, Heung-Yeung Shum, Shijun Yin, Yuqing Tang
Title: The Vertical Challenge of Low-Altitude Economy: Why We Need a Unified Height System?
Abstract:
The explosive growth of the low‑altitude economy, driven by eVTOLs and UAVs, demands a unified digital infrastructure to ensure safety and scalability. However, the current aviation vertical references are dangerously fragmented: manned aviation relies on barometric pressure, cartography uses Mean Sea Level (MSL), and obstacle avoidance depends on Above Ground Level (AGL). This fragmentation creates significant ambiguity for autonomous systems and hinders cross‑stakeholder interoperability. In this article, we propose Height Above Ellipsoid (HAE) as the standardized vertical reference for lower airspace. Unlike legacy systems prone to environmental drift and inconsistent datums, HAE provides a globally consistent, GNSS‑native, and mathematically stable reference. We present a pragmatic bidirectional transformation framework to bridge HAE with legacy systems and demonstrate its efficacy through (1) real‑world implementation in Shenzhen's partitioned airspace management, and (2) a probabilistic risk assessment driven by empirical flight logs from the PX4 ecosystem. Results show that transitioning to HAE reduces the required vertical separation minimum, effectively increasing dynamic airspace capacity while maintaining a target safety level. This work offers a roadmap for transitioning from analog height keeping to a digital‑native vertical standard.

Authors:Chih-Fan Pai, Yuto Watanabe, Yujie Tang, Yang Zheng
Title: Policy Optimization of Mixed H2/H-infinity Control: Benign Nonconvexity and Global Optimality
Abstract:
Mixed H2/H‑infinity control balances performance and robustness by minimizing an H2 cost bound subject to an H‑infinity constraint. However, classical Riccati/LMI solutions offer limited insight into the nonconvex optimization landscape and do not readily scale to large‑scale or data‑driven settings. In this paper, we revisit mixed H2/H‑infinity control from a modern policy optimization viewpoint, including the general two‑channel and single‑channel cases. One central result is that both cases enjoy a benign nonconvex structure: every stationary point is globally optimal. We characterize the H‑infinity‑constrained feasible set, which is open, path‑connected, with boundary given exactly by policies saturating the H‑infinity constraint. We also show that the mixed objective is real analytic in the interior with explicit gradient formulas. Our key analysis builds on an Extended Convex Lifting (ECL) framework that bridges nonconvex policy optimization and convex reformulations. The ECL constructions rely on non‑strict Riccati inequalities that allow us to characterize global optimality. These insights reveal hidden convexity in mixed H2/H‑infinity control and facilitate the design of scalable policy iteration methods in large‑scale settings.

Authors:Akiyuki Koyama, Hiroaki Kawashima
Title: Data-Driven Control of a Magnetically Actuated Fish-Like Robot
Abstract:
Magnetically actuated fish‑like robots offer promising solutions for underwater exploration due to their miniaturization and agility; however, precise control remains a significant challenge because of nonlinear fluid dynamics, flexible fin hysteresis, and the variable‑duration control steps inherent to the actuation mechanism. This paper proposes a comprehensive data‑driven control framework to address these complexities without relying on analytical modeling. Our methodology comprises three core components: 1) developing a forward dynamics model (FDM) using a neural network trained on real‑world experimental data to capture state transitions under varying time steps; 2) integrating this FDM into a gradient‑based model predictive control (G‑MPC) architecture to optimize control inputs for path following; and 3) applying imitation learning to approximate the G‑MPC policy, thereby reducing the computational cost for real‑time implementation. We validate the approach through simulations utilizing the identified dynamics model. The results demonstrate that the G‑MPC framework achieves accurate path convergence with minimal root mean square error (RMSE), and the imitation learning controller (ILC) effectively replicates this performance. This study highlights the potential of data‑driven control strategies for the precise navigation of miniature, fish‑like soft robots.

Authors:Haruki Izawa, Takeshi Takai, Shingo Kitano, Mikita Miyaguchi, Hiroaki Kawashima
Title: Adaptive Policy Switching of Two-Wheeled Differential Robots for Traversing over Diverse Terrains
Abstract:
Exploring lunar lava tubes requires robots to traverse without human intervention. Because pre‑trained policies cannot fully cover all possible terrain conditions, our goal is to enable adaptive policy switching, where the robot selects an appropriate terrain‑specialized model based on its current terrain features. This study investigates whether terrain types can be estimated effectively using posture‑related observations collected during navigation. We fine‑tuned a pre‑trained policy using Proximal Policy Optimization (PPO), and then collected the robot's 3D orientation data as it moved across flat and rough terrain in a simulated lava‑tube environment. Our analysis revealed that the standard deviation of the robot's pitch data shows a clear difference between these two terrain types. Using Gaussian mixture models (GMM), we evaluated terrain classification across various window sizes. An accuracy of more than 98% was achieved when using a 70‑step window. The result suggests that short‑term orientation data are sufficient for reliable terrain estimation, providing a foundation for adaptive policy switching.

Authors:Daniel Mastropietro, Vyacheslav Kungurtsev
Title: Multistage Stochastic Programming for Rare Event Risk Mitigation in Power Systems Management
Abstract:
High intermittent renewable penetration in the energy mix presents challenges in robustness for the management of power systems' operation. If a tail realization of the distribution of weather yields a prolonged period of time during which solar irradiation and wind speed are insufficient for satisfying energy demand, then it becomes critical to ramp up the generation of conventional power plants with adequate foresight. This event trigger is costly, and inaccurate forecasting can either be wasteful or yield catastrophic undersupply. This encourages particular attention to accurate modeling of the noise and the resulting dynamics within the aforementioned scenario. In this work we present a method for rare event‑aware control of power systems using multi‑stage scenario‑based stochastic programming. A Fleming‑Viot particle approach is used to bias the scenario generation towards rare realizations of very low wind power, in order to obtain a cost‑effective control of conventional power plants that is robust under prolonged renewable energy shortfalls.

Authors:Anton S. Shiriaev, Leonid B. Freidovich, Alexander I. Shepeljavyi, Anders Robertsson, Rolf Johansson
Title: On boundedness of solutions of three-state Moore-Greitzer compressor model with nonlinear proportional-integral controller for the surge subsystem
Abstract:
The work focuses on Lagrange stability of the origin for the three‑state Moore‑Greitzer compressor model in closed loop with a nonlinear PI controller, tuned only to stabilize a lower‑dimensional invariant surge‑dynamics subsystem.The linearization of the system is not stabilizable but the static nonlinearity satisfies a sector condition, and together with a structural property of the stall‑dynamics subsystem, this plays an essential role in the analysis. The main contribution provides explicit conditions on the controller parameters together with analytical arguments that guarantee boundedness of all solutions of the closed‑loop system. The analysis employs a non‑standard application of circle‑criterion‑based arguments. Together with the additional arguments developed in the work, this stability test also shows that the closed‑loop system is robust to certain perturbations and model uncertainties.

Authors:Boxuan Xie, Yifan Zhang, Kalle Koskinen, Alexis A. Dowhuszko, Jiacheng Wang, Ruichen Zhang, Zehui Xiong, Dusit Niyato, Zhu Han, Riku Jäntti
Title: Joint Visible Light and RF Backscatter Communications for Ambient IoT Network: Fundamentals, Applications, and Opportunities
Abstract:
The rapid growth of the Internet of Things (IoT) devices in the sixth‑generation (6G) wireless networks raises significant generality and scalability challenges due to energy consumption, deployment complexity, and environmental impact. Ambient IoT (A‑IoT), leveraging ambient energy harvesting (EH) for batteryless device operation, has emerged as a promising solution to address these challenges.Among various EH and communication techniques, visible light communication (VLC) integrated with ambient backscatter communication (AmBC) offers remarkable advantages, including energy neutrality, high reliability, and enhanced security. In this paper, we propose a joint VLC‑AmBC architecture, emphasizing fundamental concepts, system designs, and practical implementations. We explore potential applications in environmental monitoring, healthcare, smart logistics, and secure communications. We present proof‑of‑concept demonstrations for three distinct types of ambient backscatter devices (AmBDs): EH‑Only, VLC‑Relay, and VLC‑Control. Experimental results demonstrate the feasibility of implementing joint VLC‑AmBC systems, highlighting their practical viability across various deployment scenarios. Finally, we outline future research directions, including integrated sensing and communication, as well as optimized energy‑efficient deployment. Open issues, such as large‑scale deployment challenges, are also discussed, thereby providing a clear roadmap for future developments in joint VLC‑AmBC‑enabled A‑IoT ecosystems.

Authors:Tichakorn Wongpiromsarn
Title: Risk-Aware Rulebooks for Multi-Objective Trajectory Evaluation under Uncertainty
Abstract:
We present a risk‑aware formalism for evaluating system trajectories in the presence of uncertain interactions between the system and its environment. The proposed formalism supports reasoning under uncertainty and systematically handles complex relationships among requirements and objectives, including hierarchical priorities and non‑comparability. Rather than treating the environment as exogenous noise, we explicitly model how each system trajectory influences the environment and evaluate trajectories under the resulting distribution of environment responses. We prove that the formalism induces a preorder on the set of system trajectories, ensuring consistency and preventing cyclic preferences. Finally, we illustrate the approach with an autonomous driving example that demonstrates how the formalism enhances explainability by clarifying the rationale behind trajectory selection.

Authors:Emmanuel Bamidele
Title: AMV-L: Lifecycle-Managed Agent Memory for Tail-Latency Control in Long-Running LLM Systems
Abstract:
Long‑running LLM agents require persistent memory to preserve state across interactions, yet most deployed systems manage memory with age‑based retention (e.g., TTL). While TTL bounds item lifetime, it does not bound the computational footprint of memory on the request path: as retained items accumulate, retrieval candidate sets and vector similarity scans can grow unpredictably, yielding heavy‑tailed latency and unstable throughput. We present AMV‑L (Adaptive Memory Value Lifecycle), a memory‑management framework that treats agent memory as a managed systems resource. AMV‑L assigns each memory item a continuously updated utility score and uses value‑driven promotion, demotion, and eviction to maintain lifecycle tiers; retrieval is restricted to a bounded, tier‑aware candidate set that decouples the request‑path working set from total retained memory. We implement AMV‑L in a full‑stack LLM serving system and evaluate it under identical long‑running workloads against two baselines: TTL and an LRU working‑set policy, with fixed prompt‑injection caps. Relative to TTL, AMV‑L improves throughput by 3.1x and reduces latency by 4.2x (median), 4.7x (p95), and 4.4x (p99), while reducing the fraction of requests exceeding 2s from 13.8% to 0.007%. Compared to LRU, AMV‑L trades a small regression in median/p95 latency (+26% / +3%) for improved extreme‑tail behavior (‑15% p99; ‑98% >2s) and lower token overhead (approximately 6% fewer tokens/request), while matching retrieval quality (value means within approximately 0‑2%). The gains arise primarily from bounding retrieval‑set size and vector‑search work, not from shortening prompts. Our results show that predictable performance for long‑running LLM agents requires explicit control of memory working‑set size and value‑driven lifecycle management, rather than retention time alone.

Authors:Henrique O. Caetano, Rahul K. Gupta, Carlos D. Maciel
Title: bayesgrid: An Open-Source Python Tool for Generating Probabilistic Synthetic Transmission-Distribution Grids Using Bayesian Hierarchical Models
Abstract:
In this work, we present bayesgrid, an open‑source python toolbox for generating synthetic power transmission‑distribution systems for any geographical location worldwide, using the publicly available data from OpenStreetMap (OSM). The toolbox is based on Bayesian Hierarchical Models (BHM) which is trained on existing distribution network databases to develop a probabilistic model and can be applied to any geographical location worldwide, leveraging transfer learning. Thanks to the BHM, the tool is capable of generating multiple instances of the distribution system for a same region. The generated networks contain three‑phase phase‑consistent unbalanced networks, radial topology and information on the nodal demand distributions. The generated network also contain the critical reliability indices, specifically the interruption duration and frequency of failure for individual grid components, allowing its application in reliability‑related studies. The tool is demonstrated for different case studies generating synthetic network datasets for different geographical regions around the world. The framework allows saving the generated networks into open‑source platforms: PandaPower and OpenDSS. We also present an application for computation of probabilistic hosting capacity using the synthetic networks.

Authors:Weipeng Liu, Upendra Prasad, Yutian Liu, Yong Dong, Haoran Zhao, Lei Wu
Title: On Theoretical Stability Proof and Stability Margin Analysis of Enhanced Droop-Free Control Schemes for Islanded Microgrids
Abstract:
This paper studies enhanced droop‑free control strategies with sparse neighboring communication for achieving effective active power sharing of distributed energy resources (DERs) while maintaining the frequency stability of islanded microgrids. The normalized active power consensus (NAPC) based droop‑free control can share the load among controllable DERs in proportion to their available capacities. However, existing literature exclusively takes the asymptotic stability of the NAPC based droop‑free control for granted, lacking a comprehensive theoretical proof that is critical for ensuring its effective design and practical implementation. This paper, for the first time, provides a thorough theoretical proof of the asymptotic stability of two NAPC‑based droop‑free control schemes: ordinary NAPC (ONAPC) and amplifier‑equipped NAPC (A‑NAPC), by testifying that all effective eigenvalues have negative real parts. The effect of various system settings on the stability margins is further analyzed with respect to the average admittance of the electrical network, the sparseness of the communication network, and the average available capacity of controllable DERs. Based on the sensitivity of eigenvalues with respect to perturbations, a vulnerability analysis is conducted to identify the weaknesses in the microgrids. Case studies demonstrate that the available capacity of controllable DERs has the most decisive influence on the stability margin of NAPC‑based droop‑free control, while O‑NAPC/ANAPC control scheme is more suitable for microgrids with DERs of larger/ smaller available capacities.

Authors:Ali Rajaei, Jochen L. Cremer
Title: Security-Constrained Substation Reconfiguration Considering Busbar and Coupler Contingencies
Abstract:
Substation reconfiguration via busbar splitting can mitigate transmission grid congestion and reduce operational costs. However, existing approaches neglect the security of substation topology, particularly for substations without busbar splitting (i.e., closed couplers), which can lead to severe consequences. Additionally, the computational complexity of optimizing substation topology remains a challenge. This paper introduces a MILP formulation for security‑constrained substation reconfiguration (SC‑SR), considering N‑1 line, coupler and busbar contingencies to ensure secure substation topology. To efficiently solve this problem, we propose a heuristic approach with multiple master problems (HMMP). A central master problem optimizes dispatch, while independent substation master problems determine individual substation topologies in parallel. Linear AC power flow equations ensure PF accuracy, while feasibility and optimality sub‑problems evaluate contingency cases. The proposed HMMP significantly reduces computational complexity and enables scalability to large‑scale power systems. Case studies on the IEEE 14‑bus, 118‑bus, and PEGASE 1354‑bus system show the effectiveness of the approach in mitigating the impact of coupler and busbar tripping, balancing system security and cost, and computational efficiency.

Authors:Patrick Bachmann, Andrii Mironchenko
Title: Lyapunov characterization of boundedness of reachability sets for infinite-dimensional systems
Abstract:
We prove a converse Lyapunov theorem for boundedness of reachability sets for a general class of control systems whose flow is Lipschitz continuous on compact intervals with respect to trajectory‑dominated inputs. We show that this condition is satisfied by many semi‑linear evolution equations. For ordinary differential equations, as a consequence of our results, we obtain a converse Lyapunov theorem for forward completeness, without a priori restrictions on the magnitude of inputs.

Authors:Ginevra Larroux, Matthieu Jacobs, Keyu Jia, Fabrizio Sossan, Mario Paolone
Title: Enhancing Power Systems Transmission Adequacy via Optimal BESS Siting and Sizing using Benders Decomposition with Feasibility Cuts
Abstract:
This work presents a general framework for the operationally driven optimal siting and sizing of battery energy storage systems in power transmission networks, aimed at enhancing their resource adequacy. The approach considers multi‑period planning horizons, enforces network constraints at high temporal resolution, and targets large‑scale meshed systems. The resulting computationally complex mixed‑integer non‑linear programming problem is reformulated as a mixed‑integer second‑order cone programming problem and solved via Generalized Benders Decomposition, with feasibility cuts enabling congestion management and voltage regulation under binding network limits. A tailored heuristic recovers an alternating‑current power‑flow‑feasible operating point from the relaxed solution. The proposed formulation is parallelizable, yielding excellent computational performance, while featuring rigorous guarantees of convergence.

Authors:Mahdi Hejrati, Amir Hossein Barjini, Gokhan Alcan, Jouni Mattila
Title: Adaptive Modular Geometric Control of Robotic Manipulators
Abstract:
This paper proposes an adaptive modular geometric control framework for robotic manipulators. The proposed methodology decomposes the overall manipulator dynamics into individual modules, enabling the design of local geometric control laws at the module level. To address parametric uncertainties, geometric adaptation law is incorporated into the control structure, requiring only a single adaptation gain for the entire system while ensuring physically consistent and drift‑free parameter estimates. Exponential stability of the proposed controller is established in the nominal case. Numerical simulations on a complex redundant robotic manipulator are conducted to evaluate the proposed approach against existing modular and geometric control methods. The results show that the proposed method reduces the RMS position error by at least 12.2% compared with state‑of‑the‑art controllers under almost the same control effort. In addition, the adaptive extension demonstrates strong capability in compensating for parametric uncertainties and preserving high tracking performance.

Authors:Ramachandran Anantharaman, Renato Vizuete, Julien M. Hendrickx, Alexandre Mauroy
Title: Identification of Nonlinear Acyclic Networks in Continuous Time from Nonzero Initial Conditions and Full Excitations
Abstract:
We propose a method to identify nonlinear acyclic networks in continuous time when the dynamics are located on the edges and all the nodes are excited. We show that it is necessary and sufficient to measure all the sinks to identify any tree in continuous time when the functions associated with the dynamics are analytic and satisfy f(0)=0, which is analogous to the discrete‑time case. For general directed acyclic graphs (DAGs), we show that it is necessary and sufficient to measure all sinks, assuming that the dynamics are not linear (a condition that can be relaxed for trees). Then, based on the measurement of higher order derivatives and nonzero initial conditions, we introduce a method for the identification of trees, which allows us to recover the nonlinear functions located in the edges of the network under the assumption of dictionary functions. Finally, we propose a method to identify multiple parallel paths of the same length between two nodes, which allow us to identify any DAG when combined with the algorithm for the identification of trees. Several examples are added to illustrate the results.

Authors:Nicolas Helson, Pegah Alizadeh, Anastasios Giovanidis
Title: Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control
Abstract:
Offline Reinforcement Learning (RL) is a promising approach for next‑generation wireless networks, where online exploration is unsafe and large amounts of operational data can be reused across the model lifecycle. However, the behavior of offline RL algorithms under genuinely stochastic dynamics ‑‑ inherent to wireless systems due to fading, noise, and traffic mobility ‑‑ remains insufficiently understood. We address this gap by evaluating Bellman‑based (Conservative Q‑Learning), sequence‑based (Decision Transformers), and hybrid (Critic‑Guided Decision Transformers) offline RL methods in an open‑access stochastic telecom environment (mobile‑env). Our results show that Conservative Q‑Learning consistently produces more robust policies across different sources of stochasticity, making it a reliable default choice in lifecycle‑driven AI management frameworks. Sequence‑based methods remain competitive and can outperform Bellman‑based approaches when sufficient high‑return trajectories are available. These findings provide practical guidance for offline RL algorithm selection in AI‑driven network control pipelines, such as O‑RAN and future 6G functions, where robustness and data availability are key operational constraints.

Authors:Maxime Grosso, Pierre Riedinger, Jamal Daafouz, Serge Pierfederici, Hicham Janati Idrissi, Blaise Lapôtre
Title: Harmonic Modeling and Control under Variable-Frequency
Abstract:
This paper develops a harmonic‑domain framework for systems with variable fundamental frequency. A variable‑frequency sliding Fourier decomposition is introduced in the phase domain, together with necessary and sufficient conditions for time‑ domain realizability. An exact harmonic‑domain differential model is derived for general nonlinear systems under variable frequency, without assumptions on the frequency variation. An explicit parameter‑varying approximation is then obtained, along with a tight error bound expressed in terms of local relative frequency variation, providing a non‑conservative validity criterion and clarifying the limitations of classical heuristics. A main result shows that, for linear phase‑periodic systems with affine frequency dependence, stability analysis and control synthesis can be carried out without approximation and without assumptions on the frequency variation, provided the frequency evolves within a prescribed interval. As a consequence, both problems reduce to harmonic Lyapunov inequalities evaluated at the two extreme frequency values, yielding a convex LMI characterization. The framework is illustrated on a variable‑speed permanent magnet synchronous motor.

Authors:Zhengxuan Liu, Yuxin Cai, Yijing Wang, Xiangkun He, Chen Lv, Zhiqiang Zuo
Title: Dual-Interaction-Aware Cooperative Control Strategy for Alleviating Mixed Traffic Congestion
Abstract:
As Intelligent Transportation System (ITS) develops, Connected and Automated Vehicles (CAVs) are expected to significantly reduce traffic congestion through cooperative strategies, such as in bottleneck areas. However, the uncertainty and diversity in the behaviors of Human‑Driven Vehicles (HDVs) in mixed traffic environments present major challenges for CAV cooperation. This paper proposes a Dual‑Interaction‑Aware Cooperative Control (DIACC) strategy that enhances both local and global interaction perception within the Multi‑Agent Reinforcement Learning (MARL) framework for Connected and Automated Vehicles (CAVs) in mixed traffic bottleneck scenarios. The DIACC strategy consists of three key innovations: 1) A Decentralized Interaction‑Adaptive Decision‑Making (D‑IADM) module that enhances actor's local interaction perception by distinguishing CAV‑CAV cooperative interactions from CAV‑HDV observational interactions. 2) A Centralized Interaction‑Enhanced Critic (C‑IEC) that improves critic's global traffic understanding through interaction‑aware value estimation, providing more accurate guidance for policy updates. 3) A reward design that employs softmin aggregation with temperature annealing to prioritize interaction‑intensive scenarios in mixed traffic. Additionally, a lightweight Proactive Safety‑based Action Refinement (PSAR) module applies rule‑based corrections to accelerate training convergence. Experimental results demonstrate that DIACC significantly improves traffic efficiency and adaptability compared to rule‑based and benchmark MARL models.

Authors:Emile Anand, Ishani Karmarkar
Title: Learning Approximate Nash Equilibria in Cooperative Multi-Agent Reinforcement Learning via Mean-Field Subsampling
Abstract:
Many large‑scale platforms and networked control systems have a centralized decision maker interacting with a massive population of agents under strict observability constraints. Motivated by such applications, we study a cooperative Markov game with a global agent and n homogeneous local agents in a communication‑constrained regime, where the global agent only observes a subset of k local agent states per time step. We propose an alternating learning framework (\textttALTERNATING‑MARL), where the global agent performs subsampled mean‑field Q‑learning against a fixed local policy, and local agents update by optimizing in an induced MDP. We prove that these approximate best‑response dynamics converge to an \widetildeO(1/\sqrtk)‑approximate Nash Equilibrium, while separating the sample complexities between the joint state and action spaces. Finally, we validate our results in numerical simulations for multi‑robot control.

Authors:Rohan Khatavkar, The Bach Nguyen, Inseung Kang, Hyunglae Lee, Jiefeng Sun
Title: Soft Semi-active Back Support Device with Adaptive Force Profiles using Variable-elastic Actuation and Weight Feedback
Abstract:
Portable active back support devices (BSDs) offer tunable assistance but are often bulky and heavy, limiting their usability. In contrast, passive BSDs are lightweight and compact but lack the ability to adapt their assistance to different back movements. We present a soft, lightweight, and compact BSD that combines a variable‑stiffness passive element and an active element (an artificial muscle) in parallel. The device provides tunable assistance through discrete changes in stiffness values and active force levels. We validate the device's tuning capabilities through bench testing and on‑body characterization. Further, we use the device's tuning capabilities to provide weight‑adaptive object lifting and lowering assistance. We detect the weight handled by the user based on forearm force myography and upper‑back inertial measurement unit data. Furthermore, electromyography analyses in five participants performing symmetric object lifting and lowering tasks showed reductions in back extensor activity. Preliminary results in one participant also indicated reduced muscle activity during asymmetric lifting.

Authors:Samiran Ghosh, V Anil Kumar
Title: Internet malware propagation: Dynamics and control through SEIRV epidemic model with relapse and intervention
Abstract:
Malware attacks in today's vast digital ecosystem pose a serious threat. Understanding malware propagation dynamics and designing effective control strategies are therefore essential. In this work, we propose a generic SEIRV model formulated using ordinary differential equations to study malware spread. We establish the positivity and boundedness of the system, derive the malware propagation threshold, and analyze the local and global stability of the malware‑free equilibrium. The separatrix defining epidemic regions in the control space is identified, and the existence of a forward bifurcation is demonstrated. Using normalized forward sensitivity indices, we determine the parameters most influential to the propagation threshold. We further examine the nonlinear dependence of key epidemic characteristics on the transmission rate, including the maximum number of infected, time to peak infection, and total number of infected. We propose a hybrid gradient‑based global optimization framework using simulated annealing approach to identify effective and cost‑efficient control strategies. Finally, we calibrate the proposed model using infection data from the "Windows Malware Dataset with PE API Calls" and investigated the effect of intervention onset time on averted cases, revealing an exponential decay relationship between delayed intervention and averted cases.

Authors:Jie Zhu, Yiwei Qiu, Yangjun Zeng, Shahab Dehghan, Sheng Wang, Shi Chen, Buxiang Zhou
Title: Frequency Security-Aware Production Scheduling of Utility-Scale Off-Grid Renewable P2H Systems Coordinating Heterogeneous Electrolyzers
Abstract:
Renewable power‑to‑hydrogen (ReP2H) enables large‑scale renewable energy utilization and supports the decarbonization of hard‑to‑abate sectors, such as chemicals and maritime transport, via hydrogen‑based renewable ammonia and methanol fuels. As a result, utility‑scale ReP2H projects are expanding worldwide. However, off‑grid ReP2H systems exhibit low inertia due to their converter‑dominated nature, making frequency security a critical concern. Although recent studies show that electrolyzers can contribute to frequency regulation (FR), their support capability depends on operating states and loading levels, creating a trade‑off between hydrogen output and frequency security. To address this challenge, this work develops a unified co‑optimization framework for frequency security‑aware production scheduling of utility‑scale off‑grid ReP2H systems coordinating heterogeneous electrolyzers. A system‑level frequency response model is established to capture multi‑stage FR from alkaline water electrolyzers (AWEs), proton exchange membrane electrolyzers (PEMELs), and other resources, including ammonia‑fueled generators retrofitted in co‑located chemical plants, battery energy storage, and wind turbines (WTs). Stage‑wise transient frequency security constraints are derived, reformulated into tractable forms, and embedded into production scheduling, enabling coordinated on/off switching and load allocation across electrolyzers to maximize hydrogen output under uncertain renewable power input while enforcing frequency security constraints. Case studies based on real‑world systems demonstrate that the proposed approach allows HPs to replace 55.52% and 96.85% of FR reserves from WTs and AFGs, respectively, while maintaining comparable hydrogen output. Year‑long simulations show an average 28.96% increase in annual net profit resulting from reduced reliance on conventional reserves.

Authors:Xiangyu Liu, Haoyi You, Kaiqing Zhang
Title: Principled Learning-to-Communicate with Quasi-Classical Information Structures
Abstract:
Learning‑to‑communicate (LTC) in partially observable environments has received increasing attention in deep multi‑agent reinforcement learning, where the control and communication strategies are jointly learned. Meanwhile, the impact of communication on decision‑making has been extensively studied in control theory. In this paper, we seek to formalize and better understand LTC by bridging these two lines of work, through the lens of information structures (ISs). To this end, we formalize LTC in decentralized partially observable Markov decision processes (Dec‑POMDPs) under the common‑information‑based framework from decentralized stochastic control, and classify LTC problems based on the ISs before (additional) information sharing. We first show that non‑classical LTCs are computationally intractable in general, and thus focus on quasi‑classical (QC) LTCs. We then propose a series of conditions for QC LTCs, under which LTC preserves the QC IS after information sharing, whereas violating them can cause computational hardness in general. Further, we develop provable planning and learning algorithms for QC LTCs, and establish quasi‑polynomial time and sample complexities for several QC LTC examples that satisfy the above conditions. Along the way, we also establish new results on a relationship between (strictly) QC IS and the condition of having strategy‑independent common‑information‑based beliefs (SI‑CIBs), as well as on solving Dec‑POMDPs without computationally intractable oracles but beyond those with SI‑CIBs, which may be of independent interest.

Authors:Zhiheng Shi, Xiaohan Xu, Wei Ma, Kairui Feng, Bin He
Title: The Evolution of Eco-routing under Population Growth: Evidence from Six U.S. Cities
Abstract:
Rapid urban population growth drives car travel demand, increasing transport carbon emissions and posing a critical challenge to sustainable development. Although existing studies have demonstrated that eco‑routing can reduce individual emissions, research gaps remain. On the one hand, such personal reductions have a negligible impact on overall emissions, and cannot be simply aggregated to capture the complex effects of large‑scale eco‑routing. On the other hand, under population growth, the long‑term effectiveness of eco‑routing, as well as the evolution of its efficiency and traveler route choice, remain underexplored. To address these limitations, this study proposes Time‑Only and Time‑Carbon user equilibrium (UE) models, integrates them with a demand forecasting method for simulating future network traffic, and designs multi‑dimensional metrics to characterize urban dynamics. Using real‑world road networks, commuting origin‑destination (OD) demand, and population projections under various shared socioeconomic pathways (SSPs) for six representative U.S. cities as a case study, we conduct a comprehensive analysis of urban dynamics across different routing strategies and population sizes. The results reveal that while eco‑routing mitigates total emissions, emissions in most cities scale superlinearly with population, a scaling order that remains invariant regardless of routing and construction strategies. Moreover, under population growth, travelers using eco‑routing tend to increasingly select shorter routes, giving rise to carbon bottlenecks. A strategy of targeted capacity expansion on these critical bottlenecks (0.46% of links) significantly reduces both emissions (3%) and travel time (28%) without compromising eco‑routing efficiency. This study provides a foundation for formulating low‑carbon urban transport planning and emission reduction policies.

Authors:Emiliano Dall'Anese
Title: Local Safety Filters for Networked Systems via Two-Time-Scale Design
Abstract:
Safety filters based on Control Barrier Functions (CBFs) provide formal guarantees of forward invariance, but are often difficult to implement in networked dynamical systems. This is due to global coupling and communication requirements. This paper develops locally implementable approximations of networked CBF safety filters that require no coordination across subsystems. The proposed approach is based on a two‑time‑scale dynamic implementation inspired by singular perturbation theory, where a small parameter ε separates fast filter dynamics from the plant dynamics; then, a local implementation is enabled via derivative estimation. Explicit bounds are derived to quantify the mismatch between trajectories of the systems with dynamic filter and with the ideal centralized safety filter. These results characterize how safety degradation depends on the time‑scale parameter ε, estimation errors, and filter activation time, thereby quantifying trade‑offs between safety guarantees and local implementability.

Authors:Kiruthiga Chandra Shekar, Aliasghar Moj Arab
Title: Safety-Centered Scenario Generation for Autonomous Vehicles
Abstract:
This paper presents a scenario generation framework that creates diverse, parametrized, and safety‑critical driving situations to validate the safety features of autonomous vehicles in simulation [15]. By modeling factors such as road geometry, traffic participants, environmental conditions, and perception uncertainties, the framework enables repeatable and scalable testing of safety mechanisms, including emergency braking, evasive maneuvers, and vulnerable road user protection. The framework supports both regulatory and edge case scenarios, mapped to hazards and safety goals derived from Hazard Analysis and Risk Assessment (HARA), ensuring traceability to ISO 26262 functional safety requirements and performance limitations. The output from these simulations provides quantitative safety metrics such as time‑to‑collision, minimum distance, braking and steering performance, and residual collision severity. These metrics enable the systematic evaluation of evasive maneuvering as a safety feature, while highlighting system limitations and edge‑case vulnerabilities. Integration of scenario‑based simulation with safety engineering principles offers accelerated validation cycles, improved test coverage at reduced cost, and stronger evidence for regulatory and stakeholder confidence.

Authors:Radu-Andrei Cioaca, Paul Irofti, Cristian Rusu, Gianluca Caparra, Andrei-Alexandru Marinache, Florin Stoican
Title: Real-time tightly coupled GNSS and IMU integration via Factor Graph Optimization
Abstract:
Reliable positioning in dense urban environments remains challenging due to frequent GNSS signal blockage, multipath, and rapidly varying satellite geometry. While factor graph optimization (FGO)‑based GNSS‑IMU fusion has demonstrated strong robustness and accuracy, most formulations remain offline. In this work, we present a real‑time tightly coupled GNSS‑IMU FGO method that enables causal state estimation via incremental optimization with fixed‑lag marginalization, and we evaluate its performance in a highly urbanized GNSS‑degraded environment using the UrbanNav dataset.

Authors:Radu-Andrei Cioaca, Cristian Rusu, Paul Irofti, Gianluca Caparra, Andrei-Alexandru Marinache, Florin Stoican
Title: Real-time loosely coupled GNSS and IMU integration via Factor Graph Optimization
Abstract:
Accurate positioning, navigation, and timing (PNT) is fundamental to the operation of modern technologies and a key enabler of autonomous systems. A very important component of PNT is the Global Navigation Satellite System (GNSS) which ensures outdoor positioning. Modern research directions have pushed the performance of GNSS localization to new heights by fusing GNSS measurements with other sensory information, mainly measurements from Inertial Measurement Units (IMU). In this paper, we propose a loosely coupled architecture to integrate GNSS and IMU measurements using a Factor Graph Optimization (FGO) framework. Because the FGO method can be computationally challenging and often used as a post‑processing method, our focus is on assessing its localization accuracy and service availability while operating in real‑time in challenging environments (urban canyons). Experimental results on the UrbanNav‑HK‑MediumUrban‑1 dataset show that the proposed approach achieves real‑time operation and increased service availability compared to batch FGO methods. While this improvement comes at the cost of reduced positioning accuracy, the paper provides a detailed analysis of the trade‑offs between accuracy, availability, and computational efficiency that characterize real‑time FGO‑based GNSS/IMU fusion.

Authors:Yejin Moon, Gabor Orosz, Hosam K. Fathy
Title: Designing Barrier Functions for Graceful Safety Control
Abstract:
This paper examines the problem of achieving "grace" when controlling dynamical systems for safety, which is defined in terms of providing multi‑layered safety assurances. Namely, two safety layers are created: a primary layer that represents a desirable degree of safety, and a secondary failsafe layer. Graceful control then involves ensuring that even if the primary layer is breached, the failsafe layer remains forward invariant. The paper pursues this goal by constructing a safety constraint that combines the concepts of zeroing and reciprocal control barrier functions with regard to the primary and secondary safe sets, respectively. This constraint is analogous to a stiffening spring, making it possible to construct energy‑based analytical proofs of the resulting graceful safety guarantees. The proposed approach is developed for systems with a relative degree of either 1 or 2, the latter case being particularly useful for mechanical systems. We demonstrate the applicability of the method using a wall collision avoidance example. This demonstration highlights the benefits of the proposed approach compared to traditional benchmarks from the literature.

Authors:Mohammad Alikhani
Title: DKD-KAN: A Lightweight knowledge-distilled KAN intrusion detection framework, based on MLP and KAN
Abstract:
Cyber‑security systems often operate in resource‑constrained environments, such as edge environments and real‑time monitoring systems, where model size and inference time are crucial. A light‑weight intrusion detection framework is proposed that utilizes the Kolmogorov‑Arnold Network (KAN) to capture complex features in the data, with the efficiency of decoupled knowledge distillation (DKD) training approach. A high‑capacity KAN network is first trained to detect attacks performed on the test bed. This model then serves as a teacher to guide a much smaller multilayer perceptron (MLP) student model via DKD. The resulting DKD‑MLP model contains only 2,522 and 1,622 parameters for WADI and SWaT datasets, which are significantly smaller than the number of parameters of the KAN teacher model. This is highly appropriate for deployment in resource‑constrained devices with limited computational resources. Despite its low size, the student model maintains a high performance. Our approach demonstrate the practicality of using KAN as a knowledge‑rich teacher to train much smaller student models, without considerable drop in accuracy in intrusion detection frameworks. We have validated our approach on two publicly available datasets. We report F1‑score improvements of 4.18% on WADI and 3.07% on SWaT when using the DKD‑MLP model, compared to the bare student model. The implementation of this paper is available on our GitHub repository.

Authors:Yacob Medhin, Tushar Sial, Simone Servadio
Title: Multidisciplinary Design Optimization of a Low-Thrust Asteroid Orbit Insertion Using Electric Propulsion
Abstract:
Low‑thrust electric propulsion missions are often designed under simplifying assumptions such as constant thrust or fixed specific impulse, neglecting the strong coupling between trajectory dynamics, spacecraft power availability, and propulsion performance. In deep‑space environments with reduced solar irradiance, these assumptions can lead to suboptimal or infeasible designs, underscoring the need to simultaneously optimize the trajectory and power subsystem. This paper presents a multidisciplinary design optimization (MDO) framework for the simultaneous design of low‑thrust trajectories and spacecraft power systems, with explicit coupling to electric propulsion performance. The framework incorporates a high‑fidelity variable‑specific impulse model of the SPT‑140 Hall thruster, in which thrust and efficiency are directly constrained by time‑varying solar power availability and solar array degradation, rather than treated as fixed parameters. The coupled problem is posed as a time‑optimal control problem and addressed using a framework built on top of OpenMDAO and Dymos toolchains, where Dymos employs a collocation‑based direct‑transcription approach for trajectory optimization. OpenMDAO provides accurate analytic partial derivatives, enabling efficient gradient‑based optimization. A Fast Fourier Series shape‑based method is used to generate dynamically feasible initial guess trajectories, and the resulting nonlinear programming problem is solved using IPOPT. The proposed framework is demonstrated through a low‑thrust orbit insertion scenario around asteroid 16‑Psyche, a regime in which reduced solar irradiance makes power‑aware trajectory design particularly critical. Simulation results demonstrate the framework's ability to capture key power‑propulsion‑trajectory trade‑offs, highlighting the importance of integrated power optimization for realistic electric propulsion mission design.

Authors:Ji-Eun Byun, Se-Hyeok Lee
Title: A New System Function for Maximum Processable Flow in Process Plants and Application to Reliability Assessment
Abstract:
This study presents a new system performance function for process plant reliability analysis, formulated to capture both structural topology and process sequencing constraints. Built on a modified maximum‑flow framework and solved via linear programming, the proposed function efficiently quantifies the maximum feasible flow through a series of interconnected stages. It addresses limitations of existing models, such as fault trees and event trees, that often overlook flow continuity and topological dependencies. The system function is integrated into a reliability assessment framework, enabling the evaluation of system failure probability and reliability‑based component importance measures. Application to two benchmark examples, including a gas supply plant with 57 nodes and 102 edges, demonstrates the effectiveness of the proposed system function and the validity of the resulting reliability assessment for risk‑informed layout planning. A reconfiguration guided by component importance measures yields up to a 20% reduction in system failure probability, underscoring the importance of effective equipment and pipeline layout. The proposed framework offers a promising direction for reliability‑based management of industrial process facilities.

Authors:Hassan Abdelraouf, Jeff S. Shamma
Title: Can a Learner Regret Using a No-Regret Algorithm? A Control-Theoretic Study of Performance Dominance
Abstract:
No‑regret learning dynamics ensure that a learner asymptotically achieves an average reward no worse than that of any fixed strategy. This no‑regret guarantee does not determine the value of the asymptotic average reward. Indeed, it is possible for different no‑regret learning dynamics to exhibit different asymptotic average rewards when facing the same environment while both assure the no‑regret guarantee. This paper asks whether a "free‑lunch" phenomenon can arise among no‑regret algorithms. Namely, is it possible for one no‑regret learning rule to uniformly outperform another no‑regret learning rule across all payoff environments. Stated differently, can a learner regret not using a particular no‑regret algorithm? We consider generalized replicator dynamics (RD) as a cascade interconnection between a linear time‑invariant (LTI) system and the softmax nonlinearity. Varying this LTI system leads to different realizations of replicator dynamics, including so‑called anticipatory RD, exponential RD, and other forms of higher‑order RD. Setting the LTI system to be an integrator realizes standard RD, which is known to satisfy the no‑regret property. Within this framework, we analyze and compare various realizations of these generalized realizations RD by varying the LTI system. We first formulate performance comparison as a passivity property of an associated comparison system and establish "local" dominance results, i.e., comparing the asymptotic performance near an equilibrium payoff vector. We then cast performance comparison between a form of anticipatory RD and standard RD as an optimal‑control problem. We show that the minimal achievable cumulative reward gap is zero, thereby establishing global dominance of anticipatory RD across all payoff environments and establishing a "free lunch" among no‑regret learning dynamics.

Authors:Hossein Rastgoftar, Muhammad J. H. Zahed
Title: Deep Q-Learning-Based Gain Scheduling for Nonlinear Quadcopter Dynamics
Abstract:
This paper presents a deep Q‑network (DQN)‑based gain‑scheduling framework for safety‑critical quadcopter trajectory tracking. Instead of directly learning control inputs, the proposed approach selects from a finite set of pre‑certified stabilizing gain vectors, enabling reinforcement learning to operate within a structured and stability‑preserving control architecture. By exploiting the isotropic structure of the translational dynamics, feedback gains are shared across spatial axes to reduce dimensionality while preserving performance. The learned policy adapts feedback aggressiveness in real time, applying high authority during large transients and reducing gains near convergence to limit control effort. Simulation results using a high‑fidelity nonlinear quadcopter model demonstrate accurate trajectory tracking, bounded attitude excursions, smooth transition to hover after the final time, and consistent reward improvement, validating the effectiveness and robustness of the proposed learning‑based gain scheduling strategy.

Authors:Mohamed Serry, Maxwell Fitzsimmons, Jun Liu
Title: Safe and Robust Domains of Attraction for Discrete-Time Systems: A Set-Based Characterization and Certifiable Neural Network Estimation
Abstract:
Analyzing nonlinear systems with attracting robust invariant sets (RISs) requires estimating their domains of attraction (DOAs). Despite extensive research, accurately characterizing DOAs for general nonlinear systems remains challenging due to both theoretical and computational limitations, particularly in the presence of uncertainties and state constraints. In this paper, we propose a novel framework for the accurate estimation of safe (state‑constrained) and robust DOAs for discrete‑time nonlinear uncertain systems with continuous dynamics, open safe sets, compact disturbance sets, and uniformly locally \ell_p‑stable compact RISs. The notion of uniform \ell_p stability is quite general and encompasses, as special cases, uniform exponential and polynomial stability. The DOAs are characterized via newly introduced value functions defined on metric spaces of compact sets. We establish their fundamental mathematical properties and derive the associated Bellman‑type (Zubov‑type) functional equations. Building on this characterization, we develop a physics‑informed neural network (NN) framework to learn the corresponding value functions by embedding the derived Bellman‑type equations directly into the training process. To obtain certifiable estimates of the safe robust DOAs from the learned neural approximations, we further introduce a verification procedure that leverages existing formal verification tools. The effectiveness and applicability of the proposed methodology are demonstrated through four numerical examples involving nonlinear uncertain systems subject to state constraints, and its performance is compared with existing methods from the literature.

Authors:Bhathiya Rathnayake, Sijia Geng
Title: Grid-Forming Control with Assignable Voltage Regulation Guarantees and Safety-Critical Current Limiting
Abstract:
This paper develops a nonlinear grid‑forming (GFM) controller with provable voltage‑formation guarantees, with over‑current limiting enforced via a control‑barrier‑function (CBF)‑based safety filter. The nominal controller follows a droop‑based inner‑outer architecture, in which voltage references and frequency are generated by droop laws, an outer‑loop voltage controller produces current references using backstepping (BS), and an inner‑loop current controller synthesizes the terminal voltage. The grid voltage is treated as an unknown bounded disturbance, without requiring knowledge of its bound, and the controller design does not rely on any network parameters beyond the point of common coupling (PCC). To robustify voltage formation against the grid voltage, a deadzone‑adapted disturbance suppression (DADS) framework is incorporated, yielding practical voltage regulation characterized by asymptotic convergence of the PCC voltage errors to an assignably small and known residual set. Furthermore, the closed‑loop system is proven to be globally well posed, with all physical and adaptive states bounded and voltage error transients (due to initial conditions) decaying exponentially at an assignable rate. On top of the nominal controller, hard over‑current protection is achieved through a minimally invasive CBF‑based safety filter that enforces strict current limits via a single‑constraint quadratic program. The safety filter is compatible with any locally Lipschitz nominal controller. Rigorous analysis establishes forward invariance of the safe‑current set and boundedness of all states under current limiting. Numerical results demonstrate improved transient performance and faster recovery during current‑limiting events when the proposed DADS‑BS controller is used as the nominal control law, compared with conventional PI‑based GFM control.

Authors:Shenghua Feng, Jie An, Naijun Zhan, Fanjiang Xu
Title: Exact Moment Estimation of Stochastic Differential Dynamics
Abstract:
Moment estimation for stochastic differential equations (SDEs) is fundamental to the formal reasoning and verification of stochastic dynamical systems, yet remains challenging and is rarely available in closed form. In this paper, we study time‑homogeneous SDEs with polynomial drift and diffusion, and investigate when their moments can be computed exactly. We formalize the notion of moment‑solvable SDEs and propose a generic symbolic procedure that, for a given monomial, attempts to construct a finite linear ordinary differential equation (ODE) system governing its moment, thereby enabling exact computation. We introduce a syntactic class of pro‑solvable SDEs, characterized by a block‑triangular structure, and prove that all polynomial moments of any pro‑solvable SDE admit such finite ODE representations. This class strictly generalizes linear SDEs and includes many nonlinear models. Experimental results demonstrate the effectiveness of our approach.

Authors:Mohamad Izdin Hlal, Hussien Elharati, Ahmed Altaher
Title: Optimum Battery Depth of Discharge of Stand-alone Hybrid System Using the MOPSO Method
Abstract:
This paper presents an optimized design of a Standalone Solar PV/Battery (SSPVB) system to address energy reliability and cost efficiency challenges in off‑grid environments. The proposed system integrates a Multi‑Objective Particle Swarm Optimization (MOPSO) approach and validates the results using the Non‑Dominated Sorting Genetic Algorithm II (NSGA‑II). The optimization process aims to minimize both the Cost of Energy (COE) and Loss of Load Probability (LLP), while examining the effects of Battery Depth of Discharge (DOD) on system reliability and lifecycle cost. Results indicate that an optimal DOD of approximately 70% yields a COE of 0.2059 USD/kWh with zero LLP, demonstrating strong reliability and cost‑effectiveness. Comparative analysis shows that both MOPSO and NSGA‑II methods achieve consistent outcomes, with MOPSO exhibiting faster convergence. The study provides valuable insights into optimal battery sizing for stand‑alone systems, contributing to modern optimization practices in renewable energy applications.

Authors:Jiawei Wang, Arshiya Taj Abdul, Evangelos A. Theodorou
Title: cuNRTO: GPU-Accelerated Nonlinear Robust Trajectory Optimization
Abstract:
Robust trajectory optimization enables autonomous systems to operate safely under uncertainty by computing control policies that satisfy the constraints for all bounded disturbances. However, these problems often lead to large Second Order Conic Programming (SOCP) constraints, which are computationally expensive. In this work, we propose the CUDA Nonlinear Robust Trajectory Optimization (cuNRTO) framework by introducing two dynamic optimization architectures that have direct application to robust decision‑making and are implemented on CUDA. The first architecture, NRTO‑DR, leverages the Douglas‑Rachford (DR) splitting method to solve the SOCP inner subproblems of NRTO, thereby significantly reducing the computational burden through parallel SOCP projections and sparse direct solves. The second architecture, NRTO‑FullADMM, is a novel variant that further exploits the problem structure to improve scalability using the Alternating Direction Method of Multipliers (ADMM). Finally, we provide GPU implementations of the proposed methodologies using custom CUDA kernels for SOC projection steps and cuBLAS GEMM chains for feedback gain updates. We validate the performance of cuNRTO through simulated experiments on unicycle, quadcopter, and Franka manipulator models, demonstrating speedups of up to 139.6×. More details are available at https://cunrto.github.io.

Authors:Mengru Wu, Jiawei Li, Jiaqi Wei, Bin Lyu, Kai-Kit Wong, Hyundong Shin
Title: Joint Optimization of Model Partitioning and Resource Allocation for Anti-Jamming Collaborative Inference Systems
Abstract:
With the increasing computational demands of deep neural network (DNN) inference on resource‑constrained devices, DNN partitioning‑based device‑edge collaborative inference has emerged as a promising paradigm. However, the transmission of intermediate feature data is vulnerable to malicious jamming, which significantly degrades the overall inference performance. To counter this threat, this letter focuses on an anti‑jamming collaborative inference system in the presence of a malicious jammer. In this system, a DNN model is partitioned into two distinct segments, which are executed by wireless devices and edge servers, respectively. We first analyze the effects of jamming and DNN partitioning on inference accuracy via data regression. Based on this, our objective is to maximize the system's revenue of delay and accuracy (RDA) under inference accuracy and computing resource constraints by jointly optimizing computation resource allocation, devices' transmit power, and DNN partitioning. To address the mixed‑integer nonlinear programming problem, we propose an efficient alternating optimization‑based algorithm, which decomposes the problem into three subproblems that are solved via Karush‑Kuhn‑Tucker conditions, convex optimization methods, and a quantum genetic algorithm, respectively. Extensive simulations demonstrate that our proposed scheme outperforms baselines in terms of RDA.

Authors:Adnane Saoud, Ryan S. Johnson, Ricardo G. Sanfelice
Title: Robust Hybrid Finite-Time Parameter Estimation Without Persistence of Excitation
Abstract:
In this paper, we consider the problem of estimating parameters of a linear regression model. Using a hybrid systems framework, a hybrid algorithm is proposed allowing the estimate to converge to the exact value of the unknown parameters in predetermined finite time. Interestingly, we show that for the case of constant parameters, the convergence property of the hybrid algorithm holds while only requiring the regressor to be exciting on a given interval. For the case of piecewise constant parameters, the classical persistency of excitation condition is required to guarantee the convergence. Robustness of the proposed algorithm with respect to measurements noise is analysed. Finally, illustrative examples are provided showing the merits of the proposed approach in terms of scalability and the applicability for the general class of time‑varying unknown parameters

Authors:Miroslav Krstic
Title: Contractor-Expander and Universal Inverse Optimal Positive Nonlinear Control
Abstract:
For general control‑affine nonlinear systems in the positive orthant, and with positive controls, we show how strict CLFs can be utilized for inverse optimal stabilization. Conventional ``LgV'' inverse optimal feedback laws, for systems with unconstrained states and controls, assume sign‑unconstrained inputs and input penalties that are class‑K in the input magnitude, hence symmetric about zero. Such techniques do not extend to positive‑state‑and‑control systems. Major customizations are needed, and introduced in this paper, for positive systems where highly asymmetric (or unconventionally symmetric) costs not only on the state but also on control are necessary. With the predator‑prey positive‑state positive‑input benchmark system as inspiration, using a strict CLF built in our previous paper, we prototype two general inverse optimal methodological frameworks that employ particular ``contractor and expander functions.'' One framework (A) employs a triple consisting of a CLF, a stabilizing feedback, and an expander, whereas the other framework (B) employs a pair of a CLF and a contractor function. Both frameworks yield inverse optimal stabilizer constructions, on positive orthants of arbitrary dimensions. A stronger construction results from a stronger CLF condition. Biological interpretation for the predator‑prey model illuminates that such inverse optimal control constructions are bio‑ecologically meaningful. In addition to general frameworks, we present two fully explicit designs: two Sontag‑like universal formulae for stabilization of positive‑orthant systems by positive feedback, one of them with inverse optimality.

Authors:Yi Zhang, Yichao Wang, Wei Xiao, Mohamadamin Rajabinezhad, Shan Zuo
Title: Event-Driven Safe and Resilient Control of Automated and Human-Driven Vehicles under EU-FDI Attacks
Abstract:
This paper studies the safe and resilient control of Connected and Automated Vehicles (CAVs) operating in mixed traffic environments where they must interact with Human‑Driven Vehicles (HDVs) under uncertain dynamics and exponentially unbounded false data injection (EU‑FDI) attacks. These attacks pose serious threats to safety‑critical applications. While resilient control strategies can mitigate adversarial effects, they often overlook collision avoidance requirements. Conversely, safety‑critical approaches tend to assume nominal operating conditions and lack resilience to adversarial inputs. To address these challenges, we propose an event‑driven safe and resilient (EDSR) control framework that integrates event‑driven Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) with adaptive attack‑resilient control. The framework further incorporates data‑driven estimation of HDV behaviors to ensure safety and resilience against EU‑FDI attacks. Specifically, we focus on the lane‑changing maneuver of CAVs in the presence of unpredictable HDVs and EU‑FDI attacks on acceleration inputs. The event‑driven approach reduces computational load while maintaining real‑time safety guarantees. Simulation results, including comparisons with conventional safety‑critical control methods that lack resilience, validate the effectiveness and robustness of the proposed EDSR framework in achieving collision‑free maneuvers, stable velocity regulation, and resilient operation under adversarial conditions.

Authors:Xiaoyu Ma, Sean Qian
Title: Joint Estimation of Dynamic O-D Demand and Choice Models for Dynamic Multi-modal Networks: Computational Graph-Based Learning and Hypothesis Tests
Abstract:
Understanding travel demand and behavior, particularly route and mode choices, is critical for effective transportation planning and policy design in multi‑modal systems with emerging mobility options. Multi‑modal system‑level data, such as traffic counts, probe speeds, and transit ridership, offer scalable, cost‑effective, and privacy‑preserving advantages for inferring and analyzing travel behavior. This research uses such system‑level data to infer travel demand and choices that vary by time of day, origin/destination location, and mode. Existing studies focus on a single transportation mode, consider limited behavioral factors in disutility functions, rely on static travel time functions, and face computational challenges when applied to large‑scale networks. This research addresses these gaps by proposing a joint estimation framework for dynamic origin‑destination demand and disutility functions within a multi‑modal transportation system that includes both private driving and public transit, using multi‑source system‑level data. A multi‑modal dynamic traffic assignment model that accounts for both route and mode choices is integrated into the framework, with detailed travel time modeling for multiple modes. Alternative‑specific and zone‑specific factors are incorporated into generic disutility functions to capture heterogeneous traveler perceptions. The estimation problem is formulated and solved using a computational graph‑based approach, enabling dynamic network modeling and scalable inference across large‑scale networks and diverse data sets. Furthermore, we propose a hypothesis testing framework tailored to this complex estimation setting to assess the statistical significance of behavioral parameters, thereby enabling model selection and statistically rigorous insights for real‑world applications.

Authors:Alessandro Melis, Tarek Bouazza, Hassan Alnahhal, Sifeddine Benahmed, Soulaimane Berkane, Tarek Hamel
Title: Scalar-Measurement Attitude Estimation on $\mathbf{SO}(3)$ with Bias Compensation
Abstract:
Attitude estimation methods typically rely on full vector measurements from inertial sensors such as accelerometers and magnetometers. This paper shows that reliable estimation can also be achieved using only scalar measurements, which naturally arise either as components of vector readings or as independent constraints from other sensing modalities. We propose nonlinear deterministic observers on \mathbfSO(3) that incorporate gyroscope bias compensation and guarantee uniform local exponential stability under suitable observability conditions. A key feature of the framework is its robustness to partial sensing: accurate estimation is maintained even when only a subset of vector components is available. Experimental validation on the BROAD dataset confirms consistent performance across progressively reduced measurement configurations, with estimation errors remaining small even under severe information loss. To the best of our knowledge, this is the first work to establish fundamental observability results showing that two scalar measurements under suitable excitation suffice for attitude estimation, and that three are enough in the static case. These results position scalar‑measurement‑based observers as a practical and reliable alternative to conventional vector‑based approaches.

Authors:Alexander Schperberg, Yeping Wang, Stefano Di Cairano
Title: Safe Whole-Body Loco-Manipulation via Combined Model and Learning-based Control
Abstract:
Simultaneous locomotion and manipulation enables robots to interact with their environment beyond the constraints of a fixed base. However, coordinating legged locomotion with arm manipulation, while considering safety and compliance during contact interaction remains challenging. To this end, we propose a whole‑body controller that combines a model‑based admittance control for the manipulator arm with a Reinforcement Learning (RL) policy for legged locomotion. The admittance controller maps external wrenches‑‑such as those applied by a human during physical interaction‑‑into desired end‑effector velocities, allowing for compliant behavior. The velocities are tracked jointly by the arm and leg controllers, enabling a unified 6‑DoF force response. The model‑based design permits accurate force control and safety guarantees via a Reference Governor (RG), while robustness is further improved by a Kalman filter enhanced with neural networks for reliable base velocity estimation. We validate our approach in both simulation and hardware using the Unitree Go2 quadruped robot with a 6‑DoF arm and wrist‑mounted 6‑DoF Force/Torque sensor. Results demonstrate accurate tracking of interaction‑driven velocities, compliant behavior, and safe, reliable performance in dynamic settings.

Authors:Liya Huang, Federico Milano, Georgios Tzounas
Title: Ill-Conditioned Power Flow Analysis Using a Quantized State-Based Approach
Abstract:
This paper focuses on power flow analysis through the lens of the Newton flow, a continuous‑time formulation of Newton's method. Within this framework, we explore how quantized‑state concepts, originally developed as an alternative to time discretization, can be incorporated to govern the evolution of the Newton flow toward the power flow solution. This approach provides a novel perspective on adaptive step‑size control and shows how state quantization can enhance robustness in illconditioned cases. The performance of the proposed approach is discussed with the ACTIVSg70k synthetic test system.

Authors:Zahra Zahedi, Xinyue Hu, Shashank Mehrotra, Mark Steyvers, Kumar Akash
Title: Strategic Shaping of Human Prosociality: A Latent-State POMDP Framework
Abstract:
We propose a decision‑theoretic framework in which a robot strategically can shape inferred human's prosocial state during repeated interactions. Modeling the human's prosociality as a latent state that evolves over time, the robot learns to infer and influence this state through its own actions, including helping and signaling. We formalize this as a latent‑state POMDP with limited observations and learn the transition and observation dynamics using expectation maximization. The resulting belief‑based policy balances task and social objectives, selecting actions that maximize long‑term cooperative outcomes. We evaluate the model using data from user studies and show that the learned policy outperforms baseline strategies in both team performance and increasing observed human cooperative behavior.

Authors:Moh Kamalul Wafi, Milad Siami
Title: A Passivity-Agnostic Framework for Distributed Adaptive Synchronization under Unknown Leader Dynamics
Abstract:
We present a passivity‑agnostic framework for distributed adaptive synchronization under position‑only communication, bounded disturbances, and unknown leader dynamics. By passivity‑agnostic we mean the design does not require the closed loop system to be strictly positive real (SPR) a priori: it certifies SPR when present and recovers it by frequency shaping when absent. Followers are heterogeneous second‑order systems with unknown (possibly unstable) dynamics. In the SPR regime, a structured reparameterization yields gradient‑based adaptive error dynamics; Lyapunov analysis guarantees global asymptotic synchronization in the disturbance‑free case, exact rejection of constant disturbances, and bounded responses to time‑varying disturbances, with parameter convergence under persistent excitation. In the non‑SPR regime, frequency shaping recovers effective passivity of the unshaped transfer function, enabling the same stability guarantees via standard passivity/Lyapunov arguments using Meyer‑Kalman‑Yakubovich (MKY) Lemma. Simulations across star, cyclic, path, and arbitrary graphs demonstrate scalable synchronization, robust tracking, and parameter adaptation under multiple disturbance profiles, confirming that the frequency‑shaped non‑SPR designs match the performance of the SPR case.

Authors:Emirhan Tekez, Melih Bastopcu, Sinan Gezici
Title: Characterizing Information Accuracy in Timeliness-Based Gossip Networks
Abstract:
We investigate information accuracy in timeliness‑based gossip networks where the source evolves according to a continuous‑time Markov chain (CTMC) with M states and disseminates status updates to a network of n nodes. In addition to direct source updates, nodes exchange their locally stored packets via gossip and accept incoming packets solely based on whether the incoming packet is fresher than their local copy. As a result, a node can possess the freshest packet in the network while still not having the current source state. To quantify the amount of accurate information flowing in the network under such a gossiping scheme, we introduce two accuracy metrics, average accuracy, defined as the expected fraction of nodes carrying accurate information in any given subset, and freshness‑based accuracy, defined as the accuracy of the freshest node in any given subset. Using a stochastic hybrid systems (SHS) framework, we first derive steady‑state balance equations and obtain matrix‑valued recursions that characterize these metrics in fully connected gossip networks under binary CTMCs. We then extend our analysis to the general multi‑state information source using a joint CTMC approach. Finally, we quantify the fraction of nodes whose information is accurate due to direct source pushes versus gossip exchanges. We verify our findings with numerical analyses and provide asymptotic insights.

Authors:Dhrumil Bhatt, Anakha Kurup, R. C. Mala
Title: Resilient Chaotic Cross-Layer Routing for Smart Grid IoT Networks
Abstract:
This paper presents the Distributed Adaptive Multi‑Radio Cross‑Layer Routing (DAMCR) protocol, designed to enhance reliability, adaptability, and energy efficiency in smart grid and industrial Internet of Things (IoT) communication networks. DAMCR integrates Chaotic Frequency‑Hopping Spread Spectrum (C‑FHSS) to improve physical‑layer security and jamming resilience with Link‑Adaptive Quality Power Control (LAQPC) to dynamically regulate transmission power based on instantaneous link quality and residual node energy. To meet heterogeneous traffic requirements, the protocol incorporates priority‑aware message classification that differentiates between periodic monitoring data and time‑critical fault and protection messages. The proposed framework is implemented and evaluated in MATLAB using a heterogeneous network composed of LoRa, Wi‑Fi, and dual‑radio nodes operating under AWGN, Rayleigh, and Rician fading environments. Extensive simulation results demonstrate that DAMCR consistently achieves a Packet Delivery Ratio (PDR) exceeding 95% across all evaluated scenarios, while maintaining end‑to‑end latency between 17 and 23 ms, even in the presence of controlled jamming attacks. These results confirm that the tight integration of chaos‑based spectrum agility, cross‑technology routing, and energy‑aware cross‑layer adaptation significantly improves communication reliability, latency stability, and resilience compared to conventional single‑radio and static‑routing protocols.

Authors:Megan S. Harris, Mohammad Mahdi Naderi, Ehsanoddin Ghorbanichemazkati, Sina Jangjoo, Emily Lapan, Seyed Amirreza Hosseini, Fabian Schipfer, Stephen Craig, Enayat Moallemi, Inas Khayal, Laura M. Arpan, Tian Tang, John C. Little, Amro M. Farid
Title: A System-of-Systems Convergence Paradigm for Societal Challenges of the Anthropocene
Abstract:
Modern societal challenges, such as climate change, urbanization, and water resource management, demand integrated, multi‑discipline, multi‑problem approaches to frame and address their complexity. Unfortunately, current methodologies often operate within disciplinary silos, leading to fragmented insights and missed opportunities for convergence. A critical barrier to cross‑disciplinary integration lies in the disparate ontologies that shape how different fields conceptualize and communicate knowledge. To address these limitations, this paper proposes a system‑of‑systems (SoS) convergence paradigm grounded in a meta‑cognition map, a framework that integrates five complementary domains: real‑world observations, systems thinking, visual modeling, mathematics, and computing. The paradigm is based on the Systems Modeling Language (SysML), offering a standardized, domain‑neutral approach for representing and analyzing complex systems. The proposed methodology is demonstrated through a case study of the Chesapeake Bay Watershed, a socio‑environmental system requiring coordination across land use, hydrology, economic and policy domains. By modeling this system with SysML, the study illustrates practical strategies for navigating interdisciplinary challenges and highlights the potential of agile SoS modeling to support large‑scale, multi‑dimensional decision‑making.

Authors:Megan S. Harris, John C. Little, Amro M. Farid
Title: A Hetero-functional Graph State Estimator for Watershed Systems: Application to the Chesapeake Bay
Abstract:
Regional watersheds are complex systems of systems encompassing hydrology, land‑use decision‑making, estuarine ecological feedbacks, and overlapping governance jurisdictions. Their effective management underlies many modern societal challenges and therefore requires models that capture interdependencies between natural and institutional systems. Regional‑specific models such as the Chesapeake Assessment Scenario Tool, used in this paper's case study, provide valuable nutrient estimates but rely on structurally opaque watershed routing that limits integration into broader systems‑level analyses. This paper introduces a modeling framework for watershed systems. First, a region‑independent reference architecture is developed. Second, the Weighted Least Squares Error Hetero‑functional Graph State Estimator, an extension of Hetero‑functional Graph Theory (HFGT), is adapted to estimate nutrient flows from uncertain data. The framework is demonstrated through instantiation in the Chesapeake Bay Watershed. By establishing a shared ontology grounded in Systems Modeling Language and HFGT, the approach enables integration of economic and governance systems to support sustainable watershed management.

Authors:Taoran Wu, Dominik Wagner, C. -H. Luke Ong, Bai Xue
Title: PAC Finite-Time Safety Guarantees for Stochastic Systems with Unknown Disturbance Distributions
Abstract:
We investigate the problem of establishing finite‑time probabilistic safety guarantees for discrete‑time stochastic dynamical systems subject to unknown disturbance distributions, using barrier certificate methods. Our approach develops a data‑driven safety certification framework that relies only on a finite collection of independent and identically distributed (i.i.d.) disturbance samples. Within this framework, we propose a certification procedure such that, with confidence at least 1‑δ over the sampled disturbances, if the output of the certification procedure is accepted, the probability that the system remains within a prescribed safe set over a finite horizon is at least 1‑ε. A key challenge lies in formally characterizing the probably approximately correct (PAC) generalization behavior induced by finite samples. To address this, we derive PAC generalization bounds using tools from VC dimension, scenario optimization, and Rademacher complexity. These results illuminate the fundamental trade‑offs between sample size, model complexity, and safety tolerance, providing both theoretical insight and practical guidance for designing reliable, data‑driven safety certificates in discrete‑time stochastic systems.

Authors:Bruno Pinheiro, Daniel Dotta
Title: Dynamic Connectivity and Local Frequency Strength under Stochastic Variations
Abstract:
This paper introduces a novel metric, termed the Generalized Fiedler Vector (GFV), to evaluate the dynamic connectivity in power systems. The proposed metric leverages the network connectivity, represented by the system Laplacian matrix, together with the nodal inertia distribution, following a formulation previously developed by the first author. By capturing the interplay between system topology and dynamic properties, the GFV provides valuable insights for the optimal siting of stochastic generation to mitigate its impact on local and system‑wide frequency variability. The effectiveness of the proposed approach is demonstrated through Monte Carlo simulations performed on the IEEE 68‑bus test system.

Authors:Ilenia Carboni, Elia Cereda, Lorenzo Lamberti, Daniele Malpetti, Francesco Conti, Daniele Palossi
Title: Tiny-DroNeRF: Tiny Neural Radiance Fields aboard Federated Learning-enabled Nano-drones
Abstract:
Sub‑30g nano‑sized aerial robots can leverage their agility and form factor to autonomously explore cluttered and narrow environments, like in industrial inspection and search and rescue missions. However, the price for their tiny size is a strong limit in their resources, i.e., sub‑100 mW microcontroller units (MCUs) delivering ～100 GOps/s at best, and memory budgets well below 100 MB. Despite these strict constraints, we aim to enable complex vision‑based tasks aboard nano‑drones, such as dense 3D scene reconstruction: a key robotic task underlying fundamental capabilities like spatial awareness and motion planning. Top‑performing 3D reconstruction methods leverage neural radiance fields (NeRF) models, which require GBs of memory and massive computation, usually delivered by high‑end GPUs consuming 100s of Watts. Our work introduces Tiny‑DroNeRF, a lightweight NeRF model, based on Instant‑NGP, and optimized for running on a GAP9 ultra‑low‑power (ULP) MCU aboard our nano‑drones. Then, we further empower our Tiny‑DroNeRF by leveraging a collaborative federated learning scheme, which distributes the model training among multiple nano‑drones. Our experimental results show a 96% reduction in Tiny‑DroNeRF's memory footprint compared to Instant‑NGP, with only a 5.7 dB drop in reconstruction accuracy. Finally, our federated learning scheme allows Tiny‑DroNeRF to train with an amount of data otherwise impossible to keep in a single drone's memory, increasing the overall reconstruction accuracy. Ultimately, our work combines, for the first time, NeRF training on an ULP MCU with federated learning on nano‑drones.

Authors:Dewan Mahnaaz Mahmud, Vinu Thomas, Bogdan Marinescu, Mickaël Hilairet
Title: Critical Clearing Time Enhancement of Droop-Controlled Grid-Forming Inverters with Adaptive Function-Based Parameters
Abstract:
With the increasing penetration of renewable energy sources, grid‑forming (GFM) inverters are becoming essential for voltage and frequency regulation. However, the transient stability of GFM inverter is critically affected by the current limiters that are embedded with the standard control schemes. This paper proposes a novel adaptive function to enhance the transient stability of droop‑controlled GFM inverters. The proposed method autonomously adjusts the active power reference and the droop gain based on the terminal voltage of the inverter. Also, the acceleration of the phase angle is prevented, leading to the maximization of critical clearing time (CCT). The proposed method is benchmarked against two state‑of‑the‑art GFM inverter CCT enhancement methods. Effectiveness of the proposed method is validated through electromagnetic transient (EMT) simulations in MATLAB/Simulink\textsuperscript\textregistered.

Authors:Peng Yu, Xin Wang, Ning Tan
Title: Shape-Interpretable Visual Self-Modeling Enables Geometry-Aware Continuum Robot Control
Abstract:
Continuum robots possess high flexibility and redundancy, making them well suited for safe interaction in complex environments, yet their continuous deformation and nonlinear dynamics pose fundamental challenges to perception, modeling, and control. Existing vision‑based control approaches often rely on end‑to‑end learning, achieving shape regulation without explicit awareness of robot geometry or its interaction with the environment. Here, we introduce a shape‑interpretable visual self‑modeling framework for continuum robots that enables geometry‑aware control. Robot shapes are encoded from multi‑view planar images using a Bezier‑curve representation, transforming visual observations into a compact and physically meaningful shape space that uniquely characterizes the robot's three‑dimensional configuration. Based on this representation, neural ordinary differential equations are employed to self‑model both shape and end‑effector dynamics directly from data, enabling hybrid shape‑position control without analytical models or dense body markers. The explicit geometric structure of the learned shape space allows the robot to reason about its body and surroundings, supporting environment‑aware behaviors such as obstacle avoidance and self‑motion while maintaining end‑effector objectives. Experiments on a cable‑driven continuum robot demonstrate accurate shape‑position regulation and tracking, with shape errors within 1.56% of image resolution and end‑effector errors within 2% of robot length, as well as robust performance in constrained environments. By elevating visual shape representations from two‑dimensional observations to an interpretable three‑dimensional self‑model, this work establishes a principled alternative to vision‑based end‑to‑end control and advances autonomous, geometry‑aware manipulation for continuum robots.

Authors:Tanlu Liang, Ting Bai, Andreas A. Malikopoulos
Title: Predictive Lane-Change and Routing Coordination in Bus-Priority Mixed Traffic Corridors
Abstract:
In this paper, we investigate the coordination of vehicle maneuvers in mixed‑traffic corridors where connected and automated vehicles, human‑driven vehicles, and buses interact under dedicated bus lane operations. We develop a segment‑based network coordination framework that jointly optimizes lane‑change and routing decisions of connected and automated vehicles to improve dedicated lane utilization while preserving bus priority. The proposed framework incorporates a predictive bus‑protection mechanism that restricts vehicle access to protected lane segments within a monitoring horizon, together with a utility‑driven lane‑change strategy that accounts for anticipated travel time gains, downstream routing feasibility, and lane‑change stability. By explicitly coupling network‑level routing decisions with lane‑level interaction control, the method proactively mitigates conflicts on dedicated lanes before congestion effects materialize. The proposed approach is evaluated through microscopic traffic simulations in SUMO using a realistic urban corridor. Simulation results demonstrate that the framework enhances bus schedule adherence and reduces average travel times for both automated and human‑driven vehicles, while maintaining stable lane‑change behavior without increasing maneuver frequency.

Authors:Praharshitha Aryasomayajula, Ting Bai, Andreas A. Malikopoulos
Title: Battery Discharge Modeling for Electric Vehicles: A Hybrid Physics-based Residual Learning Approach
Abstract:
The growing integration of electric vehicle (EV) fleets into transportation services and energy systems requires accurate modeling of battery discharge and state‑of‑charge (SoC) evolution to ensure reliable vehicle operation and grid coordination. Existing approaches face a trade‑off between interpretable but simplified physics‑based models and data‑driven methods that demand large datasets and may lack physical consistency. In this paper, we propose a hybrid physics‑based residual learning framework for EV battery discharge modeling. A vehicle dynamics model based on force‑balance equations provides an interpretable baseline estimate of energy consumption and SoC evolution, capturing aerodynamic drag, rolling resistance, and regenerative braking. A neural network residual learner then corrects discrepancies caused by complex factors such as traffic conditions and driver behavior. Experimental results on 1,500 trip scenarios demonstrate that the proposed approach reduces the mean absolute percentage error to approximately 0.8%, significantly outperforming physics‑only models while preserving physical interpretability and computational efficiency.

Authors:Amir Ali Farzin, Yuen-Man Pun, Philipp Braun, Iman Shames
Title: On the Stability Connection Between Discrete-Time Algorithms and Their Resolution ODEs: Applications to Min-Max Optimisation
Abstract:
This work establishes a rigorous connection between stability properties of discrete‑time algorithms (DTAs) and corresponding continuous‑time dynamical systems derived through O(s^r) ‑resolution ordinary differential equations (ODEs). We show that for discrete‑ and continuous‑time dynamical systems satisfying a mild error assumption, exponential stability of a common equilibrium with respect to the continuous time dynamics implies exponential stability of the corresponding equilibrium for the discrete‑time dynamics, provided that the step size is chosen sufficiently small. We extend this result to common compact invariant sets. We prove that if an equilibrium is exponentially stable for the O(s^r) ‑resolution ODE, then it is also exponentially stable for the associated DTA. We apply this framework to analyse the limit point properties of several prominent optimisation algorithms, including Two‑Timescale Gradient Descent‑‑Ascent (TT‑GDA), Generalised Extragradient (GEG), Two‑Timescale Proximal Point (TT‑PPM), Damped Newton (DN), Regularised Damped Newton (RDN), and the Jacobian method (JM), by studying their O(1) ‑ and O(s) ‑resolution ODEs. We show that under a proper choice of hyperparameters, the set of saddle points of the objective function is a subset of the set of exponentially stable equilibria of GEG, TT‑PPM, DN, and RDN. We relax the common Hessian invariance assumption through direct analysis of the resolution ODEs, broadening the applicability of our results. Numerical examples illustrate the theoretical findings.

Authors:Wentao Tang, Xiuzhen Ye
Title: Koopman-based Estimation of Lyapunov Functions: Theory on a Reproducing Kernel Hilbert Space
Abstract:
Koopman operator provides a general linear description of nonlinear systems, whose estimation from data (via extended dynamic mode decomposition) has been extensively studied. However, the elusiveness between the Koopman spectrum and the stability of equilibrium point poses a challenge to utilizing the Koopman operator for stability analysis, which further hinders the construction of a universal theory of Koopman‑based control. In our prior work, we defined the Koopman operator on a reproducing kernel Hilbert space (RKHS) using a linear‑‑radial product kernel, and proved that the Koopman spectrum is confined in the unit disk of the complex plane when the origin is an asymptotically stable equilibrium point. Building on this fundamental spectrum‑‑stability relation, here we consider the problem of Koopman operator‑based Lyapunov function estimation with a given decay rate function. The decay rate function and the Lyapunov function are both specified by positive operators on the RKHS and are related by an operator algebraic Lyapunov equation (ALE), whose solution exists uniquely. The error bound of such a Lyapunov function estimate, obtained via kernel extended dynamic mode decomposition (kEDMD), are established based on statistical learning theory and verified by a numerical study.

Authors:Homayoun Honari, Roger Creus Castanyer, Michael Przystupa, Michael Noukhovitch, Pablo Samuel Castro, Glen Berseth
Title: Align and Filter: Improving Performance in Asynchronous On-Policy RL
Abstract:
Distributed training and increasing the gradient update frequency are practical strategies to accelerate learning and improve performance, but both exacerbate a central challenge: policy lag, which is the mismatch between the behavior policy generating data and the learning policy being updated. Policy lag can hinder the scaling of on‑policy learning algorithms to larger problems. In this paper, we identify the sources of policy lag caused by distributed learning and high update frequency. We use the findings to propose total Variation‑based Advantage aligned Constrained policy Optimization (\methodacronym) as a practical approach to mitigate policy lag. We empirically validate our method and show that it offers better robustness to policy lag in classic RL tasks and a modern RL for LLM math reasoning task.

Authors:Gabriele Dessena, Marco Civera, Oscar E. Bonilla-Manrique
Title: Operational Modal Analysis of Aeronautical Structures via Tangential Interpolation
Abstract:
Over the last decades, progress in modal analysis has enabled increasingly routine use of modal parameters for applications such as structural health monitoring and finite element model updating. For output‑only identification, or Operational Modal Analysis (OMA), widely adopted approaches include Stochastic Subspace Identification (SSI) methods and the Natural Excitation Technique combined with the Eigensystem Realization Algorithm (NExT‑ERA). Nevertheless, SSI‑based techniques may become cumbersome on large systems, while NExT‑ERA fitting can struggle when measurements are contaminated by noise. To alleviate these, this work investigates an OMA frequency‑domain formulation for aeronautical structures by coupling the Loewner Framework (LF) with NExT, yielding the proposed NExT‑LF method. The method exploits the computational efficiency of LF, due to the effectiveness of tangential interpolation, together with the impulse response function retrieval enabled by NExT. NExT‑LF is assessed on two experimental benchmarks: the eXperimental BeaRDS 2 high‑aspect‑ratio wing main spar and an Airbus Helicopters H135 bearingless main rotor blade. The identified modal parameters are compared against available experimental references and results obtained via SSI with Canonical Variate Analysis and NExT‑ERA. The results show that the modes identified by NExT‑LF correlate well with benchmark data, particularly for high‑amplitude tests and in the low‑frequency range.

Authors:Kalliopi Kleisarchaki
Title: Opponent State Inference Under Partial Observability: An HMM-POMDP Framework for 2026 Formula 1 Energy Strategy
Abstract:
The 2026 Formula 1 technical regulations introduce a fundamental change to energy strategy: under a 50/50 internal combustion engine / battery power split with unlimited regeneration and a driver‑controlled Override Mode, the optimal energy deployment policy depends not only on a driver's own state but on the hidden state of rival cars. This creates a Partially Observable Stochastic Game that cannot be solved by single‑agent optimisation methods. We present a tractable two‑layer inference and decision framework. The first layer is a 40‑state Hidden Markov Model (HMM) that infers a probability distribution over each rival's ERS charge level (four modes: H, M, L_harvest, L_derate), Override Mode status, and tyre degradation state from six publicly observable telemetry signals. The second layer is a Deep Q‑Network (DQN) policy that takes the HMM belief state as input and selects between energy deployment strategies. We formally characterise the counter‑harvest trap, a deceptive strategy in which a car deliberately suppresses observable deployment signals to induce a rival into a failed attack, and show that detecting it requires belief‑state inference over both ERS level and the harvest/derate sub‑mode. On synthetic races, the HMM achieves 96.8% ERS‑level accuracy (random baseline 25%), classifies L_harvest vs. L_derate with 89.4% accuracy, and detects counter‑harvest trap conditions with 96.3% recall. Pre‑season analysis indicates circuit‑dependent recharge availability (1.0x to 2.2x per lap) as the primary confound; Melbourne is the hardest‑case validation environment. Baum‑Welch calibration on 2026 race telemetry begins with the Australian Grand Prix (8 March 2026).

Authors:Rahul K. Gupta
Title: AC-Informed DC Optimal Transmission Switching via Admittance Sensitivity-Augmented Constraints and Repair Costs
Abstract:
AC optimal transmission switching (AC‑OTS) is a computationally challenging problem due to the nonconvexity and nonlinearity of AC power‑flow (PF) equations coupled with a large number of binary variables. A computationally efficient alternative is the DC‑OTS model, which uses the DC PF equations, but it can yield infeasible or suboptimal switching decisions when evaluated under the full AC optimal power flow (AC‑OPF). To tackle this issue, we propose an AC‑Informed DC Optimal Transmission Switching (AIDC‑OTS) scheme that enhances the DC‑OTS model by leveraging first‑ and second‑order admittance sensitivities‑based constraints and repair/penalty costs that guide the DC OTS towards AC‑feasible topologies. The resulting model initially is a Mixed‑Integer Quadratically Constrained Quadratic Program (MIQCQP), which we further reformulate into solver‑friendly representations, such as a Mixed‑Integer Second‑Order Cone Program (MISOCP) and a Mixed‑Integer Linear Program (MILP). This proposed scheme yields switching topologies that are AC‑feasible, while maintaining computational tractability. We validate the proposed scheme using extensive simulations across a large set of PGlib test cases, demonstrating its effectiveness, with performance benchmarks against original DC‑OTS and other OTS formulations such as LPAC‑OTS and QC‑OTS.

Authors:Andrea Espinosa del Pozo, Araceli Hernandez, Luis Badesa
Title: Least-Cost Overvoltage Control in PV-Rich Distribution Networks via Unbalanced Optimal Power Flow
Abstract:
The increasing penetration of photovoltaic (PV) generation in low‑voltage distribution networks presents operational challenges, with overvoltages being among the most critical. This study introduces a tool based on Unbalanced Optimal Power Flow (UBOPF) to assess cost‑effective local inverter control strategies specifically aimed at mitigating overvoltage issues. Two approaches are examined: dynamic active power curtailment and combined active and reactive power control. These strategies are tested on a residential low‑voltage network with high PV penetration, where the UBOPF model with voltage‑magnitude constraints was implemented in Julia using the JuMP optimization package. The results demonstrate that both methods are effective in maintaining voltage levels within regulatory limits, with the latter leading to lower PV curtailment. The analysis highlights the need to consider these control actions as ancillary services to the grid, which should be properly compensated given their effect on generator revenues.

Authors:Shrenik Jadhav, Zheng Liu
Title: Digital Twin-Based Cooling System Optimization for Data Center
Abstract:
Data center cooling systems consume significant auxiliary energy, yet optimization studies rarely quantify the gap between theoretically optimal and operationally deployable control strategies. This paper develops a digital twin of the liquid cooling infrastructure at the Frontier exascale supercomputer, in which a hot‑temperature water system comprises three parallel subloops, each serving dedicated coolant distribution unit clusters through plate heat exchangers and variable‑speed pumps. The surrogate model is built based on Modelica and validated through one full calendar year of 10‑minute operational data following ASHRAE Guideline 14. The model achieves a subloop coefficient of variation of the root mean square error below 2.7% and a normalized mean bias error within 2.5%. Using this validated surrogate model, a layered optimization framework evaluates three progressively constrained strategies: an analytical flow‑only optimization achieves 20.4% total energy saving, unconstrained joint optimization of flow rate and supply temperature demonstrates 30.1% total energy saving, and ramp‑constrained optimization of flow rate and supply temperature, enforcing actuator rate limits, can reach total energy saving of 27.8%. The analysis reveals that the baseline system operates at 2.9 times the minimum thermally safe flow rate, and the co‑optimizing supply temperature with flow rate nearly doubles the savings achievable by flow reduction alone.

Authors:Lotfi Ben Othmane, Yasaswini Konapalli, Naga Prudhvi Mareedu
Title: Extending Adaptive Cruise Control with Machine Learning Intrusion Detection Systems
Abstract:
An Adaptive Cruise Control (ACC) system automatically adjusts the host vehicle's speed to maintain a safe following distance from a lead vehicle. In typical implementations, a feedback controller (e.g., a Proportional‑Integral‑Derivative (PID) controller) computes the host vehicle's acceleration using a target speed and a spacing error, defined as the difference between the measured inter‑vehicle distance and a desired safe distance. ACC is often assumed to be resilient to fault‑injection attacks because a Kalman filter (KF) can smooth noisy speed measurements. However, we show‑‑through analytical proofs and simulation results‑‑that a KF can tolerate injected speed values only up to a bounded threshold. When injected values exceed this threshold, the filter can be driven off track, causing the ACC controller to make unsafe acceleration decisions and potentially leading to collisions. Our main contribution is to augment the PID‑based controller with Intrusion Detection System (IDS) outputs, yielding Intrusion Detection Systems‑Based Adaptive Cruise Control (ACC‑IDS). The ACC‑IDS controller is simple and implementable: a binary intrusion flag switches the control law to emergency braking. We prove that augmenting ACC with an IDS, under assumed detection‑performance and latency constraints, can mitigate these attacks and help preserve ACC's collision‑avoidance guarantees.

Authors:Boumediene Hamzi, Houman Owhadi, Umesh Vaidya
Title: Kernel Methods for Stochastic Dynamical Systems with Application to Koopman Eigenfunctions: Feynman-Kac Representations and RKHS Approximation
Abstract:
We extend the unified kernel framework for transport equations and Koopman eigenfunctions, developed in previous work by the authors for deterministic systems, to stochastic differential equations (SDEs). In the deterministic setting, three analytically grounded constructions‑Lions‑type variational principles, Green's function convolution, and resolvent operators along characteristic flows‑‑were shown to yield identical reproducing kernels. For stochastic systems, the Koopman generator includes a second‑order diffusion term, transforming the first‑order hyperbolic transport equation into a second‑order elliptic‑parabolic PDE. This fundamental change necessitates replacing the method of characteristics with probabilistic representations based on the Feynman‑‑Kac formula. Our main contributions include: (i) extension of all three kernel constructions to stochastic systems via Feynman‑‑Kac path‑integral representations; (ii) proof of kernel equivalence under uniform ellipticity assumptions; (iii) a collocation‑based computational framework incorporating second‑order differential operators; (iv) error bounds separating RKHS approximation error from Monte Carlo sampling error; (v) analysis of how diffusion affects numerical conditioning; and (vi) connections to generator EDMD, diffusion maps, and kernel analog forecasting. Numerical experiments on Ornstein‑‑Uhlenbeck processes, nonlinear SDEs with varying diffusion strength, and multi‑dimensional systems validate the theoretical developments and demonstrate that moderate diffusion can improve numerical stability through elliptic regularization.

Authors:Weijie Ren, Guang-Ren Duan, Ping Li, He Kong
Title: Observer-Based Active Fault/Disturbance Compensation Control for Fully Actuated Systems
Abstract:
This paper is concerned with fault/disturbance compensation control for fully actuated systems. In particular, we explore observer‑based control, incorporating an active compensation mechanism. First, we propose a novel observer with enhanced design flexibility for the fully actuated system model, enabling simultaneous estimation of system states and exogenous unknown signals, such as faults or disturbances. Then, a nonlinear controller is developed with an active fault or disturbance compensation term, leveraging the fully actuated system approach. The asymptotic stability of both the state estimation error and the closed‑loop control system is systematically established. Finally, the feasibility and merits of the proposed method are validated through comparative simulations and experiments.

Authors:Jiahao Fu, Feng Yang
Title: Intent-Context Synergy Reinforcement Learning for Autonomous UAV Decision-Making in Air Combat
Abstract:
Autonomous UAV infiltration in dynamic contested environments remains a significant challenge due to the partially observable nature of threats and the conflicting objectives of mission efficiency versus survivability. Traditional Reinforcement Learning (RL) approaches often suffer from myopic decision‑making and struggle to balance these trade‑offs in real‑time. To address these limitations, this paper proposes an Intent‑Context Synergy Reinforcement Learning (ICS‑RL) framework. The framework introduces two core innovations: (1) An LSTM‑based Intent Prediction Module that forecasts the future trajectories of hostile units, transforming the decision paradigm from reactive avoidance to proactive planning via state augmentation; (2) A Context‑Analysis Synergy Mechanism that decomposes the mission into hierarchical sub‑tasks (safe cruise, stealth planning, and hostile breakthrough). We design a heterogeneous ensemble of Dueling DQN agents, each specialized in a specific tactical context. A dynamic switching controller based on Max‑Advantage values seamlessly integrates these agents, allowing the UAV to adaptively select the optimal policy without hard‑coded rules. Extensive simulations demonstrate that ICS‑RL significantly outperforms baselines (Standard DDQN) and traditional methods (PSO, Game Theory). The proposed method achieves a mission success rate of 88% and reduces the average exposure frequency to 0.24 per episode, validating its superiority in ensuring robust and stealthy penetration in high‑dynamic scenarios.

Authors:Vikram C Patil
Title: Battery Lifetime Prediction using Data-driven Modeling Approaches
Abstract:
Batteries are ubiquitous today, with applications ranging from smartphones, watches, and laptops to electric cars, drones, and electric aircraft. Lithium‑ion batteries are widely used in these applications due to their high energy density, rechargeability, and low lifecycle cost. Understanding the lifetime of lithium‑ion batteries is essential for their effective utilization across many domains. In this study, data‑driven modeling approaches are explored to predict the lifetime of lithium‑ion batteries using various measurable battery parameters. A battery dataset from NASA's electric aircraft experiments was used, which included 17 predictor variables and remaining flight time as the response variable representing battery lifetime. The dataset contained more than 4,000,000 rows. However, the original dataset provided limited directly useful information about battery utilization over time; therefore, feature engineering was performed to generate more informative variables. Additionally, dimensionality reduction using principal component analysis (PCA) was applied to reduce computational cost and model complexity by selecting a smaller number of principal components as predictors for model development. Random forest and neural network models were explored for battery lifetime prediction using the engineered features. Multiple neural network configurations were evaluated, including single‑ and double‑hidden‑layer architectures with varying numbers of nodes. Mean squared error (MSE) on the test dataset was used as the performance metric for model comparison. The results indicate that data‑driven modeling approaches are effective for battery lifetime prediction, with neural network models outperforming other models based on the MSE metric. Furthermore, neural networks demonstrate robustness in handling high‑dimensional battery data.

Authors:Huan Cai, Ziqing Lu, Catherine Xu, Weiyu Xu, Jie Zheng
Title: Artificial Superintelligence May be Useless: Equilibria in the Economy of Multiple AI Agents
Abstract:
With recent development of artificial intelligence, it is more common to adopt AI agents in economic activities. This paper explores the economic actions of agents, including human agents and AI agents, in an economic game of trading products/services, and the equilibria in this economy involving multiple agents. We derive a range of equilibrium results and their corresponding conditions using a Markov chain stationary distribution based model. One distinct feature of our model is that we consider the long‑term utility generated by economic activities instead of their short‑term benefits. For the model consisting of two agents, we fully characterize all the possible economic equilibria and conditions. Interestingly, we show that unless each agent can at least double (not merely increase) its marginal utility by purchasing the other agent's products/services, purchasing the other agent's products/services will not happen in any economic equilibrium. We further extend our results to three and more agents, where we characterize more economic equilibria. We find that in some equilibria, the ``more powerful'' AI agents contribute zero utility to ``less capable'' agents.

Authors:Marek Chalupa, Thomas A. Henzinger, N. Ege Saraç, Emily Yu
Title: Quantitative Monitoring of Signal First-Order Logic
Abstract:
Runtime monitoring checks, during execution, whether a partial signal produced by a hybrid system satisfies its specification. Signal First‑Order Logic (SFO) offers expressive real‑time specifications over such signals, but currently comes only with Boolean semantics and has no tool support. We provide the first robustness‑based quantitative semantics for SFO, enabling the expression and evaluation of rich real‑time properties beyond the scope of existing formalisms such as Signal Temporal Logic. To enable online monitoring, we identify a past‑time fragment of SFO and give a pastification procedure that transforms bounded‑response SFO formulas into equisatisfiable formulas in this fragment. We then develop an efficient runtime monitoring algorithm for this past‑time fragment and evaluate its performance on a set of benchmarks, demonstrating the practicality and effectiveness of our approach. To the best of our knowledge, this is the first publicly available prototype for online quantitative monitoring of full SFO.

Authors:Laurent Pagnier, Melvyn Tyloo, Akshita Jindal, Pragati Thakur, Kyle C. A. Wedgwood
Title: A Closed-loop Framework to Discriminate Models Using Optimal Control
Abstract:
Predicting the response of an observed system to a known input is a fruitful first step to accurately control the system's dynamics. Despite the recent advances in fully data‑driven algorithms, the most interpretable way to reach this goal is through mechanistic mathematical modeling. Here, we leverage optimal control and propose a closed‑loop iterative method to choose among a set of candidate models the one that most accurately predict an observed system. We assume that one has control over an input of the observed system and access to measurements of its response. Our approach is to identify the input control that maximally discriminates the response of the candidate models, allowing us to determine which model is best by comparing such responses with the observed data. We demonstrate our proposed framework in numerical simulations before applying it during an electrophysiology experiment, successfully discriminating between different models for photocurrents produced via opsin dynamics.

Authors:Debarpita Banerjee, Debasmita Lohar, Sumana Ghosh
Title: Precision Switching Schedule for Efficient Control Implementations
Abstract:
Modern cyber‑physical systems, such as automotive control, rely on feedback controllers that regulate the system towards desired a setpoint. In practice, however, the controller must also be scheduled efficiently on resource‑constrained processors, where the choice of numerical precision for controller implementation directly affects both control quality and computational cost. This trade‑off is critical: higher precision improves control performance but increases runtime, while lower precision executes faster in the processor but may degrade overall system performance. In this work, we propose the first approach for a precision switching schedule, where the controller switches between different floating‑point precisions to balance control performance and enhance computational efficiency. We formulate this problem as a multi‑objective optimization, expressed as a Mixed‑Integer Quadratic Program (MIQP) with sound linearizations and error bounds that capture roundoff effects from different precision implementations. Our method efficiently computes a switching schedule that ensures the system output remains within a specified reference band. Through experimental evaluation on standard benchmark control systems, we demonstrate that switching between 32‑bit and 16‑bit floating‑point implementations offers an average runtime reduction of 26.5% compared to 32‑bit execution and a 27.6% improvement in control performance over 16‑bit execution, while maintaining near‑optimal overall performance.

Authors:Qi Hu, Jingyu Wang, Huriye Atilgan, Armin Lak, Martin J. Booth
Title: Depth-adapted adaptive optics for three-photon microscopy
Abstract:
Three‑photon (3‑P) fluorescence microscopy enables deep in vivo imaging with subcellular resolution, but its performance is fundamentally constrained by the maximum permissible laser power required to avoid tissue heating and photodamage. Under these power‑limited conditions, fluorescence signal generation, image contrast, and achievable imaging depth are strongly affected by the illumination beam profile and aberration correction strategy. In this paper, we showed that using a fixed illumination beam size was suboptimal across different imaging depths. We further showed that conventional Zernike‑based adaptive optics (AO) correction degrades under reduced Gaussian illumination beam sizes due to loss of modal orthogonality. This degradation results in slow convergence, unintended focal and field‑of‑view shifts, and excessive wavefront deformations. To overcome these limitations, we introduced a depth‑adapted AO framework in which both the illumination beam profile and the aberration correction basis were dynamically matched to the imaging conditions. By combining depth‑optimised beam underfilling with a bespoke set of illumination‑matched aberration modes, we achieved faster and more stable AO convergence, enhanced fluorescence signal and image quality during deep in vivo multi‑channel neuroimaging. Together, these results established a practical and robust AO‑enabled three‑photon microscopy strategy that maximised imaging performance under realistic power constraints.

Authors:Manuella Christelle Tossa, Fernando Madrigal, Ryan Blosser, Asma Jodeiri Akbarfam
Title: Time Stepped Cyber Physical Simulation of DoS, DoD, and FDI Attacks on the IEEE 14 Bus System
Abstract:
Reliable grid operation depends on accurate and timely telemetry, making modern power systems vulnerable to communication layer cyberattacks. This paper evaluates how Denial of Service (DoS), Denial of Data (DoD), and False Data Injection (FDI) attacks disrupt the IEEE 14 bus system using a MATLAB only, time stepped simulation framework built on MATPOWER. The framework emulates a 24 hour operating cycle with sinusoidal load variation, introduces attack specific manipulation of load and voltage data, and performs full AC power flow solves with reactive limit enforcement (PV PQ switching). At each timestep, the system logs true and measured voltages, generator P/Q output, system losses, and voltage limit violations to capture transient cyber physical effects. Results show that DoD causes the largest physical distortions and reactive power stress, DoS masks natural variability and degrades situational awareness, and FDI creates significant discrepancies between true and perceived voltages. The study provides a compact, reproducible benchmark for analyzing cyber induced instability and informing future defense strategies.

Authors:Yiwei Qiu, Jiahao Hu, Yi Zhou, Jie Zhu, Li Jiang, Shi Chen, Buxiang Zhou
Title: Enhanced Hydrogen Electrolyzer with Integrated Energy Storage to Provide Grid-Forming Services for Off-Grid ReP2H Application
Abstract:
This article proposes an energy storage‑enhanced hydrogen electrolyzer (ESEHE) to provide grid‑forming (GFM) services for off‑grid renewable power to hydrogen (ReP2H) systems. Unlike conventional ReP2H systems that use a centralized energy storage (ES) plant, the proposed topology directly connects batteries to the DC buses of electrolysis rectifiers. A tailored virtual synchronous machine (VSM) control framework enables the electrolyzer to autonomously provide real and reactive power support. A coordinated frequency‑splitting energy extraction strategy is designed to exploit both the battery and the electrolysis stack's electrical double‑layer (EDL) effect on different timescales, maximizing active power support while mitigating battery and stack degradation. An adaptive equalization control strategy is further developed to balance the battery state of charge (SOC) among multiple ESEHEs operating in parallel, which optimizes energy distribution and extends battery life. Real‑time simulations on StarSim validate the proposed topology and control strategies. Techno‑economic analysis shows that, compared with conventional off‑grid ReP2H systems based on a centralized ES plant, the ESEHE improves overall energy efficiency by 0.23% and reduces the initial total converter investment cost by roughly 6%, mainly due to the elimination of bidirectional AC/DC conversion and its associated losses in the centralized ES plant.

Authors:Ram Milan Kumar Verma, Shashi Ranjan Kumar, Hemendra Arya
Title: Integrated Guidance and Control for Path-Following with Bounded Inputs
Abstract:
Precise motion control of underactuated surface vessels is a crucial task in various maritime applications. In this work, we develop a nonlinear motion control strategy for surface vessels inspired by the pursuit guidance philosophy. Any sufficiently smooth path can be seen as a continuum of virtual targets moving along a specified path, which the pursuer is trying to catch. Contrary to the traditional path‑following methods, this work develops an integrated guidance and control approach capable of following any smooth path (unlike the ones composed of a finite number of straight lines and circles). The approach relies on steering the vehicle such that its velocity vector aligns with the line‑of‑sight (the line joining the moving virtual target and the surface vessel), resulting in a tail‑chase scenario. This leads to a path‑following behavior. This integrated approach also overcomes the disadvantages inherent in the traditional two‑loop‑based approaches. Additionally, the proposed work takes into account the asymmetric actuator constraints in the design, which makes the design close to realistic scenarios. Furthermore, the control law has been derived within a nonlinear framework using sliding mode, and thus remains applicable for a wider envelope. The stability of the proposed control strategy is formally proven. Numerical simulations for various specified paths validate the controller's accurate path‑following performance.

Authors:Zhiyi Zhao, Ye Guo, Zhenjia Lin, Yinliang Xu
Title: Curtail Renewables to Enhance Flexibility: A Regulated Forecast-based Dispatch Approach
Abstract:
This paper considers the flexibility degradation problem caused by excessive flexible ramping product (FRP) requirements with high variable energy resource (VER) penetration. Based on the rolling‑window co‑optimization model of energy and FRP, theoretical analysis of this paper reveals a unit dispatch transfer effect, in which high FRP requirements under forecast‑based dispatch (FBD) constrain real‑time flexibility and distort economic efficiency. To alleviate this effect, a regulated forecast‑based dispatch (RFBD) approach is proposed, which moderately caps VER outputs and enhances system flexibility. Simulation results demonstrate that the proposed approach effectively lowers FRP requirements and reduces operating cost compared with FBD.

Authors:Ruijie Tang, Chi Kit Ng, Kaixuan Wu, Long Bai, Guankun Wang, Yiming Huang, Yupeng Wang, Hongliang Ren
Title: TMR-VLA:Vision-Language-Action Model for Magnetic Motion Control of Tri-leg Silicone-based Soft Robot
Abstract:
In‑vivo environments, magnetically actuated soft robots offer advantages such as wireless operation and precise control, showing promising potential for painless detection and therapeutic procedures. We developed a trileg magnetically driven soft robot (TMR) whose multi‑legged design enables more flexible gaits and diverse motion patterns. For the silicone made of reconfigurable soft robots, its navigation ability can be separated into sequential motions, namely squatting, rotation, lifting a leg, walking and so on. Its motion and behavior depend on its bending shapes. To bridge motion type description and specific low‑level voltage control, we introduced TMR‑VLA, an end‑to‑end multi‑modal system for a trileg magnetic soft robot capable of performing hybrid motion types, which is promising for developing a navigation ability by adapting its shape to language‑constrained motion types. The TMR‑VLA deploys embodied endoluminal localization ability from EndoVLA, and fuses sequential frames and natural language commands as input. Low‑level voltage output is generated based on the current observation state and specific motion type description. The result shows the TMR‑VLA can predict how the voltage applied to TMR will change the dynamics of a silicon‑made soft robot. The TMR‑VLA reached a 74% average success rate.

Authors:Sina Mohammadi, Wayne Wang, Marcus Chen I Wada, Rouzbeh Haghighi, Ali Hassan, Hualong Liu, Archit Bhatnagar, Ang Chen, Wencong Su
Title: Grid Integration of AI Data Centers: A Critical Review of Energy Storage Solutions
Abstract:
Artificial intelligence (AI) is driving unprecedented growth in data center (DC) scale and power demand. AI workloads impose highly dynamic, difficult‑to‑forecast power profiles on the utility grid, creating reliability and stability challenges that conventional DC architectures are not designed to address. This paper provides a critical review of energy storage systems (ESSs) as the key enabling technology for reliable grid integration of AI DCs. We organize the review around a four‑layer hierarchical taxonomy, namely chip‑level buffering, rack/server‑level ESSs, facility‑level uninterruptible power supply (UPS) systems, and grid‑scale battery energy storage systems (BESSs), supplemented by non‑battery technologies including fuel cells (FCs) and thermal energy storage (TES). Each layer is analyzed with respect to response timescale, power and energy ratings, operational role, integration challenges, and coordination requirements. Key findings include: (i) AI DC load profiles differ fundamentally from traditional loads in their sub‑second variability, making conventional ESS dispatch strategies insufficient; (ii) hierarchical, coordinated ESS deployment across all layers is necessary for effective load smoothing and grid support; and (iii) significant gaps remain in simulation tools, degradation modeling, load forecasting, and optimal multi‑layer sizing. This review identifies open research challenges and future directions at the intersection of AI computing infrastructure and power system integration.

Authors:Tianyou Li, Haifeng Hu, Dapeng Li
Title: Adaptive Channel Estimation and Hybrid Beamforming for RIS aided Vehicular Communication
Abstract:
Reconfigurable intelligent surface (RIS) constitutes a disruptive technology for enhancing vehicular communication performance through reconfigurable propagation environments. In this paper, we propose an adaptive channel estimation framework and hybrid beamforming optimization strategy for RIS‑aided vehicular multiple‑input multiple‑output (MIMO) systems operating in high‑mobility scenarios. To address severe Doppler effects and rapid channel variations, we design a velocity‑aware pilot scheme that progressively estimates cascaded channels across two timescales, leveraging tensor decomposition and adaptive grouping of passive elements. This framework dynamically balances channel estimation accuracy and spectral efficiency, significantly reducing training overhead. Furthermore, we develop a low‑complexity hybrid beamforming algorithm for both narrowband single vehicle user equipment (VUE) and broadband multi‑VUE systems. For single‑VUE scenarios, we derive closed‑form active beamforming solutions and optimize passive beamforming via alternating optimization. For multi‑VUE broadband systems, we jointly optimize subcarrier allocation, power distribution, and beamforming to maximize system throughput while mitigating inter‑carrier interference (ICI) caused by Doppler spread, subject to quality‑of‑service (QoS) constraints and RIS hardware limitations. Our simulation results demonstrate that the proposed methods achieve substantial performance gains in channel estimation efficiency, beamforming robustness, and system throughput compared to conventional schemes, particularly under high mobility conditions.

Authors:Paul Nitschke, Shahriar Talebi
Title: Hereditary Geometric Meta-RL: Nonlocal Generalization via Task Symmetries
Abstract:
Meta‑Reinforcement Learning (Meta‑RL) commonly generalizes via smoothness in the task encoding. While this enables local generalization around each training task, it requires dense coverage of the task space and leaves richer task space structure untapped. In response, we develop a geometric perspective that endows the task space with a "hereditary geometry" induced by the inherent symmetries of the underlying system. Concretely, the agent reuses a policy learned at the train time by transforming states and actions through actions of a Lie group. This converts Meta‑RL into symmetry discovery rather than smooth extrapolation, enabling the agent to generalize to wider regions of the task space. We show that when the task space is inherited from the symmetries of the underlying system, the task space embeds into a subgroup of those symmetries whose actions are linearizable, connected, and compact‑‑properties that enable efficient learning and inference at the test time. To learn these structures, we develop a differential symmetry discovery method. This collapses functional invariance constraints and thereby improves numerical stability and sample efficiency over functional approaches. Empirically, on a two‑dimensional navigation task, our method efficiently recovers the ground‑truth symmetry and generalizes across the entire task space, while a common baseline generalizes only near training tasks.

Authors:Gabriel Mantegna, Emil Dimanchev, Filippo Pecci, Neha Patankar, Jesse Jenkins
Title: Uncertainty-Aware Grid Planning in the Real World: A Method Enabling Large-Scale, Two-Stage Adaptive Robust Optimization for Capacity Expansion Planning
Abstract:
Capacity expansion models are frequently used to inform multi‑billion dollar grid infrastructure decisions, a context in which there is significant uncertainty surrounding the future need for and performance of such infrastructure. However, despite much academic literature on the topic, virtually no grid planning processes use capacity expansion models that endogenously consider uncertainty, an oversight which frequently leads to short‑sighted infrastructure decisions. This is partially due to a technology transfer gap, but it is also due to a lack of methods that work at large scale. In this paper we introduce a method for endogenizing uncertainty into capacity expansion models, a variant of adaptive robust optimization, that addresses this gap. We apply the method to a real‑world capacity expansion planning problem, that of the State of California, and compare its performance to that of traditional adaptive robust optimization. We find that both the traditional method and our method identify increased transmission investment as a key lever for increasing robustness and adaptability, while helping to avoid downside risks that current deterministic planning processes may be exposing ratepayers to. Our method performs similarly to the traditional method in terms of outcomes, while significantly reducing computational complexity, making it scalable to real‑world planning problems.

Authors:Om Tailor
Title: Verifier-Bound Communication for LLM Agents: Certified Bounds on Covert Signaling
Abstract:
Colluding language‑model agents can hide coordination in messages that remain policy‑compliant at the surface level. We present CLBC, a protocol where generation and admission are separated: a message is admitted to transcript state only if a small verifier accepts a proof‑bound envelope under a pinned predicate Π. The predicate binds policy hash, public randomness schedule, transcript chaining, latent schema constraints, canonical metadata/tool fields, and deterministic rejection codes. We show how this protocol yields an upper bound on transcript leakage in terms of latent leakage plus explicit residual channels, derive adaptive composition guarantees, and state a semantic lower bound when policy‑valid alternatives remain choosable. We report extensive empirically grounded evidence: aggregate evaluation satisfies all prespecified thresholds; strict lane decoder advantage is bounded at 0.0000 with MI proxy 0.0636; adaptive‑colluder stress tests remain below attacker thresholds; and baseline separation shows large gaps between reject‑by‑default semantics and audit‑only controls. We further quantify operational tradeoffs. Strict full‑proof mode has median turn latency 27.53s (p95 28.08s), while sampled proving reduces non‑proved‑turn latency to 0.327ms. The central finding is that bottlenecks alone are insufficient: security claims depend on verifiable admission semantics that are online, deterministic, and fail‑closed.

Authors:Mohammed Adib Oumer, Vishnu Murali, Majid Zamani
Title: Vector Certificates for $ω$-regular Specifications
Abstract:
The recently introduced notions of ranking functions and closure certificates utilize well‑foundedness arguments to facilitate the verification of dynamical systems against ω‑regular properties. A ranking function and a closure certificate are real‑valued functions defined over states and state pairs of a dynamical system whose zero superlevel sets are inductive state invariant and inductive transition invariant, respectively. The search for such certificates can be automated by fixing a specific template class, such as a polynomial of a fixed degree, and then using optimization techniques such as sum‑of‑squares (SOS) programming to find it. Unfortunately, such certificates may not be found for a fixed template. In such a case, one must change the template; for example, increase the degree of the polynomial. In this paper, we consider a notion of multiple functions in the form of vector certificates. Taking inspiration from the literature on vector barrier certificates as generalizations of standard barrier certificates for safety verification, we propose vector co‑Büchi ranking functions and vector closure certificates as nontrivial generalizations of ranking functions and closure certificates, respectively. Both notions consist of a set of functions that jointly overapproximate an inductive invariant by considering each function to be a linear combination of the others. The advantage of such certificates is that they allow us to prove properties even when a single function for a fixed template cannot be found using standard approaches. We present an SOS programming approach to search for these functions and demonstrate the effectiveness of our proposed method in verifying ω‑regular specifications in several case studies.

Authors:Jin Lee, Zhonghao Chen, Xuhang He, Robert Underwood, Bogdan Nicolae, Franck Cappello, Xiaoyi Lu, Sheng Di, Zheng Zhang
Title: SPARe: Stacked Parallelism with Adaptive Reordering for Fault-Tolerant LLM Pretraining Systems with 100k+ GPUs
Abstract:
In large‑scale LLM pre‑training systems with 100k+ GPUs, failures become the norm rather than the exception, and restart costs can dominate wall‑clock training time. However, existing fault‑tolerance mechanisms are largely unprepared for this restart‑dominant regime. To address this challenge, we propose SPARe ‑ Stacked Parallelism with Adaptive Reordering ‑ a fault‑tolerance framework that masks node failures during gradient synchronization by stacking redundant data shards across parallelism groups and adaptively reordering execution. SPARe achieves availability comparable to traditional replication while maintaining near‑constant computation overhead of only 2~3x, even under high redundancy where traditional replication would require linearly inflating overhead. We derive closed‑form expressions for endurable failure count and computation overhead, validate them via SimGrid‑based discrete‑event simulation, and jointly optimize redundancy and checkpointing to minimize time‑to‑train. At extreme scale with up to 600k GPUs, SPARe reduces time‑to‑train by 40~50% compared to traditional replication.

Authors:Amit Shivam, Manuel C. R. M. Fernandes, Sergio Vinha, Fernando A. C. C. Fontes
Title: Geometric Look-Angle Shaping Strategy for Enclosed Inspection
Abstract:
This paper introduces inspection through GLASS, a Geometric Look‑Angle Shaping Strategy for enclosed regions using unmanned aerial vehicles. In doing so, the vehicles guidance command is constructed through a bounded, geometry‑consistent shaping of the look angle relative to a desired standoff path. By embedding a smooth, hyperbolic‑tangent‑type shaping function within a polar geometric framework, GLASS ensures global existence of the guidance dynamics. It avoids the far‑field limitations inherent to conventional formulations. Lyapunov stability analysis establishes asymptotic convergence to a prescribed inspection standoff under explicit curvature feasibility conditions, along with analytical settling‑time characteristics. The proposed strategy incorporates maximum turn‑rate constraints without inducing singularities throughout the workspace. High‑fidelity six‑degree‑of‑freedom quadrotor simulations demonstrate the effectiveness of GLASS in representative enclosed inspection scenarios, highlighting a practically viable guidance framework for autonomous enclosed inspection missions.

Authors:Sumesh Sharma, Marcel Moll, Timo Oksanen
Title: Smart Prism with Tilt Compensation for CAN bus on Mobile Machinery Using Robotic Total Stations
Abstract:
Accurate reference trajectories are required to validate autonomous agricultural robots and highly automated off‑road vehicles under real‑world field conditions. In practice, robotic total stations provide millimeter‑level prism center coordinates, but the point of interest on the vehicle is typically displaced by a lever arm, ranging from decimeters to multiple meters. Roll and pitch motions, as typically observed in off‑road machinery, therefore introduce horizontal point of interest errors far exceeding the measurement accuracy of robotic total stations observations. This paper presents the design, implementation, and validation of a Smart Prism prototype that augments a robotic total station prism with an inertial measurement unit to enable real‑time tilt compensation. The prototype integrates an STM32H7 microcontroller and a Murata SCH16T‑series IMU and estimates roll and pitch angles using an adaptive complementary filter. The tilt‑compensated point of interest coordinates are obtained by transforming a calibrated lever arm from the body frame into the navigation frame and combining it with robotic total station prism positions. To support vehicle‑side integration, the system can transmit prism and tilt‑compensated point of interest coordinates on the Controller Area Network bus, allowing the point of interest to be treated as a virtual position sensor (e.g., co‑located with a rear‑axle reference point). Experiments with a fixed ground reference point, using a prism to point of interest lever arm of approximately 1.07m and manual roll/pitch excursions of up to 60 deg, yield three‑dimensional root‑mean‑square errors between 2.9mm and 23.6mm across five test series. The results demonstrate that IMU‑based tilt compensation enables reference measurements suitable for validating centimeter‑level navigation systems under dynamic field conditions.

Authors:Vidyut Pradeep, Shirantha Welikala
Title: Modeling PWM-Time-SOC Interaction in a Simulated Robot
Abstract:
Accurate prediction of battery state of charge is needed for autonomous robots to plan movements without using up all available power. This work develops a physics and data‑informed model from a simulation that predicts SOC depletion as a function of time and PWM duty cycle for a simulated 4‑wheel Arduino robot. A forward‑motion simulation incorporating motor electrical characteristics (resistance, inductance, back‑EMF, torque constant) and mechanical dynamics (mass, drag, rolling resistance, wheel radius) was used to generate SOC time‑series data across PWM values from 1‑100%. Sparse Identification of Nonlinear Dynamics (SINDy), combined with least‑squares regression, was applied to construct a unified nonlinear model that captures SOC(t, p). The framework allows for energy‑aware planning for similar robots and can be extended to incorporate arbitrary initial SOC levels and environment‑dependent parameters for real‑world deployment.

Authors:Ran Tao, Pan Zhao, Ilya Kolmanovsky, Naira Hovakimyan
Title: Robust Adaptive MPC in the Presence of Nonlinear Time-Varying Uncertainties: An Uncertainty Compensation Approach
Abstract:
This paper introduces an uncertainty compensation‑based robust adaptive model predictive control (MPC) framework for linear systems with nonlinear time‑varying uncertainties. The framework integrates an L1 adaptive controller to compensate for the matched uncertainty and a robust feedback controller, designed using linear matrix inequalities, to mitigate the effect of unmatched uncertainty on target output channels. Uniform bounds on the errors between the system's states and control inputs and those of a nominal (i.e., uncertainty‑free) system are derived. These error bounds are then used to tighten the actual system's state and input constraints, enabling the design of an MPC for the nominal system under these tightened constraints. Referred to as uncertainty compensation‑based MPC (UC‑MPC), this approach ensures constraint satisfaction while delivering enhanced performance compared to existing methods. Simulation results for a flight control example and a spacecraft landing on an asteroid demonstrate the effectiveness of the proposed framework.

Authors:H. Sinan Bank, Daniel R. Herber
Title: GENAI WORKBENCH: AI-Assisted Analysis and Synthesis of Engineering Systems from Multimodal Engineering Data
Abstract:
Modern engineering design platforms excel at discipline‑specific tasks such as CAD, CAM, and CAE, but often lack native systems engineering frameworks. This creates a disconnect where system‑level requirements and architectures are managed separately from detailed component design, hindering holistic development and increasing integration risks. To address this, we present the conceptual framework for the GenAI Workbench, a Model‑Based Systems Engineering (MBSE) environment that integrates systems engineering principles into the designer's workflow. Built on an open‑source PLM platform, it establishes a unified digital thread by linking semantic data from documents, physical B‑rep geometry, and relational system graphs. The workbench facilitates an AI‑assisted workflow where a designer can ingest source documents, from which the system automatically extracts requirements and uses vision‑language models to generate an initial system architecture, such as a Design Structure Matrix (DSM). This paper presents the conceptual architecture, proposed methodology, and anticipated impact of this work‑in‑progress framework, which aims to foster a more integrated, data‑driven, and informed engineering design methodology.

Authors:Kei Suzuki, Jing Liu, Ye Wang, Chiori Hori, Matthew Brand, Diego Romeres, Toshiaki Koike-Akino
Title: Embedding Morphology into Transformers for Cross-Robot Policy Learning
Abstract:
Cross‑robot policy learning ‑‑ training a single policy to perform well across multiple embodiments ‑‑ remains a central challenge in robot learning. Transformer‑based policies, such as vision‑language‑action (VLA) models, are typically embodiment‑agnostic and must infer kinematic structure purely from observations, which can reduce robustness across embodiments and even limit performance within a single embodiment. We propose an embodiment‑aware transformer policy that injects morphology via three mechanisms: (1) kinematic tokens that factorize actions across joints and compress time through per‑joint temporal chunking; (2) a topology‑aware attention bias that encodes kinematic topology as an inductive bias in self‑attention, encouraging message passing along kinematic edges; and (3) joint‑attribute conditioning that augments topology with per‑joint descriptors to capture semantics beyond connectivity. Across a range of embodiments, this structured integration consistently improves performance over a vanilla pi0.5 VLA baseline, indicating improved robustness both within an embodiment and across embodiments.

Authors:Kazi Sher Ahmed, Bekir Bediz
Title: Design Framework and Manufacturing of an Active Magnetic Bearing Spindle for Micro-Milling Applications
Abstract:
Micro‑milling spindles require high rotational speeds where conventional rolling element bearings face limitations such as friction and thermal expansion. Active magnetic bearings (AMBs) address these challenges by providing non‑contact and lubrication‑free operation at ultra‑high speeds with the ability to actively regulate spindle dynamics. The existing literature on AMB spindles has mainly reported specific prototype realizations or control system implementations for specific spindle dynamics. Consequently, design knowledge remains fragmented across isolated successful studies. This paper addresses this gap by presenting a systematic and iterative framework to design and manufacture a micro‑milling AMB spindle. The process involves a multidisciplinary design flow with a focus on critical practical aspects of manufacturing. The realized spindle is reported as a case study.

Authors:Miao Zhang, Ruixiao Zhang, Jianxin Shi, Hengzhi Wang, Hao Fang, Jiangchuan Liu
Title: QuickGrasp: Responsive Video-Language Querying Service via Accelerated Tokenization and Edge-Augmented Inference
Abstract:
Video‑language models (VLMs) are reshaping video querying services, bringing unified solutions to complex perception and reasoning tasks. However, deploying large VLMs in real‑world systems remains challenging due to their high resource demands, and remote‑based deployment often results in unacceptable response delays. Although small, locally deployable VLMs offer faster responses, they unavoidably fall short in accuracy. To reconcile this trade‑off, we propose QuickGrasp, a responsive, quality of service (QoS)‑aware system that bridges this gap through a local‑first architecture with on‑demand edge augmentation. Built upon the highly modular architecture of VLMs, QuickGrasp shares the vision representation across model variants to avoid redundant computation. To maximize system‑wide efficiency, QuickGrasp introduces three key designs: accelerated video tokenization, query‑adaptive edge augmentation, and delay‑aware, accuracy‑preserving vision token density configuration. We implement a prototype of QuickGrasp and evaluate it across multiple video understanding benchmarks. The results show that QuickGrasp matches the accuracy of large VLMs while achieving up to a 12.8x reduction in response delay. QuickGrasp represents a key advancement toward building responsive video querying services for open‑world understanding that fully leverage the capabilities of VLMs.

Authors:Gerald Ebmer, Minh Nhat Vu, Tobias Glück, Wolfgang Kemmetmüller
Title: Autonomous Block Assembly for Boom Cranes with Passive Joint Dynamics: Integrated Vision MPC Control
Abstract:
This paper presents an autonomous control framework for articulated boom cranes performing prefabricated block assembly in construction environments. The key challenge addressed is precise placement control under passive joint dynamics that cause pendulum‑like sway, complicating the accurate positioning of building components. Our integrated approach combines real‑time vision‑based pose estimation of building blocks, collision‑aware B‑spline path planning, and nonlinear model predictive control (NMPC) to achieve autonomous pickup, placement, and obstacle‑avoidance assembly operations. The framework is validated on a laboratory‑scale testbed that emulates crane kinematics and passive dynamics while enabling rapid experimentation. The collision‑aware planner generates feasible B‑spline references in real‑time on CPU hardware with anytime performance, while the NMPC controller actively suppresses passive joint sway and tracks the planned trajectory under continuous vision feedback. Experimental results demonstrate autonomous block stacking and obstacle‑avoidance assembly, with sway damping reducing settling times by more than an order of magnitude compared to uncontrolled passive dynamics, confirming the real‑time feasibility of the integrated approach for construction automation.

Authors:Mariam Elnour, Mohammad AlShaikh Saleh, Rachad Atat, Xiang Huo, Abdulrahman Takiddin, Muhammad Ismail, Hasan Kurban, Katherine R. Davis, Erchin Serpedin
Title: Joint Sensor Deployment and Physics-Informed Graph Transformer for Smart Grid Attack Detection
Abstract:
This paper proposes a joint multi‑objective optimization framework for strategic sensor placement in power systems to enhance attack detection. A novel physics‑informed graph transformer network (PIGTN)‑based detection model is proposed. Non‑dominated sorting genetic algorithm‑II (NSGA‑II) jointly optimizes sensor locations and the PIGTN's detection performance, while considering practical constraints. The combinatorial space of feasible sensor placements is explored using NSGA‑II, while concurrently training the proposed detector in a closed‑loop setting. Compared to baseline sensor placement methods, the proposed framework consistently demonstrates robustness under sensor failures and improvements in detection performance in seven benchmark cases, including the 14, 30, IEEE‑30, 39, 57, 118 and the 200 bus systems. By incorporating AC power flow constraints, the proposed PIGTN‑based detection model generalizes well to unseen attacks and outperforms other graph network‑based variants (topology‑aware models), achieving improvements up to 37% in accuracy and 73% in detection rate, with a mean false alarms rate of 0.3%. In addition, optimized sensor layouts significantly improve the performance of power system state estimation, achieving a 61%‑‑98% reduction in the average state error.

Authors:Alexandre Anahory Simoes, Leonardo Colombo, Juan Giribet, Efstratios Stratoglou
Title: Virtual Constraint for a Quadrotor UAV Enforcing a Body-Axis Pointing Direction
Abstract:
We propose a geometric control framework on SE(3) for quadrotors that enforces pointing‑driven missions without completing a full attitude reference. The mission is encoded through virtual constraints defining a task manifold and an associated set of admissible velocities, and invariance is achieved by a feedback law obtained from a linear system in selected inputs. Under a transversality condition with the effective actuation distribution, the invariance‑enforcing input is uniquely defined, yielding a constructive control law and, for relevant tasks, closed‑form expressions. We further derive a local off‑manifold stabilization extension. As a case study, we lock a body axis to a prescribed line‑of‑sight direction while maintaining fixed altitude.

Authors:Jacob Goodman, Leonardo Colombo, Juan Giribet
Title: Observer-Based Estimation and Hydrostatic Inertia Modeling for Cooperative Transport of Variable-Inertia Loads with Quadrotors
Abstract:
We address load‑parameter estimation in cooperative aerial transport with time‑varying mass and inertia, as in fluid‑carrying payloads. Using an intrinsic manifold model of the multi‑quadrotor‑load dynamics, we combine a geometric tracking controller with an observer for parameter identification. We estimate mass from measurable kinematics and commanded forces, and handle variable inertia via an inertia surrogate that reproduces the load's rotational dynamics for control and state propagation. Instead of real‑time identification of the true inertia tensor, driven by high‑dimensional internal fluid motion, we leverage known tank geometry and fluid‑mechanical structure to pre‑compute inertia tensors and update them through a lookup table indexed by fill level and attitude. The surrogate is justified via the incompressible Navier‑Stokes equations in the translating/rotating load frame: when effective forcing is gravity‑dominated (i.e., translational/rotational accelerations and especially jerk are limited), the fluid approaches hydrostatic equilibrium and the free surface is well approximated by a plane orthogonal to the body‑frame gravity direction.

Authors:Shihao Li, Jiachen Li, Christopher Martin, Zijun Chen, Dongmei Chen, Wei Li
Title: Curriculum-Based Soft Actor-Critic for Multi-Section R2R Tension Control
Abstract:
Precise tension control in roll‑to‑roll (R2R) manufacturing is difficult under varying operating conditions and process uncertainty. This paper presents a curriculum‑based Soft Actor‑Critic (SAC) controller for multi‑section R2R tension control. The policy is trained in three phases with progressively wider reference ranges, from 27 to 33 N to the full operating envelope of 20 to 40 N, so it can generalize across nominal and disturbed conditions. On a three‑section R2R benchmark, the learned controller achieves accurate tracking in nominal operation and handles large disturbances, including 20 N to 40 N step changes, with a single policy and no scenario‑specific retuning. These results indicate that curriculum‑trained SAC is a practical alternative to model‑based control when system parameters vary and process uncertainty is significant.

Authors:Kriti Thakur, Alivelu Manga Parimi, Mayukha Pal
Title: FaultXformer: A Transformer-Encoder Based Fault Classification and Location Identification model in PMU-Integrated Active Electrical Distribution System
Abstract:
Accurate fault detection and localization in electrical distribution systems is crucial, especially with the increasing integration of distributed energy resources (DERs), which inject greater variability and complexity into grid operations. In this study, FaultXformer is proposed, a Transformer encoder‑based architecture developed for automatic fault analysis using real‑time current data obtained from phasor measurement unit (PMU). The approach utilizes time‑series current data to initially extract rich temporal information in stage 1, which is crucial for identifying the fault type and precisely determining its location across multiple nodes. In Stage 2, these extracted features are processed to differentiate among distinct fault types and identify the respective fault location within the distribution system. Thus, this dual‑stage transformer encoder pipeline enables high‑fidelity representation learning, considerably boosting the performance of the work. The model was validated on a dataset generated from the IEEE 13‑node test feeder, simulated with 20 separate fault locations and several DER integration scenarios, utilizing current measurements from four strategically located PMUs. To demonstrate robust performance evaluation, stratified 10‑fold cross‑validation is performed. FaultXformer achieved average accuracies of 98.76% in fault type classification and 98.92% in fault location identification across cross‑validation, consistently surpassing conventional deep learning baselines convolutional neural network (CNN), recurrent neural network (RNN). long short‑term memory (LSTM) by 1.70%, 34.95%, and 2.04% in classification accuracy and by 10.82%, 40.89%, and 6.27% in location accuracy, respectively. These results demonstrate the efficacy of the proposed model with significant DER penetration.

Authors:Moritz Woelk, Jarod Morris, Wentao Tang
Title: Neural Luenberger state observer for nonautonomous nonlinear systems
Abstract:
This work proposes a method for model‑free synthesis of a state observer for nonlinear systems with manipulated inputs, where the observer is trained offline using a historical or simulation dataset of state measurements. We use the structure of the Kazantzis‑Kravaris/Luenberger (KKL) observer, extended to nonautonomous systems by adding an additional input‑affine term to the linear time‑invariant (LTI) observer‑state dynamics, which determines a nonlinear injective mapping of the true states. Both this input‑affine term and the nonlinear mapping from the observer states to the system states are learned from data using fully connected feedforward multi‑layer perceptron neural networks. Furthermore, we theoretically prove that trained neural networks, when given new input‑output data, can be used to observe the states with a guaranteed error bound. To validate the proposed observer synthesis method, case studies are performed on a bioreactor and a Williams‑Otto reactor.

Authors:Mihir Sinha, Kriti Thakur, Prasanta K. Panigrahi, Alivelu Manga Parimi, Mayukha Pal
Title: Data-Driven Linearization based Arc Fault Prediction in Medium Voltage Electrical Distribution System
Abstract:
High‑impedance arc faults (HIAFs) in medium‑voltage electrical distribution systems are difficult to detect due to their low fault current levels and nonlinear transient behavior. Traditional detection algorithms generally struggle with predictions under dynamic waveform scenarios. This research provides our approach of using a unique data‑driven linearization (DDL) framework for early prediction of HIAFs, giving both interpretability and scalability. The proposed method translates nonlinear current waveforms into a linearized space using coordinate embeddings and polynomial transformation, enabling precise modelling of fault precursors.The total duration of the test waveform is 0.5 seconds, within which the arc fault occurs between 0.2 seconds to 0.3 seconds. Our proposed approach using DDL, trained solely on the pre‑fault healthy region (0.10 seconds to 0.18 seconds) effectively captures certain invisible fault precursors, to accurately predict the onset of fault at 0.189 seconds, which is approximately 0.011 seconds (i.e., 11 milliseconds) earlier than the actual fault occurrence. In particular, the framework predicts the start of arc faults at 0.189 seconds, significantly earlier of the actual fault incidence at 0.200 seconds, demonstrating substantial early warning capability. Performance evaluation comprises eigenvalue analysis, prediction error measures, error growth rate and waveform regeneration fidelity. Such early prediction proves that the model is capable of correctly foreseeing faults which is especially helpful in preventing real‑world faults and accidents. It confirms that our proposed approach reliably predicts arc faults in medium‑voltage power distribution systems

Authors:Luca Giangrande
Title: A 200 dB Dynamic Range Radiation-Hard Delta-Sigma Current Digitizer for Beam Loss Monitoring
Abstract:
This manuscript describes a radiation‑hardened current‑mode delta‑sigma ADC fabricated in a standard 130 nm CMOS technology and qualified for total ionizing doses up to 100 Mrad. The operational signal range achieved with a 100 s integration window exceeds 200 dB. The converter is designed for beam loss monitoring applications in high‑energy physics, where it must handle input currents spanning nine decades, from 1 mA down to 1 pA, while providing a fast 10 us response time for machine protection. To meet these conflicting requirements, the architecture exploits the inherent trade‑off between resolution and acquisition time provided by delta‑sigma conversion: a first‑order architecture, sampling at 20 MHz, delivers 11‑bit effective resolution within the critical 10 us window for critical currents around 1 mA. Integration times above 10 s enable the sub‑picoampere resolution required for precise beam alignment and background monitoring. The chip integrates two independent channels, consumes 25 mW from a 1.2 V supply, and relies on radiation‑hardening techniques such as triple‑redundant digital logic, custom ESD protections, and manual enclosed layout for critical analog transistors. Post‑irradiation measurements up to 100 Mrad show no significant performance degradation, and the uncalibrated integral nonlinearity remains within [+4, ‑5] LSBs over the 1 mA to 5 uA range. The converter's flexibility and radiation tolerance make it suitable not only for the HL‑LHC beam loss monitoring upgrade but also for other precision current measurement applications in harsh environments.

Authors:Xinyao Wang, Jonathan Realmuto
Title: Physics-Embedded Neural ODEs for Learning Antagonistic Pneumatic Artificial Muscle Dynamics
Abstract:
Pneumatic artificial muscles (PAMs) enable compliant actuation for soft wearable, assistive, and interactive robots. When arranged antagonistically, PAMs can provide variable impedance through co‑contraction but exhibit coupled, nonlinear, and hysteretic dynamics that challenge modeling and control. This paper presents a hybrid neural ordinary differential equation (Neural ODE) framework that embeds physical structure into a learned model of antagonistic PAM dynamics. The formulation combines parametric joint mechanics and pneumatic state dynamics with a neural network force component that captures antagonistic coupling and rate‑dependent hysteresis. The forward model predicts joint motion and chamber pressures with a mean R^2 of 0.88 across 225 co‑contraction conditions. An inverse formulation, derived from the learned dynamics, computes pressure commands offline for desired motion and stiffness profiles, tracked in closed loop during execution. Experimental validation demonstrates reliable stiffness control across 126‑176 N/mm and consistent impedance behavior across operating velocities, in contrast to a static model, which shows degraded stiffness consistency at higher velocities.

Authors:Yihan, Wen, Xin Chen
Title: PseudoAct: Leveraging Pseudocode Synthesis for Flexible Planning and Action Control in Large Language Model Agents
Abstract:
Large language model (LLM) agents typically rely on reactive decision‑making paradigms such as ReAct, selecting actions conditioned on growing execution histories. While effective for short tasks, these approaches often lead to redundant tool usage, unstable reasoning, and high token consumption in complex long‑horizon tasks involving branching, iteration, or multi‑tool coordination. To address these limitations, this paper introduces PseudoAct, a novel framework for flexible planning and action control in LLM agents through pseudocode synthesis. Leveraging the ability of LLMs to express task‑solving strategies as code, PseudoAct synthesizes a structured pseudocode plan that decomposes a task into subtasks and explicitly encodes control flow, including sequencing, conditionals, loops, parallel composition, and combinations of these logic primitives. Actions are then executed by following this global plan, making the decision logic explicit and temporally coherent. This design reduces redundant actions, prevents infinite loops, and avoids uninformative alternative exploration, enabling consistent and efficient long‑horizon decision‑making. Experiments on benchmark datasets show that our method significantly outperforms existing reactive agent approaches, achieving a 20.93% absolute gain in success rate on FEVER and setting a new state‑of‑the‑art on HotpotQA.

Authors:Yanda Yang, Sambeeta Das
Title: MicroPush: A Simulator and Benchmark for Contact-Rich Cell Pushing and Assembly with a Magnetic Rolling Microrobot
Abstract:
Magnetic rolling microrobots enable gentle manipulation in confined microfluidic environments, yet autonomy for contact‑rich behaviors such as cell pushing and multi‑target assembly remains difficult to develop and evaluate reproducibly. We present MicroPush, an open‑source simulator and benchmark suite for magnetic rolling microrobots in cluttered 2D scenes. MicroPush combines an overdamped interaction model with contact‑aware stick‑‑slip effects, lightweight near‑field damping, optional Poiseuille background flow, and a calibrated mapping from actuation frequency to free‑space rolling speed. On top of the simulator core, we provide a modular planning‑‑control stack with a two‑phase strategy for contact establishment and goal‑directed pushing, together with a deterministic benchmark protocol with fixed tasks, staged execution, and unified CSV logging for single‑object transport and hexagonal assembly. We report success, time, and tracking metrics, and an actuation‑variation measure E_Δω. Results show that controller stability dominates performance under flow disturbances, while planner choice can influence command smoothness over long‑horizon sequences via waypoint progression. MicroPush enables reproducible comparison and ablation of planning, control, and learning methods for microscale contact‑rich micromanipulation.

Authors:Chun-Wei Kong, Sebastian Escobar, Ibon Gracia, Jay McMahon, Morteza Lahijanian
Title: Training with Hard Constraints: Learning Neural Certificates and Controllers for SDEs
Abstract:
Due to their expressive power, neural networks (NNs) are promising templates for functional optimization problems, particularly for reach‑avoid certificate generation for systems governed by stochastic differential equations (SDEs). However, ensuring hard‑constraint satisfaction remains a major challenge. In this work, we propose two constraint‑driven training frameworks with guarantees for supermartingale‑based neural certificate construction and controller synthesis for SDEs. The first approach enforces certificate inequalities via domain discretization and a bound‑based loss, guaranteeing global validity once the loss reaches zero. We show that this method also enables joint NN controller‑certificate synthesis with hard guarantees. For high‑dimensional systems where discretization becomes prohibitive, we introduce a partition‑free, scenario‑based training method that provides arbitrarily tight PAC guarantees for certificate constraint satisfaction. Benchmarks demonstrate scalability of the bound‑based method up to 5D, outperforming the state of the art, and scalability of the scenario‑based approach to at least 10D with high‑confidence guarantees.

Authors:Sander Tonkens, Sosuke Kojima, Chenhao Liu, Judy Masri, Sylvia Herbert
Title: Refining Almost-Safe Value Functions on the Fly
Abstract:
Control Barrier Functions (CBFs) are a powerful tool for ensuring robotic safety, but designing or learning valid CBFs for complex systems is a significant challenge. While Hamilton‑Jacobi Reachability provides a formal method for synthesizing safe value functions, it scales poorly and is typically performed offline, limiting its applicability in dynamic environments. This paper bridges the gap between offline synthesis and online adaptation. We introduce refineCBF for refining an approximate CBF ‑ whether analytically derived, learned, or even unsafe ‑ via warm‑started HJ reachability. We then present its computationally efficient successor, HJ‑Patch, which accelerates this process through localized updates. Both methods guarantee the recovery of a safe value function and can ensure monotonic safety improvements during adaptation. Our experiments validate our framework's primary contribution: in‑the‑loop, real‑time adaptation, in simulation (with detailed value function analysis) and on physical hardware. Our experiments on ground vehicles and quadcopters show that our framework can successfully adapt to sudden environmental changes, such as new obstacles and unmodeled wind disturbances, providing a practical path toward deploying formally guaranteed safety in real‑world settings.

Authors:Mohammad Sabouri
Title: Cybersecurity of Teleoperated Quadruped Robots: A Systematic Survey of Vulnerabilities, Threats, and Open Defense Gaps
Abstract:
Teleoperated quadruped robots are increasingly deployed in safety‑critical missions ‑‑ industrial inspection, military reconnaissance, and emergency response ‑‑ yet the security of their communication and control infrastructure remains insufficiently characterized. Quadrupeds present distinct security challenges arising from dynamic stability constraints, gait‑dependent vulnerability windows, substantial kinetic energy, and elevated operator cognitive load. This survey synthesizes peer‑reviewed literature and vulnerability disclosures (2019‑‑2025) to provide comprehensive analysis of cybersecurity threats, consequences, and countermeasures for teleoperated quadruped systems. We contribute: (i) a six‑layer attack taxonomy spanning perception manipulation, VR/AR operator targeting, communication disruption, control signal attacks, localization spoofing, and network intrusion; (ii) systematic attack‑to‑consequence mapping with timing characterization; (iii) Technology Readiness Level classification exposing critical maturity gaps between field‑deployed communication protections (TRL 7‑‑9) and experimental perception/operator‑layer defenses (TRL 3‑‑5); (iv) comparative security analysis of six commercial platforms; (v) pragmatic deployment guidance stratified by implementation timeline; and (vi) eight prioritized research gaps with implementation roadmaps. Limitations: Platform assessments rely on publicly available information. Attack success rates derive from cited studies under controlled conditions and require domain‑specific validation.

Authors:S M Zia Ur Rashid, Deepa Gurung, Sonam Raj Gupta, Suman Rath
Title: Lifecycle-Integrated Security for AI-Cloud Convergence in Cyber-Physical Infrastructure
Abstract:
The convergence of Artificial Intelligence (AI) inference pipelines with cloud infrastructure creates a dual attack surface where cloud security standards and AI governance frameworks intersect without unified enforcement mechanisms. AI governance, cloud security, and industrial control system standards intersect without unified enforcement, leaving hybrid deployments exposed to cross‑layer attacks that threaten safety‑critical operations. This paper makes three primary contributions: (i) we synthesize these frameworks into a lifecycle‑staged threat taxonomy structured around explicit attacker capability tiers, (ii) we propose a Unified Reference Architecture spanning a Secure Data Factory, a hardened model supply chain, and a runtime governance layer, (iii) we present a case study through Grid‑Guard, a hybrid Transmission System Operator scenario in which coordinated defenses drawn from NIST AI RMF, MITRE ATLAS, OWASP AI Exchange and GenAI, CSA MAESTRO, and NERC CIP defeat a multi‑tier physical‑financial manipulation campaign without human intervention. Controls are mapped against all five frameworks and current NERC CIP standards to demonstrate that a single cloud‑native architecture can simultaneously satisfy AI governance, adversarial robustness, agentic safety, and industrial regulatory compliance obligations.

Authors:Ruiqi Wang, Pinjun Zheng, Yiming Yang, Xiarui Su, Mohammad Vaseem, Anas Chaaban, Md. Jahangir Hossain, Tareq Y. Al-Naffouri, Atif Shamim
Title: Millimeter-Wave RIS: Hardware Design and System-Level Considerations
Abstract:
Reconfigurable intelligent surfaces have emerged as a promising hardware platform for shaping wireless propagation environments at millimeter‑wave (mm‑Wave) frequencies and beyond. While many existing studies emphasize channel modeling and signal processing, practical RIS deployment is fundamentally governed by hardware design choices and their system‑level implications. This paper presents a hardware‑centric overview of recent mm‑Wave RIS developments, covering wideband realizations, high‑resolution phase‑quantized designs, fully printed low‑cost implementations, optically transparent surfaces, RIS‑on‑chip solutions, and emerging three‑dimensional architectures. Key challenges including mutual coupling, calibration, multi‑RIS interaction, and frequency‑dependent phase control are discussed to bridge hardware realization with system‑level optimization. This overview provides practical design insights and aims to guide future RIS research toward scalable, efficient, and practically deployable intelligent surface architectures.

Authors:Joonwon Choi, Kartik Anand Pant, Youngim Nam, Henry Hellmann, Karthik Nune, Inseok Hwang
Title: Signal Temporal Logic Verification and Synthesis Using Deep Reachability Analysis and Layered Control Architecture
Abstract:
We propose a signal temporal logic (STL)‑based framework that rigorously verifies the feasibility of a mission described in STL and synthesizes control to safely execute it. The proposed framework ensures safe and reliable operation through two phases. First, the proposed framework assesses the feasibility of STL by computing a backward reachable tube (BRT), which captures all states that can satisfy the given STL, regardless of the initial state. The proposed framework accommodates the multiple reach‑avoid (MRA) problem to address more general STL specifications and leverages a deep neural network to alleviate the computation burden for reachability analysis, reducing the computation time by about 1000 times compared to a baseline method. We further propose a layered planning and control architecture that combines mixed‑integer linear programming (MILP) for global planning with model predictive control (MPC) as a local controller for the verified STL. Consequently, the proposed framework can robustly handle unexpected behavior of obstacles that are not described in the environment information or STL, thereby providing reliable mission performance. Our numerical simulations demonstrate that the proposed framework can successfully compute BRT for a given STL and perform the mission.

Authors:Rafael R. Baptista, André de Lima Salgado, Ricardo V. Godoy, Marcelo Becker, Thiago Boaventura, Gustavo J. G. Lahr
Title: Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction
Abstract:
Leader‑follower interaction is an important paradigm in human‑robot interaction (HRI). Yet, assigning roles in real time remains challenging for resource‑constrained mobile and assistive robots. While large language models (LLMs) have shown promise for natural communication, their size and latency limit on‑device deployment. Small language models (SLMs) offer a potential alternative, but their effectiveness for role classification in HRI has not been systematically evaluated. In this paper, we present a benchmark of SLMs for leader‑follower communication, introducing a novel dataset derived from a published database and augmented with synthetic samples to capture interaction‑specific dynamics. We investigate two adaptation strategies: prompt engineering and fine‑tuning, studied under zero‑shot and one‑shot interaction modes, compared with an untrained baseline. Experiments with Qwen2.5‑0.5B reveal that zero‑shot fine‑tuning achieves robust classification performance (86.66% accuracy) while maintaining low latency (22.2 ms per sample), significantly outperforming baseline and prompt‑engineered approaches. However, results also indicate a performance degradation in one‑shot modes, where increased context length challenges the model's architectural capacity. These findings demonstrate that fine‑tuned SLMs provide an effective solution for direct role assignment, while highlighting critical trade‑offs between dialogue complexity and classification reliability on the edge.

Authors:Giona Fieni, Joschua Wüthrich, Marc-Philippe Neumann, Christopher H. Onder
Title: Learning-based Multi-agent Race Strategies in Formula 1
Abstract:
In Formula 1, race strategies are adapted according to evolving race conditions and competitors' actions. This paper proposes a reinforcement learning approach for multi‑agent race strategy optimization. Agents learn to balance energy management, tire degradation, aerodynamic interaction, and pit‑stop decisions. Building on a pre‑trained single‑agent policy, we introduce an interaction module that accounts for the behavior of competitors. The combination of the interaction module and a self‑play training scheme generates competitive policies, and agents are ranked based on their relative performance. Results show that the agents adapt pit timing, tire selection, and energy allocation in response to opponents, achieving robust and consistent race performance. Because the framework relies only on information available during real races, it can support race strategists' decisions before and during races.

Authors:Chong-Yi Sun, Heling Yuan, Xu Fang, Yan He, Xi-Ming Sun
Title: Integrated Flight and Propulsion Control for Fixed-Wing UAVs via Thrust and Disturbance Compensation
Abstract:
This paper investigates the position‑tracking control problem for fixed‑wing unmanned aerial vehicles (UAVs) equipped with a turbojet engine via an integrated flight and propulsion control scheme. To this end, a hierarchical control framework with thrust and disturbance compensation is proposed. In particular, we first propose a perturbed fixed‑wing UAV model with turbojet engine dynamics, accounting for both unmodeled dynamics and external disturbances. Second, a versatile extended observer is designed to handle both unmeasurable thrust dynamics and external disturbances. Third, a hierarchical control framework is implemented using three observer‑based controllers to guarantee position‑tracking performance. With the proposed control strategy, we prove that the closed‑loop system asymptotically converges to the desired trajectory. Finally, a comparative simulation is performed to illustrate the proposed control strategy.

Authors:Yosuke Inoue, Masaki Inoue
Title: Steady State Covariance Steering via Sparse Intervention
Abstract:
This paper addresses the steady state covariance steering for linear dynamical systems via structural intervention on the system matrix. We formulate the covariance steering problem as the minimization of the Kullback‑Leibler (KL) divergence between the steady state and target Gaussian distributions. To solve the problem, we develop a solution method, hereafter referred to as the proximal gradient‑based algorithm, of promoting sparsity in the structural intervention by integrating the objective into a proximal gradient framework with L1 regularization. The main contribution of this paper lies in the analytical expression of the KL divergence gradient with respect to the intervention matrix: the gradient is characterized by the solutions to two Lyapunov equations related to the state covariance equation and its adjoint. Numerical simulations demonstrate that the proximal gradient‑based algorithm effectively identifies sparse, structurally‑constrained interventions to achieve precise covariance steering.

Authors:Maryam Ansarifard, Mohit K. Sharma, Kishor C. Joshi, George Exarchakos
Title: Transformer Actor-Critic for Efficient Freshness-Aware Resource Allocation
Abstract:
Emerging applications such as autonomous driving and industrial automation demand ultra‑reliable and low‑latency communication (URLLC), where maintaining fresh and timely information is critical. A key performance metric in such systems is the age of information (AoI). This paper addresses AoI minimization in a multi‑user uplink wireless network using non‑orthogonal multiple access (NOMA), where users offload tasks to a base station. The system must handle user heterogeneity in task sizes, AoI thresholds, and penalty sensitivities, while adhering to NOMA constraints on user scheduling. We propose a deep reinforcement learning (DRL) framework based on proximal policy optimization (PPO), enhanced with a Transformer encoder. The attention mechanism allows the agent to focus on critical user states and capture inter‑user dependencies, improving policy performance and scalability. Extensive simulations show that our method reduces average AoI compared to baselines. We also analyze the evolution of attention weights during training and observe that the model progressively learns to prioritize high‑importance users. Attention maps reveal meaningful structure: early‑stage policies exhibit uniform attention, while later stages show focused patterns aligned with user priority and NOMA constraints. These results highlight the promise of attention‑driven DRL for intelligent, priority‑aware resource allocation in next‑generation wireless systems.

Authors:Philipp Schitz, Paolo Mercorelli, Johann C. Dauer
Title: Robust Helicopter Ship Deck Landing With Guaranteed Timing Using Shrinking-Horizon Model Predictive Control
Abstract:
We present a runtime efficient algorithm for autonomous helicopter landings on moving ship decks based on Shrinking‑Horizon Model Predictive Control (SHMPC). First, a suitable planning model capturing the relevant aspects of the full nonlinear helicopter dynamics is derived. Next, we use the SHMPC together with a touchdown controller stage to ensure a pre‑specified maneuver time and an associated landing time window despite the presence of disturbances. A high disturbance rejection performance is achieved by designing an ancillary controller with disturbance feedback. Thus, given a target position and time, a safe landing with suitable terminal conditions is be guaranteed if the initial optimization problem is feasible. The efficacy of our approach is shown in simulation where all maneuvers achieve a high landing precision in strong winds while satisfying timing and operational constraints with maximum computation times in the millisecond range.

Authors:Xiang Yin
Title: Opacity in Discrete Event Systems: A Perspective and Overview
Abstract:
Opacity has emerged as a central confidentiality notion for information‑flow security in discrete event systems (DES), capturing the requirement that an external observer (intruder) should never be able to determine with certainty whether the system is, was, or will be in a secret state. This article provides a concise, newcomer‑friendly overview of opacity in DES, emphasizing core definitions and the unifying estimation viewpoint behind major opacity notions,. We summarize representative verification techniques and highlight how different observation models reshape both the problem formulation and algorithmic structure. We then review principal enforcement paradigms, ranging from opacity‑enforcing supervisory control to sensor activation/information release optimization and obfuscation/editing mechanisms. Beyond finite automata, we outline how opacity has been studied in richer models such as stochastic systems, timed systems, Petri nets, and continuous/hybrid dynamics, and we briefly survey applications in robotics, location privacy, and information services. Finally, we discuss selected open challenges, including solvability under incomparable information, scalable methods beyond worst‑case complexity, and opacity under intelligent or data‑driven adversaries.

Authors:Gaoyang Pang, Wanchun Liu, Chentao Yue, Daniel E. Quevedo, Karl H. Johansson, Branka Vucetic, Yonghui Li
Title: Toward Wireless Human-Machine Collaboration in the 6G Era
Abstract:
The next industrial revolution, Industry 5.0, will be driven by advanced technologies that foster human‑machine collaboration (HMC). It will leverage human creativity, judgment, and dexterity with the machine's strength, precision, and speed to improve productivity, quality of life, and sustainability. Wireless communications, empowered by the emerging capabilities of sixth‑generation (6G) wireless networks, will play a central role in enabling flexible, scalable, and low‑cost deployment of geographically distributed HMC systems. In this article, we first introduce the generic architecture and key components of wireless HMC (WHMC). We then present the network topologies of WHMC and highlight impactful applications across various industry sectors. Driven by the prospective applications, we elaborate on new performance metrics that researchers and practitioners may consider during the exploration and implementation of WHMC and discuss new design methodologies. We then summarize the communication requirements and review promising state‑of‑the‑art technologies that can support WHMC. Finally, we present a proof‑of‑concept case study and identify several open challenges.

Authors:Yahia Salaheldin Shaaban, Salem Lahlou, Abdelrahman Sayed Sayed
Title: HyperKKL: Enabling Non-Autonomous State Estimation through Dynamic Weight Conditioning
Abstract:
This paper proposes HyperKKL, a novel learning approach for designing Kazantzis‑Kravaris/Luenberger (KKL) observers for non‑autonomous nonlinear systems. While KKL observers offer a rigorous theoretical framework by immersing nonlinear dynamics into a stable linear latent space, its practical realization relies on solving Partial Differential Equations (PDE) that are analytically intractable. Current existing learning‑based approximations of the KKL observer are mostly designed for autonomous systems, failing to generalize to driven dynamics without expensive retraining or online gradient updates. HyperKKL addresses this by employing a hypernetwork architecture that encodes the exogenous input signal to instantaneously generate the parameters of the KKL observer, effectively learning a family of immersion maps parameterized by the external drive. We rigorously evaluate this approach against a curriculum learning strategy that attempts to generalize from autonomous regimes via training heuristics alone. The novel approach is illustrated on four numerical simulations in benchmark examples including the Duffing, Van der Pol, Lorenz, and Rössler systems.

Authors:Yuto Watanabe, Yang Zheng
Title: Gradient Dominance in the Linear Quadratic Regulator: A Unified Analysis for Continuous-Time and Discrete-Time Systems
Abstract:
Despite its nonconvexity, policy optimization for the Linear Quadratic Regulator (LQR) admits a favorable structural property known as gradient dominance, which facilitates linear convergence of policy gradient methods to the globally optimal gain. While gradient dominance has been extensively studied, continuous‑time and discrete‑time LQRs have largely been analyzed separately, relying on slightly different assumptions, proof strategies, and resulting guarantees. In this paper, we present a unified gradient dominance property for both continuous‑time and discrete‑time LQRs under mild stabilizability and detectability assumptions. Our analysis is based on a convex reformulation derived from a common Lyapunov inequality representation and a unified change‑of‑variables procedure. This convex‑lifting perspective yields a single proof framework applicable to both time models. The unified treatment clarifies how differences between continuous‑time and discrete‑time dynamics influence theoretical guarantees and reveals a deeper structural symmetry between the two formulations. Numerical examples illustrate and support the theoretical findings.

Authors:Xinyu Tan, Ningwei Bai, Harry Gardener, Zhengyang Zhong, Luoyu Zhang, Liuhaichen Yang, Zhekai Duan, Monkgogi Galeitsiwe, Zezhi Tang
Title: SignVLA: A Gloss-Free Vision-Language-Action Framework for Real-Time Sign Language-Guided Robotic Manipulation
Abstract:
We present, to our knowledge, the first sign language‑driven Vision‑Language‑Action (VLA) framework for intuitive and inclusive human‑robot interaction. Unlike conventional approaches that rely on gloss annotations as intermediate supervision, the proposed system adopts a gloss‑free paradigm and directly maps visual sign gestures to semantic instructions. This design reduces annotation cost and avoids the information loss introduced by gloss representations, enabling more natural and scalable multimodal interaction. In this work, we focus on a real‑time alphabet‑level finger‑spelling interface that provides a robust and low‑latency communication channel for robotic control. Compared with large‑scale continuous sign language recognition, alphabet‑level interaction offers improved reliability, interpretability, and deployment feasibility in safety‑critical embodied environments. The proposed pipeline transforms continuous gesture streams into coherent language commands through geometric normalization, temporal smoothing, and lexical refinement, ensuring stable and consistent interaction. Furthermore, the framework is designed to support future integration of transformer‑based gloss‑free sign language models, enabling scalable word‑level and sentence‑level semantic understanding. Experimental results demonstrate the effectiveness of the proposed system in grounding sign‑derived instructions into precise robotic actions under diverse interaction scenarios. These results highlight the potential of the framework to advance accessible, scalable, and multimodal embodied intelligence.

Authors:Arash J. Khabbazi, Kevin J. Kircher
Title: Small HVAC Control Demonstrations in Larger Buildings Often Overestimate Savings
Abstract:
How much energy, money, and emissions can advanced control of heating and cooling equipment save in real buildings? To address this question, researchers sometimes control a small number of thermal zones within a larger multi‑zone building, then report savings for the controlled zones only. That approach can overestimate savings by neglecting heat transfer between controlled zones and adjacent zones. This paper mathematically characterizes the overestimation error when the dynamics are linear and the objectives are linear in the thermal load, as usually holds when optimizing energy efficiency, energy costs, or emissions. Overestimation errors can be large even in seemingly innocuous situations. For example, when controlling only interior zones that have no direct thermal contact with the outdoors, all perceived savings are fictitious. This paper provides an alternative estimation method based on the controlled and adjacent zones' temperature measurements. The new method does not require estimating how much energy the building would have used under baseline operations, so it removes the additional measurement and verification challenge of accurate baseline estimation.

Authors:Yicheng Chen, Jinjie Li, Haokun Liu, Zicheng Luo, Kotaro Kaneko, Moju Zhao
Title: Hierarchical Trajectory Planning of Floating-Base Multi-Link Robot for Maneuvering in Confined Environments
Abstract:
Floating‑base multi‑link robots can change their shape during flight, making them well‑suited for applications in confined environments such as autonomous inspection and search and rescue. However, trajectory planning for such systems remains an open challenge because the problem lies in a high‑dimensional, constraint‑rich space where collision avoidance must be addressed together with kinematic limits and dynamic feasibility. This work introduces a hierarchical trajectory planning framework that integrates global guidance with configuration‑aware local optimization. First, we exploit the dual nature of these robots ‑ the root link as a rigid body for guidance and the articulated joints for flexibility ‑ to generate global anchor states that decompose the planning problem into tractable segments. Second, we design a local trajectory planner that optimizes each segment in parallel with differentiable objectives and constraints, systematically enforcing kinematic feasibility and maintaining dynamic feasibility by avoiding control singularities. Third, we implement a complete system that directly processes point‑cloud data, eliminating the need for handcrafted obstacle models. Extensive simulations and real‑world experiments confirm that this framework enables an articulated aerial robot to exploit its morphology for maneuvering that rigid robots cannot achieve. To the best of our knowledge, this is the first planning framework for floating‑base multi‑link robots that has been demonstrated on a real robot to generate continuous, collision‑free, and dynamically feasible trajectories directly from raw point‑cloud inputs, without relying on handcrafted obstacle models.

Authors:Alexander Benvenuti, Brandon Fallin, Calvin Hawkins, Brendan Bialy, Miriam Dennis, Warren Dixon, Matthew Hale
Title: Differentially Private Data-Driven Markov Chain Modeling
Abstract:
Markov chains model a wide range of user behaviors. However, generating accurate Markov chain models requires substantial user data, and sharing these models without privacy protections may reveal sensitive information about the underlying user data. We introduce a method for protecting user data used to formulate a Markov chain model. First, we develop a method for privatizing database queries whose outputs are elements of the unit simplex, and we prove that this method is differentially private. We quantify its accuracy by bounding the expected KL divergence between private and non‑private queries. We extend this method to privatize stochastic matrices whose rows are each a simplex‑valued query of a database, which includes data‑driven Markov chain models. To assess their accuracy, we analytically bound the change in the stationary distribution and the change in the convergence rate between a non‑private Markov chain model and its private form. Simulations show that under a typical privacy implementation, our method yields less than 2% error in the stationary distribution, indicating that our approach to private modeling faithfully captures the behavior of the systems we study.

Authors:Md Hasanur Rashid, Dong Dai
Title: AdapTBF: Decentralized Bandwidth Control via Adaptive Token Borrowing for HPC Storage
Abstract:
Modern high‑performance computing (HPC) applications run on compute resources but share global storage systems. This design can cause problems when applications consume a disproportionate amount of storage bandwidth relative to their allocated compute resources. For example, an application running on a single compute node can issue many small, random writes and consume excessive I/O bandwidth from a storage server. This can hinder larger jobs that write to the same storage server and are allocated many compute nodes, resulting in significant resource waste. A straightforward solution is to limit each application's I/O bandwidth on storage servers in proportion to its allocated compute resources. This approach has been implemented in parallel file systems using Token Bucket Filter (TBF). However, strict proportional limits often reduce overall I/O efficiency because HPC applications generate short, bursty I/O. Limiting bandwidth can waste server capacity when applications are idle or prevent applications from temporarily using higher bandwidth during bursty phases. We argue that I/O control should maximize per‑application performance and overall storage efficiency while ensuring fairness (e.g., preventing small jobs from blocking large‑scale ones). We propose AdapTBF, which builds on TBF in modern parallel file systems (e.g., Lustre) and introduces a decentralized bandwidth control approach using adaptive borrowing and lending. We detail the algorithm, implement AdapTBF in Lustre, and evaluate it using synthetic workloads modeled after real‑world scenarios. Results show that AdapTBF manages I/O bandwidth effectively while maintaining high storage utilization, even under extreme conditions.

Authors:Alfonso Sciacchitano, Douglas L. Van Bossuyt
Title: A Mission Engineering Framework for Uncrewed Aerial Vehicle Design in GNSS-Denied Environments for Intelligence, Surveillance, and Reconnaissance Mission Sets
Abstract:
Small, low‑size, weight, power, and cost (SWaP‑C) uncrewed aerial vehicles (UAVs) are increasingly used for intelligence, surveillance, and reconnaissance (ISR) missions due to their affordability, attritability, and suitability for distributed operations. However, their design poses challenges including limited endurance, constrained payload capacity, and reliance on simple sensing modalities such as fixed‑field‑of‑view, bearing‑only cameras. Traditional platform‑centric methods cannot capture the coupled performance, cost, and coordination trade‑offs that emerge at the system‑of‑systems level. This paper presents a mission engineering framework for early‑phase design of low‑SWaP‑C UAV ISR architectures. The framework integrates design of experiments, multi‑objective optimization, and high‑fidelity simulation into a closed‑loop process linking design variables to estimator‑informed performance and mission cost. Candidate architectures are explored via Latin hypercube sampling and refined using a genetic algorithm, with performance evaluated through Monte Carlo trials of a federated Kalman filter benchmarked against the posterior Cramer‑Rao lower bound. Validation follows the Validation Square methodology, combining theoretical, empirical, and structural assessments. A case study on man‑overboard localization in a GNSS‑denied maritime environment shows that localization accuracy saturates at sub‑meter levels, while higher‑cost configurations primarily add redundancy and resilience. The framework thus quantifies mission trade‑offs between performance, affordability, and robustness, providing a scalable decision‑support tool for contested, resource‑constrained ISR missions.

Authors:Xuanhao Mu, Jakob Geiges, Nan Liu, Thorsten Schlachter, Veit Hagenmeyer
Title: Improving Spatial Allocation for Energy System Coupling with Graph Neural Networks
Abstract:
In energy system analysis, coupling models with mismatched spatial resolutions is a significant challenge. A common solution is assigning weights to high‑resolution geographic units for aggregation, but traditional models are limited by using only a single geospatial attribute. This paper presents an innovative method employing a self‑supervised Heterogeneous Graph Neural Network to address this issue. This method models high‑resolution geographic units as graph nodes, integrating various geographical features to generate physically meaningful weights for each grid point. These weights enhance the conventional Voronoi‑based allocation method, allowing it to go beyond simply geographic proximity by incorporating essential geographic information.In addition, the self‑supervised learning paradigm overcomes the lack of accurate ground‑truth data. Experimental results demonstrate that applying weights generated by this method to cluster‑based Voronoi Diagrams significantly enhances scalability, accuracy, and physical plausibility, while increasing precision compared to traditional methods.

Authors:Lan Zhang, Chengsi Liang, Zeming Zhuang, Yao Sun, Fang Fang, Xiaoyong Yuan, Dusit Niyato
Title: Secure Semantic Communications via AI Defenses: Fundamentals, Solutions, and Future Directions
Abstract:
Semantic communication (SemCom) redefines wireless communication from reproducing symbols to transmitting task‑relevant semantics. However, this AI‑native architecture also introduces new vulnerabilities, as semantic failures may arise from adversarial perturbations to models, corrupted training data, desynchronized priors, or misaligned inference even when lower‑layer transmission reliability and cryptographic protection remain intact. This survey provides a defense‑centered and system‑oriented synthesis of security in SemCom via AI defense. We analyze AI‑centric threat models by consolidating existing studies and organizing attack surfaces across model‑level, channel‑realizable, knowledge‑based, and networked inference vectors. Building on this foundation, we present a structured taxonomy of defense strategies organized by where semantic integrity can be compromised in SemCom systems despite correct symbol delivery, spanning semantic encoding, wireless transmission, knowledge integrity, and coordination among multiple agents. These categories correspond to distinct security failure modes, including representation fragility, channel‑realizable manipulation, semantic prior poisoning or desynchronization, and adversarial propagation through distributed inference. We also examine security utility operating envelopes that capture tradeoffs among semantic fidelity, robustness, latency, and energy under realistic constraints, survey evaluation frameworks and representative applications, and identify open challenges in cross‑layer composition and deployment‑time certification. Overall, this survey offers a unified system‑level perspective that enables readers to understand major threat and defense mechanisms in AI‑native SemCom systems and to leverage emerging security techniques in the design and deployment of robust SemCom architectures for next‑generation intelligent networks.

Authors:Mahsa Ebadat-Parast, Xiaozhe Wang
Title: Tempered Christoffel-Weighted Polynomial Chaos Expansion for Resilience-Oriented Uncertainty Quantification
Abstract:
Accurate and efficient uncertainty quantification is essential for resilience assessment of modern power systems under high impact and low probability disturbances. Data driven sparse polynomial chaos expansion (DDSPCE) provides a computationally efficient surrogate framework but may suffer from ill conditioned regression and loss of accuracy in the distribution tails that determine system risk. This paper studies the impact of regression weighting schemes on the stability and tail accuracy of DD‑SPCE surrogates by introducing a tempered Christoffel weighted least squares (T‑CWLS) formulation that balances numerical stability and tail fidelity. The tempering exponent is treated as a hyperparameter whose influence is examined with respect to distributional accuracy compared with Monte Carlo simulations. Case studies on distribution system load shedding show that the proposed method reduces 95th percentile deviation by 16%, 5th percentile deviation by 6%, and improves the regression stability index by over 130%. The results demonstrate that controlling the weighting intensity directly influences both stability index and the accuracy of tail prediction.

Authors:Leonardo Colombo, Thomas Beckers, Juan Giribet
Title: Aggressiveness-Aware Learning-based Control of Quadrotor UAVs with Safety Guarantees
Abstract:
This paper presents an aggressiveness‑aware control framework for quadrotor UAVs that integrates learning‑based oracles to mitigate the effects of unknown disturbances. Starting from a nominal tracking controller on \mathrmSE(3), unmodeled generalized forces and moments are estimated using a learning‑based oracle and compensated in the control inputs. An aggressiveness‑aware gain scheduling mechanism adapts the feedback gains based on probabilistic model‑error bounds, enabling reduced feedback‑induced aggressiveness while guaranteeing a prescribed practical exponential tracking performance. The proposed approach makes explicit the trade‑off between model accuracy, robustness, and control aggressiveness, and provides a principled way to exploit learning for safer and less aggressive quadrotor maneuvers.

Authors:Jie Han, Arash Khalatbarisoltani, Hai L. Vu, Xiaosong Hu, Jun Yang
Title: Traffic-aware Hierarchical Integrated Thermal and Energy Management for Connected HEVs
Abstract:
The energy and thermal management systems of hybrid electric vehicles (HEVs) are inherently interdependent. With the ongoing deployment of intelligent transportation systems (ITSs) and increasing vehicle connectivity, the integration of traffic information has become crucial for improving both energy efficiency and thermal comfort in modern vehicles. To enhance fuel economy, this paper proposes a novel traffic‑aware hierarchical integrated thermal and energy management (TA‑ITEM) strategy for connected HEVs. In the upper layer, global reference trajectories for battery state of charge (SOC) and cabin temperature are planned using traffic flow speed information obtained from ITSs. In the lower layer, a real‑time model predictive control (MPC)‑based ITEM controller is developed, which incorporates a novel Transformer‑based speed predictor with driving condition recognition (TF‑DCR) to enable anticipatory tracking of the reference trajectories. Numerical simulations are conducted under various driving cycles and ambient temperature conditions. The results demonstrate that the proposed TA‑ITEM approach outperforms conventional rule‑based and MPC‑SP approaches, with average fuel consumption reductions of 56.36% and 5.84%, respectively, while maintaining superior thermal regulation and cabin comfort. These findings confirm the effectiveness and strong generalization capability of TA‑ITEM and underscore the advantages of incorporating traffic information.

Authors:Lucas Souza e Silva, Luis Rodrigues
Title: On the airspace complexity metrics for predecessor-follower operations
Abstract:
This technical note proposes a novel airspace complexity metric that quantifies the air traffic controller workload and coordination effort for pairwise predecessor‑follower aircraft operations in cruise. The pairwise dynamic workload (PDW) is proposed as a continuous function that depends on the relevant parameters of these operations, such as the aircraft separation and separation rate. A comparison of this metric with the dynamic density (DD) shows that it is capable of continuously evaluating the variation of airspace complexity over time and monitoring the aircraft parameters that might lead to conflicts. This metric can be used to support the implementation of autonomous and supervised aircraft procedures, to achieve a more structured and coordinated airspace.

Authors:Haoran Su, Hanxiao Deng
Title: LightSim: A Lightweight Cell Transmission Model Simulator for Traffic Signal Control Research
Abstract:
Reinforcement learning for traffic signal control is bottlenecked by simulators: training in SUMO takes hours, reproducing results often requires days of platform‑specific setup, and the slow iteration cycle discourages the multi‑seed experiments that rigorous evaluation demands. Much of this cost is unnecessary, since for signal timing optimization the relevant dynamics are queue formation and discharge, which the Cell Transmission Model (CTM) captures as a macroscopic flow model. We introduce LightSim, a pure Python, pip‑installable traffic simulator with Gymnasium and PettingZoo interfaces that runs over 20000 steps per second on a single CPU. Across cross‑simulator experiments spanning single intersections, grid networks, arterial corridors, and six real‑world city networks, LightSim preserves controller rankings from SUMO for both classical and reinforcement learning strategies while training 3 to 7 times faster. LightSim is released as an open‑source benchmark with nineteen built‑in scenarios, seven controllers, and full reinforcement learning pipelines, lowering the barrier to signal control research from days to minutes.

Authors:Omayra Yago Nieto, Leonardo Colombo
Title: Learning-Based Geometric Leader-Follower Control for Cooperative Rigid-Payload Transport with Aerial Manipulators
Abstract:
This paper presents a learning‑based tracking control framework for cooperative transport of a rigid payload by multiple aerial manipulators under rigid grasp constraints. A unified geometric model is developed, yielding a coupled agent‑‑payload differential‑‑algebraic system that explicitly captures contact wrenches, payload dynamics, and internal force redundancy. A leader‑‑follower architecture is adopted in which a designated leader generates a desired payload wrench based on geometric tracking errors, while the remaining agents realize this wrench through constraint‑consistent force allocation. Unknown disturbances and modeling uncertainties are compensated using Gaussian Process (GP) regression. High‑probability bounds on the learning error are explicitly incorporated into the control design, combining GP feedforward compensation with geometric feedback. Lyapunov analysis establishes uniform ultimate boundedness of the payload tracking errors with high probability, with an ultimate bound that scales with the GP predictive uncertainty.

Authors:Minjie Tang, Zunqi Li, Photios A. Stavrou, Marios Kountouris
Title: Pilot-Free Optimal Control over Wireless Networks: A Control-Aided Channel Prediction Approach
Abstract:
A recurring theme in optimal controller design for wireless networked control systems (WNCS) is the reliance on real‑time channel state information (CSI). However, acquiring accurate CSI a priori is notoriously challenging due to the time‑varying nature of wireless channels. In this work, we propose a pilot‑free framework for optimal control over wireless channels in which control commands are generated from plant states together with control‑aided channel prediction. For linear plants operating over an orthogonal frequency‑division multiplexing (OFDM) architecture, channel prediction is performed via a Kalman filter (KF), and the optimal control policy is derived from the Bellman principle. To alleviate the curse of dimensionality in computing the optimal control policy, we approximate the solution using a coupled algebraic Riccati equation (CARE), which can be computed efficiently via a stochastic approximation (SA) algorithm. Rigorous performance guarantees are established by proving the stability of both the channel predictor and the closed‑loop system under the resulting control policy, providing sufficient conditions for the existence and uniqueness of a stabilizing approximate CARE solution, and establishing convergence of the SA‑based control algorithm. The framework is further extended to nonlinear plants under general wireless architectures by combining a KalmanNet‑based predictor with a Markov‑modulated deep deterministic policy gradient (MM‑DDPG) controller. Numerical results show that the proposed pilot‑free approach outperforms benchmark schemes in both control performance and channel prediction accuracy for linear and nonlinear scenarios.

Authors:Pelin Sekercioglu, Angela Fontan, Dimos V. Dimarogonas
Title: Stability of Open Multi-agent Systems over Dynamic Signed Digraphs
Abstract:
We address the synchronization problem in open multi‑agent systems (OMAS) containing both cooperative and antagonistic interactions. In these systems, agents can join or leave the network over time, and the interaction structure may evolve accordingly. To capture these dynamical structural changes, we represent the network as a switched system interconnected over a dynamic and directed signed graph. Additionally, the network may contain one or multiple leader groups that influence the behavior of the remaining agents. In general, we show that the OMAS exhibit a more general form of synchronization, including trivial consensus, bipartite consensus and containment. Our approach uses the signed edge‑based agreement protocol, and constructs strict Lyapunov functions for signed networks described by signed edge‑Laplacian matrices containing multiple zero eigenvalues. Numerical simulations validate our theoretical results.

Authors:Xu Yang, Chenhui Lin, Xiang Ma, Dong Liu, Ran Zheng, Haotian Liu, Wenchuan Wu
Title: Two-Stage Active Distribution Network Voltage Control via LLM-RL Collaboration: A Hybrid Knowledge-Data-Driven Approach
Abstract:
The growing integration of distributed photovoltaics (PVs) into active distribution networks (ADNs) has exacerbated operational challenges, making it imperative to coordinate diverse equipment to mitigate voltage violations and enhance power quality. Although existing data‑driven approaches have demonstrated effectiveness in the voltage control problem, they often require extensive trial‑and‑error exploration and struggle to incorporate heterogeneous information, such as day‑ahead forecasts and semantic‑based grid codes. Considering the operational scenarios and requirements in real‑world ADNs, in this paper, we propose a hybrid knowledge‑data‑driven approach that leverages dynamic collaboration between a large language model (LLM) agent and a reinforcement learning (RL) agent to achieve two‑stage voltage control. In the day‑ahead stage, the LLM agent receives coarse region‑level forecasts and generates scheduling strategies for on‑load tap changer (OLTC) and shunt capacitors (SCs) to regulate the overall voltage profile. Then in the intra‑day stage, based on accurate node‑level measurements, the RL agent refines terminal voltages by deriving reactive power generation strategies for PV inverters. On top of the LLM‑RL collaboration framework, we further propose a self‑evolution mechanism for the LLM agent and a pretrain‑finetune pipeline for the RL agent, effectively enhancing and coordinating the policies for both agents. The proposed approach not only aligns more closely with practical operational characteristics but also effectively utilizes the inherent knowledge and reasoning capabilities of the LLM agent, significantly improving training efficiency and voltage control performance. Comprehensive comparisons and ablation studies demonstrate the effectiveness of the proposed method.

Authors:Haoyang Li, Weidong Liu, Zhensheng Chen, Chaoyun Song, Gaojie Chen
Title: Geometry-Dependent Radiation of Pinching Antennas: Theory, Simulation, and Measurement
Abstract:
Most existing studies achieve beamforming by adjusting the positions of pinching antennas (PAs) and typically model PAs as isotropic radiators. However, under the dielectric scatterer model, the PA radiation pattern depends on its geometry. This letter investigates the radiation patterns of PAs with different geometries through full‑wave simulations and measurements, and demonstrates how geometry influences the radiation directivity. In addition, an arc‑shaped PA is introduced to enable transmit‑direction control in PA systems. A PA system prototype consisting of a dielectric waveguide, waveguide transitions, and a PA element is proposed. Prototype measurements are used to validate the simulations and to characterize the directivity of square and triangular PAs, and the measurement procedure can be applied to obtain radiation patterns for PAs with general geometries. The simulation and measurement results jointly demonstrate that PA geometry is critical in PA systems because it influences the radiation characteristics significantly.

Authors:Miroslav Krstic
Title: Asymmetry Demystified: Strict CLFs and Feedbacks for Predator-Prey Interconnections
Abstract:
The difficulty with control of population dynamics, besides the states being positive and the control having to also be positive, is the extreme difference in the dynamics near extinction and at overpopulated states. As hard as global stabilization is, even harder is finding CLFs that are strict, don't require LaSalle arguments, and permit quantification of convergence. Among the three canonical types of two‑population dynamics (mutualism, which borders on trivial, predator‑prey, and competition, which makes global stabilization with positive harvesting impossible), predator‑prey is the ``sweet spot'' for the study of stabilization. Even when the predator‑prey interaction is neutrally stable, global asymptotic stabilization with strict CLFs has proven very difficult, except by conservative, hard‑to‑gain‑insight‑from Matrosov‑like techniques. In this little note we show directions for the design of clean, elegant, insight‑bearing, majorization‑free strict CLFs. They generalize the classical Volterra‑style Lyapunov functions for population dynamics to non‑separable Volterra‑style constructions. As a bonus to strictification as an analysis activity, we provide examples of concurrent designs of feedback and CLFs, using customized versions of forwarding and backstepping (note that, in suitable coordinates, predator‑prey is both strict‑feedforward and strict‑feedback), where the striking deviations from these methods' conventional forms is necessitated by the predator‑prey's states and inputs needing to be kept positive.

Authors:Linhan Fang, Elias Raffoul, Xingpeng Li
Title: Diagnosis-Driven Co-planning of Network Reinforcement and BESS for Distribution Grid with High Penetration of Electric Vehicles
Abstract:
While the rapid proliferation of electric vehicles (EVs) accelerates net‑zero goals, uncoordinated charging activities impose severe operational challenges on distribution grids, including exacerbated peak loads, thermal overloading, and voltage violations. To overcome the computational intractability of jointly optimizing grid infrastructure reinforcements and battery energy storage system (BESS) installations, this paper proposes a novel three‑stage diagnosis‑driven co‑planning (DDCP) framework. The methodology integrates a violation detection and quantification (VDQ) model to systematically identify system breaches, and a violation‑mitigated BESS planning (VMBP) model for optimal BESS sitting and sizing. Specifically, Stage I of the DDCP framework diagnoses critical bottleneck lines that render standalone BESS solutions infeasible. Stage II targets cable upgrades exclusively at the Top‑N prioritized bottleneck lines and Stage III then executes the optimal BESS deployment using a network‑enhanced VMBP model. Furthermore, this study quantifies the EV hosting capacity thresholds before and after BESS integration across varying EV adoption rates and base voltages. Finally, a comprehensive comparative analysis evaluates four mitigation approaches: the VDQ‑driven cable upgrade (VCU) model, the VMBP model, system‑wide voltage uprating, and the proposed DDCP framework. The results demonstrate that the DDCP framework not only resolves the complex joint‑optimization hurdle but also achieves the high techno‑economic superiority in addressing high‑EV‑penetration challenges.

Authors:Chuanghong Weng, Ehsan Nekouei
Title: Optimal Real-Time Fusion of Time-Series Data Under Rényi Differential Privacy
Abstract:
In this paper, we investigate the optimal real‑time fusion of data collected by multiple sensors. In our set‑up, the sensor measurements are considered to be private and are jointly correlated with an underlying process. A fusion center combines the private sensor measurements and releases its output to an honest‑but‑curious party, which is responsible for estimating the state of the underlying process based on the fusion center's output. The privacy leakage incurred by the fusion policy is quantified using Rényi differential privacy. We formulate the privacy‑aware fusion design as a constrained finite‑horizon optimization problem, in which the fusion policy and the state estimation are jointly optimized to minimize the state estimation error subject to a total privacy budget constraint. We derive the constrained optimality conditions for the proposed optimization problem and use them to characterize the structural properties of the optimal fusion policy. Unlike classical differential privacy mechanisms, the optimal fusion policy is shown to adaptively allocates the privacy budget and regulates the adversary's belief in a closed‑loop manner. To reduce the computational burden of solving the resulting constrained optimality equations, we parameterize the fusion policy using a structured Gaussian distribution and show that the parameterized fusion policy satisfies the privacy constraint. We further develop a numerical algorithm to jointly optimize the fusion policy and state estimator. Finally, we demonstrate the effectiveness of the proposed fusion framework through a traffic density estimation case study.

Authors:Felipe Bartelt, Luciano C. A. Pimenta, Weijia Yao, Vinicius M. Gonçalves
Title: Vector Fields for Path Following on Lie Groups with Application in Robot Control
Abstract:
Many robotic systems allow independent control of position and orientation (pose), including omnidirectional aerial vehicles, underwater robots, and manipulator end‑effectors. In many applications, these systems must follow a continuous sequence of poses, leading to either trajectory‑tracking or path following formulations. Compared to trajectory‑tracking, path following offers important practical advantages. In particular, we focus on the problem of path following on Lie groups. Considering the robots as rigid bodies moving in the 3D space, this path‑following problem can be posed as a problem of designing guiding vector fields on the matrix Lie group SE(3). In this paper, we develop a general vector‑field framework for path following on connected matrix Lie groups, of which SE(3) is a prominent special case. The proposed vector field guarantees convergence to a desired parametric curve from almost all initial conditions while ensuring continuous motion along the path. Furthermore, another interesting feature is that, as opposed to previous works, the control input is "minimal" in terms of representation and closer to the engineering application (e.g., the body twist in the case SE(3)). After establishing the general case, the framework is then specialized to SE(3), of special interest in robotics, yielding an efficient algorithm suitable for real‑time robotic control. Experiments with a robotic manipulator tracking complex pose paths demonstrate the effectiveness of the approach. An open‑source implementation is also provided.

Authors:Darshan Gadginmath, Ahmed Allibhoy, Fabio Pasqualetti
Title: Provably Safe Generative Sampling with Constricting Barrier Functions
Abstract:
Flow‑based generative models, such as diffusion models and flow matching models, have achieved remarkable success in learning complex data distributions. However, a critical gap remains for their deployment in safety‑critical domains: the lack of formal guarantees that generated samples will satisfy hard constraints. We address this by proposing a safety filtering framework that acts as an online shield for any pre‑trained generative model. Our key insight is to cooperate with the generative process rather than override it. We define a constricting safety tube that is relaxed at the initial noise distribution and progressively tightens to the target safe set at the final data distribution, mirroring the coarse‑to‑fine structure of the generative process itself. By characterizing this tube via Control Barrier Functions (CBFs), we synthesize a feedback control input through a convex Quadratic Program (QP) at each sampling step. As the tube is loosest when noise is high and intervention is cheapest in terms of control energy, most constraint enforcement occurs when it least disrupts the model's learned structure. We prove that this mechanism guarantees safe sampling while minimizing the distributional shift from the original model at each sampling step, as quantified by the KL divergence. Our framework applies to any pre‑trained flow‑based generative scheme requiring no retraining or architectural modifications. We validate the approach across constrained image generation, physically‑consistent trajectory sampling, and safe robotic manipulation policies, achieving 100% constraint satisfaction while preserving semantic fidelity.

Authors:Sunki Hong, Jisoo Lee
Title: Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting
Abstract:
Selecting the right deep learning model for power grid forecasting is challenging, as performance heavily depends on the data available to the operator. This paper presents a comprehensive benchmark of five modern neural architectures: two state space models (PowerMamba, S‑Mamba), two Transformers (iTransformer, PatchTST), and a traditional LSTM. We evaluate these models on hourly electricity demand across six diverse US power grids for forecast windows between 24 and 168 hours. To ensure a fair comparison, we adapt each model with specialized temporal processing and a modular layer that cleanly integrates weather covariates. Our results reveal that there is no single best model for all situations. When forecasting using only historical load, PatchTST and the state space models provide the highest accuracy. However, when explicit weather data is added to the inputs, the rankings reverse: iTransformer improves its accuracy three times more efficiently than PatchTST. By controlling for model size, we confirm that this advantage stems from the architecture's inherent ability to mix information across different variables. Extending our evaluation to solar generation, wind power, and wholesale prices further demonstrates that model rankings depend on the forecast task: PatchTST excels on highly rhythmic signals like solar, while state space models are better suited for the chaotic fluctuations of wind and price. Ultimately, this benchmark provides grid operators with actionable guidelines for selecting the optimal forecasting architecture based on their specific data environments.

Authors:Oscar Jed R. Chuy, Matthew T. Hale, Vignesh Sivaramakrishnan, Sean Phillips, Ricardo G. Sanfelice
Title: Autonomous Satellite Rendezvous via Hybrid Feedback Optimization
Abstract:
As satellites have proliferated, interest has increased in autonomous rendezvous, proximity operations, and docking (ARPOD). A fundamental challenge in these tasks is the uncertainties when operating in space, e.g., in measurements of satellites' states, which can make future states difficult to predict. Another challenge is that satellites' onboard processors are typically much slower than their terrestrial counterparts. Therefore, to address these challenges we propose to solve an ARPOD problem with feedback optimization, which computes inputs to a system by measuring its outputs, feeding them into an optimization algorithm in the loop, and computing some number of iterations towards an optimal input. We focus on satellite rendezvous, and satellites' dynamics are modeled using the continuous‑time Clohessy‑Wiltshire equations, which are marginally stable. We develop an asymptotically stabilizing controller for them, and we use discrete‑time gradient descent in the loop to compute inputs to them. Then, we analyze the hybrid feedback optimization system formed by the stabilized Clohessy‑Wiltshire equations with gradient descent in the loop. We show that this model is well‑posed and that maximal solutions are both complete and non‑Zeno. Then, we show that solutions converge exponentially fast to a ball around a rendezvous point, and we bound the radius of that ball in terms of system parameters. Simulations show that this approach provides up to a 98.4% reduction in the magnitude of disturbances across a range of simulations, which illustrates the viability of hybrid feedback optimization for autonomous satellite rendezvous.

Authors:Xinxin Wang, Lixian Yan, Shuhan Liu, Luke Upton, Zhuoqi Cai, Yiming Tan, Shengman Li, Koustav Jana, Peijing Li, Jesse Cirimelli-Low, Thierry Tambe, Matthew Guthaus, H. -S. Philip Wong
Title: Heterogeneous Memory Design Exploration for AI Accelerators with a Gain Cell Memory Compiler
Abstract:
As memory increasingly dominates system cost and energy, heterogeneous on‑chip memory systems that combine technologies with complementary characteristics are becoming essential. Gain Cell RAM (GCRAM) offers higher density, lower power, and tunable retention, expanding the design space beyond conventional SRAM. To this end, we create an OpenGCRAM compiler supporting both SRAM and GCRAM. It generates macro‑level designs and layouts for commercial CMOS processes and characterizes area, delay, and power across user‑defined configurations. The tool enables systematic identification of optimal heterogeneous memory configurations for AI tasks under specified performance metrics.

Authors:Sanchita Ghosh, Tanushree Roy
Title: KAN-Koopman Based Rapid Detection Of Battery Thermal Anomalies With Diagnostics Guarantees
Abstract:
Early diagnosis of battery thermal anomalies is crucial to ensure safe and reliable battery operation by preventing catastrophic thermal failures. Battery diagnostics primarily rely on battery surface temperature measurements and/or estimation of core temperatures. However, aging‑induced changes in the battery model and limited training data remain major challenges for model‑based and machine‑learning based battery state estimation and diagnostics. To address these issues, we propose a Kolomogorov‑Arnold network (KAN) in conjunction with a Koopman‑based detection algorithm that leverages the unique advantages of both methods. Firstly, the lightweight KAN provides a model‑free estimation of the core temperature to ensure rapid detection of battery thermal anomalies. Secondly, the Koopman operator is learned in real time using the estimated core temperature from KAN and the measured surface temperature of the battery to provide the core and surface temperature prediction for diagnostic residual generation. This online learning approach overcomes the challenges of model changes. Furthermore, we derive analytical conditions to obtain diagnostic guarantees on our KAN‑Koopman detection scheme. Our simulation results illustrate a significant reduction in detection time with the proposed algorithm compared to the baseline Koopman‑only algorithm.

Authors:Huy Trinh
Title: A Survey of Recent Developments in SYCL Compiler Implementations
Abstract:
This survey discusses recent advancements in SYCL compiler implementations, one of the crucial aspects of compiler construction for heterogeneous computing systems. We explore the transition from traditional compiler construction, from Single‑Source Multiple Compiler Passes (SMCP) to a more advanced approach to Single‑Source Single Compiler Pass (SSCP). The survey analyzes multiple papers that researched the different developments of SYCL implementation based on SSCP and their approach to enhancing performance and addressing separate challenges.

Authors:Alexander J Gallo, Massimiliano Zoggia, Alessandro Falsone, Maria Prandini, Simone Garatti
Title: Robustness certificates in data-driven non-convex optimization with additively-uncertain constraints
Abstract:
We consider decision‑making problems that are formulated as non‑convex optimization programs where uncertainty enters the constraints through an additive term, independent of the decision variables, and robustness is imposed using a finite data‑set, according to the scenario robust optimization paradigm. By exploiting the structure of the constraints, we show that both a priori and a posteriori distribution‑free probabilistic robustness certificates for a possibly sub‑optimal solution to the resulting data‑driven optimization problem can be obtained with minimal computational effort. Building on these results, we also discuss a one‑shot and an incremental procedure to determine the size of the data‑set so as to guarantee a user‑chosen robustness level. Notably, both the a posteriori robustness assessment and incremental data‑set sizing do not require to solve the non‑convex scenario program. A comparative analysis performed on the unit commitment problem using real data reveals a limited increase in conservativeness with a significant computational saving with respect to the application of scenario theory results for general, non necessarily structured, non‑convex problems.

Authors:Marvin Dorn, Julian Hoffmann, André Weber, Veit Hagenmeyer
Title: Fast-Response Balancing Capacity of Alkaline Electrolyzers
Abstract:
The energy transition requires flexible technologies to maintain grid stability, and electrolyzers are playing an increasingly important role in meeting this need. While previous studies often question the dynamic capabilities of large‑scale alkaline electrolyzer systems, we assess their potential to provide balancing services using real manufacturer data. Unlike common approaches, we propose the decoupling between the total electrolyzer power and a smaller fractions of power actually offered on balancing markets. Adapting an existing methodology, we analyze alkaline electrolyzer systems and extend the assessment to Germany and Europe. Our results show that large‑scale electrolyzers are technically capable of delivering fast‑response balancing services, with significantly lower dynamic requirements than previously assumed. The planned electrolyzers in Germany could cover the entire balancing capacity market, potentially saving around 13 % of their electricity costs, excluding energy balancing revenues. The decoupling also resolves part of the trade‑off for electrolyzer manufacturers, enabling the design of less dynamic but more stable systems.

Authors:Xinhui Rong, Girish N. Nair
Title: Hawkes Identification with a Prescribed Causal Basis: Closed-Form Estimators and Asymptotics
Abstract:
Driven by the recent surge in neural‑inspired modeling, point processes have gained significant traction in systems and control. While the Hawkes process is the standard model for characterizing random event sequences with memory, identifying its unknown kernels is often hindered by nonlinearity. Approaches using prescribed basis kernels have emerged to enable linear parameterization, yet they typically rely on iterative likelihood methods and lack rigorous analysis under model misspecification. This paper justifies a closed‑form Least Squares identification framework for Hawkes processes with prescribed kernels. We guarantee estimator existence via the almost‑sure positive definiteness of the empirical Gram matrix and prove convergence to the true parameters under correct specification, or to well‑defined pseudo‑true parameters under misspecification. Furthermore, we derive explicit Central Limit Theorems for both regimes, providing a complete and interpretable asymptotic theory. We demonstrate these theoretical findings through comparative numerical simulations.

Authors:Mohamed Shamseldein
Title: Grid-Mind: An LLM-Orchestrated Multi-Fidelity Agent for Automated Connection Impact Assessment
Abstract:
Large language models (LLMs) have demonstrated remarkable tool‑use capabilities, yet their application to power system operations remains largely unexplored. This paper presents Grid‑Mind, a domain‑specific LLM agent that interprets natural‑language interconnection requests and autonomously orchestrates multi‑fidelity power system simulations. The LLM‑first architecture positions the language model as the central decision‑making entity, employing an eleven‑tool registry to execute Connection Impact Assessment (CIA) studies spanning steadystate power flow, N‑1 contingency analysis, transient stability, and electromagnetic transient screening. A violation inspector grounds every decision in quantitative simulation outputs, while a three‑layer anti‑hallucination defence mitigates numerical fabrication risk through forced capacity‑tool routing and post‑response grounding validation. A prompt‑level self‑correction mechanism extracts distilled lessons from agent failures, yielding progressive accuracy improvements without model retraining. End‑to‑end evaluation on 50 IEEE 118‑bus scenarios (DeepSeek‑V3, 2026‑02‑23) achieved 84.0% tool‑selection accuracy and 100% parsing accuracy. A separate 56‑scenario self‑correction suite passed 49 of 56 cases (87.5%) with a mean score of 89.3. These results establish a reproducible baseline for continued refinement while maintaining auditable, simulation‑grounded decision support.

Authors:Haizum Hanim Ab Halim, Dalila Alias, Akmal Zaini Arsad, Lewis Tee Jen Looi, Rosdiadee Nordin, Denny Ng Kok Sum
Title: IoT-Driven Building Energy Management Systems (BEMS) for Net Zero Energy Buildings: Concept, Integration and Future Directions
Abstract:
Construction and operating of buildings is one of the major contributors to global greenhouse emissions. With the inefficient usage of energy due to human behavior and manual operation, the energy consumption of buildings is further increased. These challenges highlight the need for improved Building Energy Management Systems (BEMS) integrated with Internet of Things (IoT) and data driven intelligence to enhance energy‑efficiency in a building and contribute to Net‑Zero Energy Buildings (NZEB) targets. This paper offers four keys contributions: i) a systematic review of IoT enabled BEMS including components, network architecture and functional capabilities, ii) an evaluation of real‑world BEMS datasets to support Artificial Intelligence (AI) based predictive control, iii) an analysis of integration challenges related to interoperability, smart grids and net‑zero energy strategies, and iv) a case study highlighting global best practices, performances outcomes, and lesson learned for scaling advanced BEMS solutions.

Authors:Rayan Mazouz, Frederik Baymler Mathiesen, Luca Laurenti, Morteza Lahijanian
Title: StochasticBarrier.jl: A Toolbox for Stochastic Barrier Function Synthesis
Abstract:
We present StochasticBarrier.jl, an open‑source Julia‑based toolbox for generating Stochastic Barrier Functions (SBFs) for safety verification of discrete‑time stochastic systems with additive Gaussian noise. StochasticBarrier.jl certifies linear, polynomial, and piecewise affine (PWA) systems. The latter enables verification for a wide range of system dynamics, including general nonlinear types. The toolbox implements a Sum‑of‑Squares (SOS) optimization approach, as well as methods based on piecewise constant (PWC) functions. For SOS‑based SBFs, StochasticBarrier.jl leverages semi‑definite programming solvers, while for PWC SBFs, it offers three engines: two using linear programming (LP) and one based on gradient descent (GD). Benchmarking StochasticBarrier.jl against the state‑of‑the‑art shows that the tool outperforms existing tools in computation time, safety probability bounds, and scalability across over 30 case studies. Compared to its closest competitor, StochasticBarrier.jl is up to four orders of magnitude faster, achieves significant safety probability improvements, and supports higher‑dimensional systems.

Authors:Zehao Wang, Mingzhe Han, Wei Cheng, Yue-Kai Huang, Philip Ji, Denton Wu, Mahdi Safari, Flemming Holtorf, Kenaish AlQubaisi, Norbert M. Linke, Danyang Zhuo, Yiran Chen, Ting Wang, Dirk Englund, Tingjun Chen
Title: Agentic AI for Scalable and Robust Optical Systems Control
Abstract:
We present AgentOptics, an agentic AI framework for high‑fidelity, autonomous optical system control built on the Model Context Protocol (MCP). AgentOptics interprets natural language tasks and executes protocol‑compliant actions on heterogeneous optical devices through a structured tool abstraction layer. We implement 64 standardized MCP tools across 8 representative optical devices and construct a 410‑task benchmark to evaluate request understanding, role‑aware responses, multi‑step coordination, robustness to linguistic variation, and error handling. We assess two deployment configurations‑‑commercial online LLMs and locally hosted open‑source LLMs‑‑and compare them with LLM‑based code generation baselines. AgentOptics achieves 87.7%‑‑99.0% average task success rates, significantly outperforming code‑generation approaches, which reach up to 50% success. We further demonstrate broader applicability through five case studies extending beyond device‑level control to system orchestration, monitoring, and closed‑loop optimization. These include DWDM link provisioning and coordinated monitoring of coherent 400 GbE and analog radio‑over‑fiber (ARoF) channels; autonomous characterization and bias optimization of a wideband ARoF link carrying 5G fronthaul traffic; multi‑span channel provisioning with launch power optimization; closed‑loop fiber polarization stabilization; and distributed acoustic sensing (DAS)‑based fiber monitoring with LLM‑assisted event detection. These results establish AgentOptics as a scalable, robust paradigm for autonomous control and orchestration of heterogeneous optical systems.

Authors:Anders Hansson, João Victor Galvão da Mata, Martin S. Andersen
Title: Informativity and Identifiability for Identification of Networks of Dynamical Systems
Abstract:
In this paper, we show how informativity and identifiability for networks of dynamical systems can be investigated using Gröbner bases. We provide a sufficient condition for informativity in terms of positive definiteness of the spectrum of external signals and full generic rank of the transfer function relating the external signals to the inputs of the predictor. Moreover, we show how generic local network identifiability can be investigated by computing the dimension of the fiber associated with the closed loop transfer function from external measurable signals to the measured outputs.

Authors:Wei Xiao, Christos Cassandras, Anni Li
Title: Robust Taylor-Lagrange Control for Safety-Critical Systems
Abstract:
Solving safety‑critical control problem has widely adopted the Control Barrier Function (CBF) method. However, the existence of a CBF is only a sufficient condition for system safety. The recently proposed Taylor‑Lagrange Control (TLC) method addresses this limitation, but is vulnerable to the feasibility preservation problem (e.g., inter‑sampling effect). In this paper, we propose a robust TLC (rTLC) method to address the feasibility preservation problem. Specifically, the rTLC method expands the safety function at an order higher than the relative degree of the function using Taylor's expansion with Lagrange remainder, which allows the control to explicitly show up at the current time instead of the future time in the TLC method. The rTLC method naturally addresses the feasibility preservation problem with only one hyper‑parameter (the discretization time interval size during implementation), which is much less than its counterparts. Finally, we illustrate the effectiveness of the proposed rTLC method through an adaptive cruise control problem, and compare it with existing safety‑critical control methods.

Authors:Pelin Sekercioglu, Angela Fontan, Dimos V. Dimarogonas
Title: Edge-based Synchronization over Signed Digraphs with Multiple Leaders
Abstract:
This work addresses the edge‑based synchronization problem in first‑order multi‑agent systems containing both cooperative and antagonistic interactions with one or multiple leader groups. The presence of multiple leaders and antagonistic interactions means that the multi‑agent system typically does not achieve consensus, unless specific conditions (on the number of leaders and on the signed graph) are met, in which case the agents reach a trivial form of consensus. In general, we show that the multi‑agent system exhibits a more general form of synchronization, including bipartite consensus and containment. Our approach proposes a signed edge‑based agreement protocol for signed networks described by signed edge‑Laplacian matrices. In particular, in this work, we present new spectral properties of signed edge‑Laplacian matrices containing multiple zero eigenvalues and establish global exponential stability of the synchronization errors. Moreover, we explicitly compute the equilibrium to which all edge states converge, thereby characterizing the resulting synchronization behavior. Numerical simulations validate our theoretical results.

Authors:Carlo Karam, Matteo Tacchi, Mirko Fiacchini
Title: A Stochastic Tube-Based MPC Framework with Hard Input Constraints
Abstract:
This work presents a stochastic tube‑based model predictive control framework that guarantees hard input constraint satisfaction for linear systems subject to unbounded additive disturbances. The approach relies on a structured design of probabilistic reachable sets that explicitly incorporates actuator saturation into the error dynamics and bounds the resulting nonlinearity within a convex embedding. The proposed controller retains the computational efficiency and structural advantages of stochastic tube‑based approaches while ensuring state chance constraint satisfaction alongside hard input limits. Recursive feasibility and mean‑square stability are established for our scheme, and a numerical example illustrates its effectiveness.

Authors:Lars Fischer, Daniel Flögel, Sören Hohmann
Title: Rendezvous and Docking of Mobile Ground Robots for Efficient Transportation Systems
Abstract:
In‑Motion physical coupling of multiple mobile ground robots has the potential to enable new applications like in‑motion transfer that improves efficiency in handling and transferring goods, which tackles current challenges in logistics. A key challenge lies in achieving reliable autonomous in‑motion physical coupling of two mobile ground robots starting at any initial position. Existing approaches neglect the modeling of the docking interface and the strategy for approaching it, resulting in uncontrolled collisions that make in‑motion physical coupling either impossible or inefficient. To address this challenge, we propose a central mpc approach that explicitly models the dynamics and states of two omnidirectional wheeled robots, incorporates constraints related to their docking interface, and implements an approaching strategy for rendezvous and docking. This novel approach enables omnidirectional wheeled robots with a docking interface to physically couple in motion regardless of their initial position. In addition, it makes in‑motion transfer possible, which is 19.75% more time‑ and 21.04% energy‑efficient compared to a non‑coupling approach in a logistic scenario.

Authors:Bang Huang, Baha Eddine Youcef Belmekki, Mohamed-Slim Alouini
Title: High-Altitude Platforms in the Low-Altitude Economy: Bridging Communication, Computing, and Regulation
Abstract:
The Low‑Altitude Economy (LAE) is rapidly emerging as a new technological and industrial frontier, with unmanned aerial vehicles (UAVs), electric vertical takeoff and landing (eVTOL) aircraft, and aerial swarms increasingly deployed in logistics, infrastructure inspection, security, and emergency response. However, the large‑scale development of the LAE demands a reliable aerial foundation that ensures not only real‑time connectivity and computational support, but also navigation integrity and safe airspace management for safety‑critical operations. High‑Altitude Platforms (HAPs), positioned at around 20 km, provide a unique balance between wide‑area coverage and low‑latency responsiveness. Compared with low earth orbit (LEO) satellites, HAPs are closer to end users and thus capable of delivering millisecond‑level connectivity, fine‑grained regulatory oversight, and powerful onboard computing and caching resources. Beyond connectivity and computation, HAPs‑assisted sensing and regulation further enable navigation integrity and airspace trust, which are essential for safety‑critical UAV and eVTOL operations in the LAE. This article proposes a five‑stage evolutionary roadmap for HAPs in the LAE: from serving as aerial infrastructure bases, to becoming super back‑ends for UAV, to acting as frontline support for ground users, further enabling swarm‑scale UAV coordination, and ultimately advancing toward edge‑air‑cloud closed‑loop autonomy. In parallel, HAPs complement LEO satellites and cloud infrastructures to form a global‑regional‑local three‑tier architecture. Looking forward, HAPs are expected to evolve from simple platforms into intelligent hubs, emerging as pivotal nodes for air traffic management, intelligent logistics, and emergency response. By doing so, they will accelerate the transition of the LAE toward large‑scale deployment, autonomy, and sustainable growth.

Authors:Timon Conrad, Changhun Kim, Johann Jäger, Andreas Maier, Siming Bayer
Title: Impact of Training Dataset Size for ML Load Flow Surrogates
Abstract:
Efficient and accurate load flow calculations are a bedrock of modern power system operation. Classical numerical methods such as the Newton‑Raphson algorithm provide highly precise results but are computationally demanding, which limits their applicability in large‑scale scenario studies and optimization in time‑critical contexts. Research has shown that machine learning approaches can approximate load flow results with high accuracy while substantially reducing computation time. Sample efficiency, i.e., the ability to achieve high accuracy with limited training dataset size, is still insufficiently researched, especially in grids with a fixed topology. This paper presents a systematic investigation of the sample efficiency of a Multilayer Perceptron and two Graph Neural Network variants on a dataset based on a modified IEEE 5‑bus system. The results for this grid size show that Graph Neural Networks achieve the lowest losses. However, the availability of large training datasets remains the dominant factor influencing performance compared to architecture choice.

Authors:Mario di Bernardo
Title: Multicellular Feedback Control Strategies in Synthetic Microbial Consortia: From Embedded to Distributed Control
Abstract:
Living organisms rely on endogenous feedback mechanisms to maintain homeostasis in the presence of uncertainty and environmental fluctuations. An emerging challenge at the interface of control systems engineering and synthetic biology is the design of reliable feedback strategies to regulate cellular behavior and collective biological functions. In this article, we review recent advances in multicellular feedback control, where sensing, computation, and actuation are distributed across different cell populations within synthetic microbial consortia, giving rise to biological multiagent control systems governed by molecular communication. From a control‑theoretic perspective, these consortia can be interpreted as distributed biomolecular control systems, where coordination among populations replace embedded regulation. We survey theoretical frameworks, control architectures, and modeling approaches, ranging from aggregate population‑level dynamics to spatially aware agent‑based simulations, and discuss experimental demonstrations in engineered Escherichia coli consortia. We highlight how distributing control functions across populations can reduce metabolic burden, mitigate retroactivity, improve robustness to uncertainty, and enable modular reuse of control components. Beyond regulation of gene expression, we discuss the emerging problem of population composition control, where coordination among growing and competing cell populations becomes an integral part of the control objective. Finally, we outline key open challenges that must be addressed before multicellular control strategies can be deployed in real‑world applications such as biomanufacturing, environmental remediation, and therapeutic systems. These challenges span modeling and simulation, experimental platform development, coordination and composition control, and long‑term evolutionary stability.

Authors:Junseon Park, Hyeongon Park, Rahul K. Gupta
Title: Co-Optimization of Network Topology and Variable Impedance Devices under Dynamic Line Ratings in Power Transmission Systems
Abstract:
Power system operators are increasingly deploying Grid Enhancing Technologies (GETs) to mitigate operational challenges such as line and transformer congestion, and voltage violations. These technologies, including Network Topology Optimization (NTO), Variable Impedance Devices (VIDs), and Dynamic Line Rating (DLR), enhance system flexibility and enable better utilization of existing network assets. However, as the deployment of multiple GETs grows, effective coordination among them becomes essential to fully realize their potential benefits. This paper presents a co‑optimization framework that models and coordinates NTO, VID, and DLR within a unified optimization scheme to alleviate network congestion and minimize operational costs. The NTO formulation is developed using a node‑breaker model, offering finer switching granularity and improved operational flexibility. The inclusion of VIDs introduces nonlinear and non‑convex relationships in the optimization problem. DLR takes into account of weather conditions, primarily wind speed and ambient temperature, enabling adaptive utilization of transmission capacity. The proposed framework is validated on standard IEEE benchmark test systems, demonstrating its effectiveness under varying numbers and placements of impedance controllers.

Authors:William Sharpless, Oswin So, Dylan Hirsch, Sylvia Herbert, Chuchu Fan
Title: Bellman Value Decomposition for Task Logic in Safe Optimal Control
Abstract:
Real‑world tasks involve nuanced combinations of goal and safety specifications. In high dimensions, the challenge is exacerbated: formal automata become cumbersome, and the combination of sparse rewards tends to require laborious tuning. In this work, we consider the innate structure of the Bellman Value as a means to naturally organize the problem for improved automatic performance. Namely, we prove the Bellman Value for a complex task defined in temporal logic can be decomposed into a graph of Bellman Values, connected by a set of well‑known Bellman equations (BEs): the Reach‑Avoid BE, the Avoid BE, and a novel type, the Reach‑Avoid‑Loop BE. To solve the Value and optimal policy, we propose VDPPO, which embeds the decomposed Value graph into a two‑layer neural net, bootstrapping the implicit dependencies. We conduct a variety of simulated and hardware experiments to test our method on complex, high‑dimensional tasks involving heterogeneous teams and nonlinear dynamics. Ultimately, we find this approach greatly improves performance over existing baselines, balancing safety and liveness automatically.

Authors:Xinyi Yi, Ioannis Lestas
Title: A mixed Hinfty-Passivity approach for Leveraging District Heating Systems as Frequency Ancillary Service in Electric Power Systems
Abstract:
This paper introduces a mixed H‑infinity‑passivity framework that enables district heating systems (DHSs) with heat pumps to support electric‑grid frequency regulation. The analysis illustrates how the DHS regulator influences coupled electro‑thermal frequency dynamics and provides LMI conditions for efficient controller design. We also present a disturbance‑independent temperature regulator that ensures stability and robustness against heat‑demand uncertainty. Simulations demonstrate improved frequency‑control dynamics in the electrical power grid while maintaining good thermal performance in the DHS.

Authors:Taiyo Mantani, Hikaru Hoshino, Tomonari Kanazawa, Eiko Furutani
Title: Sizing of Battery Considering Renewable Energy Bidding Strategy with Reinforcement Learning
Abstract:
This paper proposes a novel computationally efficient algorithm for optimal sizing of Battery Energy Storage Systems (BESS) considering renewable energy bidding strategies. Unlike existing two‑stage methods, our algorithm enables the cooptimization of both by updating the BESS size during the training of the bidding policy, leveraging an extended reinforcement learning (RL) framework inspired by advancements in embodied cognition. By integrating the Deep Recurrent Q‑Network (DRQN) with a distributed RL framework, the proposed algorithm effectively manages uncertainties in renewable generation and market prices while enabling parallel computation for efficiently handling long‑term data.

Authors:Tomonari Kanazawa, Hikaru Hoshino, Eiko Furutani
Title: A Reinforcement Learning-based Transmission Expansion Framework Considering Strategic Bidding in Electricity Markets
Abstract:
Transmission expansion planning in electricity markets is tightly coupled with the strategic bidding behaviors of generation companies. This paper proposes a Reinforcement Learning (RL)‑based co‑optimization framework that simultaneously learns transmission investment decisions and generator bidding strategies within a unified training process. Based on a multiagent RL framework for market simulation, the proposed method newly introduces a design policy layer that jointly optimizes continuous/discrete transmission expansion decisions together with strategic bidding policies. Through iterative interaction between market clearing and investment design, the framework effectively captures their mutual influence and achieves consistent co‑optimization of expansion and bidding decisions. Case studies on the IEEE 30‑bus system are provided for proof‑of‑concept validation of the proposed co‑optimization framework.

Authors:Fei Chen, Jorge Cortés, Sonia Martínez
Title: Enhancing network resilience through topological switching
Abstract:
This work studies how to preemptively increase the resilience of a network by means of time‑varying topological actuation. To do this, we focus on linear dynamical systems that are compatible with a given network, and consider policies that switch periodically between the given one and an alternative, topologically‑compatible dynamics. In particular, we seek to solve design problems aimed at finding a) the optimal switching schedule between two preselected topologies, and b) an optimal topology and optimal switching schedule. By imposing periodicity, we first provide a metric of resilience in terms of the spectral abscissa of the averaged linear time‑invariant dynamics. By restricting our policies to commutative networks, we then show how the optimal scheduling problem reduces to a convex optimization, providing a bound on the net resilience that can be achieved. After this, we find that the optimal, sparse commutative network to switch with is fully disconnected and allocates the spectral sum among the nodes of the network equally. We then impose additional restrictions on topology edge selection, which leads to a biconvex optimization for which certain matrix rank conditions guide the choice of weighting parameters to obtain desirable solutions. Finally, we provide two methods to solve this problem efficiently (based on a McCormick relaxation, and alternating minimization), and illustrate the results in simulations.

Authors:Nazal Mohamed, Ayush Mohanty, Nagi Gebraeel
Title: Federated Causal Representation Learning in State-Space Systems for Decentralized Counterfactual Reasoning
Abstract:
Networks of interdependent industrial assets (clients) are tightly coupled through physical processes and control inputs, raising a key question: how would the output of one client change if another client were operated differently? This is difficult to answer because client‑specific data are high‑dimensional and private, making centralization of raw data infeasible. Each client also maintains proprietary local models that cannot be modified. We propose a federated framework for causal representation learning in state‑space systems that captures interdependencies among clients under these constraints. Each client maps high‑dimensional observations into low‑dimensional latent states that disentangle intrinsic dynamics from control‑driven influences. A central server estimates the global state‑transition and control structure. This enables decentralized counterfactual reasoning where clients predict how outputs would change under alternative control inputs at others while only exchanging compact latent states. We prove convergence to a centralized oracle and provide privacy guarantees. Our experiments demonstrate scalability, and accurate cross‑client counterfactual inference on synthetic and real‑world industrial control system datasets.

Authors:Mohamadamin Rajabinezhad, Muratkhan Abdirash, Xiaofan Cui, Shan Zuo
Title: Decentralized Attack-Resilient CLF-Based Control of Nonlinear DC Microgrids under FDI Attacks
Abstract:
The growing deployment of nonlinear, converter interfaced distributed energy resources (DERs) in DC microgrids demands decentralized controllers that remain stable and resilient under a wide range of cyber‑physical attacks and disturbances. Traditional droop or linearized control methods lack resilience and scalability, especially when the system operates in its nonlinear region or faces diverse false‑data‑injection (FDI) attacks on control inputs. In this work, we develop a Decentralized Attack‑Resilient Control Lyapunov Function (AR‑CLF) based Quadratic Program (QP) control framework for nonlinear DC microgrids that ensures large‑signal stability in a fully decentralized manner. Built upon the port‑Hamiltonian representation, the proposed controller dynamically compensates diverse attacks including exponentially unbounded control‑input perturbations beyond the bounded‑attack regime commonly assumed in existing methods, through an adaptive resilience term, without requiring global information. Simulations validate that the AR‑CLF based QP controller achieves superior stability and resilience against unbounded attacks, paving the way for scalable, attack‑resilient, and physically consistent control of next‑generation DC microgrids.

Authors:Zirui Xu, Vasileios Tzoumas
Title: Self-Configurable Mesh-Networks for Scalable Distributed Submodular Bandit Optimization
Abstract:
We study how to scale distributed bandit submodular coordination under realistic communication constraints in bandwidth, data rate, and connectivity. We are motivated by multi‑agent tasks of active situational awareness in unknown, partially‑observable, and resource‑limited environments, where the agents must coordinate through agent‑to‑agent communication. Our approach enables scalability by (i) limiting information relays to only one‑hop communication and (ii) keeping inter‑agent messages small, having each agent transmit only its own action information. Despite these information‑access restrictions, our approach enables near‑optimal action coordination by optimizing the agents' communication neighborhoods over time, through distributed online bandit optimization, subject to the agents' bandwidth constraints. Particularly, our approach enjoys an anytime suboptimality bound that is also strictly positive for arbitrary network topologies, even disconnected. To prove the bound, we define the Value of Coordination (VoC), an information‑theoretic metric that quantifies for each agent the benefit of information access to its neighbors. We validate in simulations the scalability and near‑optimality of our approach: it is observed to converge faster, outperform benchmarks for bandit submodular coordination, and can even outperform benchmarks that are privileged with a priori knowledge of the environment.

Authors:Erik Garcia Oyono, Jialin Lin, Dandan Zhang
Title: Design and Control of Modular Magnetic Millirobots for Multimodal Locomotion and Shape Reconfiguration
Abstract:
Modular small‑scale robots offer the potential for on‑demand assembly and disassembly, enabling task‑specific adaptation in dynamic and constrained environments. However, existing modular magnetic platforms often depend on workspace collisions for reconfiguration, employ bulky three‑dimensional electromagnetic systems, and lack robust single‑module control, which limits their applicability in biomedical settings. In this work, we present a modular magnetic millirobotic platform comprising three cube‑shaped modules with embedded permanent magnets, each designed for a distinct functional role: a free module that supports self‑assembly and reconfiguration, a fixed module that enables flip‑and‑walk locomotion, and a gripper module for cargo manipulation. Locomotion and reconfiguration are actuated by programmable combinations of time‑varying two‑dimensional uniform and gradient magnetic field inputs. Experiments demonstrate closed‑loop navigation using real‑time vision feedback and A path planning, establishing robust single‑module control capabilities. Beyond locomotion, the system achieves self‑assembly, multimodal transformations, and disassembly at low field strengths. Chain‑to‑gripper transformations succeeded in 90% of trials, while chain‑to‑square transformations were less consistent, underscoring the role of module geometry in reconfiguration reliability. These results establish a versatile modular robotic platform capable of multimodal behavior and robust control, suggesting a promising pathway toward scalable and adaptive task execution in confined environments.

Authors:Xinling Li, Gioele Zardini
Title: Where Should Robotaxis Operate? Strategic Network Design for Autonomous Mobility-on-Demand
Abstract:
The emergence of Autonomous Mobility‑on‑Demand (AMoD) services creates new opportunities to improve the efficiency and reliability of on‑demand mobility systems. Unlike human‑driven Mobility‑on‑Demand (MoD), AMoD enables fully centralized fleet control, but it also requires appropriate infrastructure, so that vehicles can operate safely only on a suitably instrumented subnetwork of the roads. Most existing AMoD research focuses on fleet control (matching, rebalancing, ridepooling) on a fixed road network and does not address the joint design of the service network and fleet capacity. In this paper, we formalize this strategic design problem as the Autonomous Mobility‑on‑Demand Network Design Problem (AMoD‑NDP), in which an operator selects an operation subnetwork and routes all passengers, subject to infrastructure and fleet constraints and route‑level quality‑of‑service requirements. We propose a path‑based mixed‑integer formulation of the AMoD‑NDP and develop a column‑generation‑based algorithm that scales to city‑sized networks. The master problem optimizes over a restricted set of paths, while the pricing problem reduces to an elementary shortest path with resource constraints, solved exactly by a tailored label‑correcting algorithm. The method provides an explicit certificate of the optimality gap and extends naturally to a robust counterpart under box uncertainty in travel times and demand. Using real‑world data from Manhattan, New York City, we show that the framework produces stable and interpretable operation subnetworks, quantifies trade‑offs between infrastructure investment and fleet time, and accommodates additional path‑level constraints, such as limits on left turns as a proxy for operational risk. These results illustrate how the proposed approach can support strategic planning and policy analysis for future AMoD deployments.

Authors:Yihsu Chen, Abel Souza, Fargol Nematkhah, Andrew L. Liu
Title: A Power Market Model with Hypersaclers and Modular Datacenters
Abstract:
The rapid adoption of AI has led the growth of computational demand, with large language models (LLMs) at the forefront since ChatGPT's debut in 2022. Meanwhile, large amounts of renewable energy have been deployed but, ultimately, curtailed due to transmission congestion and inadequate demand. This work develops a power market model that allows hyperscalers to spatially migrate LLM inference workloads to geo‑distributed modular datacenters (MDCs), which are co‑located with near renewable sources of energy at the edge of the network. We introduce the optimization problems faced by the hyperscaler and MDCs in addition to consumers, producers, and the electric grid operator, where the hyerscaler enters an agreement to lease MDCs while ensuring that the required service level objectives (SLOs) are met. The overall market model is formulated as a complementarity problem, where the proof is provided showing the existence and uniqueness of the solutions. When applying the model to an IEEE RTS‑24 bus case study, we show that even with a provision that requires MDCs to disclose the CO_2 emissions associated with their energy supply sources, renting less polluting MDCs is unlikely to yield meaningful emission reductions due to so‑called contract‑reshuffling. The situation can be mitigated when conventional loads are supplied by forward contracts through power purchase agreements. This also leads to a decline in system congestion when the hyperscaler becomes increasingly cost‑aware.

Authors:Hassan Munif, Vineeth Satheeskumar Varma, Samson Lasaulce
Title: Strategic Gaussian Signaling under Linear Sensitivity Mismatch
Abstract:
We analyze Stackelberg Gaussian signaling games where the encoder and decoder have a linear sensitivity mismatch. Unlike the standard additive‑bias model, a sensitivity mismatch means the encoder prefers the decoder to track a linear transformation of the state rather than a shifted one. We derive the equilibrium structure for both noiseless (cheap‑talk) and noisy signaling channels. In the noiseless case, the equilibrium admits a spectral characterization: the encoder transmits information only along eigenspaces associated with the negative eigenvalues of a mismatch matrix. In the noisy regime, we derive analytical thresholds for informative signaling, showing that communication collapses if the sensitivity mismatch or transmission cost exceeds a channel‑dependent threshold.

Authors:Mingliang Xiong, Zeqian Guo, Qingqing Zhang, Qingwen Liu, Gang Wang, Gang Li, Bin He
Title: On the Stability of Spatially Distributed Cavity Laser and Boundary of Resonant Beam SLIPT
Abstract:
Spatially distributed cavity (SDC) lasers are a promising technology for simultaneous light information and power transfer (SLIPT), offering benefits such as increased mobility and intrinsic safety, which are advantageous for various Internet of Things (IoT) devices. \mll However, achieving beam transmission over meter‑level long working distances presents significant challenges from cavity stability constraints, manufacturing/assembly tolerances, and diffraction losses\mrr. This paper conducts a theoretical investigation of the fundamental restrictions limiting long‑range resonant beam generation. We investigate cavity stability and beam characteristics, and propose a binary‑search‑based Monte Carlo simulation algorithm as well as a linear approximation algorithm to quantify the maximum acceptable tolerances for stable operation. \mll Numerical results indicate that the stable region contracts sharply as distance increases. For fixed‑component systems, an acceptable tolerance of 0.01 mm restricts the achievable transmission distance to less than 2 m. \mrr To address this limitation, we also prove the feasibility of long‑range beam formation using precision adjustable elements, paving the way for advanced engineering applications. \mll Experimental results verified this assumption, demonstrating that by tuning the stable region during assembly, the transmission distance could be extended to 2.8 m. \mrr This work provides essential theoretical insights and practical design guidelines for realizing stable, long‑range SDC systems.

Authors:Ziwei Kang, Yizhi Zhou
Title: Distributed and Consistent Multi-Robot Visual-Inertial-Ranging Odometry on Lie Groups
Abstract:
Reliable localization is a fundamental requirement for multi‑robot systems operating in GPS‑denied environments. Visual‑inertial odometry (VIO) provides lightweight and accurate motion estimation but suffers from cumulative drift in the absence of global references. Ultra‑wideband (UWB) ranging offers complementary global observations, yet most existing UWB‑aided VIO methods are designed for single‑robot scenarios and rely on pre‑calibrated anchors, which limits their robustness in practice. This paper proposes a distributed collaborative visual‑inertial‑ranging odometry (DC‑VIRO) framework that tightly fuses VIO and UWB measurements across multiple robots. Anchor positions are explicitly included in the system state to address calibration uncertainty, while shared anchor observations are exploited through inter‑robot communication to provide additional geometric constraints. By leveraging a right‑invariant error formulation on Lie groups, the proposed approach preserves the observability properties of standard VIO, ensuring estimator consistency. Simulation results with multiple robots demonstrate that DC‑VIRO significantly improves localization accuracy and robustness, while simultaneously enabling anchor self‑calibration in distributed settings.

Authors:Jie Song, Yang Bai, Naoki Wakamiya
Title: Cooperative Transportation Without Prior Object Knowledge via Adaptive Self-Allocation and Coordination
Abstract:
This work proposes a novel cooperative transportation framework for multi‑agent systems that does not require any prior knowledge of cargo locations or sizes. Each agent relies on local sensing to detect cargos, recruit nearby agents, and autonomously form a transportation team with an appropriate size. The core idea is that once an agent detects a cargo within its sensing range, it generates an attraction field represented by a density function, which pulls neighboring agents toward the cargo. When multiple cargos are present, the attraction fields generated by different agents are adaptively weighted and combined with Centroidal Voronoi Tessellation (CVT), enabling agents to self‑organize into balanced formations while automatically allocating more agents to larger cargos. To prevent agents from clustering on one side of a large cargo, a Control Barrier Function (CBF)‑based mechanism is introduced to enforce safe inter‑agent distances and promote a uniform, symmetric distribution of agents around each cargo, which is essential for stable transportation. Simulation results demonstrate that the proposed framework can simultaneously transport multiple cargos of different sizes in a coordinated and collision‑free manner.

Authors:Laurenţiu Lucian Anton, Marija Ilić
Title: Unified Diagnostics for Quantifying AC Operating-Point Robustness Under Injection and Topological Uncertainties with Regime Changes
Abstract:
In the presence of uncertainties in load, generation, and network topology, power system planning must reflect operational conditions, while operations require situational awareness over credible uncertainty sets. Existing methods screen, analyze, embed, and propagate uncertainty in power flow and optimal power flow settings, but provide only partial insight into how physical constraints, controls, and economic interactions shape steady‑state operating‑point robustness. By formulating operating‑point robustness as a post‑solution physical response problem around a solved AC optimal power flow (AC‑OPF) equilibrium, this paper presents a unified framework for assessing robustness under injection and topological uncertainty without re‑optimization. We construct a primal physical response mapping that accounts for connectivity changes, active power redistribution, generator saturation including PV \rightarrow PQ transitions, and AC network propagation, and introduce quasi‑duals that provide a geometric interpretation of shadow prices for off‑optimal equilibria. Using these mappings, we develop deterministic screening procedures that generalize N‑k contingency analysis to include cost vulnerability C‑k, and local analogs N+δ(k) and C+δ(k) defined through sensitivity‑normalized margins and risk tolerances. The framework is extended to probabilistic screening for distribution‑ and moment‑based uncertainties, with sequentially‑pruned mixture modeling and α‑stressed regime constructions to manage combinatorial branching. A case study on the Puerto Rican bulk power system demonstrates integration with geospatial data to enhance operational and planning awareness.

Authors:Shiyu Liu, Dylan Lester, Husnu Narman, Ammar Alzarrad, Pingping Zhu
Title: Depth-Enhanced YOLO-SAM2 Detection for Reliable Ballast Insufficiency Identification
Abstract:
This paper presents a depth‑enhanced YOLO‑SAM2 framework for detecting ballast insufficiency in railway tracks using RGB‑D data. Although YOLOv8 provides reliable localization, the RGB‑only model shows limited safety performance, achieving high precision (0.99) but low recall (0.49) due to insufficient ballast, as it tends to over‑predict the sufficient class. To improve reliability, we incorporate depth‑based geometric analysis enabled by a sleeper‑aligned depth‑correction pipeline that compensates for RealSense spatial distortion using polynomial modeling, RANSAC, and temporal smoothing. SAM2 segmentation further refines region‑of‑interest masks, enabling accurate extraction of sleeper and ballast profiles for geometric classification. Experiments on field‑collected top‑down RGB‑D data show that depth‑enhanced configurations substantially improve the detection of insufficient ballast. Depending on bounding‑box sampling (AABB or RBB) and geometric criteria, recall increases from 0.49 to as high as 0.80, and F1‑score improves from 0.66 to over 0.80. These results demonstrate that integrating depth correction with YOLO‑SAM2 yields a more robust and reliable approach for automated railway ballast inspection, particularly in visually ambiguous or safety‑critical scenarios.

Authors:Bowen Song, Simon Weissmann, Mathias Staudigl, Andrea Iannelli
Title: A Stochastic Gradient Descent Approach to Design Policy Gradient Methods for LQR
Abstract:
In this work, we propose a stochastic gradient descent (SGD) framework to design data‑driven policy gradient descent algorithms for the linear quadratic regulator problem. Two alternative schemes are considered to estimate the policy gradient from stochastic trajectory data: (i) an indirect online identification based approach, in which the system matrices are first estimated and subsequently used to construct the gradient, and (ii) a direct zeroth‑order approach, which approximates the gradient using empirical cost evaluations. In both cases, the resulting gradient estimates are random due to stochasticity in the data, allowing us to use SGD theory to analyze the convergence of the associated policy gradient methods. A key technical step consists of modeling the gradient estimates as suitable stochastic gradient oracles, which, because of the way they are computed, are inherently based. We derive sufficient conditions under which SGD with a biased gradient oracle converges asymptotically to the optimal policy, and leverage these conditions to design the parameters of the gradient estimation schemes. Moreover, we compare the advantages and limitations of the two data‑driven gradient estimators. Numerical experiments validate the effectiveness of the proposed methods.

Authors:Nicola Cigarini, Giulia Michieletto, Angelo Cenedese
Title: RotorSuite: A MATLAB/Simulink Toolbox for Tilt Multi-Rotor UAV Modeling
Abstract:
In recent years, aerial platforms have evolved from passive flying sensors into versatile, contact‑aware robotic systems, leading to rapid advances in platform design. Standard coplanar and collinear quadrotors have been complemented by modern tilted and tilting multi‑rotor platforms with enhanced maneuverability. To properly analyze, control, and validate the performance of these emerging platforms, an accurate modeling step is required; however, this can be time‑consuming, user‑dependent and error‑prone. To address this issue, we propose a MATLAB/Simulink toolbox for modeling and simulating the dynamics of a broad class of multi‑rotor platforms through both an analytical and physics‑based approaches. The toolbox, named RotorSuite, is provided with comprehensive documentation and example use cases, representing a valuable tool for didactic, research, and industrial development purposes.

Authors:Kaichen Jiang, Yuyue Yan, Mingda Yue, Yuhu Wu
Title: Seeking Nash Equilibrium in Non-cooperative Quadratic Games Under Delayed Information Exchange
Abstract:
In this paper, we investigate the seeking of Nash equilibrium (NE) in a non‑cooperative quadratic game where all agents exchange their delayed strategy information with their neighbors. To extend best‑response algorithms to the delayed information setting, an estimation mechanism for each agent to estimate the current strategy profile is designed. Based on the best‑response strategy to the estimations, the strategy profile dynamics of all agents is established, which is revealed to converge asymptotically to the NE when agents exchange multi‑step‑delay information via the Lyapunov‑Krasovskii functional approach. In the scenario where agents exchange one‑step‑delay information, the exponential convergence of the strategy profile dynamics to the NE can be guaranteed by restricting the learning rate to less than an upper bound. Moreover, a lower bound on the learning rate for instability of the NE is proposed. Numerical simulations are provided for verifying the developed results.

Authors:Ziyan Zhang, Changxin Wan, Peng Hao, Kanok Boriboonsomsin, Matthew J. Barth, Yongkang Liu, Seyhan Ucar, Guoyuan Wu
Title: HONEST-CAV: Hierarchical Optimization of Network Signals and Trajectories for Connected and Automated Vehicles with Multi-Agent Reinforcement Learning
Abstract:
This study presents a hierarchical, network‑level traffic flow control framework for mixed traffic consisting of Human‑driven Vehicles (HVs), Connected and Automated Vehicles (CAVs). The framework jointly optimizes vehicle‑level eco‑driving behaviors and intersection‑level traffic signal control to enhance overall network efficiency and decrease energy consumption. A decentralized Multi‑Agent Reinforcement Learning (MARL) approach by Value Decomposition Network (VDN) manages cycle‑based traffic signal control (TSC) at intersections, while an innovative Signal Phase and Timing (SPaT) prediction method integrates a Machine Learning‑based Trajectory Planning Algorithm (MLTPA) to guide CAVs in executing Eco‑Approach and Departure (EAD) maneuvers. The framework is evaluated across varying CAV proportions and powertrain types to assess its effects on mobility and energy performance. Experimental results conducted in a 44 real‑world network demonstrate that the MARL‑based TSC method outperforms the baseline model (i.e., Webster method) in speed, fuel consumption, and idling time. In addition, with MLTPA, HONEST‑CAV benefits the traffic system further in energy consumption and idling time. With a 60% CAV proportion, vehicle average speed, fuel consumption, and idling time can be improved/saved by 7.67%, 10.23%, and 45.83% compared with the baseline. Furthermore, discussions on CAV proportions and powertrain types are conducted to quantify the performance of the proposed method with the impact of automation and electrification.

Authors:Zeinab Salehi, Elizabeth L. Ratnam, Yijun Chen, Ian R. Petersen, Guodong Shi, Duncan S. Callaway
Title: An Electricity Market with Reactive Power Trading: Incorporating Dynamic Operating Envelopes
Abstract:
Electricity market design that accounts for grid constraints such as voltage and thermal limits at the distribution level can increase opportunities for the grid integration of Distributed Energy Resources (DERs). In this paper, we consider rooftop solar backed by battery storage connected to a distribution grid. We design an electricity market to support customers sharing rooftop generation in excess of their energy demand, where customers earn a profit through peer‑to‑peer (P2P) energy trading. Our proposed electricity market also incorporates P2P reactive power trading to improve the voltage profile across a distribution feeder. We formulate the electricity market as an optimization‑based problem, where voltage and thermal limits across a feeder are managed through the assignment of customer‑specific dynamic operating envelopes (DOEs). The electricity market equilibrium is referred to as a competitive equilibrium, which is equivalent to a Nash equilibrium in a standard game. Our proposed market design is benchmarked using the IEEE 13‑node test feeder.

Authors:Carina Veil, Moritz Flaschel, Ellen Kuhl, Cosimo Della Santina
Title: Infinite-Dimensional Closed-Loop Inverse Kinematics for Soft Robots via Neural Operators
Abstract:
For fully actuated rigid robots, kinematic inversion is a purely geometric problem, efficiently solved by closed‑loop inverse kinematics (CLIK) schemes that compute joint configurations to position the robot body in space. For underactuated soft robots, however, not all configurations are attainable through control action, making kinematic inversion extremely challenging. Extensions of CLIK address this by introducing end‑to‑end mappings from actuation to task space for the controller to operate on, but typically assume finite dimensions of the underlying virtual configuration space. In this work, we formulate CLIK in the infinite‑dimensional domain to reason about the entire soft robot shape while solving tasks. We do this by composing an actuation‑to‑shape map with a shape‑to‑task map, deriving the differential end‑to‑end kinematics via an infinite‑dimensional chain rule, and thereby obtaining a Jacobian‑based CLIK algorithm. Since this actuation‑to‑shape mapping is rarely available in closed form, we propose to learn it using differentiable neural operator networks. We first present an analytical study on a constant‑curvature segment, and then apply the neural version of the algorithm to a three‑fiber soft robotic arm whose underlying model relies on morphoelasticity and active filament theory.

Authors:Josée Mallah, Zakii Javed, Zafer Azak, Thomas Stone, Luigi G. Occhipinti
Title: Design and Biomechanical Evaluation of a Lightweight Low-Complexity Soft Bilateral Ankle Exoskeleton
Abstract:
Many people could benefit from exoskeleton assistance during gait, for either medical or nonmedical purposes. But exoskeletons bring added mass and structure, which in turn require compensating for. In this work, we present a lightweight, low‑complexity, soft bilateral ankle exoskeleton for plantarflexion assistance, with a shoe attachment design that can be mounted on top of any pair of shoes. Experimental tests show no significant difference in lower limb kinematics and kinetics when wearing the exoskeleton in zero‑torque mode relative to not wearing an exoskeleton, showing that our device does not obstruct healthy gait, and proving it as a compliant and comfortable device, promising to provide effective assistance. Hence, a control system was developed, and additional tests are underway.

Authors:Kenshiro Oguri, Gregory Lantoine
Title: Convex Block-Cholesky Approach to Risk-Constrained Low-thrust Trajectory Design under Operational Uncertainty
Abstract:
Designing robust trajectories under uncertainties is an emerging technology that may represent a key paradigm shift in space mission design. As we pursue more ambitious scientific goals (e.g., multi‑moon tours, missions with extensive components of autonomy), it becomes more crucial that missions are designed with navigation (Nav) processes in mind. The effect of Nav processes is statistical by nature, as they consist of orbit determination (OD) and flight‑path control (FPC). Thus, this mission design paradigm calls for techniques that appropriately quantify statistical effects of Nav, evaluate associated risks, and design missions that ensure sufficiently low risk while minimizing a statistical performance metric; a common metric is Delta‑V99: worst‑case (99%‑quantile) Delta‑V expenditure including statistical FPC efforts. In response to the need, this paper develops an algorithm for risk‑constrained trajectory optimization under operational uncertainties due to initial state dispersion, navigation error, maneuver execution error, and imperfect dynamics modeling. We formulate it as a nonlinear stochastic optimal control problem and develop a computationally tractable algorithm that combines optimal covariance steering and sequential convex programming (SCP). Specifically, the proposed algorithm takes a block‑Cholesky approach for convex formulation of optimal covariance steering, and leverages a recent SCP algorithm, SCvx, for reliable numerical convergence. We apply the developed algorithm to risk‑constrained, statistical trajectory optimization for exploration of dwarf planet Ceres with a Mars gravity assist, and demonstrate the robustness of the statistically‑optimal trajectory and FPC policies via nonlinear Monte Carlo simulation.

Authors:Mohamed Elgouhary, Amr S. El-Wakeel
Title: Learning to Tune Pure Pursuit in Autonomous Racing: Joint Lookahead and Steering-Gain Control with PPO
Abstract:
Pure Pursuit (PP) is widely used in autonomous racing for real‑time path tracking due to its efficiency and geometric clarity, yet performance is highly sensitive to how key parameters‑lookahead distance and steering gain‑are chosen. Standard velocity‑based schedules adjust these only approximately and often fail to transfer across tracks and speed profiles. We propose a reinforcement‑learning (RL) approach that jointly chooses the lookahead Ld and a steering gain g online using Proximal Policy Optimization (PPO). The policy observes compact state features (speed and curvature taps) and outputs (Ld, g) at each control step. Trained in F1TENTH Gym and deployed in a ROS 2 stack, the policy drives PP directly (with light smoothing) and requires no per‑map retuning. Across simulation and real‑car tests, the proposed RL‑PP controller that jointly selects (Ld, g) consistently outperforms fixed‑lookahead PP, velocity‑scheduled adaptive PP, and an RL lookahead‑only variant, and it also exceeds a kinematic MPC raceline tracker under our evaluated settings in lap time, path‑tracking accuracy, and steering smoothness, demonstrating that policy‑guided parameter tuning can reliably improve classical geometry‑based control.

Authors:Yu Kawano, Simone Betteti, Alexander Davydov, Francesco Bullo
Title: Incremental Input-to-State Stability and Equilibrium Tracking for Stochastic Contracting Dynamics
Abstract:
In this paper, we study the contractivity of nonlinear stochastic differential equations (SDEs) driven by deterministic inputs and Brownian motions. Given a weighted \ell_2‑norm for the state space, we show that an SDE is incrementally noise‑ and input‑to‑state stable if its vector field is uniformly contracting in the state and uniformly Lipschitz in the input. This result is applied to error estimation for time‑varying equilibrium tracking in the presence of noise affecting both the system dynamics and the input signals. We consider both Ornstein‑Uhlenbeck processes modeling unbounded noise and Jacobi diffusion processes modeling bounded noise. Finally, we turn our attention to the associated Fokker‑Planck equation of an SDE. For this context, we prove incremental input‑to‑state stability with respect to an arbitrary p‑Wasserstein metric when the drift vector field is uniformly contracting in the state and uniformly Lipschitz in the input with respect to an arbitrary norm.

Authors:Ashwin P. Dani
Title: Parameter Update Laws for Adaptive Control with Affine Equality Parameter Constraints
Abstract:
In this paper, constrained parameter update laws for adaptive control with convex equality constraint on the parameters are developed, one based on a gradient only update and the other incorporating concurrent learning (CL) update. The update laws are derived by solving a constrained optimization problem with affine equality constraints. This constrained problem is reformulated as an equivalent unconstrained problem in a new variable, thereby eliminating the equality constraints. The resulting update law is integrated with an adaptive trajectory tracking controller, enabling online learning of the unknown system parameters. Lyapunov stability of the closed‑loop system with the equality‑constrained parameter update law is established. The effectiveness of the proposed equality‑constrained adaptive control law is demonstrated through simulations, validating its ability to maintain constraints on the parameter estimates, achieving convergence to the true parameters for CL‑based update law, and achieving asymptotic and exponential tracking performance for constrained gradient and constrained CL‑based update laws, respectively.

Authors:Feng Zhao, Tongxin Zheng, Dane Schiro, Xiaochu Wang
Title: A Marginal Reliability Impact Based Accreditation Framework for Capacity Markets
Abstract:
This paper presents a Marginal Reliability Impact (MRI) based resource accreditation framework for capacity market design. Under this framework, a resource is accredited based on its marginal impact on system reliability, thus aligning the resource accreditation value with its reliability contribution. A key feature of the MRI based accreditation is that the accredited capacities supplied by different resources to the capacity market are substitutable in reliability contribution, a desired feature of homogeneous products. Moreover, with MRI based capacity demand, substitutability between supply and demand for capacity is also achieved. As a result, a capacity market with the MRI based capacity product can better characterize the underlying resource adequacy problem and lead to more efficient market outcomes.

Authors:Liang Wu, Wallace Gian Yion Tan, Richard D. Braatz, Ján Drgoňa
Title: Koopman-BoxQP: Solving Large-Scale NMPC at kHz Rates
Abstract:
Solving large‑scale nonlinear model predictive control (NMPC) problems at kilohertz (kHz) rates on standard processors remains a formidable challenge. This paper proposes a Koopman‑BoxQP framework that i) learns a linear Koopman high‑dimensional model, ii) eliminates the high‑dimensional observables to construct a multi‑step prediction model of the states and control inputs, iii) penalizes the multi‑step prediction model into the objective, which results in a structured box‑constrained quadratic program (BoxQP) whose decision variables include both the system states and control inputs, iv) develops a structure‑exploited and warm‑starting‑supported variant of the feasible Mehrotra's interior‑point algorithm for BoxQP. Numerical results demonstrate that Koopman‑BoxQP can solve a large‑scale NMPC problem with 1040 variables and 2080 inequalities at a kHz rate.

Authors:Philippe Jacquod, Laurent Pagnier, Daniel J. Gauthier
Title: Accurate Data-Based State Estimation from Power Loads Inference in Electric Power Grids
Abstract:
Accurate state estimation is a crucial requirement for the reliable operation and control of electric power systems. Here, we construct a data‑driven, numerical method to infer missing power load values in large‑scale power grids. Given partial observations of power demands, the method estimates the operational state using a linear regression algorithm, exploiting statistical correlations within synthetic training datasets. We evaluate the performance of the method on three synthetic transmission grid test systems. Numerical experiments demonstrate the high accuracy achieved by the method in reconstructing missing demand values under various operating conditions. We further apply the method to real data for the transmission power grid of Switzerland. Despite the restricted number of observations in this dataset, the method infers missing power loads rather accurately. Furthermore, Newton‑Raphson power flow solutions show that deviations between true and inferred values for power loads result in smaller deviations between true and inferred values for flows on power lines. This ensures that the estimated operational state correctly captures potential line contingencies. Overall, our results indicate that simple data‑based regression techniques can provide an efficient and reliable alternative for state estimation in modern power grids.

Authors:Fen Wu, Chengzhi Yuan
Title: Hybrid Control of ADT Switched Linear Systems subject to Actuator Saturation
Abstract:
This paper develops a hybrid output‑feedback control framework for average dwell‑time (ADT) switched linear systems subject to actuator saturation. The considered subsystems may be exponentially unstable, and the saturation nonlinearity is explicitly handled through a deadzone‑based representation. The proposed hybrid controller combines mode‑dependent full‑order dynamic output‑feedback controllers with a supervisory reset mechanism that updates controller states at switching instants. By incorporating the reset rule directly into the synthesis conditions, switching boundary constraints and performance requirements are addressed in a unified convex formulation. Sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to guarantee exponential stability under ADT switching and a prescribed weighted \cal L_2‑gain disturbance attenuation level for energy‑bounded disturbances. An explicit controller construction algorithm is provided based on feasible LMI solutions. Simulation results demonstrate the effectiveness and computational tractability of the proposed approach and highlight its advantages over existing output‑feedback saturation control methods.

Authors:Rong Fu, Yibo Meng, Guangzhen Yao, Jiaxuan Lu, Zeyu Zhang, Zhaolu Kang, Ziming Guo, Jia Yee Tan, Xiaojing Du, Simon James Fong
Title: TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs
Abstract:
Real‑time schedulers must reason about tight deadlines under strict compute budgets. We present TempoNet, a reinforcement learning scheduler that pairs a permutation‑invariant Transformer with a deep Q‑approximation. An Urgency Tokenizer discretizes temporal slack into learnable embeddings, stabilizing value learning and capturing deadline proximity. A latency‑aware sparse attention stack with blockwise top‑k selection and locality‑sensitive chunking enables global reasoning over unordered task sets with near‑linear scaling and sub‑millisecond inference. A multicore mapping layer converts contextualized Q‑scores into processor assignments through masked‑greedy selection or differentiable matching. Extensive evaluations on industrial mixed‑criticality traces and large multiprocessor settings show consistent gains in deadline fulfillment over analytic schedulers and neural baselines, together with improved optimization stability. Diagnostics include sensitivity analyses for slack quantization, attention‑driven policy interpretation, hardware‑in‑the‑loop and kernel micro‑benchmarks, and robustness under stress with simple runtime mitigations; we also report sample‑efficiency benefits from behavioral‑cloning pretraining and compatibility with an actor‑critic variant without altering the inference pipeline. These results establish a practical framework for Transformer‑based decision making in high‑throughput real‑time scheduling.

Authors:Junseon Park, Hyeongon Park, Rahul K. Gupta
Title: Iterative McCormick Relaxation for Joint Impedance Control and Network Topology Optimization
Abstract:
Power system operators are increasingly deploying Variable Impedance Devices (VIDs), e.g., Smart Wires, and Network Topology Optimization (NTO) schemes for mitigating operational challenges such as line and transformer congestion, and voltage violations. This work aims to optimize and coordinate the operation of distributed VIDs considering fixed and optimized topologies. This problem is inherently non‑linear due to power flow equations as well as bilinear terms introduced due to variable line impedance of VIDs. Furthermore, the topology optimization scheme makes it a mixed integer nonlinear problem. To tackle this, we introduce using McCormick relaxation scheme, which converts the bilinear constraints into a linear set of constraints along with the DC power flow equations. We propose an iterative correction of the McCormick relaxation to enhance its accuracy. The proposed framework is validated on standard IEEE benchmark test systems, and we present a performance comparison of the iterative McCormick method against the non‑linear, SOS2 piecewise linear approximation, and original McCormick relaxation.

Authors:Ishan Paranjape, Tarun Hejmadi, Suman Chakravorty
Title: Probabilistic Methods for Initial Orbit Determination and Orbit Determination in Cislunar Space
Abstract:
In orbital mechanics, Gauss's method for orbit determination (OD) is a popular, minimal assumption solution for obtaining the initial state estimate of a passing resident space object (RSO). Since much of the cislunar domain relies on three‑body dynamics, a key assumption of Gauss's method is rendered incompatible, creating a need for a new, minimal assumption method for initial orbit determination (IOD). In this work, we present a framework for short and long term probabilistic target tracking in cislunar space which produces an initial state estimate with as few assumptions as possible. Specifically, we propose an IOD method involving the kinematic fitting of several series of noisy, consecutive ground‑based observations. Once a probabilistic initial state estimate in the form of a particle cloud is formed, we apply the powerful Particle Gaussian Mixture (PGM) Filter to reduce the uncertainty of our state estimate over time. This combined IOD/OD framework is demonstrated for several classes of trajectories in cislunar space and compared to better‑known filtering frameworks.

Authors:N. Naveen Mukesh, Debraj Chakraborty
Title: Incremental Data Driven Transfer Identification
Abstract:
We introduce a geometric method for online transfer identification of a deterministic linear time‑invariant system. At the beginning of the identification process, we assume access to abundant data from a system that is similar, though not identical, to the true system. In the early stages of data collection from the true system, the dataset generated is still not sufficiently informative to enable precise identification. Consequently, multiple candidate models remain consistent with the observations available at that point. Our method picks, at each instant, the model closest to the similar system that is consistent with the current data. As more data are collected, the proposed model gradually moves away from the initial similar system and eventually converges to the true system when the data set grows to be informative. Numerical examples demonstrate the effectiveness of the incremental transfer identification paradigm, where identified models with minimal data are used to solve the pole placement problem.

Authors:Yan Chen, Ruyi Huang, Cheng Liu
Title: Decision Support under Prediction-Induced Censoring
Abstract:
In many data‑driven online decision systems, actions determine not only operational costs but also the data availability for future learning ‑‑ a phenomenon termed Prediction‑Induced Censoring (PIC). This challenge is particularly acute in large‑scale resource allocation for generative AI (GenAI) serving: insufficient capacity triggers shortages but hides the true demand, leaving the system with only a "greater‑than" constraint. Standard decision‑making approaches that rely on uncensored data suffer from selection bias, often locking the system into a self‑reinforcing low‑provisioning trap. To break this loop, this paper proposes an adaptive approach named PIC‑Reinforcement Learning (PIC‑RL), a closed‑loop framework that transforms censoring from a data quality problem into a decision signal. PIC‑RL integrates (1) Uncertainty‑Aware Demand Prediction to manage the information‑cost trade‑off, (2) Pessimistic Surrogate Inference to construct decision‑aligned conservative feedback from shortage events, and (3) Dual‑Timescale Adaptation to stabilize online learning against distribution drift. The analysis provides theoretical guarantees that the feedback design corrects the selection bias inherent in naive learning. Experiments on production Alibaba GenAI traces demonstrate that PIC‑RL consistently outperforms state‑of‑the‑art baselines, reducing service degradation by up to 50% while maintaining cost efficiency.

Authors:Unnati Nigam, Radhendushka Srivastava, Faezeh Marzbanrad, Michael Burke
Title: Quasi-Periodic Gaussian Process Predictive Iterative Learning Control
Abstract:
Repetitive motion tasks are common in robotics, but performance can degrade over time due to environmental changes and robot wear and tear. Iterative learning control (ILC) improves performance by using information from previous iterations to compensate for expected errors in future iterations. This work incorporates the use of Quasi‑Periodic Gaussian Processes (QPGPs) into a predictive ILC framework to model and forecast disturbances and drift across iterations. Using a recent structural equation formulation of QPGPs, the proposed approach enables efficient inference with complexity \mathcalO(p^3) instead of \mathcalO(i^2p^3), where p denotes the number of points within an iteration and i represents the total number of iterations, specially for larger i. This formulation also enables parameter estimation without loss of information, making continual GP learning computationally feasible within the control loop. By predicting next‑iteration error profiles rather than relying only on past errors, the controller achieves faster convergence and maintains this under time‑varying disturbances. We benchmark the method against both standard ILC and conventional Gaussian Process (GP)‑based predictive ILC on three tasks, autonomous vehicle trajectory tracking, a three‑link robotic manipulator, and a real‑world Stretch robot experiment. Across all cases, the proposed approach converges faster and remains robust under injected and natural disturbances while reducing computational cost. This highlights its practicality across a range of repetitive dynamical systems.

Authors:Shubham Bhardwaj, Chandrajit Bajaj
Title: PHAST: Port-Hamiltonian Architecture for Structured Temporal Dynamics Forecasting
Abstract:
Real physical systems are dissipative ‑‑ a pendulum slows, a circuit loses charge to heat ‑‑ and forecasting their dynamics from partial observations is a central challenge in scientific machine learning. We address the \emphposition‑only (q‑only) problem: given only generalized positions~q_t at discrete times (momenta~p_t latent), learn a structured model that (a)~produces stable long‑horizon forecasts and (b)~recovers physically meaningful parameters when sufficient structure is provided. The port‑Hamiltonian framework makes the conservative‑dissipative split explicit via \dotx=(J‑R)\nabla H(x), guaranteeing dH/dt\le 0 when R\succeq 0. We introduce PHAST (Port‑Hamiltonian Architecture for Structured Temporal dynamics), which decomposes the Hamiltonian into potential~V(q), mass~M(q), and damping~D(q) across three knowledge regimes (KNOWN, PARTIAL, UNKNOWN), uses efficient low‑rank PSD/SPD parameterizations, and advances dynamics with Strang splitting. Across thirteen q‑only benchmarks spanning mechanical, electrical, molecular, thermal, gravitational, and ecological systems, PHAST achieves the best long‑horizon forecasting among competitive baselines and enables physically meaningful parameter recovery when the regime provides sufficient anchors. We show that identification is fundamentally ill‑posed without such anchors (gauge freedom), motivating a two‑axis evaluation that separates forecasting stability from identifiability.

Authors:Robert Parker
Title: Generating adversarial inputs for a graph neural network model of AC power flow
Abstract:
This work formulates and solves optimization problems to generate input points that yield high errors between a neural network's predicted AC power flow solution and solutions to the AC power flow equations. We demonstrate this capability on an instance of the CANOS‑PF graph neural network model, as implemented by the PFΔ benchmark library, operating on a 14‑bus test grid. Generated adversarial points yield errors as large as 3.4 per‑unit in reactive power and 0.08 per‑unit in voltage magnitude. When minimizing the perturbation from a training point necessary to satisfy adversarial constraints, we find that the constraints can be met with as little as an 0.04 per‑unit perturbation in voltage magnitude on a single bus. This work motivates the development of rigorous verification and robust training methods for neural network surrogate models of AC power flow.

Authors:Markus Heinrichs, Aydin Sezgin, Rainer Kronberger
Title: A Scalable Reconfigurable Intelligent Surface with 3 Bit Phase Resolution and High Bandwidth for 3.6 GHz 5G/6G Applications
Abstract:
Reconfigurable Intelligent Surfaces enable active control of wireless propagation channels, which is crucial for future 5G and 6G networks. This work presents a scalable RIS design operating at 3.6 GHz with both 1 bit and 3 bit phase resolution, supporting wideband applications. The unit cells employ low‑cost printed circuit board technology with an innovative spring‑contact feeding structure, enabling efficient assembly and reduced manufacturing complexity for large‑area arrays. The design achieves broadband phase control, low power consumption, and high scalability, with experimental results demonstrating phase tunability across the n78 frequency band and competitive reflection performance compared to existing solutions. This RIS architecture provides a practical platform for experimental studies of smart radio environments, beam steering, and sensing applications in next‑generation wireless networks.

Authors:J. Liu, F. Milano
Title: Modal Energy for Power System Analysis: Definitions and Requirements
Abstract:
Modal energy provides information complementary to and based on conventional eigenvalues and participation factors for power system modal analysis. However, modal energy definition is not unique. This letter clarifies the definitions and applicability of mainstream modal energy approaches, focusing on their mappings to eigenvalues and to the total system energy. It is shown that these mappings hold only under restrictive conditions, notably system normality, which limits their applicability in inverter‑dominated power systems.

Authors:Zijian Zhang, Linglong Dai
Title: Wavenumber-domain signal processing for holographic MIMO: Foundations, methods, and future directions
Abstract:
Holographic multiple‑input multiple‑output (H‑MIMO) systems represent a paradigm shift in wireless communications by enabling quasi‑continuous apertures. Unlike conventional MIMO systems, H‑MIMO with subwavelength antenna spacing operates in both far‑field and near‑field regimes, where classical discrete Fourier transform (DFT) representations fail to sufficiently capture the channel characteristics. To address this challenge, this article provides an overview of the emerging wavenumber‑domain signal processing framework. Specifically, by leveraging spatial Fourier plane‑wave decomposition to model H‑MIMO channels, the wavenumber domain offers a unified and physically consistent basis for characterizing subwavelength‑level spatial correlation and spherical wave propagation. This article first introduces the concept of H‑MIMO and the wavenumber representation of H‑MIMO channels. Next, it elaborates on wavenumber‑domain signal processing technologies reported in the literature, including multiplexing, channel estimation, and waveform designs. Finally, it highlights open challenges and outlines future research directions in wavenumber‑domain signal processing for next‑generation wireless systems.

Authors:Peter Seiler, Mark E. Pittelkau, Felix Biertümpfel
Title: Method to Compute Pointing Displacement, Smear, and Jitter Covariances for Optical Payloads
Abstract:
This paper presents a method to assess the pointing and image motion performance of optical payloads in the presence of image displacement (shift), smear, and jitter. The method assumes the motion is a stationary random process over an image exposure interval. Displacement, smear, and jitter covariances are computed from the solution to a Lyapunov differential equation. These covariances parameterize statistical image motion modulation transfer functions (MTFs), and they can be used to verify pointing and image motion MTF requirements. The method in the present paper extends a previous method to include smear, as well as displacement, and hence jitter. The approach in the present paper also leads, as a special case, to a more efficient method to compute the displacement covariance than the previous method. Numerical examples illustrate the proposed method.

Authors:Joshua A. Robbins, Andrew F. Thompson, Jonah J. Glunt, Herschel C. Pangborn
Title: Hybrid System Planning using a Mixed-Integer ADMM Heuristic and Hybrid Zonotopes
Abstract:
Embedded optimization‑based planning for hybrid systems is challenging due to the use of mixed‑integer programming, which is computationally intensive and often sensitive to the specific numerical formulation. To address that challenge, this article proposes a framework for motion planning of hybrid systems that pairs hybrid zonotopes ‑ an advanced set representation ‑ with a new alternating direction method of multipliers (ADMM) mixed‑integer programming heuristic. A general treatment of piecewise affine (PWA) system reachability analysis using hybrid zonotopes is presented and extended to formulate optimal planning problems. Sets produced using the proposed identities have lower memory complexity and tighter convex relaxations than equivalent sets produced from preexisting techniques. The proposed ADMM heuristic makes efficient use of the hybrid zonotope structure. For planning problems formulated as hybrid zonotopes, the proposed heuristic achieves improved convergence rates as compared to state‑of‑the‑art mixed‑integer programming heuristics. The proposed methods for hybrid system planning on embedded hardware are experimentally applied in a combined behavior and motion planning scenario for autonomous driving.

Authors:Leila Gharavi, Simone Baldi, Yuki Hosomi, Tona Sato, Bart De Schutter, Binh-Minh Nguyen, Hiroshi Fujimoto
Title: Dodging the Moose: Experimental Insights in Real-Life Automated Collision Avoidance
Abstract:
The sudden appearance of a static obstacle on the road, i.e. the moose test, is a well‑known emergency scenario in collision avoidance for automated driving. Model Predictive Control (MPC) has long been employed for planning and control of automated vehicles in the state of the art. However, real‑time implementation of automated collision avoidance in emergency scenarios such as the moose test remains unaddressed due to the high computational demand of MPC for evasive action in such hazardous scenarios. This paper offers new insights into real‑time collision avoidance via the experimental imple‑ mentation of MPC for motion planning after a sudden and unexpected appearance of a static obstacle. As the state‑of‑the‑art nonlinear MPC shows limited capability to provide an acceptable solution in real‑time, we propose a human‑like feed‑forward planner to assist when the MPC optimization problem is either infeasible or unable to find a suitable solution due to the poor quality of its initial guess. We introduce the concept of maximum steering maneuver to design the feed‑forward planner and mimic a human‑like reaction after detecting the static obstacle on the road. Real‑life experiments are conducted across various speeds and level of emergency using FPEV2‑Kanon electric vehicle. Moreover, we demonstrate the effectiveness of our planning strategy via comparison with the state‑of‑ the‑art MPC motion planner.

Authors:Mark Spiller, Lennart Kracke, Johannes Autenrieb
Title: Robust Adaptive Sliding-Mode Control for Damaged Fixed-Wing UAVs
Abstract:
Many unmanned aerial vehicles (UAVs) can remain aerodynamically flyable after sustaining structural or control surface damage, yet insufficient robustness in conventional autopilots often leads to mission failure. This paper proposes a robust adaptive sliding mode controller (RASMC) for fixed‑wing UAVs subject to aerodynamic coefficient perturbations and partial loss of control surface effectiveness. A damage‑aware flight dynamics model is developed to systematically analyze the impact of such impairments on the closed‑loop behavior. The RASMC is designed to ensure reliable tracking and stabilization, while a gain adaptation law maintains low control effort under nominal conditions and increases the gains as needed in the presence of aerodynamic damage. Lyapunov‑based stability guarantees are derived, and assumptions on admissible uncertainty bounds are formulated to characterize the limits within which closed‑loop stability and performance can be ensured. The proposed controller is implemented within an existing UAV autopilot framework, where outer‑loop guidance and speed control modules provide reference commands to the RASMC for attitude stabilization. Simulations demonstrate that, despite significant damage, all closed‑loop states remain stable with bounded tracking errors.

Authors:Eleftherios E. Vlahakis, Arash Bahari Kordabad, Lars Lindemann, Pantelis Sopasakis, Sadegh Soudjani, Dimos V. Dimarogonas
Title: Multi-Agent Temporal Logic Planning via Penalty Functions and Block-Coordinate Optimization
Abstract:
Multi‑agent planning under Signal Temporal Logic (STL) is often hindered by collaborative tasks that lead to computational challenges due to the inherent high‑dimensionality of the problem, preventing scalable synthesis with satisfaction guarantees. To address this, we formulate STL planning as an optimization program under arbitrary multi‑agent constraints and introduce a penalty‑based unconstrained relaxation that can be efficiently solved via a Block‑Coordinate Gradient Descent (BCGD) method, where each block corresponds to a single agent's decision variables, thereby mitigating complexity. By utilizing a quadratic penalty function defined via smooth STL semantics, we show that BCGD iterations converge to a stationary point of the penalized problem under standard regularity assumptions. To enforce feasibility, the BCGD solver is embedded within a two‑layer optimization scheme: inner BCGD updates are performed for a fixed penalty parameter, which is then increased in an outer loop to progressively improve multi‑agent STL robustness. The proposed framework enables scalable computations and is validated through various complex multi‑robot planning scenarios.

Authors:Yi Zhang, Omar Faris, Chapa Sirithunge, Kai-Fung Chu, Fumiya Iida, Fulvio Forni
Title: Distributed Virtual Model Control for Scalable Human-Robot Collaboration in Shared Workspace
Abstract:
We present a decentralized, agent agnostic, and safety‑aware control framework for human‑robot collaboration based on Virtual Model Control (VMC). In our approach, both humans and robots are embedded in the same virtual‑component‑shaped workspace, where motion is the result of the interaction with virtual springs and dampers rather than explicit trajectory planning. A decentralized, force‑based stall detector identifies deadlocks, which are resolved through negotiation. This reduces the probability of robots getting stuck in the block placement task from up to 61.2% to zero in our experiments. The framework scales without structural changes thanks to the distributed implementation: in experiments we demonstrate safe collaboration with up to two robots and two humans, and in simulation up to four robots, maintaining inter‑agent separation at around 20 cm. Results show that the method shapes robot behavior intuitively by adjusting control parameters and achieves deadlock‑free operation across team sizes in all tested scenarios.

Authors:Glen Hjelmerud Mørkbak Sørensen, Torleiv H. Bryne, Kristoffer Gryte, Tor Arne Johansen
Title: Bluetooth Phased-array Aided Inertial Navigation Using Factor Graphs: Experimental Verification
Abstract:
Phased‑array Bluetooth systems have emerged as a low‑cost alternative for performing aided inertial navigation in GNSS‑denied use cases such as warehouse logistics, drone landings, and autonomous docking. Basing a navigation system off of commercial‑off‑the‑shelf components may reduce the barrier of entry for phased‑array radio navigation systems, albeit at the cost of significantly noisier measurements and relatively short feasible range. In this paper, we compare robust estimation strategies for a factor graph optimisation‑based estimator using experimental data collected from multirotor drone flight. We evaluate performance in loss‑of‑GNSS scenarios when aided by Bluetooth angular measurements, as well as range or barometric pressure.

Authors:Renato Quartullo, Andrea Garulli, Mirko Leomanni
Title: Robust Model Predictive Control for Linear Systems with Interval Matrix Model Uncertainty
Abstract:
This paper proposes a novel robust Model Predictive Control (MPC) scheme for linear discrete‑time systems affected by model uncertainty described by interval matrices. The key feature of the proposed method is a bound on the uncertainty propagation along the prediction horizon which exploits a set‑theoretic over‑approximation of each term of the uncertain system impulse response. Such an approximation is based on matrix zonotopes and leverages the interval matrix structure of the uncertainty model. Its main advantage is that all the relevant bounds are computed offline, thus making the online computational load independent of the number of uncertain parameters. A variable‑horizon MPC formulation is adopted to guarantee recursive feasibility and to ensure robust asymptotic stability of the closed‑loop system. Numerical simulations demonstrate that the proposed approach is able to match the feasibility regions of the most effective state‑of‑the‑art methods, while significantly reducing the computational burden, thereby enabling the treatment of nontrivial dimensional systems with multiple uncertain parameters.

Authors:Max Bruninx, Seyed Soroush Karimi Madahi, Timothy Verstraeten, Jan Decuyper, Chris Develder, Jan Helsen
Title: Herd Behavior in Decentralized Balancing Models: A Case Study in Belgium
Abstract:
In a decentralized balancing model, Balance Responsible Parties (BRPs) are encouraged by the Transmission System Operator (TSO) to deviate from their schedule to help the system restore balance, also referred to as implicit balancing. This could reduce balancing costs for the grid operator and lower the entry barrier for flexible assets compared to explicit balancing services. However, these implicit reactions may overshoot when their total capacity is high, potentially requiring more explicit activations. This study analyses the effect of increased participation in the decentralized balancing model in Belgium. To this end, we develop a market simulator that produces price signals on minute‑level and simulate the implicit reactions for battery assets with different risk profiles. Besides the current price formula, we also study two potential candidates for the near‑term presented by the TSO. A simulation study is conducted using Belgian market data for the year 2023. The findings indicate that, while having a significant positive effect on the balancing costs at first, the risk of overshoots can outweigh the potential benefits when the total capacity of the implicit reactions becomes too large. Furthermore, even when the balancing costs start to increase for the TSO, BRPs were still found to benefit from implicit balancing.

Authors:Hsin-Jung Yang, Zhanhong Jiang, Prajwal Koirala, Qisai Liu, Cody Fleming, Soumik Sarkar
Title: LexiSafe: Offline Safe Reinforcement Learning with Lexicographic Safety-Reward Hierarchy
Abstract:
Offline safe reinforcement learning (RL) is increasingly important for cyber‑physical systems (CPS), where safety violations during training are unacceptable and only pre‑collected data are available. Existing offline safe RL methods typically balance reward‑safety tradeoffs through constraint relaxation or joint optimization, but they often lack structural mechanisms to prevent safety drift. We propose LexiSafe, a lexicographic offline RL framework designed to preserve safety‑aligned behavior. We first develop LexiSafe‑SC, a single‑cost formulation for standard offline safe RL, and derive safety‑violation and performance‑suboptimality bounds that together yield sample‑complexity guarantees. We then extend the framework to hierarchical safety requirements with LexiSafe‑MC, which supports multiple safety costs and admits its own sample‑complexity analysis. Empirically, LexiSafe demonstrates reduced safety violations and improved task performance compared to constrained offline baselines. By unifying lexicographic prioritization with structural bias, LexiSafe offers a practical and theoretically grounded approach for safety‑critical CPS decision‑making.

Authors:Jan H. Hoekstra, Bendegúz M. Györök, Roland Tóth, Maarten Schoukens
Title: Learning-based augmentation of first-principle models: A linear fractional representation-based approach
Abstract:
Nonlinear system identificationhas proven to be effective in obtaining accurate models from data for complex real‑world systems. In particular, recent encoder‑based methods with artificial neural network state‑space (ANN‑SS) models have achieved state‑of‑the‑art performance on various benchmarks, using computationally efficient methods and offering consistent model estimation in the presence of noisy data. However, inclusion of prior knowledge of the system can be further exploited to increase (i) estimation speed, (ii) accuracy, and (iii) interpretability of the resulting models. This paper proposes a model augmentation method that incorporates prior knowledge from first‑principles (FP) models in a flexible manner. We introduce a novel linear‑fractional‑representation (LFR) model structure that allows for the general representation of various augmentation structures including the ones that are commonly used in the literature, and an encoder‑based identification algorithm for estimating the proposed structures together with appropriate initialisation methods. The performance and generalisation capabilities of the proposed method are demonstrated on the identification of a hardening mass‑spring‑damper system in a simulation study and on the data‑driven modelling of the dynamics of an F1Tenth electric car using measured data.

Authors:Boris Sedlak, Víctor Casamayor Pujol, Schahram Dustdar
Title: Visual Insights into Agentic Optimization of Pervasive Stream Processing Services
Abstract:
Processing sensory data close to the data source, often involving Edge devices, promises low latency for pervasive applications, like smart cities. This commonly involves a multitude of processing services, executed with limited resources; this setup faces three problems: first, the application demand and the resource availability fluctuate, so the service execution must scale dynamically to sustain processing requirements (e.g., latency); second, each service permits different actions to adjust its operation, so they require individual scaling policies; third, without a higher‑level mediator, services would cannibalize any resources of services co‑located on the same device. This demo first presents a platform for context‑aware autoscaling of stream processing services that allows developers to monitor and adjust the service execution across multiple service‑specific parameters. We then connect a scaling agent to these interfaces that gradually builds an understanding of the processing environment by exploring each service's action space; the agent then optimizes the service execution according to this knowledge. Participants can revisit the demo contents as video summary and introductory poster, or build a custom agent by extending the artifact repository.

Authors:Ziyan Zhang, Chuheng Wei, Xuanpeng Zhao, Siyan Li, Will Snyder, Mike Stas, Peng Hao, Kanok Boriboonsomsin, Guoyuan Wu
Title: A Multi-modal Detection System for Infrastructure-based Freight Signal Priority
Abstract:
Freight vehicles approaching signalized intersections require reliable detection and motion estimation to support infrastructure‑based Freight Signal Priority (FSP). Accurate and timely perception of vehicle type, position, and speed is essential for enabling effective priority control strategies. This paper presents the design, deployment, and evaluation of an infrastructure‑based multi‑modal freight vehicle detection system integrating LiDAR and camera sensors. A hybrid sensing architecture is adopted, consisting of an intersection‑mounted subsystem and a midblock subsystem, connected via wireless communication for synchronized data transmission. The perception pipeline incorporates both clustering‑based and deep learning‑based detection methods with Kalman filter tracking to achieve stable real‑time performance. LiDAR measurements are registered into geodetic reference frames to support lane‑level localization and consistent vehicle tracking. Field evaluations demonstrate that the system can reliably monitor freight vehicle movements at high spatio‑temporal resolution. The design and deployment provide practical insights for developing infrastructure‑based sensing systems to support FSP applications.

Authors:Sehani Siriwardana, Jean Michel de Souza Sant'Ana, Richard Demo Souza, Abolfazl Zakeri, Onel Luis Alcaraz López
Title: On the Value of Base Station Motion Knowledge for Goal-Oriented Remote Monitoring with Energy-Harvesting Sensors
Abstract:
This paper investigates goal‑oriented remote monitoring of an unobservable Markov source using energy‑harvesting sensors that communicate with a mobile receiver, such as a Low Earth Orbit (LEO) satellite or Unmanned Aerial Vehicle (UAV). Unlike conventional systems that assume stationary base stations, the proposed framework explicitly accounts for receiver mobility, which induces time‑varying channel characteristics modeled as a finite‑state Markov process. The remote monitoring problem is formulated as a partially observable Markov decision process (POMDP), which is transformed into a tractable belief‑state MDP and solved using relative value iteration to obtain optimal sampling and transmission policies. Two estimation strategies are considered: Maximum Likelihood (ML) and Minimum Mean Distortion (MMD). Numerical results demonstrate that incorporating receiver mobility and channel state information into the optimization reduces the average distortion by 10% to 42% compared to baseline policies and constant‑channel assumptions, highlighting the importance of base station motion knowledge for effective goal‑oriented communication.

Authors:Alessandro Quattrociocchi, Manisha Talukdar, Pere Izquierdo Gómez, Tomislav Dragicevic
Title: Distributionally Robust Scheduling of Electrified Heating Under Heat Demand Forecast Uncertainty
Abstract:
Electrified heating systems with thermal storage, such as electric boilers and heat pumps, represent a major source of demand‑side flexibility. Under current electricity market designs, balance responsible parties (BRPs) operating such assets are required to submit binding day‑ahead electricity consumption schedules, and they typically do it based on forecasts of heat demand and electricity prices. Common scheduling approaches implicitly assume that forecast uncertainty can be well characterized using historical forecast errors. In practice, however, the cumulative effect of uncertainty creates significant exposure to imbalance‑price risk when the committed schedule cannot be followed. To address this, we propose a distributionally robust chance‑constrained optimization framework for the day‑ahead scheduling of a multi‑MW electric boiler using only limited residual forecast samples. We derive a tractable convex reformulation of the problem and calibrate the ambiguity set directly from historical forecast‑error data through an a priori tunable risk parameter. Numerical results show that enforcing performance guarantees on the heat‑demand balance constraint reduces demand violations by 40% compared to a deterministic forecast‑based scheduler and up to 10% relative to a nominal chance‑constrained model with a fixed error distribution. Further, we show that modeling the real‑time rebound cost of demand violations as a second‑stage term can reduce the overall daily operating cost by up to 34% by hedging against highly volatile day‑ahead electricity prices.

Authors:Antonio Rapuano, Yaolei Shen, Federico Califano, Chiara Gabellieri, Antonio Franchi
Title: Nonlinear Predictive Control of the Continuum and Hybrid Dynamics of a Suspended Deformable Cable for Aerial Pick and Place
Abstract:
This paper presents a framework for aerial manipulation of an extensible cable that combines a high‑fidelity model based on partial differential equations (PDEs) with a reduced‑order representation suitable for real‑time control. The PDEs are discretised using a finite‑difference method, and proper orthogonal decomposition is employed to extract a reduced‑order model (ROM) that retains the dominant deformation modes while significantly reducing computational complexity. Based on this ROM, a nonlinear model predictive control scheme is formulated, capable of stabilizing cable oscillations and handling hybrid transitions such as payload attachment and detachment. Simulation results confirm the stability, efficiency, and robustness of the ROM, as well as the effectiveness of the controller in regulating cable dynamics under a range of operating conditions. Additional simulations illustrate the application of the ROM for trajectory planning in constrained environments, demonstrating the versatility of the proposed approach. Overall, the framework enables real‑time, dynamics‑aware control of unmanned aerial vehicles (UAVs) carrying suspended flexible cables.

Authors:Heisei Yonezawa, Ansei Yonezawa, Itsuro Kajiwara
Title: Continual uncertainty learning
Abstract:
Robust control of mechanical systems with multiple uncertainties remains a fundamental challenge, particularly when nonlinear dynamics and operating‑condition variations are intricately intertwined. Although deep reinforcement learning (DRL) combined with domain randomization has shown promise in mitigating the sim‑to‑real gap, simultaneously handling all the sources of uncertainty often leads to sub‑optimal policies and poor learning efficiency. This study formulates a new curriculum‑based continual learning framework for robust control problems involving nonlinear dynamical systems in which multiple sources of uncertainty are simultaneously superimposed. The key idea is to decompose a complex control problem with multiple uncertainties into a sequence of continual learning tasks, in which the strategies for handling each uncertainty are acquired sequentially. The original system is extended into a finite set of plants whose dynamic uncertainties are gradually expanded and diversified as learning progresses. The policy is stably updated across the entire plant sets associated with tasks defined by different uncertainty configurations without catastrophic forgetting. To ensure high learning efficiency, we jointly incorporate a model‑based controller (MBC), which guarantees a shared baseline performance across the plant sets, into the learning process in order to accelerate the convergence. This residual learning scheme facilitates task‑specific optimization of the DRL agent for each uncertainty, thereby enhancing sample efficiency. Finally, this study adopts the proposed method to design an active vibration controller for automotive powertrains as a practical industrial application. We verify that the resulting controller is robust against structural nonlinearities and dynamic variations; thus, it can realize successful sim‑to‑real transfer.

Authors:Antonio Franchi, Chiara Gabellieri
Title: Geometric Inverse Flight Dynamics on SO(3) and Application to Tethered Fixed-Wing Aircraft
Abstract:
We present a robotics‑oriented, coordinate‑free formulation of inverse flight dynamics for fixed‑wing aircraft on SO(3). Translational force balance is written in the world frame and rotational dynamics in the body frame; aerodynamic directions (drag, lift, side) are defined geometrically, avoiding local attitude coordinates. Enforcing coordinated flight (no sideslip), we derive a closed‑form trajectory‑to‑input map yielding the attitude, angular velocity, and thrust‑angle‑of‑attack pair, and we recover the aerodynamic moment coefficients component‑wise. Applying such a map to tethered flight on spherical parallels, we obtain analytic expressions for the required bank angle and identify a specific zero‑bank locus where the tether tension exactly balances centrifugal effects, highlighting the decoupling between aerodynamic coordination and the apparent gravity vector. Under a simple lift/drag law, the minimal‑thrust angle of attack admits a closed form. These pointwise quasi‑steady inversion solutions become steady‑flight trim when the trajectory and rotational dynamics are time‑invariant. The framework bridges inverse simulation in aeronautics with geometric modeling in robotics, providing a rigorous building block for trajectory design and feasibility checks.

Authors:Shankar Ramharack, Rajiv Sahadeo
Title: Validation of KESTREL EMT for Industrial Capacitor Switching Transient Studies
Abstract:
Electromagnetic transient (EMT) simulation is essential for analyzing sub‑cycle switching phenomena in industrial power systems; however, commercial EMT platforms present significant cost barriers for smaller utilities, consultancies, and academic institutions, particularly in developing regions. This paper validates KESTREL EMT, a free and open‑source electromagnetic transient solver with Python integration, through three progressive case studies involving industrial capacitor switching transients. This work investigates energization, switching resonance and VFD interactions with capacitor banks. The results demonstrate that KESTREL, when supported by appropriate circuit modeling techniques, produces EMT responses consistent with analytical predictions and established IEEE benchmarks. This work establishes a validated and reproducible methodology for conducting industrial EMT studies using freely available, open‑source tools.

Authors:Xiaocai Zhang, Neema Nassir, Milad Haghani
Title: Spatio-temporal dual-stage hypergraph MARL for human-centric multimodal corridor traffic signal control
Abstract:
Human‑centric traffic signal control in corridor networks must increasingly account for multimodal travelers, particularly high‑occupancy public transportation, rather than focusing solely on vehicle‑centric performance. This paper proposes STDSH‑MARL (Spatio‑Temporal Dual‑Stage Hypergraph based Multi‑Agent Reinforcement Learning), a scalable multi‑agent deep reinforcement learning framework that follows a centralized training and decentralized execution paradigm. The proposed method captures spatio‑temporal dependencies through a novel dual‑stage hypergraph attention mechanism that models interactions across both spatial and temporal hyperedges. In addition, a hybrid discrete action space is introduced to jointly determine the next signal phase configuration and its corresponding green duration, enabling more adaptive signal timing decisions. Experiments conducted on a corridor network under five traffic scenarios demonstrate that STDSH‑MARL consistently improves multimodal performance and provides clear benefits for public transportation priority. Compared with state‑of‑the‑art baseline methods, the proposed approach achieves superior overall performance. Further ablation studies confirm the contribution of each component of STDSH‑MARL, with temporal hyperedges identified as the most influential factor driving the observed performance gains.

Authors:Ki-Hyun Kim, Yeongung Kim, Shenghui Cui, Jae-Jung Jung
Title: Decoupled Internal Energy Regulation and Inertial Response Provision for Grid-Forming Multilevel-Converter-Based E-STATCOMs
Abstract:
As power systems accommodate higher shares of renewable generation, short‑term power imbalances become more frequent and can manifest as pronounced voltage and frequency excursions under low‑inertia conditions. E‑STATCOMs (STATCOMs equipped with energy storage) offer a practical means to provide both voltage support and fast frequency assistance under grid‑forming control. Among candidate implementations, double‑star multilevel‑converter (DS‑MC)‑based E‑STATCOMs enable centralized energy‑storage integration at the dc link, which improves thermal management and maintainability. Nevertheless, conventional dc‑side power‑based internal‑energy regulation in DS‑MCs can undesirably couple loss compensation to the energy‑storage path, accelerating storage cycling and constraining operation when the storage is unavailable. This paper introduces a control strategy that assigns DS‑MC total internal‑energy regulation to the ac‑side active‑power path, while reserving dc‑side storage power solely for frequency support. By decoupling internal‑energy management from inertial‑response provision, the proposed scheme enables flexible operation as either a STATCOM or an E‑STATCOM according to storage availability and mitigates unnecessary storage cycling. The proposed strategy is verified through offline simulations and laboratory‑scale experiments.

Authors:Gitae Park, Kisong Lee
Title: Beyond Average-Channel-Based Rate Approximations: UAV Trajectory and Scheduling Optimization With Expected Rate Consideration
Abstract:
This paper investigates the joint optimization of trajectory, user scheduling, and time‑slot duration in unmanned aerial vehicle (UAV)‑assisted wireless communication systems under minimum expected spectral efficiency (SE) constraints. Unlike most existing studies that approximate the expected SE by substituting the random channel gain with its mean value, thereby evaluating the SE at the average channel realization and overestimating the true expected SE due to Jensen's inequality, we approximate the expected SE by numerically integrating the SE over the channel distributions. Specifically, instead of relying on average‑channel‑based approximations, we develop a conservative yet tractable quadrature‑based approximation by discretizing the associated cumulative distribution functions. The resulting finite‑sum representation explicitly accounts for the probabilistic LoS structure and channel fading effects, while remaining tractable for optimization. Leveraging this lower bound, we formulate a mission completion time minimization problem subject to minimum expected‑SE requirements for all ground nodes. The resulting problem is a mixed‑integer nonconvex optimization, which is tackled via a penalty‑based block coordinate descent framework. The proposed algorithm alternately optimizes the scheduling decisions and the UAV trajectory along with adaptive time‑slot durations, and maintains feasibility with respect to the original expected‑SE constraints by leveraging successive convex approximation and quadratic transform techniques. Simulation results demonstrate that the proposed method strictly satisfies the minimum expected‑SE constraints and achieves a significantly shorter mission completion time than conventional average‑channel‑based approaches, which are shown to yield infeasible or overly conservative solutions.

Authors:Ginevra Larroux, Matthieu Jacobs, Mario Paolone
Title: On the Tightness of the Second-Order Cone Relaxation of the Optimal Power Flow with Angles Recovery in Meshed Networks
Abstract:
This letter investigates properties of the second‑order cone relaxation of the optimal power flow (OPF) problem, with emphasis on relaxation tightness, nodal voltage angles recovery, and alternating‑current‑OPF feasibility in meshed networks. The theoretical discussion is supported by numerical experiments on standard IEEE test cases. Implications for power system planning are briefly outlined.

Authors:Omer Burak Iskender, Keck Voon Ling, Mengu Cho, Sangkyun Kim, Necmi Cihan Orger
Title: Low-Thrust Trajectory Optimization for Cubesat Lunar Mission: HORYU-VI
Abstract:
This paper presents a low‑thrust trajectory optimization strategy to achieve a near‑circular lunar orbit for a CubeSat injected into a lunar flyby trajectory. The 12U CubeSat HORYU‑VI is equipped with four Hall‑effect thrusters and designed as a secondary payload on NASA's Space Launch System under the Artemis program. Upon release, the spacecraft gains sufficient energy to escape the Earth‑Moon system after a lunar flyby. The proposed trajectory is decomposed into three phases: (1) pre‑flyby deceleration to avoid heliocentric escape, (2) lunar gravitational capture, and (3) orbit circularization to the science orbit. For each phase, an impulsive‑burn solution is first computed as an initial guess, which is then refined through finite‑burn optimization using Sequential Quadratic Programming (SQP). The dynamical model incorporates Earth‑Moon‑Sun‑Jupiter gravitational interactions and a high‑fidelity lunar gravity field. All trajectories are independently verified with NASA's General Mission Analysis Tool (GMAT). Results demonstrate that HORYU‑VI achieves lunar capture within 200 days, establishes a stable science orbit at 280 days, and can spiral down to a near‑circular 100 km orbit by 450 days, using a total Delta‑V of 710 m/s, well within the capability of the electric propulsion system.

Authors:Alex Moody, Penina Axelrad, Rebecca Russell
Title: Machine Learning Argument of Latitude Error Model for LEO Satellite Orbit and Covariance Correction
Abstract:
Low Earth orbit (LEO) satellites are leveraged to support new position, navigation, and timing (PNT) service alternatives to GNSS. These alternatives require accurate propagation of satellite position and velocity with a realistic quantification of uncertainty. It is commonly assumed that the propagated uncertainty distribution is Gaussian; however, the validity of this assumption can be quickly compromised by the mismodeling of atmospheric drag. We develop a machine learning approach that corrects error growth in the argument of latitude for a diverse set of LEO satellites. The improved orbit propagation accuracy extends the applicability of the Gaussian assumption and modeling of the errors with a corrected mean and covariance. We compare the performance of a time‑conditioned neural network and a Gaussian Process on datasets computed with an open source orbit propagator and publicly available Vector Covariance Message (VCM) ephemerides. The learned models predict the argument of latitude error as a Gaussian distribution given parameters from a single VCM epoch and reverse propagation errors. We show that this one‑dimensional model captures the effect of mismodeled drag, which can be mapped to the Cartesian state space. The correction method only updates information along the dimensions of dominant error growth, while maintaining the physics‑based propagation of VCM covariance in the remaining dimensions. We therefore extend the utility of VCM ephemerides to longer time horizons without modifying the functionality of the existing propagator.

Authors:Sina Akhbari, Mehran Mahboubkhah
Title: Smooth trajectory generation and hybrid B-splines-Quaternions based tool path interpolation for a 3T1R parallel kinematic milling robot
Abstract:
This paper presents a smooth trajectory generation method for a four‑degree‑of‑freedom parallel kinematic milling robot. The proposed approach integrates B‑spline and Quaternion interpolation techniques to manage decoupled position and orientation data points. The synchronization of orientation and arc‑length‑parameterized position data is achieved through the fitting of smooth piece‑wise Bezier curves, which describe the non‑linear relationship between path length and tool orientation, solved via sequential quadratic programming. By leveraging the convex hull properties of Bezier curves, the method ensures spatial and temporal separation constraints for multi‑agent trajectory generation. Unit quaternions are employed for orientation interpolation, providing a robust and efficient representation that avoids gimbal lock and facilitates smooth, continuous rotation. Modifier polynomials are used for position interpolation. Temporal trajectories are optimized using minimum jerk, time‑optimal piece‑wise Bezier curves in two stages: task space followed by joint space, implemented on a low‑cost microcontroller. Experimental results demonstrate that the proposed method offers enhanced accuracy, reduced velocity fluctuations, and computational efficiency compared to conventional interpolation methods.

Authors:Saud Alghumayjan, Ming Yi, Bolun Xu
Title: A Few-Shot LLM Framework for Extreme Day Classification in Electricity Markets
Abstract:
This paper proposes a few‑shot classification framework based on Large Language Models (LLMs) to predict whether the next day will have spikes in real‑time electricity prices. The approach aggregates system state information, including electricity demand, renewable generation, weather forecasts, and recent electricity prices, into a set of statistical features that are formatted as natural‑language prompts and fed to an LLM along with general instructions. The model then determines the likelihood that the next day would be a spike day and reports a confidence score. Using historical data from the Texas electricity market, we demonstrate that this few‑shot approach achieves performance comparable to supervised machine learning models, such as Support Vector Machines and XGBoost, and outperforms the latter two when limited historical data are available. These findings highlight the potential of LLMs as a data‑efficient tool for classifying electricity price spikes in settings with scarce data.

Authors:H. Sinan Bank, Daniel R. Herber
Title: Retrieval Augmented (Knowledge Graph), and Large Language Model-Driven Design Structure Matrix (DSM) Generation of Cyber-Physical Systems
Abstract:
We explore the potential of Large Language Models (LLMs), Retrieval‑Augmented Generation (RAG), and Graph‑based RAG (GraphRAG) for generating Design Structure Matrices (DSMs). We test these methods on two distinct use cases ‑‑ a power screwdriver and a CubeSat with known architectural references ‑‑ evaluating their performance on two key tasks: determining relationships between predefined components, and the more complex challenge of identifying components and their subsequent relationships. We measure the performance by assessing each element of the DSM and overall architecture. Despite design and computational challenges, we identify opportunities for automated DSM generation, with all code publicly available for reproducibility and further feedback from the domain experts.

Authors:Harrison Perone, Christopher W. Hays
Title: Consensus Based Task Allocation for Angles-Only Local Catalog Maintenance of Satellite Systems
Abstract:
In order for close proximity satellites to safely perform their missions, the relative states of all satellites and pieces of debris must be well understood. This presents a problem for ground based tracking and orbit determination since it may not be practical to achieve the required accuracy. Using space‑based sensors allows for more accurate relative state estimates, especially if multiple satellites are allowed to communicate. Of interest to this work is the case where several communicating satellites each need to maintain a local catalog of communicating and non‑communicating objects using angles‑only limited field of view (FOV) measurements. However, this introduces the problem of efficiently scheduling and coordinating observations among the agents. This paper presents a decentralized task allocation algorithm to address this problem and quantifies its performance in terms of fuel usage and overall catalog uncertainty via numerical simulation. It was found that the new method significantly outperforms the uncertainty‑fuel Pareto frontier formed by current approaches.

Authors:Emily Logan, Ning Qi, Bolun Xu
Title: Nonparametric Kernel Regression for Coordinated Energy Storage Peak Shaving with Stacked Services
Abstract:
Developing effective control strategies for behind‑the‑meter energy storage to coordinate peak shaving and stacked services is essential for reducing electricity costs and extending battery lifetime in commercial buildings. This work proposes an end‑to‑end, two‑stage framework for coordinating peak shaving and energy arbitrage with a theoretical decomposition guarantee. In the first stage, a non‑parametric kernel regression model constructs state‑of‑charge trajectory bounds from historical data that satisfy peak‑shaving requirements. The second stage utilizes the remaining capacity for energy arbitrage via a transfer learning method. Case studies using New York City commercial building demand data show that our method achieves a 1.3 times improvement in performance over the state‑of‑the‑art forecast‑based method, achieving cost savings and effective peak management without relying on predictions.

Authors:Abhinav Sharma, Pratyush Chakraborty, Manoj Datta, Kazi N. Hasan
Title: Optimal Placement and Sizing of PV-Based DG Units in a Distribution Network Considering Loading Capacity
Abstract:
This research paper proposes an efficient methodology for the allocation of multiple photovoltaic (PV)‑based distributed generation (DG) units in the radial distribution network (RDN), while considering the loading capacity of the network. The proposed method is structured using a two‑stage approach. In the first stage, the additional active power loading capacity of the network and each individual bus is determined using an iterative approach. This analysis quantifies the network's additional active loadability limits and identifies buses with high active power loading capacity, which are considered candidate nodes for the placement of DG units. Subsequently, in the second stage, the optimal locations and sizes of DG units are determined using the Monte Carlo method, with the objectives of minimizing voltage deviation and reducing active power losses in the network. The methodology is validated on the standard IEEE 33‑bus RDN to determine the optimal locations and sizes of DG units. The results demonstrate that the optimal allocation of one, two, and three DG units, achieved from proposed method, reduces network active power losses by 50.37%, 58.62%, and 65.16%, respectively, and also significantly enhances the voltage profile across all buses. When the obtained results are compared with the results of several existing studies, it is found that the proposed method allows for larger DG capacities and maintains better voltage profiles throughout the RDN.

Authors:Prashant Solanki, Isabelle El-Hajj, Jasper J. van Beers, Erik-Jan van Kampen, Coen C. de Visser
Title: Certifying Hamilton-Jacobi Reachability Learned via Reinforcement Learning
Abstract:
We present a framework to \emphcertify Hamilton‑‑Jacobi (HJ) reachability learned by reinforcement learning (RL). Building on a discounted initial time \emphtravel‑cost formulation that makes small‑step RL value iteration provably equivalent to a forward Hamilton‑‑Jacobi (HJ) equation with damping, we convert certified learning errors into calibrated inner/outer enclosures of strict backward reachable tube. The core device is an additive‑offset identity: if W_λ solves the discounted travel‑cost Hamilton‑‑Jacobi‑‑Bellman (HJB) equation, then W_\varepsilon:=W_λ+ \varepsilon solves the same PDE with a constant offset λ\varepsilon. This means that a uniform value error is \emphexactly equal to a constant HJB offset. We establish this uniform value error via two routes: (A) a Bellman operator‑residual bound, and (B) a HJB PDE‑slack bound. Our framework preserves HJ‑level safety semantics and is compatible with deep RL. We demonstrate the approach on a double‑integrator system by formally certifying, via satisfiability modulo theories (SMT), a value function learned through reinforcement learning to induce provably correct inner and outer backward‑reachable set enclosures over a compact region of interest.

Authors:MHD Saria Allahham, Hossam S. Hassanein
Title: How Reliable is Your Service at the Extreme Edge? Analytical Modeling of Computational Reliability
Abstract:
Extreme Edge Computing (XEC) distributes streaming workloads across consumer‑owned devices, exploiting their proximity to users and ubiquitous availability. Many such workloads are AI‑driven, requiring continuous neural network inference for tasks like object detection and video analytics. Distributed Inference (DI), which partitions model execution across multiple edge devices, enables these streaming services to meet strict throughput and latency requirements. Yet consumer devices exhibit volatile computational availability due to competing applications and unpredictable usage patterns. This volatility poses a fundamental challenge: how can we quantify the probability that a device, or ensemble of devices, will maintain the processing rate required by a streaming service? This paper presents an analytical framework for computational reliability in XEC, defined as the probability that instantaneous capacity meets demand at a specified Quality of Service (QoS) threshold. We derive closed‑form reliability expressions under two information regimes: Minimal Information (MI), requiring only declared operational bounds, and historical data, which refines estimates via Maximum Likelihood Estimation from past observations. The framework extends to multi‑device deployments, providing reliability expressions for series, parallel, and partitioned workload configurations. We derive optimal workload allocation rules and analytical bounds for device selection, equipping orchestrators with tractable tools to evaluate deployment feasibility and configure distributed streaming systems. We validate the framework using real‑time object detection with YOLO11m model as a representative DI streaming workload; experiments on emulated XED environments demonstrate close agreement between analytical predictions, Monte Carlo sampling, and empirical measurements across diverse capacity and demand configurations.

Authors:Miguel A. Trujillo, Rodrigo Aldana-López, David Gomez Gutierrez, Michael Defoort, Javier Ruiz Leon, Hector M. Becerra
Title: Autonomous and non-autonomous fixed-time leader-follower consensus for second-order multi-agent systems
Abstract:
This paper addresses the problem of consensus tracking with fixed‑time convergence, for leader‑follower multi‑agent systems with double‑integrator dynamics, where only a subset of followers has access to the state of the leader. The control scheme is divided into two steps. The first one is dedicated to the estimation of the leader state by each follower in a distributed way and in a fixed‑time. Then, based on the estimate of the leader state, each follower computes its control law to track the leader in a fixed‑time. In this paper, two control strategies are investigated and compared to solve the two mentioned steps. The first one is an autonomous protocol which ensures a fixed‑time convergence for the observer and for the controller parts where the Upper Bound of the Settling‑Time (UBST) is set a priory by the user. Then, the previous strategy is redesigned using time‑varying gains to obtain a non‑autonomous protocol. This enables to obtain less conservative estimates of the UBST while guaranteeing that the time‑varying gains remain bounded. Some numerical examples show the effectiveness of the proposed consensus protocols.

Authors:Alina Castell Blasco, Maxime Bouton
Title: Collaborative Safe Bayesian Optimization
Abstract:
Mobile networks require safe optimization to adapt to changing conditions in traffic demand and signal transmission quality, in addition to improving service performance metrics. With the increasing complexity of emerging mobile networks, traditional parameter tuning methods become too conservative or complex to evaluate. For the first time, we apply safe Bayesian optimization to mobile networks. Moreover, we develop a new safe collaborative optimization algorithm called CoSBO, leveraging information from multiple optimization tasks in the network and considering multiple safety constraints. The resulting algorithm is capable of safely tuning the network parameter online with very few iterations. We demonstrate that the proposed method improves sample efficiency in the early stages of the optimization process by comparing it against the SafeOpt‑MC algorithm in a mobile network scenario.

Authors:Ahmad Amine, Kabir Puri, Viet-Anh Le, Rahul Mangharam
Title: Nonplanar Model Predictive Control for Autonomous Vehicles with Recursive Sparse Gaussian Process Dynamics
Abstract:
This paper proposes a nonplanar model predictive control (MPC) framework for autonomous vehicles operating on nonplanar terrain. To approximate complex vehicle dynamics in such environments, we develop a geometry‑aware modeling approach that learns a residual Gaussian Process (GP). By utilizing a recursive sparse GP, the framework enables real‑time adaptation to varying terrain geometry. The effectiveness of the learned model is demonstrated in a reference‑tracking task using a Model Predictive Path Integral (MPPI) controller. Validation within a custom Isaac Sim environment confirms the framework's capability to maintain high tracking accuracy on challenging 3D surfaces.

Authors:Zirui Zang, Ahmad Amine, Nick-Marios T. Kokolakis, Truong X. Nghiem, Ugo Rosolia, Rahul Mangharam
Title: SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks
Abstract:
Robots executing iterative tasks in complex, uncertain environments require control strategies that balance robustness, safety, and high performance. This paper introduces a safe information‑theoretic learning model predictive control (SIT‑LMPC) algorithm for iterative tasks. Specifically, we design an iterative control framework based on an information‑theoretic model predictive control algorithm to address a constrained infinite‑horizon optimal control problem for discrete‑time nonlinear stochastic systems. An adaptive penalty method is developed to ensure safety while balancing optimality. Trajectories from previous iterations are utilized to learn a value function using normalizing flows, which enables richer uncertainty modeling compared to Gaussian priors. SIT‑LMPC is designed for highly parallel execution on graphics processing units, allowing efficient real‑time optimization. Benchmark simulations and hardware experiments demonstrate that SIT‑LMPC iteratively improves system performance while robustly satisfying system constraints.

Authors:Jialin Zheng, Ruhaan Batta, Zhong Liu, Xiaonan Lu
Title: Discovering Unknown Inverter Governing Equations via Physics-Informed Sparse Machine Learning
Abstract:
Discovering the unknown governing equations of grid‑connected inverters from external measurements holds significant attraction for analyzing modern inverter‑intensive power systems. However, existing methods struggle to balance the identification of unmodeled nonlinearities with the preservation of physical consistency. To address this, this paper proposes a Physics‑Informed Sparse Machine Learning (PISML) framework. The architecture integrates a sparse symbolic backbone to capture dominant model skeletons with a neural residual branch that compensates for complex nonlinear control logic. Meanwhile, a Jacobian‑regularized physics‑informed training mechanism is introduced to enforce multi‑scale consistency including large/small‑scale behaviors. Furthermore, by performing symbolic regression on the neural residual branch, PISML achieves a tractable mapping from black‑box data to explicit control equations. Experimental results on a high‑fidelity Hardware‑in‑the‑Loop platform demonstrate the framework's superior performance. It not only achieves high‑resolution identification by reducing error by over 340 times compared to baselines but also realizes the compression of heavy neural networks into compact explicit forms. This restores analytical tractability for rigorous stability analysis and reduces computational complexity by orders of magnitude. It also provides a unified pathway to convert structurally inaccessible devices into explicit mathematical models, enabling stability analysis of power systems with unknown inverter governing equations.

Authors:Théo Ayral, Saifeddine Aloui, Mathieu Grossard
Title: Reactive Slip Control in Multifingered Grasping: Hybrid Tactile Sensing and Internal-Force Optimization
Abstract:
We build a low‑level reflex control layer driven by fast tactile feedback for multifinger grasp stabilization. Our hybrid approach combines learned tactile slip detection with model‑based internal‑force control to halt in‑hand slip while preserving the object‑level wrench. The multimodal tactile stack integrates piezoelectric sensing (PzE) for fast slip cues and piezoresistive arrays (PzR) for contact localization, enabling online construction of a contact‑centric grasp representation without prior object knowledge. Experiments demonstrate reactive stabilization of multifingered grasps under external perturbations, without explicit friction models or direct force sensing. In controlled trials, slip onset is detected after 20.4 +/‑ 6 ms. The framework yields a theoretical grasp response latency on the order of 30 ms, with grasp‑model updates in less than 5 ms and internal‑force selection in about 4 ms. The analysis supports the feasibility of sub‑50 ms tactile‑driven grasp responses, aligned with human reflex baselines.

Authors:Ian Anderson, Agham Posadas, Alexander A. Demkov, Ruochen Lu
Title: Tunable Ferroelectric Acoustic Resonators in Monolithic Thin-Film Barium Titanate
Abstract:
The increasing development of wireless communication bands has motivated the development of compact, low‑loss, and frequency adjustable RF filtering technologies. Acoustic resonators are the ideal solution to these requirements, and tunable implementations offer a path toward reconfigurable front ends. In this work, we investigate epitaxial barium titanate (BTO) grown on silicon as a platform for tunable acoustic resonators operating in the sub‑GHz regime. We demonstrate lateral excitation of symmetric lamb (S0) modes in X‑cut BTO membranes, in contrast to prior thickness‑defined ferroelectric resonators. Devices are designed using finite‑element simulations and fabricated with laterally patterned electrodes that enable overtone coupling to multiple resonant modes. Under applied DC bias, ferroelectric domains align, allowing electrical excitation, frequency tuning, and quality‑factor enhancement of acoustic modes. Resonances near 300 MHz and 700 MHz exhibit electromechanical coupling up to 8% and bias‑dependent frequency tuning, with a distinct transition in behavior near 20 V. These results highlight monolithic BTO on silicon as a promising material system for laterally excited, tunable acoustic resonators for reconfigurable RF applications.

Authors:Nelson Salazar-Pena, Alejandra Tabares, Andres Gonzalez-Mancera
Title: Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets
Abstract:
This paper proposes implicit cooperation, a framework enabling decentralized agents to approximate optimal coordination in local energy markets without explicit peer‑to‑peer communication. We formulate the problem as a decentralized partially observable Markov decision problem that is solved through a multi‑agent reinforcement learning task in which agents use stigmergic signals (key performance indicators at the system level) to infer and react to global states. Through a 3x3 factorial design on an IEEE 34‑node topology, we evaluated three training paradigms (CTCE, CTDE, DTDE) and three algorithms (PPO, APPO, SAC). Results identify APPO‑DTDE as the optimal configuration, achieving a coordination score of 91.7% relative to the theoretical centralized benchmark (CTCE). However, a critical trade‑off emerges between efficiency and stability: while the centralized benchmark maximizes allocative efficiency with a peer‑to‑peer trade ratio of 0.6, the fully decentralized approach (DTDE) demonstrates superior physical stability. Specifically, DTDE reduces the variance of grid balance by 31% compared to hybrid architectures, establishing a highly predictable, import‑biased load profile that simplifies grid regulation. Furthermore, topological analysis reveals emergent spatial clustering, where decentralized agents self‑organize into stable trading communities to minimize congestion penalties. While SAC excelled in hybrid settings, it failed in decentralized environments due to entropy‑driven instability. This research proves that stigmergic signaling provides sufficient context for complex grid coordination, offering a robust, privacy‑preserving alternative to expensive centralized communication infrastructure.

Authors:Amirhossein Iraniparast, Dominic Groß
Title: Stability and convergence of multi-converter systems using projection-free power-limiting droop control
Abstract:
In this paper, we propose a projection‑free power‑limiting droop control for grid‑connected power electronics and an associated constrained flow problem. In contrast to projection‑based power‑limiting droop control, the novel projection‑free power‑limiting droop control results in networked dynamics that are semi‑globally exponentially stable with respect to the set of optimizers of the constrained flow problem. Under a change to edge coordinates, the overall networked dynamics arising from projection‑free power‑limiting droop control coincide with the projection‑free primal‑dual dynamics associated with an augmented Lagrangian of the constrained flow problem. Leveraging this result, we (i) provide a bound on the convergence rate of the projection‑free networked dynamics, (ii) propose a tuning method for controller parameters to improve the bound on the convergence rate, and (iii) analyze the relationship of the bound on the convergence rate and connectivity of the network. Finally, the analytical results are illustrated using an Electromagnetic transient (EMT) simulation.

Authors:Huan Liu, Michel Gendreau, Binjie Xu, Guohua Wu, Yi Gu
Title: Close-enough general routing problem for multiple unmanned aerial vehicles in monitoring missions
Abstract:
In this paper, we introduce a close‑enough multi‑UAV general routing problem (CEMUAVGRP) where a fleet of homogeneous UAVs conduct monitoring tasks containing nodes, each of which has its disk neighborhood, and edges, aiming to minimize the total distance. A two‑phase iterative method is proposed, partitioning the CEMUAVGRP into a general routing phase where a satisfactory route including required nodes and edges for each UAV is obtained without considering the disk neighborhoods of required nodes, and a close‑enough routing phase where representative points are optimized for each required node in the determined route. To be specific, a variable neighborhood descent (VND) heuristic is proposed for the general routing phase, while a second‑order cone programming (SOCP) procedure is applied in the close‑enough routing phase. These two phases are performed in an iterative fashion under the framework of an adaptive iterated local search (AILS) algorithm until the predefined termination criteria are satisfied. Extensive experiments and comparative studies are conducted, demonstrating the efficiency of the proposed AILS‑VND‑SOCP algorithm and the superiority of disk neighborhoods.

Authors:Rohit Kaushik, Eva Kaushik
Title: A Koopman-Bayesian Framework for High-Fidelity, Perceptually Optimized Haptic Surgical Simulation
Abstract:
We introduce a unified framework that combines nonlinear dynamics, perceptual psychophysics and high frequency haptic rendering to enhance realism in surgical simulation. The interaction of the surgical device with soft tissue is elevated to an augmented state space with a Koopman operator formulation, allowing linear prediction and control of the dynamics that are nonlinear by nature. To make the rendered forces consistent with human perceptual limits, we put forward a Bayesian calibration module based on WeberFechner and Stevens scaling laws, which progressively shape force signals relative to each individual's discrimination thresholds. For various simulated surgical tasks such as palpation, incision, and bone milling, the proposed system attains an average rendering latency of 4.3 ms, a force error of less than 2.8% and a 20% improvement in perceptual discrimination. Multivariate statistical analyses (MANOVA and regression) reveal that the system's performance is significantly better than that of conventional spring‑damper and energy, based rendering methods. We end by discussing the potential impact on surgical training and VR, based medical education, as well as sketching future work toward closed, loop neural feedback in haptic interfaces.

Authors:Zhen Wu, Xiaoyu Huang, Lujie Yang, Yuanhang Zhang, Xi Chen, Pieter Abbeel, Rocky Duan, Angjoo Kanazawa, Carmelo Sferrazza, Guanya Shi, C. Karen Liu
Title: Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching
Abstract:
While recent advances in humanoid locomotion have achieved stable walking on varied terrains, capturing the agility and adaptivity of highly dynamic human motions remains an open challenge. In particular, agile parkour in complex environments demands not only low‑level robustness, but also human‑like motion expressiveness, long‑horizon skill composition, and perception‑driven decision‑making. In this paper, we present Perceptive Humanoid Parkour (PHP), a modular framework that enables humanoid robots to autonomously perform long‑horizon, vision‑based parkour across challenging obstacle courses. Our approach first leverages motion matching, formulated as nearest‑neighbor search in a feature space, to compose retargeted atomic human skills into long‑horizon kinematic trajectories. This framework enables the flexible composition and smooth transition of complex skill chains while preserving the elegance and fluidity of dynamic human motions. Next, we train motion‑tracking reinforcement learning (RL) expert policies for these composed motions, and distill them into a single depth‑based, multi‑skill student policy, using a combination of DAgger and RL. Crucially, the combination of perception and skill composition enables autonomous, context‑aware decision‑making: using only onboard depth sensing and a discrete 2D velocity command, the robot selects and executes whether to step over, climb onto, vault or roll off obstacles of varying geometries and heights. We validate our framework with extensive real‑world experiments on a Unitree G1 humanoid robot, demonstrating highly dynamic parkour skills such as climbing tall obstacles up to 1.25m (96% robot height), as well as long‑horizon multi‑obstacle traversal with closed‑loop adaptation to real‑time obstacle perturbations.

Authors:Boris Sedlak, Víctor Casamayor Pujol, Ildefons Magrans de Abril, Praveen Kumar Donta, Adel N. Toosi, Schahram Dustdar
Title: Service Orchestration in the Computing Continuum: Structural Challenges and Vision
Abstract:
The Computing Continuum (CC) integrates different layers of processing infrastructure, from Edge to Cloud, to optimize service quality through ubiquitous and reliable computation. Compared to central architectures, however, heterogeneous and dynamic infrastructure increases the complexity for service orchestration. To guide research, this article first summarizes structural problems of the CC, and then, envisions an ideal solution for autonomous service orchestration across the CC. As one instantiation, we show how Active Inference, a concept from neuroscience, can support self‑organizing services in continuously interpreting their environment to optimize service quality. Still, we conclude that no existing solution achieves our vision, but that research on service orchestration faces several structural challenges. Most notably: provide standardized simulation and evaluation environments for comparing the performance of orchestration mechanisms. Together, the challenges outline a research roadmap toward resilient and scalable service orchestration in the CC.

Authors:Maximino Linares, Guillaume Doras, Thomas Hélie
Title: Controlled oscillation modeling using port-Hamiltonian neural networks
Abstract:
Learning dynamical systems through purely data‑driven methods is challenging as they do not learn the underlying conservation laws that enable them to correctly generalize. Existing port‑Hamiltonian neural network methods have recently been successfully applied for modeling mechanical systems. However, even though these methods are designed on power‑balance principles, they usually do not consider power‑preserving discretizations and often rely on Runge‑Kutta numerical methods. In this work, we propose to use a second‑order discrete gradient method embedded in the learning of dynamical systems with port‑Hamiltonian neural networks. Numerical results are provided for three systems deliberately selected to span different ranges of dynamical behavior under control: a baseline harmonic oscillator with quadratic energy storage; a Duffing oscillator, with a non‑quadratic Hamiltonian offering amplitude‑dependent effects; and a self‑sustained oscillator, which can stabilize in a controlled limit cycle through the incorporation of a nonlinear dissipation. We show how the use of this discrete gradient method outperforms the performance of a Runge‑Kutta method of the same order. Experiments are also carried out to compare two theoretically equivalent port‑Hamiltonian systems formulations and to analyze the impact of regularizing the Jacobian of port‑Hamiltonian neural networks during training.

Authors:Feras Kiki, Pouya P. Niaz, Alireza Madani, Cagatay Basdogan
Title: Estimating Human Muscular Fatigue in Dynamic Collaborative Robotic Tasks with Learning-Based Models
Abstract:
Assessing human muscle fatigue is critical for optimizing performance and safety in physical human‑robot interaction(pHRI). This work presents a data‑driven framework to estimate fatigue in dynamic, cyclic pHRI using arm‑mounted surface electromyography(sEMG). Subject‑specific machine‑learning regression models(Random Forest, XGBoost, and Linear Regression predict the fraction of cycles to fatigue(FCF) from three frequency‑domain and one time‑domain EMG features, and are benchmarked against a convolutional neural network(CNN) that ingests spectrograms of filtered EMG. Framing fatigue estimation as regression (rather than classification) captures continuous progression toward fatigue, supporting earlier detection, timely intervention, and adaptive robot control. In experiments with ten participants, a collaborative robot under admittance control guided repetitive lateral (left‑right) end‑effector motions until muscular fatigue. Average FCF RMSE across participants was 20.8+/‑4.3% for the CNN, 23.3+/‑3.8% for Random Forest, 24.8+/‑4.5% for XGBoost, and 26.9+/‑6.1% for Linear Regression. To probe cross‑task generalization, one participant additionally performed unseen vertical (up‑down) and circular repetitions; models trained only on lateral data were tested directly and largely retained accuracy, indicating robustness to changes in movement direction, arm kinematics, and muscle recruitment, while Linear Regression deteriorated. Overall, the study shows that both feature‑based ML and spectrogram‑based DL can estimate remaining work capacity during repetitive pHRI, with the CNN delivering the lowest error and the tree‑based models close behind. The reported transfer to new motion patterns suggests potential for practical fatigue monitoring without retraining for every task, improving operator protection and enabling fatigue‑aware shared autonomy, for safer fatigue‑adaptive pHRI control.

Authors:Liang Wu, Yunhong Che, Bo Yang, Kangyu Lin, Ján Drgoňa
Title: Time-Certified and Efficient NMPC via Koopman Operator
Abstract:
Certifying and accelerating execution times of nonlinear model predictive control (NMPC) implementations are two core requirements. Execution‑time certificate guarantees that the NMPC controller returns a solution before the next sampling time, and achieving faster worst‑case and average execution times further enables its use in a wider set of applications. However, NMPC produces a nonlinear program (NLP) for which it is challenging to derive its execution time certificates. Our previous works, \citepwu2025direct,wu2025time provide data‑independent execution time certificates (certified number of iterations) for box‑constrained quadratic programs (BoxQP). To apply the time‑certified BoxQP algorithm \citepwu2025time for state‑input constrained NMPC, this paper i) learns a linear model via Koopman operator; ii) proposes a dynamic‑relaxation construction approach yields a structured BoxQP rather than a general QP; iii) exploits the structure of BoxQP, where the dimension of the linear system solved in each iteration is reduced from 5N(n_u+n_x) to Nn_u (where n_u, n_x, N denote the number of inputs, states, and length of prediction horizon), yielding substantial speedups (when n_x \gg n_u, as in PDE control).

Authors:Algo Carè, Marco C. Campi, Simone Garatti
Title: Scenario Approach with Post-Design Certification of User-Specified Properties
Abstract:
The scenario approach is an established data‑driven design framework that comes equipped with a powerful theory linking design complexity to generalization properties. In this approach, data are simultaneously used both for design and for certifying the design's reliability, without resorting to a separate test dataset. This paper takes a step further by guaranteeing additional properties, useful in post‑design usage but not considered during the design phase. To this end, we introduce a two‑level framework of appropriateness: baseline appropriateness, which guides the design process, and post‑design appropriateness, which serves as a criterion for a posteriori evaluation. We provide distribution‑free upper bounds on the risk of failing to meet the post‑design appropriateness; these bounds are computable without using any additional test data. Under additional assumptions, lower bounds are also derived. As part of an effort to demonstrate the usefulness of the proposed methodology, the paper presents two practical examples in H2 and pole‑placement problems. Moreover, a method is provided to infer comprehensive distributional knowledge of relevant performance indexes from the available dataset.

Authors:Ferdinand Geuss, Orcun Karaca, Mario Schweizer, Ognjen Stanojev
Title: The role of VSG parameters in shaping small-signal SG dynamics
Abstract:
We derive a small‑signal transfer function for a system comprising a virtual synchronous generator (VSG), a synchronous generator (SG), and a load, capturing voltage and frequency dynamics. Using this model, we analyze the sensitivity of SG dynamics to VSG parameters, highlighting trade‑offs in choosing virtual inertia and governor lag, the limited effect of damper‑winding emulation, and several others.

Authors:Shuichi Yahagi, Ansei Yonezawa, Heisei Yonezawa, Hiroki Seto, Itsuro Kajiwara
Title: Generalized bilinear Koopman realization from input-output data for multi-step prediction with metaheuristic optimization of lifting function and its application to real-world industrial system
Abstract:
This paper introduces an input‑output bilinear Koopman realization with an optimization algorithm of lifting functions. For nonlinear systems with inputs, Koopman‑based modeling is effective because the Koopman operator enables a high‑dimensional linear representation of nonlinear dynamics. However, traditional approaches face significant challenges in industrial applications. Measuring all system states is often impractical due to constraints on sensor installation. Moreover, the predictive performance of a Koopman model strongly depends on the choice of lifting functions, and their design typically requires substantial manual effort. In addition, although a linear time‑invariant (LTI) Koopman model is the most commonly used model structure in the Koopman framework, such model exhibit limited predictive accuracy. To address these limitations, we propose an input‑output bilinear Koopman modeling in which the design parameters of radial basis function (RBF)‑based lifting functions are optimized using a global metaheuristic algorithm to improve long‑term prediction performance. Consideration of the long‑term prediction performance enhances the reliability of the resulting model. The proposed methodology is validated in simulations and experimental tests, with the airpath control system of a diesel engine as the plant to be modeled. This plant represents a challenging industrial application because it exhibits strong nonlinearities and coupled multi‑input multi‑output (MIMO) dynamics. These results demonstrate that the proposed input‑output bilinear Koopman model significantly outperforms traditional linear Koopman models in predictive accuracy.

Authors:Hannah Michalska, Miguel Torres-Torriti
Title: A Geometric Approach to Feedback Stabilization of Nonlinear Systems with Drift
Abstract:
The paper presents an approach to the construction of stabilizing feedback for strongly nonlinear systems. The class of systems of interest includes systems with drift which are affine in control and which cannot be stabilized by continuous state feedback. The approach is independent of the selection of a Lyapunov type function, but requires the solution of a nonlinear programming 'satisficing problem' stated in terms of the logarithmic coordinates of flows. As opposed to other approaches, point‑to‑point steering is not required to achieve asymptotic stability. Instead, the flow of the controlled system is required to intersect periodically a certain reachable set in the space of the logarithmic coordinates.

Authors:Jose Luis Peralta-Cabezas, Miguel Torres-Torriti, Marcelo Guarini-Hermann
Title: A Comparison of Bayesian Prediction Techniques for Mobile Robot Trajectory Tracking
Abstract:
This paper presents a performance comparison of different estimation and prediction techniques applied to the problem of tracking multiple robots. The main performance criteria are the magnitude of the estimation or prediction error, the computational effort and the robustness of each method to non‑Gaussian noise. Among the different techniques compared are the well known Kalman filters and their different variants (e.g. extended and unscented), and the more recent techniques relying on Sequential Monte Carlo Sampling methods, such as particle filters and Gaussian Mixture Sigma Point Particle Filter.

Authors:Mohamad Chehade, Hao Zhu
Title: Fine-Tuning LLMs to Generate Economical and Reliable Actions for the Power Grid
Abstract:
Public Safety Power Shutoffs (PSPS) force rapid topology changes that can render standard operating points infeasible, requiring operators to quickly identify corrective transmission switching actions that reduce load shedding while maintaining acceptable voltage behavior. We present a verifiable, multi‑stage adaptation pipeline that fine‑tunes an instruction‑tuned large language model (LLM) to generate \emphopen‑only corrective switching plans from compact PSPS scenario summaries under an explicit switching budget. First, supervised fine‑tuning distills a DC‑OPF MILP oracle into a constrained action grammar that enables reliable parsing and feasibility checks. Second, direct preference optimization refines the policy using AC‑evaluated preference pairs ranked by a voltage‑penalty metric, injecting voltage‑awareness beyond DC imitation. Finally, best‑of‑N selection provides an inference‑time addition by choosing the best feasible candidate under the target metric. On IEEE 118‑bus PSPS scenarios, fine‑tuning substantially improves DC objective values versus zero‑shot generation, reduces AC power‑flow failure from 50% to single digits, and improves voltage‑penalty outcomes on the common‑success set. Code and data‑generation scripts are released to support reproducibility.

Authors:Jaehan Im
Title: Noncooperative Coordination for Decentralized Air Traffic Management
Abstract:
Decentralized air traffic management requires coordination among self‑interested stakeholders operating under shared safety and capacity constraints, where conventional centralized or implicitly cooperative models do not adequately capture this setting. We develop a unified perspective on noncooperative coordination, in which system‑level outcomes emerge by designing incentives and assigning signals that reshape individual optimality rather than imposing cooperation or enforcement. We advance this framework along three directions: scalable equilibrium engineering via reduced‑rank and uncertainty‑aware correlated equilibria, decentralized mechanism design for equilibrium selection without enforcement, and structured noncooperative dynamics with convergence guarantees. Beyond these technical contributions, we discuss core design principles that govern incentive‑compatible coordination in decentralized systems. Together, these results establish a foundation for scalable, robust coordination in safety‑critical air traffic systems.

Authors:Fen Wu
Title: State Feedback Control of State-Delayed LPV Systems using Dynamic IQCs
Abstract:
This paper develops a new control framework for linear parameter‑varying (LPV) systems with time‑varying state delays by integrating parameter‑dependent Lyapunov functions with integral quadratic constraints (IQCs). A novel delay‑dependent state‑feedback controller structure is proposed, consisting of a linear state‑feedback law augmented with an additional term that captures the delay‑dependent dynamics of the plant. Closed‑loop stability and \mathcalL_2‑gain performance are analyzed using dynamic IQCs and parameter‑dependent quadratic Lyapunov functions, leading to convex synthesis conditions that guarantee performance in terms of parameter‑dependent linear matrix inequalities (LMIs). Unlike traditional delay control approaches, the proposed IQC‑based framework provides a flexible and systematic methodology for handling delay effects, enabling enhanced control capability, reduced conservatism, and improved closed‑loop performance.

Authors:Mohammad Mhadi Naderi, Megan S. Harris, John C. Little, Amro M. Farid
Title: Embedding Economic Input-Output Models in Systems of Systems: An MBSE and Hetero-functional Graph Theory Approach
Abstract:
Characterizing the interdependent nature of Anthropocene systems of systems is fundamental to making informed decisions to address challenges across complex ecological, environmental, and coupled human‑natural systems. This paper presents the first application of Model‑Based Systems Engineering (MBSE) and Hetero‑functional Graph Theory (HFGT) to economic systems, establishing a scalable and extensible methodology for integrating economic input‑output (EIO) models within a unified system‑of‑systems modeling framework. Integrating EIO models into the MBSE‑HFGT workflow demonstrates how the structural form and function of economic systems can be expressed through SysML's graphical ontology and subsequently translated into the computational structure of HFGT. Using a synthetic Rectangular Choice of Technology (RCOT) example as a pedagogical foundation, the study confirms that the dynamics captured by basic EIO models, as well as other complex economic models grounded in EIO theory, can be equivalently reproduced within the MBSE‑HFGT framework. The integration with MBSE and HFGT thus preserves analytical precision while offering enhanced graphical clarity and system‑level insight through a shared ontological structure. By integrating modeling languages and mathematical frameworks, the proposed methodology establishes a foundation for knowledge co‑production and integrated decision‑making to address the multifaceted sustainability challenges associated with Anthropocene systems of systems.

Authors:Amirhossein Taherpour, Abbas Taherpour, Tamer Khattab
Title: Secure High-Resolution ISAC via Multi-Layer Intelligent Metasurfaces: A Layered Optimization Framework
Abstract:
Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next‑generation wireless networks, enabling simultaneous data transmission and environmental sensing. However, existing ISAC systems face fundamental limitations in achieving high‑resolution sensing while maintaining robust communication security and spectral efficiency. This paper introduces a transformative approach leveraging stacked intelligent metasurfaces (SIM) to overcome these challenges. We propose a multi‑functional SIM‑assisted system that jointly optimizes communication secrecy and sensing accuracy through a novel layered optimization framework. Our solution employs a multi‑objective optimization formulation that balances secrecy rate maximization with sensing error minimization under practical hardware constraints. The proposed layered block coordinate descent algorithm efficiently coordinates sensing configuration, secure beamforming, communication metasurface optimization, and resource allocation while ensuring robustness to channel uncertainties. Extensive simulations demonstrate significant performance gains over conventional approaches, achieving 32‑61% improvement in sensing accuracy and 15‑35% enhancement in secrecy rates while maintaining computational efficiency. This work establishes a new paradigm for secure and high‑precision multi‑functional wireless systems.

Authors:Stefano Boccelli, William M. Farrell, Prabal Saxena, Orenthal J. Tucker
Title: Venting and Outgassing Simulations of Pressurized Lunar Modules: Contamination of the Lunar Environment
Abstract:
One objective of Artemis science is to determine the impact human activities have on the lunar environment, which might compromise science objectives and measurements. We perform a preliminary analysis of the contamination associated with airlock venting and outgassing from a prototype lunar‑module geometry intended to host astronauts on the lunar surface. The air flow generated by the depressurization of the airlock, expanding in the lunar exosphere, is studied using the Direct Simulation Monte Carlo (DSMC) method for two different venting configurations and the particle flux on the surface is computed as a function of the distance from the the module. Outgassing from the main body of the module ‑‑ assumed to be covered with a Multi‑Layer Insulation (MLI) blanketing ‑‑ and from the solar panels is then analyzed using a view‑factor method, employing outgassing rates from the literature.Our results give preliminary indications of the distance at which contamination levels fall below the values characteristic of native species in the lunar atmosphere. Scientific measurements targeting 40Ar should be carried farther than 30‑‑100 meters from the module, while the detection of lower‑abundance species such as polar‑crater water might require to travel up to and beyond 3 km from the module.

Authors:Balram Kandoria, Aryaman Singh Samyal
Title: Kalman Filtering Based Flight Management System Modeling for AAM Aircraft
Abstract:
Advanced Aerial Mobility (AAM) operations require strategic flight planning services that predict both spatial and temporal uncertainties to safely validate flight plans against hazards such as weather cells, restricted airspaces, and CNS disruption areas. Current uncertainty estimation methods for AAM vehicles rely on conservative linear models due to limited real‑world performance data. This paper presents a novel Kalman Filter‑based uncertainty propagation method that models AAM Flight Management System (FMS) architectures through sigmoid‑blended measurement noise covariance. Unlike existing approaches with fixed uncertainty thresholds, our method continuously adapts the filter's measurement trust based on progress toward waypoints, enabling FMS correction behavior to emerge naturally. The approach scales proportionally with control inputs and is tunable to match specific aircraft characteristics or route conditions. We validate the method using real ADS‑B data from general aviation aircraft divided into training and verification sets. Uncertainty propagation parameters were tuned on the training set, achieving 76% accuracy in predicting arrival times when compared against the verification dataset, demonstrating the method's effectiveness for strategic flight plan validation in AAM operations.

Authors:Junyi Li, Tim Foissner, Floran Martin, Antti Piippo, Marko Hinkkanen
Title: Gradient Networks for Universal Magnetic Modeling of Synchronous Machines
Abstract:
This paper presents a physics‑informed neural network approach for dynamic modeling of saturable synchronous machines, including cases with spatial harmonics. We introduce an architecture that incorporates gradient networks directly into the fundamental machine equations, enabling accurate modeling of the nonlinear and coupled electromagnetic constitutive relationship. By learning the gradient of the magnetic field energy, the model inherently satisfies energy balance (reciprocity conditions). The proposed architecture can universally approximate any physically feasible magnetic behavior and offers several advantages over lookup tables and standard machine learning models: it requires less training data, ensures monotonicity and reliable extrapolation, and produces smooth outputs. These properties further enable robust model inversion and optimal trajectory generation, often needed in control applications. We validate the proposed approach using measured and finite‑element method (FEM) datasets from a 5.6‑kW permanent‑magnet (PM) synchronous reluctance machine. Results demonstrate accurate and physically consistent models, even with limited training data.

Authors:Sidharthenee Nayak, Victor Sam Moses Babu, Chandrashekhar Narayan Bhende, Pratyush Chakraborty, Mayukha Pal
Title: Fault Detection in Electrical Distribution System using Autoencoders
Abstract:
In recent times, there has been considerable interest in fault detection within electrical power systems, garnering attention from both academic researchers and industry professionals. Despite the development of numerous fault detection methods and their adaptations over the past decade, their practical application remains highly challenging. Given the probabilistic nature of fault occurrences and parameters, certain decision‑making tasks could be approached from a probabilistic standpoint. Protective systems are tasked with the detection, classification, and localization of faulty voltage and current line magnitudes, culminating in the activation of circuit breakers to isolate the faulty line. An essential aspect of designing effective fault detection systems lies in obtaining reliable data for training and testing, which is often scarce. Leveraging deep learning techniques, particularly the powerful capabilities of pattern classifiers in learning, generalizing, and parallel processing, offers promising avenues for intelligent fault detection. To address this, our paper proposes an anomaly‑based approach for fault detection in electrical power systems, employing deep autoencoders. Additionally, we utilize Convolutional Autoencoders (CAE) for dimensionality reduction, which, due to its fewer parameters, requires less training time compared to conventional autoencoders. The proposed method demonstrates superior performance and accuracy compared to alternative detection approaches by achieving an accuracy of 97.62% and 99.92% on simulated and publicly available datasets.

Authors:Tyrone Fernando
Title: Unified Eigenvalue-Eigenspace Criteria for Functional Properties of Linear Systems and the Generalized Separation Principle
Abstract:
Classical controllability and observability characterise reachability and reconstructibility of the full system state and admit equivalent geometric and eigenvalue‑based Popov‑Belevitch‑Hautus (PBH) tests. Motivated by large‑scale and networked systems where only selected linear combinations of the state are of interest, this paper studies functional generalisations of these properties. A PBH‑style framework for functional system properties is developed, providing necessary and sufficient spectral characterisations. The results apply uniformly to diagonalizable and non‑diagonalizable systems and recover the classical PBH tests as special cases. Two new intrinsic notions are introduced: intrinsic functional controllability, and intrinsic functional stabilizability. These intrinsic properties are formulated directly in terms of invariant subspaces associated with the functional and provide verifiable conditions for the existence of admissible augmentations required for functional controller design and observer‑based functional controller design. The intrinsic framework enables the generalized separation principle at the functional level, establishing that functional controllers and functional observers can be designed independently. Illustrative examples demonstrate the theory and highlight situations where functional control and estimation are possible despite lack of full‑state controllability or observability.

Authors:Souvik Das, Luca Schenato, Subhrakanti Dey
Title: On Convergence Analysis of Network-GIANT: An approximate Hessian-based fully distributed optimization algorithm
Abstract:
In this paper, we present a detailed convergence analysis of a recently developed approximate Newton‑type fully distributed optimization method for smooth, strongly convex local loss functions, called Network‑GIANT, which has been empirically illustrated to show faster linear convergence properties while having the same communication complexity (per iteration) as its first order distributed counterparts. By using consensus based parameter updates, and a local Hessian based descent direction at the individual nodes with gradient tracking, we first explicitly characterize a global linear convergence rate for Network‑GIANT, which can be computed as the spectral radius of a 3 × 3 matrix dependent on the Lipschitz continuity (L) and strong convexity (μ) parameters of the objective functions, and the spectral norm (σ) of the underlying undirected graph represented by a doubly stochastic consensus matrix. We provide an explicit bound on the step size parameter η, below which this spectral radius is guaranteed to be less than 1. Furthermore, we derive a mixed linear‑quadratic inequality based upper bound for the optimality gap norm, which allows us to conclude that, under small step size values, asymptotically, as the algorithm approaches the global optimum, it achieves a locally linear convergence rate of 1‑η(1 ‑\fracγμ) for Network‑GIANT, provided the Hessian approximation error γ (between the harmonic mean of the local Hessians and the global hessian (the arithmetic mean of the local Hessians) is smaller than μ. This asymptotically linear convergence rate of \approx 1‑η explains the faster convergence rate of Network‑GIANT for the first time. Numerical experiments are carried out with a reduced CovType dataset for binary logistic regression over a variety of graphs to illustrate the above theoretical results.

Authors:Anna-Lena Schlamp, Jeremias Gerner, Klaus Bogenberger, Werner Huber, Stefanie Schmidtner
Title: ROSA: Roundabout Optimized Speed Advisory with Multi-Agent Trajectory Prediction in Multimodal Traffic
Abstract:
We present ROSA ‑‑ Roundabout Optimized Speed Advisory ‑‑ a system that combines multi‑agent trajectory prediction with coordinated speed guidance for multimodal, mixed traffic at roundabouts. Using a Transformer‑based model, ROSA jointly predicts the future trajectories of vehicles and Vulnerable Road Users (VRUs) at roundabouts. Trained for single‑step prediction and deployed autoregressively, it generates deterministic outputs, enabling actionable speed advisories. Incorporating motion dynamics, the model achieves high accuracy (ADE: 1.29m, FDE: 2.99m at a five‑second prediction horizon), surpassing prior work. Adding route intention further improves performance (ADE: 1.10m, FDE: 2.36m), demonstrating the value of connected vehicle data. Based on predicted conflicts with VRUs and circulating vehicles, ROSA provides real‑time, proactive speed advisories for approaching and entering the roundabout. Despite prediction uncertainty, ROSA significantly improves vehicle efficiency and safety, with positive effects even on perceived safety from a VRU perspective. The source code of this work is available under: github.com/urbanAIthi/ROSA.

Authors:Ariana R. Mendez-Castillo, Rodrigo Aldana-Lopez, Antonio Ramirez-Trevino, Rosario Aragues, David Gomez-Gutierrez
Title: Hierarchical parameter estimation for distributed networked systems: a dynamic consensus approach
Abstract:
This work introduces a novel two‑stage distributed framework to globally estimate constant parameters in a networked system, separating shared information from local estimation. The first stage uses dynamic average consensus to aggregate agents' measurements into surrogates of centralized data. Using these surrogates, the second stage implements a local estimator to determine the parameters. By designing an appropriate consensus gain, the persistence of excitation of the regressor matrix is achieved, and thus, exponential convergence of a local Gradient Estimator (GE) is guaranteed. The framework facilitates its extension to switched network topologies, quantization, and the heterogeneous substitution of the GE with a Dynamic Regressor Extension and Mixing (DREM) estimator, which supports relaxed excitation requirements.

Authors:Hadi Nemati, Álvaro Ortega, Enrique Lobato, Luis Rouco
Title: A Multi-Bound Robust Optimization Approach for Renewable-Based VPP Market Participation Considering Intra-Hourly Uncertainty Exposure
Abstract:
With the ongoing transition of electricity markets worldwide from hourly to intra‑hourly bidding, market participants‑‑especially Renewable Energy Sources (RES)‑‑gain improved opportunities to adjust energy and reserve schedules and to benefit from more accurate higher‑resolution forecasts. However, this shift requires participants to update decision‑making frameworks and to strengthen uncertainty management in order to fully exploit the new market potential. In particular, Renewable‑Based Virtual Power Plants (RVPPs) aggregating dispatchable and non‑dispatchable RES must account for these changes through market‑oriented scheduling methods that efficiently address multiple uncertainties, including electricity prices, RES generation, and demand consumption. In this vein, this paper proposes a multi‑bound robust optimization framework to simultaneously capture these uncertainties, explicitly incorporate intra‑hourly variability, and differentiate the deviation levels (frequent, moderate deviations and rare, extreme ones) of uncertain parameters. The proposed approach yields less conservative and more implementable bidding and scheduling decisions, thus improving RVPP profitability in both energy and reserve markets. Simulation studies compare the proposed method with standard robust optimization and evaluate the operational, market‑strategy, and economic impacts of quarter‑hourly versus hourly market resolution. Results indicate that the normalized absolute differences, across different uncertainty‑handling strategies, between hourly and 15‑minute schedules are 18.0‑‑34.2% for day‑ahead traded energy, and 28.7‑‑65.6% and 10.1‑‑16.3% for upward and downward reserve traded in the secondary reserve market, respectively. Furthermore, relative to classic robust optimization, the proposed multi‑bound approach increases profit by 24.9‑‑49.2% across the considered strategies.

Authors:Cornelia Skaga, Mahdieh S. Sadabadi, Gilbert Bergna-Diaz
Title: DC Microgrids with Nested Nonlinear Distributed Control: Scalable Large-Signal Stability and Voltage Containment
Abstract:
This paper investigates a cyber‑physical DC microgrid employing a nonlinear distributed consensus‑based control scheme for coordinated integration and management of distributed generating units within an expandable framework. Relying on nested primary andsecondary control loops; a (distributed) outer‑loop and a (decentralized) inner‑loop, the controller achieves proportional current sharing among all distributed generation units, while dynamically operating within predefined voltage limits. A rigorous Lyapunov‑based stability analysis establishes a scalable global exponential stability certificate under some tuning conditions and sufficient time‑scale separation between the control loops, based on singular perturbation theory. An optimization‑based tuning strategy is then formulated to identify and subsequently diminish unstable operating conditions. In turn, various practical tuning strategies are introduced to provide stable operations while facilitating near‑optimal proportional current sharing. The effectiveness of the proposed control framework and tuning approaches are finally supported through time‑domain simulations of a case‑specific low‑voltage DC microgrid.

Authors:Fan Zhang, Deyuan Meng, Ying Tan
Title: Segment-Based Two-Loop Adaptive Iterative Learning Control for Spacecraft Position and Attitude Tracking
Abstract:
Proximity operations of rigid bodies, such as spacecraft rendezvous and docking, require precise tracking of both position and attitude over finite time intervals. These operations are often repeated under uncertain conditions, with unknown but repeatable parameters and disturbances. Adaptive iterative learning control (ILC) is well suited to such tasks, as it can track desired trajectories while learning unknown, iteration‑invariant signals or parameters. However, conventional adaptive ILC faces two challenges: (i) the coupling between rotational and translational dynamics complicates the design of the two coordinated learning loops for position and attitude, and (ii) standard adaptive ILC designs cannot guarantee bounded control inputs. To address these issues, we propose a dual‑number‑based, segment‑based two‑loop adaptive ILC framework for simultaneous high‑precision position and attitude tracking. The framework employs two learning loops that interact through a dual‑number representation of tracking errors, combining position and attitude errors into a single mathematical object for unified control design. A segment‑based dynamic projection mechanism ensures that both parameter estimates and control inputs remain bounded without prior knowledge of uncertainties. Mathematical analysis and numerical simulations demonstrate that the proposed framework significantly enhances tracking performance under unknown but repeatable uncertainties and strong rotational‑translational coupling.

Authors:Anqi Dong, Karl H. Johansson, Johan Karlsson
Title: Temporally Flexible Transport Scheduling on Networks with Departure-Arrival Constriction and Nodal Capacity Limits
Abstract:
We investigate the optimal transport (OT) problem over networks, wherein supply and demand are conceptualized as temporal marginals governing departure rates of particles from source nodes and arrival rates at sink nodes. This setting extends the classical OT framework, where all mass is conventionally assumed to depart at t = 0 and arrive at t = t_f. Our generalization accommodates departures and arrivals at specified times, referred as departure‑‑arrival(DA) constraints. In particular, we impose nodal‑temporal flux constraints at source and sink nodes, characterizing two distinct scenarios: (i) Independent DA constraints, where departure and arrival rates are prescribed independently, and (ii) Coupled DA constraints, where each particle's transportation time span is explicitly specified. We establish that OT with independent DA constraints admits a multi‑marginal optimal transport formulation, while the coupled DA case aligns with the unequal‑dimensional OT framework. For line graphs, we analyze the existence and uniqueness of the solution path. For general graphs, we use a constructive path‑based reduction and optimize over a prescribed set of paths. From a computational perspective, we consider entropic regularization of the original problem to efficiently provide solutions based on multi‑marginal Sinkhorn method, making use of the graphical structure of the cost to further improve scalability. Our numerical simulation further illustrates the linear convergence rate in terms of marginal violation.

Authors:Xu Shang, Masih Haseli, Jorge Cortés, Yang Zheng
Title: On the Existence of Koopman Linear Embeddings for Controlled Nonlinear Systems
Abstract:
Koopman linear representations have become a popular tool for control design of nonlinear systems, yet it remains unclear when such representations are exact. In this paper, we establish sufficient and necessary conditions under which a controlled nonlinear system admits an exact finite‑dimensional Koopman linear representation, which we term Koopman linear embedding. We show that such a system must be transformable into a special control‑affine preserved (CAP) structure, which enforces affine dependence of the state on the control input and isolates all nonlinearities into an autonomous subsystem. We further prove that this autonomous subsystem must itself admit a finite‑dimensional Koopman linear model with a sufficiently‑rich Koopman invariant subspace. Finally, we introduce a symbolic procedure to determine whether a given controlled nonlinear system admits the CAP structure, thereby elucidating whether Koopman approximation errors arise from intrinsic system dynamics or from the choice of lifting functions.

Authors:Jaehan Im, David Fridovich-Keil, Ufuk Topcu
Title: Noncooperative Virtual Queue Coordination via Uncertainty-Aware Correlated Equilibria
Abstract:
Collaborative virtual queueing has been proposed as a mechanism to mitigate airport surface congestion while preserving airline autonomy over aircraft‑level pushback decisions. A central coordinator can regulate aggregate pushback capacity but cannot directly control which specific aircraft are released, limiting its ability to steer system‑level performance. We propose a noncooperative coordination mechanism for collaborative virtual queueing based on the correlated equilibrium concept, which enables the coordinator to provide incentive‑compatible recommendations on aircraft‑level pushback decisions without overriding airline autonomy. To account for uncertainty in airlines' internal cost assessments, we introduce chance constraints into the correlated equilibrium formulation. This formulation provides explicit probabilistic guarantees on incentive compatibility, allowing the coordinator to adjust the confidence level with which airlines are expected to follow the recommended actions. We further propose a scalable algorithm for computing chance‑constrained correlated equilibria by exploiting a reduced‑rank structure. Numerical experiments demonstrate that the proposed method scales to realistic traffic levels up to 210 eligible pushbacks per hour, reduces accumulated delay by up to approximately 8.9% compared to current first‑come‑first‑served schemes, and reveals a trade‑off between confidence level, deviation robustness, and achievable cost efficiency.

Authors:Amit Shivam, Kiran Kumari, Fernando A. C. C. Fontes
Title: Prescribed-Performance-Aware Hybrid-Gain-Based Robust Controller
Abstract:
This paper proposes a prescribed performance function aware hybrid gain finite time sliding mode control framework for a class of nonlinear systems subject to matched disturbances. The hybrid gain structure ensures bounded control effort while retaining finite time convergence, and the incorporation of PPFs enables explicit enforcement of transient performance requirements. Theoretical guarantees are first established for first order systems, characterizing finite time convergence, disturbance rejection, and residual bounds. The approach is then extended to second order dynamics, where a sliding manifold is designed using PPF constraints to facilitate controlled shaping of position and velocity transients. Simulation studies illustrate the proposed design under matched peak control conditions. Comparative results for second‑order systems demonstrate that, while a well tuned non‑PPF hybrid gain controller achieves competitive tracking performance, the PPF‑aware formulation strictly enforces prescribed transient constraints and yields consistent reductions of approximately 9 to 12 percent in integral error and control energy metrics without increasing peak actuation effort.

Authors:Jean Zhu, Shuang Gao
Title: Data-Driven Network LQG Mean Field Games with Heterogeneous Populations via Integral Reinforcement Learning
Abstract:
This paper establishes a data‑driven solution for infinite horizon linear quadratic Gaussian Mean Field Games with network‑coupled heterogeneous agent populations where the dynamics of the agents are unknown. The solution technique relies on Integral Reinforcement Learning and Kleinman's iteration for solving algebraic Riccati equations (ARE). The resulting algorithm uses trajectory data to generate network‑coupled MFG strategies for agents and does not require parameters of agents' dynamics. Under technical conditions on the persistency of excitation and on the existence of unique stabilizing solution to the corresponding AREs, the learned network‑coupled MFG strategies are shown to converge to their true values.

Authors:Maria Conceição, António Grilo, Meysam Basiri
Title: Path Planning Optimisation for SParse, AwaRe and Cooperative Networked Aerial Robot Teams (SpArC-NARTs): Optimisation Tool and Ground Sensing Coverage Use Cases
Abstract:
A networked aerial robot team (NART) comprises a group of agents (e.g., unmanned aerial vehicles (UAVs), ground control stations, etc.) interconnected by wireless links. Inter‑agent connectivity, even if intermittent (i.e. sparse), enables data exchanges between agents and supports cooperative behaviours in several NART missions. It can benefit online decentralised decision‑making and group resilience, particularly when prior knowledge is inaccurate or incomplete. These requirements can be accounted for in the offline mission planning stages to incentivise cooperative behaviours and improve mission efficiency during the NART deployment. This paper proposes a novel path planning tool for a Sparse, Aware, and Cooperative Networked Aerial Robot Team (SpArC‑NART) in exploration missions. It simultaneously considers different levels of prior information regarding the environment, limited agent energy, sensing, and communication, as well as distinct NART constitutions. The communication model takes into account the limitations of user‑defined radio technology and physical phenomena. The proposed tool aims to maximise the mission goals (e.g., finding one or multiple targets, covering the full area of the environment, etc.), while cooperating with other agents to reduce agent reporting times, increase their global situational awareness (e.g., their knowledge of the environment), and facilitate mission replanning, if required. The developed cooperation mechanism leverages soft‑motion constraints and dynamic rewards based on the Value of Movement and the expected communication availability between the agents at each time step. A ground sensing coverage use case was chosen to illustrate the current capabilities of this tool.

Authors:Antonio Franchi
Title: Muscle Coactivation in the Sky: Geometry and Pareto Optimality of Energy vs. Aerodynamic Promptness and Multirotors as Variable Stiffness Actuators
Abstract:
In robotics and biomechanics, trading metabolic cost for kinematic readiness is a well‑established principle. This paper formalizes this concept for aerial multirotors through the introduction of aerodynamic promptness ‑‑ a dynamic metric analogous to dynamic manipulability in robotics. By formulating redundancy resolution as a geometric multi‑objective optimization along task fibers, we rigorously characterize the topological trade‑off between energy consumption and promptness. We demonstrate that this interplay is fundamentally governed by fiber geometry. Cooperative actuation regime yields compact fibers with bounded, compatible Pareto fronts. Conversely, antagonistic actuation regime unlocks unbounded fibers, enabling aerodynamic co‑contraction that drives promptness to hardware limits at the expense of flight endurance. We establish a structural isomorphism between aerodynamic co‑contraction and biologically inspired variable stiffness actuators, introducing a dynamic ``flying muscle'' paradigm. Ultimately, this framework transitions multirotor allocation from heuristic energy minimization to principled, geometry‑aware Pareto navigation, laying foundational theory for the design and control of highly agile aerial platforms.

Authors:Xinan Rong, Changhuang Wan, Aochen He, Xiaolong Li, Gangshan Jing
Title: Rigidity-Based Multi-Finger Coordination for Precise In-Hand Manipulation of Force-Sensitive Objects
Abstract:
Precise in‑hand manipulation of force‑sensitive objects typically requires judicious coordinated force planning as well as accurate contact force feedback and control. Unlike multi‑arm platforms with gripper end effectors, multi‑fingered hands rely solely on fingertip point contacts and are not able to apply pull forces, therefore poses a more challenging problem. Furthermore, calibrated torque sensors are lacking in most commercial dexterous hands, adding to the difficulty. To address these challenges, we propose a dual‑layer framework for multi‑finger coordination, enabling high‑precision manipulation of force‑sensitive objects through joint control without tactile feedback. This approach solves coordinated contact force planning by incorporating graph rigidity and force closure constraints. By employing a force‑to‑position mapping, the planned force trajectory is converted to a joint trajectory. We validate the framework on a custom dexterous hand, demonstrating the capability to manipulate fragile objects‑including a soft yarn, a plastic cup, and a raw egg‑with high precision and safety.

Authors:Jan-Hendrik Ewering, Max Bartholdt, Simon F. G. Ehlers, Niklas Wahlström, Thomas B. Schön, Thomas Seel
Title: Simultaneous State Estimation and Online Model Learning in a Soft Robotic System
Abstract:
Operating complex real‑world systems, such as soft robots, can benefit from precise predictive control schemes that require accurate state and model knowledge. This knowledge is typically not available in practical settings and must be inferred from noisy measurements. In particular, it is challenging to simultaneously estimate unknown states and learn a model online from sequentially arriving measurements. In this paper, we show how a recently proposed gray‑box system identification tool enables the estimation of a soft robot's current pose while at the same time learning a bending stiffness model. For estimation and learning, we only need a nominal constant‑curvature robot model and measurements of the robot's base reactions (e.g., base forces). The estimation scheme ‑‑ relying on a marginalized particle filter ‑‑ allows us to conveniently interface nominal constant‑curvature equations with a Gaussian Process (GP) bending stiffness model to be learned. This, in contrast to estimation via a random walk over stiffness values, enables prediction of bending stiffness and improves overall model quality. We demonstrate, using a real‑world soft robot, that the method learns a bending‑stiffness model online while accurately estimating the robot's pose. Notably, reduced error in multi‑step forward predictions indicates that the learned bending‑stiffness GP improves overall model quality.

Authors:Xiaojie Li, Xunyuan Yin
Title: Learning-based data-enabled moving horizon estimation with application to membrane-based biological wastewater treatment process
Abstract:
In this paper, we propose a data‑enabled moving horizon estimation (MHE) approach for a class of nonlinear systems without explicit modeling, by leveraging Koopman operator theory and Willems fundamental lemma. Specifically, the nonlinear system is lifted to a linear parameter‑varying Koopman surrogate, in which the lifting functions and scheduling mappings are learned directly from data using neural networks. Willems fundamental lemma is then employed to construct a trajectory‑based representation of the Koopman surrogate, which bypasses the explicit identification of the matrices of the Koopman surrogate. Based on this representation, we formulate a convex data‑enabled MHE design, which provides real‑time estimates of the Koopman surrogate states, from which the states of the original nonlinear system are reconstructed. Sufficient conditions are derived to ensure the stability of the estimation error. The effectiveness of the proposed method is illustrated using a simulated membrane‑based biological wastewater treatment process.

Authors:Naoki Hashima, Hikaru Hoshino, Luis David Pabón Ospina, Eiko Furutani
Title: Probabilistic Reachability Analysis of Multi-scale Voltage Dynamics Using Reinforcement Learning
Abstract:
Voltage stability in modern power systems involves coupled dynamics across multiple time scales. Conventional methods based on time‑scale separation or static stability margins may overlook instabilities caused by the coupling of slow and fast transients. Uncertainty in operating conditions further complicates stability assessment, and high computational cost of Monte Carlo simulations limit its applicability to multi‑scale dynamics. This paper presents a deep reinforcement learning‑based framework for probabilistic reachability analysis of multi‑scale voltage dynamics. By formulating each instability mechanism as a distinct absorbing state and introducing a multi‑critic architecture for mechanism‑specific learning, the proposed method enables consistent learning of risk probabilities associated with multiple instability types within a unified framework. The approach is demonstrated on a four‑bus system with load tap changers and over‑excitation limiters, illustrating effectiveness of the proposed learning‑based reachability analysis in identifying and quantifying the mechanisms leading to voltage collapse.

Authors:Mal Fazliu, Matthew Coombes, Sen Wang, Cunjia Liu
Title: XIT: Exploration and Exploitation Informed Trees for Active Gas Distribution Mapping in Unknown Environments
Abstract:
Mobile robotic gas distribution mapping (GDM) provides critical situational awareness during emergency responses to hazardous gas releases. However, most systems still rely on teleoperation, limiting scalability and response speed. Autonomous active GDM is challenging in unknown and cluttered environments, because the robot must simultaneously explore traversable space, map the environment, and infer the gas distribution belief from sparse chemical measurements. We address this by formulating active GDM as a next‑best‑trajectory informative path planning (IPP) problem and propose XIT (Exploration‑Exploitation Informed Trees), a sampling‑based planner that balances exploration and exploitation by generating concurrent trajectories toward exploration‑rich goals while collecting informative gas measurements en route. XIT draws batches of samples from an Upper Confidence Bound (UCB) information field derived from the current gas posterior and expands trees using a cost that trades off travel effort against gas concentration and uncertainty. To enable plume‑aware exploration, we introduce the gas frontier concept, defined as unobserved regions adjacent to high gas concentrations, and propose the Wavefront Gas Frontier Detection (WGFD) algorithm for their identification. High‑fidelity simulations and real‑world experiments demonstrate the benefits of XIT in terms of GDM quality and efficiency. Although developed for active GDM, XIT is readily applicable to other robotic information‑gathering tasks in unknown environments that face the exploration and exploitation trade‑off.

Authors:Yaoyu Li, Chaosheng Huang, Jun Li
Title: SPLIT: Sparse Incremental Learning of Error Dynamics for Control-Oriented Modeling in Autonomous Vehicles
Abstract:
Accurate, computationally efficient, and adaptive vehicle models are essential for autonomous vehicle control. Hybrid models that combine a nominal model with a Gaussian Process (GP)‑based residual model have emerged as a promising approach. However, the GP‑based residual model suffers from the curse of dimensionality, high evaluation complexity, and the inefficiency of online learning, which impede the deployment in real‑time vehicle controllers. To address these challenges, we propose SPLIT, a sparse incremental learning framework for control‑oriented vehicle dynamics modeling. SPLIT integrates three key innovations: (i) Model Decomposition. We decompose the vehicle model into invariant elements calibrated by experiments, and variant elements compensated by the residual model to reduce feature dimensionality. (ii) Local Incremental Learning. We define the valid region in the feature space and partition it into subregions, enabling efficient online learning from streaming data. (iii) GP Sparsification. We use bayesian committee machine to ensure scalable online evaluation. Integrated into model‑based controllers, SPLIT is evaluated in aggressive simulations and real‑vehicle experiments. Results demonstrate that SPLIT improves model accuracy and control performance online. Moreover, it enables rapid adaptation to vehicle dynamics deviations and exhibits robust generalization to previously unseen scenarios.

Authors:Iasson Karafyllis, Miroslav Krstic
Title: On the Existence of Periodic Solutions with Applications to Extremum-Seeking
Abstract:
This paper provides two results that are useful in the study of the existence and the stability properties of a periodic solution for a given dynamical system. The first result deals with scalar time‑periodic systems and establishes the equivalence of the existence of a periodic solution and the existence of a bounded solution. The second result provides sufficient conditions for the existence and the stability of a periodic solution for a time‑periodic dynamical system. Both results are applied to extremum seeking problems for a static output map with no plant dynamics and novel non‑local results are provided without the use of averaging theorems and singular perturbation arguments.

Authors:Henrique O. Caetano, Rahul K. Gupta, Cristhian G. da R. de Oliveira, João B. A. London, Carlos Dias Maciel
Title: Bayesian Model-based Generation of Synthetic Unbalanced Distribution Networks Incorporating Reliability Indices
Abstract:
Real‑world power distribution data are often inaccessible due to privacy and security concerns, highlighting the need for tools for generating realistic synthetic networks. Existing methods typically overlook critical reliability metrics such as the Customer Average Interruption Frequency Index (CAIFI) and the Customer Average Interruption Duration Index (CAIDI). Moreover, these methods often neglect phase consistency during the design stage, necessitating the use of a separate phase assignment algorithm. This work proposes a Bayesian Hierarchical Model (BHM) that generates phase‑consistent unbalanced three‑phase distribution systems, and incorporates reliability indices. The BHM learns the joint distribution of phase configuration, power demand, and reliability indices from a reference network, conditioning these attributes on topological features. We apply the proposed methodology to generate synthetic power distribution networks in Brazil, and validated it on known Brazilian networks. The results show that the BHM accurately reproduces the distributions of phase allocation, power demand, and reliability metrics on the training system. Furthermore, in out‑of‑sample validation on unseen data, the model generates phase‑consistent networks and accurately predicts the reliability indices for the synthetic systems. The generated networks are also electrically feasible: three‑phase power flows converge and voltages remain within typical operating limits, enabling studies of planning, reliability, and resilience.

Authors:Ziyi Zhang, Xiyu Deng, Guannan Qu, Yorie Nakahira
Title: UAVGENT: A Language-Guided Distributed Control Framework
Abstract:
We study language‑in‑the‑loop control for multi‑drone systems that execute evolving, high‑level missions while retaining formal robustness guarantees at the physical layer. We propose a three‑layer architecture in which (i) a human operator issues natural‑language instructions, (ii) an LLM‑based supervisor periodically interprets, verifies, and corrects the commanded task in the context of the latest state and target estimates, and (iii) a distributed inner‑loop controller tracks the resulting reference using only local relative information. We derive a theoretical guarantee that characterizes tracking performance under bounded disturbances and piecewise‑smooth references with discrete jumps induced by LLM updates. Overall, our results illustrate how centralized language‑based task reasoning can be combined with distributed feedback control to achieve complex behaviors with provable robustness and stability.

Authors:Sean Bowerfind, Matthew R. Kirchner, Gary Hewer
Title: A Data-Driven Algorithm for Model-Free Control Synthesis
Abstract:
Presented is an algorithm to synthesize the optimal infinite‑horizon LQR feedback controller for continuous‑time systems. The algorithm does not require knowledge of the system dynamics but instead uses only a finite‑length sampling of arbitrary input‑output data. The algorithm is based on a constrained optimization problem that enforces a necessary condition on the dynamics of the optimal value function along any trajectory. In addition to calculating the standard LQR gain matrix, a feedforward gain can be found to implement a reference tracking controller. This paper presents a theoretical justification for the method and shows several examples, including a validation test on a real scale aircraft.

Authors:Ruiqi Wang, Yiming Yang, Atif Shamim
Title: 3-D Reconfigurable Intelligent Surface: From Reflection to Transmission and From Single Hemisphere to Full 3-D Coverage
Abstract:
Reconfigurable intelligent surfaces (RIS) are conventionally implemented as two‑dimensional (2D) electromagnetic (EM) structures to steer incident waves toward desired reflection angles. This approach limits the reflection to a single hemisphere, and the beam‑scanning range is relatively small. In this work, a novel three‑dimensional (3D) RIS concept is proposed, where beam‑scanning can be realized not only through reflection from the illuminated surface but also through controlled transmission toward adjacent surfaces, enabling near blind‑spot‑free coverage in the full 3D spatial domain. A cube‑based 3D‑RIS design operating at millimeter‑wave (mm‑Wave) frequencies and consisting of six interconnected RIS surfaces is presented. Each surface integrates reconfigurable receiving and reflecting arrays with orthogonal polarizations to ensure intrinsic EM isolation, while a reconfigurable feeding network supports dynamic operation. A subarray‑based synthesis approach with binary amplitude gating and predefined phase offsets is developed through a unified theoretical model. This model, validated through full‑wave simulations, enables efficient beam switching through a shared aperture. Based on this framework, an 8 x 12 element surface comprising six 4 x 4 subarrays is designed, with each surface covering an angular range from ‑30 deg to +30 deg. The experimental prototype has been characterized in the 24 to 30 GHz band, and the results demonstrate a gain enhancement of 14.7 dB for reflection, while 14.1 dB is achieved for transmission to the neighboring surface. Finally, wireless communication trials using the Pluto software‑defined radio platform combined with frequency up/down converters confirm improved constellation quality and a 6‑7 dB improvement in error vector magnitude (EVM) for both reflection and neighboring surface transmission scenarios.

Authors:J. H. Hoekstra, B. Györök, R. Töth, M. Schoukens
Title: Encoder initialisation methods in the model augmentation setting
Abstract:
Nonlinear system identification (NL‑SI) has proven to be effective in obtaining accurate models for highly complex systems. Recent encoder‑based methods for artificial neural network state‑space (ANN‑SS) models have shown state‑of‑the‑art performance with improved computational efficiency, where the encoder is used to estimate the initial state allowing for batch optimisation methods. To address the lack of interpretability of these black‑box ANN models, model augmentation approaches can be used. These combine prior available baseline models with the ANN learning components, resulting in faster convergence and more interpretable models. The combination of the encoder‑based method with model augmentation has shown potential. Thus far, however, the encoder has still been treated as a black‑box function in the overall estimation process, while additional information in the form of the baseline model is available to predict the model state from past input‑output data. In this paper, we propose novel encoder initialisation approaches based on the available baseline model, resulting in improved noise robustness and faster convergence compared to black‑box initialisation. The performance of these initialisation methods is demonstrated on a mass‑spring‑damper system.

Authors:E. Javier Olucha, Arash Sadeghzadeh, Amritam Das, Roland Tóth
Title: Bayesian Optimization Based Grid Point Allocation for LPV and Robust Control
Abstract:
This paper investigates systematic selection of optimal grid points for grid‑based Linear Parameter‑Varying (LPV) and robust controller synthesis. In both settings, the objective is to identify a set of local models such that the controller synthesized for these local models will satisfy global stability and performance requirements for the entire system. Here, local models correspond to evaluations of the LPV or uncertain plant at fixed values of the scheduling signal or realizations of the uncertainty set, respectively. Then, Bayesian optimization is employed to discover the most informative points that govern the closed‑loop performance of the designed LPV or robust controller for the complete system until no significant further performance increase or a user specified limit is reached. Furthermore, when local model evaluations are computationally demanding or difficult to obtain, the proposed method is capable to minimize the number of evaluations and adjust the overall computational cost to the available budget. Lastly, the capabilities of the proposed method in automatically obtaining a sufficiently informative grid set are demonstrated on three case‑studies: a robust controller design for an unbalanced disk, a multi‑objective robust attitude controller design for a satellite with uncertain parameters and two flexible rotating solar arrays, and an LPV controller design for a robotic arm.

Authors:Luisa Schuhmacher, Hazem Sallouha, Ihsane Gryech, Sofie Pollin
Title: Data Augmentation and Attention for massive MIMO-based Indoor Localization in Changing Environments
Abstract:
The demand for high‑precision indoor localization has grown significantly with the rise of smart environments, industrial automation, and location‑aware applications. While massive Multiple‑Input and Multiple‑Output (MIMO) systems enable millimeter‑level accuracy by leveraging rich Channel State Information (CSI), most existing solutions are optimized for static environments, where users or devices remain fixed during data collection and inference. Real‑world applications, however, often require real‑time localization in changing environments, where rapid movement, unpredictable blockages, and dynamic channel conditions pose significant challenges. To address these challenges, we introduce two data augmentation techniques designed to resemble blocked antennas, enhancing the generalizability of localization models to dynamic scenarios. Additionally, we enhance an existing Deep Learning (DL) model by incorporating attention modules, improving its ability to focus on relevant channel features and antennas. We train our model on data from a static scenario, augmented with the proposed techniques, and evaluate it on a dataset collected in changing scenarios. We investigate the performance enhancements achieved by the data augmentation techniques and the Attention modules, and observe a localization accuracy improvement from a mean error of 286 mm, when trained without Attention and without data augmentations, to 66 mm, when trained with Attention and data augmentation. This shows that high localization accuracy can be maintained in changing environments, even without training data from those scenarios.

Authors:Zilin Ge, Ozan Alp Topal, Irshad Ahmad Meer, Pei Xiao, Cicek Cavdar
Title: EARL: Energy-Aware Adaptive Antenna Control with Reinforcement Learning in O-RAN Cell-Free Massive MIMO Networks
Abstract:
Cell‑free massive multi‑input multi‑output (MIMO) promises uniform high performance across the network, but also brings a high energy cost due to joint transmission from distributed radio units (RUs) and centralized processing in the cloud. Leveraging the resource‑sharing capabilities of Open Radio Access Network (O‑RAN), we propose EARL, an energy‑aware adaptive antenna control framework based on reinforcement learning. EARL dynamically configures antenna elements in RUs to minimize radio, optical fronthaul, and cloud processing power consumption while meeting user spectral efficiency demands. Numerical results show power savings of up to 81% and 50% over full‑on and heuristic baselines, respectively. The RL‑based approach operates within 220 ms, satisfying O‑RAN's near‑real‑time limit, and a greedy refinement further halves power consumption at a 2 s runtime.

Authors:Houssem Eddine Mohamadi, Nadjia Kara
Title: SKYSURF: A Self-learning Framework for Persistent Surveillance using Cooperative Aerial Gliders
Abstract:
The success of surveillance applications involving small unmanned aerial vehicles (UAVs) depends on how long the limited on‑board power would persist. To cope with this challenge, alternative renewable sources of lift are sought. One promising solution is to extract energy from rising masses of buoyant air. This paper proposes a local‑global behavioral management and decision‑making approach for the autonomous deployment of soaring‑capable UAVs. The cooperative UAVs are modeled as non‑deterministic finite state‑based rational agents. In addition to a mission planning module for assigning tasks and issuing dynamic navigation waypoints for a new path planning scheme, in which the concepts of visibility and prediction are applied to avoid the collisions. Moreover, a delayed learning and tuning strategy is employed optimize the gains of the path tracking controller. Rigorous comparative analyses carried out with three benchmarking baselines and 15 evolutionary algorithms highlight the adequacy of the proposed approach for maintaining the surveillance persistency (staying aloft for longer periods without landing) and maximizing the detection of targets (two times better than non‑cooperative and semi‑cooperative approaches) with less power consumption (almost 6% of battery consumed in six hours).

Authors:Huang Zhenyu, Yuan Zhao
Title: Empirical Validation of a Dual-Defense Mechanism Reshaping Wholesale Electricity Price Dynamics in Singapore
Abstract:
While ex‑ante screening and static price caps are global standards for mitigating price volatility, Singapore's electricity market employs a unique dual‑defense mechanism integrating vesting contracts (VC) with a temporary price cap (TPC). Using high‑frequency data from 2021 to 2024, this paper evaluates this mechanism and yields three primary findings. First, a structural trade‑off exists within the VC framework: while VC quantity (VCQ) suppresses average prices, it paradoxically exacerbates instability via liquidity squeezes. Conversely, VC price (VCP) functions as a tail‑risk anchor, dominating at extreme quantiles where VCQ efficacy wanes. Second, a structural break around the 2023 reform reveals a fundamental re‑mapping of price dynamics; the previously positive pass‑through from offer ratios to clearing prices was largely neutralized post‑reform. Furthermore, diagnostics near the TPC threshold show no systematic evidence of strategic bid shading, confirming the TPC's operational integrity. Third, the dual‑defense mechanism exhibits a critical synergy that resolves the volatility trade‑off. The TPC reverses the volatility penalty of high VCQ, shifting the elasticity of conditional volatility from a destabilizing 0.636 to a stabilizing ‑0.213. This synergy enables the framework to enhance tail‑risk control while eliminating liquidity‑related stability costs. We conclude that this dual‑defense mechanism successfully decouples price suppression from liquidity risks, thereby maximizing market stability.

Authors:Volodymyr Sydorskyi
Title: Lung nodule classification on CT scan patches using 3D convolutional neural networks
Abstract:
Lung cancer remains one of the most common and deadliest forms of cancer worldwide. The likelihood of successful treatment depends strongly on the stage at which the disease is diagnosed. Therefore, early detection of lung cancer represents a critical medical challenge. However, this task poses significant difficulties for thoracic radiologists due to the large number of studies to review, the presence of multiple nodules within the lungs, and the small size of many nodules, which complicates visual assessment. Consequently, the development of automated systems that incorporate highly accurate and computationally efficient lung nodule detection and classification modules is essential. This study introduces three methodological improvements for lung nodule classification: (1) an advanced CT scan cropping strategy that focuses the model on the target nodule while reducing computational cost; (2) target filtering techniques for removing noisy labels; (3) novel augmentation methods to improve model robustness. The integration of these techniques enables the development of a robust classification subsystem within a comprehensive Clinical Decision Support System for lung cancer detection, capable of operating across diverse acquisition protocols, scanner types, and upstream models (segmentation or detection). The multiclass model achieved a Macro ROC AUC of 0.9176 and a Macro F1‑score of 0.7658, while the binary model reached a Binary ROC AUC of 0.9383 and a Binary F1‑score of 0.8668 on the LIDC‑IDRI dataset. These results outperform several previously reported approaches and demonstrate state‑of‑the‑art performance for this task.

Authors:Umair Zulfiqar
Title: From Data $H(jω_i)$ to Balanced Truncation Family: A Projection-based Non-intrusive Approach
Abstract:
This paper presents data‑driven implementations of balanced truncation and several of its generalizations that rely exclusively on transfer function samples on the imaginary axis. Rather than implicitly approximating the Gramians via numerical quadrature, the proposed approach approximates them implicitly through projection. This enables multiple members of the balanced truncation family to be implemented non‑intrusively using practically measurable data, without requiring spectral factorizations. Using this projection‑based framework, data‑driven implementations are developed for standard balanced truncation, frequency‑limited balanced truncation, time‑limited balanced truncation, self‑weighted balanced truncation, LQG balanced truncation, H‑infinity balanced truncation, positive‑real balanced truncation, bounded‑real balanced truncation, and stochastic balanced truncation. Numerical results demonstrate that the proposed non‑intrusive implementations achieve performance comparable to their intrusive counterparts and accurately capture the dominant Hankel singular values.

Authors:Gaoxin Zhang, Ruixing Ren, Junhui Zhao, Xiaoke Sun
Title: Dual-Channel Feature Fusion for Joint Prediction in Dynamic Signed Weighted Networks
Abstract:
Link prediction is central to unraveling social network evolution and node relationships, as well as understanding the characteristic mechanisms of complex networks. Currently, research on link prediction for complex dynamic networks integrating temporal evolution, relational polarity and edge weight information remains significantly underexplored, failing to meet practical demands. For dynamic signed‑weighted networks, this paper proposes a tripartite joint prediction framework for unified forecasting of links, signs and weights. First, the dynamic network is decomposed into temporal snapshots, and node semantic embeddings are generated via sign‑aware weighted random walks. We then design multi‑hop structural balance and temporal difference features to capture the structural characteristics and dynamic evolution laws of the network, respectively. The model adopts a dual‑channel feature decoupling mechanism: node semantic embeddings are used for link existence prediction, while relational sign features are fed into a Transformer encoder to model temporal dependencies. Finally, prediction results are output synergistically through a multi‑task unit. Simulation experiments demonstrate that, compared with baseline methods, the proposed framework achieves an average 2%‑4% improvement in the performance of link existence and relational sign prediction, and a significant 40%‑50% reduction in edge weight prediction error.

Authors:Sunandan Adhikary, Soumyajit Dey
Title: Safe Controller Synthesis Using Lyapunov-based Barriers for Linear Hybrid Systems with Simplex Architecture
Abstract:
Modern cyber‑physical systems often have a two‑layered design, where the primary controller is AI‑enabled or an analytical controller optimising some specific cost function. If the resulting control action is perceived as unsafe, a secondary safety‑focused backup controller is activated. The existing backup controller design schemes do not consider a real‑time deadline for the course correction of a potentially unsafe system trajectory or constrain maximisation of the safe operating region as a synthesis criterion. This essentially implies an eventual safety guarantee over a small operating region. This paper proposes a novel design method for backup safe controllers (BSCs) that ensure invariance across the largest possible region in the safe state space, along with a guarantee for timely recovery when the system states deviate from their usual behaviour. This is the first work to synthesise safe controllers that ensure maximal safety and timely recovery while aiming at minimal resource usage by switching between BSCs with different execution rates. An online safe controller activation policy is also proposed to switch between BSCs (and the primary optimal controller) to optimise processing bandwidth for control computation. To establish the efficacy of the proposed method, we evaluate the safety and recovery time of the proposed safe controllers, as well as the activation policy, in closed loops with linear hybrid dynamical systems under budgeted bandwidth.

Authors:Kaizer Rahaman, Jyotirmoy V. Deshmukh, Ashish R. Hota, Lars Lindemann
Title: When Environments Shift: Safe Planning with Generative Priors and Robust Conformal Prediction
Abstract:
Autonomous systems operate in environments that may change over time. An example is the control of a self‑driving vehicle among pedestrians and human‑controlled vehicles whose behavior may change based on factors such as traffic density, road visibility, and social norms. Therefore, the environment encountered during deployment rarely mirrors the environment and data encountered during training ‑‑ a phenomenon known as distribution shift ‑‑ which can undermine the safety of autonomous systems. Conformal prediction (CP) has recently been used along with data from the training environment to provide prediction regions that capture the behavior of the environment with a desired probability. When embedded within a model predictive controller (MPC), one can provide probabilistic safety guarantees, but only when the deployment and training environments coincide. Once a distribution shift occurs, these guarantees collapse. We propose a planning framework that is robust under distribution shifts by: (i) assuming that the underlying data distribution of the environment is parameterized by a nuisance parameter, i.e., an observable, interpretable quantity such as traffic density, (ii) training a conditional diffusion model that captures distribution shifts as a function of the nuisance parameter, (iii) observing the nuisance parameter online and generating cheap, synthetic data from the diffusion model for the observed nuisance parameter, and (iv) designing an MPC that embeds CP regions constructed from such synthetic data. Importantly, we account for discrepancies between the underlying data distribution and the diffusion model by using robust CP. Thus, the plans computed using robust CP enjoy probabilistic safety guarantees, in contrast with plans obtained from a single, static set of training data. We empirically demonstrate safety under diverse distribution shifts in the ORCA simulator.

Authors:Ziyan Lin, Liang Xu
Title: Curvature-Guided Safety Filters: State-Dependent Hessian-Weighted Projection with Provable Performance Bounds
Abstract:
Safety filters provide a lightweight mechanism for enforcing state and input safety in learning‑enabled control. However, common Euclidean projections onto the safe set disregard long‑term performance, while directly optimizing the action‑value function within the safe set can be nonconvex and computationally prohibitive. This paper proposes a state‑dependent, Hessian‑guided projection for safety filtering that preserves convexity while improving performance. The key idea is to select a weighted projection matrix from the curvature of the action‑value function, thereby biasing the correction toward action directions with higher value sensitivity. We establish (i) a uniform bound on the performance gap between the weighted projection and the safe value‑optimal action, and (ii) a condition under which the weighted projection outperforms the Euclidean projection in long‑term value. To support black‑box controllers, we further present a data‑driven construction of the weighted projection matrix via an iterative Q‑function learning algorithm with quadratic feature blocks and regularization that enforces curvature dominance and bounded higher‑order terms. Simulations on a quadrotor tracking‑and‑avoidance task indicate that the proposed filter maintains safety while reducing value degradation relative to Euclidean projection, with computational overhead compatible with real‑time operation.

Authors:Jiawei Zhang, Gregor Verbic, Frederik Geth, Mohsen Aldaadi, Rahmat Heidari, Julio Braslavsky
Title: Dynamic Network Prices for Prosumer-aware Hosting Capacity Management
Abstract:
The fast uptake of distributed energy resources (DERs) presents increasing challenges for managing hosting capacity in distribution networks. Existing solutions include direct load control, operating envelopes, and price‑based control through dynamic energy prices. Despite their effectiveness, these methods often rely on assumed prosumer behavioural patterns and overlook prosumers' desire to retain control over their devices. Additionally, current fixed or Time‑of‑Use (ToU) prices are based on spatial and temporal averages, having limited impact on network conditions and DER operation. To address these limitations, this paper proposes a bilevel optimisation framework that explicitly models prosumer decision‑making in the design of dynamic network prices. The upper level represents the distribution system operator (DSO), setting network prices under cost‑recovery and network constraints, while the lower level models prosumers optimising DER operation in response. The proposed framework preserves customer prerogative, enhances DER flexibility, and offers actionable insights for network hosting capacity management and the evolution of network tariff structures under high DER penetration.

Authors:Bikram Panthee, Haoming Yang, Corey D. Harper, Amritanshu Pandey
Title: Grid-ECO: Grid Aware Electric Vehicle Charging Stations Placement Optimizer
Abstract:
The paper develops a methodology, Grid‑ECO, to optimally allocate electric vehicle charging stations (EVCS) within a distribution feeder, while considering EV charging demand at census‑level granularity. The underlying problem is NP‑hard and requires satisfying nonlinear, nonconvex, three‑phase unbalanced AC network constraints while including integer decision variables. Existing works cannot guarantee AC feasibility nor optimality of this problem without either i) relaxing the integer decision variable space or ii) convexifying AC constraints. Proposed Grid‑ECO exactly solves the underlying mixed‑integer nonlinear program (MINLP) to near‑zero optimality gap while prioritizing candidate locations based on grid voltage and current sensitivities. To solve the MINLP exactly, Grid‑ECO exactly reformulates it into mixed‑integer bilinear program (MIBLP), enabling global optimization using the spatial branch‑and‑bound algorithm (sBnB). To ensure computational tractability for large‑scale feeders, we develop and include a novel presolving strategy based on Sequential Bound Tightening (SBT) with variable filtering and decomposition. Case studies demonstrate that Grid‑ECO outperforms the off‑the‑shelf Gurobi sBnB solver by solving cases where no feasible solution is found within 167 hours. When feasible solution is found by off‑the‑shelf solver, Grid‑ECO reduces solution time by up to 73% and sBnB node exploration by up to 97%, while achieving a 0% optimality gap and guaranteed AC feasibility.

Authors:Jonathan E. Swindell, David W. Cox, Rebekah Edwards, Emma Lever, Adam C. Goad, Austin Egbert, Charles Baylis, Robert J. Marks
Title: Implementation of a Directional Modulation Testbed for Reconfigurable Transmitters for Spatially Agile MIMO Systems
Abstract:
This paper demonstrates the implementation and validation of a microwave testbed for directionally modulated transmission. Directional modulation enables multiple communication and/or radar signals to be transmitted in multiple directions simultaneously using a single phased array aperture, helping to relieve spectral congestion. A two‑element transmitter array is driven by a Xilinx ZCU208 Radio Frequency System on a Chip (RFSoC). Our testbed provides a foundation for developing a fully reconfigurable array transmitter for multi‑user multiple‑input multiple‑output (MU‑MIMO) radar and communications, which will incorporate in‑situ measurement, reconfigurable matching circuitry, and fast tuning algorithms for frequency and directional selectivity. This testbed enables development and validation of reconfigurable techniques for adaptive spectral and spatial coexistence.

Authors:Mohammed Adib Oumer, Vishnu Murali, Majid Zamani
Title: Interpolation-Inspired Closure Certificates
Abstract:
Barrier certificates, a form of state invariants, provide an automated approach to the verification of the safety of dynamical systems. Similarly to barrier certificates, recent works explore the notion of closure certificates, a form of transition invariants, to verify dynamical systems against ω‑regular properties including safety. A closure certificate, defined over state pairs of a dynamical system, is a real‑valued function whose zero superlevel set characterizes an inductive transition invariant of the system. The search for such a certificate can be effectively automated by assuming it to be within a specific template class, e.g. a polynomial of a fixed degree, and then using optimization techniques such as sum‑of‑squares (SOS) programming to find it. Unfortunately, one may not be able to find such a certificate for a fixed template. In such a case, one must change the template, e.g. increase the degree of the polynomial. In this paper, we consider a notion of multiple closure certificates dubbed interpolation‑inspired closure certificates. An interpolation‑inspired closure certificate consists of a set of functions which jointly over‑approximate a transition invariant by first considering one‑step transitions, then two, and so on until a transition invariant is obtained. The advantage of interpolation‑inspired closure certificates is that they allow us to prove properties even when a single function for a fixed template cannot be found using standard approaches. We present SOS programming and a scenario program to find these sets of functions and demonstrate the effectiveness of our proposed method to verify persistence and general ω‑regular specifications in some case studies.

Authors:Johannes Mootz, Reza Akhavian
Title: Control Barrier Functions with Audio Risk Awareness for Robot Safe Navigation on Construction Sites
Abstract:
Construction automation increasingly requires autonomous mobile robots, yet robust autonomy remains challenging on construction sites. These environments are dynamic and often visually occluded, which complicates perception and navigation. In this context, valuable information from audio sources remains underutilized in most autonomy stacks. This work presents a control barrier function (CBF)‑based safety filter that provides safety guarantees for obstacle avoidance while adapting safety margins during navigation using an audio‑derived risk cue. The proposed framework augments the CBF with a lightweight, real‑time jackhammer detector based on signal envelope and periodicity. Its output serves as an exogenous risk that is directly enforced in the controller by modulating the barrier function. The approach is evaluated in simulation with two CBF formulations (circular and goal‑aligned elliptical) with a unicycle robot navigating a cluttered construction environment. Results show that the CBF safety filter eliminates safety violations across all trials while reaching the target in 40.2% (circular) vs. 76.5% (elliptical), as the elliptical formulation better avoids deadlock. This integration of audio perception into a CBF‑based controller demonstrates a pathway toward richer multimodal safety reasoning in autonomous robots for safety‑critical and dynamic environments.

Authors:Maojiang Deng, Shoufeng Lu, Jiazhao Shi, Wen Zhang
Title: Adaptive traffic signal control optimization using a novel road partition and multi-channel state representation method
Abstract:
This study proposes a novel adaptive traffic signal control method leveraging a Deep Q‑Network (DQN) and Proximal Policy Optimization (PPO) to optimize signal timing by integrating variable cell length and multi‑channel state representation. A road partition formula consisting of the sum of logarithmic and linear functions was proposed. The state variables are a vector composed of three channels: the number of vehicles, the average speed, and space occupancy. The set of available signal phases constitutes the action space, the selected phase is executed with a fixed green time. The reward function is formulated using the absolute values of key traffic state metrics ‑ waiting time, speed, and fuel consumption. Each metric is normalized by a typical maximum value and assigned a weight that reflects its priority and optimization direction. The simulation results, using Sumo‑TensorFlow‑Python, demonstrate a cross‑range transferability evaluation and show that the proposed variable cell length and multi‑channel state representation method excels compared to fixed cell length in optimization performance.

Authors:Ayrton Almada, Laurent Pagnier, Igal Goldshtein, Saif R. Kazi, Michael, Chertkov
Title: Real-Time Dynamic N-1 Screening: Identifying High-Risk Lines and Transformers After Common Faults
Abstract:
Power system operators routinely perform N‑1 contingency analysis, yet conventional tools provide limited guidance on which lines or transformers deserve heightened attention during fast post‑fault transients. In particular, static screening does not reveal whether (1) the same faulted line repeatedly triggers severe downstream overloads, or (2) a specific transformer emerges as vulnerable across many distinct fault scenarios. This paper introduces a real‑time dynamic N‑1 screening framework that addresses this gap by estimating, for each counterfactual single‑phase transmission fault, the probability of transient overcurrent on critical grid elements. The output is an operator‑facing dashboard that ranks (a) faulted lines whose outages most frequently lead to dangerous transformer overloads, and (b) transformers that consistently overload across top‑risk scenarios, both of which are actionable indicators for real‑time situational awareness. The approach models post‑fault electromechanical dynamics using a linear stochastic formulation of the swing equations with short‑lived, fault‑localized uncertainty, and combines analytic transient evaluation with cross‑entropy based importance sampling to efficiently estimate rare but high‑impact events. All N‑1 contingencies are evaluated in parallel with linear computational complexity. The framework is demonstrated on the IEEE 118‑bus system, where it reveals latent high‑risk lines and transformers that remain invisible under deterministic dynamic or static N‑1 analysis. Results show orders‑of‑magnitude computational speedup relative to brute‑force Monte Carlo, enabling practical deployment within real‑time operational cycles.

Authors:Yuanliang Li, Xun Gong, Reza Iravani, Bo Cao, Heng Liu, Ziming Chen
Title: String-Level Ground Fault Localization for TN-Earthed Three-Phase Photovoltaic Systems
Abstract:
The DC‑side ground fault (GF) poses significant risks to three‑phase TN‑earthed photovoltaic (PV) systems, as the resulting high fault current can directly damage both PV inverters and PV modules. Once a fault occurs, locating the faulty string through manual string‑by‑string inspection is highly time‑consuming and inefficient. This work presents a comprehensive analysis of GF characteristics through fault‑current analysis and a simulation‑based case study covering multiple fault locations. Building on these insights, we propose an edge‑AI‑based GF localization approach tailored for three‑phase TN‑earthed PV systems. A PLECS‑based simulation model that incorporates PV hysteresis effects is developed to generate diverse GF scenarios, from which correlation‑based features are extracted throughout the inverter's four‑stage shutdown sequence. Using the simulated dataset, a lightweight Variational Information Bottleneck (VIB)‑based localization model is designed and trained, achieving over 93% localization accuracy at typical sampling rates with low computational cost, demonstrating strong potential for deployment on resource‑constrained PV inverters.

Authors:Xiaowen Tao, Yinuo Wang, Haitao Ding, Yuanyang Qi, Ziyu Song
Title: Energy-Aware Reinforcement Learning for Robotic Manipulation of Articulated Components in Infrastructure Operation and Maintenance
Abstract:
With the growth of intelligent civil infrastructure and smart cities, operation and maintenance (O&M) increasingly requires safe, efficient, and energy‑conscious robotic manipulation of articulated components, including access doors, service drawers, and pipeline valves. However, existing robotic approaches either focus primarily on grasping or target object‑specific articulated manipulation, and they rarely incorporate explicit actuation energy into multi‑objective optimisation, which limits their scalability and suitability for long‑term deployment in real O&M settings. Therefore, this paper proposes an articulation‑agnostic and energy‑aware reinforcement learning framework for robotic manipulation in intelligent infrastructure O&M. The method combines part‑guided 3D perception, weighted point sampling, and PointNet‑based encoding to obtain a compact geometric representation that generalises across heterogeneous articulated objects. Manipulation is formulated as a Constrained Markov Decision Process (CMDP), in which actuation energy is explicitly modelled and regulated via a Lagrangian‑based constrained Soft Actor‑Critic scheme. The policy is trained end‑to‑end under this CMDP formulation, enabling effective articulated‑object operation while satisfying a long‑horizon energy budget. Experiments on representative O&M tasks demonstrate 16%‑30% reductions in energy consumption, 16%‑32% fewer steps to success, and consistently high success rates, indicating a scalable and sustainable solution for infrastructure O&M manipulation.

Authors:Shunsei Yamagishi, Lei Jing
Title: A Lightweight Cubature Kalman Filter for Attitude and Heading Reference Systems Using Simplified Prediction Equations
Abstract:
Attitude and Heading Reference Systems (AHRSs) are broadly applied wherever reliable orientation and motion sensing is required. In this paper, we present an improved Cubature Kalman Filter (CKF) with lower computational cost while maintaining estimation accuracy, which is named "Kaisoku Cubature Kalman Filter (KCKF)". The computationally efficient equations of the KCKF are derived by simplifying those of the CKF, while preserving equivalent mathematical relations. The lightweight prediction equations in the KCKF are derived by expanding the summation terms in the CKF and simplifying the result. This paper shows that the KCKF requires fewer floating‑point operations (FLOPs) than the CKF. The controlled experimental results show that the KCKF reduces the computation time by approximately 19% compared to the CKF on a high‑performance computer, whereas the KCKF reduces the computation time by approximately 15% compared to the CKF on a low‑cost single‑board computer. In addition, the KCKF maintains the attitude estimation accuracy of the CKF.

Authors:Jacky Kwok, Xilun Zhang, Mengdi Xu, Yuejiang Liu, Azalia Mirhoseini, Chelsea Finn, Marco Pavone
Title: Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment
Abstract:
The long‑standing vision of general‑purpose robots hinges on their ability to understand and act upon natural language instructions. Vision‑Language‑Action (VLA) models have made remarkable progress toward this goal, yet their generated actions can still misalign with the given instructions. In this paper, we investigate test‑time verification as a means to shrink the "intention‑action gap." We first characterize the test‑time scaling laws for embodied instruction following and demonstrate that jointly scaling the number of rephrased instructions and generated actions greatly increases test‑time sample diversity, often recovering correct actions more efficiently than scaling each dimension independently. To capitalize on these scaling laws, we present CoVer, a contrastive verifier for vision‑language‑action alignment, and show that our architecture scales gracefully with additional computational resources and data. We then introduce CoVer‑VLA, a hierarchical test‑time verification pipeline using the trained verifier. At deployment, our framework precomputes a diverse set of rephrased instructions from a Vision‑Language‑Model (VLM), repeatedly generates action candidates for each instruction, and then uses the verifier to select the optimal high‑level prompt and low‑level action chunks. Compared to scaling policy pre‑training on the same data, our verification approach yields 22% gains in‑distribution and 13% out‑of‑distribution on the SIMPLER benchmark, with a further 45% improvement in real‑world experiments. On the PolaRiS benchmark, CoVer‑VLA achieves 14% gains in task progress and 9% in success rate.

Authors:Ambuj Gupta, Balarko Chaudhuri, Mark O'Malley
Title: Equivalent Circuit Modeling of Grid-Forming Inverters in (Sub)-Transient Time-Frame
Abstract:
The widely accepted definition of grid‑forming (GFM) inverter states that it should behave as a (nearly) constant voltage source behind an impedance by maintaining a (nearly) constant internal voltage phasor in the sub‑transient to transient time frame. Some system operators further mandate permissible ranges for this effective impedance. However, these specifications do not clearly define the location of the internal voltage source, and no systematic method exists to quantify its effective impedance for a black‑box GFM model. To address this, we first compare the transient responses of an ideal voltage source and a GFM to show that an idealistic GFM maintains a (nearly) constant voltage across the filter capacitor, rather than at the inverter switches. Then we propose a systematic method to quantify the effective impedance of a GFM from its black‑box model using frequency‑domain admittance plots. Using standard PSCAD GFM models developed by NLR (formerly NREL), we demonstrate that the GFM's equivalent impedance model captures the sub‑transient response and static voltage stability limit accurately. Further, replacing the GFM with the proposed equivalent circuit model in the modified IEEE‑39 bus system is shown to reproduce the small‑signal stability characteristics with reasonable accuracy.

Authors:Anutam Srinivasan, Antoine Leeman, Glen Chou
Title: Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis
Abstract:
We present a novel framework for robust out‑of‑distribution planning and control using conformal prediction (CP) and system level synthesis (SLS), addressing the challenge of ensuring safety and robustness when using learned dynamics models beyond the training data distribution. We first derive high‑confidence model error bounds using weighted CP with a learned, state‑control‑dependent covariance model. These bounds are integrated into an SLS‑based robust nonlinear model predictive control (MPC) formulation, which performs constraint tightening over the prediction horizon via volume‑optimized forward reachable sets. We provide theoretical guarantees on coverage and robustness under distributional drift, and analyze the impact of data density and trajectory tube size on prediction coverage. Empirically, we demonstrate our method on nonlinear systems of increasing complexity, including a 4D car and a 12D quadcopter, improving safety and robustness compared to fixed‑bound and non‑robust baselines, especially outside of the data distribution.

Authors:Tam W. Nguyen
Title: Adaptive Behavioral Predictive Control: State-Free Regulation Without Hankel Weights
Abstract:
This paper presents adaptive behavioral predictive control (ABPC), an indirect adaptive predictive control framework operating on streaming data. An LPV‑‑ARX predictor is identified online via kernel‑‑recursive least squares and used to compute closed‑form predictive control sequences over a finite horizon, avoiding batch Hankel constructions and iterative optimization. Nonlinear kernel dictionaries extend model expressiveness within a behavioral formulation. Numerical studies on Hammerstein and NARX systems demonstrate effective performance when the dictionary aligns with the plant class and highlight conditioning and feature‑selection effects. The paper emphasizes numerical simulation, computational feasibility, and reproducibility.

Authors:Yujing Liu, Xin Zheng, Zhixin Liu, Lei Guo
Title: Gradient-Based Adaptive Prediction and Control for Nonlinear Dynamical Systems
Abstract:
This paper investigates gradient‑based adaptive prediction and control for nonlinear stochastic dynamical systems under a weak convexity condition on the prediction‑based loss. This condition accommodates a broad range of nonlinear models in control and machine learning such as saturation functions, sigmoid, ReLU and tanh activation functions, and standard classification models. Without requiring any persistent excitation of the data, we establish global convergence of the proposed adaptive predictor and derive explicit rates for its asymptotic performance. Furthermore, under a classical nonlinear minimum‑phase condition and with a linear growth bound on the nonlinearities, we establish the convergence rate of the resulting closed‑loop control error. Finally, we demonstrate the effectiveness of the proposed adaptive prediction algorithm on a real‑world judicial sentencing dataset. The adaptive control performance will also be evaluated via a numerical simulation.

Authors:Andrej Stankovski, Blazhe Gjorgiev, James Ciyu Qin, Giovanni Sansavini
Title: A day-ahead market model for power systems: benchmarking and security implications
Abstract:
Power system security assessments, e.g. via cascading outage models, often use operational set‑points based on optimal power flow (OPF) dispatch. However, driven by cost minimization, OPF provides an ideal, albeit unrealistic, clearing of the generating units that disregards the complex interactions among market participants. In addition, existing market modeling tools often utilize economic dispatch and unit commitment to minimize total system costs, often disregarding the profit‑driven behavior of market participants. The security of the system, therefore, may be overestimated. To address this gap, we introduce a social‑welfare‑based day‑ahead market‑clearing model. The security implications are analyzed using Cascades, a model for cascading failure analysis. We apply this model to the IEEE‑118 bus system with three independent control zones. The results show that market dispatch leads to an increase in demand not served (DNS) of up to 80% higher than OPF, highlighting a significant security overestimation. This is especially pronounced in large‑scale cascading events with DNS above 100MW. A key driver is the increased dispatch of storage and gas units, which can place the system in critical operating conditions. Operators can use this information to properly estimate the impact of the market on system security and plan efficient expansion strategies.

Authors:Tianmu Niu, Bing Mao, Xiaoqun Wu, Tingwen Huang
Title: Non-Trivial Consensus on Directed Matrix-Weighted Networks with Cooperative and Antagonistic Interactions
Abstract:
This paper investigates the non‑trivial consensus problem on directed signed matrix‑weighted networks\textemdash a novel convergence state that has remained largely unexplored despite prior studies on bipartite consensus and trivial consensus. Notably, we first prove that for directed signed matrix‑weighted networks, every eigenvalue of the grounded Laplacians has positive real part under certain conditions. This key finding ensures the global asymptotic convergence of systems states to the null spaces of signed matrix‑weighted Laplacians, providing a foundational tool for analyzing dynamics on rooted signed matrix‑weighted networks. To achieve non‑trivial consensus, we propose a systematic approach involving the strategic selection of informed agents, careful design of external signals, and precise determination of coupling terms. Crucially, we derive the lower bounds of the coupling coefficients. Our consensus algorithm operates under milder connectivity conditions, and does not impose restrictions on whether the network is structurally balanced or unbalanced. Moreover, the non‑trivial consensus state can be preset arbitrarily as needed. We also carry out the above analysis for undirected networks, with more relaxed conditions on the coupling coefficients comparing to the directed case. This paper further studies non‑trivial consensus with switching topologies, and propose the necessary condition for the convergence of switching networks. The work in this paper demonstrates that groups with both cooperative and antagonistic multi‑dimensional interactions can achieve consensus, which was previously deemed exclusive to fully cooperative groups.

Authors:Neha Gupta, Hamed Alimohammadi, Mohammad Shojafar, De Mi, Muhammad N. M. Bhutta
Title: Solving the Post-Quantum Control Plane Bottleneck: Energy-Aware Cryptographic Scheduling in Open RAN
Abstract:
The Open Radio Access Network (O‑RAN) offers flexibility and innovation but introduces unique security vulnerabilities, particularly from cryptographically relevant quantum computers. While Post‑Quantum Cryptography (PQC) is the primary scalable defence, its computationally intensive handshakes create a significant bottleneck for the RAN control plane, posing sustainability challenges. This paper proposes an energy‑aware framework to solve this PQC bottleneck, ensuring quantum resilience without sacrificing operational energy efficiency. The system employs an O‑RAN aligned split: a Crypto Policy rApp residing in the Non‑Real‑Time (Non‑RT) RIC defines the strategic security envelope (including PQC suites), while a Security Operations Scheduling (SOS) xApp in the Near‑RT RIC converts these into tactical timing and placement intents. Cryptographic enforcement remains at standards‑compliant endpoints: the Open Fronthaul utilizes Media Access Control Security (MACsec) at the O‑DU/O‑RU, while the xhaul (midhaul and backhaul) utilizes IP Security (IPsec) at tunnel terminators. The SOS xApp reduces PQC overhead by batching non‑urgent handshakes, prioritizing session resumption, and selecting parameters that meet slice SLAs while minimizing joules per secure connection. We evaluate the architecture via a Discrete‑Event Simulation (DES) using 3GPP‑aligned traffic profiles and verified hardware benchmarks from literature. Results show that intelligent scheduling can reduce per‑handshake energy by approximately 60 percent without violating slice latency targets.

Authors:David Hogan, Andrew Doherty, Boon Kien Khoo, Johnson Zhou, Richard Salib, James Stewart, Kiaran Lawson, Alon Loeffler, Brett Kagan
Title: CL API: Real-Time Closed-Loop Interactions with Biological Neural Networks
Abstract:
Biological neural networks (BNNs) are increasingly explored for their rich dynamics, parallelism, and adaptive behavior. Beyond understanding their function as a scientific endeavour, a key focus has been using these biological systems as a novel computing substrate. However, BNNs can only function as reliable information‑processing systems if inputs are delivered in a temporally and structurally consistent manner. In practice, this requires stimulation with precisely controlled structure, microsecond‑scale timing, multi‑channel synchronization, and the ability to observe and respond to neural activity in real‑time. Existing approaches to interacting with BNNs face a fundamental trade‑off: they either depend on low‑level hardware mechanisms, imposing prohibitive complexity for rapid iteration, or they sacrifice temporal and structural control, undermining consistency and reproducibility ‑ particularly in closed‑loop experiments. The Cortical Labs Application Programming Interface (CL API) enables real‑time, sub‑millisecond closed‑loop interactions with BNNs. Taking a contract‑based API design approach, the CL API provides users with precise stimulation semantics, transactional admission, deterministic ordering, and explicit synchronization guarantees. This contract is presented through a declarative Python interface, enabling non‑expert programmers to express complex stimulation and closed‑loop behavior without managing low‑level scheduling or hardware details. Ultimately, the CL API provides an accessible and reproducible foundation for real‑time experimentation with BNNs, supporting both fundamental biological research and emerging neurocomputing applications.

Authors:Yoshiyuki Ohmura, Yasuo Kuniyoshi
Title: Defining causal mechanism in dual process theory and two types of feedback control
Abstract:
Mental events are considered to supervene on physical events. A supervenient event does not change without a corresponding change in the underlying subvenient physical events. Since wholes and their parts exhibit the same supervenience‑subvenience relations, inter‑level causation has been expected to serve as a model for mental causation. We proposed an inter‑level causation mechanism to construct a model of consciousness and an agent's self‑determination. However, a significant gap exists between this mechanism and cognitive functions. Here, we demonstrate how to integrate the inter‑level causation mechanism with the widely known dual‑process theories. We assume that the supervenience level is composed of multiple supervenient functions (i.e., neural networks), and we argue that inter‑level causation can be achieved by controlling the feedback error defined through changing algebraic expressions combining these functions. Using inter‑level causation allows for a dual laws model in which each level possesses its own distinct dynamics. In this framework, the feedback error is determined independently by two processes: (1) the selection of equations combining supervenient functions, and (2) the negative feedback error reduction to satisfy the equations through adjustments of neurons and synapses. We interpret these two independent feedback controls as Type 1 and Type 2 processes in the dual process theories. As a result, theories of consciousness, agency, and dual process theory are unified into a single framework, and the characteristic features of Type 1 and Type 2 processes are naturally derived.

Authors:Yilun Huang, Asal Mehradfar, Salman Avestimehr, Hamidreza Aghasi
Title: EM-Aware Physical Synthesis: Neural Inductor Modeling and Intelligent Placement & Routing for RF Circuits
Abstract:
This paper presents an ML‑driven framework for automated RF physical synthesis that transforms circuit netlists into manufacturable GDSII layouts. While recent ML approaches demonstrate success in topology selection and parameter optimization, they fail to produce manufacturable layouts due to oversimplified component models and lack of routing capabilities. Our framework addresses these limitations through three key innovations: (1) a neural network framework trained on 18,210 inductor geometries with frequency sweeps from 1‑100 GHz, generating 7.5 million training samples, that predicts inductor Q‑factor with less than 2% error and enables fast gradient‑based layout optimization with a 93.77% success rate in producing high‑Q layouts; (2) an intelligent P‑Cell optimizer that reduces layout area while maintaining design‑rule‑check (DRC) compliance; and (3) a complete placement and routing engine with frequency‑dependent EM spacing rules and DRC‑aware synthesis. The neural inductor model demonstrates superior accuracy across 1‑100 GHz, enabling EM‑accurate component synthesis with real‑time inference. The framework successfully generates DRC‑aware GDSII layouts for RF circuits, representing a significant step toward automated RF physical design.

Authors:Zitian Qiu, Yunjie Gu
Title: Power Margin Ratio -- A Large-Signal System Strength Metric for Inverter-Based Resources-Dominated Power Systems
Abstract:
As the growing penetration of inverter‑based resources (IBRs) in modern power systems, the system strength is decreasing. Due to the inherent difference in short‑circuit capacity contributions of synchronous generators and inverters, the short‑circuit ratio is not a one‑size‑fit‑all metric to assess the system strength. Following the distinct dynamic behavior of the IBR in small‑ and large‑signal disturbance, the system strength is separated accordingly. To address the large‑signal system strength assessment, a control type‑dependent metric, Power Margin Ratio (PMR), is proposed in this paper. PMR is defined as the ratio between the maximum power that can be injected to the system without causing any instability and the nominal power of the IBR. It can be obtained via power flow calculation with a modified algorithm. The theoretical foundation of PMR is established from the viewpoint of dynamical systems. PMR is identical to SCR for the single‑plant‑infinite‑bus system, while presents advancement for multi‑infeed power systems. Comprehensive case studies and discussions have validated that PMR reveals the large‑signal system strength from a static perspective.

Authors:Michael Menezes, Anastasios Kyrillidis
Title: GHOST: Unmasking Phantom States in Mamba2 via Grouped Hidden-state Output-aware Selection & Truncation
Abstract:
While Mamba2's expanded state dimension enhances temporal modeling, it incurs substantial inference overhead that saturates bandwidth during autoregressive generation. Standard pruning methods fail to address this bottleneck: unstructured sparsity leaves activations dense, magnitude‑based selection ignores runtime dynamics, and gradient‑based methods impose prohibitive costs. We introduce GHOST (Grouped Hidden‑state Output‑aware Selection and Truncation), a structured pruning framework that approximates control‑theoretic balanced truncation using only forward‑pass statistics. By jointly measuring controllability and observability, GHOST rivals the fidelity of gradient‑based methods without requiring backpropagation. As a highlight, on models ranging from 130M to 2.7B parameters, our approach achieves a 50% state‑dimension reduction with approximately 1 perplexity point increase on WikiText‑2. Code is available at https://anonymous.4open.science/r/mamba2_ghost‑7BCB/.

Authors:Shaojie Zhang, Ozgur B. Akan
Title: Chemo Hydrodynamic Transceivers for the Internet of Bio-Nano Things, Modeling the Joint Propulsion Transmission trade-off
Abstract:
The Internet of Bio‑Nano Things (IoBNT) requires mobile nanomachines that navigate complex fluids while exchanging molecular signals under external supervision. We introduce the chemo‑hydrodynamic transceiver, a unified model for catalytic Janus particles in which an external optical control simultaneously drives molecular emission and active self‑propulsion. Unlike common abstractions that decouple mobility and communication, we derive a stochastic channel model that captures their physicochemical coupling and shows that actuation‑induced distance jitter can dominate the received‑signal variance, yielding a fundamental trade‑off: stronger actuation increases emission but can sharply reduce reliability through motion‑induced fading. Numerical results reveal a unimodal reliability profile with a critical actuation level beyond which the signal‑to‑noise ratio collapses, and an optimal control level that scales approximately linearly with link distance. Compared with Brownian‑mobility baselines, the model exposes a pronounced estimation gap: neglecting active motility noise can underestimate the bit error probability by orders of magnitude. These findings provide physical‑layer guidelines for mobility‑aware IoBNT protocol design and closed‑loop control of nanorobotic swarms.

Authors:Liraz Mudrik, Yaakov Oshman
Title: Unified Estimation--Guidance Framework Based on Bayesian Decision Theory
Abstract:
Using Bayesian decision theory, we modify the perfect‑information, differential game‑based guidance law (DGL1) to address the inevitable estimation error occurring when driving this guidance law with a separately‑designed state estimator. This yields a stochastic guidance law complying with the generalized separation theorem, as opposed to the common approach, that implicitly, but unjustifiably, assumes the validity of the regular separation theorem. The required posterior probability density function of the game's state is derived from the available noisy measurements using an interacting multiple model particle filter. When the resulting optimal decision turns out to be nonunique, this feature is harnessed to appropriately shape the trajectory of the pursuer so as to enhance its estimator's performance. In addition, certain properties of the particle‑based computation of the Bayesian cost are exploited to render the algorithm amenable to real‑time implementation. The performance of the entire estimation‑decision‑guidance scheme is demonstrated using an extensive Monte Carlo simulation study.

Authors:Adam Evans, Roberto Armellin
Title: Sample-Free Safety Assessment of Neural Network Controllers via Taylor Methods
Abstract:
In recent years, artificial neural networks have been increasingly studied as feedback controllers for guidance problems. While effective in complex scenarios, they lack the verification guarantees found in classical guidance policies. Their black‑box nature creates significant concerns regarding trustworthiness, limiting their adoption in safety‑critical spaceflight applications. This work addresses this gap by developing a method to assess the safety of a trained neural network feedback controller via automatic domain splitting and polynomial bounding. The methodology involves embedding the trained neural network into the system's dynamical equations, rendering the closed‑loop system autonomous. The system flow is then approximated by high‑order Taylor polynomials, which are subsequently manipulated to construct polynomial maps that project state uncertainties onto an event manifold. Automatic domain splitting ensures the polynomials are accurate over their relevant subdomains, whilst also allowing an extensive state‑space to be analysed efficiently. Utilising polynomial bounding techniques, the resulting event values may be rigorously constrained and analysed within individual subdomains, thereby establishing bounds on the range of possible closed‑loop outcomes from using such neural network controllers and supporting safety assessment and informed operational decision‑making in real‑world missions.

Authors:Yin Tang, Jiawei Ma, Jinrui Zhang, Alex Jinpeng Wang, Deyu Zhang
Title: Mitigating Error Accumulation in Continuous Navigation via Memory-Augmented Kalman Filtering
Abstract:
Continuous navigation in complex environments is critical for Unmanned Aerial Vehicle (UAV). However, the existing Vision‑Language Navigation (VLN) models follow the dead‑reckoning, which iteratively updates its position for the next waypoint prediction, and subsequently construct the complete trajectory. Then, such stepwise manner will inevitably lead to accumulated errors of position over time, resulting in misalignment between internal belief and objective coordinates, which is known as "state drift" and ultimately compromises the full trajectory prediction. Drawing inspiration from classical control theory, we propose to correct for errors by formulating such sequential prediction as a recursive Bayesian state estimation problem. In this paper, we design NeuroKalman, a novel framework that decouples navigation into two complementary processes: a Prior Prediction, based on motion dynamics and a Likelihood Correction, from historical observation. We first mathematically associate Kernel Density Estimation of the measurement likelihood with the attention‑based retrieval mechanism, which then allows the system to rectify the latent representation using retrieved historical anchors without gradient updates. Comprehensive experiments on TravelUAV benchmark demonstrate that, with only 10% of the training data fine‑tuning, our method clearly outperforms strong baselines and regulates drift accumulation.

Authors:Alfonso Sciacchitano, Liraz Mudrik, Sean Kragelund, Isaac Kaminer
Title: Multi-UAV Trajectory Optimization for Bearing-Only Localization in GPS Denied Environments
Abstract:
Accurate localization of maritime targets by unmanned aerial vehicles (UAVs) remains challenging in GPS‑denied environments. UAVs equipped with gimballed electro‑optical sensors are typically used to localize targets, however, reliance on these sensors increases mechanical complexity, cost, and susceptibility to single‑point failures, limiting scalability and robustness in multi‑UAV operations. This work presents a new trajectory optimization framework that enables cooperative target localization using UAVs with fixed, non‑gimballed cameras operating in coordination with a surface vessel. This estimation‑aware optimization generates dynamically feasible trajectories that explicitly account for mission constraints, platform dynamics, and out‑of‑frame events. Estimation‑aware trajectories outperform heuristic paths by reducing localization error by more than a factor of two, motivating their use in cooperative operations. Results further demonstrate that coordinated UAVs with fixed, non‑gimballed cameras achieve localization accuracy that meets or exceeds that of single gimballed systems, while substantially lowering system complexity and cost, enabling scalability, and enhancing mission resilience.

Authors:Chih-Yuan Chiu, Devansh Jalota, Marco Pavone
Title: Credit-Based vs. Discount-Based Congestion Pricing: A Comparison Study
Abstract:
Credit‑based congestion pricing (CBCP) and discount‑based congestion pricing (DBCP), which respectively allot travel credits and toll discounts to subsidize low‑income users' access to tolled roads, have emerged as promising policies for alleviating the societal inequity concerns of congestion pricing. However, since real‑world implementations of CBCP and DBCP are nascent, their relative merits remain unclear. In this work, we compare the efficacy of deploying CBCP and DBCP in reducing user costs and increasing toll revenues. We first formulate a non‑atomic congestion game in which low‑income users receive a travel credit or toll discount for accessing tolled lanes. We establish that, in our formulation, Nash equilibrium flows always exist and can be computed or well approximated via convex programming. Our main result establishes a set of practically relevant conditions under which DBCP provably outperforms CBCP in inducing equilibrium outcomes that minimize a given societal cost, which encodes user cost reduction and toll revenue maximization. Finally, we validate our theoretical contributions via a case study of the 101 Express Lanes Project, a CBCP program implemented in the San Francisco Bay Area.

Authors:Kavan Fatehi, Mostafa Rahmani Ghourtani, Amir Sonee, Poonam Yadav, Alessandra M Russo, Hamed Ahmadi, Radu Calinescu
Title: Interpretable Attention-Based Multi-Agent PPO for Latency Spike Resolution in 6G RAN Slicing
Abstract:
Sixth‑generation (6G) radio access networks (RANs) must enforce strict service‑level agreements (SLAs) for heterogeneous slices, yet sudden latency spikes remain difficult to diagnose and resolve with conventional deep reinforcement learning (DRL) or explainable RL (XRL). We propose \emphAttention‑Enhanced Multi‑Agent Proximal Policy Optimization (AE‑MAPPO), which integrates six specialized attention mechanisms into multi‑agent slice control and surfaces them as zero‑cost, faithful explanations. The framework operates across O‑RAN timescales with a three‑phase strategy: predictive, reactive, and inter‑slice optimization. A URLLC case study shows AE‑MAPPO resolves a latency spike in 18ms, restores latency to 0.98ms with 99.9999% reliability, and reduces troubleshooting time by 93% while maintaining eMBB and mMTC continuity. These results confirm AE‑MAPPO's ability to combine SLA compliance with inherent interpretability, enabling trustworthy and real‑time automation for 6G RAN slicing.

Authors:Fan Jiang, Xingpeng Li, Pascal Van Hentenryck
Title: Deep Neural Network-Enhanced Frequency-Constrained Optimal Power Flow with Multi-Governor Dynamics
Abstract:
To ensure frequency security in power systems, both the rate of change of frequency (RoCoF) and the frequency nadir (FN) must be explicitly accounted for in real‑time frequency‑constrained optimal power flow (FCOPF). However, accurately modeling sys‑tem frequency dynamics through analytical formulations is chal‑lenging due to their inherent nonlinearity and complexity. To address this issue, deep neural networks (DNNs) are utilized to capture the nonlinear mapping between system operating condi‑tions and key frequency performance metrics. In this paper, a DNN‑based frequency prediction model is developed and trained using the high‑fidelity time‑domain simulation data generated in PSCAD/EMTDC. The trained DNN is subsequently transformed into an equivalent mixed‑integer linear programming (MILP) form and embedded into the FCOPF problem as additional con‑straints to explicitly enforce frequency security, leading to the proposed DNN‑FCOPF formulation. For benchmarking, two alternative models are considered: a conventional optimal power flow without frequency constraints and a linearized FCOPF in‑corporating system‑level RoCoF and FN constraints. The effec‑tiveness of the proposed method is demonstrated by comparing the solutions of these three models through extensive PSCAD/EMTDC time‑domain simulations under various loading scenarios.

Authors:Liangkai Liu, Kang G. Shin, Jinkyu Lee, Chengmo Yang, Weisong Shi
Title: Enhancing Predictability of Multi-Tenant DNN Inference for Autonomous Vehicles' Perception
Abstract:
Autonomous vehicles (AVs) rely on sensors and deep neural networks (DNNs) to perceive their surrounding environment and make maneuver decisions in real time. However, achieving real‑time DNN inference in the AV's perception pipeline is challenging due to the large gap between the computation requirement and the AV's limited resources. Most, if not all, of existing studies focus on optimizing the DNN inference time to achieve faster perception by compressing the DNN model with pruning and quantization. In contrast, we present a Predictable Perception system with DNNs (PP‑DNN) that reduce the amount of image data to be processed while maintaining the same level of accuracy for multi‑tenant DNNs by dynamically selecting critical frames and regions of interest (ROIs). PP‑DNN is based on our key insight that critical frames and ROIs for AVs vary with the AV's surrounding environment. However, it is challenging to identify and use critical frames and ROIs in multi‑tenant DNNs for predictable inference. Given image‑frame streams, PP‑DNN leverages an ROI generator to identify critical frames and ROIs based on the similarities of consecutive frames and traffic scenarios. PP‑DNN then leverages a FLOPs predictor to predict multiply‑accumulate operations (MACs) from the dynamic critical frames and ROIs. The ROI scheduler coordinates the processing of critical frames and ROIs with multiple DNN models. Finally, we design a detection predictor for the perception of non‑critical frames. We have implemented PP‑DNN in an ROS‑based AV pipeline and evaluated it with the BDD100K and the nuScenes dataset. PP‑DNN is observed to significantly enhance perception predictability, increasing the number of fusion frames by up to 7.3x, reducing the fusion delay by >2.6x and fusion‑delay variations by >2.3x, improving detection completeness by 75.4% and the cost‑effectiveness by up to 98% over the baseline.

Authors:Srishti Siddharth, Vivek Natarajan, Ravi N. Banavar
Title: Lie Group Variational Integrator for the Geometrically Exact Rod with Circular Cross-Section Incorporating Cross-Sectional Deformation
Abstract:
In this paper, we derive the continuous space‑time equations of motion of a three‑dimensional geometrically exact rod, or the Cosserat rod, incorporating planar cross‑sectional deformation. We then adopt the Lie group variational integrator technique to obtain a discrete model of the rod incorporating both rotational motion and cross‑sectional deformation as well. The resulting discrete model possesses several desirable features: it ensures volume conservation of the discrete elements by considering cross‑sectional deformation through a local dilatation factor, it demonstrates the beneficial properties associated with the variational integrator technique, such as the preservation of the rotational configuration, and energy conservation with a bounded error. An exhaustive set of numerical results under various initial conditions of the rod demonstrates the efficacy of the model in replicating the physics of the system.

Authors:Levi D. Reyes Premer, Arash J. Khabbazi, Kevin J. Kircher
Title: Trajectory-based data-driven predictive control and the state-space predictor
Abstract:
We define trajectory predictive control (TPC) as a family of output‑feedback indirect data‑driven predictive control (DDPC) methods that represent the output trajectory of a discrete‑time system as a linear function of the recent input/output history and the planned input trajectory. This paper shows that for different choices of the trajectory predictor, TPC encompasses a wide variety of DDPC methods, including subspace predictive control (SPC), closed‑loop SPC, γ‑DDPC, causal‑γ‑DDPC, transient predictive control, and others. This paper introduces a trajectory predictor that corresponds to a linear state‑space model with the recent input/output history as the state. With this state‑space predictor, TPC is a special case of linear model predictive control and therefore inherits its mature theory. In numerical experiments, TPC performance approaches the limit of oracle H_2‑optimal control with perfect knowledge of the underlying system model. For TPC with small training datasets, the state‑space predictor outperforms other predictors because it has fewer parameters.

Authors:Julian D. Schiller, Malte Heinrich, Victor G. Lopez, Matthias A. Müller
Title: Tuning the burn-in phase in training recurrent neural networks improves their performance
Abstract:
Training recurrent neural networks (RNNs) with standard backpropagation through time (BPTT) can be challenging, especially in the presence of long input sequences. A practical alternative to reduce computational and memory overhead is to perform BPTT repeatedly over shorter segments of the training data set, corresponding to truncated BPTT. In this paper, we examine the training of RNNs when using such a truncated learning approach for time series tasks. Specifically, we establish theoretical bounds on the accuracy and performance loss when optimizing over subsequences instead of the full data sequence. This reveals that the burn‑in phase of the RNN is an important tuning knob in its training, with significant impact on the performance guarantees. We validate our theoretical results through experiments on standard benchmarks from the fields of system identification and time series forecasting. In all experiments, we observe a strong influence of the burn‑in phase on the training process, and proper tuning can lead to a reduction of the prediction error on the training and test data of more than 60% in some cases.

Authors:Marc Gillioz, Guillaume Dubuis, Étienne Voutaz, Philippe Jacquod
Title: Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids
Abstract:
We apply several machine learning algorithms to the problem of anomaly detection in operational data for large‑scale, high‑voltage electric power grids. We observe important differences in the performance of the algorithms. Neural networks typically outperform classical algorithms such as k‑nearest neighbors and support vector machines, which we explain by the strong contextual nature of the anomalies. We show that unsupervised learning algorithm work remarkably well and that their predictions are robust against simultaneous, concurring anomalies.

Authors:Alexander Demin, Gleb Pogudin
Title: Simple generators of rational function fields
Abstract:
Consider a subfield of the field of rational functions in several indeterminates. We present an algorithm that, given a set of generators of such a subfield, finds a simple generating set. We provide an implementation of the algorithm and show that it improves upon the state of the art both in efficiency and the quality of the results. Furthermore, we demonstrate the utility of simplified generators through several case studies from different application domains, such as structural parameter identifiability. The main algorithmic novelties include performing only partial Gröbner basis computation via sparse interpolation and efficient search for polynomials of a fixed degree in a subfield of the rational function field.

Authors:Mohamed Camil Belhadjoudja
Title: Backstepping Control of PDEs on Domains with Graph-Monotone Boundaries
Abstract:
Despite the extensive body of work on backstepping for one‑dimensional PDEs, results in higher dimensions remain comparatively limited. Most available methods either exploit particular symmetries of the PDE or address problems posed on parallelepiped domains. To the best of our knowledge, the only approach that enables the design of backstepping controllers on non‑parallelepiped regions without symmetry assumptions is the domain extension technique. This method, however, presents several drawbacks. In particular, the control input at each time instant is obtained by simulating a PDE on an extended domain, from which the actual input on the original domain is approximated. By contrast, in the one‑dimensional setting, once the time‑independent backstepping gain kernel is known, the control input can be computed in closed form as a feedback depending solely on the state at that same instant. Moreover, problems such as output‑feedback design or adaptive and robust control do not appear straightforward to address with the domain extension method, at least to the best of our knowledge. These considerations motivate the search, whenever possible, for alternatives that preserve the main advantages of one‑dimensional backstepping. A motivating example for the domain extension method is the control of the heat equation on a piano‑shaped domain, with actuation applied at the tail of the piano. In this extended abstract, we show through a simple calculation that the domain extension method is not required in this setting. Instead, a strategy akin to that used for parallelepiped domains can be adopted. This result constitutes a first instance of a broader framework for backstepping control of asymmetric PDEs posed on non‑parallelepiped regions, which we refer to as domains with graph‑monotone boundaries. The general framework is developed in a forthcoming paper.

Authors:Henrik Sandberg, Kamil Hassan, Heng Wu
Title: Singular Port-Hamiltonian Systems Beyond Passivity
Abstract:
In this paper, we investigate a class of port‑Hamiltonian systems with singular vector fields. We show that, under suitable conditions, their interconnection with passive systems ensures convergence to a prescribed non‑equilibrium steady state. At first glance, this behavior appears to contradict the seemingly passive structure of port‑Hamiltonian systems, since sustaining a non‑equilibrium steady state requires continuous power injection. We resolve this apparent paradox by showing that the singularity in the vector field induces a sliding mode that contributes effective energy, enabling maintenance of the steady state and demonstrating that the system is not passive. Furthermore, we consider regularizations of the singular dynamics and show that the resulting systems are cyclo‑passive, while still capable of supplying the required steady‑state power. These results clarify the role of singularities in port‑Hamiltonian systems and provide new insight into their energetic properties.

Authors:Giorgia Disarò, Maria Elena Valcher
Title: Reference Output Tracking in Boolean Control Networks
Abstract:
In this paper, the problem of tracking a given reference output trajectory is investigated for the class of Boolean control networks, by resorting to their algebraic representation. First, the case of a finite‑length reference trajectory is addressed, and the analysis and algorithm first proposed in [17] are extended to be able to deal with arbitrary initial conditions and to identify all possible solutions. The approach developed for the finite‑length case is then adjusted to cope with periodic reference output trajectories. The results of the paper are illustrated through an example.

Authors:Pradyumna Kumar Bishoyi, Chia Chia Lee, Navid Keshtiarast, Marina Petrova
Title: Dynamic Interference Management for TN-NTN Coexistence in the Upper Mid-Band
Abstract:
The coexistence of terrestrial networks (TN) and non‑terrestrial networks (NTN) in the frequency range 3 (FR3) upper mid‑band presents considerable interference concerns, as dense TN deployments can severely degrade NTN downlink performance. Existing studies rely on interference‑nulling beamforming, precoding, or exclusion zones that require accurate channel state information (CSI) and static coordination, making them unsuitable for dynamic NTN scenarios. To overcome these limitations, we develop an optimization framework that jointly controls TN downlink power, uplink power, and antenna downtilt to protect NTN links while preserving terrestrial performance. The resultant non‑convex coupling between TN and NTN parameters is addressed by a Proximal Policy Optimization (PPO)‑based reinforcement learning method that develops adaptive power and tilt control strategies. Simulation results demonstrate a reduction up to 8 dB in the median interference‑to‑noise ratio (INR) while maintaining over 87% TN basestation activity, outperforming conventional baseline methods and validating the feasibility of the proposed strategy for FR3 coexistence.

Authors:Charlotte Cambier van Nooten, Christos Aronis, Yuliya Shapovalova, Lucia Cavallaro
Title: Exploring the impact of adaptive rewiring in Graph Neural Networks
Abstract:
This paper explores sparsification methods as a form of regularization in Graph Neural Networks (GNNs) to address high memory usage and computational costs in large‑scale graph applications. Using techniques from Network Science and Machine Learning, including Erdős‑Rényi for model sparsification, we enhance the efficiency of GNNs for real‑world applications. We demonstrate our approach on N‑1 contingency assessment in electrical grids, a critical task for ensuring grid reliability. We apply our methods to three datasets of varying sizes, exploring Graph Convolutional Networks (GCN) and Graph Isomorphism Networks (GIN) with different degrees of sparsification and rewiring. Comparison across sparsification levels shows the potential of combining insights from both research fields to improve GNN performance and scalability. Our experiments highlight the importance of tuning sparsity parameters: while sparsity can improve generalization, excessive sparsity may hinder learning of complex patterns. Our adaptive rewiring approach, particularly when combined with early stopping, proves promising by allowing the model to adapt its connectivity structure during training. This research contributes to understanding how sparsity can be effectively leveraged in GNNs for critical applications like power grid reliability analysis.

Authors:Mischa Huisman, Thomas Arnold, Erjen Lefeber, Nathan van de Wouw, Carlos Murguia
Title: Improving CACC Robustness to Parametric Uncertainty via Plant Equivalent Controller Realizations
Abstract:
Cooperative Adaptive Cruise Control (CACC) enables vehicle platooning through inter‑vehicle communication, improving traffic efficiency and safety. Conventional CACC relies on feedback linearization, assuming exact vehicle parameters; however, longitudinal vehicle dynamics are nonlinear and subject to parametric uncertainty. Applying feedback linearization with a nominal model yields imperfect cancellation, leading to model mismatch and degraded performance with off‑the‑shelf CACC controllers. To improve robustness without redesigning the CACC law, we explicitly model the mismatch between the ideal closed‑loop dynamics assumed by the CACC design and the actual dynamics under parametric uncertainties. Robustness is formulated as an \mathcalL_2 trajectory‑matching problem, minimizing the energy of this mismatch to make the uncertain system behave as closely as possible to the ideal model. This objective is addressed by optimizing over plant equivalent controller (PEC) realizations that preserve the nominal closed‑loop behavior while mitigating the effects of parametric uncertainty. Stability and performance are enforced via linear matrix inequalities, yielding a convex optimization problem applicable to heterogeneous platoons. Experimental results demonstrate improved robustness and performance under parametric uncertainty while preserving nominal CACC behavior.

Authors:Aditya Natu, Xiaozhe Hu, Hassan HosseinNia
Title: Integrating Active Damping with Shaping-Filtered Reset Tracking Control for Piezo-Actuated Nanopositioning
Abstract:
Piezoelectric nanopositioning systems are often limited by lightly damped structural resonances and the gain‑‑phase constraints of linear feedback, which restrict achievable bandwidth and tracking performance. This paper presents a dual‑loop architecture that combines an inner‑loop non‑minimum‑phase resonant controller (NRC) for active damping with an outer‑loop tracking controller augmented by a constant‑gain, lead‑in‑phase (CgLp) reset element to provide phase lead at the targeted crossover without increasing loop gain. We show that aggressively tuned CgLp designs with larger phase lead can introduce pronounced higher‑order harmonics, degrading error sensitivity in specific frequency bands and causing multiple‑reset behavior. To address this, a shaping filter is introduced in the reset‑trigger path to regulate the reset action and suppress harmonic‑induced effects while preserving the desired crossover‑phase recovery. The proposed controllers are implemented in real time on an industrial piezo nanopositioner, demonstrating an experimental open‑loop crossover increase of approximately 55~Hz and a closed‑loop bandwidth improvement of about 34~Hz relative to a well‑tuned linear baseline.

Authors:Fei Long, Kaihui Gao, Li Chen, Dan Li, Yiwei Zhang, Fei Gui, Yitao Xing, Wenjia Wei, Bingyang Liu
Title: Supercharging Packet-level Network Simulation of Large Model Training via Memoization and Fast-Forwarding
Abstract:
Packet‑level discrete‑event simulation (PLDES) is a prevalent tool for evaluating detailed performance of large model training. Although PLDES offers high fidelity and generality, its slow performance has plagued networking practitioners. Existing optimization techniques either simplify the network model, resulting in large errors; or execute it in parallel using multiple processors, with an upper bound on speedup. This paper explores an alternative optimization direction that reduces the computational loads of PLDES while maintaining high fidelity. Our key insight is that, in distributed LLM training, packet‑level traffic behaviors often exhibit repetitive contention patterns and steady‑states where flow rates stabilize, ignoring these redundant discrete events speeds up the simulation considerably and the error is negligible. We realize this idea by proposing Wormhole, a user‑transparent PLDES kernel capable of automatically memoization for unsteady‑states and skipping for steady‑states. Wormhole adopts network partitioning, state memoization and reuse, and rate‑based steady‑state identification to accurately determine the periods of each flow's steady‑state, while maintaining simulation consistency after fast‑forwarding. Experiments demonstrate that Wormhole can achieve a 744x speedup over the original ns‑3 (510x for MoE workload), with a bounded error of <1%. Applying current multithreading parallel techniques and Wormhole together allows a 1012x speedup, reducing the simulation time for one GPT‑13B training under 128 GPUs from 9 hours to 5 minutes.

Authors:Xingzhi Huang, Ji Wang
Title: Rapid Boundary Stabilization of Two-Dimensional Elastic Plates with In-Domain Aeroelastic Instabilities
Abstract:
Motivated by active wing flutter suppression in high‑Mach‑number flight, this paper presents a rapid boundary stabilization strategy for a two‑dimensional PDE‑modeled elastic plate with in‑domain instabilities, where the exponential stability is achieved with a decay rate that can be arbitrarily assigned by the users. First, the aeroelastic system is modeled as two‑dimensional coupled wave PDEs with internal anti‑damping terms, derived by Piston theory and Hamilton's principle. Using Fourier series expansion, the 2‑D problem is decomposed into a large‑scale 1‑D system, based on which full‑state boundary feedback control is designed via PDE backstepping transformation. To enable output‑feedback implementation, a state observer is further designed to estimate the distributed states over the two‑dimensional spatial domain using the available boundary measurements. Through Lyapunov analysis, the exponential stability of the 2‑D elastic plate PDE under the proposed boundary control is established with a designer‑tunable decay rate. Numerical simulations verify the effectiveness of the control strategy in suppressing flow‑induced vibrations in a 2‑D elastic plate.

Authors:Mickey M. Rogers, William M. Ota, Nathaniel Burola, Tepring Piquado
Title: The Infrastructure Equation: Water, Energy, and Community Policy for Georgia's Data Center Boom
Abstract:
The rapid growth of data centers driven by cloud computing and artificial intelligence is reshaping infrastructure planning and environmental governance in the United States. Georgia has emerged as a major market for data center development, particularly in the Atlanta metropolitan region, creating economic opportunity alongside significant challenges. Data centers are water‑intensive, energy‑intensive, and land‑intensive infrastructure whose cumulative impacts strain municipal water systems, electric grids, and local land‑use frameworks. Unlike single industrial projects, data centers are often proposed in clusters, amplifying community and infrastructure impacts. This report draws on insights from a Georgia‑based expert convening to describe the implications of data center growth for water management, energy reliability, ratepayer equity, zoning, and community engagement, identify potential gaps in transparency and regulatory coordination, and present a policy roadmap to help Georgia balance digital infrastructure growth with sustainability, equity, and community protection.

Authors:Anton A. Stoorvogel, Ali Saberi, Donya Nojavanzadeh
Title: Scale-Free delta-Level Coherent Output Synchronization of Multi-Agent Systems with Adaptive Protocols and Bounded Disturbances
Abstract:
In this paper, we investigate scale‑free delta‑level coherent output synchronization for multi‑agent systems (MAS) operating under bounded disturbances or noises. We introduce an adaptive scale‑free framework designed solely based on the knowledge of agent models and completely agnostic to both the communication topology and the size of the network. We define the level of coherency for each agent as the norm of the weighted sum of the disagreement dynamics with its neighbors. We define each agents coherency level as the norm of a weighted sum of its disagreement dynamics relative to its neighbors. The goal is to ensure that the networks coherency level remains below a prescribed threshold delta, without requiring any a priori knowledge of the disturbance.

Authors:Sanchita Ghosh, Tanushree Roy
Title: Resilient Voltage Estimation for Battery Packs Using Self-Learning Koopman Operator
Abstract:
Cloud‑based battery management systems (BMSs) rely on real‑time voltage measurement data to ensure coordinated bi‑directional charging of electric vehicles (EVs) with vehicle‑to‑grid technology. Unfortunately, an adversary can corrupt the measurement data during transmission from the local‑BMS to the cloud‑BMS, leading to disrupted EV charging. Therefore, to ensure reliable voltage data under such sensor attacks, this paper proposes a two‑stage error‑corrected self‑learning Koopman operator‑based secure voltage estimation scheme for large‑format battery packs. The first stage of correction compensates for the Koopman approximation error. The second stage aims to recover the error amassing from the lack of higher‑order battery dynamics information in the self‑learning feedback, using two alternative methods: an adaptable empirical strategy that uses cell‑level knowledge of open circuit voltage to state‑of‑charge mapping for pack‑level estimation, and a Gaussian process regression‑based data‑driven method that leverages minimal data‑training. During our comprehensive case studies using the high‑fidelity battery simulation package 'PyBaMM‑liionpack', our proposed secure estimator reliably generated real‑time voltage estimation with high accuracy under varying pack topologies, charging settings, battery age‑levels, and attack policies. Thus, the scalable and adaptable algorithm can be easily employed to diverse battery configurations and operating conditions, without requiring significant modifications, excessive data or sensor redundancy, to ensure optimum charging of EVs under compromised sensing.

Authors:Jie Feng, Yuanyuan Shi, Deepjyoti Deka
Title: Efficient Policy Adaptation for Voltage Control Under Unknown Topology Changes
Abstract:
Reinforcement learning (RL) has shown great potential for designing voltage control policies, but their performance often degrades under changing system conditions such as topology reconfigurations and load variations. We introduce a topology‑aware online policy optimization framework that leverages data‑driven estimation of voltage‑reactive power sensitivities to achieve efficient policy adaptation. Exploiting the sparsity of topology‑switching events, where only a few lines change at a time, our method efficiently detects topology changes and identifies the affected lines and parameters, enabling fast and accurate sensitivity updates without recomputing the full sensitivity matrix. The estimated sensitivity is subsequently used for online policy optimization of a pre‑trained neural‑network‑based RL controller. Simulations on both the IEEE 13‑bus and SCE 56‑bus systems demonstrate over 90 percent line identification accuracy, using only 15 data points. The proposed method also significantly improves voltage regulation performance compared with non‑adaptive policies and adaptive policies that rely on regression‑based online optimization methods for sensitivity estimation.

Authors:Supriyo Bandyopadhyay
Title: Device Applications of Heterogeneously Integrated Strain-Switched Ferrimagnets/Topological Insulator/Piezoelectric Stacks
Abstract:
A family of ferrimagnets (CoV2O4, GdCo, TbCo) exhibits out‑of‑plane magnetic anisotropy when strained compressively and in‑plane magnetic anisotropy when strained expansively (or vice versa). If such a ferrimagnetic thin film is placed on top of a topological insulator (TI) thin film and its magnetic anisotropy is modulated with strain, then interfacial exchange coupling between the ferrimagnet (FM) and the underlying TI will modulate the surface current flowing through the latter. If the strain is varied continuously, the current will also vary continuously and if the strain alternates in time, the current will also alternate with the frequency of the strain modulation, as long as the frequency is not so high that the period is smaller than the switching time of the FM. If the strain is generated with a gate voltage by integrating a piezoelectric underneath the FM/TI stack, then that can implement a transconductance amplifier or a synapse for neuromorphic computation.

Authors:Nicolai Maisch, Shengjian Chen, Alexander Robertus, Samed Ajdinović, Armin Lechler, Alexander Verl, Oliver Riedel
Title: Non-Fungible Blockchain Tokens for Traceable Online-Quality Assurance of Milled Workpieces
Abstract:
This work presents a concept and implementation for the secure storage and transfer of quality‑relevant data of milled workpieces from online‑quality assurance processes enabled by real‑time simulation models. It utilises Non‑Fungible Tokens (NFT) to securely and interoperably store quality data in the form of an Asset Administration Shell (AAS) on a public Ethereum blockchain. Minted by a custom smart contract, the NFTs reference the metadata saved in the Interplanetary File System (IPFS), allowing new data from additional processing steps to be added in a flexible yet secure manner. The concept enables automated traceability throughout the value chain, minimising the need for time‑consuming and costly repetitive manual quality checks.

Authors:Chenhan Xiao, Yang Weng
Title: Limits of Residual-Based Detection for Physically Consistent False Data Injection
Abstract:
False data injection attacks (FDIAs) pose a persistent challenge to AC power system state estimation. In current practice, detection relies primarily on topology‑aware residual‑based tests that assume malicious measurements can be distinguished from normal operation through physical inconsistency reflected in abnormal residual behavior. This paper shows that this assumption does not always hold: when FDIA scenarios produce manipulated measurements that remain on the measurement manifold induced by AC power flow relations and measurement redundancy, residual‑based detectors may fail to distinguish them from nominal data. The resulting detectability limitation is a property of the measurement manifold itself and does not depend on the attacker's detailed knowledge of the physical system model. To make this limitation observable in practice, we present a data‑driven constructive mechanism that incorporates the generic functional structure of AC power flow to generate physically consistent, manifold‑constrained perturbations, providing a concrete witness of how residual‑based detectors can be bypassed. Numerical studies on multiple AC test systems characterize the conditions under which detection becomes challenging and illustrate its failure modes. The results highlight fundamental limits of residual‑based detection in AC state estimation and motivate the need for complementary defenses beyond measurement consistency tests.

Authors:Akshay Mete, Shahid Aamir Sheikh, Tzu-Hsiang Lin, Dileep Kalathil, P. R. Kumar
Title: Optimistic World Models: Efficient Exploration in Model-Based Deep Reinforcement Learning
Abstract:
Efficient exploration remains a central challenge in reinforcement learning (RL), particularly in sparse‑reward environments. We introduce Optimistic World Models (OWMs), a principled and scalable framework for optimistic exploration that brings classical reward‑biased maximum likelihood estimation (RBMLE) from adaptive control into deep RL. In contrast to upper confidence bound (UCB)‑style exploration methods, OWMs incorporate optimism directly into model learning by augmentation with an optimistic dynamics loss that biases imagined transitions toward higher‑reward outcomes. This fully gradient‑based loss requires neither uncertainty estimates nor constrained optimization. Our approach is plug‑and‑play with existing world model frameworks, preserving scalability while requiring only minimal modifications to standard training procedures. We instantiate OWMs within two state‑of‑the‑art world model architectures, leading to Optimistic DreamerV3 and Optimistic STORM, which demonstrate significant improvements in sample efficiency and cumulative return compared to their baseline counterparts.

Authors:Giannis Delimpaltadakis, Pol Mestres, Jorge Cortés, W. P. M. H. Heemels
Title: Safe Feedback Optimization through Control Barrier Functions
Abstract:
Feedback optimization refers to a class of methods that steer a control system to a steady state that solves an optimization problem. Despite tremendous progress on the topic, an important problem remains open: enforcing state constraints at all times. The difficulty in addressing it lies on mediating between the safety enforcement and the closed‑loop stability, and ensuring the equivalence between closed‑loop equilibria and the optimization problem's critical points. In this work, we present a feedback‑optimization method that enforces state constraints at all times employing high‑order control‑barrier functions. We provide several results on the proposed controller dynamics, including well‑posedness, safety guarantees, equivalence between equilibria and critical points, and local and global (in certain convex cases) asymptotic stability of optima. Various simulations illustrate our results.

Authors:Gian Carlo Maffettone, Davide Salzano, Mario di Bernardo
Title: Robust Macroscopic Density Control of Heterogeneous Multi-Agent Systems
Abstract:
Modern applications, such as orchestrating the collective behavior of robotic swarms or traffic flows, require the coordination of large groups of agents evolving in unstructured environments, where disturbances and unmodeled dynamics are unavoidable. In this work, we develop a scalable macroscopic density control framework in which a feedback law is designed directly at the level of an advection‑‑diffusion partial differential equation. We formulate the control problem in the density space and prove global exponential convergence towards the desired behavior in \mathcalL^2 with guaranteed asymptotic rejection of bounded unknown drift terms, explicitly accounting for heterogeneous agent dynamics, unmodeled behaviors, and environmental perturbations. Our theoretical findings are corroborated by numerical experiments spanning heterogeneous oscillators, traffic systems, and swarm robotics in partially unknown environments.

Authors:Arya Abdollahi
Title: Community-Centered Resilience Enhancement of Urban Power and Gas Networks via Microgrid Partitioning, Mobile Energy Storage, and Data-Driven Risk Assessment
Abstract:
Urban energy systems face increasing challenges due to high penetration of renewable energy sources, extreme weather events, and other high‑impact, low‑probability disruptions. This project proposes a community‑centered, open‑access framework to enhance the resilience and reliability of urban power and gas networks by integrating microgrid partitioning, mobile energy storage deployment, and data‑driven risk assessment. The approach involves converting passive distribution networks into active, self‑healing microgrids using distributed energy resources and remotely controlled switches to enable flexible reconfiguration during normal and emergency operations. To address uncertainties from intermittent renewable generation and variable load, an adjustable interval optimization method combined with a column and constraint generation algorithm is developed, providing robust planning solutions without requiring probabilistic information. Additionally, a real‑time online risk assessment tool is proposed, leveraging 25 multi‑dimensional indices including load, grid status, resilient resources, emergency response, and meteorological factors to support operational decision‑making during extreme events. The framework also optimizes the long‑term sizing and allocation of mobile energy storage units while incorporating urban traffic data for effective routing during emergencies. Finally, a novel time‑dependent resilience and reliability index is introduced to quantify system performance under diverse operating conditions. The proposed methodology aims to enable resilient, efficient, and adaptable urban energy networks capable of withstanding high‑impact disruptions while maximizing operational and economic benefits.

Authors:Shinhoo Kang, Sangwook Kim, Sehyun Yun
Title: Knowledge Integration in Differentiable Models: A Comparative Study of Data-Driven, Soft-Constrained, and Hard-Constrained Paradigms for Identification and Control of the Single Machine Infinite Bus System
Abstract:
Integrating domain knowledge into neural networks is a central challenge in scientific machine learning. Three paradigms have emerged ‑‑ data‑driven (Neural Ordinary Differential Equations, NODEs), soft‑constrained (Physics‑Informed Neural Networks, PINNs), and hard‑constrained (Differentiable Programming, DP) ‑‑ each encoding physical knowledge at different levels of structural commitment. However, how these strategies impact not only predictive accuracy but also downstream tasks such as control synthesis remains insufficiently understood. This paper presents a comparative study of NODEs, PINNs, and DP for dynamical system modeling, using the Single Machine Infinite Bus power system as a benchmark. We evaluate these paradigms across three tasks: trajectory prediction, parameter identification, and Linear Quadratic Regulator control synthesis. Our results yield three principal findings. First, knowledge representation determines generalization: NODE, which learns the system operator, enables robust extrapolation, whereas PINN, which approximates a solution map, restricts generalization to the training horizon. Second, hard‑constrained formulations (DP) reduce learning to a low‑dimensional physical parameter space, achieving faster and more reliable convergence than soft‑constrained approaches. Third, knowledge fidelity propagates to control performance: DP produces controllers that closely match those obtained from true system parameters, while NODE provides a viable data‑driven alternative by recovering control‑relevant Jacobians with 3‑4% relative error and yielding LQR gains within 0.36% of the ground truth. Based on these findings, we propose a practical decision framework for selecting knowledge integration strategies in neural modeling of dynamical systems.

Authors:Philipp C. Böttcher, Carsten Hartmann, Andrea Benigni, Thiemo Pesch, Dirk Witthaut
Title: Impact of Market Reforms on Deterministic Frequency Deviations in the European Power Grid
Abstract:
Deterministic frequency deviations (DFDs) are systematic and predictable excursions of grid frequency that arise from synchronized generation ramps induced by electricity market scheduling. In this paper, we analyze the impact of the European day‑ahead market reform of 1 October 2025, which replaced hourly trading blocks with quarter‑hourly blocks, on DFDs in the Central European synchronous area. Using publicly available frequency measurements, we compare periods before and after the reform based on daily frequency profiles, indicators characterizing frequency deviations, principal component analysis, Fourier‑based functional data analysis, and power spectral density analysis. We show that the reform substantially reduces characteristic hourly frequency deviations and suppresses dominant spectral components at hourly and half‑hourly time scales, while quarter‑hourly structures gain relative importance. While the likelihood of large frequency deviations decreases overall, reductions for extreme events are less clear and depend on the metric used. Our results demonstrate that market design reforms can effectively mitigate systematic frequency deviations, but also highlight that complementary technical and regulatory measures are required to further reduce large frequency excursions in low‑inertia power systems.

Authors:Timo Oksanen
Title: ISO FastLane: Faster ISO 11783 with Dual Stack Approach as a Short Term Solution
Abstract:
The agricultural industry has been searching for a high‑speed successor to the 250~kbit/s CAN bus backbone of ISO~11783 (ISOBUS) for over a decade, yet no protocol‑level solution has reached standardization. Meanwhile, modern planters, sprayers, and Virtual Terminals are already constrained by the bus bandwidth. This paper presents ISO FastLane, a gateway‑less dual‑stack approach that routes point‑to‑point ISOBUS traffic over Ethernet while keeping broadcast messages on the existing CAN bus. The solution requires no new state machines, no middleware, and no changes to application layer code: only a simple Layer~3 routing decision and a lightweight peer discovery mechanism called Augmented Address Claim (AACL). Legacy devices continue to operate unmodified and unaware of FastLane traffic. Preliminary tests reported on the paper demonstrate that ISO FastLane accelerates Virtual Terminal object pool uploads by factor of 8 and sustains Task Controller message rates over 100 times beyond the current specification limit. Because ISO FastLane builds entirely on existing J1939 and ISO~11783 conventions, it can be implemented by ISOBUS engineers in a matter of weeks. This is delivering tangible performance gains today, without waiting for the long‑term High Speed ISOBUS solution.

Authors:Moa Johannesson, Lina Brink, Alvin Combrink, Sabino Francesco Roselli, Martin Fabian
Title: A General Formulation for the Teaching Assignment Problem: Computational Analysis Over a Real-World Dataset
Abstract:
The Teacher Assignment Problem is a combinatorial optimization problem that involves assigning teachers to courses while guaranteeing that all courses are covered, teachers do not teach too few or too many hours, teachers do not switch assigned courses too often and possibly teach the courses they favor. Typically the problem is solved manually, a task that requires several hours every year. In this work we present a mathematical formulation for the problem and an experimental evaluation of the model implemented using state‑of‑the‑art SMT, CP, and MILP solvers. The implementations are tested over a real‑world dataset provided by the Division of Systems and Control at Chalmers University of Technology, and produce teacher assignments with smaller workload deviation, a more even workload distribution among the teachers, and a lower number of switched courses.

Authors:Aamer Mohamed Huroon, Li-Chun Wang
Title: UAV-Assisted 6G Communication Networks for Railways: Technologies, Applications, and Challenges
Abstract:
Unmanned Aerial Vehicles (UAVs) are crucial for advancing railway communication by offering reliable connectivity, adaptive coverage, and mobile edge services . This survey examines UAV‑assisted approaches for 6G railway needs including ultra‑reliable low‑latency communication (URLLC) and integrated sensing and communication (ISAC). We cover railway channel models, reconfigurable intelligent surfaces (RIS), and UAV‑assisted mobile edge computing (MEC). Key challenges include coexistence with existing systems, handover management, Doppler effect, and security. The roadmap suggests work on integrated communication‑control systems and AI‑driven optimization for intelligent railway networks.

Authors:Yuma Abe, Mariko Sekiguchi, Amane Miura
Title: QoS Identifier and Slice Mapping in 5G and Non-Terrestrial Network Interconnected Systems
Abstract:
The interconnection of 5G and non‑terrestrial networks (NTNs) has been actively studied to expand connectivity beyond conventional terrestrial infrastructure. In the 3GPP standardization of 5G systems, the 5G Quality of Service (QoS) Identifier (5QI) is defined to characterize the QoS requirements of different traffic requirements. However, it falls short in capturing the diverse latency, capacity, and reliability profiles of NTN environments, particularly when NTNs are used as backhaul. Furthermore, it is difficult to manage individual traffic flows and perform efficient resource allocation and routing when a large number of 5G traffic flows are present in NTN systems. To address these challenges, we propose an optimization framework that enhances QoS handling by introducing an NTN QoS Identifier (NQI) and grouping 5G traffic into NTN slices based on similar requirements. This enables unified resource control and routing for a large number of 5G flows in NTN systems. In this paper, we present the detailed procedure of the proposed framework, which consists of 5QI to NQI mapping, NTN traffic to NTN slice mapping, and slice‑level flow and routing optimization. We evaluate the framework by comparing multiple mapping schemes through numerical simulations and analyze their impact on overall network performance.

Authors:Yuejie Wang, Chenchen Shou, Jiaxu Qian, Guyue Liu
Title: XLB: A High Performance Layer-7 Load Balancer for Microservices using eBPF-based In-kernel Interposition
Abstract:
L7 load balancers are a fundamental building block in microservices as they enable fine‑grained traffic distribution. Compared to monolithic applications, microservices demand higher performance and stricter isolation from load balancers. This is due to the increased number of instances, longer service chains, and the necessity for co‑location with services on the same host. Traditional sidecar‑based load balancers are ill‑equipped to meet these demands, often resulting in significant performance degradation. In this work, we present XLB, a novel architecture that reshapes L7 load balancers as in‑kernel interposition operating on the socket layer. We leverage eBPF to implement the core load balancing logic in the kernel, and address the connection management and state maintenance challenges through novel socket layer redirection and nested eBPF maps designs. XLB eliminates the extra overhead of scheduling, communication, and data movement, resulting in a more lightweight, scalable, and efficient L7 load balancer architecture. Compared to the widely used microservices load balancers (Istio and Cilium), over 50 microservice instances, XLB achieves up to 1.5x higher throughput and 60% lower end‑to‑end latency.

Authors:Luigi Romano, Ole Morten Aamo, Jan Åslund, Erik Frisk
Title: First-order friction models with bristle dynamics: lumped and distributed formulations
Abstract:
Dynamic models, particularly rate‑dependent models, have proven effective in capturing the key phenomenological features of frictional processes, whilst also possessing important mathematical properties that facilitate the design of control and estimation algorithms. However, many rate‑dependent formulations are built on empirical considerations, whereas physical derivations may offer greater interpretability. In this context, starting from fundamental physical principles, this paper introduces a novel class of first‑order dynamic friction models that approximate the dynamics of a bristle element by inverting the friction characteristic. Amongst the developed models, a specific formulation closely resembling the LuGre model is derived using a simple rheological equation for the bristle element. This model is rigorously analyzed in terms of stability and passivity ‑‑ important properties that support the synthesis of observers and controllers. Furthermore, a distributed version, formulated as a hyperbolic partial differential equation (PDE), is presented, which enables the modeling of frictional processes commonly encountered in rolling contact phenomena. The tribological behavior of the proposed description is evaluated through classical experiments and validated against the response predicted by the LuGre model, revealing both notable similarities and key differences.

Authors:Luigi Romano, Ole Morten Aamo, Jan Åslund, Erik Frisk
Title: Lateral tracking control of all-wheel steering vehicles with intelligent tires
Abstract:
The accurate characterization of tire dynamics is critical for advancing control strategies in autonomous road vehicles, as tire behavior significantly influences handling and stability through the generation of forces and moments at the tire‑road interface. Smart tire technologies have emerged as a promising tool for sensing key variables such as road friction, tire pressure, and wear states, and for estimating kinematic and dynamic states like vehicle speed and tire forces. However, most existing estimation and control algorithms rely on empirical correlations or machine learning approaches, which require extensive calibration and can be sensitive to variations in operating conditions. In contrast, model‑based techniques, which leverage infinite‑dimensional representations of tire dynamics using partial differential equations (PDEs), offer a more robust approach. This paper proposes a novel model‑based, output‑feedback lateral tracking control strategy for all‑wheel steering vehicles that integrates distributed tire dynamics with smart tire technologies. The primary contributions include the suppression of micro‑shimmy phenomena at low speeds and path‑following via force control, achieved through the estimation of tire slip angles, vehicle kinematics, and lateral tire forces. The proposed controller and observer are based on formulations using ODE‑PDE systems, representing rigid body dynamics and distributed tire behavior. This work marks the first rigorous control strategy for vehicular systems equipped with distributed tire representations in conjunction with smart tire technologies.

Authors:Nazanin S. Hashkavaei, Abhijit Dongare, Neon Srinivasu, Amit K. Sanyal
Title: Finite-time Stable Pose Estimation on TSE(3) using Point Cloud and Velocity Sensors
Abstract:
This work presents a finite‑time stable pose estimator (FTS‑PE) for rigid bodies undergoing rotational and translational motion in three dimensions, using measurements from onboard sensors that provide position vectors to inertially‑fixed points and body velocities. The FTS‑PE is a full‑state observer for the pose (position and orientation) and velocities and is obtained through a Lyapunov analysis that shows its stability in finite time and its robustness to bounded measurement noise. Further, this observer is designed directly on the state space, the tangent bundle of the Lie group of rigid body motions, SE(3), without using local coordinates or (dual) quaternion representations. Therefore, it can estimate arbitrary rigid body motions without encountering singularities or the unwinding phenomenon and be readily applied to autonomous vehicles. A version of this observer that does not need translational velocity measurements and uses only point clouds and angular velocity measurements from rate gyros, is also obtained. It is discretized using the framework of geometric mechanics for numerical and experimental implementations. The numerical simulations compare the FTS‑PE with a dual‑quaternion extended Kalman filter and our previously developed variational pose estimator (VPE). The experimental results are obtained using point cloud images and rate gyro measurements obtained from a Zed 2i stereo depth camera sensor. These results validate the stability and robustness of the FTS‑PE.

Authors:Hansini Ramachandran, Bhaskar Krishnamachari
Title: Escaping Local Minima: A Finite-Time Markov Chain Analysis of Constant-Temperature Simulated Annealing
Abstract:
Simulated Annealing (SA) is a widely used stochastic optimization algorithm, yet much of its theoretical understanding is limited to asymptotic convergence guarantees or general spectral bounds. In this paper, we develop a finite‑time analytical framework for constant‑temperature SA by studying a piecewise linear cost function that permits exact characterization. We model SA as a discrete‑state Markov chain and first derive a closed‑form expression for the expected time to escape a single linear basin in a one‑dimensional landscape. We show that this expression also accurately predicts the behavior of continuous‑state searches up to a constant scaling factor, which we analyze empirically and explain via variance matching, demonstrating convergence to a factor of sqrt(3) in certain regimes. We then extend the analysis to a two‑basin landscape containing a local and a global optimum, obtaining exact expressions for the expected time to reach the global optimum starting from the local optimum, as a function of basin geometry, neighborhood radius, and temperature. Finally, we demonstrate how the predicted basin escape time can be used to guide the design of a simple two‑temperature switching strategy.

Authors:Md Mahfuzur Rahman Chy, Md Rifat Al Amin Khan, Md Sultan Mahamud, Anwarul Islam Sifat, Fiona J. Stevens McFadden
Title: Real-time Load Current Monitoring of Overhead Lines Using GMR Sensors
Abstract:
Non‑contact current monitoring has emerged as a prominent research focus owing to its non‑intrusive characteristics and low maintenance requirements. However, while they offer high sensitivity, contactless sensors necessitate sophisticated design methodologies and thorough experimental validation. In this study, a Giant Magneto‑Resistance (GMR) sensor is employed to monitor the instantaneous currents of a three‑phase 400‑volt overhead line, and its performance is evaluated against that of a conventional contact‑based Hall effect sensor. A mathematical framework is developed to calculate current from the measured magnetic field signals. Furthermore, a MATLAB‑based dashboard is implemented to enable real‑time visualization of current measurements from both sensors under linear and non‑linear load conditions. The GMR current sensor achieved a relative accuracy of 64.64% to 91.49%, with most phases above 80%. Identified improvements over this are possible, indicating that the sensing method has potential as a basis for calculating phase currents.

Authors:Branislav Radeljic
Title: Genocide by Algorithm in Gaza: Artificial Intelligence, Countervailing Responsibility, and the Corruption of Public Discourse
Abstract:
The accelerating militarization of artificial intelligence has transformed the ethics, politics, and governance of warfare. This article interrogates how AI‑driven targeting systems function as epistemic infrastructures that classify, legitimize, and execute violence, using Israel's conduct in Gaza as a paradigmatic case. Through the lens of responsibility, the article examines three interrelated dimensions: (a) political responsibility, exploring how states exploit AI to accelerate warfare while evading accountability; (b) professional responsibility, addressing the complicity of technologists, engineers, and defense contractors in the weaponization of data; and (c) personal responsibility, probing the moral agency of individuals who participate in or resist algorithmic governance. This is complemented by an examination of the position and influence of those participating in public discourse, whose narratives often obscure or normalize AI‑enabled violence. The Gaza case reveals AI not as a neutral instrument but as an active participant in the reproduction of colonial hierarchies and the normalization of atrocity. Ultimately, the paper calls for a reframing of technological agency and accountability in the age of automated warfare. It concludes that confronting algorithmic violence demands a democratization of AI ethics, one that resists technocratic fatalism and centers the lived realities of those most affected by high‑tech militarism.

Authors:Andrey Gorbunov, Youhong Chen, Petr Vorobev, Jin Ma, Gregor Verbic
Title: Dynamic Passivity Multipliers for Plug-and-Play Stability Certificates of Converter-Dominated Grids
Abstract:
Ensuring small‑signal stability in power systems with a high share of inverter‑based resources (IBRs) is hampered by two factors: (i) device and network parameters are often uncertain or completely unknown, and (ii) brute‑force enumeration of all topologies is computationally intractable. These challenges motivate plug‑and‑play (PnP) certificates that verify stability locally yet hold globally. Passivity is an attractive property because it guarantees stability under feedback and network interconnections; however, strict passivity rarely holds for practical controllers such as Grid Forming Inverters (GFMs) employing P‑Q droop. This paper extends the passivity condition by constructing a dynamic, frequency‑dependent multiplier that enables PnP stability certification of each component based solely on its admittance, without requiring any modification to the controller design. The multiplier is parameterised as a linear filter whose coefficients are tuned under a passivity goal. Numerical results for practical droop gains confirm the PnP rules, substantially enlarging the certified stability region while preserving the decentralised, model‑agnostic nature of passivity‑based PnP tests.

Authors:Jasper Juchem, Mia Loccufier
Title: Shaping Energy Exchange with Gyroscopic Interconnections: a geometric approach
Abstract:
Gyroscopic interconnections enable redistribution of energy among degrees of freedom while preserving passivity and total energy, and they play a central role in controlled Lagrangian methods and IDA‑PBC. Yet their quantitative effect on transient energy exchange and subsystem performance is not well characterised. We study a conservative mechanical system with constant skew‑symmetric velocity coupling. Its dynamics are integrable and evolve on invariant two‑tori, whose projections onto subsystem phase planes provide geometric description of energy exchange. When the ratio of normal‑mode frequencies is rational, these projections become closed resonant Lissajous curves, enabling structured analysis of subsystem trajectories. To quantify subsystem behaviour, we introduce the inscribed‑radius metric: the radius of the largest origin‑centred circle contained in a projected trajectory. This gives a lower bound on attainable subsystem energy and acts as an internal performance measure. We derive resonance conditions and develop an efficient method to compute or certify the inscribed radius without time‑domain simulation. Our results show that low‑order resonances can strongly restrict energy depletion through phase‑locking, whereas high‑order resonances recover conservative bounds. These insights lead to an explicit interconnection‑shaping design framework for both energy absorption and containment control strategies, while taking responsiveness into account.

Authors:Hamed Faghihian, Arman Sargolzaei
Title: An Actor-Critic-Identifier Control Design for Increasing Energy Efficiency of Automated Electric Vehicles
Abstract:
Electric vehicles (EVs) are increasingly deployed, yet range limitations remain a key barrier. Improving energy efficiency via advanced control is therefore essential, and emerging vehicle automation offers a promising avenue. However, many existing strategies rely on indirect surrogates because linking power consumption to control inputs is difficult. We propose a neural‑network (NN) identifier that learns this mapping online and couples it with an actor‑critic reinforcement learning (RL) framework to generate optimal control commands. The resulting actor‑critic‑identifier architecture removes dependence on explicit models relating total power, recovered energy, and inputs, while maintaining accurate speed tracking and maximizing efficiency. Update laws are derived using Lyapunov stability analysis, and performance is validated in simulation. Compared to a traditional controller, the method increases total energy recovery by 12.84%, indicating strong potential for improving EV energy efficiency.

Authors:Jackson Habala, Gabriel B. Margolis, Tianyu Wang, Pratyush Bhatt, Juntao He, Naheel Naeem, Zhaochen Xu, Pulkit Agrawal, Daniel I. Goldman, Di Luo, Baxi Chong
Title: Agile asymmetric multi-legged locomotion: contact planning via geometric mechanics and spin model duality
Abstract:
Legged robot research is presently focused on bipedal or quadrupedal robots, despite capabilities to build robots with many more legs to potentially improve locomotion performance. This imbalance is not necessarily due to hardware limitations, but rather to the absence of principled control frameworks that explain when and how additional legs improve locomotion performance. In multi‑legged systems, coordinating many simultaneous contacts introduces a severe curse of dimensionality that challenges existing modeling and control approaches. As an alternative, multi‑legged robots are typically controlled using low‑dimensional gaits originally developed for bipeds or quadrupeds. These strategies fail to exploit the new symmetries and control opportunities that emerge in higher‑dimensional systems. In this work, we develop a principled framework for discovering new control structures in multi‑legged locomotion. We use geometric mechanics to reduce contact‑rich locomotion planning to a graph optimization problem, and propose a spin model duality framework from statistical mechanics to exploit symmetry breaking and guide optimal gait reorganization. Using this approach, we identify an asymmetric locomotion strategy for a hexapod robot that achieves a forward speed of 0.61 body lengths per cycle (a 50% improvement over conventional gaits). The resulting asymmetry appears at both the control and hardware levels. At the control level, the body orientation oscillates asymmetrically between fast clockwise and slow counterclockwise turning phases for forward locomotion. At the hardware level, two legs on the same side remain unactuated and can be replaced with rigid parts without degrading performance. Numerical simulations and robophysical experiments validate the framework and reveal novel locomotion behaviors that emerge from symmetry reforming in high‑dimensional embodied systems.

Authors:Lucca Maitan, Lucas Toschi, Cícero Zanette, Elisa G. Vergamini, Leonardo F. Santos, Thiago Boaventura
Title: Contraction Metric Based Safe Reinforcement Learning Force Control for a Hydraulic Actuator with Real-World Training
Abstract:
Force control in hydraulic actuators is notoriously difficult due to strong nonlinearities, uncertainties, and the high risks associated with unsafe exploration during learning. This paper investigates safe reinforcement learning (RL) for hy draulic force control with real‑world training using contraction metric certificates. A data‑driven model of a hydraulic actuator, identified from experimental data, is employed for simulation based pretraining of a Soft Actor‑Critic (SAC) policy that adapts the PI gains of a feedback‑linearization (FL) controller. To reduce instability during online training, we propose a quadratic‑programming (QP) contraction filter that leverages a learned contraction metric to enforce approximate exponential convergence of trajectories, applying minimal corrections to the policy output. The approach is validated on a hydraulic test bench, where the RL controller is trained directly on hardware and benchmarked against a simulation‑trained agent and a fixed‑gain baseline. Experimental results show that real‑hardware training improves force‑tracking performance compared to both alternatives, while the contraction filter mitigates chattering and instabilities. These findings suggest that contraction‑based certificates can enable safe RL in high force hydraulic systems, though robustness at extreme operating conditions remains a challenge.

Authors:Edoardo Giusti, Krishan Kumar Tiwari, C. J. Reddy, Danilo Brizi, Agostino Monorchio, Giuseppe Caire
Title: Artificial Magnetic Conductor Frame to Improve Impedance Matching and Radiation Symmetry in 2$\times$2 Array for 6G Applications
Abstract:
An Artificial Magnetic Conductor (AMC) frame capable of improving the impedance matching of a 2×2 array for 6G applications without degrading isolation performance is presented. The proposed frame is integrated into the array without modifying the single radiating element design. By relying on accurate full‑wave simulations, it results that the addition of the frame restores the impedance matching performance, achieving a bandwidth of 1.5 GHz at 28 GHz. The isolation between each port remains under ‑15 dB within the operating band, thanks to the vias in the rectangular patch metasurface. Moreover, the overall structure exhibits a gain of 11.81 dBi with an aperture efficiency of 69%, satisfactorily for broadband communication purposes. The proposed AMC frame represents an effective method for improving array performance without the need to alter the shape or dimensions of the single radiating element.

Authors:Brycen D. Pearl, Joshua G. Warner, Hang Woon Lee
Title: Automating the Wildfire Detection and Scheduling Pipeline with Maneuverable Earth Observation Satellites
Abstract:
Wildfires are becoming increasingly frequent, with potentially devastating consequences, including loss of life, infrastructure destruction, and severe environmental damage. Low Earth orbit satellites equipped with onboard sensors can capture critical information relative to active wildfires and enable near real‑time detection through machine learning algorithms applied to the acquired data. We propose a framework that automates the complete wildfire detection and satellite scheduling pipeline, entitled the WildFire‑applicable Intelligent and Responsive Ensemble for Detection and Scheduling (WildFIRE‑DS). This paper develops an algorithm to realize the vision of the WildFIRE‑DS as a proof of concept, integrating three key components: wildfire detection in satellite imagery, statistical updating that incorporates data from repeated flyovers, and multi‑satellite scheduling optimization. The algorithm enables wildfire detection using convolutional neural networks with sensor fusion techniques, incorporates subsequent flyover information via Bayesian statistics, and schedules a constellation of satellites using the state‑of‑the‑art Reconfigurable Earth Observation Satellite Scheduling Problem. Simulated experiments conducted using real‑world wildfire locations and the orbits of operational Earth observation satellites to demonstrate that this autonomous detection and scheduling approach effectively enhances wildfire monitoring capabilities.

Authors:Moussa Labbadi, Andrey Polyakov, Denis Efimov
Title: Accelerated Stabilization of Switched Linear MIMO Systems using Generalized Homogeneity
Abstract:
This paper addresses the problem of exponential and accelerated finite‑time, as well as nearly fixed‑time, stabilization of switched linear MIMO systems. The proposed approach relies on a generalized homogenization framework for switched linear systems and employs implicit Lyapunov functions for control design, covering both common and multiple Lyapunov function settings. Linear matrix equations and inequalities are derived to characterize the dilation generator and to synthesize the controller gains. Robustness of the resulting control laws with respect to system uncertainties and external disturbances is analyzed. The effectiveness of the proposed approach is illustrated through numerical examples.

Authors:Leonardo Colombo
Title: Quantum Riemannian Cubics with Obstacle Avoidance for Quantum Geometric Model Predictive Control
Abstract:
We propose a geometric model predictive control framework for quantum systems subject to smoothness and state constraints. By formulating quantum state evolution intrinsically on the projective Hilbert space, we penalize covariant accelerations to generate smooth trajectories in the form of Riemannian cubics, while incorporating state‑dependent constraints through potential functions. A structure‑preserving variational discretization enables receding‑horizon implementation, and a Lyapunov‑type stability result is established for the closed‑loop system. The approach is illustrated on the Bloch sphere for a two‑level quantum system, providing a viable pathway toward predictive feedback control of constrained quantum dynamics.

Authors:Jean-Pierre Busch, Lukas Ostendorf, Guido Linden, Lennart Reiher, Till Beemelmanns, Bastian Lampe, Timo Woopen, Lutz Eckstein
Title: karl. - A Research Vehicle for Automated and Connected Driving
Abstract:
As highly automated driving is transitioning from single‑vehicle closed‑access testing to commercial deployments of public ride‑hailing in selected areas (e.g., Waymo), automated driving and connected cooperative intelligent transport systems (C‑ITS) remain active fields of research. Even though simulation is omnipresent in the development and validation life cycle of automated and connected driving technology, the complex nature of public road traffic and software that masters it still requires real‑world integration and testing with actual vehicles. Dedicated vehicles for research and development allow testing and validation of software and hardware components under real‑world conditions early on. They also enable collecting and publishing real‑world datasets that let others conduct research without vehicle access, and support early demonstration of futuristic use cases. In this paper, we present karl., our new research vehicle for automated and connected driving. Apart from major corporations, few institutions worldwide have access to their own L4‑capable research vehicles, restricting their ability to carry out independent research. This paper aims to help bridge that gap by sharing the reasoning, design choices, and technical details that went into making karl. a flexible and powerful platform for research, engineering, and validation in the context of automated and connected driving. More impressions of karl. are available at https://karl.ac.

Authors:Luigi Romano, Ole Morten Aamo, Miroslav Krstić, Jan Åslund, Erik Frisk
Title: Passivity-exploiting stabilization of semilinear single-track vehicle models with distributed tire friction dynamics
Abstract:
This paper addresses the local stabilization problem for semilinear single‑track vehicle models with distributed tire friction dynamics, represented as interconnections of ordinary differential equations (ODEs) and hyperbolic partial differential equations (PDEs). A passivity‑exploiting backstepping design is presented, which leverages the strict dissipativity properties of the PDE subsystem to achieve exponential stabilization of the considered ODE‑PDE interconnection around a prescribed equilibrium. Sufficient conditions for local well‑posedness and exponential convergence are derived by constructing a Lyapunov functional combining the lumped and distributed states. Both state‑feedback and output‑feedback controllers are synthesized, the latter relying on a cascaded observer. The theoretical results are corroborated with numerical simulations, considering non‑ideal scenarios and accounting for external disturbances and uncertainties. Simulation results confirm that the proposed control strategy can effectively and robustly stabilize oversteer vehicles at high speeds, demonstrating the relevance of the approach for improving the safety and performance in automotive applications.

Authors:Luigi Romano, Ole Morten Aamo, Miroslav Krstić, Jan Åslund, Erik Frisk
Title: Stability and stabilization of semilinear single-track vehicle models with distributed tire friction dynamics via singular perturbation analysis
Abstract:
This paper investigates the stability and stabilization of semilinear single‑track vehicle models with distributed tire friction dynamics, modeled as interconnections of ordinary differential equations (ODEs) and hyperbolic partial differential equations (PDEs). Motivated by the long‑standing practice of neglecting transient tire dynamics in vehicle modeling and control, a rigorous justification is provided for such simplifications using singular perturbation theory. A perturbation parameter, defined as the ratio between a characteristic rolling contact length and the vehicle's longitudinal speed, is introduced to formalize the time‑scale separation between rigid‑body motion and tire dynamics. For sufficiently small values of this parameter, it is demonstrated that standard finite‑dimensional techniques can be applied to analyze the local stability of equilibria and to design stabilizing controllers. Both state‑feedback and output‑feedback designs are considered, under standard stabilizability and detectability assumptions. Whilst the proposed controllers follow classical approaches, the novelty of the work lies in establishing the first mathematical framework that rigorously connects distributed tire models with conventional vehicle dynamics. The results reconcile decades of empirical findings with a formal theoretical foundation and open new perspectives for the analysis and control of ODE‑PDE systems with distributed friction in automotive applications.

Authors:Wasif H. Syed, Juan E. Machado, Johannes Schiffer
Title: A Primal-Dual-Based Active Fault-Tolerant Control Scheme for Cyber-Physical Systems: Application to DC Microgrids
Abstract:
We consider the problem of active fault‑tolerant control in cyber‑physical systems composed of strictly passive linear‑time invariant dynamic subsystems. We cast the problem as a constrained optimization problem and propose an augmented primal‑dual gradient dynamics‑based fault‑tolerant control framework that enforces network‑level constraints and provides optimality guarantees for the post‑fault steady‑state operation. By suitably interconnecting the primal‑dual algorithm with the cyber‑physical dynamics, we provide sufficient conditions under which the resulting closed‑loop system possesses a unique and exponentially stable equilibrium point that satisfies the Karush‑‑Kuhn‑‑Tucker (KKT) conditions of the constrained problem. The framework's effectiveness is illustrated through numerical experiments on a DC microgrid.

Authors:Katharina Kaiser, Gustavo Valverde, Gabriela Hug
Title: Residential Peak Load Reduction via Direct Load Control under Limited Information
Abstract:
Thermostatically controlled loads and electric vehicles offer flexibility to reduce power peaks in low‑voltage distribution networks. This flexibility can be maximized if the devices are coordinated centrally, given some level of information about the controlled devices. In this paper, we propose novel optimization‑based control schemes with prediction capabilities that utilize limited information from heat pumps, electric water heaters, and electric vehicles. The objective is to flatten the total load curve seen by the distribution transformer by restricting the times at which the available flexible loads are allowed to operate, subject to the flexibility constraints of the loads to preserve customers' comfort. The original scheme was tested in a real‑world setup, considering both winter and summer days. The pilot results confirmed the technical feasibility but also informed the design of an improved version of the controller. Computer simulations using the adjusted controller show that, compared to the original formulation, the improved scheme achieves greater peak reductions in summer. Additionally, comparisons were made with an ideal controller, which assumes perfect knowledge of the inflexible load profile, the models of the controlled devices, the hot water and space heating demand, and future electric vehicle charging sessions. The proposed scheme with limited information achieves almost half of the potential average daily peak reduction that the ideal controller with perfect knowledge would achieve.

Authors:Lukas Flad, Felix Sebastian Nitz, Tobias Krawutschke
Title: A Comparative Analysis of the CERN ATLAS ITk MOPS Readout: A Feasibility Study on Production and Development Setups
Abstract:
The upcoming High‑Luminosity upgrade of the Large Hadron Collider (LHC) necessitates a complete replacement of the ATLAS Inner Detector with the new Inner Tracker (ITk). This upgrade imposes stringent requirements on the associated Detector Control System (DCS), which is responsible for the monitoring, control, and safety of the detector. A critical component of the ITk DCS is the Monitoring of Pixel System (MOPS), which supervises the local voltages and temperatures of the new pixel detector modules. This paper introduces a dedicated testbed and verification methodology for the MOPS readout, defining a structured set of test cases for two DCS‑readout architectures: a preliminary Raspberry Pi‑based controller, the "MOPS‑Hub Mock‑up"(MH Mock‑up), and the final production FPGA‑based "MOPS‑Hub" (MH). The methodology specifies the measurement chain for end‑to‑end latency, jitter, and data integrity across CAN and UART interfaces, including a unified time‑stamping scheme, non‑intrusive signal taps, and a consistent data‑logging and analysis pipeline. This work details the load profiles and scalability scenarios (baseline operation, full‑crate stress, and CAN Interface Card channel isolation), together with acceptance criteria and considerations for measurement uncertainty to ensure reproducibility. The objective is to provide a clear, repeatable procedure to qualify the MH architecture for production and deployment in the ATLAS ITk DCS. A companion paper will present the experimental results and the comparative analysis obtained using this testbed.

Authors:Akbar Anbar Jafari, Gholamreza Anbarjafari
Title: A Multi-physics Simulation Framework for High-power Microwave Counter-unmanned Aerial System Design and Performance Evaluation
Abstract:
The proliferation of small unmanned aerial systems (sUAS) operating under autonomous guidance has created an urgent need for non‑kinetic neutralization methods that are immune to conventional radio‑frequency jamming. This paper presents a comprehensive multi‑physics simulation framework for the design and performance evaluation of a high‑power microwave (HPM) counter‑UAS system operating at 2.45\,GHz. The framework integrates electromagnetic propagation modelling, antenna pattern analysis, electromagnetic coupling to unshielded drone wiring harnesses, and a sigmoid‑based semiconductor damage probability model calibrated to published CMOS latchup thresholds. A 10,000‑trial Monte Carlo analysis incorporating stochastic variations in transmitter power, antenna pointing error, target wire orientation, polarization mismatch, and component damage thresholds yields system‑level kill probabilities with 95% confidence intervals. For a baseline configuration of 25\,kW continuous‑wave power and a 60\,cm parabolic reflector (21.2\,dBi gain), the Monte Carlo simulation predicts a kill probability of 51.4\pm1.0% at 20\,m, decreasing to 13.1\pm0.7% at 40\,m. Pulsed operation at 500\,kW peak power (1% duty cycle) extends the 90% kill range from approximately 18\,m to 88\,m. The framework further provides parametric design maps, safety exclusion zone calculations compliant with ICNIRP 2020 guidelines, thermal management requirements, and waveguide mode analysis. All simulation codes and results are provided for full reproducibility.

Authors:Samsaptak Ghosh, M. Felix Orlando, Sohom Chakrabarty
Title: Post-Collision Trajectory Restoration for a Single-track Ackermann Vehicle using Heuristic Steering and Tractive Force Functions
Abstract:
Post‑collision trajectory restoration is a safety‑critical capability for autonomous vehicles, as impact‑induced lateral motion and yaw transients can rapidly drive the vehicle away from the intended path. This paper proposes a structured heuristic recovery control law that jointly commands steering and tractive force for a generalized single‑track Ackermann vehicle model. The formulation explicitly accounts for time‑varying longitudinal velocity in the lateral‑yaw dynamics and retains nonlinear steering‑coupled interaction terms that are commonly simplified in the literature. Unlike approaches that assume constant longitudinal speed, the proposed design targets the transient post‑impact regime where speed variations and nonlinear coupling significantly influence recovery. The method is evaluated in simulation on the proposed generalized single‑track model and a standard 3DOF single‑track reference model in MATLAB, demonstrating consistent post‑collision restoration behaviour across representative initial post‑impact conditions.

Authors:Davide Tebaldi, Roberto Zanasi
Title: An Approach for the Qualitative Graphical Representation of the Describing Function in Nonlinear Systems Stability Analysis
Abstract:
The describing function method is a useful tool for the qualitative analysis of limit cycles in the stability analysis of nonlinear systems. This method is inherently approximate; therefore, it should be used for a fast qualitative analysis of the considered systems. However, plotting the exact describing function requires heavy mathematical calculations, reducing interest in this method especially from the point of view of control education. The objective of this paper is to enhance the describing function method by providing a new approach for the qualitative plotting of the describing function for piecewise nonlinearities involving discontinuities. Unlike the standard method, the proposed approach allows for a straightforward, hand‑drawn plotting of the describing function using the rules introduced in this paper, simply by analyzing the shape of the nonlinearity. The proposed case studies show that the limit cycles estimation performed using the standard exact plotting of the describing function yields the same qualitative results as those obtained using the proposed qualitative method for plotting the describing function.

Authors:Yi-Hsuan Hsiao, Quang Phuc Kieu, Zhongtao Guan, Suhan Kim, Jiaze Cai, Owen Matteson, Jonathan P. How, Elizabeth Farrell Helbling, YuFeng Chen
Title: Controlled Flight of an Insect-Scale Flapping-Wing Robot via Integrated Onboard Sensing and Computation
Abstract:
Aerial insects can effortlessly navigate dense vegetation, whereas similarly sized aerial robots typically depend on offboard sensors and computation to maintain stable flight. This disparity restricts insect‑scale robots to operation within motion capture environments, substantially limiting their applicability to tasks such as search‑and‑rescue and precision agriculture. In this work, we present a 1.29‑gram aerial robot capable of hovering and tracking trajectories with solely onboard sensing and computation. The combination of a sensor suite, estimators, and a low‑level controller achieved centimeter‑scale positional flight accuracy. Additionally, we developed a hierarchical controller in which a human operator provides high‑level commands to direct the robot's motion. In a 30‑second flight experiment conducted outside a motion capture system, the robot avoided obstacles and ultimately landed on a sunflower. This level of sensing and computational autonomy represents a significant advancement for the aerial microrobotics community, further opening opportunities to explore onboard planning and power autonomy.

Authors:Shun Hirose, Shiu Mochiyama, Yoshihiko Susuki
Title: Experimental Realization of Koopman-Model Predictive Control for an AC-DC Converter
Abstract:
This paper experimentally demonstrates the Koopman‑Model Predictive Control (K‑MPC) for a real AC‑DC converter. The converter is typically modeled with a nonlinear time‑variant plant. We introduce a new dynamical approach to lifting measurable dynamics from the plant and constructing a linear time‑invariant model that is consistent with control objectives of the converter. We show that the lifting approach, combined with the K‑MPC controller, performs well across the full experimental system and outperforms existing control strategies in terms of both steady‑state and transient responses.

Authors:Melone Nyoba Tchonkeu, Soulaimane Berkane, Tarek Hamel
Title: Pitot-Aided Attitude and Air Velocity Estimation with Almost Global Asymptotic Stability Guarantees
Abstract:
This paper investigates the problem of attitude and air velocity estimation for fixed‑wing unmanned aerial vehicles (UAVs) using IMU measurements and at least one Pitot tube measurement, with almost global asymptotic stability (AGAS) guarantees. A cascade observer architecture is developed, in which a Riccati/Kalman‑type filter estimates the body‑fixed frame air velocity and the vehicle's tilt using IMU data as inputs and Pitot measurements as outputs. Under mild excitation conditions, the resulting air velocity and tilt estimation error dynamics are shown to be uniformly observable. The estimated tilt is then combined with magnetometer measurements in a nonlinear observer on SO(3) to recover the full attitude. Rigorous analysis establishes AGAS of the overall cascade structure under the uniform observability (UO) condition. The effectiveness of the proposed approach is demonstrated through validation on real flight data.

Authors:Fen Wu
Title: Robust and Gain-Scheduling ${\cal H}_2$ Control Techniques for LFT Uncertain and Parameter-Dependent Systems
Abstract:
This paper addresses the robust \cal H_2 synthesis problem for linear fractional transformation (LFT) systems subject to structured uncertainty (parameter) and white‑noise disturbances. By introducing an intermediate matrix variable, we derive convex synthesis conditions in terms of linear matrix inequalities (LMIs) that enable both robust and gain‑scheduled controller design for parameter‑dependent systems. The proposed framework preserves the classical white‑noise and impulse‑response interpretation of the \cal H_2 criterion while providing certified robustness guarantees, thereby extending optimal \cal H_2 control beyond the linear time‑invariant setting. Numerical and application examples demonstrate that the resulting robust \cal H_2 controllers achieve significantly reduced conservatism and improved disturbance rejection compared with conventional robust \cal H_\infty‑based designs.

Authors:Xihang Yu, Rajat Talak, Lorenzo Shaikewitz, Luca Carlone
Title: Picasso: Holistic Scene Reconstruction with Physics-Constrained Sampling
Abstract:
In the presence of occlusions and measurement noise, geometrically accurate scene reconstructions ‑‑ which fit the sensor data ‑‑ can still be physically incorrect. For instance, when estimating the poses and shapes of objects in the scene and importing the resulting estimates into a simulator, small errors might translate to implausible configurations including object interpenetration or unstable equilibrium. This makes it difficult to predict the dynamic behavior of the scene using a digital twin, an important step in simulation‑based planning and control of contact‑rich behaviors. In this paper, we posit that object pose and shape estimation requires reasoning holistically over the scene (instead of reasoning about each object in isolation), accounting for object interactions and physical plausibility. Towards this goal, our first contribution is Picasso, a physics‑constrained reconstruction pipeline that builds multi‑object scene reconstructions by considering geometry, non‑penetration, and physics. Picasso relies on a fast rejection sampling method that reasons over multi‑object interactions, leveraging an inferred object contact graph to guide samples. Second, we propose the Picasso dataset, a collection of 10 contact‑rich real‑world scenes with ground truth annotations, as well as a metric to quantify physical plausibility, which we open‑source as part of our benchmark. Finally, we provide an extensive evaluation of Picasso on our newly introduced dataset and on the YCB‑V dataset, and show it largely outperforms the state of the art while providing reconstructions that are both physically plausible and more aligned with human intuition.

Authors:Antonio Alcántara, Spyros Chatzivasileiadis
Title: Trustworthiness Layer for Foundation Models in Power Systems: Application to N-k Contingency Screening
Abstract:
We propose a model‑agnostic trustworthiness layer that equips any foundation model (FM) for power systems with statistically valid prediction intervals. The layer offers two calibration approaches: (i) stratified conformal prediction (SCP), which partitions residuals by contingency severity and grid element, and (ii) kernel‑weighted conformal prediction (KCP), which localizes the calibration to each test scenario via scenario representations, yielding tighter, approximately conditional bounds. Using GridFM as a guiding example, we demonstrate the framework on N‑k contingency screening for IEEE 24‑ and 118‑bus systems. The trustworthiness layer ensures that over 90% of all critical violations are captured across N‑k levels, minimizing missed detections while maintaining up to 5 times fewer false alarms than DC Power Flow. With negligible computational overhead over the underlying FM, this approach enables reliable large‑scale security assessment beyond routine N‑1 screening.

Authors:Yuhan Chen, Tao Liu, Jie Huang
Title: Leader-following Consensus over Jointly Connected Switching Networks is Achievable for Exponentially Unstable Linear Systems
Abstract:
The leader‑following consensus problem for general linear multi‑agent systems over jointly connected switching networks has been a challenging problem and the solvability of the problem has been limited to the class of linear multi‑agent systems whose system matrix is marginally stable. This condition is restrictive since it even excludes the most commonly used double‑integrator system. This paper presents a breakthrough by demonstrating that leader‑following exponential consensus is achievable for general linear multi‑agent systems over jointly connected switching networks, even when the system matrix is exponentially unstable. The degree of instability can be explicitly characterized by two key quantities that arise from the jointly connected condition on a switching graph. By exploiting duality, we further show that the output‑based distributed observer design problem for a general leader system is solvable over jointly connected switching networks, even when the system matrix is exponentially unstable. This is also in sharp contrast to the existing distributed observers, which rely on the assumption that the leader system is marginally stable.

Authors:Hyeonsu Lyu, Yumin Kim, Hyun Jong Yang
Title: End-to-End Secure Connection Probability in MultiLayer Networks with Heterogeneous Rician Fading
Abstract:
Ensuring physical‑layer security in non‑terrestrial networks (NTNs) is challenging due to their global coverage and multi‑hop relaying across heterogeneous network layers, where the locations and channels of potential eavesdroppers are typically unknown. In this work, we derive a tractable closedform expression of the end‑to‑end secure connection probability (SCP) of multi‑hop relay routes under heterogeneous Rician fading. The resulting formula shares the same functional form as prior Rayleigh‑based approximations but for the coefficients, thereby providing analytical support for the effectiveness of heuristic posterior coefficient calibration adopted in prior work. Numerical experiments under various conditions show that the proposed scheme estimates the SCP with an 1%p error in most cases; and doubles the accuracy compared with the conventional scheme even in the worst case. As a case study, we apply the proposed framework to real‑world space‑air‑groundsea integrated network dataset, showing that the derived SCP accurately captures observed security trends in practical settings.

Authors:Yumin Kim, Hyeonsu Lyu, Minjae Lee, Hyun Jong Yang
Title: Accuracy-Delay Trade-Off in LLM Offloading via Token-Level Uncertainty
Abstract:
Large language models (LLMs) offer significant potential for intelligent mobile services but are computationally intensive for resource‑constrained devices. Mobile edge computing (MEC) allows such devices to offload inference tasks to edge servers (ESs), yet introduces latency due to communication and serverside queuing, especially in multi‑user environments. In this work, we propose an uncertainty‑aware offloading framework that dynamically decides whether to perform inference locally or offload it to the ES, based on token‑level uncertainty and resource constraints. We define a margin‑based token‑level uncertainty metric and demonstrate its correlation with model accuracy. Leveraging this metric, we design a greedy offloading algorithm (GOA) that minimizes delay while maintaining accuracy by prioritizing offloading for highuncertainty queries. Our experiments show that GOA consistently achieves a favorable trade‑off, outperforming baseline strategies in both accuracy and latency across varying user densities, and operates with practical computation time. These results establish GOA as a scalable and effective solution for LLM inference in MEC environments.

Authors:Najiya Fatma, Varun Ramamohan
Title: Healthcare Facility Assignment Using Real-Time Length-of-Stay Predictions: Queuing-Theoretic and Simulation-driven Machine Learning Approaches
Abstract:
Longer stays at healthcare facilities, driven by uncertain patient load, inefficient patient flow, and lack of real‑time information about medical care, pose significant challenges for patients and healthcare providers. Providing patients with estimates of their expected real‑time length of stay (RT‑LOS), generated as a function of the operational state of the healthcare facility at their anticipated time of arrival (as opposed to estimates of average LOS), can help them make informed decisions regarding which facility to visit within a network. In this study, we develop a healthcare facility assignment (HFA) algorithm that assigns healthcare facilities to patients using RT‑LOS predictions at facilities within the network of interest. We describe the generation of RT‑LOS predictions via two methodologies: (a) an analytical queuing‑theoretic approach, and (b) a hybrid simulation‑driven machine learning approach. Because RT‑LOS predictors are highly specific to the queuing system in question, we illustrate the development of RT‑LOS predictors using both approaches by considering the outpatient experience at primary health centers. Via computational experiments, we compare outcomes from the implementation of the RT‑HFA algorithm with both RT‑LOS predictors to the case where patients visit the facility of their choice. Computational experiments also indicated that the RT‑HFA algorithm substantially reduced patient wait times and LOS at congested facilities and led to more equitable utilization of medical resources at facilities across the network. Finally, we show numerically that the effectiveness of the RT‑HFA algorithm in improving outcomes is contingent on the level of compliance with the assignment decision.

Authors:Hongzhao Zheng, Mohamed Atia, Halim Yanikomeroglu
Title: Optimized Deployment of HAPS Systems for GNSS Localization Enhancement in Urban Environments
Abstract:
While high altitude platform stations (HAPS) have been primarily explored as network infrastructure for communication services, their advantageous characteristics also make them promising candidates for augmenting GNSS localization. This paper proposes a metaheuristic framework to jointly optimize the number and placement of HAPS for GNSS enhancement in dense urban environments, considering practical constraints such as elevation masks, altitude limits, and ray‑traced visibility from 3D city models. The problem is highly nonconvex due to the discrete HAPS count and the environment‑dependent 3D Cramer‑Rao lower bound (CRLB). To address this, we develop a tailored version of the adaptive special‑crowding distance non‑dominated sorting genetic algorithm II (ASDNSGA‑II). Simulations show the method successfully identifies the minimum number of HAPS needed to satisfy a CRLB threshold and selects the configuration with the lowest CRLB within that minimum, offering a cost‑effective and scalable solution for future HAPS‑aided positioning systems.

Authors:Siyu Lu, Chenhan Xiao, Yang Weng
Title: Dynamic Load Model for Data Centers with Pattern-Consistent Calibration
Abstract:
The rapid growth of data centers has made large electronic load (LEL) modeling increasingly important for power system analysis. Such loads are characterized by fast workload‑driven variability and protection‑driven disconnection and reconnection behavior that are not captured by conventional load models. Existing data center load modeling includes physics‑based approaches, which provide interpretable structure for grid simulation, and data‑driven approaches, which capture empirical workload variability from data. However, physics‑based models are typically uncalibrated to facility‑level operation, while trajectory alignment in data‑driven methods often leads to overfitting and unrealistic dynamic behavior. To resolve these limitations, we design the framework to leverage both physics‑based structure and data‑driven adaptability. The physics‑based structure is parameterized to enable data‑driven pattern‑consistent calibration from real operational data, supporting facility‑level grid planning. We further show that trajectory‑level alignment is limited for inherently stochastic data center loads. Therefore, we design the calibration to align temporal and statistical patterns using temporal contrastive learning (TCL). This calibration is performed locally at the facility, and only calibrated parameters are shared with utilities, preserving data privacy. The proposed load model is calibrated by real‑world operational load data from the MIT Supercloud, ASU Sol, Blue Waters, and ASHRAE datasets. Then it is integrated into the ANDES platform and evaluated on the IEEE 39‑bus, NPCC 140‑bus, and WECC 179‑bus systems. We find that interactions among LELs can fundamentally alter post‑disturbance recovery behavior, producing compound disconnection‑reconnection dynamics and delayed stabilization that are not captured by uncalibrated load models.

Authors:Jianhua Sun, Kaihong Lu, Xin Yu
Title: Convergence Analysis of Continuous-Time Distributed Stochastic Gradient Algorithms
Abstract:
In this paper, we propose a new framework to study distributed optimization problems with stochastic gradients by employing a multi‑agent system with continuous‑time dynamics. Here the goal of the agents is to cooperatively minimize the sum of convex objective functions. When making decisions, each agent only has access to a stochastic gradient of its own objective function rather than the real gradient, and can exchange local state information with its immediate neighbors via a time‑varying directed graph. Particularly, the stochasticity is depicted by the Brownian motion. To handle this problem, we propose a continuous‑time distributed stochastic gradient algorithm based on the consensus algorithm and the gradient descent strategy. Under mild assumptions on the connectivity of the graph and objective functions, using convex analysis theory, the Lyapunov theory and Ito formula, we prove that the states of the agents asymptotically reach a common minimizer in expectation. Finally, a simulation example is worked out to demonstrate the effectiveness of our theoretical results.

Authors:Xiaohan Xu, Wei Ma, Zhiheng Shi, Xiaotong Xu, Bin He, Kairui Feng
Title: Urban Congestion Patterns under High Electric Vehicle Penetration: A Case Study of 10 U.S. Cities
Abstract:
With the global energy transition and the rapid penetration of electric vehicles (EVs), the widening travel cost gap between EVs and gasoline vehicles (GVs) increasingly affects commuters' route choices and may reshape urban congestion patterns. Existing research remains in its preliminary exploratory phase. On the one hand, multi‑class models do not account for fixed user class scenarios, which may not align with actual commuters; on the other hand, there is a lack of systematic quantitative analysis based on real‑world complex road networks across multiple cities. As a result, the congestion effects induced by heterogeneous GV‑EV cost structures may be mischaracterized or substantially underestimated. To address these limitations, this paper proposes a multi‑user equilibrium (MUE) assignment model for mixed GV‑EV traffic, constructs a dual algorithm with convergence guarantees, and designs multi‑dimensional evaluation metrics for congestion patterns. Using 10 representative U.S. cities as a case study, this research explores the evolution trends of traffic congestion under different EV penetration scenarios based on real city‑level road networks and block‑level commuter origin‑destination (OD) demand. The results show that full EV penetration reduces average system travel time by 2.27%‑‑10.78% across the 10 cities, with New Orleans achieving the largest reduction (10.78%) and San Francisco the smallest (2.27%), but the effectiveness of alleviating congestion exhibits urban heterogeneity. Moreover, for cities with sufficient network redundancy, benefits are primarily concentrated during the low to medium EV penetration stage (0‑0.5), though cities with topological constraints (e.g., San Francisco) show more limited improvements throughout all penetration levels. This paper can provide a foundation for formulating differentiated urban planning and congestion management policies.

Authors:Arslan Ahmad, Ian Dobson
Title: Quantifying resilience for distribution system customers with SALEDI
Abstract:
The impact of routine smaller outages on distribution system customers in terms of customer minutes interrupted can be tracked using conventional reliability indices. However, the customer minutes interrupted in large blackout events are extremely variable, and this makes it difficult to quantify the customer impact of these extreme events with resilience metrics. We solve this problem with the System Average Large Event Duration Index SALEDI that logarithmically transforms the customer minutes interrupted. We explain how this new resilience metric works, compare it with alternatives, quantify its statistical accuracy, and illustrate its practical use with standard outage data from five utilities.

Authors:Aron Mathias, Mohammad Ghufran, Jack Hughes, Hossein Rastgoftar
Title: Affine Transformable Unmanned Ground Vehicle
Abstract:
This paper develops the proof of concept for a novel affine transformable unmanned ground vehicle (ATUGV) with the capability of safe and aggressive deformation while carrying multiple payloads. The ATUGV is a multi‑body system with mobile robots that can be used to power the ATUGV morphable motion, powered cells to enclose the mobile robots, unpowered cells to contain payloads, and a deformable structure to integrate cells through bars and joints. The objective is that all powered and unpowered cells motion can safely track a desired affine transformation, where an affine transformation can be decomposed into translation, rigid body rotation, and deformation. To this end, the paper first uses a deep neural network to structure cell interconnection in such a way that every cell can freely move over the deformation plane, and the entire structure can reconfigurably deform to track a desired affine transformation. Then, the mobile robots, contained by the powered cells and stepper motors, regulating the connections of the powered and unpowered cells, design the proper controls so that all cells safely track the desired affine transformation. The functionality of the proposed ATUGV is validated through hardware experimentation and simulation.

Authors:Thomas Beckers, Ján Drgoňa, Truong X. Nghiem
Title: $\partial$CBDs: Differentiable Causal Block Diagrams
Abstract:
Modern cyber‑physical systems (CPS) integrate physics, computation, and learning, demanding modeling frameworks that are simultaneously composable, learnable, and verifiable. Yet existing approaches treat these goals in isolation: causal block diagrams (CBDs) support modular system interconnections but lack differentiability for learning; differentiable programming (DP) enables end‑to‑end gradient‑based optimization but provides limited correctness guarantees; while contract‑based verification frameworks remain largely disconnected from data‑driven model refinement. To address these limitations, we introduce differentiable causal block diagrams (\partialCBDs), a unifying formalism that integrates these three perspectives. Our approach (i) retains the compositional structure and execution semantics of CBDs, (ii) incorporates assume‑‑guarantee (A‑‑G) contracts for modular correctness reasoning, and (iii) introduces residual‑based contracts as differentiable, trajectory‑level certificates compatible with automatic differentiation (AD), enabling gradient‑based optimization and learning. Together, these elements enable a scalable, verifiable, and trainable modeling pipeline that preserves causality and modularity while supporting data‑, physics‑, and constraint‑informed optimization for CPS.

Authors:Yuang Chen, Fengqian Guo, Chang Wu, Mingyu Peng, Hancheng Lu, Chang Wen Chen
Title: NOMA-Assisted Multi-BS MEC Networks for Delay-Sensitive and Computation-Intensive IoT Applications
Abstract:
The burgeoning and ubiquitous deployment of the Internet of Things (IoT) landscape struggles with ultra‑low latency demands for computation‑intensive tasks in massive connectivity scenarios. In this paper, we propose an innovative uplink non‑orthogonal multiple access (NOMA)‑assisted multi‑base station (BS) mobile edge computing (BS‑MEC) network tailored for massive IoT connectivity. To fulfill the quality‑of‑service (QoS) requirements of delay‑sensitive and computation‑intensive IoT applications, we formulate a joint task offloading, user grouping, and power allocation optimization problem with the overarching objective of minimizing the system's total delay, aiming to address issues of unbalanced subchannel access, inter‑group interference, computational load disparities, and device heterogeneity. To effectively tackle this problem, we first reformulate task offloading and user grouping into a non‑cooperative game model and propose an exact potential game‑based joint decision‑making (EPG‑JDM) algorithm, which dynamically selects optimal task offloading and subchannel access decisions for each IoT device based on its channel conditions, thereby achieving the Nash Equilibrium. Then, we propose a majorization‑minimization (MM)‑based power allocation algorithm, which transforms the original subproblem into a tractable convex optimization paradigm. Extensive simulation experiments demonstrate that our proposed EPG‑JDM algorithm significantly outperforms state‑of‑the‑art decision‑making algorithms and classic heuristic algorithms, yielding performance improvements of up to 19.3% and 14.7% in terms of total delay and power consumption, respectively.

Authors:Linyu Lin
Title: In-Context System Identification for Nonlinear Dynamics Using Large Language Models
Abstract:
Sparse Identification of Nonlinear Dynamics (SINDy) is a powerful method for discovering parsimonious governing equations from data, but it often requires expert tuning of candidate libraries. We propose an LLM‑aided SINDy pipeline that iteratively refines candidate equations using a large language model (LLM) in the loop through in‑context learning. The pipeline begins with a baseline SINDy model fit using an adaptive library and then enters a LLM‑guided refinement cycle. At each iteration, the current best equations, error metrics, and domain‑specific constraints are summarized in a prompt to the LLM, which suggests new equation structures. These candidate equations are parsed against a defined symbolic form and evaluated on training and test data. The pipeline uses simulation‑based error as a primary metric, but also assesses structural similarity to ground truth, including matching functional forms, key terms, couplings, qualitative behavior. An iterative stopping criterion ends refinement early if test error falls below a threshold (NRMSE < 0.1) or if a maximum of 10 iterations is reached. Finally, the best model is selected, and we evaluate this LLM‑aided SINDy on 63 dynamical system datasets (ODEBench) and march leuba model for boiling nuclear reactor. The results are compared against classical SINDy and show the LLM‑loop consistently improves symbolic recovery with higher equation similarity to ground truth and lower test RMSE than baseline SINDy for cases with complex dynamics. This work demonstrates that an LLM can effectively guide SINDy's search through equation space, integrating data‑driven error feedback with domain‑inspired symbolic reasoning to discover governing equations that are not only accurate but also structurally interpretable.

Authors:Minduli C. Wijayatunga, Richard Linares, Roberto Armellin
Title: Meta-Reinforcement Learning for Robust and Non-greedy Control Barrier Functions in Spacecraft Proximity Operations
Abstract:
Autonomous spacecraft inspection and docking missions require controllers that can guarantee safety under thrust constraints and uncertainty. Input‑constrained control barrier functions (ICCBFs) provide a framework for safety certification under bounded actuation; however, conventional ICCBF formulations can be overly conservative and exhibit limited robustness to uncertainty, leading to high fuel consumption and reduced mission feasibility. This paper proposes a framework in which the full hierarchy of class‑\mathcalK functions defining the ICCBF recursion is parameterized and learned, enabling localized shaping of the safe set and reduced conservatism. A control margin is computed efficiently using differential algebra to enable the learned continuous‑time ICCBFs to be implemented on time‑sampled dynamical systems typical of spacecraft proximity operations. A meta‑reinforcement learning scheme is developed to train a policy that generates ICCBF parameters over a distribution of hidden physical parameters and uncertainties, using both multilayer perceptron (MLP) and recurrent neural network (RNN) architectures. Simulation results on cruise control, spacecraft inspection, and docking scenarios demonstrate that the proposed approach maintains safety while reducing fuel consumption and improving feasibility relative to fixed class‑\mathcalK ICCBFs, with the RNN showing a particularly strong advantage in the more complex inspection case.

Authors:Edgar Lee, Junho Choi, Taemin Kim, Changjoo Nam, Seokhwan Jeong
Title: Why Look at It at All?: Vision-Free Multifingered Blind Grasping Using Uniaxial Fingertip Force Sensing
Abstract:
Grasping under limited sensing remains a fundamental challenge for real‑world robotic manipulation, as vision and high‑resolution tactile sensors often introduce cost, fragility, and integration complexity. This work demonstrates that reliable multifingered grasping can be achieved under extremely minimal sensing by relying solely on uniaxial fingertip force feedback and joint proprioception, without vision or multi‑axis/tactile sensing. To enable such blind grasping, we employ an efficient teacher‑student training pipeline in which a reinforcement‑learned teacher exploits privileged simulation‑only observations to generate demonstrations for distilling a transformer‑based student policy operating under partial observation. The student policy is trained to act using only sensing modalities available at real‑world deployment. We validate the proposed approach on real hardware across 18 objects, including both in‑distribution and out‑of‑distribution cases, achieving a 98.3~% overall grasp success rate. These results demonstrate strong robustness and generalization beyond the simulation training distribution, while significantly reducing sensing requirements for real‑world grasping systems.

Authors:Ganghui Cao, Xunyuan Yin
Title: Distributed Omniscient Observers for Multi-Agent Systems: Design and Applications
Abstract:
This paper proposes distributed omniscient observers for both heterogeneous and homogeneous linear multi‑agent systems, such that each agent can correctly estimate the states of all agents. The observer design is based on local input‑output information available to each agent, and knowledge of the global communication graph among agents is not necessarily required. The proposed observers can contribute to distributed Nash equilibrium seeking in multi‑player games and the emergence of self‑organized social behaviors in artificial swarms. Simulation results demonstrate that artificial swarms can emulate animal social behaviors, including sheepdog herding and honeybee dance‑based navigation.

Authors:Rivaaj Monsia, Daniel Young, Olivier Francon, Risto Miikkulainen
Title: Optimizing Chlorination in Water Distribution Systems via Surrogate-assisted Neuroevolution
Abstract:
Ensuring the microbiological safety of large, heterogeneous water distribution systems (WDS) typically requires managing appropriate levels of disinfectant residuals including chlorine. WDS include complex fluid interactions that are nonlinear and noisy, making such maintenance a challenging problem for traditional control algorithms. This paper proposes an evolutionary framework to this problem based on neuroevolution, multi‑objective optimization, and surrogate modeling. Neural networks were evolved with NEAT to inject chlorine at strategic locations in the distribution network at select times. NSGA‑II was employed to optimize four objectives: minimizing the total amount of chlorine injected, keeping chlorine concentrations homogeneous across the network, ensuring that maximum concentrations did not exceed safe bounds, and distributing the injections regularly over time. Each network was evaluated against a surrogate model, i.e.\ a neural network trained to emulate EPANET, an industry‑level hydraulic WDS simulator that is accurate but infeasible in terms of computational cost to support machine learning. The evolved controllers produced a diverse range of Pareto‑optimal policies that could be implemented in practice, outperforming PPO, a standard reinforcement learning method. The results thus suggest a pathway toward improving urban water systems, and highlight the potential of using evolution with surrogate modeling to optimize complex real‑world systems.

Authors:Haiyuan Li, Hari Madhukumar, Shuangyi Yan, Yulei Wu, Dimitra Simeonidou
Title: Multi-Agentic AI for Fairness-Aware and Accelerated Multi-modal Large Model Inference in Real-world Mobile Edge Networks
Abstract:
Generative AI (GenAI) has transformed applications in natural language processing and content creation, yet centralized inference remains hindered by high latency, limited customizability, and privacy concerns. Deploying large models (LMs) in mobile edge networks emerges as a promising solution. However, it also poses new challenges, including heterogeneous multi‑modal LMs with diverse resource demands and inference speeds, varied prompt/output modalities that complicate orchestration, and resource‑limited infrastructure ill‑suited for concurrent LM execution. In response, we propose a Multi‑Agentic AI framework for latency‑ and fairness‑aware multi‑modal LM inference in mobile edge networks. Our solution includes a long‑term planning agent, a short‑term prompt scheduling agent, and multiple on‑node LM deployment agents, all powered by foundation language models. These agents cooperatively optimize prompt routing and LM deployment through natural language reasoning over runtime telemetry and historical experience. To evaluate its performance, we further develop a city‑wide testbed that supports network monitoring, containerized LM deployment, intra‑server resource management, and inter‑server communications. Experiments demonstrate that our solution reduces average latency by over 80% and improves fairness (Normalized Jain index) to 0.90 compared to other baselines. Moreover, our solution adapts quickly without fine‑tuning, offering a generalizable solution for optimizing GenAI services in edge environments.

Authors:Tong Jian, Tianyu Dai, Tao Yu
Title: Learning Nonlinear Systems In-Context: From Synthetic Data to Real-World Motor Control
Abstract:
LLMs have shown strong in‑context learning (ICL) abilities, but have not yet been extended to signal processing systems. Inspired by their design, we have proposed for the first time ICL using transformer models applicable to motor feedforward control, a critical task where classical PI and physics‑based methods struggle with nonlinearities and complex load conditions. We propose a transformer based model architecture that separates signal representation from system behavior, enabling both few‑shot finetuning and one‑shot ICL. Pretrained on a large corpus of synthetic linear and nonlinear systems, the model learns to generalize to unseen system dynamics of real‑world motors only with a handful of examples. In experiments, our approach generalizes across multiple motor load configurations, transforms untuned examples into accurate feedforward predictions, and outperforms PI controllers and physics‑based feedforward baselines. These results demonstrate that ICL can bridge synthetic pretraining and real‑world adaptability, opening new directions for data efficient control of physical systems.

Authors:H. Emre Tekaslan, Ella M. Atkins
Title: Airspace-aware Contingency Landing Planning
Abstract:
This paper develops a real‑time, search‑based aircraft contingency landing planner that minimizes traffic disruptions while accounting for ground risk. The airspace model captures dense air traffic departure and arrival flows, helicopter corridors, and prohibited zones and is demonstrated with a Washington, D.C., area case study. Historical Automatic Dependent Surveillance‑Broadcast (ADS‑B) data are processed to estimate air traffic density. A low‑latency computational geometry algorithm generates proximity‑based heatmaps around high‑risk corridors and restricted regions. Airspace risk is quantified as the cumulative exposure time of a landing trajectory within congested regions, while ground risk is assessed from overflown population density to jointly guide trajectory selection. A landing site selection module further mitigates disruption to nominal air traffic operations. Benchmarking against minimum‑risk Dubins solutions demonstrates that the proposed planner achieves lower joint risk and reduced airspace disruption while maintaining real‑time performance. Under airspace‑risk‑only conditions, the planner generates trajectories within an average of 2.9 seconds on a laptop computer. Future work will incorporate dynamic air traffic updates to enable spatiotemporal contingency landing planning that minimizes the need for real‑time traffic rerouting.

Authors:Abanoub M. Girgis, Ibtissam Labriji, Mehdi Bennis
Title: Hierarchical JEPA Meets Predictive Remote Control in Beyond 5G Networks
Abstract:
In wireless networked control systems, ensuring timely and reliable state updates from distributed devices to remote controllers is essential for robust control performance. However, when multiple devices transmit high‑dimensional states (e.g., images or video frames) over bandwidth‑limited wireless networks, a critical trade‑off emerges between communication efficiency and control performance. To address this challenge, we propose a Hierarchical Joint‑Embedding Predictive Architecture (H‑JEPA) for scalable predictive control. Instead of transmitting states, device observations are encoded into low‑dimensional embeddings that preserve essential dynamics. The proposed architecture employs a three‑level hierarchical prediction, with high‑level, medium‑level, and low‑level predictors operating across different temporal resolutions, to achieve long‑term prediction stability, intermediate interpolation, and fine‑grained refinement, respectively. Control actions are derived within the embedding space, removing the need for state reconstruction. Simulation results on inverted cart‑pole systems demonstrate that H‑JEPA enables up to 42.83 % more devices to be supported under limited wireless capacity without compromising control performance.

Authors:Katayoun Eshkofti, Matthieu Barreau
Title: Curriculum-Learned Vanishing Stacked Residual PINNs for Hyperbolic PDE State Reconstruction
Abstract:
Modeling distributed dynamical systems governed by hyperbolic partial differential equations (PDEs) remains challenging due to discontinuities and shocks that hinder the convergence of traditional physics‑informed neural networks (PINNs). The recently proposed vanishing stacked residual PINN (VSR‑PINN) embeds a vanishing‑viscosity mechanism within stacked residual refinements to enable a smooth transition from the parabolic to hyperbolic regime. This paper integrates three curriculum‑learning methods as primal‑dual (PD) optimization, causality progression, and adaptive sampling into the VSR‑PINN. The PD strategy balances physics and data losses, the causality scheme unlocks deeper stacks by respecting temporal and gradient evolution, and adaptive sampling targets high residuals. Numerical experiments on traffic reconstruction confirm that enforcing causality systematically reduces the median point‑wise MSE and its variability across runs, yielding improvements of nearly one order of magnitude over non‑causal training in both the baseline and PD variants.

Authors:Saber Omidi, Rene Akupan Ebunle, Se Young Yoon
Title: Optimal Derivative Feedback Control for an Active Magnetic Levitation System: An Experimental Study on Data-Driven Approaches
Abstract:
This paper presents the design and implementation of data‑driven optimal derivative feedback controllers for an active magnetic levitation system. A direct, model‑free control design method based on the reinforcement learning framework is compared with an indirect optimal control design derived from a numerically identified mathematical model of the system. For the direct model‑free approach, a policy iteration procedure is proposed, which adds an iteration layer called the epoch loop to gather multiple sets of process data, providing a more diverse dataset and helping reduce learning biases. This direct control design method is evaluated against a comparable optimal control solution designed from a plant model obtained through the combined Dynamic Mode Decomposition with Control (DMDc) and Prediction Error Minimization (PEM) system identification. Results show that while both controllers can stabilize and improve the performance of the magnetic levitation system when compared to controllers designed from a nominal model, the direct model‑free approach consistently outperforms the indirect solution when multiple epochs are allowed. The iterative refinement of the optimal control law over the epoch loop provides the direct approach a clear advantage over the indirect method, which relies on a single set of system data to determine the identified model and control.

Authors:Yunlu Xiao, Marina Petrova, Ljiljana Simić
Title: UnifSrv: AP Selection for Achieving Uniformly Good Performance of CF-MIMO in Realistic Urban Networks
Abstract:
Under the ideal assumption of uniform propagation, cell‑free massive MIMO (CF‑mMIMO) provides uniformly high throughput over the network by effectively surrounding each user with its serving access point (AP) set. However, in realistic non‑uniform urban propagation environments, it is difficult to consistently select good limited serving AP sets, resulting in significantly degraded throughput, reintroducing "edge‑effect" for the worst‑served users. To restore the uniformly good performance of scalable CF‑mMIMO in realistic urban networks, we formulate a novel multi‑objective optimization problem to jointly achieve high throughput by maximizing the sum data rate, uniform throughput by maximizing Jain's fairness index of the throughput per user, and scalability by minimizing the serving AP set size. We then propose the UnifSrv AP selection algorithms to solve this optimization problem, consisting of a deep reinforcement learning (DRL)‑based algorithm UnifSrv‑DRL and a heuristic algorithm UnifSrv‑heu. We conduct a comprehensive performance evaluation of scalable CF‑mMIMO under realistic urban network distributions, propagation, and mobility patterns, showing that the prior benchmark AP selection schemes fail to provide uniformly high throughput in practice. By contrast, UnifSrv at least doubles the throughput compared to prior benchmarks, or achieves comparable throughput but with half of the serving AP set size. Importantly, our heuristic algorithm achieves equivalent throughput to our DRL one, but with orders of magnitude lower complexity. We thus for the first time propose an AP selection algorithm that achieves uniformly good CF‑mMIMO performance in realistic urban networks with low complexity.

Authors:Davide Tebaldi, Roberto Zanasi
Title: Efficient and Robust Modeling of Nonlinear Mechanical Systems
Abstract:
The development of efficient and robust dynamic models is fundamental in the field of systems and control engineering. In this paper, a new formulation for the dynamic model of nonlinear mechanical systems, that can be applied to different automotive and robotic case studies, is proposed, together with a modeling procedure allowing to automatically obtain the model formulation. Compared with the Euler‑Lagrange formulation, the proposed model is shown to give superior performances in terms of robustness against measurement noise for systems exhibiting dependence on some external variables, as well as in terms of execution time when computing the inverse dynamics of the system.

Authors:Hiroshi Sato, Sho Sakaino, Toshiaki Tsuji
Title: Force Generative Imitation Learning: Bridging Position Trajectory and Force Commands through Control Technique
Abstract:
In contact‑rich tasks, while position trajectories are often easy to obtain, appropriate force commands are typically unknown. Although it is conceivable to generate force commands using a pretrained foundation model such as Vision‑Language‑Action (VLA) models, force control is highly dependent on the specific hardware of the robot, which makes the application of such models challenging. To bridge this gap, we propose a force generative model that estimates force commands from given position trajectories. However, when dealing with unseen position trajectories, the model struggles to generate accurate force commands. To address this, we introduce a feedback control mechanism. Our experiments reveal that feedback control does not converge when the force generative model has memory. We therefore adopt a model without memory, enabling stable feedback control. This approach allows the system to generate force commands effectively, even for unseen position trajectories, improving generalization for real‑world robot writing tasks.

Authors:Antonio Bernardes, Jasan Zughaibi, Michael Muehlebach, Bradley J. Nelson
Title: Structured Learning for Electromagnetic Field Modeling and Real-Time Inversion
Abstract:
Precise magnetic field modeling is fundamental to the closed‑loop control of electromagnetic navigation systems (eMNS) and the analytical Multipole Expansion Model (MPEM) is the current standard. However, the MPEM relies on strict physical assumptions regarding source symmetry and isolation, and requires optimization‑based calibration that is highly sensitive to initialization. These constraints limit its applicability to systems with complex or irregular coil geometries. This work introduces an alternative modeling paradigm based on multi‑layer perceptrons that learns nonlinear magnetic mappings while strictly preserving the linear dependence on currents. As a result, the field models enable fast, closed‑form minimum‑norm inversion with evaluation times of approximately 1 ms, which is critical for high‑bandwidth magnetic control. For model training and evaluation we use large‑scale, high‑density datasets collected from the research‑grade OctoMag and clinical‑grade Navion systems. Our results demonstrate that data‑driven models achieve predictive fidelity equivalent to the MPEM while maintaining comparable data efficiency. Furthermore, we demonstrate that straightforward design choices effectively eliminate spurious workspace ill‑conditioning frequently reported in MPEM‑based calibration. To facilitate future research, we release the complete codebase and datasets open source.

Authors:Omid Akbarzadeh, MohammadHossein Ashoori, Amy Nejati, Abolfazl Lavaei
Title: Safety Controller Synthesis for Stochastic Polynomial Time-Delayed Systems
Abstract:
This work develops a theoretical framework for safety controller synthesis in discrete‑time stochastic nonlinear polynomial systems subject to time‑invariant delays (dt‑SNPS‑td). While safety analysis of stochastic systems using control barrier certificates (CBC) has been widely studied, developing safety controllers for stochastic systems with time delays remains largely unexplored. The main challenge arises from the need to account for the influence of delayed components when formulating and enforcing safety conditions. To address this, we employ Krasovskii control barrier certificates, which extend the conventional CBC framework by augmenting it with an additional summation term that captures the influence of delayed states. This formulation integrates both the current and delayed components into a unified barrier structure, enabling safety synthesis for stochastic systems with time delays. The proposed approach synthesizes safety controllers under input constraints, offering probabilistic safety guarantees robust to such delays: it ensures that all trajectories of the dt‑SNPS‑td remain within the prescribed safe region while fulfilling a quantified probabilistic bound. To achieve this, our method reformulates the safety constraints as a sum‑of‑squares optimization program, enabling the systematic construction of Krasovskii CBC together with their associated safety controllers. We validate the proposed framework through three case studies, including two physical systems, demonstrating its effectiveness and practical applicability.

Authors:Xinran Li, Shuaikang Zheng, Pengcheng Zheng, Xinyang Wang, Jiacheng Li, Zhitian Li, Xudong Zou
Title: A Consistency-Improved LiDAR-Inertial Bundle Adjustment
Abstract:
Simultaneous Localization and Mapping (SLAM) using 3D LiDAR has emerged as a cornerstone for autonomous navigation in robotics. While feature‑based SLAM systems have achieved impressive results by leveraging edge and planar structures, they often suffer from the inconsistent estimator associated with feature parameterization and estimated covariance. In this work, we present a consistency‑improved LiDAR‑inertial bundle adjustment (BA) with tailored parameterization and estimator. First, we propose a stereographic‑projection representation parameterizing the planar and edge features, and conduct a comprehensive observability analysis to support its integrability with consistent estimator. Second, we implement a LiDAR‑inertial BA with Maximum a Posteriori (MAP) formulation and First‑Estimate Jacobians (FEJ) to preserve the accurate estimated covariance and observability properties of the system. Last, we apply our proposed BA method to a LiDAR‑inertial odometry.

Authors:Venkata Rajesh Chundru, Shreshta Rajakumar Deshpande, Stanislav A Gankov
Title: Advances in Battery Energy Storage Management: Control and Economic Synergies
Abstract:
The existing literature on Battery Energy Storage Systems (BESS) predominantly focuses on two main areas: control system design aimed at achieving grid stability and the techno‑economic analysis of BESS dispatch on power grid. However, with the increasing incorporation of ancillary services into power grids, a more comprehensive approach to energy management systems is required. Such an approach should not only optimize revenue generation from BESS but also ensure the safe, efficient, and reliable operation of lithium‑ion batteries. This research seeks to bridge this gap by exploring literature that addresses both the economic and operational dimensions of BESS. Specifically, it examines how economic aspects of grid duty cycles can align with control schemes deployed in BESS systems. This alignment, or synergy, could be instrumental in creating robust digital twins virtual representations of BESS systems that enhance both grid stability and revenue potential. The literature review is organized into five key categories: (1) ancillary services for BESS, exploring support functions that BESS can provide to power grids; (2) control systems developed for real‑time BESS power flow management, ensuring smooth operations under dynamic grid conditions; (3) optimization algorithms for BESS dispatch, focusing on efficient energy allocation strategies; (4) techno‑economic analyses of BESS and battery systems to assess their financial viability; and (5) digital twin technologies for real‑world BESS deployments, enabling advanced predictive maintenance and performance optimization. This review will identify potential synergies, research gaps, and emerging trends, paving the way for future innovations in BESS management and deployment strategies.

Authors:Gang He, Zhenyang Liu, Kepeng Xu, Li Xu, Tong Qiao, Wenxin Yu, Chang Wu, Weiying Xie
Title: Nipping the Drift in the Bud: Retrospective Rectification for Robust Vision-Language Navigation
Abstract:
Vision‑Language Navigation (VLN) requires embodied agents to interpret natural language instructions and navigate through complex continuous 3D environments. However, the dominant imitation learning paradigm suffers from exposure bias, where minor deviations during inference lead to compounding errors. While DAgger‑style approaches attempt to mitigate this by correcting error states, we identify a critical limitation: Instruction‑State Misalignment. Forcing an agent to learn recovery actions from off‑track states often creates supervision signals that semantically conflict with the original instruction. In response to these challenges, we introduce BudVLN, an online framework that learns from on‑policy rollouts by constructing supervision to match the current state distribution. BudVLN performs retrospective rectification via counterfactual re‑anchoring and decision‑conditioned supervision synthesis, using a geodesic oracle to synthesize corrective trajectories that originate from valid historical states, ensuring semantic consistency. Experiments on the standard R2R‑CE and RxR‑CE benchmarks demonstrate that BudVLN consistently mitigates distribution shift and achieves state‑of‑the‑art performance in both Success Rate and SPL.

Authors:Evgeny Kagan, Kyle Hyndman, Andrew Davis
Title: Chasing Tails: How Do People Respond to Wait Time Distributions?
Abstract:
We use a series of pre‑registered, incentive‑compatible online experiments to investigate how people evaluate and choose among different waiting time distributions. Our main findings are threefold. First, consistent with prior literature, people show an aversion to both longer expected waits and higher variance. Second, and more surprisingly, moment‑based utility models fail to capture preferences when distributions have thick‑right tails: indeed, decision‑makers strongly prefer distributions with long‑right tails (where probability mass is more evenly distributed over a larger support set) relative to tails that exhibit a spike near the maximum possible value, even when controlling for mean, variance, and higher moments. Conditional Value at Risk (CVaR) utility models commonly used in portfolio theory predict these choices well. Third, when given a choice, decision‑makers overwhelmingly seek information about right‑tail outcomes. These results have practical implications for service operations: (1) service designs that create a spike in long waiting times (such as priority or dedicated queue designs) may be particularly aversive; (2) when informativeness is the goal, providers should prioritize sharing right‑tail probabilities or percentiles; and (3) to increase service uptake, providers can strategically disclose (or withhold) distributional information depending on right‑tail shape.

Authors:Kejun Chen, Bernard Knueven, Wesley Jones
Title: A hard-constrained NN learning framework for rapidly restoring AC-OPF from DC-OPF
Abstract:
This paper proposes a hard‑constrained unsupervised learning framework for rapidly solving the non‑linear and non‑convex AC optimal power flow (AC‑OPF) problem in real‑time operation. Without requiring ground‑truth AC‑OPF solutions, feasibility and optimality are ensured through a properly designed learning environment and training loss. Inspired by residual learning, the neural network (NN) learns the correction mapping from the DC‑OPF solution to the active power setpoints of the generators through re‑dispatch. A subsequent optimization model is utilized to restore the optimal AC‑OPF solution, and the resulting projection difference is employed as the training loss. A replay buffer is utilized to enhance learning efficiency by fully leveraging past data pairs. The optimization model is cast as a differentiable optimization layer, where the gradient is derived by applying the implicit function theorem to the KKT conditions at the optimal solution. Tested on IEEE‑118 and PEGASE‑9241 bus systems, numerical results demonstrate that the proposed NN can obtain strictly feasible and near‑optimal solutions with reduced computational time compared to conventional optimization solvers. In addition, aided by the updated DC‑OPF solution under varying topologies, the trained NN, together with the PF solver, can rapidly find the corresponding AC solution. The proposed method achieves a 40× time speedup, while maintaining an average constraint violation on the order of 10^‑4 and an optimization gap below 1%.

Authors:Milad Hasanzadeh, Amin Kargarian
Title: Dynamic Quantum Optimal Communication Topology Design for Consensus Control in Linear Multi-Agent Systems
Abstract:
This paper proposes a quantum framework for the design of communication topologies in consensus‑based multi‑agent systems. The communication graph is selected online by solving a mixed‑integer quadratic program (MIQP) that minimizes a cost combining communication and distance penalties with degree‑regularization terms, while enforcing exact connectivity through a flow‑based formulation. To cope with the combinatorial complexity of this NP‑hard problem, we develop a three‑block ADMM scheme that decomposes the MIQP into a convex quadratic program in relaxed edge and flow variables, a pure binary unconstrained subproblem, and a closed‑form auxiliary update. The binary subproblem is mapped to a quadratic unconstrained binary optimization (QUBO) Hamiltonian and approximately solved via quantum imaginary time evolution (QITE). The resulting time‑varying, optimizer‑generated Laplacians are applied to linear first‑ and second‑order consensus dynamics. Numerical simulations on networks demonstrate that the proposed method produces connected topologies that satisfy degree constraints, achieve consensus, and incur costs comparable to those of classical mixed‑integer solvers, thereby illustrating how quantum algorithms can be embedded as topology optimizers within closed‑loop distributed control architectures.

Authors:Ege Yuceel, Daniel Liberzon, Sayan Mitra
Title: Active Localization of Unstable Systems with Coarse Information
Abstract:
We study localization and control for unstable systems under coarse, single‑bit sensing. Motivated by understanding the fundamental limitations imposed by such minimal feedback, we identify sufficient conditions under which the initial state can be recovered despite instability and extremely sparse measurements. Building on these conditions, we develop an active localization algorithm that integrates a set‑based estimator with a control strategy derived from Voronoi partitions, which provably estimates the initial state while ensuring the agent remains in informative regions. Under the derived conditions, the proposed approach guarantees exponential contraction of the initial‑state uncertainty, and the result is further supported by numerical experiments. These findings can offer theoretical insight into localization in robotics, where sensing is often limited to coarse abstractions such as keyframes, segmentations, or line‑based features.

Authors:Kuo Chen, Minghao Dou, Qianqi Liu, Yang An, Kai Ren, Zeming WU, Yu Tian, Jie Sun, Xinping Wang, Zhier Chen, Jiancheng Yu
Title: Dynamic Modeling, Parameter Identification and Numerical Analysis of Flexible Cables in Flexibly Connected Dual-AUV Systems
Abstract:
This research presents a dynamic modeling framework and parameter identification methods for describing the highly nonlinear behaviors of flexibly connected dual‑AUV systems. The modeling framework is established based on the lumped mass method, integrating axial elasticity, bending stiffness, added mass and hydrodynamic forces, thereby accurately capturing the time‑varying response of the forces and cable configurations. To address the difficulty of directly measuring material‑related and hydrodynamic coefficients, this research proposes a parameter identification method that combines the physical model with experimental data. High‑precision inversion of the equivalent Youngs modulus and hydrodynamic coefficients is performed through tension experiments under multiple configurations, effectively demonstrating that the identified model maintains predictive consistency in various operational conditions. Further numerical analysis indicates that the dynamic properties of flexible cable exhibit significant nonlinear characteristics, which are highly dependent on material property variations and AUV motion conditions. This nonlinear dynamic behavior results in two typical response states, slack and taut, which are jointly determined by boundary conditions and hydrodynamic effects, significantly affecting the cable configuration and endpoint loads. In this research, the dynamics of flexible cables under complex boundary conditions is revealed, providing a theoretical foundation for the design, optimization and further control research of similar systems.

Authors:Nikola Stankovic
Title: Computationally Efficient Laplacian CL-colME
Abstract:
Decentralized collaborative mean estimation (colME) is a fundamental task in heterogeneous networks. Its graph‑based variants B‑colME and C‑colME achieve high scalability of the problem. This paper evaluates the consensus‑based C‑colME framework, which relies on doubly stochastic averaging matrices to ensure convergence to the oracle solution. We propose CL‑colME, a novel variant utilizing Laplacian‑based consensus to avoid the computationally expensive normalization processes. Simulation results show that the proposed CL‑colME maintains the convergence behavior and accuracy of C‑colME while improving computational efficiency.

Authors:Nicholas Rober, Alex Rose, Jonathan P. How
Title: GUARDIAN: Safety Filtering for Systems with Perception Models Subject to Adversarial Attacks
Abstract:
Safety filtering is an effective method for enforcing constraints in safety‑critical systems, but existing methods typically assume perfect state information. This limitation is especially problematic for systems that rely on neural network (NN)‑based state estimators, which can be highly sensitive to noise and adversarial input perturbations. We address these problems by introducing GUARDIAN: Guaranteed Uncertainty‑Aware Reachability Defense against Adversarial INterference, a safety filtering framework that provides formal safety guarantees for systems with NN‑based state estimators. At runtime, GUARDIAN uses neural network verification tools to provide guaranteed bounds on the system's state estimate given possible perturbations to its observation. It then uses a modified Hamilton‑Jacobi reachability formulation to construct a safety filter that adjusts the nominal control input based on the verified state bounds and safety constraints. The result is an uncertainty‑aware filter that ensures safety despite the system's reliance on an NN estimator with noisy, possibly adversarial, input observations. Theoretical analysis and numerical experiments demonstrate that GUARDIAN effectively defends systems against adversarial attacks that would otherwise lead to a violation of safety constraints.

Authors:Akul Bansal, Simge Küçükyavuz
Title: Normalization of ReLU Dual for Cut Generation in Stochastic Mixed-Integer Programs
Abstract:
We study the Rectified Linear Unit (ReLU) dual, an existing dual formulation for stochastic programs that reformulates non‑anticipativity constraints using ReLU functions to generate tight, non‑convex, and mixed‑integer representable cuts. While this dual reformulation guarantees convergence with mixed‑integer state variables, it admits multiple optimal solutions that can yield weak cuts. To address this issue, we propose normalizing the dual in the extended space to identify solutions that yield stronger cuts. We prove that the resulting normalized cuts are tight and Pareto‑optimal in the original state space. We further compare normalization with existing regularization‑based approaches for handling dual degeneracy and explain why normalization offers key advantages. In particular, we show that normalization can recover any cut obtained via regularization, whereas the converse does not hold. Computational experiments demonstrate that the proposed approach outperforms existing methods by consistently yielding stronger cuts and reducing solution times on harder instances.

Authors:Mohamad Amin Jamshidi, Mehrbod Zarifi, Zolfa Anvari, Hamed Ghafarirad, Mohammad Zareinejad
Title: A Hybrid Data-Driven Algorithm for Real-Time Friction Force Estimation in Hydraulic Cylinders
Abstract:
Hydraulic systems are widely utilized in industrial applications due to their high force generation, precise control, and ability to function in harsh environments. Hydraulic cylinders, as actuators in these systems, apply force and position through the displacement of hydraulic fluid, but their operation is significantly influenced by friction force. Achieving precision in hydraulic cylinders requires an accurate friction model under various operating conditions. Existing analytical models, often derived from experimental tests, necessitate the identification or estimation of influencing factors but are limited in adaptability and computational efficiency. This research introduces a data‑driven, hybrid algorithm based on Long Short‑Term Memory (LSTM) networks and Random Forests for nonlinear friction force estimation. The algorithm effectively combines feature detection and estimation processes using training data acquired from an experimental hydraulic test setup. It achieves a consistent and stable model error of less than 10% across diverse operating conditions and external load variations, ensuring robust performance in complex situations. The computational cost of the algorithm is 1.51 milliseconds per estimation, making it suitable for real‑time applications. The proposed method addresses the limitations of analytical models by delivering high precision and computational efficiency. The algorithm's performance is validated through detailed analysis and experimental results, including direct comparisons with the LuGre model. The comparison highlights that while the LuGre model offers a theoretical foundation for friction modeling, its performance is limited by its inability to dynamically adjust to varying operational conditions of the hydraulic cylinder, further emphasizing the advantages of the proposed hybrid approach in real‑time applications.

Authors:Mihitha Maithripala, Zongli Lin
Title: Privacy-Preserving Dynamic Average Consensus by Masking Reference Signals
Abstract:
In multi‑agent systems, dynamic average consensus (DAC) is a decentralized estimation strategy in which a set of agents tracks the average of time‑varying reference signals. Because DAC requires exchanging state information with neighbors, attackers may gain access to these states and infer private information. In this paper, we develop a privacy‑preserving method that protects each agent's reference signal from external eavesdroppers and honest‑but‑curious agents while achieving the same convergence accuracy and convergence rate as conventional DAC. Our approach masks the reference signals by having each agent draw a random real number for each neighbor, exchanges that number over an encrypted channel at the initialization, and computes a masking value to form a masked reference. Then the agents run the conventional DAC algorithm using the masked references. Convergence and privacy analyses show that the proposed algorithm matches the convergence properties of conventional DAC while preserving the privacy of the reference signals. Numerical simulations validate the effectiveness of the proposed privacy‑preserving DAC algorithm.

Authors:Chuwei Wang, Eduardo Sebastián, Amanda Prorok, Anastasia Bizyaeva
Title: From Vision to Decision: Neuromorphic Control for Autonomous Navigation and Tracking
Abstract:
Robotic navigation has historically struggled to reconcile reactive, sensor‑based control with the decisive capabilities of model‑based planners. This duality becomes critical when the absence of a predominant option among goals leads to indecision, challenging reactive systems to break symmetries without computationally‑intense planners. We propose a parsimonious neuromorphic control framework that bridges this gap for vision‑guided navigation and tracking. Image pixels from an onboard camera are encoded as inputs to dynamic neuronal populations that directly transform visual target excitation into egocentric motion commands. A dynamic bifurcation mechanism resolves indecision by delaying commitment until a critical point induced by the environmental geometry. Inspired by recently proposed mechanistic models of animal cognition and opinion dynamics, the neuromorphic controller provides real‑time autonomy with a minimal computational burden, a small number of interpretable parameters, and can be seamlessly integrated with application‑specific image processing pipelines. We validate our approach in simulation environments as well as on an experimental quadrotor platform.

Authors:Zhang Hengyu, Maryam Cheraghy, Liu Wei, Armin Farhadi, Meysam Soltanpour, Zhong Zhuoqing
Title: UAV Trajectory Optimization via Improved Noisy Deep Q-Network
Abstract:
This paper proposes an Improved Noisy Deep Q‑Network (Noisy DQN) to enhance the exploration and stability of Unmanned Aerial Vehicle (UAV) when applying deep reinforcement learning in simulated environments. This method enhances the exploration ability by combining the residual NoisyLinear layer with an adaptive noise scheduling mechanism, while improving training stability through smooth loss and soft target network updates. Experiments show that the proposed model achieves faster convergence and up to +40 higher rewards compared to standard DQN and quickly reach to the minimum number of steps required for the task 28 in the 15 15 grid navigation environment set up. The results show that our comprehensive improvements to the network structure of NoisyNet, exploration control, and training stability contribute to enhancing the efficiency and reliability of deep Q‑learning.

Authors:Tommaso Zaccherini, Siyuan Liu, Dimos V. Dimarogonas
Title: Observer-based Control of Multi-agent Systems under STL Specifications
Abstract:
This paper proposes a decentralized controller for large‑scale heterogeneous multi‑agent systems subject to bounded external disturbances, where agents must satisfy Signal Temporal Logic (STL) specifications requiring cooperation among non‑communicating agents. To address the lack of direct communication, we employ a decentralized k‑hop Prescribed Performance State Observer (k‑hop PPSO) to provide each agent with state estimates of those agents it cannot communicate with. By leveraging the performance bounds on the state estimation errors guaranteed by the k‑hop PPSO, we first modify the space robustness of the STL tasks to account for these errors, and then exploit the modified robustness to design a decentralized continuous‑time feedback controller that ensures satisfaction of the STL tasks even under worst‑case estimation errors. A simulation result is provided to validate the proposed framework.

Authors:Lisa Piccinin, Camilla Quaresmini, Edoardo Vitale, Mara Tanelli, Valentina Breschi
Title: Fairness-aware design of nudging policies under stochasticity and prejudices
Abstract:
We present an injustice‑aware innovation‑diffusion model extending the Generalized Linear Threshold framework by assigning agents activation thresholds drawn from a Beta distribution to capture the stochastic nature of adoption shaped by inequalities. Because incentive policies themselves can inadvertently amplify these inequalities, building on this model, we design a fair Model Predictive Control (MPC) scheme that incorporates equality and equity objectives for allocating incentives. Simulations using real mobility‑habit data show that injustice reduces overall adoption, while equality smooths incentive distribution and equity reduces disparities in the final outcomes. Thus, incorporating fairness ensures effective diffusion without exacerbating existing social inequalities.

Authors:Jiachao Liu, Sean Qian
Title: On Path-based Marginal Cost of Heterogeneous Traffic Flow for General Networks
Abstract:
Path marginal cost (PMC) is a crucial component in solving path‑based system‑optimal dynamic traffic assignment (SO‑DTA), dynamic origin‑destination demand estimation (DODE), and network resilience analysis. However, accurately evaluating PMC in heterogeneous traffic conditions poses significant challenges. Previous studies often focus on homogeneous traffic flow of single vehicle class and do not well address the interactive effect of heterogeneous traffic flows and the resultant computational issues. This study proposes a novel but simple method for approximately evaluating PMC in complex heterogeneous traffic condition. The method decomposes PMC into intra‑class and inter‑class terms and uses conversion factor derived from heterogeneous link dynamics to explicitly model the intricate relationships between vehicle classes. Additionally, the method considers the non‑differentiable issue that arises when mixed traffic flow approaches system optimum conditions. The proposed method is tested on a small corridor network with synthetic demand and a large‑scale network with calibrated demand from real‑world data. Results demonstrated that our method exhibits superior performance in solving bi‑class SO‑DTA problems, yielding lower total travel cost and capturing the multi‑class flow competition at the system optimum state.

Authors:Anatoly A. Krasnovsky
Title: Toward Operationalizing Rasmussen: Drift Observability on the Simplex for Evolving Systems
Abstract:
Monitoring drift into failure is hindered by Euclidean anomaly detection that can conflate safe operational trade‑offs with risk accumulation in signals expressed as shares, and by architectural churn that makes fixed schemas (and learned models) stale before rare boundary events occur. Rasmussen's dynamic safety model motivates drift under competing pressures, but operationalizing it for software is difficult because many high‑value operational signals (effort, remaining margin, incident impact) are compositional and their parts evolve. We propose a vision for drift observability on the simplex: model drift and boundary proximity in Aitchison geometry to obtain coordinate‑invariant direction and distance‑to‑safety in interpretable balance coordinates. To remain comparable under churn, a monitor would continuously refresh its part inventory and policy‑defined boundaries from engineering artifacts and apply lineage‑aware aggregation. We outline early‑warning diagnostics and falsifiable hypotheses for future evaluation.

Authors:Anatoly A. Krasnovsky
Title: Emergence-as-Code for Self-Governing Reliable Systems
Abstract:
SLO‑as‑code has made per‑service reliability declarative, but user experience is defined by journeys whose reliability is an emergent property of microservice topology, routing, redundancy, timeouts/fallbacks, shared failure domains, and tail amplification. As a result, journey objectives (e.g., "checkout p99 < 400 ms") are often maintained outside code and drift as the system evolves, forcing teams to either miss user expectations or over‑provision and gate releases with ad‑hoc heuristics. We propose Emergence‑as‑Code (EmaC), a vision for making journey reliability computable and governable via intent plus evidence. An EmaC spec declares journey intent (objective, control‑flow operators, allowed actions) and binds it to atomic SLOs and telemetry. A runtime inference component consumes operational artifacts (e.g., tracing and traffic configuration) to synthesize a candidate journey model with provenance and confidence. From the last accepted model, the EmaC compiler/controller derives bounded journey SLOs and budgets under explicit correlation assumptions (optimistic independence vs. pessimistic shared fate), and emits control‑plane artifacts (burn‑rate alerts, rollout gates, action guards) that are reviewable in a Git workflow. An anonymized artifact repository provides a runnable example specification and generated outputs.

Authors:Maksym Figat, Ryan M. Mackey, Michel D. Ingham
Title: Ontology-Driven Robotic Specification Synthesis
Abstract:
This paper addresses robotic system engineering for safety‑ and mission‑critical applications by bridging the gap between high‑level objectives and formal, executable specifications. The proposed method, Robotic System Task to Model Transformation Methodology (RSTM2) is an ontology‑driven, hierarchical approach using stochastic timed Petri nets with resources, enabling Monte Carlo simulations at mission, system, and subsystem levels. A hypothetical case study demonstrates how the RSTM2 method supports architectural trades, resource allocation, and performance analysis under uncertainty. Ontological concepts further enable explainable AI‑based assistants, facilitating fully autonomous specification synthesis. The methodology offers particular benefits to complex multi‑robot systems, such as the NASA CADRE mission, representing decentralized, resource‑aware, and adaptive autonomous systems of the future.

Authors:Giorgio Riva, Gian Paolo Incremona, Simone Formentin, Antonella Ferrara
Title: Robust data-driven model-reference control of linear perturbed systems via sliding mode generation
Abstract:
This paper introduces a data‑based integral sliding mode control scheme for robustification of model‑reference controllers, accommodating generic multivariable linear systems with unknown dynamics and affected by matched disturbances. Specifically, an integral sliding mode control (ISMC) law is recast into a data‑based framework relying on an integral sliding variable depending only on the reference model, without the need of modeling the plant. The main strength of the proposed approach is the enforcement of the desired reference model in closed‑loop under sliding mode conditions, despite the lack of knowledge of the model dynamics and the presence of the matched disturbances. Moreover, the conditions required to guarantee an integral sliding mode generation and the closed‑loop stability are formally analyzed in the paper, remarking the generality of the proposed data‑driven integral sliding mode control (DD‑ISMC) with respect to the related model‑based counterpart. Finally, the main practices for the data‑based design of the proposed control scheme are deeply discussed in the paper, and the proposed method is tested in simulation on a benchmark example, and experimentally on a real laboratory setup. Simulation and experimental evidence fully corroborates the theoretical analysis, thus motivating further research in this direction.

Authors:Hongyi Li, Jun Xu, Jinfeng Liu
Title: Distributed Model Predictive Control for Energy and Comfort Optimization in Large Buildings Using Piecewise Affine Approximation
Abstract:
The control of large buildings encounters challenges in computational efficiency due to their size and nonlinear components. To address these issues, this paper proposes a Piecewise Affine (PWA)‑based distributed scheme for Model Predictive Control (MPC) that optimizes energy and comfort through PWA‑based quadratic programming. We utilize the Alternating Direction Method of Multipliers (ADMM) for effective decomposition and apply the PWA technique to handle the nonlinear components. To solve the resulting large‑scale nonconvex problems, the paper introduces a convex ADMM algorithm that transforms the nonconvex problem into a series of smaller convex problems, significantly enhancing computational efficiency. Furthermore, we demonstrate that the convex ADMM algorithm converges to a local optimum of the original problem. A case study involving 36 zones validates the effectiveness of the proposed method. Our proposed method reduces execution time by 86% compared to the centralized version.

Authors:Yuma Abe, Mariko Sekiguchi, Go Otsuru, Amane Miura
Title: Policy-Driven Orchestration Framework for Multi-Operator Non-Terrestrial Networks
Abstract:
Non‑terrestrial networks (NTNs) have gained significant attention for their scalability and wide coverage in next‑generation communication systems. A large number of NTN nodes, such as satellites, are required to establish a global NTN, but not all operators have the capability to deploy such a system. Therefore, cooperation among multiple operators, facilitated by an orchestrator, enables the construction of virtually large‑scale constellations. In this paper, we propose a weak‑control‑based orchestration framework that coordinates multiple NTN operators while ensuring that operations align with the policies of both the orchestrator and the individual operators. Unlike centralized orchestration frameworks, where the orchestrator determines the entire route from source to destination, the proposed framework allows each operator to select preferred routes from multiple candidates provided by the orchestrator. To evaluate the effectiveness of our proposed framework, we conducted numerical simulations under various scenarios and network configurations including dynamic NTN environments with time‑varying topologies, showing that inter‑operator cooperation improves the availability of feasible end‑to‑end routes. Furthermore, we analyzed the iterative negotiation process to address policy conflicts and quantitatively demonstrated the "price of autonomy," where strict individual policies degrade global feasibility and performance. The results also demonstrate that outcomes of the proposed framework depend on the operators' policies and that hop count and latency increase as the number of operators grows. These findings validate the proposed framework's ability to deliver practical benefits of orchestrated multi‑operator collaboration in future NTN environments.

Authors:Mahdis Rabbani, Navid Mojahed, Shima Nazari
Title: A Data Driven Structural Decomposition of Dynamic Games via Best Response Maps
Abstract:
Dynamic games are powerful tools to model multi‑agent decision‑making, yet computing Nash (generalized Nash) equilibria remains a central challenge in such settings. Complexity arises from tightly coupled optimality conditions, nested optimization structures, and poor numerical conditioning. Existing game‑theoretic solvers address these challenges by directly solving the joint game, typically requiring explicit modeling of all agents' objective functions and constraints, while learning‑based approaches often decouple interaction through prediction or policy approximation, sacrificing equilibrium consistency. This paper introduces a conceptually novel formulation for dynamic games by restructuring the equilibrium computation. Rather than solving a fully coupled game or decoupling agents through prediction or policy approximation, a data‑driven structural reduction of the game is proposed that removes nested optimization layers and derivative coupling by embedding an offline‑compiled best‑response map as a feasibility constraint. Under standard regularity conditions, when the best‑response operator is exact, any converged solution of the reduced problem corresponds to a local open‑loop Nash (GNE) equilibrium of the original game; with a learned surrogate, the solution is approximately equilibrium‑consistent up to the best‑response approximation error. The proposed formulation is supported by mathematical proofs, accompanying a large‑scale Monte Carlo study in a two‑player open‑loop dynamic game motivated by the autonomous racing problem. Comparisons are made against state‑of‑the‑art joint game solvers, and results are reported on solution quality, computational cost, and constraint satisfaction.

Authors:Chengxiao Wang, Haoze Wu, Gagandeep Singh
Title: Formal Synthesis of Certifiably Robust Neural Lyapunov-Barrier Certificates
Abstract:
Neural Lyapunov and barrier certificates have recently been used as powerful tools for verifying the safety and stability properties of deep reinforcement learning (RL) controllers. However, existing methods offer guarantees only under fixed ideal unperturbed dynamics, limiting their reliability in real‑world applications where dynamics may deviate due to uncertainties. In this work, we study the problem of synthesizing \emphrobust neural Lyapunov barrier certificates that maintain their guarantees under perturbations in system dynamics. We formally define a robust Lyapunov barrier function and specify sufficient conditions based on Lipschitz continuity that ensure robustness against bounded perturbations. We propose practical training objectives that enforce these conditions via adversarial training, Lipschitz neighborhood bound, and global Lipschitz regularization. We validate our approach in two practically relevant environments, Inverted Pendulum and 2D Docking. The former is a widely studied benchmark, while the latter is a safety‑critical task in autonomous systems. We show that our methods significantly improve both certified robustness bounds (up to 4.6 times) and empirical success rates under strong perturbations (up to 2.4 times) compared to the baseline. Our results demonstrate effectiveness of training robust neural certificates for safe RL under perturbations in dynamics.

Authors:Feng Zhu, Robert W. Heath, Aritra Mitra
Title: A Short and Unified Convergence Analysis of the SAG, SAGA, and IAG Algorithms
Abstract:
Stochastic variance‑reduced algorithms such as Stochastic Average Gradient (SAG) and SAGA, and their deterministic counterparts like the Incremental Aggregated Gradient (IAG) method, have been extensively studied in large‑scale machine learning. Despite their popularity, existing analyses for these algorithms are disparate, relying on different proof techniques tailored to each method. Furthermore, the original proof of SAG is known to be notoriously involved, requiring computer‑aided analysis. Focusing on finite‑sum optimization with smooth and strongly convex objective functions, our main contribution is to develop a single unified convergence analysis that applies to all three algorithms: SAG, SAGA, and IAG. Our analysis features two key steps: (i) establishing a bound on delays due to stochastic sub‑sampling using simple concentration tools, and (ii) carefully designing a novel Lyapunov function that accounts for such delays. The resulting proof is short and modular, providing the first high‑probability bounds for SAG and SAGA that can be seamlessly extended to non‑convex objectives and Markov sampling. As an immediate byproduct of our new analysis technique, we obtain the best known rates for the IAG algorithm, significantly improving upon prior bounds.

Authors:Rami Abdulelah Alhazmi, Achinth Suresh Babu, Syed Aseem Ul Islam, Dennis S. Bernstein
Title: Nonlinear Predictive Cost Adaptive Control of Pseudo-Linear Input-Output Models Using Polynomial, Fourier, and Cubic Spline Observables
Abstract:
Control of nonlinear systems with high levels of uncertainty is practically relevant and theoretically challenging. This paper presents a numerical investigation of an adaptive nonlinear model predictive control (MPC) technique that relies entirely on online system identification without prior modeling, training, or data collection. In particular, the paper considers predictive cost adaptive control (PCAC), which is an extension of generalized predictive control. Nonlinear PCAC (NPCAC) uses recursive least squares (RLS) with subspace of information forgetting (SIFt) to identify a discrete‑time, pseudo‑linear, input‑output model, which is used with iterative MPC for nonlinear receding‑horizon optimization. The performance of NPCAC is illustrated using polynomial, Fourier, and cubic‑spline basis functions.

Authors:Alireza Abbaspour, Shabin Mahadevan, Kilian Zwirglmaier, Jeff Stafford
Title: The Necessity of a Holistic Safety Evaluation Framework for AI-Based Automation Features
Abstract:
The intersection of Safety of Intended Functionality (SOTIF) and Functional Safety (FuSa) analysis of driving automation features has traditionally excluded Quality Management (QM) components (components that has no ASIL requirements allocated from vehicle‑level HARA) from rigorous safety impact evaluations. While QM components are not typically classified as safety‑relevant, recent developments in artificial intelligence (AI) integration reveal that such components can contribute to SOTIF‑related hazardous risks. Compliance with emerging AI safety standards, such as ISO/PAS 8800, necessitates re‑evaluating safety considerations for these components. This paper examines the necessity of conducting holistic safety analysis and risk assessment on AI components, emphasizing their potential to introduce hazards with the capacity to violate risk acceptance criteria when deployed in safety‑critical driving systems, particularly in perception algorithms. Using case studies, we demonstrate how deficiencies in AI‑driven perception systems can emerge even in QM‑classified components, leading to unintended functional behaviors with critical safety implications. By bridging theoretical analysis with practical examples, this paper argues for the adoption of comprehensive FuSa, SOTIF, and AI standards‑driven methodologies to identify and mitigate risks in AI components. The findings demonstrate the importance of revising existing safety frameworks to address the evolving challenges posed by AI, ensuring comprehensive safety assurance across all component classifications spanning multiple safety standards.

Authors:Dong Ho Kang, Aaron Kim, Mingyo Seo, Kazuto Yokoyama, Tetsuya Narita, Luis Sentis
Title: PLATO Hand: Shaping Contact Behavior with Fingernails for Precise Manipulation
Abstract:
We present the PLATO Hand, a dexterous robotic hand with a hybrid fingertip that combines a rigid fingernail, embedded distal phalanx, and compliant pulp to shape contact behavior during manipulation. \rrevBy mechanically organizing how contact is initiated, supported, and transmitted at the fingertip, this structure creates stable and task‑relevant contact conditions across diverse object geometries and grasp orientations. We develop a strain‑energy‑based bending‑‑indentation model to guide the fingertip design and to explain how material stiffness and contact geometry govern deformation partitioning within the fingertip. \rrevExperiments show improved pinch stability, improved fingernail‑mediated dorsal‑contact force transmission and proprioceptive observability, and successful execution of edge‑sensitive manipulation tasks, including paper singulation, card picking, and orange peeling. These results show that coupling a mechanically structured contact interface with a force‑motion‑transparent finger mechanism provides a principled approach to precise manipulation. Our project page is at: https://platohand.github.io

Authors:Farbod Younesi, Walter Lucia, Amr Youssef
Title: Trojan Attacks on Neural Network Controllers for Robotic Systems
Abstract:
Neural network controllers are increasingly deployed in robotic systems for tasks such as trajectory tracking and pose stabilization. However, their reliance on potentially untrusted training pipelines or supply chains introduces significant security vulnerabilities. This paper investigates backdoor (Trojan) attacks against neural controllers, using a differential‑drive mobile robot platform as a case study. In particular, assuming that the robot's tracking controller is implemented as a neural network, we design a lightweight, parallel Trojan network that can be embedded within the controller. This malicious module remains dormant during normal operation but, upon detecting a highly specific trigger condition defined by the robot's pose and goal parameters, compromises the primary controller's wheel velocity commands, resulting in undesired and potentially unsafe robot behaviours. We provide a proof‑of‑concept implementation of the proposed Trojan network, which is validated through simulation under two different attack scenarios. The results confirm the effectiveness of the proposed attack and demonstrate that neural network‑based robotic control systems are subject to potentially critical security threats.

Authors:Zhirui Liang, Jae-Won Chung, Mosharaf Chowdhury, Jiasi Chen, Vladimir Dvorkin
Title: GPU-to-Grid: Voltage Regulation via GPU Utilization Control
Abstract:
While the rapid expansion of data centers poses challenges for power grids, it also offers new opportunities as potentially flexible loads. Existing power system research often abstracts data centers as aggregate resources, while computer system research primarily focuses on optimizing GPU energy efficiency and largely ignores the grid impacts of optimized GPU power consumption. To bridge this gap, we develop a GPU‑to‑Grid framework that couples device‑level GPU control with power system objectives. We study distribution‑level voltage regulation enabled by flexibility in LLM inference, using batch size as a control knob that trades off the voltage impacts of GPU power consumption against inference latency and token throughput. We first formulate this problem as an optimization problem and then realize it as an online feedback optimization controller that leverages measurements from both the power grid and GPU systems. Our key insight is that reducing GPU power consumption alleviates violations of lower voltage limits, while increasing GPU power mitigates violations near upper voltage limits in distribution systems; this runs counter to the common belief that minimizing GPU power consumption is always beneficial to power grids.

Authors:Peter Amorese, Morteza Lahijanian
Title: Learning Nonlinear Continuous-Time Systems for Formal Uncertainty Propagation and Probabilistic Evaluation
Abstract:
Nonlinear ordinary differential equations (ODEs) are powerful tools for modeling real‑world dynamical systems. However, propagating initial state uncertainty through nonlinear dynamics, especially when the ODE is unknown and learned from data, remains a major challenge. This paper introduces a novel continuum dynamics perspective for model learning that enables formal uncertainty propagation by constructing Taylor series approximations of probabilistic events. We establish sufficient conditions for the soundness of the approach and prove its asymptotic convergence. Empirical results demonstrate the framework's effectiveness, particularly when predicting rare events.

Authors:Xuting Gao, Guillem Pascual, Scott Brown, Sonia Martínez
Title: Banach Control Barrier Functions for Large-Scale Swarm Control
Abstract:
This paper studies the safe control of very large multi‑agent systems via a generalized framework that employs so‑called Banach Control Barrier Functions (B‑CBFs). Modeling a large swarm as probability distribution over a spatial domain, we show how B‑CBFs can be used to appropriately capture a variety of macroscopic constraints that can integrate with large‑scale swarm objectives. Leveraging this framework, we define stable and filtered gradient flows for large swarms, paying special attention to optimal transport algorithms. Further, we show how to derive agent‑level, microscopical algorithms that are consistent with macroscopic counterparts in the large‑scale limit. We then identify conditions for which a group of agents can compute a distributed solution that only requires local information from other agents within a communication range. Finally, we showcase the theoretical results over swarm systems in the simulations section.

Authors:Minjie Tang, Photios A. Stavrou, Marios Kountouris
Title: Multi-Sensor Scheduling for Remote State Estimation over Wireless MIMO Fading Channels with Semantic Over-the-Air Aggregation
Abstract:
In this work, we study multi‑sensor scheduling for remote state estimation over wireless multiple‑input multiple‑output (MIMO) fading channels using a novel semantic over‑the‑air (SemOTA) aggregation approach. We first revisit Kalman filtering with conventional over‑the‑air (OTA) aggregation and highlight its transmit power limitations. To balance power efficiency and estimation performance, we formulate the scheduling task as a finite‑horizon dynamic programming (DP) problem. By analyzing the structure of the optimal Q‑function, we show that the resulting scheduling policy exhibits a semantic structure that adapts online to the estimation error covariance and channel variations. To obtain a practical solution, we derive a tractable upper bound on the Q‑function via a positive semidefinite (PSD) cone decomposition, which enables an efficient approximate scheduling policy and a low‑complexity remote estimation algorithm. Numerical results confirm that the proposed scheme outperforms existing methods in both estimation accuracy and power efficiency.

Authors:Hiu Yung Wong
Title: Review of Superconducting Qubit Devices and Their Large-Scale Integration
Abstract:
The superconducting qubit quantum computer is one of the most promising quantum computing architectures for large‑scale integration due to its maturity and close proximity to the well‑established semiconductor manufacturing infrastructure. From an education perspective, it also bridges classical microwave electronics and quantum electrodynamics. In this paper, we will review the basics of quantum computers, superconductivity, and Josephson junctions. We then introduce important technologies and concepts related to DiVincenzo's criteria, which are the necessary conditions for the superconducting qubits to work as a useful quantum computer. Firstly, we will discuss various types of superconducting qubits formed with Josephson junctions, from which we will understand the trade‑off across multiple design parameters, including their noise immunity. Secondly, we will discuss different schemes to achieve entanglement gate operations, which are a major bottleneck in achieving more efficient fault‑tolerant quantum computing. Thirdly, we will review readout engineering, including the implementations of the Purcell filters and quantum‑limited amplifiers. Finally, we will discuss the nature and review the studies of two‑level system defects, which are currently the limiting factor of qubit coherence time. DiVincenzo's criteria are only the necessary conditions for a technology to be eligible for quantum computing. To have a useful quantum computer, large‑scale integration is required. We will review proposals and developments for the large‑scale integration of superconducting qubit devices. By comparing with the application of electronic design automation (EDA) in semiconductors, we will also review the use of EDA in superconducting qubit quantum computer design, which is necessary for its large‑scale integration.

Authors:Lamberto Vazquez-Soqui, Fatima Oliva-Palomo, Diego Mercado-Ravell, Pedro Castillo
Title: SQP-Based Cable-Tension Allocation for Multi-Drone Load Transport
Abstract:
Multi‑Agent Aerial Load Transport Systems (MAATS) offer greater payload capacity and fault tolerance than single‑drone solutions. However, they have an underdetermined tension allocation problem that leads to uneven energy distribution, cable slack, or collisions between drones and cables. This paper presents a real‑time optimization layer that improves a hierarchical load‑position‑attitude controller by incorporating a Sequential Quadratic Programming (SQP) algorithm. The SQP formulation minimizes the sum of squared cable tensions while imposing a cable‑alignment penalty that discourages small inter‑cable angles, thereby preventing tether convergence without altering the reference trajectory. We tested the method under nominal conditions by running numerical simulations of four quadrotors. Computational experiments based on numerical simulations demonstrate that the SQP routine runs in a few milliseconds on standard hardware, indicating feasibility for real‑time use. A sensitivity analysis confirms that the gain of the cable‑alignment penalty can be tuned online, enabling a controllable trade‑off between safety margin and energy consumption with no measurable degradation of tracking performance in simulation. This framework provides a scalable path to safe and energy‑balanced cooperative load transport in practical deployments.

Authors:Joscha F. Bongard, Boris Lohmann
Title: Control Lyapunov Functions for Optimality in Sontag-Type Control
Abstract:
Given a Control Lyapunov Function (CLF), Sontag's famous Formula provides a nonlinear state‑feedback guaranteeing asymptotic stability of the setpoint. At the same time, a cost function that depends on the CLF is minimized. While there exist methods to construct CLFs for certain classes of systems, the impact on the resulting performance is unclear. This article aims to make two contributions to this problem: (1) We show that using the value function of an LQR design as CLF, the resulting Sontag‑type controller minimizes a classical quadratic cost around the setpoint and a CLF‑dependent cost within the domain where the CLF condition holds. We also show that the closed‑loop system is stable within a local region at least as large as that generated by the LQR. (2) We show a related CLF design for feedback‑linearizable systems resulting in a global CLF in a straight‑forward manner; The Sontag design then guarantees global asymptotic stability while minimizing a quadratic cost at the setpoint and a CLF‑dependent cost in the whole state‑space. Both designs are constructive and easily applicable to nonlinear multi‑input systems under mild assumptions.

Authors:Joscha F. Bongard, Valentin L. Krieger, Boris Lohmann
Title: Dynamic Constraint Tightening for Nonlinear MPC for Autonomous Racing via Contraction Analysis
Abstract:
This work develops a robust nonlinear Model Predictive Control (MPC) framework for path tracking in autonomous vehicles operating at the limits of handling utilizing a Control Contraction Metric (CCM) derived from a perturbed dynamic single track model. We first present a nonlinear MPC scheme for autonomous vehicles. Building on this nominal scheme, we assume limited uncertainty in tire parameters as well as bounded force disturbances in both lateral and longitudinal directions. By simplifying the perturbed model, we optimize a CCM for the uncertain model, which is validated through simulations at the dynamic limits of vehicle performance. This CCM is subsequently employed to parameterize a homothetic tube used for constraint tightening within the MPC formulation. The resulting robust nonlinear MPC is computationally more efficient than competing methods, as it introduces only a single additional state variable into the prediction model compared to the nominal scheme. Simulation results demonstrate that the homothetic tube expands most significantly in regions where the nominal scheme would otherwise violate constraints, illustrating its ability to capture all uncertain trajectories while avoiding unnecessary conservatism.

Authors:Roland Schurig, Pieter van Goor, Karl Worthmann, Rolf Findeisen
Title: On Data-Driven Unbiased Predictors using the Koopman Operator
Abstract:
The Koopman operator and its data‑driven approximations, such as extended dynamic mode decomposition (EDMD), are widely used for analysing, modelling, and controlling nonlinear dynamical systems. However, when the true Koopman eigenfunctions cannot be identified from finite data, multi‑step predictions may suffer from structural inaccuracies and systematic bias. To address this issue, we analyse the first and second moments of the multi‑step prediction residual. By decomposing the residual into contributions from the one‑step approximation error and the propagation of accumulated inaccuracies, we derive a closed‑form expression characterising these effects. This analysis enables the development of a novel and computationally efficient algorithm that enforces unbiasedness and reduces variance in the resulting predictor. The proposed method is validated in numerical simulations, showing improved uncertainty properties compared to standard EDMD. These results lay the foundation for uncertainty‑aware and unbiased Koopman‑based prediction frameworks that can be extended to controlled and stochastic systems.

Authors:Yun Jiang, Ji Wang
Title: Safe Adaptive Control of Parabolic PDE-ODE Cascades
Abstract:
In this paper, we propose a safe adaptive boundary control strategy for a class of parabolic partial differential equation‑ordinary differential equation (PDE‑ODE) cascaded systems with parametric uncertainties in both the PDE and ODE subsystems. The proposed design is built upon an adaptive Control Barrier Function (aCBF) framework that incorporates high‑relative‑degree CBFs together with a batch least‑squares identification (BaLSI)‑based adaptive control that guarantees exact parameter identification in finite time. The proposed control law ensures that: (i) if the system output state initially lies within a prescribed safe set, safety is maintained for all time; otherwise, the output is driven back into the safe region within a preassigned finite time; and (ii) convergence to zero of all plant states is achieved. Numerical simulations are provided to demonstrate the effectiveness of the proposed approach.

Authors:Chenxi Hu, Yue Ma, Yifan Wu, Yunhe Hou
Title: Resilient Load Forecasting under Climate Change: Adaptive Conditional Neural Processes for Few-Shot Extreme Load Forecasting
Abstract:
Extreme weather can substantially change electricity consumption behavior, causing load curves to exhibit sharp spikes and pronounced volatility. If forecasts are inaccurate during those periods, power systems are more likely to face supply shortfalls or localized overloads, forcing emergency actions such as load shedding and increasing the risk of service disruptions and public‑safety impacts. This problem is inherently difficult because extreme events can trigger abrupt regime shifts in load patterns, while relevant extreme samples are rare and irregular, making reliable learning and calibration challenging. We propose AdaCNP, a probabilistic forecasting model for data‑scarce condition. AdaCNP learns similarity in a shared embedding space. For each target data, it evaluates how relevant each historical context segment is to the current condition and reweights the context information accordingly. This design highlights the most informative historical evidence even when extreme samples are rare. It enables few‑shot adaptation to previously unseen extreme patterns. AdaCNP also produces predictive distributions for risk‑aware decision‑making without expensive fine‑tuning on the target domain. We evaluate AdaCNP on real‑world power‑system load data and compare it against a range of representative baselines. The results show that AdaCNP is more robust during extreme periods, reducing the mean squared error by 22% relative to the strongest baseline while achieving the lowest negative log‑likelihood, indicating more reliable probabilistic outputs. These findings suggest that AdaCNP can effectively mitigate the combined impact of abrupt distribution shifts and scarce extreme samples, providing a more trustworthy forecasting for resilient power system operation under extreme events.

Authors:Meng Yuan, Ye Wang, Xinghuo Yu, Torsten Wik, Changfu Zou
Title: Reinforcement Learning-based Home Energy Management with Heterogeneous Batteries and Stochastic EV Behaviour
Abstract:
The widespread adoption of photovoltaic (PV), electric vehicles (EVs), and stationary energy storage systems (ESS) in households increases system complexity while simultaneously offering new opportunities for energy regulation. However, effectively coordinating these resources under uncertainties remains challenging. This paper proposes a novel home energy management framework based on deep reinforcement learning (DRL) that can jointly minimise energy expenditure and battery degradation while guaranteeing occupant comfort and EV charging requirements. Distinct from existing studies, we explicitly account for the heterogeneous degradation characteristics of stationary and EV batteries in the optimisation, alongside stochastic user behaviour regarding arrival time, departure time, and driving distance. The energy scheduling problem is formulated as a constrained Markov decision process (CMDP) and solved using a Lagrangian soft actor‑critic (SAC) algorithm. This approach enables the agent to learn optimal control policies that enforce physical constraints, including indoor temperature bounds and target EV state of charge upon departure, despite stochastic uncertainties. Numerical simulations over a one‑year horizon demonstrate the effectiveness of the proposed framework in satisfying physical constraints while eliminating thermal oscillations and achieving significant economic benefits. Specifically, the method reduces the cumulative operating cost substantially compared to two standard rule‑based baselines while simultaneously decreasing battery degradation costs by 8.44%.

Authors:Axel Stafström, Daniel Arnström, Adam Miksits, David Umsonst
Title: Peak Bounds for the Estimation Error under Sensor Attacks
Abstract:
This paper investigates bounds on the estimation error of a linear system affected by norm‑bounded disturbances and full sensor attacks. The system is equipped with a detector that evaluates the norm of the innovation signal to detect faults, and the attacker wants to avoid detection. We utilize induced L_\infty system norms, also called \emphpeak‑to‑peak norms, to compare the estimation error bounds under nominal operations and under attack. This leads to a sufficient condition for when the bound on the estimation error is smaller during an attack than during nominal operation. This condition is independent of the attack strategy and depends only on the attacker's desire to remain undetected and (indirectly) the observer gain. Therefore, we investigate both an observer design method, that seeks to reduce the error bound under attack while keeping the nominal error bound low, and detector threshold tuning. As a numerical illustration, we show how a sensor attack can deactivate a robust safety filter based on control barrier functions if the attacked error bound is larger than the nominal one. We also statistically evaluate our observer design method and the effect of the detector threshold.

Authors:Léa Ninite, Adrien Banse, Guillaume O. Berger, Raphaël M. Jungers
Title: A Path-Complete Approach for Optimal Control of Switched Systems
Abstract:
We study the problem of estimating the value function of discrete‑time switched systems under arbitrary switching. Unlike the switched LQR problem, where both inputs and mode sequences are optimized, we consider the case where switching is exogenous. For such systems, the number of possible mode sequences grows exponentially with time, making the exact computation of the value function intractable. This motivates the development of tractable bounds that approximate it. We propose a novel framework, based on path‑complete graphs, for constructing computable upper bounds on the value function. In this framework, multiple quadratic functions are combined through a directed graph that encodes dynamic programming inequalities, yielding convex and sound formulations. For example, for switched linear systems with quadratic cost, we derive tractable LMI‑based formulations and provide computational complexity bounds. We further establish approximation guarantees for the upper bounds and show asymptotic non‑conservativeness using concepts from graph theory. Finally, we extend the approach to controller synthesis for systems with affine control inputs and demonstrate its effectiveness on numerical examples.

Authors:Haokun Yu, Jingyuan Zhou, Kaidi Yang
Title: Parameter Privacy-Preserving Data Sharing: A Particle-Belief MDP Formulation
Abstract:
This paper investigates parameter‑privacy‑preserving data sharing in continuous‑state dynamical systems, where a data owner designs a data‑sharing policy to support downstream estimation and control while preventing adversarial inference of a sensitive parameter. This data‑sharing problem is formulated as an optimization problem that trades off privacy leakage and the impact of data sharing on the data owner's utility, subject to a data‑usability constraint. We show that this problem admits an equivalent belief Markov decision process (MDP) formulation, which provides a simplified representation of the optimal policy. To efficiently characterize information‑theoretic privacy leakage in continuous state and action spaces, we propose a particle‑belief MDP formulation that tracks the parameter posterior via sequential Monte Carlo, yielding a tractable belief‑state approximation that converges asymptotically as the number of particles increases. We further derive a tractable closed‑form upper bound on particle‑based MI via Gaussian mixture approximations, which enables efficient optimization of the particle‑belief MDP. Experiments on a mixed‑autonomy platoon show that the learned continuous policy substantially impedes inference attacks on human‑driving behavior parameters while maintaining data usability and system performance.

Authors:Jaekeun Lee, Jae-Jung Jung, Shenghui Cui
Title: Mitigation of Structural Harmonic Instability in Virtual Admittance-Based Grid-Forming Inverters via Mimicking Skin Effect
Abstract:
The virtual admittance‑current controller (VA‑CC) scheme is widely employed to emulate an equivalent inductance in front of the internal voltage source of grid‑forming inverters. However, recent studies have reported harmonic instabilities associated with VA‑CC, motivating the need for a more physically interpretable understanding of their origin. This letter identifies a delay‑independent structural mechanism of harmonic instability in the VA‑CC scheme, wherein the interaction between the filter and virtual inductances introduces a non‑passive second‑order transfer‑function term exhibiting negative resistance. To address this issue, a simple yet effective modification is proposed by integrating a parallel virtual resistor into the VA structure. This reconfiguration enhances the passivity of VA‑CC scheme across the harmonic range by mimicking the skin effect which augments damping in high‑frequency range, without altering the wellestablished current controller or voltage feedforward control. Experimental results validate that the proposed method achieves robust harmonic stability, whereas the conventional approach fails under identical grid conditions.

Authors:Chung-Han Hsieh
Title: Sampled-Data Wasserstein Distributionally Robust Control of Multiplicative Systems: A Convex Relaxation with Performance Guarantees
Abstract:
This paper investigates the robust optimal control of sampled‑data stochastic systems with multiplicative noise and distributional ambiguity. We consider a class of discrete‑time optimal control problems where the controller \emphjointly selects a feedback policy and a sampling period to maximize the worst‑case expected concave utility of the inter‑sample growth factor. Modeling uncertainty via a Wasserstein ambiguity set, we confront the structural obstacle of~``concave‑max'' geometry arising from maximizing a concave utility against an adversarial distribution. Unlike standard convex loss minimization, the dual reformulation here requires a minimax interchange within the semi‑infinite constraints, where the utility's concavity precludes exact strong duality. To address this, we utilize a general minimax inequality to derive a tractable convex relaxation. Our approach yields a rigorous lower bound that functions as a probabilistic performance guarantee. We establish an explicit, non‑asymptotic bound on the resulting duality gap, proving that the approximation error is uniformly controlled by the Lipschitz‑smoothness of the stage reward and the diameter of the disturbance support. Furthermore, we introduce necessary and sufficient conditions for \emphrobust viability, ensuring state positivity invariance across the entire ambiguity set. Finally, we bridge the gap between static optimization and dynamic performance, proving that the optimal value of the relaxation serves as a rigorous deterministic floor for the asymptotic average utility rate almost surely. The framework is illustrated on a log‑optimal portfolio control problem, which serves as a canonical instance of multiplicative stochastic control.

Authors:Dhruv S. Kushwaha, Zoleikha A. Biron
Title: Lyapunov Constrained Soft Actor-Critic (LC-SAC) using Koopman Operator Theory for Quadrotor Trajectory Tracking
Abstract:
Reinforcement Learning (RL) has achieved remarkable success in solving complex sequential decision‑making problems. However, its application to safety‑critical physical systems remains constrained by the lack of stability guarantees. Standard RL algorithms prioritize reward maximization, often yielding policies that may induce oscillations or unbounded state divergence. There has significant work in incorporating Lyapunov‑based stability guarantees in RL algorithms with key challenges being selecting a candidate Lyapunov function, computational complexity by using excessive function approximators and conservative policies by incorporating stability criterion in the learning process. In this work we propose a novel Lyapunov‑constrained Soft Actor‑Critic (LC‑SAC) algorithm using Koopman operator theory. We propose use of extended dynamic mode decomposition (EDMD) to produce a linear approximation of the system and use this approximation to derive a closed form solution for candidate Lyapunov function. This derived Lyapunov function is incorporated in the SAC algorithm to further provide guarantees for a policy that stabilizes the nonlinear system. The results are evaluated trajectory tracking of a 2D Quadrotor environment based on safe‑control‑gym. The proposed algorithm shows training convergence and decaying violations for Lyapunov stability criterion compared to baseline vanilla SAC algorithm. GitHub Repository: https://github.com/DhruvKushwaha/LC‑SAC‑Quadrotor‑Trajectory‑Tracking

Authors:Riming Xu, Obadah Wali, Yasmine Marani, Eric Feron
Title: Control and State Estimation of Vehicle-Mounted Aerial Systems in GPS-Denied, Non-Inertial Environments
Abstract:
We present a robust control and estimation framework for quadrotors operating in Global Navigation Satellite System(GNSS)‑denied, non‑inertial environments where inertial sensors such as Inertial Measurement Units (IMUs) become unreliable due to platform‑induced accelerations. In such settings, conventional estimators fail to distinguish whether the measured accelerations arise from the quadrotor itself or from the non‑inertial platform, leading to drift and control degradation. Unlike conventional approaches that depend heavily on IMU and GNSS, our method relies exclusively on external position measurements combined with a Extended Kalman Filter with Unknown Inputs (EKF‑UI) to account for platform motion. The estimator is paired with a cascaded PID controller for full 3D tracking. To isolate estimator performance from localization errors, all tests are conducted using high‑precision motion capture systems. Experimental results in a moving‑cart testbed validate our approach under both translational in X‑axis and Y‑axis dissonance. Compared to standard EKF, the proposed method significantly improves stability and trajectory tracking without requiring inertial feedback, enabling practical deployment on moving platforms such as trucks or elevators.

Authors:Joonkyung Kim, Wenxi Chen, Davood Soleymanzadeh, Yi Ding, Xiangbo Gao, Zhengzhong Tu, Ruqi Zhang, Fan Fei, Sushant Veer, Yiwei Lyu, Minghui Zheng, Yan Gu
Title: Modular Safety Guardrails Are Necessary for Foundation-Model-Enabled Robots in the Real World
Abstract:
The integration of foundation models (FMs) into robotics has accelerated real‑world deployment, while introducing new safety challenges arising from open‑ended semantic reasoning and embodied physical action. These challenges require safety notions beyond physical constraint satisfaction. In this paper, we characterize FM‑enabled robot safety along three dimensions: action safety (physical feasibility and constraint compliance), decision safety (semantic and contextual appropriateness), and human‑centered safety (conformance to human intent, norms, and expectations). We argue that existing approaches, including static verification, monolithic controllers, and end‑to‑end learned policies, are insufficient in settings where tasks, environments, and human expectations are open‑ended, long‑tailed, and subject to adaptation over time. To address this gap, we propose modular safety guardrails, consisting of monitoring (evaluation) and intervention layers, as an architectural foundation for comprehensive safety across the autonomy stack. Beyond modularity, we highlight possible cross‑layer co‑design opportunities through representation alignment and conservatism allocation to enable faster, less conservative, and more effective safety enforcement. We call on the community to explore richer guardrail modules and principled co‑design strategies to advance safe real‑world physical AI deployment.

Authors:Nikolaos Bousias, Charalampia Stamouli, Anastasios Tsiamis, George Pappas
Title: On transferring safety certificates across dynamical systems
Abstract:
Control barrier functions (CBFs) provide a powerful tool for enforcing safety constraints in control systems, but their direct application to complex, high‑dimensional dynamics is often challenging. In many settings, safety certificates are more naturally designed for simplified or alternative system models that do not exactly match the dynamics of interest. This paper addresses the problem of transferring safety guarantees between dynamical systems with mismatched dynamics. We propose a transferred control barrier function (tCBF) framework that enables safety constraints defined on one system to be systematically enforced on another system using a simulation function and an explicit margin term. The resulting transferred barrier accounts for model mismatch and induces a safety condition that can be enforced on the target system via a quadratic‑program‑based safety filter. The proposed approach is general and does not require the two systems to share the same state dimension or dynamics. We demonstrate the effectiveness of the framework on a quadrotor navigation task with the transferred barrier ensuring collision avoidance for the target system, while remaining minimally invasive to a nominal controller. These results highlight the potential of transferred control barrier functions as a general mechanism for enforcing safety across heterogeneous dynamical systems.

Authors:Nikolaos Bousias, Lars Lindemann, George Pappas
Title: eCP: Equivariant Conformal Prediction with pre-trained models
Abstract:
Conformal prediction, a post‑hoc, distribution‑free, finite‑sample method of uncertainty quantification that offers formal coverage guarantees under the assumption of data exchangeability. Unfortunately, the resulting uncertainty regions can grow significantly in long horizon missions, rendering the statistical guarantees uninformative. To that end, we propose infusing CP with geometric information via group‑averaging of the pretrained predictor to distribute the non‑conformity mass across the orbits. Each sample now is treated as a representative of an orbit, thus uncertainty can be mitigated by other samples entangled to it via the orbit inducing elements of the symmetry group. Our approach provably yields contracted non‑conformity scores in increasing convex order, implying improved exponential‑tail bounds and sharper conformal prediction sets in expectation, especially at high confidence levels. We then propose an experimental design to test these theoretical claims in pedestrian trajectory prediction.

Authors:Hossein Rastgoftar
Title: Safety-Critical Reinforcement Learning with Viability-Based Action Shielding for Hypersonic Longitudinal Flight
Abstract:
This paper presents a safety‑critical reinforcement learning framework for nonlinear dynamical systems with continuous state and input spaces operating under explicit physical constraints. Hard safety constraints are enforced independently of the reward through action shielding and reachability‑based admissible action sets, ensuring that unsafe behaviors are never intentionally selected during learning or execution. To capture nominal operation and recovery behavior within a single control architecture, the state space is partitioned into safe and unsafe regions based on membership in a safety box, and a mode‑dependent reward is used to promote accurate tracking inside the safe region and recovery toward it when operating outside. To enable online tabular learning on continuous dynamics, a finite‑state abstraction is constructed via state aggregation, and action selection and value updates are consistently restricted to admissible actions. The framework is demonstrated on a longitudinal point‑mass hypersonic vehicle model with aerodynamic and propulsion couplings, using angle of attack and throttle as control inputs.

Authors:Chongyang Shi, Michael Dorothy, Jie Fu
Title: C-IDS: Solving Contextual POMDP via Information-Directed Objective
Abstract:
We study the policy synthesis problem in contextual partially observable Markov decision processes (CPOMDPs), where the environment is governed by an unknown latent context that induces distinct POMDP dynamics. Our goal is to design a policy that simultaneously maximizes cumulative return and actively reduces uncertainty about the underlying context. We introduce an information‑directed objective that augments reward maximization with mutual information between the latent context and the agent's observations. We develop the C‑IDS algorithm to synthesize policies that maximize the information‑directed objective. We show that the objective can be interpreted as a Lagrangian relaxation of the linear information ratio and prove that the temperature parameter is an upper bound on the information ratio. Based on this characterization, we establish a sublinear Bayesian regret bound over K episodes. We evaluate our approach on a continuous Light‑Dark environment and show that it consistently outperforms standard POMDP solvers that treat the unknown context as a latent state variable, achieving faster context identification and higher returns.

Authors:Arkaprava Sain, Sunandan Adhikary, Soumyajit Dey
Title: Mitigating Timing-Based Attacks in Real-Time Cyber-Physical Systems
Abstract:
Real‑time cyber‑physical systems depend on deterministic task execution to guarantee safety and correctness. Unfortunately, this determinism can unintentionally expose timing information that enables adversaries to infer task execution patterns and carry out timing‑based attacks targeting safety‑critical control tasks. While prior defenses aim to obscure schedules through randomization or isolation, they typically neglect the implications of such modifications on closed‑loop control behavior and real‑time feasibility. This work studies the problem of securing real‑time control workloads against timing inference attacks while explicitly accounting for both schedulability constraints and control performance requirements. We present a scheduling‑based mitigation approach that introduces bounded timing perturbations to control task executions in a structured manner, reducing adversarial opportunities without violating real‑time guarantees. The framework jointly considers worst‑case execution behavior and the impact of execution delays on control performance, enabling the system to operate within predefined safety and performance limits. Through experimental evaluation on representative task sets and control scenarios, the proposed approach demonstrates that exposure to timing‑based attacks can be significantly reduced while preserving predictable execution and acceptable control quality.

Authors:Max H. Cohen, Pio Ong, Aaron D. Ames
Title: Input-to-State Safe Backstepping: Robust Safety-Critical Control with Unmatched Uncertainties
Abstract:
Guaranteeing safety in the presence of unmatched disturbances ‑‑ uncertainties that cannot be directly canceled by the control input ‑‑ remains a key challenge in nonlinear control. This paper presents a constructive approach to safety‑critical control of nonlinear systems with unmatched disturbances. We first present a generalization of the input‑to‑state safety (ISSf) framework for systems with these uncertainties using the recently developed notion of an Optimal Decay CBF, which provides more flexibility for satisfying the associated Lyapunov‑like conditions for safety. From there, we outline a procedure for constructing ISSf‑CBFs for two relevant classes of systems with unmatched uncertainties: i) strict‑feedback systems; ii) dual‑relative‑degree systems, which are similar to differentially flat systems. Our theoretical results are illustrated via numerical simulations of an inverted pendulum and planar quadrotor.

Authors:Nico Holzinger, Matthias Althoff
Title: A Comparison of Set-Based Observers for Nonlinear Systems
Abstract:
Set‑based state estimation computes sets of states consistent with a system model given bounded sets of disturbances and noise. Bounding the set of states is crucial for safety‑critical applications so that one can ensure that all specifications are met. While numerous approaches have been proposed for nonlinear discrete‑time systems, a unified evaluation under comparable conditions is lacking. This paper reviews and implements a representative selection of set‑based observers within the CORA framework. To provide an objective comparison, the methods are evaluated on common benchmarks, and we examine computational effort, scalability, and the conservatism of the resulting state bounds. This study highlights characteristic trade‑offs between observer categories and set representations, as well as practical considerations arising in their implementation. All implementations are made publicly available to support reproducibility and future development. This paper thereby offers the first broad, tool‑supported comparison of guaranteed state estimators for nonlinear discrete‑time systems.

Authors:Manuel Treutlein, Pascal Bothe, Marc Schmidt, Roman Hahn, Oliver Neumann, Ralf Mikut, Veit Hagenmeyer
Title: Real-world energy data of 200 feeders from low-voltage grids with metadata in Germany over two years
Abstract:
The last mile of the distribution grid is crucial for a successful energy transition, as more low‑carbon technology like photovoltaic systems, heat pumps, and electric vehicle chargers connect to the low‑voltage grid. Despite considerable challenges in operation and planning, researchers often lack access to suitable low‑voltage grid data. To address this, we present the FeederBW dataset with data recorded by the German distribution system operator Netze BW. It offers real‑world energy data from 200 low‑voltage feeders over two years (2023‑2025) with weather information and detailed metadata, including changes in low‑carbon technology installations. The dataset includes feeder‑specific details such as the number of housing units, installed power of low‑carbon technology, and aggregated industrial energy data. Furthermore, high photovoltaic feed‑in and one‑minute temporal resolution makes the dataset unique. FeederBW supports various applications, including machine learning for load forecasting, conducting non‑intrusive load monitoring, generating synthetic data, and analyzing the interplay between weather, feeder measurements, and metadata. The dataset reveals insightful patterns and clearly reflects the growing impact of low‑carbon technology on low‑voltage grids.

Authors:Galina Sidorenko, Johan Thunberg
Title: A necessary and sufficient condition for discrete-time consensus on star boundaries
Abstract:
It is intuitive and well known, that if agents in a multi‑agent system iteratively update their states in the Euclidean space as convex combinations of neighbors' states, all states eventually converge to the same value (consensus), provided the interaction graph is sufficiently connected. However, this seems to be also true in practice if the convex combinations of states are mapped or radially projected onto any unit l_p‑sphere or even boundaries of star‑convex sets, herein referred to as star boundaries. In this paper, we present insight into this matter by providing a necessary and sufficient condition for asymptotic consensus of the normalized states (directions) for strongly connected directed graphs, which is equivalent to asymptotic consensus of states when the star boundaries are the same for all agents. Furthermore, we show that when asymptotic consensus occurs, the states converge linearly and the point of convergence is continuous in the initial states. Assuming a directed strongly connected graph provides a more general setting than that considered, for example, in gradient‑based consensus protocols, where symmetric graphs are assumed. Illustrative examples and a vast number of numerical simulations showcase the theoretical results.

Authors:Julia Adlercreutz, Richard Pates
Title: Cholesky factorisation, and intrinsically sparse linear quadratic regulation
Abstract:
We classify a family of matrices of shift operators that can be factorised in a computationally tractable manner with the Cholesky algorithm. Such matrices arise in the linear quadratic regulator problem, and related areas. We use the factorisation to uncover intrinsic sparsity properties in the control laws for transportation problems with an underlying tree structure. This reveals that the optimal control can be applied in a distributed manner that is obscured by standard solution methods.

Authors:Komeil Nosrati, Aleksei Tepljakov, Juri Belikov, Eduard Petlenkov
Title: When control meets large language models: From words to dynamics
Abstract:
While large language models (LLMs) are transforming engineering and technology through enhanced control capabilities and decision support, they are simultaneously evolving into complex dynamical systems whose behavior must be regulated. This duality highlights a reciprocal connection in which prompts support control system design while control theory helps shape prompts to achieve specific goals efficiently. In this study, we frame this emerging interconnection of LLM and control as a bidirectional continuum, from prompt design to system dynamics. First, we investigate how LLMs can advance the field of control in two distinct capacities: directly, by assisting in the design and synthesis of controllers, and indirectly, by augmenting research workflows. Second, we examine how control concepts help LLMs steer their trajectories away from undesired meanings, improving reachability and alignment via input optimization, parameter editing, and activation‑level interventions. Third, we look into deeper integrations by treating LLMs as dynamic systems within a state‑space framework, where their internal representations are closely linked to external control loops. Finally, we identify key challenges and outline future research directions to understand LLM behavior and develop interpretable and controllable LLMs that are as trustworthy and robust as their electromechanical counterparts, thereby ensuring they continue to support and safeguard society.

Authors:Sreeshma Markkassery, Ton van den Boom, Bart De Schutter
Title: Dynamics of Implicit Time-Invariant Max-Min-Plus-Scaling Discrete-Event Systems
Abstract:
Max‑min‑plus‑scaling (MMPS) systems generalize max‑plus, min‑plus and max‑min‑plus models with more flexibility in modelling discrete‑event dynamics. Especially, implicit MMPS models capture a wide range of real world discrete‑event applications. This article analyzes the dynamics of an autonomous, time‑invariant implicit MMPS system in a discrete‑event framework. First, we provide sufficient conditions under which an implicit MMPS system admits at least one solution to its state‑space representation. Then, we analyze its global behavior by determining the key parameters; the growth rates and fixed points. For a solvable MMPS system, we assess the local behavior of the system around its set of fixed points via a normalization procedure. Further, we present the notion of stability for the normalized system. A case study of the urban railway network substantiates the theoretical results.

Authors:Nicola Ramseyer, Matthieu Jacobs, Mario Paolone
Title: Power Reserve Procurement Considering Dependent Random Variables with PCE
Abstract:
This paper presents an approach for the modelling of dependent random variables using generalised polynomial chaos. This allows to write chance‑constrained optimization problems with respect to a joint distribution modelling dependencies between different stochastic inputs. Arbitrary dependencies are modelled by using Gaussian copulas to construct the joint distribution. The paper exploits the problem structure and develops suitable transformations to ensure tractability. The proposed method is applied to a probabilistic power reserve procurement problem. The effectiveness of the method to capture dependencies is shown by comparing the approach with a standard approach considering independent random variables.

Authors:Eymen Ipek, Mario Hirz
Title: Impact of Physics-Informed Features on Neural Network Complexity for Li-ion Battery Voltage Prediction in Electric Vertical Takeoff and Landing Aircrafts
Abstract:
The electrification of vertical takeoff and landing aircraft demands high‑fidelity battery management systems capable of predicting voltage response under aggressive power dynamics. While data‑driven models offer high accuracy, they often require complex architectures and extensive training data. Conversely, equivalent circuit models (ECMs), such as the second‑order model, offer physical interpretability but struggle with high C‑rate non‑linearities. This paper investigates the impact of integrating physics‑based information into data‑driven surrogate models. Specifically, we evaluate whether physics‑informed features allow for the simplification of neural network architectures without compromising accuracy. Using the open‑source electric vertical takeoff and landing (eVTOL) battery dataset, we compare pure data‑driven models against physics‑informed data models. Results demonstrate that physics‑informed models achieve comparable accuracy to complex pure data‑driven models while using up to 75% fewer trainable parameters, significantly reducing computational overhead for potential on‑board deployment.

Authors:Tamoghna Sarkar, Bhaskar Krishnamachari
Title: Joint Network-and-Server Congestion in Multi-Source Traffic Allocation: A Convex Formulation and Price-Based Decentralization
Abstract:
This paper studies an important rate allocation problem that arises in many networked and distributed systems: steady‑state traffic rate allocation from multiple sources to multiple service nodes when both (i) the access‑path delay on each source‑node route is rate‑dependent (capacity‑constrained) and convex, and (ii) each service node (also capacity‑constrained) experiences a load‑dependent queueing delay driven by aggregate load from all sources. We show that the resulting flow‑weighted end‑to‑end delay minimization is a convex program, yielding a global system‑optimal solution characterized by KKT conditions that equalize total marginal costs (a path marginal access term plus a node congestion price) across all utilized routes. This condition admits a Wardrop‑type interpretation: for each source, all utilized options equalize total marginal cost, while any option with strictly larger total marginal cost receives no flow. Building on this structure, we develop a lightweight distributed pricing‑based algorithm in which each service node locally computes and broadcasts a scalar congestion price from its observed aggregate load, while each source updates its traffic split by solving a small separable convex allocation problem under the advertised prices. Numerical illustrations demonstrate convergence of the distributed iteration to the centralized optimum and highlight the trade‑offs induced by jointly modeling access and service congestion.

Authors:Karthik Elamvazhuthi, Shiba Biswal, Kian Rosenblum, Arushi Katyal, Tianli Qu, Grady Ma, Rishi Sonthalia
Title: Geometry-Preserving Neural Architectures on Manifolds with Boundary
Abstract:
Preserving geometric structure is important in learning. We propose a unified class of geometry‑aware architectures that interleave geometric updates between layers, where both projection layers and intrinsic exponential map updates arise as discretizations of projected dynamical systems on manifolds (with or without boundary). Within this framework, we establish universal approximation results for constrained neural ODEs. We also analyze architectures that enforce geometry only at the output, proving a separate universal approximation property that enables direct comparison to interleaved designs. When the constraint set is unknown, we learn projections via small‑time heat‑kernel limits, showing diffusion/flow‑matching can be used as data‑based projections. Experiments on dynamics over S^2 and SO(3), and diffusion on S^d‑1‑valued features demonstrate exact feasibility for analytic updates and strong performance for learned projections

Authors:Chao Shen, Zihan Guo, Xu Wan, Zhenghao Yang, Yifan Zhang, Wengi Huang, Jie Song, Zongyan Zhang, Mingyang Sun
Title: ProOPF: Benchmarking and Improving LLMs for Professional-Grade Power Systems Optimization Modeling
Abstract:
Growing renewable penetration introduces substantial uncertainty into power system operations, necessitating frequent adaptation of dispatch objectives and constraints and challenging expertise‑intensive, near‑real‑time modeling workflows. Large Language Models (LLMs) provide a promising avenue for automating this process by translating natural‑language (NL) operational requirements into executable optimization models via semantic reasoning and code synthesis. Yet existing LLM datasets and benchmarks for optimization modeling primarily target coarse‑grained cross‑domain generalization, offering limited, rigorous evaluation in power‑system settings, particularly for Optimal Power Flow (OPF). We therefore introduce ProOPF‑D and ProOPF‑B, a dataset and benchmark for professional‑grade OPF modeling: ProOPF‑D contains 12K instances pairing NL requests with parameter adjustments and structural extensions to a canonical OPF, together with executable implementations; ProOPF‑B provides 121 expert‑annotated test cases with ground‑truth code, enabling end‑to‑end evaluation under both concrete and abstract OPF modeling regimes.

Authors:Masashi Wakaiki
Title: Data-driven stabilization of continuous-time systems with noisy input-output data
Abstract:
We study data‑driven stabilization of continuous‑time systems in autoregressive form when only noisy input‑output data are available. First, we provide an operator‑based characterization of the set of systems consistent with the data. Next, combining this characterization with behavioral theory, we derive a necessary and sufficient condition for the noisy data to be informative for quadratic stabilization. This condition is formulated as linear matrix inequalities, whose solution yields a stabilizing controller. Finally, we characterize data informativity for system identification in the noise‑free setting.

Authors:Xiaocai Zhang, Neema Nassir, Lok Sang Chan, Milad Haghani
Title: Human-Centric Traffic Signal Control for Equity: A Multi-Agent Action Branching Deep Reinforcement Learning Approach
Abstract:
Coordinating traffic signals along multimodal corridors is challenging because many multi‑agent deep reinforcement learning (DRL) approaches remain vehicle‑centric and struggle with high‑dimensional discrete action spaces. We propose MA2B‑DDQN, a human‑centric multi‑agent action‑branching double Deep Q‑Network (DQN) framework that explicitly optimizes traveler‑level equity. Our key contribution is an action‑branching discrete control formulation that decomposes corridor control into (i) local, per‑intersection actions that allocate green time between the next two phases and (ii) a single global action that selects the total duration of those phases. This decomposition enables scalable coordination under discrete control while reducing the effective complexity of joint decision‑making. We also design a human‑centric reward that penalizes the number of delayed individuals in the corridor, accounting for pedestrians, vehicle occupants, and transit passengers. Extensive evaluations across seven realistic traffic scenarios in Melbourne, Australia, demonstrate that our approach significantly reduces the number of impacted travelers, outperforming existing DRL and baseline methods. Experiments confirm the robustness of our model, showing minimal variance across diverse settings. This framework not only advocates for a fairer traffic signal system but also provides a scalable solution adaptable to varied urban traffic conditions.

Authors:David William Marques Guerra, Taufik Abrao
Title: Hybrid-Field Channel Estimation for XL-MIMO Systems: Dictionary-based Sparse Signal Recovery
Abstract:
Extremely large‑scale multiple‑input multiple‑output (XL‑MIMO) systems are a key technology for future wireless networks, but the large array aperture naturally creates a hybrid‑field (HF) propagation regime in which far‑field (FF) planar‑wave and near‑field (NF) spherical‑wave components coexist. This work considers the problem of HF channel estimation (CE) and introduces a unified model that superimposes FF and NF contributions according to the Rayleigh distance boundary. By exploiting the inherent sparsity of the channel in the angular and polar domains, we formulate the estimation task as a sparse recovery problem. Unlike conventional approaches that require prior knowledge of the channel sparsity level, the proposed method operates without requiring knowledge of the sparsity level L and the NF/FF ratio γ, which are used only for synthetic channel generation in simulations. The channel estimator determines the number of paths adaptively through a residual‑based stopping rule. A combined FF/NF dictionary is employed to initialize the support, and each selected atom undergoes continuous parameter refinement to mitigate grid mismatch. Simulation results demonstrate that the proposed estimator achieves accurate HF channel reconstruction under both line‑of‑sight (LoS) and non‑line‑of‑sight (NLoS) conditions, offering a practical and computationally efficient solution for XL‑MIMO systems. Extremely Large‑Scale MIMO (XL‑MIMO); Channel State Information (CSI); Channel estimation (CE); hybrid‑field (HF) wave propagation; near‑field (NF) spherical wave model; far‑field (FF) planar wave model

Authors:Xiaoyuan Cheng, Wenxuan Yuan, Boyang Li, Yuanchao Xu, Yiming Yang, Hao Liang, Bei Peng, Robert Loftin, Zhuo Sun, Yukun Hu
Title: How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?
Abstract:
Diffusion policy sampling enables reinforcement learning (RL) to represent multimodal action distributions beyond suboptimal unimodal Gaussian policies. However, existing diffusion‑based RL methods primarily focus on offline settings for reward maximization, with limited consideration of safety in online settings. To address this gap, we propose Augmented Lagrangian‑Guided Diffusion (ALGD), a novel algorithm for off‑policy safe RL. By revisiting optimization theory and energy‑based model, we show that the instability of primal‑dual methods arises from the non‑convex Lagrangian landscape. In diffusion‑based safe RL, the Lagrangian can be interpreted as an energy function guiding the denoising dynamics. Counterintuitively, direct usage destabilizes both policy generation and training. ALGD resolves this issue by introducing an augmented Lagrangian that locally convexifies the energy landscape, yielding a stabilized policy generation and training process without altering the distribution of the optimal policy. Theoretical analysis and extensive experiments demonstrate that ALGD is both theoretically grounded and empirically effective, achieving strong and stable performance across diverse environments.

Authors:Haoran Su, Yandong Sun, Hanxiao Deng
Title: Spatiotemporal Decision Transformer for Traffic Coordination
Abstract:
Traffic signal control is a critical challenge in urban transportation, requiring coordination among multiple intersections to optimize network‑wide traffic flow. While reinforcement learning has shown promise for adaptive signal control, existing methods struggle with multi‑agent coordination and sample efficiency. We introduce MADT (Multi‑Agent Decision Transformer), a novel approach that reformulates multi‑agent traffic signal control as a sequence modeling problem. MADT extends the Decision Transformer paradigm to multi‑agent settings by incorporating: (1) a graph attention mechanism for modeling spatial dependencies between intersections, (2) a|temporal transformer encoder for capturing traffic dynamics, and (3) return‑to‑go conditioning for target performance specification. Our approach enables offline learning from historical traffic data, with architecture design that facilitates potential online fine‑tuning. Experiments on synthetic grid networks and real‑world traffic scenarios demonstrate that MADT achieves state‑of‑the‑art performance, reducing average travel time by 5‑6% compared to the strongest baseline while exhibiting superior coordination among adjacent intersections.

Authors:Qinan Zhou, Jing Sun
Title: Estimation of Cell-to-Cell Variation and State of Health for Battery Modules with Parallel-Connected Cells
Abstract:
Estimating cell‑to‑cell variation (CtCV) and state of health (SoH) for battery modules composed of parallel‑connected cells is challenging when only module‑level signals are measurable and individual cell behaviors remain unobserved. Although progress has been made in SoH estimation, CtCV estimation remains unresolved in the literature. This paper proposes a unified framework that accurately estimates both CtCV and SoH for modules using only module‑level information extracted from incremental capacity analysis (ICA) and differential voltage analysis (DVA). With the proposed framework, CtCV and SoH estimations can be decoupled into two separate tasks, allowing each to be solved with dedicated algorithms without mutual interference and providing greater design flexibility. The framework also exhibits strong versatility in accommodating different CtCV metrics, highlighting its general‑purpose nature. Experimental validation on modules with three parallel‑connected cells demonstrates that the proposed framework can systematically select optimal module‑level features for CtCV and SoH estimations, deliver accurate CtCV and SoH estimates with high confidence and low computational complexity, remain effective across different C‑rates, and be suitable for onboard implementation.

Authors:Tiago Leite, Maria Conceição, António Grilo
Title: IMAGINE: Intelligent Multi-Agent Godot-based Indoor Networked Exploration
Abstract:
The exploration of unknown, Global Navigation Satellite System (GNSS) denied environments by an autonomous communication‑aware and collaborative group of Unmanned Aerial Vehicles (UAVs) presents significant challenges in coordination, perception, and decentralized decision‑making. This paper implements Multi‑Agent Reinforcement Learning (MARL) to address these challenges in a 2D indoor environment, using high‑fidelity game‑engine simulations (Godot) and continuous action spaces. Policy training aims to achieve emergent collaborative behaviours and decision‑making under uncertainty using Network‑Distributed Partially Observable Markov Decision Processes (ND‑POMDPs). Each UAV is equipped with a Light Detection and Ranging (LiDAR) sensor and can share data (sensor measurements and a local occupancy map) with neighbouring agents. Inter‑agent communication constraints include limited range, bandwidth and latency. Extensive ablation studies evaluated MARL training paradigms, reward function, communication system, neural network (NN) architecture, memory mechanisms, and POMDP formulations. This work jointly addresses several key limitations in prior research, namely reliance on discrete actions, single‑agent or centralized formulations, assumptions of a priori knowledge and permanent connectivity, inability to handle dynamic obstacles, short planning horizons and architectural complexity in Recurrent NNs/Transformers. Results show that the scalable training paradigm, combined with a simplified architecture, enables rapid autonomous exploration of an indoor area. The implementation of Curriculum‑Learning (five increasingly complex levels) also enabled faster, more robust training. This combination of high‑fidelity simulation, MARL formulation, and computational efficiency establishes a strong foundation for deploying learned cooperative strategies in physical robotic systems.

Authors:Berk Bozkurt, Aditya Mahajan, Ashutosh Nayyar, Yi Ouyang
Title: Sub-optimality bounds for certainty equivalent policies in partially observed systems
Abstract:
In this paper, we present a generalization of the certainty equivalence principle of stochastic control. One interpretation of the classical certainty equivalence principle for linear systems with output feedback and quadratic costs is as follows: the optimal action at each time is obtained by evaluating the optimal state‑feedback policy of the stochastic linear system at the minimum mean square error (MMSE) estimate of the state. Motivated by this interpretation, we consider certainty equivalent policies for general (non‑linear) partially observed stochastic systems that allow for any state estimate rather than restricting to MMSE estimates. In such settings, the certainty equivalent policy is not optimal. For models where the cost and the dynamics are smooth in an appropriate sense, we derive upper bounds on the sub‑optimality of certainty equivalent policies. We present several examples to illustrate the results.

Authors:Ali Forootani, Raffaele Iervolino
Title: Learnable Koopman-Enhanced Transformer-Based Time Series Forecasting with Spectral Control
Abstract:
This paper proposes a unified family of learnable Koopman operator parameterizations that integrate linear dynamical systems theory with modern deep learning forecasting architectures. We introduce four learnable Koopman variants‑scalar‑gated, per‑mode gated, MLP‑shaped spectral mapping, and low‑rank Koopman operators which generalize and interpolate between strictly stable Koopman operators and unconstrained linear latent dynamics. Our formulation enables explicit control over the spectrum, stability, and rank of the linear transition operator while retaining compatibility with expressive nonlinear backbones such as Patchtst, Autoformer, and Informer. We evaluate the proposed operators in a large‑scale benchmark that also includes LSTM, DLinear, and simple diagonal State‑Space Models (SSMs), as well as lightweight transformer variants. Experiments across multiple horizons and patch lengths show that learnable Koopman models provide a favorable bias‑variance trade‑off, improved conditioning, and more interpretable latent dynamics. We provide a full spectral analysis, including eigenvalue trajectories, stability envelopes, and learned spectral distributions. Our results demonstrate that learnable Koopman operators are effective, stable, and theoretically principled components for deep forecasting.

Authors:Saba Samadi, Brooks A. Butler, Philip E. Paré
Title: Robust Safety-Critical Control of Networked SIR Dynamics
Abstract:
We present a robust safety‑critical control framework tailored for networked susceptible‑infected‑recovered (SIR) epidemic dynamics, leveraging control barrier functions (CBFs) and robust control barrier functions to address the challenges of epidemic spread and mitigation. In our networked SIR model, each node must keep its infection level below a critical threshold, despite dynamic interactions with neighboring nodes and inherent uncertainties in the epidemic parameters and measurement errors, to ensure public health safety. We first derive a CBF‑based controller that guarantees infection thresholds are not exceeded in the nominal case. We enhance the framework to handle realistic epidemic scenarios under uncertainties by incorporating compensation terms that reinforce safety against uncertainties: an independent method with constant bounds for uniform uncertainty, and a novel approach that scales with the state to capture increased relative noise in early or suppressed outbreak stages. Simulation results on a networked SIR system illustrate that the nominal CBF controller maintains safety under low uncertainty, while the robust approaches provide formal safety guarantees under higher uncertainties; in particular, the novel method employs more conservative control efforts to provide larger safety margins, whereas the independent approach optimizes resource allocation by allowing infection levels to approach the boundaries in steady epidemic regimes.

Authors:Subhankar Banerjee, Sennur Ulukus
Title: Preemptive Scheduling for Age of Job Minimization in Task-Specific Machine Networks
Abstract:
We consider a time‑slotted job‑assignment system consisting of a central server, N task‑specific networks of machines, and multiple users. Each network specializes in executing a distinct type of task. Users stochastically generate jobs of various types and forward them to the central server, which routes each job to the appropriate network of machines. Due to resource constraints, the server cannot serve all users' jobs simultaneously, which motivates the design of scheduling policies with possible preemption. To evaluate scheduling performance, we introduce a novel timeliness metric, the age of job, inspired by the well‑known metric, the age of information. We study the problem of minimizing the long‑term weighted average age of job. We first propose a max‑weight policy by minimizing the one‑step Lyapunov drift and then derive the Whittle index (WI) policy when the job completion times of the networks of machines follow geometric distributions. For general job completion time distributions, we introduce a Whittle index with max‑weight fallback (WIMWF) policy. We also investigate the Net‑gain maximization (NGM) policy. Numerically, we show that the proposed WIMWF policy achieves the best performance in the general job completion time setting. We also observe a scaling trend: two different max‑weight policies can outperform the NGM policy in small systems, whereas the NGM policy improves as we scale the system size and becomes asymptotically better than max‑weight policies. For geometric service times, the WI policy yields the lowest age across all considered system sizes.

Authors:Jan Olucak, Torbjørn Cunis
Title: On the Practical Implementation of a Sequential Quadratic Programming Algorithm for Nonconvex Sum-of-squares Problems
Abstract:
Sum‑of‑squares (SOS) optimization provides a computationally tractable framework for certifying polynomial nonnegativity. If the considered problem is convex, the SOS problem can be transcribed into and solved by semi‑definite programs. However, in case of nonconvex problems iterative procedures are needed. Yet tractable and efficient solution methods are still lacking, limiting their application, for instance, in control engineering. To address this gap, we propose a filter line search algorithm that solves a sequence of quadratic subproblems. Numerical benchmarks demonstrate that the algorithm can significantly reduce the number of iterations, resulting in a substantial decrease in computation time compared to established methods for nonconvex SOS programs

Authors:Yiwei Zou, Huan-Cheng Liao, Wei Wang, Wonjune Kim, Yumin Su, Jacob T. Robinson, Kaiyuan Yang
Title: An Efficient Power Management Unit With Continuous MPPT and Energy Recycling for Wireless Millimetric Biomedical Implants
Abstract:
Biomedical implants offer transformative tools to improve medical outcomes. To realize minimally invasive implants with miniaturized volume and weight, wireless power transfer has been extensively studied to replace bulky batteries that dominate the volume of traditional implants and require surgical replacements. Ultra‑sonic and magnetoelectric WPT modalities, which leverage low frequency acoustic electrical coupling for energy transduction, become viable solutions for mm‑scale receivers. This work presents a fully integrated power management unit for ME WPT in millimetric implants. The PMU achieves load independent maximum power extraction and usage by continuously matching the impedance of the transducer, dynamically optimizing the power stage across varying input divided by load conditions, and reusing the storage energy to sustain the system when input power drops. Its parallel‑input regulation and storing stages architecture prevent the cascading power loss. With the skewed‑duty‑cycle MPPT technique and regulation efficiency optimizer, the PMU achieves a peak MPPT efficiency of 98.5 percent and a peak system overall efficiency of 73.33 percent. Additionally, the PMU includes an adaptive high‑voltage charging stage that charges the stimulation capacitor up to 12 V with an improved efficiency of 37.88 percent.

Authors:Jon Škerlj, Seongjin Bien, Abdeldjallil Naceri, Sami Haddadin
Title: Bridging the Sim-to-Real Gap with multipanda ros2: A Real-Time ROS2 Framework for Multimanual Systems
Abstract:
We present multipanda\_ros2, a novel open‑source ROS2 architecture for multi‑robot control of Franka Robotics robots. Leveraging ros2 control, this framework provides native ROS2 interfaces for controlling any number of robots from a single process. Our core contributions address key challenges in real‑time torque control, including interaction control and robot‑environment modeling. A central focus of this work is sustaining a 1kHz control frequency, a necessity for real‑time control and a minimum frequency required by safety standards. Moreover, we introduce a controllet‑feature design pattern that enables controller‑switching delays of \le 2 ms, facilitating reproducible benchmarking and complex multi‑robot interaction scenarios. To bridge the simulation‑to‑reality (sim2real) gap, we integrate a high‑fidelity MuJoCo simulation with quantitative metrics for both kinematic accuracy and dynamic consistency (torques, forces, and control errors). Furthermore, we demonstrate that real‑world inertial parameter identification can significantly improve force and torque accuracy, providing a methodology for iterative physics refinement. Our work extends approaches from soft robotics to rigid dual‑arm, contact‑rich tasks, showcasing a promising method to reduce the sim2real gap and providing a robust, reproducible platform for advanced robotics research.

Authors:Julian Lemmel, Felix Resch, Mónika Farsang, Ramin Hasani, Daniela Rus, Radu Grosu
Title: Adaptive Control in Autonomous Driving via Real-Time Recurrent RL
Abstract:
We study online fine‑tuning of pretrained control policies for autonomous driving using Real‑Time Recurrent Reinforcement Learning (RTRRL), a memory‑efficient algorithm that updates policy parameters at every time step without backpropagation through time. We extend RTRRL to support LrcSSM, a recently proposed nonlinear diagonal state‑space model, and combine offline behavioral cloning with online RTRRL fine‑tuning to adapt policies to distribution shifts at deployment. We validate the approach in the CarRacing simulation and on a 1:10‑scale RoboRacer platform equipped with an event camera, where a pretrained policy is fine‑tuned online during real‑world line‑following. To our knowledge, this is the first demonstration of online RL fine‑tuning with event‑camera observations on standard (non‑spiking) hardware in closed‑loop control. LrcSSM‑based policies improve fastest and most consistently across both settings.

Authors:Aniq Ur Rahman, Justin P. Coon
Title: Generating Causal Temporal Interaction Graphs for Counterfactual Validation of Temporal Link Prediction
Abstract:
Temporal link prediction (TLP) models are commonly evaluated based on predictive accuracy, yet such evaluations do not assess whether these models capture the causal mechanisms that govern temporal interactions. In this work, we propose a framework for counterfactual validation of TLP models by generating causal temporal interaction graphs (CTIGs) with known ground‑truth causal structure. We first introduce a structural equation model for continuous‑time event sequences that supports both excitatory and inhibitory effects, and then extend this mechanism to temporal interaction graphs. To compare causal models, we propose a distance metric based on cross‑model predictive error, and empirically validate the hypothesis that predictors trained on one causal model degrade when evaluated on sufficiently distant models. Finally, we instantiate counterfactual evaluation under (i) controlled causal shifts between generating models and (ii) timestamp shuffling as a stochastic distortion with measurable causal distance. Our framework provides a foundation for causality‑aware benchmarking.

Authors:Minghao Li, Ruihang Wang, Rui Tan, Yonggang Wen
Title: DCoPilot: Generative AI-Empowered Policy Adaptation for Dynamic Data Center Operations
Abstract:
Modern data centers (DCs) hosting artificial intelligence (AI)‑dedicated devices operate at high power densities with rapidly varying workloads, making minute‑level adaptation essential for safe and energy‑efficient operation. However, manually designing piecewise deep reinforcement learning (DRL) agents cannot keep pace with frequent dynamics shifts and service‑level agreement (SLA) changes of an evolving DC. This specification‑to‑policy lag causes a lack of timely, effective control policies, which may lead to service outages. To bridge the gap, we present DCoPilot, a hybrid framework for generative control policies in dynamic DC operation. DCoPilot synergizes two distinct generative paradigms, i.e., a large language model (LLM) that performs symbolic generation of structured reward forms, and a hypernetwork that conducts parametric generation of policy weights. DCoPilot operates through three coordinated phases: (i) simulation scale‑up, which stress‑tests reward candidates across diverse simulation‑ready (SimReady) scenes; (ii) meta policy distillation, where a hypernetwork is trained to output policy weights conditioned on SLA and scene embeddings; and (iii) online adaptation, enabling zero‑shot policy generation in response to updated specifications. Evaluated across five control task families spanning diverse DC components, DCoPilot achieves near‑zero constraint violations and outperforms all baselines across specification variations. Ablation studies validate the effectiveness of LLM‑based unified reward generation in enabling stable hypernetwork convergence.

Authors:Duc Hoang, Aarush Gupta, Philip Harris
Title: Ultrafast On-chip Online Learning via Spline Locality in Kolmogorov-Arnold Networks
Abstract:
Ultrafast online learning is essential for high‑frequency systems, such as controls for quantum computing and nuclear fusion, where adaptation must occur on sub‑microsecond timescales. Meeting these requirements demands low‑latency, fixed‑precision computation under strict memory constraints, a regime in which conventional Multi‑Layer Perceptrons (MLPs) are both inefficient and numerically unstable. We identify key properties of Kolmogorov‑Arnold Networks (KANs) that align with these constraints. Specifically, we show that: (i) KAN updates exploiting B‑spline locality are sparse, enabling superior on‑chip resource scaling, and (ii) KANs are inherently robust to fixed‑point quantization. By implementing fixed‑point online training on Field‑Programmable Gate Arrays (FPGAs), a representative platform for on‑chip computation, we demonstrate that KAN‑based online learners are significantly more efficient and expressive than MLPs across a range of low‑latency and resource‑constrained tasks. To our knowledge, this work is the first to demonstrate model‑free online learning at sub‑microsecond latencies.

Authors:Duc Hoang
Title: Position: The Need for Ultrafast Training
Abstract:
Domain‑specialized FPGAs have delivered unprecedented performance for low‑latency inference across scientific and industrial workloads, yet nearly all existing accelerators assume static models trained offline, relegating learning and adaptation to slower CPUs or GPUs. This separation fundamentally limits systems that must operate in non‑stationary, high‑frequency environments, where model updates must occur at the timescale of the underlying physics. In this paper, I argue for a shift from inference‑only accelerators to ultrafast on‑chip learning, in which both inference and training execute directly within the FPGA fabric under deterministic, sub‑microsecond latency constraints. Bringing learning into the same real‑time datapath as inference would enable closed‑loop systems that adapt as fast as the physical processes they control, with applications spanning quantum error correction, cryogenic qubit calibration, plasma and fusion control, accelerator tuning, and autonomous scientific experiments. Enabling such regimes requires rethinking algorithms, architectures, and toolflows jointly, but promises to transform FPGAs from static inference engines into real‑time learning machines.

Authors:Alexandre Lombard, Florent Perronnet, Nicolas Gaud, Abdeljalil Abbas-Turki
Title: Path Tracking with Dynamic Control Point Blending for Autonomous Vehicles: An Experimental Study
Abstract:
This paper presents an experimental study of a path‑tracking framework for autonomous vehicles in which the lateral control command is applied to a dynamic control point along the wheelbase. Instead of enforcing a fixed reference at either the front or rear axle, the proposed method continuously interpolates between both, enabling smooth adaptation across driving contexts, including low‑speed maneuvers and reverse motion. The lateral steering command is obtained by barycentric blending of two complementary controllers: a front‑axle Stanley formulation and a rear‑axle curvature‑based geometric controller, yielding continuous transitions in steering behavior and improved tracking stability. In addition, we introduce a curvature‑aware longitudinal control strategy based on virtual track borders and ray‑tracing, which converts upcoming geometric constraints into a virtual obstacle distance and regulates speed accordingly. The complete approach is implemented in a unified control stack and validated in simulation and on a real autonomous vehicle equipped with GPS‑RTK, radar, odometry, and IMU. The results in closed‑loop tracking and backward maneuvers show improved trajectory accuracy, smoother steering profiles, and increased adaptability compared to fixed control‑point baselines.

Authors:Rodrigo Aldana-López, Irene Perez Salesa, David Gomez Gutierrez, Rosario Aragues, Carlos Sagues
Title: Super-twisting over networks: A Lyapunov approach for distributed differentiation
Abstract:
We study distributed differentiation, where agents in a networked system estimate the average of local time‑varying signals and their derivatives under mild assumptions on the agents' signals and their first and second derivatives. Existing sliding‑mode methods provide only local stability guarantees and lack systematic gain selection. By isolating the structural features shared with the super‑twisting algorithm and encoding them into an abstract model, we construct a Lyapunov function enabling systematic gain design and proving global finite‑time convergence to consensus for the distributed differentiator. Building on this framework, we develop an event‑triggered hybrid system implementation using time‑varying and state dependent threshold rules and derive minimum inter‑event time guarantees and accuracy bounds that quantify the trade‑off between estimation accuracy and communication effort.

Authors:Qiqing Wang, Haokun Yu, Kaidi Yang
Title: Fostering Data Collaboration in Digital Transportation Marketplaces: The Role of Privacy-Preserving Mechanisms
Abstract:
Data collaboration between municipal authorities (MA) and mobility providers (MPs) has brought tremendous benefits to transportation systems in the era of big data. Engaging in collaboration can improve the service operations (e.g., reduced delay) of these data owners, however, it can also raise privacy concerns and discourage data‑sharing willingness. Specifically, data owners may be concerned that the shared data may leak sensitive information about their customers' mobility patterns or business secrets, resulting in the failure of collaboration. This paper investigates how privacy‑preserving mechanisms can foster data collaboration in such settings. We propose a game‑theoretic framework to investigate data‑sharing among transportation stakeholders, especially considering perturbation‑based privacy‑preserving mechanisms. Numerical studies demonstrate that lower data quality expectations can incentivize voluntary data sharing, improving transport‑related welfare for both MAs and MPs. Our findings provide actionable insights for policymakers and system designers on how privacy‑preserving technologies can help bridge data silos and promote collaborative, privacy‑aware transportation systems.

Authors:Minseok Kim, Masato Yomoda
Title: Synthesized-Isotropic Narrowband Channel Parameter Extraction from Angle-Resolved Wideband Channel Measurements
Abstract:
Angle‑resolved channel sounding using antenna arrays or mechanically steered high‑gain antennas is widely employed at millimeter‑wave and terahertz bands. To extract antenna‑independent large‑scale channel parameters such as path loss, delay spread, and angular spread, the radiation‑pattern effects embedded in the measured responses must be properly compensated. This paper revisits the technical challenges of path‑gain calculation from angle‑resolved wideband measurements, with emphasis on angular‑domain power integration where the scan beams are inherently non‑orthogonal and simple power summation leads to biased omni‑equivalent power estimates. We first formulate the synthesized‑isotropic narrowband power in a unified matrix form and introduce a beam‑accumulation correction factor, including an offset‑averaged variant to mitigate scalloping due to off‑grid angles. The proposed framework is validated through simulations using channel models and 154~GHz corridor measurements.

Authors:Renukanandan Tumu, Aditya Singh, Rahul Mangharam
Title: AdaptNC: Adaptive Nonconformity Scores for Conformal Prediction under Distribution Shift
Abstract:
Rigorous uncertainty quantification is essential for the safe deployment of autonomous systems in unconstrained environments. Conformal Prediction (CP) provides a distribution‑free framework for this task, yet its standard formulations rely on exchangeability assumptions that are violated by the distribution shifts inherent in real‑world robotics. Existing online CP methods maintain target coverage by adaptively scaling the conformal threshold, but typically employ a static nonconformity score function. We show that this fixed geometry leads to highly conservative, volume‑inefficient prediction regions when environments undergo structural shifts. To address this, we propose AdaptNC, a framework for the joint online adaptation of both the nonconformity score parameters and the conformal threshold. AdaptNC leverages an adaptive reweighting scheme to optimize score functions, and introduces a replay buffer mechanism to mitigate the coverage instability that occurs during score transitions. We evaluate AdaptNC on diverse robotic benchmarks involving multi‑agent policy changes, environmental changes and sensor degradation. Our results demonstrate that AdaptNC significantly reduces prediction region volume compared to state‑of‑the‑art threshold‑only baselines while maintaining target coverage levels.

Authors:Hiroshi Okajima
Title: LMI Optimization Based Multirate Steady-State Kalman Filter Design
Abstract:
This paper presents an LMI‑based design framework for multirate steady‑state Kalman filters in systems with sensors operating at different sampling rates. The multirate system is formulated as a periodic time‑varying system, where the Kalman gains converge to periodic steady‑state values that repeat every frame period. Cyclic reformulation transforms this into a time‑invariant problem; however, the resulting measurement noise covariance becomes semidefinite rather than positive definite, preventing direct application of standard Riccati equation methods. I address this through a dual LQR formulation with LMI optimization that naturally handles semidefinite covariances. The framework enables multi‑objective design, supporting pole placement for guaranteed convergence rates and l_2‑induced norm constraints for balancing average and worst‑case performance. Numerical validation using an automotive navigation system with GPS and wheel speed sensors, including Monte Carlo simulation with 500 independent noise realizations, demonstrates that the proposed filter achieves a position RMSE well below the GPS noise level through effective multirate sensor fusion, and that the LMI solution provides valid upper bounds on the estimation error covariance.

Authors:Fen Wu
Title: Hybrid Control Technique for Switched LPV Systems and Its Application to Active Magnetic Bearing System
Abstract:
This paper proposes a novel hybrid control framework for switched linear parameter‑varying (LPV) systems under hysteresis switching logic. By introducing a controller state‑reset mechanism, the hybrid LPV synthesis problem is reformulated as a convex optimization problem expressed in terms of linear matrix inequalities (LMIs), enabling efficient computation of both switching LPV controller gains and reset matrices. The proposed approach is then applied to active magnetic bearing (AMB) systems, whose rotor dynamics exhibit strong dependence on rotational speed. Conventional LPV designs are often conservative due to large speed variations. The proposed hybrid gain‑scheduled controller explicitly accounts for bounds on parameter variation rates, employs multiple LPV controllers over distinct operating regions, and uses hysteresis switching to reduce chattering and ensure stability. The effectiveness of the approach is demonstrated through a detailed AMB control design example.

Authors:Enzo Nicolas Spotorno, Matheus Wagner, Antonio Augusto Medeiros Frohlich
Title: White-Box Neural Ensemble for Vehicular Plasticity: Quantifying the Efficiency Cost of Symbolic Auditability in Adaptive NMPC
Abstract:
We present a white‑box adaptive NMPC architecture that resolves vehicular plasticity (adaptation to varying operating regimes without retraining) by arbitrating among frozen, regime‑specific neural specialists using a Modular Sovereignty paradigm. The ensemble dynamics are maintained as a fully traversable symbolic graph in CasADi, enabling maximal runtime auditability. Synchronous simulation validates rapid adaptation (~7.3 ms) and near‑ideal tracking fidelity under compound regime shifts (friction, mass, drag) where non‑adaptive baselines fail. Empirical benchmarking quantifies the transparency cost: symbolic graph maintenance increases solver latency by 72‑102X versus compiled parametric physics models, establishing the efficiency price of strict white‑box implementation.

Authors:Yingrui Fan, Junbo Zhao
Title: Harnessing Flexible Spatial and Temporal Data Center Workloads for Grid Regulation Services
Abstract:
Data centers (DCs) are increasingly recognized as flexible loads that can support grid frequency regulation. Yet, most existing methods treat workload scheduling and regulation capacity bidding separately, overlooking how queueing dynamics and spatial‑temporal dispatch decisions affect the ability to sustain real‑time regulation. As a result, the committed regulation may become infeasible or short‑lived. To address this issue, we propose a unified day‑ahead co‑optimization framework that jointly decides workload distribution across geographically distributed DCs and regulation capacity commitments. We construct a space‑time network model to capture workload migration costs, latency requirements, and heterogeneous resource limits. To ensure that the committed regulation remains deliverable, we introduce chance constraints on instantaneous power flexibility based on interactive load forecasts, and apply Value‑at‑Risk queue‑state constraints to maintain sustainable response under cumulative regulation signals. Case studies on a modified IEEE 68‑bus system using real data center traces show that the proposed framework lowers system operating costs, enables more viable regulation capacity, and achieves better revenue‑risk trade‑offs compared to strategies that optimize scheduling and regulation independently.

Authors:M. Izadi, D. Fernandez Zapico, M. Salazar, T. Hofman
Title: Optimal Sizing of Charging Energy Hubs for Heavy-Duty Electric Transport through Co-Optimization
Abstract:
Electrification of heavy‑duty vehicles places substantial stress on distribution grids, and Charging Energy Hubs (CEHs) mitigate these impacts by integrating charging infrastructure with renewable energy sources and battery storage. Optimal sizing of CEH components is therefore a critical investment decision, yet challenging because design choices depend strongly on operational dynamics. This work presents a mixed‑integer linear programming model for the optimal sizing of CEH components, using a co‑design approach that jointly optimizes component sizing and operational decisions. A case study for a heavy‑duty fleet demonstrates the effectiveness of the method for cost‑efficient, scalable, and grid‑compliant CEH planning.

Authors:Azadeh Aghaeeyan, Pouria Ramazi
Title: From Discrete to Continuous Mixed Populations of Conformists, Nonconformists, and Imitators
Abstract:
In two‑strategy decision‑making problems, individuals often imitate the highest earners or choose either the common or rare strategy. Individuals who benefit from the common strategy are conformists, whereas those who profit by choosing the less common one are called nonconformists. The population proportions of the two strategies may undergo perpetual fluctuations in finite, discrete, heterogeneous populations of imitators, conformists, and nonconformists. How these fluctuations evolve as population size increases was left as an open question and is addressed in this paper. We show that the family of Markov chains describing the discrete population dynamics forms a generalized stochastic approximation process for a differential inclusion‑‑the continuous‑time dynamics. Furthermore, we prove that the continuous‑time dynamics always equilibrate. Then, by leveraging results from the stochastic approximation theory, we show that the amplitudes of fluctuations in the proportions of the two strategies in the population approach zero with probability one when the population size grows to infinity. Our results suggest that large‑scale perpetual fluctuations are unlikely in large, well‑mixed populations consisting of these three types, particularly when imitators follow the highest earners.

Authors:Rollen S. D'Souza
Title: The Dynamic Search for the Minimal Dynamic Extension
Abstract:
Identifying the dynamic precompensator that renders a nonlinear control system feedback linearizable is a challenging problem. Researchers have explored the problem ‑‑ dynamic feedback linearization ‑‑ and produced existence conditions and constructive procedures for the dynamic precompensator. These remain, in general, either computationally expensive or restrictive. Treating the challenge as intrinsic, this article views the problem as a search problem over a category. Dynamic programming applies and, upon restriction to a finite category, classic search algorithms find the minimal dynamic extension. Alternatively, a heuristic aiming towards feedback linearizable systems can be employed to select amongst the infinitely‑many extensions. This framing provides a distinctive, birds‑eye view of the search for the dynamic precompensator.

Authors:Jietian Liu, Peter Seiler
Title: Regret of $H_\infty$ Preview Controllers
Abstract:
This paper studies preview control in both the H_\infty and regret‑optimal settings. The plant is modeled as a discrete‑time, linear time‑invariant system subject to external disturbances. The performance baseline is the optimal non‑causal controller that has full knowledge of the disturbance sequence. We first review the construction of the H_\infty preview controller with p‑steps of disturbance preview. We then show that the closed‑loop H_\infty performance of this preview controller converges as p\to \infty to the performance of the optimal non‑causal controller. Furthermore, we prove that the optimal regret of the preview controller converges to zero. These results demonstrate that increasing preview length allows controllers to asymptotically achieve non‑causal performance in both the H_\infty and regret frameworks. A numerical example illustrates the theoretical results.

Authors:T. J. Meijer, V. S. Dolk, W. P. M. H. Heemels
Title: On Poly-Quadratic Stabilizability and Detectability of Polytopic LPV Systems
Abstract:
In this technical communique, we generalize the well‑known Lyapunov‑based stabilizability and detectability tests for discrete‑time linear time‑invariant systems to polytopic linear parameter‑varying systems using the class of so‑called poly‑quadratic Lyapunov functions.

Authors:Yifan Wang
Title: Scientific Machine Learning for Resilient EV-Grid Planning and Decision Support Under Extreme Events
Abstract:
Electric vehicle (EV) charging infrastructure introduces complex challenges to urban distribution networks, particularly under extreme demand events. A critical barrier to resilience assessment is the scale gap between micro‑level charging physics and city‑scale planning: minute‑resolution deliverability constraints remain invisible in hourly aggregated datasets, causing purely data‑driven models to exhibit non‑physical behavior in high‑stress regimes. This paper develops a five‑stage scientific machine learning framework bridging this gap through physics‑informed knowledge transfer. Stage 1 learns a temperature‑pressure deliverability surface from Swiss DC fast‑charging telemetry with monotonicity constraints. Stage 2 performs cross‑scale injection via anchored quantile mapping. Stage 3 deploys a dual‑head spatio‑temporal graph neural network for joint forecasting of demand and service loss rate. Stage 4 simulates backlog dynamics under stress shocks and evaluates policy interventions. Stage 5 couples service outcomes to distribution‑grid stress via transformer loading analysis. Validation on the Shenzhen UrbanEV dataset demonstrates that physics injection restores monotone stress‑to‑risk response (Spearman correlation coefficient equals +1.0 versus ‑0.8 without injection) and improves forecasting accuracy. Under a representative demand shock, the hybrid policy reduces backlog by 79.1%, restores full service within the study horizon, and limits grid stress to only 2 additional hours. The derived resilience boundary m_crit as a function of epsilon approximately equals 1.7 minus 1.0 times epsilon, providing actionable guidance linking demand flexibility to maximum absorbable stress, enabling risk‑aware emergency planning under extreme events.

Authors:Astik Srivastava, Thomas J Chackenkulam, Bitla Bhanu Teja, Antony Thomas, Madhava Krishna
Title: SPOT: Spatio-Temporal Obstacle-free Trajectory Planning for UAVs in an Unknown Dynamic Environment
Abstract:
We address the problem of reactive motion planning for quadrotors operating in unknown environments with dynamic obstacles. Our approach leverages a 4‑dimensional spatio‑temporal planner, integrated with vision‑based Safe Flight Corridor (SFC) generation and trajectory optimization. Unlike prior methods that rely on map fusion, our framework is mapless, enabling collision avoidance directly from perception while reducing computational overhead. Dynamic obstacles are detected and tracked using a vision‑based object segmentation and tracking pipeline, allowing robust classification of static versus dynamic elements in the scene. To further enhance robustness, we introduce a backup planning module that reactively avoids dynamic obstacles when no direct path to the goal is available, mitigating the risk of collisions during deadlock situations. We validate our method extensively in both simulation and real‑world hardware experiments, and benchmark it against state‑of‑the‑art approaches, showing significant advantages for reactive UAV navigation in dynamic, unknown environments.

Authors:Martin Doff-Sotta, Zaheen A-Rahman, Mark Cannon
Title: Computationally Tractable Robust Nonlinear Model Predictive Control using DC Programming
Abstract:
We propose a computationally tractable, tube‑based robust nonlinear model predictive control (MPC) framework using difference‑of‑convex (DC) functions and sequential convex programming. For systems with differentiable discrete time dynamics, we show how to construct systematic, data‑driven DC model representations using polynomials and machine learning techniques. We develop a robust tube MPC scheme that convexifies the online optimization by linearizing the concave components of the model, and we provide guarantees of recursive feasibility and robust stability. We present three data‑driven procedures for computing DC models and compare performance using a planar vertical take‑off and landing (PVTOL) aircraft case study.

Authors:Yousef Abudyak, Mohsen Alizadeh, Wei Sun
Title: Mitigating Data Centers Load Risks and Enabling Grid Support Functions through Grid-Forming Control
Abstract:
The rapid growth of hyperscale data centers driven by Large Language Models and Artificial Intelligence workloads has introduced new challenges for power systems. These facilities experience abrupt power variations during model training and check‑point‑saving events, causing voltage deviations and frequency disturbances. Moreover, they operate as passive loads that draw power without offering any grid support. This paper presents an integrated architecture that combines Battery Energy Storage Systems (BESSs) within data centers using Grid‑Forming inverters to provide active grid‑support functions. Simulation results through MATLAB/Simulink demonstrate accurate power reference tracking under dynamic loading, with eight coordinated BESS units supplying instantaneous power during training and saving conditions. Under single‑phase voltage depression near the data center bus, the BESS delivered reactive power support similar to a Static Synchronous Compensator. During grid disconnection, seamless islanded operation was achieved with stable voltage, frequency, and continuous power delivery at the data center bus.

Authors:Shuai Gao, Dong Shen, Abdelhamid Tayebi
Title: Robust Adaptive Learning Control for a Class of Non-affine Nonlinear Systems
Abstract:
We address the tracking problem for a class of uncertain non‑affine nonlinear systems with high relative degrees, performing non‑repetitive tasks. We propose a rigorously proven, robust adaptive learning control scheme that relies on a gradient descent parameter adaptation law to handle the unknown time‑varying parameters of the system, along with a state estimator that estimates the unmeasurable state variables. Furthermore, despite the inherently complex nature of the non‑affine system, we provide an explicit iterative computation method to facilitate the implementation of the proposed control scheme. The paper includes a thorough analysis of the performance of the proposed control strategy, and simulation results are presented to demonstrate the effectiveness of the approach.

Authors:M. Reza J. Harandi, Mehrzad Namvar
Title: Reduction of Velocity-Dependent Terms in Total Energy Shaping Approach
Abstract:
Total energy shaping through interconnection and damping assignment passivity‑based control (IDA‑PBC) provides a powerful and systematic framework for stabilizing underactuated mechanical systems. Despite its theoretical appeal, incorporating actuator limitations into total energy shaping remains a largely open problem, with only limited results reported in the existing literature. In practice, the closed‑loop behavior of energy‑shaping controllers is strongly affected by the kinetic energy shaping terms. In this paper, a simultaneous IDA‑PBC (SIDA‑PBC) framework is employed to systematically attenuate the kinetic energy shaping terms by exploiting generalized forces, without altering the matching partial differential equations (PDEs). The free component of the generalized forces is derived analytically via an \ell_\infty‑norm optimization formulation. Although a reduction in kinetic energy shaping terms does not necessarily guarantee a decrease in the overall control effort, the proposed approach effectively suppresses kinetic energy shaping components and achieves a reduced control magnitude whenever such a reduction is structurally feasible. Unlike existing approaches based on gyroscopic terms, which require multiple actuators, the proposed method is applicable to mechanical systems with a single actuator. Simulation and experimental results are provided to validate the effectiveness of the proposed approach.

Authors:M. Reza J. Harandi, Mehrzad Namvar
Title: Robust Energy Shaping Control of an Underactuated Inverted Pendulum
Abstract:
Although the stabilization of underactuated systems remains a challenging problem, the total energy shaping approach provides a general framework for addressing this objective. However, the practical implementation of this method is hindered by the need to analytically solve a set of partial differential equations (PDEs), which constitutes a major obstacle. In this paper, a rotary inverted pendulum system is considered, and an interconnection and damping assignment passivity‑based control (IDA‑PBC) scheme is developed by deriving concise analytical solutions to the kinetic and potential energy PDEs. Furthermore, a novel robust term is incorporated into the control law to compensate for a specific class of disturbances that has not been addressed within the existing IDA‑PBC literature. The effectiveness of the proposed method is validated through numerical simulations, demonstrating satisfactory control performance.

Authors:Spyridon Syntakas, Kostas Vlachos
Title: Ocean Current-Harnessing Stage-Gated MPC: Monotone Cost Shaping and Speed-to-Fly for Energy-Efficient AUV Navigation
Abstract:
Autonomous Underwater Vehicles (AUVs) are a highly promising technology for ocean exploration and diverse offshore operations, yet their practical deployment is constrained by energy efficiency and endurance. To address this, we propose Current‑Harnessing Stage‑Gated MPC, which exploits ocean currents via a per‑stage scalar which indicates the "helpfulness" of ocean currents. This scalar is computed along the prediction horizon to gate lightweight cost terms only where the ocean currents truly aids the control goal. The proposed cost terms, that are merged in the objective function, are (i) a Monotone Cost Shaping (MCS) term, a help‑gated, non‑worsening modification that relaxes along‑track position error and provides a bounded translational energy rebate, guaranteeing the shaped objective is never larger than a set baseline, and (ii) a speed‑to‑fly (STF) cost component that increases the price of thrust and softly matches ground velocity to the ocean current, enabling near zero water‑relative "gliding". All terms are C1 and integrate as a plug‑and‑play in MPC designs. Extensive simulations with the BlueROV2 model under realistic ocean current fields show that the proposed approach achieves substantially lower energy consumption than conventional predictive control while maintaining comparable arrival times and constraint satisfaction.

Authors:Thanana Nuchkrua, Sudchai Boonto
Title: Cognitive-Flexible Control via Latent Model Reorganization with Predictive Safety Guarantees
Abstract:
Learning‑enabled control systems must maintain safety when system dynamics and sensing conditions change abruptly. Although stochastic latent‑state models enable uncertainty‑aware control, most existing approaches rely on fixed internal representations and can degrade significantly under distributional shift. This letter proposes a \emphcognitive‑flexible control framework in which latent belief representations adapt online, while the control law remains explicit and safety‑certified. We introduce a Cognitive‑Flexible Deep Stochastic State‑Space Model (CF‑‑DeepSSSM) that reorganizes latent representations subject to a bounded \emphCognitive Flexibility Index (CFI), and embeds the adapted model within a Bayesian model predictive control (MPC) scheme. We establish guarantees on bounded posterior drift, recursive feasibility, and closed‑loop stability. Simulation results under abrupt changes in system dynamics and observations demonstrate safe representation adaptation with rapid performance recovery, highlighting the benefits of learning‑enabled, rather than learning‑based, control for nonstationary cyber‑‑physical systems.

Authors:Martin Doff-Sotta, Florian Cech, Rishabh Manjunatha, Costantino Citro, Matthew Williams, Thomas Morstyn
Title: Modeling and Control of Hybrid Distribution Transformers for Simultaneous Grid Services
Abstract:
Hybrid distribution transformers (HDTs) integrate conventional transformers with partially rated power electronic converters to improve power quality, enable advanced ancillary services and increase penetration of renewable energy sources in the national power grid. In this paper, we present an averaged mathematical model of a three‑phase HDT equipped with two back‑to‑back voltage source converters connected in a series‑shunt configuration. Cascaded PI controllers are designed in the synchronously rotating dq0 reference frame to regulate load voltage, compensate reactive power, achieve grid frequency regulation, and perform load phase balancing. Simulation results implemented in Python confirm that these simple yet effective control mechanisms allow HDTs to offer simultaneous grid services without introducing complexity. The complete model, control architecture, and implementation steps are detailed, enabling further validation and adoption.

Authors:Yujie Yang, Zhilong Zheng, Shengbo Eben Li
Title: On the Equilibrium between Feasible Zone and Uncertain Model in Safe Exploration
Abstract:
Ensuring the safety of environmental exploration is a critical problem in reinforcement learning (RL). While limiting exploration to a feasible zone has become widely accepted as a way to ensure safety, key questions remain unresolved: what is the maximum feasible zone achievable through exploration, and how can it be identified? This paper, for the first time, answers these questions by revealing that the goal of safe exploration is to find the equilibrium between the feasible zone and the environment model. This conclusion is based on the understanding that these two components are interdependent: a larger feasible zone leads to a more accurate environment model, and a more accurate model, in turn, enables exploring a larger zone. We propose the first equilibrium‑oriented safe exploration framework called safe equilibrium exploration (SEE), which alternates between finding the maximum feasible zone and the least uncertain model. Using a graph formulation of the uncertain model, we prove that the uncertain model obtained by SEE is monotonically refined, the feasible zones monotonically expand, and both converge to the equilibrium of safe exploration. Experiments on classic control tasks show that our algorithm successfully expands the feasible zones with zero constraint violation, and achieves the equilibrium of safe exploration within a few iterations.

Authors:Ludvig Svedlund, Constantin Cronrath, Jonas Fredriksson, Bengt Lennartson
Title: Model-Based Data-Efficient and Robust Reinforcement Learning
Abstract:
A data‑efficient learning‑based control design method is proposed in this paper. It is based on learning a system dynamics model that is then leveraged in a two‑level procedure. On the higher level, a simple but powerful optimization procedure is performed such that, for example, energy consumption in a vehicle can be reduced when hard state and action constraints are also introduced. Load disturbances and model errors are compensated for by a feedback controller on the lower level. In that regard, we briefly examine the robustness of both model‑free and model‑based learning approaches, and it is shown that the model‑free approach greatly suffers from the inclusion of unmodeled dynamics. In evaluating the proposed method, it is assumed that a path is given, while the velocity and acceleration can be modified such that energy is saved, while still keeping speed limits and completion time. Compared with two well‑known actor‑critic reinforcement learning strategies, the suggested learning‑based approach saves more energy and reduces the number of evaluated time steps by a factor of 100 or more.

Authors:Reiji Terunuma, Yuta Nakamura, Takuma Abe, Takeshi Hatanaka
Title: Stealthy Coverage Control for Human-enabled Real-Time 3D Reconstruction
Abstract:
In this paper, we propose a novel semi‑autonomous image sampling strategy, called stealthy coverage control, for human‑enabled 3D structure reconstruction. The present mission involves a fundamental problem: while the number of images required to accurately reconstruct a 3D model depends on the structural complexity of the target scene to be reconstructed, it is not realistic to assume prior knowledge of the spatially non‑uniform structural complexity. We approach this issue by leveraging human flexible reasoning and situational recognition capabilities. Specifically, we design a semi‑autonomous system that leaves identification of regions that need more images and navigation of the drones to such regions to a human operator. To this end, we first present a way to reflect the human intention in autonomous coverage control. Subsequently, in order to avoid operational conflicts between manual control and autonomous coverage control, we develop the stealthy coverage control that decouples the drone motion for efficient image sampling from navigation by the human. Simulation studies on a Unity/ROS2‑based simulator demonstrate that the present semi‑autonomous system outperforms the one without human interventions in the sense of the reconstructed model quality.

Authors:Andrew F. Thompson, Joshua A. Robbins, Jonah J. Glunt, Sean B. Brennan, Herschel C. Pangborn
Title: Motion Planning with Metric Temporal Logic Using Reachability Analysis and Hybrid Zonotopes
Abstract:
Metric temporal logic (MTL) provides a formal framework for defining time‑dependent mission requirements on autonomous vehicles. However, optimizing control decisions subject to these constraints is often computationally expensive. This article presents a method that uses reachability analysis to implicitly express the set of states satisfying an MTL specification and then optimizes to find a motion plan. The hybrid zonotope set representation is used to efficiently and conveniently encode MTL specifications into reachable sets. A numerical benchmark highlights the proposed method's computational advantages as compared to existing methods in the literature. Further numerical examples and an experimental application demonstrate the ability to address time‑varying environments, region‑dependent disturbances, and multi‑agent coordination.

Authors:Lasse Kötz, Jonas Sjöberg, Knut Åkesson
Title: Optimal Control-Based Falsification of Learnt Dynamics via Neural ODEs and Symbolic Regression
Abstract:
We present a falsification framework that integrates learned surrogate dynamics with optimal control to efficiently generate counterexamples for cyber‑physical systems specified in signal temporal logic (STL). The unknown system dynamics are identified using neural ODEs, while known a‑priori structure is embedded directly into the model, reducing data requirements. The learned neural ODE is converted into an analytical form via symbolic regression, enabling fast and interpretable trajectory optimization. Falsification is cast as minimizing STL robustness over input trajectories; negative robustness yields candidate counterexamples, which are validated on the original system. Spurious traces are iteratively used to refine the surrogate, while true counterexamples are returned as final results. Experiments on ARCH‑COMP 2024 benchmarks show that this method requires orders of magnitude fewer experiments of the system under test than optimization‑based approaches that do not model system dynamics.

Authors:Zhenyu Pu, Yu Yang, Lun Yang, Qing-Shan Jia, Xiaohong Guan, Costas J. Spanos
Title: Representation Learning Enhanced Deep Reinforcement Learning for Optimal Operation of Hydrogen-based Multi-Energy Systems
Abstract:
Hydrogen‑based multi‑energy systems (HMES) have emerged as a promising low‑carbon and energy‑efficient solution, as it can enable the coordinated operation of electricity, heating and cooling supply and demand to enhance operational flexibility, improve overall energy efficiency, and increase the share of renewable integration. However, the optimal operation of HMES remains challenging due to the nonlinear and multi‑physics coupled dynamics of hydrogen energy storage systems (HESS) (consisting of electrolyters, fuel cells and hydrogen tanks) as well as the presence of multiple uncertainties from supply and demand. To address these challenges, this paper develops a comprehensive operational model for HMES that fully captures the nonlinear dynamics and multi‑physics process of HESS. Moreover, we propose an enhanced deep reinforcement learning (DRL) framework by integrating the emerging representation learning techniques, enabling substantially accelerated and improved policy optimization for spatially and temporally coupled complex networked systems, which is not provided by conventional DRL. Experimental studies based on real‑world datasets show that the comprehensive model is crucial to ensure the safe and reliable of HESS. In addition, the proposed SR‑DRL approaches demonstrate superior convergence rate and performance over conventional DRL counterparts in terms of reducing the operation cost of HMES and handling the system operating constraints. Finally, we provide some insights into the role of representation learning in DRL, speculating that it can reorganize the original state space into a well‑structured and cluster‑aware geometric representation, thereby smoothing and facilitating the learning process of DRL.

Authors:Marko Nonhoff, Mohammad Taher Al Torshan, Matthias A. Müller
Title: Robust Control of Constrained Linear Systems using Online Convex Optimization and a Reference Governor
Abstract:
This article develops a control method for linear time‑invariant systems subject to time‑varying and a priori unknown cost functions, that satisfies state and input constraints, and is robust to exogenous disturbances. To this end, we combine the online convex optimization framework with a reference governor and a constraint tightening approach. The proposed framework guarantees recursive feasibility and robust constraint satisfaction. Its closed‑loop performance is studied in terms of its dynamic regret, which is bounded linearly by the variation of the cost functions and the magnitude of the disturbances. The proposed method is illustrated by a numerical case study of a tracking control problem.

Authors:Ruibin Chen, Jayadev Joy, Yaqi Hu, Mingsheng Yin, Marco Mezzavilla, Sundeep Rangan
Title: Interpolation Techniques for Fast Channel Estimation in Ray Tracing
Abstract:
Ray tracing is increasingly utilized in wireless system simulations to estimate channel paths. In large‑scale simulations with complex environments, ray tracing at high resolution can be computationally demanding. To reduce the computation, this paper presents a novel method for conducting ray tracing at a coarse set of reference points and interpolating the channels at other locations. The key insight is to interpolate the images of reflected points. In addition to the computational savings, the method directly captures the spherical nature of each wavefront enabling fast and accurate computation of channels using line‑of‑sight MIMO and other wide aperture techniques. Through empirical validation and comparison with exhaustive ray tracing, we demonstrate the efficacy and practicality of our approach in achieving high‑fidelity channel predictions with reduced computational resources.

Authors:Finn Vehlhaber, Mauro Salazar
Title: Energy Management Strategies for Electric Aircraft Charging Leveraging Active Landside Vehicle-to-Grid
Abstract:
The deployment of medium‑range battery electric aircraft is a promising pathway to improve the environmental footprint of air mobility. Yet such a deployment would be accompanied by significant electric power requirements at airports due to aircraft charging. Given the growing prevalence of electric vehicles and their bi‑directional charging capabilities‑‑so‑called vehicle‑to‑grid (V2G)‑‑we study energy buffer capabilities of parked electric vehicles to alleviate pressure on grid connections. To this end, we present energy management strategies for airports providing cost‑optimal apron and landside V2G charge scheduling. Specifically, we first formulate the optimal energy management problem of joint aircraft charging and landside V2G coordination as a linear program, whereby we use partial differential equations to model the aggregated charging dynamics of the electric vehicle fleet. Second, we consider a shuttle flight network with a single hub of a large Dutch airline, real‑world grid prices, and synthetic parking garage occupancy data to test our framework. Our results show that V2G at even a single airport can indeed reduce energy costs to charge the aircraft fleet: Compared to a baseline scenario without V2G, the proposed concept yields cost savings of up to 32%, depending on the schedule and amount of participating vehicles, and has other potential beneficial effects on the local power grid, e.g., the reduction of potential power peaks.

Authors:Jayadev Joy, Sundeep Rangan
Title: Learning-Based Signal Recovery in Nonlinear Systems with Spectrally Separated Interference
Abstract:
Upper Mid‑Band (FR3, 7‑24 GHz) receivers for 6G must operate over wide bandwidths in dense spectral environments, making them particularly vulnerable to strong adjacent‑band interference and front‑end nonlinearities. While conventional linear receivers can suppress spectrally separated interferers under ideal hardware assumptions, receiver saturation and finite‑resolution quantization cause nonlinear spectral leakage that severely degrades performance in practical wideband radios. We study the recovery of a desired signal from nonlinear receiver observations corrupted by a high‑power out‑of‑band interferer. The receiver front‑end is modeled as a smooth, memoryless nonlinearity followed by additive noise and optional quantization. To mitigate these nonlinear and quantization‑induced distortions, we propose a learned multi‑layer Vector Approximate Message Passing (LMLVAMP) algorithm that incorporates spectral priors with neural network based denoising. Simulation results demonstrate significant performance gains over conventional methods, particularly in high‑interference regimes representative of FR3 coexistence scenarios.

Authors:Tanay Raghunandan Srinivasa, Vivek Deulkar, Aviruch Bhatia, Vishal Garg
Title: Reinforcement Learning-Based Co-Design and Operation of Chiller and Thermal Energy Storage for Cost-Optimal HVAC Systems
Abstract:
We study the joint operation and sizing of cooling infrastructure for commercial HVAC systems using reinforcement learning, with the objective of minimizing life‑cycle cost over a 30‑year horizon. The cooling system consists of a fixed‑capacity electric chiller and a thermal energy storage (TES) unit, jointly operated to meet stochastic hourly cooling demands under time‑varying electricity prices. The life‑cycle cost accounts for both capital expenditure and discounted operating cost, including electricity consumption and maintenance. A key challenge arises from the strong asymmetry in capital costs: increasing chiller capacity by one unit is far more expensive than an equivalent increase in TES capacity. As a result, identifying the right combination of chiller and TES sizes, while ensuring zero loss‑of‑cooling‑load under optimal operation, is a non‑trivial co‑design problem. To address this, we formulate the chiller operation problem for a fixed infrastructure configuration as a finite‑horizon Markov Decision Process (MDP), in which the control action is the chiller part‑load ratio (PLR). The MDP is solved using a Deep Q Network (DQN) with a constrained action space. The learned DQN RL policy minimizes electricity cost over historical traces of cooling demand and electricity prices. For each candidate chiller‑TES sizing configuration, the trained policy is evaluated. We then restrict attention to configurations that fully satisfy the cooling demand and perform a life‑cycle cost minimization over this feasible set to identify the cost‑optimal infrastructure design. Using this approach, we determine the optimal chiller and thermal energy storage capacities to be 700 and 1500, respectively.

Authors:Tanay Raghunandan Srinivasa, Vivek Deulkar, Jia Bhargava, Mohammad Hajiesmaili, Prashant Shenoy
Title: Degradation-Aware Frequency Regulation of a Heterogeneous Battery Fleet via Reinforcement Learning
Abstract:
Battery energy storage systems are increasingly deployed as fast‑responding resources for grid balancing services such as frequency regulation and for mitigating renewable generation uncertainty. However, repeated charging and discharging induces cycling degradation and reduces battery lifetime. This paper studies the real‑time scheduling of a heterogeneous battery fleet that collectively tracks a stochastic balancing signal subject to per‑battery ramp‑rate and capacity constraints, while minimizing long‑term cycling degradation. Cycling degradation is fundamentally path‑dependent: it is determined by charge‑discharge cycles formed by the state‑of‑charge (SoC) trajectory and is commonly quantified via rainflow cycle counting. This non‑Markovian structure makes it difficult to express degradation as an additive per‑time‑step cost, complicating classical dynamic programming approaches. We address this challenge by formulating the fleet scheduling problem as a Markov decision process (MDP) with constrained action space and designing a dense proxy reward that provides informative feedback at each time step while remaining aligned with long‑term cycle‑depth reduction. To scale learning to large state‑action spaces induced by fine‑grained SoC discretization and asymmetric per‑battery constraints, we develop a function‑approximation reinforcement learning method using an Extreme Learning Machine (ELM) as a random nonlinear feature map combined with linear temporal‑difference learning. We evaluate the proposed approach on a toy Markovian signal model and on a Markovian model trained from real‑world regulation signal traces obtained from the University of Delaware, and demonstrate consistent reductions in cycle‑depth occurrence and degradation metrics compared to baseline scheduling policies.

Authors:Joshua Kartzman, Calvin Hawkins, Matthew Hale
Title: Approximately Optimal Multi-Stream Quickest Change Detection
Abstract:
This paper considers the constrained sampling multi‑stream quickest change detection problem, also known as the bandit quickest change detection problem. One stream contains a change‑point that shifts its mean by an unknown amount. The goal is to quickly detect this change while controlling for false alarms, while being only able to sample one stream at each time. We propose an algorithm that combines a decaying‑ε‑greedy stream switching rule with a Generalized Likelihood Ratio detection procedure for unknown post‑change means. We provide performance bounds for our algorithm and show it achieves approximate asymptotic first‑order optimality with respect to a commonly used surrogate. We are the first to provide guarantees in this setting without assumptions such as a discretized post‑change parameter set or a lower bound on the magnitude of change. We provide guarantees for a wide range of light‑tailed distributions, including sub‑Gaussian and bounded support distributions.

Authors:Khalid Mahmud Labib, Shabbir Ahmed
Title: Modeling of Non-linear Dynamics of Lithium-ion Batteries via Delay-Embedded Dynamic Mode Decomposition
Abstract:
The complex electrochemical behavior of lithium‑ion batteries results in non‑linear dynamics and appropriate modeling of this non‑linear dynamical system is of interest for better management and control. In this work, we proposed a family of dynamic mode decomposition (DMD)‑based data‑driven models that do not require detailed knowledge of the composition of the battery materials but can essentially capture the non‑linear dynamics with higher computational efficiency. Only voltage and current data obtained from hybrid pulse power characterization (HPPC) tests were utilized to form the state space matrices and subsequently used for predicting the future terminal voltage at different state of charge (SoC) and aging levels. To construct the system model, 60% of the data from a single HPPC test was utilized to generate time‑delay embedded snapshots, with embedding dimension ranging from 40 to 2000. Among these, an embedding dimension of 1810 resulted in the least residual sum of squares (RSS) error of 3.86 for the dynamic mode decomposition with control (DMDc) model and 30 for the standard DMD model. For DMDc model, delay embeddings (ranging from 1 to 12) were also incorporated into the input current signals. For the input matrix, an embedding dimension of 6 resulted in a minimum RSS error of 1.74. Furthermore, the system matrices A and B, identified from the HPPC test when the cell is in its healthy state, were held fixed and used to simulate the system dynamics for aged batteries by updating only the control input. Despite the presence of nonlinear degradation effects in later cycles, the DMDc model effectively captured key inner dynamics such as voltage dips and transient responses for subsequent charge and discharge cycles.

Authors:Ismaeel Babur, Jane Macfarlane
Title: Regional Transportation Modeling for Equitable Electric Vehicle Charging Infrastructure Design
Abstract:
The widespread adoption of battery electric vehicles (BEVs) holds promise for mitigating emission‑related health impacts, particularly for low‑income communities disproportionately affected by exposure to traffic‑related air pollution. However, designing effective charging infrastructure necessitates a regional modeling approach that accounts for the inherent cross‑jurisdictional nature of mobility patterns. This study underscores the importance of regional modeling in optimizing charging station deployment and evaluating the environmental justice implications for equity priority communities. We present a large‑scale regional transportation modeling analysis leveraging Mobiliti, a cloud‑based platform that employs parallel discrete event simulation to enable rapid computation. Our approach identifies the spatial demand density for charging infrastructure by analyzing over 19 million trips in the San Francisco Bay Area and determining the threshold points where BEVs may require charging across a typical day. By transitioning these trips that originate outside equity priority communities to BEVs, we quantify the potential emission reductions within these vulnerable areas. The regional modeling framework captures the complex interactions between travel behavior, vehicle characteristics, and charging needs, while accounting for the interconnectivity of infrastructure across municipal boundaries. This study demonstrates the critical role of regional modeling in designing equitable BEV charging networks that address environmental justice concerns. The findings inform strategies for deploying charging infrastructure that maximizes accessibility, minimizes range anxiety, and prioritizes the health and well‑being of communities disproportionately burdened by transportation emissions.

Authors:Serhii Kryhin, Tatiana Mouzykantskii, Vivishek Sudhir
Title: Forecasting in the presence of scale-free noise
Abstract:
The extraction of signals from noise is a common problem in all areas of science and engineering. A particularly useful version is that of forecasting: determining a causal filter that estimates a future value of a hidden process from past observations. Current techniques for deriving the filter require that the noise be well described by rational power spectra. However, scale‑free noises, whose spectra scale as a non‑integer power of frequency, are ubiquitous in practice. We establish a method, together with performance guarantees, that solves the forecasting problem in the presence of scale‑free noise. Via the duality between estimation and control, our technique can be used to design control for distributed systems. These results will have wide‑ranging applications in neuroscience, finance, fluid dynamics, and quantum measurements.

Authors:Qian Zhang, Le Xie, Long Zhao, Congcong Wang
Title: Comparative Assessment of Look-Ahead Economic Dispatch and Ramp Products for Grid Flexibility
Abstract:
High renewable penetration increases the frequency and magnitude of net‑load ramps, stressing real‑time flexibility. Two commonly deployed remedies are look‑ahead economic dispatch (LAED) and ramp products (RPs), yet their operational equivalence under the industry‑standard rolling‑window dispatch implementation is not well understood. This paper develops linear optimization models for multi‑interval LAED and RP‑based co‑optimization, and proves that an enhanced RP formulation can match LAED's dispatch feasible region at a single time step when additional intertemporal deliverability constraints are enforced. We then show that this equivalence does not generally persist under rolling‑window operation because LAED and RP formulations optimize different intertemporal objectives, leading to divergent end‑of‑window states. Using different test systems under stressed ramping conditions and multiple load levels, we show LAED achieves similar or lower load shedding than RP implementations with the same look‑ahead horizon, with the most pronounced differences under high‑load, ramp‑limited conditions. The study highlights the limitations of current ramp product implementations and suggests enhancements, such as introducing more mid‑duration RPs.

Authors:Saoud Aldowaish, Yashwanth Karumanchi, Kai-Chen Chiang, Soroosh Noorzad, Morteza Fayazi
Title: SINA: A Circuit Schematic Image-to-Netlist Generator Using Artificial Intelligence
Abstract:
Current methods for converting circuit schematic images into machine‑readable netlists struggle with component recognition and connectivity inference. In this paper, we present SINA, an open‑source, fully automated circuit schematic image‑to‑netlist generator. SINA integrates deep learning for accurate component detection, Connected‑Component Labeling (CCL) for precise connectivity extraction, and Optical Character Recognition (OCR) for component reference designator retrieval, while employing a Vision‑Language Model (VLM) for reliable reference designator assignments. In our experiments, SINA achieves 96.47% overall netlist‑generation accuracy, which is 2.72x higher than state‑of‑the‑art approaches.

Authors:Abdullah Tasim, Wei Sun
Title: Physics Informed Reconstruction of Four-Dimensional Atmospheric Wind Fields Using Multi-UAS Swarm Observations in a Synthetic Turbulent Environment
Abstract:
Accurate reconstruction of atmospheric wind fields is essential for applications such as weather forecasting, hazard prediction, and wind energy assessment, yet conventional instruments leave spatio‑temporal gaps within the lower atmospheric boundary layer. Unmanned aircraft systems (UAS) provide flexible in situ measurements, but individual platforms sample wind only along their flight trajectories, limiting full wind‑field recovery. This study presents a framework for reconstructing four‑dimensional atmospheric wind fields using measurements obtained from a coordinated UAS swarm. A synthetic turbulence environment and high‑fidelity multirotor simulation are used to generate training and evaluation data. Local wind components are estimated from UAS dynamics using a bidirectional long short‑term memory network (Bi‑LSTM) and assimilated into a physics‑informed neural network (PINN) to reconstruct a continuous wind field in space and time. For local wind estimation, the bidirectional LSTM achieves root‑mean‑square errors (RMSE) of 0.064 and 0.062 m/s for the north and east components in low‑wind conditions, increasing to 0.122 to 0.129 m/s under moderate winds and 0.271 to 0.273 m/s in high‑wind conditions, while the vertical component exhibits higher error, with RMSE values of 0.029 to 0.091 m/s. The physics‑informed reconstruction recovers the dominant spatial and temporal structure of the wind field up to 1000 m altitude while preserving mean flow direction and vertical shear. Under moderate wind conditions, the reconstructed mean wind field achieves an overall RMSE between 0.118 and 0.154 m/s across evaluated UAS configurations, with the lowest error obtained using a five‑UAS swarm. These results demonstrate that coordinated UAS measurements enable accurate and scalable four‑dimensional wind‑field reconstruction without dedicated wind sensors or fixed infrastructure.

Authors:Sahan Liyanaarachchi, Sennur Ulukus, Nail Akar
Title: Beyond Martingale Estimators: Structured Estimators for Maximizing Information Freshness in Query-Based Update Systems
Abstract:
This paper investigates information freshness in a remote estimation system in which the remote information source is a continuous‑time Markov chain (CTMC). For such systems, estimators have been mainly restricted to the class of martingale estimators in which the remote estimate at any time is equal to the value of the most recently received update. This is mainly due to the simplicity and ease of analysis of martingale estimators, which however are far from optimal, especially in query‑based (i.e., pull‑based) update systems. In such systems, maximum a‑posteriori probability (MAP) estimators are optimal. However, MAP estimators can be challenging to analyze in continuous‑time settings. In this paper, we introduce a new class of estimators, called structured estimators, which can seamlessly shift from a martingale estimator to a MAP estimator, enabling them to retain useful characteristics of the MAP estimate, while still being analytically tractable. Particularly, we introduce a new estimator termed as the p‑MAP estimator which is a piecewise‑constant approximation of the MAP estimator with finitely many discontinuities, bringing us closer to a full characterization of MAP estimators when modeling information freshness. In fact, we show that for time‑reversible CTMCs, the MAP estimator reduces to a p‑MAP estimator. Using the binary freshness (BF) process for the characterization of information freshness, we derive the freshness expressions and provide optimal state‑dependent sampling policies (i.e., querying policies) for maximizing the mean BF (MBF) for pull‑based remote estimation of a single CTMC information source, when structured estimators are used. Moreover, we provide optimal query rate allocation policies when a monitor pulls information from multiple heterogeneous CTMCs with a constraint on the overall query rate.

Authors:Qian Zhang, Feng Zhao, Tongxin Zheng, Le Xie
Title: Reformulating Energy Storage Capacity Accreditation Problem with Marginal Reliability Impact
Abstract:
To enhance the efficiency of capacity markets, many electricity markets in the U.S. are adopting or planning to implement marginal capacity accreditation reforms. This paper provides new insights into energy storage capacity accreditation using Marginal Reliability Impact (MRI). We reformulate the commonly used reliability‑based storage dispatch model as an optimization problem, enabling direct calculation of the MRI from the Lagrange multipliers, rather than using brute‑force perturbation analysis. The analysis demonstrates that the EUE is a piecewise linear function and the storage MRI retains a non‑negative property across various system scenarios. We further explore the influence of qualified capacity (QC), storage dispatch rules, and other key factors on storage accreditation, providing practical insights for system operators. Additionally, comparisons of storage capacity accreditation under different reliability criteria offer valuable guidance for policymakers in setting future standards. Numerical results from a modified California system validate our findings and highlight several important phenomena associated with the MRI‑based accreditation scheme.

Authors:Qian Zhang, Feng Zhao, Gord Stephen, Chanan Singh, Le Xie
Title: A Gradient-Based Capacity Accreditation Framework in Resource Adequacy: Formulation, Computation, and Practical Implications
Abstract:
Probabilistic resource adequacy assessment is a cornerstone of modern capacity accreditation. This paper develops a gradient‑based framework, in which capacity accreditation is interpreted as the directional derivative of a probabilistic resource adequacy metric with respect to resource capacity, that unifies two widely used accreditation approaches: Effective Load Carrying Capability (ELCC) and Marginal Reliability Impact (MRI). Under mild regularity conditions, we show that marginal ELCC and MRI yield equivalent accreditation factors, while their numerical implementations exhibit markedly different computational characteristics. Building on this framework, we demonstrate how infinitesimal perturbation analysis enables up to a 1000× speedup in gradient estimation for capacity accreditation, and we implement gradient‑informed search algorithms that significantly accelerate ELCC computations relative to standard bisection methods. Large‑scale Monte Carlo experiments show that MRI achieves substantial runtime reductions compared to ELCC and exhibits greater robustness to perturbation step‑size selection. These results provide practical guidance for implementing efficient and scalable capacity accreditation in large‑scale power systems.

Authors:Shuaicheng Tong, Michael A. Boateng, Mathieu Tanneau, Pascal Van Hentenryck
Title: Volt/VAR Optimization in Transmission Networks with Discrete-Control Devices
Abstract:
Voltage (Volt) and reactive‑power (VAR) control in transmission networks is critical for reliability and increasingly needs fast, implementable decisions. This paper presents a transmission Volt/VAR Optimization (VVO) framework that co‑optimizes discrete control of on‑load tap‑changing transformers (OLTCs) and capacitor banks (CBs) with AC power flow (ACPF) physics to improve voltage stability and minimize VAR generation. The framework follows a relax‑round‑resolve pipeline: a continuous relaxation proposes targets, a rounding step selects feasible discrete settings, and a final solve enforces AC power flow physics. Extensive experiments on IEEE, PEGASE, and RTE systems show consistent improvements in voltage and VAR quality metrics with modest generator redispatch while preserving economic operation and achieving compatible runtimes with real‑time transmission operations.

Authors:Sten Elling Tingstad Jacobsen, Attila Lischka, Balázs Kulcsár, Anders Lindman
Title: Learning to Dial-a-Ride: A Deep Graph Reinforcement Learning Approach to the Electric Dial-a-Ride Problem
Abstract:
Urban mobility systems are transitioning toward electric, on‑demand services, creating operational challenges for fleet management under energy and service‑quality constraints. The Electric Dial‑a‑Ride Problem (E‑DARP) extends the classical dial‑a‑ride problem by incorporating limited battery capacity and nonlinear charging dynamics, increasing computational complexity and limiting the scalability of exact methods for real‑time use. This paper proposes a deep reinforcement learning approach based on an edge‑centric graph neural network encoder and an attention‑driven route construction policy. By operating directly on edge attributes such as travel time and energy consumption, the method captures non‑Euclidean, asymmetric, and energy‑dependent routing costs in real road networks. The learned policy jointly optimizes routing, charging, and service quality without relying on Euclidean assumptions or handcrafted heuristics. The approach is evaluated on two case studies using ride‑sharing data from San Francisco. On benchmark instances, the method achieves solutions within 0.4% of best‑known results while reducing computation times by orders of magnitude. A second case study considers large‑scale instances with up to 250 request pairs, realistic energy models, and nonlinear charging. On these instances, the learned policy outperforms Adaptive Large Neighborhood Search (ALNS) by 9.5% in solution quality while achieving 100% service completion, with inference times under 10 seconds compared to hours for the metaheuristic. Finally, sensitivity analyses quantify the impact of battery capacity, fleet size, ride‑sharing capacity, and reward weights, while robustness experiments show that deterministically trained policies generalize effectively under stochastic conditions.

Authors:Fernando Palafox, Jingqi Li, Jesse Milzman, David Fridovich-Keil
Title: Generalized Information Gathering Under Dynamics Uncertainty
Abstract:
An agent operating in an unknown dynamical system must learn its dynamics from observations. Active information gathering accelerates this learning, but existing methods derive bespoke costs for specific modeling choices: dynamics models, belief update procedures, observation models, and planners. We present a unifying framework that decouples these choices from the information‑gathering cost by explicitly exposing the causal dependencies between parameters, beliefs, and controls. Using this framework, we derive a general information‑gathering cost based on Massey's directed information that assumes only Markov dynamics with additive noise and is otherwise agnostic to modeling choices. We prove that the mutual information cost used in existing literature is a special case of our cost. Then, we leverage our framework to establish an explicit connection between the mutual information cost and information gain in linearized Bayesian estimation, thereby providing theoretical justification for mutual information‑based active learning approaches. Finally, we illustrate the practical utility of our framework through experiments spanning linear, nonlinear, and multi‑agent systems.

Authors:Chuan-Chi Lai
Title: Spatiotemporal Continual Learning for Mobile Edge UAV Networks: Mitigating Catastrophic Forgetting
Abstract:
This paper addresses catastrophic forgetting in mobile edge UAV networks within dynamic spatiotemporal environments. Conventional deep reinforcement learning often fails during task transitions, necessitating costly retraining to adapt to new user distributions. We propose the spatiotemporal continual learning (STCL) framework, realized through the group‑decoupled multi‑agent proximal policy optimization (G‑MAPPO) algorithm. The core innovation lies in the integration of a group‑decoupled policy optimization (GDPO) mechanism with a gradient orthogonalization layer to balance heterogeneous objectives including energy efficiency, user fairness, and coverage. This combination employs dynamic z‑score normalization and gradient projection to mitigate conflicts without offline resets. Furthermore, 3D UAV mobility serves as a spatial compensation layer to manage extreme density shifts. Simulations demonstrate that the STCL framework ensures resilience, with service reliability recovering to over 0.9 for moderate loads of up to 100 users. Even under extreme saturation with 140 users, G‑MAPPO maintains a significant performance lead over the multi‑agent deep deterministic policy gradient (MADDPG) baseline by preventing policy stagnation. The algorithm delivers an effective capacity gain of 20 percent under high traffic loads, validating its potential for scalable aerial edge swarms.

Authors:Italo Napolitano, Mario di Bernardo
Title: Optimal Transport for Time-Varying Multi-Agent Coverage Control
Abstract:
Coverage control algorithms have traditionally focused on static target densities, where agents are deployed to optimally cover a fixed spatial distribution. However, many applications involve time‑varying densities, including environmental monitoring, surveillance, and adaptive sensor deployment. Although time‑varying coverage strategies have been studied within Voronoi‑based frameworks, recent works have reformulated static coverage control as a semi‑discrete optimal transport problem. Extending this optimal transport perspective to time‑varying scenarios has remained an open challenge. This paper presents a rigorous optimal transport formulation for time‑varying coverage control, in which agents minimize the instantaneous Wasserstein distance to a continuously evolving target density. The proposed solution relies on a coupled system of differential equations governing agent positions and the dual variables that define Laguerre regions. In one‑dimensional domains, the resulting system admits a closed‑form analytical solution, offering both computational benefits and theoretical insight into the structure of optimal time‑varying coverage. Numerical simulations demonstrate improved tracking performance compared to quasi‑static and Voronoi‑based methods, validating the proposed framework.

Authors:Nan Lin, Yanbo Wang, Jacco Heres, Peter Palensky, Pedro P. Vergara
Title: SmartMeterFM: Unifying Smart Meter Data Generative Tasks Using Flow Matching Models
Abstract:
Smart meter data is the foundation for planning and operating the distribution network. Unfortunately, such data are not always available due to privacy regulations. Meanwhile, the collected data may be corrupted due to sensor or transmission failure, or it may not have sufficient resolution for downstream tasks. A wide range of generative tasks is formulated to address these issues, including synthetic data generation, missing data imputation, and super‑resolution. Despite the success of machine learning models on these tasks, dedicated models need to be designed and trained for each task, leading to redundancy and inefficiency. In this paper, by recognizing the powerful modeling capability of flow matching models, we propose a new approach to unify diverse smart meter data generative tasks with a single model trained for conditional generation. The proposed flow matching models are trained to generate challenging, high‑dimensional time series data, specifically monthly smart meter data at a 15 min resolution. By viewing different generative tasks as distinct forms of partial data observations and injecting them into the generation process, we unify tasks such as imputation and super‑resolution with a single model, eliminating the need for re‑training. The data generated by our model not only are consistent with the given observations but also remain realistic, showing better performance against interpolation and other machine learning based baselines dedicated to the tasks.

Authors:Yuansheng Lian, Ke Zhang, Yaming Guo, Shen Li, Meng Li
Title: BAP-SRL: Bayesian Adaptive Priority Safe Reinforcement Learning for Vehicle Motion Planning at Mixed Traffic Intersections
Abstract:
Navigating urban intersections, especially when interacting with heterogeneous traffic participants, presents a formidable challenge for autonomous vehicles (AVs). In such environments, safety risks arise simultaneously from multiple sources, each carrying distinct priority levels and sensitivities that necessitate differential protection preferences. While safe reinforcement learning (RL) offers a robust paradigm for constrained decision‑making, existing methods typically model safety as a single constraint or employ static, heuristic weighting schemes for multiple constraints. These approaches often fail to address the dynamic nature of multi‑source risks, leading to gradient cancellation that hampers learning, and suboptimal trade‑offs in critical dilemma zones. To address this, we propose a Bayesian adaptive priority safe reinforcement learning (BAP‑SRL) based motion planning framework. Unlike heuristic weighting schemes, BAP formulates constraint prioritization as a probabilistic inference task. By modeling historical optimization difficulty as a Bayesian prior and instantaneous risk evidence as a likelihood, BAP dynamically gates gradient updates using a Bayesian inference mechanism on latent constraint criticality. Extensive experiments demonstrate that our approach outperforms state‑of‑the‑art baselines in handling interactions with stochastic, heterogeneous agents, achieving lower collision rates and smoother conflict resolution.

Authors:Andrew Savchenko
Title: Authenticated encryption for space telemetry
Abstract:
We explore how command stack protection requirements outlined in NASA‑STD‑1006A can be satisfied within the context of emergency space telemetry. Proposed implementation of lightweight authenticated encryption offers strong security without sacrificing performance in resource‑constrained environments. It produces fixed‑length messages, maintaining compatibility with the underlying data transport protocols. By focusing on predictable properties and robust authentication, we create a scheme that protects the confidentiality, integrity and authenticity of telemetry data in emergency communications while balancing security requirements with the operational constraints.

Authors:Savvas Panagi, Chrysovalantis Spanias, Petros Aristidou
Title: A Thermal-Electrical Co-Optimization Framework for Active Distribution Grids with Electric Vehicles and Heat Pumps
Abstract:
The growing electrification of transportation and heating through Electric Vehicles (EVs) and Heat Pumps (HPs) introduces both flexibility and complexity to Active Distribution Networks (ADNs). These resources provide substantial operational flexibility but also create tightly coupled thermal‑electrical dynamics that challenge conventional network management. This paper proposes a unified co‑optimization framework that integrates a calibrated 3R2C grey‑box building thermal model into a network‑constrained Optimal Power Flow (OPF). The framework jointly optimizes EVs, HPs, and photovoltaic systems while explicitly enforcing thermal comfort, Distributed Energy Resource (DER) limits, and full power flow physics. To maintain computational tractability, Second‑Order Cone Programming (SOCP) relaxations are evaluated on a realistic low‑voltage feeder. The analysis shows that, despite network heterogeneity violating some theoretical exactness conditions, the relaxation remains exact in practice. Comparative assessments of convex DistFlow, bus injection, and branch flow formulations reveal that convex DistFlow achieves sub‑second runtimes and near‑optimal performance even at high DER penetration levels. Simulations confirm the effectiveness of coordinated scheduling, yielding reductions of 41% in transformer aging, 54% in losses, and complete elimination of voltage violations, demonstrating the value of integrated thermal‑electrical coordination in future smart grids.

Authors:Junhao He, Feiran You, Hongyang Du
Title: StarSD: One-for-Many Speculative Decoding
Abstract:
Speculative decoding accelerates autoregressive generation by separating token proposal from verification, but most existing approaches are designed for single‑node execution and do not scale well to multi‑accelerator clusters used for serving modern Large Language Models (LLMs). We present StarSD, a one‑for‑many speculative decoding framework that uses a single draft model to serve multiple target models across distributed nodes via a star topology. StarSD decouples drafting and verification, enabling effective sharing of draft computation, and preventing distributed accelerators from remaining idle under bursty workloads. We provide a system‑level analysis that characterizes when and why a single draft model can remain fully utilized by multiple verifiers, yielding predictable latency and utilization gains. Extensive experiments in real‑world distributed inference settings demonstrate that StarSD simplifies deployment and supports flexible resource allocation across heterogeneous accelerators, while maintaining output quality. These results indicate that StarSD is a practical and scalable framework for bringing speculative decoding to modern cloud and edge inference infrastructures.

Authors:Xiang Zhu, Xiuqiang He, Hongyang Qing, Hua Geng
Title: Decentralized Analysis Approach for Oscillation Damping in Grid-Forming and Grid-Following Heterogeneous Power Systems
Abstract:
This letter proposes a decentralized local gain condition (LGC) to guarantee oscillation damping in inverter‑based resource (IBR)‑dominated power systems. The LGC constrains the dynamic gain between each IBR and the network at its point of connection. By satisfying the LGC locally, the closed‑loop poles are confined to a desired region, thereby yielding system‑wide oscillation damping without requiring global information. Notably, the LGC is agnostic to different IBR dynamics, well‑suited for systems with heterogeneous IBRs, and flexible to various damping requirements. Moreover, a low‑complexity algorithm is proposed to parameterize LGC, providing scalable and damping‑constrained parameter tuning guidance for IBRs.

Authors:Joonhee Lee, Hyunseung Shin, Jeonggil Ko
Title: IROS: A Dual-Process Architecture for Real-Time VLM-Based Indoor Navigation
Abstract:
Indoor mobile robot navigation requires fast responsiveness and robust semantic understanding, yet existing methods struggle to provide both. Classical geometric approaches such as SLAM offer reliable localization but depend on detailed maps and cannot interpret human‑targeted cues (e.g., signs, room numbers) essential for indoor reasoning. Vision‑Language‑Action (VLA) models introduce semantic grounding but remain strictly reactive, basing decisions only on visible frames and failing to anticipate unseen intersections or reason about distant textual cues. Vision‑Language Models (VLMs) provide richer contextual inference but suffer from high computational latency, making them unsuitable for real‑time operation on embedded platforms. In this work, we present IROS, a real‑time navigation framework that combines VLM‑level contextual reasoning with the efficiency of lightweight perceptual modules on low‑cost, on‑device hardware. Inspired by Dual Process Theory, IROS separates fast reflexive decisions (System One) from slow deliberative reasoning (System Two), invoking the VLM only when necessary. Furthermore, by augmenting compact VLMs with spatial and textual cues, IROS delivers robust, human‑like navigation with minimal latency. Across five real‑world buildings, IROS improves decision accuracy and reduces latency by 66% compared to continuous VLM‑based navigation.

Authors:B. da Costa Paulo, N. Aginako, J. Ugartemendia, I. Landa del Barrio, M. Quartulli, H. Camblong
Title: Surrogate model of a HVAC system for PV self-consumption maximisation
Abstract:
In the last few years, energy efficiency has become a challenge. Not only mitigating environmental impact but reducing energy waste can lead to financial advantages. Buildings play an important role in this: they are among the biggest consumers. So, finding manners to reduce energy consumption is a way to minimise energy waste, and a technique for that is creating Demand Response (DR) strategies. This paper proposes a novel way to decrease computational effort of simulating the behaviour of a building using surrogate models based on active learning. Before going straight to the problem of a building, which is complex and computationally costly, the paper proposes the approach of active learning to a smaller problem: with reduced simulations, regress the curve of voltage versus current of a thermo‑resistor. Then, the paper implements a surrogate model of energy consumption of a building. The goal is to be able to learn the consumption pattern based on a limited number of simulations. The result given by the surrogate can be used to set the reference temperature, maximising the PV self‑consumption, and reducing energy usage from the grid. Thanks to the surrogate, the total time spent to map all possible consumption scenarios is reduced around 7 times.

Authors:Sifat Chowdhury, Yihsu Chen, Yu Zhang
Title: Resilient Grid Hardening against Multiple Hazards: An Adaptive Two-Stage Stochastic Optimization Approach
Abstract:
The growing prevalence of extreme weather events driven by climate change poses significant challenges to power system resilience. Infrastructure damage and prolonged power outages highlight the urgent need for effective grid‑hardening strategies. While some measures provide long‑term protection against specific hazards, they can become counterproductive under conflicting threats. In this work, we develop an adaptive two‑stage stochastic optimization framework to support dynamic decision‑making for hardening critical grid components under multiple hazard exposures. Unlike traditional approaches, our model adapts to evolving climate conditions, enabling more resilient investment strategies. Furthermore, we integrate long‑term (undergrounding) and short‑term (vegetation management) hardening actions to jointly minimize total system costs. Extensive simulation results validate the effectiveness of the proposed framework in reducing outage and repair costs while enhancing the adaptability and robustness of grid infrastructure planning.

Authors:Richard Zeng, Anthony Chan Carusone, Xilin Liu
Title: A Time-Domain Dual-Edge Asynchronous Pipelined SAR ADC Featuring Reset-Free Quantization at Multi-GS/s
Abstract:
Time‑domain ADCs are attractive for high‑speed wireline receivers, as time resolution scales favorably with advanced CMOS technologies, enabling multi‑GS/s single‑channel sampling rates. However, conventional time‑domain ADCs require explicit reset of voltage‑to‑time and time‑domain signal paths between samples, introducing dead time that fundamentally limits resolution, speed, and energy efficiency. This paper introduces a dual‑edge reset‑free quantization concept for asynchronous pipelined SAR time‑domain ADCs, in which both rising and falling signal edges are exploited to enable reset‑free quantization within a single conversion period. By eliminating explicit reset phases, the proposed approach expands the effective conversion window and relaxes the resolution‑speed tradeoff at high sampling rates. An 8‑bit dual‑edge asynchronous pipelined SAR time‑domain ADC is implemented in 22‑nm FD‑SOI, incorporating a linearity‑compensated dual‑edge voltage‑to‑time converter and a dual‑edge time‑to‑digital converter with independently tunable rising‑ and falling‑edge delays. The prototype occupies a core area of 0.0089 mm^2 and achieves continuous single‑channel operation at 3.5 GS/s, with architectural scalability demonstrated through intermittent operation at 10.5 GS/s and higher. At 3.5 GS/s, the ADC achieves 21.6 dB SNDR and 32.2 dB SFDR. The measured performance is primarily limited by identifiable implementation‑level factors rather than by architectural constraints, demonstrating the feasibility of dual‑edge reset‑free quantization for high‑speed time‑domain ADCs.

Authors:Kushal Pratap Singh, Manvi Bengani, Darshit Mittal, Twinkle Tripathy
Title: Distributed Circumnavigation Using Bearing Based Control with Limited Target Information
Abstract:
In this paper, we address the problem of circumnavigation of a stationary target by a heterogeneous group comprising of n autonomous agents, having unicycle kinematics. The agents are assumed to have constant linear speeds, we control only the angular speeds. Assuming limited sensing capabilities of the agents, in the proposed framework, only a subset of agents, termed as leaders, know the target location. The rest, termed as followers, do not. We propose a distributed guidance law which drives all the agents towards the desired objective; global asymptotic stability (GAS) is ensured by using Zubov's theorem. The efficacy of the approach is demonstrated through both numerical simulations and hardware experiments on the TurtleBots utilising OptiTrack motion capture system.

Authors:Byeongjun Kim, H. Jin Kim
Title: Deep QP Safety Filter: Model-free Learning for Reachability-based Safety Filter
Abstract:
We introduce Deep QP Safety Filter, a fully data‑driven safety layer for black‑box dynamical systems. Our method learns a Quadratic‑Program (QP) safety filter without model knowledge by combining Hamilton‑Jacobi (HJ) reachability with model‑free learning. We construct contraction‑based losses for both the safety value and its derivatives, and train two neural networks accordingly. In the exact setting, the learned critic converges to the viscosity solution (and its derivative), even for non‑smooth values. Across diverse dynamical systems ‑‑ even including a hybrid system ‑‑ and multiple RL tasks, Deep QP Safety Filter substantially reduces pre‑convergence failures while accelerating learning toward higher returns than strong baselines, offering a principled and practical route to safe, model‑free control.

Authors:Hengde Zhang, Yunxiao Ren, Zhisheng Duan, Zhiyong Sun, Guanrong Chen
Title: Deep Koopman Iterative Learning and Stability-Guaranteed Control for Unknown Nonlinear Time-Varying Systems
Abstract:
This paper proposes a Koopman‑based framework for modeling, prediction, and control of unknown nonlinear time‑varying systems. We present a novel Koopman‑based learning method for predicting the state of unknown nonlinear time‑varying systems, upon which a robust controller is designed to ensure that the resulting closed‑loop system is input‑to‑state stable with respect to the Koopman approximation error. The error of the lifted system model learned through the Koopman‑based method increases over time due to the time‑varying nature of the nonlinear time‑varying system. To address this issue, an online iterative update scheme is incorporated into the learning process to update the lifted system model, aligning it more precisely with the time‑varying nonlinear system by integrating the updated data and discarding the outdated data. A necessary condition for the feasibility of the proposed iterative learning method is derived. In order to reduce unnecessary system updates while ensuring the prediction accuracy of the lifted system, the update mechanism is enhanced to determine whether to update the lifted system and meanwhile to reduce updates that deteriorate the fitting performance. Furthermore, based on the online‑updated lifted system, a controller is designed to ensure the closed‑loop controlled system be input‑to‑state stable with respect to the Koopman approximation error. Numerical simulations on the Duffing oscillator, the serial manipulator, and the synthetic biological network system are presented to demonstrate the effectiveness of the proposed method for the approximation and control of unknown nonlinear time‑varying systems. The results show that the proposed approach outperforms existing methods in terms of approximation accuracy and computational efficiency, even under significant system variations.

Authors:Ruixing Ren, Junhui Zhao, Xiaoke Sun, Shanjin Ni
Title: Towards Governance of Localized VANET: An Adjustable Degree Distribution Model
Abstract:
Vehicular Ad‑hoc Networks (VANETs) serve as a critical enabler for intelligent transportation systems. However, their practical deployment faces a core governance dilemma: the network topology requires a dynamic trade‑off between robustness against targeted attacks and ensuring low‑latency information transmission. Most existing models generate fixed degree distributions, lacking the ability to adapt autonomously to the demands of diverse traffic scenarios. To address this challenge, this paper innovatively proposes a schedulable degree distribution model for localized VANETs. The core of this model lies in introducing a hybrid connection mechanism. When establishing connections, newly joining nodes do not follow a single rule but instead collaboratively perform random attachment and preferential attachment. Through theoretical derivation and simulation validation, this study demonstrates that by adjusting the cooperative weighting between these two mechanisms, the overall network degree distribution can achieve a continuous and controllable transition between a uniform distribution and a power‑law distribution. The former effectively disperses attack risks and enhances robustness, while the latter facilitates the formation of hub nodes, shortening transmission paths to reduce latency. Experimental results based on the real‑world road network of Beijing indicate that this model can precisely regulate node connection heterogeneity, attack resistance, and average transmission path length through the reshaping of the underlying topology. This provides a forward‑looking and practical governance paradigm for constructing next‑generation VANETs capable of dynamically adapting to complex environments.

Authors:Jason Lu, Tejas Santanam, Hongzhao Guan, Connor Riley, Meen-Sung Kim, Anthony Trasatti, Neda Masoud, Pascal Van Hentenryck
Title: The Impact of Shared Autonomous Vehicles in Microtransit Systems: A Case Study in Atlanta
Abstract:
Microtransit systems represent an enhancement to solve the first‑ and last‑mile problem, integrating traditional rail and bus networks with on‑demand shuttles into a flexible, integrated system. This type of demand responsive transport provides greater accessibility and higher quality levels of service compared to conventional fixed‑route transit services. Advances in technology offer further opportunities to enhance microtransit performance. In particular, shared autonomous vehicles (SAVs) have the potential to transform the mobility landscape by enabling more sustainable operations, enhanced user convenience, and greater system reliability. This paper investigates the integration of SAVs in microtransit systems, advancing the technological capabilities of on‑demand shuttles. A shuttle dispatching optimization model is enhanced to accommodate for driver behavior and SAV functionalities. A model predictive control approach is proposed that dynamically rebalances on‑demand shuttles towards areas of higher demand without relying on vast historical data. Scenario‑driven experiments are conducted using data from the MARTA Reach microtransit pilot. The results demonstrate that SAVs can elevate both service quality and user experience compared to traditional on‑demand shuttles in microtransit systems.

Authors:Jingguan Liu, Cong Chen, Xiaomeng Ai, Jiakun Fang, Jinsong Wang, Jinyu Wen
Title: Mean-Field Learning for Storage Aggregation
Abstract:
Distributed energy storage devices can be aggregated to provide operational flexibility for power systems. This requires representing a massive device population as a single, tractable surrogate that is computationally efficient and accurate. However, surrogate identification is challenging due to heterogeneity, nonconvexity, and high dimensionality of storage devices. To address these challenges, this paper develops a mean‑field learning framework for storage aggregation. We interpret aggregation as the average behavior of a large storage population and show that, as the population grows, aggregate performance converges to a unique, convex mean‑field limit, enabling tractable population‑level modeling. This convexity further yields a price‑responsive characterization of aggregate storage behavior and allows us to bound the mean‑field approximation error. We construct a convex surrogate model with physically interpretable parameters that approximates the aggregate behavior of large storage populations and can be embedded directly into power system operations. Surrogate parameter identification is formulated as an optimization problem using historical price‑response data, and we adopt a gradient‑based algorithm for efficient learning. Case studies validate the theoretical findings and demonstrate the effectiveness of the proposed framework in approximation accuracy and data efficiency.

Authors:Chonghuan Wang
Title: Experimental Design for Matching
Abstract:
Matching mechanisms play a central role in operations management across diverse fields including education, healthcare, and online platforms. However, experimentally comparing a new matching algorithm against a status quo presents some fundamental challenges due to matching interference, where assigning a unit in one matching may preclude its assignment in the other. In this work, we take a design‑based perspective to study the design of randomized experiments to compare two predetermined matching plans on a finite population, without imposing outcome or behavioral models. We introduce the notation of a disagreement set, which captures the difference between the two matching plans, and show that it admits a unique decomposition into disjoint alternating paths and cycles with useful structural properties. Based on these properties, we propose the Alternating Path Randomized Design, which sequentially randomizes along these paths and cycles to effectively manage interference. Within a minimax framework, we optimize the conditional randomization probability and show that, for long paths, the optimal choice converges to \sqrt2‑1, minimizing worst‑case variance. We establish the unbiasedness of the Horvitz‑Thompson estimator and derive a finite‑population Central Limit Theorem that accommodates complex and unstable path and cycle structures as the population grows. Furthermore, we extend the design to many‑to‑one matchings, where capacity constraints fundamentally alter the structure of the disagreement set. Using graph‑theoretic tools, including finding augmenting paths and Euler‑tour decomposition on an auxiliary unbalanced directed graph, we construct feasible alternating path and cycle decompositions that allow the design and inference results to carry over.

Authors:Xiaoxia Xu, Xidong Mu, Yuanwei Liu, Arumugam Nallanathan
Title: Multi-Mode Pinching Antenna Systems Enabled Multi-User Communications
Abstract:
This paper proposes a novel multi‑mode pinching‑antenna systems (PASS) framework. Multiple data streams can be transmitted within a single waveguide through multiple guided modes, thus facilitating efficient multi‑user communications through the mode‑domain multiplexing. A physic model is derived, which reveals the mode‑selective power radiation feature of pinching antennas (PAs). A two‑mode PASS enabled two‑user downlink communication system is investigated. Considering the mode selectivity of PA power radiation, a practical PA grouping scheme is proposed, where each PA group matches with one specific guided mode and mainly radiates its signal sequentially. Depending on whether the guided mode leaks power to unmatched PAs or not, the proposed PA grouping scheme operates in either the non‑leakage or weak‑leakage regime. Based on this, the baseband beamforming and PA locations are jointly optimized for sum rate maximization, subject to each user's minimum rate requirement. 1) A simple two‑PA case in non‑leakage regime is first considered. To solve the formulated problem, a channel orthogonality based solution is proposed. The channel orthogonality is ensured by large‑scale and wavelength‑scale equality constraints on PA locations. Thus, the optimal beamforming reduces to maximum‑ratio transmission (MRT). Moreover, the optimal PA locations are obtained via a Newton‑based one‑dimension search algorithm that enforces two‑scale PA‑location constraints by Newton's method. 2) A general multi‑PA case in both non‑leakage and weak‑leakage regimes is further considered. A low‑complexity particle‑swarm optimization with zero‑forcing beamforming (PSO‑ZF) algorithm is developed, thus effectively tackling the high‑oscillatory and strong‑coupled problem. Simulation results demonstrate the superiority of the proposed multi‑mode PASS over conventional single‑mode PASS and fixed‑antenna structures.

Authors:Emrah Akyol, Marcos Vasconcelos
Title: Distributed Learning over Noisy Communication Networks
Abstract:
We study binary coordination games over graphs under log‑linear learning when neighbor actions are conveyed through explicit noisy communication links. Each edge is modeled as either a binary symmetric channel (BSC) or a binary erasure channel (BEC). We analyze two operational regimes. For binary symmetric and binary erasure channels, we provide a structural characterization of the induced learning dynamics. In a fast‑communication regime, agents update using channel‑averaged payoffs; the resulting learning dynamics coincide with a Gibbs sampler for a scaled coordination potential, where channel reliability enters only through a scalar attenuation coefficient. In a snapshot regime, agents update from a single noisy realization and ignore channel statistics; the induced Markov chain is generally nonreversible, but admits a high‑temperature expansion whose drift matches that of the fast Gibbs sampler with the same attenuation. We further formalize a finite‑K communication budget, which interpolates between snapshot and fast behavior as the number of channel uses per update grows. This viewpoint yields a communication‑theoretic interpretation in terms of retransmissions and repetition coding, and extends naturally to heterogeneous link reliabilities via effective edge weights. Numerical experiments illustrate the theory and quantify the tradeoff between communication resources and steady‑state coordination quality.

Authors:Alejandro Luque-Cerpa, Mengyuan Wang, Emil Carlsson, Sanjit A. Seshia, Devdatt Dubhashi, Hazem Torfah
Title: Learning Contextual Runtime Monitors for Safe AI-Based Autonomy
Abstract:
We introduce a novel framework for learning context‑aware runtime monitors for AI‑based control ensembles. Machine‑learning (ML) controllers are increasingly deployed in (autonomous) cyber‑physical systems because of their ability to solve complex decision‑making tasks. However, their accuracy can degrade sharply in unfamiliar environments, creating significant safety concerns. Traditional ensemble methods aim to improve robustness by averaging or voting across multiple controllers, yet this often dilutes the specialized strengths that individual controllers exhibit in different operating contexts. We argue that, rather than blending controller outputs, a monitoring framework should identify and exploit these contextual strengths. In this paper, we reformulate the design of safe AI‑based control ensembles as a contextual monitoring problem. A monitor continuously observes the system's context and selects the controller best suited to the current conditions. To achieve this, we cast monitor learning as a contextual learning task and draw on techniques from contextual multi‑armed bandits. Our approach comes with two key benefits: (1) theoretical safety guarantees during controller selection, and (2) improved utilization of controller diversity. We validate our framework in two simulated autonomous driving scenarios, demonstrating significant improvements in both safety and performance compared to non‑contextual baselines.

Authors:Jilles S. van Hulst, Erik M. Franken, Bart J. Janssen, W. P. M. H. Heemels, Duarte J. Antunes
Title: Tilt-based Aberration Estimation in Transmission Electron Microscopy
Abstract:
Transmission electron microscopes (TEMs) enable atomic‑scale imaging but suffer from aberrations caused by lens imperfections and environmental conditions, reducing image quality. These aberrations can be compensated by adjusting electromagnetic lenses, but this requires accurate estimates of the aberration coefficients, which can drift over time. This paper introduces a method for the estimation of aberrations in TEM by leveraging the relationship between an induced tilt of the electron beam and the resulting image shift. The method uses a Kalman filter (KF) to estimate the aberration coefficients from a sequence of image shifts, while accounting for the drift of the aberrations over time. The applied tilt sequence is optimized by minimizing the trace of the predicted error covariance in the KF, which corresponds to the A‑optimality criterion in experimental design. We show that this optimization can be performed offline, as the cost criterion is independent of the actual measurements. The resulting non‑convex optimization problem is solved using a gradient‑based, receding‑horizon approach with multi‑starts. Additionally, we develop an approach to estimate specimen‑dependent noise properties using expectation maximization (EM), which are then used to tailor the tilt pattern optimization to the specific specimen being imaged. The proposed method is validated on a real TEM set‑up with several optimized tilt patterns. The results show that optimized patterns significantly outperform naive approaches and that the aberration and drift model accurately captures the underlying physical phenomena. A direct comparison with the widely used Zemlin tableau shows that the proposed method achieves comparable or higher image quality on amorphous specimens, while additionally extending to non‑amorphous specimens where the Zemlin tableau cannot operate.

Authors:Yixuan Zhu, Yinkang Gao, Lei Gong, Binze Jiang, Xiaohang Gong, Zihan Wang, Cheng Tang, Wenqi Lou, Teng Wang, Chao Wang, Xi Li, Xuehai Zhou
Title: A Timing-Anomaly Free Dynamic Scheduling on Heterogeneous Systems
Abstract:
Heterogeneous systems commonly adopt dynamic scheduling algorithms to improve resource utilization and enhance scheduling flexibility. However, such flexibility may introduce timing anomalies, wherein locally reduced execution times can lead to an increase in the overall system execution time. This phenomenon significantly complicates the analysis of Worst‑Case Response Time (WCRT), rendering conventional analysis either overly pessimistic or unsafe, and often necessitating exhaustive state‑space exploration to ensure correctness. To address this challenge, this paper presents the first timing‑anomaly‑free dynamic scheduling algorithm for heterogeneous systems, referred to as Deterministic Dynamic Execution. It achieves a safe and tight WCRT estimate through a single offline simulation execution. The core idea is to apply deterministic execution constraints, which partially restrict the resource allocation and execution order of tasks at runtime. Based on a formally defined execution progress model for heterogeneous system scheduling, we prove the correctness of the proposed execution constraints and their ability to eliminate timing anomalies. Furthermore, we propose two methods to generate execution constraints. The first method derives execution constraints directly from the execution traces produced by existing scheduling algorithms. The second method is a heuristic‑based approach that constructs execution constraints, enabling further reduction of the WCRT. Experimental results on synthetically generated DAG task sets under various system configurations demonstrate that, compared to traditional dynamic scheduling algorithms, our approach not only eliminates timing anomalies but also effectively reduces both the WCRT and response time jitter.

Authors:Yixuan Zhu, Yinkang Gao, Bo Zhang, Xiaohang Gong, Binze Jiang, Lei Gong, Wenqi Lou, Teng Wang, Chao Wang, Xi Li, Xuehai Zhou
Title: Reducing End-to-End Latency of Cause-Effect Chains with Shared Cache Analysis
Abstract:
Cause‑effect chains, as a widely used modeling method in real‑time embedded systems, are extensively applied in various safety‑critical domains. End‑to‑end latency, as a key real‑time attribute of cause‑effect chains, is crucial in many applications. But the analysis of end‑to‑end latency for cause‑effect chains on multicore platforms with shared caches still presents an unresolved issue. Traditional methods typically assume that the worst‑case execution time (WCET) of each task in the cause‑effect chain is known. However, in the absence of scheduling information, these methods often assume that all shared cache accesses result in misses, leading to an overestimation of WCET and, consequently, affecting the accuracy of end‑to‑end latency. However, effectively integrating scheduling information into the WCET analysis process of the chains may introduce two challenges: first, how to leverage the structural characteristics of the chains to optimize shared cache analysis, and second, how to improve analysis accuracy while avoiding state space explosion. To address these issues, this paper proposes a novel end‑to‑end latency analysis framework designed for multi‑chain systems on multicore platforms with shared caches. This framework extracts scheduling information and structural characteristics of cause‑effect chains, constructing fine‑grained and scalable inter‑core memory access contexts at the basic block level for time‑sensitive shared cache analysis. This results in more accurate WCET (TSC‑WCET) estimates, which are then used to derive the end‑to‑end latency. Finally, we conduct experiments on dual‑core and quad‑core systems with various cache configurations, which show that under certain settings, the average maximum end‑to‑end latency of cause‑effect chains is reduced by up to 34% and 26%.

Authors:Qing Lyu, Zhe Fu, Alexandre Bayen
Title: Unsupervised Anomaly Detection in Multi-Agent Trajectory Prediction via Transformer-Based Models
Abstract:
Identifying safety‑critical scenarios is essential for autonomous driving, but the rarity of such events makes supervised labeling impractical. Traditional rule‑based metrics like Time‑to‑Collision are too simplistic to capture complex interaction risks, and existing methods lack a systematic way to verify whether statistical anomalies truly reflect physical danger. To address this gap, we propose an unsupervised anomaly detection framework based on a multi‑agent Transformer that models normal driving and measures deviations through prediction residuals. A dual evaluation scheme has been proposed to assess both detection stability and physical alignment: Stability is measured using standard ranking metrics in which Kendall Rank Correlation Coefficient captures rank agreement and Jaccard index captures the consistency of the top‑K selected items; Physical alignment is assessed through correlations with established Surrogate Safety Measures (SSM). Experiments on the NGSIM dataset demonstrate our framework's effectiveness: We show that the maximum residual aggregator achieves the highest physical alignment while maintaining stability. Furthermore, our framework identifies 388 unique anomalies missed by Time‑to‑Collision and statistical baselines, capturing subtle multi‑agent risks like reactive braking under lateral drift. The detected anomalies are further clustered into four interpretable risk types, offering actionable insights for simulation and testing.

Authors:Jingyuan Zhou, Haoze Wu, Kaidi Yang
Title: Neural Cooperative Reach-While-Avoid Certificates for Interconnected Systems
Abstract:
Providing formal guarantees for neural network‑based controllers in large‑scale interconnected systems remains a fundamental challenge. In particular, using neural certificates to capture cooperative interactions and verifying these certificates at scale is crucial for the safe deployment of such controllers. However, existing approaches fall short on both fronts. To address these limitations, we propose neural cooperative reach‑while‑avoid certificates with Dynamic‑Localized Vector Control Lyapunov and Barrier Functions, which capture cooperative dynamics through state‑dependent neighborhood structures and provide decentralized certificates for global exponential stability and safety. Based on the certificates, we further develop a scalable training and verification framework that jointly synthesizes controllers and neural certificates via a constrained optimization objective, and leverages a sufficient condition to ensure formal guarantees considering modeling error. To improve scalability, we introduce a structural reuse mechanism to transfer controllers and certificates between substructure‑isomorphic systems. The proposed methodology is validated with extensive experiments on multi‑robot coordination and vehicle platoons. Results demonstrate that our framework ensures certified cooperative reach‑while‑avoid while maintaining strong control performance.

Authors:Xinran Wang, Peng Wu, Xiaopeng Yuan, Yulin Hu, Anke Schmeink
Title: Efficient Trajectory Design and Communication Scheduling for Dual-UAV Jamming-Aided Secure Communication Networks
Abstract:
We study dual‑unmanned aerial vehicle (UAV) jamming‑aided secure communication networks, in which one UAV delivers confidential data to multiple ground users (GUs), while a cooperative UAV provides protective interference against a ground eavesdropper. To enforce fairness, we maximize the minimum secrecy throughput across GUs by jointly designing trajectories and communication scheduling. The key difficulty lies in the continuous‑time nature of UAV trajectories and the tight space‑time coupling between the transmitter and the jammer, which jointly render the problem infinite‑dimensional and nonconvex. To address these challenges, we characterize, for the first time, the structure of the optimal trajectories and rigorously prove that they follow a collaborative successive hover‑and‑fly (co‑SHF) structure, where the two UAVs visit a limited number of synchronized co‑hovering point pairs, and during each flight segment at least one UAV moves at maximum speed. Leveraging this structure, we reformulate the problem into a finite‑dimensional form, without loss of optimality, over hovering and turning points, hovering durations, and scheduling. For tractability, we adopt a minimum‑distance approximation of continuous anti‑collision constraints and employ concave lower bounds on secrecy throughput within a successive convex approximation (SCA) method, which converges and, thanks to the co‑SHF reduction in optimization variables and constraints, achieves low computational complexity. Numerical results show that, compared with time‑discretization and no‑jamming benchmarks, the proposed co‑SHF design improves the min‑secrecy and user fairness while requiring significantly less runtime.

Authors:Omid Akbarzadeh, MohammadHossein Ashoori, Amy Nejati, Abolfazl Lavaei
Title: A Data-Driven Krasovskii-Based Approach for Safety Controller Design of Time-Delayed Uncertain Polynomial Systems
Abstract:
We develop a data‑driven framework for the synthesis of robust Krasovskii control barrier certificates (RK‑CBC) and corresponding robust safety controllers (R‑SC) for discrete‑time input‑affine uncertain polynomial systems with unknown dynamics, while explicitly accounting for unknown‑but‑bounded disturbances and time‑invariant delays using only observed input‑state data. Although control barrier certificates have been extensively studied for safety analysis of control systems, existing work on unknown systems with time delays, particularly in the presence of disturbances, remains limited. The challenge of safety synthesis for such systems stems from two main factors: first, the system's mathematical model is unavailable; and second, the safety conditions should explicitly incorporate the effects of time delays on system evolution during the synthesis process, while remaining robust to unknown disturbances. To address these challenges, we develop a data‑driven framework based on Krasovskii control barrier certificates, extending the classical CBC formulation for delay‑free systems to explicitly account for time delays by aggregating delayed components within the barrier construction. The proposed framework relies solely on input‑state data collected over a finite time horizon, enabling the direct synthesis of RK‑CBC and R‑SC from observed trajectories without requiring an explicit system model. The synthesis is cast as a data‑driven sum‑of‑squares (SOS) optimization program, yielding a structured design methodology. As a result, robust safety is guaranteed in the presence of unknown disturbances and time delays over an infinite time horizon. The effectiveness of the proposed method is demonstrated through three case studies, including two physical systems.

Authors:Julian Gutierrez, Redouane Silvente
Title: Parametric and Generative Forecasts of Day-Ahead Market Curves for Storage Optimization
Abstract:
We present two machine learning frameworks for forecasting aggregated curves and optimizing storage in the EPEX SPOT day‑ahead market. First, a fast parametric model forecasts hourly demand and supply curves in a low‑dimensional and grid‑robust representation, with minimum and maximum volumes combined with a Chebyshev polynomial for the elastic segment. The model enables daily use with low error and clear interpretability. Second, for a more comprehensive analysis, though less suited to daily operation, we employ generative models that learn the joint distribution of 24‑hour order‑level submissions given weather and fuel variables. These models generate synthetic daily scenarios of individual buy and sell orders, which, once aggregated, yield hourly supply and demand curves. Based on these forecasts, we optimize a price‑making storage strategy, quantify revenue distributions, and highlight the price‑compression effect with lower peaks, higher off‑peak levels, and diminishing returns as capacity expands.

Authors:Yuhua Zhao, Tiejun Lv, Ke Wang
Title: C-AoEI-Aware Cross-Layer Optimization in Satellite IoT Systems: Balancing Data Freshness and Transmission Efficiency
Abstract:
Satellite‑based Internet of Things (S‑IoT) faces a fundamental trilemma: propagation delay, dynamic fading, and bandwidth scarcity. While Layer‑coded Hybrid ARQ (L‑HARQ) enhances reliability, its backtracking decoding introduces age ambiguity, undermining the standard Age of Information (AoI) metric and obscuring the critical trade‑off between data freshness and transmission efficiency. To bridge this gap, we propose a novel cross‑layer optimization framework centered on a new metric, the Cross‑layer Age of Error Information (C‑AoEI). We derive a closed‑form expression for C‑AoEI, explicitly linking freshness to system parameters, establishing an explicit analytical connection between freshness degradation and channel dynamics. Building on this, we develop a packet‑level encoded L‑HARQ scheme for multi‑GBS scenarios and an adaptive algorithm that jointly optimizes coding and decision thresholds. Extensive simulations demonstrate the effectiveness of our proposed framework: it achieves 31.8% higher transmission efficiency and 17.2% lower C‑AoEI than conventional schemes. The framework also proves robust against inter‑cell interference and varying channel conditions, providing a foundation for designing efficient, latency‑aware next‑generation S‑IoT protocols.

Authors:Muzaffar Qureshi, Tochukwu Elijah Ogri, Kyle Volle, Rushikesh Kamalapurkar
Title: A Taylor Series Approach to Correct Localization Errors in Robotic Field Mapping using Gaussian Processes
Abstract:
Gaussian Processes (GPs) are powerful non‑parametric Bayesian models for regression of scalar fields, formulated under the assumption that measurement locations are perfectly known and the corresponding field measurements have Gaussian noise. However, many real‑world scalar field mapping applications rely on sensor‑equipped mobile robots to collect field measurements, where imperfect localization introduces state uncertainty. Such discrepancies between the estimated and true measurement locations degrade GP mean and covariance estimates. To address this challenge, we propose a method for updating the GP models when improved estimates become available. Leveraging the differentiability of the kernel function, a second‑order correction algorithm is developed using the precomputed Jacobians and Hessians of the GP mean and covariance functions for real‑time refinement based on measurement location discrepancy data. Simulation results demonstrate improved prediction accuracy and computational efficiency compared to full model retraining.

Authors:Domitilla Del Vecchio
Title: Control systems for synthetic biology and a case-study in cell fate reprogramming
Abstract:
This paper gives an overview of the use of control systems engineering in synthetic biology, motivated by applications such as cell therapy and cell fate reprogramming for regenerative medicine. A ubiquitous problem in these and other applications is the ability to control the concentration of specific regulatory factors in the cell accurately despite environmental uncertainty and perturbations. The paper describes the origin of these perturbations and how they affect the dynamics of the biomolecular ``plant'' to be controlled. A variety of biomolecular control implementations are then introduced to achieve robustness of the plant's output to perturbations and are grouped into feedback and feedforward control architectures. Although sophisticated control laws can be implemented in a computer today, they cannot be necessarily implemented inside the cell via biomolecular processes. This fact constraints the set of feasible control laws to those realizable through biomolecular processes that can be engineered with synthetic biology. After reviewing biomolecular feedback and feedforward control implementations, mostly focusing on the author's own work, the paper illustrates the application of such control strategies to cell fate reprogramming. Within this context, a master regulatory factor needs to be controlled at a specific level inside the cell in order to reprogram skin cells to pluripotent stem cells. The article closes by highlighting on‑going challenges and directions of future research for biomolecular control design.

Authors:Zixin Jiang, Weili Xu, Bing Dong
Title: OptAgent: an Agentic AI framework for Intelligent Building Operations
Abstract:
The urgent need for building decarbonization calls for a paradigm shift in future autonomous building energy operation, from human‑intensive engineering workflows toward intelligent agents that interact with physics‑grounded digital environments. This study proposes an end‑to‑end agentic AI‑enabled Physics‑Informed Machine Learning (PIML) environment for scalable building energy modeling, simulation, control, and automation. The framework consists of (1) a modular and physics‑consistent PIML digital environment spanning building thermal dynamics, Heating, Ventilation, and Air Conditioning (HVAC), and distributed energy resources (DER) for grid‑interactive energy management; and (2) an agentic AI layer with 11 specialist agents and 72 Model Context Protocol (MCP) tools that enable end‑to‑end execution of multi‑step energy analytics. A representative case study demonstrates multi‑domain, multi‑agent coordination for assessing how system and control upgrades affect energy use, operating cost, thermal comfort, and flexibility. In addition, a large‑scale benchmark (about 4000 runs) systematically evaluates workflow performance in terms of accuracy, token consumption, execution time, and inference cost. The results quantify the impacts of intelligence mode design, model size, task complexity, and orchestrator‑specialist coordination, and provide key lessons for building future agentic AI systems in real‑world building energy applications. This work establishes a scalable, physics‑grounded foundation for deploying agentic AI in decarbonized and grid‑interactive building operations.

Authors:Ibrahim Albulushi, Saleh Bunaiyan, Suraj S. Cheema, Hesham ElSawy, Feras Al-Dirini
Title: Probabilistic Sensing: Intelligence in Data Sampling
Abstract:
Extending the intelligence of sensors to the data‑acquisition process ‑ deciding whether to sample or not ‑ can result in transformative energy‑efficiency gains. However, making such a decision in a deterministic manner involves risk of losing information. Here we present a sensing paradigm that enables making such a decision in a probabilistic manner. The paradigm takes inspiration from the autonomous nervous system and employs a probabilistic neuron (p‑neuron) driven by an analog feature extraction circuit. The response time of the system is on the order of microseconds, over‑coming the sub‑sampling‑rate response time limit and enabling real‑time intelligent autonomous activation of data‑sampling. Validation experiments on active seismic survey data demonstrate lossless probabilistic data acquisition, with a normalized mean squared error of 0.41%, and 93% saving in the active operation time of the system and the number of generated samples.

Authors:Ganesh Sundaram, Tobias Gehra, Jonas Ulmen, Mirjan Heubaum, Daniel Görges, Michael Günthner
Title: A Latent Space Framework for Modeling Transient Engine Emissions Using Joint Embedding Predictive Architectures
Abstract:
Accurately modeling and controlling vehicle exhaust emissions during transient events, such as rapid acceleration, is critical for meeting environmental regulations and optimizing powertrains. Conventional data‑driven methods, such as Multilayer Perceptrons (MLPs) and Long Short‑Term Memory (LSTM) networks, improve upon phenomenological models but often struggle with the complex nonlinear dynamics of emission formation. These monolithic architectures are sensitive to dataset variability and typically require deep, computationally expensive structures to perform well, limiting their practical utility. This paper introduces a novel approach that overcomes these limitations by modeling emission dynamics within a structured latent space. Leveraging a Joint Embedding Predictive Architecture (JEPA), the proposed framework learns from a rich dataset that combines real‑world Portable Emission Measurement System (PEMS) data with high‑frequency hardware‑in‑the‑loop measurements. The model abstracts away irrelevant noise, encoding only the key factors governing emission behavior into a compact, robust representation. This results in superior data efficiency and predictive accuracy across diverse transient regimes, significantly outperforming high‑performing LSTM baselines in generalization. To ensure suitability for real‑world deployment, the JEPA framework is structured to support pruning and post‑training quantization. This strategy drastically reduces the computational footprint, minimizing inference time and memory demand with negligible accuracy loss. The result is a highly efficient model ideal for on‑board implementation of advanced strategies, such as model predictive control or model‑based reinforcement learning, in conventional and hybrid powertrains. These findings offer a clear pathway toward more robust emission control systems for next‑generation vehicles.

Authors:Michael S. Ackermann, Linus Balicki, Serkan Gugercin, Steffen W. R. Werner
Title: A refined nonlinear least-squares method for the rational approximation problem
Abstract:
The adaptive Antoulas‑Anderson (AAA) algorithm for rational approximation is a widely used method for the efficient construction of highly accurate rational approximations to given data. While AAA can often produce rational approximations accurate to any prescribed tolerance, these approximations may have degrees larger than what is actually required to meet the given tolerance. In this work, we consider the adaptive construction of interpolating rational approximations while aiming for the smallest feasible degree to satisfy a given error tolerance. To this end, we introduce refinement approaches to the linear least‑squares step of the classical AAA algorithm that aim to minimize the true nonlinear least‑squares error with respect to the given data. Furthermore, we theoretically analyze the derived approaches in terms of the corresponding gradients from the resulting minimization problems and use these insights to propose a new greedy framework that ensures monotonic error convergence. Numerical examples from function approximation and model order reduction verify the effectiveness of the proposed algorithm to construct accurate rational approximations of small degrees.

Authors:Ganesh Sundaram, Jonas Ulmen, Daniel Görges
Title: Component-Aware Pruning Framework for Neural Network Controllers via Gradient-Based Importance Estimation
Abstract:
The transition from monolithic to multi‑component neural architectures in advanced neural network controllers poses substantial challenges due to the high computational complexity of the latter. Conventional model compression techniques for complexity reduction, such as structured pruning based on norm‑based metrics to estimate the relative importance of distinct parameter groups, often fail to capture functional significance. This paper introduces a component‑aware pruning framework that utilizes gradient information to compute three distinct importance metrics during training: Gradient Accumulation, Fisher Information, and Bayesian Uncertainty. Experimental results with an autoencoder and a TD‑MPC agent demonstrate that the proposed framework reveals critical structural dependencies and dynamic shifts in importance that static heuristics often miss, supporting more informed compression decisions.

Authors:Ali Jnadi, Hadi Salloum, Yaroslav Kholodov, Alexander Gasnikov, Karam Almaghout
Title: SCOPE: Smooth Convex Optimization for Planned Evolution of Deformable Linear Objects
Abstract:
We present SCOPE, a fast and efficient framework for modeling and manipulating deformable linear objects (DLOs). Unlike conventional energy‑based approaches, SCOPE leverages convex approximations to significantly reduce computational cost while maintaining smooth and physically plausible deformations. This trade‑off between speed and accuracy makes the method particularly suitable for applications requiring real‑time or near‑real‑time response. The effectiveness of the proposed framework is demonstrated through comprehensive simulation experiments, highlighting its ability to generate smooth shape trajectories under geometric and length constraints.

Authors:Yan Jiang, Wei Chen, Zhaomin Lyu, Xunning Zhang, Dan Wang, Shinji Hara
Title: Frequency Shaping Control for Oscillation Damping in Weakly-Connected Power Network: A Root Locus Method
Abstract:
Frequency control following a contingency event is of vital concern in power system operations. Leveraging inverter‑based resources, it is not hard to shape the center of inertia (COI) frequency nicely. However, under weak grid conditions, it becomes insufficient to solely shape the COI frequency since this aggregate signal fails to reveal the inter‑area oscillations. In this manuscript, we advocate for foolproof fine‑tuning rules for \emphfrequency shaping control (FS) based on a systematic analysis of damping ratio and decay rate of inter‑area oscillations to simultaneously meet specified metrics for frequency security and oscillatory stability. To this end, building on a modal decomposition, we simplify the oscillation damping problem into a pole‑placement task for a set of scalar subsystems, which can be efficiently solved by only investigating the root locus of a scalar subsystem associated with the main mode, while FS inherently guarantees a Nadir‑less COI frequency response. Through our proposed root‑locus‑based oscillatory stability analysis, we derive closed‑form expressions for the minimum damping ratio and decay rate among inter‑area oscillations in terms of networked system and control parameters under FS. Moreover, we propose useful tuning guidelines for FS which need only simple calculations or visualized tuning to not only shape the COI frequency into a first‑order response that converges to a steady‑state value within the allowed range but also ensure a satisfactory damping ratio and decay rate of inter‑area oscillations following disturbances. As for the common virtual inertia control (VI), although similar oscillatory stability analysis becomes intractable, one can still glean some insights via the root locus method. Numerical simulations validate the proposed tuning for FS as well as the superiority of FS over VI in exponential convergence rate.

Authors:Giacomo Mastroddi, Jan Poland, Mats Larsson, Keith Moffat
Title: Data-Driven Predictive Control for Wide-Area Power Oscillation Damping
Abstract:
We study damping of inter‑area oscillations in transmission grids using voltage‑source‑converter‑based high‑voltage direct‑current (VSC‑HVDC) links. Conventional power oscillation damping controllers rely on system models that are difficult to obtain in practice. Data‑driven Predictive Control (DPC) addresses this limitation by replacing explicit models with data. We apply AutoRegressive with eXogenous inputs (ARX)‑based predictive control and its Transient Predictive Control (TPC) variant, and compare them with Data‑enabled Predictive Control (DeePC) and two standard model‑based controllers. The methods are evaluated in simulation on a system exhibiting both inter‑area and local oscillation modes. ARX‑based predictive control and DeePC both achieve effective damping, while the ARX‑based methods require less online computation. Using warm‑started, pre‑factorized operator‑splitting solvers, ARX/TPC control actions are computed in less than 1ms. These results demonstrate that DPC is a viable approach for power‑system oscillation damping for the given test case.

Authors:G. J. E. van Otterdijk, S. Weiland, M. Schoukens
Title: Improved Initialization for Port-Hamiltonian Neural Network Models
Abstract:
Port‑Hamiltonian neural networks have shown promising results in the identification of nonlinear dynamics of complex systems, as their combination of physical principles with data‑driven learning allows for accurate modelling. However, due to the non‑convex optimization problem inherent in learning the correct network parameters, the training procedure is prone to converging to local minima, potentially leading to poor performance. In order to avoid this issue, this paper proposes an improved initialization for port‑Hamiltonian neural networks. The core idea is to first estimate a linear port‑Hamiltonian system to be used as an initialization for the network, after which the neural network adapts to the system nonlinearities, reducing the training times and improving convergence. The effectiveness of this method is tested on a chained mass‑spring‑damper setup for varying noise levels and compared to the original approach.

Authors:An T. Le
Title: Rarity of rocket-driven Penrose extraction in Kerr spacetime
Abstract:
We study rocket‑driven Penrose extraction in the test‑particle limit on a fixed Kerr background for equatorial prograde flybys under explicit steering prescriptions. A spacecraft ejects exhaust inside the ergosphere; when the exhaust attains negative Killing energy, the remaining spacecraft gains energy by 4‑momentum conservation. Across 320,000 simulated trajectories spanning black‑hole spin, exhaust velocity, and orbital parameters, extraction with escape is rare in broad parameter scans (at most ～1%) and requires high spin (a/M\gtrsim 0.89), highly relativistic exhaust (v_e\gtrsim 0.91c), and finely tuned initial conditions. Under optimal tuning the success rate reaches ～70% at a/M = 0.95. For representative escape trajectories, a single periapsis impulse is more propellant‑efficient than the continuous‑thrust controllers studied here. All quoted thresholds are empirical and specific to the orbit family, prior, and steering protocol studied.

Authors:Riccardo Zanella, Federico Califano, Stefano Stramigioli
Title: Physical Human-Robot Interaction: A Critical Review of Safety Constraints
Abstract:
This paper aims to provide a clear and rigorous understanding of commonly recognized safety constraints in physical human‑robot interaction, particularly regarding ISO/TS 15066. We investigate the derivation of these constraints, critically examine the underlying assumptions, and evaluate their practical implications for system‑level safety and performance in industrially relevant scenarios. Key design parameters within safety‑critical control architectures are identified, and numerical examples are provided to quantify performance degradation arising from typical approximations and design decisions in manufacturing environments. Within this analysis, the fundamental role of energy in safety assessment is emphasized, providing focused insights into energy‑based safety methodologies for collaborative industrial robot systems.

Authors:Suiyi He, Zongxuan Sun
Title: Eco-Driving Control for Electric Vehicles with Multi-Speed Transmission: Optimizing Vehicle Speed and Powertrain Operation in Dynamic Environments
Abstract:
This article presents an eco‑driving algorithm for electric vehicles featuring multi‑speed transmissions. The proposed controller is formulated as a co‑optimization problem, simultaneously optimizing both vehicle longitudinal speed and powertrain operation to maximize energy efficiency. Constraints derived from a connected vehicle based traffic prediction algorithm are used to ensure traffic safety and smooth traffic flow in dynamic environments with multiple signalized intersections and mixed traffic. By simplifying the complex, nonlinear mixed integer problem, the proposed controller achieves computational efficiency, enabling real‑time implementation. To evaluate its performance, traffic scenarios from both Simulation of Urban MObility (SUMO) and real‑world road tests are employed. The results demonstrate a notable reduction in energy consumption by up to 11.36% over an \SI18\km drive.

Authors:Ankang Zhang, Ming Chi, Xiaoling Wang, Lintao Ye
Title: Model-Free Output Feedback Stabilization via Policy Gradient Methods
Abstract:
Stabilizing a dynamical system is a fundamental problem that serves as a cornerstone for many complex tasks in the field of control systems. The problem becomes challenging when the system model is unknown. Among the Reinforcement Learning (RL) algorithms that have been successfully applied to solve problems pertaining to unknown linear dynamical systems, the policy gradient (PG) method stands out due to its ease of implementation and can solve the problem in a model‑free manner. However, most of the existing works on PG methods for unknown linear dynamical systems assume full‑state feedback. In this paper, we take a step towards model‑free learning for partially observable linear dynamical systems with output feedback and focus on the fundamental stabilization problem of the system. We propose an algorithmic framework that stretches the boundary of PG methods to the problem without global convergence guarantees. We show that by leveraging zeroth‑order PG update based on system trajectories and its convergence to stationary points, the proposed algorithms return a stabilizing output feedback policy for discrete‑time linear dynamical systems. We also explicitly characterize the sample complexity of our algorithm and verify the effectiveness of the algorithm using numerical examples.

Authors:Ziyao Zhou, Zhuoran Sun, Xinyi Shen, Yang Li, Zhenhao Shi, Yixuan Yu, Hen-Wei Huang
Title: Reevaluating Bluetooth Low Energy for Ingestible Electronics
Abstract:
Bluetooth Low Energy (BLE) has been widely adopted in wearable devices; however, it has not been widely used in ingestible electronics, primarily due to concerns regarding severe tissue attenuation at the 2.4 GHz band. In this work, we systematically reevaluate the feasibility of BLE for ingestible applications by benchmarking its performance against representative sub‑GHz communication schemes across power consumption, throughput, tissue‑induced attenuation, latency, and system‑level integration constraints. We demonstrate that incorporating an RF amplifier enables BLE to maintain robust communication links through tissue‑mimicking media while preserving favorable energy efficiency. We further quantify the relationship between throughput and energy consumption over a wide operating range and demonstrate that, for the majority of ingestible sensing applications with throughput requirements below 100 kbps, BLE achieves substantially lower power consumption than sub‑GHz alternatives. End‑to‑end latency measurements show that BLE offers significantly lower latency than sub‑GHz solutions due to its native compatibility with modern computing infrastructure. Finally, we analyze antenna form factor and ecosystem integration, highlighting the mechanical and translational advantages of BLE in ingestible system design. Collectively, these results demonstrate that BLE, when appropriately configured, represents a compelling and scalable wireless communication solution for next‑generation ingestible electronics.

Authors:Ziyao Zhou, Zhuoran Sun, Chen Shen, Xinyi Shen, Zhehao Lu, Sikkandar, Hen-Wei Huang
Title: A Hybrid BLE-Wi-Fi Communication Architecture for Adaptive Imaging in Wireless Capsule Endoscopy
Abstract:
Wireless capsule endoscopy (WCE) is fundamentally constrained by limited wireless bandwidth, resulting in low imaging resolution and frame rate, which can cause motion blur and missed lesions. Although adaptive frame‑rate schemes have been explored to accommodate transient gastrointestinal (GI) motility, these approaches typically require sacrificing image resolution. The use of higher‑frequency communication bands is further limited by increased tissue attenuation. To address these challenges, we propose a hybrid Bluetooth Low Energy (BLE) and WiFi communication architecture that combines the low‑power operation of BLE with the high data throughput of WiFi. We systematically evaluate the performance of BLE and WiFi under tissue‑mimicking conditions by measuring throughput, received signal strength indicator (RSSI), and power consumption. The results demonstrate that amplified BLE with an adaptive transmission power control strategy provides a stable frame rate at low power consumption, while 2.4 GHz WiFi operating in station mode is the most suitable high‑throughput communication configuration for WCE. Compared with WiFi, BLE reduces power consumption by approximately ten times, whereas WiFi achieves up to ten times higher throughput. To reconcile these complementary trade‑offs, we further introduce a hybrid system with a frame‑boundary‑synchronized switching mechanism to ensure lossless data transmission during BLE and WiFi transitions. Experimental results show that the switching latency from BLE to WiFi is approximately 92.66 ms, which is longer than the WiFi‑to‑BLE switching latency of 15.49 ms when transmitting 10 kB image payloads. Overall, the proposed hybrid BLE and WiFi system enables robust, lossless, and energy‑efficient mode switching, supports adaptive imaging, and advances the development of next‑generation autonomous WCE platforms.

Authors:Torsten Hoefler, Mikhail Khalilov, Josiah Clark, Surendra Anubolu, Mohan Kalkunte, Karen Schramm, Eric Spada, Duncan Roweth, Keith Underwood, Adrian Caulfield, Abdul Kabbani, Amirreza Rastegari
Title: In-Network Collective Operations: Game Changer or Challenge for AI Workloads?
Abstract:
This paper summarizes the opportunities of in‑network collective operations (INC) for accelerated collective operations in AI workloads. We provide sufficient detail to make this important field accessible to non‑experts in AI or networking, fostering a connection between these communities. Consider two types of INC: Edge‑INC, where the system is implemented at the node level, and Core‑INC, where the system is embedded within network switches. We outline the potential performance benefits as well as six key obstacles in the context of both Edge‑INC and Core‑INC that may hinder their adoption. Finally, we present a set of predictions for the future development and application of INC.

Authors:Hampei Sasahara
Title: Structural Monotonicity in Transmission Scheduling for Remote State Estimation with Hidden Channel Mode
Abstract:
This study treats transmission scheduling for remote state estimation over unreliable channels with a hidden mode. A local Kalman estimator selects scheduling actions, such as power allocation and resource usage, and communicates with a remote estimator based on acknowledgement feedback, balancing estimation performance and communication cost. The resulting problem is naturally formulated as a partially observable Markov decision process (POMDP). In settings with observable channel modes, it is well known that monotonicity of the value function can be established via investigating order‑preserving property of transition kernels. In contrast, under partial observability, the transition kernels generally lack this property, which prevents the direct application of standard monotonicity arguments. To overcome this difficulty, we introduce a novel technique, referred to as state‑space folding, which induces transformed transition kernels recovering order preservation on the folded space. This transformation enables a rigorous monotonicity analysis in the partially observable setting. As a representative implication, we focus on an associated optimal stopping formulation and show that the resulting optimal scheduling policy admits a threshold structure.

Authors:Xuguang Zhang, Hexiang Zhang, Hanqing Liu, Xiaoli Li, Mu Ying, Yutian Yang, Marilyn L. Minus, Ming Su, Yi Zheng
Title: Passive Daytime Radiative Cooling Enabled by Bio-Derived Ceramic-Polymer Coatings on Rapid-Curing Fiberglass Casts
Abstract:
Passive daytime radiative cooling (PDRC) provides an energy‑free approach to suppress surface temperatures by reflecting solar irradiation while emitting thermal radiation through the mid‑infrared atmospheric window. Despite rapid progress in optical performance, most PDRC systems remain limited by rigid, fragile, or planar substrates, restricting their use on flexible, curved, or wearable surfaces. Here, we report a biocompatible and structurally robust PDRC system integrated onto a commercial rapid‑curing fiberglass cast, a conformal substrate widely used in orthopedic and industrial applications. The cooling architecture adopts a bilayer polymer design consisting of a polyvinyl alcohol (PVA) adhesion layer and a polymethyl methacrylate (PMMA) protective layer, both embedded with calcium pyrophosphate (CPP) ceramic particles derived from processed animal bone waste. The bio‑derived CPP simultaneously enables broadband solar scattering and high mid‑infrared emittance, while offering sustainability and biocompatibility advantages. The resulting composite exhibits over 90% solar reflectance and achieves up to 15 C sub‑ambient cooling under direct outdoor sunlight.

Authors:Menghan Zhang, Caisheng Wang
Title: Dual Analysis of Continuous-time Economic Dispatch and Its Price Implications
Abstract:
As load varies continuously over time, it is essential to provide continuous‑time price signals that accurately reflect supply‑demand balance. However, conventional discrete‑time economic dispatch fails to capture the intra‑temporal variations in load and generation. Its dual solution‑‑the marginal price‑‑may distort economic signals, leading to inefficient market incentives. To analyze these issues, this paper develops a continuous‑time dispatch model and derives its dual formulation for price analysis. The continuous‑time dispatch produces dual variables that can be interpreted as price signals. Piecewise time‑indexed generation and price trajectories are then constructed through a parametric programming approach. The resulting price, represented by the Lagrange multiplier of the system‑wide power balance constraint, evolves piecewise along the continuous‑time load profile. Each segment corresponds to a critical region characterized by a set of active constraints. Furthermore, we discuss the impact of unit‑specific ramping constraints on price implications. Results indicate that continuous‑time generation and price trajectories provide deeper insights into the price distortions and inefficient incentives inherent in discrete‑time formulations. The proposed methodology is validated on an illustrative 5‑bus system and a modified IEEE RTS‑2019.

Authors:Mohammed E Eltayeb
Title: Passive Beam Shaping via Binary-Coded Apertures
Abstract:
This paper presents a coded‑aperture reflector for indoor mmWave coverage enhancement in obstructed or blocked LoS settings. We model the reflecting aperture using an equivalent array‑factor formulation, where each passive reflecting cell contributes a reradiated field with phase set by the incident and departure directions. Building on this model, we develop two fabrication‑friendly passive synthesis methods: (i) binary (1‑bit) spatial coding that enables deterministic non‑specular beam formation and multibeam patterns by selecting cell participation on a dense λ/2 lattice via an ON/OFF metallization mask, and (ii) diffraction‑order (periodic) steering that exploits aperture periodicity to place selected diffraction orders at prescribed angles. We analytically characterize the proposed cosine‑threshold quantization rule, including its asymptotic activation ratio and a distribution‑free lower bound on non‑specular gain relative to ideal continuous‑phase control. To validate the proposed designs, we fabricate and metallize low‑cost prototypes in‑house using a copper‑backed 3D‑printed "inkwell" substrate with stencil‑guided conductive ink deposition. 60 GHz over‑the‑air measurements show non‑specular power enhancements on the order of +14‑20 dB relative to passive, non‑engineered (all‑ON) reflector baselines. Results also demonstrate that fully passive, binary‑coded apertures can deliver beam control with rapid in‑lab manufacturability and offer a practical alternative to power‑consuming reconfigurable surfaces for static indoor mmWave links.

Authors:Yiliang Li, Jun-e Feng, Abdelhamid Tayebi
Title: A Leader-Follower Approach for The Attitude Synchronization of Multiple Rigid Body Systems on $SO(3)$
Abstract:
This paper deals with the leader‑follower attitude synchronization problem for a group of heterogeneous rigid body systems on SO(3) under an undirected, connected, and acyclic graph communication topology. The proposed distributed control strategy, endowed with almost global asymptotic stability guarantees, allows the synchronization of the rigid body systems to a constant desired orientation known only to a single rigid body. Some simulation results are also provided to validate the theoretical developments and illustrate the performance of the proposed control strategy.

Authors:Sachin Ravikant Trankatwar, Heiko Straulino, Petar Djukic, Burak Kantarci
Title: FTA-NTN: Fairness and Throughput Assurance in Non-Terrestrial Networks
Abstract:
Designing optimal non‑terrestrial network (NTN) constellations is essential for maximizing throughput and ensuring fair resource distribution. This paper presents FTA‑NTN (Fairness and Throughput Assurance in Non‑Terrestrial Networks), a multi‑objective optimization framework that jointly maximizes throughput and fairness under realistic system constraints. The framework integrates multi‑layer Walker Delta constellations, a parametric mobility model for user distributions across Canadian land regions, adaptive K‑Means clustering for beamforming and user association, and Bayesian optimization for parameter tuning. Simulation results with 500 users show that FTA‑NTN achieves over 9.88 Gbps of aggregate throughput with an average fairness of 0.42, corresponding to an optimal configuration of 9 planes with 15 satellites per plane in LEO and 7 planes with 3 satellites per plane in MEO. These values align with 3GPP NTN evaluation scenarios and representative system assumptions, confirming their relevance for realistic deployments. Overall, FTA‑NTN demonstrates that throughput and fairness can be jointly optimized under practical constraints, advancing beyond throughput‑centric designs in the literature and offering a scalable methodology for next‑generation NTN deployments that supports efficient and equitable global connectivity.

Authors:Yuji Sakamoto
Title: Machine Learning-Based Evaluation of Attitude Sensor Characteristics Using Microsatellite Flight Data
Abstract:
Using actual flight data from a 50‑cm‑class microsatellite whose mission and operations have already been completed, this study re‑evaluates satellite attitude determination performance and the error characteristics of onboard attitude sensors. While conventional approaches rely on batch estimation or Kalman filtering based on predefined physical models and white‑noise assumptions, this research introduces a machine‑learning‑based approach to extract and correct structural and nonlinear error patterns embedded in real observational data. In this study, high‑quality attitude determination results obtained from star sensors and a fiber optical gyro (FOG) are treated as ground truth, and machine learning is applied to coarse attitude sensor data consisting of Sun sensors and magnetic field sensors. A one‑dimensional convolutional neural network (Conv1D) is employed to regressively predict attitude from short sequences of time‑series sensor measurements. The model is trained and evaluated using five sets of on‑orbit observation logs, with four passes used for training and one independent pass used for testing. The results show that, while conventional coarse attitude determination using the TRIAD method yields attitude errors on the order of 7 deg RMS, the proposed machine‑learning approach achieves RMS errors of approximately 0.7 deg for the training data and 2‑3 deg for the test data, depending on the sensor combination.

Authors:Noor Alhuda Ameen, Omid Arfaie, Ramazan Unal
Title: i-Socket: Design, Development, and Pilot Evaluation of an Individualized Multi-Material, Multi-Thickness Transtibial Prosthetic Socket
Abstract:
The prosthetic socket is an essential part in ensuring comfort and stability for the overall prosthesis system. This study proposes a multi‑material/thickness individualized transtibial prosthetic socket that focuses on providing comfort. This study aims to identify the proper material and thickness to protect Calf areas and reduce the pressure around bone areas. First, the socket is divided into four parts depending on the pressure‑sensitive/tolerant regions. After identifying the thickness range for each concerned area, the thickness and material are selected based on the Pressure‑Pain Threshold (PPT) test, and the finalized design is then prototyped. The prototyped individualized socket (i‑Socket) is 22% lighter than the participant's own socket. Results of the pilot experiments with an amputee participant showed that the pressure inside the socket decreased by 45% and 31% for the Tibia and Fibula regions, respectively. Additionally, the self‑selected CoM velocity for the walking experiment is increased by 15% compared to the similar studies in the literature. Regarding the kinematic results, symmetry in the knee and ankle joints increased by 65% and 2% when using the i‑Socket compared to the results with the participant's own socket.

Authors:Dahlia Saba, Dominic Groß
Title: Improving Stability Margins with Grid-Forming Damper Winding Emulation
Abstract:
This work presents (i) a framework for certifying small‑signal frequency stability of a power system with line dynamics and heterogeneous bus dynamics, (ii) a novel reduced‑order model of damper windings in synchronous machines, and (iii) a proportional‑derivative (PD) damper winding emulation control for voltage‑source converters (VSCs). Damper windings have long been understood to improve the frequency synchronization between machines. However, the dynamics of the damper windings are complex, making them difficult to analyze and directly emulate in the control of VSCs. This paper derives a reduced‑order model of the damper windings as a PD term that allows grid‑forming controls for VSCs to emulate their effect on frequency dynamics. Next, a framework for certifying small‑signal frequency stability of a network with heterogeneous bus dynamics is developed that extends prior results by incorporating line dynamics. Finally, we analytically demonstrate that PD damper winding emulation can improve the stability of grid‑forming converter controls. These results are validated with electromagnetic‑transient (EMT) simulation.

Authors:Nima Leclerc, Chris Miller, Nicholas Brawand
Title: When Does Adaptation Win? Scaling Laws for Meta-Learning in Quantum Control
Abstract:
Quantum hardware suffers from intrinsic device heterogeneity and environmental drift, forcing practitioners to choose between suboptimal non‑adaptive controllers or costly per‑device recalibration. We derive a scaling law lower bound for meta‑learning showing that the adaptation gain (expected fidelity improvement from task‑specific gradient steps) saturates exponentially with gradient steps and scales linearly with task variance, providing a quantitative criterion for when adaptation justifies its overhead. Validation on quantum gate calibration shows negligible benefits for low‑variance tasks but >40% fidelity gains on two‑qubit gates under extreme out‑of‑distribution conditions (10× the training noise), with implications for reducing per‑device calibration time on cloud quantum processors. Further validation on classical linear‑quadratic control confirms these laws emerge from general optimization geometry rather than quantum‑specific physics. We further introduce a few‑shot pre‑adaptation protocol that estimates the optimal adaptation budget from N=3‑5 probe steps within 3‑19% relative error across out‑of‑distribution regimes.

Authors:Ioannis G. Polyzos, Konstantinos J. Kyriakopoulos
Title: A Switching Nonlinear Model Predictive Control Strategy for Safe Collision Handling by an Underwater Vehicle-Manipulator System
Abstract:
For active intervention tasks in underwater environments, the use of autonomous vehicles is just now emerging as an active area of research. During operation, for various reasons, the robot might find itself on a collision course with an obstacle in its environment. In this paper, a switching Nonlinear Model Predictive Control (NMPC) strategy is proposed to safely handle collisions for an Underwater Vehicle‑Manipulator System (UVMS). When avoiding the collision is impossible, the control algorithm takes advantage of the manipulator, using it to push against the obstacle, and deflect away from the collision. Virtual experiments are performed to demonstrate the algorithm's capability to successfully detect collisions and either avoid them, or use the manipulator to handle them appropriately without damaging sensitive areas of the vehicle.

Authors:Xuguang Zhanga, Michael C. Halbig, Amjad Almansour, Mrityunjay Singh, Meelad Ranaiefar, Yi Zheng
Title: 3D-Printed Hybrid Liquid-CPCM Cooling Modules for High-Performance Thermal Management of Lithium-Ion Pouch Cells
Abstract:
Efficient thermal management is critical for ensuring the safety, performance, and durability of lithium ion pouch cells (LIPCs), particularly under high power operating conditions where conventional battery thermal management systems (BTMS) struggle to balance cooling effectiveness, structural simplicity, and weight. Here, we report a lightweight hybrid BTMS that synergistically integrates active liquid cooling with composite phase change material (CPCM) based thermal buffering through a 3D printed hexagonal architecture. The system is fabricated via a two step additive manufacturing process that enables sealed CPCM encapsulation and isolated liquid cooling pathways within a single carbon fiber reinforced nylon module, effectively eliminating leakage risks while allowing precise geometric control. Hexagonally partitioned CPCM cavities maximize the CPCM wall interfacial area and shorten internal conduction paths, accelerating latent heat absorption, while embedded serpentine liquid channels provide continuous convective heat removal and prevent CPCM saturation. A nanocarbon enhanced CPCM is employed to overcome the intrinsic low thermal conductivity of conventional paraffin based materials.

Authors:Donghwan Lee, Hyukjun Yang
Title: Bellman Residual Minimization for Control: Geometry, Stationarity, and Convergence
Abstract:
Markov decision problems are most commonly solved via dynamic programming. Another approach is Bellman residual minimization, which directly minimizes the squared Bellman residual objective function. However, compared to dynamic programming, this approach has received relatively less attention, mainly because it is often less efficient in practice and can be more difficult to extend to model‑free settings such as reinforcement learning. Nonetheless, Bellman residual minimization has several advantages that make it worth investigating, such as more stable convergence with function approximation for value functions. While Bellman residual methods for policy evaluation have been widely studied, methods for policy optimization (control tasks) have been scarcely explored. In this paper, we establish foundational results for the control Bellman residual minimization for policy optimization.

Authors:Seyed Bagher Hashemi Natanzi, Hossein Mohammadi, Bo Tang, Vuk Marojevic
Title: AI-Driven Fuzzing for Vulnerability Assessment of 5G Traffic Steering Algorithms
Abstract:
Traffic Steering (TS) dynamically allocates user traffic across cells to enhance Quality of Experience (QoE), load balance, and spectrum efficiency in 5G networks. However, TS algorithms remain vulnerable to adversarial conditions such as interference spikes, handover storms, and localized outages. To address this, an AI‑driven fuzz testing framework based on the Non‑Dominated Sorting Genetic Algorithm II (NSGA‑II) is proposed to systematically expose hidden vulnerabilities. Using NVIDIA Sionna, five TS algorithms are evaluated across six scenarios. Results show that AI‑driven fuzzing detects 34.2% more total vulnerabilities and 5.8% more critical failures than traditional testing, achieving superior diversity and edge‑case discovery. The observed variance in critical failure detection underscores the stochastic nature of rare vulnerabilities. These findings demonstrate that AI‑driven fuzzing offers an effective and scalable validation approach for improving TS algorithm robustness and ensuring resilient 6G‑ready networks.

Authors:Yilie Huang, Wenpin Tang, Xunyu Zhou
Title: ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule
Abstract:
We consider time discretization for score‑based diffusion models to generate samples from a learned reverse‑time dynamic on a finite grid. Uniform and hand‑crafted grids can be suboptimal given a budget on the number of time steps. We introduce Adaptive Reparameterized Time (ART), which controls the clock speed of a reparameterized time variable to redistribute computation along the sampling trajectory while preserving the terminal time, with the objective of minimizing the aggregate Euler discretization error. We derive a randomized companion ART‑RL that recasts ART as a continuous‑time reinforcement learning problem with Gaussian policies, and prove a two‑directional bridge between the two: the deterministic ART optimum lifts to an optimal Gaussian policy, and conversely any optimal Gaussian policy must recover the ART control through its mean. This bridge turns continuous‑time actor‑‑critic learning into a principled, rather than heuristic, route to the deterministic timestep optimum. Within the official EDM pipeline, ART‑RL improves FID on CIFAR‑‑10 across a wide range of budgets; after one‑time offline training, the distilled deterministic schedule transfers without retraining to AFHQv2, FFHQ, and ImageNet at no extra inference cost.

Authors:Dominik Neudert-Schulz, Thomas Dallmann
Title: Synchronization and Localization in Ad-Hoc ICAS Networks Using a Two-Stage Kuramoto Method
Abstract:
To enable Integrated Communications and Sensing (ICAS) in a peer‑to‑peer vehicular network, precise synchronization in frequency and phase among the communicating entities is required. In addition, self‑driving cars need accurate position estimates of the surrounding vehicles. In this work, we propose a joint, distributed synchronization and localization scheme for a network of communicating entities. Our proposed scheme is mostly signal‑agnostic and therefore can be applied to a wide range of possible ICAS signals. We also mitigate the effect of finite sampling frequencies, which otherwise would degrade the synchronization and localization performance severely.

Authors:Zhuangzhuang Cui, Rizqi Hersyandika, Haoqiu Xiong, Sofie Pollin
Title: Experimental Characterization of ISAC Channel Mapping and Environment Awareness
Abstract:
In the context of integrated sensing and communications (ISAC), this paper presents an experimental investigation of the relationship between monostatic sensing and naturally bistatic communication channels in an indoor millimeter‑wave environment. We characterize the propagation channel in the joint delay‑‑angle domain, extract dominant multipath components (MPCs) and associate them with physical scatterers in the environment, and demonstrate how communication MPCs can be explicitly recovered from sensing channels. Finally, the radar cross‑sections (RCSs) of two key scatterers, namely the wall and metal plate, are obtained based on calibrated channel power and reconstructed propagation distances.

Authors:Josée Mallah, Yu Zhu, Kailang Xu, Gurvinder S. Virk, Shaoping Bai, Luigi G. Occhipinti
Title: Real-Time Prediction of Lower Limb Joint Kinematics, Kinetics, and Ground Reaction Force using Wearable Sensors and Machine Learning
Abstract:
Walking is a key movement of interest in biomechanics, yet gold‑standard data collection methods are time‑ and cost‑expensive. This paper presents a real‑time, multimodal, high sample rate lower‑limb motion capture framework, based on wireless wearable sensors and machine learning algorithms. Random Forests are used to estimate joint angles from IMU data, and ground reaction force (GRF) is predicted from instrumented insoles, while joint moments are predicted from angles and GRF using deep learning based on the ResNet‑16 architecture. All three models achieve good accuracy compared to literature, and the predictions are logged at 1 kHz with a minimal delay of 23 ms for 20s worth of input data. The present work fully relies on wearable sensors, covers all five major lower limb joints, and provides multimodal comprehensive estimations of GRF, joint angles, and moments with minimal delay suitable for biofeedback applications.

Authors:Teruki Kato, Ryotaro Shima, Kenji Kashima
Title: Convex Chance-Constrained Stochastic Control under Uncertain Specifications with Application to Learning-Based Hybrid Powertrain Control
Abstract:
This paper presents a strictly convex chance‑constrained stochastic control framework that accounts for uncertainty in control specifications such as reference trajectories and operational constraints. By jointly optimizing control inputs and risk allocation under general (possibly non‑Gaussian) uncertainties, the proposed method guarantees probabilistic constraint satisfaction while ensuring strict convexity, leading to uniqueness and continuity of the optimal solution. The formulation is further extended to nonlinear model‑based control using exactly linearizable models identified through machine learning. The effectiveness of the proposed approach is demonstrated through model predictive control applied to a hybrid powertrain system.

Authors:Geunsik Lim
Title: A Generative AI-Driven Reliability Layer for Action-Oriented Disaster Resilience
Abstract:
As climate‑related hazards intensify, conventional early warning systems (EWS) disseminate alerts rapidly but often fail to trigger timely protective actions, leading to preventable losses and inequities. We introduce Climate RADAR (Risk‑Aware, Dynamic, and Action Recommendation system), a generative AI‑based reliability layer that reframes disaster communication from alerts delivered to actions executed. It integrates meteorological, hydrological, vulnerability, and social data into a composite risk index and employs guardrail‑embedded large language models (LLMs) to deliver personalized recommendations across citizen, volunteer, and municipal interfaces. Evaluation through simulations, user studies, and a municipal pilot shows improved outcomes, including higher protective action execution, reduced response latency, and increased usability and trust. By combining predictive analytics, behavioral science, and responsible AI, Climate RADAR advances people‑centered, transparent, and equitable early warning systems, offering practical pathways toward compliance‑ready disaster resilience infrastructures.

Authors:Samuel Mallick, Gianpietro Battocletti, Dimitris Boskos, Azita Dabiri, Bart De Schutter
Title: Reinforcement Learning with Distributed MPC for Fuel-Efficient Platoon Control with Discrete Gear Transitions
Abstract:
Cooperative control of groups of autonomous vehicles (AVs), i.e., platoons, is a promising direction to improving the efficiency of autonomous transportation systems. In this context, distributed co‑optimization of both vehicle speed and gear position can offer benefits for fuel‑efficient driving. To this end, model predictive control (MPC) is a popular approach, optimizing the speed and gear‑shift schedule while explicitly considering the vehicles' dynamics over a prediction window. However, optimization over both the vehicles' continuous dynamics and discrete gear positions is computationally intensive, and may require overly long sample times or high‑end hardware for real‑time implementation. This work proposes a reinforcement learning (RL)‑based distributed MPC approach to address this issue. For each vehicle in the platoon, a policy is trained to select and fix the gear positions across the prediction window of a local MPC controller, leaving a significantly simpler continuous optimization problem to be solved as part of a distributed MPC scheme. In order to reduce the computational cost of training and facilitate the scalability of the proposed approach to large platoons, the policies are parameterized such that the emergent multi‑agent RL problem can be decoupled into single‑agent learning tasks. In addition, a recurrent neural‑network (RNN) architecture is proposed for the gear selection policy, such that the learning is scalable even as the number of possible gear‑shift schedules grows exponentially with the MPC prediction horizon. In highway‑driving simulations, the proposed approach is shown to have a significantly lower computation burden and a comparable performance in terms of fuel‑efficient platoon control, with respect to pure MPC‑based co‑optimization.

Authors:Federico Villani, Christian Vogt, Luca Specht, Jero Schmid, Xiang Liu, Andrea Cossettini, Daniel Razansky, Luca Benini
Title: Validation of a Software-Defined 100-Gb/s RDMA Streaming Architecture for Ultrafast Optoacoustic and Ultrasound Imaging
Abstract:
Optoacoustic (OA) imaging has emerged as a powerful investigation tool, with demonstrated applicability in oncology, neuroscience, and cardiovascular biology. However, its clinical translation is limited with the existing OA systems, which often rely on bulky and expensive acquisition hardware mainly optimized for pulse‑echo ultrasound (US) imaging. Despite the fact that OA imaging has different requirements for receive bandwidths and timing synchronization with external laser sources, there is a strong need for unified OA‑US imaging platforms, as pulse‑echo US remains the standard tool for visualizing soft tissues. To address these challenges, we propose a new data acquisition architecture for ultrafast OA and US imaging that fully covers the requirements for large channel counts, wide bandwidth, and software‑defined operation. LtL combines state‑of‑the‑art wideband analog front‑ends, a Zynq UltraScale+ MPSoC integrating FPGA fabric with an Application Processing Unit, and a 100 GbE Remote Direct Memory Access (RDMA) backend enabling raw‑data streaming at up to 95.6 Gb/s. The architecture avoids local buffers followed by burst transfers, which commonly constrain sustainable frame rate and recording intervals, thus achieving true continuous and sustained streaming of raw data. We validate the core elements of the LtL architecture using a 16‑channel demonstration system built from commercial evaluation boards. We further verify the signal chain for up to 256‑channel scalability, confirming the wide bandwidth capabilities to support state‑of‑the‑art data transmission speeds.

Authors:Daniel Russo
Title: Success Conditioning as Policy Improvement: The Optimization Problem Solved by Imitating Success
Abstract:
A widely used technique for improving policies is success conditioning, in which one collects trajectories, identifies those that achieve a desired outcome, and updates the policy to imitate the actions taken along successful trajectories. This principle appears under many names ‑‑ rejection sampling with SFT, goal‑conditioned RL, Decision Transformers ‑‑ yet what optimization problem it solves, if any, has remained unclear. We prove that success conditioning exactly solves a trust‑region optimization problem, maximizing policy improvement subject to a χ^2 divergence constraint whose radius is determined automatically by the data. This yields an identity: relative policy improvement, the magnitude of policy change, and a quantity we call action‑influence ‑‑ measuring how random variation in action choices affects success rates ‑‑ are exactly equal at every state. Success conditioning thus emerges as a conservative improvement operator. Exact success conditioning cannot degrade performance or induce dangerous distribution shift, but when it fails, it does so observably, by hardly changing the policy at all. We apply our theory to the common practice of return thresholding, showing this can amplify improvement, but at the cost of potential misalignment with the true objective.

Authors:Bokan Chen, Raiden Hasegawa, Adriaan Hilbers, Ross Koningstein, Ana Radovanović, Utkarsh Shah, Gabriela Volpato, Mohamed Ahmed, Tim Cary, Rod Frowd
Title: A Cherry-Picking Approach to Large Load Shaping for More Effective Carbon Reduction
Abstract:
Shaping multi‑megawatt loads, such as data centers, impacts generator dispatch on the electric grid, which in turn affects system CO2 emissions and energy cost. Substantiating the effectiveness of prevalent load shaping strategies, such as those based on grid‑level average carbon intensity, locational marginal price, or marginal emissions, is challenging due to the lack of detailed counterfactual data required for accurate attribution. This study uses a series of calibrated granular ERCOT day‑ahead direct current optimal power flow (DC‑OPF) simulations for counterfactual analysis of a broad set of load shaping strategies on grid CO2 emissions and cost of electricity. In terms of annual grid level CO2 emissions reductions, LMP‑based shaping outperforms other common strategies, but can be significantly improved upon. Examining the performance of practicable strategies under different grid conditions motivates a more effective load shaping approach: one that "cherry‑picks" a daily strategy based on observable grid signals and historical data. The cherry‑picking approach to power load shaping is applicable to any large flexible consumer on the electricity grid, such as data centers, distributed energy resources and Virtual Power Plants (VPPs).

Authors:Lucu M., Martinez-Laserna E., Gandiaga I., Liu K., Camblong H., Widanage W. D., Marco J
Title: Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data -- Part B: Cycling operation
Abstract:
Conventional Li‑ion battery ageing models, such as electrochemical, semi‑empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real‑world battery operation data. In this context, the development of ageing models able to learn from in‑field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data‑driven ageing model is developed for Li‑ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 26 tested cells achieves an overall mean‑absolute‑error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth‑of‑Discharge, middle‑SOC, charging and discharging C‑rates.

Authors:Darsana U, Atreyee Kundu
Title: On maximum hands-off restricted hybrid control for discrete-time switched linear systems
Abstract:
This paper deals with design of maximum hands‑off hybrid control sequences for discrete‑time switched linear systems. It is a sparsest combination of a discrete control sequence (i.e. the switching sequence) and a continuous control sequence, both satisfying pre‑specified restrictions on the admissible actions, that steers a given initial state of the switched system to the origin of the state‑space in a pre‑specified duration of time. Given the subsystems dynamics, the sets of admissible continuous and discrete control, the initial state and the time horizon, we present a new algorithm that, under certain conditions on the subsystems dynamics and the admissible control, designs maximum hands‑off hybrid control sequences for the resulting switched system. The key apparatuses for our analysis are graph theory and linear algebra. Numerical examples are presented to demonstrate our results.

Authors:Lucu M., Martinez-Laserna E., Gandiaga I., Liu K., Camblong H., Widanage W. D., Marco J
Title: Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data -- Part A: Storage operation
Abstract:
Conventional Li‑ion battery ageing models, such as electrochemical, semi‑empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real‑world battery operation data. In this context, the development of ageing models able to learn from in‑field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data‑driven ageing model is developed for Li‑ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. This first paper focusses on the systematic modelling and experimental verification of cell degradation through calendar ageing. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 32 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 18 tested cells achieves an overall mean‑absolute‑error of 0.53% in the capacity curves prediction, after being validated under a broad window of both dynamic and static temperature and SOC storage conditions.

Authors:Camblong H., Curea O., Ugartemendia J. J., Boussaada Z., Lizarralde I., Etxegarai G
Title: Photovoltaic energy sharing: Implementation and tests on a real collective self-consumption system
Abstract:
This research study analyses different types of photovoltaic (PV) energy sharing in a collective self‑consumption (CSC) real‑case in the Izarbel technological park in France. The analysis is carried out above all from the point of view of the self‑consumption rate (SCR) and the savings. After explaining the emergence of the self‑consumption concept for the integration of renewable energies, the study case is described. The PV energy is produced in ESTIA1 building and consumed in ESTIA1, 2 and 4 buildings. The main IoT components used to implement the CSC are smart meters and the Tecsol TICs; devices based on the LoRa protocol to retrieve production and consumption data. Then, the characteristics of PV energy sharing in France are explained, in particular the three possible types of energy sharing/allocation (static, dynamic by default and customised dynamic) and the structure of the electricity bill. Finally, the three types of sharing are compared in four scenarios (without and with a data centre, for low and high solar radiation). The results show that the dynamic allocations lead to increases of the SCR and that the customised dynamic sharing increases savings.

Authors:Yaniv Brick, Francesco P. Andriulli, Mats Gustafsson
Title: Interpreting Moment Matrix Blocks Spectra using Mutual Shadow Area
Abstract:
The mutual shadow area of pairs of surface regions is used for guiding the study of the spectral components and rank of their wave interaction, as captured by the corresponding moment matrix blocks. It is demonstrated that the mutual shadow area provides an asymptotically accurate predictor of the location of the singular value curve knee. This predicted knee index is shown to partition the interacting parts of the range and domain of blocks into two subspaces that can be associated with different wave phenomena: an "aperture" subspace of dimension that scales with the subdomains area (or length in 2‑D) and a remainder "diffraction" subspace of dimension that scales much slower with the electrical length, depending on the geometric configuration. For interactions between open surface domains typical for the common hierarchical partitioning in most fast solvers, the latter can be attributed to the domain edges visible by its interacting counterpart. For interactions in 3‑D with a small aspect angles between the source and observers, the diffraction subspace dimension is dominant in determining the rank until fairly large electrical lengths are reached. This explains the delayed asymptotic scaling of ranks and impressive fast solver performance observed in recent literature for seemingly arbitrary scatterers with no special geometric characteristics. In the extreme cases of "endfire" reduced dimensionality interactions, where the shadow area vanishes, the diffraction governs also the asymptotic rank, which translates to superior asymptotic solver performance.

Authors:Chao Zhang, Kunlun Li, Chao Xu, Lie-Liang Yang, Lajos Hanzo
Title: Space-Air-Ground-Integrated Networks: The BER vs. Residual Delay and Doppler Analysis
Abstract:
Perfect Doppler compensation and synchronization is nontrivial due to multi‑path Doppler effects and Einstein's theory of relativity in the space‑air‑ground‑integrated networks (SAGINs). Hence, by considering the residual Doppler and the synchronization delay, this paper investigates the bit‑error‑rate (BER) performance attained under time‑varying correlated Shadowed‑Rician SAGIN channels. First, a practical SAGIN model is harnessed, encompassing correlated Shadowed‑Rician channels, the Snell's law‑based path loss, atmospheric absorption, the line‑of‑sight Doppler compensation, elliptical satellite orbits, and Einstein's theory of relativity. Then, a specific correlation coefficient between the pilot and data symbols is derived in the context of correlated Shadowed‑Rician channels. By exploiting this correlation coefficient, the channel distribution is mimicked by a bi‑variate Gamma distribution. Then, a closed‑form BER formula is derived under employing least‑square channel estimation and equalization for 16‑QAM. Our analytical results indicate for a 300‑km‑altitude LEO that 1) the period of realistic elliptical orbits is around 0.8 seconds longer than that of the idealized circular orbits; and 2) the relativistic delay is lower than 1 microsecond over a full LEO pass (from rise to set). Our numerical results for the L bands quantify the effects of: 1) the residual Doppler; 2) atmospheric shadowing; 3) synchronization errors; and 4) pilot overhead.

Authors:Mingtian Du, Suhas Raghavendra Kulkarni, Bernardo Noronha, Domenico Campolo
Title: Delay-Compensated Stiffness Estimation for Robot-Mediated Dyadic Interaction
Abstract:
Robot‑mediated human‑human (dyadic) interactions enable therapists to provide physical therapy remotely, yet an accurate perception of patient stiffness remains challenging due to network‑induced haptic delays. Conventional stiffness estimation methods, which neglect delay, suffer from temporal misalignment between force and position signals, leading to significant estimation errors as delays increase. To address this, we propose a robust, delay‑compensated stiffness estimation framework by deriving an algebraic estimator based on quasi‑static equilibrium that explicitly accounts for temporally aligning the expert's input with the novice's response. A Normalised Weighted Least Squares (NWLS) implementation is then introduced to robustly filter dynamic bias resulting from the algebraic derivation. Experiments using commercial rehabilitation robots (H‑MAN) as the platform demonstrate that the proposed method significantly outperforms the standard estimator, maintaining consistent tracking accuracy under multiple introduced delays. These findings offer a promising solution for achieving high‑fidelity haptic perception in remote dyadic interaction, potentially facilitating reliable stiffness assessment in therapeutic settings across networks.

Authors:Yunhao Bian, Dawei Wang, Mingyang Shen, Xinze Li, Jiayi Shi, Ziyao Zhou, Tiancheng Cao, Hen-Wei Huang
Title: A Capsule-Sized Multi-Wavelength Wireless Optical System for Edge-AI-Based Classification of Gastrointestinal Bleeding Flow Rate
Abstract:
Post‑endoscopic gastrointestinal (GI) rebleeding frequently occurs within the first 72 hours after therapeutic hemostasis and remains a major cause of early morbidity and mortality. Existing non‑invasive monitoring approaches primarily provide binary blood detection and lack quantitative assessment of bleeding severity or flow dynamic, limiting their ability to support timely clinical decision‑making during this high‑risk period. In this work, we developed a capsule‑sized, multi‑wavelength optical sensing wireless platform for order‑of‑magnitude‑level classification of GI bleeding flow rate, leveraging transmission spectroscopy and low‑power edge artificial intelligence. The system performs time‑resolved, multi‑spectral measurements and employs a lightweight two‑dimensional convolutional neural network for on‑device flow‑rate classification, with physics‑based validation confirming consistency with wavelength‑dependent hemoglobin absorption behavior. In controlled in vitro experiments under simulated gastric conditions, the proposed approach achieved an overall classification accuracy of 98.75% across multiple bleeding flow‑rate levels while robustly distinguishing diverse non‑blood gastrointestinal interference. By performing embedded inference directly on the capsule electronics, the system reduced overall energy consumption by approximately 88% compared with continuous wireless transmission of raw data, making prolonged, battery‑powered operation feasible. Extending capsule‑based diagnostics beyond binary blood detection toward continuous, site‑specific assessment of bleeding severity, this platform has the potential to support earlier identification of clinically significant rebleeding and inform timely re‑intervention during post‑endoscopic surveillance.

Authors:Mohammadreza Kamaldar
Title: Composite Adaptive Control Barrier Functions for Safety-Critical Systems with Parametric Uncertainty
Abstract:
Control barrier functions guarantee safety but typically require accurate system models. Parametric uncertainty invalidates these guarantees. Existing robust methods maintain safety via worst‑case bounds, limiting performance, while modular learning schemes decouple estimation from safety, permitting state violations during training. This paper presents the composite adaptive control barrier function (CaCBF) algorithm for nonlinear control‑affine systems subject to linear parametric uncertainty. We derive adaptation laws from a composite energy function comprising a logarithmic safety barrier, a control Lyapunov function, and a parameter error term. We prove that CaCBF guarantees the forward invariance of the safe set and the uniform boundedness of the closed‑loop system. This safety guarantee holds without requiring parameter convergence. Simulations of adaptive cruise control, an omnidirectional robot, and a planar drone demonstrate the efficacy of the CaCBF algorithm.

Authors:Roshan Nepal, Brandon Brown, Shishangbo Yu, Roozbeh Abbasi, Norman Zhou, George Shaker
Title: Battery-less Long-Range LTE-M Water Leak Detector
Abstract:
This work presents a self powered water leak sensor that eliminates both batteries and local gateways. The design integrates a dual compartment electrochemical harvester, a low input boost converter with supercapacitor storage, and a comparator gated LTE‑M radio built on the Nordic Thingy:91 platform. Laboratory tests confirm that the system can be awakened from a dormant state in the presence of water, harvest sufficient energy, and issue repeated cloud beacons using the water exposure as the power source. Beyond conventional LTE‑M deployments, the system's compatibility with 3GPP standard cellular protocols paves the way for future connectivity via non terrestrial 5G networks, enabling coverage in infrastructure scarce regions.

Authors:Roshan Nepal, Brandon Brown, Shishangbo Yu, Roozbeh Abbasi, Norman Zhou, George Shaker
Title: Battery-Free and Gateway-Free Cellular IoT Water Leak Detection System
Abstract:
This paper presents a battery‑free and gateway‑free water leak detection system capable of direct communication over LTE‑M (Cat‑M1). The system operates solely on energy harvested through a hydroelectric mechanism driven by an electrochemical sensor, thereby removing the need for conventional batteries. To address the stringent startup and operational power demands of LTE‑M transceivers, the architecture incorporates a compartmentalized sensing module and a dedicated power management subsystem, comprising a boost converter, supercapacitor based energy storage, and a hysteresis controlled load isolation circuit. This design enables autonomous, direct to cloud data transmission without reliance on local networking infrastructure. Experimental results demonstrate consistent LTE‑M beacon transmissions triggered by water induced energy generation, underscoring the system's potential for sustainable, maintenance free, and globally scalable IoT leak detection applications in smart infrastructure.

Authors:Felipe Arenas-Uribe
Title: Higher-Order Gravitational Models: A Tutorial on Spherical Harmonics and the Newtonian Model
Abstract:
Accurate modeling of gravitational interactions is fundamental to the analysis, prediction, and control of space systems. While the Newtonian point‑mass approximation suffices for many preliminary studies, real celestial bodies exhibit deviations from spherical symmetry, including oblateness, localized mass concentrations, and higher‑order shape irregularities. These features can significantly perturb spacecraft trajectories, especially in low‑altitude or long‑duration missions, leading to cumulative orbit prediction errors and increased control demands. This article presents a tutorial introduction to spherical harmonic gravity models, outlining their theoretical foundations and underlying assumptions. Higher‑order gravitational fields are derived as solutions to the Laplace equation, providing a systematic framework to capture the effects of non‑uniform mass distributions. The impact of these higher‑order terms on orbital dynamics is illustrated through examples involving Low Earth Orbit satellites and spacecraft near irregularly shaped asteroids, highlighting the practical significance of moving beyond the point‑mass approximation.

Authors:Dong Yoon Lee, Alyssa Weakley, Hui Wei, Daniel Cardona, Shijia Pan
Title: Home Health System Deployment Experience for Geriatric Care Remote Monitoring
Abstract:
To support aging‑in‑place, adult children often provide care to their aging parents from a distance. These informal caregivers desire plug‑and‑play remote care solutions for privacy‑preserving continuous monitoring that enabling real‑time activity monitoring and intuitive, actionable information. This short paper presents insights from three iterations of deployment experience for remote monitoring system and the iterative improvement in hardware, modeling, and user interface guided by the Geriatric 4Ms framework (matters most, mentation, mobility, and medication). An LLM‑assisted solution is developed to balance user experience (privacy‑preserving, plug‑and‑play) and system performance.

Authors:Maurice Filo, Nicolò Rossi, Zhou Fang, Mustafa Khammash
Title: GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design
Abstract:
Biomolecular networks underpin emerging technologies in synthetic biology‑from robust biomanufacturing and metabolic engineering to smart therapeutics and cell‑based diagnostics‑and also provide a mechanistic language for understanding complex dynamics in natural and ecological systems. Yet designing chemical reaction networks (CRNs) that implement a desired dynamical function remains largely manual: while a proposed network can be checked by simulation, the reverse problem of discovering a network from a behavioral specification is difficult, requiring substantial human insight to navigate a vast space of topologies and kinetic parameters with nonlinear and possibly stochastic dynamics. Here we introduce GenAI‑Net, a generative AI framework that automates CRN design by coupling an agent that proposes reactions to simulation‑based evaluation defined by a user‑specified objective. GenAI‑Net efficiently produces novel, topologically diverse solutions across multiple design tasks, including dose responses, complex logic gates, classifiers, oscillators, and robust perfect adaptation in deterministic and stochastic settings (including noise reduction). By turning specifications into families of circuit candidates and reusable motifs, GenAI‑Net provides a general route to programmable biomolecular circuit design and accelerates the translation from desired function to implementable mechanisms.

Authors:Liwen Jiang, Andrew B. Kahng, Zhiang Wang, Zhiyu Zheng
Title: Invited: Toward Sustainable and Transparent Benchmarking for Academic Physical Design Research
Abstract:
This paper presents RosettaStone 2.0, an open benchmark translation and evaluation framework built on OpenROAD‑Research. RosettaStone 2.0 provides complete RTL‑to‑GDS reference flows for both conventional 2D designs and Pin‑3D‑style face‑to‑face (F2F) hybrid‑bonded 3D designs, enabling rigorous apples‑to‑apples comparison across planar and three‑dimensional implementation settings. The framework is integrated within OpenROAD‑flow‑scripts (ORFS)‑Research; it incorporates continuous integration (CI)‑based regression testing and provides a standardized evaluation pipeline based on the METRICS2.1 convention, with structured logs and reports generated by ORFS‑Research. To support transparent and reproducible research, RosettaStone 2.0 further provides a community‑facing leaderboard, which is governed by verified pull requests and enforced through Developer Certificate of Origin (DCO) compliance.

Authors:Jinmeng Zha, Zhen Zhang
Title: Robust Output Regulation of Uncertain Linear Time-Varying Systems
Abstract:
Robust output regulation for linear time‑varying systems has remained an open problem for decades. To address this, we propose the trajectory‑matching system immersion framework, by reformulating the regulator equation into a more insightful form. This perspective demonstrates that finding an internal model is equivalent to reproducing the steady‑state output trajectories of a given forced system by constructing an unforced one. This reveals the fundamental influence of parametric uncertainties, yielding the precise algebraic boundary for robust regulation, termed finite linear parameterization. With this, we further demonstrate that uncertainties in time‑varying systems can easily excite infinite‑dimensional families of functions, making it impossible for a finite‑dimensional regulator to achieve exact robust regulation. Hence, we establish a comprehensive approximate robust design, which yields a bounded tracking error that can be arbitrarily small, and avoids explicitly solving the regulator equation. Additionally, it can ensure exact regulation when the uncertainty influences the system in some specified ways. Overall, these results provide a general, executable framework for constructing an internal model‑based design, and simplify the robust control implementation process.

Authors:Corrado Sgadari, Alessio La Bella, Marcello Farina
Title: A new approach for combined model class selection and parameters learning for auto-regressive neural models
Abstract:
This work introduces a novel approach for the joint selection of model structure and parameter learning for nonlinear dynamical systems identification. Focusing on a specific Recurrent Neural Networks (RNNs) family, i.e., Nonlinear Auto‑Regressive with eXogenous inputs Echo State Networks (NARXESNs), the method allows to simultaneously select the optimal model class and learn model parameters from data through a new set‑membership (SM) based procedure. The results show the effectiveness of the approach in identifying parsimonious yet accurate models suitable for control applications. Moreover, the proposed framework enables a robust training strategy that explicitly accounts for bounded measurement noise and enhances model robustness by allowing data‑consistent evaluation of simulation performance during parameter learning, a process generally NP‑hard for models with autoregressive components.

Authors:Nyi Nyi Aung, Bradley Wight, Adrian Stein
Title: Adaptive Input Shaper Design for Unknown Second-Order Systems with Real-Time Parameter Estimation
Abstract:
We propose a feedforward input‑shaping framework with online parameter estimation for unknown second‑order systems. The proposed approach eliminates the need for prior knowledge of system parameters when designing input shaping for precise switching times by incorporating online estimation for a black‑box system. The adaptive input shaping scheme accounts for the system's periodic switching behavior and enables reference shaping even when initial switching instants are missed. The proposed framework is evaluated in simulation and is intended for vibration suppression in motion control applications such as gantry cranes and 3D printer headers.

Authors:Johannes Güttler, Karan Baker, Premjit Saha, James Warner, Adrian Stein
Title: Polynomial Chaos-based Input Shaper Design under Time-Varying Uncertainty
Abstract:
The work presented here investigates the application of polynomial chaos expansion toward input shaper design in order to maintain robustness in dynamical systems subject to uncertainty. Furthermore, this work intends to specifically address time‑varying uncertainty by employing intrusive polynomial chaos expansion. The methodology presented is validated through numerical simulation of intrusive polynomial chaos expansion formulation applied to spring mass system experiencing time‑varying uncertainty in the spring stiffness. The system also evaluates non‑robust and robust input shapers through the framework in order to identify designs that minimize residual energy. Results indicate that vibration mitigation is achieved at a similar accuracy, yet at higher efficiency compared to a Monte Carlo framework.

Authors:Arshiya Rezaie Hezaveh, Peng Hu
Title: AstroTimer: Rethinking Non-Access Stratum Timers in LEO Constellations
Abstract:
Low‑Earth Orbit (LEO) constellations expand 5G coverage to remote regions but differ fundamentally from terrestrial networks due to rapidly changing topologies, fluctuating delays, and constrained onboard resources. Existing 3GPP Non‑Access Stratum (NAS) timers, inherited from terrestrial and geostationary (GEO) or medium Earth orbit (MEO) systems, fail to accommodate these dynamics, leading to signaling storms and inefficiency. This paper introduces AstroTimer, a lightweight, adaptive framework for sizing NAS timers based on LEO‑specific parameters such as link variability, processing delays, and network‑function placement. AstroTimer derives a closed‑form timer model with low computational cost and optimizes both watchdog and backoff timers for the 5G registration procedure. Simulation results demonstrate that AstroTimer significantly reduces registration time, retry frequency, and user equipment (UE) energy consumption compared to 3GPP defaults, while preventing signaling overloads. The proposed approach provides an operator‑ready foundation for reliable, efficient, and scalable non‑terrestrial 5G/6G deployments.

Authors:Jack Umenberger, Anna Osguthorpe Rasmussen
Title: Robust and learning-augmented algorithms for degradation-aware battery optimization
Abstract:
This paper studies the problem of maximizing revenue from a grid‑scale battery energy storage system, accounting for uncertain future electricity prices and the effect of degradation on battery lifetime. We formulate this task as an online resource allocation problem. We propose an algorithm, based on online mirror descent, that is no‑regret in the stochastic i.i.d. setting and attains finite asymptotic competitive ratio in the adversarial setting (robustness). When untrusted advice about the opportunity cost of degradation is available, we propose a learning‑augmented algorithm that performs well when the advice is accurate (consistency) while still retaining robustness properties when the advice is poor.

Authors:Bibek Adhikari, Rishab Rijal, Rakesh Yadav, Nikchey Khatri, Sandesh Dhakal
Title: Autonomous Mars Rover Module for Soil Sampling and Life Component Analysis
Abstract:
The search for extraterrestrial life has long been a primary focus of scientific exploration, driven by rapid advancements in technology and our understanding of the universe. The discovery of water on Mars has sparked significant interest, raising the question of whether life could exist on the planet. This study proposes a novel approach to simulate and illustrate the detection of life using a proof‑of‑life module integrated into a Mars rover. The module is an autonomous system capable of traveling to designated regions, excavating soil, collecting samples, and performing biochemical testing onboard the rover itself. The project is inherently multidisciplinary, integrating mechanical systems such as a drill mechanism and a vacuum system, alongside biochemical analysis for soil testing. The module is capable of successfully detecting the presence or absence of living components of life from the collected soil particles. This proof‑of‑life module serves as a proof‑of‑concept for autonomous life detection in extraterrestrial environments and lays the foundation for future exploration missions.

Authors:Jinaykumar Patel, Kamesh Subbarao
Title: Set-Based Reachability for Low-Thrust Spacecraft in Two-Body and Cislunar Dynamical Systems
Abstract:
This paper investigates the application of zonotope‑based reachability analysis to low‑thrust spacecraft in both two‑body and cislunar environments. Reachable sets are generated under two‑body and circular restricted three‑body (CR3BP) dynamics using set‑based methods that approximate nonlinear systems via Taylor expansions. A state‑dependent coefficient (SDC) parameterization is also explored to represent nonlinear dynamics in a pseudo‑linear form, enabling efficient matrix based propagation of reachable sets. Applications include Earth‑Mars transfer and cislunar scenarios such as L1 and L2 Halo orbits and Near Rectilinear Halo Orbits (NRHOs). The resulting reachable sets are used for safe trajectory generation and tracking, with comparisons drawn between model predictive control (MPC) and LQR‑based station‑keeping. The proposed approach provides a scalable framework for analyzing spacecraft behavior under complex dynamics and control constraints.

Authors:Shivanshu Tripathi, Hossein Mohsenzadeh Yazdi, Maziar Raissi, Hamed Mohsenian-Rad
Title: Data-Efficient Physics-Informed Learning to Model Synchro-Waveform Dynamics of Grid-Integrated Inverter-Based Resources
Abstract:
Inverter‑based resources (IBRs) exhibit fast transient dynamics during network disturbances, which often cannot be properly captured by phasor and SCADA measurements. This shortcoming has recently been addressed with the advent of waveform measurement units (WMUs), which provide high‑resolution, time‑synchronized raw voltage and current waveform samples from multiple locations in the power system. However, transient model learning based on synchro‑waveform measurements remains constrained by the scarcity of network disturbances and the complexity of the underlying nonlinear dynamics of IBRs. We propose to address these problems by developing a data‑efficient physics‑informed machine learning (PIML) framework for synchro‑waveform analytics that estimates the IBR terminal current response from only a few network disturbance signatures. Here, the physics of the electrical circuits are used to compensate for limited data availability by constraining the learning process through known circuit relationships. Two cases are considered, with known and unknown circuit parameters. In the latter case, the framework jointly learns the transient dynamics of the IBRs and the parameters of the electrical circuit. Case studies using WMU disturbance data across multiple sampling rates shows consistently lower current estimation error with substantially fewer training events than a purely data‑driven baseline.

Authors:Yanhua Zhao
Title: Online parameter estimation for the Crazyflie quadcopter through an EM algorithm
Abstract:
Drones are becoming more and more popular nowadays. They are small in size, low in cost, and reliable in operation. They contain a variety of sensors and can perform a variety of flight tasks, reaching places that are difficult or inaccessible for humans. Earthquakes damage a lot of infrastructure, making it impossible for rescuers to reach some areas. But drones can help. Many amateur and professional photographers like to use drones for aerial photography. Drones play a non‑negligible role in agriculture and transportation too. Drones can be used to spray pesticides, and they can also transport supplies. A quadcopter is a four‑rotor drone and has been studied in this paper. In this paper, random noise is added to the quadcopter system and its effects on the drone system are studied. An extended Kalman filter has been used to estimate the state based on noisy observations from the sensor. Based on a SDE system, a linear quadratic Gaussian controller has been implemented. The expectation maximization algorithm has been applied for parameter estimation of the quadcopter. The results of offline parameter estimation and online parameter estimation are presented. The results show that the online parameter estimation has a slightly larger range of convergence values than the offline parameter estimation.

Authors:Moisés J. B. B. Davi, Felipe V. Lopes, Vinícius A. Lacerda, Mário Oleskovicz, Oriol Gomis-Bellmunt
Title: Performance of Differential Protection Applied to Collector Cables of Offshore Wind Farms with MMC-HVDC Transmission
Abstract:
The ongoing global transition towards low‑carbon energy has propelled the integration of offshore wind farms, which, when combined with Modular Multilevel Converter‑based High‑Voltage Direct Current (MMC‑HVDC) transmission, present unique challenges for power system protection. In collector cables connecting wind turbines to offshore MMC, both ends are supplied by Inverter‑Based Resources (IBRs), which modify the magnitude and characteristics of fault currents. In this context, this paper investigates the limitations of conventional differential protection schemes under such conditions and compares them with enhanced strategies that account for sequence components. Using electromagnetic transient simulations of a representative offshore wind farm modeled in PSCAD/EMTDC software, internal and external fault scenarios are assessed, varying fault types and resistances. The comparative evaluation provides insights into the sensitivity and selectivity of differential protection and guides a deeper conceptual understanding of the evolving protection challenges inherent to future converter‑dominated grids.

Authors:Tims Pecerskis, Aivars Smirnovs
Title: Mixture-of-Models: Unifying Heterogeneous Agents via N-Way Self-Evaluating Deliberation
Abstract:
This paper introduces the N‑Way Self‑Evaluating Deliberation (NSED) protocol, a Runtime Mixture‑of‑Models (MoM) architecture that constructs emergent composite models from a plurality of distinct expert agents. Unlike traditional Mixture‑of‑Experts (MoE) which rely on static gating networks, NSED employs a Dynamic Expertise Broker ‑ a runtime optimization engine that treats model selection as a variation of the Knapsack Problem, binding heterogeneous checkpoints to functional roles based on live telemetry and cost constraints. At the execution layer, we formalize deliberation as a Macro‑Scale Recurrent Neural Network (RNN), where the consensus state loops back through a semantic forget gate to enable iterative refinement without proportional VRAM scaling. Key components include an orchestration fabric for trustless N‑to‑N peer review, a Quadratic Voting activation function for non‑linear consensus, and a feedback‑driven state update. Empirical validation on challenging benchmarks (AIME 2025, LiveCodeBench) demonstrates that this topology allows ensembles of small (less than 20B) consumer‑grade models to match or exceed the performance of state‑of‑the‑art 100B+ parameter models, establishing a new hardware arbitrage efficiency frontier. Furthermore, testing on the DarkBench safety suite reveals intrinsic alignment properties, with peer‑mediated correction reducing sycophancy scores below that of any individual agent.

Authors:N. Hagelaars, G. J. E. van Otterdijk, S. Moradi, R. Tóth, N. O. Jaensson, M. Schoukens
Title: Identification of Port-Hamiltonian Differential-Algebraic Equations from Input-Output Data
Abstract:
Many models of physical systems, such as mechanical and electrical networks, exhibit algebraic constraints that arise from subsystem interconnections and underlying physical laws. Such systems are commonly formulated as differential‑algebraic equations (DAEs), which describe both the dynamic evolution of system states and the algebraic relations that must hold among them. Within this class, port‑Hamiltonian differential‑algebraic equations (pH‑DAEs) offer a structured, energy‑based representation that preserves interconnection and passivity properties. This work introduces a data‑driven identification method that combines port‑Hamiltonian neural networks (pHNNs) with a differential‑algebraic solver to model such constrained systems directly from noisy input‑output data. The approach preserves the passivity and interconnection structure of port‑Hamiltonian systems while employing a backward Euler discretization with Newton's method to solve the coupled differential and algebraic equations consistently. The performance of the proposed approach is demonstrated on a DC power network, where the identified model accurately captures system behaviour and maintains errors proportional to the noise amplitude, while providing reliable parameter estimates.

Authors:Xingchen Li, Keyou You
Title: ReLU Networks for Model Predictive Control: Network Complexity and Performance Guarantees
Abstract:
Recent years have witnessed a resurgence in using ReLU neural networks (NNs) to represent model predictive control (MPC) policies. However, determining the required network complexity to ensure closed‑loop performance remains a fundamental open problem. This involves a critical precision‑complexity trade‑off: undersized networks may fail to capture the MPC policy, while oversized ones may outweigh the benefits of ReLU network approximation. In this work, we propose a projection‑based method to enforce hard constraints and establish a state‑dependent Lipschitz continuity property for the optimal MPC cost function, which enables sharp convergence analysis of the closed‑loop system. For the first time, we derive explicit bounds on ReLU network width and depth for approximating MPC policies with guaranteed closed‑loop performance. To further reduce network complexity and enhance closed‑loop performance, we propose a non‑uniform error framework with a state‑aware scaling function to adaptively adjust both the input and output of the ReLU network. Our contributions provide a foundational step toward certifiable ReLU NN‑based MPC.

Authors:Mirko Legnini, Julian Berberich
Title: Noise Resilience and Robust Convergence Guarantees for the Variational Quantum Eigensolver
Abstract:
Variational Quantum Algorithms (VQAs) are a class of hybrid quantum‑classical algorithms that leverage on classical optimization tools to find the optimal parameters for a parameterized quantum circuit. One relevant application of VQAs is the Variational Quantum Eigensolver (VQE), which aims at steering the output of the quantum circuit to the ground state of a certain Hamiltonian. Recent works have provided global convergence guarantees for VQEs under suitable local surjectivity and smoothness hypotheses, but little has been done in characterizing convergence of these algorithms when the underlying quantum circuit is affected by noise. In this work, we characterize the effect of different coherent and incoherent noise processes on the optimal parameters and the optimal cost of the VQE, and we study their influence on the convergence guarantees of the algorithm. Our work provides novel theoretical insight into the behavior of parameterized quantum circuits. Furthermore, we accompany our results with numerical simulations implemented via Pennylane.

Authors:Martina Alutto, Qiulin Xu, Fabrizio Dabbene, Hideaki Ishii, Chiara Ravazzi
Title: Model Predictive Control for Coupled Adoption-Opinion Dynamics
Abstract:
This paper investigates an optimal control problem for an adoption‑opinion model that couples opinion dynamics with a compartmental adoption framework on a multilayer network to study the diffusion of sustainable behaviors. Adoption evolves through social contagion and perceived benefits, while opinions are shaped by social interactions and feedback from adoption levels. Individuals may also stop adopting virtuous behavior due to external constraints or shifting perceptions, affecting overall diffusion. After the stability analysis of equilibria, both in the presence and absence of adopters, we introduce a Model Predictive Control (MPC) framework that optimizes interventions by shaping opinions rather than directly enforcing adoption. This nudge‑based control strategy allows policymakers to influence diffusion indirectly, making interventions more effective and scalable. Numerical simulations demonstrate that, in the absence of control, adoption stagnates, whereas MPC‑driven interventions sustain and enhance adoption across communities.

Authors:Martina Alutto, Fabrizio Dabbene, Angela Fontan, Karl H. Johansson, Chiara Ravazzi
Title: On a Coupled Adoption-Opinion Framework for Competing Innovations
Abstract:
In this paper, we propose a two‑layer adoption‑opinion model to study the diffusion of two competing technologies within a population whose opinions evolve under social influence and adoption‑driven feedback. After adopting one technology, individuals may become dissatisfied and switch to the alternative. We prove the existence and uniqueness of the adoption‑diffused equilibrium, showing that both technologies coexist and that neither partial‑adoption nor monopoly can arise. Numerical simulations show that while opinions shape the equilibrium adoption levels, the relative market share between the two technologies depends solely on their user‑experience. As a consequence, interventions that symmetrically boost opinions or adoption can disproportionately favor the higher‑quality technology, illustrating how symmetric control actions may generate asymmetric outcomes.

Authors:Hazem Ibrahim, Julius Beerwerth, Lorenz Dörschel, Bassam Alrifaee
Title: Computation-Accuracy Trade-Off in Service-Oriented Model-Based Control
Abstract:
Representing a control system as a Service‑Oriented Architecture (SOA)‑referred to as Service‑Oriented Model‑Based Control (SOMC)‑enables runtime‑flexible composition of control loop elements. This paper presents a framework that optimizes the computation‑accuracy trade‑off by formulating service orchestration as an A^\starsearch problem, complemented by Contextual Bayesian Optimization (BO) to tune the multi‑objective cost weights. A vehicle longitudinal‑velocity control case study demonstrates online, performancedriven reconfiguration of the control architecture. We show that our framework not only combines control and software structure but also considers the real‑time requirements of the control system during performance optimization.

Authors:Zoltan Nagy, Ryan Wisnesky, Kevin Carlson, Eswaran Subrahmanian, Gioele Zardini
Title: A Categorical Approach to Semantic Interoperability across Building Lifecycle
Abstract:
Buildings generate heterogeneous data across their lifecycle, yet integrating these data remains a critical unsolved challenge. Despite three decades of standardization efforts, over 40 metadata schemas now span the building lifecycle, with fragmentation accelerating rather than resolving. Current approaches rely on point‑to‑point mappings that scale quadratically with the number of schemas, or universal ontologies that become unwieldy monoliths. The fundamental gap is the absence of mathematical foundations for structure‑preserving transformations across heterogeneous building data. Here we show that category theory provides these foundations, enabling systematic data integration with O(n) specification complexity for n ontologies. We formalize building ontologies as first‑order theories and demonstrate two proof‑of‑concept implementations in Categorical Query Language (CQL): 1) generating BRICK models from IFC design data at commissioning, and 2) three‑way integration of IFC, BRICK, and RealEstateCore where only two explicit mappings yield the third automatically through categorical composition. Our correct‑by‑construction approach treats property sets as first‑class schema entities and provides automated bidirectional migrations, and enables cross‑ontology queries. These results establish feasibility of categorical methods for building data integration and suggest a path toward an app ecosystem for buildings, where mathematical foundations enable reliable component integration analogous to smartphone platforms.

Authors:Francesco Guidi, Jingfeng Shan, Mehrdad Saeidi, Enrico Testi, Elia Favarelli, Andrea Giorgetti, Davide Dardari, Alberto Zanella, Giorgio Li Pira, Francesca Starita, Anna Guerra
Title: A Cognitive Framework for Autonomous Agents: Toward Human-Inspired Design
Abstract:
This work introduces a human‑inspired reinforcement learning (RL) architecture that integrates Pavlovian and instrumental processes to enhance decision‑making in autonomous systems. While existing engineering solutions rely almost exclusively on instrumental learning, neuroscience shows that humans use Pavlovian associations to leverage predictive cues to bias behavior before outcomes occur. We translate this dual‑system mechanism into a cue‑guided RL framework in which radio‑frequency (RF) stimuli act as conditioned (Pavlovian) cues that modulate action selection. The proposed architecture combines Pavlovian values with instrumental policy optimization, improving navigation efficiency and cooperative behavior in unknown, partially observable environments. Simulation results demonstrate that cue‑driven agents adapt faster, achieving superior performance compared to traditional instrumental‑solo agents. This work highlights the potential of human learning principles to reshape digital agents intelligence.

Authors:Antonio Bracale, Jakub Janowicz, Piotr Kuwałek, Grzegorz Wiczyński
Title: Challenges in the Proper Metrological Verification of Smart Energy Meters
Abstract:
The most common instruments currently used to measure active/reactive energy and power quality indicators are smart energy meters (EM). Unfortunately, the verification of such meters is currently performed under ideal conditions or with simple signal models, which do not recreate actual states occurring in the power grid and do not ensure the verification of the properties of their signal chains. This paper presents challenges in proper metrological verification of smart EM. It presents existing legal and normative requirements and scientific research directions regarding these meters. Although the meters tested comply with the normative and legal requirements, the results reveal numerous imperfections in the signal and measurement chains for the selected test signal. Based on the results of the research results, further directions have been determined in the field of smart EM.

Authors:Julius Beerwerth, Jianye Xu, Simon Schäfer, Fynn Belderink, Bassam Alrifaee
Title: Zero-Shot MARL Benchmark in the Cyber-Physical Mobility Lab
Abstract:
We present a reproducible benchmark for evaluating sim‑to‑real transfer of Multi‑Agent Reinforcement Learning (MARL) policies for Connected and Automated Vehicles (CAVs). The platform, based on the Cyber‑Physical Mobility Lab (CPM Lab) [1], integrates simulation, a high‑fidelity digital twin, and a physical testbed, enabling structured zero‑shot evaluation of MARL motion‑planning policies. We demonstrate its use by deploying a SigmaRL‑trained policy [2] across all three domains, revealing two complementary sources of performance degradation: architectural differences between simulation and hardware control stacks, and the sim‑to‑real gap induced by increasing environmental realism. The open‑source setup enables systematic analysis of sim‑to‑real challenges in MARL under realistic, reproducible conditions.

Authors:Lingxiao Sun, Zhaoyang Zhang, Zihan Lin, Zirui Chen, Weijie Zhou, Zhaohui Yang, Tony Q. S. Quek
Title: Agentic AI-RAN Empowering Synergetic Sensing, Communication, Computing, and Control
Abstract:
Future sixth‑generation (6G) networks are expected to support low‑altitude wireless networks (LAWNs), where unmanned aerial vehicles (UAVs) and aerial robots operate in highly dynamic three‑dimensional environments under stringent latency, reliability, and autonomy requirements. In such scenarios, autonomous task execution at the network edge demands holistic coordination among sensing, communication, computing, and control (SC3) processes. Agentic Artificially Intelligent Radio Access Networks (Agentic AI‑RAN) offer a promising paradigm by enabling the edge network to function as an autonomous decision‑making entity for low‑altitude agents with limited onboard resources. In this article, we propose and design a task‑oriented Agentic AI‑RAN architecture that enables SC3 task execution within a single edge node. This integrated design tackles the fundamental problem of coordinating heterogeneous workloads in resource‑constrained edge environments. Furthermore, a representative low‑altitude embodied intelligence system is prototyped based on a general‑purpose Graphics Processing Unit (GPU) platform to demonstrate autonomous drone navigation in realistic settings. By leveraging the Multi‑Instance GPU (MIG) partitioning technique and the containerized deployment, the demonstration system achieves physical resource isolation while supporting tightly coupled coordination between real‑time communication and multimodal inference under a unified task framework. Experimental results demonstrate low closed‑loop latency, robust bidirectional communication, and stable performance under dynamic runtime conditions, highlighting its viability for mission‑critical low‑altitude wireless networks in 6G.

Authors:Jian Xu, Xinxiong Jiang, Yi Bao, Yuchen Zheng, Xuhui Chen, Qiang Xu, Siyang Liao, Deping Ke, Xiaoqi Gao
Title: Sequential Operating Simulation of Solid State Transformer-Driven Next-Generation 800 VDC Data Center
Abstract:
Artificial‑intelligence (AI) workloads are driving rapid growth in data‑center electricity use and rack power density, increasing demand for power‑delivery systems that are efficient and robust to fast load transients. Conventional uninterruptible power supply (UPS) based AC distribution chains involve multiple conversion stages and line‑frequency transformers, which compound losses and are less compatible with dynamic AI power profiles. Although solid‑state transformers (SSTs) and 800 VDC distribution architecture are widely discussed, implementable topology/control details, and long‑horizon validation with realistic operating profiles remain limited. This paper develops an SST‑driven 800 VDC architecture that converts 10 kV MVAC to an 800V LVDC bus using a three‑phase H‑bridge AC/DC stage cascaded with a dual‑active‑bridge (DAB) DC/DC stage. A coordinated closed‑loop control scheme, combining rectifier voltage/current regulation and DAB phase‑shift control, is designed to maintain DC‑bus voltage stability. The proposed system is implemented on the real‑time digital simulation (RTDS) platform and evaluated via sequential simulations using real‑world day‑ and month‑scale operating profiles of data centers, benchmarked against a UPS supply chain. Numerical studies demonstrate tight 800 VDC regulation, reduced input‑side energy consumption compared with the UPS baseline, and satisfactory power‑quality performance. A capacitance sensitivity test quantifies tradeoffs between DC‑bus ripple and low‑frequency input‑power oscillations, yielding a practical capacitance range for design. Overall, the work provides a reproducible evaluation workflow and actionable guidance for next‑generation AI data centers.

Authors:Xueqing Gao, Jun Zhang, Tao Li, Mingming Zhang
Title: Robust Grid-Forming Control Based on Virtual Flux Observer
Abstract:
This paper investigates the design and analysis of a novel grid‑forming (GFM) control method for grid‑connected converters (GCCs). The core novelty lies in a virtual flux observer‑based synchronization and load angle control method. The terminal voltage of the converter is directly regulated to provide voltage‑source behavior. The control parameters are designed for decoupling and pole placement. The proposed method exhibits strong robustness in stability and dynamical performance across varying and uncertain grid strengths. The robust control performance of the proposed method is first demonstrated by small‑signal analysis, then validated by experiments on a 20 kVA power conversion system.

Authors:Bryce L. Ferguson, Chinmay Maheshwari, Manxi Wu, Shankar Sastry
Title: Game-to-Real Gap: Quantifying the Effect of Model Misspecification in Network Games
Abstract:
Game‑theoretic models and solution concepts provide rigorous tools for predicting collective behavior in multi‑agent systems. In practice, however, different agents may rely on different game‑theoretic models to design their strategies. As a result, when these heterogeneous models interact, the realized outcome can deviate substantially from the outcome each agent expects based on its own local model. In this work, we introduce the game‑to‑real gap, a new metric that quantifies the impact of such model misspecification in multi‑agent environments. The game‑to‑real gap is defined as the difference between the utility an agent actually obtains in the multi‑agent environment (where other agents may have misspecified models) and the utility it expects under its own game model. Focusing on quadratic network games, we show that misspecifications in either (i) the external shock or (ii) the player interaction network can lead to arbitrarily large game‑to‑real gaps. We further develop novel network centrality measures that allow exact evaluation of this gap in quadratic network games. Our analysis reveals that standard network centrality measures fail to capture the effects of model misspecification, underscoring the need for new structural metrics that account for this limitation. Finally, through illustrative numerical experiments, we show that existing centrality measures in network games may provide a counterintuitive understanding of the impact of model misspecification.

Authors:Belal Korany, Peerapol Tinnakornsrisuphap, Saadallah Kassir, Prashanth Hande, Hyun Yong Lee, Thomas Stockhammer, Hemanth Sampath
Title: Multi-User Content Diversity in Wireless Networks
Abstract:
Immersive applications such as eXtended Reality (XR), cloud gaming, and real‑time video streaming are central to the vision of 6G networks. These applications require not only low latency and high data rates, but also consistent and high‑quality User Experience (UX). Traditional rate allocation and congestion control mechanisms in wireless networks treat users uniformly based on channel conditions, rely only on network‑centric Key Performance Indicators (KPIs), and ignore the content diversity, which can lead to inefficient resource utilization and degraded UX. In this paper, we introduce the concept of Multi‑User Content Diversity, which recognizes that different users concurrently consume media with varying complexity, and therefore have different bitrate requirements to achieve satisfactory UX. We propose multiple different frameworks that exploit multi‑user content diversity and lead to overall network‑wide gains in terms of UX. For each framework, we demonstrate the required information exchange between Application Servers (ASs), Application Clients (ACs), and the network, and the algorithms that run in each of these components to optimize a network‑wide UXbased objective. Simulation results demonstrate that exploiting multi‑user content diversity leads to significant gains in UX capacity, UX fairness, and network utilization, when compared to conventional rate control methods. These findings highlight the potential of content‑aware networking as a key enabler for emerging wireless systems.

Authors:Maria Vabson, Muhy Eddin Zater, Amir Sajadi, Kyri Baker, Bri-Mathias Hodge
Title: Optimal County-Level Siting of Data Centers in the United States
Abstract:
Data centers are growing rapidly, creating the pressing need for the development of critical infrastructure build out to support these resource‑intensive large loads. Their immense consumption of electricity and, often, freshwater, continues to stress an already constrained and aging power grid and water resources. This paper presents a comprehensive modeling approach to determine the optimal locations to construct such facilities by quantifying their resource use and minimizing associated costs. The interdisciplinary modeling approach incorporates a number of factors including the power grid, telecommunications, climate, water use, and collocated generation potential. This work establishes the base model whose functionality is shown through several test cases focusing on carbon‑free generation collocation on a county‑level in the United States. The results suggest that while capital costs are the biggest driver, having a longer future outlook and allowing more variable generation collocation influences the model to choose sites with higher renewable potential.

Authors:Anh-Duy Pham
Title: AMBER: A Columnar Architecture for High-Performance Agent-Based Modeling in Python
Abstract:
Python is widely used for agent‑based modelling because it is accessible and has a mature scientific ecosystem, but object‑per‑agent execution incurs interpreter overhead that restricts the population sizes feasible in interactive modelling, calibration, and parameter sweeps. This paper presents AMBER, a Python framework that stores agent state in a Polars‑backed columnar table and exposes population operations through a compact view API. The framework preserves conventional model and agent abstractions while translating common population updates into compiled column operations; behaviours that do not vectorise remain expressible through a buffered object‑oriented path. We evaluate AMBER on wealth transfer, random walk, and spatial SIR benchmarks against Mesa, AgentPy, SimPy, Melodie, Agents.jl, and AMBER's own loop path, using invariant checks to verify comparable model outputs before timing. Across the tested workloads, AMBER has the lowest execution time among Python‑hosted implementations and achieves speedups of up to 1118× over Mesa; on the largest SIR benchmark it is also faster than the Julia‑based Agents.jl implementation.

Authors:Niklas Schmid, Jaeyoun Choi, Oswin So, Chuchu Fan
Title: Maximizing Reach-Avoid Probabilities for Linear Stochastic Systems via Control Architectures
Abstract:
The maximization of reach‑avoid probabilities for stochastic systems is a central topic in the control literature. Yet, the available methods are either restricted to low‑dimensional systems or suffer from conservative approximations. To address these limitations, we propose control architectures that combine the flexibility of Markov Decision Processes with the scalability of Model Predictive Controllers. The Model Predictive Controller tracks reference signals while remaining agnostic to the stochasticity and reach‑avoid objective. Instead, the reach‑avoid probability is maximized by optimally updating the controller's reference online. To achieve this, the closed‑loop system, consisting of the system and Model Predictive Controller, is abstracted as a Markov Decision Process in which a new reference can be chosen at every time‑step. A feedback policy generating optimal references is then computed via Dynamic Programming. If the state space of the system is continuous, the Dynamic Programming algorithm must be executed on a finite system approximation. Modifications to the Model Predictive Controller enable a computationally efficient robustification of the Dynamic Programming algorithm to approximation errors, preserving bounds on the achieved reach‑avoid probability. The approach is validated on a perturbed 12D quadcopter model in cluttered reach‑avoid environments proving its flexibility and scalability.

Authors:Zixin Jiang, Ruizhi Song, Guowen Li, Yuhang Zhang, Zheng O'Neill, Xuezheng Wang, Judah Goldfeder, Bing Dong
Title: BESTOpt: A Modular, Physics-Informed Machine Learning based Building Modeling, Control and Optimization Framework
Abstract:
Modern buildings are increasingly interconnected with occupancy, heating, ventilation, and air‑conditioning (HVAC) systems, distributed energy resources (DERs), and power grids. Modeling, control, and optimization of such multi‑domain systems play a critical role in achieving building‑sector decarbonization. However, most existing tools lack scalability and physical consistency for addressing these complex, multi‑scale ecosystem problems. To bridge this gap, this study presents BESTOpt, a modular, physics‑informed machine learning (PIML) framework that unifies building applications, including benchmarking, evaluation, diagnostics, control, optimization, and performance simulation. The framework adopts a cluster‑domain‑system/building‑component hierarchy and a standardized state‑action‑disturbance‑observation data typology. By embedding physics priors into data‑driven modules, BESTOpt improves model accuracy and physical consistency under unseen conditions. Case studies on single‑building and cluster scenarios demonstrate its capability for multi‑level centralized and decentralized control. Looking ahead, BESTOpt lays the foundation for an open, extensible platform that accelerates interdisciplinary research toward smart, resilient, and decarbonized building ecosystems.

Authors:Ruoyu Lin, Magnus Egerstedt
Title: Stochastic Control Barrier Functions under State Estimation: From Euclidean Space to Lie Groups
Abstract:
Ensuring safety for autonomous systems under uncertainty remains challenging, particularly when safety of the true state is required despite the true state not being fully known. Control barrier functions (CBFs) have become widely adopted as safety filters. However, standard CBF formulations do not explicitly account for state estimation uncertainty and its propagation, especially for stochastic systems evolving on manifolds. In this paper, we propose a safety‑critical control framework with a provable bound on the finite‑time safety probability for stochastic systems under noisy state information. The proposed framework explicitly incorporates the uncertainty arising from both process and measurement noise, and synthesizes controllers that adapt to the level of uncertainty. The framework admits closed‑form solutions in linear settings, and experimental results demonstrate its effectiveness on systems whose state spaces range from Euclidean space to Lie groups.

Authors:Pan-Yang Su, Arwa Alanqary, Bryce L. Ferguson, Manxi Wu, Alexandre M. Bayen, Shankar Sastry
Title: Average Unfairness in Routing Games
Abstract:
We propose average unfairness as a new measure of fairness in routing games, defined as the ratio between the average latency and the minimum latency experienced by users. This measure is a natural complement to two existing unfairness notions: loaded unfairness, which compares maximum and minimum latencies of routes with positive flow, and user equilibrium (UE) unfairness, which compares maximum latency with the latency of a Nash equilibrium. We show that the worst‑case values of all three unfairness measures coincide and are characterized by a steepness parameter intrinsic to the latency function class. We show that average unfairness is always no greater than loaded unfairness, and the two measures are equal only when the flow is fully fair. Besides that, we offer a complete comparison of the three unfairness measures, which, to the best of our knowledge, is the first theoretical analysis in this direction. Finally, we study the constrained system optimum (CSO) problem, where one seeks to minimize total latency subject to an upper bound on unfairness. We prove that, for the same tolerance level, the optimal flow under an average unfairness constraint achieves lower total latency than any flow satisfying a loaded unfairness constraint. We show that such improvement is always strict in parallel‑link networks and establish sufficient conditions for general networks. We further illustrate the latter with numerical examples. Our results provide theoretical guarantees and valuable insights for evaluating fairness‑efficiency tradeoffs in network routing.

Authors:Zirui Niu, Giordano Scarciotti, Alessandro Astolfi
Title: Interconnection-based Model Reduction for Linear Hybrid Systems
Abstract:
In this paper, we address the model reduction problem for linear hybrid systems via the interconnection‑based technique called moment matching. We consider two classical interconnections, namely the direct and swapped interconnections, in the hybrid setting, and we present families of reduced‑order models for each interconnection via a hybrid characterisation of the steady‑state responses. By combining the results for each interconnection, the design of a reduced‑order model that achieves moment matching simultaneously for both interconnections is studied. In addition, we show that the presented results have simplified counterparts when the jumps of the hybrid system are periodic. A numerical simulation is finally given to illustrate the results.

Authors:Maosong Wang, Jiarui Cui, Wenqi Wu, Peiqi Li, Xianfei Pan
Title: Improve the autonomy of the SE2(3) group based Extended Kalman Filter for Integrated Navigation: Application
Abstract:
One of the core advantages of SE2(3) Lie group framework for navigation modeling lies in the autonomy of error propagation. In the previous paper, the theoretical analysis of autonomy property of navigation model in inertial, earth and world frames was given. A construction method for SE2(3) group navigation model is proposed to improve the non‑inertial navigation model toward full autonomy. This paper serves as a counterpart to previous paper and conducts the real‑world strapdown inertial navigation system (SINS)/odometer(ODO) experiments as well as Monte‑Carlo simulations to demonstrate the performance of improved SE2(3) group based high‑precision navigation models.

Authors:Jiarui Cui, Maosong Wang, Wenqi Wu, Peiqi Li, Xianfei Pan
Title: Improve the autonomy of the SE2(3) group based Extended Kalman Filter for Integrated Navigation: Theoretical Analysis
Abstract:
One of core advantages of the SE2(3) Lie group framework for navigation modeling lies in the autonomy of error propagation. Current research on Lie group based extended Kalman filters has demonstrated that error propagation autonomy holds in low‑precision applications, such as in micro electromechanical system (MEMS) based integrated navigation without considering earth rotation and inertial device biases. However, in high‑precision navigation state estimation, maintaining autonomy is extremely difficult when considering with earth rotation and inertial device biases. This paper presents the theoretical analysis on the autonomy of SE2(3) group based high‑precision navigation models under inertial, earth and world frame respectively. Through theoretical analysis, we find that the limitation of the traditional, trivial SE2(3) group navigation modeling method is that the presence of Coriolis force terms introduced by velocity in non‑inertial frame. Therefore, a construction method for SE2(3) group navigation models is proposed, which brings the navigation models closer to full autonomy.

Authors:John Bannan, Nazia Rahman, Chang-Hee Won
Title: Dynamic Tactile Sensing System and Soft Actor Critic Reinforcement Learning for Inclusion Characterization
Abstract:
This paper presents the Dynamic Tactile Sensing System that utilizes robotic tactile sensing in conjunction with reinforcement learning to locate and characterize embedded inclusions. A dual arm robot is integrated with an optical Tactile Imaging Sensor that utilizes the Soft Actor Critic Algorithm to acquire tactile data based on a pixel intensity reward. A Dynamic Interrogation procedure for tactile exploration is developed that enables the robot to first localize inclusion and refine their positions for precise imaging. Experimental validation conducted on Polydimethylsiloxane phantoms demonstrates that the robot using the Tactile Soft Actor Critic Model was able to achieve size estimation errors of 2.61% and 5.29% for soft and hard inclusions compared to 7.84% and 6.87% for expert human operators. Results also show that Dynamic Tactile Sensing System was able to locate embedded inclusions and autonomously determine their mechanical properties, useful in applications such as breast tumor characterization.

Authors:Eder Baron-Prada, Adolfo Anta, Florian Dörfler
Title: Stability Analysis of Power-Electronics-Dominated Grids Using Scaled Relative Graphs
Abstract:
This paper presents a novel approach to stability analysis for grid‑connected converters utilizing Scaled Relative Graphs (SRG). Our method effectively decouples grid and converter dynamics, thereby establishing a comprehensive and efficient framework for evaluating closed‑loop stability. Our analysis accommodates both linear and non‑linear loads, enhancing its practical applicability. Furthermore, we demonstrate that our stability assessment remains unaffected by angular variations resulting from dq‑frame transformations, significantly increasing the method's robustness and versatility. The effectiveness of our approach is validated in several simulation case studies, which illustrate its broad applicability in modern power systems.

Authors:Yunan Wang, Chuxiong Hu, Zhao Jin
Title: Time-Optimal Switching Surfaces for Triple Integrator under Full Box Constraints
Abstract:
Time‑optimal control for triple integrator under full box constraints is a fundamental problem in the field of optimal control, which has been widely applied in the industry. However, scenarios involving asymmetric constraints, non‑stationary boundary conditions, and active position constraints pose significant challenges. This paper provides a complete characterization of time‑optimal switching surfaces for the problem, leading to novel insights into the geometric structure of the optimal control. The active condition of position constraints is derived, which is absent from the literature. An efficient algorithm is proposed, capable of planning time‑optimal trajectories under asymmetric full constraints and arbitrary boundary states, with a 100% success rate. Computational time for each trajectory is within approximately 10μs, achieving a 5‑order‑of‑magnitude reduction compared to optimization‑based baselines.

Authors:Shiqi Wei, Qiqing Wang, Kaidi Yang
Title: Virtual Traffic Police: Large Language Model-Augmented Traffic Signal Control for Unforeseen Incidents
Abstract:
Adaptive traffic signal control (TSC) has demonstrated strong effectiveness in managing dynamic traffic flows. However, conventional methods often struggle when unforeseen traffic incidents occur (e.g., accidents and road maintenance), which typically require labor‑intensive and inefficient manual interventions by traffic police officers. Large Language Models (LLMs) appear to be a promising solution thanks to their remarkable reasoning and generalization capabilities. Nevertheless, existing works often propose to replace existing TSC systems with LLM‑based systems, which can be (i) unreliable due to the inherent hallucinations of LLMs and (ii) costly due to the need for system replacement. To address the issues of existing works, we propose a hierarchical framework that augments existing TSC systems with LLMs, whereby a virtual traffic police agent at the upper level dynamically fine‑tunes selected parameters of signal controllers at the lower level in response to real‑time traffic incidents. To enhance domain‑specific reliability in response to unforeseen traffic incidents, we devise a self‑refined traffic language retrieval system (TLRS), whereby retrieval‑augmented generation is employed to draw knowledge from a tailored traffic language database that encompasses traffic conditions and controller operation principles. Moreover, we devise an LLM‑based verifier to update the TLRS continuously over the reasoning process. Our results show that LLMs can serve as trustworthy virtual traffic police officers that can adapt conventional TSC methods to unforeseen traffic incidents with significantly improved operational efficiency and reliability.

Authors:Rui Yang, Lei Zheng, Ruoyu Yao, Jun Ma
Title: DualShield: Safe Model Predictive Diffusion via Reachability Analysis for Interactive Autonomous Driving
Abstract:
Diffusion models have emerged as a powerful approach for multimodal motion planning in autonomous driving. However, their practical deployment is typically hindered by the inherent difficulty in enforcing vehicle dynamics and a critical reliance on accurate predictions of other agents, making them prone to safety issues under uncertain interactions. To address these limitations, we introduce DualShield, a planning and control framework that leverages Hamilton‑Jacobi (HJ) reachability value functions in a dual capacity. First, the value functions act as proactive guidance, steering the diffusion denoising process towards safe and dynamically feasible regions. Second, they form a reactive safety shield using control barrier‑value functions (CBVFs) to modify the executed actions and ensure safety. This dual mechanism preserves the rich exploration capabilities of diffusion models while providing principled safety assurance under uncertain and even adversarial interactions. Simulations in challenging unprotected U‑turn scenarios demonstrate that DualShield significantly improves both safety and task efficiency compared to leading methods from different planning paradigms under uncertainty.

Authors:Lili Chen, Winn Wing-Yiu Chow, Stella Peng, Bencheng Fan, Sachitha Bandara
Title: Bridging Qualitative Rubrics and AI: A Binary Question Framework for Criterion-Referenced Grading in Engineering
Abstract:
PURPOSE OR GOAL: This study investigates how GenAI can be integrated with a criterion‑referenced grading framework to improve the efficiency and quality of grading for mathematical assessments in engineering. It specifically explores the challenges demonstrators face with manual, model solution‑based grading and how a GenAI‑supported system can be designed to reliably identify student errors, provide high‑quality feedback, and support human graders. The research also examines human graders' perceptions of the effectiveness of this GenAI‑assisted approach. ACTUAL OR ANTICIPATED OUTCOMES: The study found that GenAI achieved an overall grading accuracy of 92.5%, comparable to two experienced human graders. The two researchers, who also served as subject demonstrators, perceived the GenAI as a helpful second reviewer that improved accuracy by catching small errors and provided more complete feedback than they could manually. A central outcome was the significant enhancement of formative feedback. However, they noted the GenAI tool is not yet reliable enough for autonomous use, especially with unconventional solutions. CONCLUSIONS/RECOMMENDATIONS/SUMMARY: This study demonstrates that GenAI, when paired with a structured, criterion‑referenced framework using binary questions, can grade engineering mathematical assessments with an accuracy comparable to human experts. Its primary contribution is a novel methodological approach that embeds the generation of high‑quality, scalable formative feedback directly into the assessment workflow. Future work should investigate student perceptions of GenAI grading and feedback.

Authors:Sampson E. Nwachukwu
Title: Design, Modelling, and Control of Magnetic Ball Suspension System
Abstract:
This paper presents the modeling, control design, and performance analysis of a Magnetic Ball Suspension System (MBSS), a nonlinear and inherently unstable electromechanical system used in various precision applications. The system's primary objective is to levitate a steel ball using electromagnetic force without physical contact, thereby eliminating frictional losses. A comprehensive state‑space model was developed, capturing both the mechanical and electrical dynamics. The equilibrium points of the system were determined through feedback linearization using the Jacobian matrix. To ensure system stability, controllability and observability analyses were conducted, confirming that state feedback and observer‑based control strategies could be effectively implemented. Three distinct control methods were explored: pole placement‑based state feedback control, full‑order observer design, and optimal state feedback control using the Linear Quadratic Regulator (LQR). Each control strategy was validated through Simulink simulations for both linearized and nonlinear models. Simulation results demonstrated that the linearized system consistently achieved desired performance with minimal oscillations, whereas the nonlinear system exhibited significant transient oscillations before stabilization. The full‑order observer enhanced estimation accuracy, enabling effective control where direct state measurement was impractical. The LQR‑based control offered improved robustness and minimized control effort, though its performance was comparable to standard state feedback in some cases.

Authors:Bowen Ou, Bin Wang, Slava Maslennikov, Hanchao Liu, Jim Follum
Title: Applicability and Limitation Analysis of PMU Data and Phasor Concept for Low- and High- Frequency Oscillations
Abstract:
Phasor Measurement Units (PMUs) convert high‑speed waveform data into low‑speed phasor data, which are fundamental to wide‑area monitoring and control in power systems, with oscillation detection and localization among their most prominent applications. However, representing electrical waveform signals with oscillations using PMU phasors is effective only for low‑frequency oscillations. This paper investigates the root causes of this limitation, focusing on errors introduced by Discrete Fourier Transform (DFT)‑based signal processing, in addition to the attenuation effects of anti‑aliasing filters, and the impact of low reporting rates. To better represent and estimate waveform signals with oscillations, we propose a more general signal model and a multi‑step estimation method that leverages one‑cycle DFT, the Matrix Pencil Method, and the Least Squares Method. Numerical experiments demonstrate the superior performance of the proposed signal model and estimation method. Furthermore, this paper reveals that the phasor concept, let alone PMU phasors, can become invalid for waveform signals with high‑frequency oscillations characterized by asymmetric sub‑ and super‑synchronous components. These findings highlight the fundamental limitations of PMU data and phasor concept, and emphasize the need to rely on waveform data for analyzing high‑frequency oscillations in modern power systems.

Authors:Jiaqing Chang, Song Gao, Chaowei Dong, zhaobang Li, Yang Liu
Title: Preparation and Motion Study of Magnetically Driven Micro Soft Robot Mimicking the Cownose Ray
Abstract:
In narrow, unstructured underwater environments such as environmental monitoring and minimally invasive medical procedures, micro soft robots exhibit unique advantages due to their flexible movement capabilities and small size. At the same time, applying bionic technology to the structural design of micro soft robots can significantly improve their swimming performance. However, limited by their miniaturization, these robots are difficult to power internally and usually adopt a wireless power supply method. This study designs and fabricates a magnetically responsive, cownose ray‑inspired micro soft robot based on the swimming principle of the cownose ray. The robot is made of a certain proportion of NdFeB and PDMS. Then, a three‑dimensional Helmholtz coil is used to generate an oscillating harmonic magnetic field to conduct swimming experiments on the robot, exploring the influence of magnetic field parameters on the robot's swimming performance. The experimental results show that the swimming speed is the fastest at B = 5 mT and f = 11 Hz, reaching 5.25 mm/s, which is about 0.5 body lengths per second. In addition, by adjusting the current direction and frequency of the coil, the robot can perform different swimming modes such as straight swimming, turning swimming, and directional swimming. By employing a stepwise adjustment method, the impact of response errors on the robot's trajectory can be effectively reduced. This study demonstrates a method for magnetically driven micro soft robots, laying a foundation for the application of wireless‑driven robots in underwater narrow spaces.

Authors:Jianye Xu, Bassam Alrifaee
Title: TTCBF: A Truncated Taylor Control Barrier Function for High-Order Safety Constraints
Abstract:
Control Barrier Functions (CBFs) enforce safety by rendering a prescribed safe set forward invariant. However, standard CBFs are limited to safety constraints with relative degree one, while High‑Order CBF (HOCBF) methods address higher relative degree at the cost of introducing a chain of auxiliary functions and multiple class K functions whose tuning scales with the relative degree. In this paper, we introduce a Truncated Taylor Control Barrier Function (TTCBF), which generalizes standard discrete‑time CBFs to consider high‑order safety constraints and requires only one class K function, independent of the relative degree. We also propose an adaptive variant, adaptive TTCBF (aTTCBF), that optimizes an online gain on the class K function to improve adaptability, while requiring fewer control design parameters than existing adaptive HOCBF variants. Numerical experiments in a relative‑degree‑six spring‑mass system and a cluttered corridor navigation validate the above theoretical findings.

Authors:Natanon Tongamrak, Kannapha Amaruchkul, Wijarn Wangdee, Jitkomut Songsiri
Title: Stochastic EMS for Optimal 24/7 Carbon-Free Energy Operations
Abstract:
This paper proposes a two‑stage stochastic optimization formulation to determine optimal operation and procurement plans for achieving a 24/7 carbon‑free energy (CFE) compliance at minimized cost. The system in consideration follows primary energy technologies in Thailand including solar power, battery storage, and a diverse portfolio of renewable and carbon‑based energy procurement sources. Unlike existing literature focused on long‑term planning, this study addresses near real‑time operations using a 15‑minute resolution. A novel feature of the formulation is the explicit treatment of CFE compliance as a model parameter, enabling flexible targets such as a minimum percentage of hourly matching or a required number of carbon‑free days within a multi‑day horizon. The mixed‑integer linear programming formulation accounts for uncertainties in load and solar generation by integrating deep learning‑based forecasting within a receding horizon framework. By optimizing battery profiles and multi‑source procurement simultaneously, the proposed system provides a feasible pathway for transitioning to carbon‑free operations in emerging energy markets.

Authors:A. Vaca, J. Gutierrez Florensa, F. Milano
Title: Instantaneous Frequency in Power Systems using the Teager-Kaiser Energy Operator
Abstract:
This paper develops an instantaneous‑frequency (IF) local estimator calculated with the complex Teager‑Kaiser energy operator (CTKEO) and the dynamic‑signal identity. The contribution is a novel IF expression that makes the envelope‑curvature terms explicit, thus correcting the bias that affects conventional estimators used in power systems. The estimator aligns with complex‑frequency (CF) kinematics and admits a geometric interpretation (curvature/torsion) without phase unwrapping. Simulations and data‑driven examples demonstrate the accuracy of the proposed approach.

Authors:Oleg Romanchuk, Roman Bondar
Title: The Responsibility Vacuum: Organizational Failure in Scaled Agent Systems
Abstract:
Modern CI/CD pipelines integrating agent‑generated code exhibit a structural failure in responsibility attribution. Decisions are executed through formally correct approval processes, yet no entity possesses both the authority to approve those decisions and the epistemic capacity to meaningfully understand their basis. We define this condition as responsibility vacuum: a state in which decisions occur, but responsibility cannot be attributed because authority and verification capacity do not coincide. We show that this is not a process deviation or technical defect, but a structural property of deployments where decision generation throughput exceeds bounded human verification capacity. We identify a scaling limit under standard deployment assumptions, including parallel agent generation, CI‑based validation, and individualized human approval gates. Beyond a throughput threshold, verification ceases to function as a decision criterion and is replaced by ritualized approval based on proxy signals. Personalized responsibility becomes structurally unattainable in this regime. We further characterize a CI amplification dynamic, whereby increasing automated validation coverage raises proxy signal density without restoring human capacity. Under fixed time and attention constraints, this accelerates cognitive offloading in the broad sense and widens the gap between formal approval and epistemic understanding. Additional automation therefore amplifies, rather than mitigates, the responsibility vacuum. We conclude that unless organizations explicitly redesign decision boundaries or reassign responsibility away from individual decisions toward batch‑ or system‑level ownership, responsibility vacuum remains an invisible but persistent failure mode in scaled agent deployments.

Authors:Maiken Borud Omtveit, Qian Long, Valentin Chabaud, Marte Ruud-Olsen, Steinar Halsne, Tor-Christian Ystgaard
Title: Electrical Design of a Clean Offshore Heat and Power (CleanOFF) Hub
Abstract:
This paper presents an innovative offshore solution where oil & gas platform clusters are powered by a wind farm and a hydrogen hub. The results show a feasible off‑grid design as an alternative to conventional electrification solutions. To address the challenges of design and operation of such a system, a power system model of the equipment and control was developed in a power system simulator called Process Power Simulator (PPSim). Power fluctuations in the wind farm are modelled using a state‑of‑the‑art method encompassing turbulence and wakes. Various operation scenarios were used to evaluate the system design and find the right equipment size. An expensive component to over dimension is the battery energy storage system (BESS). The BESS power rating and energy capacity were found by running a combination of scenarios with extreme and natural wind variations, and contingencies. The control strategy and ramp rates of electrolyzers have significant impact on both system performance and design. A ramp rate in the order of seconds as opposed to minutes will decrease the required BESS size by 60‑70%. Choosing synchronized control of the electrolyzers can further reduce the BESS size by 15‑20%. The simulations also revealed challenges to achieve self‑sufficiency of hydrogen and potential design improvements are suggested.

Authors:Jingwei Dong, André M. H. Teixeira
Title: Stealthy bias injection attack detection based on Kullback-Leibler divergence in stochastic linear systems
Abstract:
This paper studies the design of detection observers against stealthy bias injection attacks in stochastic linear systems under Gaussian noise, considering adversaries that exploit noise and inject crafted bias signals into a subset of sensors in a slow and coordinated manner, thereby achieving malicious objectives while remaining stealthy. To address such attacks, we formulate the observer design as a max‑min optimization problem to enhance the detectability of worst‑case BIAs, which attain a prescribed attack impact with the least detectability evaluated via Kullback‑Leibler divergence. To reduce the computational complexity of the derived non‑convex design problem, we consider the detectability of worst‑case BIAs at three specific time instants: attack onset, one step after attack occurrence, and the steady state. We prove that the Kalman filter is optimal for maximizing the BIA detectability at the attack onset, regardless of the subset of attacked sensors. For the one‑step and steady‑state cases, the observer design problems are approximated by bi‑convex optimization problems, which can be efficiently solved using alternating optimization and alternating direction method of multipliers. Moreover, more tractable linear matrix inequality relaxations are developed. Finally, the effectiveness of the proposed stealth‑aware detection framework is demonstrated through an application to a thermal system.

Authors:Mehdi Golestani, Yongduan Song, Weizhen Liu, Guangren Duan, He Kong
Title: Practical prescribed-time prescribed performance control with asymptotic convergence -- A vanishing sigma-modification approach
Abstract:
In this paper, we present a method capable of ensuring practical prescribed‑time control with guaranteed performance for a class of nonlinear systems in the presence of time‑varying parametric and dynamic uncertainties, and uncertain control coefficients. Our design consists of two key steps. First, we construct a performance‑rate function that freezes at and after a user‑specified time T, playing a crucial role in achieving desired precision within prescribed time T and dealing with unmodeled dynamics. Next, based on this function and a sigma‑modification strategy in which the leakage term starts to vanish at t > T, we develop an adaptive dynamic surface control framework to reduce control complexity, deal with uncertainties, ensure prescribed performance, practical prescribed‑time convergence to a specific region, and ultimately achieve asymptotic convergence. The effectiveness of the proposed control method is validated through numerical simulations.

Authors:Lei Zheng, Luyao Zhang, Peiqi Yu, Yifan Sun, Sergio Grammatico, Jun Ma, Changliu Liu
Title: Contingency Planning for Safety-Critical Autonomous Vehicles: A Review and Perspectives
Abstract:
Contingency planning is the architectural capability that enables autonomous vehicles (AVs) to anticipate and mitigate discrete, high‑impact hazards, such as sensor outages and adversarial interactions. This paper presents a comprehensive survey of the field, synthesizing fragmented literature into a unified logic‑conditioned hybrid control framework. Within this formalism, we categorize approaches into two distinct paradigms: Reactive Safety, which responds to realized hazards by enforcing safety constraints or executing fail‑safe maneuvers; and Proactive Safety, which optimizes for future recourse by branching over potential modal transitions. In addition, we propose a fine‑grained taxonomy that partitions the landscape into external contingencies (environmental and interactive hazards) and internal contingencies (system faults). Through a critical comparative analysis, we reveal a fundamental structural divergence: internal faults are predominantly addressed via reactive fail‑safe mechanisms, whereas external interaction uncertainties increasingly require proactive branching strategies. Furthermore, we identify a critical methodological divergence: whereas physical hazards are typically managed with formal guarantees, semantic and out‑of‑distribution anomalies currently rely heavily on empirical validation. We conclude by identifying the open challenges in bridging the gap between theoretical guarantees and practical validation, advocating for hybrid architectures and standardized benchmarking to transition contingency planning from formulation to certifiable real‑world deployment.

Authors:Qinghao Wen, Zihao Ren, Lei Wang, Hyungbo Shim, Guodong Shi
Title: Blended Dynamics and Emergence in Open Quantum Networks
Abstract:
In this paper, we develop a blended dynamics framework for open quantum networks with diffusive couplings. The network consists of qubits interconnected through Hamiltonian couplings, environmental dissipation, and consensus‑like diffusive interactions. Such networks commonly arise in spontaneous emission processes and non‑Hermitian quantum computing, and their evolution follows a Lindblad master equation. Blended dynamics theory is well established in the classical setting as a tool for analyzing emergent behaviors in heterogeneous networks with diffusive couplings. Its key insight is to blend the local dynamics rather than the trajectories of individual nodes. Perturbation analysis then shows that, under sufficiently strong coupling, all node trajectories tend to stay close to those of the blended system over time. We first show that this theory extends naturally to the reduced‑state dynamics of quantum networks, revealing classical‑like clustering phenomena in which qubits converge to a shared equilibrium or a common trajectory determined by the quantum blended reduced‑state dynamics. We then extend the analysis to qubit coherent states using quantum Laplacians and induced graphs, proving orbit attraction of the network density operator toward the quantum blended coherent dynamics, establishing the emergence of intrinsically quantum and dynamically clustering behaviors. Finally, numerical examples validate the theoretical results.

Authors:Zihao Ren, Lei Wang, Deming Yuan, Guodong Shi
Title: Differential Privacy on Affine Manifolds: Geometrically Confined Privacy in Linear Dynamical Systems
Abstract:
In this paper, we present a comprehensive framework for differential privacy over affine manifolds and validate its usefulness in the contexts of differentially private cloud‑based control and average consensus. We consider differential privacy mechanisms for linear queries when the input data are constrained to lie on affine manifolds, a structural property that is assumed to be available as prior knowledge to adversaries. In this setting, the definition of neighborhood adjacency must be formulated with respect to the intrinsic geometry of the manifolds. We demonstrate that such affine‑manifold constraints can fundamentally alter the attainable privacy levels relative to the unconstrained case. In particular, we derive necessary and sufficient conditions under which differential privacy can be realized via structured noise injection mechanisms, wherein correlated Gaussian or Laplace noise distributions, rather than i.i.d. perturbations, are calibrated to the dataset. Based on these characterizations, we develop explicit noise calibration procedures that guarantee the tight realization of any prescribed privacy budget with a matching noise magnitude. Finally, we show that the proposed framework admits direct applications to linear dynamical systems ranging from differentially private cloud‑based control to privacy‑preserving average consensus, all of which naturally involve affine‑manifold constraints. The established theoretical results are illustrated through numerical examples.

Authors:Ruixing Ren, Minqi Tao, Junhui Zhao, Xiaoke Sun, Qiuping Li
Title: Hierarchical Optimization Based Multi-objective Dynamic Regulation Scheme for VANET Topology
Abstract:
As a core technology of intelligent transportation systems, vehicular ad‑hoc networks support latency‑sensitive services such as safety warning and cooperative perception via vehicle‑to‑everything communications. However, their highly dynamic topology increases average path length, raises latency, and reduces throughput, severely limiting communication performance. Existing topology optimization methods lack capabilities in multi‑objective coordination, dynamic adaptation, and global‑local synergy. To address this, this paper proposes a two‑layer dynamic topology regulation scheme combining local feature aggregation and global adjustment. The scheme constructs a dynamic multi‑objective optimization model integrating average path length, end‑to‑end latency, and network throughput, and achieves multi‑index coordination via link adaptability metrics and a dynamic normalization mechanism. it quickly responds to local link changes via feature fusion of local node feature extraction and dynamic neighborhood sensing, and balances optimization accuracy and real‑time performance using a dual‑mode adaptive solving strategy for global topology adjustment. It reduces network oscillation risks by introducing a performance improvement threshold and a topology validity verification mechanism. Simulation results on real urban road networks via the SUMO platform show that the proposed scheme outperforms traditional methods in average path length (stabilizing at ~4 hops), end‑to‑end latency (remaining ~0.01 s), and network throughput.

Authors:Hyeongon Park, Daniel K. Molzahn, Rahul K. Gupta
Title: Ramping-aware Enhanced Flexibility Aggregation of Distributed Generation with Energy Storage in Power Distribution Networks
Abstract:
Power distribution networks are increasingly hosting controllable and flexible distributed energy resources (DERs) that, when aggregated, can provide ancillary support to transmission systems. However, existing aggregation schemes often ignore the ramping constraints of these DERs, which can render them impractical in real deployments. This work proposes a ramping‑aware flexibility aggregation scheme, computed at the transmission‑distribution boundary, that explicitly accounts for DER ramp limits and yields flexibility envelopes that are provably disaggregable. To further enhance the attainable flexibility region, we introduce a novel pre‑ramping strategy, which proactively adjusts resource operating points to enlarge the aggregated flexibility envelope while preserving both network feasibility and disaggregation guarantees. The proposed method demonstrates a 5.2% to 19.2% improvement in flexibility relative to the baseline model, depending on system conditions. We validate the scheme on an IEEE‑33 bus distribution system and provide formal proofs showing that both aggregation strategies are disaggregable for all feasible trajectories within the aggregate flexibility envelope.

Authors:Yogesh Pipada Sunil Kumar, S. Ali Pourmousavi, Jon A. R. Liisberg, Julian Lesmos-Vinasco
Title: Efficient reformulations of ReLU deep neural networks for surrogate modelling in power system optimisation
Abstract:
The ongoing decarbonisation of power systems is driving an increasing reliance on distributed energy resources, which introduces complex and nonlinear interactions that are difficult to capture in conventional optimisation models. As a result, machine learning based surrogate modelling has emerged as a promising approach, but integrating machine learning models such as ReLU deep neural networks (DNNs) directly into optimisation often results in nonconvex and computationally intractable formulations. This paper proposes a linear programming (LP) reformulation for a class of convexified ReLU DNNs with non‑negative weight matrices beyond the first layer, enabling a tight and tractable embedding of learned surrogate models in optimisation. We evaluate the method using a case study on learning the prosumer's responsiveness within an aggregator bidding problem in the Danish tertiary capacity market. The proposed reformulation is benchmarked against state‑of‑the‑art alternatives, including piecewise linearisation (PWL), MIP‑based embedding, and other LP relaxations. Across multiple neural network architectures and market scenarios, the convexified ReLU DNN achieves solution quality comparable to PWL and MIP‑based reformulations while significantly improving computational performance and preserving model fidelity, unlike penalty‑based reformulations. The results demonstrate that convexified ReLU DNNs offer a scalable and reliable methodology for integrating learned surrogate models in optimisation, with applicability to a wide range of emerging power system applications.

Authors:Sharaf K. Magableh, Caisheng Wang, Oraib Dawaghreh
Title: A Two-Stage Risk-Averse DRO-MILP Methodological Framework for Managing AI/Data Center Demand Shocks
Abstract:
The rapid growth of artificial intelligence (AI)‑driven data centers is reshaping electricity demand patterns. This is achieved by introducing fast, multi‑gigawatt load ramps that challenge the stability and resilience of modern power systems. Traditional resilience frameworks focus mainly on physical outages and largely overlook these emerging digital‑era disturbances. This paper proposes a unified two‑stage, risk‑aware distributionally robust optimization (DRO)‑MILP framework that coordinates the pre‑allocation and post‑event dispatch of Flexible Capacity Modules (FCMs), including BESS, fast‑ramping generation, demand response, and potential long‑duration storage. Stage‑I optimally positions FCMs using DRO with CVaR to hedge against uncertain AI load surges. Stage‑II models real‑time stabilization following stochastic demand‑shock scenarios, minimizing imbalance, unserved energy, and restoration penalties. The framework is designed to be applied on IEEE 33‑bus system or expanded for scalability to larger IEEE test feeders capable of representing AI‑scale loads. This contributes a scalable planning tool for resilient, AI‑integrated distribution grids.

Authors:Yogesh Pipada Sunil Kumar, S. Ali Pourmousavi, Jon A. R. Liisberg, Julian Lesmos-Vinasco
Title: Calibrated uncertainty quantification for prosumer flexibility aggregation in ancillary service markets
Abstract:
Reliable forecasting of prosumer flexibility is critical for demand response aggregators participating in frequency controlled ancillary services market, where strict reliability requirements such as the P90 standard are enforced. Limited historical data, dependence on exogeneous factors, and heterogenous prosumer behaviour introduce significant epistemic uncertainty, making deterministic or poorly calibrated probabilistic models unsuitable for market bidding. This paper proposes the use of scalable uncertainty quantification framework that integrates Monte Carlo dropout (MCD) with conformal prediction (CP) to produce calibrated, finite sample prediction intervals for aggregated prosumer flexibility. The proposed framework is applied to a behind‑the‑meter aggregator participating in the Danish manual frequency restoration reserve capacity market. A large‑scale synthetic dataset is generated using a modified industry‑grade home energy management system, combined with publicly available load, solar, price, activation and device‑level data. The resulting machine learning surrogate model captures aggregate prosumer price responsiveness and provides uncertainty‑aware estimates suitable for market bidding. Multiple multivariate CP strategies are evaluated and benchmarked against conventional MCD‑based methods. Results show that standalone MCD systematically overestimates available flexibility and violates P90 compliance, whereas the proposed MCD‑CP framework achieves reliable coverage with controlled conservatism. When embedded in aggregator bidding model, conformalised methods substantially reduce overbidding risk and achieve upto 70% of perfect‑information profit while satisfying regulatory reliability constraints, providing practical, computationally efficient, and market‑compliant solution for aggregator flexibility forecasting under uncertainty.

Authors:Bhabani Shankar Dey, Ahan Basu, Pushpak Jagtap
Title: Input-to-State Stabilizing Neural Controllers for Unknown Switched Nonlinear Systems within Compact Sets
Abstract:
This paper develops a neural network based control framework that ensures system safety and input‑to‑state stability (ISS) for general nonlinear switched systems with unknown dynamics. Leveraging the concept of dwell time, we derive Lyapunov based sufficient conditions under which both safety and ISS of the closed‑loop switched system are guaranteed. The feedback controllers and the associated Lyapunov functions are parameterized using neural networks and trained from data collected over a compact state space via deterministic sampling. To provide formal stability guarantees under the learned controllers, we introduce a validity condition based on Lipschitz continuity assumptions, which is embedded directly into the training framework. This ensures that the resulting neural network controllers satisfy provable correctness and stability guarantees beyond the sampled data. As a special case, the proposed framework recovers ISS and safety under arbitrary switching when a common Lyapunov function exists. Simulation results on a representative switched nonlinear system demonstrate the effectiveness of the proposed approach.

Authors:Guillaume Ambal, Max Stupple, Brijesh Dongol, Azalea Raad
Title: Specifying and Verifying RDMA Synchronisation (Extended Version)
Abstract:
Remote direct memory access (RDMA) allows a machine to directly read from and write to the memory of remote machine, enabling high‑throughput, low‑latency data transfer. Ensuring correctness of RDMA programs has only recently become possible with the formalisation of \textRDMA^\textTSO semantics (describing the behaviour of RDMA networking over a TSO CPU). However, this semantics currently lacks a formalisation of remote synchronisation, meaning that the implementations of common abstractions such as locks cannot be verified. In this paper, we close this gap by presenting \textRDMA^\textTSO_\textRMW, the first semantics for remote `read‑modify‑write' (RMW) instructions over TSO. It turns out that remote RMW operations are weak and only ensure atomicity against other remote RMWs. We therefore build a set of composable synchronisation abstractions starting with the \textRDMA^\textWAIT_\textRMW library. Underpinned by \textRDMA^\textWAIT_\textRMW, we then specify, implement and verify three classes of remote locks that are suitable for different scenarios. Additionally, we develop the notion of a strong RDMA model, \textRDMA^\textSC_\textRMW, which is akin to sequential consistency in shared memory architectures. Our libraries are built to be compatible with an existing set of high‑performance libraries called LOCO, which ensures compositionality and verifiability.

Authors:Tingwei Zhang, Jiahui Liu, David Allstot, Huaping Liu
Title: An Ion-Intercalation Memristor for Enabling Full Parallel Writing in Crossbar Networks
Abstract:
Crossbar architectures have long been seen as a promising foundation for in‑memory computing, using memristor arrays for high‑density, energy‑efficient analog computation. However, this conventional architecture suffers from a fundamental limitation: the inability to perform parallel write operations due to the sneak path problem. This arises from the structural overlap of read and write paths, forcing sequential or semi‑parallel updates and severely limiting scalability. To address this, we introduce a new memristor design that decouples read and write operations at the device level. This design enables orthogonal conductive paths, and employs a reversible ion doping mechanism, inspired by lithium‑ion battery principles, to modulate resistance states independently of computation. Fabricated devices exhibit near‑ideal memristive characteristics and stable performance under isolated read/write conditions.

Authors:Victor Hugo Pereira Rodrigues, Tiago Roux Oliveira, Miroslav Krstic, Paulo Tabuada
Title: Event-Triggered Newton Extremum Seeking for Multivariable Optimization
Abstract:
This paper presents a static event‑triggered control strategy for multivariable Newton‑based extremum seeking. The proposed method integrates event‑triggered actuation into the Newton‑based optimization framework to reduce control updates while maintaining rapid convergence to the extremum. Unlike traditional gradient‑based extremum seeking, where the convergence rate depends on the unknown Hessian of the cost function, the proposed approach employs a dynamic estimator of the Hessian inverse, formulated as a Riccati equation, enabling user‑assignable convergence rates. The event‑triggering mechanism is designed to minimize unnecessary actuation updates while preserving stability and performance. Using averaging theory, we establish local stability results and exponential convergence to a neighborhood of the unknown extremum point. Additionally, numerical simulations illustrate the benefits of the proposed approach over gradient‑based and continuously actuated Newton‑based extremum seeking, showing improved convergence rates and reduced control update frequency, leading to more efficient implementation in real‑time optimization scenarios.

Authors:Liang Wu, Bo Yang, Junheng Li, Xu Yang, Yilin Mo, Yang Shi, Aaron D. Ames, Ján Drgoňa
Title: $π$MPC: A Parallel-in-horizon and Construction-free NMPC Solver
Abstract:
The alternating direction method of multipliers (ADMM) has gained increasing popularity in embedded model predictive control (MPC) due to its code simplicity and pain‑free parameter selection. However, existing ADMM solvers either target general quadratic programming (QP) problems or exploit sparse MPC formulations via Riccati recursions, which are inherently sequential and therefore difficult to parallelize for long prediction horizons. This technical note proposes a novel parallel‑in‑horizon and construction‑free nonlinear MPC algorithm, termed πMPC, which combines a new variable‑splitting scheme with a velocity‑based system representation in the ADMM framework, enabling horizon‑wise parallel execution while operating directly on system matrices without explicit MPC‑to‑QP construction. Numerical experiments and accompanying code are provided to validate the effectiveness of the proposed method.

Authors:William Pan, Guiran Liu, Binrong Zhu, Qun Wang, Yingzhou Lu, Beiyu Lin, Rose Qingyang Hu
Title: Rethinking On-Device LLM Reasoning: Why Analogical Mapping Outperforms Abstract Thinking for IoT DDoS Detection
Abstract:
The rapid expansion of IoT deployments has intensified cybersecurity threats, notably Distributed Denial of Service (DDoS) attacks, characterized by increasingly sophisticated patterns. Leveraging Generative AI through On‑Device Large Language Models (ODLLMs) provides a viable solution for real‑time threat detection at the network edge, though limited computational resources present challenges for smaller ODLLMs. This paper introduces a novel detection framework that integrates Chain‑of‑Thought (CoT) reasoning with Retrieval‑Augmented Generation (RAG), tailored specifically for IoT edge environments. We systematically evaluate compact ODLLMs, including LLaMA 3.2 (1B, 3B) and Gemma 3 (1B, 4B), using structured prompting and exemplar‑driven reasoning strategies. Experimental results demonstrate substantial performance improvements with few‑shot prompting, achieving macro‑average F1 scores as high as 0.85. Our findings highlight the significant advantages of incorporating exemplar‑based reasoning, underscoring that CoT and RAG approaches markedly enhance small ODLLMs' capabilities in accurately classifying complex network attacks under stringent resource constraints.

Authors:Manuel Kohli, Jean Teissier
Title: VCSEL-based CPO for Scale-Up in A.I. Datacenter. Status and Perspectives
Abstract:
The drastic increase of bandwidth demands in AI datacenters requires new solutions with low power consumption and high bandwidth densities. Further increasing the total bandwidth with copper interconnects is challenging, thus it is crucial to introduce optics into the scale‑up network. For this purpose, the most important metrics are the energy efficiency of the total link in addition to the spatial bandwidth‑density and reach. With the inefficiency of copper traces at high speeds, transitioning to co‑packaged optics is no longer optional but essential. Here, we show that the mature VCSEL technology offers the ideal combination of low‑cost, low‑latency, high‑reliability, and energy efficiency at all bitrates, thanks to their unique versatility and high wall‑plug‑efficiency. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

Authors:Yichen Guo, Tao Peng, Yujie Zhao, Yijing Niu, Wenbo Wang
Title: The Impact of Interference Cognition on the Reliability and Capacity of Industrial Wireless Communications
Abstract:
Interference significantly impacts the performance of industrial wireless networks, particularly n severe interference environments with dense networks reusing spectrum resources intensively. Although delicate interference information is often unavailable in conventional networks, emerging interference cognition techniques can compensate this critical problem with possibly different precision. This paper investigates the relationship between precision of interference cognition and system performance. We propose a novel performance analysis framework that quantifies the impact of varying interference information precision on achievable rate. Specifically, leveraging the Nakagami‑\mathbfm fading channel model, we analytically and asymptotically analyze the average achievable rate in the finite blocklength regime for different precision levels of signal and interference information. Our findings reveal the critical importance of identifying per‑link interference information for achieving optimal performance. Additionally, obtaining instantaneous information is more beneficial for signal links.

Authors:Cristian Sestito, Panagiota Kontou, Pratibha Verma, Atish Dixit, Alexandros D. Keros, Michael O'Boyle, Christos-Savvas Bouganis, Themis Prodromakis
Title: A flexible language model-assisted electronic design automation framework
Abstract:
Large language models (LLMs) are transforming electronic design automation (EDA) by enhancing design stages such as schematic design, simulation, netlist synthesis, and place‑and‑route. Existing methods primarily focus these optimisations within isolated open‑source EDA tools and often lack the flexibility to handle multiple domains, such as analogue, digital, and radio‑frequency design. In contrast, modern systems require to interface with commercial EDA environments, adhere to tool‑specific operation rules, and incorporate feedback from design outcomes while supporting diverse design flows. We propose a versatile framework that uses LLMs to generate files compatible with commercial EDA tools and optimise designs using power‑performance‑area reports. This is accomplished by guiding the LLMs with tool constraints and feedback from design outputs to meet tool requirements and user specifications. Case studies on operational transconductance amplifiers, microstrip patch antennas, and FPGA circuits show that the framework is effective as an EDA‑aware assistant, handling diverse design challenges reliably.

Authors:Zhenxu Zhao, Ji Wang, Weiyao Lan
Title: Data-Driven Safe Output Regulation of Strict-Feedback Linear Systems with Input Delay
Abstract:
This paper develops a data‑driven safe control framework for linear systems possessing a known strict‑feedback structure, but with most plant parameters, external disturbances, and input delay being unknown. By leveraging Koopman operator theory, we utilize Krylov dynamic mode decomposition (DMD) to extract the system dynamics from measured data, enabling the reconstruction of the system and disturbance matrices. Concurrently, the batch least‑squares identification (BaLSI) method is employed to identify other unknown parameters in the input channel. Using control barrier functions (CBFs) and backstepping, we first develop a full‑state safe controller. Based on this, we build an output‑feedback controller by performing system identification using only the output data and actuation signals as well as constructing an observer to estimate the unmeasured plant states. The proposed approach achieves: 1) finite‑time identification of a substantial set of unknown system quantities, and 2) exponential convergence of the output state (the state furthest from the control input) to a reference trajectory while rigorously ensuring safety constraints. The effectiveness of the proposed method is demonstrated through a safe vehicle platooning application.

Authors:Sahan Liyanaarachchi, Sennur Ulukus, Nail Akar
Title: Utilizing the Perceived Age to Maximize Freshness in Query-Based Update Systems
Abstract:
Query‑based sampling has become an increasingly popular technique for monitoring Markov sources in pull‑based update systems. However, most of the contemporary literature on this assumes an exponential distribution for query delay and often relies on the assumption that the feedback or replies to the queries are instantaneous. In this work, we relax both of these assumptions and find optimal sampling policies for monitoring continuous‑time Markov chains (CTMC) under generic delay distributions. In particular, we show that one can obtain significant gains in terms of mean binary freshness (MBF) by employing a waiting based strategy for query‑based sampling.

Authors:Sander Doodeman, Paula Chanfreut Palacio, Elena Torta, Duarte Antunes
Title: Where to Place a Heavy Payload on a Multirotor UAV for Best Control Performance
Abstract:
This paper studies the impact of rigidly attached heavy payload placement ‑ where the payload mass significantly influences the UAV's dynamics ‑ on the stability and control performance of a multirotor unmanned aerial vehicle (UAV). In particular, we focus on how the position of such a payload relative to the vehicle's Center of Gravity (CoG) affects the stability and control performance at an arbitrary point of interest on the UAV, such as the payload position, and on how this position can be optimized. Our conclusions are based on two key contributions. First, we analyze the stability of the zero‑dynamics of a complete nonlinear model of the UAV with payload. We demonstrate that the stability of the zero dynamics depends on the vertical signed distance in the body‑fixed frame between the controlled output position and the combined CoG of the UAV with payload. Specifically, positioning the output below the CoG yields unstable zero dynamics, while the linearized zero dynamics are marginally stable when placing it above, indicating reduced sensitivity to input disturbances. Second, we analyze the performance of the linearized UAV model with payload by providing an analytical expression for the H2‑norm, from which we can quantify the system's attenuation to white noise input disturbances. We conclude that less control authority leads to a higher optimal position of the controlled output with respect to the CoG for closed‑loop white‑noise disturbance rejection capabilities, also when the heavy payload is the controlled output. The results are illustrated through numerical examples.

Authors:Yongqiang Zhang, Mustafa A. Kishk, Mohamed-Slim Alouini
Title: Small Models, Big Impact: Tool-Augmented AI Agents for Wireless Network Planning
Abstract:
Large Language Models (LLMs) such as ChatGPT promise revolutionary capabilities for Sixth‑Generation (6G) wireless networks but their massive computational requirements and tendency to generate technically incorrect information create deployment barriers. In this work, we introduce MAINTAINED: autonomous artificial intelligence agent for wireless network deployment. Instead of encoding domain knowledge within model parameters, our approach orchestrates specialized computational tools for geographic analysis, signal propagation modeling, and network optimization. In a real‑world case study, MAINTAINED outperforms state‑of‑the‑art LLMs including ChatGPT‑4o, Claude Sonnet 4, and DeepSeek‑R1 by up to 100‑fold in verified performance metrics while requiring less computational resources. This paradigm shift, moving from relying on parametric knowledge towards externalizing domain knowledge into verifiable computational tools, eliminates hallucination in technical specifications and enables edge‑deployable Artificial Intelligence (AI) for wireless communications.

Authors:Ruixing Ren, Shan Chen, Xuehan Bao, Pingzheng Ge, Dongming Wang, Junhui Zhao
Title: Base Station Sleeping Strategy Based on Load Sharing in Ultra-Dense Networks
Abstract:
To address the issues of high operational costs and low energy efficiency (EE) caused by the dense deployment of small base stations (s‑BSs) in 5G ultra‑dense networks (UDNs), this paper first constructs a multi‑objective mathematical optimization model targeting maximizing EE and minimizing the number of active BSs. The model incorporates key constraints including BS operational state, user equipment (UE)‑BS connection relationship, and load threshold, laying a theoretical foundation for the coordinated optimization of energy conservation and quality of service. Based on this model, an integrated solution combining UE‑BS initial connection optimization and load‑sharing based BS sleeping is proposed. In the initial connection phase, with communication quality and BS load as dual constraints, efficient matching between UEs and optimal BSs is achieved through three sequential steps: communication feasibility screening, redundant connection removal, and overload load redistribution. This resolves the problems of load imbalance and difficult identification of redundant BSs in UDNs arising from unordered initial connections. In the BS sleeping phase, a BS sleeping index, comprehensively considering UE transferability and backup BS resources, is innovatively introduced to quantify BS dormancy priority. Through a closed‑loop process involving low‑load BS screening, adjacent BS load evaluation, and load sharing by two takeover BSs based on their capacity, accurate dormancy of redundant BSs and collaborative load migration are realized. Simulation results in a typical UDNs scenario demonstrate that, compared with the traditional baseline scheme, the proposed solution exhibits significant advantages in convergence speed, optimization of the number of active BSs, and EE improvement.

Authors:Ruixing Ren
Title: Integrated Sensing and Communication for Low-Altitude Security
Abstract:
The dense concentration of low‑altitude, slow‑speed, and small‑size targets in the complex low‑altitude environment poses significant security challenges, including failures in continuous wide‑area sensing and ambiguous target intent, which existing regulatory frameworks struggle to address. Integrated sensing and communication (ISAC), a hallmark of next‑generation mobile communication, offers a transformative approach to low‑altitude security governance. By leveraging existing cellular infrastructure and spectrum resources, ISAC enables the construction of a seamless wide‑area sensing network, supports intelligent feature extraction and intent inference, facilitates real‑time collaborative decision‑making, and establishes a dynamic trust authentication framework. This article systematically reviews the technical system, analyzes the security challenges, forecasts the enabling value of ISAC, and discusses the resulting open problems and challenges, thereby laying a foundation for future research and industrial implementation.

Authors:Luigi Romano, Ole Morten Aamo, Jan Åslund, Erik Frisk
Title: Linear viscoelastic rheological FrBD models
Abstract:
In [1], a new modeling paradigm for developing rate‑and‑state‑dependent, control‑oriented friction models was introduced. The framework, termed Friction with Bristle Dynamics (FrBD), combines nonlinear analytical expressions for the friction coefficient with constitutive equations for bristle‑like elements. Within the FrBD framework, this letter introduces two novel formulations based on the two most general linear viscoelastic models for solids: the Generalized Maxwell (GM) and Generalized Kelvin‑Voigt (GKV) elements. Both are analyzed in terms of boundedness and passivity, revealing that these properties are satisfied for any physically meaningful parametrization. An application of passivity for control design is also illustrated, considering an example from robotics. The findings of this letter systematically integrate rate‑and‑state dynamic friction models with linear viscoelasticity.

Authors:Matteo Vaccargiu, Azmat Ullah, Pierluigi Gallo
Title: A Blockchain-Oriented Software Engineering Architecture for Carbon Credit Certification Systems
Abstract:
Carbon credit systems have emerged as a policy tool to incentivize emission reductions and support the transition to clean energy. Reliable carbon‑credit certification depends on mechanisms that connect actual, measured renewable‑energy production to verifiable emission‑reduction records. Although blockchain and IoT technologies have been applied to emission monitoring and trading, existing work offers limited support for certification processes, particularly for small and medium‑scale renewable installations. This paper introduces a blockchain‑based carbon‑credit certification architecture, demonstrated through a 100 kWp photovoltaic case study, that integrates real‑time IoT data collection, edge‑level aggregation, and secure on‑chain storage on a permissioned blockchain with smart contracts. Unlike approaches focused on trading mechanisms, the proposed system aligns with European legislation and voluntary carbon‑market standards, clarifying the practical requirements and constraints that apply to photovoltaic operators. The resulting architecture provides a structured pathway for generating verifiable carbon‑credit records and supporting third‑party verification.

Authors:Yiwei Zhou, Zhongcheng Lei, Xiaoran Dai, Wenshan Hu, Hong Zhou
Title: Research on Adaptive Inertial Control in Synchronization Systems: Based on Variational Optimization Methods and Their Applications in the Stability of Complex Networks
Abstract:
Aiming at the core problem that it is difficult for a fixed inertia coefficient to balance transient disturbance suppression and long‑term stability in complex network synchronization systems, an adaptive inertia control strategy based on variational optimization is proposed. Taking the Kuramoto model with inertia as the research carrier, the analytical expression of the time‑varying inertia coefficient M(t) is strictly derived by the functional variational method, and a hierarchical control structure of "benchmark inertia + disturbance feedback" is constructed to achieve the organic unity of minimizing the vulnerability performance function H(T) and stability constraints. A multimodal decoupling control strategy based on Laplacian eigenvector projection is designed to enhance the feedback strength of the dominant mode by eigenvalue weighting, improving the control accuracy and dynamic response speed. Simulation verification is carried out in complex network systems, and the control performance of regular networks (RG), random networks (ER), small‑world networks (SW), scale‑free networks (SF) and spider webs (SP) under three typical disturbances of pulses, monotonic decays and oscillatory decays is systematically analyzed. The results show that the proposed strategy reduces H(T) of the five networks by 19%‑25%, shortens the relaxation time by 15%‑24%, and the real parts of all system eigenvalues are less than ‑0.25s^‑1 , meeting the asymptotic stability criterion. This study provides a new theoretical framework and engineering implementation scheme for the stability control of complex network synchronization systems, which can be widely applied to fields such as power grids, communication networks, and neural networks.

Authors:Joon Lee, Jeongyoon Han, Doyoung Kim, Seokhwan Jeong
Title: RIM Hand : A Robotic Hand with an Accurate Carpometacarpal Joint and Nitinol-Supported Skeletal Structure
Abstract:
This paper presents the flexible RIM Hand, a biomimetic robotic hand that precisely replicates the carpometacarpal (CMC) joints and employs superelastic Nitinol wires throughout its skeletal framework. By modeling the full carpal‑to‑metacarpal anatomy, the design enables realistic palm deformation through tendon‑driven fingers while enhancing joint restoration and supports skeletal structure with Nitinol‑based dorsal extensors. A flexible silicone skin further increases contact friction and contact area, enabling stable grasps for diverse objects. Experiments show that the palm can deform up to 28%, matching human hand flexibility, while achieving more than twice the payload capacity and three times the contact area compared to a rigid palm design. The RIM Hand thus offers improved dexterity, compliance, and anthropomorphism, making it promising for prosthetic and service‑robot applications.

Authors:Xun Feng, Chao Zhai
Title: Distributed Coverage Control on Poriferous Surface via Poly-Annulus Conformal Mapping
Abstract:
The inherent non‑convexity of poriferous surfaces typically entraps agents in local minima and complicates workload distribution. To resolve this, we propose a distributed diffeomorphic coverage control framework for the multi‑agent system (MAS) in such surfaces. First, we establish a distributed poly‑annulus conformal mapping that transforms arbitrary poriferous surfaces into a multi‑hole disk. Leveraging this topological equivalence, a collision‑free sectorial partition mechanism is designed in the multi‑hole disk, which rigorously induces strictly connected subregions and workload balance on the poriferous surfaces. This mechanism utilizes a buffer‑based sequence mechanism to ensure strict topological safety when bypassing obstacles. Furthermore, a pull‑back Riemannian metric is constructed to define the length metric that encodes safety constraints. Based on this metric, a distributed gradient‑based control law is synthesized to drive agents toward optimal configurations, ensuring simultaneous obstacle avoidance and coverage optimization. Theoretical analyses guarantee the Input‑to‑State Stability (ISS) of the partition dynamics and the asymptotic convergence of the closed‑loop system. Numerical simulations confirm the reachability and robustness of the proposed coverage algorithm, offering a scalable solution for distributed coverage in poriferous surfaces.

Authors:Lifu Ding, Chunhui Hou, Yutong Li, Qinmin Yang
Title: Resilient Hierarchical Power Control for Hybrid GFL/GFM Microgrids Under Mixed Cyber-Attacks and Physical Constraints
Abstract:
Hybrid microgrids integrating Grid‑Following (GFL) and Grid‑Forming (GFM) inverters present complex control challenges arising from the decoupling between long‑term economic dispatch and real‑time dynamic regulation, as well as the distinct physical limitations of heterogeneous inverters under cyber uncertainties. This paper proposes a Resilient Hierarchical Power Control (RHPC) strategy to unify these conflicting requirements within a cohesive framework. A standardized power increment mechanism is developed to bridge the tertiary and secondary layers, ensuring that real‑time load fluctuations are compensated strictly according to the optimal economic ratios derived from the tertiary layer. To address the strict active power saturation constraints of GFL units, a dynamic activation scheme coupled with projection operators is introduced, which actively isolates saturated nodes from the consensus loop to prevent integrator wind‑up and preserve the stability of the GFM backbone. Furthermore, the proposed framework incorporates a multi‑scale attention mechanism and LSTM‑based predictors into the secondary control protocol, endowing the system with robustness against unbounded False Data Injection (FDI) attacks and packet losses. Rigorous theoretical analysis confirms that the system achieves Uniformly Ultimately Bounded (UUB) convergence, and simulations on a modified IEEE 33‑bus system demonstrate that the proposed strategy significantly improves power sharing accuracy and operational resilience in both grid‑connected and islanded modes compared to conventional methods.

Authors:Milad Hoseinpour, Shubhanshu Shekhar, Vladimir Dvorkin
Title: Outage Identification from Electricity Market Data: Quickest Change Detection Approach
Abstract:
Power system outages expose market participants to significant financial risk unless promptly detected and hedged. We develop an outage identification method from public market signals grounded in the parametric quickest change detection (QCD) theory. Parametric QCD operates on stochastic data streams, distinguishing pre‑ and post‑change regimes using the ratio of their respective probability density functions. To derive the density functions for normal and post‑outage market signals, we exploit multi‑parametric programming to decompose complex market signals into parametric random variables with a known density. These densities are then used to construct a QCD‑based statistic that triggers an alarm as soon as the statistic exceeds an appropriate threshold. Numerical experiments on a stylized PJM testbed demonstrate rapid line outage identification from public streams of electricity demand and price data.

Authors:Shuwen Lu, Mark E. Lewis, Jamol Pender
Title: Balancing Independent and Collaborative Service
Abstract:
We study a two‑type server queueing system where flexible Type‑I servers, upon their initial interaction with jobs, decide in real time whether to process them independently or in collaboration with dedicated Type‑II servers. Independent processing begins immediately, as does collaborative service if a Type‑II server is available. Otherwise, the job and its paired Type‑I server wait in queue for collaboration. Type‑I servers are non‑preemptive and cannot engage with new jobs until their current job is completed. We provide a complete characterization of the structural properties of the optimal policy for the clearing system. In particular, an optimal control is shown to follow a threshold structure based on the number of jobs in the queue before a Type‑I first interaction and on the number of jobs in either independent or collaborative service. We propose simple threshold heuristics, based on linear approximations, for real‑time decision‑making. In much of the parameter and state spaces, we establish theoretical bounds that compare the thresholds proposed by our heuristics to those of optimal policies and identify parameter configurations where these bounds are attained. Outside of these regions, the optimal thresholds are infinite. Numerical experiments further demonstrate the accuracy and robustness of our heuristics, particularly when the initial queue length is high. Our proposed heuristics achieve costs within 0.5% of the optimal policy on average and significantly outperform benchmark policies that exhibit extreme sensitivity to system parameters, sometimes incurring costs exceeding 100% of the optimal.

Authors:Shuwen Lu, Jamol Pender, Mark E. Lewis
Title: Control policies for a two-stage queueing system with parallel and single server options
Abstract:
We study a two‑stage tandem service queue attended by two servers. Each job‑server pair must complete both service phases together, with the server unable to begin a new job until the current one is fully processed after two stages. Immediately after the first phase of service, the server decides whether to send the job/customer to a downstream station that allows parallel processing or to a single‑service facility that offers faster or higher‑quality service but handles only one job at a time. This choice determines whether the second phase commences immediately or (potentially) after waiting in a queue for the single‑service facility to become available. The decision‑making scenario is modeled via a Markov decision process formulation, of a clearing system with holding costs at each station. We fully characterize the structural properties of an optimal control policy based on the relationship between the service rates at the downstream stations. A numerical study highlights the significance of optimal control by comparing its performance against several natural heuristic policies.

Authors:Szabolcs Szentpéteri, Balázs Csanád Csáji
Title: Distribution-Free Confidence Ellipsoids for Ridge Regression with PAC Bounds
Abstract:
Linearly parametrized models are widely used in control and signal processing, with the least‑squares (LS) estimate being the archetypical solution. When the input is insufficiently exciting, the LS problem may be unsolvable or numerically unstable. This issue can be resolved through regularization, typically with ridge regression. Although regularized estimators reduce the variance error, it remains important to quantify their estimation uncertainty. A possible approach for linear regression is to construct confidence ellipsoids with the Sign‑Perturbed Sums (SPS) ellipsoidal outer approximation (EOA) algorithm. The SPS EOA builds non‑asymptotic confidence ellipsoids under the assumption that the noises are independent and symmetric about zero. This paper introduces an extension of the SPS EOA algorithm to ridge regression, and derives probably approximately correct (PAC) upper bounds for the resulting region sizes. Compared with previous analyses, our result explicitly show how the regularization parameter affects the region sizes, and provide tighter bounds under weaker excitation assumptions. Finally, the practical effect of regularization is also demonstrated via simulation experiments.

Authors:Aydin Ghojogh, M. Hadi Sepanj, Benyamin Ghojogh
Title: On the Relation of State Space Models and Hidden Markov Models
Abstract:
State Space Models (SSMs) and Hidden Markov Models (HMMs) are foundational frameworks for modeling sequential data with latent variables and are widely used in signal processing, control theory, and machine learning. Despite their shared temporal structure, they differ fundamentally in the nature of their latent states, probabilistic assumptions, inference procedures, and training paradigms. Recently, deterministic state space models have re‑emerged in natural language processing through architectures such as S4 and Mamba, raising new questions about the relationship between classical probabilistic SSMs, HMMs, and modern neural sequence models. In this paper, we present a unified and systematic comparison of HMMs, linear Gaussian state space models, Kalman filtering, and contemporary NLP state space models. We analyze their formulations through the lens of probabilistic graphical models, examine their inference algorithms ‑‑ including forward‑backward inference and Kalman filtering ‑‑ and contrast their learning procedures via Expectation‑Maximization and gradient‑based optimization. By highlighting both structural similarities and semantic differences, we clarify when these models are equivalent, when they fundamentally diverge, and how modern NLP SSMs relate to classical probabilistic models. Our analysis bridges perspectives from control theory, probabilistic modeling, and modern deep learning.

Authors:Theodor-Gabriel Nicu, Florin Stoican, Daniel-Mihail Ioan, Ionela Prodan
Title: Safe Navigation in Cluttered Environments Via Spline-Based Harmonic Potential Fields
Abstract:
We provide a complete motion‑planning mechanism that ensures target tracking and obstacle avoidance in a cluttered environment. For a given polyhedral decomposition of the feasible space, we adopt a novel procedure that constrains the agent to move only through a prescribed sequence of cells via a suitable control policy. For each cell, we construct a harmonic potential surface induced by a Dirichlet boundary condition given as a cardinal B‑spline curve. A detailed analysis of the curve behavior (periodicity, support) and of the associated control point selection allows us to explicitly compute these harmonic potential surfaces, from which we subsequently derive the corresponding control policy. We illustrate that the resulting construction funnels the agent safely along the chain of cells from the starting point to the target.

Authors:Mahmud S. Zango, Jianglin Lan
Title: Autonomous Navigation at the Nano-Scale: Algorithms, Architectures, and Constraints
Abstract:
Autonomous navigation for nano‑scale unmanned aerial vehicles (nano‑UAVs) is governed by extreme Size, Weight, and Power (SWaP) constraints (with the weight < 50 g and sub‑100 mW onboard processor), distinguishing it fundamentally from standard robotic paradigms. This review synthesizes the state‑of‑the‑art in sensing, computing, and control architectures designed specifically for these sub‑ 100mW computational envelopes. We critically analyse the transition from classical geometry‑based methods to emerging "Edge AI" paradigms, including quantized deep neural networks deployed on ultra‑low‑power System‑on‑Chips (SoCs) and neuromorphic event‑based control. Beyond algorithms, we evaluate the hardware‑software co‑design requisite for autonomy, covering advancements in dense optical flow, optimized Simultaneous Localization and Mapping (SLAM), and learning‑based flight control. While significant progress has been observed in visual navigation and relative pose estimation, our analysis reveals persistent gaps in long‑term endurance, robust obstacle avoidance in dynamic environments, and the "Sim‑to‑Real" transfer of reinforcement learning policies. This survey provides a roadmap for bridging these gaps, advocating for hybrid architectures that fuse lightweight classical control with data‑driven perception to enable fully autonomous, agile nano‑UAVs in GPS‑denied environments.

Authors:Yaniv Proselkov, Liming Xu, Alexandra Brintrup
Title: Modelling viable supply networks with cooperative adaptive financing
Abstract:
We propose a financial liquidity policy sharing method for firm‑to‑firm supply networks, introducing a scalable autonomous control function for viable complex adaptive supply networks. Cooperation and competition in supply chains is reconciled through overlapping collaborative sets, making firms interdependent and enabling distributed risk governance. How cooperative range ‑ visibility ‑ affects viability is studied using dynamic complex adaptive systems modelling. We find that viability needs cooperation; visibility and viability grow together in scale‑free supply networks; and distributed control, where firms only have limited partner information, outperforms centralised control. This suggests that policy toward network viability should implement distributed supply chain financial governance, supporting interfirm collaboration, to enable autonomous control.

Authors:Michael Giovanniello, Dharik S. Mallapragada
Title: Emissions and cost tradeoffs of time-matched clean electricity procurement under inter-annual weather variability -- case study of hydrogen production
Abstract:
Regulators and voluntary corporate sustainability efforts are increasingly adopting time‑matching requirements (TMRs) for clean electricity procurement for large loads, such as data centers, and electricity‑intensive fuel production, such as hydrogen. We use a stochastic capacity expansion model (CEM) framework to assess how inter‑annual weather variability affects the cost, composition, and emissions of procurement‑driven infrastructure to meet annual and hourly TMRs using the case study of a grid‑connected hydrogen producer in Texas. Our approach, which relies on co‑optimizing investments and hourly operations over nine weather scenarios, reveals that hourly TMR comes at a higher cost premium compared to annual TMR than previously estimated by single‑scenario deterministic modeling, while emissions outcomes remain directionally consistent. Demand flexibility and partial hourly TMR (80‑90%) lower the cost premium while preserving emissions benefits. We further examine how binding renewable portfolio standards (RPS) interact with TMR costs and emissions outcomes. When an RPS is applied to non‑H2 electricity demand, annual TMR reduces emissions comparably to hourly TMR at a lower cost. Incorporating H2‑related electricity demand directly into the RPS constraint, rather than imposing a separate TMR, achieves similar emissions outcomes at still lower cost, suggesting that TMR‑based clean electricity procurement, particularly hourly matching, offers limited additional value in regions with stringent grid decarbonization policies.

Authors:Maryam Salamatmoghadasi, Amir Mehrabian, Halim Yanikomeroglu, Georges Kaddoum
Title: QoS-Aware Energy Optimization via Cell Switching in Heterogeneous Networks
Abstract:
The growing demand for mobile data services in dense urban areas has intensified the need for energy‑efficient radio access networks (RANs) in future 6G systems. In this context, one promising strategy is cell switching (CS), which dynamically deactivates underutilized small base stations (SBSs) to reduce power consumption. However, while previous research explored CS primarily based on traffic load, ensuring user quality of service (QoS) under realistic channel conditions remains a challenge. In this paper, we propose a novel optimization‑driven CS framework that jointly minimizes network power consumption and guarantees user QoS by enforcing a minimum received power threshold as part of offloading decisions. In contrast to prior load‑based or learning‑based approaches, our method explicitly integrates channel‑aware information into the CS process, thus ensuring reliable service quality for offloaded users. Furthermore, flexibility of the proposed framework enables operators to adapt system behavior between energy‑saving and QoS‑preserving modes by tuning a single design parameter. Simulation results demonstrate that the proposed approach achieves up to 30% power savings as compared to baseline methods while fully maintaining QoS under diverse network conditions. Scalability and robustness of the proposed method in realistic heterogeneous networks (HetNets) further highlight its potential as a practical solution for sustainable 6G deployments.

Authors:Abdelrahman Soliman, Ahmed Refaey, Aiman Erbad, Amr Mohamed
Title: IntAgent: NWDAF-Based Intent LLM Agent Towards Advanced Next Generation Networks
Abstract:
Intent‑based networks (IBNs) are gaining prominence as an innovative technology that automates network operations through high‑level request statements, defining what the network should achieve. In this work, we introduce IntAgent, an intelligent intent LLM agent that integrates NWDAF analytics and tools to fulfill the network operator's intents. Unlike previous approaches, we develop an intent tools engine directly within the NWDAF analytics engine, allowing our agent to utilize live network analytics to inform its reasoning and tool selection. We offer an enriched, 3GPP‑compliant data source that enhances the dynamic, context‑aware fulfillment of network operator goals, along with an MCP tools server for scheduling, monitoring, and analytics tools. We demonstrate the efficacy of our framework through two practical use cases: ML‑based traffic prediction and scheduled policy enforcement, which validate IntAgent's ability to autonomously fulfill complex network intents.

Authors:Harry Huang, Talia Xu, Marco Zúñiga Zamalloa
Title: Exploiting Light To Enhance The Endurance and Navigation of Lighter-Than-Air Micro-Drones
Abstract:
Micro‑Unmanned Aerial Vehicles (UAVs) are rapidly expanding into tasks from inventory to environmental sensing, yet their short endurance and unreliable navigation in GPS‑denied spaces limit deployment. Lighter‑Than‑Air (LTA) drones offer an energy‑efficient alternative: they use a helium envelope to provide buoyancy, which enables near‑zero‑power drain during hovering and much longer operation. LTAs are promising, but their design is complex, and they lack integrated solutions to enable sustained autonomous operations and navigation with simple, low‑infrastructure. We propose a compact, self‑sustaining LTA drone that uses light for both energy harvesting and navigation. Our contributions are threefold: (i) a high‑fidelity simulation framework to analyze LTA aerodynamics and select a stable, efficient configuration; (ii) a framework to integrate solar cells on the envelope to provide net‑positive energy; and (iii) a point‑and‑go navigation system with three light‑seeking algorithms operating on a single light beacon. Our LTA‑analysis, together with the integrated solar panels, not only saves energy while flying, but also enables sustainable operation: providing 1 minute of flying time for every 4 minutes of energy harvesting, under illuminations of 80klux. We also demonstrate robust single‑beacon navigation towards a light source that can be up to 7m away, in indoor and outdoor environments, even with moderate winds. The resulting system indicates a plausible path toward persistent, autonomous operation for indoor and outdoor monitoring. More broadly, this work provides a practical pathway for translating the promise of LTA drones into a persistent, self‑sustaining aerial system.

Authors:Shaya Garjani, Ashish Cherukuri, Bayu Jayawardhana, Nima Monshizadeh
Title: Stability of Information-Based Routing in Dynamic Transportation Networks
Abstract:
Recent studies on transportation networks have shown that real‑time route guidance can inadvertently induce congestion or oscillatory traffic patterns. Nevertheless, such technologies also offer a promising opportunity to manage traffic non‑intrusively by shaping the information delivered to users, thereby mitigating congestion and enhancing network stability. A key step toward this goal is to identify information signals that ensure the existence of an equilibrium with desirable stability and convergence properties. This challenge is particularly relevant when traffic density and routing dynamics evolve concurrently, as increasingly occurs with digital signaling and real‑time navigation technologies. To address this, we analyze a parallel‑path transportation network with a single origin‑destination pair, incorporating joint traffic density and logit‑based routing dynamics that evolve at the same timescale. We characterize a class of density‑dependent traffic information that guarantees a unique equilibrium in the free‑flow regime, ensures its asymptotic stability, and keeps traffic densities within the free‑flow region for all time. The theoretical results are complemented by a numerical case study demonstrating how the framework can inform the design of traffic information that reduces total travel time without compromising credibility.

Authors:Chao Wang, Shuyuan Zhang, Lei Wang
Title: Convex Model Predictive Control for Safe Output Consensus of Nonlinear Multi-Agent Systems
Abstract:
Nonlinear dynamics and safety constraints typically result in a nonlinear programming problem when applying model predictive control to achieve safe output consensus. To avoid the heavy computational burden of solving a nonlinear programming problem directly, this paper proposes a novel Convex Model Predictive Control (CMPC) approach based on a Sequential Quadratic Programming (SQP) scheme. The core of our method lies in transforming the nonlinear constraints into linear forms: we linearize the system dynamics and convexify the discrete‑time high‑order control barrier functions using a proposed tangent‑line projection method. Consequently, the original problem is reduced to a quadratic program that can be iteratively solved within the SQP scheme at each time step of CMPC. Furthermore, we provide the formal guarantee of the convergence of the SQP scheme, and subsequently guarantee the recursive feasibility and stability of CMPC. Simulations on multi‑agent systems with unicycle dynamics demonstrate a 35‑52 times reduction in computation time compared with baseline methods, confirming the suitability of the proposed approach for real‑time safe output consensus control.

Authors:Imran Sayyed, Nandan Kumar Sinha
Title: Feedforward-Feedback Integration in Flight Control: Reinforcement Learning with Sliding Mode Control
Abstract:
Learning‑based controllers leverage nonlinear couplings and enhance transients but seldom offer guarantees under tight input constraints. Robust feedback like sliding‑mode control (SMC) provides these guarantees but is conservative in isolation. This paper creates a learning‑augmented framework where a deep reinforcement learning policy produces feedforward commands and an SMC law imposes actuator limits, bounds learned authority, and guarantees robustness. The policy is modeled as a matched, bounded input, and Lyapunov‑based conditions link SMC gains to the admissible feedforward bound, guaranteeing stability under saturation. This formulation is applicable to nonlinear, underactuated plants with hard constraints. To illustrate the methodology, the method is applied to a six‑degree‑of‑freedom aircraft model and compared with Reinforcement Learning and isolated SMC. Simulation results show that the hybrid controller improves transient behavior and reduces control oscillations compared to standalone RL and SMC controllers, while preserving robustness under modeling uncertainties and disturbances. Even using it with partially trained policies, SMC component of the control stabilizes transients, whereas fully trained policies provide faster convergence, reduced constraint violations, and robustness. These results illustrate that learning‑augmented control offers superior performance with robustness guarantees under tight input constraints.

Authors:Evangelos Ntouros, Ewoud J. J. Smeur
Title: Guiding vector field-based guidance under wind disturbances applied to a tailsitter UAV
Abstract:
This paper develops a guidance control law based on a parametric Guiding Vector Field (GVF) and integrates it with a state‑of‑the‑art acceleration and attitude control architecture for tailsitters. The resulting framework enables a direct comparison between traditional trajectory‑tracking guidance and GVF‑based path‑following guidance using a realistic tailsitter model operating under windy conditions. Through extensive simulations, it is shown that for agile flight scenarios with wind and small initial position error, both guidance strategies achieve comparable tracking performance, indicating that the additional complexity introduced by the GVF formulation is not always justified. However, the GVF‑based approach exhibits an advantage when initial deviation from the path is present, yielding smooth and well‑behaved convergence toward the desired path. Two additional contributions support this evaluation. First, a modification of the parametric GVF is proposed that guarantees exponential stability of the tracking error dynamics for a single integrator system. Second, the differential flatness transform of a tailsitter vehicle is extended to account for explicit knowledge of the wind velocity vector.

Authors:Shima Sadat Mousavi, Xiao Tan, Aaron D. Ames
Title: From Vertices to Convex Hulls: Certifying Set-Wise Compatibility for CBF Constraints
Abstract:
This paper develops certificates that propagate compatibility of multiple control barrier function (CBF) constraints from sampled vertices to their convex hull. Under mild concavity and affinity assumptions, we present three sufficient feasibility conditions under which feasible inputs over the convex hull can be obtained per coordinate, with a common input, or via convex blending. We also describe the associated computational methods, based on interval intersections or an offline linear program (LP). Beyond certifying compatibility, we give conditions under which the quadratic‑program (QP) safety filter is affine in the state. This enables explicit implementations via convex combinations of vertex‑feasible inputs. Case studies illustrate the results.

Authors:Yuji Sakamoto
Title: Report on Earth Observation Missions and Ground Station Management using On-Demand Satellite Operation System
Abstract:
Since the launch of its first satellite in 2009, Tohoku University has continuously developed and operated Earth observation satellites and engineering demonstration satellites in the 50cm‑class and CubeSat‑class (up to 3U). The 50cm‑class satellite launched into operation in 2021 enabled efficient operations through cloud‑based management functions for both the satellite and ground stations, including automatic command generation. By 2022, up to eight operational satellites were simultaneously managed on a daily basis using three ground stations (Sendai, Hakodate, and Sweden). This paper presents the operational achievements to date and introduces the system that supports efficient satellite operations

Authors:Yuji Sakamoto, Masaki Aoi, Sho Suzuki, Takumi Haga, Shumpei Hosokawa, Yuma Abe, Yuya Tasaki, Tsuyoshi Totani, Sou Nakamura, Masaharu Uchiumi, Shinya Fujita
Title: System Analysis and Pre-Flight Evaluation of Deployable Solar Panels for 3U CubeSat HOKUSHIN-1
Abstract:
This paper describes the system design methodology derived from the development and evaluation tests of deployable solar panels to be mounted on a 3U CubeSat. The study mainly includes structural analysis, thermal analysis, and a review of vibration test results. Hokkaido University is developing the 3U CubeSat HOKUSHIN‑1 in collaboration with Tohoku University and Muroran Institute of Technology. Deployable solar panels are a key technology for future planned lunar exploration missions, as they enable power‑intensive communication and propulsion required for orbit control. The satellite also demonstrates a newly developed compact and efficient propulsion system. The satellite has dimensions of approximately 10x10x34 cm, a mass of 3.99 kg, and will be deployed into a circular orbit at an altitude of about 400 km with an orbital inclination of 51.6 degrees from the International Space Station.

Authors:Yuji Sakamoto, Junichi Kurihara, Shinya Fujita, Yuji Sato, Toshinori Kuwahara
Title: Lessons Learned from Structural Design and Vibration Testing of 50-kg Microsatellites Deployed from the International Space Station
Abstract:
Hokkaido University and Tohoku University have been developing and operating a constellation of 50‑cm‑class microsatellites for Earth observation. DIWATA‑1, launched in 2016, was deployed into a circular orbit at an altitude of approximately 400 km from the International Space Station (ISS). For the subsequent satellite developed in 2021, the structural design and vibration test campaign were optimized to meet a strict one‑year development schedule. This paper summarizes how the structural design of the previous satellite was reviewed and updated, and how the vibration test was successfully completed in a single trial to minimize schedule and technical risks. These lessons learned provide valuable insights, as there are only a limited number of reported cases of 50‑kg‑class microsatellites deployed from the ISS.

Authors:Ming Li, Fan Liu, Yifeng Xiong, Jie Xu, Tao Liu
Title: Sensing-Limited Control of Noiseless Linear Systems Under Nonlinear Observations
Abstract:
This paper investigates the fundamental information‑theoretic limits for the control and sensing of noiseless linear dynamical systems subject to a broad class of nonlinear observations. We analyze the interactions between the control and sensing components by characterizing the minimum information flow required for stability. Specifically, we derive necessary conditions for mean‑square observability and stabilizability, demonstrating that the average directed information rate from the state to the observations must exceed the intrinsic expansion rate of the unstable dynamics. Furthermore, to address the challenges posed by non‑Gaussian distributions inherent to nonlinear observation channels, we establish sufficient conditions by imposing regularity assumptions, specifically log‑concavity, on the system's probabilistic components. We show that under these conditions, the divergence of differential entropy implies the convergence of the estimation error, thereby closing the gap between information‑theoretic bounds and estimation performance. By establishing these results, we unveil the fundamental performance limits imposed by the sensing layer, extending classical data‑rate constraints to the more challenging regime of nonlinear observation models.

Authors:Arkaprava Gupta, Nicholas Carter, William Zellers, Prateek Ganguli, Benedikt Dietrich, Vibhor Krishna, Parasara Sridhar Duggirala, Samarjit Chakraborty
Title: Bandit Algorithms for Deep Brain Stimulation
Abstract:
Deep Brain Stimulation (DBS) is an effective treatment for Parkinson's disease, but conventional fixed‑parameter stimulation can reduce battery life and cause side effects while failing to adapt to changing neural dynamics. Recent reinforcement learning approaches improve adaptability, yet most rely on deep neural networks that require offline training and are computationally too expensive for implantable hardware. This paper presents a resource‑conscious adaptive DBS framework based on a Time‑ and Threshold‑Triggered Pruned Multi‑Armed Bandit (T3P MAB) algorithm. The proposed method jointly tunes stimulation frequency and amplitude, avoids prior training, and remains transparent enough to support clinician‑guided adjustment. Using a computational basal ganglia‑thalamic model, we show that T3P converges faster than competing MAB methods and outperforms deep‑RL baselines in suppressing pathological beta‑band activity while reducing stimulation power. We implemented it on different microcontrollers and report detailed energy measurements, showing convergence in under two minutes and suitability for resource‑constrained implantable systems. These results support lightweight bandit‑based control as a practical path toward personalized, energy‑efficient DBS.

Authors:Hiroshi Okajima, Shun Shirahama, Tatsunori Hayashi, Nobutomo Matsunaga
Title: From Noise to Knowledge: System Identification with Systematic Polytope Construction via Cyclic Reformulation
Abstract:
Model‑based robust control requires not only accurate nominal models but also systematic uncertainty representations to guarantee stability and performance. However, constructing polytopic uncertainty models typically demands multiple experiments or a priori structural assumptions.This paper proposes an identification framework based on intentional periodicity induction, in which cyclic reformulation with period N is applied to a linear time‑invariant system to interpret noise‑induced parameter fluctuations as a structured manifestation of estimation uncertainty. The N parameter sets obtained from a single identification experiment ‑‑ which would coincide in the noise‑free case ‑‑ are used as polytope vertices, providing systematic control over the granularity of the uncertainty description through the choice of N. The practical utility of the constructed polytope is demonstrated through robust H_\infty state‑feedback synthesis via LMI optimization at the polytope vertices; the synthesis uses only noisy identification data and is shown across Monte Carlo trials to stabilize the true plant with only marginal conservatism. Complementarily, a diagnostic assessment based on the best in‑polytope point confirms that the polytope captures meaningful uncertainty information. For a third‑order system under Gaussian and uniform noise, a comparison with bootstrap‑inspired resampling baselines indicates that cyclic reformulation provides a competitive or favorable trade‑off by utilizing the full data record; the construction is further validated on a fourth‑order MIMO system.

Authors:Manobendu Sarker, Md. Zoheb Hassan, Xianbin Wang
Title: Closed-loop Uplink Radio Resource Management in CF-O-RAN Empowered 5G Aerial Corridor
Abstract:
In this paper, we investigate the uplink (UL) radio resource management for 5G aerial corridors with an open‑radio access network (O‑RAN)‑enabled cell‑free (CF) massive multiple‑input multiple‑output (mMIMO) system. Our objective is to maximize the minimum spectral efficiency (SE) by jointly optimizing unmanned aerial vehicle (UAV)‑open radio unit (O‑RU) association and UL transmit power under quality‑of‑service (QoS) constraints. Owing to its NP‑hard nature, the formulated problem is decomposed into two tractable sub‑problems solved via alternating optimization (AO) using two computationally efficient algorithms. We then propose (i) a QoS‑driven and multi‑connectivity‑enabled association algorithm incorporating UAV‑centric and O‑RU‑centric criteria with targeted refinement for weak UAVs, and (ii) a bisection‑guided fixed‑point power control algorithm achieving global optimality with significantly reduced complexity, hosted as xApp at the near‑real‑time (near‑RT) RAN intelligent controller (RIC) of O‑RAN. Solving the resource‑allocation problem requires global channel state information (CSI), which incurs substantial measurement and signaling overhead. To mitigate this, we leverage a channel knowledge map (CKM) within the O‑RAN non‑RT RIC to enable efficient environment‑aware CSI inference. Simulation results show that the proposed framework achieves up to 440% improvement in minimum SE, 100% QoS satisfaction and fairness, while reducing runtime by up to 99.7% compared to an interior point solver‑based power allocation solution, thereby enabling O‑RAN compliant real‑time deployment.

Authors:Manobendu Sarker, Soumaya Cherkaoui
Title: Priority-Based Bandwidth Allocation in Network Slicing-Enabled Cell-Free Massive MIMO Systems
Abstract:
This paper addresses joint admission control and per‑user equipment (UE) bandwidth allocation to maximize weighted sum‑rate in network slicing‑enabled user‑centric cell‑free (CF) massive multiple‑input multiple‑output (mMIMO) systems when aggregate quality‑of‑service (QoS) demand may exceed available bandwidth. Specifically, we optimize bandwidth allocation while satisfying heterogeneous QoS requirements across enhanced mobile broadband (eMBB) and ultra‑reliable low‑latency communication (URLLC) slices in the uplink. The formulated problem is NP‑hard, rendering global optimality computationally intractable. We decompose it into two sub‑problems and solve them via computationally efficient heuristics within a sequential framework. We propose (i) a hierarchical admission control scheme that selectively admits UEs under bandwidth scarcity, prioritizing URLLC to ensure latency‑sensitive QoS compliance, and (ii) an iterative gradient‑based bandwidth allocation scheme that transfers bandwidth across slices guided by marginal utility and reallocates resources within slices. Simulation results demonstrate that the proposed scheme achieves near‑optimal performance, deviating from an interior point solver‑based benchmark by at most 2.2% in weighted sum‑rate while reducing runtime by 99.7%, thereby enabling practical real‑time deployment. Compared to a baseline round‑robin scheme without admission control, the proposed approach achieves up to 1085% and 7% higher success rates for eMBB and URLLC slices, respectively, by intentionally sacrificing sum‑rate to guarantee QoS. Sensitivity analysis further reveals that the proposed solution adapts effectively to diverse eMBB/URLLC traffic compositions, maintaining 47‑51% eMBB and 93‑94% URLLC success rates across varying load scenarios, confirming its robustness for resource‑constrained large‑scale deployments.

Authors:Manobendu Sarker, Soumaya Cherkaoui
Title: Network Slicing Resource Management in Uplink User-Centric Cell-Free Massive MIMO Systems
Abstract:
This paper addresses the joint optimization of per‑user equipment (UE) bandwidth allocation and UE‑access point (AP) association to maximize weighted sum‑rate while satisfying heterogeneous quality‑of‑service (QoS) requirements across enhanced mobile broadband (eMBB) and ultra‑reliable low‑latency communication (URLLC) slices in the uplink of a network slicing‑enabled user‑centric cell‑free (CF) massive multiple‑input multiple‑output (mMIMO) system. The formulated problem is NP‑hard, rendering global optimality computationally intractable. To address this challenge, it is decomposed into two sub‑problems, each solved by a computationally efficient heuristic scheme, and jointly optimized through an alternating optimization framework. We then propose (i) a bandwidth allocation scheme that balances UE priority, spectral efficiency, and minimum bandwidth demand under limited resources to ensure fair QoS distribution, and (ii) a priority‑based UE‑AP association assignment approach that balances UE service quality with system capacity constraints. Together, these approaches provide a practical and computationally efficient solution for resource‑constrained network slicing scenarios, where QoS feasibility is often violated under dense deployments and limited bandwidth, necessitating graceful degradation and fair QoS preservation rather than solely maximizing the aggregate sum‑rate. Simulation results demonstrate that the proposed scheme achieves up to 52% higher weighted sum‑rate, 140% and 58% higher QoS success rates for eMBB and URLLC slices, respectively, while reducing runtime by up to 97% compared to the considered benchmarks.

Authors:Yin Wu, Wei-Yu Chiu, Yuan-Po Tsai, Shangyuan Liu, Weiqi Hua
Title: Multiagent Reinforcement Learning in Enhancing Resilience of Microgrids under Extreme Weather Events
Abstract:
Grid resilience is crucial in light of power interruptions caused by increasingly frequent extreme weather events. Well‑designed energy management systems (EMS) have made progress in improving microgrid resilience through the coordination of distributed energy resources (DERs), but still face significant challenges in addressing the uncertainty of load demand caused by extreme weather. The integration of deep reinforcement learning (DRL) into EMS design enables optimized microgrid control strategies for coordinating DERs. Building on this, we proposed a cooperative multi‑agent deep reinforcement learning (MADRL)‑based EMS framework to provide flexible scalability for microgrids, enhance resilience and reduce operational costs during power outages. Specifically, the gated recurrent unit with a gating mechanism was introduced to extract features from temporal data, which enables the EMS to coordinate DERs more efficiently. Next, the proposed MADRL method incorporating action masking techniques was evaluated in the IEEE 33‑Bus system using real‑world data on renewable generation and power load. Finally, the numerical results demonstrated the superiority of the proposed method in reducing operating costs as well as the effectiveness in enhancing microgrid resilience during power interruptions.

Authors:Parisa Ansari Bonab, Elisabeth Andarge Gedefaw, Mohammad Khajenejad
Title: Resilient Interval Observer-Based Control for Cooperative Adaptive Cruise Control under FDI Attack
Abstract:
Connectivity in connected and autonomous vehicles (CAVs) introduces vulnerability to cyber threats such as false data injection (FDI) attacks, which can compromise system reliability and safety. To ensure resilience, this paper proposes a control framework combining a nonlinear controller with an interval observer for robust state estimation under measurement noise. The observer bounds leader's states, while a neural network‑based estimator estimates the unknown FDI attacks in real time. These estimates are then used to mitigate FDI attack effects maintaining safe inter‑vehicle spacing. The proposed approach leverages an idea of interval observer‑based estimation and merges model‑based and learning‑based methods to achieve accurate estimations and real‑time performance. MATLAB/Simulink results confirm resilient tracking, precise FDI attack estimation, and robustness to noise, demonstrating potential for real‑world CACC applications under cyberattacks, disturbance, and bounded measurement noise.

Authors:Johnathan Corbin, Sarah H. Q. Li, Jonathan Rogers
Title: Allocating Corrective Control to Mitigate Multi-agent Safety Violations Under Private Preferences
Abstract:
We propose a novel framework that computes the corrective control efforts to ensure joint safety in multi‑agent dynamical systems. This framework efficiently distributes the required corrective effort without revealing individual agents' private preferences. Our framework integrates high‑order control barrier functions (HOCBFs), which enforce safety constraints with formal guarantees of safety for complex dynamical systems, with a privacy‑preserving resource allocation mechanism based on the progressive second price (PSP) auction. When a joint safety constraint is violated, agents iteratively bid on new corrective efforts via 'avoidance credits' rather than explicitly solving for feasible corrective efforts that remove the safety violation. The resulting correction, determined via a second price payment rule, coincides with the socially optimal safe distribution of corrective actions. Critically, the bidding process achieves this optimal allocation efficiently and without revealing private preferences of individual agents. We demonstrate this method through multi‑robot hardware experiments on the Robotarium platform.

Authors:Maxim Yudayev, Juha Carlon, Diwas Lamsal, Vayalet Stefanova, Benjamin Filtjens
Title: HERMES: A Unified Open-Source Framework for Realtime Multimodal Physiological Sensing, Edge AI, and Intervention in Closed-Loop Smart Healthcare Applications
Abstract:
Intelligent assistive technologies are increasingly recognized as critical daily‑use enablers for people with disabilities and age‑related functional decline. Longitudinal studies, curation of quality datasets, live monitoring in activities of daily living, and intelligent intervention devices, share the largely unsolved need in reliable high‑throughput multimodal sensing and processing. Streaming large heterogeneous data from distributed sensors, historically closed‑source environments, and limited prior works on realtime closed‑loop AI methodologies, inhibit such applications. To accelerate the emergence of clinical deployments, we deliver HERMES ‑ an open‑source high‑performance Python framework for continuous multimodal sensing and AI processing at the edge. It enables synchronized data collection, and realtime streaming inference with user PyTorch models, on commodity computing devices. HERMES is applicable to fixed‑lab and free‑living environments, of distributed commercial and custom sensors. It is the first work to offer a holistic methodology that bridges cross‑disciplinary gaps in real‑world implementation strategies, and guides downstream AI model development. Its application on the closed‑loop intelligent prosthesis use case illustrates the process of suitable AI model development from the generated constraints and trade‑offs. Validation on the use case, with 4 synchronized hosts cooperatively capturing 18 wearable and off‑body modalities, demonstrates performance and relevance of HERMES to the trajectory of the intelligent healthcare domain.

Authors:Maaz Qureshi, Mohammad Omid Bagheri, Abdelrahman Elbadrawy, William Melek, George Shaker
Title: Automated Angular Received-Power Characterization of Embedded mmWave Transmitters Using Geometry-Calibrated Spatial Sampling
Abstract:
This paper presents an automated measurement methodology for angular received‑power characterization of embedded millimeter‑wave transmitters using geometry‑calibrated spatial sampling. Characterization of integrated mmWave transmitters remains challenging due to limited angular coverage and alignment variability in conventional probe‑station techniques, as well as the impracticality of anechoic‑chamber testing for platform‑mounted active modules. To address these challenges, we introduce RAPTAR, an autonomous measurement system for angular received‑power acquisition under realistic installation constraints. A collaborative robot executes geometry‑calibrated, collision‑aware hemispherical trajectories while carrying a calibrated receive probe, enabling controlled and repeatable spatial positioning around a fixed device under test. A spectrum‑analyzer‑based receiver chain acquires amplitude‑only received power as a function of angle and distance following quasi‑static pose stabilization. The proposed framework enables repeatable angular received‑power mapping and power‑domain comparison against idealized free‑space references derived from full‑wave simulation. Experimental results for a 60‑GHz radar module demonstrate a mean absolute received‑power error below 2 dB relative to simulation‑derived references and a 36.5 % reduction in error compared to manual probe‑station measurements, attributed primarily to reduced alignment variability and consistent spatial sampling. The proposed method eliminates the need for coherent field measurements and near‑field transformations, enabling practical power‑domain characterization of embedded mmWave modules. It is well suited for angular validation in real‑world platforms where conventional anechoic measurements are impractical.

Authors:Luis Cervantes-Pérez, Víctor Santibáñez, Jesús Sandoval, Romeo Ortega, Jose Guadalupe Romero
Title: An Experimental Comparison of Sliding Mode and Immersion and Invariance Adaptive Controllers forPosition-feedback Tracking of a Simple Mechanical System with Friction
Abstract:
The purpose of this paper is to illustrate, in an experimental facility consisting of a simple pendular device, the performance of a sliding mode adaptive position‑feedback tracking controller of mechanical systems with friction reported in the literature. To put this experimental evidence in perspective, we compare the performance of the sliding mode scheme with the one obtained by an adaptive controller designed following the well‑known immersion and invariance technique.

Authors:Rezky Kam, Coddy N. Siswanto
Title: Time-Continuous Modeling for Temporal Affective Pattern Recognition in LLMs
Abstract:
This paper introduces a dataset and conceptual framework for LLMs to mimic real world emotional dynamics through time and in‑context learning leveraging physics‑informed neural network, opening a possibility for interpretable dialogue modeling.

Authors:Sahil Aziz, Wajid Ali, Khaliqur Rahman
Title: Analyzing the Impact of EV Battery Charging on the Distribution Network
Abstract:
Many countries are rapidly adopting electric vehicles (EVs) due to their meager running cost and environment‑friendly nature. EVs are likely to dominate the internal combustion (IC) engine cars entirely over the next few years. With the rise in popularity of EVs, adverse effects of EV charging loads on the grid system have been observed. Since the distribution system (DS) does not cope with the high overloading capacity, the negative impact of EV charging load on the distribution network (DN) cannot be neglected. A high level of EV penetration with uncoordinated charging is the primary cause of voltage instability, increased peak load demand, and reliability issues of the DN. In this paper, a detailed overview of all the notable impacts of EV charging on voltage profile, power quality, and DS performance is discussed. This work also reviews the different topologies of EV chargers and the issues introduced by power converters on the utility grid. Finally, the strategies for improving the charging of EVs proposed in the literature to consider the random nature of EVs charging, the management of peak loads, and bidirectional power flow are summarized.

Authors:Alexander Medvedev, Anton V. Proskurnikov
Title: Solvability of The Output Corridor Control Problem by Pulse-Modulated Feedback
Abstract:
The problem of maintaining the output of a positive time‑invariant single‑input single‑output system within a predefined corridor of values is treated. For third‑order plants possessing a certain structure, it is proven that the problem is always solvable under stationary conditions by means of pulse‑modulated feedback. The obtained result is utilized to assess the feasibility of patient‑specific pharmacokinetic‑pharmacodynamic models with respect to patient safety. A population of Wiener models capturing the dynamics of a neuromuscular blockade agent is studied to investigate whether or not they can be driven into the desired output corridor by clinically acceptable sequential drug doses (boluses). It is demonstrated that low values of a parameter in the nonlinear pharmacodynamic part lie behind the detected model infeasibility.

Authors:Tomás Tapia, Yury Dvorkin
Title: Reachability Guarantees for Energy Arbitrage
Abstract:
This paper introduces a unified framework for battery energy arbitrage under uncertain market prices that integrates chance‑constrained terminal state‑of‑charge requirements with online threshold policies. We first cast the multi‑interval arbitrage problem as a stochastic dynamic program enhanced by a probabilistic end‑of‑horizon state‑of‑charge (SoC) constraint, ensuring with high confidence that the battery terminates within a prescribed energy band. We then apply a k‑search algorithm to derive explicit charging (buying) and discharging (selling) thresholds with provable worst‑case competitive ratio, and compute the corresponding action probabilities over the decision horizon. To compute exact distributions under operational limits, we develop a probability redistribution pruning method and use it to quantify the likelihood of meeting the terminal SoC band. Leveraging the resulting SoC distribution, we estimate the minimum stopping‑time required to satisfy the SoC chance constraint. Computational experiments on historical real price data demonstrate that the proposed framework substantially improves the estimation of SoC evolution and supports chance‑constraint satisfaction.

Authors:Ruslan Zakirzyanov
Title: A method for optimizing the structure of the software and hardware complex of a distributed process control system for large industrial enterprises
Abstract:
The article proposes a method for optimizing the structure of the software and hardware complex of an automated control system for continuous technological processes for large industrial enterprises. General information is given on the relevance of the problem of choosing the structure of a system built on the basis of serially produced components, a formal description of the optimization problem is given, the criterion and limitations are highlighted. A solution method using the metaheuristic algorithm of ant colonies is described. A numerical example of the solution is given, the results of the algorithm are analyzed, and directions for further research are determined.

Authors:Yue Yang, Christoph Leuze, Brian Hargreaves, Bruce Daniel, Fred M Baik
Title: Multimodal Feedback for Handheld Tool Guidance: Combining Wrist-Based Haptics with Augmented Reality
Abstract:
We investigate how vibrotactile wrist feedback can enhance spatial guidance for handheld tool movement in optical see‑through augmented reality (AR). While AR overlays are widely used to support surgical tasks, visual occlusion, lighting conditions, and interface ambiguity can compromise precision and confidence. To address these challenges, we designed a multimodal system combining AR visuals with a custom wrist‑worn haptic device delivering directional and state‑based cues. A formative study with experienced surgeons and residents identified key tool maneuvers and preferences for reference mappings, guiding our cue design. In a cue identification experiment (N=21), participants accurately recognized five vibration patterns under visual load, with higher recognition for full‑actuator states than spatial direction cues. In a guidance task (N=27), participants using both AR and haptics achieved significantly higher spatial precision (5.8 mm) and usability (SUS = 88.1) than those using either modality alone, despite having modest increases in task time. Participants reported that haptic cues provided reassuring confirmation and reduced cognitive effort during alignment. Our results highlight the promise of integrating wrist‑based haptics into AR systems for high‑precision, visually complex tasks such as surgical guidance. We discuss design implications for multimodal interfaces supporting confident, efficient tool manipulation.

Authors:Kun-Yan Jiang, Wei-Yu Chiu, Yuan-Po Tsai
Title: Profit Maximization for Electric Vehicle Charging Stations Using Multiagent Reinforcement Learning
Abstract:
Electric vehicles (EVs) are increasingly integrated into power grids, offering economic and environmental benefits but introducing challenges due to uncoordinated charging. This study addresses the profit maximization problem for multiple EV charging stations (EVCSs) equipped with energy storage systems (ESS) and renewable energy sources (RES), with the capability for energy trading. We propose a Double Hypernetwork QMIX‑based multi‑agent reinforcement learning (MARL) framework to optimize cooperative energy management under uncertainty in EV demand, renewable generation, and real‑time electricity prices. The framework mitigates overestimation bias in value estimation, enables distributed decision‑making, and incorporates an internal energy trading mechanism. Numerical experiments using real‑world data demonstrate that, compared to standard QMIX, the proposed method achieves approximately 5.3% and 12.7% higher total profit for the two regions, respectively, highlighting its economic and operational efficiency. Additionally, the approach maintains robust performance under varying levels of EV demand uncertainty and renewable energy fluctuations.

Authors:Angel Y. He, David Parker
Title: Robust Verification of Concurrent Stochastic Games
Abstract:
Autonomous systems often operate in multi‑agent settings and need to make concurrent, strategic decisions, typically in uncertain environments. Verification and control problems for these systems can be tackled with concurrent stochastic games (CSGs), but this model requires transition probabilities to be precisely specified ‑ an unrealistic requirement in many real‑world settings. We introduce robust CSGs and their subclass interval CSGs (ICSGs), which capture epistemic uncertainty about transition probabilities in CSGs. We propose a novel framework for robust verification of these models under worst‑case assumptions about transition uncertainty. Specifically, we develop the underlying theoretical foundations and efficient algorithms, for finite‑ and infinite‑horizon objectives in both zero‑sum and nonzero‑sum settings, the latter based on (social‑welfare optimal) Nash equilibria. We build an implementation in the PRISM‑games model checker and demonstrate the feasibility of robust verification of ICSGs across a selection of large benchmarks.

Authors:Oliver Schön, Zhengang Zhong, Sadegh Soudjani
Title: Kernel-Based Learning of Safety Barriers
Abstract:
The rapid integration of AI algorithms in safety‑critical applications such as autonomous driving and healthcare is raising significant concerns about the ability to meet stringent safety standards. Traditional tools for formal safety verification struggle with the black‑box nature of AI‑driven systems and lack the flexibility needed to scale to the complexity of real‑world applications. In this paper, we present a data‑driven approach for safety verification and synthesis of black‑box systems with discrete‑time stochastic dynamics. We employ the concept of control barrier certificates, which can guarantee safety of the system, and learn the certificate directly from a set of system trajectories. We use conditional mean embeddings to embed data from the system into a reproducing kernel Hilbert space (RKHS) and construct an RKHS ambiguity set that can be inflated to robustify the result to out‑of‑distribution behavior. We provide the theoretical results on how to apply the approach to general classes of temporal logic specifications beyond safety. For the data‑driven computation of safety barriers, we leverage a finite Fourier expansion to cast a typically intractable semi‑infinite optimization problem as a linear program. The resulting spectral barrier allows us to leverage the fast Fourier transform to generate the relaxed problem efficiently, offering a scalable yet distributionally robust framework for verifying safety. Our work moves beyond restrictive assumptions on system dynamics and uncertainty, as demonstrated on two case studies including a black‑box system with a neural network controller.

Authors:Aditya Natu, Hassan HosseinNia
Title: Decentralized Motion and Resonant Damping Control for High-Bandwidth and Cross-Coupling Reduction in MIMO Nanopositioners
Abstract:
Piezoelectric nanopositioning systems are widely used in precision applications that require nanometer accuracy and high‑speed motion; however, lightly damped resonances and pronounced cross‑axis coupling severely limit bandwidth and disturbance rejection. This paper presents a decentralized dual‑loop control strategy for a two‑axis nanopositioner, combining an inner non‑minimum‑phase resonant damping controller with an outer motion controller on each axis. The dominant diagonal resonance is actively damped to enable closed‑loop bandwidths beyond the first structural mode, while a parallel band‑pass damping path is specifically tuned to a higher‑order resonance that predominantly affects the cross‑coupling channels. Experimental results demonstrate that this targeted band‑pass damping substantially reduces cross‑axis coupling and enhances disturbance rejection, without compromising tracking accuracy.

Authors:Margarida Caleiras, Samuel Moniz, Paulo Jorge Nascimento
Title: A Constraint Programming Model for the Super-Agile Earth Observation Satellite Imaging Scheduling Problem
Abstract:
As the dependence on satellite imaging continues to grow, modern satellites have become increasingly agile, with the new generation, namely super‑agile Earth observation satellites (SAEOS), providing unprecedented imaging flexibility. The highly dynamic capabilities of these satellites introduce additional challenges to the scheduling of observation tasks, as existing approaches for conventional agile satellites do not account for variable observation durations and multiple imaging directions. Although some efforts have been made in this regard, the SAEOS imaging scheduling problem (SAEOS‑ISP) remains largely unexplored, and no exact approaches have yet been proposed. In this context, this study presents the first exact Constraint Programming formulation for the SAEOS‑ISP, considering flexible observation windows, multiple pointing directions and sequence‑dependent transition times across multiple satellites. Computational experiments on a newly generated benchmark set demonstrate that the model can be solved efficiently and within very short computational times. Moreover, the results also show that the proposed approach has the potential to achieve higher computational performance compared to the non‑exact approaches that are currently considered state‑of‑the‑art.

Authors:Manavi Araga, Aditya Natu, Hassan HosseinNia
Title: Structured μ-Synthesis for Nanopositioners under Payload-Induced Uncertainties: Minimising Conservatism for Robust Performance
Abstract:
Most systems exhibit significant variability in their dynamics, including variations in system parameters and large high‑frequency dynamic uncertainties. Traditional uncertainty modelling techniques consolidate all such variations into a single uncertainty block, often yielding overly conservative representations of the true plant behaviour. This paper introduces an uncertainty modelling framework that employs multiple structured and unstructured uncertainty blocks to reduce this conservatism. The methodology is evaluated for an industrial piezoelectric nanopositioner subject to payload‑induced variations, using uncertainty models of differing complexity. A bandpass controller is synthesised via structured mixed‑μ synthesis, and the resulting designs are compared in terms of conservatism of the uncertainty model, robust performance, and computational effort.

Authors:Liang Wu, Wallace Gian Yion Tan, Leqi Zhou, Richard D. Braatz, Jan Drgona
Title: Least-Squares Multi-Step Koopman Operator Learning for Model Predictive Control
Abstract:
MPC is widely used in real‑time applications, but practical implementations are typically restricted to convex QP formulations to ensure fast and certified execution. Koopman‑based MPC enables QP‑based control of nonlinear systems by lifting the dynamics to a higher‑dimensional linear representation. However, existing approaches rely on single‑step EDMD. Consequently, prediction errors may accumulate over long horizons when the EDMD operator is applied recursively. Moreover, the multi‑step prediction loss is nonconvex with respect to the single‑step EDMD operator, making long‑horizon model identification particularly challenging. This paper proposes a multi‑step EDMD framework that directly learns the condensed multi‑step state‑control mapping required for Koopman‑MPC, thereby bypassing explicit identification of the lifted system matrices and subsequent model condensation. The resulting identification problem admits a convex least‑squares formulation. We further show that the problem decomposes across prediction horizons and state coordinates, enabling parallel computation and row‑wise \ell_1‑regularization for automatic dictionary pruning. A non‑asymptotic finite‑sample analysis demonstrates that, unlike one‑step EDMD, the proposed method avoids error compounding and yields error bounds that depend only on the target multi‑step mapping. Numerical examples validate improved long‑horizon prediction accuracy and closed‑loop performance.

Authors:Christopher Kao, Akhil Pathapati, James Davis
Title: AI for Green Spaces: Leveraging Autonomous Navigation and Computer Vision for Park Litter Removal
Abstract:
There are 50 billion pieces of litter in the U.S. alone. Grass fields contribute to this problem because picnickers tend to leave trash on the field. We propose building a robot that can autonomously navigate, identify, and pick up trash in parks. To autonomously navigate the park, we used a Spanning Tree Coverage (STC) algorithm to generate a coverage path the robot could follow. To navigate this path, we successfully used Real‑Time Kinematic (RTK) GPS, which provides a centimeter‑level reading every second. For computer vision, we utilized the ResNet50 Convolutional Neural Network (CNN), which detects trash with 94.52% accuracy. For trash pickup, we tested multiple design concepts. We select a new pickup mechanism that specifically targets the trash we encounter on the field. Our solution achieved an overall success rate of 80%, demonstrating that autonomous trash pickup robots on grass fields are a viable solution.

Authors:Suguru Sato, Kamesh Subbarao
Title: Three Dimensional Hydrodynamic Flow-Based Collision Avoidance for UAV Formations Facing Emergent Dynamic Obstacles
Abstract:
This paper presents a three‑dimensional, hydrodynamics‑inspired collision avoidance framework for uncrewed aerial vehicle (UAV) formations operating in dynamic environments. When moving obstacles enter a UAV's sensing region, they are modeled as three dimensional doublets or ellipsoids that generate local velocity fields, guiding nearby UAVs to execute smooth, collision‑free maneuvers without trajectory discontinuities or explicit trajectory replanning. This flow‑based approach enables real‑time operation and interpretable behavior by leveraging the nature of fluid flow around obstacles via the harmonic properties of Laplace's equation, inherently avoiding local minima common in traditional potential field methods. To establish and maintain coordination among the UAVs, a Virtual Rigid Body (VRB) formation strategy is integrated, ensuring that formation geometry and trajectory tracking are preserved. Simulation results demonstrate the feasibility and scalability of the method for both individual and multi‑UAV scenarios with multiple formation geometries encountering moving obstacles. The proposed approach achieves safe, smooth, and computationally efficient avoidance maneuvers suitable for real‑time and practical applications.

Authors:Suguru Sato, Jinaykumar Patel, Kamesh Subbarao
Title: Optimal Thruster Configuration for 6-DOF Control of a Small Satellite
Abstract:
With the growing deployment of small satellites (such as CubeSats, Nanosats, Picosats, and Femtosats) in Low Earth Orbit (LEO) for targeted applications like imaging, communication, data storage, and rendezvous‑docking mission, there is increasing attention on orbit maintenance and attitude control. A common approach for active orbit control involves the use of multiple thrusters, which, when properly arranged, can also generate the required torque for attitude control. Starting from a 24‑thruster configuration, this paper presents a set of thruster configurations (referred to as a viable configuration group) that enable full six degrees of freedom (6‑DOF) control. Further, configuration group that requires minimum total thrust to achieve 6‑DOF commands are found among the viable configuration group. One configuration from each of these groups is further evaluated for its attitude control performance through a representative rendezvous‑docking mission, demonstrating that even with a reduced thruster count, sufficient maneuverability can be achieved.

Authors:Suguru Sato, Kamesh Subbarao
Title: Modeling and Simulation of Virtual Rigid Body Formations and Their Applications Using Multiple Air Vehicles
Abstract:
This paper presents thorough mathematical modeling, control law development, and simulation of virtual structure formations which are inspired by the characteristics of rigid bodies. The stable constraint forces that establish the rigidity in the formation are synthesized by utilizing d'Alembert's principle of virtual work, constraint sensitivities (Lagrange multipliers) and constraint stabilization using Baumgarte stabilization. The governing equations of motion of a multiagent system are derived via Newton's and Euler's equations to include these constraint forces and to enable inputs regarding the formation as if it were an independent rigid body. The performance of this framework is evaluated under multiple cases including waypoint following missions, and using different number of agents.

Authors:Stavros Mitrolaris, Subhankar Banerjee, Sennur Ulukus
Title: Age-Based Scheduling for a Memory-Constrained Quantum Switch
Abstract:
In a time‑slotted system, we study the problem of scheduling multipartite entanglement requests in a quantum switch with a finite number of quantum memory registers. Specifically, we consider probabilistic link‑level entanglement (LLE) generation for each user, probabilistic entanglement swapping, and one‑slot decoherence. To evaluate the performance of the proposed scheduling policies, we introduce a novel age‑based metric, coined age of entanglement establishment (AoEE). We consider two families of low‑complexity policies for which we obtain closed‑form expressions for their corresponding AoEE performance. Optimizing over each family, we obtain two policies. Further, we propose one more low‑complexity policy and provide its performance guarantee. Finally, we numerically compare the performance of the proposed policies.

Authors:Deepak Babu Piskala
Title: From Everything-is-a-File to Files-Are-All-You-Need: How Unix Philosophy Informs the Design of Agentic AI Systems
Abstract:
A core abstraction in early Unix systems was the principle that 'everything is a file', enabling heterogeneous devices and kernel resources to be manipulated via uniform read/write interfaces. This paper explores how an analogous unification is emerging in contemporary agentic AI. We trace the evolution from Unix to DevOps, Infrastructure‑as‑Code, and finally autonomous software agents, highlighting how file‑like abstractions and code‑based specifications collapse diverse resources into consistent, composable interfaces. The resulting perspective suggests that adopting file‑ and code‑centric interaction models may enable agentic systems that are more maintainable, auditable, and operationally robust.

Authors:Miriam K. Wolff, Peter Calhoun, Eleonora Maria Aiello, Yao Qin, Sam F. Royston
Title: MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Management
Abstract:
Progress in Type 1 Diabetes (T1D) algorithm development is limited by the fragmentation and lack of standardization across existing T1D management datasets. Current datasets differ substantially in structure and are time‑consuming to access and process, which impedes data integration and reduces the comparability and generalizability of algorithmic developments. This work aims to establish a unified and accessible data resource for T1D algorithm development. Multiple publicly available T1D datasets were consolidated into a unified resource, termed the MetaboNet dataset. Inclusion required the availability of both continuous glucose monitoring (CGM) data and corresponding insulin pump dosing records. Additionally, auxiliary information such as reported carbohydrate intake and physical activity was retained when present. The MetaboNet dataset comprises 3135 subjects and 1228 patient‑years of overlapping CGM and insulin data, making it substantially larger than existing standalone benchmark datasets. The resource is distributed as a fully public subset available for immediate download at https://metabo‑net.org/ , and with a Data Use Agreement (DUA)‑restricted subset accessible through their respective application processes. For the datasets in the latter subset, processing pipelines are provided to automatically convert the data into the standardized MetaboNet format. A consolidated public dataset for T1D research is presented, and the access pathways for both its unrestricted and DUA‑governed components are described. The resulting dataset covers a broad range of glycemic profiles and demographics and thus can yield more generalizable algorithmic performance than individual datasets.

Authors:Matt Baughman, Marena Trujillo, Bri-Mathias Hodge, Emily Jensen
Title: Implications of Grid-Forming Inverter Parameters on Disturbance Localization and Controllability
Abstract:
The shift from traditional synchronous generator (SG) based power generation to generation driven by power electronic devices introduces new dynamic phenomena and considerations for the control of large‑scale power systems. In this paper, two aspects of all‑inverter power systems are investigated: greater localization of system disturbance response and greater system controllability. The prevalence of both of these aspects are shown to be related to the lower effective inertia of inverters and have implications for future widearea control system design. Greater disturbance localization implies the need for feedback measurement placement close to generator nodes to properly reject disturbances in the system while increased system controllability implies that widearea control systems should preferentially actuate inverters to most efficiently control the system. This investigation utilizes reduced‑order linear time‑invariant models of both SGs and inverters that are shown to capture the frequency dynamics of interest in both all‑SG and all‑inverter systems, allowing for the efficient use of both frequency and time domain analysis methods.

Authors:Johan Thunberg, Galina Sidorenko
Title: Projection-based discrete-time consensus on the unit sphere
Abstract:
We address discrete‑time consensus on the Euclidean unit sphere. For this purpose we consider a distributed algorithm comprising the iterative projection of a conical combination of neighboring states. Neighborhoods are represented by a strongly connected directed graph, and the conical combinations are represented by a (non‑negative) weight matrix with a zero structure corresponding to the graph. A first result mirrors earlier results for gradient flows. Under the assumptions that each diagonal element of the weight matrix is more than \sqrt2 larger than the sum of the other elements in the corresponding row, the sphere dimension is greater or equal to 2, and the graph, as well as the weight matrix, is symmetric, we show that the algorithm comprises gradient ascent, stable fixed points are consensus points, and the set of initial points for which the algorithm converges to a non‑consensus fixed point has measure zero. The second result is that for the unit circle and a strongly connected graph or for any unit sphere with dimension greater than or equal to 1 and the complete graph, only for a measure zero set of weight matrices there are fixed points for the algorithm which do not have consensus or antipodal configurations.

Authors:Abdelrahman Ramadan, Sidney Givigi
Title: Learning-Based Shrinking Disturbance-Invariant Tubes for State- and Input-Dependent Uncertainty
Abstract:
We develop a learning‑based framework for constructing shrinking disturbance‑invariant tubes under state‑ and input‑dependent uncertainty, intended as a building block for tube Model Predictive Control (MPC), and certify safety via a lifted, isotone (order‑preserving) fixed‑point map. Gaussian Process (GP) posteriors become (1‑α) credible ellipsoids, then polytopic outer sets for deterministic set operations. A two‑time‑scale scheme separates learning epochs, where these polytopes are frozen, from an inner, outside‑in iteration that converges to a compact fixed point Z^\star\!\subseteq\!\mathcal G; its state projection is RPI for the plant. As data accumulate, disturbance polytopes tighten, and the associated tubes nest monotonically, resolving the circular dependence between the set to be verified and the disturbance model while preserving hard constraints. A double‑integrator study illustrates shrinking tube cross‑sections in data‑rich regions while maintaining invariance.

Authors:Henrik Hose, Paul Brunzema, Devdutt Subhasish, Sebastian Trimpe
Title: The Mini Wheelbot Dataset: High-Fidelity Data for Robot Learning
Abstract:
The development of robust learning‑based control algorithms for unstable systems requires high‑quality, real‑world data, yet access to specialized robotic hardware remains a significant barrier for many researchers. This paper introduces a comprehensive dynamics dataset for the Mini Wheelbot, an open‑source, quasi‑symmetric balancing reaction wheel unicycle. The dataset provides 1 kHz synchronized data encompassing all onboard sensor readings, state estimates, ground‑truth poses from a motion capture system, and third‑person video logs. To ensure data diversity, we include experiments across multiple hardware instances and surfaces using various control paradigms, including pseudo‑random binary excitation, nonlinear model predictive control, and reinforcement learning agents. We include several example applications in dynamics model learning, state estimation, and time‑series classification to illustrate common robotics algorithms that can be benchmarked on our dataset.

Authors:Akhilesh Raj, Swann Perarnau, Aniruddha Gokhale, Solomon Bekele Abera
Title: Offline Reinforcement-Learning-Based Power Control for Application-Agnostic Energy Efficiency
Abstract:
Energy efficiency has become an integral aspect of modern computing infrastructure design, impacting the performance, cost, scalability, and durability of production systems. The incorporation of power actuation and sensing capabilities in CPU designs is indicative of this, enabling the deployment of system software that can actively monitor and adjust energy consumption and performance at runtime. While reinforcement learning (RL) would seem ideal for the design of such energy efficiency control systems, online training presents challenges ranging from the lack of proper models for setting up an adequate simulated environment, to perturbation (noise) and reliability issues, if training is deployed on a live system. In this paper we discuss the use of offline reinforcement learning as an alternative approach for the design of an autonomous CPU power controller, with the goal of improving the energy efficiency of parallel applications at runtime without unduly impacting their performance. Offline RL sidesteps the issues incurred by online RL training by leveraging a dataset of state transitions collected from arbitrary policies prior to training. Our methodology applies offline RL to a gray‑box approach to energy efficiency, combining online application‑agnostic performance data (e.g., heartbeats) and hardware performance counters to ensure that the scientific objectives are met with limited performance degradation. Evaluating our method on a variety of compute‑bound and memory‑bound benchmarks and controlling power on a live system through Intel's Running Average Power Limit, we demonstrate that such an offline‑trained agent can substantially reduce energy consumption at a tolerable performance degradation cost.

Authors:Tyler Paine, Brendan Long, Jeremy Wenger, Michael DeFilippo, James Usevitch, Michael Benjamin
Title: Distributed Control Barrier Functions for Safe Multi-Vehicle Navigation in Heterogeneous USV Fleets
Abstract:
Collision avoidance in heterogeneous fleets of uncrewed vessels is challenging because the decision‑making processes and controllers often differ between platforms, and it is further complicated by the limitations on sharing trajectories and control values in real‑time. This paper presents a pragmatic approach that addresses these issues by adding a control filter on each autonomous vehicle that assumes worst‑case behavior from other contacts, including crewed vessels. This distributed safety control filter is developed using control barrier function (CBF) theory and the application is clearly described to ensure explainability of these safety‑critical methods. This work compares the worst‑case CBF approach with a Collision Regulations (COLREGS) behavior‑based approach in simulated encounters. Real‑world experiments with three different uncrewed vessels and a human operated vessel were performed to confirm the approach is effective across a range of platforms and is robust to uncooperative behavior from human operators. Results show that combining both CBF methods and COLREGS behaviors achieves the best safety and efficiency.

Authors:Luisa Schuhmacher, Jimmy Fernandez Landivar, Ihsane Gryech, Hazem Sallouha, Michele Rossi, Sofie Pollin
Title: Machine Learning on the Edge for Sustainable IoT Networks: A Systematic Literature Review
Abstract:
The Internet of Things (IoT) has become integral to modern technology, enhancing daily life and industrial processes through seamless connectivity. However, the rapid expansion of IoT systems presents significant sustainability challenges, such as high energy consumption and inefficient resource management. Addressing these issues is critical for the long‑term viability of IoT networks. Machine learning (ML), with its proven success across various domains, offers promising solutions for optimizing IoT operations. ML algorithms can learn directly from raw data, uncovering hidden patterns and optimizing processes in dynamic environments. Executing ML at the edge of IoT networks can further enhance sustainability by reducing bandwidth usage, enabling real‑time decision‑making, and improving data privacy. Additionally, testing ML models on actual hardware is essential to ensure satisfactory performance under real‑world conditions, as it captures the complexities and constraints of real‑world IoT deployments. Combining ML at the edge and actual hardware testing, therefore, increases the reliability of ML models to effectively improve the sustainability of IoT systems. The present systematic literature review explores how ML can be utilized to enhance the sustainability of IoT networks, examining current methodologies, benefits, challenges, and future opportunities. Through our analysis, we aim to provide insights that will drive future innovations in making IoT networks more sustainable.

Authors:Botao Zhu, Xianbin Wang
Title: Composite and Staged Trust Evaluation for Multi-Hop Collaborator Selection
Abstract:
Multi‑hop collaboration offers new perspectives for enhancing task execution efficiency by increasing available distributed collaborators for resource sharing. Consequently, selecting trustworthy collaborators becomes critical for realizing effective multi‑hop collaboration. However, evaluating device trust requires the consideration of multiple factors, including relatively stable factors, such as historical interaction data, and dynamic factors, such as varying resources and network conditions. This differentiation makes it challenging to achieve the accurate evaluation of composite trust factors using one identical evaluation approach. To address this challenge, this paper proposes a composite and staged trust evaluation (CSTE) mechanism, where stable and dynamic factors are separately evaluated at different stages and then integrated for a final trust decision. First, a device interaction graph is constructed from stable historical interaction data to represent direct trust relationships between devices. A graph neural network framework is then used to propagate and aggregate these trust relationships to produce the historical trustworthiness of devices. In addition, a task‑specific trust evaluation method is developed to assess the dynamic resources of devices based on task requirements, which generates the task‑specific resource trustworthiness of devices. After these evaluations, CSTE integrates their results to identify devices within the network topology that satisfy the minimum trust thresholds of tasks. These identified devices then establish a trusted topology. Finally, within this trusted topology, an A search algorithm is employed to construct a multi‑hop collaboration path that satisfies the task requirements. Experimental results demonstrate that CSTE outperforms the comparison algorithms in identifying paths with the highest average trust values.

Authors:Victor G. Lopez, Matthias A. Müller
Title: On Data-based Nash Equilibria in LQ Nonzero-sum Differential Games
Abstract:
This paper considers data‑based solutions of linear‑quadratic nonzero‑sum differential games. Two cases are considered. First, the deterministic game is solved and Nash equilibrium strategies are obtained by using persistently excited data from the multiagent system. Then, a stochastic formulation of the game is considered, where each agent measures a different noisy output signal and state observers must be designed for each player. It is shown that the proposed data‑based solutions of these games are equivalent to known model‑based procedures. The resulting data‑based solutions are validated in a numerical experiment.

Authors:Haoyang Zhang
Title: Analysis of Full Order Observer Based Control for Spacecraft Orbit Maneuver Trajectory Under Solar Radiation Pressure
Abstract:
This study investigates the application of modern control theory to improve the precision of spacecraft orbit maneuvers in low Earth orbit under the influence of solar radiation pressure. A full order observer based feedback control framework is developed to estimate system states and compensate for external disturbances during the trajectory correction phase following main engine cut off. The maneuver trajectory is generated using Lambert guidance, while the observer based controller ensures accurate tracking of the target orbit despite SRP perturbations. The effectiveness of the proposed design is assessed through stability, observability, and controllability analyses. Stability is validated by step‑response simulations and eigenvalue distributions of the system dynamics. Observability is demonstrated through state matrix rank analysis, confirming complete state estimation. Controllability is verified using state feedback rank conditions and corresponding control performance plots. Comparative simulations highlight that, in contrast to uncontrolled or conventional control cases, the observer based controller achieves improved trajectory accuracy and robust disturbance rejection with moderate control effort. These findings indicate that observer‑based feedback control offers a reliable and scalable solution for precision orbital maneuvering in LEO missions subject to environmental disturbances.

Authors:Savvas Papaioannou, Panayiotis Kolios, Christos G. Panayiotou, Marios M. Polycarpou
Title: Adaptive Monitoring of Stochastic Fire Front Processes via Information-seeking Predictive Control
Abstract:
We consider the problem of adaptively monitoring a wildfire front using a mobile agent (e.g., a drone), whose trajectory determines where sensor data is collected and thus influences the accuracy of fire propagation estimation. This is a challenging problem, as the stochastic nature of wildfire evolution requires the seamless integration of sensing, estimation, and control, often treated separately in existing methods. State‑of‑the‑art methods either impose linear‑Gaussian assumptions to establish optimality or rely on approximations and heuristics, often without providing explicit performance guarantees. To address these limitations, we formulate the fire front monitoring task as a stochastic optimal control problem that integrates sensing, estimation, and control. We derive an optimal recursive Bayesian estimator for a class of stochastic nonlinear elliptical‑growth fire front models. Subsequently, we transform the resulting nonlinear stochastic control problem into a finite‑horizon Markov decision process and design an information‑seeking predictive control law obtained via a lower confidence bound‑based adaptive search algorithm with asymptotic convergence to the optimal policy.

Authors:W. P. M. H. Heemels, R. Postoyan, P. Bernard, K. J. A. Scheres, R. G. Sanfelice
Title: Solution Concepts and Existence Results for Hybrid Systems with Continuous-time Inputs
Abstract:
In many scenarios, it is natural to model a plant's dynamical behavior using a hybrid dynamical system influenced by exogenous continuous‑time inputs. While solution concepts and analytical tools for existence and completeness are well established for autonomous hybrid systems, corresponding results for hybrid dynamical systems involving continuous‑time inputs are generally lacking. This work aims to address this gap. We first formalize notions of a solution for such systems. We then provide conditions that guarantee the existence and forward completeness of solutions. Moreover, we leverage results and ideas from viability theory to present more explicit conditions in terms of various tangent cone formulations. Variants are provided that depend on the regularity of the exogenous input signals.

Authors:Ju-Hong Oh, Seon-In Kim, Eui-Jong Kim
Title: Determining optimal thermal energy storage charging temperature for cooling using integrated building and coil modeling
Abstract:
Thermal energy storage (TES) systems coupled with heat pumps offer significant potential for improving building energy efficiency by shifting electricity demand to off‑peak hours. However, conventional operating strategies maintain conservatively low chilled water temperatures throughout the cooling season, a practice that results in suboptimal heat pump performance. This study proposes a physics‑based integrated simulation framework to determine the maximum feasible chilled water supply temperature while ensuring cooling stability. The framework integrates four submodels: relative humidity prediction, dynamic cooling load estimation, cooling coil performance prediction, and TES discharge temperature prediction. Validation against measured data from an office building demonstrates reliable accuracy across all sub‑models (e.g., CVRMSE of 9.3% for cooling load and R2 of 0.91 for peak‑time discharge temperature). The integrated simulation reveals that the proposed framework can increase the daily initial TES charging temperature by an average of 2.55 °C compared to conventional fixed‑temperature operation, enabling the heat pump to operate at a higher coefficient of performance. This study contributes a practical methodology for optimizing TES charging temperatures in building heating, ventilation, and air conditioning (HVAC) systems while maintaining indoor setpoint temperatures.

Authors:Yujia Yuan, Chuanzhen Zhao, Margherita Ronchini, Yuya Nishio, Donglai Zhong, Can Wu, Hyukmin Kweon, Zehao Sun, Rachael K. Mow, Yuran Shi, Lukas Michalek, Haotian Wu, Qianhe Liu, Weichen Wang, Yating Yao, Zelong Yin, Junyi Zhao, Zihan He, Ke Chen, Ruiheng Wu, Jiuyun Shi, Jian Pei, Zhenan Bao
Title: A monolithic fabrication platform for intrinsically stretchable polymer transistors and complementary circuits
Abstract:
Soft, stretchable organic field‑effect transistors (OFETs) can provide powerful on‑skin signal conditioning, but current fabrication methods are often material‑specific: each new polymer semiconductor (PSC) requires a tailored process. The challenge is even greater for complementary OFET circuits, where two PSCs must be patterned sequentially, which often leads to device degradation. Here, we introduce a universal, monolithic photolithography process that enables high‑yield, high‑resolution stretchable complementary OFETs and circuits. This approach is enabled by a process‑design framework that includes (i) a direct, photopatternable, solvent‑resistant, crosslinked dielectric/semiconductor interface, (ii) broadly applicable crosslinked PSC blends that preserve high mobility, and (iii) a patterning strategy that provides simultaneous etch masking and encapsulation. Using this platform, we achieve record integration density for stretchable OTFTs (55,000 cm^‑2), channel lengths down to 2 um, and low‑voltage operation at 5 V. We demonstrate photopatterning across multiple PSC types and realize complementary circuits, including 3 kHz stretchable ring oscillators, the first to exceed 1 kHz and representing more than a 60‑fold increase in stage switching speed over the state of the art. Finally, we demonstrate the first stretchable complementary OTFT neuron circuit, where the output frequency is modulated by the input current to mimic neuronal signal processing. This scalable approach can be readily extended to diverse high‑performance stretchable materials, accelerating the development and manufacturing of skin‑like electronics.

Authors:Zain ul Abdeen, Waris Gill, Ming Jin
Title: Toward Adaptive Grid Resilience: A Gradient-Free Meta-RL Framework for Critical Load Restoration
Abstract:
Restoring critical loads after extreme events demands adaptive control to maintain distribution‑grid resilience, yet uncertainty in renewable generation, limited dispatchable resources, and nonlinear dynamics make effective restoration difficult. Reinforcement learning (RL) can optimize sequential decisions under uncertainty, but standard RL often generalizes poorly and requires extensive retraining for new outage configurations or generation patterns. We propose a meta‑guided gradient‑free RL (MGF‑RL) framework that learns a transferable initialization from historical outage experiences and rapidly adapts to unseen scenarios with minimal task‑specific tuning. MGF‑RL couples first‑order meta‑learning with evolutionary strategies, enabling scalable policy search without gradient computation while accommodating nonlinear, constrained distribution‑system dynamics. Experiments on IEEE 13‑bus and IEEE 123‑bus test systems show that MGF‑RL outperforms standard RL, MAML‑based meta‑RL, and model predictive control across reliability, restoration speed, and adaptation efficiency under renewable forecast errors. MGF‑RL generalizes to unseen outages and renewable patterns while requiring substantially fewer fine‑tuning episodes than conventional RL. We also provide sublinear regret bounds that relate adaptation efficiency to task similarity and environmental variation, supporting the empirical gains and motivating MGF‑RL for real‑time load restoration in renewable‑rich distribution grids.

Authors:Dalibor Sain, Thomas Rosenstatter, Olaf Saßnick, Christian Schäfer, Stefan Huber
Title: Secure Data Bridging in Industry 4.0: An OPC UA Aggregation Approach for Including Insecure Legacy Systems
Abstract:
The increased connectivity of industrial networks has led to a surge in cyberattacks, emphasizing the need for cybersecurity measures tailored to the specific requirements of industrial systems. Modern Industry 4.0 technologies, such as OPC UA, offer enhanced resilience against these threats. However, widespread adoption remains limited due to long installation times, proprietary technology, restricted flexibility, and formal process requirements (e.g. safety certifications). Consequently, many systems do not yet implement these technologies, or only partially. This leads to the challenge of dealing with so‑called brownfield systems, which are often placed in isolated security zones to mitigate risks. However, the need for data exchange between secure and insecure zones persists. This paper reviews existing solutions to address this challenge by analysing their approaches, advantages, and limitations. Building on these insights, we identify three key concepts, evaluate their suitability and compatibility, and ultimately introduce the SigmaServer, a novel TCP‑level aggregation method. The developed proof‑of‑principle implementation is evaluated in an operational technology (OT) testbed, demonstrating its applicability and effectiveness in bridging secure and insecure zones.

Authors:Maryam Salamatmoghadasi, Amir Mehrabian, Halim Yanikomeroglu, Georges Kaddoum
Title: Sustainable Vertical Heterogeneous Networks: A Cell Switching Approach with High Altitude Platform Station
Abstract:
The rapid growth of radio access networks (RANs) is increasing energy consumption and challenging the sustainability of future systems. We consider a dense‑urban vertical heterogeneous network (vHetNet) comprising a high‑altitude platform station (HAPS) acting as a super macro base station, a terrestrial macro base station (MBS), and multiple small base stations (SBSs). We propose a HAPS‑enhanced cell‑switching algorithm that selectively deactivates SBSs based on their traffic load and the capacity and channel conditions of both the MBS and HAPS. The resulting energy‑minimization problem, subject to an outage‑based quality‑of‑service (QoS) constraint, is formulated as a mixed‑integer nonlinear program and reformulated into a mixed‑integer program for efficient solution. Using realistic 3GPP channel models, simulations show substantial energy savings versus All‑ON, terrestrial cell switching, and sorting benchmarks. Relative to All‑ON, the proposed method reduces power consumption by up to 77% at low loads and about 40% at high loads; a NoQoS variant achieves up to 90% and 47%, respectively. The approach maintains high served‑traffic levels and provides a tunable trade‑off between power efficiency and outage‑based QoS, supporting scalable and sustainable 6G deployments.

Authors:Amer Diwan, Prabhakar Raghavan, Eli Upfal
Title: Balanced allocation: considerations from large scale service environments
Abstract:
We study d‑way balanced allocation, which assigns each incoming job to the lightest loaded among d randomly chosen servers. While prior work has extensively studied the performance of the basic scheme, there has been less published work on adapting this technique to many aspects of large‑scale systems. Based on our experience in building and running planet‑scale cloud applications, we extend the understanding of d‑way balanced allocation along the following dimensions: (i) Bursts: Events such as breaking news can produce bursts of requests that may temporarily exceed the servicing capacity of the system. Thus, we explore what happens during a burst and how long it takes for the system to recover from such bursts. (ii) Priorities: Production systems need to handle jobs with a mix of priorities (e.g., user facing requests may be high priority while other requests may be low priority). We extend d‑way balanced allocation to handle multiple priorities. (iii) Noise: Production systems are often typically distributed and thus d‑way balanced allocation must work with stale or incorrect information. Thus we explore the impact of noisy information and their interactions with bursts and priorities. We explore the above using both extensive simulations and analytical arguments. Specifically we show, (i) using simulations, that d‑way balanced allocation quickly recovers from bursts and can gracefully handle priorities and noise; and (ii) that analysis of the underlying generative models complements our simulations and provides insight into our simulation results.

Authors:Davide Mannini, James B. Rawlings
Title: Disturbance Attenuation Regulator II: Stage Bound Finite Horizon Solution
Abstract:
This paper develops a generalized finite horizon recursive solution to the discrete time stage bound disturbance attenuation regulator (StDAR) for state feedback control. This problem addresses linear dynamical systems subject to stage bound disturbances, i.e., disturbance sequences constrained independently at each time step through stagewise squared two‑norm bounds. The term generalized indicates that the results accommodate arbitrary initial states. By combining game theory and dynamic programming, this work derives a recursive solution for the optimal state feedback policy. The optimal policy is nonlinear in the state and requires solving a tractable convex optimization for the Lagrange multiplier vector at each stage; the control is then explicit. For systems with constant stage bound, the problem admits a steady‑state optimization expressed as a tractable linear matrix inequality (LMI) with O(n^3) complexity. Numerical examples illustrate the properties of the solution. This work provides a complete feedback solution to the StDAR for arbitrary initial states. Companion papers address the signal bound disturbance attenuation regulator (SiDAR): the finite horizon solution in Part~I‑A and convergence properties in Part~I‑B.

Authors:Davide Mannini, James B. Rawlings
Title: Disturbance Attenuation Regulator I-B: Signal Bound Convergence and Steady-State
Abstract:
This paper establishes convergence and steady‑state properties for the signal bound disturbance attenuation regulator (SiDAR). Building on the finite horizon recursive solution developed in a companion paper, we introduce the steady‑state SiDAR and derive its tractable linear matrix inequality (LMI) with O(n^3) complexity. Systems are classified as degenerate or nondegenerate based on steady‑state solution properties. For nondegenerate systems, the finite horizon solution converges to the steady‑state solution for all states as the horizon approaches infinity. For degenerate systems, convergence holds in one region of the state space, while a turnpike arises in the complementary region. When convergence holds, the optimal multiplier and control gain are obtained directly from the LMI solution. Numerical examples illustrate convergence behavior and turnpike phenomena. Companion papers address the finite horizon SiDAR solution and the stage bound disturbance attenuation regulator (StDAR).

Authors:Davide Mannini, James B. Rawlings
Title: Disturbance Attenuation Regulator I-A: Signal Bound Finite Horizon Solution
Abstract:
This paper develops a generalized finite horizon recursive solution to the discrete time signal bound disturbance attenuation regulator (SiDAR) for state feedback control. This problem addresses linear dynamical systems subject to signal bound disturbances, i.e., disturbance sequences whose squared signal two‑norm is bounded by a fixed budget. The term generalized indicates that the results accommodate arbitrary initial states. By combining game theory and dynamic programming, we derive a recursive solution for the optimal state feedback policy valid for arbitrary initial states. The optimal policy is nonlinear in the state and requires solving a tractable convex scalar optimization for the Lagrange multiplier at each stage; the control is then explicit. For fixed disturbance budget α, the state space partitions into two distinct regions: \mathcalX_L(α), where the optimal control policy is linear and coincides with the standard linear H_\infty state feedback control, and \mathcalX_NL(α), where the optimal control policy is nonlinear. We establish monotonicity and boundedness of the associated Riccati recursions and characterize the geometry of the solution regions. A numerical example illustrates the theoretical properties. This work provides a complete feedback solution to the finite horizon SiDAR for arbitrary initial states. Companion papers address the steady‑state problem and convergence properties for the signal bound case, and the stage bound disturbance attenuation regulator (StDAR).

Authors:Brandon D'Agostino, Jimmy Chen, Ram Rajagopal
Title: Beyond Uptime: Actionable Performance Metrics for EV Charging Site Operators
Abstract:
The transition to electric vehicles (EVs) depends heavily on the reliability of charging infrastructure, yet approximately 1 in 5 drivers report being unable to charge during station visits due to inoperable equipment. While regulatory efforts such as the National Electric Vehicle Infrastructure (NEVI) program have established uptime requirements, these metrics are often simplistic, delayed, and fail to provide the diagnostic granularity needed by Charging Site Operators (CSOs). Despite their pivotal role in maintaining and improving site performance, CSOs have been largely overlooked by existing reporting standards. In this paper, we propose a suite of readily computable, actionable performance metrics‑Fault Time, Fault‑Reason Time, and Unreachable Time‑that decompose charger behavior into operationally meaningful states. Unlike traditional uptime, these metrics are defined over configurable periods and distinguish between hardware malfunctions and network connectivity issues. We demonstrate the implementation of these metrics via an open‑source tool that derives performance data from existing infrastructure without requiring hardware modifications. A case study involving 98 chargers at a California academic institution spanning 2018‑2024 demonstrates that these metrics reveal persistent "zombie chargers" and high‑frequency network instability that remain hidden in standard annual reporting.

Authors:Simin Liu, Tong Zhao, Bernhard Paus Graesdal, Peter Werner, Jiuguang Wang, John Dolan, Changliu Liu, Tao Pang
Title: Approximately Optimal Global Planning for Contact-Rich SE(2) Manipulation on a Graph of Reachable Sets
Abstract:
If we consider human manipulation, it is clear that contact‑rich manipulation (CRM)‑the ability to use any surface of the manipulator to make contact with objects‑can be far more efficient and natural than relying solely on end‑effectors (i.e., fingertips). However, state‑of‑the‑art model‑based planners for CRM are still focused on feasibility rather than optimality, limiting their ability to fully exploit CRM's advantages. We introduce a new paradigm that computes approximately optimal manipulator plans. This approach has two phases. Offline, we construct a graph of mutual reachable sets, where each set contains all object orientations reachable from a starting object orientation and grasp. Online, we plan over this graph, effectively computing and sequencing local plans for globally optimized motion. On a challenging, representative contact‑rich task, our approach outperforms a leading planner, reducing task cost by 61%. It also achieves a 91% success rate across 250 queries and maintains sub‑minute query times, ultimately demonstrating that globally optimized contact‑rich manipulation is now practical for real‑world tasks.

Authors:Yuxin Yang, Hang Zhou, Hourong Song, Branislav Hredzak
Title: On the static and small signal analysis of DAB converter
Abstract:
This document develops a method to solve the periodic operating point of Dual‑Active‑Bridge (DAB).

Authors:Trager Joswig-Jones, Baosen Zhang
Title: Safe Trajectory Gradient Flow Control of a Grid-Interfacing Inverter
Abstract:
Grid‑interfacing inverters serve as the interface between renewable energy resources and the electric power grid, offering fast, programmable control capabilities. However, their operation is constrained by hardware limitations, such as bounds on the current magnitude. Existing control methods for these systems often neglect these constraints during controller design and instead rely on ad hoc limiters, which can introduce instability or degrade performance. In this work, we present a control framework that directly incorporates constraints into the control of a voltage‑source inverter. We propose a safe trajectory gradient flow controller, which applies the safe gradient flow method to a rolling horizon trajectory optimization problem to ensure that the states remain within a safe set defined by the constraints while directing the trajectory towards an optimal equilibrium point of a nonlinear program. Simulation results demonstrate that our approach can drive the outputs of a simulated inverter system to optimal values and maintain state constraints, even when using a limited number of optimization steps per control cycle.

Authors:José-Ramón Vidal, Luis Guijarro, Vicent Pla
Title: A user subscription model in mobile radio access networks with network slicing
Abstract:
Network slicing is an architectural enabling technology that logically decouples the current cellular networks into infrastructure providers (InPs) and Network Slice Tenants (NSTs). The network resources (e.g., radio access resources at each cell) are owned by the InP, and are shared by the NSTs to provide a service to their mobile users. In this context, we proposed a business model that includes resource allocation and user subscription to NSTs in a competitive setting, and provides, among other things, closed‑form expressions for the subscription indicators in equilibrium of each NST at each cell. This model relies on the widely adopted logit model to characterize user subscriptions. However, as a consequence of user mobility and radio propagation, some of the underlying assumptions in the logit model do not hold. Therefore, further research is needed to assess the accuracy of the results provided by the logit model in a mobile radio scenario. We carry out a thorough evaluation of the validity of the model by comparing its results against those obtained through computer simulation. Our simulation model includes complete and realistic characterizations of user mobility and radio propagation. From the results, we conclude in most cases the logit model provides valid results in a mobile radio scenario.

Authors:He Ren, Gaowei Yan, Hang Liu, Lifeng Cao, Zhijun Zhao, Gang Dang
Title: Online identification of nonlinear time-varying systems with uncertain information
Abstract:
Digital twins (DTs), serving as the core enablers for real‑time monitoring and predictive maintenance of complex cyber‑physical systems, impose critical requirements on their virtual models: high predictive accuracy, strong interpretability, and online adaptive capability. However, existing techniques struggle to meet these demands simultaneously: Bayesian methods excel in uncertainty quantification but lack model interpretability, while interpretable symbolic identification methods (e.g., SINDy) are constrained by their offline, batch‑processing nature, which make real‑time updates challenging. To bridge this semantic and computational gap, this paper proposes a novel Bayesian Regression‑based Symbolic Learning (BRSL) framework. The framework formulates online symbolic discovery as a unified probabilistic state‑space model. By incorporating sparse horseshoe priors, model selection is transformed into a Bayesian inference task, enabling simultaneous system identification and uncertainty quantification. Furthermore, we derive an online recursive algorithm with a forgetting factor and establish precise recursive conditions that guarantee the well‑posedness of the posterior distribution. These conditions also function as real‑time monitors for data utility, enhancing algorithmic robustness. Additionally, a rigorous convergence analysis is provided, demonstrating the convergence of parameter estimates under persistent excitation conditions. Case studies validate the effectiveness of the proposed framework in achieving interpretable, probabilistic prediction and online learning.

Authors:Georgia Psychogiou, Donal P. Lynch, Spyridon N. Daskalakis, Manos M. Tentzeris, George Goussetis, Stylianos D. Asimonis
Title: Single-Feed Circularly Polarized Super Realized Gain Antenna
Abstract:
This paper presents a super realized gain, circularly polarized strip‑crossed dipole antenna operating at 3.5 GHz. Superdirective behavior is achieved by leveraging strong inter‑element mutual coupling through careful adjustment of the strip dimensions. The antenna features a single driven element, with the other element passively loaded with a reactive impedance. The structure is optimized to maximize left‑hand circularly polarized (LHCP) realized gain, ensuring high polarization purity and good impedance matching. The optimized design exhibits a 50 Ω impedance bandwidth of 3.29 ‑ 4.17 GHz (23.75%) and an axial‑ratio bandwidth of 3.43 ‑ 3.57 GHz (4%). At 3.5 GHz, the antenna achieves a peak realized gain of 6.1 dB (ka \approx 1.65), with an axial ratio of 1.4 dB. These results demonstrate that circular polarization and superdirectivity can be simultaneously realized in a geometrically simple, low‑profile (0.15λ) antenna, rendering it suitable for integration into compact sub‑6~GHz wireless and sensing platforms.

Authors:Jonathan Vieth, Annika Eichler, Arne Speerforck
Title: Model Predictive Control of Thermo-Hydraulic Systems Using Primal Decomposition
Abstract:
Decarbonizing the global energy supply requires more efficient heating and cooling systems. Model predictive control enhances the operation of cooling and heating systems but depends on accurate system models, often based on control volumes. We present an automated framework including time discretization to generate model predictive controllers for such models. To ensure scalability, a primal decomposition exploiting the model structure is applied. The approach is validated on an underground heating system with varying numbers of states, demonstrating the primal decomposition's advantage regarding scalability.

Authors:Andres Intriago, Rongxing Hu, Nabil Mohammed, S. Gokul Krishnan, Konstantinos Kotsovos, Issam Gereige, Nesren Attiah, Ali Basaheeh, Sarah Aqeel, Hamad A. Saiari, Shehab Ahmed, Charalambos Konstantinou
Title: HyMGP: A Customized MILP-Based Tool for Techno-Economic Planning of Islanded Microgrids
Abstract:
This paper presents a customized microgrid planning algorithm and tool, HyMGP, for remote sites in arid regions, which is formulated as a Mixed Integer Linear Programming (MILP) problem. HyMGP is compared with HOMER Pro to evaluate its performance in optimizing the sizing of microgrid components, including photovoltaic panels (PVs), vertical axis wind turbines (VAWTs), and battery energy storage systems (BESS), for remote and off‑grid applications. The study focuses on a standalone microgrid in the Saudi Arabia, considering high solar irradiance, limited wind availability, and a constant load profile composed of continuous cathodic protection and daytime cooling. In the simulation environment, comparisons with HOMER solutions demonstrate the advantages of HyMGP, which provides optimal and more flexible solutions by allowing user‑defined component specifications and strictly enforcing all constraints. Further analysis shows that incorporating wind turbines reduces the Net Present Cost (NPC) by decreasing the required PV and battery capacities. Increasing battery autonomy leads to a higher NPC in both PV‑only and hybrid systems due to the need for larger storage. Finally, lithium iron phosphate (Li‑ion LFP) batteries are found to be more cost effective than lead acid, offering lower NPCs due to their longer lifespan, deeper discharge capability, and fewer replacement cycles.

Authors:Hideki Nishizawa, Kazuya Anazawa, Tetsuro Inui, Toru Mano, Takeo Sasai, Giacomo Borraccini, Tatsuya Matsumura, Hiroyuki Ishihara, Sae Kojima, Yoshiaki Sone, Koichi Takasugi
Title: Leveraging Digital Twin Technologies: All-Photonics Networks-as-a-Service for Data Center Xchange in the Era of AI [Invited Tutorial]
Abstract:
This paper presents a data center exchange (Data Center Xchange, DCX) architecture for all‑photonics networks‑as‑a‑service in distributed data center infrastructures, enabling the creation of a virtual large‑scale data center by directly interconnecting distributed data centers in metropolitan areas. Key requirements for such an architecture are identified: support for low‑latency operations, scalability, reliability, and flexibility within a single network architecture; the ability to add new operator‑driven automation functionalities based on an open networking approach; and the ability to control and manage remotely deployed transponders connected via access links with unknown physical parameters. We propose a set of technologies that enable digital twin operations for optical networks, including a cloud‑native architecture for coherent transceivers, remote transponder control, fast end‑to‑end optical path provisioning, transceiver‑based physical‑parameter estimation incorporating digital longitudinal monitoring, and optical line system calibration, demonstrating their feasibility through field validations.

Authors:Yuda Li, Shaoyuan Li, Xiang Yin
Title: On the Computation and Approximation of Backward Reachable Sets for Max-Plus Linear Systems using Polyhedras
Abstract:
This paper investigates reachability analysis for max‑plus linear systems (MPLS), an important class of dynamical systems that model synchronization and delay phenomena in timed discrete‑event systems. We specifically focus on backward reachability analysis, i.e., determining the set of states that can reach a given target set within a certain number of steps. Computing backward reachable sets presents significant challenges due to the non‑convexity of max‑plus dynamics and the complexity of set complement operations. To address these challenges, we propose a novel approximation framework that efficiently computes backward reachable sets by exploiting the structure of tropical polyhedra. Our approach reformulates the problem as a sequence of symbolic operations and approximates non‑convex target sets through closure operations on unions of tropical polyhedra. We develop a systematic algorithm that constructs both outer (M‑form) and inner (V‑form) representations of the resulting sets, incorporating extremal filtering to reduce computational complexity. The proposed method offers a scalable alternative to traditional DBM‑based approaches, enabling reliable approximate backward reachability analysis for general target regions in MPLS.

Authors:Farshad Amani, Faezeh Ardali, Amin Kargarian
Title: Event-Driven Deep RL Dispatcher for Post-Storm Distribution System Restoration
Abstract:
Natural hazards such as hurricanes and floods damage power grid equipment, forcing operators to replan restoration repeatedly as new information becomes available. This paper develops a deep reinforcement learning (DRL) dispatcher that serves as a real‑time decision engine for crew‑to‑repair assignments. We model restoration as a sequential, information‑revealing process and learn an actor‑critic policy over compact features such as component status, travel/repair times, crew availability, and marginal restoration value. A feasibility mask blocks unsafe or inoperable actions, such as power flow limits, switching rules, and crew‑time constraints, before they are applied. To provide realistic runtime inputs without relying on heavy solvers, we use lightweight surrogates for wind and flood intensities, fragility‑based failure, spatial clustering of damage, access impairments, and progressive ticket arrivals. In simulated hurricane and flood events, the learned policy updates crew decisions in real time as new field reports arrive. Because the runtime logic is lightweight, it improves online performance (energy‑not‑supplied, critical‑load restoration time, and travel distance) compared with mixed‑integer programs and standard heuristics. The proposed approach is tested on the IEEE 13‑ and 123‑bus feeders with mixed hurricane/flood scenarios.

Authors:Sahba Baniasadi, Paul M. Griffin, Prakash Chakraborty
Title: Emergency Department Patient Flow Optimization with an Alternative Care Threshold Policy
Abstract:
Emergency department (ED) overcrowding and patient boarding represent critical systemic challenges that compromise care quality. We propose a threshold‑based admission policy that redirects non‑urgent patients to alternative care pathways, such as telemedicine, during peak congestion. The ED is modeled as a two‑class M/M/c preemptive‑priority queuing system, where high‑acuity patients are prioritized and low‑acuity patients are subject to state‑dependent redirection. Analyzed via a level‑dependent Quasi‑Birth‑Death (QBD) process, the model determines the optimal threshold by maximizing a long‑run time‑averaged objective function comprising redirection‑affected revenue and costs associated with patient balking and system occupancy. Numerical analysis using national healthcare data reveals that optimal policies are highly context‑dependent. While rural EDs generally optimize at lower redirection thresholds, urban EDs exhibit performance peaks at moderate thresholds. Results indicate that our optimal policy yields significant performance gains of up to 4.84% in rural settings and 5.90% in urban environments. This research provides a mathematically rigorous framework for balancing clinical priority with operational efficiency across diverse ED settings.

Authors:Kaixin Lu, Ziliang Lyu, Yanfang Mo, Yiguang Hong, Haoyong Yu
Title: Extremum Seeking Nonovershooting Control of Strict-Feedback Systems Under Unknown Control Direction
Abstract:
This paper addresses the nonovershooting control problem for strict‑feedback nonlinear systems with unknown control direction. We propose a method that integrates extremum seeking with Lie bracket‑based design to achieve approximately nonovershooting tracking. The approach ensures that arbitrary reference trajectories can be tracked from below for any initial condition, with the overshoot reducible to arbitrarily small levels through parameter tuning. The method further provides a mechanism for enforcing high‑relative‑degree nonovershooting constraints in safety‑critical scenarios involving unknown control directions.

Authors:Yerzhan Mustafa, Selçuk Köse
Title: Interfacing Superconductor and Semiconductor Digital Electronics
Abstract:
Interface circuits are the key components that enable the hybrid integration of superconductor and semiconductor digital electronics. The design requirements of superconductor‑semiconductor interface circuits vary depending on the application, such as high‑performance classical computing, superconducting quantum computing, and digital signal processing. In this survey, various interface circuits are categorized based on the working principle and structure. The superconducting output drivers are explored, which are capable of converting and amplifying, e.g., single flux quantum (SFQ) voltage pulses, to voltage levels that semiconductor circuits can process. Several trade‑offs between circuit‑ and system‑level design parameters are examined. Accordingly, parameters such as the data rate, output voltage, power dissipation, layout area, thermal/heat load of cryogenic cables, and bit‑error rate are considered.

Authors:Karolina Schmidt, Luis Rodrigues
Title: Collision Avoidance for Non-Cooperative Multi-Swarm Coverage Control with Bounded Disturbance Measurements
Abstract:
This paper proposes a new algorithm for collision‑free coverage control of multiple non‑cooperating swarms in the presence of bounded disturbances. A new methodology is introduced that accounts for uncertainties in disturbance measurements. The proposed methodology is used to develop an algorithm that ensures collision‑free motion in multi‑swarm coverage control, specifically for cases where disturbances are present and their measurements are subject to bounded uncertainty. The theoretical results are validated through simulations of multiple swarms that independently aim to cover a given region in an environment with disturbances.

Authors:Zixuan He, Mohammad Reza Deylam Salehi, Derya Malak, Photios A. Stavrou
Title: Learning-Augmented Perfectly Secure Collaborative Matrix Multiplication
Abstract:
This paper presents a perfectly secure matrix multiplication (PSMM) protocol for multiparty computation (MPC) of \mathrmA^\top\mathrmB over finite fields. The proposed scheme guarantees correctness and information‑theoretic privacy against threshold‑bounded, semi‑honest colluding agents, under explicit local storage constraints. Our scheme encodes submatrices as evaluations of sparse masking polynomials and combines coefficient alignment with Beaver‑style randomness to ensure perfect secrecy. We demonstrate that any colluding set of parties below the security threshold observes uniformly random shares, and that the recovery threshold is optimal, matching existing information‑theoretic limits. Building on this framework, we introduce a learning‑augmented extension that integrates tensor‑decomposition‑based local block multiplication, capturing both classical and learned low‑rank methods. We demonstrate that the proposed learning‑based PSMM preserves privacy and recovery guarantees for MPC, while providing scalable computational efficiency gains (up to 80%) as the matrix dimensions grow.

Authors:Ge Lei, Ferran Brosa Planella, Sterling G. Baird, Samuel J. Cooper
Title: From Prompt to Protocol: Fast Charging Batteries with Large Language Models
Abstract:
Efficiently optimizing battery charging protocols is challenging because each evaluation is slow, costly, and non‑differentiable. Many existing approaches address this difficulty by heavily constraining the protocol search space, which limits the diversity of protocols that can be explored, preventing the discovery of higher‑performing solutions. We introduce two gradient‑free, LLM‑driven closed‑loop methods: Prompt‑to‑Optimizer (P2O), which uses an LLM to propose the code for small neural‑network‑based protocols, which are then trained by an inner loop, and Prompt‑to‑Protocol (P2P), which simply writes an explicit function for the current and its scalar parameters. Across our case studies, LLM‑guided P2O outperforms neural networks designed by Bayesian optimization, evolutionary algorithms, and random search. In a realistic fast charging scenario, both P2O and P2P yield around a 4.2 percent improvement in state of health (capacity retention based health metric under fast charging cycling) over a state‑of‑the‑art multi‑step constant current (CC) baseline, with P2P achieving this under matched evaluation budgets (same number of protocol evaluations). These results demonstrate that LLMs can expand the space of protocol functional forms, incorporate language‑based constraints, and enable efficient optimization in high cost experimental settings.

Authors:Shen Chen, Chaohou Liu, Wei Yao, Jisong Wang, Shuaipo Guo, Zeng Liu, Jinjun Liu
Title: A Novel $αβ$-Approximation Method Based on Numerical Integration for Discretizing Continuous Systems
Abstract:
In this article, we propose a novel discretization method based on numerical integration for discretizing continuous systems, termed the αβ‑approximation or Scalable Bilinear Transformation (SBT). In contrast to existing methods, the proposed method consists of two factors, i.e., shape factor (α) and time factor (β). Depending on the discretization technique applied, we identify two primary distortion modes in discrete resonant controllers: frequency warping and resonance damping. We further provide a theoretical explanation for these distortion modes, and demonstrate that the performance of the method is superior to all typical methods. The proposed method is implemented to discretize a quasi‑resonant (QR) controller on a control board, achieving 25% reduction in the root‑mean‑square error (RMSE) compared to the SOTA method. Finally, the approach is extended to discretizing a resonant controller of a grid‑tied inverter. The efficacy of the proposed method is conclusively validated through favorable comparisons among the theory, simulation, and experiments.

Authors:Jochen Stiasny, Jochen Cremer
Title: Residual Power Flow for Neural Solvers
Abstract:
The energy transition challenges operational tasks based on simulations and optimisation. These computations need to be fast and flexible as the grid is ever‑expanding, and renewables' uncertainty requires a flexible operational environment. Learned approximations, proxies or surrogates ‑‑ we refer to them as Neural Solvers ‑‑ excel in terms of evaluation speed, but are inflexible with respect to adjusting to changing tasks. Hence, neural solvers are usually applicable to highly specific tasks, which limits their usefulness in practice; a widely reusable, foundational neural solver is required. Therefore, this work proposes the Residual Power Flow (RPF) formulation. RPF formulates residual functions based on Kirchhoff's laws to quantify the infeasibility of an operating condition. The minimisation of the residuals determines the voltage solution; an additional slack variable is needed to achieve AC‑feasibility. RPF forms a natural, foundational subtask of tasks subject to power flow constraints. We propose to learn RPF with neural solvers to exploit their speed. Furthermore, RPF improves learning performance compared to common power flow formulations. To solve operational tasks, we integrate the neural solver in a Predict‑then‑Optimise (PO) approach to combine speed and flexibility. The case study investigates the IEEE 9‑bus system and three tasks (AC Optimal Power Flow (OPF), power‑flow and quasi‑steady state power flow) solved by PO. The results demonstrate the accuracy and flexibility of learning with RPF.

Authors:Marouan Mizmizi, Stefano Tebaldini, Umberto Spagnolini
Title: Echo-Side Integrated Sensing and Communication via Space-Time Reconfigurable Intelligent Surfaces
Abstract:
This paper presents an echo‑side modulation framework for integrated sensing and communication (ISAC) systems. A space‑time reconfigurable intelligent surface (ST‑RIS) impresses a continuous‑phase modulation onto the radar echo, enabling uplink data transmission with a phase modulation of the transmitted radar‑like waveform. The received signal is a multiplicative composition of the sensing waveform and the phase for communication. Both functionalities share the same physical signal and perceive each other as impairments. The achievable communication rate is expressed as a function of a coupling parameter that links sensing accuracy to phase error accumulation. Under a fixed bandwidth constraint, the sensing and communication figures of merit define a convex Pareto frontier. The optimal bandwidth allocation satisfying a minimum sensing requirement is derived in closed form. The modified Cramer‑Rao bound (MCRB) for range estimation is derived in closed form; this parameter must be estimated to compensate for the frequency offset before data demodulation. Frame synchronization is formulated as a generalized likelihood ratio test (GLRT), and the detection probability is obtained through characteristic function inversion, accounting for residual frequency errors from imperfect range estimation. Numerical results validate the theoretical bounds and characterize the trade‑off across the operating range.

Authors:Ying Gao, Qingqing Wu, Ziyuan Zheng, Yanze Zhu, Wen Chen, Xin Lin, Shanpu Shen
Title: Two-Scale Spatial Deployment for Cost-Effective Wireless Networks via Cooperative IRSs and Movable Antennas
Abstract:
This paper proposes a two‑scale spatial deployment strategy to ensure reliable coverage for multiple target areas, integrating macroscopic intelligent reflecting surfaces (IRSs) and fine‑grained movable antennas (MAs). Specifically, IRSs are selectively deployed from candidate sites to shape the propagation geometry, while MAs are locally repositioned among discretized locations to exploit small‑scale channel variations. The objective is to minimize the total deployment cost of MAs and IRSs by jointly optimizing the IRS site selection, MA positions, transmit precoding, and IRS phase shifts, subject to the signal‑to‑noise ratio (SNR) requirements for all target areas. This leads to a challenging mixed‑integer non‑convex optimization problem that is intractable to solve directly. To address this, we first formulate an auxiliary problem to verify the feasibility. A penalty‑based double‑loop algorithm integrating alternating optimization and successive convex approximation (SCA) is developed to solve this feasibility issue, which is subsequently adapted to obtain a suboptimal solution for the original cost minimization problem. Finally, based on the obtained solution, we formulate an element refinement problem to further reduce the deployment cost, which is solved by a penalty‑based SCA algorithm. Simulation results demonstrate that the proposed designs consistently outperform benchmarks relying on independent area planning or full IRS deployment in terms of cost‑efficiency. Moreover, for cost minimization, MA architectures are preferable in large placement apertures, whereas fully populated FPA architectures excel in compact ones; for worst‑case SNR maximization, MA architectures exhibit a lower cost threshold for feasibility, while FPA architectures can attain peak SNR at a lower total cost.

Authors:Abhishek Kumar, José-Ramón Vidal, Jorge Martinez-Bauset, Frank Y. Li
Title: Semi-Contention-Free Access in IoT NOMA Networks: A Reinforcement Learning Framework
Abstract:
The unprecedented surge of massive Internet of things (mIoT) traffic in beyond fifth generation (B5G) communication systems calls for transformative approaches for multiple access and data transmission. While classical model‑based tools have been proven to be powerful and precise, an imminent trend for resource management in B5G networks is promoting solutions towards data‑driven design. Considering an IoT network with devices spread in clusters covered by a base station, we present in this paper a novel model‑free multiple access and data transmission framework empowered by reinforcement learning, designed for power‑domain non‑orthogonal multiple access networks to facilitate uplink traffic of small data packets. The framework supports two access modes referred to as contention‑based and semi‑contention‑free, with its core component being a policy gradient algorithm executed at the base station. The base station performs access control and optimal radio resource allocation by periodically broadcasting two control parameters to each cluster of devices that considerably reduce data detection failures with a minimum computation requirement on devices. Numerical results, in terms of system and cluster throughput, throughput fairness, access delay, and energy consumption, demonstrate the efficiency and scalability of the framework as network size and traffic load vary.

Authors:Haohao Shi, Huy Truong-Ba, Michael E. Cholette, Brenden Harris, Juan Montes, Tommy Chan
Title: Semi-physical Gamma-Process Degradation Modeling and Performance-Driven Opportunistic Maintenance Optimization for LED Lighting Systems
Abstract:
Large‑scale LED lighting systems degrade through gradual package degradation and abrupt driver outages, while acceptability is determined by spatio‑temporal illuminance compliance rather than component reliability alone. This paper proposes a performance‑driven, simulation‑in‑the‑loop framework for opportunistic maintenance optimization of LED lighting systems. LED package degradation is modeled by a semi‑physical non‑homogeneous Gamma process whose mean follows an exponential lumen‑maintenance trend, and driver outages are described by a Weibull lifetime model. Parameters are calibrated from LM‑80 accelerated degradation data via Bayesian inference, enabling uncertainty propagation to operating conditions. System performance is evaluated using ray‑tracing‑based illuminance mapping, and static indices (average illuminance and uniformity) are converted into a long‑term dynamic deficiency‑ratio metric via performance‑deficiency durations over event intervals. To enable scalable Monte Carlo policy evaluation and search, a surrogate‑based performance mapping replaces repeated ray‑tracing with negligible loss of fidelity. An opportunistic policy is optimized in a multi‑objective setting to balance performance deficiency, site visits, and replacements. A case study demonstrates the practicality of the framework and the resulting Pareto trade‑offs for maintenance decision support.

Authors:Ro'i Lang, Elon Rimon
Title: Feedback-Based Mobile Robot Navigation in 3-D Environments Using Artificial Potential Functions Technical Report
Abstract:
This technical report presents the construction and analysis of polynomial navigation functions for motion planning in 3‑D workspaces populated by spherical and cylindrical obstacles. The workspace is modeled as a bounded spherical region, and obstacles are encoded using smooth polynomial implicit functions. We establish conditions under which the proposed navigation functions admit a unique non‑degenerate minimum at the target while avoiding local minima, including in the presence of pairwise intersecting obstacles. Gradient and Hessian analyses are provided, and the theoretical results are validated through numerical simulations in obstacle rich 3‑D environments.

Authors:Jixiang Zhang, Han Xu, Daming Cao, Yinfei Xu, Minghao Chen, Chengyu Lin
Title: On Discrete Age of Information of Status Updating System With General Packet Arrival Processes
Abstract:
Characterizing Age of Information (AoI) in status updating systems with general arrival and service processes has great significance considering that the interarrival and service time of updates can possibly be arbitrary in a real world. While expressions of average continuous AoI under G/G/1/1 queues have been derived in the paper by Soysal and Ulukus, the discrete case remained unsolved. To address it, this paper gives a fully characterization of probability generation functions (PGF) of discrete AoI under G/G/1/1 settings when preemption is allowed. In the non‑preemptive case, this paper gives the expressions of PGF of discrete AoI under G/Geo/1/1 settings, which also extends the former results. The average discrete AoI is derived and discussed based on these new theoretical findings.

Authors:Luigi Romano, Ole Morten Aamo, Miroslav Krstić, Jan Åslund, Erik Frisk
Title: Boundary adaptive observer design for semilinear hyperbolic rolling contact ODE-PDE systems with uncertain friction
Abstract:
This paper presents an adaptive observer design for semilinear hyperbolic rolling contact ODE‑PDE systems with uncertain friction characteristics parameterized by a matrix of unknown coefficients appearing in the nonlinear (and possibly non‑smooth) PDE source terms. Under appropriate assumptions of forward completeness and boundary sensing, an adaptive observer is synthesized to simultaneously estimate the lumped and distributed states, as well as the uncertain friction parameters, using only boundary measurements. The observer combines a finite‑dimensional parameter estimator with an infinite‑dimensional description of the state error dynamics, and achieves exponential convergence under persistent excitation. The effectiveness of the proposed design is demonstrated in simulation by considering a relevant example borrowed from road vehicle dynamics.

Authors:Zahra Sobhani, Mahmoud Shahrokhi
Title: System Availability Optimization: Integrating Quantity Discounts and Delivery Lead Time Considerations
Abstract:
Purpose: The model allocates the system components orders to the suppliers to minimize the parts price and the system construction delay penalties and maximize the system availability during its use. It considers the quantity‑based discount and variation of delivery lead time by ordering similar components. The model also reflects the prerequisite relationships between construction activities and calculates the delay penalty resulting from parts delivery lead time. Design/methodology/approach: This research presents a model for selecting suppliers of components of an industrial series‑parallel multi‑state system. A nonlinear binary mathematical program uses the Markov process results to select system components. It minimizes the total system construction phase costs, including the components' price, and the system construction delay penalty, and the system exploitation phase costs, including the system shutdown and working at half capacity. Findings: The model allocates the optimal orders for a typical industrial system's components, composing four elements. The proposed approach combines the nonlinear binary program and the Markov process results to optimize the system life cycle parameters, including the system construction cost and operational availability. Originality/value: Using the Markov chain results in binary nonlinear mathematical programming, this study attempts to strike the right balance between the construction phase's objectives and an industrial unit's operation phase.

Authors:Marcus Greiff, Ray Zhang, Thomas Lew, John Subosits
Title: Dynamic Association of Semantics and Parameter Estimates by Filtering
Abstract:
We propose a probabilistic semantic filtering framework in which parameters of a dynamical system are inferred and associated with a closed set of semantic classes in a map. We extend existing methods to a multi‑parameter setting using a posterior that tightly couples semantics with the parameter likelihoods, and propose a filter to compute this posterior sequentially, subject to dynamics in the map's state. Using Bayesian moment matching, we show that the computational complexity of measurement updates scales linearly in the dimension of the parameter space. Finally, we demonstrate limitations of applying existing methods to a problem from the driving domain, and show that the proposed framework better captures time‑varying parameter‑to‑semantic associations.

Authors:Liu Cao, Zisheng Gong, Ziyue Xiao, Zhaoyu Liu, Houtianfu Wang, Lyutianyang Zhang
Title: RIS-Aided E2E Multi-Path Uplink Transmission Optimization for 6G Time-Sensitive Services
Abstract:
The Access Traffic Steering, Switching, and Splitting (ATSSS) defined in the latest 3GPP Release 19 enables traffic flow over the multiple access paths to achieve the lower‑latency End‑to‑end (E2E) delivery for 6G time‑sensitive services. However, the existing E2E multi‑path operation often falls short of more stringent QoS requirements for 6G time‑sensitive services. This work proposes a Reconfigurable Intelligent Surfaces (RIS)‑aided E2E multi‑path uplink (UL) transmission architecture that explicitly accounts for both radio link latency and N3 backhaul latency, via the coupled designs of the UL traffic‑splitting ratio, transmit power, receive combining, and RIS phase shift under practical constraints to achieve the minimum average E2E latency. We develop an alternating optimization framework that updates the above target parameters to be optimized. The simulations were conducted to compare the effectiveness of the proposed E2E optimization framework that lowers the average E2E latency up to 43% for a single user and 32% for the whole system compared with baselines in our prior work [1].

Authors:Rishav Sen, Amutheezan Sivagnanam, Aron Laszka, Ayan Mukhopadhyay, Abhishek Dubey
Title: Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit
Abstract:
The rapid growth of urban populations and the increasing need for sustainable transportation solutions have prompted a shift towards electric buses in public transit systems. However, the effective management of mixed fleets consisting of both electric and diesel buses poses significant operational challenges. One major challenge is coping with dynamic electricity pricing, where charging costs vary throughout the day. Transit agencies must optimize charging assignments in response to such dynamism while accounting for secondary considerations such as seating constraints. This paper presents a comprehensive mixed‑integer linear programming (MILP) model to address these challenges by jointly optimizing charging schedules and trip assignments for mixed (electric and diesel bus) fleets while considering factors such as dynamic electricity pricing, vehicle capacity, and route constraints. We address the potential computational intractability of the MILP formulation, which can arise even with relatively small fleets, by employing a hierarchical approach tailored to the fleet composition. By using real‑world data from the city of Chattanooga, Tennessee, USA, we show that our approach can result in significant savings in the operating costs of the mixed transit fleets.

Authors:Alexandre Sanfelici Bazanella, Mateus Gaspary de Freitas
Title: Improving the GMAW process through current control
Abstract:
A control strategy for the electrical current in GMAW processes is proposed. The control is in closed‑loop, designed by formal methods, based on a mathematical model of the electrical behavior of the GMAW process, and implemented in C+ language in a microcontroller. The model consists of a switched equivalent electrical circuit whose parameters are obtained in a data‑driven manner. The strategy is tested in numerous experiments with both manual and robot welding, showing improvements in the overall welding process.

Authors:Mohamed Afouene Melki, Mohammad Shehab, Mohamed-Slim Alouini
Title: AUV Trajectory Learning for Underwater Acoustic Energy Transfer and Age Minimization
Abstract:
Internet of underwater things (IoUT) is increasingly gathering attention with the aim of monitoring sea life and deep ocean environment, underwater surveillance as well as maintenance of underwater installments. However, conventional IoUT devices, reliant on battery power, face limitations in lifespan and pose environmental hazards upon disposal. This paper introduces a sustainable approach for simultaneous information uplink from the IoUT devices and acoustic energy transfer (AET) to the devices via an autonomous underwater vehicle (AUV), potentially enabling them to operate indefinitely. To tackle the time‑sensitivity, we adopt age of information (AoI), and Jain's fairness index. We develop two deep‑reinforcement learning (DRL) algorithms, offering a high‑complexity, high‑performance frequency division duplex (FDD) solution and a low‑complexity, medium‑performance time division duplex (TDD) approach. The results elucidate that the proposed FDD and TDD solutions significantly reduce the average AoI and boost the harvested energy as well as data collection fairness compared to baseline approaches.

Authors:Zezhi Tang, Christopher Passmore, Andrew I Campbell, Jonathan Howse, J Anthony Rossiter, Stephen Ebbens, George Panoutsos
Title: Disturbance observer-based tracking control for roll-to-roll slot die coating systems under gap and pump rate disturbances
Abstract:
Roll‑to‑roll slot die coating is a widely used industrial manufacturing technique applied in a diverse range of applications such as the production of lithium‑ion batteries, solar cells and optical films. The efficiency of roll‑to‑roll slot die coating depends on the precise control of various input parameters such as pump rate, substrate velocity and coating gap. However, these inputs are sensitive to disturbances in process conditions, leading to inconsistencies in the various characteristics of the produced film. To address this challenge, a \glsDO is utilized for detecting disturbances, which may occur in the same or different channels as the control signal within the system. A generalized compensator is then implemented to mitigate the impact of these disturbances on the output, thereby enhancing uncertainty suppression. Additionally, integrating the disturbance rejection system with an output tracking controller enables the coating system to maintain the desired thickness under varying input conditions and disturbances. The effectiveness of this approach is then validated using a test rig equipped with a camera system, which facilitates the development of a data‑driven model of the dynamic process, represented by state‑space equations. The simulation results were demonstrated to showcase the effectiveness of the DOBOTC system, which provides a resilient solution for the output tracking issue in a data‑driven model with generalized disturbances.

Authors:Joseph Ross, Damien Frost, Efstratios Chatzinikolaou, Stephen Duncan, David Howey
Title: Current and temperature imbalances in parallel-connected grid storage battery modules
Abstract:
A key challenge with large battery systems is heterogeneous currents and temperatures in modules with parallel‑connected cells. Although extreme currents and temperatures are detrimental to the performance and lifetime of battery cells, there is not a consensus on the scale of typical imbalances within grid storage modules. Here, we quantify these imbalances through simulations and experiments on an industrially representative grid storage battery module consisting of prismatic lithium iron phosphate cells, elucidating the evolution of current and temperature imbalances and their dependence on individual cell and module parameter variations. Using a sensitivity analysis, we find that varying contact resistances and cell resistances contribute strongly to temperature differences between cells, from which we define safety thresholds on cell‑to‑cell variability. Finally, we investigate how these thresholds change for different applications, to outline a set of robustness metrics that show how cycling at lower C‑rates and narrower SOC ranges can mitigate failures.

Authors:Guan-Ting Lin, Wei-Yu Chiu, Chien-Feng Wu, Asef Nazari, Dhananjay Thiruvady
Title: Multiobjective Model Predictive Control for Residential Demand Response Management Under Uncertainty
Abstract:
Residential users in demand response programs must balance electricity costs and user dissatisfaction under real‑time pricing. This study proposes a multiobjective model predictive control approach for home energy management systems with battery storage, aiming to minimize both objectives while mitigating uncertainties. Laguerre functions parameterize control signals, transforming the optimization problem into one with linear inequalities for efficient exploration. A constrained multiobjective evolutionary algorithm, incorporating convex sampler‑based crossover and mutation, is developed to ensure feasible solutions. Simulations show that the proposed method outperforms existing approaches, limiting cost increases to 0.52% under uncertainties, compared to at least 2.3% with other methods.

Authors:Yizhan Feng, Hichem Snoussi, Jing Teng, Abel Cherouat, Tian Wang
Title: Large Language Models to Enhance Multi-task Drone Operations in Simulated Environments
Abstract:
Benefiting from the rapid advancements in large language models (LLMs), human‑drone interaction has reached unprecedented opportunities. In this paper, we propose a method that integrates a fine‑tuned CodeT5 model with the Unreal Engine‑based AirSim drone simulator to efficiently execute multi‑task operations using natural language commands. This approach enables users to interact with simulated drones through prompts or command descriptions, allowing them to easily access and control the drone's status, significantly lowering the operational threshold. In the AirSim simulator, we can flexibly construct visually realistic dynamic environments to simulate drone applications in complex scenarios. By combining a large dataset of (natural language, program code) command‑execution pairs generated by ChatGPT with developer‑written drone code as training data, we fine‑tune the CodeT5 to achieve automated translation from natural language to executable code for drone tasks. Experimental results demonstrate that the proposed method exhibits superior task execution efficiency and command understanding capabilities in simulated environments. In the future, we plan to extend the model functionality in a modular manner, enhancing its adaptability to complex scenarios and driving the application of drone technologies in real‑world environments.

Authors:Hiroki Sakamoto, Kazuhiro Sato
Title: Data-Driven Regularized Time-Limited h2 Model Reduction from Noisy Impulse Responses
Abstract:
This paper develops a data‑driven time‑limited h2 model reduction method for discrete‑time linear time‑invariant systems. Specifically, we formulate and solve a regularized time‑limited h2 model reduction problem using only noisy impulse response data. Furthermore, we show that the objective function and its gradient can be represented using only noisy impulse response data. Numerical experiments using SLICOT benchmarks demonstrate that the proposed regularized method achieves lower relative time‑limited h2 errors than the tested alternatives and is effective in situations where the unregularized method may deteriorate under noise.

Authors:Wei-Jen Liu, Wei-Yu Chiu, Weiqi Hua
Title: Blockchain-Enabled Renewable Energy Certificate Trading: A Secure and Privacy-Preserving Approach
Abstract:
In the 21st century, transitioning to renewable energy sources is imperative, with fossil fuel reserves depleting rapidly and recognizing critical environmental issues such as climate change, air pollution, water pollution, and habitat destruction. Embracing renewable energy is not only an environmental necessity but also a strategic move with multiple benefits. By shifting to renewable energy sources and supporting their production through the acquisition of renewable energy certificates, we foster innovation and drive economic growth in the renewable energy sector. This, in turn, reduces greenhouse gas emissions, aligning with global efforts to mitigate climate change. Additionally, renewable energy certificates ensure compliance with regulations that mandate the use of renewable energy, enhancing legal adherence while promoting transparency and trust in energy sourcing. To monitor the uptake of renewable energy, governments have implemented Renewable Energy Certificates (RECs) as a tracking mechanism for the production and consumption of renewable energy. However, there are two main challenges to the existing REC schema: 1) The RECs have not been globally adopted due to inconsistent design; 2) The consumer privacy has not been well incorporated in the design of blockchain. In this study, we investigate the trading of RECs between suppliers and consumers using the directed acyclic graph (DAG) blockchain system and introduce a trading schema to help protect consumer information. Our results demonstrate lower transaction time by 41% and energy consumption by 65% compared to proof‑of‑stake.

Authors:Luca Ballotta, Geethu Joseph
Title: Minimal Actuator Selection
Abstract:
Selecting a few available actuators to ensure the controllability of a linear system is a fundamental problem in control theory. Previous works either focus on optimal performance, simplifying the controllability issue, or make the system controllable under structural assumptions, such as in graphs or when the input matrix is a design parameter. We generalize these approaches to offer a precise characterization of the general minimal actuator selection problem where a set of actuators is given, described by a fixed input matrix, and goal is to choose the fewest actuators that make the system controllable. We show that this problem can be equivalently cast as an integer linear program and, if actuation channels are sufficiently independent, as a set multicover problem under multiplicity constraints. The latter equivalence is always true if the state matrix has all distinct eigenvalues, in which case it simplifies to the set cover problem. Such characterizations hold even when a robust selection that tolerates a given number of faulty actuators is desired. Our established connection legitimates a designer to use algorithms from the rich literature on the set multicover problem to select the smallest subset of actuators, including exact solutions that do not require brute‑force search.

Authors:Moussa Labbadi, Christophe Roman, Yacine Chitour
Title: On Robust Fixed-Time Stabilization of the Cauchy Problem in Hilbert Spaces
Abstract:
This paper presents finite‑time and fixed‑time stabilization results for inhomogeneous abstract evolution problems, extending existing theories. We prove well‑posedness for strong and weak solutions, and estimate upper bounds for settling times for both homogeneous and inhomogeneous systems. We generalize finite‑dimensional results to infinite‑dimensional systems and demonstrate partial state stabilization with actuation on a subset of the domain. The interest of these results are illustrated through an application of a heat equation with memory term.

Authors:Abdikarim Mohamed Ibrahim, Rosdiadee Nordin
Title: Large Artificial Intelligence Model Guided Deep Reinforcement Learning for Resource Allocation in Non Terrestrial Networks
Abstract:
Large AI Model (LAM) have been proposed to applications of Non‑Terrestrial Networks (NTN), that offer better performance with its great generalization and reduced task specific trainings. In this paper, we propose a Deep Reinforcement Learning (DRL) agent that is guided by a Large Language Model (LLM). The LLM operates as a high level coordinator that generates textual guidance that shape the reward of the DRL agent during training. The results show that the LAM‑DRL outperforms the traditional DRL by 40% in nominal weather scenarios and 64% in extreme weather scenarios compared to heuristics in terms of throughput, fairness, and outage probability.

Authors:Jinghao Cao, Wanchun Liu, Yonghui Li, Branka Vucetic
Title: Hybrid Centralized Distributed Control for Lifelong MAPF over Wireless Connections
Abstract:
In lifelong multi‑agent path finding (MAPF) with many robots, unreliable wireless links and stochastic executions are the norm. Existing approaches typically either rely on centralized planning under idealized communication, or run fully distributed local controllers with fixed communication patterns; they rarely couple communication scheduling with policy learning, and thus struggle when bandwidth is scarce or packets are frequently dropped. We address this joint control‑‑communication problem and propose a hybrid centralized‑‑distributed scheme: a centralized cloud policy sends small residual corrections only when selected, while a lightweight on‑board Gated recurrent unit (GRU) policy provides a safe default fallback when wireless connection is not available.

Authors:Hongbing Yu, Jiyu Wang, Xiaojun Zhang, Mingsheng Zhao
Title: Research on Mechanical Properties and Deformation-Fracture Energy Consumption Characteristics of Plateau Frozen Rocks
Abstract:
The exploitation of mineral resources in plateau regions is confronted with critical challenges including low blasting efficiency, excessive energy consumption,and compromised operational safety when dealing with low‑temperature water‑bearing frozen rock masses.This study systematically investigates the dynamic‑static mechanical properties,deformation‑fracture behaviors,and energy consumption characteristics of plateau frozen sandstone under the coupled effects of temperature and moisture content (5%‑15%).The research methodology integrates field sampling, low‑pressure low‑temperature simulation tests, graded impact loading tests, and numerical inversion analysis. Results demonstrate that freezing significantly enhances the dynamic strength and brittleness of saturated sandstone.The pore structure undergoes substantial evolution with decreasing temperature, with the porosity increasing by 63.15%.Based on PFC3D microscopic simulations, the mechanism of frost heave damage and the regulatory effect of water‑ice phase transition on rock mechanical behaviors are elucidated.A quantitative analysis method for energy dissipation is proposed, revealing that the energy absorption increment of frozen rocks is higher than that of room‑temperature samples.The findings provide a theoretical basis and technical support for optimizing blasting parameters, realizing directional energy release,and promoting green construction of frozen rock masses in high‑altitude areas.

Authors:Syue-Cian Lin, Wei-Yu Chiu, Chien-Feng Wu
Title: Memetic Covariance Matrix Adaptation Evolution Strategy for Bilinear Matrix Inequality Problems in Control System Design
Abstract:
Bilinear Matrix Inequalities (BMIs) are fundamental to control system design but are notoriously difficult to solve due to their nonconvexity. This study addresses BMI‑based control optimization problems by adapting and integrating advanced evolutionary strategies. Specifically, a memetic Covariance Matrix Adaptation Evolution Strategy (memetic CMA‑ES) is proposed, which incorporates a local refinement phase via a (1+1)‑CMA‑ES within the global search process. While these algorithmic components are established in evolutionary computing, their tailored integration and specific tuning for control design tasks represent a novel application in this context. Experimental evaluations on H_\infty controller synthesis and spectral abscissa optimization demonstrate that the proposed method achieves superior performance compared to existing BMI solvers in terms of both solution quality and robustness. This work bridges the gap between evolutionary computation and control theory, providing a practical and effective approach to tackling challenging BMI‑constrained problems.

Authors:Zeyang Li, Sunbochen Tang, Navid Azizan
Title: Reverse Flow Matching: A Unified Framework for Online Reinforcement Learning with Diffusion and Flow Policies
Abstract:
Diffusion and flow policies are gaining prominence in online reinforcement learning (RL) due to their expressive power, yet training them efficiently remains a critical challenge. A fundamental difficulty in online RL is the lack of direct samples from the target distribution; instead, the target is an unnormalized Boltzmann distribution defined by the Q‑function. To address this, two seemingly distinct families of methods have been proposed for diffusion policies: a noise‑expectation family, which utilizes a weighted average of noise as the training target, and a gradient‑expectation family, which employs a weighted average of Q‑function gradients. Yet, it remains unclear how these objectives relate formally or if they can be synthesized into a more general formulation. In this paper, we propose a unified framework, reverse flow matching (RFM), which rigorously addresses the problem of training diffusion and flow models without direct target samples. By adopting a reverse inferential perspective, we formulate the training target as a posterior mean estimation problem given an intermediate noisy sample. Crucially, we introduce Langevin Stein operators to construct zero‑mean control variates, deriving a general class of estimators that effectively reduce importance sampling variance. We show that existing noise‑expectation and gradient‑expectation methods are two specific instances within this broader class. This unified view yields two key advancements: it extends the capability of targeting Boltzmann distributions from diffusion to flow policies, and enables the principled combination of Q‑value and Q‑gradient information to derive an optimal, minimum‑variance estimator, thereby improving training efficiency and stability. We instantiate RFM to train a flow policy in online RL, and demonstrate improved performance on continuous‑control benchmarks compared to diffusion policy baselines.

Authors:Gabriele Dessena, Alessandro Pontillo
Title: Modal Parameter Extraction via Propeller-Driven Vibration Testing
Abstract:
Ground Vibration Testing (GVT) supports aircraft certification but often requires lengthy and costly campaigns. Propeller‑driven Vibration Testing (PVT) is assessed here as an output‑only alternative, in line with Operational Modal Analysis approaches such as Taxi Vibration Testing and Flight Vibration Testing. A cantilever Aluminium 7075‑T6 wing spar is instrumented with seven accelerometers and excited by an outboard electric motor and propeller. Seven runs are carried out: a motor‑off baseline, five constant‑throttle cases, and a manual up‑down throttle sweep. The acquired spectra indicate that the dominant resonances remain observable under propeller excitation, while low‑throttle conditions introduce narrowband harmonics that may mask structural peaks; the sweep reduces persistent overlap. Modal parameters are identified for the baseline and sweep cases using the Natural Excitation Technique with the Loewner Framework (NExT‑LF). The first two modes remain closely matched (Modal Assurance Criterion (MAC) > 0.99), whereas the third mode shows reduced repeatability (MAC = 0.827) and a larger frequency shift, consistent with propeller‑induced bending‑‑torsion coupling and non‑ideal sweep control. Overall, PVT provides a viable complement to GVT for extracting low‑frequency modal information and motivates pursuing future work on automated throttle scheduling and coupling‑aware test planning.

Authors:Nardos Belay Abera, Yize Chen
Title: Coordinated Cooling and Compute Management for AI Datacenters
Abstract:
The AI datacenters are currently being deployed on a large scale to support the training and deployment of power‑intensive large‑language models (LLMs). Extensive amount of computation and cooling required in datacenters increase concerns about the energy use and carbon emissions of AI datacenters. Although current state‑of‑the‑art has examined the energy efficiency of LLM inference, most prior research focused on optimizing compute‑side scheduling without considering thermal objectives or constraints. Since GPU‑intensive inference generates substantial heat that can degrade datacenter performance, ignoring thermal effects can increase total energy consumption and reduce the efficiency of LLM serving. To fill this gap, we profile the characteristics of GPU servers under varying cooling and AI jobs, and develop a joint cooling and computing modeling approach for AI datacenters. Built upon such workload and thermal dynamics models, a novel hierarchical control framework is proposed to co‑optimize computing and thermal management by identifying the optimal GPU parallelism, frequency (DVFS), and cooling control knobs. Using real Azure inference traces and detailed GPU profiling, our model balances serving latency and thermal constraints in AI datacenters while significantly improving AI datacenters' energy efficiency.

Authors:Reza Arablouei
Title: Efficient Incremental SLAM via Information-Guided and Selective Optimization
Abstract:
We present an efficient incremental SLAM back‑end that achieves the accuracy of full batch optimization while substantially reducing computational cost. The proposed approach combines two complementary ideas: information‑guided gating (IGG) and selective partial optimization (SPO). IGG employs an information‑theoretic criterion based on the log‑determinant of the information matrix to quantify the contribution of new measurements, triggering global optimization only when a significant information gain is observed. This avoids unnecessary relinearization and factorization when incoming data provide little additional information. SPO executes multi‑iteration Gauss‑Newton (GN) updates but restricts each iteration to the subset of variables most affected by the new measurements, dynamically refining this active set until convergence. Together, these mechanisms retain all measurements to preserve global consistency while focusing computation on parts of the graph where it yields the greatest benefit. We provide theoretical analysis showing that the proposed approach maintains the convergence guarantees of full GN. Extensive experiments on benchmark SLAM datasets show that our approach consistently matches the estimation accuracy of batch solvers, while achieving significant computational savings compared to conventional incremental approaches. The results indicate that the proposed approach offers a principled balance between accuracy and efficiency, making it a robust and scalable solution for real‑time operation in dynamic data‑rich environments.

Authors:Chengyang Gu, Yuxin Pan, Hui Xiong, Yize Chen
Title: STO-RL: Offline RL under Sparse Rewards via LLM-Guided Subgoal Temporal Order
Abstract:
Offline reinforcement learning (RL) enables policy learning from pre‑collected datasets, avoiding costly and risky online interactions, but it often struggles with long‑horizon tasks involving sparse rewards. Existing goal‑conditioned and hierarchical offline RL methods decompose such tasks and generate intermediate rewards to mitigate limitations of traditional offline RL, but usually overlook temporal dependencies among subgoals and rely on imprecise reward shaping, leading to suboptimal policies. To address these issues, we propose STO‑RL (Offline RL using LLM‑Guided Subgoal Temporal Order), an offline RL framework that leverages large language models (LLMs) to generate temporally ordered subgoal sequences and corresponding state‑to‑subgoal‑stage mappings. Using this temporal structure, STO‑RL applies potential‑based reward shaping to transform sparse terminal rewards into dense, temporally consistent signals, promoting subgoal progress while avoiding suboptimal solutions. The resulting augmented dataset with shaped rewards enables efficient offline training of high‑performing policies. Evaluations on four discrete and continuous sparse‑reward benchmarks demonstrate that STO‑RL consistently outperforms state‑of‑the‑art offline goal‑conditioned and hierarchical RL baselines, achieving faster convergence, higher success rates, and shorter trajectories. Ablation studies further confirm STO‑RL's robustness to imperfect or noisy LLM‑generated subgoal sequences, demonstrating that LLM‑guided subgoal temporal structures combined with theoretically grounded reward shaping provide a practical and scalable solution for long‑horizon offline RL.

Authors:Ahmed A. Hassan, Ahmad Adnan Qidan, Taisir Elgorashi, Jaafar Elmirghani
Title: DRL-based Power Allocation in LiDAL-Assisted RLNC-NOMA OWC Systems
Abstract:
Non‑orthogonal multiple access (NOMA) is a promising technique for optical wireless communication (OWC), enabling multiple users to share the optical spectrum simultaneously through the power domain. However, imperfect channel state information (CSI) and residual decoding errors deteriorate NOMA performance, especially in realistic dense‑user indoor scenarios. In this work, we model an OWC system that integrates light detection and localization (LiDAL) and random linear network coding (RLNC) within a NOMA framework. LiDAL exploits spatio‑temporal information to improve user CSI, while RLNC enhances data resilience in the successive decoding process, resulting in a LiDAL‑assisted RLNC‑NOMA OWC system. Power allocation (PA) is crucial in this system due to complex interactions between multiple users and the coding and detection processes, but optimizing continuous PA dynamically can be computationally prohibitive. To address this, we adopt a deep reinforcement learning (DRL) framework to efficiently learn near‑optimal PA strategies. In particular, a DRL‑based normalized advantage function (NAF) algorithm is proposed to maximize the average sum rate, and its performance is compared to deep deterministic policy gradient (DDPG), gain ratio PA (GRPA), and exhaustive search. The results indicate that NAF closely matches exhaustive search, is 39% faster than DDPG, and improves the average sum rate by 4.6% over GRPA, while accounting for user location estimation errors.

Authors:Prashant Solanki, Isabelle El-Hajj, Jasper van Beers, Erik-Jan van Kampen, Coen de Visser
Title: Unifying Hamilton-Jacobi Reachability and Reinforcement Learning
Abstract:
We unify Hamilton‑Jacobi (HJ) reachability and Reinforcement Learning (RL) through a proposed running cost formulation. We prove that the resultant travel‑cost value function is the unique bounded viscosity solution of a time‑dependent Hamilton‑Jacobi Bellman (HJB) Partial Differential Equation (PDE) with zero terminal data, whose negative sublevel set equals the strict backward‑reachable tube. Using a forward reparameterization and a contraction inducing Bellman update, we show that fixed points of small‑step RL value iteration converge to the viscosity solution of the forward discounted HJB. Experiments on a classical benchmark validate this connection by demonstrating convergence of learned value functions toward semi‑Lagrangian HJB solutions and by quantifying approximation error across the state space. These results empirically support the theoretical analysis, showing that the proposed framework preserves reachability‑based safety semantics while remaining compatible with deep RL implementations.

Authors:Yuwen Ma, Sarah K. Spurgeon, Tao Li, Boli Chen
Title: Can Inherent Communication Noise Guarantee Privacy in Distributed Cooperative Control ?
Abstract:
This paper investigates privacy‑preserving distributed cooperative control for multi‑agent systems within the framework of differential privacy. In cooperative control, communication noise is inevitable and is usually regarded as a disturbance that impairs coordination. This work revisits such noise as a potential privacy‑enhancing factor. A linear quadratic regulator (LQR)‑based framework is proposed for agents communicating over noisy channels, \textcolorblackwhere the noise variance depends on the relative state differences between neighbouring agents. The resulting controller achieves formation while protecting the reference signals from inference attacks. It is analytically proven that the inherent communication noise can guarantee bounded (ε,δ)‑differential privacy without adding dedicated privacy noise, while the \textcolorblacksystem cooperative tracking error remains bounded and convergent in both the mean‑square and almost‑sure sense.

Authors:Harrison M. Bonner, Matthew R. Kirchner
Title: Human as an Actuator Dynamic Model Identification
Abstract:
This paper presents a method for estimating parameters that form a general model for human pilot response for specific tasks. The human model is essential for the dynamic analysis of piloted vehicles. Data are generated on a simulator with multiple trials being incorporated to find the single model that best describes the data. The model is found entirely in the time domain by constructing a constrained optimization problem. This optimization problem implicitly represents the state of the underlying system, making it robust to natural variation in human responses. It is demonstrated by estimating the human response model for a position control task with a quadcopter drone.

Authors:Zhiting Mei, Tenny Yin, Ola Shorinwa, Apurva Badithela, Zhonghe Zheng, Joseph Bruno, Madison Bland, Lihan Zha, Asher Hancock, Jaime Fernández Fisac, Philip Dames, Anirudha Majumdar
Title: Video Generation Models in Robotics -- Applications, Research Challenges, Future Directions
Abstract:
Video generation models have emerged as high‑fidelity models of the physical world, capable of synthesizing high‑quality videos capturing fine‑grained interactions between agents and their environments conditioned on multi‑modal user inputs. Their impressive capabilities address many of the long‑standing challenges faced by physics‑based simulators, driving broad adoption in many problem domains, e.g., robotics. For example, video models enable photorealistic, physically consistent deformable‑body simulation without making prohibitive simplifying assumptions, which is a major bottleneck in physics‑based simulation. Moreover, video models can serve as foundation world models that capture the dynamics of the world in a fine‑grained and expressive way. They thus overcome the limited expressiveness of language‑only abstractions in describing intricate physical interactions. In this survey, we provide a review of video models and their applications as embodied world models in robotics, encompassing cost‑effective data generation and action prediction in imitation learning, dynamics and rewards modeling in reinforcement learning, visual planning, and policy evaluation. Further, we highlight important challenges hindering the trustworthy integration of video models in robotics, which include poor instruction following, hallucinations such as violations of physics, and unsafe content generation, in addition to fundamental limitations such as significant data curation, training, and inference costs. We present potential future directions to address these open research challenges to motivate research and ultimately facilitate broader applications, especially in safety‑critical settings.

Authors:Chaoyi Lin Yang, Gabriele Dessena, Oscar E. Bonilla-Manrique
Title: Affordable Data Collection System for UAVs Taxi Vibration Testing
Abstract:
Structural vibration testing plays a key role in aerospace engineering for evaluating dynamic behaviour, ensuring reliability and verifying structural integrity. These tests rely on accurate and robust data acquisition systems (DAQ) to capture high‑quality acceleration data. However, commercial DAQs that provide the required performance and features are often expensive and complex, limiting their accessibility for small‑scale research and experimental applications. This work presents the design and experimental validation of an affordable and in‑house‑developed acceleration DAQ, tested on a small fixed‑wing UAV through several Taxi Vibration Test (TVT) runs and ambient vibration measurements. The proposed system integrates several OrangePi 3 LTS single‑board computers with multiple LSM6DS3TR‑C MEMS inertial measurement units operating simultaneously via an Inter‑Integrated Circuit (I2C) communication interface, managed under a Python‑based master/slave architecture. Data is acquired at a stable sampling rate of approximately 208 Hz and post‑processed using Welch's method to estimate their Power Spectral Density (PSD). Results confirm the system ability to provide consistent multi‑sensor acceleration data and repeatable PSD profiles under the same test conditions; thus, demonstrating its reliability. With a total hardware cost below 600 EUR (approximately 690 USD), the developed DAQ offers a compact, scalable and cost‑effective alternative for aerospace vibration analysis and structural testing.

Authors:Lohitvel Gopikannan, Shashi Ranjan Kumar, Abhinav Sinha
Title: Predefined-time One-Shot Cooperative Estimation, Guidance, and Control for Simultaneous Target Interception
Abstract:
This work develops a unified nonlinear estimation‑guidance‑control framework for cooperative simultaneous interception of a stationary target under a heterogeneous sensing topology, where sensing capabilities are non‑uniform across interceptors. Specifically, only a subset of agents is instrumented with onboard seekers (informed/seeker‑equipped agents), whereas the rest of them (seeker‑less agents) acquire the information about the target indirectly via the informed agents and execute a distributed cooperative guidance for simultaneous target interception. To address the resulting partial observability, a predefined‑time distributed observer is leveraged, guaranteeing convergence of the target state estimates for seeker‑less agents through information exchange with seeker‑equipped neighbors over a directed communication graph. Thereafter, an improved time‑to‑go estimate accounting for wide launch envelopes is utilized to design the distributed cooperative guidance commands. This estimate is coupled with a predefined‑time consensus protocol, ensuring consensus in the agents' time‑to‑go values. The temporal upper bounds within which both observer error and time‑to‑go consensus error converge to zero can be prescribed as design parameters. Furthermore, the cooperative guidance commands are realized by means of an autopilot, wherein the interceptor is steered by canard actuation. The corresponding fin deflection commands are generated using a predefined‑time convergent sliding mode control law. This enables the autopilot to precisely track the commanded lateral acceleration within a design‑specified time, while maintaining non‑singularity of the overall design. Theoretical guarantees are supported by numerical simulations across diverse engagement geometries, verifying the estimation accuracy, the cooperative interception performance, and the autopilot response using the proposed scheme.

Authors:Hugo Matias, Daniel Silvestre
Title: Safe Navigation under Uncertain Obstacle Dynamics using Control Barrier Functions and Constrained Convex Generators
Abstract:
This paper presents a sampled‑data framework for the safe navigation of controlled agents in environments cluttered with obstacles governed by uncertain linear dynamics. Collision‑free motion is achieved by combining Control Barrier Function (CBF)‑based safety filtering with set‑valued state estimation using Constrained Convex Generators (CCGs). At each sampling time, a CCG estimate of each obstacle is obtained using a finite‑horizon guaranteed estimation scheme and propagated over the sampling interval to obtain a CCG‑valued flow that describes the estimated obstacle evolution. However, since CCGs are defined indirectly ‑ as an affine transformation of a generator set subject to equality constraints, rather than as a sublevel set of a scalar function ‑ converting the estimated obstacle flows into CBFs is a nontrivial task. One of the main contributions of this paper is a procedure to perform this conversion, ultimately yielding a CBF via a convex optimization problem whose validity is established by the Implicit Function Theorem. The resulting obstacle‑specific CBFs are then merged into a single CBF that is used to design a safe controller through the standard Quadratic Program (QP)‑based approach. Since CCGs support Minkowski sums, the proposed framework also naturally handles rigid‑body agents and generalizes existing CBF‑based rigid‑body navigation designs to arbitrary agent and obstacle geometries. While the main contribution is general, the paper primarily focuses on agents with first‑order control‑affine dynamics and second‑order strict‑feedback dynamics. Simulation examples demonstrate the effectiveness of the proposed method.

Authors:Liyang Feng, Hanlin Sun, Yu Marco Nie, Jun Xie, Jiayang Li
Title: Enforcing Priority in Schedule-based User Equilibrium Transit Assignment
Abstract:
Denied boarding in congested transit systems induces queuing delays and departure‑time shifts that can reshape passenger flows. Correctly modeling these responses in transit assignment hinges on the enforcement of two priority rules: continuance priority for onboard passengers and first‑come‑first‑served (FCFS) boarding among waiting passengers. Existing schedule‑based models typically enforce these rules through explicit dynamic loading and group‑level expected costs, yet discrete vehicle runs can induce nontrivial within‑group cost differences that undermine behavioral consistency. We revisit the implicit‑priority framework of Nguyen et al. (2001), which, by encoding boarding priority through the notion of available capacity, characterizes route and departure choices based on realized personal (rather than group‑averaged) travel experiences. However, the framework lacks an explicit mathematical formulation and exact computational methods for finding equilibria. Here, we derive an equivalent nonlinear complementarity problem (NCP) formulation and establish equilibrium existence under mild conditions. We also show that multiple equilibria may exist, including behaviorally questionable ones. To rule out these artifacts, we propose a refined arc‑level NCP formulation that not only corresponds to a tighter, behaviorally consistent equilibrium concept but also is more computationally tractable. We reformulate the NCP as a continuously differentiable mathematical program with equilibrium constraints (MPEC) and propose two solution algorithms. Numerical studies on benchmark instances and a Hong Kong case study demonstrate that the model reproduces continuance priority and FCFS queuing and captures departure‑time shifts driven by the competition for boarding priority.

Authors:Andrea Martin, Giuseppe Belgioioso
Title: Learning to accelerate Krasnosel'skii-Mann fixed-point iterations with guarantees
Abstract:
We introduce a principled learning to optimize (L2O) framework for solving fixed‑point problems involving general nonexpansive mappings. Our idea is to deliberately inject summable perturbations into a standard Krasnosel'skii‑Mann iteration to improve its average‑case performance over a specific distribution of problems while retaining its convergence guarantees. Under a metric sub‑regularity assumption, we prove that the proposed parametrization includes only iterations that locally achieve linear convergence‑up to a vanishing bias term‑and that it encompasses all iterations that do so at a sufficiently fast rate. We then demonstrate how our framework can be used to augment several widely‑used operator splitting methods to accelerate the solution of structured monotone inclusion problems, and validate our approach on a best approximation problem using an L2O‑augmented Douglas‑Rachford splitting algorithm.

Authors:José Pulido, Francesc Wilhelmi, Sergio Fortes, Alfonso Fernández-Durán, Lorenzo Galati Giordano, Raquel Barco
Title: Studying the Role of Synthetic Data for Machine Learning-based Wireless Networks Traffic Forecasting
Abstract:
Synthetic data generation is an appealing tool for augmenting and enriching datasets, playing a crucial role in advancing artificial intelligence (AI) and machine learning (ML). Not only does synthetic data help build robust AI/ML datasets cost‑effectively, but it also offers privacy‑friendly solutions and bypasses the complexities of storing large data volumes. This paper proposes a novel method to generate synthetic data, based on first‑order auto‑regressive noise statistics, for large‑scale Wi‑Fi deployments. The approach operates with minimal real data requirements while producing statistically rich traffic patterns that effectively mimic real Access Point (AP) behavior. Experimental results show that ML models trained on synthetic data achieve Mean Absolute Error (MAE) values within 10 to 15 of those obtained using real data when trained on the same APs, while requiring significantly less training data. Moreover, when generalization is required, synthetic‑data‑trained models improve prediction accuracy by up to 50 percent compared to real‑data‑trained baselines, thanks to the enhanced variability and diversity of the generated traces. Overall, the proposed method bridges the gap between synthetic data generation and practical Wi‑Fi traffic forecasting, providing a scalable, efficient, and real‑time solution for modern wireless networks.

Authors:Hao Wu, Shengtian Yang, Huiguo Gao, Diao Wang, Jun Chen, Guanding Yu
Title: Clipped Affine Policy: Low-Complexity Near-Optimal Online Power Control for Energy Harvesting Communications over Fading Channels
Abstract:
This paper investigates online power control for point‑to‑point energy harvesting communications over wireless fading channels. A linear‑policy‑based approximation is derived for the relative‑value function in the Bellman equation of the power control problem. This approximation leads to two fundamental power control policies: optimistic and robust clipped affine policies, both taking the form of a clipped affine function of the battery level and the reciprocal of channel signal‑to‑noise ratio coefficient. They are essentially battery‑limited weighted directional waterfilling policies operating between adjacent time slots. By leveraging the relative‑value approximation and derived policies, a domain‑knowledge‑enhanced reinforcement learning (RL) algorithm is proposed for online power control. The proposed approach is further extended to scenarios with energy and/or channel lookahead. Comprehensive simulation results demonstrate that the proposed methods achieve a good balance between computational complexity and optimality. In particular, the robust clipped affine policy (combined with RL, using at most five parameters) outperforms all existing approaches across various scenarios, with less than 2% performance loss relative to the optimal policy.

Authors:Jimin Wang, Jieming Ke, Jin Guo, Yanlong Zhao
Title: Recursive Binary Identification with Differential Privacy and Data Tampering Attacks
Abstract:
In this paper, we consider the parameter estimation in a bandwidth‑constrained sensor network communicating through an insecure medium. The sensor performs a local quantization, and transmits a 1‑bit message to an estimation center through a wireless medium where the transmission of information is vulnerable to attackers. Both eavesdroppers and data tampering attackers are considered in our setting. A differential privacy method is used to protect the sensitive information against eavesdroppers. Then, a recursive projection algorithm is proposed such that the estimation center achieves the almost sure convergence and mean‑square convergence when quantized measurements, differential privacy, and data tampering attacks are considered in a uniform framework. A privacy analysis including the convergence rate with privacy or without privacy is given. Further, we extend the problem to multi‑agent systems. For this case, a distributed recursive projection algorithm is proposed with guaranteed almost sure and mean square convergence. A simulation example is provided to illustrate the effectiveness of the proposed algorithms.

Authors:Josip Kir Hromatko, Šandor Ileš, Branimir Škugor, Joško Deur
Title: Energy-efficient torque allocation for straight-line driving of electric vehicles based on pseudoconvex polynomials
Abstract:
Electric vehicles with multiple motors provide a flexibility in meeting the driver torque demand, which calls for minimizing the battery energy consumption through torque allocation. In this paper, we present an approach to this problem based on approximating electric motor losses using higher‑order polynomials with specific properties. To ensure a well‑behaved optimization landscape, monotonicity and positivity constraints are imposed on the polynomial models using sum of squares programming. This methodology provides robustness against noisy or sparse data, while retaining the computational efficiency of a polynomial function approximation. The torque allocation problem based on such polynomials is formulated as a constrained nonlinear optimization problem and solved efficiently using readily available solvers. In the nominal case, the first‑order necessary conditions for optimality can also be used to obtain a global solution. The performance of the proposed method is evaluated on several certification driving cycles against a grid search‑based benchmark. Results show a modest influence on electric energy consumption, while enabling real‑time optimization and integration with other vehicle control systems.

Authors:Emiel Vanspranghels, Zhuangzhuang Cui, Sofie Pollin
Title: Frequency-Adaptive Multi-Band Architecture for Upper Mid-Band MIMO Systems
Abstract:
FR3 (\approx7‑24 GHz), also referred to as the upper mid‑band, has recently emerged as promising spectrum for 6G; however, its propagation and MIMO characteristics vary significantly with frequency and environment, and spectrum availability may be intermittent due to incumbents. Using site‑specific ray tracing (Sionna RT) in representative indoor and outdoor scenarios, we evaluate 7, 10, 14, 20, and 24 GHz under SISO and MIMO configurations. The results show that FR3 exhibits propagation characteristics intermediate between sub‑6 GHz and mmWave bands while supporting meaningful spatial multiplexing, albeit with strong site dependence. Motivated by these findings, we propose a fully digital frequency‑adaptive multi‑band MIMO architecture that repurposes ADCs/DACs and baseband processing resources across FR3 subbands via switching, enabling dynamic trade‑offs between bandwidth (spectrum gain) and antenna consolidation (MIMO gain) under availability and channel constraints. Simulation results demonstrate that exploiting additional spectrum is often optimal, while adaptive resource repurposing becomes beneficial when subbands are unavailable or when multiplexing gains are concentrated at specific frequencies.

Authors:Andrea Cristofaro, Luca Zaccarian
Title: Nonquadratic global asymptotic stability certificates for saturated linear feedbacks
Abstract:
We establish sufficient conditions for positive (semi‑)definiteness, with or without radial unboundedness, for nonquadratic Lyapunov function constructed as sign‑indefinite quadratic forms involving the state and the deadzone of a suitable input. We then use these conditions to build weak nonquadratic Lyapunov functions establishing global asymptotic stability of linear systems in feedback through a saturation, leveraging invariance principles. Our results are shown to be non‑conservative (necessary and sufficient) for a family of well known prototypical examples of linear SISO feedbacks that are not globally exponentially stabilizable (the so‑called ANCBI plants). Our multi‑input extension leads to convex stability analysis tests, formulated as linear matrix inequalities that are applicable to ANCBI non‑globally‑exponentially‑stabilizable plants.

Authors:Emre Balci, Timucin Aydede, Gorkem Yilmaz, Ece Gelal Soyak
Title: Examining the Effectiveness of Transformer-Based Smart Contract Vulnerability Scan
Abstract:
Smart contract technology facilitates self‑executing agreements on the blockchain, eliminating dependency on an external trusted authority. However, smart contracts may expose vulnerabilities that can lead to financial losses and disruptions in decentralized applications. In this work, we evaluate deep learning‑based approaches for vulnerability scanning of Ethereum smart contracts. We propose VASCOT, a Vulnerability Analyzer for Smart COntracts using Transformers, which performs sequential analysis of Ethereum Virtual Machine (EVM) bytecode and incorporates a sliding window mechanism to overcome input length constraints. To assess VASCOT's detection efficacy, we construct a dataset of 16,469 verified Ethereum contracts deployed in 2022, and annotate it using trace analysis with concrete validation to mitigate false positives. VASCOT's performance is then compared against a state‑of‑the‑art LSTM‑based vulnerability detection model on both our dataset and an older public dataset. Our findings highlight the strengths and limitations of each model, providing insights into their detection capabilities and generalizability.

Authors:Rongxing Hu, Charalambos Konstantinou
Title: Stochastic Power-Water Coordination: Unlocking Flexibility in Hybrid RO Desalination Plants via Variable-Speed Pumps and Tank Mixing
Abstract:
Water desalination plants (DPs) are among the most critical infrastructures and largest electricity loads in water‑scarce regions worldwide. Although reverse osmosis (RO) desalination is the most energy‑efficient and dominant technology, it remains energy‑intensive but can offer substantial flexibility potential for power systems. This paper proposes a coordinated operation framework for power systems and DPs that explicitly accounts for both systems' operational constraints and fully unlocks DP flexibility. To achieve this, a detailed DP model is developed, incorporating the characteristics of an actual high‑pressure pump with variable‑speed operation, on‑off operation with flushing requirements, water quality constraints, and water dynamics and salt mixing in the storage tank. By proactively managing freshwater storage and tank salinity in a closed‑loop coordinated scheduling framework, the operational flexibility of the DP is significantly enhanced. With appropriate simplification and linearization, the resulting coordinated scheduling problem is formulated as a tractable mixed‑integer linear programming (MILP) model, and a two‑step decomposed commitment‑scheduling stochastic optimization (TDCSO) is proposed to efficiently address uncertainties. Case studies validate the proposed approach and demonstrate up to a 6% operating cost reduction.

Authors:Siddhartha Ganguly, Kenji Kashima
Title: Robust maximum hands-off optimal control: existence, maximum principle, and $L^{0}$-$L^1$ equivalence
Abstract:
This work advances the maximum hands‑off sparse control framework by developing a robust counterpart for constrained linear systems with parametric uncertainties. The resulting optimal control problem minimizes an L^0 objective subject to an uncountable, compact family of constraints, and is therefore a nonconvex, nonsmooth robust optimization problem. To address this, we replace the L^0 objective with its convex L^1 surrogate and, using a nonsmooth variant of the robust Pontryagin maximum principle, show that the L^0 and L^1 formulations have identical sets of optimal solutions ‑‑ we call this the robust hands‑off principle. Building on this equivalence, we propose an algorithmic framework ‑‑ drawing on numerically viable techniques from the semi‑infinite robust optimization literature ‑‑ to solve the resulting problems. An illustrative example is provided to demonstrate the effectiveness of the approach.

Authors:Mehran Moghadam, Sercan Aygun, M. Hassan Najafi
Title: TranSC: Hardware-Aware Design of Transcendental Functions Using Stochastic Logic
Abstract:
The hardware‑friendly implementation of transcendental functions remains a longstanding challenge in design automation. These functions, which cannot be expressed as finite combinations of algebraic operations, pose significant complexity in digital circuit design. This study introduces a novel approach, TranSC, that utilizes stochastic computing (SC) for lightweight yet accurate implementation of transcendental functions. Building on established SC techniques, our method explores alternative random sources‑specifically, quasi‑random Van der Corput low‑discrepancy (LD) sequences‑instead of conventional pseudo‑randomness. This shift enhances both the accuracy and efficiency of SC‑based computations. We validate our approach through extensive experiments on various function types, including trigonometric, hyperbolic, and activation functions. The proposed design approach significantly reduces MSE by up to 98% compared to the state‑of‑the‑art solutions while reducing hardware area, power consumption, and energy usage by 33%, 72%, and 64%, respectively.

Authors:Mouaad Boughellaba, Abdelhamid Tayebi, James R. Forbes, Soulaimane Berkane
Title: Nonlinear Observer Design for Visual-Inertial Odometry
Abstract:
This paper addresses the problem of Visual‑Inertial Odometry (VIO) for rigid body systems evolving in three‑dimensional space. We introduce a novel matrix Lie group structure, denoted SE_3+n(3), that unifies the pose, gravity, linear velocity, and landmark positions within a consistent geometric framework tailored to the VIO problem. Building upon this formulation, we design an almost globally asymptotically stable nonlinear geometric observer that tightly integrates data from an Inertial Measurement Unit (IMU) and visual sensors. Unlike conventional Extended Kalman Filter (EKF)‑based estimators that rely on local linearization and thus ensure only local convergence, the proposed observer achieves almost global stability through the decoupling of the rotational and translational dynamics. A globally exponentially stable Riccati‑based translational observer along with an almost global input‑to‑state stable attitude observer are designed such that the overall cascaded observer enjoys almost global asymptotic stability. This cascaded architecture guarantees robust and consistent estimation of the extended state, including orientation, position, velocity, gravity, and landmark positions, up to the VIO unobservable directions (i.e., a global translation and rotation about gravity). The effectiveness of the proposed scheme is demonstrated through numerical simulations as well as experimental validation on the EuRoC MAV dataset, highlighting its robustness and suitability for real‑world VIO applications.

Authors:Linlin Li, Steven X. Ding, Maiying Zhong, Dong Zhao, Yang Shi
Title: Analysis, detection and control of secure and safe cyber-physical control systems in a unified framework
Abstract:
This paper deals with analysis, simultaneous detection of faults and attacks, fault‑tolerant control and attack‑resilient of cyber‑physical control systems. In our recent work, it has been observed that an attack detector driven by an input residual signal is capable of reliably detecting attacks. In particular, observing system dynamics from the perspective of the system input‑output signal space reveals that attacks and system uncertainties act on different system subspaces. These results motivate our exploration of secure and safe cyber‑physical control systems in the unified framework of control and detection. The unified framework is proposed to handle control and detection issues uniformly and in subspaces of system input‑output data. Its mathematical and control‑theoretic basis is system coprime factorizations with Bezout identity at its core. We firstly explore those methods and schemes of the unified framework, which serve as the major control‑theoretic tool in our work. It is followed by re‑visiting and examining established attack detection and resilient control schemes. The major part of our work is the endeavours to develop a control‑theoretic paradigm, in which analysis, simultaneous detection of faults and attacks, fault‑tolerant and attack‑resilient control of cyber‑physical control systems are addressed in a unified manner.

Authors:Abdikarim Mohamed Ibrahim, Rosdiadee Nordin
Title: Geometry-Aware LoRaWAN Gateway Placement in Dense Urban Cities Using Digital Twins
Abstract:
LoRaWAN deployments rely on rough range estimates or simplified propagation models to decide where to place/mount gateways. As a result, operators have limited visibility into how rooftop choice, streets, and building shadowing jointly affect coverage and reliability. This paper addresses the problem of gateway placement in dense urban environments by combining a geometry accurate Digital Twin (DT) with a GPU accelerated ray tracing engine. Existing studies optimize placement on abstract grids or tune models with sparse measurements; few works evaluate LoRaWAN gateways on a full 3D city model using a realistic link budget. In this paper, we develop a DT with ITU radio materials and evaluate eight candidate rooftops for RAK7289 WisGate Edge Pro gateways under a sub‑GHz link budget derived from the data sheet. For each rooftop, we obtain Signal‑to‑Noise Ratios (SNR) on a 5 meter grid, derive robust and edge coverage indicators, and apply a greedy maximum coverage algorithm to rank sites and quantify the benefit of incremental densification. Results show that a single rooftop gateway covers one fifth of the full Sunway twin (i.e., the DT) at a robust SNR threshold, and that six sites still leave large areas of single gateway or out of coverage cells in surrounding residential streets. The findings from this paper shows that DT and ray tracing tools enable network operators to bridge the gap of expensive real‑world trials and planning to identify if the planned LoRaWAN gateway is sufficient or additional sites are required.

Authors:Abdikarim Mohamed Ibrahim, Rosdiadee Nordin
Title: Digital Twin for Ultra-Reliable & Low-Latency 6G Wireless Communications in Dense Urban City
Abstract:
High‑frequency deployments in dense cities are difficult to plan because coverage, interference, and service reliability depend sensitively on local morphology. This paper develops a geometric Digital Twin (DT) of the Sunway City and uses it to study the service implications of a multi‑site mmWave deployment. The DT is constructed from geo‑referenced three‑dimensional meshes of buildings, roads, and open areas, assembled in Blender and exported as a mesh scene. A seven‑transmitter downlink at 10 GHz is then embedded into this geometry and evaluated using a GPU accelerated ray tracing engine that returns path‑gain and Signal‑to‑Interference‑plus‑Noise Ratio (SINR) fields over a dense grid of user locations. These fields are mapped to achievable throughput and compared against representative target rates for immersive extended reality (XR), vehicle‑to‑everything (V2X) services, and ultra‑reliable low‑latency communication (URLLC). The resulting maps show that favourable streets and courtyards form narrow high rate corridors surrounded by deep shadows, even within a dense area. In the baseline deployment, one fifth of the simulated area can maintain 100 Mbps URLLC rates, and less than 10% of cells can reach 1.7 Gbps for XR, despite the presence of several rooftop sites. By exploiting the DT, we further quantify the macro‑diversity margin between the best and second best serving sites and show that most URLLC‑feasible cells have several decibels of SINR headroom that could be harvested through dual connectivity. The study shows how a city DT can translate ray tracing output into service centric metrics and planning insights, complementing both analytical models and expensive measurement campaigns.

Authors:Verena Häberle, Kehao Zhuang, Xiuqiang He, Linbin Huang, Gabriela Hug, Florian Dörfler
Title: Next-Generation Grid Codes: Towards a New Paradigm for Dynamic Ancillary Services
Abstract:
This paper introduces a conceptual foundation for Next Generation Grid Codes (NGGCs) based on stability and performance certificates, enabling the provision of dynamic ancillary services such as fast frequency and voltage regulation through decentralized frequency‑domain criteria. The NGGC framework offers two key benefits: (i) rigorous closed‑loop stability guarantees, and (ii) explicit performance guarantees for frequency and voltage dynamics in power systems. Regarding (i) stability, we employ loop‑shifting and passivity‑based techniques to derive local frequency‑domain stability certificates for individual device dynamics. These certificates ensure the closed‑loop stability of the entire interconnected power system through fully decentralized verification. Concerning (ii) performance, we establish quantitative bounds on critical time‑domain indicators of system dynamics, including the average‑mode frequency and voltage nadirs, the rate‑of‑change‑of‑frequency (RoCoF), steady‑state deviations, and oscillation damping capabilities. The bounds are obtained by expressing the performance metrics as frequency‑domain conditions on local device behavior. The NGGC framework is non‑parametric, model‑agnostic, and accommodates arbitrary device dynamics under mild assumptions. It thus provides a unified, decentralized approach to certifying both stability and performance without requiring explicit device‑model parameterizations. Moreover, the NGGC framework can be directly used as a set of specifications for control design, offering a principled foundation for future stability‑ and performance‑oriented grid codes in power systems.

Authors:Xuehui Ma, Shiliang Zhang, Zhiyong Sun, Xiaohui Zhang, Sabita Maharjan
Title: Adaptive Robust Control for Uncertain Systems with Ellipsoid-Set Learning
Abstract:
Despite the celebrated success of stochastic control approaches for uncertain systems, such approaches are limited in the ability to handle non‑Gaussian uncertainties. This work presents an adaptive robust control for linear uncertain systems, whose process noise, observation noise, and system states are depicted by ellipsoid sets rather than Gaussian distributions. We design an ellipsoid‑set learning method to estimate the boundaries of state sets, and incorporate the learned sets into the control law derivation to reduce conservativeness in robust control. Further, we consider the parametric uncertainties in state‑space matrices. Particularly, we assign finite candidates for the uncertain parameters, and construct a bank of candidate‑conditional robust control problems for each candidate. We derive the final control law by aggregating the candidate‑conditional control laws. In this way, we separate the control scheme into parallel robust controls, decoupling the learning and control, which otherwise renders the control unattainable. We demonstrate the effectiveness of the proposed control in numerical simulations in the cases of linear quadratic regulation and tracking control.

Authors:Alessandro Fontanella, Kristjan Milic, Alan Facchinetti, Sara Muggiasca, Marco Belloli
Title: Hardware-in-the-loop wind-tunnel testing of wake interactions between two floating wind turbines
Abstract:
Wake interactions in floating wind farms are inherently coupled to platform motion, yet most experimental studies to date neglect this two‑way coupling by prescribing platform movements. This work presents a hardware‑in‑the‑loop (HIL) wind‑tunnel methodology to investigate wake interactions between two floating wind turbines with fully coupled aerodynamic loading and platform dynamics. The approach integrates physical wind‑tunnel testing of two scaled rotors with a real‑time numerical model that accounts for platform motion, mooring restoring forces, and hydrodynamic loads. Experiments conducted under low‑turbulence inflow conditions show that a downstream turbine operating in the wake of an upstream turbine experiences reduced mean thrust and platform deflections due to the decreased inflow velocity, alongside enhanced low‑frequency platform motions driven by increased turbulent energy in the wake. The proposed HIL framework provides a controlled experimental basis for studying wake‑induced excitation mechanisms and supports the validation of floating wind farm models and control strategies.

Authors:Jinwoo Hwang, Yeongmin Hwang, Tadiwos Meaza, Hyeonbin Bae, Jongse Park
Title: Understanding the Performance Behaviors of End-to-End Protein Design Pipelines on GPUs
Abstract:
Recent computational advances enable protein design pipelines to run end‑to‑end on GPUs, yet their heterogeneous computational behaviors remain undercharacterized at the system level. We implement and profile a representative pipeline at both component and full‑pipeline granularities across varying inputs and hyperparameters. Our characterization identifies generally low GPU utilization and high sensitivity to sequence length and sampling strategies. We outline future research directions based on these insights and release an open‑source pipeline and profiling scripts to facilitate further studies.

Authors:Jialun Zhang, Felix Kottmann, Jimmy Chih-Hsien Peng, Adrian Perrig, Gabriela Hug
Title: Fast frequency response with heterogeneous communication delay management under the SCION Internet architecture
Abstract:
System operators can increasingly exploit distributed energy resources (DERs) and controllable loads (CLs) to provide frequency response services. In conventional practice, communication between the system operator and flexible devices relies on the Border Gateway Protocol (BGP)‑based Internet. However, existing BGP‑based architectures face challenges in providing latency‑guaranteed control, while direct private and proprietary communication networks lead to additional deployment and maintenance costs. In contrast, the SCION‑based Internet architecture supports latency‑minimum path selection, which makes it suitable for latency‑sensitive frequency contingency services such as fast frequency response (FFR). Hence, this paper proposes a real‑time reserve dispatch framework to optimally select a portfolio of flexible devices to deliver FFR services using the SCION‑based Internet. First, an analytical expression of the system frequency dynamics with respect to heterogeneous communication latencies is derived. Next, a cyber‑physical co‑optimization model is formulated to jointly schedule communication paths and physical flexibility resources for real‑time FFR provision. To improve the computation efficiency, we propose a heuristic FFR allocation algorithm to approximate the optimal response portfolio, integrating contributions from both DERs and CLs. Numerical case studies demonstrate the benefits of the proposed algorithm and its capability to approximate the optimality of the reserves allocation while significantly reducing the computation time.

Authors:Xian Wu, Jan H. van Schuppen, Hai Xiang Lin
Title: Control and Stability of a Multilevel Power System for a Future Distribution Network
Abstract:
The growing integration of renewable energy sources into distribution networks poses significant challenges to frequency and voltage stability due to their intermittent nature and low‑inertia dynamics. This paper proposes a multilevel control framework for a future decarbonized power system, using energy storage systems as power buffers to mitigate frequency and voltage fluctuations. A nonlinear interconnected model is formulated to characterize the complex dynamics across multiple levels of the distribution network. To reduce operational complexity and communication overhead of these dynamics, a distributed linear quadratic regulator control strategy is developed for information exchange in a bottom‑up approach, where each level implements local feedback control within a short time horizon. Stability conditions for both open‑loop and closed‑loop systems are established using Lyapunov‑based analysis. In addition, explicit performance bounds are derived to quantify the optimal difference between the proposed distributed strategy and the centralized control method, demonstrating the effectiveness of the proposed framework.

Authors:Sai Krishna Kanth Hari, Russell Bent
Title: Water Demand Maximization: Quick Recovery of Nonlinear Physics Solutions
Abstract:
Determining the maximum demand a water distribution network can satisfy is crucial for ensuring reliable supply and planning network expansion. This problem, typically formulated as a mixed‑integer nonlinear program (MINLP), is computationally challenging. A common strategy to address this challenge is to solve mixed‑integer linear program (MILP) relaxations derived by partitioning variable domains and constructing linear over‑ and under‑estimators to nonlinear constraints over each partition. While MILP relaxations are easier to solve up to a modest level of partitioning, their solutions often violate nonlinear water flow physics. Thus, recovering feasible MINLP solutions from the MILP relaxations is crucial for enhancing MILP‑based approaches. In this paper, we propose a robust solution recovery method that efficiently computes feasible MINLP solutions from MILP relaxations, regardless of partition granularity. Combined with iterative partition refinement, our method generates a sequence of feasible solutions that progressively approach the optimum. Through extensive numerical experiments, we demonstrate that our method outperforms baseline methods and direct MINLP solves by consistently recovering high‑quality feasible solutions with significantly reduced computation times.

Authors:Kun-Chih, Chen, Chia-Hsin Chen, Lei-Qi Wang, Chun-Chieh Wang
Title: Entropy-based Thermal Sensor Placement and Temperature Reconstruction based on Adaptive Compressive Sensing Theory
Abstract:
This paper addresses the challenges of thermal sensor allocation and full‑chip temperature reconstruction in multi‑core systems by leveraging an entropy‑based sensor placement strategy and an adaptive compressive sensing approach. By selecting sensor locations that capture diverse thermal behaviors and dynamically adjusting the measurement matrix, our method significantly enhances the accuracy of the full‑chip temperature reconstruction. Experimental results demonstrate that our approach reduces full‑chip temperature reconstruction error by 18% to 95%. In addition to the full‑chip temperature reconstruction efficiency enhancement, our proposed method improves hardware efficiency by 5% to 514% over the related works. These findings highlight the potential of our method for more effective dynamic temperature management in future high‑performance multi‑core systems.

Authors:Darius Jakobeit, Oliver Wallscheid
Title: A Power Electronic Converter Control Framework Based on Graph Neural Networks -- An Early Proof-of-Concept
Abstract:
Power electronic converter control is typically tuned per topology, limiting transfer across heterogeneous designs. This letter proposes a topology‑agnostic meta‑control framework that encodes converter netlists as typed bipartite graphs and uses a task‑conditioned graph neural network backbone with distributed control heads. The policy is trained end‑to‑end via differentiable predictive control to amortize constrained optimal control over a distribution of converter parameters and reference‑tracking tasks. In simulation on randomly sampled buck converters, the learned controller achieves near‑optimal tracking performance relative to an online optimal‑control baseline, motivating future extension to broader topologies, objectives, and real‑time deployment.

Authors:Zimao Sheng
Title: Robustness Quantification of MIMO-PI Controller From the Perspective of \(γ\)-Dissipativity
Abstract:
The proportional‑integral‑derivative (PID) controller and its variants are widely used in control engineering, but they often rely on linearization around equilibrium points and empirical parameter tuning, making them ineffective for multi‑input‑multi‑output (MIMO) systems with strong coupling, intense external disturbances, and high nonlinearity. Moreover, existing methods rarely explore the intrinsic stabilization mechanism of PID controllers for disturbed nonlinear systems from the perspective of modern robust control theories such as dissipativity and \mathcalL_2‑gain. To address this gap, this study focuses on γ‑dissipativity (partially equivalent to \mathcalL_2‑gain) and investigates the optimal parameter tuning of MIMO‑PI controllers for general disturbed nonlinear MIMO systems. First, by integrating dissipativity theory with the Hamilton‑Jacobi‑Isaacs (HJI) inequality, sufficient conditions for the MIMO‑PI‑controlled system to achieve γ‑dissipativity are established, and the degree of γ‑dissipativity in a local region containing the origin is quantified. Second, an optimal parameter tuning strategy is proposed, which reformulates the γ‑dissipativity optimization problem into a class of standard eigenvalue problems (EVPs) and further converts it into linear matrix inequality (LMI) formulations for efficient online computation. Comprehensive simulation experiments validate the effectiveness and optimality of the proposed approach. This work provides a theoretical basis for the robust stabilization of general disturbed nonlinear MIMO systems and enriches the parameter tuning methods of PID controllers from the perspective of dissipativity.

Authors:Chao Li, Ilia Derevitskii, Sergey Kovalchuk
Title: Modeling Descriptive Norms in Multi-Agent Systems: An Auto-Aggregation PDE Framework with Adaptive Perception Kernels
Abstract:
This paper presents a PDE‑based auto‑aggregation model for simulating descriptive norm dynamics in autonomous multi‑agent systems, capturing convergence and violation through non‑local perception kernels and external potential fields. Extending classical transport equations, the framework represents opinion popularity as a continuous distribution, enabling direct interactions without Bayesian guessing of beliefs. Applied to a real‑world COVID‑19 dataset from a major medical center, the experimental results demonstrate that: when clinical guidelines serve as a top‑down constraint mechanism, it effectively generates convergence of novel descriptive norms consistent with the dataset; in the bottom‑up experiment, potential field guidance successfully promotes the system's reconstruction of descriptive norms aligned with the dataset through violation‑and‑recoupling; whereas fully autonomous interaction leads to the emergence of multi‑centric normative structures independent of the dataset.

Authors:Roya Khalili Amirabadi, Mohsen Jalaeian Farimani, Omid Solaymani Fard
Title: Self-Organizing Dual-Buffer Adaptive Clustering Experience Replay (SODACER) for Safe Reinforcement Learning in Optimal Control
Abstract:
This paper proposes a novel reinforcement learning framework, named Self‑Organizing Dual‑buffer Adaptive Clustering Experience Replay (SODACER), designed to achieve safe and scalable optimal control of nonlinear systems. The proposed SODACER mechanism consisting of a Fast‑Buffer for rapid adaptation to recent experiences and a Slow‑Buffer equipped with a self‑organizing adaptive clustering mechanism to maintain diverse and non‑redundant historical experiences. The adaptive clustering mechanism dynamically prunes redundant samples, optimizing memory efficiency while retaining critical environmental patterns. The approach integrates SODACER with Control Barrier Functions (CBFs) to guarantee safety by enforcing state and input constraints throughout the learning process. To enhance convergence and stability, the framework is combined with the Sophia optimizer, enabling adaptive second‑order gradient updates. The proposed SODACER‑Sophia's architecture ensures reliable, effective, and robust learning in dynamic, safety‑critical environments, offering a generalizable solution for applications in robotics, healthcare, and large‑scale system optimization. The proposed approach is validated on a nonlinear Human Papillomavirus (HPV) transmission model with multiple control inputs and safety constraints. Comparative evaluations against random and clustering‑based experience replay methods demonstrate that SODACER achieves faster convergence, improved sample efficiency, and a superior bias‑variance trade‑off, while maintaining safe system trajectories, validated via the Friedman test.

Authors:Lingrui Chen, Xu Zhang, Fanpeng Song, Fang Wang, Cunquan Qu, Zhixin Liu
Title: Convergence Analysis of Weighted Median Opinion Dynamics with Higher-Order Effects
Abstract:
The weighted median mechanism provides a robust alternative to weighted averaging in opinion dynamics. Existing models, however, are predominantly formulated on pairwise interaction graphs, which limits their ability to represent higher‑order environmental effects. In this work, a generalized weighted median opinion dynamics model is proposed by incorporating high‑order interactions through a simplicial complex representation. The resulting dynamics are formulated as a nonlinear discrete‑time system with synchronous opinion updates, in which intrinsic agent interactions and external environmental influences are jointly modeled. Sufficient conditions for asymptotic consensus are established for heterogeneous systems composed of opinionated and unopinionated agents. For homogeneous opinionated systems, convergence and convergence rates are rigorously analyzed using the Banach fixed‑point theorem. Theoretical results demonstrate the stability of the proposed dynamics under mild conditions, and numerical simulations are provided to corroborate the analysis. This work extends median‑based opinion dynamics to high‑order interaction settings and provides a system‑level framework for stability and consensus analysis.

Authors:Andrei A. Korigodskii, Artem E. Vasiunik, Georgii A. Varin, Adilia M. Zukhurova, Matvei V. Urvantsev, Semen A. Osipenkov, Igor S. Efremov, Georgii E. Bondar
Title: Precision Meets Art: Autonomous Multi-UAV System for Large Scale Mural Drawing
Abstract:
The integration of autonomous unmanned aerial vehicles (UAVs) into large‑scale artistic projects has emerged as a new application in robotics. This paper presents the design, deployment, and testing of a novel multi‑drone system for automated mural painting in outdoor settings. This technology makes use of new software that coordinates multiple drones simultaneously, utilizing state‑machine algorithms for task execution. Key advancements are the complex positioning system that combines 2D localization using a single motion tracking camera with onboard LiDAR for precise positioning, and a novel flight control algorithm, which works differently along the trajectory and normally to it, ensuring smoothness and high precision of the drawings at the same time. A 100 square meters mural was created using the developed multi‑drone system, validating the system's efficacy. Compared to single‑drone approaches, our multi‑UAV solution significantly improves scalability and operational speed while maintaining high stability even in harsh weather conditions. The findings highlight the potential of autonomous robotic swarms in creative applications, paving the way for further advancements in large‑scale robotic art.

Authors:Yi Zhan, Iván Martínez-Estévez, Min Luo, Alejandro J. C. Crespo, Abbas Khayyer
Title: Coupling Smoothed Particle Hydrodynamics with Multi-Agent Deep Reinforcement Learning for Cooperative Control of Point Absorbers
Abstract:
Wave Energy Converters, particularly point absorbers, have emerged as one of the most promising technologies for harvesting ocean wave energy. Nevertheless, achieving high conversion efficiency remains challenging due to the inherently complex and nonlinear interactions between incident waves and device motion dynamics. This study develops an optimal adaptive damping control model for the power take‑off (PTO) system by coupling Smoothed Particle Hydrodynamics (SPH) with multi‑agent deep reinforcement learning. The proposed framework enables real‑time communication between high‑fidelity SPH simulations and intelligent control agents that learn coordinated policies to maximise energy capture. In each training episode, the SPH‑based environment provides instantaneous hydrodynamic states to the agents, which output continuous damping actions and receive rewards reflecting power absorption. The Multi‑Agent Soft Actor Critic algorithm is employed within a centralised‑training and decentralised‑execution scheme to ensure stable learning in continuous, multi‑body systems. The entire platform is implemented in a unified GPU‑accelerated C++ environment, allowing long‑horizon training and large‑scale three‑dimensional simulations. The approach is validated through a series of two‑dimensional and three‑dimensional benchmark cases under regular and irregular wave conditions. Compared with constant PTO damping, the learned control policy increases overall energy capture by 23.8% and 21.5%, respectively, demonstrating the strong potential of intelligent control for improving the performance of wave energy converter arrays. The developed three‑dimensional GPU‑accelerated multi‑agent platform in computational hydrodynamics, is extendable to other fluid‑structure interaction engineering problem that require real‑time, multi‑body coordinated control.

Authors:Mundla Narasimhappa, Praveen Kumar
Title: Hybrid LSTM-UKF Framework: Ankle Angle and Ground Reaction Force Estimation
Abstract:
Accurate prediction of joint kinematics and kinetics is essential for advancing gait analysis and developing intelligent assistive systems such as prosthetics and exoskeletons. This study presents a hybrid LSTM‑UKF framework for estimating ankle angle and ground reaction force (GRF) across varying walking speeds. A multimodal sensor fusion strategy integrates force plate data, knee angle, and GRF signals to enrich biomechanical context. Model performance was evaluated using RMSE and R^2 under subject‑specific validation. The LSTM‑UKF consistently outperformed standalone LSTM and UKF models, achieving up to 18.6% lower RMSE for GRF prediction at 3 km/h. Additionally, UKF integration improved robustness, reducing ankle angle RMSE by up to 22.4% compared to UKF alone at 1 km/h. These results underscore the effectiveness of hybrid architectures for reliable gait prediction across subjects and walking conditions.

Authors:Imran Sayyed, Aayush Konar, Nandan Kumar Sinha
Title: Deep Reinforcement Learning based Control Design for Aircraft Recovery from Loss-of-Control Scenario
Abstract:
Loss‑of‑control (LOC) remains a leading cause of fixed‑wing aircraft accidents, especially in post‑stall and flat‑spin regimes where conventional gain‑scheduled or logic‑based recovery laws may fail. This study formulates spin‑recovery as a continuous‑state, continuous‑action Markov Decision Process and trains a Proximal Policy Optimization (PPO) agent on a high‑fidelity six‑degree‑of‑freedom F‑18/HARV model that includes nonlinear aerodynamics, actuator saturation and rate coupling. A two‑phase potential‑based reward structure first penalizes large angular rates and then enforces trimmed flight. After 6,000 simulated episodes, the policy generalities to unseen upset initializations. Results show that the learned policy successfully arrests the angular rates and stabilizes the angle of attack. The controller performance is observed to be satisfactory for recovery from spin condition which was compared with a state‑of‑the‑art sliding mode controller. The findings demonstrate that deep reinforcement learning can deliver interpretable, dynamically feasible manoeuvres for real‑time loss of control mitigation and provide a pathway for flight‑critical RL deployment.

Authors:Matthew Hilsenrath, Daniel R. Herber
Title: A Data-Driven Surrogate Modeling and Sensor/Actuator Placement Framework for Flexible Spacecraft
Abstract:
Flexible spacecraft structures present significant challenges for physical and control system design due to nonlinear dynamics, mission constraints, environmental variables, and changing operational conditions. This paper presents a data‑driven framework for constructing reduced‑order surrogate models of a flexible spacecraft using the method of Dynamic Mode Decomposition (DMD), followed by optimal sensor/actuator pair placement. Highfidelity simulation data from a nonlinear flexible spacecraft model, including coupled rigid‑body and elastic modes, are captured by defining a mesh of nodes over the spacecraft body. The data‑driven methods are then used to construct a modal model from the time histories of these node points. Optimal sensor/actuator placement for controllability and observability is performed via a nonlinear programming technique that maximizes the singular values of the Hankel matrix. Finally, the sensor placement and dynamics modeling approach is iterated to account for changes in the dynamic system introduced by sensor/actuator physical mass. The proposed methodology enables initialization of physical modeling without requiring a direct analytical model and provides a practical solution for onboard implementation in model‑based control and estimation systems. Results demonstrate optimal design methodology with substantial model‑order reduction while preserving dynamic fidelity, and provide insight into effective sensor‑actuator configurations for estimation and control.

Authors:Selin Ezgi Ozcan, Mustafa Mert Ankarali
Title: Koopman Model Dimension Reduction via Variational Bayesian Inference and Graph Search
Abstract:
Koopman operator recently gained increasing attention in the control systems community for its abilities to bridge linear and nonlinear systems. Data driven Koopman operator approximations have established themselves as key enablers for system identification and model predictive control. Nonetheless, such methods commonly entail a preselected definition of states in the function space leading to high dimensional overfitting models and degraded long term prediction performances. We address this problem by proposing a hierarchical probabilistic approach for the Koopman model identification problem. In our method, elements of the model are treated as random variables and the posterior estimates are found using variational Bayesian (VB) inference updates. Our model distinguishes from others in the integration of inclusion flags. By the help of the inclusion flags, we intuitively threshold the probability of each state in the model. We then propose a graph search based algorithm to reduce the preselected states of the Koopman model. We demonstrate that our reduction method overcomes numerical instabilities and the reduced models maintain performance in simulated and real experiments.

Authors:Mostafa Darvishi
Title: Timing Fragility Aware Selective Hardening of RISCV Soft Processors on SRAM Based FPGAs
Abstract:
Selective hardening is widely employed to improve the reliability of FPGA based soft processors while limiting the overhead of full redundancy. However, existing approaches primarily rely on architectural criticality or functional fault analysis, overlooking the impact of routing dependent timing sensitivity on processor robustness. This paper introduces a timing fragility aware selective hardening methodology for RISCV soft processors implemented on SRAM based FPGAs. Building on recent advances in in situ timing observability, the proposed approach quantifies the statistical timing sensitivity of pipeline components under controlled routing perturbations and uses this information to guide hardening decisions. Experimental results on a RISCV processor implemented on a commercial FPGA platform show that components exhibiting higher timing fragility also demonstrate increased vulnerability to routing induced delay effects. Leveraging this correlation, the proposed selective hardening strategy achieves robustness comparable to full hardening while significantly reducing area and timing overhead. These results demonstrate that timing fragility provides a practical and effective metric for reliability aware design optimization in FPGA based processor architectures.

Authors:Samiya A Alkhairy
Title: Auditory Filter Behavior and Updated Estimated Constants
Abstract:
Filters from the Gammatone family are often used to model auditory signal processing, but the filter constant values used to mimic human hearing are largely set to values based on historical psychoacoustic data collected several decades ago. Here, we move away from this long‑standing convention, and estimate filter constants using a range of more recent reported filter characteristics (such as quality factors and ratios between quality factors and peak group delay) within a characteristics‑based framework that clarifies how filter behavior is related to the underlying constants. Using a sharp‑filter approximation that captures shared peak‑region behavior across certain classes of filters, we analyze the range of behaviors accessible when the full degrees of freedom of the filter are utilized rather than fixing the filter order or exponent to historically prescribed values. Filter behavior is characterized using magnitude‑based and phase‑based characteristics and their ratios, which reveal which characteristics are informative for constraining filter constants and which are only weakly constraining. We show that these insights and estimation methods extend to multiple realizable filter classes from the Gammatone family and apply them, together with recent physiological and psychoacoustic observations, to derive constraints on and estimates for filter constants for human auditory filters. More broadly, this framework supports the design of auditory filters with arbitrary characteristic‑level specifications and enables systematic assessment of how variations in filter characteristics influence auditory models, perceptual findings, and technologies that rely on auditory filterbanks.

Authors:Md Sharif Hossen, Anil Gurses, Ozgur Ozdemir, Mihail Sichitiu, Ismail Guvenc
Title: Resilient UAV Data Mule via Adaptive Sensor Association under Timing Constraints
Abstract:
Unmanned aerial vehicles (UAVs) can be critical for time‑sensitive data collection missions, yet existing research often relies on simulations that fail to capture real‑world complexities. Many studies assume ideal wireless conditions or focus only on path planning, neglecting the challenge of making real‑time decisions in dynamic environments. To bridge this gap, we address the problem of adaptive sensor selection for a data‑gathering UAV, considering both the buffered data at each sensor and realistic propagation conditions. We introduce the Hover‑based Greedy Adaptive Download (HGAD) strategy, designed to maximize data transfer by intelligently hovering over sensors during periods of peak signal quality. We validate HGAD using both a digital twin (DT) and a real‑world (RW) testbed at the NSF‑funded AERPAW platform. Our experiments show that HGAD significantly improves download stability and successfully meets per‑sensor data targets. When compared with the traditional Greedy approach that simply follows the strongest signal, HGAD is shown to outperform in the cumulative data download. This work demonstrates the importance of integrating signal‑to‑noise ratio (SNR)‑aware and buffer‑aware scheduling with DT and RW signal traces to design resilient UAV data‑mule strategies for realistic deployments.

Authors:Gerald Ogbonna, C. Lindsay Anderson
Title: Generalized Spectral Clustering of Low-Inertia Power Networks
Abstract:
Large‑scale integration of distributed energy resources has led to a rapid increase in the number of controllable devices and a significant change in system dynamics. This has necessitating the shift towards more distributed and scalable control strategies to manage the increasing system complexity. In this work, we address the problem of partitioning a low‑inertia power network into dynamically coherent subsystems to facilitate the utilization of distributed control schemes. We show that an embedding of the power network using the spectrum of the linearized synchronization dynamics matrix results in a natural decomposition of the network. We establish the connection between our approach and the broader framework of spectral clustering using the Laplacian matrix of the admittance network. The proposed method is demonstrated on the IEEE 30‑bus test system. We consider the robustness of the clusters by analyzing the sensitivity of the small eigenvalues and their corresponding eigenspaces to perturbations caused by variation in the steady‑state operating points of the network.

Authors:Kavita Shekhawat, Nandan Kumar Sinha
Title: Modeling and Bifurcation Analysis of Longitudinal Dynamics of an Air-Breathing Hypersonic Vehicle
Abstract:
A nonlinear model of an Air‑Breathing Hypersonic Vehicle (ABHV) longitudinal dynamics characterized by coupling of aerodynamic and propulsive terms is presented in this paper. The model is verified using modal analysis carried out around a design operating condition with results available in the literature. Further, parametric dynamic behavior is computed for the model as steady states with local stability with respect to its control inputs, elevator and fuel‑equivalence ratio in four different cases using a numerical continuation algorithm. Detailed analysis of the qualitative longitudinal dynamics of the model is carried out based on bifurcation theory methodology. Numerical simulation results are presented to verify bifurcation analysis results.

Authors:Thomas Stegen, Julien Allard, Noé Diffels, François Vallée, Mevludin Glavic, Zacharie De Grève, Bertrand Cornélusse
Title: Explicit Reward Mechanisms for Local Flexibility in Renewable Energy Communities
Abstract:
Incentivizing flexible consumption of end‑users is key to maximizing the value of local exchanges within Renewable Energy Communities. If centralized coordination for flexible resources planning raises concerns regarding data privacy and fair benefits distribution, state‑of‑the‑art approaches (e.g., bi‑level, ADMM) often face computational complexity and convexity challenges, limiting the precision of embedded flexible models. This work proposes an iterative resolution procedure to solve the decentralized flexibility planning with a central operator as a coordinator within a community. The community operator asks for upward or downward flexibility depending on the global needs, while members can individually react with an offer for flexible capacity. This approach ensures individual optimality while converging towards a global optimum, as validated on a 20‑member domestic case study for which the gap in terms of collective bill is not more than 3.5% between the decentralized and centralized coordination schemes.

Authors:Emily Cheng, Carmen Amo Alonso, Federico Danieli, Arno Blaas, Luca Zappella, Pau Rodriguez, Xavier Suau
Title: GenCtrl -- A Formal Controllability Toolkit for Generative Models
Abstract:
As generative models become ubiquitous, there is a critical need for fine‑grained control over the generation process. Yet, while controlled generation methods from prompting to fine‑tuning proliferate, a fundamental question remains unanswered: are these models truly controllable in the first place? In this work, we provide a theoretical framework to formally answer this question. Framing human‑model interaction as a control process, we propose a novel algorithm to estimate the controllable sets of models in a dialogue setting. Notably, we provide formal guarantees on the estimation error as a function of sample complexity: we derive probably‑approximately correct bounds for controllable set estimates that are distribution‑free, employ no assumptions except for output boundedness, and work for any black‑box nonlinear control system (i.e., any generative model). We empirically demonstrate the theoretical framework on different tasks in controlling dialogue processes, for both language models and text‑to‑image generation. Our results show that model controllability is surprisingly fragile and highly dependent on the experimental setting. This highlights the need for rigorous controllability analysis, shifting the focus from simply attempting control to first understanding its fundamental limits.

Authors:Chao Shen, Zihan Guo, Ke Zuo, Wenqi Huang, Mingyang Sun
Title: LLM-DMD: Large Language Model-based Power System Dynamic Model Discovery
Abstract:
Current model structural discovery methods for power system dynamics impose rigid priors on the basis functions and variable sets of dynamic models while often neglecting algebraic constraints, thereby limiting the formulation of high‑fidelity models required for precise simulation and analysis. This letter presents a novel large language model (LLM)‑based framework for dynamic model discovery (LLM‑DMD) which integrates the reasoning and code synthesis capabilities of LLMs to discover dynamic equations and enforce algebraic constraints through two sequential loops: the differential‑equation loop that identifies state dynamics and associated variables, and the algebraic‑equation loop that formulates algebraic constraints on the identified algebraic variables. In each loop, executable skeletons of power system dynamic equations are generated by the LLM‑based agent and evaluated via gradient‑based optimizer. Candidate models are stored in an island‑based archive to guide future iterations, and evaluation stagnation activates a variable extension mechanism that augments the model with missing algebraic or input variables, such as stator currents to refine the model. Validation on synchronous generator benchmarks of the IEEE 39‑bus system demonstrates the superiority of LLM‑DMD in complete dynamic model discovery.

Authors:Yu Zhou, Andrey Polyakov, Gang Zheng, Masaaki Nagahara
Title: Discrete Homogeneity and Quantizer Design for Nonlinear Homogeneous Control Systems
Abstract:
This paper proposes a framework for analysis of generalized homogeneous control systems under state quantization. In particular, it addresses the challenge of maintaining finite/fixed‑time stability of nonlinear systems in the presence of quantized measurements. To analyze the behavior of quantized control system, we introduce a new type of discrete homogeneity, where the dilation is defined by a discrete group. The converse Lyapunov function theorem is established for homogeneous systems with respect to discrete dilations. By extending the notion of sector‑boundedness to a homogeneous vector space, we derive a generalized homogeneous sector‑boundedness condition that guarantees finite/fixed‑time stability of nonlinear control system under quantized measurements. A geometry‑aware homogeneous static vector quantizer is then designed using generalized homogeneous coordinates, enabling an efficient quantization scheme. The resulting homogeneous control system with the proposed quantizer is proven to be homogeneous with respect to discrete dilation and globally finite‑time, nearly fixed‑time, or exponentially stable, depending on the homogeneity degree. Numerical examples validate the effectiveness of the proposed approach.

Authors:Luis A. Garcia-Reyes, Oriol Gomis-Bellmunt, Eduardo Prieto-Araujo, Vinícius A. Lacerda, Marc Cheah-Mañe
Title: SIaD-Tool: A Comprehensive Frequency-Domain Tool for Small-Signal Stability and Interaction Assessment in Modern Power Systems
Abstract:
This paper presents SIaD‑Tool, an open‑source frequency‑domain (FD) scanning solution for stability and interaction assessment in modern power systems. The tool enables multi‑sequence identification in the abc, dq0, and 0pn frames and supports both series voltage and parallel current perturbation strategies. A novel perturbation scheme allows direct scanning in the target frame, simplifying the analysis of coupling effects and mirrored frequencies. SIaD‑Tool is implemented on a multi‑platform architecture, including MATLAB/Simulink and Python‑PSCAD/EMTDC. Beyond system identification, it integrates automated stability evaluation through four standardized methods: Generalized Nyquist Criterion (GNC), modal impedance analysis, phase margin assessment, and passivity checks. Validation is carried out via extensive case studies involving passive elements, grid‑following and grid‑forming converters, offshore wind power plants, and the IEEE 9‑bus system. Results confirm high accuracy, scalability, and robustness in detecting critical modes, interaction frequencies, oscillatory behavior, and stability margins.

Authors:Joseph Nyangon, Brecht Seifi
Title: How Carbon Border Adjustment Mechanism is Energizing the EU Carbon Market and Industrial Transformation
Abstract:
The global carbon market is fragmented and characterized by limited pricing transparency and empirical evidence, creating challenges for investors and policymakers in identifying carbon management opportunities. The European Union is among several regions that have implemented emissions pricing through an Emissions Trading System (EU ETS). While the EU ETS has contributed to emissions reductions, it has also raised concerns related to international competitiveness and carbon leakage, particularly given the strong integration of EU industries into global value chains. To address these challenges, the European Commission proposed the Carbon Border Adjustment Mechanism (CBAM) in 2021. CBAM is designed to operate alongside the EU ETS by applying a carbon price to selected imported goods, thereby aligning carbon costs between domestic and foreign producers. It will gradually replace existing carbon leakage mitigation measures, including the allocation of free allowances under the EU ETS. The initial scope of CBAM covers electricity, cement, fertilizer, aluminium, iron, and steel. As climate policies intensify under the Paris Agreement, CBAM‑like mechanisms are expected to play an increasingly important role in managing carbon‑related trade risks and supporting the transition to net zero emissions.

Authors:Sumit S. Kamat, Ajin Sunny, T. Michael Seigler, Jesse B. Hoagg
Title: Experimental Demonstration of a Decentralized Electromagnetic Formation Flying Control Using Alternating Magnetic Field Forces
Abstract:
Electromagnetic formation flying (EMFF) is challenging due to the complex coupling between the electromagnetic fields generated by each satellite in the formation. To address this challenge, this article uses alternating magnetic field forces (AMFF) to decouple the electromagnetic forces between each pair of satellites. Each satellite's electromagnetic actuation system is driven by a sum of amplitude‑modulated sinusoids, where amplitudes are controlled to achieve desired forces between each pair of satellites. The main contribution of this article is a 3‑satellite experimental demonstration of decentralized closed‑loop EMFF using AMFF. To our knowledge, this is the first demonstration of AMFF with at least 3 satellites in open or closed loop. This is noteworthy because the coupling challenges of EMFF are only present with more than 2 satellites, and thus, a formation of at least 3 is necessary to evaluate the effectiveness of AMFF. The experiments are conducted on a ground‑based testbed consisting of 3 electromagnetically actuated satellites on linear air tracks. The closed‑loop experimental results are compared with behavior from numerical simulations.

Authors:Janina Schaa, Thomas Berger
Title: Data-Based Analysis of Relative Degree and Zero Dynamics in Linear Systems
Abstract:
Data‑driven control offers a powerful alternative to traditional model‑based methods, particularly when accurate system models are unavailable or prohibitively complex. While existing data‑driven control methods primarily aim to construct controllers directly from measured data, our approach uses the available data to assess fundamental system‑theoretic properties. This allows the informed selection of suitable control strategies without explicit model identification. We provide data‑based conditions characterizing the (vector) relative degree and the stability of the zero dynamics, which are critical for ensuring proper performance of modern controllers. Our results cover both single‑ and multi‑input/output settings of discrete‑time linear systems. We further show how a continuous‑time system can be reconstructed from three sampling discretizations obtained via Zero‑order Hold at suitable sampling times, thus allowing the extension of the results to the combined data collected from these discretizations. All results can be applied directly to observed data sets using the proposed algorithms.

Authors:Francesco Bianchin, Robert Lefringhausen, Sandra Hirche
Title: Online Bayesian Learning of Agent Behavior in Differential Games
Abstract:
This work introduces an online Bayesian game‑theoretic method for behavior identification in multi‑agent dynamical systems. By casting Hamilton‑Jacobi‑Bellman optimality conditions as linear‑in‑parameter residuals, the method enables fast sequential Bayesian updates, uncertainty‑aware inference, and robust prediction from limited, noisy data‑without history stacks. The approach accommodates nonlinear dynamics and nonquadratic value functions through basis expansions, providing flexible models. Experiments, including linear‑quadratic and nonlinear shared‑control scenarios, demonstrate accurate prediction with quantified uncertainty, highlighting the method's relevance for adaptive interaction and real‑time decision making.

Authors:Niloufar Alavi, Swati Shah, Rezvan Alamian, Stefan Goetz
Title: Driver-Intention Prediction with Deep Learning: Real-Time Brain-to-Vehicle Communication
Abstract:
Brain‑computer interfaces (BCIs) allow direct communication between the brain and electronics without the need for speech or physical movement. Such interfaces can be particularly beneficial in applications requiring rapid response times, such as driving, where a vehicle's advanced driving assistance systems could benefit from immediate understanding of a driver's intentions. This study presents a novel method for predicting a driver's intention to steer using electroencephalography (EEG) signals through deep learning. A driving simulator created a controlled environment in which participants imagined controlling a vehicle during various driving scenarios, including left and right turns, as well as straight driving. A convolutional neural network (CNN) classified the detected EEG data with minimal pre‑processing. Our model achieved an accuracy of 83.7% in distinguishing between the three steering intentions and demonstrated the ability of CNNs to process raw EEG data effectively. The classification accuracy was highest for right‑turn segments, which suggests a potential spatial bias in brain activity. This study lays the foundation for more intuitive brain‑to‑vehicle communication systems.

Authors:Maitraya Avadhut Desai, Ognjen Stanojev, Simon Muntwiler, Gabriela Hug
Title: Effect of Dispatch Decisions on Small-Signal Stability of Converter-Dominated Power Systems
Abstract:
Small‑signal stability of modern converter‑dominated power systems has been the subject of extensive research, particularly from the perspective of device‑level control design for grid‑forming (GFM) and grid‑following (GFL) converters. However, the influence of power flow variables on system stability has received limited attention. Conventional small‑signal stability analyses are typically conducted at a specific operating point, emphasizing the selection of control or system design parameters while neglecting the sensitivity of stability characteristics to operating conditions. This paper seeks to bridge this gap by systematically investigating the impact of dispatch decisions on the small‑signal stability of converter‑based power systems. Our findings are first illustrated on a three‑bus system and then validated on the standard IEEE 39‑bus test system to demonstrate scalability. Across the test systems, we find that high‑voltage capacitive operation of GFL converters limits its active power injection, whereas inductive operation permits higher injections, and it is generally preferable for the GFM converter to supply more active power.

Authors:Silan Zhang, Yujie Tang
Title: ZIVR: An Incremental Variance Reduction Technique For Zeroth-Order Composite Problems
Abstract:
This paper investigates zeroth‑order (ZO) finite‑sum composite optimization. Recently, variance reduction techniques have been applied to ZO methods to mitigate the non‑vanishing variance of 2‑point estimators in constrained/composite optimization, yielding improved convergence rates. However, existing ZO variance reduction methods typically involve batch sampling of size at least Θ(n) or Θ(d), which can be computationally prohibitive for large‑scale problems. In this work, we propose a general variance reduction framework, Zeroth‑Order Incremental Variance Reduction (ZIVR), which supports flexible implementations\unicodex2014including a pure 2‑point zeroth‑order algorithm that eliminates the need for large batch sampling. Furthermore, we establish comprehensive convergence guarantees for ZIVR across strongly‑convex, convex, and non‑convex settings that match their first‑order counterparts. Numerical experiments validate the effectiveness of our proposed algorithm.

Authors:Xinyi Tao, Panfeng Huang, Fan Zhang
Title: Safe Reinforcement Learning Beyond Baseline Control: A Hierarchical Framework for Space Triangle Tethered Formation System
Abstract:
Triangular tethered formation system (TTFS) provide a promising platform for deep space exploration and distributed sensing due to its intrinsic spatial‑orientation stability and capability of adjusting distances among node satellites through deployment and retrieval of tethers. However, due to the coupled tether‑satellite dynamics and disturbance sensitivity of TTFS, traditional control methods struggle to achieve a balanced trade‑off among configuration accuracy requirements, tension constraints, and energy efficiency consumption throughout the deployment process.In this paper, a novel model‑reference reinforcement learning control framework is proposed for TTFS. By integrating baseline model‑based control with a Soft Actor‑Critic (SAC) compensator, the proposed method simultaneously achieves high‑precision tracking, fuel efficiency, and compliance with tension limits. A hierarchical training scheme is developed to address the convergence difficulties arising from strongly coupled states in centralized training, while tailored reward functions, reset conditions, and normalization criteria are designed to accelerate training convergence. Closed‑loop stability of the overall control law is rigorously proven using Lyapunov methods. Simulation results demonstrate that the proposed controller reduces steady‑state tracking errors by over 96% for tethers and 99% for node satellites, while cutting fuel consumption by two orders of magnitude compared with the baseline method. These results validate the effectiveness and stability of the proposed approach for TTFS deployment control.

Authors:Mattia Merluzzi, Olivier Bouchet, Ali Balador, Gilles Callebaut, Anastasius Gavras, Liesbet Van der Perre, Albert Banchs, Mauro Renato Boldi, Emilio Calvanese Strinati, Bahare M Khorsandi, Marja Matinmikko-Blue, Lars Christoph Schmelz
Title: Towards Sustainable 6G: A Holistic View of Trade-offs and Enablers
Abstract:
The sixth generation of mobile networks (6G) can play a central role in shaping a sustainable future, the most compelling contemporary challenge. Connecting the unconnected, reducing carbon emissions of vertical sectors, and allowing heterogeneous types of intelligence (including humans) to safely and constructively interact in complex environments, are only a few of the several challenges that can be supported by 6G. However, this requires a careful design that balances positive and negative impacts of 6G, towards a sustainable and sustainability‑enabling technology. This paper presents a holistic view that translates the complex interplay between the 6G enabling effects and the sustainability of 6G by design, into concrete trade‑offs and research questions. Starting from today's challenges for society and associated key values, we unfold the dilemma into a set of technical trade‑offs, whose solutions span from technological innovations to standardization actions towards applicability.

Authors:Xi Ru, Xiaoyu Peng, Xinghua Chen, Zhaojian Wang, Peng Yang, Feng Liu
Title: Matrix-Valued Passivity Indices: Foundations, Properties, and Stability Implications
Abstract:
The passivity index, a quantitative measure of a system's passivity deficiency or excess, has been widely used in stability analysis and control. Existing studies mostly rely on scalar forms of indices, which are restrictive for multi‑input, multi‑output (MIMO) systems. This paper extends the classical scalar indices to a systematic matrix‑valued framework, referred to as passivity matrices. A broad range of classical results in passivity theory can be naturally generalized in this framework. We first show that, under the matrix representation, passivity indices essentially correspond to the curvature of the dissipativity functional under a second‑variation interpretation. This result reveals that the intrinsic geometric structure of passivity consists of its directions and intensities, which a scalar index cannot fully capture. For linear time‑invariant (LTI) systems, we examine the structural properties of passivity matrices with respect to the Loewner partial order and propose two principled criteria for selecting representative matrices. Compared with conventional scalar indices, the matrix‑valued indices capture the passivity coupling among different input‑output channels in MIMO systems and provide a more comprehensive description of system passivity. This richer information leads to lower passivation effort and less conservative stability assessment.

Authors:Sojin Park, Ross Baldick, Hunyoung Shin
Title: Definition and Formulation of Inertia Service Incorporating Inverter-Based Resources
Abstract:
Increasing concerns over the scarcity of inertia have motivated the procurement of inertia as an ancillary service (AS). Despite numerous academic and practical efforts, there remains a lack of consensus regarding the definition and treatment of inertia service in market operations, particularly the specification of inertia variables and the separation between synchronous inertia (SI) from synchronous generators and virtual inertia (VI) from inverter‑based resources. To address these issues, this paper proposes a power‑oriented (P‑oriented) definition based on inertial response, which establishes conceptual consistency between SI and VI and makes the inertia service commensurable with other ASs. This definition explicitly incorporates both positive and negative inertial responses during frequency drop events. We then formulate a security‑constrained economic dispatch framework based on this P‑oriented definition and demonstrate its practical effectiveness through simulations. Case studies on a modified IEEE 30‑bus system show that the proposed bidirectional service definition ensures price signals that reflect the economic value of inertial provision.

Authors:Mohsen Jalaeian-Farimani, Xiong Xiong, Luca Bascetta
Title: Enhanced-FQL($λ$), an Efficient and Interpretable RL with novel Fuzzy Eligibility Traces and Segmented Experience Replay
Abstract:
This paper introduces a fuzzy reinforcement learning framework, Enhanced‑FQL(λ), that integrates novel Fuzzified Eligibility Traces (FET) and Segmented Experience Replay (SER) into fuzzy Q‑learning with the Fuzzified Bellman Equation (FBE) for continuous control. The proposed approach employs an interpretable fuzzy rule base instead of complex neural architectures, while maintaining competitive performance through two key innovations: a fuzzified Bellman equation with eligibility traces for stable multi‑step credit assignment, and a memory‑efficient segment‑based experience replay mechanism for enhanced sample efficiency. Theoretical analysis proves the proposed method convergence under standard assumptions. On the Cart‑‑Pole benchmark, Enhanced‑FQL(λ) improves sample efficiency and reduces variance relative to n‑step fuzzy TD and fuzzy SARSA(λ), while remaining competitive with the tested DDPG baseline. These results support the proposed framework as an interpretable and computationally compact alternative for moderate‑scale continuous control problems.

Authors:Zabir Ahmed, Xiang Li, Kanika Sarna, Harshvardhan Gupta, Vishal Jain, Maysamreza Chamanzar
Title: Ultra-sensitive graphene-based electro-optic sensors for optically-multiplexed neural recording
Abstract:
Large‑scale neural recording with high spatio‑temporal resolution is essential for understanding information processing in brain, yet current neural interfaces fall far short of comprehensively capturing brain activity due to extremely high neuronal density and limited scalability. Although recent advances have miniaturized neural probes and increased channel density, fundamental design constraints still prevent dramatic scaling of simultaneously recorded channels. To address this limitation, we introduce a novel electro‑optic sensor that directly converts ultra‑low‑amplitude neural electrical signals into optical signals with high signal‑to‑noise ratio. By leveraging the ultra‑high bandwidth and intrinsic multiplexing capability of light, this approach offers a scalable path toward massively parallel neural recording beyond the limits of traditional electrical interfaces. The sensor integrates an on‑chip photonic microresonator with a graphene layer, enabling direct detection of neural signals without genetically encoded optical indicators or tissue modification, making it suitable for human translation. Neural signals are locally transduced into amplified optical modulations and transmitted through on‑chip waveguides, enabling interference‑free recording without bulky electromagnetic shielding. Arrays of wavelength‑selective sensors can be multiplexed on a single bus waveguide using wavelength‑division multiplexing (WDM), greatly improving scalability while maintaining a minimal footprint to reduce tissue damage. We demonstrate detection of evoked neural signals as small as 25 μV with 3 dB SNR from mouse brain tissue and show multiplexed recording from 10 sensors on a single waveguide. These results establish a proof‑of‑concept for optically multiplexed neural recording and point toward scalable, high‑density neural interfaces for neurological research and clinical applications.

Authors:Midhun T. Augustine
Title: Predictive Controlled Music
Abstract:
This paper presents a new approach to algorithmic composition, called predictive controlled music (PCM), which combines model predictive control (MPC) with music generation. PCM uses dynamic models to predict and optimize the music generation process, where musical notes are computed in a manner similar to an MPC problem by optimizing a performance measure. A feedforward neural network‑based assessment function is used to evaluate the generated musical score, which serves as the objective function of the PCM optimization problem. Furthermore, a recurrent neural network model is employed to capture the relationships among the variables in the musical notes, and this model is then used to define the constraints in the PCM. Similar to MPC, the proposed PCM computes musical notes in a receding‑horizon manner, leading to feedback controlled prediction. Numerical examples are presented to illustrate the PCM generation method.

Authors:Léo Simpson, Moritz Diehl
Title: Identification of a Kalman filter: consistency of local solutions
Abstract:
Prediction error and maximum likelihood methods are powerful tools for identifying linear dynamical systems and, in particular, enable the joint estimation of model parameters and the Kalman filter used for state estimation. A key limitation, however, is that these methods require solving a generally non‑convex optimization problem to global optimality. This paper analyzes the statistical behavior of local minimizers in the special case where only the Kalman gain is estimated. We prove that these local solutions are statistically consistent estimates of the true Kalman gain. This follows from asymptotic unimodality: as the dataset grows, the objective function converges to a limit with a unique local (and therefore global) minimizer. We further provide guidelines for designing the optimization problem for Kalman filter tuning and discuss extensions to the joint estimation of additional linear parameters and noise covariances. Finally, the theoretical results are illustrated using three examples of increasing complexity. The main practical takeaway of this paper is that difficulties caused by local minimizers in system identification are, at least, not attributable to the tuning of the Kalman gain.

Authors:Juan F. Gutierrez, Nhung Nguyen, Jesus M. Quintero, Andres Gomez
Title: Solar Panel-based Visible Light Communication for Batteryless Systems
Abstract:
This paper presents a batteryless wireless communication node for the Internet of Things, powered entirely by ambient light and capable of receiving data through visible light communication. A solar panel serves dual functions as an energy harvester and an optical antenna, capturing modulated signals from LED light sources. A lightweight analog front‑end filters and digitizes the signals for an 8‑bit low‑power processor, which manages the system's operational states based on stored energy levels. The main processor is selectively activated to minimize energy consumption. Data reception is synchronized with the harvester's open‑circuit phase, reducing interference and improving signal quality. The prototype reliably decodes 32‑bit VLC frames at 800\,Herz, consuming less than 2.8\,mJ, and maintains sleep‑mode power below 30\,uW.

Authors:Haoran Su
Title: Hierarchical GNN-Based Multi-Agent Learning for Dynamic Queue-Jump Lane and Emergency Vehicle Corridor Formation
Abstract:
Emergency vehicles require rapid passage through congested traffic, yet existing strategies fail to adapt to dynamic conditions. We propose a novel hierarchical graph neural network (GNN)‑based multi‑agent reinforcement learning framework to coordinate connected vehicles for emergency corridor formation. Our approach uses a high‑level planner for global strategy and low‑level controllers for trajectory execution, utilizing graph attention networks to scale with variable agent counts. Trained via Multi‑Agent Proximal Policy Optimization (MAPPO), the system reduces emergency vehicle travel time by 28.3% compared to baselines and 44.6% compared to uncoordinated traffic in simulations. The design achieves near‑zero collision rates (0.3%) while maintaining 81% of background traffic efficiency. Ablation and generalization studies confirm the framework's robustness across diverse scenarios. These results demonstrate the effectiveness of combining GNNs with hierarchical learning for intelligent transportation systems.

Authors:Alessandro Lo Schiavo, Luigi Costanzo, Massimo Vitelli
Title: A Load Impedance Emulation Active Interface for Piezoelectric Vibration Energy Harvesters
Abstract:
A single stage active AC/DC interface able to emulate the optimal load impedance of a Resonant Piezoelectric Vibration Energy Harvester (RPVEH) is proposed. As theoretically shown, unlike an electronic interface that emulates an optimal load generator, an interface that emulates an optimal load impedance does not require adaptation to the acceleration of input vibrations. This allows the use of a very simple control, avoiding the implementation of Maximum Power Point Tracking (MPPT) algorithms that require lossy microcontrollers. Thus, the proposed interface is equipped with a simple analog controller allowing the RPVEH to work in its Maximum Power Point (MPP) in both steady‑state and variable conditions of vibrations, without recurring to multivariable perturbative approaches, as it happens for the most of single stage AC/DC interfaces proposed in the literature. The absence of perturbative techniques allows a significant improvement of both stationary and dynamic performances. Experimental tests of a prototype of the proposed interface confirm the theoretical findings and the predicted behavior.

Authors:Vishaal Krishnan, L. Mahadevan
Title: Stigmergic optimal transport
Abstract:
Efficient navigation in swarms often relies on the emergence of decentralized approaches that minimize traversal time or energy. Stigmergy, where agents modify a shared environment that then modifies their behavior, is a classic mechanism that can encode this strategy. We develop a theoretical framework for stigmergic transport by casting it as a stochastic optimal control problem: agents (collectively) lay and (individually) follow trails while minimizing expected traversal time. Simulations and analysis reveal two emergent behaviors: path straightening in homogeneous environments and path refraction at material interfaces, both consistent with experimental observations of insect trails. While reminiscent of Fermat's principle, our results show how local, noisy agent+field interactions can give rise to geodesic trajectories in heterogeneous environments, without centralized coordination or global knowledge, relying instead on an embodied slow fast dynamical mechanism.

Authors:Gorka Nieto, Idoia de la Iglesia, Cristina Perfecto, Unai Lopez-Novoa
Title: On-Device Deep Reinforcement Learning for Decentralized Task Offloading Performance trade-offs in the training process
Abstract:
Allowing less capable devices to offload computational tasks to more powerful devices or servers enables the development of new applications that may not run correctly on the device itself. Deciding where and why to run each of those applications is a complex task. Therefore, different approaches have been adopted to make offloading decisions. In this work, we propose a decentralized Deep Reinforcement Learning (DRL) agent to address the selection of computing locations. Unlike most existing work, we analyze it in a real testbed composed of various edge devices running the agent to determine where to execute each task. These devices are connected to a Multi‑Access Edge Computing (MEC) server and a Cloud server through 5G communications. We evaluate not only the agent's performance in meeting task requirements but also the implications of running this type of agent locally, assessing the trade‑offs of training locally versus remotely in terms of latency and energy consumption.

Authors:Josie König, Han Cheng Lie
Title: Posterior error bounds for prior-driven balancing in linear Gaussian inverse problems
Abstract:
In large‑scale Bayesian inverse problems, it is often necessary to apply approximate forward models to reduce the cost of forward model evaluations, while controlling approximation quality. In the context of Bayesian inverse problems with linear forward models, Gaussian priors, and Gaussian noise, we use perturbation theory for inverses to bound the error in the approximate posterior mean and posterior covariance resulting from a linear approximate forward model. We then focus on the smoothing problem of inferring the initial condition of linear time‑invariant dynamical systems, using finitely many partial state observations. For such problems, and for a specific model order reduction method based on balanced truncation, we show that the impulse response of a certain prior‑driven system is closely related to the prior‑preconditioned Hessian of the inverse problem. This reveals a novel connection between systems theory and inverse problems. We exploit this connection to prove the first a priori error bounds for system‑theoretic model order reduction methods applied to smoothing problems. The bounds control the approximation error of the posterior mean and covariance in terms of the truncated Hankel singular values of the underlying system.

Authors:Jad Wehbeh, Eric C. Kerrigan
Title: Exact Continuous Reformulations of Logic Constraints in Nonlinear Optimization and Optimal Control Problems
Abstract:
Many nonlinear optimal control and optimization problems involve constraints that combine continuous dynamics with discrete logic conditions. Standard approaches typically rely on mixed‑integer programming, which introduces scalability challenges and requires specialized solvers. This paper presents an exact reformulation of broad classes of logical constraints as binary‑variable‑free expressions whose differentiability properties coincide with those of the underlying predicates, enabling their direct integration into nonlinear programming models. Our approach rewrites arbitrary logical propositions into conjunctive normal form, converts them into equivalent max‑‑min constraints, and applies a smoothing procedure that preserves the exact feasible set. The method is evaluated on two benchmark problems, a quadrotor trajectory optimization with obstacle avoidance and a hybrid two‑tank system with temporal logic constraints, and is shown to obtain optimal solutions more consistently and efficiently than existing binary variable elimination techniques.

Authors:Markus Walker, Marcel Reith-Braun, Tai Hoang, Gerhard Neumann, Uwe D. Hanebeck
Title: Smooth Sampling-Based Model Predictive Control Using Deterministic Samples
Abstract:
Sampling‑based model predictive control (MPC) is effective for nonlinear systems but often produces non‑smooth control inputs due to random sampling. To address this issue, we extend the model predictive path integral (MPPI) framework with deterministic sampling and improvements from cross‑entropy method (CEM)‑‑MPC, such as iterative optimization, proposing deterministic sampling MPPI (dsMPPI). This combination leverages the exponential weighting of MPPI alongside the efficiency of deterministic samples. Experiments demonstrate that dsMPPI achieves smoother trajectories compared to state‑of‑the‑art methods.

Authors:Asitha Lakruwan Kulasekera
Title: A Systems-Engineered ESP32 DAQ Architecture and FAIR Data Workflow for Small-Scale Wind Turbine Performance Measurement in Tropical Environments
Abstract:
Small‑scale wind turbine research in resource‑constrained academic settings frequently produces unreliable or unpublishable datasets due to ad‑hoc instrumentation, inadequate time synchronization, storage failures, and weak data governance. This paper presents a systematic data acquisition (DAQ) methodology and ESP32‑based reference implementation design for field characterization of small wind turbines (100~W‑‑5~kW), emphasizing tropical/coastal deployment constraints typical of Low‑ and Middle‑Income Countries (LMIC). We integrate (i)~a student‑adapted V‑model with requirements traceability, (ii)~hardware selection strategies for high‑humidity and salt‑spray environments, (iii)~an embedded firmware architecture featuring interrupt‑driven rotor speed measurement, state‑machine fault handling, and NTP‑based time synchronization, (iv)~a local‑first hybrid storage design combining SD‑card persistence with optional MQTT cloud telemetry, and (v)~a data‑management workflow adapting CRISP‑DM and FAIR principles with explicit quality dimensions and publication templates. A detailed helical vertical‑axis wind turbine (VAWT) design scenario for coastal Sri Lanka illustrates the complete methodology, targeting >90% data completeness over six‑month campaigns. The methodology is accompanied by open‑source firmware, hardware templates, and data‑publication workflow artifacts released via GitHub and Zenodo.

Authors:Woraphrut Kornmaneesang, Tsu-Chin Tsao, Niloufar Esfandi, Shyh-Leh Chen
Title: Unified and Efficient Analysis of Machining Chatter and Surface Location Error
Abstract:
Although machining chatter can be suppressed by the choice of stable cutting parameters through means of stability lobe diagram (SLD), surface roughness still remains due to the forced vibration, which limits surface quality, especially in the surface finish. Better cutting parameters can be achieved considering surface location error (SLE) together with SLD. This paper proposes an innovative modeling framework of the machining dynamic system that enables efficient computation of the chatter stability and SLE. The framework mainly embodies two techniques, namely semi‑discretization method (SDM) and lifting method. The machining dynamics system is mathematically expressed as an angle‑varying delay differential equation (DDE). The SDM approximates the angle‑varying and delayed terms to ordinary terms using zero‑phase interpolations and governs the discrete angle‑varying dynamics system. Then, the system is merged over the tooth passing angle using the lifted approach to establish an explicit dynamic system in the compact state‑space form. Based on the compact state‑space model, the chatter stability and SLE prediction are easily and efficiently conducted. Simulation results show the improved efficiency of the proposed method over other well‑known methods.

Authors:Md Nafees Fuad Rafi, Zhaomiao Guo
Title: Multi-agent Optimization of Non-cooperative Multimodal Mobility Systems
Abstract:
While multimodal mobility systems have the potential to bring many benefits to travelers, drivers, the environment, and traffic congestion, such systems typically involve multiple non‑cooperative decision‑makers who may selfishly optimize their own objectives without considering the overall system benefits. This paper aims to investigate market‑based interactions of travelers and ride‑sourcing drivers in the context of multimodal mobility systems. We propose a unified mathematical modeling framework to capture the decentralized travelers and drivers' decision‑making process and balance the network's demand and supply by equilibrium pricing. Such a model allows analyses of the impact of decentralized decision‑making on multimodal mobility efficiencies. The proposed formulation can be further convexified to efficiently compute the equilibrium ride‑sourcing prices. We conduct numerical experiments on different settings of transportation networks to gain policy insights. We find that travelers prefer ride‑sourcing and multimodal transportation more than the driving option when they are more sensitive to prices. We also find that travelers may need to be subsidized to use multimodal transportation when there is fewer transit hubs in the network or, ride‑sourcing drivers become too sensitive to the prices. However, we find that more transit hubs in the network increases the total empty VMT of ride‑sourcing drivers by increasing the total relocation time. The proposed model can be used by policymakers and platform operators to design pricing and subsidy schemes that align individual decision‑making with system‑level efficiency and evaluate the trade‑offs between accessibility and environmental impacts in multimodal transportation networks.

Authors:Shibo Han, Bonan Hou, Chong Jin Ong
Title: Output Consensus on Periodic References for Constrained Multi-agent Systems Under a Switching Network
Abstract:
This work addresses the output consensus problem of constrained heterogeneous multi‑agent systems under a switching network with potential communication delays, where outputs are periodic and characterized by an exosystem. Since periodic references have more complex dynamics, it is more challenging to track periodic references and achieve consensus on them. In this paper, a model predictive control method incorporating an artificial reference and a modified cost function is proposed to track periodic references, which maintains recursive feasibility even when references switch. Moreover, consensus protocols are proposed to achieve consensus on periodic references in different scenarios, in which global information such as the set of globally admissible references and the global time index are not involved. Theoretical analysis proves that constrained output consensus is asymptotically achieved with the proposed algorithm as the references of each agent converge and agents track their references while maintaining constraint satisfaction. Finally, numerical examples are provided to verify the effectiveness of the proposed algorithm.

Authors:Lauritz Zendel, Chiara Springer, Frank Dammel, Peter Stephan
Title: Derivation of the Thermal Conductivity in a Latent Thermal Energy Storage Unit for Use in Simplified System Models
Abstract:
Latent Thermal Energy Storages (LTES) can store thermal energy in a narrow temperature range. Therefore, they are favorable for integration into Rankine‑based Carnot Batteries. For the design of such systems, simulations based on accurate models are desirable. However, physical phenomena such as natural convection in LTES units cannot be modeled directly in transient system models. Simplified models are required. Therefore, the objective of this work is to derive simplified LTES unit models for use in system models. In transient simulations the state of charge of the LTES influences its temperature profile. The temperature profile depends on the geometry of the LTES unit. Therefore, the geometry must be considered to model the transient behavior of an LTES unit. The LTES unit under investigation has a shell and tube heat exchanger structure. The phase change material (PCM) is located between the hexagonal fins and in the space between the finned tubes. Aluminum fins are used. They have a high thermal conductivity and thus compensate for the low thermal conductivity of the sodium nitrate used as PCM. The interaction between fins and PCM is complex. Therefore, a numerical approach can be used to gain insight into the behavior of the LTES unit. To transfer the results of a complex model to a simplified model where fins and PCM are not considered individually, the effective thermal conductivity of a single finned tube can be used to approximate the performance of the LTES unit. In this study, a model of a section with a single finned tube is developed using the COMSOL software. The effective thermal conductivity of the system is determined by varying the effective thermal conductivity in a simplified model and comparing the results with reference cases based on a complex modeling approach. The results can serve as model input for simplified system models of Carnot Batteries, among others.

Authors:Simon Halvdansson, Lucas Ferreira Bernardino, Brage Rugstad Knudsen
Title: Accounting for Optimal Control in the Sizing of Isolated Hybrid Renewable Energy Systems Using Imitation Learning
Abstract:
Decarbonization of isolated or off‑grid energy systems through phase‑in of large shares of intermittent solar or wind generation requires co‑installation of energy storage or continued use of existing fossil dispatchable power sources to balance supply and demand. The effective CO2 emission reduction depends on the relative capacity of the energy storage and renewable sources, the stochasticity of the renewable generation, and the optimal control or dispatch of the isolated energy system. While the operations of the energy storage and dispatchable sources may impact the optimal sizing of the system, it is challenging to account for the effect of finite horizon, optimal control at the stage of system sizing. Here, we present a flexible and computationally efficient sizing framework for energy storage and renewable capacity in isolated energy systems, accounting for uncertainty in the renewable generation and the optimal feedback control. To this end, we implement an imitation learning approach to stochastic neural model predictive control (MPC) which allows us to relate the battery storage and wind peak capacities to the emissions reduction and investment costs while accounting for finite horizon, optimal control. Through this approach, decision makers can evaluate the effective emission reduction and costs of different storage and wind capacities at any price point while accounting for uncertainty in the renewable generation with limited foresight. We evaluate the proposed sizing framework on a case study of an offshore energy system with a gas turbine, a wind farm and a battery energy storage system (BESS). In this case, we find a nonlinear, nontrivial relationship between the investment costs and reduction in gas usage relative to the wind and BESS capacities, emphasizing the complexity and importance of accounting for optimal control in the design of isolated energy systems.

Authors:Jaeyeon Park, Jiyu Lee, Junyeol Maeng, Shenghui Cui
Title: DSP-Based Sub-Switching-Period Current-Limiting Control for Grid-Tied Inverter under Grid Faults
Abstract:
This paper presents a sub‑switching period current‑limiting control for a grid‑tied inverter to prevent transient overcurrents during grid faults and enable seamless fault ride‑through (FRT). Sudden grid‑voltage disturbances, such as voltage sags or phase jumps, can induce large transient currents within a switching period, particularly at low switching frequencies. Upon disturbance detection, the proposed method immediately modifies the pulse‑width modulation carrier, enabling continuous regulation of the inverter output current within a time much shorter than a switching period without interrupting current flow. The proposed method can be implemented on commonly used digital signal processors without requiring specialized analog or digital circuits or high‑speed computing devices. Experimental results from a 2‑level, 3‑phase inverter switching at 3.6 kHz validate the effectiveness of the proposed method under symmetric and asymmetric voltage sags and phase jumps.

Authors:Apoorva Khairnar, Yogesh Phalak, Jun Wang, Ziyang Zhou, Benjamin Jantzen, Suyi Li, Noel Naughton
Title: Spider web-inspired sensing and computation with fiber network physical reservoirs
Abstract:
Physical reservoir computing leverages the intrinsic dynamics of mechanical systems to perform computation through their natural responses to input signals. Here, we study a compliant fiber network inspired by orb‑weaving spider webs and investigate how its mechanical design and operating conditions shape its computational capability. Using Cosserat rod‑based simulations, we identify how network topology, geometry, actuation, and axial tension impact the nonlinear computation and memory capacity of the network. We further evaluate several readout reduction strategies to assess how computational performance varies with the number and placement of measured outputs. We then experimentally validate these results using a physical fiber‑network prototype. Overall, results provide insights and guidance on design, actuation, and sensing choices to enable fiber networks for mechano‑intelligent computation. They demonstrate the ability of structured compliant fibers networks to serve as physical reservoirs capable of nonlinear transformation and input‑history retention.

Authors:Osasumwen Cedric Ogiesoba-Eguakun, Suman Rath
Title: Cyberattack Detection in Virtualized Microgrids Using LightGBM and Knowledge-Distilled Classifiers
Abstract:
Modern microgrids depend on distributed sensing and communication interfaces, making them increasingly vulnerable to cyber physical disturbances that threaten operational continuity and equipment safety. In this work, a complete virtual microgrid was designed and implemented in MATLAB/Simulink, integrating heterogeneous renewable sources and secondary controller layers. A structured cyberattack framework was developed using MGLib to inject adversarial signals directly into the secondary control pathways. Multiple attack classes were emulated, including ramp, sinusoidal, additive, coordinated stealth, and denial of service behaviors. The virtual environment was used to generate labeled datasets under both normal and attack conditions. The datasets trained Light Gradient Boosting Machine (LightGBM) models to perform two functions: detecting the presence of an intrusion (binary) and distinguishing among attack types (multiclass). The multiclass model attained 99.72% accuracy and a 99.62% F1 score, while the binary model attained 94.8% accuracy and a 94.3% F1 score. A knowledge‑distillation step reduced the size of the multiclass model, allowing faster predictions with only a small drop in performance. Real‑time tests showed a processing delay of about 54 to 67 ms per 1000 samples, demonstrating suitability for CPU‑based edge deployment in microgrid controllers. The results confirm that lightweight machine learning based intrusion detection methods can provide fast, accurate, and efficient cyberattack detection without relying on complex deep learning models. Key contributions include: (1) development of a complete MATLAB‑based virtual microgrid, (2) structured attack injection at the control layer, (3) creation of multiclass labeled datasets, and (4) design of low‑cost AI models suitable for practical microgrid cybersecurity.

Authors:Zeyu Dong, Yuke Tian, Yu Sun
Title: Adaptive Model-Based Reinforcement Learning for Orbit Feedback Control in NSLS-II Storage Ring
Abstract:
The National Synchrotron Light Source II (NSLS‑II) uses highly stable electron beam to produce high‑quality X‑ray beams with high brightness and low‑emittance synchrotron radiation. The traditional algorithm to stabilize the beam applies singular value decomposition (SVD) on the orbit response matrix to remove noise and extract actions. Supervised learning has been studied on NSLS‑II storage ring stabilization and other accelerator facilities recently. Several problems, for example, machine status drifting, environment noise, and non‑linear accelerator dynamics, remain unresolved in the SVD‑based and supervised learning algorithms. To address these problems, we propose an adaptive training framework based on model‑based reinforcement learning. This framework consists of two types of optimizations: trajectory optimization attempts to minimize the expected total reward in a differentiable environment, and online model optimization learns non‑linear machine dynamics through the agent‑environment interaction. Through online training, this framework tracks the internal status drifting in the electron beam ring. Simulation and real in‑facility experiments on NSLS‑II reveal that our method stabilizes the beam position and minimizes the alignment error, defined as the root mean square (RMS) error between adjusted beam positions and the reference position, down to ~1μm.

Authors:Rishav Sen, Yunuo Zhang, Fangqi Liu, Jose Paolo Talusan, Ava Pettet, Yoshinori Suzue, Ayan Mukhopadhyay, Abhishek Dubey
Title: Online Decision-Making Under Uncertainty for Vehicle-to-Building Systems
Abstract:
Vehicle‑to‑building (V2B) systems integrate physical infrastructures, such as smart buildings and electric vehicles (EVs) connected to chargers at the building, with digital control mechanisms to manage energy use. By utilizing EVs as flexible energy reservoirs, buildings can dynamically charge and discharge them to optimize energy use and cut costs under time‑variable pricing and demand charge policies. This setup leads to the V2B optimization problem, where buildings coordinate EV charging and discharging to minimize total electricity costs while meeting users' charging requirements. However, the V2B optimization problem is challenging because of: (1) fluctuating electricity pricing, which includes both energy charges (/kWh) and demand charges (/kW); (2) long planning horizons (typically over 30 days); (3) heterogeneous chargers with varying charging rates, controllability, and directionality (i.e., unidirectional or bidirectional); and (4) user‑specific battery levels at departure to ensure user requirements are met. In contrast to existing approaches that often model this setting as a single‑shot combinatorial optimization problem, we highlight critical limitations in prior work and instead model the V2B optimization problem as a Markov decision process (MDP), i.e., a stochastic control process. Solving the resulting MDP is challenging due to the large state and action spaces. To address the challenges of the large state space, we leverage online search, and we counter the action space by using domain‑specific heuristics to prune unpromising actions. We validate our approach in collaboration with Nissan Advanced Technology Center ‑ Silicon Valley. Using data from their EV testbed, we show that the proposed framework significantly outperforms state‑of‑the‑art methods.

Authors:Vincent P. Paglioni, Graeme Troxell, Aaron Brown, Steve Conrad, Mazdak Arabi
Title: Developing a Quantitative Resiliency Approach
Abstract:
Resiliency has garnered attention in the management of critical infrastructure as a metric of system performance, but there are significant roadblocks to its implementation in a realistic decision‑making framework. Contrasted to risk and reliability, which have robust quantification approaches and undergird many regulatory approaches to system safety (e.g., "risk‑informed decision‑making"), resiliency is a diffuse, qualitatively‑understood characteristic, often treated differently or distinctly. However, in the emerging context of highly‑complex, highly‑interdependent critical systems, the idea of reliability (as the probability of non‑failure) may not be an appropriate metric of system health. As a result, focus is shifting towards resiliency‑centered approaches that value the response to failure as much as the avoidance of failure. Supporting this approach requires a robustly‑defined, quantitative understanding of resiliency. In this paper, we explore the foundations of reliability and resiliency engineering, and propose an approach to resiliency‑informed decision‑making bolstered by a quantitative understanding of resiliency.

Authors:Negar Monir, Sadegh Soudjani
Title: Policy Synthesis for Interval MDPs via Polyhedral Lyapunov Functions
Abstract:
Decision‑making under uncertainty is central to many safety‑critical applications, where decisions must be guided by probabilistic modeling formalisms. This paper introduces a novel approach to policy synthesis in multi‑objective interval Markov decision processes using polyhedral Lyapunov functions. Unlike previous Lyapunov‑based methods that mainly rely on quadratic functions, our method utilizes polyhedral functions to enhance accuracy in managing uncertainties within value iteration of dynamic programming. We reformulate the value iteration algorithm as a switched affine system with interval uncertainties and apply control‑theoretic stability principles to synthesize policies that guide the system toward a desired target set. By constructing an invariant set of attraction, we ensure that the synthesized policies provide convergence guarantees while minimizing the impact of transition uncertainty in the underlying model. Our methodology removes the need for computationally intensive Pareto curve computations by directly determining a policy that brings objectives within a specified range of their target values. We validate our approach through numerical case studies, including a recycling robot and an electric vehicle battery, demonstrating its effectiveness in achieving policy synthesis under uncertainty.

Authors:Zuang Wang, Yongqiang Wang
Title: Local Updates in Distributed Optimization: Provable Acceleration and Topology Effects
Abstract:
Inspired by the success of performing multiple local optimization steps between communication rounds in federated learning, incorporating such local updates into distributed optimization has recently attracted growing interest. However, unlike federated learning, where local updates can accelerate training by reducing gradient estimation error under minibatch settings, it remains unclear whether similar benefits persist when exact gradients are available. Moreover, existing theoretical results typically require reducing the step size when multiple local updates are employed, which can entirely offset any potential benefit of these additional local updates. In this paper, we focus on the classic DIGing algorithm and leverage the tight performance bounds provided by Performance Estimation Problems (PEP) to show that incorporating local updates can indeed accelerate distributed optimization. To the best of our knowledge, this is the first rigorous demonstration of such acceleration for a broad class of objective functions. Our analysis further reveals that, under an appropriate step size, performing only two local updates is sufficient to achieve the maximal possible improvement, and that additional local updates provide no further gains. Because more updates increase computational cost, these findings offer practical guidance for efficient implementation. We also show that these speed gains depend critically on the network structure, with sparser or less connected graphs, characterized by the spectral properties of the mixing matrix, yielding smaller improvements. Extensive experiments on both synthetic and real‑world datasets corroborate the theoretical findings.

Authors:Yigal Koifman, Eran Iceland, Erez Koifman, Ariel Barel, Alfred M. Bruckstein
Title: Sensor to Pixels: Decentralized Swarm Gathering via Image-Based Reinforcement Learning
Abstract:
This study highlights the potential of image‑based reinforcement learning methods for addressing swarm‑related tasks. In multi‑agent reinforcement learning, effective policy learning depends on how agents sense, interpret, and process inputs. Traditional approaches often rely on handcrafted feature extraction or raw vector‑based representations, which limit the scalability and efficiency of learned policies concerning input order and size. In this work we propose an image‑based reinforcement learning method for decentralized control of a multi‑agent system, where observations are encoded as structured visual inputs that can be processed by Neural Networks, extracting its spatial features and producing novel decentralized motion control rules. We evaluate our approach on a multi‑agent convergence task of agents with limited‑range and bearing‑only sensing that aim to keep the swarm cohesive during the aggregation. The algorithm's performance is evaluated against two benchmarks: an analytical solution proposed by Bellaiche and Bruckstein, which ensures convergence but progresses slowly, and VariAntNet, a neural network‑based framework that converges much faster but shows medium success rates in hard constellations. Our method achieves high convergence, with a pace nearly matching that of VariAntNet. In some scenarios, it serves as the only practical alternative.

Authors:Zongyang Lv, Yanmei Jia, Yongqing Liu, Alan F. Lynch, Qing Zhao, Yuhu Wu
Title: Modeling and Control for UAV with Off-center Slung Load
Abstract:
Unmanned aerial vehicle (UAV) with slung load system is a classic air transportation system. In practical applications, the suspension point of the slung load does not always align with the center of mass (CoM) of the UAV due to mission requirements or mechanical interference. This offset creates coupling in the system's nonlinear dynamics which leads to a complicated motion control problem. In existing research, modeling of the system are performed about the UAV's CoM. In this work we use the point of suspension instead. Based on the new model, a cascade control strategy is developed. In the middle‑loop controller, the acceleration of the suspension point is used to regulate the swing angle of the slung load without the need for considering the coupling between the slung load and the UAV. An inner‑loop controller is designed to track the UAV's attitude without the need of simplification on the coupling effects. We prove local exponential stability of the closed‑loop using Lyapunov approach. Finally, simulations and experiments are conducted to validate the proposed control system.

Authors:Timothy Barfoot, Cedric Le Gentil, Sven Lilge
Title: Revisiting Continuous-Time Trajectory Estimation via Gaussian Processes and the Magnus Expansion
Abstract:
Continuous‑time state estimation has been shown to be an effective means of (i) handling asynchronous and high‑rate measurements, (ii) introducing smoothness to the estimate, (iii) post hoc querying the estimate at times other than those of the measurements, and (iv) addressing certain observability issues related to scanning‑while‑moving sensors. A popular means of representing the trajectory in continuous time is via a Gaussian process (GP) prior, with the prior's mean and covariance functions generated by a linear time‑varying (LTV) stochastic differential equation (SDE) driven by white noise. When the state comprises elements of Lie groups, previous works have resorted to a patchwork of local GPs each with a linear time‑invariant SDE kernel, which while effective in practice, lacks theoretical elegance. Here we revisit the full LTV GP approach to continuous‑time trajectory estimation, deriving a global GP prior on Lie groups via the Magnus expansion, which offers a more elegant and general solution. We provide a numerical comparison between the two approaches and discuss their relative merits.

Authors:Boshuai Zhao, Adam Abdin, Jakob Puchinger
Title: New Formulations and Discretization Insights for the Electric Autonomous Dial-a-Ride Problem
Abstract:
The Electric Autonomous Dial‑a‑Ride Problem (E‑ADARP) involves routing and scheduling electric autonomous vehicles under battery capacity and partial recharging constraints, aiming to minimize total travel cost and excess ride time. In practice, operational data for time and state‑of‑charge (SoC) are often available only at a coarse granularity. This raises a natural question: can discretization be exploited to improve computational performance by enabling alternative formulation structures? To investigate this question, we develop three formulations reflecting different levels of discretization. The first is an improved event‑based formulation (IEBF) with arc‑flow SoC variables for the continuous‑parameter E‑ADARP, serving as a strengthened baseline. The latter two are fragment‑based formulations designed for discretized inputs. The second is a time‑space fragment‑based formulation with continuous SoC arc‑flow variables (TSFFCS), which discretizes time while keeping SoC continuous. The third is a battery‑time‑space fragment‑based formulation (BTSFF), which discretizes both time and SoC. Here, an event denotes a tuple consisting of a location and a set of onboard customers, while a fragment denotes a partial path. Computational results show that IEBF improves upon the existing event‑based formulation for the original E‑ADARP. Under discretized settings, TSFFCS tends to outperform IEBF, particularly when recharging is frequent and time discretization is relatively coarse, indicating that time discretization can improve computational performance across a wide range of settings. In contrast, BTSFF rarely outperforms TSFFCS unless the number of reachable SoC levels is limited, suggesting that explicit SoC discretization is beneficial only in relatively restricted settings.

Authors:Leonardo Bettini, Amirhossein Kazemipour, Robert K. Katzschmann, George Haller
Title: Nonlinear Spectral Modeling and Control of Soft-Robotic Muscles from Data
Abstract:
Artificial muscles are essential for compliant musculoskeletal robotics but complicate control due to nonlinear multiphysics dynamics. Hydraulically amplified electrostatic (HASEL) actuators, a class of soft artificial muscles, offer high performance but exhibit memory effects and hysteresis. Here we present a data‑driven reduction and control strategy grounded in spectral submanifold (SSM) theory. In the adiabatic regime, where inputs vary slowly relative to intrinsic transients, trajectories rapidly converge to a low‑dimensional slow manifold. We learn an explicit input‑to‑output map on this manifold from forced‑response trajectories alone, avoiding decay experiments that can trigger hysteresis. We deploy the SSM‑based model for real‑time control of an antagonistic HASEL‑clutch joint. This approach yields a substantial reduction in tracking error compared to feedback‑only and feedforward‑only baselines under identical settings. This record‑and‑control workflow enables rapid characterization and high‑performance control of soft muscles and muscle‑driven joints without detailed physics‑based modeling.

Authors:Amy Hodgkin, Nick Pepper, Marc Thomas
Title: Conditioning Aircraft Trajectory Prediction on Meteorological Data with a Physics-Informed Machine Learning Approach
Abstract:
Accurate aircraft trajectory prediction (TP) in air traffic management systems is confounded by a number of epistemic uncertainties, dominated by uncertain meteorological conditions and operator specific procedures. Handling this uncertainty necessitates the use of probabilistic, machine learned models for generating trajectories. However, the trustworthiness of such models is limited if generated trajectories are not physically plausible. For this reason we propose a physics‑informed approach in which aircraft thrust and airspeed are learned from data and are used to condition the existing Base of Aircraft Data (BADA) model, which is physics‑based and enforces energy‑based constraints on generated trajectories. A set of informative features are identified and used to condition a probabilistic model of aircraft thrust and airspeed, with the proposed scheme demonstrating a 20% improvement in skilfulness across a set of six metrics, compared against a baseline probabilistic model that ignores contextual information such as meteorological conditions.

Authors:Yuta Takahashi, Hiraku Sakamoto, Shin-ichiro Sakai
Title: Time-Varying Kinematics Control for Magnetically-Actuated Satellite Swarm without Additional Actuator
Abstract:
Electromagnetic Formation Flight is a technology that uses electromagnetic forces and torques to control multiple satellites without conventional fuel‑based propulsion. In this paper, the controllability of the system is discussed based on the conservation of the entire system's angular momentum, which constitutes a nonholonomic constraint. This paper designs a new controller for multiple satellites without an additional attitude actuator.

Authors:Mintae Kim
Title: Finite Memory Belief Approximation for Optimal Control in Partially Observable Markov Decision Processes
Abstract:
We study finite memory belief approximation for partially observable (PO) stochastic optimal control (SOC) problems. While belief states are sufficient for SOC in partially observable Markov decision processes (POMDPs), they are generally infinite‑dimensional and impractical. We interpret truncated input‑output (IO) histories as inducing a belief approximation and develop a metric‑based theory that directly relates information loss to control performance. Using the Wasserstein metric, we derive policy‑conditional performance bounds that quantify value degradation induced by finite memory along typical closed‑loop trajectories. Our analysis proceeds via a fixed‑policy comparison: we evaluate two cost functionals under the same closed‑loop execution and isolate the effect of replacing the true belief by its finite memory approximation inside the belief‑level cost. For linear quadratic Gaussian (LQG) systems, we provide closed‑form belief mismatch evaluation and empirically validate the predicted mechanism, demonstrating that belief mismatch decays approximately exponentially with memory length and that the induced performance mismatch scales accordingly. Together, these results provide a metric‑aware characterization of what finite memory belief approximation can and cannot achieve in PO settings.

Authors:Omayra Yago Nieto, Alexandre Anahory Simoes, Juan I. Giribet, Leonardo Colombo
Title: Dual-quaternion learning control for autonomous vehicle trajectory tracking with safety guarantees
Abstract:
We propose a learning‑based trajectory tracking controller for autonomous robotic platforms whose motion can be described kinematically on \mathrmSE(3). The controller is formulated in the dual quaternion framework and operates at the velocity level, assuming direct command of angular and linear velocities, as is standard in many aerial vehicles and omnidirectional mobile robots. Gaussian Process (GP) regression is integrated into a geometric feedback law to learn and compensate online for unknown, state‑dependent disturbances and modeling imperfections affecting both attitude and position, while preserving the algebraic structure and coupling properties inherent to rigid‑body motion. The proposed approach does not rely on explicit parametric models of the unknown effects, making it well‑suited for robotic systems subject to sensor‑induced disturbances, unmodeled actuation couplings, and environmental uncertainties. A Lyapunov‑based analysis establishes probabilistic ultimate boundedness of the pose tracking error under bounded GP uncertainty, providing formal stability guarantees for the learning‑based controller. Simulation results demonstrate accurate and smooth trajectory tracking in the presence of realistic, localized disturbances, including correlated rotational and translational effects arising from magnetometer perturbations. These results illustrate the potential of combining geometric modeling and probabilistic learning to achieve robust, data‑efficient pose control for autonomous robotic systems.

Authors:Jingbo Qu, Yijie Wang, Yujie Fu, Putai Zhang, Weihan Li, Mian Li
Title: From inconsistency to decision: explainable operation and maintenance of battery energy storage systems
Abstract:
Battery Energy Storage Systems (BESSs) are increasingly critical to power‑system stability, yet their operation and maintenance remain dominated by reactive, expert‑dependent diagnostics. While cell‑level inconsistencies provide early warning signals of degradation and safety risks, the lack of scalable and interpretable decision‑support frameworks prevents these signals from being effectively translated into operational actions. Here we introduce an inconsistency‑driven operation and maintenance paradigm for large‑scale BESSs that systematically transforms routine monitoring data into explainable, decision‑oriented guidance. The proposed framework integrates multi‑dimensional inconsistency evaluation with large language model‑based semantic reasoning to bridge the gap between quantitative diagnostics and practical maintenance decisions. Using eight months of field data from an in‑service battery system comprising 3,564 cells, we demonstrate how electrical, thermal, and aging‑related inconsistencies can be distilled into structured operational records and converted into actionable maintenance insights through a multi‑agent framework. The proposed approach enables accurate and explainable responses to real‑world operation and maintenance queries, reducing response time and operational cost by over 80% compared with conventional expert‑driven practices. These results establish a scalable pathway for intelligent operation and maintenance of battery energy storage systems, with direct implications for reliability, safety, and cost‑effective integration of energy storage into modern power systems.

Authors:Ziyao Zhou, Hen-Wei Huang
Title: Closed-Loop Transmission Power Control for Reliable and Low-Power BLE Communication in Dynamic IoT Settings
Abstract:
Reliable and energy‑efficient Bluetooth Low Energy (BLE) communication is crucial for Internet of Things (IoT) applications in dynamic environments. However, the Received Signal Strength Indicator (RSSI) and data throughput in BLE are highly susceptible to environmental variability, which degrades communication performance. In this work, we systematically analyze the interdependence among RSSI, throughput, transmission power (TXP), and the peripheral device system power consumption under diverse real‑world conditions. We observe that adjusting the TXP effectively influences both RSSI and throughput. We propose a robust closed‑loop TXP control framework based on Proportional‑Integral‑Derivative (PID) controllers. Two initial control strategies are investigated: an RSSI‑based approach and a throughput‑based approach, each exhibiting distinct advantages and limitations. The RSSI‑based method provides rapid responsiveness to signal fluctuations but lacks direct correlation with data throughput, whereas the throughput‑based method offers more accurate feedback on effective throughput at the cost of slower response. To address these limitations, a hybrid RSSI‑throughput control strategy is developed, combining the responsiveness of RSSI feedback with the accuracy of throughput measurements. Experimental results demonstrate that the proposed hybrid approach maintains data throughput close to the target level with minimal variance, even under rapidly changing environmental conditions.

Authors:Mingxuan Li, Wei Wei, Yin Xu, Chengeng Zhang, Shanshan Shi
Title: Post-Earthquake Restoration of Electricity-Gas Distribution Systems with Damage Information Collection and Repair Vehicle Routing
Abstract:
Extreme events such as earthquakes pose significant threats to integrated electricity‑gas distribution systems (IEGDS) by causing widespread damage. Existing restoration approaches typically assume full awareness of damage, which may not be true if monitoring and communication infrastructures are impaired. In such circumstances, field inspection is necessary. This paper presents a novel adaptive restoration framework for IEGDS, considering dynamic damage assessment and repair. The restoration problem is formulated as a partially observable Markov decision process (POMDP), capturing the gradually revealed contingency and the evolving impact of field crew actions. To address the computational challenges of POMDPs in real‑time applications, an advanced belief tree search (BTS) algorithm is introduced. This algorithm enables crew members to continuously update their actions based on evolving belief states, leveraging comprehensive simulations to evaluate potential future trajectories and identify optimal inspection and repair strategies. Based on the BTS algorithm, a unified real‑time decision‑making framework is developed for IEGDS restoration. Case studies on two distinct IEGDS systems demonstrate the effectiveness and scalability of the proposed method. The results indicate that the proposed approach achieves an outage cost comparable to the ideal solution, and reduces the total outage cost by more than 15% compared to strategies based on stochastic programming and heuristic methods.

Authors:Zhuangzhuang Cui, Rudranil Chattopadhyay, Emiel Vanspranghels, Sofie Pollin
Title: Site-Specific and Frequency-Dependent Channel Characterization and MIMO Performance in FR3
Abstract:
Next‑generation wireless systems aim to enable on‑demand connectivity through dynamic spectrum utilization. Motivated by this vision, this paper investigates the propagation characteristics and MIMO performance of the upper mid‑band, spanning approximately 7‑24 GHz and unofficially referred to as FR3. Using site‑specific ray‑tracing (RT) simulations based on the Sionna framework, we analyze indoor and outdoor environments at representative frequencies across FR1, FR3, and FR2, including 3.5, 7, 10, 14, 20, 24, and 28 GHz, under both single‑antenna and multi‑antenna configurations. The results show that FR3 exhibits intermediate propagation behavior between sub‑6 GHz and millimeter‑wave bands while sustaining effective spatial multiplexing and favorable spectral efficiency. Furthermore, large‑array analysis indicates that performance gains in FR3 are closely tied to antenna scaling, highlighting the importance of large‑size or large‑aperture MIMO architectures for practical deployments.

Authors:Yoshiyuki Ohmura, Earnest Kota Carr, Yasuo Kuniyoshi
Title: A Mathematical Formalization of Self-Determining Agency
Abstract:
Defining agency is an extremely important challenge for cognitive science and artificial intelligence. Physics generally describes mechanical happenings, but there remains an unbridgeable gap between these and the acts of agents. To discuss the morality and responsibility of agents, it is necessary to model acts; whether such responsible acts can be fully explained by physical determinism remains an ongoing debate. Although we have already proposed a physical agent determinism model that appears to go beyond mere mechanical happenings, we have not yet established a strict mathematical formalism to eliminate ambiguity. Here, we explain why a physical system can follow coarse‑graining agent‑level determination without violating physical laws by formulating supervenient causation. Generally, supervenience including coarse graining does not change without a change in its lower base; therefore, a single supervenience alone cannot define supervenient causation. We define supervenient causation as the causal efficacy from the supervenience level to its lower base level. Although an algebraic expression composed of the multiple supervenient functions does supervenes on the base, an index sequence that determines the algebraic expression does not supervene on the base. Therefore, the sequence can possess unique dynamical laws that are independent of the lower base level. This independent dynamics creates the possibility for temporally preceding changes at the supervenience level to cause changes at the lower base level. Such a dual‑laws system is considered useful for modeling self‑determining agents such as humans.

Authors:Xunqiang Lan, Xiao Tang, Ruonan Zhang, Bin Li, Qinghe Du, Dusit Niyato, Zhu Han
Title: Distributionally Robust Game for Proof-of-Work Blockchain Mining Under Resource Uncertainties
Abstract:
Blockchain plays a crucial role in ensuring the security and integrity of decentralized systems, with the proof‑of‑work (PoW) mechanism being fundamental for achieving distributed consensus. As PoW blockchains see broader adoption, an increasingly diverse set of miners with varying computing capabilities participate in the network. In this paper, we consider the PoW blockchain mining, where the miners are associated with resource uncertainties. To characterize the uncertainty computing resources at different mining participants, we establish an ambiguous set representing uncertainty of resource distributions. Then, the networked mining is formulated as a non‑cooperative game, where distributionally robust performance is calculated for each individual miner to tackle the resource uncertainties. We prove the existence of the equilibrium of the distributionally robust mining game. To derive the equilibrium, we propose the conditional value‑at‑risk (CVaR)‑based reinterpretation of the best response of each miner. We then solve the individual strategy with alternating optimization, which facilitates the iteration among miners towards the game equilibrium. Furthermore, we consider the case that the ambiguity of resource distribution reduces to Gaussian distribution and the case that another uncertainties vanish, and then characterize the properties of the equilibrium therein along with a distributed algorithm to achieve the equilibrium. Simulation results show that the proposed approaches effectively converge to the equilibrium, and effectively tackle the uncertainties in blockchain mining to achieve a robust performance guarantee.

Authors:Ting Peng
Title: Hierarchical Preemptive Holistic Collaborative Systems for Embodied Multi-Agent Systems: Framework, Hybrid Stability, and Scalability Analysis
Abstract:
The coordination of Embodied Multi‑Agent Systems in constrained physical environments requires a rigorous balance between safety, scalability, and efficiency. Traditional decentralized approaches, e.g., reactive collision avoidance, are prone to local minima or reciprocal yielding standoffs due to the lack of future intent awareness. In contrast, centralized planning suffers from intractable computational complexity and single‑point‑of‑failure vulnerabilities. To address these limitations, we propose the Hierarchical Preemptive Holistic Collaborative (Prollect) framework, which generalizes the Preemptive Holistic Collaborative System (PHCS) by decomposing the global coordination problem into topologically connected subspace optimizations. We formalize the system as a Hybrid Automaton and introduce a three‑stage receding horizon mechanism (frozen execution, preliminary planning, proactive look‑ahead windows) with explicit padding to prevent races between coordination dissemination and intent updates. Notably, we design a robust timing protocol with a mandatory Idle Buffer that acts as a dwell‑time constraint to eliminate Zeno behaviors and ensure computational stability under jitter. Furthermore, we formalize a Shadow Agent protocol to guarantee seamless trajectory consistency across subspace boundaries, which we treat as an Input‑to‑State Stability (ISS) problem.

Authors:Zihan Yang, Jindou Jia, Meng Wang, Yuhang Liu, Kexin Guo, Xiang Yu
Title: Unified Meta-Representation and Feedback Calibration for General Disturbance Estimation
Abstract:
Precise control in modern robotic applications is always an open issue due to unknown time‑varying disturbances. Existing meta‑learning‑based approaches require a shared representation of environmental structures, which lack flexibility for realistic non‑structural disturbances. Besides, representation error and the distribution shifts can lead to heavy degradation in prediction accuracy. This work presents a generalizable disturbance estimation framework that builds on meta‑learning and feedback‑calibrated online adaptation. By extracting features from a finite time window of past observations, a unified representation that effectively captures general non‑structural disturbances can be learned without predefined structural assumptions. The online adaptation process is subsequently calibrated by a state‑feedback mechanism to attenuate the learning residual originating from the representation and generalizability limitations. Theoretical analysis shows that simultaneous convergence of both the online learning error and the disturbance estimation error can be achieved. Through the unified meta‑representation, our framework effectively estimates multiple rapidly changing disturbances, as demonstrated by quadrotor flight experiments. See the project page for video, supplementary material and code: https://nonstructural‑metalearn.github.io.

Authors:Kexin Guo, Zihan Yang, Yuhang Liu, Jindou Jia, Xiang Yu
Title: Optimizing Control-Friendly Trajectories with Self-Supervised Residual Learning
Abstract:
Real‑world physics can only be analytically modeled with a certain level of precision for modern intricate robotic systems. As a result, tracking aggressive trajectories accurately could be challenging due to the existence of residual physics during controller synthesis. This paper presents a self‑supervised residual learning and trajectory optimization framework to address the aforementioned challenges. At first, unknown dynamic effects on the closed‑loop model are learned and treated as residuals of the nominal dynamics, jointly forming a hybrid model. We show that learning with analytic gradients can be achieved using only trajectory‑level data while enjoying accurate long‑horizon prediction with an arbitrary integration step size. Subsequently, a trajectory optimizer is developed to compute the optimal reference trajectory with the residual physics along it minimized. It ends up with trajectories that are friendly to the following control level. The agile flight of quadrotors illustrates that by utilizing the hybrid dynamics, the proposed optimizer outputs aggressive motions that can be precisely tracked.

Authors:Xinyi Liu, Xuan He, Yize Chen
Title: Scaling Laws of Machine Learning for Optimal Power Flow
Abstract:
Optimal power flow (OPF) is one of the fundamental tasks for power system operations. While machine learning (ML) approaches such as deep neural networks (DNNs) have been widely studied to enhance OPF solution speed and performance, their practical deployment faces two critical scaling questions: What is the minimum training data volume required for reliable results? How should ML models' complexity balance accuracy with real‑time computational limits? Existing studies evaluate discrete scenarios without quantifying these scaling relationships, leading to trial‑and‑error‑based ML development in real‑world applications. This work presents the first systematic scaling study for ML‑based OPF across two dimensions: data scale (0.1K‑40K training samples) and compute scale (multiple NN architectures with varying FLOPs). Our results reveal consistent power‑law relationships on both DNNs and physics‑informed NNs (PINNs) between each resource dimension and three core performance metrics: prediction error (MAE), constraint violations and speed. We find that for ACOPF, the accuracy metric scales with dataset size and training compute. These scaling laws enable predictable and principled ML pipeline design for OPF. We further identify the divergence between prediction accuracy and constraint feasibility and characterize the compute‑optimal frontier. This work provides quantitative guidance for ML‑OPF design and deployments.

Authors:Anh Le, Phat K. Huynh, Om P. Yadav, Harun Pirim, Chau Le, Trung Q. Le
Title: Topology-Aware Spatio-Temporal Graph Transformer for Predicting Smart Grid Failures
Abstract:
Smart grid infrastructure needs improved resilience and preventive maintenance through more accurate predictions. Current methodologies lack accurate representation of spatio‑temporal‑causal interdependencies and class imbalance in failure prediction tasks. This study introduces a Topology‑Aware Spatio‑Temporal Graph Transformer (ST‑GT) architecture that overcomes existing limitations by using three main innovations: (1) directly incorporating physical transmission network topology into the transformer attention mechanism to identify spatial failure propagation patterns; (2) unified processing of static topological descriptors and (3) temporal Phasor Measurement Units (PMU) sequences in an end‑to‑end framework. The ST‑GT model exhibited outstanding performance in five‑fold cross‑validation across 10 substations, attaining perfect recall (1.000 \pm 0.001) and an F1‑score of 0.858 \pm 0.009, markedly surpassing XGBoost baselines (0.683 accuracy/F1). Perfect recall guarantees that no critical failures are overlooked, which is essential for grid safety; however, it may lead to an increase in false alarm rates. This framework integrates temporal dynamics modeling with spatial graph awareness for critical infrastructure monitoring. It offers interpretable insights into failure propagation pathways and enhances maintenance strategies. Future research focuses on developing cost‑weighted loss functions for precision‑recall trade‑off enhancement, implementing real‑time monitoring systems with uncertainty quantification, and creating cost‑sensitive frameworks balancing false alarm expenditures with failure consequences. The methodology success suggests its potential for wider application in critical infrastructure areas requiring spatial temporal failure prediction.

Authors:Elisio Juvenal Muchave, Pedro Henrique Silva Coutinho, Tiago Roux Oliveira, Miroslav Krstić
Title: Extremum Seeking Control for Wave-PDE Actuation with Distributed Effects
Abstract:
This paper deals with the gradient‑based extremum seeking control (ESC) with actuation dynamics governed by distributed wave partial differential equations (PDEs). To achieve the control objective of real‑time optimization for this class of infinite‑dimensional systems, we first solve the trajectory generation problem to re‑design the additive perturbation signal of the ESC system. Then, we develop a boundary control law through the backstepping method to compensate for the wave PDE with distributed effects, which ensures the exponential stability of the average closed‑loop system by means of a Lyapunov‑based analysis. At last, by employing the averaging theory for infinite‑dimensional systems, we prove that the closed‑loop trajectories converge to a small neighborhood surrounding the optimal point. Numerical simulations are presented to illustrate the effectiveness of the proposed method.

Authors:Otman Basir
Title: AI Social Responsibility as Reachability: Execution-Level Semantics for the Social Responsibility Stack
Abstract:
Artificial intelligence systems are increasingly embedded as persistent, closed‑loop components within cyber‑physical, social, and institutional processes. Rather than producing isolated outputs, such systems operate continuously under feedback, adaptation, and scale, reshaping physical flows, human behavior, and institutional practice over time. In these settings, socially unacceptable outcomes rarely arise from singular faults or explicit policy violations. Instead, they emerge through cumulative execution trajectories enabled by repetition, concurrency, and feedback. This paper advances the formal foundation of the Social Responsibility Stack (SRS) by making its central requirement explicit: responsibility is fundamentally a reachability property of system execution. A system is responsible iff its execution semantics prevent entry into inadmissible global configurations, regardless of local performance gains or optimization objectives. Responsibility failures are therefore not objective‑level errors, but execution‑level failures of trajectory control. To operationalize this perspective, we introduce Petri nets as an execution‑level formalism for responsible autonomous systems. We show how SRS value commitments correspond to forbidden markings, safeguards to structural constraints on transition firing, auditing to monitoring of reachability pressure, and governance to legitimate modification of execution structure. Embedding Petri‑net reachability within the SRS architecture internalizes responsibility as a structural invariant rather than an external objective or post‑hoc mechanism. These results establish the Social Responsibility Stack as an executable responsibility architecture and position reachability‑based execution semantics as a necessary foundation for responsible autonomy in feedback‑rich cyber‑physical and socio‑technical systems.

Authors:Azuka Chiejina, Divyadharshini Muruganandham, Vini Chaudhary, Kaushik Chowdhury, Vijay K. Shah
Title: O-DSS: An Open Dynamic Spectrum Sharing Framework for Cellular-Radar Coexistence in Mid-band Frequencies
Abstract:
The growing demand for mid‑band spectrum necessitates efficient Dynamic Spectrum Sharing (DSS) to ensure coexistence between cellular networks and incumbent radar systems. Existing Spectrum Access System (SAS) frameworks rely on fixed Environmental Sensing Capability (ESC) sensors, which are latency‑prone and inflexible. This paper introduces O‑DSS, an O‑RAN‑compliant, Machine Learning (ML)‑driven DSS framework that enables real‑time cellular‑radar coexistence in mid‑band frequencies with shipborne and fast‑moving airborne radars. O‑DSS integrates radar detection from low‑overhead Key Performance Metrics (KPMs) with spectrogram‑based localization to drive fine‑grained RAN control, including PRB blanking and radar‑aware MCS adaptation. Deployed as a modular xApp, O‑DSS achieves 60~ms detection and 700~ms evacuation latencies, outperforming existing baselines. Evaluations across simulations and Over‑The‑Air (OTA) testbeds show that O‑DSS ensures robust incumbent protection while maintaining cellular performance by achieving radar detection of \geq 99% at SINR \geq ‑4~dB and localization recall of \geq 95% at SINR \geq 8~dB.

Authors:Emre Sariyildiz
Title: AMC26: High-performance DOb for robust position control
Abstract:
This paper presents a new HPDOb that significantly improves disturbance estimation accuracy and robustness in motion control systems, surpassing the capabilities of conventional DObs. The proposed observer is analysed and synthesised in the discrete‑time domain, providing a realistic representation of their dynamic behaviour and enabling enhanced controller design for practical applications. The core contribution of the HPDOb is a novel synthesis method that incorporates higher‑order truncation error dynamics into disturbance estimation. Unlike conventional DObs, which are limited to zero‑order truncation error, the HPDOb achieves first‑order truncation error, yielding markedly improved estimation accuracy and robustness against disturbances in motion control systems. Simulation and experiments verify the stability and performance of HPDOb.

Authors:Emre Sariyildiz
Title: AMC26: VSSEA robust position control
Abstract:
This paper presents robust position control strategies for the novel VSSEA. By employing a constructed state‑space model, two control schemes are developed in a unified framework: a state‑feedback controller and a sliding mode controller, both integrated with a second‑order DOb. The proposed framework achieves high‑performance motion control by precisely estimating and compensating for internal and external disturbances, while preserving the nominal dynamic response. Simulation results demonstrate that pole‑placement‑based controllers are highly sensitive to disturbances, whereas LQR‑based controllers offer improved robustness at the expense of slower dynamics. By incorporating DOb, robustness is significantly enhanced without degrading time response, and the LQR controller can be tuned solely for performance optimization. Experimental results confirm that the proposed robust position controllers can be implemented in real world applications. These results highlight the effectiveness of the proposed approach and lay the foundation for future investigations on robust stability and performance under different stiffness settings.

Authors:Shijin Chen, Zeyi Liu, Chenyang Li, Dongliang Zou, Xiao He, Donghua Zhou
Title: Multi-mode Fault Diagnosis Datasets of Three-phase Asynchronous Motor Under Variable Working Conditions
Abstract:
Three‑phase asynchronous motor are fundamental components in industrial systems, and their failure can lead to significant operational downtime and economic losses. Vibration and current signals are effective indicators for monitoring motor health and diagnosing faults. However, motors in real applications often operate under variable conditions such as fluctuating speeds and loads, which complicate the fault diagnosis process. This paper presents a comprehensive dataset collected from a three‑phase asynchronous motor under various fault types and severities, operating under diverse speed and load conditions. The dataset includes both single faults and mechanical‑electrical compound faults, such as rotor unbalance, stator winding short circuits, bearing faults, and their combinations. Data were acquired under both steady and transitional conditions, with signals including triaxial vibration, three‑phase currents, torque, and key‑phase signals. This dataset supports the development and validation of robust fault diagnosis methods for electric motors under realistic operating conditions.

Authors:Shrenik Jadhav, Zheng Liu
Title: Machine Learning Guided Cooling System Optimization for Data Center
Abstract:
Effective data center cooling is crucial for reliable operation; however, cooling systems often exhibit inefficiencies that result in excessive energy consumption. This paper presents a three‑stage, physics‑guided machine learning framework for identifying and reducing cooling energy waste in high‑performance computing facilities. Using one year of 10‑minute resolution operational data from the Frontier exascale supercomputer, we first train a monotonicity‑constrained gradient boosting surrogate that predicts facility accessory power from coolant flow rates, temperatures, and server power. The surrogate achieves a mean absolute error of 0.026 MW and predicts power usage effectiveness within 0.01 of measured values for 98.7% of test samples. In the second stage, the surrogate serves as a physics‑consistent baseline to quantify excess cooling energy, revealing approximately 85 MWh of annual inefficiency concentrated in specific months, hours, and operating regimes. The third stage evaluates guardrail‑constrained counterfactual adjustments to supply temperature and subloop flows, demonstrating that up to 96% of identified excess can be recovered through small, safe setpoint changes while respecting thermal limits and operational constraints. The framework yields interpretable recommendations, supports counterfactual analyses such as flow reduction during low‑load periods and redistribution of thermal duty across cooling loops, and provides a practical pathway toward quantifiable reductions in accessory power. The developed framework is readily compatible with model predictive control and provides a template that, with site‑specific recalibration, could be adapted to other liquid‑cooled data centers with different configurations and cooling requirements.

Authors:Luca Furieri
Title: Characterizing all locally exponentially stabilizing controllers as a linear feedback plus learnable nonlinear Youla dynamics
Abstract:
We derive a state‑space characterization of all dynamic state‑feedback controllers that make an equilibrium of a nonlinear input‑affine continuous‑time system locally exponentially stable. Specifically, any controller obtained as the sum of a linear state‑feedback u=Kx, with K stabilizing the linearized system, and the output of internal locally exponentially stable controller dynamics is itself locally exponentially stabilizing. Conversely, every dynamic state‑feedback controller that locally exponentially stabilizes the equilibrium admits such a decomposition. The result can be viewed as a state‑space nonlinear Youla‑type parametrization specialized to local, rather than global, and exponential, rather than asymptotic, closed‑loop stability. The residual locally exponentially stable controller dynamics can be implemented with stable recurrent neural networks and trained as neural ODEs to achieve high closed‑loop performance in nonlinear control tasks.

Authors:Ahmed S. Alahmed, Audun Botterud, Saurabh Amin, Ali T. Al-Awami
Title: Optimal Scheduling of Electricity and Water in Renewable-Colocated Desalination Plants
Abstract:
We develop a mathematical framework for the optimal scheduling of flexible water desalination plants (WDPs) as hybrid generator‑load resources. WDPs integrate thermal generation, membrane‑based controllable loads, and renewable energy sources, offering unique operational flexibility for power system operations. They can simultaneously participate in two markets: selling desalinated water to a water utility, and bidirectionally transacting electricity with the grid based on their net electricity demand. We formulate the scheduling decision problem of a profit‑maximizing WDP, capturing operational, technological, and market‑based coupling between water and electricity flows. The threshold‑based structure we derive provides computationally tractable coordination suitable for large‑scale deployment, offering operational insights into how thermal generation and membrane‑based loads complementarily provide continuous bidirectional flexibility. The thresholds are analytically characterized in closed form as explicit functions of technology and tariff parameters. We examine how small changes in the exogenous tariff and technology parameters affect the WDP's profit. Extensive simulations illustrate the optimal WDP's operation, profit, and water‑electricity exchange, demonstrating significant improvements relative to benchmark algorithms.

Authors:Hossein Rastgoftar
Title: Finite-State Decentralized Policy-Based Control With Guaranteed Ground Coverage
Abstract:
We propose a finite‑state, decentralized decision and control framework for multi‑agent ground coverage. The approach decomposes the problem into two coupled components: (i) the structural design of a deep neural network (DNN) induced by the reference configuration of the agents, and (ii) policy‑based decentralized coverage control. Agents are classified as anchors and followers, yielding a generic and scalable communication architecture in which each follower interacts with exactly three in‑neighbors from the preceding layer, forming an enclosing triangular communication structure. The DNN training weights implicitly encode the spatial configuration of the agent team, thereby providing a geometric representation of the environmental target set. Within this architecture, we formulate a computationally efficient decentralized Markov decision process (MDP) whose components are time‑invariant except for a time‑varying cost function defined by the deviation from the centroid of the target set contained within each agent communication triangle. By introducing the concept of Anyway Output Controllability (AOC), we assume each agent is AOC and establish decentralized convergence to a desired configuration that optimally represents the environmental target.

Authors:Leandro Lanzieri, Jiri Kral, Goerschwin Fey, Holger Schlarb, Thomas C. Schmidt
Title: Ageing Monitoring for Commercial Microcontrollers Based on Timing Windows
Abstract:
Microcontrollers are increasingly present in embedded deployments and dependable systems, for which malfunctions due to hardware ageing can have severe impact. The lack of deployable techniques for ageing monitoring on these devices has spread the application of guard bands to prevent timing errors due to degradation. Applying this static technique can limit performance and lead to sudden failures as devices age. In this paper, we follow a software‑based self‑testing approach to design monitoring of hardware degradation for microcontrollers. Deployable in the field, our technique leverages timing windows of variable lengths to determine the maximum operational frequency of the devices. We empirically validate the method on real hardware and find that it consistently detects temperature‑induced degradations in maximum operating frequency of up to 13.79 % across devices for 60 °C temperature increase.

Authors:Mihails Milehins, Dan B. Marghitu
Title: Asymptotic Behavior of an Unforced Duhem-Type Hysteretic Oscillator
Abstract:
The article describes fundamental analytical properties of an unforced mechanical oscillator with a Duhem‑type viscoelastoplastic hysteretic element. These properties include global existence of solutions, uniqueness of solutions, and convergence of each solution to an equilibrium point.

Authors:Riccardo Zuliani, Efe C. Balta, John Lygeros
Title: Policy Optimization with Differentiable MPC: Convergence Analysis under Uncertainty
Abstract:
Model‑based policy optimization is a well‑established framework for designing reliable and high‑performance controllers across a wide range of control applications. Recently, this approach has been extended to model predictive control policies, where explicit dynamical models are embedded within the control law. However, the performance of the resulting controllers, and the convergence of the associated optimization algorithms, critically depends on the accuracy of the models. In this paper, we demonstrate that combining gradient‑based policy optimization with recursive system identification ensures convergence to an optimal controller design and showcase our finding in several control examples.

Authors:Shamik Bhattacharyya, Rachel Kalpana Kalaimani
Title: Distributed Federated Learning by Alternating Periods of Training
Abstract:
Federated learning is a privacy‑focused approach towards machine learning where models are trained on client devices with locally available data and aggregated at a central server. However, the dependence on a single central server is challenging in the case of a large number of clients and even poses the risk of a single point of failure. To address these critical limitations of scalability and fault‑tolerance, we present a distributed approach to federated learning comprising multiple servers with inter‑server communication capabilities. While providing a fully decentralized approach, the designed framework retains the core federated learning structure where each server is associated with a disjoint set of clients with server‑client communication capabilities. We propose a novel DFL (Distributed Federated Learning) algorithm which uses alternating periods of local training on the client data followed by global training among servers. We show that the DFL algorithm, under a suitable choice of parameters, ensures that all the servers converge to a common model value within a small tolerance of the ideal model, thus exhibiting effective integration of local and global training models. Finally, we illustrate our theoretical claims through numerical simulations.

Authors:Milad Hasanzadeh, Ali Rajabi, Amin Kargarian
Title: A Survey on Applications of Quantum Computing for Unit Commitment
Abstract:
Unit Commitment (UC) is a core optimization problem in power system operation and electricity market scheduling. It determines the optimal on/off status and dispatch of generating units while satisfying system, operational, and market constraints. Traditionally, UC has been solved using mixed‑integer programming, dynamic programming, or metaheuristic methods, all of which face scalability challenges as systems grow in size and uncertainty. Recent advances in quantum computing, spanning quantum annealing, variational algorithms, and hybrid quantum classical optimization, have opened new opportunities to accelerate UC solution processes by exploiting quantum parallelism and entanglement. This paper presents a comprehensive survey of existing research on the applications of quantum computing for solving the UC problem. The reviewed works are categorized based on the employed quantum paradigms, including annealing‑based, variational hybrid, quantum machine learning, and quantum‑inspired methods. Key modeling strategies, hardware implementations, and computational trade‑offs are discussed, highlighting the current progress, limitations, and potential future directions for large‑scale quantum‑enabled UC.

Authors:Manh-Dat Nguyen, Thomas Do, Nguyen Thanh Trung Le, Xuan-The Tran, Fred Chang, Chin-Teng Lin
Title: Design and Quantitative Evaluation of an Embedded EEG Instrumentation Platform for Real-Time SSVEP Decoding
Abstract:
This paper presents an embedded EEG instrumentation platform for real‑time steady‑state visually evoked potential (SSVEP) decoding based on an ESP32‑S3 microcontroller and an ADS1299 analog front end. The system performs 8‑channel EEG acquisition, zero‑phase bandpass filtering, and canonical correlation analysis entirely on‑device, while supporting wireless communication and closed‑loop operation without external computation. A central contribution is the quantitative characterization of the platform's measurement integrity. Reported results demonstrate a stable shorted‑input noise floor (\approx 0.08~μ\textV_\textRMS), tightly bounded sampling jitter (0.56~μ\texts standard deviation), and negligible long‑term drift (< 1~\textppm). Numerical fidelity analysis shows 100% decision agreement between the mixed‑precision embedded pipeline and a 64‑bit double‑precision reference. Effective common‑mode attenuation exceeded 112~\textdB under balanced conditions, with a localized 26.9~\textdB degradation observed under source‑impedance mismatch. Closed‑loop validation achieved 99.17% online accuracy and an information transfer rate of 27.66~\textbits/min. These results position the proposed system as a quantitatively characterized embedded EEG measurement and processing platform for real‑time SSVEP decoding.

Authors:Wenhui Chu, Aobo Jin, Hardik A. Gohel
Title: Simulations and Advancements in MRI-Guided Power-Driven Ferric Tools for Wireless Therapeutic Interventions
Abstract:
Designing a robotic system that functions effectively within the specific environment of a Magnetic Resonance Imaging (MRI) scanner requires solving numerous technical issues, such as maintaining the robot's precision and stability under strong magnetic fields. This research focuses on enhancing MRI's role in medical imaging, especially in its application to guide intravascular interventions using robot‑assisted devices. A newly developed computational system is introduced, designed for seamless integration with the MRI scanner, including a computational unit and user interface. This system processes MR images to delineate the vascular network, establishing virtual paths and boundaries within vessels to prevent procedural damage. Key findings reveal the system's capability to create tailored magnetic field gradient patterns for device control, considering the vessel's geometry and safety norms, and adapting to different blood flow characteristics for finer navigation. Additionally, the system's modeling aspect assesses the safety and feasibility of navigating pre‑set vascular paths. Conclusively, this system, based on the Qt framework and C/C++, with specialized software modules, represents a major step forward in merging imaging technology with robotic aid, significantly enhancing precision and safety in intravascular procedures.

Authors:Liam Perreault, Idris Kempf, Kirill Sechkar, Jean-Baptiste Lugagne, Antonis Papachristodoulou
Title: Host-Aware Control of Gene Expression using Data-Enabled Predictive Control
Abstract:
Cybergenetic gene expression control in bacteria enables applications in engineering biology, drug development, and biomanufacturing. AI‑based controllers offer new possibilities for real‑time, single‑cell‑level regulation but typically require large datasets and re‑training for new systems. Data‑enabled Predictive Control (DeePC) offers better sample efficiency without prior modelling. We apply DeePC to a system with two inputs (optogenetic control and media concentration) and two outputs (expression of gene of interest and host growth rate). Using basis functions to address nonlinearities, we demonstrate that DeePC remains robust to parameter variations and performs among the best control strategies while using the least data.

Authors:Hyuntae Kim, Idris Kempf
Title: Cross-Directional Modelling and Control of Slot-Die Battery Electrode Coating
Abstract:
As global battery demand increases, real‑time process control becomes increasingly important for battery electrode manufacturing, yet slot‑die lines are still mostly manually operated in open loop. This paper develops a physics‑based modelling‑and‑control pipeline for film‑thickness regulation. Computational fluid dynamics (CFD) simulations provide the data from which a low‑order cross‑directional model is identified and calibrated. Numerical simulations demonstrate close agreement between the CFD and the cross‑directional model, which is used to design a controller that can be used in both real‑time, automated feedback operation and manual feedforward operation during line commissioning.

Authors:Qingzuo Meng, Pengfeng Lin, Yujie Wang, Miao Zhu, Amer M. Ghias, Syed Islam, Frede Blaabjerg
Title: Full-Time-Scale Power Management Strategy for Hybrid AC/DC/DS Microgrid with Dynamic Concatenation and Autonomous Frequency / Voltage Restorations
Abstract:
Hybrid AC/DC microgrids with distributed energy storage (DS) improve power reliability in remote areas. Existing power management methods either focus on steady‑state power sharing or transient inertia support, but rarely combine both. They also often ignore frequency and voltage deviations caused by droop control, which can harm sensitive loads. To overcome these issues, this paper proposes a full‑time‑scale (FTS) power management strategy that unifies transient inertia sharing and steady‑state power allocation through a novel dynamic concatenator. It also introduces autonomous frequency/voltage restoration to eliminate steady‑state deviations in each subgrid. Additionally, a global equivalent circuit model (GECM) is developed to simplify system analysis and design. Experiments confirm that the approach maintains nominal frequency and voltage in steady state while enabling seamless transition between transient inertia support and proportional power sharing across all time scales.

Authors:Rishav Sen, Fangqi Liu, Jose Paolo Talusan, Ava Pettet, Yoshinori Suzue, Mark Bailey, Ayan Mukhopadhyay, Abhishek Dubey
Title: CONSENT: A Negotiation Framework for Leveraging User Flexibility in Vehicle-to-Building Charging under Uncertainty
Abstract:
The growth of Electric Vehicles (EVs) creates a conflict in vehicle‑to‑building (V2B) settings between building operators, who face high energy costs from uncoordinated charging, and drivers, who prioritize convenience and a full charge. To resolve this, we propose a negotiation‑based framework that, by design, guarantees voluntary participation, strategy‑proofness, and budget feasibility. It transforms EV charging into a strategic resource by offering drivers a range of incentive‑backed options for modest flexibility in their departure time or requested state of charge (SoC). Our framework is calibrated with user survey data and validated using real operational data from a commercial building and an EV manufacturer. Simulations show that our negotiation protocol creates a mutually beneficial outcome: lowering the building operator's costs by over 3.5% compared to an optimized, non‑negotiating smart charging policy, while simultaneously reducing user charging expenses by 22% below the utility's retail energy rate. By aligning operator and EV user objectives, our framework provides a strategic bridge between energy and mobility systems, transforming EV charging from a source of operational friction into a platform for collaboration and shared savings.

Authors:Declan S. Jagt, Matthew M. Peet
Title: Lyapunov Functions can Exactly Quantify Rate Performance of Nonlinear Differential Equations
Abstract:
Pointwise‑in‑time stability notions for Ordinary Differential Equations (ODEs) provide quantitative metrics for system performance by establishing bounds on the rate of decay of the system state in terms of initial condition ‑‑ allowing stability to be quantified by e.g. the maximum provable decay rate. Such bounds may be obtained by finding suitable Lyapunov functions using, e.g. Sum‑of‑Squares (SOS) optimization. While Lyapunov tests have been proposed for numerous pointwise‑in‑time stability notions, including exponential, rational, and finite‑time stability, it is unclear whether these characterizations are able to provide accurate bounds on system performance. In this paper, we start by proposing a generalized notion of rate performance ‑‑ with exponential, rational, and finite‑time decay rates being special cases. Then, for any such notion and rate, we associate a Lyapunov condition which is shown to be necessary and sufficient for a system to achieve that rate. Finally, we show how the proposed conditions can be enforced using SOS programming in the case of exponential, rational, and finite‑time stability. Numerical examples in each case demonstrate that the corresponding SOS test can achieve tight bounds on the rate performance with accurate inner bounds on the associated regions of performance.

Authors:Poorvi Joshi, Mohan Gurusamy
Title: Context-Aware Information Transfer via Digital Semantic Communication in UAV-Based Networks
Abstract:
In smart cities, bandwidth‑constrained Unmanned Aerial Vehicles (UAVs) often fail to relay mission‑critical data in time, compromising real‑time decision‑making. This highlights the need for faster and more efficient transmission of only the most relevant information. To address this, we propose DSC‑UAV model, leveraging a context‑adaptive Digital Semantic Communication (DSC) framework. This model redefines aerial data transmission through three core components: prompt‑aware encoding, dynamic UAV‑enabled relaying, and user mobility‑optimized reinforcement learning. Ground users transmit context‑driven visual content. Images are encoded via Vision Transformer combined with a prompt‑text encoder to generate semantic features based on the desired context (generic or object‑specific). These features are then quantized and transmitted over a UAV network that dynamically relays the data. Joint trajectory and resource allocation are optimized using Truncated Quantile Critic (TQC)‑aided reinforcement learning technique, which offers greater stability and precision over standard SAC and TD3 due to its resistance to overestimation bias. Simulations demonstrate significant performance improvement, up to 22% gain in semantic‑structural similarity and 14% reduction in Age of Information (AoI) compared to digital and prior UAV‑semantic communication baselines. By integrating mobility control with context‑driven visual abstraction, DSC‑UAV advances resilient, information‑centric surveillance for next‑generation UAV networks in bandwidth‑constrained environments.

Authors:Sunki Hong, Jisoo Lee
Title: Reliable Grid Forecasting: State Space Models for Safety-Critical Energy Systems
Abstract:
Accurate grid load forecasting is safety‑critical: under‑predictions risk supply shortfalls, while symmetric error metrics can mask this operational asymmetry. We introduce an operator‑legible evaluation framework ‑‑ Under‑Prediction Rate (UPR), tail \textReserve_99.5^% requirements, and explicit inflation diagnostics (\textBias_24h/OPR) ‑‑ to quantify one‑sided reliability risk beyond MAPE. Using this framework, we evaluate five neural architectures ‑‑ two state space models (S‑Mamba, PowerMamba), two Transformers (iTransformer, PatchTST), an LSTM, and a probabilistic SSM variant (Mamba‑ProbTSF) ‑‑ on a weather‑aligned California Independent System Operator (CAISO) dataset spanning Nov 2023‑‑Nov 2025 (84,498 hourly records across 5 regional transmission areas) under a rolling‑origin walk‑forward backtest. We develop and evaluate thermal‑lag‑aligned weather fusion strategies matched to each architecture's inductive bias. Our results demonstrate that standard accuracy metrics are insufficient proxies for operational safety: models with comparable MAPE can imply materially different tail reserve requirements (\textReserve_99.5^%). We show that explicit weather integration narrows error distributions, with the magnitude of improvement being architecturally determined ‑‑ iTransformer's cross‑variate attention benefits significantly more than PatchTST's channel‑independent design. Crucially, we identify a widespread susceptibility to "fake safety" in risk‑averse forecasting: while probabilistic calibration reduces upper‑tail errors, it achieves this by systematically inflating schedules (e.g., increasing bias by over 1,700 MW in severe cases) if left unconstrained. To solve this, we introduce Bias/OPR‑constrained objectives that enable auditable trade‑offs between minimizing tail risk and preventing trivial over‑forecasting.

Authors:Chuyuan Tao, Fanxin Wang, Haolong Jiang, Jia He, Yiyang Chen, Qinglei Bu
Title: Sampling Strategy Design for Model Predictive Path Integral Control on Legged Robot Locomotion
Abstract:
Model Predictive Path Integral (MPPI) control has emerged as a powerful sampling‑based optimal control method for complex, nonlinear, and high‑dimensional systems. However, directly applying MPPI to legged robotic systems presents several challenges. This paper systematically investigates the role of sampling strategy design within the MPPI framework for legged robot locomotion. Based upon the idea of structured control parameterization, we explore and compare multiple sampling strategies within the framework, including both unstructured and spline‑based approaches. Through extensive simulations on a quadruped robot platform, we evaluate how different sampling strategies affect control smoothness, task performance, robustness, and sample efficiency. The results provide new insights into the practical implications of sampling design for deploying MPPI on complex legged systems.

Authors:Zihan Li, Ziming Wang, Chenning Liu, Xin Wang
Title: Neural-network-based Self-triggered Observed Platoon Control for Autonomous Vehicles
Abstract:
This paper investigates autonomous vehicle (AV) platoon control under uncertain dynamics and intermittent communication, which remains a critical challenge in intelligent transportation systems. To address these issues, this paper proposes an adaptive consensus tracking control framework for nonlinear multi‑agent systems (MASs). The proposed approach integrates backstepping design, a nonlinear sampled‑data observer, radial basis function neural networks, and a self‑triggered communication mechanism. The radial basis function neural networks approximate unknown nonlinearities and time‑varying disturbances, thereby enhancing system robustness. A distributed observer estimates neighboring states based on limited and intermittent measurements, thereby reducing dependence on continuous communication. Moreover, self‑triggered mechanism is developed to determine triggering instants, guaranteeing a strictly positive minimum inter‑event time and preventing Zeno behavior. The theoretical analysis proves that all closed‑loop signals are uniformly ultimately bounded (UUB), and tracking errors converge to a compact set. Simulation results demonstrate that the proposed approach achieves high robustness, adaptability, and communication efficiency, making it suitable for real‑world networked vehicle systems.

Authors:Pouria Sarhadi
Title: Simple yet Effective Anti-windup Techniques for Amplitude and Rate Saturation: An Autonomous Underwater Vehicle Case Study
Abstract:
Actuator amplitude and rate saturation (A\&RSat), together with their consequent windup problem, have long been recognised as challenges in control systems. Anti‑windup (AW) solutions have been developed over the past decades, which can generally be categorised into two main groups: classical and modern anti‑windup (CAW and MAW) approaches. Classical methods have provided simple and effective results, mainly addressing amplitude saturation. In contrast, modern approaches offer powerful and theoretically sound solutions capable of handling both amplitude and rate saturations. However, MAW's derivation process often imposes restrictive conditions and can be complex to apply in practical engineering problems. Nevertheless, the literature has paid limited attention (if not entirely ignored) to the potential of simple yet effective CAW schemes that can operate in the presence of both A\&RSat elements. This paper revisits this issue and proposes modifications to two well‑known controllers: PID and LQI. The obtained results, benchmarked on the REMUS AUV yaw control problem and compared with constrained MPC, indicate that these classical techniques can still provide simple yet effective solutions with comparable performance, at least for SISO systems. These findings may stimulate further research into solutions that achieve comparable performance with only one (or a limited number of) additional tuning parameters and straightforward implementation.

Authors:Jinzhou Xu, Yuanxin Zhuo, Paola Tapia
Title: Multi-Objective Operational Optimization of Energy Storage Systems in User-Side Microgrids
Abstract:
An operational optimization strategy for microgrid energy storage systems (ESSs) is developed to address practical user‑oriented application requirements, and its effectiveness is validated using real‑world operational data. First, a fundamental ESS model is established to characterize system dynamics and operational constraints, providing a theoretical basis for optimization. Subsequently, a multi‑objective operational optimization framework is formulated to simultaneously minimize electricity cost, reduce carbon emissions, and enhance renewable energy utilization. To ensure computational efficiency and scalability, the commercial optimization solver Gurobi is employed. The proposed strategy is evaluated using actual microgrid operational data, demonstrating that the developed ESS model accurately represents real system constraints. Compared with existing user operational strategies, the proposed approach achieves an average reduction of 13.47% in electricity cost. Moreover, by dynamically adjusting the weighting factors of the multi‑objective formulation, the strategy enables flexible operational modes and significantly improves adaptability to varying operating scenarios. In addition, the proposed framework provides decision support for user‑side microgrids participating in surplus electricity feed‑in policies. The main contribution of this work lies in its user‑centric optimization design, which enhances operational flexibility and scenario adaptability through multi‑objective weight allocation, offering a practical and scalable solution for real‑world microgrid ESS operation.

Authors:Sokipriala Jonah, Seong Ki Yoo, Saurav Sthapit
Title: Adaptive Scheduling: A Reinforcement Learning Whittle Index Approach for Wireless Sensor Networks
Abstract:
We propose a reinforcement learning based scheduling framework for Restless Multi‑Armed Bandit (RMAB) problems, centred on a Whittle Index Q‑Learning policy with Upper Confidence Bound (UCB) exploration, referred to as WIQL‑UCB. Unlike existing approaches that rely on fixed or adaptive epsilon‑greedy strategies and require careful hyperparameter tuning, the proposed method removes problem‑specific tuning and is therefore more generalisable across diverse RMAB settings. We evaluate WIQL‑UCB on standard RMAB benchmarks and on a practical sensor scheduling application based on the Age of Incorrect Information (AoII), using an edge‑based state estimation scheme that requires no prior knowledge of system dynamics. Experimental results show that WIQL‑UCB achieves near‑optimal performance while significantly improving computational and memory efficiency. For a representative problem size of N = 15 and M = 3, the proposed method requires only around 600 bytes of memory, compared with several kilobytes for tabular Q‑learning and hundreds of kilobytes to megabytes for deep reinforcement learning baselines. In addition, WIQL‑UCB achieves sub‑millisecond per‑decision runtimes and is several times faster than deep reinforcement learning approaches, while maintaining competitive performance. Overall, these results demonstrate that WIQL‑UCB consistently outperforms both non‑Whittle‑based and Whittle‑index learning baselines across a wide range of RMAB settings.

Authors:Pengfeng Lin, Guangjie Gao, Jianjun Ma, Miao Zhu, Xinan Zhang, Ahmed Abu-Siada
Title: Transient Power Allocation Control Scheme for Hybrid Hydrogen Electrolyzer-Supercapacitor System with Autonomous Inertia Response
Abstract:
This paper proposes a hybrid hydrogen electrolyzer‑supercapacitor system (HESS) with a novel control strategy for renewable‑dominant power grids. The HESS consists of alkaline electrolyzers (AEL), proton exchange membrane electrolyzers (PEMEL), and supercapacitors (SC). The interfacing inverters between HESS and power grid are regulated by an inertia emulation control strategy. From HESS, AEL is with conventional DC power control, whereas PEMEL and SC are designed with the proposed dynamic inertia control and capacitive inertia control, respectively. Benefitting from the coordination of three controls, within the HESS, high‑frequency transient power components are autonomously handled by SC, stable frequency power components are regulated by PEMEL, and low‑frequency steady‑state power is addressed by AEL, characterized by low operational gains and longer lifetimes. SC delivers transient power, significantly alleviating energy losses on electrolyzers and achieving adequate inertia recovery capabilities while requiring no additional communication. Implementing SOC recovery control enables the SC to withstand more than three times more stability discharge cycles compared to an SC without SOC recovery. Furthermore, a large‑signal mathematical model based on mixed potential theory is established, providing clear stability boundaries for system parameters. Dynamic analyses theoretically verify system feasibility, and extensive hardware‑in‑the‑loop experimental results fully validate the proposed HESS along with the corresponding transient power allocation controls.

Authors:Davide Carecci, Laurent Dewasme, Alessio La Bella, Gianni Ferretti, Alain Vande Wouwer
Title: Tube-based robust nonlinear model predictive control of anaerobic co-digestion
Abstract:
To match the growing demand for bio‑methane production, anaerobic digesters need to embrace the co‑digestion of different feedstocks; in addition, to improve the techno‑economic performance, an optimal and time‑varying adaptation of the input diet is required. These operation modes constitute a very hard challenge for the limited instrumentation and control equipment typically installed aboard full‑scale plants. A model‑based predictive approach may be able to handle such control problem, but the identification of reliable predictive models is limited by the low information content typical of the data available from full‑scale plants' operations, which entail high parametric uncertainty. In this work, the application of a tube‑based robust nonlinear model predictive control (NMPC) is proposed to regulate bio‑methane production over a period of diet change in time, while warranting safe operation and dealing with uncertainties. In view of its upcoming validation on a true small pilot‑scale plant, the NMPC capabilities are assessed via numerical simulations designed to resemble as much as possible the experimental setup, along with some practical final considerations.

Authors:Ridma Ganganath, Simone Servadio, David Daeyoung Lee
Title: Compensating Star-Trackers Misalignments with Adaptive Multi-Model Estimation
Abstract:
This paper presents an adaptive multi‑model framework for jointly estimating spacecraft attitude and star‑tracker misalignments in GPS‑denied deep‑space CubeSat missions. A Multiplicative Extended Kalman Filter (MEKF) estimates attitude, angular velocity, and gyro bias, while a Bayesian Multiple‑Model Adaptive Estimation (MMAE) layer operates on a discrete grid of body‑to‑sensor misalignment hypotheses. In the single‑misalignment case, the MEKF processes gyroscope measurements and TRIAD‑based attitude observations, and the MMAE updates a three‑dimensional grid over the misalignment vector. For a dual‑misalignment configuration, the same MEKF dynamics are retained, and the MMAE bank is driven directly by stacked line‑of‑sight measurements from two star trackers, forming a six‑dimensional grid over the two misalignment quaternions without augmenting the continuous‑state dimension. A novel diversity metric, Ψ, is introduced to trigger adaptive refinement of the misalignment grid around a weighted‑mean estimate, thereby preventing premature collapse of the model probabilities and concentrating computation in the most likely region of the parameter space. Monte Carlo simulations show arcsecond‑level misalignment estimation and sub‑degree attitude errors for both estimation problems, with estimation errors remaining well‑bounded, proving robustness and consistency. These results indicate that the proposed MEKF‑‑MMAE architecture enables accurate, autonomous, and computationally efficient in‑flight calibration for resource‑constrained spacecraft, and establishes dual star‑tracker misalignment estimation as a practical option for deep‑space CubeSat missions.

Authors:Salim Oyinlola, Joshua Ajayi, Gozie Ibekwe
Title: Time Series Based CO2 Emission Forecasting and Energy Mix Analysis for Net Zero Transitions: A Multi Country Study
Abstract:
This study examines long‑term CO_2 emission trajectories across five major economies: Nigeria, the United States, China, Brazil, and Russia, by integrating national energy‑mix characteristics with time‑series forecasting models. Annual emissions from 2000‑2023 were analyzed alongside energy production data to classify countries into fossil‑dependent, transition‑phase, or renewable‑accelerated profiles. Three forecasting models (ARIMA, SARIMA, and Holt‑Winters exponential smoothing) were evaluated using MAE, RMSE, MAPE, and R^2 metrics. Results show that Holt‑Winters provided the most accurate forecasts for Nigeria, the United States, China, and Brazil, while SARIMA performed best for Russia due to its relatively stable emissions. Long‑term projections from 2024 to 2060 indicate divergent decarbonization pathways. Brazil aligns most closely with a low‑emission future owing to its renewable‑dominant energy system, whereas Nigeria continues on an upward emissions trajectory driven by fossil dependence. The United States and China maintain moderate declines but require accelerated mitigation to reach their respective net‑zero commitments. Russia's emissions remain largely flat under current conditions. These findings highlight the strong influence of energy structures on national decarbonization prospects and underscore the need for targeted energy policy reforms to align with global climate objectives.

Authors:Devesh Nath, Haoran Yin, Glen Chou
Title: Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees
Abstract:
We propose a scalable reachability‑based framework for probabilistic, data‑driven safety verification of unknown nonlinear dynamics. We use Koopman theory with a neural network (NN) lifting function to learn an approximate linear representation of the dynamics and design linear controllers in this space to enable closed‑loop tracking of a reference trajectory distribution. Closed‑loop reachable sets are efficiently computed in the lifted space and mapped back to the original state space via NN verification tools. To capture model mismatch between the Koopman dynamics and the true system, we apply conformal prediction to produce statistically‑valid error bounds that inflate the reachable sets to ensure the true trajectories are contained with a user‑specified probability. These bounds generalize across references, enabling reuse without recomputation. Results on high‑dimensional MuJoCo tasks (11D Hopper, 28D Swimmer) and 12D quadcopters show improved reachable set coverage rate, computational efficiency, and conservativeness over existing methods.

Authors:Achraf Hsain, Yahya Zaki, Othman Abaakil, Hibat-allah Bekkar, Yousra Chtouki
Title: Tiny Machine Learning for Real-Time Aquaculture Monitoring: A Case Study in Morocco
Abstract:
Aquaculture, the farming of aquatic organisms, is a rapidly growing industry facing challenges such as water quality fluctuations, disease outbreaks, and inefficient feed management. Traditional monitoring methods often rely on manual labor and are time consuming, leading to potential delays in addressing issues. This paper proposes the integration of low‑power edge devices using Tiny Machine Learning (TinyML) into aquaculture systems to enable real‑time automated monitoring and control, such as collecting data and triggering alarms, and reducing labor requirements. The system provides real‑time data on the required parameters such as pH levels, temperature, dissolved oxygen, and ammonia levels to control water quality, nutrient levels, and environmental conditions enabling better maintenance, efficient resource utilization, and optimal management of the enclosed aquaculture space. The system enables alerts in case of anomaly detection. The data collected by the sensors over time can serve for important decision‑making regarding optimizing water treatment processes, feed distribution, feed pattern analysis and improve feed efficiency, reducing operational costs. This research explores the feasibility of developing TinyML‑based solutions for aquaculture monitoring, considering factors such as sensor selection, algorithm design, hardware constraints, and ethical considerations. By demonstrating the potential benefits of TinyML in aquaculture, our aim is to contribute to the development of more sustainable and efficient farming practices.

Authors:Boya Hou, Maxim Raginsky, Abhishek Pandey, Olgica Milenkovic
Title: Spatially-Coupled Network RNA Velocities: A Control-Theoretic Perspective
Abstract:
RNA velocity is an important model that combines cellular spliced and unspliced RNA counts to infer dynamical properties of various regulatory functions. Despite its wide applicability and many variants used in practice, the model has not been adequately designed to directly account for both intracellular gene regulatory network interactions and spatial intercellular communications. Here, we propose a new RNA velocity approach that jointly and directly captures two new network structures: an intracellular gene regulatory network (GRN) and an intercellular interaction network that captures interactions between (neighboring) cells, with relevance to spatial transcriptomics. We theoretically analyze this two‑level network system through the lens of control and consensus theory. In particular, we investigate network equilibria, stability, cellular network consensus, and optimal control approaches for targeted drug intervention.

Authors:Ata Akbari Asanjan, Milad Memarzadeh, Bryan Matthews, Nikunj Oza
Title: Improving Variational Autoencoder using Random Fourier Transformation: An Aviation Safety Anomaly Detection Case-Study
Abstract:
In this study, we focus on the training process and inference improvements of deep neural networks (DNNs), specifically Autoencoders (AEs) and Variational Autoencoders (VAEs), using Random Fourier Transformation (RFT). We further explore the role of RFT in model training behavior using Frequency Principle (F‑Principle) analysis and show that models with RFT turn to learn low frequency and high frequency at the same time, whereas conventional DNNs start from low frequency and gradually learn (if successful) high‑frequency features. We focus on reconstruction‑based anomaly detection using autoencoder and variational autoencoder and investigate the RFT's role. We also introduced a trainable variant of RFT that uses the existing computation graph to train the expansion of RFT instead of it being random. We showcase our findings with two low‑dimensional synthetic datasets for data representation, and an aviation safety dataset, called Dashlink, for high‑dimensional reconstruction‑based anomaly detection. The results indicate the superiority of models with Fourier transformation compared to the conventional counterpart and remain inconclusive regarding the benefits of using trainable Fourier transformation in contrast to the Random variant.

Authors:Wenhui Chu, Khang Tran, Nikolaos V. Tsekos
Title: Simulations of MRI Guided and Powered Ferric Applicators for Tetherless Delivery of Therapeutic Interventions
Abstract:
Magnetic Resonance Imaging (MRI) is a well‑established modality for pre‑operative planning and is also explored for intra‑operative guidance of procedures such as intravascular interventions. Among the experimental robot‑assisted technologies, the magnetic field gradients of the MRI scanner are used to power and maneuver ferromagnetic applicators for accessing sites in the patient's body via the vascular network. In this work, we propose a computational platform for preoperative planning and modeling of MRI‑powered applicators inside blood vessels. This platform was implemented as a two‑way data and command pipeline that links the MRI scanner, the computational core, and the operator. The platform first processes multi‑slice MR data to extract the vascular bed and then fits a virtual corridor inside the vessel. This corridor serves as a virtual fixture (VF), a forbidden region for the applicators to avoid vessel perforation or collision. The geometric features of the vessel centerline, the VF, and MRI safety compliance (dB/dt, max available gradient) are then used to generate magnetic field gradient waveforms. Different blood flow profiles can be user‑selected, and those parameters are used for modeling the applicator's maneuvering. The modeling module further generates cues about whether the selected vascular path can be safely maneuvered. Given future experimental studies that require a real‑time operation, the platform was implemented on the Qt framework (C/C++) with software modules performing specific tasks running on dedicated threads: PID controller, generation of VF, generation of MR gradient waveforms.

Authors:Austin R. Ellis-Mohr, Max Hartman, Lav R. Varshney
Title: Energy-Aware Routing to Large Reasoning Models
Abstract:
Large reasoning models (LRMs) have heterogeneous inference energy costs based on which model is used and how much it reasons. To reduce energy, it is important to choose the right LRM and operate it in the right way. As a result, the performance of systems that dispatch tasks to different individual LRMs depend on the balance between mean energy provisioning and stochastic fluctuations. The critical regime is the unique operating point at which neither auxiliary energy nor baseline energy is systematically wasted. Increasing baseline supply shifts the system toward persistent over‑supply and baseline‑energy waste, while reducing supply induces persistent reliance on auxiliary energy. Yet in this regime, performance remains volatility‑limited and so a second‑order characterization provides further insights that we develop. Here, performance is governed by how variability is absorbed across time, models, and execution choices. This perspective highlights variance‑aware routing and dispatch as a principled design axis, and provides a theoretical basis for developing energy‑aware model routing policies. Routing behavior is characterized when dispatch policies are based on training‑compute and inference‑compute scaling laws for LRMs.

Authors:Houtianfu Wang, Haofan Dong, Hanlin Cai, Ozgur B. Akan
Title: Environment-to-Link ISAC with Space-Weather Sensing for Ka-Band LEO Downlinks
Abstract:
Ka‑band low‑Earth‑orbit (LEO) downlinks can suffer second‑scale reliability collapses during flare‑driven ionospheric disturbances, where fixed fade margins and reactive adaptive coding and modulation (ACM) are either overly conservative or too slow. This paper presents a GNSS‑free, link‑internal predictive controller that senses the same downlink via a geometry‑free dual‑carrier phase observable at 10~Hz: a high‑pass filter and template‑based onset detector, followed by a four‑state nearly‑constant‑velocity Kalman filter, estimate ΔVTEC and its rate, and a short look‑ahead (60~s) yields an endpoint outage probability used as a risk gate to trigger one‑step discrete MCS down‑switch and pilot‑time update with hysteresis. Evaluation uses physics‑informed log replay driven by real GOES X‑ray flare morphologies under a disjoint‑day frozen‑calibration protocol, with uncertainty reported via paired moving‑block bootstrap. Across stressed 60~s windows, the controller reduces peak BLER by 25‑‑30% and increases goodput by 0.10‑‑0.15~bps/Hz versus no‑adaptation baselines under a unified link‑level abstraction. The loop runs in \mathcalO(1) per 0.1~s epoch (about 0.042~ms measured), making on‑board implementation feasible, and scope and deployment considerations for dispersion‑dominated events are discussed.

Authors:Houtianfu Wang, Ozgur Akan
Title: AFDM for LEO Inter-Satellite Links: Path-Level CSI Prediction and CRLB-Guided Pre-Equalization
Abstract:
Low‑Earth‑orbit (LEO) inter‑satellite links must cope with strongly doubly selective channels and aged channel state information (CSI). In this paper, the term ``sensing'' refers to the receiver‑side identifiability of a small set of dominant delay‑‑Doppler path parameters, quantified via CRLB‑type proxies, rather than a full‑fledged target‑sensing pipeline. Affine frequency division multiplexing (AFDM) provides a sparse delay‑‑Doppler (DD) representation well suited to such channels, yet most existing AFDM designs assume ideal CSI, operate on grid‑based channel coefficients, and optimize only communication performance. This paper proposes a two‑stage AFDM‑based ISAC framework for mobile LEO ISLs that explicitly operates under predicted CSI. In Stage~I, we model the channel by a small number of dominant specular paths and perform sequence prediction directly on their complex gains, delays, and Dopplers, from which we reconstruct the AFDM DD‑domain kernel used as the sole instantaneous CSI at the transmitter. In Stage~II, we design a sensing‑aware AFDM pre‑equalizer by augmenting the classical minimum mean‑square error (MMSE) solution with a term obtained from Cramér‑‑Rao‑type sensitivity measures evaluated under the predicted channel model, leading to a first‑order surrogate of a CRLB‑regularized pre‑equalizer with a single tuning parameter that controls the communication‑‑sensing tradeoff. Simulation results for representative LEO ISL trajectories show that the proposed path‑level predictor improves effective‑kernel reconstruction over AFDM‑unaware baselines, and that, under predicted CSI, the sensing‑aware pre‑equalizer significantly improves sensing‑oriented metrics over outdated‑CSI baselines while keeping symbol error rates close to a communication‑oriented MMSE design with only modest additional complexity.

Authors:Longxiang Shao, Dominik Huesener, Michael Schluse, Juergen Rossmann
Title: Application of the learning from errors principle in tufting machines
Abstract:
The principle of learning from errors is pedagogically powerful but often impractical in industrial settings due to risks to safety and equipment. This paper presents an integrated training approach specifically designed for tufting machine operators. It uses hybrid digital twins, augmented reality (AR), and Petri Net‑based modelling to apply the learning from errors principle effectively. Operator actions and errors are simulated via experimentable digital twins (EDTs), and the consequences of errors are visualized in AR, enabling safe, experiential learning. A Petri Net model formally represents the process, including typical faults and recovery paths, and is implemented in VEROSIM using SOML++. This hybrid framework provides a scalable foundation for AR‑guided training systems that reduce risk and accelerate skill acquisition.

Authors:Mayuranath SureshKumar, Hanumanthrao Kannan
Title: A formal theory on problem space as a semantic world model in systems engineering
Abstract:
Classic problem‑space theory models problem solving as a navigation through a structured space of states, operators, goals, and constraints. Systems Engineering (SE) employs analogous constructs (functional analysis, operational analysis, scenarios, trade studies), yet still lacks a rigorous systems‑theoretic representation of the problem space itself. In current practice, reasoning often proceeds directly from stakeholder goals to prescriptive artifacts. This makes foundational assumptions about the operational environment, admissible interactions, and contextual conditions implicit or prematurely embedded in architectures or requirements. This paper addresses that gap by formalizing the problem space as an explicit semantic world model containing theoretical constructs that are defined prior to requirements and solution commitments. These constructs along with the developed axioms, theorems and corollary establish a rigorous criterion for unambiguous boundary semantics, context‑dependent interaction traceability to successful stakeholder goal satisfaction, and sufficiency of problem‑space specification over which disciplined reasoning can occur independent of solution design. It offers a clear distinction between what is true of the problem domain and what is chosen as a solution. The paper concludes by discussing the significance of the theory on practitioners and provides a dialogue‑based hypothetical case study between a stakeholder and an engineer, demonstrating how the theory guides problem framing before designing any prescriptive artifacts.

Authors:Uğurcan Özalp
Title: Stochastic Actor-Critic: Mitigating Overestimation via Temporal Aleatoric Uncertainty
Abstract:
Off‑policy actor‑critic methods in reinforcement learning train a critic with temporal‑difference updates and use it as a learning signal for the policy (actor). This design typically achieves higher sample efficiency than purely on‑policy methods. However, critic networks tend to overestimate value estimates systematically. This is often addressed by introducing a pessimistic bias based on uncertainty estimates. Current methods employ ensembling to quantify the critic's epistemic uncertainty‑uncertainty due to limited data and model ambiguity‑to scale pessimistic updates. In this work, we propose a new algorithm called Stochastic Actor‑Critic (STAC) that incorporates temporal (one‑step) aleatoric uncertainty‑uncertainty arising from stochastic transitions, rewards, and policy‑induced variability in Bellman targets‑to scale pessimistic bias in temporal‑difference updates, rather than relying on epistemic uncertainty. STAC uses a single distributional critic network to model the temporal return uncertainty, and applies dropout to both the critic and actor networks for regularization. Our results show that pessimism based on a distributional critic alone suffices to mitigate overestimation, and naturally leads to risk‑averse behavior in stochastic environments. Introducing dropout further improves training stability and performance by means of regularization. With this design, STAC achieves improved computational efficiency using a single distributional critic network.

Authors:Rajiv Chaitanya M, D R Ramesh Babu
Title: ARISE: Adaptive Reinforcement Integrated with Swarm Exploration
Abstract:
Effective exploration remains a key challenge in RL, especially with non‑stationary rewards or high‑dimensional policies. We introduce ARISE, a lightweight framework that enhances reinforcement learning by augmenting standard policy‑gradient methods with a compact swarm‑based exploration layer. ARISE blends policy actions with particle‑driven proposals, where each particle represents a candidate policy trajectory sampled in the action space, and modulates exploration adaptively using reward‑variance cues. While easy benchmarks exhibit only slight improvements (e.g., +0.7% on CartPole‑v1), ARISE yields substantial gains on more challenging tasks, including +46% on LunarLander‑v3 and +22% on Hopper‑v4, while preserving stability on Walker2d and Ant. Under non‑stationary reward shifts, ARISE provides marked robustness advantages, outperforming PPO by +75 points on CartPole and improving LunarLander accordingly. Ablation studies confirm that both the swarm component and the adaptive mechanism contribute to the performance. Overall, ARISE offers a simple, architecture‑agnostic route to more exploratory and resilient RL agents without altering core algorithmic structures.

Authors:Mogens Plessen
Title: From 2D to 3D terrain-following area coverage path planning
Abstract:
An algorithm for 3D terrain‑following area coverage path planning is presented. Multiple adjacent paths are generated that are (i) locally apart from each other by a distance equal to the working width of a machinery, while (ii) simultaneously floating at a projection distance equal to a specific working height above the terrain. The complexities of the algorithm in comparison to its 2D equivalent are highlighted. These include uniformly spaced elevation data generation using an Inverse Distance Weighting‑approach and a local search. Area coverage path planning results for real‑world 3D data within an agricultural context are presented to validate the algorithm.

Authors:Mehdi Heydari Shahna, Pauli Mustalahti, Jouni Mattila
Title: NMPC-Augmented Visual Navigation and Safe Learning Control for Large-Scale Mobile Robots
Abstract:
A large‑scale mobile robot (LSMR) is a high‑order multibody system that often operates on loose, unconsolidated terrain, which reduces traction. This paper presents a comprehensive navigation and control framework for an LSMR that ensures stability and safety‑defined performance, delivering robust operation on slip‑prone terrain by jointly leveraging high‑performance techniques. The proposed architecture comprises four main modules: (1) a visual pose‑estimation module that fuses onboard sensors and stereo cameras to provide an accurate, low‑latency robot pose, (2) a high‑level nonlinear model predictive control that updates the wheel motion commands to correct robot drift from the robot reference pose on slip‑prone terrain, (3) a low‑level deep neural network control policy that approximates the complex behavior of the wheel‑driven actuation mechanism in LSMRs, augmented with robust adaptive control to handle out‑of‑distribution disturbances, ensuring that the wheels accurately track the updated commands issued by high‑level control module, and (4) a logarithmic safety module to monitor the entire robot stack and guarantees safe operation. The proposed low‑level control framework guarantees uniform exponential stability of the actuation subsystem, while the safety module ensures the whole system‑level safety during operation. Comparative experiments on a 6,000 kg LSMR actuated by two complex electro‑hydrostatic drives, while synchronizing modules operating at different frequencies.

Authors:Junyue Huang, Shaoyuan Li, Xiang Yin
Title: Stability Verification for Switched Systems using Neural Multiple Lyapunov Functions
Abstract:
Stability analysis of switched systems, characterized by multiple operational modes and switching signals, is challenging due to their nonlinear dynamics. While frameworks such as multiple Lyapunov functions (MLF) provide a foundation for analysis, their computational applicability is limited for systems without favorable structure. This paper investigates stability analysis for switched systems under state‑dependent switching conditions. We propose neural multiple Lyapunov functions (NMLF), a unified framework that combines the theoretical guarantees of MLF with the computational efficiency of neural Lyapunov functions (NLF). Our approach leverages a set of tailored loss functions and a counter‑example guided inductive synthesis (CEGIS) scheme to train neural networks that rigorously satisfy MLF conditions. Through comprehensive simulations and theoretical analysis, we demonstrate NMLF's effectiveness and its potential for practical deployment in complex switched systems.

Authors:Trung Dao, Minh Nguyen, Son Do, Hoang Tran
Title: Cyberscurity Threats and Defense Mechanisms in IoT network
Abstract:
The rapid proliferation of Internet of Things (IoT) technologies, projected to exceed 30 billion interconnected devices by 2030, has significantly escalated the complexity of cybersecurity challenges. This survey aims to provide a comprehensive analysis of vulnerabilities, threats, and defense mechanisms, specifically focusing on the integration of network and application layers within real‑time monitoring and decision‑making systems. Employing an integrative review methodology, 59 scholarly articles published between 2009 and 2024 were selected from databases such as IEEE Xplore, ScienceDirect, and PubMed, utilizing keywords related to IoT vulnerabilities and security attacks. Key findings identify critical threat categories, including sensor vulnerabilities, Denial‑of‑Service (DoS) attacks, and public cloud insecurity. Conversely, the study highlights advanced defense approaches leveraging Artificial Intelligence (AI) for anomaly detection, Blockchain for decentralized trust, and Zero Trust Architecture (ZTA) for continuous verification. This paper contributes a novel five‑layer IoT model and outlines future research directions involving quantum computing and 6G networks to bolster IoT ecosystem resilience.

Authors:Kooktae Lee
Title: Optimal Transport-Based Decentralized Multi-Agent Distribution Matching
Abstract:
This paper presents a decentralized control framework for distribution matching in multi‑agent systems (MAS), where agents collectively achieve a prescribed terminal spatial distribution. The problem is formulated using optimal transport (Wasserstein distance), which provides a principled measure of distributional discrepancy and serves as the basis for the control design. To avoid solving the global optimal transport problem directly, the distribution‑matching objective is reformulated into a tractable per‑agent decision process, enabling each agent to identify its desired terminal locations using only locally available information. A sequential weight‑update rule is introduced to construct feasible local transport plans, and a memory‑based correction mechanism is incorporated to maintain reliable operation under intermittent and range‑limited communication. Convergence guarantees are established, showing cycle‑wise improvement of a surrogate transport cost under both linear and nonlinear agent dynamics. Simulation results demonstrate that the proposed framework achieves effective and scalable distribution matching while operating fully in a decentralized manner.

Authors:Cameron Hickert, Sirui Li, Zhengbing He, Cathy Wu
Title: Probability-Aware Parking Selection
Abstract:
Current navigation systems conflate time‑to‑drive with the true time‑to‑arrive by ignoring parking search duration and the final walking leg. Such underestimation can significantly affect user experience, mode choice, congestion, and emissions. To address this issue, this paper introduces the probability‑aware parking selection problem, which aims to direct drivers to the best parking location rather than straight to their destination. An adaptable dynamic programming framework is proposed that leverages probabilistic, lot‑level availability to minimize the expected time‑to‑arrive. Closed‑form analysis determines when it is optimal to target a specific parking lot or explore alternatives, as well as the expected time cost. Sensitivity analysis and three illustrative cases are examined, demonstrating the model's ability to account for the dynamic nature of parking availability. Given the high cost of permanent sensing infrastructure, we assess the error rates of using stochastic observations to estimate availability. Experiments with real‑world data from the US city of Seattle indicate this approach's viability, with mean absolute error decreasing from 7% to below 2% as observation frequency increases. In data‑based simulations, probability‑aware strategies demonstrate time savings up to 66% relative to probability‑unaware baselines, yet still take up to 123% longer than time‑to‑drive estimates.

Authors:Marc Seidel, Mahathi Anand, Frank Allgöwer
Title: Safety for Weakly-Hard Control Systems via Graph-Based Barrier Functions
Abstract:
Despite significant advancement in technology, communication and computational failures are still prevalent in safety‑critical engineering applications. Often, networked control systems experience packet dropouts, leading to open‑loop behavior that significantly affects the behavior of the system. Similarly, in real‑time control applications, control tasks frequently experience computational overruns and thus occasionally no new actuator command is issued. This article addresses the safety verification and controller synthesis problem for a class of control systems subject to weakly‑hard constraints, i.e., a set of window‑based constraints where the number of failures are bounded within a given time horizon. The results are based on a new notion of graph‑based barrier functions that are specifically tailored to the considered system class, offering a set of constraints whose satisfaction leads to safety guarantees despite communication failures. Subsequent reformulations of the safety constraints are proposed to alleviate conservatism and improve computational tractability, and the resulting trade‑offs are discussed. Finally, several numerical case studies demonstrate the effectiveness of the proposed approach.

Authors:David Millard, Ali Baheri
Title: Can Optimal Transport Improve Federated Inverse Reinforcement Learning?
Abstract:
In robotics and multi‑agent systems, fleets of autonomous agents often operate in subtly different environments while pursuing a common high‑level objective. Directly pooling their data to learn a shared reward function is typically impractical due to differences in dynamics, privacy constraints, and limited communication bandwidth. This paper introduces an optimal transport‑based approach to federated inverse reinforcement learning (IRL). Each client first performs lightweight Maximum Entropy IRL locally, adhering to its computational and privacy limitations. The resulting reward functions are then fused via a Wasserstein barycenter, which considers their underlying geometric structure. We further prove that this barycentric fusion yields a more faithful global reward estimate than conventional parameter averaging methods in federated learning. Overall, this work provides a principled and communication‑efficient framework for deriving a shared reward that generalizes across heterogeneous agents and environments.

Authors:S. Gokul Krishnan, Mohd. Asim Aftab, Nabil Mohammed, Shehab Ahmed, Charalambos Konstantinou
Title: Impact Assessment of Heterogeneous Grid Support Functions in Smart Inverter Deployments
Abstract:
The decarbonization of the energy sector has led to a significant high penetration of distributed energy resources (DERs), particularly photovoltaic (PV) systems, in low‑voltage (LV) distribution networks. To maintain grid stability, recent standards (e.g., IEEE 1547‑2018) mandate DERs to provide grid‑support functionalities through smart inverters (SIs), which typically operate autonomously based on local measurements. However, as DER penetration increases, uncoordinated control modes of SIs can lead to adverse interactions, compromising system efficiency, voltage regulation, and overall stability. While previous studies have demonstrated the benefits of coordinated inverter control and optimal dispatch strategies, the system‑wide impacts of heterogeneous SI groups operating under different control modes remain largely unexamined. This paper addresses this gap by assessing the dynamic interactions among multiple SI groups with varying control strategies, namely: Constant Power Factor (CPF), Volt‑VAR, and Volt‑Watt modes. Furthermore, the analysis covers both resistive and inductive feeder types. The validation is performed using a real‑time setup. The CIRGE low‑voltage (LV) distribution network is simulated in the Opal‑RT platform as the test network, enabling realistic and high‑fidelity evaluation of SI control interactions under practical grid conditions.

Authors:Aly Sabri Abdalla, Vuk Marojevic
Title: Next Generation Intelligent Low-Altitude Economy Deployments: The O-RAN Perspective
Abstract:
Despite the growing interest in low‑altitude economy (LAE) applications, including UAV‑based logistics and emergency response, fundamental challenges remain in orchestrating such missions over complex, signal‑constrained environments. These include the absence of real‑time, resilient, and context‑aware orchestration of aerial nodes with limited integration of artificial intelligence (AI) specialized for LAE missions. This paper introduces an open radio access network (O‑RAN)‑enabled LAE framework that leverages seamless coordination between the disaggregated RAN architecture, open interfaces, and RAN intelligent controllers (RICs) to facilitate closed‑loop, AI‑optimized, and mission‑critical LAE operations. We evaluate the feasibility and performance of the proposed architecture via a semantic‑aware rApp that acts as a terrain interpreter, offering semantic guidance to a reinforcement learning‑enabled xApp, which performs real‑time trajectory planning for LAE swarm nodes. We survey the capabilities of UAV testbeds that can be leveraged for LAE research, and present critical research challenges and standardization needs.

Authors:Mohammadreza Doostmohammadian, Hamid R. Rabiee
Title: Impact of Clustering on the Observability and Controllability of Complex Networks
Abstract:
The increasing complexity and interconnectedness of systems across various fields have led to a growing interest in studying complex networks, particularly Scale‑Free (SF) networks, which best model real‑world systems. This paper investigates the influence of clustering on the observability and controllability of complex SF networks, framing these characteristics in the context of structured systems theory. In this paper, we show that densely clustered networks require fewer driver and observer nodes due to better information propagation within clusters. This relationship is of interest for optimizing network design in applications such as social networks and intelligent transportation systems. We first quantify the network observability/controllability requirements, and then, through Monte‑Carlo simulations and different case studies, we show how clustering affects these metrics. Our findings offer practical insights into reducing control and observer nodes for sensor/actuator placement, particularly in resource‑constrained setups. This work contributes to the understanding of network observability/controllability and presents techniques for improving these features through alterations in network structure and clustering.

Authors:Nikhil Garg, Anxiong Song, Niklas Plessnig, Nathan Savoia, Laura Bégon-Lours
Title: Personalized Spiking Neural Networks with Ferroelectric Synapses for EEG Signal Processing
Abstract:
Electroencephalography (EEG)‑based brain‑computer interfaces (BCIs) are strongly affected by non‑stationary neural signals that vary across sessions and individuals, limiting the generalization of subject‑agnostic models and motivating adaptive and personalized learning on resource‑constrained platforms. Programmable memristive hardware offers a promising substrate for such post‑deployment adaptation; however, practical realization is challenged by limited weight resolution, device variability, nonlinear programming dynamics, and finite device endurance. In this work, we show that spiking neural networks (SNNs) can be deployed on ferroelectric memristive synaptic devices for adaptive EEG‑based motor imagery decoding under realistic device constraints, achieving classification performance comparable to software‑based SNNs. We fabricate, characterize, and model the weight update in ferroelectric synapses. We then evaluate the deployment of convolutional‑recurrent SNN architecture using two strategies. First, we adapt to SNNs a mixed precision strategy in which gradient‑based updates are accumulated digitally and converted into discrete programming events only when a threshold is exceeded. Additionally, the weight update is device‑aware and accounts for the nonlinear, state‑dependent programming dynamics. During learning and adaptation, this scheme mitigates possible endurance and energy constraints. Second, we evaluate the transfer of software‑trained weights followed by low‑overhead on‑device re‑tuning. We show that, subject‑specific transfer learning achieved by retraining only the final network layers improves classification accuracy. These results demonstrate that programmable ferroelectric hardware can support robust, low‑overhead adaptation in spiking neural networks, opening a practical path toward personalized neuromorphic processing of neural signals.