arXiv Papers with Code in Systems and Control (January 2026

Abstract:
Effective ride-hailing dispatch requires anticipating demand patterns that vary substantially across time-of-day, day-of-week, season, and special events. We propose a regime-calibrated approach that (i) segments historical trip data into demand regimes, (ii) matches the current operating period to the most similar historical analogues via a similarity ensemble combining Kolmogorov-Smirnov distance, Wasserstein-1 distance, feature distance, variance ratio, event pattern similarity, and temporal proximity, and (iii) uses the resulting calibrated demand prior to drive both an LP-based fleet repositioning policy and batch dispatch with Hungarian matching. In ablation, a distributional-only metric subset achieves the strongest mean-wait reduction, while the full ensemble is retained as a robustness-oriented default that preserves calendar and event context. Evaluated on 5.2 million NYC TLC trips across 8 diverse scenarios (winter/summer, weekday/weekend/holiday, morning/evening/night) with 5 random seeds each, our method reduces mean rider wait times by 31.1% (bootstrap 95% CI: [26.5, 36.6]; Friedman chi-squared = 80.0, p = 4.25e-18; Cohen's d = 7.5-29.9). P95 wait drops 37.6% and the Gini coefficient of wait times improves from 0.441 to 0.409. The two contributions compose multiplicatively: calibration provides 16.9% reduction relative to the replay baseline; LP repositioning adds a further 15.5%. The approach requires no training, is deterministic and explainable, generalizes to Chicago (23.3% wait reduction using the NYC-built regime library without retraining), and is robust across fleet sizes (32-47% improvement for 0.5x-2.0x fleet scaling). Code is available at https://github.com/IndarKarhana/regime-calibrated-dispatch.

Paperid: 2, https://arxiv.org/pdf/2604.03086.pdf GitHub

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.

Paperid: 3, https://arxiv.org/pdf/2604.02531.pdf GitHub

Abstract:
We present \texttt{DR-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 \texttt{DR-DAQP} is up to two orders of magnitude faster than the state-of-the-art solver \texttt{PATH}. On a game-theoretic MPC benchmark, \texttt{DR-DAQP} achieves solve times several orders of magnitude below those of the mixed-integer solver \texttt{NashOpt}. A high-performing C implementation is available at \textt{https://github.com/darnstrom/daqp}, with easily-accessible interfaces to Julia, MATLAB, and Python.

Paperid: 4, https://arxiv.org/pdf/2604.00215.pdf GitHub

Abstract:
Outpatient departments (OPDs) in Indian public hospitals face severe overcrowding, with daily volumes reaching 200--8,000 patients~\cite{aiims2020annual}. 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 $\sim$236 simulated urgency drift events per session (modeled via stochastic deterioration probabilities), identifies $\sim$11.9 additional hidden-critical cases via patient memory, and recomposes queue urgency distribution from 13/36/158/161 (Critical/High/Medium/Low) to $\sim$25/178/115/50 through continuous reassessment, while maintaining comparable throughput ($\sim$40.4 patients/hour).

Paperid: 5, https://arxiv.org/pdf/2603.29658.pdf GitHub

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.

Paperid: 6, https://arxiv.org/pdf/2603.29265.pdf GitHub

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.

Paperid: 7, https://arxiv.org/pdf/2603.26264.pdf GitHub GitHub

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.

Paperid: 8, https://arxiv.org/pdf/2603.23203.pdf GitHub

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.

Paperid: 9, https://arxiv.org/pdf/2603.22992.pdf GitHub

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.

Paperid: 10, https://arxiv.org/pdf/2603.20646.pdf GitHub

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.

Paperid: 11, https://arxiv.org/pdf/2603.17843.pdf GitHub

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.

Paperid: 12, https://arxiv.org/pdf/2603.17751.pdf GitHub

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.

Paperid: 13, https://arxiv.org/pdf/2603.17497.pdf GitHub

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.

Paperid: 14, https://arxiv.org/pdf/2603.14049.pdf GitHub

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 $\mathsf{SO}(2)$ and $\mathsf{SO}(3)$. The codes and animations are publicly available at https://github.com/gradslab/SbpLieGroups.git .

Paperid: 15, https://arxiv.org/pdf/2603.13559.pdf GitHub

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 $M \in \mathbb{R}^{n \times m}$, 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 $M$.

Paperid: 16, https://arxiv.org/pdf/2603.08958.pdf GitHub GitHub GitHub

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 \href{https://nail-uh.github.io/iros2026.github.io/}{project website}\footnote{Project Website: https://nail-uh.github.io/iros2026.github.io/}.

Paperid: 17, https://arxiv.org/pdf/2603.06217.pdf GitHub

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.

Paperid: 18, https://arxiv.org/pdf/2603.03880.pdf GitHub

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.

Paperid: 19, https://arxiv.org/pdf/2603.03280.pdf GitHub

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.

Paperid: 20, https://arxiv.org/pdf/2603.03017.pdf GitHub

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.

Paperid: 21, https://arxiv.org/pdf/2603.00455.pdf GitHub

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.

Paperid: 22, https://arxiv.org/pdf/2602.21531.pdf GitHub

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/.

Paperid: 23, https://arxiv.org/pdf/2602.16073.pdf GitHub GitHub

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/.

Paperid: 24, https://arxiv.org/pdf/2602.16063.pdf GitHub

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.

Paperid: 25, https://arxiv.org/pdf/2602.10007.pdf GitHub

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

Paperid: 26, https://arxiv.org/pdf/2602.04132.pdf GitHub

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

Paperid: 27, https://arxiv.org/pdf/2602.03020.pdf GitHub

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.

Paperid: 28, https://arxiv.org/pdf/2602.00534.pdf GitHub

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.

Paperid: 29, https://arxiv.org/pdf/2601.22151.pdf GitHub

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

Paperid: 30, https://arxiv.org/pdf/2601.21094.pdf GitHub GitHub

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.

Paperid: 31, https://arxiv.org/pdf/2601.19616.pdf GitHub

Abstract:
We present a Monte Carlo study of energy extraction from rotating (Kerr) black holes via the Penrose process using rocket propulsion. Through over 250,000 trajectory simulations, we establish sharp constraints on when Penrose extraction with escape to infinity succeeds. The mechanism requires that exhaust ejected inside the ergosphere carries negative Killing energy, which is kinematically accessible only via ultra-relativistic ejection deep within the ergosphere. We find that successful extraction with escape is statistically rare ($\sim$1% in broad parameter scans) and is governed by strict thresholds: it requires high black hole spin (empirically $a/M \gtrsim 0.89$) and ultra-relativistic exhaust velocity (onset at $v_e \approx 0.91c$). When conditions are highly tuned to a specific "sweet spot," success rates can reach 88.5%, representing a narrow extraction window rather than generic behavior. Furthermore, single-impulse thrust at periapsis achieves significantly higher cumulative efficiency ($η_{\rm cum} \approx 19\%$) compared to continuous thrust ($\sim$2--4%) due to path-averaging penalties. These constraints quantify the extreme fine-tuning required for material-based Penrose extraction, consistent with the astrophysical dominance of electromagnetic mechanisms. Simulation code is available at https://github.com/anindex/penrose_process.

Paperid: 32, https://arxiv.org/pdf/2601.19354.pdf GitHub

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.

Paperid: 33, https://arxiv.org/pdf/2601.19312.pdf GitHub

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.

Paperid: 34, https://arxiv.org/pdf/2601.14933.pdf GitHub GitHub

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 $n\times n$, standard approaches for dense matrix equations typically incur $\mathcal{O}(n^3)$ cost per-iteration, while the efficient $\mathcal{O}(n^2)$ 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 $\mathcal{O}(\log(n))$ 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 $\mathcal{O}(n^2)$ arithmetic cost. We prove an $\mathcal{O}(n)$ 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 \texttt{icare}, 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 : \href{https://github.com/yogeshd-iitk/nonlinear_matrix_equation_R1RSD}{\textcolor{blue}{https://github.com/yogeshd-iitk/nonlinear\_matrix \_equation\_R1RSD}}

Paperid: 35, https://arxiv.org/pdf/2601.14468.pdf GitHub GitHub

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 \href{https://github.com/LSU-RAISE-LAB/APF-OPF}{GitHub}, along with a README describing how to run and reproduce the experiments.

Paperid: 36, https://arxiv.org/pdf/2601.12952.pdf GitHub

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.

Paperid: 37, https://arxiv.org/pdf/2601.12766.pdf GitHub

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/.

Paperid: 38, https://arxiv.org/pdf/2601.07728.pdf GitHub

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).

Paperid: 39, https://arxiv.org/pdf/2601.07721.pdf GitHub

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\footnote{https://github.com/pesslovany/Matlab-LagrangianPMF}.

Paperid: 40, https://arxiv.org/pdf/2601.07476.pdf GitHub

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 10s grams, severely limits their onboard computational resources to sub-\SI{100}{\milli\watt} microcontroller units (MCUs). The Bitcraze Crazyflie nano-drone is the \textit{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 \textit{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\%).

Paperid: 41, https://arxiv.org/pdf/2601.03281.pdf GitHub

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

Paperid: 42, https://arxiv.org/pdf/2601.01274.pdf GitHub

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

Paperid: 43, https://arxiv.org/pdf/2603.04626.pdf

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.

Paperid: 44, https://arxiv.org/pdf/2601.09463.pdf

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.

Paperid: 45, https://arxiv.org/pdf/2601.02804.pdf

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.

Paperid: 46, https://arxiv.org/pdf/2601.16565.pdf

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.

Paperid: 47, https://arxiv.org/pdf/2603.00420.pdf

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.

Paperid: 48, https://arxiv.org/pdf/2603.27976.pdf

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$^2$LoS, 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$^2$LoS 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$\times$.

Paperid: 49, https://arxiv.org/pdf/2603.18865.pdf

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 pre-trained 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, 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.

Paperid: 50, https://arxiv.org/pdf/2603.18855.pdf

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.

Paperid: 51, https://arxiv.org/pdf/2603.18853.pdf

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

Paperid: 52, https://arxiv.org/pdf/2603.17499.pdf

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.

Paperid: 53, https://arxiv.org/pdf/2601.20780.pdf

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.

Paperid: 54, https://arxiv.org/pdf/2603.18407.pdf

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.

Paperid: 55, https://arxiv.org/pdf/2603.17202.pdf

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.

Paperid: 56, https://arxiv.org/pdf/2602.14436.pdf

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.

Paperid: 57, https://arxiv.org/pdf/2604.00673.pdf

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.

Paperid: 58, https://arxiv.org/pdf/2601.21706.pdf

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.

Paperid: 59, https://arxiv.org/pdf/2603.17416.pdf

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.

Paperid: 60, https://arxiv.org/pdf/2603.10906.pdf

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.

Paperid: 61, https://arxiv.org/pdf/2603.07393.pdf

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.

Paperid: 62, https://arxiv.org/pdf/2604.03066.pdf

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.

Paperid: 63, https://arxiv.org/pdf/2603.11077.pdf

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).

Paperid: 64, https://arxiv.org/pdf/2603.11074.pdf

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.

Paperid: 65, https://arxiv.org/pdf/2603.00357.pdf

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.

Paperid: 66, https://arxiv.org/pdf/2601.18280.pdf

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.

Paperid: 67, https://arxiv.org/pdf/2603.25274.pdf

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.

Paperid: 68, https://arxiv.org/pdf/2602.22662.pdf

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.

Paperid: 69, https://arxiv.org/pdf/2602.19667.pdf

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.

Paperid: 70, https://arxiv.org/pdf/2601.08214.pdf

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.

Paperid: 71, https://arxiv.org/pdf/2602.21752.pdf

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.

Paperid: 72, https://arxiv.org/pdf/2602.04971.pdf

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.

Paperid: 73, https://arxiv.org/pdf/2602.22134.pdf

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.

Paperid: 74, https://arxiv.org/pdf/2603.14374.pdf

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.

Paperid: 75, https://arxiv.org/pdf/2603.07866.pdf

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.

Paperid: 76, https://arxiv.org/pdf/2602.23312.pdf

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.

Paperid: 77, https://arxiv.org/pdf/2601.11231.pdf

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.

Paperid: 78, https://arxiv.org/pdf/2603.25816.pdf

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.

Paperid: 79, https://arxiv.org/pdf/2602.20359.pdf

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.

Paperid: 80, https://arxiv.org/pdf/2602.04056.pdf

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.

Paperid: 81, https://arxiv.org/pdf/2604.00429.pdf

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 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.

Paperid: 82, https://arxiv.org/pdf/2603.05385.pdf

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.

Paperid: 83, https://arxiv.org/pdf/2601.22080.pdf

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.

Paperid: 84, https://arxiv.org/pdf/2604.00391.pdf

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 \emph{Behavioral 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.

Paperid: 85, https://arxiv.org/pdf/2604.00388.pdf

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 $2\times$, 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.

Paperid: 86, https://arxiv.org/pdf/2604.00380.pdf

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.

Paperid: 87, https://arxiv.org/pdf/2603.22731.pdf

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.

Paperid: 88, https://arxiv.org/pdf/2603.21545.pdf

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.

Paperid: 89, https://arxiv.org/pdf/2603.21543.pdf

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).

Paperid: 90, https://arxiv.org/pdf/2603.21539.pdf

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.

Paperid: 91, https://arxiv.org/pdf/2602.24259.pdf

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.

Paperid: 92, https://arxiv.org/pdf/2601.07744.pdf

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.

Paperid: 93, https://arxiv.org/pdf/2601.01940.pdf

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.

Paperid: 94, https://arxiv.org/pdf/2603.26009.pdf

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.

Paperid: 95, https://arxiv.org/pdf/2604.02821.pdf

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.

Paperid: 96, https://arxiv.org/pdf/2603.28011.pdf

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 $\texttt{immrax}$ 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 $X$.

Paperid: 97, https://arxiv.org/pdf/2603.19524.pdf

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.

Paperid: 98, https://arxiv.org/pdf/2602.02236.pdf

Abstract:
Deploying pretrained policies in real-world applications presents substantial challenges that fundamentally limit the practical applicability of learning-based control systems. When autonomous systems encounter environmental changes in system dynamics, sensor drift, or task objectives, fixed policies rapidly degrade in performance. We show that employing Real-Time Recurrent Reinforcement Learning (RTRRL), a biologically plausible algorithm for online adaptation, can effectively fine-tune a pretrained policy to improve autonomous agents' performance on driving tasks. We further show that RTRRL synergizes with a recent biologically inspired recurrent network model, the Liquid-Resistance Liquid-Capacitance RNN. We demonstrate the effectiveness of this closed-loop approach in a simulated CarRacing environment and in a real-world line-following task with a RoboRacer car equipped with an event camera.

Paperid: 99, https://arxiv.org/pdf/2601.10095.pdf

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.

Paperid: 100, https://arxiv.org/pdf/2601.00587.pdf

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.

Paperid: 101, https://arxiv.org/pdf/2604.03536.pdf

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 \emph{combinatorial 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 \emph{generalized combinatorial CBF} under a condition termed \emph{conjunctive 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.

Paperid: 102, https://arxiv.org/pdf/2604.03450.pdf

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.

Paperid: 103, https://arxiv.org/pdf/2604.03405.pdf

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.

Paperid: 104, https://arxiv.org/pdf/2603.17058.pdf

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.

Paperid: 105, https://arxiv.org/pdf/2603.16851.pdf

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.

Paperid: 106, https://arxiv.org/pdf/2603.00085.pdf

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.

Paperid: 107, https://arxiv.org/pdf/2602.05324.pdf

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.

Paperid: 108, https://arxiv.org/pdf/2602.03691.pdf

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.

Paperid: 109, https://arxiv.org/pdf/2604.02791.pdf

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.

Paperid: 110, https://arxiv.org/pdf/2604.02768.pdf

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.

Paperid: 111, https://arxiv.org/pdf/2604.00334.pdf

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.

Paperid: 112, https://arxiv.org/pdf/2602.15794.pdf

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.

Paperid: 113, https://arxiv.org/pdf/2603.00182.pdf

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.

Paperid: 114, https://arxiv.org/pdf/2604.01517.pdf

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.

Paperid: 115, https://arxiv.org/pdf/2602.11842.pdf

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, disregarding the complex interactions among market participants. The security of the system, therefore, may be overestimated. To address this gap, we introduce a market model with a social-welfare-based day-ahead market clearing mechanism. The security implications are analyzed via Cascades, a cascading outage analysis framework. We apply this framework 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 of up to 80% higher than OPF, highlighting a security overestimation. Operators can use this information to properly allocate reserves and perform efficient expansion planning strategies.

Paperid: 116, https://arxiv.org/pdf/2604.04486.pdf

Abstract:
The integration of the high penetration of intermittent renewable energy sources (RES) and the retirement of thermal units have significantly aggravated the flexibility scarcity and real-time balancing challenges in power systems. Low-carbon steel production systems, based on green-hydrogen ironmaking and electrified melting, 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 zero-carbon steel production system architecture is established to explicitly represent the energy-material flow coupling relationships among electricity, hydrogen, heat, iron, steel, CO2, and methanol. Second, to explicitly capture electric arc furnace (EAF) operational constraints while preserving optimization tractability, an operating feasible region model is developed and validated using field data from a pure hydrogen direct reduced iron and EAF plant, yielding an average relative error of 4.1%. Finally, a process-aware DR scheduling model is formulated by incorporating the proposed process deviation penalties to balance economic performance against process disturbance costs and operational acceptability. Additionally, dual-side evaluation metrics are developed to quantify grid-side regulation performance and load-side flexibility characteristics. Case studies demonstrate that under real-time pricing, the proposed system achieves an average DR capacity of 275.4 MW, improves the RES-load matching degree from 0.262 to 0.508, and reduces total operational costs by 17.78% compared with the baseline scheduling scheme. The proposed framework provides a theoretical foundation for RES-steel-chemical synergies.

Paperid: 117, https://arxiv.org/pdf/2604.04235.pdf

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.

Paperid: 118, https://arxiv.org/pdf/2604.03892.pdf

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.

Paperid: 119, https://arxiv.org/pdf/2603.29119.pdf

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$\times$ over a baseline approach.

Paperid: 120, https://arxiv.org/pdf/2603.29117.pdf

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.

Paperid: 121, https://arxiv.org/pdf/2603.27912.pdf

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.

Paperid: 122, https://arxiv.org/pdf/2603.24566.pdf

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.

Paperid: 123, https://arxiv.org/pdf/2603.21651.pdf

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.

Paperid: 124, https://arxiv.org/pdf/2603.18450.pdf

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.

Paperid: 125, https://arxiv.org/pdf/2603.17225.pdf

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.

Paperid: 126, https://arxiv.org/pdf/2602.17199.pdf

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.

Paperid: 127, https://arxiv.org/pdf/2602.17166.pdf

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.

Paperid: 128, https://arxiv.org/pdf/2602.15827.pdf

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.

Paperid: 129, https://arxiv.org/pdf/2601.22098.pdf

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.

Paperid: 130, https://arxiv.org/pdf/2604.00826.pdf

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.

Paperid: 131, https://arxiv.org/pdf/2603.29560.pdf

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.

Paperid: 132, https://arxiv.org/pdf/2603.17632.pdf

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.

Paperid: 133, https://arxiv.org/pdf/2602.02924.pdf

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.

Paperid: 134, https://arxiv.org/pdf/2601.15729.pdf

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.

Paperid: 135, https://arxiv.org/pdf/2601.14880.pdf

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.

Paperid: 136, https://arxiv.org/pdf/2601.08753.pdf

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.

Paperid: 137, https://arxiv.org/pdf/2601.03476.pdf

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.

Paperid: 138, https://arxiv.org/pdf/2601.01581.pdf

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.

Paperid: 139, https://arxiv.org/pdf/2601.00609.pdf

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.

Paperid: 140, https://arxiv.org/pdf/2604.04067.pdf

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.

Paperid: 141, https://arxiv.org/pdf/2604.02132.pdf

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.

Paperid: 142, https://arxiv.org/pdf/2604.00659.pdf

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.

Paperid: 143, https://arxiv.org/pdf/2603.29851.pdf

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.

Paperid: 144, https://arxiv.org/pdf/2603.28900.pdf

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.

Paperid: 145, https://arxiv.org/pdf/2603.24540.pdf

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.

Paperid: 146, https://arxiv.org/pdf/2603.12187.pdf

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.

Paperid: 147, https://arxiv.org/pdf/2602.09123.pdf

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.

Paperid: 148, https://arxiv.org/pdf/2601.18294.pdf

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.

Paperid: 149, https://arxiv.org/pdf/2604.00400.pdf

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.

Paperid: 150, https://arxiv.org/pdf/2602.21155.pdf

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 a prediction for diagnostic residual generation. This online learning approach overcomes the challenges of model changes, while the integrated structure reduces the dependence on large datasets. Furthermore, we derive analytical conditions that provide 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.

Paperid: 151, https://arxiv.org/pdf/2602.10397.pdf

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.

Paperid: 152, https://arxiv.org/pdf/2601.23119.pdf

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.

Paperid: 153, https://arxiv.org/pdf/2601.14643.pdf

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.

Paperid: 154, https://arxiv.org/pdf/2601.07823.pdf

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.

Paperid: 155, https://arxiv.org/pdf/2603.27583.pdf

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.

Paperid: 156, https://arxiv.org/pdf/2603.25276.pdf

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.

Paperid: 157, https://arxiv.org/pdf/2603.19229.pdf

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.

Paperid: 158, https://arxiv.org/pdf/2603.12662.pdf

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.

Paperid: 159, https://arxiv.org/pdf/2603.03082.pdf

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.

Paperid: 160, https://arxiv.org/pdf/2602.13615.pdf

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.

Paperid: 161, https://arxiv.org/pdf/2602.11478.pdf

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.

Paperid: 162, https://arxiv.org/pdf/2601.11323.pdf

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.

Paperid: 163, https://arxiv.org/pdf/2601.07156.pdf

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.

Paperid: 164, https://arxiv.org/pdf/2601.07090.pdf

Abstract:
This paper presents preliminary results toward a conceptual foundation for Next Generation Grid Codes (NGGCs) based on decentralized stability and performance certification for dynamic ancillary services. The proposed NGGC framework targets two core outcomes: (i) guaranteed closed-loop stability and (ii) explicit performance assurances for power-system frequency and voltage dynamics. Stability is addressed using loop-shifting and passivity-based methods that yield local frequency-domain certificates for individual devices, enabling fully decentralized verification of the interconnected system. Performance is characterized by deriving quantitative bounds on key time-domain metrics (e.g., nadirs, rate-of-change-of-frequency (RoCoF), steady-state deviations, and oscillation damping) through frequency-domain constraints on local device behavior. The framework is non-parametric and model-agnostic, accommodating a broad class of device dynamics under mild assumptions, and provides an initial unified approach to stability and performance certification without explicit device-model parameterization. As such, these results offer a principled starting point for the development of future grid codes and control design methodologies in modern power systems.

Paperid: 165, https://arxiv.org/pdf/2601.02885.pdf

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 them 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 has been debated. 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, a sequence of indices 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.

Paperid: 166, https://arxiv.org/pdf/2601.02278.pdf

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.

Paperid: 167, https://arxiv.org/pdf/2604.04280.pdf

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.

Paperid: 168, https://arxiv.org/pdf/2604.00748.pdf

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.

Paperid: 169, https://arxiv.org/pdf/2603.28938.pdf

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 $\sqrt{T}$, 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.

Paperid: 170, https://arxiv.org/pdf/2602.17312.pdf

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.

Paperid: 171, https://arxiv.org/pdf/2602.17282.pdf

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.

Paperid: 172, https://arxiv.org/pdf/2602.06620.pdf

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.

Paperid: 173, https://arxiv.org/pdf/2601.16367.pdf

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.

Paperid: 174, https://arxiv.org/pdf/2601.12782.pdf

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.

Paperid: 175, https://arxiv.org/pdf/2601.03893.pdf

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.

Paperid: 176, https://arxiv.org/pdf/2601.00521.pdf

Abstract:
Current parking navigation systems often underestimate total travel time by failing to account for the time spent searching for a parking space, which significantly affects 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 for decision-making based on probabilistic information about parking availability at the parking lot level. 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. Acknowledging the financial costs of permanent sensing infrastructure, the paper provides analytical and empirical assessments of errors incurred when leveraging stochastic observations to estimate parking 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 grows. 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 direct-to-destination estimates.

Paperid: 177, https://arxiv.org/pdf/2604.01403.pdf

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.

Paperid: 178, https://arxiv.org/pdf/2604.00774.pdf

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.

Paperid: 179, https://arxiv.org/pdf/2604.00561.pdf

Abstract:
In this paper, we derive a novel procedure for set-membership estimation of dynamical systems affected by stochastic noise with unbounded support. By 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. Through our analysis, we provide conditions under which the proposed uncertainty set converges to the true system parameters and establish an upper bound on the convergence rate. The proposed uncertainty set can be used directly for the synthesis of robust controllers with probabilistic stability and performance guarantees. Concluding numerical examples demonstrate the advantages of the proposed formulation over established approaches.

Paperid: 180, https://arxiv.org/pdf/2603.22469.pdf

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.

Paperid: 181, https://arxiv.org/pdf/2603.19648.pdf

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.

Paperid: 182, https://arxiv.org/pdf/2603.19361.pdf

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.

Paperid: 183, https://arxiv.org/pdf/2603.16865.pdf

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.

Paperid: 184, https://arxiv.org/pdf/2603.16481.pdf

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 scale well in the multi-output case, or are difficult to integrate into downstream tasks. This paper addresses these limitations by presenting a tight, distribution-free bound for multi-output kernel-based estimates. It is obtained through an unconstrained, duality-based formulation, which shares the same structure of classic Gaussian process confidence bounds and can thus be straightforwardly integrated into downstream optimization pipelines. We show that the proposed bound generalizes many existing results and illustrate its application using an example inspired by quadrotor dynamics learning.

Paperid: 185, https://arxiv.org/pdf/2603.15606.pdf

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 and at a controllable rate remains an open challenge: gradient descent is blind to curvature, stochastic perturbation methods lack deterministic guarantees, and Newton-type approaches suffer from Hessian singularity. We present Curvature-Regularized Gradient Dynamics (CRGD), which augments the objective with a smooth penalty on the most negative Hessian eigenvalue, yielding an augmented cost that serves as an optimization Lyapunov function with user-selectable convergence rates to second-order stationary points. Numerical experiments on a nonconvex matrix factorization example confirm that CRGD escapes saddle points across all tested configurations, with escape time that decreases with the eigenvalue gap, in contrast to gradient descent, whose escape time grows inversely with the gap.

Paperid: 186, https://arxiv.org/pdf/2603.14514.pdf

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.

Paperid: 187, https://arxiv.org/pdf/2603.10743.pdf

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 that dimensional-analysis and data-scaling, established techniques in physical sciences, 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. 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.

Paperid: 188, https://arxiv.org/pdf/2603.08038.pdf

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.

Paperid: 189, https://arxiv.org/pdf/2603.07095.pdf

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.

Paperid: 190, https://arxiv.org/pdf/2603.02696.pdf

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.

Paperid: 191, https://arxiv.org/pdf/2602.19532.pdf

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.

Paperid: 192, https://arxiv.org/pdf/2602.17705.pdf

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.

Paperid: 193, https://arxiv.org/pdf/2602.11116.pdf

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.

Paperid: 194, https://arxiv.org/pdf/2602.07456.pdf

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.

Paperid: 195, https://arxiv.org/pdf/2602.05116.pdf

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.

Paperid: 196, https://arxiv.org/pdf/2602.04262.pdf

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.

Paperid: 197, https://arxiv.org/pdf/2602.03521.pdf

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.

Paperid: 198, https://arxiv.org/pdf/2601.21988.pdf

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.

Paperid: 199, https://arxiv.org/pdf/2601.20324.pdf

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.

Paperid: 200, https://arxiv.org/pdf/2601.16290.pdf

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.

Paperid: 201, https://arxiv.org/pdf/2601.14689.pdf

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.

Paperid: 202, https://arxiv.org/pdf/2601.14343.pdf

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.

Paperid: 203, https://arxiv.org/pdf/2601.00221.pdf

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.

Paperid: 204, https://arxiv.org/pdf/2604.02256.pdf

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 $1.8\times 10^6$ 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.

Paperid: 205, https://arxiv.org/pdf/2603.25840.pdf

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.

Paperid: 206, https://arxiv.org/pdf/2603.25057.pdf

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.

Paperid: 207, https://arxiv.org/pdf/2603.23649.pdf

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.

Paperid: 208, https://arxiv.org/pdf/2603.18701.pdf

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.

Paperid: 209, https://arxiv.org/pdf/2603.18658.pdf

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.

Paperid: 210, https://arxiv.org/pdf/2603.16680.pdf

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.

Paperid: 211, https://arxiv.org/pdf/2603.14386.pdf

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.

Paperid: 212, https://arxiv.org/pdf/2603.14056.pdf

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/

Paperid: 213, https://arxiv.org/pdf/2603.13509.pdf

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.

Paperid: 214, https://arxiv.org/pdf/2603.05740.pdf

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.

Paperid: 215, https://arxiv.org/pdf/2603.02478.pdf

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 $\mathbf{SO}(3)$ 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.

Paperid: 216, https://arxiv.org/pdf/2602.18109.pdf

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.

Paperid: 217, https://arxiv.org/pdf/2602.17174.pdf

Abstract:
Robust control of mechanical systems with multiple uncertainties remains a fundamental challenge, particularly when nonlinear dynamics and operating-condition variations are intricately intertwined. While deep reinforcement learning (DRL) combined with domain randomization has shown promise in mitigating the sim-to-real gap, simultaneously handling all 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 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 learning efficiency, we jointly incorporate a model-based controller (MBC), which guarantees a shared baseline performance across the plant sets, into the learning process to accelerate the convergence. This residual learning scheme facilitates task-specific optimization of the DRL agent for each uncertainty, thereby enhancing sample efficiency. As a practical industrial application, this study applies the proposed method to designing an active vibration controller for automotive powertrains. We verified that the resulting controller is robust against structural nonlinearities and dynamic variations, realizing successful sim-to-real transfer.

Paperid: 218, https://arxiv.org/pdf/2602.15422.pdf

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.

Paperid: 219, https://arxiv.org/pdf/2602.10813.pdf

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.

Paperid: 220, https://arxiv.org/pdf/2602.10752.pdf

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 $\mathcal{L}_2$ 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.

Paperid: 221, https://arxiv.org/pdf/2602.09695.pdf

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 $\mathcal{L}^2$ 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.

Paperid: 222, https://arxiv.org/pdf/2602.08273.pdf

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.

Paperid: 223, https://arxiv.org/pdf/2601.07489.pdf

Abstract:
FR3 ($\approx$7-24 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.

Paperid: 224, https://arxiv.org/pdf/2601.02903.pdf

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.

Paperid: 225, https://arxiv.org/pdf/2604.04499.pdf

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 \textit{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.

Paperid: 226, https://arxiv.org/pdf/2604.03728.pdf

Abstract:
Renewable power-to-ammonia (ReP2A) production offers a viable pathway to decarbonize the power and chemical sectors and is increasingly supported by carbon-emission policies. However, a carbon-related mechanism that links ReP2A producers with fossil-based gray ammonia (GA) competitors while aligning the interests of renewable power, green hydrogen, and green ammonia producers in the ReP2A process chain remains unexplored. To fill this gap, we propose a hierarchical carbon-driven incentive mechanism (PCIM) to improve the market competitiveness of green ammonia. We first construct a trading framework in which ReP2A and GA participate in both the carbon allowance (CA) and ammonia markets, which forms the outer layer. These interactions, together with electricity and hydrogen transactions in the ReP2A chain, which form the inner layer, are modeled as a hierarchical game. For tractability, the inner layer is characterized via decomposable equivalent optimization, and the outer layer is solved as a mixed-integer linear program (MILP) derived from Karush-Kuhn-Tucker conditions. Based on the resulting equilibrium, we identify the carbon-related revenue of ReP2A and propose an incentive-compatible CA allocation mechanism (PCAM) %to ensure equitable benefit sharing across the ReP2A chain. Simulations show that the PCIM reduces carbon emissions by 12.9\% at a cost of only a 1.8% decrease in sectorwide revenue, and results from the PCIM provide guidance for carbon pricing. Furthermore, the application of the PCAM increases stakeholders' willingness to participate in ReP2A production.

Paperid: 227, https://arxiv.org/pdf/2604.03449.pdf

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.

Paperid: 228, https://arxiv.org/pdf/2604.02953.pdf

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.

Paperid: 229, https://arxiv.org/pdf/2604.02939.pdf

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.

Paperid: 230, https://arxiv.org/pdf/2604.02401.pdf

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.

Paperid: 231, https://arxiv.org/pdf/2603.29858.pdf

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.

Paperid: 232, https://arxiv.org/pdf/2603.24138.pdf

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.

Paperid: 233, https://arxiv.org/pdf/2603.23197.pdf

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.

Paperid: 234, https://arxiv.org/pdf/2603.14298.pdf

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.

Paperid: 235, https://arxiv.org/pdf/2603.11982.pdf

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.

Paperid: 236, https://arxiv.org/pdf/2603.03685.pdf

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.

Paperid: 237, https://arxiv.org/pdf/2603.00514.pdf

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.

Paperid: 238, https://arxiv.org/pdf/2602.10911.pdf

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.

Paperid: 239, https://arxiv.org/pdf/2602.06569.pdf

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.

Paperid: 240, https://arxiv.org/pdf/2601.22865.pdf

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.

Paperid: 241, https://arxiv.org/pdf/2601.20298.pdf

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.

Paperid: 242, https://arxiv.org/pdf/2601.19132.pdf

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.

Paperid: 243, https://arxiv.org/pdf/2601.11320.pdf

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.

Paperid: 244, https://arxiv.org/pdf/2601.07079.pdf

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.

Paperid: 245, https://arxiv.org/pdf/2601.01772.pdf

Abstract:
Brain-Computer Interfaces (BCIs) enable users to interact with machines directly via neural activity, yet their real-world deployment is often hindered by bulky and powerhungry hardware. We present EdgeSSVEP, a fully embedded microcontroller-based Steady-State Visually Evoked Potential (SSVEP) BCI platform that performs real-time EEG acquisition, zero-phase filtering, and on-device classification within a lowpower 240 MHz MCU operating at only 222 mW. The system incorporates an 8-channel EEG front end, supports 5-second stimulus durations, and executes the entire SSVEP decoding pipeline locally, eliminating dependence on PC-based processing. EdgeSSVEP was evaluated using six stimulus frequencies (7, 8, 9, 11, 7.5, and 8.5 Hz) with 10 participants. The device achieved 99.17% classification accuracy and 27.33 bits/min Information Transfer Rate (ITR), while consuming substantially less power than conventional desktop-based systems. The system integrates motion sensing to support artifact detection and improve robustness and signal stability in practical environments. For development and debugging, the system also provides optional TCP data streaming to external clients. Overall, EdgeSSVEP offers a scalable, energy-efficient, and secure embedded BCI platform suitable for assistive communication and neurofeedback applications, with potential extensions to accelerometer-based artifact mitigation and broader real-world deployments.

Paperid: 246, https://arxiv.org/pdf/2604.02196.pdf

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.

Paperid: 247, https://arxiv.org/pdf/2604.01805.pdf

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.

Paperid: 248, https://arxiv.org/pdf/2603.26956.pdf

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.

Paperid: 249, https://arxiv.org/pdf/2603.25982.pdf

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.

Paperid: 250, https://arxiv.org/pdf/2603.21476.pdf

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.

Paperid: 251, https://arxiv.org/pdf/2603.19632.pdf

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.

Paperid: 252, https://arxiv.org/pdf/2603.15907.pdf

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.

Paperid: 253, https://arxiv.org/pdf/2603.14450.pdf

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.

Paperid: 254, https://arxiv.org/pdf/2603.04914.pdf

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 \emph{U-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.

Paperid: 255, https://arxiv.org/pdf/2603.04189.pdf

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.

Paperid: 256, https://arxiv.org/pdf/2602.18740.pdf

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 4*4 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.

Paperid: 257, https://arxiv.org/pdf/2602.17434.pdf

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.

Paperid: 258, https://arxiv.org/pdf/2602.16866.pdf

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.

Paperid: 259, https://arxiv.org/pdf/2602.14092.pdf

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 rely solely on 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 real-world soft-robot data, that the method learns a bending stiffness model online while accurately estimating the robot's pose. Notably, reduced multi-step forward-prediction errors indicate that the learned bending-stiffness GP improves overall model quality.

Paperid: 260, https://arxiv.org/pdf/2602.12616.pdf

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.

Paperid: 261, https://arxiv.org/pdf/2602.10162.pdf

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.

Paperid: 262, https://arxiv.org/pdf/2602.07859.pdf

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.

Paperid: 263, https://arxiv.org/pdf/2602.06356.pdf

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.

Paperid: 264, https://arxiv.org/pdf/2602.03272.pdf

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.

Paperid: 265, https://arxiv.org/pdf/2602.00636.pdf

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.

Paperid: 266, https://arxiv.org/pdf/2601.21046.pdf

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.

Paperid: 267, https://arxiv.org/pdf/2601.20314.pdf

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.

Paperid: 268, https://arxiv.org/pdf/2601.20149.pdf

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.

Paperid: 269, https://arxiv.org/pdf/2601.08445.pdf

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.

Paperid: 270, https://arxiv.org/pdf/2601.05637.pdf

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.

Paperid: 271, https://arxiv.org/pdf/2601.05519.pdf

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.

Paperid: 272, https://arxiv.org/pdf/2604.03909.pdf

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.

Paperid: 273, https://arxiv.org/pdf/2604.01392.pdf

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.

Paperid: 274, https://arxiv.org/pdf/2604.01372.pdf

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 \emph{single 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 \emph{set 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.

Paperid: 275, https://arxiv.org/pdf/2604.00061.pdf

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.

Paperid: 276, https://arxiv.org/pdf/2603.23898.pdf

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.

Paperid: 277, https://arxiv.org/pdf/2603.23465.pdf

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.

Paperid: 278, https://arxiv.org/pdf/2603.21428.pdf

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.

Paperid: 279, https://arxiv.org/pdf/2603.17137.pdf

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.

Paperid: 280, https://arxiv.org/pdf/2603.02579.pdf

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.

Paperid: 281, https://arxiv.org/pdf/2602.22774.pdf

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.

Paperid: 282, https://arxiv.org/pdf/2602.18933.pdf

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.

Paperid: 283, https://arxiv.org/pdf/2602.10355.pdf

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.

Paperid: 284, https://arxiv.org/pdf/2602.03939.pdf

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.

Paperid: 285, https://arxiv.org/pdf/2601.22120.pdf

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.

Paperid: 286, https://arxiv.org/pdf/2601.22096.pdf

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.

Paperid: 287, https://arxiv.org/pdf/2601.22087.pdf

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 $1000\times$ 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.

Paperid: 288, https://arxiv.org/pdf/2601.17608.pdf

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.

Paperid: 289, https://arxiv.org/pdf/2601.16578.pdf

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.

Paperid: 290, https://arxiv.org/pdf/2601.16198.pdf

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.

Paperid: 291, https://arxiv.org/pdf/2601.15196.pdf

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.

Paperid: 292, https://arxiv.org/pdf/2601.06885.pdf

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.

Paperid: 293, https://arxiv.org/pdf/2604.04027.pdf

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.

Paperid: 294, https://arxiv.org/pdf/2604.01450.pdf

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.

Paperid: 295, https://arxiv.org/pdf/2604.00566.pdf

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

Paperid: 296, https://arxiv.org/pdf/2603.29474.pdf

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.

Paperid: 297, https://arxiv.org/pdf/2603.29445.pdf

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.

Paperid: 298, https://arxiv.org/pdf/2603.29225.pdf

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.

Paperid: 299, https://arxiv.org/pdf/2603.27382.pdf

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.

Paperid: 300, https://arxiv.org/pdf/2603.19450.pdf

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.

Paperid: 301, https://arxiv.org/pdf/2603.18364.pdf

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.

Paperid: 302, https://arxiv.org/pdf/2603.17003.pdf

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.

Paperid: 303, https://arxiv.org/pdf/2603.16808.pdf

Abstract:
We consider nonlinear model predictive control (MPC) schemes using surrogate models in the optimization step 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 with a numerical example.

Paperid: 304, https://arxiv.org/pdf/2603.15889.pdf

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.

Paperid: 305, https://arxiv.org/pdf/2603.14388.pdf

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.

Paperid: 306, https://arxiv.org/pdf/2603.09424.pdf

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.

Paperid: 307, https://arxiv.org/pdf/2603.09076.pdf

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.

Paperid: 308, https://arxiv.org/pdf/2603.06955.pdf

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.

Paperid: 309, https://arxiv.org/pdf/2603.06354.pdf

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.

Paperid: 310, https://arxiv.org/pdf/2603.05762.pdf

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.

Paperid: 311, https://arxiv.org/pdf/2603.05279.pdf

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.

Paperid: 312, https://arxiv.org/pdf/2603.01365.pdf

Abstract:
Distributed training and increasing the gradient update frequency are practical strategies to accelerate learning and improve performance, but both exacerbate a central challenge: \textit{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 \textit{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.

Paperid: 313, https://arxiv.org/pdf/2603.00283.pdf

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.

Paperid: 314, https://arxiv.org/pdf/2603.00126.pdf

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.

Paperid: 315, https://arxiv.org/pdf/2602.21429.pdf

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.

Paperid: 316, https://arxiv.org/pdf/2602.19414.pdf

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.

Paperid: 317, https://arxiv.org/pdf/2602.17504.pdf

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 aerodynamic damage and control surface degradation, all closed-loop states remain stable with bounded tracking errors.

Paperid: 318, https://arxiv.org/pdf/2601.16003.pdf

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 10$μ$s, achieving a 5-order-of-magnitude reduction compared to optimization-based baselines.

Paperid: 319, https://arxiv.org/pdf/2601.14416.pdf

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.

Paperid: 320, https://arxiv.org/pdf/2601.11394.pdf

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.

Paperid: 321, https://arxiv.org/pdf/2601.09158.pdf

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.

Paperid: 322, https://arxiv.org/pdf/2601.05087.pdf

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.

Paperid: 323, https://arxiv.org/pdf/2601.03247.pdf

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.

Paperid: 324, https://arxiv.org/pdf/2604.03046.pdf

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.

Paperid: 325, https://arxiv.org/pdf/2604.02173.pdf

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.

Paperid: 326, https://arxiv.org/pdf/2604.02157.pdf

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.

Paperid: 327, https://arxiv.org/pdf/2604.01477.pdf

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.

Paperid: 328, https://arxiv.org/pdf/2604.01373.pdf

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.

Paperid: 329, https://arxiv.org/pdf/2604.01144.pdf

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.

Paperid: 330, https://arxiv.org/pdf/2604.00379.pdf

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.

Paperid: 331, https://arxiv.org/pdf/2604.00286.pdf

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.

Paperid: 332, https://arxiv.org/pdf/2604.00283.pdf

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.

Paperid: 333, https://arxiv.org/pdf/2604.00201.pdf

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.

Paperid: 334, https://arxiv.org/pdf/2603.28971.pdf

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.

Paperid: 335, https://arxiv.org/pdf/2603.27427.pdf

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.

Paperid: 336, https://arxiv.org/pdf/2603.27306.pdf

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 \textsc{GUIDE}, 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.

Paperid: 337, https://arxiv.org/pdf/2603.26578.pdf

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.

Paperid: 338, https://arxiv.org/pdf/2603.24475.pdf

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.

Paperid: 339, https://arxiv.org/pdf/2603.22881.pdf

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.

Paperid: 340, https://arxiv.org/pdf/2603.20557.pdf

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.

Paperid: 341, https://arxiv.org/pdf/2603.19813.pdf

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.

Paperid: 342, https://arxiv.org/pdf/2603.17418.pdf

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.

Paperid: 343, https://arxiv.org/pdf/2603.12220.pdf

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.

Paperid: 344, https://arxiv.org/pdf/2603.11716.pdf

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.

Paperid: 345, https://arxiv.org/pdf/2603.06541.pdf

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.

Paperid: 346, https://arxiv.org/pdf/2603.06338.pdf

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.

Paperid: 347, https://arxiv.org/pdf/2603.03965.pdf

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, a geometric adaptive law is incorporated into the control structure. The adaptation mechanism updates only the spatial inertia parameters using a single adaptation gain for the entire system, while guaranteeing physically consistent and drift-free parameter estimates. Numerical simulations are provided to validate the effectiveness of the proposed approach in comparison to the existing modular and geometric methods.

Paperid: 348, https://arxiv.org/pdf/2603.02526.pdf

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-focused approaches tend to assume nominal operating conditions and lack resilience to adversarial inputs. To address these challenges, we propose a 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 pure event-driven methods lacking 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.

Paperid: 349, https://arxiv.org/pdf/2603.01972.pdf

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.

Paperid: 350, https://arxiv.org/pdf/2603.01403.pdf

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.

Paperid: 351, https://arxiv.org/pdf/2602.19386.pdf

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.

Paperid: 352, https://arxiv.org/pdf/2602.19070.pdf

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.

Paperid: 353, https://arxiv.org/pdf/2602.18814.pdf

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.

Paperid: 354, https://arxiv.org/pdf/2602.18382.pdf

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.

Paperid: 355, https://arxiv.org/pdf/2602.17574.pdf

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.

Paperid: 356, https://arxiv.org/pdf/2602.15254.pdf

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.

Paperid: 357, https://arxiv.org/pdf/2602.10936.pdf

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.

Paperid: 358, https://arxiv.org/pdf/2602.10615.pdf

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.

Paperid: 359, https://arxiv.org/pdf/2602.08943.pdf

Abstract:
An Artificial Magnetic Conductor (AMC) frame capable of improving the impedance matching of a 2$\times$2 array for 6G applications without degrading isolation performance is presented. The proposed frame is integrated into the array without modifying the single radiating element design. By relying on accurate full-wave simulations, it results that the addition of the frame restores the impedance matching performance, achieving a bandwidth of 1.5 GHz at 28 GHz. The isolation between each port remains under -15 dB within the operating band, thanks to the vias in the rectangular patch metasurface. Moreover, the overall structure exhibits a gain of 11.81 dBi with an aperture efficiency of 69$\%$, 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.

Paperid: 360, https://arxiv.org/pdf/2602.08842.pdf

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.

Paperid: 361, https://arxiv.org/pdf/2602.00325.pdf

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.

Paperid: 362, https://arxiv.org/pdf/2601.21622.pdf

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.

Paperid: 363, https://arxiv.org/pdf/2601.19462.pdf

Abstract:
This paper aims to provide a clear and rigorous understanding of commonly recognized safety constraints in physical human-robot interaction, i.e. ISO/TS 15066, by examining how they are obtained and which assumptions support them. We clarify the interpretation and practical impact of key simplifying assumptions, show how these modeling choices affect both safety and performance across the system, and indicate specific design parameters that can be adjusted in safety-critical control implementations. Numerical examples are provided to quantify performance degradation induced by common approximations and simplifying design choices. Furthermore, the fundamental role of energy in safety assessment is emphasized, and focused insights are offered on the existing body of work concerning energy-based safety methodologies.

Paperid: 364, https://arxiv.org/pdf/2601.17520.pdf

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.

Paperid: 365, https://arxiv.org/pdf/2601.10827.pdf

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.

Paperid: 366, https://arxiv.org/pdf/2601.08254.pdf

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.

Paperid: 367, https://arxiv.org/pdf/2601.08107.pdf

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.

Paperid: 368, https://arxiv.org/pdf/2601.07133.pdf

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.

Paperid: 369, https://arxiv.org/pdf/2601.07132.pdf

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.

Paperid: 370, https://arxiv.org/pdf/2601.03445.pdf

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.

Paperid: 371, https://arxiv.org/pdf/2601.00309.pdf

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.

Paperid: 372, https://arxiv.org/pdf/2601.00257.pdf

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.

Paperid: 373, https://arxiv.org/pdf/2604.03605.pdf

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.

Paperid: 374, https://arxiv.org/pdf/2604.03282.pdf

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.

Paperid: 375, https://arxiv.org/pdf/2604.02625.pdf

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.

Paperid: 376, https://arxiv.org/pdf/2604.02573.pdf

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.

Paperid: 377, https://arxiv.org/pdf/2604.01056.pdf

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.

Paperid: 378, https://arxiv.org/pdf/2604.00119.pdf

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.

Paperid: 379, https://arxiv.org/pdf/2603.29214.pdf

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.

Paperid: 380, https://arxiv.org/pdf/2603.25959.pdf

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.

Paperid: 381, https://arxiv.org/pdf/2603.23476.pdf

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.

Paperid: 382, https://arxiv.org/pdf/2603.23039.pdf

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.

Paperid: 383, https://arxiv.org/pdf/2603.22445.pdf

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.

Paperid: 384, https://arxiv.org/pdf/2603.21726.pdf

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.

Paperid: 385, https://arxiv.org/pdf/2603.20144.pdf

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.

Paperid: 386, https://arxiv.org/pdf/2603.19119.pdf

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.

Paperid: 387, https://arxiv.org/pdf/2603.18335.pdf

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 \emph{unknown 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.

Paperid: 388, https://arxiv.org/pdf/2603.17764.pdf

Abstract:
Dynamic pricing is commonly used to regulate congestion in shared service systems. This paper is motivated by the fact that when heterogeneaous user groups (in terms of price responsiveness) are present, conventional monotonic pricing can lead to unfair outcomes by disproportionately excluding price-elastic users, particularly under high or uncertain demand. 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 resource capacity and fairness constraints for all user type distributions consistent with aggregate measurements. By integrating integral High Order Control Barrier Functions (iHOCBFs) with a worst case robust optimization framework, 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.

Paperid: 389, https://arxiv.org/pdf/2603.17293.pdf

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.

Paperid: 390, https://arxiv.org/pdf/2603.15881.pdf

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.

Paperid: 391, https://arxiv.org/pdf/2603.15588.pdf

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.

Paperid: 392, https://arxiv.org/pdf/2603.09894.pdf

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.

Paperid: 393, https://arxiv.org/pdf/2603.08964.pdf

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.

Paperid: 394, https://arxiv.org/pdf/2603.07395.pdf

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.

Paperid: 395, https://arxiv.org/pdf/2603.03556.pdf

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.

Paperid: 396, https://arxiv.org/pdf/2603.03546.pdf

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.

Paperid: 397, https://arxiv.org/pdf/2603.01077.pdf

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.

Paperid: 398, https://arxiv.org/pdf/2603.00858.pdf

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.

Paperid: 399, https://arxiv.org/pdf/2602.23526.pdf

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.

Paperid: 400, https://arxiv.org/pdf/2602.22459.pdf

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.

Paperid: 401, https://arxiv.org/pdf/2602.22443.pdf

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.

Paperid: 402, https://arxiv.org/pdf/2602.21715.pdf

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.

Paperid: 403, https://arxiv.org/pdf/2602.18668.pdf

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.

Paperid: 404, https://arxiv.org/pdf/2602.17352.pdf

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.

Paperid: 405, https://arxiv.org/pdf/2602.15209.pdf

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.

Paperid: 406, https://arxiv.org/pdf/2602.14537.pdf

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.

Paperid: 407, https://arxiv.org/pdf/2602.12841.pdf

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.

Paperid: 408, https://arxiv.org/pdf/2602.12638.pdf

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.

Paperid: 409, https://arxiv.org/pdf/2602.12296.pdf

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.

Paperid: 410, https://arxiv.org/pdf/2602.11461.pdf

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.

Paperid: 411, https://arxiv.org/pdf/2602.10523.pdf

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.

Paperid: 412, https://arxiv.org/pdf/2602.08058.pdf

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.

Paperid: 413, https://arxiv.org/pdf/2602.05586.pdf

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.

Paperid: 414, https://arxiv.org/pdf/2602.05584.pdf

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.

Paperid: 415, https://arxiv.org/pdf/2602.05156.pdf

Abstract:
We present the PLATO Hand, a dexterous robotic hand with a hybrid fingertip that embeds a rigid fingernail within a compliant pulp. This design shapes contact behavior to enable diverse interaction modes across a range of object geometries. We develop a strain-energy-based bending-indentation model to guide the fingertip design and to explain how guided contact preserves local indentation while suppressing global bending. Experimental results show that the proposed robotic hand design demonstrates improved pinching stability, enhanced force observability, and successful execution of edge-sensitive manipulation tasks, including paper singulation, card picking, and orange peeling. Together, these results show that coupling structured contact geometry with a force-motion transparent mechanism provides a principled, physically embodied approach to precise manipulation.

Paperid: 416, https://arxiv.org/pdf/2602.03757.pdf

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.

Paperid: 417, https://arxiv.org/pdf/2602.02592.pdf

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.

Paperid: 418, https://arxiv.org/pdf/2602.01420.pdf

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.

Paperid: 419, https://arxiv.org/pdf/2602.01337.pdf

Abstract:
This paper considers the bandit quickest change detection problem in which one stream contains a change-point that shifts its distribution by an unknown amount in an unknown direction. We consider an agent that can observe only a single stream at each time, and the goal of the agent is to detect this change as quickly as possible while controlling for false alarms. We propose an algorithm that combines a decaying-$ε$-greedy stream switching rule with an efficient change-point detection algorithm for unknown post-change means. We provide bounds on the expected detection delay and average run length to false alarm for our algorithm, and based on these results we prove our algorithm is approximately optimal with respect to a commonly used surrogate. This work is the first to provide provable guarantees in this setting without strong assumptions such as a discretized post-change parameter set or a lower bound on the magnitude of change.

Paperid: 421, https://arxiv.org/pdf/2601.20666.pdf

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.

Paperid: 422, https://arxiv.org/pdf/2601.13174.pdf

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.

Paperid: 423, https://arxiv.org/pdf/2601.11698.pdf

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.

Paperid: 424, https://arxiv.org/pdf/2601.10891.pdf

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.

Paperid: 425, https://arxiv.org/pdf/2601.09484.pdf

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.

Paperid: 426, https://arxiv.org/pdf/2601.09058.pdf

Abstract:
The Access Traffic Steering, Switching, and Splitting (ATSSS) defined in the 3GPP latest Release 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 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 15% for the whole system compared with baselines.

Paperid: 427, https://arxiv.org/pdf/2601.07172.pdf

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.

Paperid: 428, https://arxiv.org/pdf/2601.05408.pdf

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.

Paperid: 429, https://arxiv.org/pdf/2601.04817.pdf

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.

Paperid: 430, https://arxiv.org/pdf/2601.01951.pdf

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.

Paperid: 432, https://arxiv.org/pdf/2604.03508.pdf

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.

Paperid: 433, https://arxiv.org/pdf/2604.03490.pdf

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.

Paperid: 434, https://arxiv.org/pdf/2604.03278.pdf

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.

Paperid: 435, https://arxiv.org/pdf/2604.02069.pdf

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.

Paperid: 436, https://arxiv.org/pdf/2604.01211.pdf

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.

Paperid: 437, https://arxiv.org/pdf/2604.01198.pdf

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.

Paperid: 438, https://arxiv.org/pdf/2604.01188.pdf

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.

Paperid: 439, https://arxiv.org/pdf/2604.01156.pdf

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.

Paperid: 440, https://arxiv.org/pdf/2604.00992.pdf

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.

Paperid: 441, https://arxiv.org/pdf/2604.00935.pdf

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.

Paperid: 442, https://arxiv.org/pdf/2604.00573.pdf

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.

Paperid: 443, https://arxiv.org/pdf/2603.29050.pdf

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.

Paperid: 444, https://arxiv.org/pdf/2603.27803.pdf

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].

Paperid: 445, https://arxiv.org/pdf/2603.27159.pdf

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 $\sqrt{T}$-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.

Paperid: 446, https://arxiv.org/pdf/2603.25979.pdf

Abstract:
Resource allocation under strategic adversarial constraints represents a fundamental challenge in control systems, from cybersecurity defense to infrastructure protection. While game-theoretic frameworks have long informed such problems, Colonel Blotto games -- despite their direct relevance to allocation decisions -- remain underutilized and underappreciated in the controls community compared to other game-theoretic models like the Prisoner's Dilemma. The disparity stems largely from analytical complexity: Colonel Blotto games typically require characterizing intricate mixed-strategy equilibria that resist the clean, closed-form solutions control theorists prefer. Yet as Golman and Page observe, this very complexity ``makes Blotto all the more compelling in its interpretations.'' The goal of this expository article is to showcase the power and versatility of Colonel Blotto game frameworks for the controls community, demonstrating how allocation problems across cybersecurity, network defense, and multi-agent systems can be modeled within this unified theoretical structure. We survey recent analytical and computational breakthroughs, highlight diverse applications, and examine extensions addressing incomplete information, network effects, and multi-stage decision-making -- illustrating how Colonel Blotto games provide both practical tools and fundamental insights for strategic resource allocation in adversarial environments.

Paperid: 447, https://arxiv.org/pdf/2603.24795.pdf

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.

Paperid: 448, https://arxiv.org/pdf/2603.19955.pdf

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.

Paperid: 449, https://arxiv.org/pdf/2603.19796.pdf

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.

Paperid: 450, https://arxiv.org/pdf/2603.18581.pdf

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.

Paperid: 451, https://arxiv.org/pdf/2603.17335.pdf

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.

Paperid: 452, https://arxiv.org/pdf/2603.17097.pdf

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.

Paperid: 453, https://arxiv.org/pdf/2603.16842.pdf

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.

Paperid: 454, https://arxiv.org/pdf/2603.16841.pdf

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.

Paperid: 455, https://arxiv.org/pdf/2603.16832.pdf

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.

Paperid: 456, https://arxiv.org/pdf/2603.15063.pdf

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.

Paperid: 457, https://arxiv.org/pdf/2603.14018.pdf

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.

Paperid: 458, https://arxiv.org/pdf/2603.12098.pdf

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.

Paperid: 459, https://arxiv.org/pdf/2603.10138.pdf

Abstract:
Power distribution systems are increasingly exposed to large voltage fluctuations driven by intermittent solar photovoltaic generation and rapidly 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 the structural properties of the nonlinear constraints. Case studies show that the proposed approach achieves fast convergence and adapts quickly to changes in net load.

Paperid: 460, https://arxiv.org/pdf/2603.09382.pdf

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.

Paperid: 461, https://arxiv.org/pdf/2603.07237.pdf

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.}

Paperid: 462, https://arxiv.org/pdf/2603.04843.pdf

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.

Paperid: 463, https://arxiv.org/pdf/2603.03664.pdf

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 LTCs preserve the QC IS after information sharing, whereas violating which 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 results on the 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.

Paperid: 464, https://arxiv.org/pdf/2602.24268.pdf

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.

Paperid: 465, https://arxiv.org/pdf/2602.23607.pdf

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.

Paperid: 466, https://arxiv.org/pdf/2602.22577.pdf

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.

Paperid: 467, https://arxiv.org/pdf/2602.22249.pdf

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.

Paperid: 468, https://arxiv.org/pdf/2602.20107.pdf

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.

Paperid: 469, https://arxiv.org/pdf/2602.19587.pdf

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.

Paperid: 470, https://arxiv.org/pdf/2602.19366.pdf

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.

Paperid: 471, https://arxiv.org/pdf/2602.18655.pdf

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.

Paperid: 472, https://arxiv.org/pdf/2602.18331.pdf

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.

Paperid: 473, https://arxiv.org/pdf/2602.18059.pdf

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.

Paperid: 474, https://arxiv.org/pdf/2602.17379.pdf

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.

Paperid: 475, https://arxiv.org/pdf/2602.12954.pdf

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.

Paperid: 476, https://arxiv.org/pdf/2602.09928.pdf

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.

Paperid: 477, https://arxiv.org/pdf/2602.09206.pdf

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 tradeoffs 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.

Paperid: 478, https://arxiv.org/pdf/2602.07684.pdf

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.

Paperid: 479, https://arxiv.org/pdf/2602.03070.pdf

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 \textbf{ProOPF-D} and \textbf{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.

Paperid: 480, https://arxiv.org/pdf/2601.22052.pdf

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 a 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 sub-second inference times 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.

Paperid: 481, https://arxiv.org/pdf/2601.19284.pdf

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.

Paperid: 482, https://arxiv.org/pdf/2601.18840.pdf

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.

Paperid: 483, https://arxiv.org/pdf/2601.15496.pdf

Abstract:
In this paper, we study the timeliness of actions in communication systems where actuation is constrained by control permissions or energy availability. Building on the Age of Actuation (AoA) metric, which quantifies the timeliness of actions independently of data freshness, we introduce a new metric, the \emph{Age of Actuated Information (AoAI)}. AoAI captures the end-to-end timeliness of actions by explicitly accounting for the age of the data packet at the moment it is actuated. We analyze and characterize both AoA and AoAI in discrete-time systems with data storage capabilities under multiple actuation scenarios. The actuator requires both a data packet and an actuation opportunity, which may be provided by a controller or enabled by harvested energy. Data packets may be stored either in a single-packet buffer or an infinite-capacity queue for future actuation. For these settings, we derive closed-form expressions for the average AoA and AoAI and investigate their structural differences. While AoA and AoAI coincide in instantaneous actuation systems, they differentiate when data buffering is present. Our results reveal counterintuitive regimes in which increasing update or actuation rates degrade action timeliness for both AoA and AoAI. Moreover, as part of the analysis, we obtain a novel closed-form characterization of the steady-state distribution of a Geo/Geo/1 queue operating under the FCFS discipline, expressed solely in terms of the queue length and the age of the head-of-line packet. The proposed metrics and analytical results provide new insights into the semantics of timeliness in systems where information ultimately serves the purpose of actuation.

Paperid: 484, https://arxiv.org/pdf/2601.14665.pdf

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.

Paperid: 485, https://arxiv.org/pdf/2601.11901.pdf

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.

Paperid: 486, https://arxiv.org/pdf/2601.11326.pdf

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.

Paperid: 487, https://arxiv.org/pdf/2601.10178.pdf

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.

Paperid: 488, https://arxiv.org/pdf/2601.10153.pdf

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.

Paperid: 489, https://arxiv.org/pdf/2601.07646.pdf

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.

Paperid: 490, https://arxiv.org/pdf/2601.03097.pdf

Abstract:
We propose a learning-based trajectory tracking controller for autonomous robotic platforms whose motion can be described kinematically on $\mathrm{SE}(3)$. 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.

Paperid: 491, https://arxiv.org/pdf/2601.01538.pdf

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.

Paperid: 492, https://arxiv.org/pdf/2601.00289.pdf

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.

Paperid: 493, https://arxiv.org/pdf/2604.04400.pdf

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.

Paperid: 494, https://arxiv.org/pdf/2604.04234.pdf

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 \emph{stability} 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.

Paperid: 495, https://arxiv.org/pdf/2604.03138.pdf

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.

Paperid: 496, https://arxiv.org/pdf/2604.02687.pdf

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.

Paperid: 497, https://arxiv.org/pdf/2604.01959.pdf

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.

Paperid: 498, https://arxiv.org/pdf/2604.01539.pdf

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.

Paperid: 499, https://arxiv.org/pdf/2604.01441.pdf

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.

Paperid: 500, https://arxiv.org/pdf/2604.00490.pdf

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 $2\times 2$ 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.

Paperid: 501, https://arxiv.org/pdf/2604.00254.pdf

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.

Paperid: 502, https://arxiv.org/pdf/2603.29708.pdf

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.

Paperid: 503, https://arxiv.org/pdf/2603.29532.pdf

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.

Paperid: 504, https://arxiv.org/pdf/2603.29523.pdf

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 \texttt{pandapower} 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.

Paperid: 505, https://arxiv.org/pdf/2603.27580.pdf

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.

Paperid: 506, https://arxiv.org/pdf/2603.27305.pdf

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.

Paperid: 507, https://arxiv.org/pdf/2603.26999.pdf

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.

Paperid: 508, https://arxiv.org/pdf/2603.26636.pdf

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.

Paperid: 509, https://arxiv.org/pdf/2603.24990.pdf

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.

Paperid: 510, https://arxiv.org/pdf/2603.24894.pdf

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.

Paperid: 511, https://arxiv.org/pdf/2603.24503.pdf

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.

Paperid: 512, https://arxiv.org/pdf/2603.24130.pdf

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.

Paperid: 513, https://arxiv.org/pdf/2603.23814.pdf

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 ($δ$IOS) 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 ($δ$ISS) implies FM semi-globally for time-invariant systems under an equibounded input assumption. Notably, Boyd and Chua's approximation theorems apply to delta-ISS 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.

Paperid: 514, https://arxiv.org/pdf/2603.23182.pdf

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.

Paperid: 515, https://arxiv.org/pdf/2603.22653.pdf

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.

Paperid: 516, https://arxiv.org/pdf/2603.20472.pdf

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.

Paperid: 517, https://arxiv.org/pdf/2603.20402.pdf

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.

Paperid: 518, https://arxiv.org/pdf/2603.20152.pdf

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.

Paperid: 519, https://arxiv.org/pdf/2603.19655.pdf

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.

Paperid: 520, https://arxiv.org/pdf/2603.19455.pdf

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.

Paperid: 521, https://arxiv.org/pdf/2603.18861.pdf

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.

Paperid: 522, https://arxiv.org/pdf/2603.18422.pdf

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.

Paperid: 523, https://arxiv.org/pdf/2603.18094.pdf

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.

Paperid: 524, https://arxiv.org/pdf/2603.18022.pdf

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.

Paperid: 525, https://arxiv.org/pdf/2603.16768.pdf

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.

Paperid: 526, https://arxiv.org/pdf/2603.11547.pdf

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.

Paperid: 527, https://arxiv.org/pdf/2603.10292.pdf

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.

Paperid: 528, https://arxiv.org/pdf/2603.07413.pdf

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.

Paperid: 529, https://arxiv.org/pdf/2603.07375.pdf

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.

Paperid: 530, https://arxiv.org/pdf/2603.07053.pdf

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.

Paperid: 531, https://arxiv.org/pdf/2603.01359.pdf

Abstract:
Over the last decades, progress in modal analysis has enabled increasingly routine use of modal parameters, including those extracted from in-situ measurements, 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 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.

Paperid: 532, https://arxiv.org/pdf/2603.00728.pdf

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.

Paperid: 533, https://arxiv.org/pdf/2602.23478.pdf

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.

Paperid: 534, https://arxiv.org/pdf/2602.21525.pdf

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.

Paperid: 535, https://arxiv.org/pdf/2602.21278.pdf

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.

Paperid: 536, https://arxiv.org/pdf/2602.14742.pdf

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.

Paperid: 537, https://arxiv.org/pdf/2602.13009.pdf

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.

Paperid: 538, https://arxiv.org/pdf/2602.12416.pdf

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.

Paperid: 539, https://arxiv.org/pdf/2602.07959.pdf

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.

Paperid: 540, https://arxiv.org/pdf/2602.07958.pdf

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.

Paperid: 541, https://arxiv.org/pdf/2602.07215.pdf

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.

Paperid: 542, https://arxiv.org/pdf/2602.05376.pdf

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.

Paperid: 543, https://arxiv.org/pdf/2602.05103.pdf

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.

Paperid: 544, https://arxiv.org/pdf/2602.03346.pdf

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.

Paperid: 545, https://arxiv.org/pdf/2601.21753.pdf

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.

Paperid: 546, https://arxiv.org/pdf/2601.20005.pdf

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.

Paperid: 547, https://arxiv.org/pdf/2601.17442.pdf

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.

Paperid: 548, https://arxiv.org/pdf/2601.16283.pdf

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.

Paperid: 549, https://arxiv.org/pdf/2601.16187.pdf

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.

Paperid: 550, https://arxiv.org/pdf/2601.14763.pdf

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.

Paperid: 551, https://arxiv.org/pdf/2601.14725.pdf

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.

Paperid: 552, https://arxiv.org/pdf/2601.13114.pdf

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.

Paperid: 553, https://arxiv.org/pdf/2601.12885.pdf

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.

Paperid: 554, https://arxiv.org/pdf/2601.12545.pdf

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.

Paperid: 555, https://arxiv.org/pdf/2601.12210.pdf

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.

Paperid: 556, https://arxiv.org/pdf/2601.10044.pdf

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.

Paperid: 557, https://arxiv.org/pdf/2601.06000.pdf

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.

Paperid: 558, https://arxiv.org/pdf/2601.04957.pdf

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.

Paperid: 559, https://arxiv.org/pdf/2601.03536.pdf

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.

Paperid: 560, https://arxiv.org/pdf/2601.03143.pdf

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.

Paperid: 561, https://arxiv.org/pdf/2604.04531.pdf

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.

Paperid: 562, https://arxiv.org/pdf/2604.04070.pdf

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.

Paperid: 563, https://arxiv.org/pdf/2604.02749.pdf

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.

Paperid: 564, https://arxiv.org/pdf/2603.29921.pdf

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.

Paperid: 565, https://arxiv.org/pdf/2603.29083.pdf

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.

Paperid: 566, https://arxiv.org/pdf/2603.28310.pdf

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.

Paperid: 568, https://arxiv.org/pdf/2603.27177.pdf

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.

Paperid: 569, https://arxiv.org/pdf/2603.25574.pdf

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.

Paperid: 570, https://arxiv.org/pdf/2603.24419.pdf

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.

Paperid: 571, https://arxiv.org/pdf/2603.22924.pdf

Abstract:
The paper shows that positive linear systems can be stabilized using positive Luenberger-type observers, contradicting previous conclusions. 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.

Paperid: 572, https://arxiv.org/pdf/2603.17686.pdf

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.

Paperid: 573, https://arxiv.org/pdf/2603.17190.pdf

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.

Paperid: 574, https://arxiv.org/pdf/2603.17015.pdf

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.

Paperid: 575, https://arxiv.org/pdf/2603.14516.pdf

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.

Paperid: 576, https://arxiv.org/pdf/2603.14047.pdf

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.

Paperid: 577, https://arxiv.org/pdf/2603.11978.pdf

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.

Paperid: 578, https://arxiv.org/pdf/2603.11390.pdf

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.

Paperid: 579, https://arxiv.org/pdf/2603.09110.pdf

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.

Paperid: 580, https://arxiv.org/pdf/2603.08931.pdf

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.

Paperid: 581, https://arxiv.org/pdf/2603.07964.pdf

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.

Paperid: 582, https://arxiv.org/pdf/2603.07823.pdf

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.

Paperid: 583, https://arxiv.org/pdf/2603.07810.pdf

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.

Paperid: 584, https://arxiv.org/pdf/2603.07499.pdf

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.

Paperid: 585, https://arxiv.org/pdf/2603.07437.pdf

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.

Paperid: 586, https://arxiv.org/pdf/2603.06964.pdf

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.

Paperid: 587, https://arxiv.org/pdf/2603.03943.pdf

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.

Paperid: 588, https://arxiv.org/pdf/2603.03929.pdf

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.

Paperid: 589, https://arxiv.org/pdf/2603.02412.pdf

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.

Paperid: 590, https://arxiv.org/pdf/2603.02379.pdf

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.

Paperid: 591, https://arxiv.org/pdf/2602.23345.pdf

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.

Paperid: 592, https://arxiv.org/pdf/2602.21567.pdf

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.

Paperid: 593, https://arxiv.org/pdf/2602.19784.pdf

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.

Paperid: 594, https://arxiv.org/pdf/2602.19428.pdf

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.

Paperid: 595, https://arxiv.org/pdf/2602.19421.pdf

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.

Paperid: 596, https://arxiv.org/pdf/2602.18569.pdf

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.

Paperid: 597, https://arxiv.org/pdf/2602.17252.pdf

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.

Paperid: 598, https://arxiv.org/pdf/2602.16166.pdf

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.

Paperid: 599, https://arxiv.org/pdf/2602.13896.pdf

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.

Paperid: 600, https://arxiv.org/pdf/2602.11063.pdf

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.

Paperid: 601, https://arxiv.org/pdf/2602.09605.pdf

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.

Paperid: 602, https://arxiv.org/pdf/2602.09429.pdf

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.

Paperid: 603, https://arxiv.org/pdf/2602.09427.pdf

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.

Paperid: 604, https://arxiv.org/pdf/2602.08477.pdf

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.

Paperid: 605, https://arxiv.org/pdf/2602.06026.pdf

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.

Paperid: 606, https://arxiv.org/pdf/2602.05311.pdf

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 \emph{robust 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.

Paperid: 607, https://arxiv.org/pdf/2602.04578.pdf

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%.

Paperid: 608, https://arxiv.org/pdf/2602.02137.pdf

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.

Paperid: 609, https://arxiv.org/pdf/2601.21679.pdf

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.

Paperid: 610, https://arxiv.org/pdf/2601.20561.pdf

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 electron beam tilt 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. In total, the alignment time is reduced from typically several minutes to less than a minute compared to the state-of-the-art.

Paperid: 611, https://arxiv.org/pdf/2601.20445.pdf

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.

Paperid: 612, https://arxiv.org/pdf/2601.20427.pdf

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%.

Paperid: 613, https://arxiv.org/pdf/2601.18690.pdf

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.

Paperid: 614, https://arxiv.org/pdf/2601.18494.pdf

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.

Paperid: 615, https://arxiv.org/pdf/2601.17859.pdf

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 $μs$ 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.

Paperid: 616, https://arxiv.org/pdf/2601.13799.pdf

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.

Paperid: 617, https://arxiv.org/pdf/2601.13057.pdf

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.

Paperid: 618, https://arxiv.org/pdf/2601.12695.pdf

Abstract:
Model-based control requires accurate mathematical models to guarantee control performance and stability. However, obtaining accurate models is challenging due to process and sensor noise. This paper proposes a novel identification algorithm that derives polytopic uncertainty models by interpreting noise-induced parameter fluctuations as intrinsic uncertainty. The method applies cyclic reformulation with period N to linear time-invariant systems, yielding N parameter sets with slight variations that serve as polytope vertices. This enables systematic polytopic model construction from a single identification experiment. Simulation results demonstrate significant improvements: the proposed method achieves higher parameter estimation accuracy and reduces prediction errors by approximately half compared to conventional approaches. The vertex count N provides systematic control over the precision of uncertainty representation.

Paperid: 619, https://arxiv.org/pdf/2601.09223.pdf

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.

Paperid: 620, https://arxiv.org/pdf/2601.07608.pdf

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.

Paperid: 621, https://arxiv.org/pdf/2601.01409.pdf

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.

Paperid: 622, https://arxiv.org/pdf/2604.04079.pdf

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.

Paperid: 623, https://arxiv.org/pdf/2604.03921.pdf

Abstract:
This paper develops a cooperative fault-tolerant control framework for heterogeneous networked linear systems subject to sensor degradation and external disturbances. Each unit employs an augmented $\mathcal{H}_\infty$ observer that jointly reconstructs its state and sensor fault, providing disturbance-attenuated estimation guarantees. An inner state-feedback gain is then synthesized via convex $\mathcal{H}_\infty$ LMIs to ensure robust closed-loop stabilization, while an outer distributed integral action drives all units to track a constant setpoint source. The resulting network error dynamics satisfy an input-to-state stability condition with respect to disturbances and estimation imperfections, and converge to zero in their absence. Simulations on star, cyclic, and path topologies with heterogeneous agents confirm reliable tracking despite abrupt sensor faults and bounded disturbances, demonstrating a scalable and resilient coordination strategy for multi-agent systems with sensing imperfections.

Paperid: 624, https://arxiv.org/pdf/2604.02634.pdf

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.

Paperid: 625, https://arxiv.org/pdf/2604.02622.pdf

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.

Paperid: 626, https://arxiv.org/pdf/2604.01991.pdf

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.

Paperid: 627, https://arxiv.org/pdf/2604.01945.pdf

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.

Paperid: 628, https://arxiv.org/pdf/2604.01928.pdf

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.

Paperid: 629, https://arxiv.org/pdf/2604.01923.pdf

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.

Paperid: 630, https://arxiv.org/pdf/2604.01173.pdf

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.

Paperid: 631, https://arxiv.org/pdf/2604.00305.pdf

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.

Paperid: 632, https://arxiv.org/pdf/2603.27909.pdf

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.

Paperid: 633, https://arxiv.org/pdf/2603.26647.pdf

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.

Paperid: 634, https://arxiv.org/pdf/2603.26335.pdf

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 mode 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 mode 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 mode 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.

Paperid: 635, https://arxiv.org/pdf/2603.25287.pdf

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.

Paperid: 636, https://arxiv.org/pdf/2603.25259.pdf

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.

Paperid: 637, https://arxiv.org/pdf/2603.25192.pdf

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.

Paperid: 638, https://arxiv.org/pdf/2603.25167.pdf

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.

Paperid: 639, https://arxiv.org/pdf/2603.25161.pdf

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.

Paperid: 640, https://arxiv.org/pdf/2603.24489.pdf

Abstract:
Model Predictive Path Integral (MPPI) control is a popular sampling-based method for trajectory optimization in nonlinear and nonconvex settings, yet its optimization structure remains only partially understood. We develop a variational, optimization-theoretic interpretation of MPPI by lifting constrained trajectory optimization to a KL-regularized problem over distributions and reducing it to a negative log-partition (free-energy) objective over a tractable sampling family. For a general parametric family, this yields a preconditioned gradient method on the distribution parameters and a natural multi-step extension of MPPI. For the fixed-covariance Gaussian family, we show that classical MPPI is recovered exactly as a preconditioned gradient descent step with unit step size. This interpretation enables a direct convergence analysis: under bounded feasible sets, we derive an explicit upper bound on the smoothness constant and a simple sufficient condition guaranteeing descent of exact MPPI. Numerical experiments support the theory and illustrate the effect of key hyperparameters on performance.

Paperid: 641, https://arxiv.org/pdf/2603.21819.pdf

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.

Paperid: 642, https://arxiv.org/pdf/2603.21635.pdf

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.

Paperid: 643, https://arxiv.org/pdf/2603.20672.pdf

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.

Paperid: 644, https://arxiv.org/pdf/2603.20256.pdf

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.

Paperid: 645, https://arxiv.org/pdf/2603.20189.pdf

Abstract:
Steering large-scale swarms in only a few control updates is challenging because real systems operate in sampled-data form: control inputs are updated intermittently and applied over finite intervals. In this regime, the natural object is not an instantaneous velocity field, but a finite-window control quantity that captures the system response over each sampling interval. Inspired by MeanFlow, we introduce a control-space learning framework for swarm steering under linear time-invariant dynamics. The learned object is the coefficient that parameterizes the finite-horizon minimum-energy control over each interval. We show that this coefficient admits both an integral representation and a local differential identity along bridge trajectories, which leads to a simple stop-gradient training objective. At implementation time, the learned coefficient is used directly in sampled-data updates, so the prescribed dynamics and actuation map are respected by construction. The resulting framework provides a scalable approach to few-step swarm steering that is consistent with the sampled-data structure of real control systems.

Paperid: 646, https://arxiv.org/pdf/2603.19188.pdf

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.

Paperid: 647, https://arxiv.org/pdf/2603.19132.pdf

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.

Paperid: 648, https://arxiv.org/pdf/2603.18454.pdf

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 nonlinear Dubins car tracking problem, the self-consistent bound captures most of the optimal cost across sensor noise levels, while the open-loop variant is vacuous at low noise.

Paperid: 649, https://arxiv.org/pdf/2603.18284.pdf

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.

Paperid: 650, https://arxiv.org/pdf/2603.17931.pdf

Abstract:
This paper proposes a real-time control policy for cascaded hydropower systems that incorporates decision-dependent uncertainty (DDU) to capture the coupling of streamflow uncertainties across the network. 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 convergence of the proposed method and show that it recovers the Bonferroni approximation under steady-state conditions. A randomized case study based on Columbia River data demonstrates that the proposed framework improves both energy generation and reservoir reliability by accounting for DDU. Sensitivity analyses on drought severity and model parameters further highlight the value of adaptive risk allocation for resilient hydropower operations.

Paperid: 651, https://arxiv.org/pdf/2603.15934.pdf

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.

Paperid: 652, https://arxiv.org/pdf/2603.15912.pdf

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.

Paperid: 653, https://arxiv.org/pdf/2603.14762.pdf

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 $\mathcal{O}(N \log N)$ steps, while simultaneously achieving finite $L_2$-gain with respect to system disturbances.

Paperid: 654, https://arxiv.org/pdf/2603.12583.pdf

Abstract:
In this work, we propose a data-driven skill-informed framework to design optimal haptic nudge feedback for high-dimensional novel motor learning tasks. We first model the stochastic dynamics of human motor learning using an Input-Output Hidden Markov Model (IOHMM), which explicitly decouples latent skill evolution from observable kinematic emissions. 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, implicitly guiding the learner toward robust regions of the skill space. We validated our approach through a human-subject study ($N=30$) using a high-dimensional hand-exoskeleton task. Results demonstrate that participants trained with the POMDP-derived policy exhibited significantly accelerated task performance compared to groups receiving heuristic-based feedback or no feedback. Furthermore, synergy analysis revealed that the POMDP group discovered efficient low-dimensional motor representations more rapidly.

Paperid: 655, https://arxiv.org/pdf/2603.09904.pdf

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.

Paperid: 656, https://arxiv.org/pdf/2603.08045.pdf

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.

Paperid: 657, https://arxiv.org/pdf/2603.07060.pdf

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.

Paperid: 658, https://arxiv.org/pdf/2603.06536.pdf

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.

Paperid: 659, https://arxiv.org/pdf/2603.05169.pdf

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.

Paperid: 660, https://arxiv.org/pdf/2603.04203.pdf

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.

Paperid: 661, https://arxiv.org/pdf/2603.02344.pdf

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.

Paperid: 662, https://arxiv.org/pdf/2603.01248.pdf

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.

Paperid: 663, https://arxiv.org/pdf/2603.00379.pdf

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.

Paperid: 664, https://arxiv.org/pdf/2602.23056.pdf

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.

Paperid: 665, https://arxiv.org/pdf/2602.22714.pdf

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.

Paperid: 666, https://arxiv.org/pdf/2602.19179.pdf

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 $\sqrt{\|Σ\|_{\mathrm{op}}}/R\approx 1/6$ 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.

Paperid: 667, https://arxiv.org/pdf/2602.17512.pdf

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.

Paperid: 668, https://arxiv.org/pdf/2602.17415.pdf

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.

Paperid: 669, https://arxiv.org/pdf/2602.17297.pdf

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.

Paperid: 670, https://arxiv.org/pdf/2602.16586.pdf

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.

Paperid: 671, https://arxiv.org/pdf/2602.14652.pdf

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.

Paperid: 672, https://arxiv.org/pdf/2602.13212.pdf

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.

Paperid: 673, https://arxiv.org/pdf/2602.13150.pdf

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.

Paperid: 674, https://arxiv.org/pdf/2602.12436.pdf

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.

Paperid: 675, https://arxiv.org/pdf/2602.12288.pdf

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.

Paperid: 676, https://arxiv.org/pdf/2602.12281.pdf

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.

Paperid: 677, https://arxiv.org/pdf/2602.11380.pdf

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.

Paperid: 678, https://arxiv.org/pdf/2602.09645.pdf

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.

Paperid: 679, https://arxiv.org/pdf/2602.08435.pdf

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.

Paperid: 680, https://arxiv.org/pdf/2602.06639.pdf

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.

Paperid: 681, https://arxiv.org/pdf/2602.06215.pdf

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.

Paperid: 682, https://arxiv.org/pdf/2602.05803.pdf

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.

Paperid: 683, https://arxiv.org/pdf/2602.02866.pdf

Abstract:
Estimating cell-to-cell variation (CtCV) and state of health (SoH) for battery modules with 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.

Paperid: 684, https://arxiv.org/pdf/2601.21562.pdf

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.

Paperid: 685, https://arxiv.org/pdf/2601.21039.pdf

Abstract:
Distributed energy storage devices can be pooled and coordinated by aggregators to participate in power system operations and market clearings. This requires representing a massive device population as a single, tractable surrogate that is computationally efficient, accurate, and compatible with market participation requirements. 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. Leveraging these results, we construct a convex surrogate model that approximates the aggregate behavior of large storage populations and can be embedded directly into power system operations and market clearing. Surrogate parameter identification is formulated as an optimization problem using historical market price-response data, and we adopt a gradient-based algorithm for efficient learning procedure. Case studies validate the theoretical findings and demonstrate the effectiveness of the proposed framework in approximation accuracy, data efficiency, and profit outcomes.

Paperid: 686, https://arxiv.org/pdf/2601.19822.pdf

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.

Paperid: 687, https://arxiv.org/pdf/2601.19794.pdf

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.

Paperid: 688, https://arxiv.org/pdf/2601.19742.pdf

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.

Paperid: 689, https://arxiv.org/pdf/2601.17812.pdf

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.

Paperid: 690, https://arxiv.org/pdf/2601.16722.pdf

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.

Paperid: 691, https://arxiv.org/pdf/2601.16719.pdf

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.

Paperid: 692, https://arxiv.org/pdf/2601.16682.pdf

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$^\star$search 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.

Paperid: 693, https://arxiv.org/pdf/2601.16315.pdf

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.

Paperid: 694, https://arxiv.org/pdf/2601.14642.pdf

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 $\text{RDMA}^\text{TSO}$ 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 $\text{RDMA}^{\text{TSO}}_{\text{RMW}}$, 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 $\text{RDMA}^{\text{WAIT}}_{\text{RMW}}$ library. Underpinned by $\text{RDMA}^{\text{WAIT}}_{\text{RMW}}$, 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, $\text{RDMA}^{\text{SC}}_{\text{RMW}}$, 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.

Paperid: 695, https://arxiv.org/pdf/2601.10605.pdf

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.

Paperid: 696, https://arxiv.org/pdf/2601.08491.pdf

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.

Paperid: 697, https://arxiv.org/pdf/2601.08136.pdf

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.

Paperid: 698, https://arxiv.org/pdf/2601.07783.pdf

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.

Paperid: 699, https://arxiv.org/pdf/2601.05632.pdf

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.

Paperid: 700, https://arxiv.org/pdf/2601.02706.pdf

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.

Paperid: 701, https://arxiv.org/pdf/2601.02607.pdf

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.

Paperid: 702, https://arxiv.org/pdf/2601.02243.pdf

Abstract:
We develop a mathematical framework for the optimal dispatch 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 dispatch 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.

Paperid: 703, https://arxiv.org/pdf/2601.01777.pdf

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.

Paperid: 704, https://arxiv.org/pdf/2601.00820.pdf

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 $Δ$VTEC 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 $\mathcal{O}(1)$ 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.

Paperid: 705, https://arxiv.org/pdf/2604.04401.pdf

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.

Paperid: 706, https://arxiv.org/pdf/2604.03868.pdf

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.

Paperid: 707, https://arxiv.org/pdf/2604.03559.pdf

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.

Paperid: 708, https://arxiv.org/pdf/2604.03132.pdf

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 $157\times$ reduction in codebook size over a uniform grid baseline while preserving invariance, and we empirically characterize the minimum sensing resolution compatible with safe operation.

Paperid: 709, https://arxiv.org/pdf/2604.01830.pdf

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 $6\times$ faster on the first benchmark, reaches $94.6\%$ of oracle reward with roughly $200\times$ 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 $2.5\times$ faster than a strong specialized engineering baseline. These results show that our method provides an effective mechanism for topology control in power grids.

Paperid: 710, https://arxiv.org/pdf/2604.01755.pdf

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.

Paperid: 711, https://arxiv.org/pdf/2604.01509.pdf

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$^2$OC) 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$^2$OC 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.

Paperid: 712, https://arxiv.org/pdf/2604.00950.pdf

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.

Paperid: 713, https://arxiv.org/pdf/2604.00825.pdf

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.

Paperid: 714, https://arxiv.org/pdf/2604.00631.pdf

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.

Paperid: 715, https://arxiv.org/pdf/2604.00553.pdf

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.

Paperid: 716, https://arxiv.org/pdf/2604.00439.pdf

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.

Paperid: 717, https://arxiv.org/pdf/2604.00179.pdf

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.

Paperid: 718, https://arxiv.org/pdf/2604.00150.pdf

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.

Paperid: 719, https://arxiv.org/pdf/2603.28917.pdf

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.

Paperid: 720, https://arxiv.org/pdf/2603.28286.pdf

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.

Paperid: 721, https://arxiv.org/pdf/2603.27939.pdf

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.

Paperid: 722, https://arxiv.org/pdf/2603.27677.pdf

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.

Paperid: 723, https://arxiv.org/pdf/2603.27548.pdf

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.

Paperid: 724, https://arxiv.org/pdf/2603.27374.pdf

Abstract:
In adaptive-sampling control, the control frequency can be adjusted during task execution. Ensuring that these on-the-fly changes do not jeopardize the safety of the system being controlled requires careful attention. We introduce robust M-step hold model predictive control (MPC) to address this. This MPC 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 use our framework 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.

Paperid: 725, https://arxiv.org/pdf/2603.25822.pdf

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.

Paperid: 726, https://arxiv.org/pdf/2603.24251.pdf

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.

Paperid: 727, https://arxiv.org/pdf/2603.23803.pdf

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.

Paperid: 728, https://arxiv.org/pdf/2603.23748.pdf

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.

Paperid: 729, https://arxiv.org/pdf/2603.23357.pdf

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.

Paperid: 730, https://arxiv.org/pdf/2603.23222.pdf

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.

Paperid: 731, https://arxiv.org/pdf/2603.22548.pdf

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.

Paperid: 732, https://arxiv.org/pdf/2603.21429.pdf

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.

Paperid: 733, https://arxiv.org/pdf/2603.20027.pdf

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.

Paperid: 734, https://arxiv.org/pdf/2603.17867.pdf

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.

Paperid: 735, https://arxiv.org/pdf/2603.17836.pdf

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.

Paperid: 736, https://arxiv.org/pdf/2603.17135.pdf

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.

Paperid: 737, https://arxiv.org/pdf/2603.16599.pdf

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.

Paperid: 738, https://arxiv.org/pdf/2603.14396.pdf

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.

Paperid: 739, https://arxiv.org/pdf/2603.14106.pdf

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.

Paperid: 740, https://arxiv.org/pdf/2603.14060.pdf

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.

Paperid: 741, https://arxiv.org/pdf/2603.10280.pdf

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.

Paperid: 742, https://arxiv.org/pdf/2603.09617.pdf

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.

Paperid: 743, https://arxiv.org/pdf/2603.09183.pdf

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

Paperid: 744, https://arxiv.org/pdf/2603.06921.pdf

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.

Paperid: 745, https://arxiv.org/pdf/2603.05661.pdf

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.

Paperid: 746, https://arxiv.org/pdf/2603.05268.pdf

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.

Paperid: 747, https://arxiv.org/pdf/2603.03641.pdf

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.

Paperid: 748, https://arxiv.org/pdf/2603.02642.pdf

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 implementation 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 speedup up to 139.6$\times$.

Paperid: 749, https://arxiv.org/pdf/2602.19486.pdf

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.

Paperid: 750, https://arxiv.org/pdf/2602.19346.pdf

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.

Paperid: 751, https://arxiv.org/pdf/2602.16735.pdf

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.

Paperid: 752, https://arxiv.org/pdf/2602.16187.pdf

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.

Paperid: 753, https://arxiv.org/pdf/2602.15596.pdf

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, \citep{wu2025direct,wu2025time} provide data-independent execution time certificates (certified number of iterations) for box-constrained quadratic programs (BoxQP). To apply the time-certified BoxQP algorithm \citep{wu2025time} 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).

Paperid: 754, https://arxiv.org/pdf/2602.15568.pdf

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.

Paperid: 755, https://arxiv.org/pdf/2602.15350.pdf

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 \emph{open-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.

Paperid: 756, https://arxiv.org/pdf/2602.14780.pdf

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.

Paperid: 757, https://arxiv.org/pdf/2602.12663.pdf

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.

Paperid: 758, https://arxiv.org/pdf/2602.12573.pdf

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.

Paperid: 759, https://arxiv.org/pdf/2602.11899.pdf

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.

Paperid: 760, https://arxiv.org/pdf/2602.11077.pdf

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.

Paperid: 761, https://arxiv.org/pdf/2602.07811.pdf

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.

Paperid: 762, https://arxiv.org/pdf/2602.07677.pdf

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.

Paperid: 763, https://arxiv.org/pdf/2602.07299.pdf

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 standard reinforcement learning methods such as PPO. 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.

Paperid: 764, https://arxiv.org/pdf/2602.07000.pdf

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.

Paperid: 765, https://arxiv.org/pdf/2602.06996.pdf

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.

Paperid: 766, https://arxiv.org/pdf/2602.06191.pdf

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.

Paperid: 767, https://arxiv.org/pdf/2602.04756.pdf

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.

Paperid: 768, https://arxiv.org/pdf/2602.04744.pdf

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.

Paperid: 769, https://arxiv.org/pdf/2602.03987.pdf

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.

Paperid: 770, https://arxiv.org/pdf/2602.03986.pdf

Abstract:
We study the effect of group symmetrization of pre-trained models on conformal prediction (CP), a post-hoc, distribution-free, finite-sample method of uncertainty quantification that offers formal coverage guarantees under the assumption of data exchangeability. Unfortunately, CP 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.

Paperid: 771, https://arxiv.org/pdf/2601.23108.pdf

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.

Paperid: 772, https://arxiv.org/pdf/2601.22880.pdf

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.

Paperid: 773, https://arxiv.org/pdf/2601.21230.pdf

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.

Paperid: 774, https://arxiv.org/pdf/2601.21176.pdf

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.

Paperid: 775, https://arxiv.org/pdf/2601.19813.pdf

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.

Paperid: 776, https://arxiv.org/pdf/2601.19086.pdf

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.

Paperid: 777, https://arxiv.org/pdf/2601.16758.pdf

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.

Paperid: 778, https://arxiv.org/pdf/2601.15816.pdf

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.

Paperid: 779, https://arxiv.org/pdf/2601.14882.pdf

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.

Paperid: 780, https://arxiv.org/pdf/2601.14704.pdf

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.

Paperid: 781, https://arxiv.org/pdf/2601.14414.pdf

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 \textit{parallel-in-horizon} and \textit{construction-free} nonlinear MPC algorithm, termed $π$MPC, 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.

Paperid: 782, https://arxiv.org/pdf/2601.12694.pdf

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.

Paperid: 783, https://arxiv.org/pdf/2601.12037.pdf

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.

Paperid: 784, https://arxiv.org/pdf/2601.11453.pdf

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.

Paperid: 785, https://arxiv.org/pdf/2601.11205.pdf

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.

Paperid: 786, https://arxiv.org/pdf/2601.10973.pdf

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.

Paperid: 787, https://arxiv.org/pdf/2601.10671.pdf

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.

Paperid: 788, https://arxiv.org/pdf/2601.09998.pdf

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.

Paperid: 789, https://arxiv.org/pdf/2601.09916.pdf

Abstract:
This paper presents a perfectly secure matrix multiplication (PSMM) protocol for multiparty computation (MPC) of $\mathrm{A}^{\top}\mathrm{B}$ 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.

Paperid: 790, https://arxiv.org/pdf/2601.09549.pdf

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.

Paperid: 791, https://arxiv.org/pdf/2601.09302.pdf

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.

Paperid: 792, https://arxiv.org/pdf/2601.07712.pdf

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.

Paperid: 793, https://arxiv.org/pdf/2601.05756.pdf

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.

Paperid: 794, https://arxiv.org/pdf/2601.05070.pdf

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.

Paperid: 795, https://arxiv.org/pdf/2601.03413.pdf

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.

Paperid: 796, https://arxiv.org/pdf/2601.01793.pdf

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.

Paperid: 797, https://arxiv.org/pdf/2604.04711.pdf

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.

Paperid: 798, https://arxiv.org/pdf/2604.04545.pdf

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.

Paperid: 799, https://arxiv.org/pdf/2604.04455.pdf

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.

Paperid: 800, https://arxiv.org/pdf/2604.04213.pdf

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.

Paperid: 801, https://arxiv.org/pdf/2604.03407.pdf

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.

Paperid: 802, https://arxiv.org/pdf/2604.02227.pdf

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.

Paperid: 803, https://arxiv.org/pdf/2604.02225.pdf

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.

Paperid: 804, https://arxiv.org/pdf/2604.01573.pdf

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.

Paperid: 805, https://arxiv.org/pdf/2604.01448.pdf

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.

Paperid: 806, https://arxiv.org/pdf/2603.29882.pdf

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.

Paperid: 807, https://arxiv.org/pdf/2603.29283.pdf

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.

Paperid: 808, https://arxiv.org/pdf/2603.28965.pdf

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.

Paperid: 809, https://arxiv.org/pdf/2603.27902.pdf

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.

Paperid: 810, https://arxiv.org/pdf/2603.27576.pdf

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.

Paperid: 811, https://arxiv.org/pdf/2603.27575.pdf

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.

Paperid: 812, https://arxiv.org/pdf/2603.25430.pdf

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.

Paperid: 813, https://arxiv.org/pdf/2603.24600.pdf

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.

Paperid: 814, https://arxiv.org/pdf/2603.23450.pdf

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.

Paperid: 815, https://arxiv.org/pdf/2603.22773.pdf

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 $\SE(3) \times \mathbb{R}$ 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.

Paperid: 816, https://arxiv.org/pdf/2603.21594.pdf

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.

Paperid: 817, https://arxiv.org/pdf/2603.21081.pdf

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.

Paperid: 818, https://arxiv.org/pdf/2603.21070.pdf

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.

Paperid: 819, https://arxiv.org/pdf/2603.20151.pdf

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.

Paperid: 820, https://arxiv.org/pdf/2603.19449.pdf

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.

Paperid: 821, https://arxiv.org/pdf/2603.17631.pdf

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.

Paperid: 822, https://arxiv.org/pdf/2603.17572.pdf

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.

Paperid: 823, https://arxiv.org/pdf/2603.16074.pdf

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.

Paperid: 824, https://arxiv.org/pdf/2603.15983.pdf

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.

Paperid: 825, https://arxiv.org/pdf/2603.13922.pdf

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.

Paperid: 826, https://arxiv.org/pdf/2603.12899.pdf

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.

Paperid: 827, https://arxiv.org/pdf/2603.12202.pdf

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.

Paperid: 828, https://arxiv.org/pdf/2603.11723.pdf

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.

Paperid: 829, https://arxiv.org/pdf/2603.11502.pdf

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.

Paperid: 830, https://arxiv.org/pdf/2603.10836.pdf

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.

Paperid: 831, https://arxiv.org/pdf/2603.10802.pdf

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.

Paperid: 832, https://arxiv.org/pdf/2603.10800.pdf

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.

Paperid: 833, https://arxiv.org/pdf/2603.09942.pdf

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.

Paperid: 834, https://arxiv.org/pdf/2603.09916.pdf

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.

Paperid: 835, https://arxiv.org/pdf/2603.09859.pdf

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.

Paperid: 836, https://arxiv.org/pdf/2603.09807.pdf

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.

Paperid: 837, https://arxiv.org/pdf/2603.09600.pdf

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.

Paperid: 838, https://arxiv.org/pdf/2603.09464.pdf

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.

Paperid: 839, https://arxiv.org/pdf/2603.06943.pdf

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.

Paperid: 840, https://arxiv.org/pdf/2603.06783.pdf

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.

Paperid: 841, https://arxiv.org/pdf/2603.05771.pdf

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.

Paperid: 842, https://arxiv.org/pdf/2603.05021.pdf

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.

Paperid: 843, https://arxiv.org/pdf/2603.05011.pdf

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.

Paperid: 844, https://arxiv.org/pdf/2603.03848.pdf

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.

Paperid: 845, https://arxiv.org/pdf/2603.02443.pdf

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.

Paperid: 846, https://arxiv.org/pdf/2603.00981.pdf

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.

Paperid: 847, https://arxiv.org/pdf/2603.00325.pdf

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.

Paperid: 848, https://arxiv.org/pdf/2603.00251.pdf

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.

Paperid: 849, https://arxiv.org/pdf/2603.00103.pdf

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.

Paperid: 850, https://arxiv.org/pdf/2602.21914.pdf

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.

Paperid: 851, https://arxiv.org/pdf/2602.21868.pdf

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.

Paperid: 852, https://arxiv.org/pdf/2602.21415.pdf

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.

Paperid: 853, https://arxiv.org/pdf/2602.20076.pdf

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.

Paperid: 854, https://arxiv.org/pdf/2602.19867.pdf

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.

Paperid: 855, https://arxiv.org/pdf/2602.19341.pdf

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.

Paperid: 856, https://arxiv.org/pdf/2602.19310.pdf

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.

Paperid: 857, https://arxiv.org/pdf/2602.18365.pdf

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.

Paperid: 858, https://arxiv.org/pdf/2602.16715.pdf

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.

Paperid: 859, https://arxiv.org/pdf/2602.16206.pdf

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.

Paperid: 860, https://arxiv.org/pdf/2602.14830.pdf

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 $3 \times 3$ 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.

Paperid: 861, https://arxiv.org/pdf/2602.14725.pdf

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.

Paperid: 862, https://arxiv.org/pdf/2602.14382.pdf

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.

Paperid: 863, https://arxiv.org/pdf/2602.11183.pdf

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.

Paperid: 864, https://arxiv.org/pdf/2602.11076.pdf

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 \emph{Attention-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 $18$ms, restores latency to $0.98$ms 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.

Paperid: 865, https://arxiv.org/pdf/2602.05565.pdf

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.

Paperid: 866, https://arxiv.org/pdf/2602.00027.pdf

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.

Paperid: 867, https://arxiv.org/pdf/2601.21297.pdf

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.

Paperid: 868, https://arxiv.org/pdf/2601.19340.pdf

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 \SI{18}{\km} drive.

Paperid: 869, https://arxiv.org/pdf/2601.19078.pdf

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.

Paperid: 870, https://arxiv.org/pdf/2601.18973.pdf

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$\times$ 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. Together, these results offer a transferable framework for decision-making in adaptive control.

Paperid: 871, https://arxiv.org/pdf/2601.18313.pdf

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.

Paperid: 872, https://arxiv.org/pdf/2601.17210.pdf

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.

Paperid: 873, https://arxiv.org/pdf/2601.16078.pdf

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.

Paperid: 874, https://arxiv.org/pdf/2601.16062.pdf

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.

Paperid: 875, https://arxiv.org/pdf/2601.16014.pdf

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.

Paperid: 876, https://arxiv.org/pdf/2601.14673.pdf

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.

Paperid: 877, https://arxiv.org/pdf/2601.14663.pdf

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.

Paperid: 878, https://arxiv.org/pdf/2601.12699.pdf

Abstract:
Deep Brain Stimulation (DBS) has proven to be a promising treatment of Parkinson's Disease (PD). DBS involves stimulating specific regions of the brain's Basal Ganglia (BG) using electric impulses to alleviate symptoms of PD such as tremors, rigidity, and bradykinesia. Although most clinical DBS approaches today use a fixed frequency and amplitude, they suffer from side effects (such as slurring of speech) and shortened battery life of the implant. Reinforcement learning (RL) approaches have been used in recent research to perform DBS in a more adaptive manner to improve overall patient outcome. These RL algorithms are, however, too complex to be trained in vivo due to their long convergence time and requirement of high computational resources. We propose a new Time & Threshold-Triggered Multi-Armed Bandit (T3P MAB) RL approach for DBS that is more effective than existing algorithms. Further, our T3P agent is lightweight enough to be deployed in the implant, unlike current deep-RL strategies, and even forgoes the need for an offline training phase. Additionally, most existing RL approaches have focused on modulating only frequency or amplitude, and the possibility of tuning them together remains greatly unexplored in the literature. Our RL agent can tune both frequency and amplitude of DBS signals to the brain with better sample efficiency and requires minimal time to converge. We implement an MAB agent for DBS for the first time on hardware to report energy measurements and prove its suitability for resource-constrained platforms. Our T3P MAB algorithm is deployed on a variety of microcontroller unit (MCU) setups to show its efficiency in terms of power consumption as opposed to other existing RL approaches used in recent work.

Paperid: 879, https://arxiv.org/pdf/2601.07997.pdf

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, \textcolor{black}{where 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 \textcolor{black}{system cooperative tracking error} remains bounded and convergent in both the mean-square and almost-sure sense.

Paperid: 880, https://arxiv.org/pdf/2601.07715.pdf

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.

Paperid: 881, https://arxiv.org/pdf/2601.07150.pdf

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.

Paperid: 882, https://arxiv.org/pdf/2601.06766.pdf

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.

Paperid: 883, https://arxiv.org/pdf/2601.05526.pdf

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.

Paperid: 884, https://arxiv.org/pdf/2601.04796.pdf

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.

Paperid: 885, https://arxiv.org/pdf/2601.02571.pdf

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.

Paperid: 886, https://arxiv.org/pdf/2601.02053.pdf

Abstract:
Microcontrollers are increasingly present in embedded deployments and dependable applications, 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.

Paperid: 887, https://arxiv.org/pdf/2601.01410.pdf

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 Reserve$_{99.5}^{\%}$ requirements, and explicit inflation diagnostics (Bias$_{24h}$/OPR) -- to quantify one-sided reliability risk beyond MAPE. Using this framework, we evaluate state space models (Mamba variants) and strong baselines 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 for these architectures. Our results demonstrate that standard accuracy metrics are insufficient proxies for operational safety: models with comparable MAPE can imply materially different tail reserve requirements (Reserve$_{99.5}^{\%}$). We show that explicit weather integration narrows error distributions, reducing the impact of temperature-driven demand spikes. Furthermore, while probabilistic calibration reduces large-error events, it can induce systematic schedule inflation. We introduce Bias/OPR-constrained objectives to enable auditable trade-offs between minimizing tail risk and preventing trivial over-forecasting.

Paperid: 888, https://arxiv.org/pdf/2601.01076.pdf

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.

Paperid: 889, https://arxiv.org/pdf/2601.01018.pdf

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.

Paperid: 890, https://arxiv.org/pdf/2601.00020.pdf

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. We fabricate, characterize, and model ferroelectric synapses. We evaluate a convolutional-recurrent SNN architecture under two complementary deployment strategies: (i) device-aware training using a ferroelectric synapse model, and (ii) transfer of software-trained weights followed by low-overhead on-device re-tuning. To enable efficient adaptation, we introduce a device-aware weight-update strategy in which gradient-based updates are accumulated digitally and converted into discrete programming events only when a threshold is exceeded, emulating nonlinear, state-dependent programming dynamics while reducing programming frequency. Both deployment strategies achieve classification performance comparable to state-of-the-art software-based SNNs. Furthermore, 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.

Paperid: 891, https://arxiv.org/pdf/2604.04266.pdf

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.

Paperid: 892, https://arxiv.org/pdf/2604.04246.pdf

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.

Paperid: 893, https://arxiv.org/pdf/2604.04225.pdf

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.

Paperid: 894, https://arxiv.org/pdf/2604.03491.pdf

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.

Paperid: 895, https://arxiv.org/pdf/2604.02727.pdf

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.

Paperid: 896, https://arxiv.org/pdf/2604.02619.pdf

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.

Paperid: 897, https://arxiv.org/pdf/2604.02575.pdf

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.

Paperid: 898, https://arxiv.org/pdf/2604.02471.pdf

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.

Paperid: 899, https://arxiv.org/pdf/2604.00926.pdf

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.

Paperid: 900, https://arxiv.org/pdf/2604.00542.pdf

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.

Paperid: 901, https://arxiv.org/pdf/2604.00524.pdf

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.

Paperid: 902, https://arxiv.org/pdf/2604.00338.pdf

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.

Paperid: 903, https://arxiv.org/pdf/2604.00186.pdf

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 O*NET 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

Paperid: 904, https://arxiv.org/pdf/2603.29959.pdf

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.

Paperid: 905, https://arxiv.org/pdf/2603.29744.pdf

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.

Paperid: 906, https://arxiv.org/pdf/2603.29255.pdf

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 $1000\times$ speedup and runs faster than real time, while the hybrid model achieves over $500\times$ 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.

Paperid: 907, https://arxiv.org/pdf/2603.28926.pdf

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.

Paperid: 908, https://arxiv.org/pdf/2603.28369.pdf

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.

Paperid: 909, https://arxiv.org/pdf/2603.28217.pdf

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.

Paperid: 910, https://arxiv.org/pdf/2603.27328.pdf

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.

Paperid: 911, https://arxiv.org/pdf/2603.27286.pdf

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.

Paperid: 912, https://arxiv.org/pdf/2603.27081.pdf

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.

Paperid: 913, https://arxiv.org/pdf/2603.27020.pdf

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.

Paperid: 914, https://arxiv.org/pdf/2603.26998.pdf

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.

Paperid: 915, https://arxiv.org/pdf/2603.26621.pdf

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.

Paperid: 916, https://arxiv.org/pdf/2603.26050.pdf

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.

Paperid: 917, https://arxiv.org/pdf/2603.24509.pdf

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.

Paperid: 918, https://arxiv.org/pdf/2603.24076.pdf

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.

Paperid: 919, https://arxiv.org/pdf/2603.23372.pdf

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.

Paperid: 920, https://arxiv.org/pdf/2603.23150.pdf

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.

Paperid: 921, https://arxiv.org/pdf/2603.23015.pdf

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.

Paperid: 922, https://arxiv.org/pdf/2603.22508.pdf

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.

Paperid: 923, https://arxiv.org/pdf/2603.20838.pdf

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.

Paperid: 924, https://arxiv.org/pdf/2603.20553.pdf

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.

Paperid: 925, https://arxiv.org/pdf/2603.20131.pdf

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.

Paperid: 926, https://arxiv.org/pdf/2603.19492.pdf

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.

Paperid: 927, https://arxiv.org/pdf/2603.18236.pdf

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.

Paperid: 928, https://arxiv.org/pdf/2603.18219.pdf

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.

Paperid: 929, https://arxiv.org/pdf/2603.17665.pdf

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.

Paperid: 930, https://arxiv.org/pdf/2603.17518.pdf

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.

Paperid: 931, https://arxiv.org/pdf/2603.17155.pdf

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.

Paperid: 932, https://arxiv.org/pdf/2603.17134.pdf

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.

Paperid: 933, https://arxiv.org/pdf/2603.16588.pdf

Abstract:
This article 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. We 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.

Paperid: 934, https://arxiv.org/pdf/2603.14509.pdf

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.

Paperid: 935, https://arxiv.org/pdf/2603.14403.pdf

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.

Paperid: 936, https://arxiv.org/pdf/2603.14384.pdf

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.

Paperid: 937, https://arxiv.org/pdf/2603.13502.pdf

Abstract:
Wirelessly-connected robotic system empowers robots with real-time intelligence by leveraging remote computing resources for decision-making. However, the data exchange between robots and base stations often overwhelms communication links, introducing latency that undermines real-time response. To tackle this, goal-oriented semantic communication (GSC) has been introduced into wirelessly-connected robotic systems to extract and transmit only goal-relevant semantic representations, enhancing communication efficiency and task effectiveness. However, existing GSC approaches focused primarily on optimizing effectiveness metrics while overlooking safety requirements, which should be treated as the top priority in real-world robotic systems. To bridge this gap, we propose safety-guaranteed and goal-oriented semantic communication for wirelessly-connected robotic system, aiming to maximize the robotic task effectiveness subject to practical operational safety requirements. We first summarize the general safety requirements and effectiveness metrics across typical robotic tasks, including robot arm grasping, unmanned aerial vehicle (UAV)-assisted tasks, and multi-robot exploration. We then systematically analyze the unique safety and effectiveness challenges faced by wirelessly-connected robotic system in sensing, communication, and control. Based on these, we further present potential safety-guaranteed and goal-oriented sensing, communication, and control solutions. Finally, a UAV target tracking case study validates that our proposed GSC solutions can significantly improve safety rate and tracking success rate by more than 2 times and 4.5 times, respectively.

Paperid: 938, https://arxiv.org/pdf/2603.13050.pdf

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.

Paperid: 939, https://arxiv.org/pdf/2603.12807.pdf

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.

Paperid: 940, https://arxiv.org/pdf/2603.12059.pdf

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.

Paperid: 941, https://arxiv.org/pdf/2603.11328.pdf

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.

Paperid: 942, https://arxiv.org/pdf/2603.11265.pdf

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.

Paperid: 943, https://arxiv.org/pdf/2603.11260.pdf

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.

Paperid: 944, https://arxiv.org/pdf/2603.10779.pdf

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.

Paperid: 945, https://arxiv.org/pdf/2603.10711.pdf

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.

Paperid: 946, https://arxiv.org/pdf/2603.10262.pdf

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 $Δt = 2~μ$s 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

Paperid: 947, https://arxiv.org/pdf/2603.06933.pdf

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.

Paperid: 948, https://arxiv.org/pdf/2603.04866.pdf

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.

Paperid: 949, https://arxiv.org/pdf/2603.04393.pdf

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.

Paperid: 950, https://arxiv.org/pdf/2603.03759.pdf

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 $(\texttt{ALTERNATING-MARL})$, 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 $\widetilde{O}(1/\sqrt{k})$-approximate Nash Equilibrium, while yielding a separation in the sample complexities between the joint state space and action space. Finally, we validate our results in numerical simulations for multi-robot control and federated optimization.

Paperid: 951, https://arxiv.org/pdf/2603.03173.pdf

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.

Paperid: 952, https://arxiv.org/pdf/2602.24254.pdf

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.

Paperid: 953, https://arxiv.org/pdf/2602.24247.pdf

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

Paperid: 954, https://arxiv.org/pdf/2602.22499.pdf

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.

Paperid: 955, https://arxiv.org/pdf/2602.22409.pdf

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.

Paperid: 956, https://arxiv.org/pdf/2602.21936.pdf

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 $\mathrm{SE}(3)$, 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.

Paperid: 957, https://arxiv.org/pdf/2602.21852.pdf

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.

Paperid: 958, https://arxiv.org/pdf/2602.19292.pdf

Abstract:
This paper analyzes Stackelberg Gaussian signaling games under linear sensitivity mismatch, generalizing standard additive and constant-bias models. We characterize the Stackelberg equilibrium structure for both noiseless and noisy signaling regimes. In the noiseless case, we show that the encoder selectively reveals information along specific eigenspaces of a cost-mismatch matrix. We then extend the analysis to the noisy regime and derive analytical thresholds for the existence of informative equilibria, demonstrating a sharp phase transition where communication collapses into silence if the sensitivity mismatch is sufficiently high, in contrast with the fully revealing equilibria often found in constant-bias models.

Paperid: 959, https://arxiv.org/pdf/2602.19238.pdf

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.

Paperid: 960, https://arxiv.org/pdf/2602.17055.pdf

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.

Paperid: 961, https://arxiv.org/pdf/2602.16062.pdf

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.

Paperid: 962, https://arxiv.org/pdf/2602.15526.pdf

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.

Paperid: 963, https://arxiv.org/pdf/2602.14765.pdf

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.

Paperid: 964, https://arxiv.org/pdf/2602.13454.pdf

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.

Paperid: 965, https://arxiv.org/pdf/2602.12452.pdf

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.

Paperid: 966, https://arxiv.org/pdf/2602.11820.pdf

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.

Paperid: 967, https://arxiv.org/pdf/2602.10855.pdf

Abstract:
In this paper, we study a class of port-Hamiltonian systems whose vector fields exhibit singularities. A representative example of this class has recently been employed in the power electronics literature to implement a grid-forming controller. We show that, under certain conditions, these port-Hamiltonian systems, when interconnected with passive systems, converge to a prescribed non-equilibrium steady state. At first glance, the apparently passive nature of the port-Hamiltonian system seems incompatible with the active power injection required to sustain this non-equilibrium condition. However, we demonstrate that the discontinuity inherent in the vector field provides the additional energy needed to maintain this operating point, indicating that the system is not globally passive. Moreover, when the discontinuity is replaced by a continuous approximation, the resulting system becomes cyclo-dissipative while still capable of supplying the required power.

Paperid: 968, https://arxiv.org/pdf/2602.10169.pdf

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.

Paperid: 969, https://arxiv.org/pdf/2602.10044.pdf

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.

Paperid: 970, https://arxiv.org/pdf/2602.09493.pdf

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.

Paperid: 971, https://arxiv.org/pdf/2602.09473.pdf

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.

Paperid: 972, https://arxiv.org/pdf/2602.06618.pdf

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.

Paperid: 973, https://arxiv.org/pdf/2602.05442.pdf

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.

Paperid: 974, https://arxiv.org/pdf/2602.05363.pdf

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.

Paperid: 975, https://arxiv.org/pdf/2602.05304.pdf

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.

Paperid: 976, https://arxiv.org/pdf/2602.05121.pdf

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.

Paperid: 977, https://arxiv.org/pdf/2602.04720.pdf

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.

Paperid: 978, https://arxiv.org/pdf/2602.04609.pdf

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.

Paperid: 979, https://arxiv.org/pdf/2602.04310.pdf

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.

Paperid: 980, https://arxiv.org/pdf/2602.04221.pdf

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.

Paperid: 981, https://arxiv.org/pdf/2602.03508.pdf

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.

Paperid: 982, https://arxiv.org/pdf/2602.03433.pdf

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.

Paperid: 983, https://arxiv.org/pdf/2602.02903.pdf

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.

Paperid: 984, https://arxiv.org/pdf/2602.01492.pdf

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.

Paperid: 985, https://arxiv.org/pdf/2602.01164.pdf

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.

Paperid: 986, https://arxiv.org/pdf/2602.00908.pdf

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.

Paperid: 987, https://arxiv.org/pdf/2602.00905.pdf

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.

Paperid: 988, https://arxiv.org/pdf/2602.00823.pdf

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.

Paperid: 989, https://arxiv.org/pdf/2602.00630.pdf

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.

Paperid: 990, https://arxiv.org/pdf/2602.00466.pdf

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.

Paperid: 991, https://arxiv.org/pdf/2601.23076.pdf

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.

Paperid: 992, https://arxiv.org/pdf/2601.22114.pdf

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.

Paperid: 993, https://arxiv.org/pdf/2601.21506.pdf

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.

Paperid: 994, https://arxiv.org/pdf/2601.21371.pdf

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.

Paperid: 995, https://arxiv.org/pdf/2601.19953.pdf

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.

Paperid: 996, https://arxiv.org/pdf/2601.19241.pdf

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.

Paperid: 997, https://arxiv.org/pdf/2601.19238.pdf

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.

Paperid: 998, https://arxiv.org/pdf/2601.17752.pdf

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.

Paperid: 999, https://arxiv.org/pdf/2601.16648.pdf

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.

Paperid: 1000, https://arxiv.org/pdf/2601.12562.pdf

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.

Paperid: 1001, https://arxiv.org/pdf/2601.11439.pdf

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 $\sqrt{2}$ 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.

Paperid: 1002, https://arxiv.org/pdf/2601.10869.pdf

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.

Paperid: 1003, https://arxiv.org/pdf/2601.10868.pdf

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).

Paperid: 1004, https://arxiv.org/pdf/2601.10867.pdf

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: $\mathcal{X}_L(α)$, where the optimal control policy is linear and coincides with the standard linear $H_{\infty}$ state feedback control, and $\mathcal{X}_{NL}(α)$, 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).

Paperid: 1005, https://arxiv.org/pdf/2601.08405.pdf

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.

Paperid: 1006, https://arxiv.org/pdf/2601.08372.pdf

Abstract:
This paper develops a data-driven h2 model reduction method for discrete-time linear time-invariant systems. Specifically, we solve the h2 model reduction problem defined over a finite horizon using only impulse response data. Furthermore, we show that the proposed data-driven algorithm converges to a stationary point under certain assumptions. Numerical experiments demonstrate that the proposed method constructs a good reduced-order model in terms of the h2 norm defined over the finite horizon using a SLICOT benchmark (the CD player model).

Paperid: 1007, https://arxiv.org/pdf/2601.08338.pdf

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.

Paperid: 1008, https://arxiv.org/pdf/2601.08123.pdf

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.

Paperid: 1009, https://arxiv.org/pdf/2601.08060.pdf

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, the imperfection of channel state information (CSI) and residual errors in decoding process deteriorate the performance of NOMA, especially when multi-parameteric and realistic dense-user indoor scenarios are considered. In this work, we model a LiDAL-assisted RLNC-NOMA OWC system, where the light detection and localization (LiDAL) technique exploits spatio-temporal information to improve user CSI, while random linear network coding (RLNC) enhances data resilience in the NOMA successive decoding process. Power allocation (PA) is a crucial issue in communication systems, particularly in the modeled system, due to the complex interactions between multiple users and the coding and detection processes. However, optimizing continuous PA dynamically requires advanced techniques to avoid excessive computational complexity. Therefore, we adopt a deep reinforcement learning (DRL) framework to efficiently learn near-optimal power allocation strategies, enabling enhanced system performance. In particular, a DRL-based normalized advantage function (NAF) algorithm is proposed to maximize the average sum rate of the system, and its performance is analyzed and compared to other widely used DRL-based and conventional PA schemes, such as deep deterministic policy gradient (DDPG), gain ratio PA (GRPA), and exhaustive search.

Paperid: 1010, https://arxiv.org/pdf/2601.03906.pdf

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.

Paperid: 1011, https://arxiv.org/pdf/2601.03495.pdf

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.

Paperid: 1012, https://arxiv.org/pdf/2601.03003.pdf

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.

Paperid: 1013, https://arxiv.org/pdf/2601.01430.pdf

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.

Paperid: 1014, https://arxiv.org/pdf/2601.01335.pdf

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.

Paperid: 1015, https://arxiv.org/pdf/2601.01016.pdf

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.

Paperid: 1016, https://arxiv.org/pdf/2604.04150.pdf

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 ($\frac{dB}{dt}$) 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.

Paperid: 1017, https://arxiv.org/pdf/2604.03655.pdf

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.

Paperid: 1018, https://arxiv.org/pdf/2604.03001.pdf

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.

Paperid: 1019, https://arxiv.org/pdf/2604.02878.pdf

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.

Paperid: 1020, https://arxiv.org/pdf/2604.02455.pdf

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.

Paperid: 1021, https://arxiv.org/pdf/2604.01956.pdf

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.

Paperid: 1022, https://arxiv.org/pdf/2604.01910.pdf

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.

Paperid: 1023, https://arxiv.org/pdf/2604.01873.pdf

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.

Paperid: 1024, https://arxiv.org/pdf/2604.01870.pdf

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.

Paperid: 1025, https://arxiv.org/pdf/2604.01355.pdf

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.

Paperid: 1026, https://arxiv.org/pdf/2604.00900.pdf

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.

Paperid: 1027, https://arxiv.org/pdf/2604.00564.pdf

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.

Paperid: 1028, https://arxiv.org/pdf/2604.00520.pdf

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.

Paperid: 1029, https://arxiv.org/pdf/2604.00329.pdf

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.

Paperid: 1030, https://arxiv.org/pdf/2604.00320.pdf

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.

Paperid: 1031, https://arxiv.org/pdf/2603.29956.pdf

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.

Paperid: 1032, https://arxiv.org/pdf/2603.29792.pdf

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.

Paperid: 1033, https://arxiv.org/pdf/2603.29512.pdf

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.

Paperid: 1034, https://arxiv.org/pdf/2603.29490.pdf

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.

Paperid: 1035, https://arxiv.org/pdf/2603.29392.pdf

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.

Paperid: 1036, https://arxiv.org/pdf/2603.28910.pdf

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 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.

Paperid: 1037, https://arxiv.org/pdf/2603.28323.pdf

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.

Paperid: 1038, https://arxiv.org/pdf/2603.27943.pdf

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.

Paperid: 1039, https://arxiv.org/pdf/2603.27708.pdf

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.

Paperid: 1040, https://arxiv.org/pdf/2603.27604.pdf

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.

Paperid: 1041, https://arxiv.org/pdf/2603.26711.pdf

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.

Paperid: 1042, https://arxiv.org/pdf/2603.26347.pdf

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.

Paperid: 1043, https://arxiv.org/pdf/2603.26155.pdf

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.

Paperid: 1044, https://arxiv.org/pdf/2603.26081.pdf

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.

Paperid: 1045, https://arxiv.org/pdf/2603.24598.pdf

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.

Paperid: 1046, https://arxiv.org/pdf/2603.24381.pdf

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.

Paperid: 1047, https://arxiv.org/pdf/2603.23648.pdf

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.

Paperid: 1048, https://arxiv.org/pdf/2603.23312.pdf

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.

Paperid: 1049, https://arxiv.org/pdf/2603.23131.pdf

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.

Paperid: 1050, https://arxiv.org/pdf/2603.22929.pdf

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.

Paperid: 1051, https://arxiv.org/pdf/2603.22710.pdf

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.

Paperid: 1052, https://arxiv.org/pdf/2603.20067.pdf

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.

Paperid: 1053, https://arxiv.org/pdf/2603.19910.pdf

Abstract:
Algorithms for Bayesian state estimation of nonlinear systems inevitably introduce approximation errors. These algorithms depend on parameters that influence the accuracy of the numerical approximations used. 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.

Paperid: 1054, https://arxiv.org/pdf/2603.19618.pdf

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.

Paperid: 1055, https://arxiv.org/pdf/2603.18841.pdf

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.

Paperid: 1056, https://arxiv.org/pdf/2603.16949.pdf

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.

Paperid: 1057, https://arxiv.org/pdf/2603.16503.pdf

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.

Paperid: 1058, https://arxiv.org/pdf/2603.16458.pdf

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.

Paperid: 1059, https://arxiv.org/pdf/2603.15961.pdf

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.

Paperid: 1060, https://arxiv.org/pdf/2603.15948.pdf

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.

Paperid: 1061, https://arxiv.org/pdf/2603.15806.pdf

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.

Paperid: 1062, https://arxiv.org/pdf/2603.14622.pdf

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.

Paperid: 1063, https://arxiv.org/pdf/2603.14606.pdf

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.

Paperid: 1064, https://arxiv.org/pdf/2603.14411.pdf

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.

Paperid: 1065, https://arxiv.org/pdf/2603.14408.pdf

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.

Paperid: 1066, https://arxiv.org/pdf/2603.13959.pdf

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.

Paperid: 1067, https://arxiv.org/pdf/2603.13678.pdf

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.

Paperid: 1068, https://arxiv.org/pdf/2603.12415.pdf

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.

Paperid: 1069, https://arxiv.org/pdf/2603.11943.pdf

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.

Paperid: 1070, https://arxiv.org/pdf/2603.11600.pdf

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.

Paperid: 1071, https://arxiv.org/pdf/2603.11349.pdf

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.

Paperid: 1072, https://arxiv.org/pdf/2603.10890.pdf

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.

Paperid: 1073, https://arxiv.org/pdf/2603.10656.pdf

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.

Paperid: 1074, https://arxiv.org/pdf/2603.10245.pdf

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.

Paperid: 1075, https://arxiv.org/pdf/2603.09952.pdf

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 \textrm{AdamW} and \textrm{Muon}, 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 \emph{rescaled} \textrm{AdamW}, row normalization, and column normalization. The resulting learning rate width-aware scaling rules recover $μ$P scaling~\cite{yang2021tensor} 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 \textrm{Muon} can suffer an $\mathcal{O}(\sqrt{w})$ 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.

Paperid: 1076, https://arxiv.org/pdf/2603.09729.pdf

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.

Paperid: 1077, https://arxiv.org/pdf/2603.09671.pdf

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.

Paperid: 1078, https://arxiv.org/pdf/2603.08860.pdf

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.

Paperid: 1079, https://arxiv.org/pdf/2603.08831.pdf

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.

Paperid: 1080, https://arxiv.org/pdf/2603.07828.pdf

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.

Paperid: 1081, https://arxiv.org/pdf/2603.06361.pdf

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.

Paperid: 1082, https://arxiv.org/pdf/2603.05363.pdf

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.

Paperid: 1083, https://arxiv.org/pdf/2603.05239.pdf

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.

Paperid: 1084, https://arxiv.org/pdf/2603.05115.pdf

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.

Paperid: 1085, https://arxiv.org/pdf/2603.04190.pdf

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.

Paperid: 1086, https://arxiv.org/pdf/2603.00513.pdf

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.

Paperid: 1087, https://arxiv.org/pdf/2603.00415.pdf

Abstract:
Artificial intelligence (AI) is driving a rapid expansion of data centers (DCs). These facilities consume large amounts of electricity and introduce new challenges for power systems. AI workloads cause rapid power changes and high peak demand. These behaviors are different from traditional data centers (TDCs) and can affect grid stability and reliability. This paper reviews how energy storage systems (ESSs) can help integrate AI data DCs with the electric grid. We examine storage solutions at multiple levels, including grid-scale batteries, UPS systems, rack-level storage, and chip-level buffering. Each layer operates at a different time scale and serves a different purpose. Grid-interactive UPS (GiUPS) systems can respond quickly to disturbances and assist with frequency regulation or voltage ride through. Large battery energy storage systems (BESSs) can smooth power demand, support renewable on-site generation, and provide grid services. Rack-level and server-level storage help manage fast power fluctuations close to computing hardware. We also discuss other technologies such as fuel cells (FCs) and thermal energy storage (TE) that can support co-generation and reduce emissions. In addition, second-life battery energy storage (SLBESS) are reviewed as a lower-cost option for large installations whether supporting UPS battery or as a backup generation. The paper compares the benefits, challenges, and coordination requirements of these solutions. Overall, the study provides a structured view of how energy storage can improve reliability, flexibility, and sustainability when connecting future AI data centers to the power grid.

Paperid: 1088, https://arxiv.org/pdf/2603.00394.pdf

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.

Paperid: 1089, https://arxiv.org/pdf/2602.24260.pdf

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.

Paperid: 1090, https://arxiv.org/pdf/2602.18031.pdf

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.

Paperid: 1091, https://arxiv.org/pdf/2602.17877.pdf

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.

Paperid: 1092, https://arxiv.org/pdf/2602.17621.pdf

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.

Paperid: 1093, https://arxiv.org/pdf/2602.17068.pdf

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.

Paperid: 1094, https://arxiv.org/pdf/2602.17019.pdf

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.

Paperid: 1095, https://arxiv.org/pdf/2602.16127.pdf

Abstract:
We present a hybrid learning and model-based approach that adapts internal grasp forces to halt in-hand slip on a multifingered robotic gripper. A multimodal tactile stack combines piezoelectric (PzE) sensing for fast slip cues with piezoresistive (PzR) arrays for contact localization, enabling online construction of the grasp matrix. Upon slip, we update internal forces computed in the null space of the grasp via a quadratic program that preserves the object wrench while enforcing actuation limits. The pipeline yields a theoretical sensing-to-command latency of 35-40 ms, with 5 ms for PzR-based contact and geometry updates and about 4 ms for the quadratic program solve. In controlled trials, slip onset is detected at 20ms. We demonstrate closed-loop stabilization on multifingered grasps under external perturbations. Augmenting efficient analytic force control with learned tactile cues yields both robustness and rapid reactions, as confirmed in our end-to-end evaluation. Measured delays are dominated by the experimental data path rather than actual computation. The analysis outlines a clear route to sub-50 ms closed-loop stabilization.

Paperid: 1096, https://arxiv.org/pdf/2602.15834.pdf

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.

Paperid: 1097, https://arxiv.org/pdf/2602.14247.pdf

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.

Paperid: 1098, https://arxiv.org/pdf/2602.13739.pdf

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.

Paperid: 1099, https://arxiv.org/pdf/2602.12293.pdf

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.

Paperid: 1100, https://arxiv.org/pdf/2602.11373.pdf

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.

Paperid: 1101, https://arxiv.org/pdf/2602.11004.pdf

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.

Paperid: 1102, https://arxiv.org/pdf/2602.10878.pdf

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.

Paperid: 1103, https://arxiv.org/pdf/2602.10754.pdf

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.

Paperid: 1104, https://arxiv.org/pdf/2602.10724.pdf

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.

Paperid: 1105, https://arxiv.org/pdf/2602.09536.pdf

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.

Paperid: 1106, https://arxiv.org/pdf/2602.08488.pdf

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.

Paperid: 1107, https://arxiv.org/pdf/2602.08328.pdf

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.

Paperid: 1108, https://arxiv.org/pdf/2602.08303.pdf

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.

Paperid: 1109, https://arxiv.org/pdf/2602.07876.pdf

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.

Paperid: 1110, https://arxiv.org/pdf/2602.06780.pdf

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.

Paperid: 1111, https://arxiv.org/pdf/2602.05683.pdf

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.

Paperid: 1112, https://arxiv.org/pdf/2602.05157.pdf

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 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.

Paperid: 1113, https://arxiv.org/pdf/2602.05011.pdf

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.

Paperid: 1114, https://arxiv.org/pdf/2602.04801.pdf

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.

Paperid: 1115, https://arxiv.org/pdf/2602.03246.pdf

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.

Paperid: 1116, https://arxiv.org/pdf/2602.03082.pdf

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

Paperid: 1117, https://arxiv.org/pdf/2602.02959.pdf

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.

Paperid: 1118, https://arxiv.org/pdf/2602.02858.pdf

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.

Paperid: 1119, https://arxiv.org/pdf/2602.02814.pdf

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.

Paperid: 1120, https://arxiv.org/pdf/2602.01189.pdf

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.

Paperid: 1121, https://arxiv.org/pdf/2602.00812.pdf

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 \emph{cognitive-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 \emph{Cognitive 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.

Paperid: 1122, https://arxiv.org/pdf/2602.00798.pdf

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.

Paperid: 1123, https://arxiv.org/pdf/2601.23160.pdf

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.

Paperid: 1124, https://arxiv.org/pdf/2601.22403.pdf

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.

Paperid: 1125, https://arxiv.org/pdf/2601.22111.pdf

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.

Paperid: 1126, https://arxiv.org/pdf/2601.21651.pdf

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.

Paperid: 1127, https://arxiv.org/pdf/2601.21390.pdf

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.

Paperid: 1128, https://arxiv.org/pdf/2601.21308.pdf

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.

Paperid: 1129, https://arxiv.org/pdf/2601.21300.pdf

Abstract:
In this paper, we address the problem of circumnavigation of a stationary target by a heterogeneous group comprising of $\textbf{n}$ 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 \textit{leaders}, know the target location. The rest, termed as \textit{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.

Paperid: 1130, https://arxiv.org/pdf/2601.19617.pdf

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.

Paperid: 1131, https://arxiv.org/pdf/2601.19010.pdf

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.

Paperid: 1132, https://arxiv.org/pdf/2601.17660.pdf

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.

Paperid: 1133, https://arxiv.org/pdf/2601.17656.pdf

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.

Paperid: 1134, https://arxiv.org/pdf/2601.17582.pdf

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.

Paperid: 1135, https://arxiv.org/pdf/2601.17209.pdf

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.

Paperid: 1136, https://arxiv.org/pdf/2601.16876.pdf

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.

Paperid: 1137, https://arxiv.org/pdf/2601.16827.pdf

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.

Paperid: 1138, https://arxiv.org/pdf/2601.16149.pdf

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.

Paperid: 1139, https://arxiv.org/pdf/2601.15059.pdf

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.

Paperid: 1140, https://arxiv.org/pdf/2601.11982.pdf

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.

Paperid: 1141, https://arxiv.org/pdf/2601.11962.pdf

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.

Paperid: 1142, https://arxiv.org/pdf/2601.11832.pdf

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.

Paperid: 1143, https://arxiv.org/pdf/2601.11802.pdf

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.

Paperid: 1144, https://arxiv.org/pdf/2601.11788.pdf

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.

Paperid: 1145, https://arxiv.org/pdf/2601.11335.pdf

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.

Paperid: 1146, https://arxiv.org/pdf/2601.10929.pdf

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.

Paperid: 1147, https://arxiv.org/pdf/2601.09969.pdf

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.

Paperid: 1148, https://arxiv.org/pdf/2601.09626.pdf

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.

Paperid: 1149, https://arxiv.org/pdf/2601.09380.pdf

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.

Paperid: 1150, https://arxiv.org/pdf/2601.08335.pdf

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.

Paperid: 1151, https://arxiv.org/pdf/2601.08050.pdf

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 compare learned values to semi-Lagrangian HJB ground truth and quantify error.

Paperid: 1152, https://arxiv.org/pdf/2601.07622.pdf

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.

Paperid: 1153, https://arxiv.org/pdf/2601.07527.pdf

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.

Paperid: 1154, https://arxiv.org/pdf/2601.07256.pdf

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.

Paperid: 1155, https://arxiv.org/pdf/2601.06515.pdf

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.

Paperid: 1156, https://arxiv.org/pdf/2601.05084.pdf

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.

Paperid: 1157, https://arxiv.org/pdf/2601.04190.pdf

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.

Paperid: 1158, https://arxiv.org/pdf/2601.03486.pdf

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 ~1$μ$m.

Paperid: 1159, https://arxiv.org/pdf/2601.02701.pdf

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.

Paperid: 1160, https://arxiv.org/pdf/2601.02275.pdf

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 can be extended to other liquid-cooled data centers with different configurations and cooling requirements.

Paperid: 1161, https://arxiv.org/pdf/2601.01065.pdf

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.

Paperid: 1162, https://arxiv.org/pdf/2604.04394.pdf

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.

Paperid: 1163, https://arxiv.org/pdf/2604.04109.pdf

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.

Paperid: 1164, https://arxiv.org/pdf/2604.03766.pdf

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.

Paperid: 1165, https://arxiv.org/pdf/2604.03617.pdf

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).

Paperid: 1166, https://arxiv.org/pdf/2604.03123.pdf

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.

Paperid: 1167, https://arxiv.org/pdf/2604.03087.pdf

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.

Paperid: 1168, https://arxiv.org/pdf/2604.02932.pdf

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.

Paperid: 1169, https://arxiv.org/pdf/2604.02762.pdf

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.

Paperid: 1170, https://arxiv.org/pdf/2604.02177.pdf

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.

Paperid: 1171, https://arxiv.org/pdf/2604.01790.pdf

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.

Paperid: 1172, https://arxiv.org/pdf/2604.01656.pdf

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.

Paperid: 1173, https://arxiv.org/pdf/2604.01459.pdf

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.

Paperid: 1174, https://arxiv.org/pdf/2604.00667.pdf

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.

Paperid: 1175, https://arxiv.org/pdf/2604.00565.pdf

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.

Paperid: 1176, https://arxiv.org/pdf/2604.00496.pdf

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.

Paperid: 1177, https://arxiv.org/pdf/2604.00287.pdf

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.

Paperid: 1178, https://arxiv.org/pdf/2603.29862.pdf

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

Paperid: 1179, https://arxiv.org/pdf/2603.29745.pdf

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.

Paperid: 1180, https://arxiv.org/pdf/2603.29499.pdf

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.

Paperid: 1181, https://arxiv.org/pdf/2603.29107.pdf

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.

Paperid: 1182, https://arxiv.org/pdf/2603.29001.pdf

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.

Paperid: 1183, https://arxiv.org/pdf/2603.28945.pdf

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.

Paperid: 1184, https://arxiv.org/pdf/2603.28192.pdf

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.

Paperid: 1185, https://arxiv.org/pdf/2603.27934.pdf

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.

Paperid: 1186, https://arxiv.org/pdf/2603.27893.pdf

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.

Paperid: 1187, https://arxiv.org/pdf/2603.27837.pdf

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.

Paperid: 1188, https://arxiv.org/pdf/2603.27442.pdf

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.

Paperid: 1189, https://arxiv.org/pdf/2603.27273.pdf

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.

Paperid: 1190, https://arxiv.org/pdf/2603.27161.pdf

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.

Paperid: 1191, https://arxiv.org/pdf/2603.26704.pdf

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.

Paperid: 1192, https://arxiv.org/pdf/2603.26080.pdf

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.

Paperid: 1193, https://arxiv.org/pdf/2603.25991.pdf

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.

Paperid: 1194, https://arxiv.org/pdf/2603.25332.pdf

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.

Paperid: 1195, https://arxiv.org/pdf/2603.25211.pdf

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.

Paperid: 1196, https://arxiv.org/pdf/2603.24785.pdf

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.

Paperid: 1197, https://arxiv.org/pdf/2603.24714.pdf

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.

Paperid: 1198, https://arxiv.org/pdf/2603.24703.pdf

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.

Paperid: 1199, https://arxiv.org/pdf/2603.24629.pdf

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.

Paperid: 1200, https://arxiv.org/pdf/2603.23777.pdf

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.

Paperid: 1201, https://arxiv.org/pdf/2603.23475.pdf

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.

Paperid: 1202, https://arxiv.org/pdf/2603.23401.pdf

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.

Paperid: 1203, https://arxiv.org/pdf/2603.23103.pdf

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.

Paperid: 1204, https://arxiv.org/pdf/2603.21514.pdf

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.

Paperid: 1205, https://arxiv.org/pdf/2603.21266.pdf

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.

Paperid: 1206, https://arxiv.org/pdf/2603.20447.pdf

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.

Paperid: 1207, https://arxiv.org/pdf/2603.20408.pdf

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.

Paperid: 1208, https://arxiv.org/pdf/2603.20265.pdf

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.

Paperid: 1209, https://arxiv.org/pdf/2603.20259.pdf

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.

Paperid: 1210, https://arxiv.org/pdf/2603.20249.pdf

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.

Paperid: 1211, https://arxiv.org/pdf/2603.20146.pdf

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.

Paperid: 1212, https://arxiv.org/pdf/2603.19965.pdf

Abstract:
This paper analyses the computational complexity of validated interval methods for uncertain nonlinear systems. 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 the dependence on the initial search volume $\mathrm{Vol}(X_0)$, the target tolerance $\varepsilon$, and the costs of validated primitives explicit (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. The bounds quantify the scaling with $\mathrm{Vol}(X_0)$ and $\varepsilon$ for validated steady-state enclosure and highlight dominant cost drivers. We also show that determinant and inverse computation for interval matrices via naive Laplace expansion is factorial in the matrix dimension, motivating specialised interval linear algebra. Finally, interval Newton and interval Krawczyk have comparable leading-order costs; Krawczyk is typically cheaper in practice because it inverts a real midpoint matrix rather than an interval matrix. These results support the practical design of solvers for validated steady-state analysis in applications such as biochemical reaction network modelling, robust parameter estimation, and other uncertainty-aware computations in systems and synthetic biology.

Paperid: 1213, https://arxiv.org/pdf/2603.19952.pdf

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.

Paperid: 1214, https://arxiv.org/pdf/2603.19895.pdf

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.

Paperid: 1215, https://arxiv.org/pdf/2603.19641.pdf

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.

Paperid: 1216, https://arxiv.org/pdf/2603.19584.pdf

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.

Paperid: 1217, https://arxiv.org/pdf/2603.19580.pdf

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

Paperid: 1218, https://arxiv.org/pdf/2603.19454.pdf

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.

Paperid: 1219, https://arxiv.org/pdf/2603.18921.pdf

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.

Paperid: 1220, https://arxiv.org/pdf/2603.18910.pdf

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.

Paperid: 1221, https://arxiv.org/pdf/2603.18200.pdf

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.

Paperid: 1222, https://arxiv.org/pdf/2603.18069.pdf

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.

Paperid: 1223, https://arxiv.org/pdf/2603.17802.pdf

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.

Paperid: 1224, https://arxiv.org/pdf/2603.17780.pdf

Abstract:
Descriptor systems arise naturally in applications governed by algebraic constraints, such as power networks and chemical processes. The singular system matrix in descriptor systems may introduce non-causal dynamics, where the current output depends on future inputs and, in the presence of stochastic process and measurement noise, on future noise realizations as well. This paper proposes a data-driven predictive control framework for stochastic descriptor systems that accommodates algebraic constraints and impulsive modes without explicit system identification. A causal innovation representation is constructed by augmenting the system state with a noise buffer that encapsulates the non-causal stochastic interactions, transforming the descriptor system into an equivalent proper state-space form. Willems' Fundamental Lemma is then extended to the innovation form with fully data-verifiable conditions. Building on these results, a practical Inno-DeePC algorithm is developed that integrates offline innovation estimation and online predictive control. Numerical experiments on a direct-current (DC) microgrid demonstrate the effectiveness of the proposed approach for stochastic descriptor systems.

Paperid: 1225, https://arxiv.org/pdf/2603.17634.pdf

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.

Paperid: 1226, https://arxiv.org/pdf/2603.17593.pdf

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.

Paperid: 1227, https://arxiv.org/pdf/2603.16523.pdf

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.

Paperid: 1228, https://arxiv.org/pdf/2603.16424.pdf

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.

Paperid: 1229, https://arxiv.org/pdf/2603.16231.pdf

Abstract:
Numerical optimal control is commonly divided between globally structured but dimensionally intractable Hamilton-Jacobi-Bellman (HJB) methods and scalable but local trajectory optimization. We introduce the Featurized Occupation Measure (FOM), a finite-dimensional primal-dual interface for the occupation-measure formulation that unifies trajectory search and global HJB-type certification. FOM is broad yet numerically tractable, covering both explicit weak-form schemes and implicit simulator- or rollout-based sampling methods. Within this framework, approximate HJB subsolutions serve as intrinsic numerical certificates to directly evaluate and guide the primal search. We prove asymptotic consistency with the exact infinite-dimensional occupation-measure problem, and show that for block-organized feasible certificates, finite-dimensional approximation preserves certified lower bounds with blockwise error and complexity control. We also establish persistence of these lower bounds under time shifts and bounded model perturbations. Consequently, these structural properties render global certificates into flexible, reusable computational objects, establishing a systematic basis for certificate-guided optimization in nonlinear control.

Paperid: 1230, https://arxiv.org/pdf/2603.16198.pdf

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.

Paperid: 1231, https://arxiv.org/pdf/2603.16182.pdf

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.

Paperid: 1232, https://arxiv.org/pdf/2603.16146.pdf

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.

Paperid: 1233, https://arxiv.org/pdf/2603.16114.pdf

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.

Paperid: 1234, https://arxiv.org/pdf/2603.14942.pdf

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.

Paperid: 1235, https://arxiv.org/pdf/2603.13862.pdf

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.

Paperid: 1236, https://arxiv.org/pdf/2603.13828.pdf

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.

Paperid: 1237, https://arxiv.org/pdf/2603.13343.pdf

Abstract:
Most vehicle predictive maintenance systems rely exclusively on internal diagnostic signals and are validated on deterministic synthetic data, limiting the credibility of reported metrics. This paper presents a simulation-validated proof-of-concept framework for V2X-augmented predictive maintenance, integrating on-board sensor streams with external contextual signals -- road quality, weather, traffic density, and driver behaviour -- acquired via V2X communication and third-party APIs, with inference at the vehicle edge. Field validation on instrumented vehicles is identified as the required next step. Three experiments address common shortcomings of prior work. A feature group ablation study shows that V2X contextual features contribute a 2.6-point F1 gain, with full context removal reducing macro F1 from 0.855 to 0.807. On the AI4I 2020 real-world industrial failure dataset (10,000 samples, five failure modes), LightGBM achieves AUC-ROC of 0.973 under 5-fold stratified CV with SMOTE confined to training folds. A noise sensitivity analysis shows macro F1 remains above 0.88 under low noise and degrades to 0.74 under very high noise. SHAP analysis confirms that V2X and engineered interaction features rank among the top 15 predictors. Edge inference is estimated to reduce latency from 3.5s to under 1.0s versus cloud-only processing.

Paperid: 1238, https://arxiv.org/pdf/2603.12828.pdf

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.

Paperid: 1239, https://arxiv.org/pdf/2603.12503.pdf

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.

Paperid: 1240, https://arxiv.org/pdf/2603.11582.pdf

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.

Paperid: 1241, https://arxiv.org/pdf/2603.10970.pdf

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.

Paperid: 1242, https://arxiv.org/pdf/2603.10880.pdf

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.

Paperid: 1243, https://arxiv.org/pdf/2603.10635.pdf

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.

Paperid: 1244, https://arxiv.org/pdf/2603.10557.pdf

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.

Paperid: 1245, https://arxiv.org/pdf/2603.10340.pdf

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.

Paperid: 1246, https://arxiv.org/pdf/2603.09421.pdf

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.

Paperid: 1247, https://arxiv.org/pdf/2603.08886.pdf

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.

Paperid: 1248, https://arxiv.org/pdf/2603.07601.pdf

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.

Paperid: 1249, https://arxiv.org/pdf/2603.06951.pdf

Abstract:
Memory disaggregation via Compute Express Link (CXL) enables multiple hosts to share remote memory, improving utilization for data-intensive workloads. 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, a hardware-software co-design that provides fine-grained, process-level isolation for shared disaggregated memory. Space-Control authenticates execution context in the hardware and enforces access control on every memory access and amortizes lookup times with a small cache. Our design allows up to 127 processes Simulation Toolkit (SST) based CXL model, Space-Control incurs minimal performance overhead of 3.3%, making shared disaggregated memory isolation practical.

Paperid: 1250, https://arxiv.org/pdf/2603.06548.pdf

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.

Paperid: 1251, https://arxiv.org/pdf/2603.06494.pdf

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.

Paperid: 1252, https://arxiv.org/pdf/2603.06042.pdf

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.

Paperid: 1253, https://arxiv.org/pdf/2603.05902.pdf

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.

Paperid: 1254, https://arxiv.org/pdf/2603.05724.pdf

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.

Paperid: 1255, https://arxiv.org/pdf/2603.05716.pdf

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.

Paperid: 1256, https://arxiv.org/pdf/2603.04988.pdf

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.

Paperid: 1257, https://arxiv.org/pdf/2603.04962.pdf

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.

Paperid: 1258, https://arxiv.org/pdf/2603.04761.pdf

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.

Paperid: 1259, https://arxiv.org/pdf/2603.03932.pdf

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.

Paperid: 1260, https://arxiv.org/pdf/2603.03479.pdf

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.

Paperid: 1261, https://arxiv.org/pdf/2603.01173.pdf

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.

Paperid: 1262, https://arxiv.org/pdf/2603.00572.pdf

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.

Paperid: 1263, https://arxiv.org/pdf/2603.00438.pdf

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.

Paperid: 1264, https://arxiv.org/pdf/2602.23668.pdf

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.

Paperid: 1265, https://arxiv.org/pdf/2602.23397.pdf

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.

Paperid: 1266, https://arxiv.org/pdf/2602.22630.pdf

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.

Paperid: 1267, https://arxiv.org/pdf/2602.22514.pdf

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.

Paperid: 1268, https://arxiv.org/pdf/2602.21768.pdf

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.

Paperid: 1269, https://arxiv.org/pdf/2602.21738.pdf

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.

Paperid: 1270, https://arxiv.org/pdf/2602.21090.pdf

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.

Paperid: 1271, https://arxiv.org/pdf/2602.20842.pdf

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.

Paperid: 1272, https://arxiv.org/pdf/2602.20795.pdf

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.

Paperid: 1273, https://arxiv.org/pdf/2602.19862.pdf

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.

Paperid: 1274, https://arxiv.org/pdf/2602.19173.pdf

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.

Paperid: 1275, https://arxiv.org/pdf/2602.18961.pdf

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.

Paperid: 1276, https://arxiv.org/pdf/2602.18386.pdf

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.

Paperid: 1277, https://arxiv.org/pdf/2602.18058.pdf

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.

Paperid: 1278, https://arxiv.org/pdf/2602.17998.pdf

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 \emph{position-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 $\dot{x}=(J-R)\nabla H(x)$, guaranteeing $dH/dt\le 0$ when $R\succeq 0$. We introduce \textbf{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.

Paperid: 1279, https://arxiv.org/pdf/2602.17247.pdf

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.

Paperid: 1280, https://arxiv.org/pdf/2602.16475.pdf

Abstract:
We present a framework to \emph{certify} Hamilton--Jacobi (HJ) reachability learned by reinforcement learning (RL). Building on a discounted initial time \emph{travel-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 \emph{exactly} 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.

Paperid: 1281, https://arxiv.org/pdf/2602.15841.pdf

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.

Paperid: 1282, https://arxiv.org/pdf/2602.15684.pdf

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.

Paperid: 1283, https://arxiv.org/pdf/2602.14660.pdf

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.

Paperid: 1284, https://arxiv.org/pdf/2602.13641.pdf

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.

Paperid: 1285, https://arxiv.org/pdf/2602.12473.pdf

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.

Paperid: 1286, https://arxiv.org/pdf/2602.12289.pdf

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.

Paperid: 1287, https://arxiv.org/pdf/2602.12283.pdf

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.

Paperid: 1288, https://arxiv.org/pdf/2602.11822.pdf

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.

Paperid: 1289, https://arxiv.org/pdf/2602.11408.pdf

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/.

Paperid: 1290, https://arxiv.org/pdf/2602.11332.pdf

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.

Paperid: 1291, https://arxiv.org/pdf/2602.10888.pdf

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.

Paperid: 1292, https://arxiv.org/pdf/2602.10835.pdf

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.

Paperid: 1293, https://arxiv.org/pdf/2602.07975.pdf

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.

Paperid: 1294, https://arxiv.org/pdf/2602.07581.pdf

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 ($\partial$CBDs), 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.

Paperid: 1295, https://arxiv.org/pdf/2602.07300.pdf

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.

Paperid: 1296, https://arxiv.org/pdf/2602.06944.pdf

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.

Paperid: 1297, https://arxiv.org/pdf/2602.06380.pdf

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.

Paperid: 1298, https://arxiv.org/pdf/2602.06255.pdf

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 $40\times$ time speedup, while maintaining an average constraint violation on the order of $10^{-4}$ and an optimization gap below $1\%$.

Paperid: 1299, https://arxiv.org/pdf/2602.05974.pdf

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.

Paperid: 1300, https://arxiv.org/pdf/2602.05967.pdf

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.

Paperid: 1301, https://arxiv.org/pdf/2602.05644.pdf

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.

Paperid: 1302, https://arxiv.org/pdf/2602.05456.pdf

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.

Paperid: 1303, https://arxiv.org/pdf/2602.05263.pdf

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.

Paperid: 1304, https://arxiv.org/pdf/2602.04656.pdf

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.

Paperid: 1305, https://arxiv.org/pdf/2602.04568.pdf

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 \emph{peak-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.

Paperid: 1306, https://arxiv.org/pdf/2602.04057.pdf

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.

Paperid: 1307, https://arxiv.org/pdf/2602.03646.pdf

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.

Paperid: 1308, https://arxiv.org/pdf/2602.03460.pdf

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.

Paperid: 1309, https://arxiv.org/pdf/2602.03256.pdf

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.

Paperid: 1310, https://arxiv.org/pdf/2602.02942.pdf

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

Paperid: 1311, https://arxiv.org/pdf/2602.01013.pdf

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.

Paperid: 1312, https://arxiv.org/pdf/2602.00968.pdf

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.

Paperid: 1313, https://arxiv.org/pdf/2602.00031.pdf

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.

Paperid: 1314, https://arxiv.org/pdf/2601.22395.pdf

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.

Paperid: 1315, https://arxiv.org/pdf/2601.22294.pdf

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.

Paperid: 1316, https://arxiv.org/pdf/2601.20367.pdf

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.

Paperid: 1317, https://arxiv.org/pdf/2601.19665.pdf

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 \emph{frequency 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.

Paperid: 1318, https://arxiv.org/pdf/2601.19116.pdf

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.

Paperid: 1319, https://arxiv.org/pdf/2601.18959.pdf

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.

Paperid: 1320, https://arxiv.org/pdf/2601.17983.pdf

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.

Paperid: 1321, https://arxiv.org/pdf/2601.17978.pdf

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.

Paperid: 1322, https://arxiv.org/pdf/2601.17155.pdf

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.

Paperid: 1323, https://arxiv.org/pdf/2601.17154.pdf

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.

Paperid: 1324, https://arxiv.org/pdf/2601.16323.pdf

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.

Paperid: 1325, https://arxiv.org/pdf/2601.15135.pdf

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.

Paperid: 1326, https://arxiv.org/pdf/2601.15099.pdf

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.

Paperid: 1327, https://arxiv.org/pdf/2601.14613.pdf

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.

Paperid: 1328, https://arxiv.org/pdf/2601.14089.pdf

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.

Paperid: 1329, https://arxiv.org/pdf/2601.13252.pdf

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.

Paperid: 1330, https://arxiv.org/pdf/2601.13066.pdf

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.

Paperid: 1331, https://arxiv.org/pdf/2601.13037.pdf

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.

Paperid: 1332, https://arxiv.org/pdf/2601.12851.pdf

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.

Paperid: 1333, https://arxiv.org/pdf/2601.12840.pdf

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.

Paperid: 1334, https://arxiv.org/pdf/2601.12689.pdf

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.

Paperid: 1335, https://arxiv.org/pdf/2601.12687.pdf

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.

Paperid: 1336, https://arxiv.org/pdf/2601.12657.pdf

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.

Paperid: 1337, https://arxiv.org/pdf/2601.12616.pdf

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.

Paperid: 1338, https://arxiv.org/pdf/2601.12081.pdf

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.

Paperid: 1339, https://arxiv.org/pdf/2601.12028.pdf

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.

Paperid: 1340, https://arxiv.org/pdf/2601.12002.pdf

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.

Paperid: 1341, https://arxiv.org/pdf/2601.11426.pdf

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.

Paperid: 1342, https://arxiv.org/pdf/2601.11352.pdf

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.

Paperid: 1343, https://arxiv.org/pdf/2601.09917.pdf

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.

Paperid: 1344, https://arxiv.org/pdf/2601.08488.pdf

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 \gls{DO} 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.

Paperid: 1345, https://arxiv.org/pdf/2601.08339.pdf

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.

Paperid: 1346, https://arxiv.org/pdf/2601.08168.pdf

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.

Paperid: 1347, https://arxiv.org/pdf/2601.07295.pdf

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.

Paperid: 1348, https://arxiv.org/pdf/2601.06755.pdf

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.

Paperid: 1349, https://arxiv.org/pdf/2601.06557.pdf

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.

Paperid: 1350, https://arxiv.org/pdf/2601.06508.pdf

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.

Paperid: 1351, https://arxiv.org/pdf/2601.06439.pdf

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.

Paperid: 1352, https://arxiv.org/pdf/2601.04505.pdf

Abstract:
Generating accurate circuit schematics from high-level natural language descriptions remains a persistent challenge in electronics design, as large language models (LLMs) frequently hallucinate in granular details, violate electrical constraints, and produce non-machine-readable outputs. We present CircuitLM, a novel multi-agent LLM-aided circuit design pipeline that translates user prompts into structured, visually interpretable CircuitJSON schematics through five sequential stages: (i) LLM-based component identification, (ii) canonical pinout retrieval, (iii) chain-of-thought reasoning by an electronics expert agent, (iv) JSON schematic synthesis, and (v) force-directed SVG visualization. Anchored by a curated, embedding-powered component knowledge base. While LLMs often violate electrical constraints, CircuitLM bridges this gap by grounding generation in a verified and dynamically extensible component database, initially comprising 50 components. To ensure safety, we incorporate a hybrid evaluation framework, namely Dual-Metric Circuit Validation (DMCV), validated against human-expert assessments, which achieves high fidelity in microcontroller-centric designs. We evaluate the system on 100 diverse embedded-systems prompts across six LLMs and introduce DMCV to assess both structural and electrical validity. This work bridges natural language input to deployable hardware designs, enabling reliable circuit prototyping by non-experts. Our code and data will be made public upon acceptance.

Paperid: 1353, https://arxiv.org/pdf/2601.04111.pdf

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.

Paperid: 1354, https://arxiv.org/pdf/2601.03767.pdf

Abstract:
This work addresses the output consensus problem of constrained heterogeneous multi-agent systems under a switching network with potential communication delay, where outputs are periodic and characterized by a linear 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 is proposed to track periodic references, which maintains recursive feasibility even when reference switches. 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.

Paperid: 1355, https://arxiv.org/pdf/2601.03638.pdf

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.

Paperid: 1356, https://arxiv.org/pdf/2601.03442.pdf

Abstract:
In conventional distributed optimization, each agent performs a single local update between two communication rounds with its neighbors to synchronize solutions. Inspired by the success of using multiple local updates in federated learning, incorporating local updates into distributed optimization has recently attracted increasing attention. However, unlike federated learning, where multiple local updates can accelerate learning by improving gradient estimation under mini-batch settings, it remains unclear whether similar benefits hold in distributed optimization when gradients are exact. 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 and obscure their true impact on convergence. 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. Extensive experiments on both synthetic and real-world datasets corroborate the theoretical findings.

Paperid: 1357, https://arxiv.org/pdf/2601.03282.pdf

Abstract:
The Electric Autonomous Dial-A-Ride Problem (E-ADARP) optimizes routing and scheduling for electric autonomous vehicles to transport customers from origins to destinations. It features a combined objective that minimizes travel cost and excess user ride time, and allows partial recharging. Motivated by practical scenarios where time and battery data are available with limited precision, we introduce a discrete variant of the problem, termed D-E-ADARP, in which all time and battery parameters are discretized. This enables the development of our alternative solution approach: the discrete battery-time-space fragment-based formulation (BTSFF). In this framework, a fragment represents a subpath with an associated cost that accounts for both travel cost and excess user ride time. The BTSFF network integrates spatial, temporal, and battery dimensions, with the latter two discretized into finite indices. Computational results show that BTSFF solves D-E-ADARP significantly more efficiently than existing methods applied to the original E-ADARP. In addition, BTSFF efficiently provides high-quality lower bounds for E-ADARP and accelerates solving its battery swap variants. For E-ADARP, a relaxed network is constructed by rounding down travel times and battery consumptions, enabling a valid lower bound. For battery swap variants, BTSFF integrates lazy constraints via callbacks to correct time discretization errors, guaranteeing optimal solutions. Experiments show BTSFF outperforms benchmark methods in efficiency.

Paperid: 1358, https://arxiv.org/pdf/2601.02958.pdf

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.

Paperid: 1359, https://arxiv.org/pdf/2601.01726.pdf

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.

Paperid: 1360, https://arxiv.org/pdf/2601.01629.pdf

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.

Paperid: 1361, https://arxiv.org/pdf/2601.01256.pdf

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.

Paperid: 1362, https://arxiv.org/pdf/2601.01170.pdf

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.

Paperid: 1363, https://arxiv.org/pdf/2601.01130.pdf

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.

Paperid: 1364, https://arxiv.org/pdf/2601.00981.pdf

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.

Paperid: 1365, https://arxiv.org/pdf/2601.00813.pdf

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.

Paperid: 1366, https://arxiv.org/pdf/2601.00494.pdf

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.

Paperid: 1367, https://arxiv.org/pdf/2604.04625.pdf

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 $\boldsymbol{0^\circ/180^\circ \pm 20^\circ}$ phase states between ON/OFF conditions. The designed RIS is in the form of a $\boldsymbol{10\times10}$ array with a compact size of $\boldsymbol{2.9λ_g \times 2.9λ_g}$. 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 \mbox{band (3.5\,GHz).}

Paperid: 1368, https://arxiv.org/pdf/2604.04602.pdf

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.

Paperid: 1369, https://arxiv.org/pdf/2604.04544.pdf

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.

Paperid: 1370, https://arxiv.org/pdf/2604.04165.pdf

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.

Paperid: 1371, https://arxiv.org/pdf/2604.04051.pdf

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.

Paperid: 1372, https://arxiv.org/pdf/2604.04041.pdf

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.

Paperid: 1373, https://arxiv.org/pdf/2604.04001.pdf

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.

Paperid: 1374, https://arxiv.org/pdf/2604.03989.pdf

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 $\He{PA} \prec 0$ (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.

Paperid: 1375, https://arxiv.org/pdf/2604.03917.pdf

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.

Paperid: 1376, https://arxiv.org/pdf/2604.03794.pdf

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.

Paperid: 1377, https://arxiv.org/pdf/2604.03791.pdf

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.

Paperid: 1378, https://arxiv.org/pdf/2604.03788.pdf

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.

Paperid: 1379, https://arxiv.org/pdf/2604.03461.pdf

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.

Paperid: 1380, https://arxiv.org/pdf/2604.03458.pdf

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 $C_{\mathrm{W}}$, 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 $K_{\mathrm{R}}$ index, and the SCR index.

Paperid: 1381, https://arxiv.org/pdf/2604.03427.pdf

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.

Paperid: 1382, https://arxiv.org/pdf/2604.03415.pdf

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.

Paperid: 1383, https://arxiv.org/pdf/2604.03399.pdf

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.

Paperid: 1384, https://arxiv.org/pdf/2604.03392.pdf

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.

Paperid: 1385, https://arxiv.org/pdf/2604.02707.pdf

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.

Paperid: 1386, https://arxiv.org/pdf/2604.02503.pdf

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.

Paperid: 1387, https://arxiv.org/pdf/2604.02273.pdf

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.

Paperid: 1388, https://arxiv.org/pdf/2604.02251.pdf

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.

Paperid: 1389, https://arxiv.org/pdf/2604.02199.pdf

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.

Paperid: 1390, https://arxiv.org/pdf/2604.02170.pdf

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.

Paperid: 1391, https://arxiv.org/pdf/2604.02025.pdf

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'.

Paperid: 1392, https://arxiv.org/pdf/2604.01914.pdf

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.

Paperid: 1393, https://arxiv.org/pdf/2604.01795.pdf

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.

Paperid: 1394, https://arxiv.org/pdf/2604.01721.pdf

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.

Paperid: 1395, https://arxiv.org/pdf/2604.01547.pdf

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.

Paperid: 1396, https://arxiv.org/pdf/2604.01406.pdf

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.

Paperid: 1397, https://arxiv.org/pdf/2604.01338.pdf

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.

Paperid: 1398, https://arxiv.org/pdf/2604.01321.pdf

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.

Paperid: 1399, https://arxiv.org/pdf/2604.01115.pdf

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.

Paperid: 1400, https://arxiv.org/pdf/2604.01104.pdf

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.

Paperid: 1401, https://arxiv.org/pdf/2604.00674.pdf

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.

Paperid: 1402, https://arxiv.org/pdf/2604.00487.pdf

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.

Paperid: 1403, https://arxiv.org/pdf/2604.00449.pdf

Abstract:
We study distributed optimization over networks with Byzantine agents that may send arbitrary adversarial messages. We propose \emph{Gradient 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 \emph{Gradient 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.

Paperid: 1404, https://arxiv.org/pdf/2604.00415.pdf

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.

Paperid: 1405, https://arxiv.org/pdf/2604.00277.pdf

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.

Paperid: 1406, https://arxiv.org/pdf/2604.00173.pdf

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.

Paperid: 1407, https://arxiv.org/pdf/2604.00135.pdf

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.

Paperid: 1408, https://arxiv.org/pdf/2603.29995.pdf

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.

Paperid: 1409, https://arxiv.org/pdf/2603.29717.pdf

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.

Paperid: 1410, https://arxiv.org/pdf/2603.29526.pdf

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.

Paperid: 1411, https://arxiv.org/pdf/2603.29354.pdf

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.

Paperid: 1412, https://arxiv.org/pdf/2603.28891.pdf

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.

Paperid: 1413, https://arxiv.org/pdf/2603.28873.pdf

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.

Paperid: 1414, https://arxiv.org/pdf/2603.28243.pdf

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.

Paperid: 1415, https://arxiv.org/pdf/2603.28016.pdf

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.

Paperid: 1416, https://arxiv.org/pdf/2603.27961.pdf

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 $[16\times10]$ 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.

Paperid: 1417, https://arxiv.org/pdf/2603.27833.pdf

Abstract:
In this work, we first prove that the separation principle holds for switched 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 switching 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 switching policy.

Paperid: 1418, https://arxiv.org/pdf/2603.27831.pdf

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.

Paperid: 1419, https://arxiv.org/pdf/2603.27816.pdf

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.

Paperid: 1420, https://arxiv.org/pdf/2603.27615.pdf

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.

Paperid: 1421, https://arxiv.org/pdf/2603.27592.pdf

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.

Paperid: 1422, https://arxiv.org/pdf/2603.27581.pdf

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.

Paperid: 1423, https://arxiv.org/pdf/2603.27560.pdf

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.

Paperid: 1424, https://arxiv.org/pdf/2603.27024.pdf

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 $g$.

Paperid: 1425, https://arxiv.org/pdf/2603.26709.pdf

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.

Paperid: 1426, https://arxiv.org/pdf/2603.26697.pdf

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.

Paperid: 1427, https://arxiv.org/pdf/2603.26339.pdf

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.

Paperid: 1428, https://arxiv.org/pdf/2603.25898.pdf

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 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.

Paperid: 1429, https://arxiv.org/pdf/2603.25602.pdf

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.

Paperid: 1430, https://arxiv.org/pdf/2603.25308.pdf

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.

Paperid: 1431, https://arxiv.org/pdf/2603.25139.pdf

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.

Paperid: 1432, https://arxiv.org/pdf/2603.24959.pdf

Abstract:
Biohybrid robots integrate living tissues with engineered artificial structures to achieve organism-inspired actuation and behavior. A persistent challenge is delivering stimulation and control signals without relying on tethered wiring or bulky hardware immersed in cell-culture media. Wireless bioelectronics addresses this limitation by enabling the remote transfer of control signals, typically via radio-frequency magnetic fields, to locally stimulate muscle tissues at tissue-electrode interfaces. In parallel, wireless optoelectronics enables remote control of optogenetically modified, muscle-based robots by embedding light emitters that initiate muscle actuation through light-gated ion channels. Further advances incorporate neuromuscular junctions, leveraging biological signal transduction to enable selective control of multiple actuators through wireless frequency- and time-division multiplexing. This perspective article summarizes recent advances in control strategies for biohybrid robots, namely, wireless electrical stimulation, wireless optical stimulation, and neuromuscular integration. Then this describes cross-cutting design principles and highlights a future direction, namely, co-integration of neural organoid-bioelectronics toward autonomous, closed-loop biohybrid robots.

Paperid: 1433, https://arxiv.org/pdf/2603.24241.pdf

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.

Paperid: 1434, https://arxiv.org/pdf/2603.23983.pdf

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.

Paperid: 1435, https://arxiv.org/pdf/2603.22654.pdf

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.

Paperid: 1436, https://arxiv.org/pdf/2603.22554.pdf

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.

Paperid: 1437, https://arxiv.org/pdf/2603.22460.pdf

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.

Paperid: 1438, https://arxiv.org/pdf/2603.21810.pdf

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.

Paperid: 1439, https://arxiv.org/pdf/2603.21580.pdf

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.

Paperid: 1440, https://arxiv.org/pdf/2603.21370.pdf

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.

Paperid: 1441, https://arxiv.org/pdf/2603.21089.pdf

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.

Paperid: 1442, https://arxiv.org/pdf/2603.20819.pdf

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 $\widetilde{O}(1/ε)$. Simulation supports the theory and illustrates the advantage of the proposed estimator for uncertainty quantification.

Paperid: 1443, https://arxiv.org/pdf/2603.20564.pdf

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.

Paperid: 1444, https://arxiv.org/pdf/2603.20525.pdf

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.

Paperid: 1445, https://arxiv.org/pdf/2603.20500.pdf

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.

Paperid: 1446, https://arxiv.org/pdf/2603.20434.pdf

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.

Paperid: 1447, https://arxiv.org/pdf/2603.20411.pdf

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.

Paperid: 1448, https://arxiv.org/pdf/2603.20277.pdf

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.

Paperid: 1449, https://arxiv.org/pdf/2603.20238.pdf

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.

Paperid: 1450, https://arxiv.org/pdf/2603.19116.pdf

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.

Paperid: 1451, https://arxiv.org/pdf/2603.18949.pdf

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.

Paperid: 1452, https://arxiv.org/pdf/2603.18776.pdf

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.

Paperid: 1453, https://arxiv.org/pdf/2603.18424.pdf

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

Paperid: 1454, https://arxiv.org/pdf/2603.18333.pdf

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.

Paperid: 1455, https://arxiv.org/pdf/2603.17640.pdf

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.

Paperid: 1456, https://arxiv.org/pdf/2603.17376.pdf

Abstract:
The solvability condition of the power flow equation is important in operational planning and control as it guarantees the existence and uniqueness of a solution for a given set of power injections. As renewable generation becomes more prevalent, the steady-state operating point of the system changes more frequently, making it increasingly challenging to verify power flow solvability by running the AC power flow solver after each change in power injections. This process can be computationally intensive, and numerical solvers do not always converge reliably to an operational solution. In this paper, we propose a sufficient condition for the solvability of the lossless real power flow equation based on the cycle space of a meshed network. The proposed condition yields a less conservative solvability certificate than existing sufficient conditions on the tested systems and can serve as a useful foundation for developing solvability conditions for the fully coupled power flow equations.

Paperid: 1457, https://arxiv.org/pdf/2603.17340.pdf

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.

Paperid: 1458, https://arxiv.org/pdf/2603.17309.pdf

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.

Paperid: 1459, https://arxiv.org/pdf/2603.17163.pdf

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.

Paperid: 1460, https://arxiv.org/pdf/2603.17089.pdf

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.

Paperid: 1461, https://arxiv.org/pdf/2603.17022.pdf

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.

Paperid: 1462, https://arxiv.org/pdf/2603.16961.pdf

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.

Paperid: 1463, https://arxiv.org/pdf/2603.16893.pdf

Abstract:
In many low-income countries, neighborhood diesel generators are widely used to compensate for unreliable or unavailable national electricity grids. These diesel-based microgrids are typically characterized by market power, significant pollution, and weak regulatory oversight. 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 developing 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 applied to a Lebanese case study using high-resolution empirical data collected via logging devices. 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.

Paperid: 1464, https://arxiv.org/pdf/2603.16887.pdf

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.

Paperid: 1465, https://arxiv.org/pdf/2603.16879.pdf

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.

Paperid: 1466, https://arxiv.org/pdf/2603.16705.pdf

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.

Paperid: 1467, https://arxiv.org/pdf/2603.16565.pdf

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.

Paperid: 1468, https://arxiv.org/pdf/2603.15993.pdf

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.

Paperid: 1469, https://arxiv.org/pdf/2603.15865.pdf

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.

Paperid: 1470, https://arxiv.org/pdf/2603.15586.pdf

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.

Paperid: 1471, https://arxiv.org/pdf/2603.15571.pdf

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.

Paperid: 1472, https://arxiv.org/pdf/2603.14616.pdf

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.

Paperid: 1473, https://arxiv.org/pdf/2603.14442.pdf

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.

Paperid: 1474, https://arxiv.org/pdf/2603.14197.pdf

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.

Paperid: 1475, https://arxiv.org/pdf/2603.13975.pdf

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

Paperid: 1476, https://arxiv.org/pdf/2603.13883.pdf

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.

Paperid: 1477, https://arxiv.org/pdf/2603.13608.pdf

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.

Paperid: 1478, https://arxiv.org/pdf/2603.13529.pdf

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.

Paperid: 1479, https://arxiv.org/pdf/2603.13136.pdf

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.

Paperid: 1480, https://arxiv.org/pdf/2603.13003.pdf

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.

Paperid: 1481, https://arxiv.org/pdf/2603.12541.pdf

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.

Paperid: 1482, https://arxiv.org/pdf/2603.12439.pdf

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.

Paperid: 1483, https://arxiv.org/pdf/2603.12399.pdf

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

Paperid: 1484, https://arxiv.org/pdf/2603.12075.pdf

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.

Paperid: 1485, https://arxiv.org/pdf/2603.12069.pdf

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.

Paperid: 1486, https://arxiv.org/pdf/2603.11905.pdf

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.

Paperid: 1487, https://arxiv.org/pdf/2603.11264.pdf

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.

Paperid: 1488, https://arxiv.org/pdf/2603.10812.pdf

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.

Paperid: 1489, https://arxiv.org/pdf/2603.10670.pdf

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.

Paperid: 1490, https://arxiv.org/pdf/2603.10562.pdf

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.

Paperid: 1491, https://arxiv.org/pdf/2603.10527.pdf

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.

Paperid: 1492, https://arxiv.org/pdf/2603.10275.pdf

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.

Paperid: 1493, https://arxiv.org/pdf/2603.10149.pdf

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.

Paperid: 1494, https://arxiv.org/pdf/2603.09918.pdf

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.

Paperid: 1495, https://arxiv.org/pdf/2603.09908.pdf

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.

Paperid: 1496, https://arxiv.org/pdf/2603.09878.pdf

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.

Paperid: 1497, https://arxiv.org/pdf/2603.09814.pdf

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.

Paperid: 1498, https://arxiv.org/pdf/2603.09395.pdf

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.

Paperid: 1499, https://arxiv.org/pdf/2603.09254.pdf

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.

Paperid: 1500, https://arxiv.org/pdf/2603.09086.pdf

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.

Paperid: 1501, https://arxiv.org/pdf/2603.08816.pdf

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.

Paperid: 1502, https://arxiv.org/pdf/2603.08078.pdf

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.

Paperid: 1503, https://arxiv.org/pdf/2603.07907.pdf

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 $\mathcal{L}_2$-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 $\mathcal{H}_\infty$ 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.

Paperid: 1504, https://arxiv.org/pdf/2603.07818.pdf

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.

Paperid: 1505, https://arxiv.org/pdf/2603.07783.pdf

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.

Paperid: 1506, https://arxiv.org/pdf/2603.07736.pdf

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.

Paperid: 1507, https://arxiv.org/pdf/2603.07721.pdf

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.

Paperid: 1508, https://arxiv.org/pdf/2603.07092.pdf

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.

Paperid: 1509, https://arxiv.org/pdf/2603.07049.pdf

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.

Paperid: 1510, https://arxiv.org/pdf/2603.06962.pdf

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.

Paperid: 1511, https://arxiv.org/pdf/2603.05489.pdf

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.

Paperid: 1512, https://arxiv.org/pdf/2603.04787.pdf

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.

Paperid: 1513, https://arxiv.org/pdf/2603.04734.pdf

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 optimization. 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.

Paperid: 1514, https://arxiv.org/pdf/2603.04661.pdf

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.

Paperid: 1515, https://arxiv.org/pdf/2603.04335.pdf

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.

Paperid: 1516, https://arxiv.org/pdf/2603.03724.pdf

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.

Paperid: 1517, https://arxiv.org/pdf/2603.03712.pdf

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.

Paperid: 1518, https://arxiv.org/pdf/2603.03574.pdf

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.

Paperid: 1519, https://arxiv.org/pdf/2603.03497.pdf

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.

Paperid: 1520, https://arxiv.org/pdf/2603.03289.pdf

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.

Paperid: 1521, https://arxiv.org/pdf/2603.03127.pdf

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.

Paperid: 1522, https://arxiv.org/pdf/2603.02975.pdf

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.

Paperid: 1523, https://arxiv.org/pdf/2603.02687.pdf

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.

Paperid: 1524, https://arxiv.org/pdf/2603.02534.pdf

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

Paperid: 1525, https://arxiv.org/pdf/2603.02503.pdf

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.

Paperid: 1526, https://arxiv.org/pdf/2603.02105.pdf

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.

Paperid: 1527, https://arxiv.org/pdf/2603.01198.pdf

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.

Paperid: 1528, https://arxiv.org/pdf/2603.00974.pdf

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.

Paperid: 1529, https://arxiv.org/pdf/2603.00709.pdf

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.

Paperid: 1530, https://arxiv.org/pdf/2603.00616.pdf

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.

Paperid: 1531, https://arxiv.org/pdf/2603.00528.pdf

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.

Paperid: 1532, https://arxiv.org/pdf/2603.00407.pdf

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.

Paperid: 1533, https://arxiv.org/pdf/2603.00396.pdf

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.

Paperid: 1534, https://arxiv.org/pdf/2603.00320.pdf

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.

Paperid: 1535, https://arxiv.org/pdf/2603.00319.pdf

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.

Paperid: 1536, https://arxiv.org/pdf/2603.00169.pdf

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.

Paperid: 1537, https://arxiv.org/pdf/2602.24252.pdf

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.

Paperid: 1538, https://arxiv.org/pdf/2602.23670.pdf

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.

Paperid: 1539, https://arxiv.org/pdf/2602.23313.pdf

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.

Paperid: 1540, https://arxiv.org/pdf/2602.23052.pdf

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.

Paperid: 1541, https://arxiv.org/pdf/2602.22939.pdf

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.

Paperid: 1542, https://arxiv.org/pdf/2602.22380.pdf

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.

Paperid: 1543, https://arxiv.org/pdf/2602.22133.pdf

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.

Paperid: 1544, https://arxiv.org/pdf/2602.21602.pdf

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.

Paperid: 1545, https://arxiv.org/pdf/2602.21450.pdf

Abstract:
This paper presents a novel vector field strategy for controlling fully-actuated systems on connected matrix Lie groups, ensuring convergence to and traversal along a curve defined on the group. Our approach generalizes our previous work (Rezende et al., 2022) and reduces to it when considering the Lie group of translations in Euclidean space. Since the proofs in Rezende et al. (2022) rely on key properties such as the orthogonality between the convergent and traversal components, we extend these results by leveraging Lie group properties. These properties also allow the control input to be non-redundant, meaning it matches the dimension of the Lie group, rather than the potentially larger dimension of the space in which the group is embedded. This can lead to more practical control inputs in certain scenarios. A particularly notable application of our strategy is in controlling systems on SE(3) -- in this case, the non-redundant input corresponds to the object's mechanical twist -- making it well-suited for controlling objects that can move and rotate freely, such as omnidirectional drones. In this case, we provide an efficient algorithm to compute the vector field. We experimentally validate the proposed method using a robotic manipulator to demonstrate its effectiveness.

Paperid: 1546, https://arxiv.org/pdf/2602.21334.pdf

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.

Paperid: 1547, https://arxiv.org/pdf/2602.20453.pdf

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.

Paperid: 1548, https://arxiv.org/pdf/2602.19420.pdf

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.

Paperid: 1549, https://arxiv.org/pdf/2602.19002.pdf

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.

Paperid: 1550, https://arxiv.org/pdf/2602.18751.pdf

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.

Paperid: 1551, https://arxiv.org/pdf/2602.18416.pdf

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.

Paperid: 1552, https://arxiv.org/pdf/2602.18261.pdf

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.

Paperid: 1553, https://arxiv.org/pdf/2602.18247.pdf

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.

Paperid: 1554, https://arxiv.org/pdf/2602.18048.pdf

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.

Paperid: 1555, https://arxiv.org/pdf/2602.18014.pdf

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 $\mathcal{O}(p^3)$ instead of $\mathcal{O}(i^2p^3)$, 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.

Paperid: 1556, https://arxiv.org/pdf/2602.17874.pdf

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.

Paperid: 1557, https://arxiv.org/pdf/2602.17407.pdf

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.

Paperid: 1558, https://arxiv.org/pdf/2602.17239.pdf

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.

Paperid: 1559, https://arxiv.org/pdf/2602.17118.pdf

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.

Paperid: 1560, https://arxiv.org/pdf/2602.16831.pdf

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.

Paperid: 1561, https://arxiv.org/pdf/2602.16764.pdf

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.

Paperid: 1562, https://arxiv.org/pdf/2602.16758.pdf

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.

Paperid: 1563, https://arxiv.org/pdf/2602.16678.pdf

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.

Paperid: 1564, https://arxiv.org/pdf/2602.16565.pdf

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.

Paperid: 1565, https://arxiv.org/pdf/2602.16362.pdf

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.

Paperid: 1566, https://arxiv.org/pdf/2602.16260.pdf

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.

Paperid: 1567, https://arxiv.org/pdf/2602.16235.pdf

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.

Paperid: 1568, https://arxiv.org/pdf/2602.16102.pdf

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.

Paperid: 1569, https://arxiv.org/pdf/2602.16036.pdf

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.

Paperid: 1570, https://arxiv.org/pdf/2602.15704.pdf

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.

Paperid: 1571, https://arxiv.org/pdf/2602.15370.pdf

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.

Paperid: 1572, https://arxiv.org/pdf/2602.15354.pdf

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.

Paperid: 1573, https://arxiv.org/pdf/2602.15046.pdf

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.

Paperid: 1574, https://arxiv.org/pdf/2602.14339.pdf

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.

Paperid: 1575, https://arxiv.org/pdf/2602.14104.pdf

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.

Paperid: 1576, https://arxiv.org/pdf/2602.13957.pdf

Abstract:
In this paper, we propose a data-enabled moving horizon estimation (MHE) approach for nonlinear systems. While the approach is formulated by leveraging Koopman theory, its implementation does not require explicit Koopman modeling. Lifting functions are learned from the state and input data of the original nonlinear system to project the system trajectories into the lifted space, where the resulting trajectories implicitly describe the Koopman representation for the original nonlinear system. A convex data-enabled MHE formulation is developed to provide real-time state estimates of the Koopman representation, from which the states of the nonlinear system can be reconstructed. Sufficient conditions are derived to ensure the stability of the estimation error. The effectiveness of the proposed method is illustrated using a membrane-based biological water treatment process.

Paperid: 1577, https://arxiv.org/pdf/2602.13157.pdf

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.

Paperid: 1578, https://arxiv.org/pdf/2602.13108.pdf

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.

Paperid: 1579, https://arxiv.org/pdf/2602.12838.pdf

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).

Paperid: 1580, https://arxiv.org/pdf/2602.12782.pdf

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.

Paperid: 1581, https://arxiv.org/pdf/2602.12603.pdf

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.

Paperid: 1582, https://arxiv.org/pdf/2602.12202.pdf

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 NREL, we demonstrate that the GFM's equivalent impedance model captures the sub-transient response and static voltage stability limit reasonably accurately.

Paperid: 1583, https://arxiv.org/pdf/2602.12047.pdf

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.

Paperid: 1584, https://arxiv.org/pdf/2602.11632.pdf

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.

Paperid: 1585, https://arxiv.org/pdf/2602.11429.pdf

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.

Paperid: 1586, https://arxiv.org/pdf/2602.10963.pdf

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.

Paperid: 1587, https://arxiv.org/pdf/2602.10567.pdf

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 parameterized family of 1-D systems. For each mode, a full-state boundary feedback controller 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. 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.

Paperid: 1588, https://arxiv.org/pdf/2602.10526.pdf

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.

Paperid: 1589, https://arxiv.org/pdf/2602.09667.pdf

Abstract:
The transition toward low-inertia power systems demands modeling frameworks that provide not only accurate state predictions but also physically consistent sensitivities for control. While scientific machine learning offers powerful nonlinear modeling tools, the control-oriented implications of different differentiable paradigms remain insufficiently understood. This paper presents a comparative study of Physics-Informed Neural Networks (PINNs), Neural Ordinary Differential Equations (NODEs), and Differentiable Programming (DP) for modeling, identification, and control of power system dynamics. Using the Single Machine Infinite Bus (SMIB) system as a benchmark, we evaluate their performance in trajectory extrapolation, parameter estimation, and Linear Quadratic Regulator (LQR) synthesis. Our results highlight a fundamental trade-off between data-driven flexibility and physical structure. NODE exhibits superior extrapolation by capturing the underlying vector field, whereas PINN shows limited generalization due to its reliance on a time-dependent solution map. In the inverse problem of parameter identification, while both DP and PINN successfully recover the unknown parameters, DP achieves significantly faster convergence by enforcing governing equations as hard constraints. Most importantly, for control synthesis, the DP framework yields closed-loop stability comparable to the theoretical optimum. Furthermore, we demonstrate that NODE serves as a viable data-driven surrogate when governing equations are unavailable.

Paperid: 1590, https://arxiv.org/pdf/2602.09414.pdf

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.

Paperid: 1591, https://arxiv.org/pdf/2602.09398.pdf

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.

Paperid: 1592, https://arxiv.org/pdf/2602.09213.pdf

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.

Paperid: 1593, https://arxiv.org/pdf/2602.09150.pdf

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.

Paperid: 1594, https://arxiv.org/pdf/2602.09144.pdf

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.

Paperid: 1595, https://arxiv.org/pdf/2602.09140.pdf

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.

Paperid: 1596, https://arxiv.org/pdf/2602.08444.pdf

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.

Paperid: 1597, https://arxiv.org/pdf/2602.07995.pdf

Abstract:
This work introduces for the first time, to our knowledge, a trustworthiness layer for foundation models in power systems. Using stratified conformal prediction, we devise adaptive, statistically valid confidence bounds for each output of a foundation model. For regression, this allows users to obtain an uncertainty estimate for each output; for screening, it supports conservative decisions that minimize false negatives. We demonstrate our method by enhancing GridFM, the first open-source Foundation Model for power systems, with statistically valid prediction intervals instead of heuristic error margins. We apply it for N-k contingency assessment, a combinatorial NP-Hard problem. We show that trustworthy GridFM can offer richer and more accurate information than DC Power Flow, having 2x-3x higher precision, while running up to 18x faster than AC Power Flow for systems up to 118 buses. Moving a step further, we also examine the ability of trustworthy GridFM to generalize to unseen high-order contingencies: through a rigorous analysis, we assess how a model trained on N-1 or N-2 outages extrapolates to unseen contingencies up to N-5.

Paperid: 1598, https://arxiv.org/pdf/2602.07921.pdf

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.

Paperid: 1599, https://arxiv.org/pdf/2602.07836.pdf

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.

Paperid: 1600, https://arxiv.org/pdf/2602.07335.pdf

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-$\mathcal{K}$ 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-$\mathcal{K}$ ICCBFs, with the RNN showing a particularly strong advantage in the more complex inspection case.

Paperid: 1601, https://arxiv.org/pdf/2602.07326.pdf

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.

Paperid: 1602, https://arxiv.org/pdf/2602.07173.pdf

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.

Paperid: 1603, https://arxiv.org/pdf/2602.07074.pdf

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.

Paperid: 1604, https://arxiv.org/pdf/2602.06365.pdf

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.

Paperid: 1605, https://arxiv.org/pdf/2602.06263.pdf

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.

Paperid: 1606, https://arxiv.org/pdf/2602.06087.pdf

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.

Paperid: 1607, https://arxiv.org/pdf/2601.20723.pdf

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.

Paperid: 1608, https://arxiv.org/pdf/2601.20226.pdf

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.

Paperid: 1609, https://arxiv.org/pdf/2601.20183.pdf

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.

Paperid: 1610, https://arxiv.org/pdf/2601.19638.pdf

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.

Paperid: 1611, https://arxiv.org/pdf/2601.19095.pdf

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.

Paperid: 1612, https://arxiv.org/pdf/2601.19000.pdf

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.

Paperid: 1613, https://arxiv.org/pdf/2601.18971.pdf

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.

Paperid: 1614, https://arxiv.org/pdf/2601.17990.pdf

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).

Paperid: 1615, https://arxiv.org/pdf/2601.17980.pdf

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.

Paperid: 1616, https://arxiv.org/pdf/2601.17974.pdf

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.

Paperid: 1617, https://arxiv.org/pdf/2601.17965.pdf

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.

Paperid: 1618, https://arxiv.org/pdf/2601.17464.pdf

Abstract:
Robust output regulation for linear time-varying systems has remained an open problem for decades. To address this, we propose intrinsic system immersion by reformulating the regulator equation in a more insightful form, indicating that finding an internal model is equivalent to reproducing the output trajectory of a forced system by constructing an unforced system. This perspective reveals the influence of parametric uncertainties, demonstrating that an infinite-dimensional controller is generally unavoidable for robustness against plant uncertainty. Consequently, a general robust design is proposed without explicitly solving the regulator equation. It ensures robustness against uncertainties in the exosystem interaction, and achieves approximate output regulation when an infinite-dimensional controller is necessary for regulation. Additionally, we study the regulator equation in a coordinate-free framework, extend the time-varying non-resonance condition, and provide a method to minimize the dimension of an internal model. Overall, these results provide a general systematic framework for constructing robust internal model-based controllers, and simplify the control implementation process.

Paperid: 1619, https://arxiv.org/pdf/2601.17195.pdf

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.

Paperid: 1620, https://arxiv.org/pdf/2601.17193.pdf

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.

Paperid: 1621, https://arxiv.org/pdf/2601.17158.pdf

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.

Paperid: 1622, https://arxiv.org/pdf/2601.16863.pdf

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.

Paperid: 1623, https://arxiv.org/pdf/2601.16764.pdf

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.

Paperid: 1624, https://arxiv.org/pdf/2601.16663.pdf

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.

Paperid: 1625, https://arxiv.org/pdf/2601.16612.pdf

Abstract:
The most common instruments currently measuring active/reactive energy and power quality indicators are smart energy meters. 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 the proper metrological verification of smart energy meters. It presents existing legal and normative requirements and scientific research directions regarding these meters. Selected test results are presented, which show that although the tested meters meet the normative and legal requirements because they have been approved for sale, numerous imperfections in the signal and measurement chains of the analyzed instruments are revealed for the selected test signal. On the basis of the presented research results, further directions of research in the field of smart energy meters have been determined.

Paperid: 1626, https://arxiv.org/pdf/2601.16502.pdf

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.

Paperid: 1627, https://arxiv.org/pdf/2601.16418.pdf

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.

Paperid: 1628, https://arxiv.org/pdf/2601.16061.pdf

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.

Paperid: 1629, https://arxiv.org/pdf/2601.15626.pdf

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.

Paperid: 1630, https://arxiv.org/pdf/2601.15529.pdf

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.

Paperid: 1631, https://arxiv.org/pdf/2601.15349.pdf

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.

Paperid: 1632, https://arxiv.org/pdf/2601.14984.pdf

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.

Paperid: 1633, https://arxiv.org/pdf/2601.14342.pdf

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.

Paperid: 1634, https://arxiv.org/pdf/2601.13753.pdf

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.

Paperid: 1635, https://arxiv.org/pdf/2601.13202.pdf

Abstract:
Time-matching requirements (TMRs) for clean electricity procurement are increasingly adopted in voluntary corporate sustainability initiatives and regulatory frameworks. While prior research has evaluated cost and emissions impacts of hourly vs. annual TMR, these studies typically rely on single-year weather scenarios that do not capture inter-annual variability in variable renewable energy (VRE) generation. We use a capacity expansion model to assess how inter-annual weather variability affects procurement-driven infrastructure investments, costs, and emissions for a grid-connected hydrogen producer under both annual and hourly time-matching strategies. Using a Texas case study, we compare deterministic (single weather scenario) and stochastic (nine weather scenarios) modeling approaches. Both procurement investments and cost and emissions outcomes are sensitive to weather scenario, with annual matching exhibiting greater sensitivity than hourly matching. Stochastic modeling finds higher cost premiums for hourly versus annual matching compared to deterministic modeling, though emissions trends remain directionally consistent. Demand flexibility through H2 storage is critical for lowering hourly matching cost premiums under weather-driven VRE variability. Partial hourly matching (e.g., 80-90% compliance) can modestly reduce costs while maintaining minimal emissions impacts. Finally, we examine how grid-level renewable portfolio standards (RPS) affect additionality and emissions. When stringent additionality is achieved via binding RPS constraints on non-H2 electricity demand, annual matching can produce emissions reductions comparable to hourly matching at lower cost.

Paperid: 1636, https://arxiv.org/pdf/2601.13088.pdf

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.

Paperid: 1637, https://arxiv.org/pdf/2601.12987.pdf

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.

Paperid: 1638, https://arxiv.org/pdf/2601.12625.pdf

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.

Paperid: 1639, https://arxiv.org/pdf/2601.12610.pdf

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.

Paperid: 1640, https://arxiv.org/pdf/2601.12341.pdf

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.

Paperid: 1642, https://arxiv.org/pdf/2601.12003.pdf

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.

Paperid: 1643, https://arxiv.org/pdf/2601.11967.pdf

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.

Paperid: 1644, https://arxiv.org/pdf/2601.11876.pdf

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.

Paperid: 1645, https://arxiv.org/pdf/2601.11505.pdf

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.

Paperid: 1646, https://arxiv.org/pdf/2601.10976.pdf

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.

Paperid: 1647, https://arxiv.org/pdf/2601.10975.pdf

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.

Paperid: 1648, https://arxiv.org/pdf/2601.10874.pdf

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.

Paperid: 1649, https://arxiv.org/pdf/2601.10861.pdf

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.

Paperid: 1650, https://arxiv.org/pdf/2601.10379.pdf

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.

Paperid: 1651, https://arxiv.org/pdf/2601.10292.pdf

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.

Paperid: 1652, https://arxiv.org/pdf/2601.10189.pdf

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.

Paperid: 1653, https://arxiv.org/pdf/2601.10041.pdf

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.

Paperid: 1654, https://arxiv.org/pdf/2601.09533.pdf

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.

Paperid: 1655, https://arxiv.org/pdf/2601.09422.pdf

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.

Paperid: 1656, https://arxiv.org/pdf/2601.09318.pdf

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.

Paperid: 1657, https://arxiv.org/pdf/2601.09194.pdf

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.

Paperid: 1658, https://arxiv.org/pdf/2601.08518.pdf

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.

Paperid: 1659, https://arxiv.org/pdf/2601.08459.pdf

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.

Paperid: 1660, https://arxiv.org/pdf/2601.08177.pdf

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.

Paperid: 1661, https://arxiv.org/pdf/2601.08113.pdf

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.

Paperid: 1662, https://arxiv.org/pdf/2601.07956.pdf

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.

Paperid: 1663, https://arxiv.org/pdf/2601.07665.pdf

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.

Paperid: 1664, https://arxiv.org/pdf/2601.07431.pdf

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.

Paperid: 1665, https://arxiv.org/pdf/2601.07334.pdf

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.

Paperid: 1666, https://arxiv.org/pdf/2601.06964.pdf

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.

Paperid: 1667, https://arxiv.org/pdf/2601.06879.pdf

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.

Paperid: 1668, https://arxiv.org/pdf/2601.06740.pdf

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.

Paperid: 1669, https://arxiv.org/pdf/2601.06686.pdf

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.

Paperid: 1670, https://arxiv.org/pdf/2601.06540.pdf

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 SODASER 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.

Paperid: 1671, https://arxiv.org/pdf/2601.06485.pdf

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.

Paperid: 1672, https://arxiv.org/pdf/2601.06473.pdf

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.

Paperid: 1673, https://arxiv.org/pdf/2601.06325.pdf

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.

Paperid: 1674, https://arxiv.org/pdf/2601.06315.pdf

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.

Paperid: 1675, https://arxiv.org/pdf/2601.05949.pdf

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, and numerical simulations show that the resulting clusters using our approach are dynamically coherent. We consider the robustness of the clusters identified in the network by analyzing the sensitivity of the small eigenvalues and their corresponding eigenspaces, which determines the coherency structure of the oscillator dynamics, to variations in the steady-state operating points of the network.

Paperid: 1676, https://arxiv.org/pdf/2601.05800.pdf

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.

Paperid: 1677, https://arxiv.org/pdf/2601.05490.pdf

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.

Paperid: 1678, https://arxiv.org/pdf/2601.05395.pdf

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.

Paperid: 1679, https://arxiv.org/pdf/2601.05056.pdf

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$\unicode{x2014}$including 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.

Paperid: 1680, https://arxiv.org/pdf/2601.04504.pdf

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.

Paperid: 1681, https://arxiv.org/pdf/2601.04354.pdf

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 $μ$V 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.

Paperid: 1682, https://arxiv.org/pdf/2601.04198.pdf

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.

Paperid: 1683, https://arxiv.org/pdf/2601.04136.pdf

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.

Paperid: 1684, https://arxiv.org/pdf/2601.03976.pdf

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.

Paperid: 1685, https://arxiv.org/pdf/2601.03971.pdf

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.

Paperid: 1686, https://arxiv.org/pdf/2601.03819.pdf

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.

Paperid: 1687, https://arxiv.org/pdf/2601.03777.pdf

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.

Paperid: 1688, https://arxiv.org/pdf/2601.03716.pdf

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.

Paperid: 1689, https://arxiv.org/pdf/2601.03679.pdf

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.

Paperid: 1690, https://arxiv.org/pdf/2601.03452.pdf

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.

Paperid: 1691, https://arxiv.org/pdf/2601.03386.pdf

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.

Paperid: 1692, https://arxiv.org/pdf/2601.03360.pdf

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.

Paperid: 1693, https://arxiv.org/pdf/2601.03007.pdf

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.

Paperid: 1694, https://arxiv.org/pdf/2601.01693.pdf

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.

Paperid: 1695, https://arxiv.org/pdf/2601.01691.pdf

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.

Paperid: 1696, https://arxiv.org/pdf/2601.01179.pdf

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.

Paperid: 1697, https://arxiv.org/pdf/2601.01157.pdf

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.

Paperid: 1698, https://arxiv.org/pdf/2601.01105.pdf

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.

Paperid: 1699, https://arxiv.org/pdf/2601.00819.pdf

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.

Paperid: 1700, https://arxiv.org/pdf/2601.00755.pdf

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.

Paperid: 1701, https://arxiv.org/pdf/2601.00693.pdf

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.

Paperid: 1702, https://arxiv.org/pdf/2601.00556.pdf

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.

Paperid: 1703, https://arxiv.org/pdf/2604.04537.pdf

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.

Paperid: 1704, https://arxiv.org/pdf/2604.04293.pdf

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.

Paperid: 1705, https://arxiv.org/pdf/2604.04116.pdf

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.

Paperid: 1706, https://arxiv.org/pdf/2604.03834.pdf

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.

Paperid: 1707, https://arxiv.org/pdf/2604.02900.pdf

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.

Paperid: 1709, https://arxiv.org/pdf/2604.01937.pdf

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

Paperid: 1710, https://arxiv.org/pdf/2604.01912.pdf

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. Through this geometric framework, classical pseudo-linear static allocation strategies are shown to inevitably intersect singular boundary hyperplanes, triggering infinite-derivative kinetic singularities and fragmenting the task space into an exponential number of singularity-separated sectors. In contrast, allocators derived from the orthogonal manifolds yield continuously differentiable global sections with only a linear number of sectors for transversal layers, or can even form a single global diffeomorphism to the task space in the case of the two extremal layers, thus completely avoiding geometric rank-loss and boundary-crossing singularities. These theoretical results directly apply to the control allocation of propeller-driven architectures, including multirotor UAVs, marine, and underwater vehicles.

Paperid: 1711, https://arxiv.org/pdf/2604.01730.pdf

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.

Paperid: 1712, https://arxiv.org/pdf/2604.01149.pdf

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.

Paperid: 1713, https://arxiv.org/pdf/2604.00540.pdf

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.

Paperid: 1714, https://arxiv.org/pdf/2604.00451.pdf

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.

Paperid: 1715, https://arxiv.org/pdf/2604.00309.pdf

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.

Paperid: 1716, https://arxiv.org/pdf/2604.00067.pdf

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 \emph{Compress--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.

Paperid: 1717, https://arxiv.org/pdf/2603.29752.pdf

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.

Paperid: 1718, https://arxiv.org/pdf/2603.29712.pdf

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.

Paperid: 1719, https://arxiv.org/pdf/2603.27446.pdf

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.

Paperid: 1720, https://arxiv.org/pdf/2603.27337.pdf

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.

Paperid: 1721, https://arxiv.org/pdf/2603.27051.pdf

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.

Paperid: 1722, https://arxiv.org/pdf/2603.24273.pdf

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.

Paperid: 1723, https://arxiv.org/pdf/2603.24096.pdf

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.

Paperid: 1724, https://arxiv.org/pdf/2603.23147.pdf

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.

Paperid: 1725, https://arxiv.org/pdf/2603.22857.pdf

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.

Paperid: 1726, https://arxiv.org/pdf/2603.22802.pdf

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.

Paperid: 1727, https://arxiv.org/pdf/2603.21713.pdf

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 acrobative 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.

Paperid: 1728, https://arxiv.org/pdf/2603.21133.pdf

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.

Paperid: 1729, https://arxiv.org/pdf/2603.21128.pdf

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.

Paperid: 1730, https://arxiv.org/pdf/2603.20013.pdf

Abstract:
One of the main challenges in set-based state estimation is the trade-off between accuracy and computational complexity, which becomes particularly critical for systems with time-varying dynamics. Accurate set representations such as polytopes, even when encoded as Constrained Zonotopes (CZs) or Constrained Convex Generators (CCGs), typically lead to a progressive growth of the set description, requiring order reduction procedures that increase the online computational burden. In this paper, we propose a fixed structure and computationally efficient approach for guaranteed state estimation of discrete-time Linear Time-Varying (LTV) systems using CCG formulations. The proposed method expresses the state enclosure explicitly in terms of a fixed number of past inputs and measurements, resulting in a constant-size set description and avoiding the need for online order reduction. Numerical results illustrate the effectiveness and computational advantages of the proposed method.

Paperid: 1731, https://arxiv.org/pdf/2603.19545.pdf

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.

Paperid: 1732, https://arxiv.org/pdf/2603.19420.pdf

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.

Paperid: 1733, https://arxiv.org/pdf/2603.19396.pdf

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 \citet{OSullivanRomaoMargellos2026}, 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.

Paperid: 1734, https://arxiv.org/pdf/2603.18947.pdf

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.

Paperid: 1735, https://arxiv.org/pdf/2603.18546.pdf

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.

Paperid: 1736, https://arxiv.org/pdf/2603.18122.pdf

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.

Paperid: 1737, https://arxiv.org/pdf/2603.18041.pdf

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 $\mathcal{S}_n(M,G)=M^n/(G\times S_n)$ 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 $d_{\mathrm{GH}}(X_x,X_y)\le d_{M,G}([x],[y])$. 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 $(\mathcal{S}_n(M,G),d_{M,G})$: 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 $\mathbb{S}^2$ and $\mathbb{T}^m$ illustrate satellite-constellation and formation settings.

Paperid: 1738, https://arxiv.org/pdf/2603.17849.pdf

Abstract:
We establish a canonical decomposition of the infinitesimal Koopman generator of any port-Hamiltonian (pH) system into skew-adjoint (energy-conserving), positive-semidefinite (dissipative), and input-port components, proving that the generator satisfies an energy-dissipation inequality on a dense subdomain of $L^2(μ)$ for any invariant measure $μ$ satisfying a mild joint-invariance condition stated in Theorem 1. This infinite-dimensional splitting carries over exactly to finite-dimensional Galerkin approximations, yielding structure-constrained surrogate models that provably inherit passivity with a quadratic storage function in the lifted observable space. Leveraging this structure, we design passivity-based controllers directly in the lifted space and establish asymptotic stability of the lifted closed-loop system via LaSalle's invariance principle under a mild detectability condition. For linear pH systems, the decomposition recovers the true pH matrices exactly, confirming that the structural constraints arise naturally from the operator theory rather than being imposed by hand. The framework unifies port-Hamiltonian systems theory and Koopman spectral methods, providing a rigorous operator-theoretic foundation for energy-consistent lifting of nonlinear pH dynamics.

Paperid: 1739, https://arxiv.org/pdf/2603.16882.pdf

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.

Paperid: 1740, https://arxiv.org/pdf/2603.16617.pdf

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.

Paperid: 1741, https://arxiv.org/pdf/2603.16595.pdf

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.

Paperid: 1742, https://arxiv.org/pdf/2603.15248.pdf

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$\leq$4) the arbitration mechanism cannot form; above it (k$\geq$8), 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.

Paperid: 1743, https://arxiv.org/pdf/2603.14868.pdf

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.

Paperid: 1744, https://arxiv.org/pdf/2603.14625.pdf

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(\sqrt{T}) 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.

Paperid: 1745, https://arxiv.org/pdf/2603.14263.pdf

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 \cite{CalafioreSIAM}, 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 \emph{offline} 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 $Q_m=I_m-\frac{1}{m}\one\one\tran$ 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 \emph{online} 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.

Paperid: 1746, https://arxiv.org/pdf/2603.12779.pdf

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.

Paperid: 1747, https://arxiv.org/pdf/2603.11091.pdf

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.

Paperid: 1748, https://arxiv.org/pdf/2603.10294.pdf

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.

Paperid: 1749, https://arxiv.org/pdf/2603.10065.pdf

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.

Paperid: 1750, https://arxiv.org/pdf/2603.09174.pdf

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.

Paperid: 1751, https://arxiv.org/pdf/2603.08736.pdf

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.

Paperid: 1752, https://arxiv.org/pdf/2603.07998.pdf

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.

Paperid: 1753, https://arxiv.org/pdf/2603.07453.pdf

Abstract:
Input delays are a common source of performance degradation and instability in control systems. This paper addresses the $\mathcal{H}_\infty$ 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.

Paperid: 1754, https://arxiv.org/pdf/2603.07266.pdf

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.

Paperid: 1755, https://arxiv.org/pdf/2603.07143.pdf

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.

Paperid: 1756, https://arxiv.org/pdf/2603.06954.pdf

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.

Paperid: 1757, https://arxiv.org/pdf/2603.06247.pdf

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.

Paperid: 1758, https://arxiv.org/pdf/2603.05441.pdf

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.

Paperid: 1759, https://arxiv.org/pdf/2603.04603.pdf

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.

Paperid: 1760, https://arxiv.org/pdf/2603.04443.pdf

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.

Paperid: 1761, https://arxiv.org/pdf/2603.03632.pdf

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.

Paperid: 1762, https://arxiv.org/pdf/2603.03486.pdf

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.

Paperid: 1763, https://arxiv.org/pdf/2603.02530.pdf

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. For the predator-prey positive-state positive-input benchmark system, with a strict CLF built in our previous paper, we prototype two 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. Framework B demands more design effort than framework A but is free of conditions that may fail to hold in general. 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 one fully explicit design: a Sontag-like universal formula for inverse optimal stabilization of positive-orthant systems by positive feedback.

Paperid: 1764, https://arxiv.org/pdf/2603.01290.pdf

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 (abbreviated MOM throughout), 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 30-state Hidden Markov Model (HMM) that infers a probability distribution over each rival's ERS charge level, Override Mode status, and tyre degradation state from five 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 rather than reactive threshold rules. On synthetic races generated from the model's own assumptions, the HMM achieves 92.3% ERS inference accuracy (random baseline: 33.3%) and detects counter-harvest trap conditions with 95.7% recall. Pre-registration -- empirical validation begins Australian Grand Prix, 8 March 2026.

Paperid: 1765, https://arxiv.org/pdf/2603.01249.pdf

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.

Paperid: 1766, https://arxiv.org/pdf/2603.00875.pdf

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.

Paperid: 1767, https://arxiv.org/pdf/2603.00381.pdf

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.

Paperid: 1768, https://arxiv.org/pdf/2602.24140.pdf

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.

Paperid: 1769, https://arxiv.org/pdf/2602.23404.pdf

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.

Paperid: 1770, https://arxiv.org/pdf/2602.22713.pdf

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.

Paperid: 1771, https://arxiv.org/pdf/2602.21594.pdf

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.

Paperid: 1772, https://arxiv.org/pdf/2602.21113.pdf

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.

Paperid: 1773, https://arxiv.org/pdf/2602.20683.pdf

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.

Paperid: 1774, https://arxiv.org/pdf/2602.19666.pdf

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 \textit{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.

Paperid: 1775, https://arxiv.org/pdf/2602.18376.pdf

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.

Paperid: 1776, https://arxiv.org/pdf/2602.17975.pdf

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 PF$Δ$ 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.

Paperid: 1777, https://arxiv.org/pdf/2602.15333.pdf

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.

Paperid: 1778, https://arxiv.org/pdf/2602.15282.pdf

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 $\mathcal{L}_2$-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.

Paperid: 1779, https://arxiv.org/pdf/2602.14909.pdf

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.

Paperid: 1780, https://arxiv.org/pdf/2602.14222.pdf

Abstract:
In robotics and human biomechanics, the tension between energy economy and kinematic readiness is well recognized; this work brings that fundamental principle to aerial multirotors. We show that the limited torque of the motors and the nonlinear aerodynamic map from rotor speed to thrust naturally give rise to the novel concept of promptness-a metric akin to dynamic aerodynamic manipulability. By treating energy consumption as a competing objective and introducing a geometric fiber-bundle formulation, we turn redundancy resolution into a principled multi-objective program on affine fibers. The use of the diffeomorphic transformation linearizing the signed-quadratic propulsion model allows us to lay the foundations for a rigorous study of the interplay between these costs. Through an illustrative case study on 4-DoF allocation on the hexarotor, we reveal that this interplay is fiber-dependent and physically shaped by hardware inequalities. For unidirectional thrusters, the feasible fibers are compact, yielding interior allocations and a short Pareto arc, while torque demands break symmetry and separate the optima. Conversely, with reversible propellers, the null space enables antagonistic rotor co-contraction that drives promptness to hardware limits, making optimal endurance and agility fundamentally incompatible in those regimes. Ultimately, rather than relying on heuristic tuning or black box algorithms to empirically improve task execution, this framework provides a foundational understanding of why and how to achieve agility through geometry-aware control allocation, offering possible guidance for vehicle design, certification metrics, and threat-aware flight operation.

Paperid: 1781, https://arxiv.org/pdf/2602.12750.pdf

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.

Paperid: 1782, https://arxiv.org/pdf/2602.12697.pdf

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.

Paperid: 1783, https://arxiv.org/pdf/2602.12016.pdf

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.

Paperid: 1784, https://arxiv.org/pdf/2602.10876.pdf

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.

Paperid: 1785, https://arxiv.org/pdf/2602.10294.pdf

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.

Paperid: 1786, https://arxiv.org/pdf/2602.09673.pdf

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.

Paperid: 1787, https://arxiv.org/pdf/2602.09633.pdf

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.

Paperid: 1788, https://arxiv.org/pdf/2602.09202.pdf

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.

Paperid: 1789, https://arxiv.org/pdf/2602.08137.pdf

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.

Paperid: 1790, https://arxiv.org/pdf/2602.07360.pdf

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.

Paperid: 1791, https://arxiv.org/pdf/2602.06070.pdf

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.

Paperid: 1792, https://arxiv.org/pdf/2602.05483.pdf

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.

Paperid: 1793, https://arxiv.org/pdf/2602.05458.pdf

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.

Paperid: 1794, https://arxiv.org/pdf/2602.04831.pdf

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.

Paperid: 1795, https://arxiv.org/pdf/2602.04219.pdf

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 \emph{jointly} 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 \emph{robust 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.

Paperid: 1796, https://arxiv.org/pdf/2602.03968.pdf

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.

Paperid: 1797, https://arxiv.org/pdf/2602.02992.pdf

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.

Paperid: 1798, https://arxiv.org/pdf/2602.01537.pdf

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.

Paperid: 1799, https://arxiv.org/pdf/2602.01524.pdf

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.

Paperid: 1800, https://arxiv.org/pdf/2602.01457.pdf

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.

Paperid: 1801, https://arxiv.org/pdf/2602.01261.pdf

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.

Paperid: 1802, https://arxiv.org/pdf/2601.21861.pdf

Abstract:
This paper addresses the critical challenge of coordinating mobile edge UAV networks to maintain robust service in highly dynamic spatiotemporal environments. Conventional Deep Reinforcement Learning (DRL) approaches often suffer from catastrophic forgetting when transitioning between distinct task scenarios, such as moving from dense urban clusters to sparse rural areas. These transitions typically necessitate computationally expensive retraining or model resets to adapt to new user distributions, leading to service interruptions. To overcome these limitations, we propose a computationally efficient Spatiotemporal Continual Learning (STCL) framework realized through a Group-Decoupled Multi-Agent Proximal Policy Optimization (G-MAPPO) algorithm. Our approach integrates a novel Group-Decoupled Policy Optimization (GDPO) mechanism that utilizes dynamic $z$-score normalization to autonomously balance heterogeneous objectives, including energy efficiency, user fairness, and coverage. This mechanism effectively mitigates gradient conflicts induced by concept drifts without requiring offline retraining. Furthermore, the framework leverages the 3D mobility of UAVs as a spatial compensation layer, enabling the swarm to autonomously adjust altitudes to accommodate extreme density fluctuations. Extensive simulations demonstrate that the proposed STCL framework achieves superior resilience, characterized by an elastic recovery of service reliability to approximately 0.95 during phase transitions. Compared to the MADDPG baseline, G-MAPPO not only prevents knowledge forgetting but also delivers an effective capacity gain of 20\% under extreme traffic loads, validating its potential as a scalable solution for edge-enabled aerial swarms.

Paperid: 1803, https://arxiv.org/pdf/2601.21657.pdf

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.

Paperid: 1804, https://arxiv.org/pdf/2601.21036.pdf

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 $\sqrt{2}-1$, 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.

Paperid: 1805, https://arxiv.org/pdf/2601.20135.pdf

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.

Paperid: 1806, https://arxiv.org/pdf/2601.19131.pdf

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.

Paperid: 1807, https://arxiv.org/pdf/2601.19087.pdf

Abstract:
This paper presents a theory-to-hardware framework for fully passive millimeter-wave beam control aimed at extending indoor coverage. The reflecting aperture is modeled using an equivalent array-factor formulation in which each passive element contributes a reradiated field determined by the incident and desired departure angles. Building on this model, we develop two implementation-friendly passive design approaches: (i) binary (1-bit) spatial masking, which enables beam steering and multi-beam synthesis by selecting element participation on a dense lattice via an ON/OFF metallization pattern, and (ii) diffraction-order (periodic) steering, which exploits controlled aperture periodicity to place selected diffraction orders at prescribed angles. Theoretical analysis characterizes the asymptotic activation behavior of the binary mask and establishes a distribution-free lower-bound on the achievable gain. Prototypes are realized using a copper-backed 3D-printed substrate with stencil-guided conductive ink deposition. 60 GHz over-the-air measurements in single- and multi-beam configurations validate the predicted steering behavior. Theoretical and experimental results demonstrate that fully passive, binary-coded apertures can provide deterministic beam control and offer a scalable alternative to power-consuming reconfigurable surfaces for static indoor links.

Paperid: 1808, https://arxiv.org/pdf/2601.19047.pdf

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.

Paperid: 1809, https://arxiv.org/pdf/2601.18308.pdf

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.

Paperid: 1810, https://arxiv.org/pdf/2601.18175.pdf

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.

Paperid: 1811, https://arxiv.org/pdf/2601.17683.pdf

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.

Paperid: 1812, https://arxiv.org/pdf/2601.17628.pdf

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.

Paperid: 1813, https://arxiv.org/pdf/2601.17009.pdf

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.

Paperid: 1814, https://arxiv.org/pdf/2601.16292.pdf

Abstract:
Agent-based modeling (ABM) has emerged as an indispensable methodology for studying complex adaptive systems across the natural and social sciences. However, Python-based ABM frameworks face a fundamental tension between the accessibility that has made Python dominant in scientific computing and the performance requirements of large-scale simulations. This paper introduces AMBER, a framework that resolves this tension through a novel architectural approach: replacing the conventional object-per-agent representation with columnar state management using the Polars DataFrame library. We analyze the computational characteristics of both paradigms, present the architectural design of AMBER including its core abstractions, spatial environments, experiment management, and optimization capabilities. Empirical evaluation on three canonical benchmarks demonstrates that AMBER achieves speedups of 1.2x to 93x depending on workload characteristics, with the greatest advantages for models dominated by population-wide attribute operations. Memory profiling reveals 30-50% reduction in peak usage compared to object-oriented frameworks. Our results establish columnar state management as a viable architectural foundation for high-performance ABM in interpreted languages.

Paperid: 1815, https://arxiv.org/pdf/2601.15622.pdf

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.

Paperid: 1816, https://arxiv.org/pdf/2601.13810.pdf

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.

Paperid: 1817, https://arxiv.org/pdf/2601.12857.pdf

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

Paperid: 1818, https://arxiv.org/pdf/2601.12334.pdf

Abstract:
This paper proposes an active-learning approach to worst-case nonlinear regression with deterministic error guarantees. Given a known nonlinear function defined over a compact set, we compute a surrogate model, such as a feedforward neural network, by minimizing the maximum absolute approximation error. To address the nonsmooth nature of the resulting minimax problem, we introduce a smooth approximation of the $L_\infty$-type loss that enables efficient gradient-based training. We iteratively enrich the training set by actively learning points of largest approximation error through global optimization. The resulting models admit certified worst-case error bounds, either constant or input-dependent, over the entire input domain. The approach is demonstrated through approximations of nonlinear functions and nonconvex sets, as well as through the derivation of uncertain models of more complex nonlinear dynamics within a given model class, and the approximation of explicit model predictive control laws.

Paperid: 1819, https://arxiv.org/pdf/2601.12070.pdf

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.

Paperid: 1820, https://arxiv.org/pdf/2601.11672.pdf

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.

Paperid: 1821, https://arxiv.org/pdf/2601.11244.pdf

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.

Paperid: 1822, https://arxiv.org/pdf/2601.08110.pdf

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.

Paperid: 1823, https://arxiv.org/pdf/2601.06568.pdf

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 $\mathcal{L}_2$-gain. To address this gap, this study focuses on $γ$-dissipativity (partially equivalent to $\mathcal{L}_2$-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.

Paperid: 1824, https://arxiv.org/pdf/2601.06308.pdf

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.

Paperid: 1825, https://arxiv.org/pdf/2601.06094.pdf

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.

Paperid: 1826, https://arxiv.org/pdf/2601.04392.pdf

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 Fuzzified Bellman Equation (FBE) for continuous control tasks. 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. Extensive evaluations in continuous control domains demonstrate that Enhanced-FQL($λ$) achieves superior sample efficiency and reduced variance compared to n-step fuzzy TD and fuzzy SARSA($λ$) baselines, while maintaining substantially lower computational complexity than deep RL alternatives such as DDPG. The framework's inherent interpretability, combined with its computational efficiency and theoretical convergence guarantees, makes it particularly suitable for safety-critical applications where transparency and resource constraints are essential.

Paperid: 1827, https://arxiv.org/pdf/2601.04221.pdf

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.

Paperid: 1828, https://arxiv.org/pdf/2601.04177.pdf

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.

Paperid: 1829, https://arxiv.org/pdf/2601.03867.pdf

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.

Paperid: 1830, https://arxiv.org/pdf/2601.03132.pdf

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.

Paperid: 1831, https://arxiv.org/pdf/2601.02779.pdf

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.

Paperid: 1832, https://arxiv.org/pdf/2601.02585.pdf

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.

Paperid: 1833, https://arxiv.org/pdf/2601.02560.pdf

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.

Paperid: 1834, https://arxiv.org/pdf/2601.02557.pdf

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.

Paperid: 1835, https://arxiv.org/pdf/2601.02244.pdf

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. Specirically, 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.

Paperid: 1836, https://arxiv.org/pdf/2601.02109.pdf

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.

Paperid: 1837, https://arxiv.org/pdf/2601.01302.pdf

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.

Paperid: 1838, https://arxiv.org/pdf/2601.00737.pdf

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.

Paperid: 1839, https://arxiv.org/pdf/2601.00614.pdf

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.

Paperid: 1840, https://arxiv.org/pdf/2601.00548.pdf

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.