TCYB2025

Abstract:
Recently, foundation models (such as ChatGPT) have emerged with powerful learning, understanding, and generalization abilities, showcasing tremendous potential to revolutionarily promote modern industry. Despite significant advancements in various fields, existing general foundation models face challenges in industry when dealing with the data of specialized modalities, the tasks of varying-scenario with multiple processes, and the requirements of trustworthy output, which makes industrial foundation model (IFM) a necessity. This article proposes a system architecture of termed IFMsys, including model training, model adaptation, and model application. Specifically, in model training, a base model is constructed by pretraining on multimodal industrial data and fine-tuning with fundamental industrial mechanisms. In model adaptation, the base model is developed into a series of task-oriented and domain-specific IFMs through fine-tuning with representative tasks and domain knowledge. In model application, an industrial agent-centric collaboration system and a comprehensive application framework of IFM are proposed to enhance the industrial product lifecycle applications. In addition, a prototype system of the IFM, namely, MetaIndux, is delivered, with application examples presented in typical industrial tasks. Finally, future research directions and open issues of IFM are prospected. We hope this article will inspire the advancements in the theories, technologies, and applications in this emerging research field of IFM.

Abstract:
Few-shot learning algorithms frequently exhibit suboptimal performance due to the limited availability of labeled data. This article presents a novel quantum few-shot image classification methodology aimed at enhancing the efficacy of few-shot learning algorithms at both the data and parameter levels. Initially, a quantum augmentation image representation technique is introduced, leveraging the local phase of quantum states to support few-shot learning algorithms at the data level. This approach enriches classical data while maintaining its intrinsic physical properties. Subsequently, a parameterized quantum circuit is employed to construct the classification model. This circuit, characterized by a reduced number of trainable parameters, shows increased resilience to overfitting, thereby offering a significant advantage at the parameter level for few-shot learning algorithms. The proposed approach is validated using three datasets, with experimental results indicating that it outperforms classical methods in few-shot learning scenarios while requiring fewer computational resources.

Affiliations: Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine and the School of Computer and Information, Hefei University of Technology, Hefei, China; School of Computer and Information, Hefei University of Technology, Hefei, Anhui, China; Department of Measurement and Control Systems, Silesian University of Technology, Gliwice, Poland; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

Abstract:
The Bandler–Kohout subproduct (BKS) method acts as one of the two representative fuzzy relational inference (FRI) strategies. Observing the BKS method using constraint modeling, two fuzzy implications, respectively, produce expression to the factors of inference mechanism and rule base. However, these two factors normally reflect different connotations from the perspectives of artificial intelligence applications and logical meaning. Enlightened by such idea, in this study, we propose and investigate the differently implicational BKS (DBKS) method. Initially, main properties of DBKS are validated. The reversibility and interpolativity of DBKS are proved under certain conditions. The equivalent relationship is verified between interpolativity and continuity for DBKS. The robustness of DBKS is confirmed from both the similarity and the extensional hull. Posteriorly, the computational performance of DBKS is analyzed. In DBKS, the preservation of the indistinguishability holds for input fuzzy sets, and it is proved that the first-aggregate-then-infer (FATI) reasoning strategy of DBKS is equivalent to the first-infer-then-aggregate (FITA) one. To improve the computational efficiency, the hierarchical DBKS method is presented. In addition, the fuzzy system is established on the strength of the DBKS method, the singleton fuzzifier and the centroid defuzzifier. Its response function is analyzed and a universal approximator is built by the fuzzy system via DBKS. At the end, we compare the results of DBKS with BKS by virtue of two examples in affective computing. It is discovered that DBKS can create superior forms of FRI in comparison to those produced by BKS.

Abstract:
This article studies event-triggered nonsingular terminal sliding-mode control (TSMC) for a class of nonlinear systems. First, a static event-triggering mechanism is implemented in the nonsingular TSMC design. It is shown that the sliding variable can reach the quasi-sliding-mode band and the states can converge to a neighborhood of the equilibrium dependent on the threshold of the event-triggering mechanism. Second, by taking advantage of the internal variable, a dynamic event-triggering mechanism is developed for the nonsingular TSMC design. Compared to the static event-triggered nonsingular TSMC, the designed dynamic event-triggered nonsingular TSMC strategy can reduce the number of events while maintaining the same upper bounds of quasi-sliding-mode and steady states. It is further shown that both event-triggered nonsingular TSMC systems have no Zeno behavior. Finally, simulation results are given to demonstrate the effectiveness of the theoretical results.

Abstract:
Multiview subspace clustering (MVSC) aims to integrate complementary information from different views to accurately reveal the subspace structure of a multiview dataset. Traditional MVSC methods often emphasize the aggregation of samples within the same subspace, while neglecting the separation of samples across different subspaces. In this article, we incorporate contrastive learning techniques into the MVSC framework, developing a contrastive data self-representation module, a contrastive regularizer for the reconstruction coefficient matrix in each view, and a contrastive alignment term to obtain a consensus coefficient matrix that fuses structural information from the reconstruction coefficient matrices. This leads to the framework of a purely contrastive MVSC (PCMVSC) approach. We elaborate on the superiority of the proposed modules in PCMVSC over similar ones in existing methods and show that the consensus reconstruction coefficient matrix obtained by PCMVSC can effectively uncover the underlying subspace structure of multiview datasets. Extensive subspace clustering experiments prove the effectiveness of PCMVSC and reveal that it outperforms various existing multiview clustering algorithms.

Abstract:
This article considers the security control problem of a safety-critical system, described by a general nonlinear uncertain system with constraints for collision avoidance and internal dynamic limitations. We design an integrated security and safety-critical control law to prevent the system from operating in the unsafe mode under denial-of-service (DoS) attacks in the signal transmission channels. By combining the internal model principle and the time- and event-triggered sampling mechanism for DoS detection, an improved dynamic compensator is first proposed and converts the safety tracking problem into the attractivity problem of the constrained error system. Then a security control is constructed for the error system by integrating the safety-critical controller in the barrier function-based framework. Finally, we prove that the integrated control design can guarantee the security, safety, and stability of the closed-loop system.

Abstract:
While reinforcement learning (RL) has shown experimental success in a number of applications, it is known to be sensitive to noise and perturbations in the parameters of the system, leading to high variability in the total reward amongst different episodes on slightly different environments. To introduce robustness, as well as sample efficiency, risk-sensitive RL methods are being thoroughly studied. In this work, we provide a definition of robust RL policies and formulate a risk-sensitive RL problem to approximate them, by solving an optimization problem with respect to a modified objective based on exponential criteria. In particular, we study a model-free risk-sensitive variation of the widely used Monte Carlo policy gradient algorithm, and introduce a novel risk-sensitive online Actor-Critic algorithm based on solving a multiplicative Bellman equation using stochastic approximation updates. Analytical results suggest that the use of exponential criteria generalizes commonly used ad-hoc regularization approaches, improves sample efficiency, and introduces robustness with respect to perturbations in the model parameters and the environment. The implementation, performance, and robustness properties of the proposed methods are evaluated in simulated experiments.

Abstract:
Evolutionary multitasking (EMT) is an emerging approach for solving multitask optimization problems (MTOPs) and has garnered considerable research interest. The implicit EMT is a significant research branch that utilizes evolution operators to enable knowledge transfer (KT) between tasks. However, current approaches in implicit EMT face challenges in adaptability, due to the limited use of different evolution operators with different parameter settings and insufficient utilization of evolutionary states for performing KT. This results in suboptimal exploitation of implicit KT’s potential to tackle a variety of MTOPs. To overcome these limitations, we propose a novel learning-to-transfer (L2T) framework to automatically discover efficient KT policies for the MTOPs at hand. Our framework conceptualizes the KT process as a learning agent’s sequence of strategic decisions within the EMT process. We propose an action formulation for deciding when and how to transfer, a state representation with informative features of evolution states, a reward formulation concerning convergence and transfer efficiency gain, and the environment for the agent to interact with MTOPs. We employ an actor-critic network structure for the agent and learn the policy via proximal policy optimization. This learned agent can be integrated with various evolutionary algorithms, enhancing their ability to address unseen MTOPs. Comprehensive empirical studies on both synthetic and real-world MTOPs, encompassing diverse intertask relationships, function classes, and task distributions are conducted to validate the proposed L2T framework. The results show a marked improvement in the adaptability and performance of implicit EMT when solving a wide spectrum of unseen MTOPs.

Abstract:
This article presents a high-confidence data-driven safe tracking control design for stochastic linear discrete-time systems. The high-confidence safe reference tracking for an ellipsoidal safe set is first formalized using the concept of probabilistic set-based \lambda -contractivity. A data-driven controller, composed of feedback and feedforward elements, is then designed to enforce the \lambda -contractivity of the safe set. The feedback control gain is learned by 1) providing a data-driven representation of the closed-loop system, which contains a decision variable that affects the control gain and 2) optimizing the decision variable to ensure the \lambda -contractivity. This feedback term can be learned using a data set that is not even rich enough to identify the full system model. A feedforward gain learning algorithm and a data-driven reference governor are provided to satisfy the required conditions on equilibrium terms. It is shown that under certain conditions on the equilibrium terms, the learned tracking controller guarantees the system’s safety and stability with high probability. The reference governor dynamically manipulates the desired reference signal based on the data quality to prevent any breach of safety constraints in a probabilistic manner. It is shown that the output of the reference governor eventually converges to the desired goal states if inside the safe set and high-quality data is available. Therefore, the tracking controller guarantees convergence of the system output to its desired goal while ensuring safety with a high probability. The simulation results on a drone hovering and a test system, comparing the results with the existing literature, confirm that the presented high-confidence data-driven safe tracking control outperforms certainty-equivalent safe control methods.

Abstract:
Current studies for brain-muscle modulation often analyze selected properties in electrophysiological signals, leading to a partial understanding. This article proposes a cross-modal generative model that converts brain activities measured by electroencephalography (EEG) to corresponding muscular responses recorded by electromyography (EMG). Examining the generation process in the model highlights how the motor cue, representing implicit motor information hidden within brain activities, modulates the interaction between brain and muscle systems. The proposed model employs a two-stage generation process to bridge the semantic gap in cross-modal signals. Initially, the shared movement-related information between EEG and EMG signals is extracted using a contrastive learning framework. These shared representations act as conditional vectors in the subsequent EMG generation stage based on generative adversarial networks (GANs). Experiments on a self-collected multimodal electrophysiological signal data set show the algorithm’s superiority over existing time series generative methods in cross-modal EMG generation. Further insights derived from the model’s inference process underscore the brain’s strategy for muscle control during movements. This research provides a data-driven approach for the neuroscience community, offering a comprehensive perspective of brain-muscular modulation.

Abstract:
The widely adopted Jacobi-Fourier moment (JFM) is limited by its inability to effectively capture spatial information. Although fractional-order JFM (FOJFM) introduces spatial information through a fractional-order parameter, the control of spatial information remains inadequate. This limitation stems from the insufficient control over zeros distribution associated with the used moment’s radial kernel. To address this issue, we generalize both JFM and FOJFM into a transformed JFM. A transformed function with four parameters is designed, and adjustable JFM (AJFM) is proposed. Two parameters correlate to increasing velocities on the left and right parts of the transformed functions, enabling zeros quantities of radial kernel fall in the left and right parts of the interval. The other two parameters segment the transformed function, adjusting regions where different quantities of zeros fall in. This refined control over the radial kernel’s zero distribution enhances the versatility of feature extraction by the AJFM, governed by the introduced parameters. Experimental results demonstrate that AJFM, with properly chosen parameters, can emphasize specific regions within an image more effectively.

Abstract:
In order to process multiview multilabel, multilabel, and multiview data, current learning algorithms are designed on the basis of data characteristics, correlations, etc. While these algorithms cannot express correlations among different features, instances, labels in within-view, cross-view, and consensus-view representations self-adaptively and relative accurately. To this end, this study takes the classical multiple correlations-based model as the basis and explores some laws of self-adaptive change for those correlations in multiple representations. The proposed algorithm is called multiple self-adaptive correlation-based multiview multilabel learning (MuSC-MVML). Extensive experiments on 38 datasets demonstrate the superiority of MuSC-MVML and some conclusions are addressed. 1) MuSC-MVML outperforms most compared algorithms in statistical in terms of AUC and its performance is also stable; 2) the computational cost of MuSC-MVML is moderate and on most datasets, MuSC-MVML has a relatively fast convergence; and 3) introducing some laws of self-adaptive change for those correlations can improve the ability of MuSC-MVML to process multiview multilabel datasets effectively and express correlations in multiple representations better. Furthermore, this study explains the reason that why we use alternating optimization strategy to optimize the model of MuSC-MVML and provides some suggestions that how to modify the model of MuSC-MVML to process incomplete multiview multilabel datasets with noise.

Abstract:
This brief proposes an optimized time-synchronized control method based on reinforcement learning for the bipartite consensus tracking problem. The study considers multiagent system comprising leaders and followers, where followers interact through signed directed graphs. Some agents track the leader’s state, while others converge to its opposite value. The proposed method employs a time-synchronized sliding mode control framework to ensure fixed-time bipartite consensus among agents with signed interaction topology. Reinforcement learning is integrated to optimize the control process, wherein an actor–critic architecture is utilized to minimize the Bellman residual, enabling optimal control performance. Theoretical analysis proves the fixed-time convergence and Bellman optimality of the system, with the upper bound of convergence time explicitly determined by controller parameters. Simulation experiments validate the effectiveness of the proposed method: all followers simultaneously achieve bipartite consensus within a fixed time, while reinforcement learning significantly and adaptively optimizes the control process.

Affiliations: Institute of Artificial Intelligence, the School of Cyber Science and Technology, the School of Computer Science and Engineering, and the Zhongguancun Laboratory, Beihang University, Beijing, China; Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China; College of Software Engineering, Xi’an Jiaotong University, Xi’an, China; School of Computer Science and Technology, Nanjing Tech University, Nanjing, China; China Academy of Railway Sciences Corporation Ltd., Beijing, China; UR-LISTN, University of Technology of Troyes, Troyes, France

Abstract:
Noncontrastive self-supervised learning methods offer an effective alternative to contrastive approaches by avoiding the need for negative samples to avoid representation collapse. Noncontrastive learning methods explicitly or implicitly optimize the representation space, yet they often require large representation dimensions, leading to dimensional inefficiency. To provide negative samples, contrastive learning methods often require large batch sizes, thus regarded as sample inefficient, while noncontrastive learning methods require large representation dimensions, thus regarded as dimension inefficient. Although we have some understanding of the noncontrastive learning method, theoretical analysis of such phenomenon still remains largely unexplored. We present a theoretical analysis of the dimensional need for noncontrastive learning. We investigate the transfer between upstream representation learning and downstream tasks’ performance, demonstrating how noncontrastive methods implicitly increase interclass distances within the representation space and how the distance affects the model performance of evaluation performance. We prove that the performance of noncontrastive methods is affected by the output dimension and the number of latent classes, and illustrate why performance degrades significantly when the output dimension is substantially smaller than the number of latent classes. We demonstrate our findings through experiments on image classification experiments, and enrich the verification in audio, graph and text modalities. We also perform empirical evaluation for image models on extensive detection and segmentation tasks beyond classification that show satisfactory correspondence to our theorem.

Abstract:
human-object interaction (HOI) detection tackles the problem of joint localization and classification of HOIs. Recent HOI detection methods are mainly based on transformer networks, where the explicit priors at the object level (e.g., scene layout, object appearance, or category) are usually fed into the transformer to improve the object query ability. Though these methods have achieved remarkable results, they did not pay enough attention to the implicit action-level information, which is the fundamental element of HOI. In this work, we propose an interaction-aware transformer network (IATN) to obtain the interaction-aware query, by jointly utilizing implicit action-level priors and explicit object-level priors. Specifically, we design an action-aware module (AAM) to aggregate implicit action priors from the scene level and instance level, respectively. Then, we design an action-oriented graph (AOG), where human feature and object feature are graph nodes and action semantics represent graph edges, to aggregate priors jointly from action level and object level. Afterwards, the interaction-aware query is acquired and finally adopted to obtain the HOI predictions. Besides, we leverage knowledge distillation to enhance the action-level priors by transferring the final HOI predictions to the intermediate features. Extensive experiments on HICO-DET and V-COCO datasets verify the effectiveness of our proposed interaction-aware model.

Abstract:
Location information is crucial for efficient task assignment in spatial crowdsourcing, but sharing such information raises privacy concerns. Differential privacy (DP) offers a solution by protecting location privacy while preserving data usefulness. Existing DP-based spatial crowdsourcing frameworks have two main limitations: 1) they fail to provide personalized privacy preservation for workers and 2) they prioritize incentive mechanisms (such as utility maximization and cost minimization) while overlooking quality control. To address these limitations, we formulate the multiobjective privacy-preserving task assignment (MP-TA) problem. This problem aims to maximize both incentives and quality while meeting service rate requirements and ensuring personalized privacy protection for workers. Accordingly, we present a three-phase framework comprising worker proposal, candidate worker selection, and task assignment optimization. To generate high-quality eligible solutions for both objectives, we introduce a distributed cooperative co-evolutionary multiobjective memetic algorithm (DCC-MMA) based on sequential subproblem division and knee-driven migration operation. Matching-based crossover, matching-based mutation, and fix operations are designed to enhance search efficiency. Experimental results demonstrate DCC-MMA’s superiority in solution quality, convergence speed, and scalability compared to state-of-the-art algorithms.

Abstract:
Utilizing thermal infrared facial imaging for fever screening in public spaces has become a common strategy to curb the spread of influenza viruses. However, it is difficult to capture larger number of faces with fever labels, which makes learning facial temperature representation extremely difficult. To overcome this limitation, we propose a self-supervised fever screening framework (SelfFS) to learn temperature representation from infrared face images. Specifically, SelfFS employs rate reduction theory to guide the network to focus on temperature features by expanding the coding rate of faces with different temperatures and compressing the coding rate of faces with the same temperature but different appearances. Furthermore, we impose sparsity constraints on the network parameters, which facilitates the extraction of simple temperature features with a limited number of neurons while filtering complex appearance features. Experiments demonstrate that our SelfFS framework outperforms existing fever screening techniques and achieves the comparable results with the supervised methods.

Abstract:
The active surge control has superiorities in expanding the stable working range and reducing the performance loss of aero-engines. Despite these benefits, ensuring fixed-time stability of the closed-loop system with model uncertainty remains a significant challenge. Conventional techniques to active surge control of aero-engines often struggle with model uncertainty and suffer from long-time surge fault. To deal with these issues, this article proposes a novel fixed-time active surge control scheme for enhancing the adaptability to the model change and extending the service life of aero-engines. First, considering the model uncertainty in aero-engines, a radial basis function (RBF) neural network is established for the approximation of complex system dynamic. Second, the adaptive law is proposed to optimize the weight vectors of the neural network. Third, a fixed-time controller is designed to ensure responsiveness and stabilize the compressor dynamics by tuning the intake air flow, where the fixed-time stability property guarantees less operation time under the surge fault. Finally, applications in the turbofan aero-engine validate the superiorities of the proposed method.

Abstract:
Multiview Subspace Clustering (MvSC) has demonstrated impressive clustering performance on multiview data. Most existing methods rely on either raw features or reduced-redundancy data for subspace representation learning, followed by spectral clustering to derive the final results. However, these methods maintain a fixed feature space during subspace learning, which limits information propagation and compromises both representation quality and clustering performance. To address this issue, this article proposes an adaptive dictionary learning approach for MvSC (AMvSC), which seamlessly integrates redundancy reduction and representation learning within a unified framework to facilitate mutual information propagation. Specifically, an adaptive dictionary learning strategy is designed to automatically reduce redundancy and noise in the original feature space during the subspace representation learning process. This strategy ensures effective information exchange, thereby enhancing the quality of the learned representations. Additionally, low-rank constraints, combined with smoothness and diversity regularization, are applied to further refine the subspace representations and comprehensively capture complex correlations among samples. Finally, an alternating optimization algorithm is developed to iteratively update the unified learning model. Extensive experiments validate the effectiveness and superiority of the proposed method.

Affiliations: Department of Systems Science, School of Mathematics, Southeast University, Nanjing, China; College of Electronic Engineering, National University of Defense Technology, Hefei, China; School of Mathematics, China University of Mining and Technology, Xuzhou, China; School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China; Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, School of Internet of Things Engineering, Jiangnan University, Wuxi, China; School of Automation, Southeast University, Nanjing, China

Abstract:
This article investigates the global predefined-time consensus (PTC) of multiagent system (MAS) via constructing a duplex communication network. Unlike the traditional finite-/fixed-time convergence, our method allows the upper-bound of settling-time to be an explicit constant, which is tunable and can be set beforehand without relating with the network information, controlling parameters, and initial conditions. In particular, our approach uses a smooth, nonchattering consensus scheme that avoids conventional discontinuous functions like signum and absolute value functions. By the Lyapunov stability analysis, the sufficient criterion is deduced for ensuring the PTC of MAS. Finally, simulations confirm the effectiveness of our proposed nonchattering scheme.

Abstract:
This article presents a distributed pinning control strategy aimed at achieving global stabilization of Markovian jump Boolean control networks. The strategy relies on network matrix information to choose controlled nodes and adopts the algebraic state space representation approach for designing pinning controllers. Initially, a sufficient criterion is established to verify the global stability of a given Markovian jump Boolean network (MJBN) with probability one at a specific state within finite time. To stabilize an unstable MJBN at a predetermined state, the selection of pinned nodes involves removing the minimal number of entries, ensuring that the network matrix transforms into a strictly lower (or upper) triangular form. For each pinned node, two types of state feedback controllers are developed: 1) mode-dependent and 2) mode-independent, with a focus on designing a minimally updating controller. The choice of controller type is determined by the feasibility condition of the mode-dependent pinning controller, which is articulated through the solvability of matrix equations. Finally, the theoretical results are illustrated by studying the T cell large granular lymphocyte survival signaling network consisting of 54 genes and 6 stimuli.

Abstract:
Safety guarantee is an important topic when training real-world tasks with reinforcement learning (RL). During online environmental exploration, any constraint violation can lead to significant property damage and risks to personnel. Existing safe RL methods either exclusively address safety concerns after reaching optimality or incorporate a certain degree of tolerance for constraint violations during training. This article proposes a feasible policy iteration framework that can guarantee absolute safety during online exploration, i.e., constraint violations never happen in real-world interactions. The key to maintaining absolute safety lies in confining the environmental exploration at each step always within the feasible region of the current policy. This feasible region is described by a newly defined constraint decay function with uncertainty, ensuring the forward invariance of the feasible region under the worst case. Within the proposed framework, the feasible region maintains its monotonic expanding property and converges to its maximum extent, even though only local samples are available, i.e., the agent only has access to samples within the feasible region. Meanwhile, the trained policy also improves monotonically within its corresponding feasible region if one can use different updating rules inside and outside the feasible region. Finally, practical algorithms are designed with the actor-critic-scenery architecture, consisting of three modules: 1) safe exploration; 2) model error estimation; and 3) network update. Experimental results indicate that our algorithms achieve performance comparable to baselines while maintaining zero constraint violation throughout the entire training process. In contrast, the baseline algorithm typically requires thousands of constraint violations to achieve the same performance. These findings suggest a substantial potential for applying feasible policy iteration in real-world tasks, enabling the online evolution of intricate systems.

Abstract:
The goal of few-shot class incremental learning (FSCIL) is to learn new concepts from a limited number of novel samples while preserving the knowledge of previously learned classes. The mainstream FSCIL framework begins with training in the base session, after which the feature extractor is frozen to accommodate novel classes. We observed that traditional base-session training approaches often lead to overfitting on challenging samples, which can lead to reduced robustness in the decision boundaries and exacerbate the forgetting phenomenon when introducing incremental data. To address this issue, we proposed the progressive learning strategy (PGLS). First, inspired by curriculum learning, we developed a covariance noise perturbation approach based on the statistical information as a difficulty measure for assessing sample robustness. We then reweighted the samples based on their robustness, initially concentrating on enhancing model stability by prioritizing robust samples and subsequently leveraging weakly robust samples to improve generalization. Second, we predefined forward compatibility for various virtual class augmentation models. Within base class training, we employed a curriculum learning strategy that progressively introduced fewer to more virtual classes in order to mitigate any adverse effects on model performance. This strategy enhances the adaptability of base classes to novel ones and alleviates forgetting problems. Finally, extensive experiments conducted on the CUB200, CIFAR100, and miniImageNet datasets demonstrate the significant advantages of our proposed method over state-of-the-art models.

Abstract:
The self-expressive coefficient plays a crucial role in the self-expressiveness-based subspace clustering method. To enhance the precision of the self-expressive coefficient, we propose a novel deep subspace clustering method, named grouping belief-based deep contrastive subspace clustering (GRESS), which integrates the clustering information and higher-order relationship into the coefficient matrix. Specifically, we develop a deep contrastive subspace clustering module to enhance the learning of both self-expressive coefficients and cluster representations simultaneously. This approach enables the derivation of relatively noiseless self-expressive similarities and cluster-based similarities. To enable interaction between these two types of similarities, we propose a unique grouping belief-based affinity refinement module. This module leverages grouping belief to uncover the higher-order relationships within the similarity matrix, and integrates the well-designed noisy similarity suppression and similarity increment regularization to eliminate redundant connections while complete absent information. Extensive experimental results on four benchmark datasets validate the superiority of our proposed method GRESS over several state-of-the-art methods.

Abstract:
With the increasing popularity of autonomous driving systems and their applications in complex transportation scenarios, collaborative perception among multiple intelligent agents has become an important research direction. Existing single-agent multimodal fusion approaches are limited by their inability to leverage additional sensory data from nearby agents. In this article, we present the collaborative multimodal fusion network (CMMFNet) for distributed perception in multiagent systems. CMMFNet first extracts modality-specific features from LiDAR point clouds and camera images for each agent using dual-stream neural networks. To overcome the ambiguity in-depth prediction, we introduce a collaborative depth supervision module that projects dense fused point clouds onto image planes to generate more accurate depth ground truths. We then present modality-aware fusion strategies to aggregate homogeneous features across agents while preserving their distinctive properties. To align heterogeneous LiDAR and camera features, we introduce a modality consistency learning method. Finally, a transformer-based fusion module dynamically captures cross-modal correlations to produce a unified representation. Comprehensive evaluations on two extensive multiagent perception datasets, OPV2V and V2XSet, affirm the superiority of CMMFNet in detection performance, establishing a new benchmark in the field.

Affiliations: School of Computer Science and Engineering, the Institute of Artificial Intelligence, the Ministry of Education Key Laboratory of Information Technology, and the Guangdong Province Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, China; State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China; School of Cyber Science and Technology, Sun Yat-sen University (Shenzhen Campus), Shenzhen, China

Abstract:
Deep neural network (DNN) models are widely used in various fields, such as pattern recognition and natural language processing, and provide considerable commercial value to their owners. Embedding a digital watermark in the model allows the legitimate owner to detect unauthorized use of the model. However, the existing DNN watermarking methods are vulnerable to model extraction attacks since the watermark task and the original model task are independent. In this article, a novel collaborative DNN watermarking framework is proposed to defend against model extraction attacks by establishing cooperation between the watermark generation and embedding. Specifically, the trigger samples are not only imperceptible to ensure perceptual stealth security but also infused with target-label information to guide the following feature associations. In the process of watermark embedding, the feature representation of trigger samples is forced to be similar to that of the task distribution samples via feature coupling. Consequently, the trigger samples from our framework can be recognized in the stolen model as task distribution samples, so that the ownership of the model can be successfully verified. Extensive experiments on CIFAR10, CIFAR100, and ImageNet demonstrate the effectiveness and superior performance of the proposed watermarking framework against various model extraction attacks.

Abstract:
Determining the optimal number of clusters in cluster analysis without prior knowledge remains a critical and challenging task. Existing methods often depend on calculating clustering validity indices (CVIs), which increases complexity and may reduce efficiency. Furthermore, different CVIs frequently suggest varying optimal cluster numbers, complicating the selection process. To address these challenges, we propose a novel clustering algorithm, self-regulating possibilistic C-means (PCM) with high-density points (SR-PCM-HDP), which simplifies cluster number determination while improving clustering efficiency. First, the density-based knowledge extraction (DBKE) method is introduced to estimate an appropriate initial cluster number and identify high-density points. DBKE enhances the density peak clustering (DPC) algorithm by removing the need for a predefined density radius. Second, SR-PCM-HDP refines the clustering process by incorporating a parameter to balance the interactions between high-density points and cluster centers, reducing sensitivity to initial configurations and accelerating convergence. Third, the parameter adjustment mechanism in classical PCM is redefined to enable adaptive updates during SR-PCM-HDP iterations. This mechanism facilitates the gradual elimination of obsolete clusters and iterative cluster formation. The theoretical foundations of the SR-PCM-HDP cluster elimination mechanism are rigorously established. Experimental results validate the accuracy and effectiveness of SR-PCM-HDP in determining cluster numbers and ensuring clustering validity, particularly for datasets with overlapping or imbalanced distributions. Comparisons are conducted against 13 state-of-the-art algorithms, including fuzzy clustering, possibilistic clustering, and CVI-based cluster determination methods.

Abstract:
Dynamic link prediction (DLP) plays a critical role in understanding and forecasting evolving relationships in real-world systems across various domains. However, accurately predicting future links remains challenging, as existing methods often overlook the independent modeling of dynamic interactions within individual nodes and the fine-grained characterization of latent interactions across node sequences. To address these challenges, we propose FineFormer (Fine-grained Interactive Transformer), a novel framework that alternates between self-attention and cross-attention mechanisms, enhanced with layer-wise contrastive learning. This design enables FineFormer to uncover fine-grained temporal dependencies both within single node sequences and across different node sequences. Specifically, self-attention captures temporal–spatial dynamics within the interaction sequences of individual nodes, while cross-attention focuses on the complex interactions across the sequences of pairs of nodes. Additionally, by strategically applying layer-wise contrastive learning, FineFormer refines node representations and enhances the model’s ability to distinguish between connected and unconnected node pairs during feature refinement. FineFormer is evaluated on five challenging and diverse real-world DLP datasets. Experimental results demonstrate that FineFormer consistently outperforms state-of-the-art baselines, particularly in capturing complex, fine-grained interactions in continuous-time dynamic networks.

Abstract:
All-in-one image restoration methods, as opposed to conventional image restoration methods, reconstruct images impaired by various degradations within a unified model, eliminating the need for separate network parameters for each task. However, current all-in-one image restoration approaches tackle various types of image degradation using an identical underlying model, neglecting the inherent variability in complexity across different image restoration tasks, resulting in inefficient allocation of computational resources. To address this limitation, this article introduces the arbitrary routing framework (ARF), designed to effectively assess the difficulty of image restoration tasks and identify the most suitable network structure based on these complexities. This framework can be integrated with existing all-in-one image restoration models, enabling efficient inference by activating various proportions of the entire network, that is subnetworks, based on their task-specific complexities. More specifically, the ARF comprises two principal components: 1) the arbitrary routing backbone (ARB) and 2) a task-specific neural architecture search (T-NAS). The ARB incorporates a routing layer between consecutive convolutional groups, offering a wide array of potential subnetwork configurations while adding only negligible extra parameters. Concurrently, T-NAS autonomously identifies the most effective subnetworks for each image restoration task, optimizing both performance and efficiency through an efficiency-aware reward function. Comprehensive experiments across various image restoration tasks demonstrate that the ARF significantly improves performance metrics, that is, an increase of 0.31 in reconstruction PSNR, while also achieving a notable reduction in computational demands by 37.1% compared with the benchmark AirNet method. The code has been made available in the supplementary materials.

Abstract:
This article addresses the annular finite-time H_2/H_\infty control for mean-field jump-diffusion systems (MFJDSs), where the state equation is influenced by both Wiener and Poisson noises. Initially, a new concept termed annular finite-time H_2/H_\infty control is introduced, which simultaneously ensures the system’s annular finite-time bounded-ness (AFTB) in the mean-square sense and the minimization of H_2 and H_\infty performance indices. Moreover, its superiority over finite-time H_2/H_\infty control is analyzed. Next, several innovative and less conservative sufficient conditions for both state feedback and observer-based annular finite-time (SFAFT and OBAFT) H_2/H_\infty control are proposed. Further, a new algorithm is devised. When \gamma is a fixed value, this algorithm can be used to obtain the range of stability parameters \mu and \pi . When \gamma is a varying value, this algorithm can be employed to determine the relationship between the H_2 and H_\infty performance indices under different values of \mu and \pi . Finally, a comprehensive design example is presented to showcase the practical advantages of the proposed methodologies.

Abstract:
Data-driven inverse reinforcement learning (RL) control aims to infer the unknown cost function of a learner system from expert demonstrations. The convergence of existing methods necessitates a data storage mechanism to maintain persistent excitation (PE), which consumes memory and induces delays in satisfying full-rank conditions. To address these problems, in this article, we propose a novel memory-efficient inverse RL algorithm for multiplayer differential game that eliminates the need for strict PE and data storage. We prove that Nash equilibrium solutions for the learner system can be guaranteed under a mild initial excitation condition. Besides, existing inverse RL control algorithms often rely on an initial admissible control policy (IACP), which is difficult to obtain in data-driven scenarios. We address this problem by designing a novel filter-based homotopic RL algorithm, which derives an IACP for learner systems by shifting unstable poles into a stable region. Moreover, we establish several properties of the designed algorithms, including convergence, nonuniqueness, and stability. Finally, the effectiveness of the proposed algorithms is verified by comparative studies and simulation results.

Affiliations: School of Information Science and Technology, the Beijing Laboratory of Smart Environmental Protection, the Beijing Key Laboratory of Computational Intelligence and Intelligent System, and the Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China; Science and Technology on Aircraft Control Laboratory and the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China; Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, China

Abstract:
We study the synchronization of delayed reaction-diffusion neural networks (RDNNs) with Neumann boundary conditions, considering both distributed and discrete delays. Particularly, boundary sampled-data (SD) control is proposed to synchronize delayed RDNNs. In the proposed synchronization strategy, boundary SD control is based on boundary and distributed SD measurements. Based on the Lyapunov stability theory and inequality techniques, some synchronization criteria via the boundary SD control are proposed for delayed RDNNs. The boundary SD control gains are obtained by solving the conditions with linear matrix inequalities. Finally, a numerical example is presented to demonstrate the feasibility and effectiveness of the proposed method.

Affiliations: School of Mathematical Sciences, Yangzhou University, Jiangsu, China; Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Ministry of Education, Wuhu, China; Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China; Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China

Abstract:
This article tackles the sampled-data control issue for a class of time-scale-type systems (TSTSs) subject to denial-of-service (DoS) attacks. A novel sampled-data control protocol, that incorporate the backward-jump-like operator (BJLO), is proposed to ensure compatibility with the discontinuity of time scales. Furthermore, a generalized Halanay-like inequality (GHLI) is proposed to address the effects of time scale discontinuities and DoS attacks on sampling intervals. Compared with the common Halanay inequality (CHI) used in continuous-time sampled-data systems, the GHLI accommodates TSTSs and permits some sampling intervals that exceed the constraints of the CHI. By leveraging the GHLI and the proposed sampled-data control protocol, the exponential stability criterion is derived for TSTSs under DoS attacks. This article culminates with two simulation examples and the micro-grid case study conducted to validate the proposed results.

Abstract:
In nature, populations of organisms (e.g., wolves) exhibit a remarkable ability to coordinate their group actions, such as hunting prey or evading predators, despite the coexistence of cooperative and competitive interactions among individuals. Motivated by this intriguing phenomenon, this article investigates the cooperative consensus tracking control problem of multiagent systems (MASs) over cooperation–competition networks with asynchronous communications. That is, all followers can simultaneously achieve trajectory tracking of the leader agent, even if there exist competitive interactions between the followers and the leader. To portray the cooperation and competition level among agents, a new distance-based weight function is designed, which is more flexible than the fixed weight values in existing research works. Theoretically, the sufficient conditions for achieving consensus tracking control are obtained based on the convergence analysis method of infinite products of super-stochastic matrices. Finally, some numerical simulations are given to verify the effectiveness of the proposed consensus tracking control scheme.

Abstract:
Concept drift, characterized by changes in data distribution over time, has always been an inevitable problem in nonstationary data stream environments. Multistream scenarios are particularly complex due to the potential alteration of interstream correlations, posing significant challenges in addressing concept drift across multiple streams. Most existing adaptation methods target single-stream data, with limited research on multistream. To address these gaps, we propose a Continuous Graph Learning-based self-adaptation framework for Multistream concept drift, termed as CGLM. Our framework introduces a novel graph neural network (GNN) structure embedded with a dynamic graph generator (AGG). This generator creates an adaptive correlation graph using small-scale historical data, capturing spatio-temporal dependencies among streams without predefined graphs during the training phase. A base prediction GNN model is then initialized. When online testing starts, real-time performance is monitored to detect concept drift. Self-adaptation process is achieved by subgraph updating, with different continuous graph learning mechanisms are applied to nondrift or drift scenarios. Lightweight adjustment of subgraphs is performed under nondrift. When drift occurs, AGG generates a new dynamic graph based on newly arriving samples. Our adaptive diffusion graph attention module (ADGAT) captures local correlation changes caused by the drift in the newly generated dynamic graph. It adaptively updates the weights of the original correlation graph based on the extent of the drift. Experimental results on three large-scale real-world datasets demonstrate the superiority of our method over all baseline methods. Additionally, when large-scale data is available for training, our proposed CGLM still surpasses baseline methods.

Affiliations: School of Future Technology and the Institute of Artificial Intelligence, Shanghai University, Shanghai, China; State Key Laboratory of Submarine Geoscience, the Department of Automation, and the Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai, China; Department of Automation, Shanghai Jiao Tong University, Shanghai, China; Department of Electrical Engineering, City University of Hong Kong, Hong Kong, SAR, China

Abstract:
This article investigates the controllability of networked sampled-data systems with various time delays on both control and transmission channels. Necessary and sufficient controllability conditions are first derived for systems with a single delay and then extended to systems with multiple delays. It is found that delays in control signals have no effects on the overall controllability. For a networked system whose topology matrix has only zero eigenvalues, delays of neither control nor transmission signals will affect the overall controllability. It is proved that an uncontrollable mode 1 of such a networked sampled-data system cannot be altered by arbitrary delays. Finally, the networked sampled-data system with first-order holders is discussed, which is modeled as a variant of time-delayed system, and some easy-to-verify algebraic conditions on the controllability are given based on matrix rank checking.

Affiliations: School of Mathematics and Statistics, Guangxi Normal University, Guilin, China; College of Information Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education, East China University of Science and Technology, Shanghai, China; Department of Automation, Zhejiang University of Technology, Hangzhou, China; Department of Electrical Engineering, Yeungnam University, Gyeongsan, Republic of Korea

Abstract:
This study investigates an asynchronous sliding-mode control (SMC) strategy tailored for interval type-2 (IT2) fuzzy switching systems, specifically addressing challenges posed by cyber-attacks. Distinct from existing stochastic switching strategies, a novel duration-time-based switching rule is proposed that integrates both sojourn probability and mode duration, significantly reducing computational complexity and aligning more closely with practical requirements. To mitigate mode-switching-induced chattering and enhance robustness against uncertainties and disturbances, an innovative fuzzy SMC law with a learning mechanism is developed. Notably, a recursive sliding-mode learning controller is introduced, replacing abrupt switching actions with iterative learning adjustments to progressively guide system states onto the sliding surface, thereby significantly improving control smoothness and reducing chattering. To effectively handle cyber-attacks disrupting mode transmission, a comprehensive mismatched model that dynamically synchronizes the modes of the system and the controller is introduced, offering improved resilience compared to traditional fixed mismatch approaches. Utilizing the proposed learning-based SMC and Lyapunov stability theory, sufficient conditions ensuring mean-square stability of the system are derived. Finally, the practical effectiveness and distinct superiority of the proposed methods are demonstrated through simulations using mass-spring–damper and tunnel diode circuit models.

Abstract:
To evaluate the energy-saving effect of the controller, obtaining upper bounds on energy consumption and control time has become a worthwhile and meaningful issue to study. This article mainly discusses three contents about the Kuramoto oscillator network, including fixed-time synchronization (FxTS), prescribed-time synchronization (PTS), and energy consumption estimation. First, to reach FxTS, two sufficient conditions are proposed to guarantee that the Kuramoto oscillator network can reach fixed-time phase agreement and frequency synchronization. Unlike finite/fixed-time controllers, the prescribed-time controller in this article includes a time-varying function term, which is essential to ensure that the system achieves the prescribed-time phase agreement and frequency synchronization. At the same time, the setting-time for PTS is independent of the system initial values or controller parameters, which expands the application prospects of the system. Then, with limited setting-time as a premise, the energy consumed during the fixed/prescribed-time control process is obtained, which helps to evaluate the working time of the system. Finally, an example of a 5-node network is used to illustrate the effectiveness of FxTS and PTS in Kuramoto-oscillator networks.

Abstract:
Neural network (NN) is a prominent intelligent model to process information through the connection and activation of multilayer neurons. However, NNs usually encounter with the incorrect activation of neurons because of the excessive coverage for the boundary of compound noises. To address this issue, this article proposes a robust reconstructed NN (RRNN) with spectral reshaping activation (SRA). Primarily, an SRA is designed to replace the original activation of NN, which shrinks the spectrums of the compound noises toward the cluster center through spectral subtraction. It enables RRNN to reshape a concentrated noise space for easy coverage. Then, a hierarchical gradient descent (HGD) algorithm is developed to update the parameters of RRNN. The HGD algorithm establishes a noise-contrastive degree of SRA to penalize the loss function of RRNN, which holds robust performance with different noises. Furthermore, the theoretical proof of RRNN is presented to validate its robustness. Finally, the experimental results confirm the superior robustness of RRNN for tackling noisy samples compared to other methods.

Abstract:
Autonomous object-searching is crucial for various applications of unmanned aerial vehicles (UAVs). Considering the fact that existing autonomous exploration methods either focus only on maximizing the exploration of unknown areas or suffer from insufficient searches due to repeated and unnecessary exploration, this article introduces an effective object-searching strategy for UAVs in large-scale and complex environments. A novel method is proposed to empower UAVs with the capability to conduct fast, secure, and efficient searches for interested objects in large-scale and complex environments. A Kalman filter-based YOLO algorithm is first proposed to achieve robust object position estimation in cluttered and occlusion-prone scenarios, and a mode-based method is then introduced to conduct a computationally efficient viewpoint generation. A hierarchical searching method is proposed, which not only can increase computational and search efficiency but also can leverage frontier data for search-planning, including coarse global searching paths and optimizing local refined searching trajectories. Experimental results in six different environments indicate that our proposed method outperforms existing techniques in terms of both reduced searching times and computing time. Moreover, the effectiveness of the proposed method is substantiated in various real-world scenarios.

Abstract:
Neighborhood rough sets are an effective model for handling numerical and categorical data entangled with vagueness, imprecision, or uncertainty. However, existing neighborhood rough set models and their feature selection methods treat each sample equally, whereas different types of samples inherently play different roles in constructing neighborhood granules and evaluating the goodness of features. In this study, the sample weight information is first introduced into neighborhood rough sets, and a novel weighted neighborhood rough set model is consequently constructed. Then, considering the lack of sample weight information in practical data, a margin-based weight optimization function is designed, based on which a gradient descent algorithm is provided to adaptively learn sample weights through maximizing sample margins. Finally, an average granule margin measure is put forward for feature selection, and a forward-adding heuristic algorithm is developed to generate an optimal feature subset. The proposed method constructs the weighted neighborhood rough sets using sample weights for the first time and is able to yield compact feature subsets with a large margin. Extensive experiments and statistical analysis on UCI datasets show that the proposed method achieves highly competitive performance in terms of feature reduction rate and classification accuracy when compared with other state-of-the-art methods.

Abstract:
Neuroimaging analysis aims to reveal the information-processing mechanisms of the human brain in a noninvasive manner. In the past, graph neural networks (GNNs) have shown promise in capturing the non-Euclidean structure of brain networks. However, existing neuroimaging studies focused primarily on spatial functional connectivity, despite temporal dynamics in complex brain networks. To address this gap, we propose a spatio-temporal interactive graph representation framework (STIGR) for dynamic neuroimaging analysis that encompasses different aspects from classification and regression tasks to interpretation tasks. STIGR leverages a dynamic adaptive-neighbor graph convolution network to capture the interrelationships between spatial and temporal dynamics. To address the limited global scope in graph convolutions, a self-attention module based on Transformers is introduced to extract long-term dependencies. Contrastive learning is used to adaptively contrast similarities between adjacent scanning windows, modeling cross-temporal correlations in dynamic graphs. Extensive experiments on six public neuroimaging datasets demonstrate the competitive performance of STIGR across different platforms, achieving state-of-the-art results in classification and regression tasks. The proposed framework enables the detection of remarkable temporal association patterns between regions of interest based on sequential neuroimaging signals, offering medical professionals a versatile and interpretable tool for exploring task-specific neurological patterns. Our codes and models are available at https://github.com/77YQ77/STIGR/.

Abstract:
Meta-reinforcement learning (meta-RL) algorithms extract task information from experienced context in order to reason about new tasks, and facilitate rapid adaptation. The quality of these contextual representations (or embeddings) is therefore crucial for a meta-RL agent to make effective decisions in unknown environments. Current methods predominantly assume the existence of a single underlying task, but using a single contextual embedding may not be expressive enough to fully capture the broader distribution of task variations that an agent might encounter. Decomposing that information into different representations can allow them to capture more relevant features in context space while applying additional structure that aids downstream exploitation. In this article, we develop global-local embeddings for contextual meta-RL (GLOBEX), an off-policy contextual meta-RL algorithm that decomposes the contextual representation into separate global and local embeddings. The learning process maximizes information retained by the embeddings and utilizes a mutual information constraint to encourage decoupling. Illustrative examples show that our method effectively adapts by identifying global task dynamics and exploiting temporally local signals. In addition, GLOBEX outperforms existing state-of-the-art meta-RL algorithms on standard MuJoCo benchmarks.

Abstract:
Owing to advancements in deep learning technology, vision transformers (ViTs) have demonstrated impressive performance in various computer vision tasks. Nonetheless, ViTs still face some challenges, such as high computational complexity and the absence of desirable inductive biases. To alleviate these issues, the potential advantages of combining eagle vision with ViTs are explored. A bi-fovea visual interaction (BFVI) structure inspired by the unique physiological and visual characteristics of eagle eyes is introduced. Based on this structural design approach, a novel bi-fovea self-attention (BFSA) mechanism and bi-fovea feedforward network (BFFN) are proposed. These components are employed to mimic the hierarchical and parallel information processing scheme of the biological visual cortex, thereby enabling networks to learn the feature representations of the targets in a coarse-to-fine manner. Furthermore, a bionic eagle vision (BEV) block is designed as the basic building unit based on the BFSA mechanism and the BFFN. By stacking the BEV blocks, a unified and efficient family of pyramid backbone networks called eagle ViTs (EViTs) is developed. Experimental results indicate that the EViTs exhibit highly competitive performance in various computer vision tasks, demonstrating their potential as backbone networks. In terms of computational efficiency and scalability, EViTs show significant advantages compared with other counterparts. The developed code is available at https://github.com/nkusyl/EViT.

Abstract:
This article explores the problem of prescribed-time stabilization for a class of high-order polynomial nonlinear systems with unknown time-varying nonlinearities. The key technique behind the proposed strategy involves fixing the time-varying components to their bounded values before the prescribed time and establishing a new lemma to suppress the time-varying continuous functions in the investigated system. We design a continuous bounded feedback controller to address the singularities induced by infinite control gains at the prescribed time and to suppress the implicit effects of time variations. Superior to the existing prescribed-time stabilization results, our strategy achieves the states’ convergence within the prescribed-time and the nontruncated run of controller simultaneously. We employ the wing rock motion to demonstrate the practicality and superiority of the developed strategies.

Abstract:
The invariant ellipsoid methodology focuses on minimizing the invariant/attractive set for a linear control system subjects to bounded external disturbances. In this note, the invariant ellipsoid methodology is adapted to multiagent systems (MASs) by leveraging the generalized homogeneous control. A necessary and sufficient condition for the optimal rejection of external disturbances using a homogeneous control protocol is presented. Compared to linear control protocols, the generalized homogeneous approach yields faster convergence and enhanced accuracy. Theoretical results are validated by the numerical simulations of the multiagent system comprised of unicycle mobile robots (UMRs).

Abstract:
This study investigates the utilization of a dynamic encoding-decoding mechanism for transferred signals to explore adaptive quantized iterative learning control. Encoding-decoding pairs for error and output are designed to adjust the quantization parameters dynamically. A uniform quantizer with a finite quantization level is employed on the system measurement side, with distinct lower bounds specified for the quantizer under two encoding-decoding pairs. Zoom-out and zoom-in strategies are incorporated into the encoder and decoder, respectively, enabling adaptation of the quantizer. These two adaptive quantization mechanisms ensure convergence of the system output toward the desired reference without saturating the quantizer under any initial input. The proposed scheme relaxes the constraints on the initial input signals, simplifies the expression for the quantizer saturation bound, and concurrently reduces the magnitude of the saturation bound itself. Finally, a numerical and an experimental examples are presented to validate the proposed learning control scheme.

Abstract:
Integrating the advantage of the unbiased finite impulse response (UFIR) filter into the Kalman filter (KF) is a practical yet challenging issue, where how to effectively borrow knowledge across domains is a core issue. Existing methods often fall short in addressing performance degradation arising from noise uncertainties. In this article, we delve into a Bayesian transfer filter (BTF) that seamlessly integrates the UFIR filter into the KF through a knowledge-constrained mechanism. Specifically, the pseudo marginal measurement likelihood of the UFIR filter is reused as a constraint to refine the Bayesian posterior distribution in the KF. To optimize this process, we exploit the Kullback-Leibler (KL) divergence to measure and reduce discrepancies between the proposal and target distributions. This approach overcomes the limitations of traditional weight-based fusion methods and eliminates the need for error covariance. Additionally, a necessary condition based on mean square error criteria is established to prevent negative transfer. Using a moving target tracking example and a quadruple water tank experiment, we demonstrate that the proposed BTF offers superior robustness against noise uncertainties compared to existing methods.

Abstract:
Due to its property of not requiring prior knowledge of the environment, reinforcement learning (RL) has significant potential for solving quantum control problems. In this work, we investigate the effectiveness of continuous control policies based on deep deterministic policy gradient. To achieve good control of quantum systems with high fidelity, we propose an auxiliary task-based deep RL (AT-DRL) for quantum control. In particular, we design an auxiliary task to predict the fidelity value, sharing partial parameters with the main network (from the main RL task). The auxiliary task learns synchronously with the main task, allowing one to extract intrinsic features of the environment, thus aiding the agent to achieve the desired state with high fidelity. To further enhance the control performance, we also design a guided reward function based on the fidelity of quantum states that enables gradual fidelity improvement. Numerical simulations demonstrate that the proposed AT-DRL can provide a good solution to the exploration of quantum dynamics. It not only achieves high task fidelities but also demonstrates fast learning rates. Moreover, AT-DRL has great potential in designing control pulses that achieve effective quantum state preparation.

Abstract:
This study investigates the performance of discrete-time systems under quantized iterative learning control. An encoding–decoding mechanism is combined with a spherical polar coordinate-based quantizer to process the signals transmitted through a control network, which introduces a quantization operation to the encoding process. A scenario involving encoding and decoding of the system output is explored before discussing the general scenario involving encoding and decoding of both the system output and control input. Unlike existing schemes, the two scenarios require no additional scaling parameter in the encoder and decoder. The radius of the support sphere is designed to vary over the iterations, and the learning control scheme is based on the output of the decoder. The results indicate that the control method enables error-free tracking performance of a system. The theoretical conclusions are verified in tests of a permanent magnet synchronous motor.

Affiliations: School of Engineering, Qufu Normal University, Rizhao, China; School of Control Science and Engineering, Tiangong University, Tianjin, China; School of Mathematics, Southeast University, Nanjing, China; Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education, East China University of Science and Technology, Shanghai, China; College of Mathematics and Statistics, Guangxi Normal University, Guilin, China; School and Hospital of Stomatology and the Liaoning Provincial Key Laboratory of Oral Diseases, China Medical University, Shenyang, Liaoning, China

Abstract:
This study investigates the synchronization of reaction-diffusion complex dynamical networks (CDNs) based on semi-Markov switching topology and an event-triggered protocol. The investigated model is rendered more practical via the introduction of a semi-Markov process for stochastic jump CDNs. Based on the internal dynamic variable history information, a dynamic-memory event-triggered strategy is proposed, wherein the primary novelty lies in its prior transmitted packets to enhance the control performance. This further reduces data transmission based on the dynamic threshold parameters. The Bessel-Legendre inequality is adopted to reduce the conservatism of the obtained results. In addition, sufficient synchronization conditions are established to ensure the stochastic stability of the error system for two different models (partial differential equations- and ordinary differential equations-based models). Furthermore, two examples are provided to illustrate the effectiveness of the theoretical results.

Abstract:
Multiview semi-supervised learning is a popular research area in which people utilize cross-view knowledge to overcome the limitation of labeled data in semi-supervised learning. Existing methods mainly utilize deep neural network, which is relatively time-consuming due to the complex network structure and back propagation iterations. In this article, co-training broad Siamese-like network (Co-BSLN) is proposed for coupled-view semi-supervised classification. Co-BSLN learns knowledge from two-view data and can be used for multiview data with the help of feature concatenation. Different from existing deep learning methods, Co-BSLN utilizes a simple shallow network based on broad learning system (BLS) to simplify the network structure and reduce training time. It replaces back propagation iterations with a direct pseudo inverse calculation to further reduce time consumption. In Co-BSLN, different views of the same instance are considered as positive pairs due to cross-view consistency. Predictions of views in positive pairs are used to guide the training of each other through a direct logit vector mapping. Such a design is fast and effectively utilizes cross-view consistency to improve the accuracy of semi-supervised learning. Evaluation results demonstrate that Co-BSLN is able to improve accuracy and reduce training time on popular datasets.

Abstract:
Developing natural, intuitive, and human-centric input systems for mobile human-machine interaction (HMI) poses significant challenges. Existing gaze or gesture-based interaction systems are often constrained by their dependence on continuous visual engagement, limited interaction surfaces, or cumbersome hardware. To address these challenges, we propose MetaSkin, a novel neurohaptic interface that uniquely integrates neural signals with on-skin interaction for bare-handed, eyes-free interaction by exploiting human’s natural proprioceptive capabilities. To support the interface, we developed a deep learning framework that employs multiscale temporal-spectral feature representation and selective feature attention to effectively decode neural signals generated by on-skin touch and motion gestures. In experiments with 12 participants, our method achieved offline accuracies of 81.95% for touch location discrimination, 71.00% for motion type identification, and 46.08% for 10-class touch-motion classification. In pseudo-online settings, accuracies reached 99.43% for touch onset detection, and 80.34% and 67.02% for classification of touch location and motion type, respectively. Neurophysiological analyses revealed distinct neural activation patterns in the sensorimotor cortex, underscoring the efficacy of our multiscale approach in capturing rich temporal and spectral dynamics. Future work will focus on optimizing the system for diverse user populations and dynamic environments, with a long-term goal of advancing human-centered, neuroadaptive interfaces for next-generation HMI systems. This work represents a significant step toward a paradigm shift in design of brain-computer interfaces, bridging sensory and motor paradigms for building more sophisticated systems.

Abstract:
In this article, two types of multiagent systems (MASs) are developed for distributed bilevel constrained optimization. Within the framework of the distributed bilevel optimization modeling, the objective function is in a summation manner of local objective functions. Multiple agents connected via a communication network are harnessed for optimizing the local objective functions cooperatively while adhering to coupled constraints with global information, and each agent is tasked with solving an individual inner problem and it is subject to multiple local constraints. To address challenges posed by the distributed computation requirement of the proposed bilevel optimization models and multiple complex constraints, first and second-order MASs are customized and proven to converge to the optimal solution. Three examples involving two numerical simulations and an economic dispatch problem are elaborated to verify and demonstrate the optimality, enhanced robustness to communication blocking, and fast convergence of the proposed approaches.

Affiliations: Australian AI Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia; Department of Systems Science, School of Mathematics, Southeast University, Nanjing, China; College of Mathematics and Econometrics, Hunan University, Changsha, China; School of Mathematics, China University of Mining and Technology, Xuzhou, China; College of Mathematics and System Science, Xinjiang University, Urumqi, China

Abstract:
Fault-tolerant control (FTC) is an effective control method designed to maintain a faulty system within an acceptable risk level while ensuring its safety. However, handling both uncertainties and faults in a system remains challenging. In this article, we propose two probabilistic model-based adaptive FTC methods for faulty nonlinear systems with unknown dynamics. We study Gaussian process (GP) regression in two cases: 1) an offline learning-based control method and 2) an event-triggered online data-driven modeling method, to learn unknown system dynamics. Considering the computational complexity of GP regression in practical applications, we discuss the case of computational delays in real-time predictions. Moreover, we develop four theoretical criteria to ensure the probabilistic stability of closed-loop systems. Finally, numerical simulations validate the effectiveness of proposed control methods and demonstrate their competitiveness compared to existing approaches.

Abstract:
Linear quadratic control with unknown value functions and dynamics is extremely challenging, and most of the existing studies have focused on the regulation problem, incapable of dealing with the tracking problem. To solve both linear quadratic regulation and tracking problems for continuous-time systems with unknown value functions, this article develops a discounted inverse reinforcement learning (DIRL) method that inherits the model-independent property of reinforcement learning (RL). More specifically, we first formulate a standard paradigm for solving linear quadratic control using DIRL. To recover the value function and the target control gain, an error metric is elaborately constructed, and a quasi-Newton algorithm is adopted to minimize it. Furthermore, three DIRL algorithms, including model-based, model-free off-policy, and model-free on-policy algorithms, are proposed. The latter two rely on the expert’s demonstration data or the online observed data, requiring no prior knowledge of the system dynamics and value function. The stability, convergence, and existence conditions of multiple solutions are thoroughly analyzed. Finally, numerical simulations demonstrate the effectiveness of the theoretical results.

Abstract:
Knowledge transfer-based evolutionary optimization has garnered significant attention, such as in multitask evolutionary optimization (MTEO), which aims to solve complex problems by simultaneously optimizing multiple tasks. While this emerging paradigm has been primarily focusing on task similarity, there remains a hugely untapped potential in harnessing the shared characteristics between different domains. For example, real-world complex systems usually share the same characteristics, such as the power-law rule, small-world property and community structure, thus making it possible to transfer solutions optimized in one system to another to facilitate the optimization. Drawing inspiration from this observation of shared characteristics within complex systems, we present a novel framework, multidomain evolutionary optimization (MDEO). First, we propose a community-level measurement of graph similarity to manage the knowledge transfer among domains. Furthermore, we develop a graph-learning-based network alignment model that serves as the conduit for effectively transferring solutions between different domains. Moreover, we devise a self-adaptive mechanism to determine the number of transferred solutions from different domains, and introduce a knowledge-guided mutation mechanism that adaptively redefines mutation candidates to facilitate the utilization of knowledge from other domains. To evaluate its performance, we use a challenging combinatorial problem known as adversarial link perturbation as the primary illustrative optimization task. Experiments on multiple real-world networks of different domains demonstrate the superiority of the proposed framework in efficacy compared to classical evolutionary optimization.

Affiliations: School of Information Science and Technology, the Beijing Laboratory of Smart Environmental Protection, the Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China; School of Electrical Engineering, University of Jinan, Jinan, China; Science and Technology on Aircraft Control Laboratory, the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China; Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong

Abstract:
In this work, we introduce a fuzzy intermittent control method for nonlinear coupled delayed partial differential equation-ordinary differential equation (PDE-ODE) systems based on spatially averaged measurements (SAMs). First, the nonlinear coupled delayed PDE-ODE systems are accurately modeled by adopting the Takagi–Sugeno (T-S) fuzzy PDE-ODE model. Then, based on the T-S fuzzy PDE-ODE model, a switching lyapunov functional (LF) is given, and fuzzy intermittent controllers are designed to ensure the exponential stability of the closed-loop fuzzy delayed coupled systems. Sufficient conditions for the exponential stability of the system are expressed through by a set of space-dependent linear matrix inequalities (SDLMIs). Finally, the simulation results are used to verify the effectiveness of the proposed approach for controlling hypersonic rocket car (HRC).

Abstract:
fault-tolerant control (FTC), due to its characteristic of fault prevention and mitigation, is an increasingly popular topic in wastewater treatment process (WWTP) for safety purpose. However, the presence of uncertainties and external disturbances inevitably leads to unknown faults in WWTP, making it challenging for FTC strategies using existing fault data to ensure continuous safe and stable operation. To address this issue, a knowledge compensation-based active fault-tolerant control (KC-AFTC) is designed in this article. First, a knowledge-based prescribed performance function (KPPF) is introduced to constrain the transient and steady-state performance of WWTP. Then, the proposed KPPF can assist in KC-AFTC to ensure the operation of WWTP with the desirable performance in the event of faults. Second, a knowledge compensation mechanism (KCM), which is extracted from KPPF and the fault data, is employed to reconstruct the control law for different fault conditions in active FTC (AFTC). Then, KC-AFTC can maintain the continuously safe and stable operation of WWTP. Third, the stability of knowledge compensation-based AFTC (KC-AFTC) is demonstrated through Lyapunov theory. Then, the stability analysis can provide theoretical basis for further application of KC-AFTC in practical experiments. Finally, the proposed control method is validated through both a simulation case and a real WWTP. The results demonstrate that KC-AFTC can achieve outstanding performance in terms of stability and fault tolerance.

Abstract:
A frequent problem in data stream mining is concept drift, meaning the data distribution changes over time. A common issue when dealing with concept drift is insufficient data. Real-world applications of data stream mining often involve multiple data streams. However, most concept drift methods handle these data streams separately. This study uses data from other data streams to handle the problem of insufficient data. We propose a novel Multistream Concept Drift Handling Framework via data sharing, containing a fuzzy membership-based drift detection (FMDD) component and a fuzzy membership-based drift adaptation (FMDA) component, to train the new learning model for drifting streams by sharing weighted data from other nondrifting streams. A stream fuzzy set is defined with membership functions that measure the degree to which samples belong to a data stream. Our Concept Drift Handling Framework can detect when and in which stream concept drift occurs, and therefore the insufficient data issue can be solved by adding the weighted data from nondrifting streams to train new learning models. Synthetic and real-world experimental results show that our method can help avoid the insufficient data issue and thereby significantly improve the prediction performance.

Abstract:
The interaction topology of multiagent systems (MASs) is crucial for effective collaboration. Recent advances in topology inference provide a better understanding of the behaviors of the systems. Nevertheless, external attackers can exploit such techniques, posing a severe privacy breach, while the challenges to the topology protection problem remain unresolved. This article proposes a distributed collaboration algorithm for MASs to defend against topology inference attacks. The novelties include: 1) Compared with traditional noise-adding methods that inject decaying random inputs, the proposed algorithm constructs a novel noise term to increase the irregularity of the agents’ states in a distributed manner while satisfying the convergence requirements. 2) A weight-selecting strategy is designed to choose the subtopologies to degrade the topology inference accuracy, further improving the topology-protecting performance. Theoretically, we derive the mean-square convergence factor and the nonasymptotic error bounds of our proposed algorithm. Extensive simulations demonstrate the effectiveness of the proposed algorithm in protecting the topology.

Abstract:
Imitation learning is an important method for the human–robot skill transfer. However, ensuring that skills learned through imitation remain effective in different environments is a challenge. This article addresses the challenge by proposing a stable autonomous dynamic system that can effectively handle obstacles and disturbances while maintaining trajectory accuracy. We introduce an energy-approximated dynamic subattractor (EADA) method that enhances disturbance resistance by dynamically selecting subattractors through Neum (an energy function derived from demonstration data). By combining velocity modulation algorithms with EADA, the system achieves global stability, precise obstacle avoidance, autonomous trajectory recovery, and rapid response. The proposed framework effectively handles complex scenarios, including environments with multiple obstacles, dynamic obstacles, and disturbances. We validate the proposed approach through simulations on the LASA dataset and real-world robotic experiments (both single-arm and dual-arm robots), demonstrating its effectiveness in achieving smooth and accurate obstacle avoidance trajectories with generalization capability.

Abstract:
Entangled states are fundamental to quantum information processing. However, many existing quantum control methods rely on predefined target states, limiting their flexibility in accommodating diverse entanglement structures. This article introduces a sliding mode control framework that utilizes an entanglement measure as the sliding surface, enabling the generation of entangled states without specifying a fixed target. By adjusting the desired entanglement level, the proposed method can generate a wide range of states, including both bipartite and multipartite configurations, as well as pure and mixed states. Since the entanglement measure is scalar-valued, the resulting control law is inherently independent of the number of subsystems—an important advantage of the proposed approach. Among various entangled states, maximally entangled states (MESs) are of particular interest. Lyapunov stability of the control scheme is established, and numerical simulations confirm its effectiveness in robustly generating MESs in both bipartite and multipartite systems.

Abstract:
This article proposes a generalized Udwadia-Kalaba control method to simultaneously handle equality and speed inequality constraints. First, a dynamic model that includes both of these constraints is established, and the state transformation is carried out through diffeomorphism state transition theory. At the same time, limitations are imposed on the difference between the estimated state variables designed in this article and the actual state variables. Then, a robust control strategy is designed to ensure that the equality constraints and speed inequality constraints are satisfied. By utilizing the Lyapunov approach, the uniform boundedness and uniform ultimate boundedness of the dynamical system are demonstrated. Finally, the feasibility of the proposed method was verified through an uncertain long-distance belt conveyor system.

Abstract:
Cloud control systems (CCSs) are evolving rapidly, requiring secure communication channels to protect critical remote control tasks. This article introduces an efficient encryption mechanism configured for securing the physical layer communication in these systems while transmitting data over optical fiber networks using orthogonal frequency division multiplexing active optical networks. The proposed scheme takes advantage of the hypersensitive chaotic properties of the Lorenz map to provide robust multidimensional encryption and confidentiality. The encryption process involves arranging bulk encoded codewords in a table structure shape, with each column representing individual codewords encoded by pipelined successive cancellation polar encoding. A Lorenz map is then utilized to generate three distinct chaotic keys, which are employed to reindex the rows and columns of the table structured codeword along with the subcarrier remapping in the constellation map. This reindexing operation provides an additional layer of security against potential brute-force attacks. Performance metrics such as computational efficiency, security robustness, and resistance to various noise and distortion sources are being evaluated. The experimental results demonstrate promising levels of security and resilience against potential threats on the physical layer of communications. The scheme’s compatibility with the technical environment ensures seamless integration into existing CCSs infrastructure, making it a resilient solution for securing critical physical layer transmissions.

Abstract:
Polygonal collision avoidance (PCA) is short for the problem of collision avoidance between two polygons (i.e., polytopes in planar) that own their dynamic equations. This problem suffers the inherent difficulty in dealing with nonsmooth boundaries and recently optimization-defined metrics, such as signed distance field (SDF) and its variants, have been proposed as control barrier functions (CBFs) to tackle PCA problems. In contrast, we propose an optimization-free smooth CBF method in this article, which is computationally efficient and proved to be nonconservative. It is achieved by three main steps: a lower bound of SDF is expressed as a nested Boolean logic composition first, then its smooth approximation is established by applying the latest log-sum-exp method, after which a specified CBF-based safety filter is proposed to address this class of problems. To illustrate its wide applications, the optimization-free smooth CBF method is extended to solve distributed collision avoidance of two underactuated nonholonomic vehicles and drive an underactuated container crane to avoid a moving obstacle, respectively, for which numerical simulations are also performed.

Abstract:
Landscape painting is a gem of cultural and artistic heritage that showcases the splendor of nature through the deep observations and imaginations of its painters. Limited by traditional techniques, these artworks were confined to static imagery in ancient times, leaving the dynamism of landscapes and the subtleties of artistic sentiment to the viewer’s imagination. Recently, emerging text-to-video (T2V) diffusion methods have shown significant promise in video generation, providing hope for the creation of dynamic landscape paintings. However, current T2V methods focus on generating natural videos, emphasizing the capture of details and the authenticity of physical laws. In contrast, landscape painting videos emphasize the overall dynamic aesthetic. Besides, challenges, such as the lack of specific datasets, the intricacy of artistic styles, and the creation of extensive, high-quality videos pose difficulties for these models in generating landscape painting videos. In this article, we propose landscape painting videos-high definition (LPV-HD), a novel T2V dataset for landscape painting videos, and noise-aware diffusion model (NADM), a T2V model that utilizes Stable Diffusion. Specifically, we present a motion module featuring a dual attention mechanism to capture the dynamic transformations of landscape imageries, alongside a noise adapter to leverage unsupervised contrastive learning in the latent space to ensure the overall beauty of the landscape painting video. Following the generation of keyframes, we employ optical flow for frame interpolation to enhance video smoothness. Our method not only retains the essence of the landscape painting imageries but also achieves dynamic transitions, significantly advancing the field of artistic video generation. Source code and dataset are available at https://github.com/llzlh21/NADM.

Abstract:
This article investigates the problem of distributed Nash equilibrium (NE) seeking in noncooperative games within a directed communication network. For promoting the efficiency of communication among players, a gradient-based dynamic event-triggered mechanism is proposed, where Zeno behavior is excluded. Moreover, based on the Lyapunov stability theory, we derive sufficient conditions for exponential convergence and demonstrate that the seeking strategy proposed facilitates the convergence of players’ actions toward the NE. To illustrate the effectiveness of the proposed strategies, simulation results are presented in a system consisting of five agents.

Affiliations: Research Center of Intelligent Control and Systems, Yongjiang Laboratory, Ningbo, China; Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China; National Research Base of Intelligent Manufacturing Service, and the Chongqing Engineering Laboratory for Detection Control and Integrated Systems, Chongqing Technology and Business University, Chongqing, China; Institute of Architecture Engineering, Guangxi City Vocational University, Chongzuo, Guangxi, China; Antal Bejczy Center for Intelligent Robotics, University Research, Innovation and Service Center, Óbuda University, Budapest, Hungary

Abstract:
Recent advances in video tracking with discriminative correlation filters leverage diverse observation models. However, fusing hand-crafted and deep convolutional neural network representations equivalently would overly constrain resolution conditions for template matching, leading to peak response slippage and jittery neighboring search processes, especially problematic in autonomous driving scenarios. This article addresses the inference conservatism issue in multitype feature tracking. We propose a target-observation constraint framework to formalize discrimination conservatism across feature map channels. A learning constraint transformation methodology is introduced to cluster similar representations while pushing dissimilar ones apart. These discriminant constraints are further fine-tuned through joint learning with correlation filters, improving the positional precision of detection responses. Additionally, we propose an updating strategy that suppresses low scores of symmetric dispersion ratio, enhancing tracking robustness. Extensive evaluations on five tracking datasets demonstrate the superior performance of our approach: UAV20L, UAVDT, OTB-100, VOT-2019, and LaSOT.

Abstract:
The interpretability of quantum machine learning (QML) refers to the capability to provide clear and understandable explanations for the predictions and decision-making processes of QML models. However, most quantum convolutional layers (QCLs) utilize closed-box structures that are inherently devoid of interpretability, leading to the opacity of principles and the suboptimal mapping of classical data. This significantly undermines the reliability of QML models. In addition, most of the current QML interpretability focuses on post hoc interpretability seriously neglecting the importance of exploring intrinsic causes. To tackle these challenges, we introduce the quantum adjoint convolution operation (QACO). It is an intrinsic interpretability scheme based on quantum evolution, as its quantum mapping precisely corresponds to the position and pixel values of the image and its principle is equivalent to the Frobenius inner product (FIP). Furthermore, we extend the QACO concept into the quantum adjoint convolutional layer (QACL) by integrating the quantum phase estimation (QPE) algorithm, enabling the parallel computation of all FIPs. Experimental results on PennyLane and TensorFlow platforms demonstrate that our method achieves a 6.3%, 3.4%, and 2.9% higher average test accuracy on Fashion MNIST, MNIST, and DermaMNIST datasets compared to classical and uninterpretable quantum counterparts, respectively, while maintaining 73.3% noise-robust accuracy under Gaussian noise, showcasing its superior generalizability and resilience in practical scenarios.

Abstract:
This article provides a new methodology to design a novel predictive control (PC) scheme for unknown nonlinear discrete-time systems, by deeply exploiting future ideal controllers and the dynamic linearization (DL) technique. The control input increment vector can be linearly parameterized with the time-varying control gain vector. The PC law is obtained by directly optimizing the control gain vector with the least square method. The system outputs are predicted through the parameterized PC law and the DL data model of the controlled system. The proposed PC scheme is data-driven, that is, it does not depend on the system dynamic model and the control gain vector is adaptively optimized by using only the measured input/output data. The monotonic convergency of the proposed PC scheme is theoretically guaranteed, and its effectiveness is validated by two illustrative examples, i.e., a complicated nonlinear system and a linear time-invariant system.

Abstract:
Oriented object detection has been crucial for rotation-sensitive tasks and has garnered significant attention. Most existing methods generate angles as detector output vectors, but this strategy can abnormally magnify visually similar differences between two boxes in certain circumstances, termed boundary discontinuity issue. To overcome this limitation, we propose a boundary continuity-aware Gaussian encoder (BCGE). Specifically, BCGE directly predicts target Gaussian distributions for proposals and learns an oriented bounding box as an integrated 2-D matrix, effectively addressing boundary discontinuity issues. We also propose a transformation from Gaussian representation back to boxes and extend this transformation theory to the complex domain to adapt to the learning characteristics of neural networks. Furthermore, BCGE serves as a versatile plug-and-play architectural encoder, directly replacing the standard coding process in various oriented detectors with adaptability. Experimental results on five popular datasets, i.e., DOTA, UCAS-AOD, HRSC2016, SSDD, and HRSID, consistently show the effectiveness of our approach.

Affiliations: College of Information Engineering, Shanghai Maritime University, Shanghai, China; School of Mathematics, Shanghai University of Finance and Economics, Shanghai, China; School of Mathematical Sciences, the Center for Applied Mathematics, the Shanghai Center for Mathematical Sciences, and the Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University, Shanghai, China; School of Mathematical Sciences, Fudan University, Shanghai, China

Abstract:
This article investigates distributed adaptive algorithms for intralayer synchronization of multiplex networks, both with and without pinning control. Two types of distributed adaptive algorithms are considered based on the parameters being adjusted: 1) node-based algorithms, which adapt the coupling strength of each node using the relative information from its neighborhood and itself, and 2) edge-based algorithms, which update the coupling weight of each edge based on the relative information between the two connected nodes. Using the Lyapunov function method, we prove that, under mild conditions on the uncoupled node dynamics, the proposed adaptive strategies guarantee intralayer synchronization for any multiplex network with strongly connected intralayer topologies.

Affiliations: Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China; College of Electronic and Information Engineering, the Department of Control Science and Engineering, and the Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction, Shanghai, China; School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

Abstract:
This article investigates the pursuit-evasion games for target capture in multiagent systems. To address this challenge, a novel data-driven optimal control policy is proposed, leveraging off-policy reinforcement learning and Nash-minmax strategies. First, a comprehensive framework for multiagent pursuit-evasion games is developed, modeled as a two-layer game structure. In this framework, interactions among agents within the same team are characterized as nonzero-sum games, while interactions between opposing teams are adversarial and treated as zero-sum games. Second, Nash-minmax strategies are introduced to solve the formulated multiagent pursuit-evasion games. These strategies effectively derive distributed Nash solutions for agents within the same team and adversarial worst-case policies for agents in opposing teams. Furthermore, to eliminate the reliance on prior knowledge of agent dynamics and initial stabilizing control gains, a data-driven optimal control policy is designed, ensuring the achievement of target capture. Finally, a numerical example is provided to demonstrate the effectiveness and practical applicability of the proposed approach.

Affiliations: Key Laboratory of Grain Information Processing and Control, Ministry of Education, the Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making, the School of Artificial Intelligence and Big Data, Institute for Complexity Science, Henan University of Technology, Zhengzhou, China; Faculty of Data Science, City University of Macau, Macau, China; School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China; School of Artificial Intelligence and Computer Science, Nantong University, Nantong, China; School of Artificial Intelligence, Optics and Electronics and the School of Cybersecurity, Northwestern Polytechnical University, Xi’an, China; School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

Abstract:
The broad learning system (BLS) is a lightweight neural network known for its efficient learning capabilities; however, it is limited by its reliance on a binary label strategy. Existing label enhancement models primarily focus on increasing the distances between labels from different classes, which inadvertently expands the distance within the same category. For classification tasks, maintaining similarity within the intraclass is essential for ensuring the model’s effectiveness. To address this issue, we propose a groupwise label enhancement BLS model that ensures both intraclass similarity and interclass disparity of labels. Specifically, we develop a novel regression target that generalizes existing label enhancement targets in BLS, increasing the distances between labels of different classes while overcoming the constraints imposed by binary labels. Moreover, we design a groupwise constraint to jointly enhance the intraclass similarity and interclass disparity of labels. Additionally, we propose a novel alternating direction method of multipliers-based optimization algorithm to solve our proposed model, ensuring both computational efficiency and theoretical convergence. Experimental results on several public datasets demonstrate the outstanding effectiveness and efficiency of our proposed model compared to other state-of-the-art methods.

Abstract:
This article proposes a novel multicontroller-based fault tolerance method to cope with a class of high-order sub-fully actuated systems (sub-FASs) with nonlinear uncertainties and actuator faults. As a promising control-oriented theory, the FAS approach is a convenient and powerful tool for nonlinear control. However, the stabilization of sub-FASs, whose input matrix function is not globally invertible, is more sophisticated and challenging due to the issue of control singularity. To address the global fault-tolerant stabilization of uncertain sub-FASs, a high-order nonlinear system model with both multiplicative and additive actuator faults is considered. By introducing the concepts of linear singular set and singularity function, the entire state space is analytically divided into several regions. Then, according to the initial states of system, three different control strategies are developed to overcome singularity and achieve global stabilization, including a FAS-based stabilizing control law, a singularity-avoid tracking control strategy, as well as a singularity-free switching control strategy. The closed-loop response of the faulty system is proven to be ultimately uniformly bounded in all cases, and the effectiveness of proposed method is illustrated through a numerical example.

Abstract:
In the field of multiagent reinforcement learning (MARL), the ability to effectively explore unknown environments and collect information and experiences that are most beneficial for policy learning represents a critical research area. However, existing work often encounters difficulties in addressing the uncertainties caused by state changes and the inconsistencies between agents’ local observations and global information, which presents significant challenges to coordinated exploration among multiple agents. To address this issue, this article proposes a novel MARL exploration method with Min-Max intrinsic motivation (E2M) that promotes the learning of joint policies of agents by introducing surprise minimization and social influence maximization. Since the agent is subject to unstable state changes in the environment, we introduce surprise minimization by computing state entropy to encourage the agents to cope with more stable and familiar situations. This method enables surprise estimation based on the low-dimensional representation of states obtained from random encoders. Furthermore, to prevent surprise minimization from leading to conservative policies, we introduce mutual information between agents’ behaviors as social influence. By maximizing social influence, the agents are encouraged to interact to facilitate the emergence of cooperative behavior. The performance of our proposed E2M is testified across a range of popular StarCraft II and Multiagent MuJoCo tasks. Comprehensive results demonstrate its effectiveness in enhancing the cooperative capability of the multiple agents.

Abstract:
Graph anomaly detection (GAD) has attracted increasing interest due to its critical role in diverse real-world applications. Graph neural networks (GNNs) offer a promising avenue for GAD, leveraging their exceptional capacity to model complex graph structures and relationships. However, existing GNN-based models encounter challenges in addressing the GAD’s fundamental issue—anomaly camouflage, where anomalies mimic normal instances, leading to indistinguishable features. In this article, we propose a novel approach, termed correlation information enhanced GAD (CIE-GAD). Specifically, drawing on the observation that the distribution of homophilic and heterophilic edges differs between abnormal and normal samples, we construct a hypergraph to learn the co-occurrence relationships among adjacent edges. By enhancing the extraction of sample correlation information, we effectively tackle feature similarity caused by anomaly camouflage, thereby enhancing the performance of GAD. Furthermore, we develop a spectral convolution mechanism based on node-level attention fusion, enabling the capture of multifrequency signals. This module performs adaptive fusion tailored to the unique frequency information requirements of each node, mitigating the local heterophily problem. Extensive experiments on various real-world GAD datasets demonstrate that the proposed CIE-GAD outperforms state-of-the-art methods. Notably, our approach achieves AUC-PR improvements of up to 3.47%, with an average gain of 1.5%, demonstrating its effectiveness in detecting anomalies in graph data.

Abstract:
The underlying time-variant and subject-specific brain dynamics lead to inconsistent distributions in electroencephalogram (EEG) topology and representations within and between individuals. However, current works primarily align the distributions of EEG representations, overlooking the topology variability in capturing the dependencies between channels, which may limit the performance of EEG emotion recognition. To tackle this issue, this article proposes an adaptive attention-modulated graph network (AdamGraph) to enhance the subject adaptability of EEG emotion recognition against connection variability and representation variability. Specifically, an attention-modulated graph connection module is proposed to explicitly capture the individual important relationships among channels adaptively. Through modulating the attention matrix of individual functional connections using spatial connections based on prior knowledge, the attention-modulated weights can be learned to construct individual connections adaptively, thereby mitigating individual differences. Besides, a deep node-graph representation learning module is designed to extract long-range interaction characteristics among channels and alleviate the over-smoothing problem of representations. Furthermore, a graph domain co-regularized learning module is imposed to tackle the individual distribution discrepancies in connection and representations across different domains. Extensive experiments on three public EEG emotion datasets, i.e., SEED, DREAMER, and MPED, validate the superior performance of AdamGraph compared with state-of-the-art methods.

Abstract:
Rhythm is an inherent aspect of human behavior, present from infancy and embedded in cultural practices. At the core of rhythm perception lies meter anticipation, a spontaneous process in the human brain that typically occurs before actual beats. This anticipation can be framed as a time series prediction problem. From the perspective of human embodied system behavior, although many models have been developed for time series prediction, most prioritize accuracy over biological realism, contrasting with the natural imprecision of human internal clocks. Neuroscientific evidence, such as infants’ natural meter synchronization, underscores the need for biologically plausible models. Therefore, we propose a neuron oscillator-based dynamic system that simulates human behavior during meter perception. The model introduces two tunable parameters for local and global adjustments, fine-tuning the oscillation combinations to emulate human-like rhythmic behavior. The experiments are conducted under three common scenarios encountered during human-machine interaction, demonstrating that the proposed model can exhibit human-like reactions. Additionally, experiments involving human-machine and interhuman interactions show that the model successfully replicates real-world rhythmic behavior, advancing toward more natural and synchronized human-machine rhythm interaction.

Affiliations: Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong, SAR, China; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China; College of Computer Science and the Chongqing Key Laboratory of Big Data Intelligence and Privacy Computing, Chongqing University, Chongqing, China; Civil Engineering School, Qingdao University of Technology, Qingdao, China; Department of Computer Science, City University of Hong Kong, Hong Kong, SAR, China

Abstract:
Despite the increasing interest in sequential transfer optimization (STO), a comprehensive benchmark suite for systematically comparing various STO algorithms remains underexplored. Existing test problems, which are often manually configured and lack scalability, can result in biased and nongeneralizable algorithm performance. In light of the above, we first introduce four concepts for characterizing STO problems (STOPs) in this study and present an important feature, namely similarity distribution, to quantitatively delineate the relationship between the optimal solutions of source and target tasks. Subsequently, we present general design guidelines for STOPs and introduce a problem generator that demonstrates strong scalability. Specifically, the similarity distribution of a problem can be easily customized through a novel inverse generation strategy, allowing for a continuous spectrum that captures the diverse similarity relationships present in real-world scenarios. Lastly, a benchmark suite comprising 12 STOPs, characterized by a range of customized similarity relationships, has been developed using the proposed generator and will serve as a platform for examining various STO algorithms. For instance, biased transferability representation, irregular mapping learning behaviors, and performance improvements unrelated to search experience are significant empirical findings that previous benchmarks failed to reveal, yet can be effectively identified through our test problems. The source code of the proposed problem generator is available at https://github.com/XmingHsueh/STOP-G.

Abstract:
Over the past decade, numerous synchronization-based identification methods have been proposed to address the challenge of identifying unknown network topologies. The linear independence condition (LIC) is an essential requirement in these methods, however, there are issues with this condition. In this article, we propose an improved LIC-free synchronization-based identification method to address above issues. Specifically, a drive network consisting of isolated nodes that satisfy specific conditions is constructed, and the network containing an unknown topology is defined as the response network. Through the design of appropriate controllers and update laws, the drive network and the response network achieve synchronization, while the estimation matrix accurately identifies the unknown topology matrix. Our method is proven to be a generalized form of the existing LIC-free identification methods. Furthermore, we introduce a novel proof framework to theoretically demonstrate the effectiveness of our method. Finally, two simulation examples demonstrate the effectiveness of the proposed method.

Abstract:
This article presents a bearing-based formation control method for autonomous aerial vehicle (AAV) swarms, allowing users to specify both convergence time and precision in advance. Unlike traditional distance-based methods, which rely on intricate distance measurements, our approach simplifies constraints using bearing information, reducing hardware and sensing requirements. It also eliminates the need to update control commands for each AAV, as formation reconfiguration can be achieved solely by adjusting the motion trajectory of formation leaders. Moreover, the strategy demonstrates enhanced robustness in addressing real-world input constraints. A continuous hyperbolic tangent saturation function and an input saturation compensation system are incorporated, ensuring system convergence and precision while addressing singularity issues. In addition, unlike conventional bearing-based strategies focusing primarily on convergence time, the proposed algorithm enables preset control over both convergence time and precision. Finally, the effectiveness of the proposed approach is validated through several illustrative examples, including a 6-degree-of-freedom (6DoF) quadrotor AAV swarm, highlighting its practical applicability and performance.

Abstract:
This article investigates the event-triggered (ET) almost output regulation (ETAOR) issue for the switched T-S fuzzy (T-SF) systems with both output regulation (OR) characteristic and L_2 gain characteristic considered. First, in order to conserve communication resources, an ET mechanism and an ET switched fuzzy feedback controller are devised. Then, the definition of the ETAOR issue for the switched T-SF systems is presented. Next, with the relaxed assumption of the same coordinate transformation, the ETAOR issue of the switched T-SF systems is transformed into the ET H_\infty control problem of the switched T-SF systems. Further, by using the multiple Lyapunov functions approach, a solvability condition on the ETAOR issue is established for the switched T-SF systems with the average dwell-time related switching signals. Such condition is also suitable for nonswitched T-SF systems. In addition, Zeno behavior may be caused by the ET programme is excluded. Finally, the presented control method is applied to an aero-engine case study to corroborate its effectiveness.

Abstract:
This article investigates a noncooperative game of multiagent systems in incomplete information scenarios. To cooperatively seek the Nash equilibrium (NE), each agent aims to minimize its own cost function by interacting with its neighbors over undirected communication networks. While existing distributed NE seeking methods alleviate the computational burden, they also entail higher communication costs. To reduce communication frequency and bandwidth, we propose a class of distributed edge-based NE seeking methods by leveraging the advantages of event-triggered mechanisms and quantization techniques. In the proposed framework, a buffer is equipped on every communication channel, thereby reducing the workload of both agents at either end. It is shown that the convergence error can be made arbitrarily small by tuning a constant threshold, and it can asymptotically converge to zero by setting an exponentially decaying threshold or a dynamic threshold. Moreover, in the case of unawareness of any global information, we further provide a fully distributed event-triggered quantized algorithm, by which the convergence error is ultimately uniformly bounded. Finally, two numerical examples are utilized to illustrate the effectiveness of the proposed algorithms.

Abstract:
The prescribed-time output regulation problem is investigated for a class of uncertain nonlinear multiagent systems (MASs) subject to limited communication resources. To address this challenge, an event-based distributed neuro-adaptive prescribed-time control scheme comprising distributed prescribed-time observers, a dynamic event-triggered mechanism (DETM), and neuro-adaptive prescribed-time controllers is developed. Specifically, a distributed prescribed-time observer is constructed for each agent using event-based communications to estimate the states of the exosystem. The constructed observer operates without requiring prior knowledge of the exosystem dynamics or global information, ensuring that observation errors converge to a small neighborhood around zero within a user-determined time interval. Additionally, the incorporation of the DETM guarantees a positive lower bound on the interexecution intervals, thereby alleviating the communication demands. Building on this observer, neuro-adaptive prescribed-time controllers are derived for each agent, capable of maintaining the system states within a user-defined compact set without the need for prior knowledge of the initial system states. It is demonstrated that the regulated outputs converge to a region arbitrarily tuned by the user within a prescribed time, with all signals remaining bounded and Zeno behavior eliminated. Finally, two examples are exhibited to verify the effectiveness of the obtained results.

Abstract:
The main challenge when solving constrained multiobjective optimization problems (CMOPs) with intricate constraints and high dimensionality is how to overcome a problem of irregular and variable-shaped objective search regions. Such regions can lead to problems of local optimization and uneven distribution of feasible solutions. To overcome these challenges, an efficacious search method is usually needed to improve the efficiency of searching optimal solution and utilization of data structure used to store nondominated vectors. The originality of this work comes with a creative and novel design of Kriging surrogate model-based simplex crossover operator (KSCO) and Kriging surrogate model-based local search of simplex crossover operator (KLSSCO). KSCO is used to calculate the speed update equation, as well as the coefficients of the equation. KLSSCO is employed to decide which particle is treated as third particle participating in the speed update equation. A constrained multiobjective particle swarm optimization (PSO) based on KSCO and KLSSCO is proposed to solve the CMOP with local optimization and uneven distribution problems, namely KSCO and KLSSCO-based constrained multiobjective PSO algorithm (KCMOPSO). This ensures that the algorithm can search the infeasible and feasible regions of constrained multiobjective problems accurately and accelerate the convergence of the algorithm. The experimental results show that the proposed algorithm is more effective compared with the existing elite method.

Abstract:
State estimation of cyber-physical power systems (CPPSs) is of great significance for power system optimization, control, and security analysis. Additionally, fractional differential calculus is based on differentiation and integration of arbitrary fractional order, which can more accurately describe the physical phenomenon model than the traditional integer calculus. Thus, this article proposes a novel fractional-order extended Kalman filter (FOEKF) based on the evolutionary algorithm and deep ensemble learning techniques for the state estimation problem of CPPSs from the fractional-order theory perspective. First, the power system is modeled as a fractional version to describe the physical phenomenon better according to the fractional differential calculus theory. Then, considering the difficulties in determining fractional orders in the fractional-order power system, a deep ensemble learning-based approach is used to design the fitness function and a genetic algorithm is developed to determine these parameters by optimizing the designed objective function. Furthermore, to solve the difficulties in estimating for fractional-order power system by integral extended Kalman filter (EKF), the evolutionary FOEKF (EFOEKF) is presented as the estimator for the designed fractional-order power system. Finally, to improve the performance of EFOEKF under bad datum scenarios caused by cyber-attacks or sudden loads, an enhanced EFOEKF method is developed by using an adapted exponential weighting function. The numerical simulation results show that the proposed EFOEKF is better than EKF and FOEKF on four different IEEE bus systems in terms of the mean absolute error.

Affiliations: Research Institute of Interdisciplinary Intelligent Science, Ningbo University of Technology, Ningbo, China; Research Center of Intelligent Control and Systems, Yongjiang Laboratory, Ningbo, China; Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China; School of Aerospace Science and Technology, Xidian University, Xi’an, China; Ningbo Institute of Intelligent Equipment Technology Company Ltd., Ningbo, China

Abstract:
In the field of smart surface mount technology (SMT) production, integrating machines through a cyber-physical system (CPS) architecture holds significant potential for improving assembly quality and efficiency. However, fully unifying inspection and production systems to effectively address assembly-related quality issues remains a challenge. This study seeks to close these gaps by introducing collaborative optimization methods to ensure seamless operations. The research is driven by the need for precise control of key assembly parameters, such as placement height, x-offset, y-offset, rotation angle deviations, and blowing durations, all of which are major contributors to defects. To address these challenges, we propose a self-adaptive collaborative optimization (SACO) framework that prioritizes enhancements based on their impact on both quality and efficiency. The SACO framework combines customized Bayesian optimization and particle swarm optimization techniques, allowing for dynamic adjustments to process parameters, guided by real-time data from automatic optical inspection (AOI) systems. The primary goal of this study is to reduce defects and improve efficiency in the SMT assembly process through these targeted improvements. Experimental results validate the effectiveness of the proposed methods, demonstrating significant advancements in placement accuracy and overall assembly efficiency. Our findings confirm that the SACO framework provides a robust solution to persistent challenges in SMT production, addressing critical gaps in quality control and process optimization.

Abstract:
Constrained multiobjective optimization problems are widespread in practical engineering fields. Scholars have proposed various effective constrained multiobjective evolutionary algorithms (CMOEAs) for such problems. However, most existing algorithms overlook the differences between different decision variables in influencing the degree of constraint violation and still lack an effective handling mechanism for constraint-sensitive variables. To address this issue, a two-stage cooperation multiobjective evolutionary algorithm guided by constraint-sensitive variables (CV-TCMOEA) is proposed. In the first stage, a relatively simple auxiliary problem with only a few dominant constraints is constructed to approximate the original problem. After obtaining a set of approximate Pareto optimal solutions by dealing with the auxiliary problem, in the second stage, a constraint-sensitive variable-guided multistrategy cooperation search method is developed. In this method, decision variables are divided into two types: 1) constraint-sensitive and 2) constraint-insensitive variables, and a variable-type-guided cooperative individual update strategy is proposed to autonomously select appropriate search strategies for different types of variables. Experimental results on 28 benchmark functions and 10 engineering problems demonstrated the superiority of the CV-TCMOEA over seven state-of-the-art CMOEAs.

Abstract:
Undoubtedly, machine learning (ML) has demonstrated a wealth of far-reaching successes present both at the level of fundamental developments, design methodologies and numerous application areas, quite often encountered in domains requiring a high level of autonomous behavior. Over the passage of time, there are growing challenges of privacy and security, interpretability, explainability, confidence (credibility), and computational sustainability, among others. In this study, we advocate that these quests could be addressed by casting them both conceptually and algorithmically in the unified environment augmented by the principles of granular computing. It is demonstrated that the level of abstraction, delivered by granular computing plays a pivotal role in the interpretation by quantifying the level of credibility of ML constructs. The study also highlights the principles of granular computing and elaborates on its landscape. The original idea of a comprehensive and unified framework of data-knowledge environment of ML is introduced along with a detailed discussion on how data and knowledge are used in a seamless fashion by invoking granular embedding and producing relevant loss functions. Key categories of knowledge-data integration realized at the levels of data and model (involving symbolic/qualitative models and physics-oriented models) and investigated.

Abstract:
The 3-D point cloud has been widely used in many mobile application scenarios, including autonomous driving and 3-D sensing on mobile devices. However, existing 3-D point cloud models tend to be large and cumbersome, making them hard to deploy on edged devices due to their high memory requirements and nonreal-time latency. There has been a lack of research on how to compress 3-D point cloud models into lightweight models. In this article, we propose a method called T3DNet (tiny 3-D network with augmentation and distillation) to address this issue. We find that the tiny model after network augmentation is much easier for a teacher to distill. Instead of gradually reducing the parameters through techniques, such as pruning or quantization, we predefine a tiny model and improve its performance through auxiliary supervision from augmented networks and the original model. We evaluate our method on several public datasets, including ModelNet40, ShapeNet, and ScanObjectNN. Our method can achieve high compression rates without significant accuracy sacrifice, achieving state-of-the-art performances on three datasets against existing methods. Amazingly, our T3DNet is 58× smaller and 54× faster than the original model yet with only 1.4% accuracy descent on the ModelNet40 dataset. Our code is available at https://github.com/Zhiyuan002/T3DNet.

Abstract:
This article investigates stochastic differential games involving multiple attackers, defenders, and a single target, with their interactions defined by a distributed topology. By leveraging principles of topological graph theory, a distributed design strategy is developed that operates without requiring global information, thereby minimizing system coupling. Additionally, this study extends the analysis to incorporate stochastic elements into the target-attackers–defenders games, moving beyond the scope of deterministic differential games. Using the direct method of completing the square and the Radon-Nikodym derivative, we derive optimal distributed control strategies for two scenarios: one where the target follows a predefined trajectory and another where it has free maneuverability. In both scenarios, our research demonstrates the effectiveness of the designed control strategies in driving the system toward a Nash equilibrium. Notably, our algorithm eliminates the need to solve the coupled Hamilton-Jacobi equation, significantly reducing computational complexity. To validate the effectiveness of the proposed control strategies, numerical simulations are presented in this article.

Abstract:
This article introduces a novel approach for spacecraft formation flying utilizing Lorentz-augmented techniques. It demonstrates that the relative motion among spacecraft, driven by the Lorentz force, possesses equilibrium states beneficial for formation maintenance. However, for effective formation reconfiguration, reliance solely on the Lorentz force is insufficient; low thrust is also necessary. To address this, this article proposes an optimal control framework based on reinforcement learning (RL). It derives the nonlinear dynamics of relative motion within the geomagnetic field, considering intersatellite Lorentz force, atmospheric drag, and Earth’s gravitational harmonics. The study employs Lagrangian coherent structure analysis to identify relative equilibrium configurations and develops an RL-based optimal control strategy for real-time formation reconfiguration. By leveraging optimal demonstrations, the framework guides the agent’s actions to match these demonstrations over time, especially when encountering out-of-distribution states. Numerical simulations confirm the method’s optimality, robustness, and real-time performance, highlighting its potential in achieving optimal control and adapting to varying environment in future space missions.

Abstract:
Underwater areas have harsh environments with poor light, limited visibility, and high levels of noise. Humans have a weak perception of position, surroundings, and exterior information when staying underwater, which makes it difficult for humans to carry out complex underwater tasks, such as rescue, observation, and construction. Wearable devices have shown good results in enhancing human sensory function on land, thus they could potentially play a role in enhancing human underwater perception ability. This perspective aims to analyze the state-of-the-art of underwater wearable systems for human perception enhancement. This work discusses the core technology and challenges of human underwater perceptual enhancement, including wearable underwater navigation, underwater environment reconstruction, and underwater sensorial information delivery. Future research could focus on designing waterproof flexible human-machine interfaces for sensing and feedback, exploiting advanced sensors and fusion algorithms for wearable underwater positioning, and studying multimodal information interaction strategies of wearable systems.

Affiliations: School of Automation, Northwestern Polytechnical University, Xi’an, China; School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, China; School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China; School of Software, Yunnan University, Kunming, China; School of Cybersecurity and the School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University, Xi’an, China; Department of Basic Theoretical Research, Shanghai Artificial Intelligence Laboratory, Shanghai, China

Abstract:
In this article, we introduce a new theory for payoff control in multichannel learning environments, where agents interact with each other over multiple channels and each channel is a repeated normal form game. We propose two payoff control strategies—partial control and full control—that allow a single agent to set an upper bound to the opponent’s expected payoffs summed across all channels, even if the opponent is a reinforcement learning agent. We prove that a partial (or full) control strategy can be obtained by solving a system of inequalities, and characterize the conditions under which such a partial (or full) control strategy exists. We show that by utilizing these control strategies, the agent can influence the opponent’s learning evolution and direct it toward a desired viable equilibrium. Our experiments confirm the effectiveness of our theory for payoff control in a wide range of multichannel learning environments.

Abstract:
Surrogate-assisted evolutionary algorithms (SAEAs) are extensively used to tackle expensive optimization problems (EOPs). The integration of surrogate-based global and local search is a prevalent hierarchical SAEA framework, which can effectively balance exploration and exploitation capabilities. However, it still faces challenges when tackling high-dimensional EOPs (HEOPs) owing to the curse of dimensionality. In this article, we propose a hierarchical surrogate-assisted differential evolution with core space localization (HSADE-CS) to solve HEOPs. Its contributions are listed as follows: 1) a top-promising sampling strategy is introduced in the global search to mitigate the challenges posed by the uncertainty in the performance of the surrogate model; 2) a core space localization (CSL) method is proposed to identify a high-potential space within the local promising region, enhancing the effectiveness of local search; and 3) a fitness-independent adaptive parameter control method based on the Minkowski distance is developed within the differential evolution (DE) optimizer to improve the performance of surrogate model-driven local search. The performance of HSADE-CS has been validated on numerous benchmark problems from the commonly used expensive optimization benchmark suite, as well as the CEC2014 and CEC2017 benchmark suites, with problem dimensions up to 500. It has also been tested on a real-world problem, i.e., circular antenna array design optimization. Experimental results demonstrate that HSADE-CS is highly competitive compared to the state-of-the-art SAEAs.

Abstract:
The event-triggered control strategy is capable of significantly reducing the number of control task executions while achieving desired control objectives, such as stability. In this article, we introduce a novel learning-based method for event-triggered model predictive control with initially unknown dynamics. The formulation of optimal control problems (OCPs) is based on predictive states derived from Gaussian process (GP) regression under terminal constraints. The event-triggered condition proposed in this article is derived from the recursive feasibility, so that the OCPs are solved only when an error between the predictive and the actual states exceeds a certain threshold. This article analyzes the convergence of the closed-loop system under the event-triggered condition, demonstrating that the system’s state will enter the terminal set within a finite time, assuming small-enough uncertainty in the GP model. We validate this approach through a tracking control problem, illustrating its practical effectiveness.

Abstract:
Feature selection (FS) is a significant research topic in machine learning and artificial intelligence, but it becomes complicated in the high dimensional search space due to the vast number of features. Evolutionary computation (EC) has been widely used in solving FS by modeling it as an expensive wrapper-form optimization task, where a classifier is used to obtain classification accuracy for fitness evaluation (FE). In this article, we propose that the FS problem can be also modeled as a cheap filter-form optimization task, where the FE is based on the relevance and redundancy of the selected features. The wrapper-form optimization task is beneficial for classification accuracy while the filter-form optimization task has the strength of a lighter computational cost. Therefore, different from existing multitask-based FS that uses various wrapper-form optimization tasks, this article uses a multiform optimization technique to model the FS problem as a wrapper-form optimization task and a filter-form optimization task simultaneously. An evolutionary multitask FS (EMTFS) algorithm for parallel tacking these two tasks is proposed followed by, in which a two-channel knowledge transfer strategy is proposed to transfer positive knowledge across the two tasks. Experiments on widely used public datasets show that EMTFS can select as few features as possible on the premise of superior classification accuracy than the compared state-of-the-art FS algorithms.

Abstract:
In this article, an integral reinforcement learning (IRL) method is developed for dynamic event-triggered nonzero-sum (NZS) games to achieve the Nash equilibrium of unmanned surface vehicles (USVs) with state and input constraints. Initially, a mapping function is designed to map the state and control of the USV into a safe environment. Subsequently, IRL-based coupled Hamilton-Jacobi equations, which avoid dependence on system dynamics, are derived to solve the Nash equilibrium. To conserve computational resources and reduce network transmission burdens, a static event-triggered control is initially designed, followed by the development of a more flexible dynamic form. Finally, a critic neural network is designed for each player to approximate its value function and control policy. Rigorous proofs are provided for the uniform ultimate boundedness of the state and the weight estimation errors. The effectiveness of the present method is demonstrated through simulation experiments.

Affiliations: State Key Laboratory of Synthetical Automation for Process Industries and the International Joint Research Laboratory of Integrated Automation, Northeastern University, Shenyang, China; Electrical and Computer Engineering Department, University of Florida, Gainesville, FL, USA; Electrical and Computer Engineering Department, Auburn University, Auburn, AL, USA; UTA Research Institute, University of Texas at Arlington, Fort Worth, TX, USA

Abstract:
This article proposes inverse reinforcement learning (IRL) algorithms for tracking control of linear networked control systems under random state dropouts during wireless transmission. The controlled system aims to track the optimal trajectory of a target system, despite the cost function governing the target’s behaviors being unknown. The problem is complicated by random state dropouts occurring in two crucial scenarios: 1) the reception of the target’s state and 2) feedback of the controlled system’s states. Our approach enables the controlled system to infer the target’s cost function and optimal control policy, thereby facilitating effective tracking. Specifically, we develop a model-based IRL algorithm that integrates the Smith predictor for state estimation. Then, we advance a state-dropout-aware inverse Q-learning algorithm that uses solely accessible system data, eliminating the need for system models. The theoretical validity of the proposed algorithms is rigorously established, and their practical effectiveness is validated through numerical simulations.

Affiliations: School of Artificial Intelligence and Automation and the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, and the Hubei Key Laboratory of Brain-Inspired Intelligent Systems, Huazhong University of Science and Technology, Wuhan, China; Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China; Science Program, Texas A&M University at Qatar, Doha, Qatar

Abstract:
This study investigates the semi-global fixed-time stability (SGFTS) and global fixed-time stability (GFTS) of nonlinear impulsive systems (NISs). A key challenge in analyzing the SGFTS of such systems lies in the evolving integration methods caused by the impulses. To address this, we dynamically partition the semi-global attraction set (SGAS) and solve the corresponding differential equations within each subset. Additionally, by constructing the transition dynamics of impulse points and iteratively computing these points, we establish the conditions for SGFTS under both stabilizing and destabilizing impulses. For GFTS, the primary difficulty arises from the distinct trajectories and dynamics of points located inside and outside the SGAS. To overcome this, we introduce the concept of the maximum-minimum impulse interval and derive a sufficient condition that ensures the system can enter the SGAS from a distance under a finite number of impulses. Furthermore, we develop a criterion for GFTS under varying impulse degrees and provide convergence time estimation based on the research on SGFTS of NIS. Finally, numerical examples are presented to validate the theoretical results. Notably, in Example 3, a fixed-time impulse controller is designed based on the proposed theoretical framework to achieve global stabilization of complex systems. This example highlights the potential applications of this work in the field of control.

Abstract:
Effective fault diagnosis is crucial for maintaining the reliability and safety of industrial systems. Incremental learning, which enables models to continuously update and adapt to new data or emerging fault classes without complete retraining, has recently gained attention as a promising solution for addressing nonstationary data streams in fault diagnosis applications. Nevertheless, most existing review articles on fault diagnosis adopt a broad perspective, primarily discussing general techniques such as deep learning and transfer learning, without providing a dedicated focus on incremental learning strategies. To the best of our knowledge, it is the first review focusing specifically on incremental learning-enabled fault diagnosis methods. In this work, state-of-the-art incremental learning-enabled fault diagnosis are systematically reviewed. These methods are categorized into distinct groups based on their incremental learning strategies and application contexts. In addition, major challenges associated with applying incremental learning to fault diagnosis, including concept drift and catastrophic forgetting, are discussed, along with emerging solutions proposed to address these issues. A novel taxonomy and perspective on incremental learning-enabled fault diagnosis approaches is presented, providing a timely and comprehensive reference for researchers and practitioners in this evolving field.

Abstract:
This article addresses the zonotope-based \mathcal L_\infty event-triggered control for switched positive systems with frequent asynchronism and interval uncertainties. In view of the advantages of zonotope in set description, we extend it to the set-based control, propose a novel zonotope-based control method, and further consider the typical model uncertainty and frequent asynchronism. First, by introducing a 1-norm-based event-triggered scheme, the time-varying state zonotope is established based on the closed-loop system and event-triggering conditions. Second, the dual convergence and \mathcal L_\infty performance of the positive state zonotope is analyzed by defining suitable center and radius functions for matched and mismatched intervals, respectively. Then, the permissible mode-dependent average dwell time signals and asynchronous controllers are jointly designed to guarantee the stability and \mathcal L_\infty performance for the underlying system. Finally, the effectiveness and advantages of the proposed method are demonstrated through a numerical example.

Abstract:
This article investigates resilient source seeking problem of second-order multirobot systems (MRSs) under mixed cyberattacks, which consist of misbehaving and Denial-of-Service (DoS) attacks. The misbehaving attacks can cover several types of malicious attacks, such as false data injection, stubborn, and Byzantine, while the network connectivity may be compromised by DoS attacks, potentially resulting in a time-varying and disconnected digraph. To this end, a resilient source seeking algorithm is proposed by designing an auxiliary point for each agent such that the coordination problem is transformed into a point tracking one. A reference velocity is calculated to guide benign robots toward the source, leveraging their historically optimal positions with the highest signal strength. This ensures the auxiliary points converge to the source, clustering benign robots nearby. When DoS attacks occur on some edges, the latest sampling data acquired before the attacks is used to hold the control signals for the robots. Then, sufficient conditions are established through rigorous stability analysis. In comparison to existing methods, the proposed approach extends the safe-kernel-based resilient consensus algorithms to a resilient source seeking algorithm for a general discrete-time second-order dynamics, while also can withstand a mixed cyberattack comprising both misbehaving and DoS attacks. Finally, simulation and experimental results are presented to validate the efficacy of the proposed algorithm.

Affiliations: College of Computer Science and the National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China; Department of Radiology, West China Hospital, Sichuan University, Chengdu, China; Saw Swee Hock School of Public Health, National University of Singapore, Cluny Road, Singapore; Department of Mathematics, Sichuan University, Chengdu, China; Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Fusionopolis, Singapore; Center for Innovation and Precision Eye Health and the Yong Loo Lin School of Medicine, National University of Singapore, Cluny Road, Singapore

Abstract:
Medical image segmentation is critical for disease diagnosis and treatment assessment. However, concerns regarding the reliability of segmentation regions persist among clinicians, mainly attributed to the absence of confidence assessment, robustness, and calibration to accuracy. To address this, we introduce deep evidential segmentation model (DEviS), an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. DEviS not only enhances the calibration and robustness of baseline segmentation accuracy but also provides high-efficiency uncertainty estimation for reliable predictions. By leveraging subjective logic theory, we explicitly model probability and uncertainty for medical image segmentation. Here, the Dirichlet distribution parameterizes the distribution of probabilities for different classes of the segmentation results. To generate calibrated predictions and uncertainty, we develop a trainable calibrated uncertainty penalty. Furthermore, DEviS incorporates an uncertainty-aware filtering (UAF) module, which designs the metric of uncertainty-calibrated error to filter out-of-distribution (OOD) data. We conducted validation studies on publicly available datasets, including ISIC2018, KiTS2021, LiTS2017, and BraTS2019, to assess the accuracy and robustness of different backbone segmentation models enhanced by DEviS, as well as the efficiency and reliability of uncertainty estimation. Additionally, two potential clinical trials were conducted using the UAF module. The clinical application conducted on the Johns Hopkins OCT and Duke OCT-DME datasets demonstrated the effectiveness of the model in filtering OOD data. The second trial evaluated its efficacy in filtering high-quality data on the FIVES datasets. At last, the proposed DEviS method was extended to semi-supervised medical image segmentation, where it exhibited strong robustness under noisy conditions. Our code has been released in https://github.com/Cocofeat/DEviS.

Abstract:
Anomaly detection for time-series data has been viewed widely in many practical applications and caused lots of research interests. A popular solution based on deep learning techniques is first building an anomaly detection model by offline learning on a specific training data and subsequently utilizing the model to detect anomalies in the online setting. On the one hand, previous works have introduced a plenty of learning methods for building anomaly detection models, but it is well known that no one can perform best in all settings. Therefore, there is usually high demand for collecting multiple detection models in practical applications. On the other hand, in view of the limited computing resources in many online anomaly detection applications, it is almost impossible to run multiple detection models simultaneously due to the high time cost. Then, a natural idea is to enhance the general anomaly detection procedure with an effective mechanism for selecting proper models to avoid the high cost caused by executing too many detection models. Previous works focusing on the recommendation either are time inefficient or usually show weak performance, suffering from the missing labels and heterogeneous data characteristics met in real applications. Therefore, it is highly needed to design effective recommendation methods for automatically choosing anomaly detection models. Motivated by the technical challenges, a novel recommendation method for anomaly detection models is proposed in this article. First, a model recommendation framework based on the concept of detectability is introduced, where the detectability of an anomaly detection model is defined using a fine-grained strategy for comparing data characteristics. Then, based on efficient techniques for computing detectability, an efficient model recommendation algorithm is designed. Finally, extensive experimental results are produced on real time series and typical anomaly detection methods, and show that the proposed method is both effective and efficient.

Abstract:
This article investigates open-loop and feedback solutions to linear quadratic mean field (MF) games with a leader and many followers. The leader first gives its strategy and then all the followers cooperate to optimize the social cost as the sum of their costs. By variational analysis with MF approximations, we obtain a set of open-loop controls of players in terms of solutions to MF forward–backward stochastic differential equations (FBSDEs), which is further shown be to an asymptotic Stackelberg-team equilibrium. By applying the matrix maximum principle, a set of decentralized feedback strategies is constructed for all the players. For open-loop and feedback solutions, the corresponding costs of all players are explicitly given by virtue of the solutions to two Riccati equations, respectively. The performances of two solutions are compared by the numerical simulation.

Abstract:
This article presents an approach to ensure the robust forward invariance of safe sets for sampled-data input nonlinear dynamical systems with model uncertainties. We first design a continuous-time composite controller structure for the uncertain system by integrating an uncertainty compensation term and a state feedback term. The uncertainty compensation term is generated by a nonlinear observer, while the feedback term is subject to linear constraints on a high order control barrier function (HOCBF) which effectively mitigates the adverse effects of the uncertainty observation error on the safety constraints. Then, inspired by the continuous-time controller, a sampled-data controller is proposed where the feedback control term is obtained by solving a new quadratic program (QP) problem with modified HOCBF constraints to address the challenges posed by sampled-data input. Sufficient conditions are derived to guarantee the robust forward invariance of the safe sets for the sampled-data nonlinear dynamical system. From the simulation experiments, it is demonstrated that the proposed method successfully ensures the safety of the sampled-data input dynamical systems with model uncertainties.

Abstract:
This article presents a comprehensive theoretical framework for addressing the problem of secure fusion estimation in energy-constrained multisensor systems, specifically targeting hybrid attacks in multiple transmission levels. The lifespan of sensor nodes is constrained by the availability of energy supply, and all the sensors have the flexibility to choose between high-energy or low-energy levels for transmitting their measurements. Sensor data becomes vulnerable to malicious tampering when operating in a low-energy level, whereas the high-energy transmission level enables accurate data transmission. By introducing a set of Bernoulli random variables and ternary random variables, a novel measurement model is proposed to characterize scenarios involving both dual-energy transmission modes and hybrid attacks, including three statuses: safe, deception attacks, and Denial-of-Service attacks. Based on the innovation analysis approach, local secure estimators are designed to ensure that the estimation errors are minimized locally. Then, an optimal secure fusion algorithm is provided to generate the final estimated value by fusing all the local estimates. Additionally, the proposed secure fusion estimation algorithm’s stability and steady-state properties are investigated. Finally, two simulation cases are conducted to provide the empirical evidence of the superior performance of the proposed approach.

Abstract:
During the distributed thermal process, frequent model updates (MUs) and controller activations can lead to worse performance due to over-computation. To address this problem, a dual event-triggered spatial model predictive control (DET-SMPC) under a data-driven framework is investigated for distributed thermal processes to achieve good global performance. The spatiotemporal model is built utilizing the time/space theorem and updated to accommodate the time-varying system dynamics. Since the controller effect will be affected when the model is switched, it is necessary to identify the preferable switching mode. Therefore, an adaptive MU approach based on an error-triggered generator is proposed. Subsequently, ET-model predictive control (MPC), the controller activation threshold derived from the Lyapunov function, is introduced. The controller will only be activated when the threshold is triggered, resulting in better performance. The availability of the dual event-triggered spatial MPC (DET-SMPC) is confirmed through both simulation studies and oven experiments.

Abstract:
In this article, the stabilization problem is investigated for input-delayed systems with unknown system dynamics. To solve this problem, a value iteration (VI)-based adaptive dynamic programming (ADP) algorithm is established to learn the state feedback controller from the data along the trajectory of the system. In order to design this control algorithm, the input-delayed system is transformed into a delay-free system at first. Thus, the algebraic Riccati matrix equation (ARE) of the delay-free system is iteratively solved in the absence of system model, and then the controller is designed by using the approximation to the solution of the ARE. In particular, the rank condition of the data-constructed matrices is satisfied by utilizing basis functions, and an initial stabilizing controller is not required in the proposed algorithm. Finally, the effectiveness of the proposed algorithm is illustrated by two practical examples.

Abstract:
This article considers the optimal consensus control for the multiagent systems problem. By developing the multiagent multistep Q-learning (MaMsQL), the methodology achieves enhanced efficiency while addressing the issue of the complex interaction dynamics between agents, environmental uncertainty, thus ultimately meeting demand of balancing exploration and exploitation. First, associated with the performance index, the Q-function is established to prove that all optimal Q-functions form a Nash equilibrium outcome, thereby the consensus problem is converted to finding the optimal Q-functions. Then, the MaMsQL method is developed with theoretical proof of its convergence. Finally, the method is implemented through a specially designed Actor-Critic network. By virtue of the comparison with multiagent single step Q-learning, the effectiveness and superiority of this method are verified through simulation examples.

Abstract:
In this article, a novel energy-efficient control method for waypoint tracking of underwater gliders is designed. The method can be divided into a planning layer and a control layer. In the planning layer, a novel steady/unsteady gliding depth intervals-based dead-reckoning is proposed to predict depth-averaged current velocity with lower consumption. Also a novel heading and depth modification strategy based on line-of-sight is proposed to implement waypoint tracking planning. In the control layer, heading control is implemented by two event-triggered extended state observers (ET-ESOs) and an event-triggered backstepping heading controller (ET-BHC). The ET-ESOs intermittently estimate the real states input to the ET-BHC, and the ET-BHC intermittently outputs the control signal. Simulation and sea trial results show that the UG achieves tracking waypoints, and in addition, energy efficiency is significantly improved.

Abstract:
In a real-world setting, several correlational data streams are active at once. An essential question is how to use the correlations between data streams to enhance the effectiveness of machine learning models. The fact that data streams are nonstationary and the correlations across data streams might change over time presents another difficulty. We suggest an ensemble chain-structured model, Evolutionary regressor chains (RCs), to track the correlations between data streams to solve these issues. We develop a heuristic order searching approach to search for the chain’s optimal order. With the ability to monitor the dynamicity of the correlations, the heuristic order searching technique can also update the chains over time. Furthermore, a way for reducing computing complexity while maintaining the ensemble’s diversity is proposed. The method’s theoretical foundation is established through a dynamic regret analysis proving optimal adaptation in the data streams. The outcomes of our experiments demonstrate the effectiveness of Evolutionary RCs.

Abstract:
This article is committed to the establishment of an integral-based high-order sliding mode (iHOSM) controller for one category of nonlinear systems when confronted with perturbations with unknown magnitudes. A striking characteristic is that an integral dynamics is deftly constructed and then incorporated into the original sliding mode system to dispose of the perturbations of unknown magnitudes. On account of the new sliding mode dynamics, we explicitly introduce a systematic design protocol to dexterously design a new iHOSM controller. This is fulfilled through amending the technique of adding a power integrator. The theoretical justification has been guaranteed by the rigorous Lyapunov analysis. The investigations of two elucidative examples are provided to validate the validity and feasibility of the designed methodology.

Abstract:
Autonomous exploration is a fundamental challenge for numerous applications of mobile robots. Traditional methods often lead to impractical and discontinuous trajectories, which may substantially deteriorate the exploration time. In this work, we propose a rapid autonomous exploration framework with a field-of-view (FOV) expansion mechanism. We present a 1-degree-of-freedom (DOF) FOV expansion mechanism, coupled with a frontier-gravitation FOV direction planning method to decouple the direction of the sensor’s FOV from the robot velocity direction. Our approach includes a rapid frontier viewpoint generation method utilizing principal component analysis (PCA). Moreover, we introduce a sliding window travelling salesman problem (TSP) for global coverage path planning, incorporating an attenuation coefficient to increase the exploration priority of independent small frontiers and reduce revisit probability. Finally, compared to state-of-the-art (SOTA) approaches, our proposed mechanism and framework beneficially reduce exploration time by 30%–44% and enhance the continuity of the robot movement in both simulation and real-world scenarios.

Abstract:
This article considers the finite-frequency (FF) H_2 – H_\infty composite control problem for continuous singularly perturbed systems. To address the performance requirements in the low- and high-frequency ranges, the FF H_2 and H_\infty norms are used to impose on the performance of the slow and fast subsystems, respectively. The FF H_2 control of the slow subsystem is analyzed using the FF Gramian matrix method. While the FF H_\infty control of the fast subsystem is studied by using the Generalized Kalman–Yakubovič–Popov Lemma. Subsequently, an H_2 – H_\infty composite controller for the singularly perturbed system is developed. Finally, two simulation examples involving an armature control direct-current motor system are demonstrated to verify the effectiveness and superiority of the proposed control scheme.

Abstract:
This article proposes a novel gain selection scheme for fractional-proportional-type iterative learning control, aiming to achieve faster convergence rates while maintaining high tracking precision. The convergence of tracking errors to adjustable limit cycles is demonstrated, and a recursive computation method is provided for these limit cycles. Furthermore, the bounds of the limit cycles are estimated in detail, and both local and global convergence rates are thoroughly analyzed. A systematic performance comparison of different gain selection rules, including tracking precision and convergence rate, is conducted. Two multistage update schemes are established through combining different gain selections to accelerate convergence quantitatively, resulting in faster convergence rates compared to the common proportional-type update rule while preserving final zero-error tracking performance. Moreover, the switching iteration of the proposed multistage schemes can be independent of system matrices. Numerical simulations and experiments are presented to validate the theoretical findings.

Abstract:
This article proposes a new separation design method for the well-known event-triggered output feedback control for networked control systems (NCSs). Considering a scenario in which the sensor and controller transmit their data via the dual-channel communication network, two independent dynamic event-triggered strategies are well-designed to reduce the consumption of network resources. Furthermore, a novel separation design method for the event-triggered output feedback control is presented, in which the controller, the observer, and their respective triggering conditions can be designed separately. Compared with the existing separation design methods, the main advantage of the proposed method is that no constraint on the dual-channel triggering thresholds is required. The simulation results exemplify the merits of the theoretical results with a comparison.

Affiliations: Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China; Department of Automation, University of Science and Technology of China, Hefei, Anhui, China; School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW, Australia; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui, China

Abstract:
This article investigates the issue of observer-based fixed-time-synchronized tracking control for Euler-Lagrange (EL) systems with uncertain dynamics, bias-actuator faults and external disturbances. A novel fixed-time observer is proposed to reconstruct the actuator faults and system uncertainties, so that the observation error can reduce to zero within a fixed time. A fixed-time stable system with fast convergence rate is developed by using switching terms to design a newly sliding mode variable with the norm-normalized sign function. Then, on the basis of the reconstructed information from the fixed-time observer and the sliding mode variable, a robust control law is developed to realize fixed-time-synchronized stability of the EL system. It is concluded by Lyapunov stability theorem that the proposed method not only can guarantee that the boundary of convergence time is irrelevant of initial values of the system states, but also make all elements of the system tracking errors reach the origin simultaneously under the influence of actuator faults, external disturbances and uncertain dynamics. Finally, several comparative simulations are carried out to validate the developed observation and control schemes as well as their effectiveness.

Abstract:
The competitive swarm optimizer (CSO) has been widely used for addressing multiobjective optimization problems owing to its diverse learning approach. However, the evolutionary process uncertainty within the algorithm weakens the optimization reliability. To deal with this concern, a robust multiobjective CSO with a predictive indicator (RMOCSO-PI), is proposed. This approach can reduce aimless and inefficient searches caused by the uncertainty to enhance algorithmic robustness. First, a predictive indicator is established based on the autoregressive model, which utilizes historical swarm distribution data to predict the evolutionary trends. Then, the particles are classified into winners and losers by evaluating their evolutionary potential, whose evolution would be guided differentially. Second, a space fusion-based competitive mechanism is designed to generate precise evolution directions for loser particles. The space fusion-based adaptive adjustment method integrates the learning cost metric in decision space with the learning worth metric in objective space for proper learning weight settings. Third, a dynamic cooperative mechanism is presented to purposefully guide the diversity exploration of particles. By estimating evolutionary states, three cooperative patterns are dynamically assigned to particles for purposeful diversity exploration. To provide theoretical support for the validity and reliability of RMOCSO-PI, a convergence analysis is given. Furthermore, experimental results verify that RMOCSO-PI has more stable and excellent optimization performance.

Abstract:
This article proposes a strategy to accelerate the convergence of iterative learning control (ILC) while maintaining robustness against stochastic noise. The strategy adaptively reweights the error signals used in conventional ILC schemes, casting greater influence to larger errors during input updates, thereby accelerating the correction of noisy inputs and improving overall convergence behavior. Furthermore, to mitigate the impact of noise-dominated small errors on weight computation, a saturation mechanism is introduced. A convergence theorem is established to characterize how the saturation parameters affect the asymptotic convergence of the input deviation-induced errors. Simulation and experimental results demonstrate that incorporating this strategy consistently improves convergence speed while maintaining tracking accuracy across different ILC implementations.

Abstract:
We consider a joint uplink and downlink scheduling problem of a fully distributed wireless networked control system (WNCS) with a limited number of frequency channels. Using elements of stochastic systems theory, we derive a sufficient stability condition of the WNCS, which is stated in terms of both the control and communication system parameters. Once the condition is satisfied, there exists a stationary and deterministic scheduling policy that can stabilize all plants of the WNCS. By analyzing and representing the per-step cost function of the WNCS in terms of a finite-length countable vector state, we formulate the optimal transmission scheduling problem into a Markov decision process and develop a deep reinforcement learning (DRL)-based framework for solving it. To tackle the challenges of a large action space in DRL, we propose novel action space reduction and action embedding methods for the DRL framework that can be applied to various algorithms, including deep Q-network (DQN), deep deterministic policy gradient (DDPG), and twin delayed DDPG (TD3). Numerical results show that the proposed algorithm significantly outperforms benchmark policies.

Affiliations: College of Information Science and Technology and Artificial Intelligence and the College of Computer Science and Engineering, Nanjing Forestry University, Nanjing, China; Key Laboratory of Knowledge Engineering With Big Data, Hefei University of Technology, Hefei, China; School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China; College of Forestry, Hebei Agricultural University, Baoding, China; Chinese Academy of Forestry, Institute of Forest Resource Information Techniques, Beijing, China; College of Information Science and Technology and Artificial Intelligence, State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China

Abstract:
Recently, interest in flat-type projection clustering methods has grown as they improve learner’s performance by exploring multiple projection subspaces. However, solvers used in previous representative works predominantly rely on greedy search strategies, which incur high computational costs and fail to consider interdependencies between projections. Moreover, these methods do not simultaneously guarantee the effective suppression of outliers and noisy data at cluster boundaries, ultimately compromising data discrimination. To address these limitations and discover a more effective subspace for each flat, we propose robust multiple flat projections clustering (RMFPC). This method computes within- and between-cluster distances using the L2,1-norm to enhance robustness against outliers. Furthermore, we propose a truncated distance maximization constraint (TDMC) to eliminate the influence of noisy data on cluster separability. The resulting objective is presented in a ratio form, which is not trivial. We provide a novel formulation to achieve a theoretically equivalent problem. Based on this reformulation, we develop an efficient non-greedy solution algorithm. In addition, a cluster center optimization mechanism is incorporated into the solution process to accurately estimate the distribution of each cluster center. The convergence analysis and proof of the proposed algorithm are provided. Experiments on both toy and real-world datasets demonstrate the effectiveness of the proposed method.

Abstract:
This article addresses the periodic event-triggered adaptive output feedback control problem for networked system with unknown nonlinear dynamics. Based on the output-dependent periodic event-triggered mechanism (PETM), a nonlinear observer is designed to estimate system states, where the fuzzy-logic systems-based approximation method and adaptive technique are employed to approximate and compensate for uncertainties. To enhance resource utilization efficiency in communication channels, a new observer and parameter estimators-dependent parallel PETM is proposed to schedule intermittent packet transmission. Then, a digital controller is designed to reduce frequent control updating. By constructing novel piecewise Lyapunov functional, it is proven that the underlying system states, the observation error signals and parameter estimation signals are semiglobally uniformly ultimately bounded. In addition, the proposed control method is applied to solve the stabilization problem of networked interconnected systems. Finally, a numerical simulation is performed to show the efficiency of the developed control method.

Abstract:
In this article, an accelerated value iteration-based safe Q-learning (SQL) algorithm is developed to design the tracking controller for unknown nonlinear systems. First, an augmented Q-function, consisting of a quadratic utility function and an adjustable positive-definite control barrier function (CBF), is devised to ensure both the optimality and safety of the tracking controller. The quadratic utility function, associated with optimality, guarantees that the tracking controller can eliminate the ultimate tracking error, regardless of the reference trajectory. The adjustable positive-definite CBF, pertaining to safety, ensures that the tracking error converges faster toward zero while remaining within the safe set at all times. Second, an accelerated iterative learning mechanism, comprising policy evaluation (PE) and policy improvement (PI), is employed to discover the safe optimal tracking control policy. Integrating the difference between two iterative Q-functions into the current PE process can expedite the convergence rate of the SQL algorithm. A policy optimization technique based on Nesterov Momentum method is utilized to accelerate the PI process of the SQL algorithm. When faced with a large amount of offline data, the two-stage accelerated learning effectively reduces computational pressure. Furthermore, convergence of the Q-function sequence and safety of the optimal tracking policy are theoretically analyzed. Finally, by using neural networks and the action-critic structure, two simulation examples are performed to verify the availability of accelerated SQL methods.

Abstract:
This article concentrates on designing the \epsilon -stealthy attack against remote state estimation in cyber-physical systems, where the intercepted information and the side information sensed by additional sensors are simultaneously exploited by the attacker. Combining the two types of information, a novel attack model based on corrupted innovation is proposed, and the attacked error covariance of the remote estimator is derived. Then, to maximize the error covariance, an analytical attack strategy is given. Compared with the existing results, the proposed attack exhibits significantly improved performance while requiring only one auxiliary filter. Finally, the numerical simulations verify the effectiveness of the results.

Affiliations: School of Engineering and Technology, University of New South Wales, Canberra, ACT, Australia; School of Engineering, The Australian National University, Canberra, ACT, Australia; Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia; Department of Control and Systems Engineering, School of Management and Engineering, Nanjing University, Nanjing, China; Department of Chemistry, Princeton University, Princeton, NJ, USA

Abstract:
Quantum state tomography (QST) is the process of reconstructing the state of a quantum system (mathematically described as a density matrix) through a series of different measurements, which can be solved by learning a parameterized function to translate experimentally measured statistics into physical density matrices. However, the specific structure of quantum measurements for characterizing a quantum state has been neglected in previous work. In this article, we explore the similarity between highly structured sentences in natural language and intrinsically structured measurements in QST. To fully leverage the intrinsic quantum characteristics involved in QST, we design a quantum-aware transformer (QAT) model to capture the complex relationship between measured frequencies and density matrices. In particular, we query quantum operators in the architecture to facilitate informative representations of quantum data and integrate the Bures distance into the loss function to evaluate quantum state fidelity, thereby enabling the reconstruction of quantum states from measured data with high fidelity. Extensive simulations and experiments (on IBM quantum computers) demonstrate the superiority of the QAT in reconstructing quantum states with favorable robustness against experimental noise.

Abstract:
Adaptive control (AC) has been extensively proved as a promising strategy for nonlinear systems, especially in the dynamic and changeable environments. However, due to the unavoidable existence of unknown interconnections in the real nonlinear systems, it is difficult for typical AC to guarantee its stability within the bounded convergence time. Thus, to solve this issue, a predefined-time adaptive neural control (PTANC) scheme is developed for nonlinear systems with unknown interconnections in this aritcle. First, an integrated control framework, where the controlled objectives not only relate with each other but also change over time, is able to catch more characteristics of nonlinear systems than the existing works. Then, the proposed control scheme can address the impact of unknown interconnections on the system stability. Second, a predefined adaptive law mechanism is employed to estimate the unknown interconnections to assist in PTANC. Then, the proposed PTANC scheme can ensure its predefined-time stable in the occurrence of unknown interconnections. Third, a neural network-based self-regulating strategy is designed to construct the Lyapunov function to prove the stability of PTANC. Then, the comprehensive stability analysis can make PTANC scheme be successful applied in nonlinear systems. Finally, the proposed PTANC scheme is tested on the numerical simulation and BSM1 simulation platform. The experimental results illustrate that the envisioned control method attains exceptional performance.

Affiliations: School of Reliability and Systems Engineering, Beihang University, Beijing, China; School of Reliability and Systems Engineering and the Key Laboratory of Reliability and Environmental Engineering, Beihang University, Beijing, China; School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore; School of Electrical and Electronic Engineering and the Lee Kong Chian School of Medicine, Nanyang Technological University, Jurong West, Singapore

Abstract:
The advent of cyber-physical production systems (CPPSs) has greatly improved production responsiveness. However, effective control and decision-making in CPPSs remain challenging due to the dynamic nature of both internal operations and external environments. We present a multiobjective optimization approach for managing operation, maintenance, and support decisions in CPPSs under time-varying demands. Specifically, a decision-making framework is developed to enable collaborative control, incorporating reliability-based risk assessment and multiobjective optimization techniques. To facilitate continuous decision-making in response to uncertainties, a biobjective optimization model is formulated using a receding horizon control architecture, addressing conflicting objectives simultaneously. An enhanced multiobjective pigeon-inspired optimization algorithm is proposed to generate Pareto-optimal solutions by co-minimizing the production risks and costs. Experimental validations are carried out through both numerical simulations and real-world experiments on a subsea production system in the South China Sea, involving two support sites, six production sites, thirty-six machines, and 288 components.

Abstract:
This article provides a new impulsive observation approach [called impulsive adaptive observer, impulsive adaptive observation (IAO)] for a class of linear systems. A discrete-time-based adaptive rule for the impulsive observer gain is designed using only output information at discrete-time intervals (or impulsive instants), overcoming the real-time data requirement of continuous-time adaptive observation frameworks. The IAO effectively estimates the states of a continuous-time system and demonstrates outstanding state tracking performance in practical implementations. Furthermore, the IAO-based feedback controller is designed to stabilize the controlled plant. Stability criteria for the IAO protocols are established, showing improved performance over existing schemes by reducing computational load and enhancing control flexibility. The simulations for the electrical system are presented finally to confirm the effectiveness of the IAO and its control approach.

Affiliations: School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China; Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China; Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC, Canada; School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, China

Abstract:
This article deals with the observer-based control problem of networked periodic piecewise systems under encoding-decoding frameworks. An encoder with a uniform quantizer, which can compress and encrypt data, is provided to process the measurements from the sensors. The processed data is transmitted over the network to the decoder to recover the original data and then to the remote control station, thereby reducing the communication burden and ensuring data security. Then, by constructing the periodic Lyapunov function with linear interpolation terms, exploiting an effective technique—singular value decomposition—sufficient conditions with linear matrix inequality (LMI) constraints for selecting the observer and controller parameters are derived to achieve the exponentially ultimate boundedness of closed-loop systems. Moreover, to eliminate extra steady-state errors caused by encoding-decoding mechanisms (EDMs), a dynamic quantization factor that can make the asymptotic upper bound tend to zero is designed. Finally, numerical examples are provided to illustrate the effectiveness of the derived theoretical results.

Abstract:
Role-based collaboration (RBC) is an innovative collaborative approach designed to enhance collaboration. Role negotiation (RN) is a critical step in RBC, during which the role set and the number of agents required for each role, i.e., role requirements, are determined. This process establishes the foundational input for group role assignment (GRA), where roles are assigned to agents to optimize group performance. Uncertainties in RN, such as task volume fluctuations, create dynamic agent requirements. However, existing RBC models typically assume RN to be static, thus failing to adequately address the substantial challenges. Three-way decision (3WD) is a robust decision-making methodology well-suited for managing uncertainty. To address the uncertainties in role requirements, this article introduces truncated discrete distribution to quantify role requirements, and presents a novel group role three-way assignment (GR3A) model. Compared with traditional RBC, our model offers an additional variable partial substitute choice that offers agents little salary during nonengagement periods but can transition to full involvement as required according to the prior agreement. GR3A is a dual-objective nonlinear optimization problem, for which a linearization strategy is proposed to achieve the optimal resolution. Additionally, sufficient and necessary conditions for these assignment problems are put forward to enhance the efficacy of the proposed solutions. To our knowledge, this study innovatively introduces a truncated discrete distribution and 3WD into the RBC framework. Empirical validation through simulations demonstrates the effectiveness and efficacy of the proposed method within the RBC context.

Abstract:
This article presents novel data-driven inverse reinforcement learning (IRL) algorithms to optimally address heterogeneous formation control problems in the presence of disturbances. We propose expert-estimator-learner multiagent systems (MASs) as independent systems with similar interaction graphs. First, a model-based IRL algorithm is introduced for the estimator MAS to determine its optimal control and reward functions. Using the estimator IRL algorithm results, a robust algorithm for model-free IRL is presented to reconstruct the learner MAS’s optimal control and reward functions without knowing the learners’ dynamics. Therefore, estimator MAS aims to estimate experts’ desired formation and learner MAS wants to track the estimators’ trajectories optimally. As a final step, data-driven implementations of these proposed IRL algorithms are presented. Consequently, this research contributes to identifying unknown reward functions and optimal controls by conducting demonstrations. Our analysis shows that the stability and convergence of MASs are thoroughly ensured. The effectiveness of the given algorithms is demonstrated via simulation results.

Abstract:
The neural activity patterns of localized brain regions are crucial for recognizing brain intentions. However, existing electroencephalogram (EEG) decoding models, especially those based on deep learning, predominantly focus on global spatial features, neglecting valuable local information, potentially leading to suboptimal performance. Therefore, this study proposed a dynamic hierarchical convolutional attention network (DH-CAN) that comprehensively learned discriminative information from both global and local spatial domains, as well as from time-frequency domains in EEG signals. Specifically, a multiscale convolutional block was designed to dynamically capture time-frequency information. The channels of EEG signals were mapped to different brain regions based on motor imagery neural activity patterns. The spatial features, both global and local, were then hierarchically extracted to fully exploit the discriminative information. Furthermore, regional connectivity was established using a graph attention network, incorporating it into the local spatial features. Particularly, this study shared network parameters between symmetrical brain regions to better capture asymmetrical motor imagery patterns. Finally, the learned multilevel features were integrated through a high-level fusion layer. Extensive experimental results on two datasets demonstrated that the proposed model performed excellently across multiple evaluation metrics, exceeding existing benchmark methods. These findings suggested that the proposed model offered a novel perspective for EEG decoding research.

Abstract:
Unsupervised skill discovery seeks to acquire different useful skills without extrinsic reward via unsupervised reinforcement learning (RL), with the discovered skills efficiently adapting to multiple downstream tasks in various ways. However, recent advanced skill discovery methods struggle to well balance state exploration and skill diversity, particularly when the potential skills are rich and hard to discern. In this article, we propose contrastive dynamic skill discovery (ComSD) which generates diverse and exploratory unsupervised skills through a novel intrinsic incentive, named contrastive dynamic reward. It contains a particle-based exploration reward to make agents access far-reaching states for exploratory skill acquisition, and a novel contrastive diversity reward to promote the discriminability between different skills. Moreover, a novel dynamic weighting mechanism between the above two rewards is proposed to balance state exploration and skill diversity, which further enhances the quality of the discovered skills. Extensive experiments and analysis demonstrate that ComSD can generate diverse behaviors at different exploratory levels for multijoint robots, enabling state-of-the-art adaptation performance on challenging downstream tasks. It can also discover distinguishable and far-reaching exploration skills in the challenging tree-like 2-D maze.

Affiliations: College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China; College of Science, China University of Petroleum (East China), Qingdao, China; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland; Technical Research, Scientific and Technological Innovation and Management of Gas Storage, West-East Gas Pipeline Company of National Petroleum and Natural Gas Pipeline Network Group, Beijing, China; Computer Science and Engineering Department, Techno India University, Kolkata, India

Abstract:
The majority of the unsupervised feature selection methods usually explore the first-order similarity of the data while ignoring the high-order similarity of the instances, which makes it easy to construct a suboptimal similarity graph. Furthermore, such methods, often are not suitable for performing feature selection due to their high complexity, especially when the dimensionality of the data is high. To address the above issues, a novel method, termed as unsupervised feature selection for high-order embedding learning and sparse learning (UFSHS), is proposed to select useful features. More concretely, UFSHS first takes advantage of the high-order similarity of the original input to construct an optimal similarity graph that accurately reveals the essential geometric structure of high-dimensional data. Furthermore, it constructs a unified framework, integrating high-order embedding learning and sparse learning, to learn an appropriate projection matrix with row sparsity, which helps to select an optimal subset of features. Moreover, we design a novel alternative optimization method that provides different optimization strategies according to the relationship between the number of instances and the dimensionality, respectively, which significantly reduces the computational complexity of the model. Even more amazingly, the proposed optimization strategy is shown to be applicable to ridge regression, broad learning systems and fuzzy systems. Extensive experiments are conducted on nine public datasets to illustrate the superiority and efficiency of our UFSHS.

Abstract:
The innovation of optimal learning control methods is profoundly propelled due to the improvement of the learning ability. In this article, we investigate the synthesis of initialization and acceleration for optimal learning control algorithms. This approach contrasts with traditional methods that concentrate solely on either the improvement of initialization or acceleration. Specifically, we establish a novel relaxed policy iteration (PI) algorithm with self-learning horizon for stochastic optimal control. Notably, by suitably utilizing self-learning horizon, we can directly evaluate inadmissible policies to reduce the initialization burden. Meanwhile, the inadmissible policy can be rapidly optimized with few learning iterations. Then, several critical conclusions of relaxed optimal control are established by discussing algorithm convergence and system stability. Furthermore, to provide the convincing application potentials, a class of unconventional problems is effectively solved by the relaxed PI algorithm, including the dynamics with external noises and nonzero equilibrium. Finally, we present a series of nonlinear benchmarks with practical applications to comprehensively evaluate the performance of relaxed PI. The experimental results obtained from these diverse benchmarks uniformly highlight the effectiveness of self-learning horizon mechanism.

Abstract:
Index tracking is a primary passive investment strategy. Many existing methods, such as cardinality-constrained and regularized regressions, need to prespecify parameters to generate sparse portfolios to track indices, which complicates the tracking procedure and may compromise tracking performance. This article addresses index tracking and enhanced index tracking via Bayesian learning and collaborative neurodynamic optimization. Specifically, we formulate a sparse Bayesian regression problem for index tracking. Furthermore, we reformulate the problem for enhanced index tracking by adding constraints based on a second-order stochastic domination rule. To overcome the nonconvexity of the objective function in the formulated problems, we propose a sparse Bayesian regression algorithm based on multiple recurrent neural networks in the collaborative neurodynamic optimization framework. We demonstrate the superiority of the proposed methods to mainstream baselines in terms of predictability, consistency, sparsity, and profitability via experimentation on the data from seven major stock markets.

Abstract:
Subspace clustering has attracted significant interest for its capacity to partition high-dimensional data into multiple subspaces. The current approaches to subspace clustering predominantly revolve around two key aspects: 1) the construction of an effective similarity matrix and 2) the pursuit of sparsity within the projection matrix. However, assessing whether the dimensionality of the projected subspace is the true dimensionality of the data is challenging. Therefore, the clustering performance may decrease when dealing with scenarios such as subspace overlap, insufficient projected dimensions, data noise, etc., since the defined dimensionality of the projected lower-dimensional space may deviate significantly from its true value. In this research, we introduce a novel reweighting strategy, which is applied to projected coordinates for the first time and propose a reweighted subspace clustering model guided by the preservation of the both local and global structural characteristics (RWSC). The projected subspaces are reweighted to augment or suppress the importance of different coordinates, so that data with overlapping subspaces can be better distinguished and the redundant coordinates produced by the predefined number of projected dimensions can be further removed. By introducing reweighting strategies, the bias caused by imprecise dimensionalities in subspace clustering can be alleviated. Moreover, global scatter structure preservation and adaptive local structure learning are integrated into the proposed model, which helps RWSC capture more intrinsic structures and its robustness and applicability can then be improved. Through rigorous experiments on both synthetic and real-world datasets, the effectiveness and superiority of RWSC are empirically verified.

Abstract:
Muscle force and morphology information offer complementary perspectives for gesture recognition and its applications. Surface Electromyography (sEMG) provides force and electrophysiological information associated with muscles, while A-mode ultrasound (AUS) reveals muscle morphological information. By leveraging these two modalities, more comprehensive muscle motor unit information relevant to gesture recognition can be obtained. In this article, we introduce the adaptive weight classification (AWC) module and its enhanced version with hierarchical classifiers, adaptive weight hierarchical soft voting (AWHSV), to integrate AUS and sEMG into a fused modality. This approach dynamically adjusts the weights of individual and fused features, compensating for lost details during fusion, leading to a richer information representation and significantly improving algorithm robustness in gesture recognition. The experimental results demonstrate that the proposed method achieves recognition rates that are 0.66%, 2.36%, and 1.30% higher than those of its counterparts using sEMG, AUS, and sEMG-AUS, respectively. Moreover, the method outperforms state-of-the-art approaches, confirming its effectiveness in gesture recognition across both single and multiple modalities. This work demonstrates the advantages of the proposed AWHSV method, providing broader application scenarios for gesture recognition.

Abstract:
This article presents attractor-transition control of complex biological networks represented by Boolean networks (BNs) wherein the BN is steered from a prescribed initial attractor toward a desired one. The proposed approach leverages the similarity between attractors and Boolean algebraic properties embedded in the underlying state transition equations. To enhance the clarity of expression regarding stabilization toward the desired attractor, a simple coordinate transformation is performed on the considered BN. Based on the characteristics of transformed state equations, self-stabilizing state variables requiring no control efforts are derived in the first. Next, by applying the feedback vertex set (FVS) control scheme, control inputs stabilizing the remaining state variables are determined. The proposed control scheme exhibits versatility by accommodating both fixed-point and cyclic attractors. We validate the effectiveness of the proposed strategy through extensive numerical experiments conducted on random BNs as well as complex biological systems. In adherence to the reproducible research initiative, detailed results of numerical experiments and all the implementation codes are provided on the authors’ website: https://github.com/choonlog/AttractorTransition.

Abstract:
This article proposes a finite-time distributed state estimation (DSE) algorithm for discrete-time stochastic nonlinear systems with heterogeneous sensors. Considering the network with heterogeneous sensors, the distributed estimate framework is designed by three phases, namely, priori prediction, measurement update, and consensus fusion. To obtain the accurate priori prediction results, the interactive multiple model (IMM) method is adopted to calculate the priori state value in the priori prediction phase. By introducing the measurement probability matrix, a novel heterogeneous measurement information fusion algorithm is designed. Then the measurement information of each sensor is used to update the priori prediction estimates to calculate the estimate results in the measurement update phase. Based on the consensus method, the estimate results of each sensor are fused with consensus weight to calculate the distributed state estimates of nonlinear systems in the consensus fusion phase. Besides, with finite consensus fusion steps, the bounds of the proposed distributed estimate algorithm are proved to be existed. Finally, distributed state estimate simulation example for nonlinear system is set to validate the performance.

Abstract:
The high-order fully actuated systems (HOFASs) approach can only completely eliminate known nonlinearities. However, the practical systems often encounter unknown nonlinearities, including disturbances and faults. Therefore, this article studies a class of nonlinear HOFAS with component faults and disturbances. By applying the HOFAS theory, a novel integrated sliding mode fault-tolerant control strategy is proposed to ensure the stability of the closed-loop system. The state feedback and output feedback controllers are designed, respectively. For output feedback control, an extended state observer is designed to estimate system states. Once the HOFAS model is established, the designed controller and extended state observer can be directly implemented, facilitating the analysis and design of the control system. And the stability analysis does not depend on the complexity of the nonlinear functions. Finally, a numerical simulation example shows the effectiveness of the proposed method.

Abstract:
Time-series classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the category to which they belong. When the class values are ordinal, classifiers that take this into account can perform better than nominal classifiers. Time-series ordinal classification (TSOC) is the field bridging this gap, yet unexplored in the literature. There are a wide range of time-series problems showing an ordered label structure, and TSC techniques that ignore the order relationship discard useful information. Hence, this article presents the first benchmarking of TSOC methodologies, exploiting the ordering of the target labels to boost the performance of current TSC state of the art. Both convolutional- and deep-learning-based methodologies (among the best performing alternatives for nominal TSC) are adapted for TSOC. For the experiments, a selection of 29 ordinal problems has been made. In this way, this article contributes to the establishment of the state of the art in TSOC. The results obtained by ordinal versions are found to be significantly better than current nominal TSC techniques in terms of ordinal performance metrics, outlining the importance of considering the ordering of the labels when dealing with this kind of problems.

Abstract:
While artificial intelligence (AI) technology has advanced in real-world applications, there is a strong motivation to develop hybrid systems where AI algorithms and humans collaborate, promoting more human-centered approaches in AI system design. This has led to the emergence of a novel human-machine computing (HMC) paradigm, which combines human cognitive abilities with machine computational power to create a collaborative computing framework that meets the demands of large-scale, complex tasks and enables human-machine symbiosis. Human processing units (HPUs) are crucial computing resources in HMC-oriented systems, and efficient HPU resource provisioning is key to boosting system performance. However, existing schemes often fail to assign tasks to the most suitable HPUs and optimize HPU utility, as they either cannot quantitatively measure skills or overlook utility concerns during task assignment and scheduling. To address these challenges, this article proposes a fine-grained HPU virtualization (FingHV) approach, which leverages virtualization techniques to improve flexibility, fairness, and utility in the provisioning process. The core idea is to use a tree-based skill model to precisely measure the levels and correlations of multiple skills within individual HPUs, and to apply a mixed time/event-based scheduling policy to maximize HPU utility. Specifically, we begin by proposing a hierarchical multiskill tree to model HPU skills and their correlations. Next, we formulate the HPU virtualization problem and present a fine-grained virtualization method, which includes a quality-driven HPU assignment process and a mixed time/event-based scheduling policy to improve resource-sharing efficiency. Finally, we evaluate FingHV on a synthetic dataset with varying task sizes and a real-world case. The results demonstrate that FingHV improves global matching quality by up to 39.7% and increases HPU utility by 11.2% compared to the baselines.

Affiliations: School of Automation, National Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing, China; School of Vehicle and Mobility, Tsinghua University, Beijing, China; School of Mechanical and Aerospace Engineering, Nanyang Technological University, Nanyang Avenue, Singapore; National Key Laboratory of Autonomous Intelligent Unmanned Systems, Harbin Institute of Technology, Harbin, China

Abstract:
This study focuses on trajectory planning and motion control policies in autonomous racing, which necessitates pushing the capacity boundaries of racing vehicles to achieve maximum speeds and minimal lap times. We propose an innovative planning control framework that integrates risk-conscious mutations in jump-start reinforcement learning (RCM-JSRL) and nonlinear model predictive control (NMPC). The RCM-JSRL algorithm incorporates jump-start curriculum learning and the risk-conscious genetic algorithm into reinforcement learning, leveraging prior expert knowledge and a curiosity-driven exploration mechanism to enhance training efficiency while avoiding excessively conservative policy generation in high-complexity and high-risk scenarios. NMPC generates locally optimal control commands that adhere to vehicle dynamics constraints while following the designated trajectory. Following training on track maps with varying difficulty levels, the proposed controller successfully executes a superior policy compared to the guide policy, providing evidence of its effectiveness and scalability. It is our belief that this technology can be applied in everyday driving scenarios, improving efficiency under special conditions, ensuring stability in critical situations, and broadening the scope of autonomous driving applications.

Affiliations: Department of Artificial Intelligence, School of Informatics, the Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education, and the Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan, Ministry of Culture and Tourism, Xiamen University, Xiamen, China; School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA

Abstract:
Bilevel optimization problems (BLOPs) are characterized by an interactive hierarchical structure, where the upper level seeks to optimize its strategy while simultaneously considering the response of the lower level. Evolutionary algorithms are commonly used to solve complex bilevel problems in practical scenarios, but they face significant resource consumption challenges due to the nested structure imposed by the implicit lower-level optimality condition. This challenge becomes even more pronounced as problem dimensions increase. Although recent methods have enhanced bilevel convergence through task-level knowledge sharing, further efficiency improvements are still hindered by redundant lower-level iterations that consume excessive resources while generating unpromising solutions. To overcome this challenge, this article proposes an efficient dynamic resource allocation framework for evolutionary bilevel optimization, named DRC-BLEA. Compared to existing approaches, DRC-BLEA introduces a novel competitive quasi-parallel paradigm, in which multiple lower-level optimization tasks, derived from different upper-level individuals, compete for resources. A continuously updated selection probability is used to prioritize execution opportunities to promising tasks. Additionally, a cooperation mechanism is integrated within the competitive framework to further enhance efficiency and prevent premature convergence. Experimental results compared with chosen state-of-the-art algorithms demonstrate the effectiveness of the proposed method. Specifically, DRC-BLEA achieves competitive accuracy across diverse problem sets and real-world scenarios, while significantly reducing the number of function evaluations and overall running time.

Abstract:
In expensive multi/many-objective optimization problems (EMOPs), the expensive objectives are generally accessed through different simulation tools, leading to different evaluation latencies and unbearable computational time for serial optimization. One promising approach to improve efficiency is to perform simulation and build surrogates separately for each objective in parallel. However, how to improve the model accuracy and select promising candidates without global information are big challenges. To alleviate these problems, this article proposes an asynchronous influence-based SAEA (AIEA) based on the client-server model. Each client approximates an objective and the server takes charge for evolution. To adaptively select promising candidates, the influence degree is introduced in candidate selection, which is calculated in the objective space to judge which candidate has more beneficial influence for evolution. With the selected candidate, the most-uncertain-first strategy is devised in objective selection for asynchronous evaluations and model improvement. To handle incomplete objective values, the nearest neighbor inheritance is adopted for unevaluated objectives. Comprehensive experiments compared with five surrogate-assisted EAs demonstrate the global optimization and scalability of AIEA.

Abstract:
In this article, a novel simulation-to-real (sim2real) multimodal learning framework is proposed for adaptive dexterous grasping and grasp status prediction. A two-stage approach is built upon the Isaac Gym and several proposed pluggable modules, which can effectively simulate dexterous grasps with multimodal sensing data, including RGB-D images of grasping scenarios, joint angles, 3-D tactile forces of soft fingertips, etc. Over 500K multimodal synthetic grasping scenarios are collected for neural network training. An adaptive dexterous grasping neural network (ADG-Net) is trained to learn dexterous grasp principles and predict grasp parameters, employing an attention mechanism and a graph convolutional neural network module to fuse multimodal information. The proposed adaptive dexterous grasping method can detect feasible grasp parameters from an RGB-D image of a grasp scene and then optimize grasp parameters based on multimodal sensing data when the dexterous hand touches a target object. Various experiments in both simulation and physical grasps indicate that our ADG-Net grasping method outperforms state-of-the-art grasping methods, achieving an average success rate of 92% for grasping isolated unseen objects and 83% for stacked objects. Code and video demos are available at https://github.com/huikul/adgnet.

Abstract:
In this article, we propose a tightly coupled visual-inertial-acoustic sensor fusion method to improve the autonomous localization accuracy of underwater vehicles. To address the performance degradation encountered by existing visual or visual-inertial simultaneous localization and mapping systems when applied in underwater environments, we integrate the Doppler velocity log (DVL), an acoustic velocity sensor, to provide additional motion information. To fully leverage the complementary characteristics among visual, inertial, and acoustic sensors, we perform multimodal information fusion in both frontend tracking and backend mapping processes. Specifically, in the frontend tracking process, we first predict the vehicle’s pose using the angular velocity measurements from the gyroscope and linear velocity measurements from the DVL. Thereafter, measurements performed by the three sensors between adjacent camera frames are utilized to construct visual reprojection error, inertial error, and DVL displacement error, which are jointly minimized to obtain a more accurate pose estimation at the current frame. In the backend mapping process, we utilize gyroscope and DVL measurements to construct relative pose change residuals between keyframes, which are minimized together with visual and inertial residuals to further refine the poses of the keyframes within the local map. Experimental results on both simulated and real-world underwater datasets demonstrate that the proposed fusion method improves the localization accuracy by more than 30% compared to the current state-of-the-art ORB-SLAM3 stereo-inertial method, validating the potential of the proposed method in practical underwater applications.

Abstract:
Discovering clusters remains challenging when dealing with complex data structures, including those with varying densities, arbitrary shapes, weak separability, or the presence of noise. In this article, we propose a novel clustering algorithm called representative point-based clustering with neighborhood information (RPC-NI), which highlights the significance of neighborhood information often overlooked by existing clustering methods. The proposed algorithm first introduces a new local centrality metric that integrates both neighborhood density and topological convergence to identify core representative points, effectively capturing the structural characteristics of the data. Subsequently, a density-adaptive distance is defined to evaluate dissimilarities between these core representative points, and such distance is used to construct a minimum spanning tree (MST) over these points. Finally, an MST-based clustering algorithm is employed to yield the desired clusters. Incorporating neighborhood information enables RPC-NI to comprehensively determine representative points, and having multiple representative points per cluster allows RPC-NI to adapt to clusters of arbitrary shapes, varying densities, and different sizes. Extensive experiments on widely used datasets demonstrate that RPC-NI outperforms baseline algorithms in terms of clustering accuracy and robustness. These results provide further evidence for the importance of incorporating neighborhood information discovering clusters with complex structures.

Abstract:
In this article, we focus on solving the problem of online multiplayer differential games (MDGs) of Markov jump systems (MJSs) using a reinforcement learning (RL) method. We consider MDGs of MJSs from the following two scenarios. In the first scenario, we propose a distributed minmax strategy, where each player can derive their optimal control policy from distributed game algebraic Riccati equations (DGAREs) without prior knowledge of the policies adopted by other players, distinguishing it from existing RL algorithms. We design a novel online distributed RL algorithm to approximate the solution of DGAREs without completely knowing system dynamics and initial admissible control policy. The second scenario involves applying Nash strategy to address MDGs of MJSs. Different from existing synchronous RL algorithm, we propose a novel online asynchronous RL algorithm that employs asynchronous iterative calculations for both policy evaluation and policy improvement, incorporating the latest information into the iterative process. The convergence of the designed RL algorithms is rigorously analyzed. Finally, two inverted pendulum system applications validate the effectiveness of the proposed methods.

Abstract:
Zero-shot anomaly detection (ZSAD) aims to develop a foundational model capable of detecting anomalies across arbitrary categories without relying on reference images. However, since “abnormality” is inherently defined in relation to “normality” within specific categories, detecting anomalies without reference images describing the corresponding normal context remains a significant challenge. As an alternative to reference images, this study explores the use of widely available product standards to characterize normal contexts and potential abnormal states. Specifically, this study introduces AnomalyVLM, which leverages generalized pretrained vision-language models (VLMs) to interpret these standards and detect anomalies. Given the current limitations of VLMs in comprehending complex textual information, AnomalyVLM generates hybrid prompts—comprising prompts for abnormal regions, symbolic rules, and region numbers—from the standards to facilitate more effective understanding. These hybrid prompts are incorporated into various stages of the anomaly detection process within the selected VLMs, including an anomaly region generator and an anomaly region refiner. By utilizing hybrid prompts, VLMs are personalized as anomaly detectors for specific categories, offering users flexibility and control in detecting anomalies across novel categories without the need for training data. Experimental results on four public industrial anomaly detection datasets, as well as a practical automotive part inspection task, highlight the superior performance and enhanced generalization capability of AnomalyVLM, especially in texture categories. An online demo of AnomalyVLM is available at https://github.com/caoyunkang/Segment-Any-Anomaly.

Abstract:
In this article, a network attack, named shifting attack, is considered for hidden Markov Boolean control networks (HMBCNs). Using semi-tensor product of matrices, the considered network and the network attack are presented in algebraic form. State feedback control (SFC) is then applied to stabilize the considered network to a desired state. A necessary and sufficient condition based on the probability matrix is presented for the stochastic stabilization of the HMBCN, based on which, the design of the SFC is given. Then shifting attack is further studied for HMBCNs. The control strategy will be shifted to another when the HMBCN is under attack. Shifting attack is also modeled in a hidden Markov process, under which, a necessary and sufficient condition for the security of the attacked HMBCN is also presented. Propositions are obtained for the security and insecurity of the attacked HMBCN. Using the change of the probability measurement approach, estimations of the states expectation and the attacked signals expectation for the attacked HMBCN are solved. At last, examples show the effectiveness of the obtained results.

Abstract:
This article addresses the fixed-time leaderless cluster synchronization of spatiotemporal community networks (SCNs) characterized by nonidentical node dynamics and reaction-diffusion feature. First, a signed SCN with reaction-diffusion effect is formulated, where the sign-based coupling is introduced to capture the dynamics of coopetition interactions among different communities. Second, to ensure the invariance of the synchronous manifold, an improved interdegree balance condition is proposed as a prerequisite for achieving cluster synchronization of the community network. Third, based on the local state information from adjacent nodes within each community, a time-limited controller is designed to enhance intracommunity coordination while avoiding the adverse effects of intercommunity competition on synchronization. Subsequently, with the help of the matrix decomposition technique and a Lyapunov-like method, several flexible leaderless cluster synchronization criteria are derived by establishing a nontrivial integral inequality and key properties of the intracommunity Laplacian matrix. Finally, the theoretical results are substantiated through a numerical example.

Affiliations: School of Electronics and Information Engineering, Tiangong University, Tianjin, China; Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, School of Computer Science and Technology, Tiangong University, Tianjin, China; School of Intelligent Manufacturing and Electrical Engineering, Guangzhou Institute of Science and Technology, Guangzhou, China; Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, China

Abstract:
This article is concerned with passivity and synchronization for fuzzy coupled reaction–diffusion neural networks (FCRDNNs) with multistate couplings or multiple spatial-diffusion couplings. First, by utilizing an adaptive state feedback controller, several passivity criteria for the FCRDNNs with multistate couplings are obtained. Moreover, a sufficient condition to guarantee the synchronization for the multistate coupled FCRDNNs is also given on the basis of the devised adaptive state feedback control scheme. Additionally, the problems of passivity and synchronization are also addressed for the FCRDNNs with multiple spatial-diffusion couplings by employing the adaptive control technique and Lyapunov functional method. Finally, two numerical examples are presented to validate the effectiveness of the devised adaptive control schemes.

Affiliations: School of Cyber Science and Engineering, Sichuan University, Chengdu, China; School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China; School of Statistics and Mathematics, Guangxi Normal University, Guilin, China; Interdisciplinary Research Institute for Intelligent Science, Ningbo University of Technology, Zhejiang, China; Key Research Center of Automation and Artificial Intelligence, Zhejiang University of Technology, Hangzhou, China; School of Mathematics, Chengdu Normal University, Chengdu, China

Abstract:
This article studies the input-to-state stability (ISS) problem of networked control systems (NCSs) subject to both Denial-of-Service (DoS) and unknown deception attacks (DAs). A neural network (NN)-based resilient event-triggered control protocol (RETCP) is first presented to mitigate resource constraints and the adverse effects of cyber attacks, where the NN technology is leveraged to neutralize and approximate the malicious data injected by unknown DAs. Then, we develop a new predictor to compensate for lost signals of NCSs during the DoS threats, so that the NCSs can further tolerate more unfavorable DoS and unknown DAs. It is shown that the resulting NCSs with the designed novel NN-based controller can achieve ISS under the complex attacks. Finally, experimental evaluations are conducted for an uncrewed ground vehicle (UGV) to verify efficacy of the proposed intelligent control protocols.

Abstract:
This article investigates the distributed tracking problem of networked cyber–physical systems (CPSs), considering system uncertainties, output constraints, and false data injection (FDI) attacks. Notably, the FDI attacks modeled here are explicitly malicious, meaning they are designed to cause the system to violate prescribed output constraints. Motivated by the lack of constrained-control studies addressing situations where the reference signal does not meet the constraint, we introduce a new form of tracking error, termed barrier tracking error (BTE). It is valuable that any conventional control scheme can be combined with the BTE idea to deal with output constraints. Subsequently, a nonsingular finite-time controller is developed using the backstepping method and neural networks. It is worth mentioning that the controller employs an event-triggered quantized control strategy, effectively reducing the burden on channels and actuators. Finally, comprehensive stability analysis and simulations are provided.

Abstract:
In this article, a linear quadratic (LQ) control based on dynamic output feedback (DOF) strategy is proposed for cyber-physical systems (CPSs) under sparse actuator and sensor attacks. The control scheme is divided into three steps. First, the studied system is transformed into a set of hybrid systems based on all possible attack sets. Second, the DOF LQ (dLQ) control scheme is studied for the case of the correct attack set, including analyzing the impact of the similarity transformation on the cost of the dLQ, determining the optimal explicit form of the similarity transformation, and giving the computational expression for the unique observable saddle point of the dLQ. Finally, two online attack detection mechanisms are proposed: 1) adaptive switching mechanism (ASM) and 2) improved ASM (IASM). The difference between the two mechanisms is that IASM detects attacks faster. The hybrid control scheme combining dLQ control method with each of the two mechanisms ensures the asymptotic stability of the closed-loop system. Distinguishing from the classical data-based optimal control method which calculates the system states online from time to time, the hybrid control scheme proposed in this article only needs to solve for the system states during a time period when the control mode is switched, which greatly reduces the computational complexity. The effectiveness and superiority of the proposed method are illustrated by two simulation examples, respectively.

Affiliations: Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang, China; Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia; Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC, Canada; Department of Applied Mathematics, Bharathiar University, Coimbatore, India

Abstract:
Recently, type-3 (T3) fuzzy logic systems (FLSs) have been widely used in various problems, such as modeling, control systems, image processing, forecasting problems, optimization algorithms, and many others. Most studies of T3-FLS focus on its different applications. However, the basic theory, the applications in real-time and online problems, learning schemes, and the robustness against non-Gaussian noises have been rarely studied. In this article, the simplification of T3-FLSs is taken into account, and the new membership functions (MFs), learning schemes, and type reduction are introduced. The concept of singleton MFs in adaptive fuzzy inference systems (ANFIS) is extended to T3-FLSs, and T3-ANFIS is proposed. The type reduction is simplified, and a noniterative learning scheme is developed. The corresponding computations for adaptation laws are derived, and all rules parameters and MF parameters are adjusted. To enhance the robustness versus impulsive noises, a T3-FLS-based correntropy Kalman filter (CKF) is designed. In the suggested algorithm, the kernel-size is not a constant value, but it is online updated by a T3-FLS. Also, to further improve robustness against noisy data, nonsingleton fuzzification for the suggested MF is formulated. By several simulations using real data sets, the feasibility of the suggested T3-FLS is shown, and its superiority is verified by comparisons. Also, the better robustness of suggested T3-FLS-based CKF versus impulsive noises is shown by comparison with traditional KFs.

Abstract:
This article focuses on designing an optimal sensor grouping transmission strategy for multiple processes over packet-dropping channels. A necessary and sufficient condition for the convergence of the estimation error is presented when employing the random access protocol (RAP) for collision-free transmission within each group, and a continuous grouping transmission strategy (CGTS) is proposed to reduce the strategy space without losing optimality. Then, based on the condition and the CGTS, the optimal grouping transmission strategy is obtained by proposing an improved Q-learning algorithm. Compared with the existing works, the proposed optimal strategy reduces channel usage while ensuring estimation accuracy. Finally, a numerical simulation is provided to validate the main results.

Abstract:
This study investigates the stability of reaction-diffusion systems (RDSs) under impulsive disturbances using an event-triggered impulsive control method. Our key contribution is providing Zeno-free conditions for the event-triggered mechanism (ETM), which is crucial due to the potential for impulsive disturbances to trigger the sampling threshold earlier than expected, leading to Zeno behavior. We address this challenge by deriving conditions that ensure the ETM operates without Zeno behavior, essential for practical control implementation. We further derive several sufficient conditions for the asymptotic stability of RDSs based on impulsive control theory. Special cases with specific disturbance rules are also discussed, offering a broader understanding of system stability under various conditions. To demonstrate the applicability of our theoretical findings, we apply our control strategies to an atmospheric pollution model. A numerical example is provided to validate the effectiveness of our approach and to offer theoretical guidance for real-world environmental pollution control.

Abstract:
The tracking control problem for strict-feedback systems with unknown dynamics has been extensively studied. However, most existing control approaches require online approximation models and associated a priori assumptions. In order to avoid the necessity of deriving online models, this article proposes a data-driven backstepping control (DBC) approach for a class of strict-feedback systems with unknown dynamics. First, unlike the widely-studied adaptive backstepping control approaches, we identify the unknown dynamics of each subsystem based on off-line data and develop a data-driven continuous-time Lyapunov equation return controller, ensuring semi-global exponential stability of the error system. Furthermore, we propose a data-driven dynamic surface control (DDSC) approach for the “complexity explosion” problem in DBC. This approach uses a data-driven linear matrix inequality to return the controller, ensuring that the error system remains semi-globally ultimately uniformly bounded, even when the derivative of the virtual controller cannot be calculated. Finally, the superiority and effectiveness of DBC and DDSC are verified by simulation examples.

Abstract:
This study introduces a new asynchronous sampled-data distributed consensus control protocol for nonlinear fractional-order multiagent systems (MASs) containing system uncertainties along with time-varying disturbances. With strict consideration of the hereditary and infinite-memory characteristics of fractional-order systems, a novel adaptive backstepping-based distributed sampled-data control scheme is developed for individual agents with asynchronous sampling mechanisms. Through Lyapunov stability analysis, it is demonstrated that the proposed strategy guarantees the stability of the entire closed-loop system, meaning that all signals will remain within bounds and each agent can achieve output consensus with the specified time-varying reference trajectory. The efficacy of the proposed approach is illustrated through simulation studies, which also serve to validate the results obtained.

Abstract:
Metric learning aims to learn a discriminative metric space, where samples of the same class stay close, and those of different classes far apart. Existing classical metric learning methods based on linear transformation have limited learning performance due to the low representation capability. Although deep metric learning learns nonlinear mappings, the training may come across convergence issues and be unstable. Additionally, many classical metric learning algorithms suffer from long computational time for iterative optimization especially when data dimension is high. Deep metric learning also requires high training cost. To learn a metric space more efficiently and effectively, this article proposes a novel broad metric learning (BML) model, which learns the data transformation by training a broad network. BML maps input data to a broad feature space by fast and convenient nonlinear feature mapping based on random weights, and learns a linear transformation to a discriminative output space. Intraclass distance is reduced by minimizing the distance between data and their class-specific reference points in the target space. The hard-triplet distance learning (HDL) is proposed to learn the distance of hard positive and negative sample pairs, which enhances the intraclass compactness and interclass separation. Closed-form solutions are adopted to solve the optimization problems efficiently when learning the linear transformation. Experiments are conducted on nine datasets to verify the efficiency and effectiveness of BML. BML learns fast and achieves high classification and clustering accuracies in the learned data space.

Abstract:
This article investigates the estimate (i.e., the invariant subset) of domain of attraction (DOA) for complex network. Starting with the quadratic Lyapunov function of isolated node, we construct a quadratic Lyapunov function of complex network for estimating the DOA of network. In this way, if the largest spherical estimate of the DOA for isolated node can be obtained, we can directly obtain the largest spherical estimate of the DOA for network. Then, for improving the existing estimate, we directly utilize the Laplacian matrix to ingeniously construct a new Lyapunov-like function, relaxing the constraint that the derivative of the Lyapunov function is negative definite in a neighborhood of the origin. Moreover, we iteratively compute Lyapunov-like functions to maximize the obtained estimate as far as possible. Afterward, for polynomial networks, the estimate problem of the DOA is transformed into a classical sum of squares (SOS) programming problem. Particularly, we use the properties of isolated node and network topology to significantly reduce the computational complexity of the above SOS programming problem, such that our estimate can be effectively obtained even for large-scale networks. Finally, four examples are given to illustrate the validity of our theoretical results and the efficiency of our computable approach.

Abstract:
Based on the hybrid-index model, this article investigates the asymptotic feedback set stabilization of Boolean control networks (BCNs) with random impulsive disturbances. In this model, it is assumed that the sequence of intervals between adjacent impulsive instants is independent and identically distributed. This assumption ensures that the subsequence of solutions sampled at impulsive moments is a Markov chain. Based on this assumption and the semi-tensor product (STP), random impulsive BCNs (RI-BCNs) can be converted into impulsive-interval driven probabilistic BCNs (ID-PBCNs), and the input-state transition probability matrix (IS-TPM) is constructed, the calculations of convergent target set in the hybrid domain and the time domain are discussed, and the necessary and sufficient conditions for asymptotic feedback set stabilizability are obtained. On this basis, we propose a design algorithm of state feedback controllers to stabilize RI-BCNs asymptotically with respect to a target set by using state-space partition, which enables the system to converge to a given set with the least number of impulsive intervals. Finally, the effectiveness of the obtained results is verified by simulations.

Abstract:
Domain adaptation (DA) techniques are becoming increasingly proficient in cross-domain fault diagnosis tasks. However, DA-based methods are not always applicable due to the target domain data is not always accessible. Although there have been some interesting domain generalization methods for fault diagnosis under unseen conditions, most of them can only be used to mine the fault features on source domain distributions, and the improvement of model generalization performance is limited. To solve this problem, the multiplicative noise Gaussian perturbation strategy and the additive noise linear fusion strategy are proposed to capture fault information beyond source domain distributions. The former is used to randomly perturb feature statistics of multisource domains to simulate the uncertainty of domain shift, while the latter is used to perform the additive noise linear operation on feature statistics of multiple source domains to ensure the authenticity of the generated feature styles. Further, the feature statistics generated by both strategies are mixed with random convex weights to obtain new feature styles, achieving the best compromise between reliability and diversity. The network can learn more fault information from features with diversified styles. Extensive experimental results on both public and real datasets verify the effectiveness of our approach.

Abstract:
This article addresses the problem of collision avoidance and obstacle avoidance in nonlinear multiagent control using a data-driven cooperative output approach. First, to avoid collisions with obstacles, a safe reference trajectory planning method is proposed, which dynamically projects unsafe segments of a reference trajectory onto the outer boundary of obstacle regions, enabling the agent to bypass the obstacle. Second, by using only input-output information, a distributed tracking controller incorporating two dynamical barrier functions is designed, which allows the agent to adaptively decelerate within the coverage range of the barrier function to avoid collisions. Finally, sufficient conditions are provided to ensure that the agent can successfully bypass obstacles using the proposed control strategy. Simulation results confirm that the proposed method enables the agent formation to timely avoid stationary/moving obstacles, prevent collisions between agents, and effectively track the desired trajectory while maintaining formation.

Abstract:
Inner convex approximation is a compelling method that enables the real-time implementation of suboptimal nonlinear model predictive controls (MPCs). However, it suffers from a slow convergence rate, which prevents suboptimal MPC from achieving better performance within a specific sample time. To address this issue, we first reformulate the conventional inner convex approximation procedure as a root-finding problem for a nonlinear equation. Then, under mild assumptions, a comprehensive functional analysis is performed on the derived nonlinear equation, focusing on its continuity, differentiability, and the invertibility of the Jacobian matrix. Building on this analysis, we propose an improved algorithm that applies Broyden’s method to accelerate the root-finding procedure of this derived nonlinear equation, thereby enhancing the convergence rate of the conventional inner convex approximation method. We also provide a detailed analysis of the proposed algorithm’s convergence properties and computational complexity, showing that it achieves a locally superlinear convergence rate without devoting much additional computational effort. Simulation experiments are performed in an obstacle avoidance scenario, and the results are compared to the conventional inner convex approximation method to assess the effectiveness and advantages of the proposed approach.

Abstract:
In industrial soft-sensor applications, labeled samples are often scarce and unable to fully represent the dynamic changes in industrial processes. Although semi-supervised methods offer a potential solution to this issue, existing feature-construction-based methods cannot ensure the effectiveness of the feature, and pseudo-label-based methods lack an established confidence evaluation standard. To address these challenges, this article first proposes a novel performance-driven distillation strategy, which designs an innovative siameseLSTM structure for training multiple teacher models. By assigning higher weights to high-performance teacher models and simultaneously leveraging the guidance of the soft sensing task, the student model is guided to learn more effective feature representations. Additionally, a new pseudo label confidence evaluation strategy is introduced, which aims to enhance the generalization of the base soft-sensor model by selecting samples with high-confidence pseudo labels. Finally, By combining the above two strategies, a semi-supervised soft-sensor framework is proposed for the soft sensing of industrial quality variables. The effectiveness of the proposed framework is validated through two real-world datasets from different stages of the alumina production process. Compared with some existing advanced soft sensor frameworks, the prediction results on different datasets show that the root-mean-square error (RMSE) and mean absolute error (MAE) are reduced by an average of 10.76% and 11.18%, respectively, while the correlation coefficient (R2) is averagely increased by 0.1203.

Abstract:
In this article, leader-following consensus of time-scale-type heterogeneous nonlinear multiagent systems (HNMASs) is investigated with dynamic periodic event-triggered mechanism (DPETM). The event detection period in DPETM is determined by a function-dependent threshold, whose initial value and the value at each periodic event detection instant are used for the update of an auxiliary function in the DPETM. Furthermore, the auxiliary function with periodic jumps serves as a detection threshold. To guarantee the nonincreasing behavior of the designed non-negative analysis function, a weighted function is devised that shares the same derivative form as the function that determines the detection period during each detection period. Then, by integrating the theory of time scales and graph theory, leader-following consensus is achieved in a periodic communication fashion with fewer sampling updates. Two examples are presented to illustrate the validity of the results.

Abstract:
This article deals with the bipartite synchronization in coupled fractional-order reaction-diffusion neural networks (CFRNN) with multiple state or multiple spatial diffusion couplings. A bipartite synchronization condition is derived for the multiple state CFRNN (MSCFRNN) with the help of the Lyapunov functional combined with inequality techniques, and an adaptive state-feedback control scheme is also devised to ensure the bipartite synchronization of the MSCFRNN. Similarly, several sufficient conditions are also given to guarantee the bipartite synchronization in the multiple spatial diffusion CFRNN (MSDCFRNN). Finally, the effectiveness of the devised control strategies is demonstrated through two numerical examples.

Abstract:
In this article, the event-triggered finite-time stabilization of time-scale delayed Takagi-Sugeno (T–S) fuzzy systems is studied. By comparing strategies, inequality techniques, and time scale theory, finite-time stabilization criteria for the systems are derived that do not require differentiability of the time delay, and the controller is designed in a simple form that does not rely on power functions or delayed state feedback controllers. Corresponding results cover both continuous-time and discrete-time cases, and construct a unified theoretical framework for the finite-time analysis of the time-scale delayed systems. Meanwhile, the proposed event-triggered mechanism can avoid Zeno behavior and reduce the consumption of communication resources. The validity of the theoretical results is verified by two simulation experiments.

Abstract:
Spatio-temporal forecasting plays a crucial role in the dynamic perception of smart cities, such as traffic flow prediction, renewable energy forecasting, and load prediction. Its objective is to understand the patterns of spatio-temporal changes under the interaction of various factors. Accurate spatio-temporal forecasting relies on sufficient high-quality data and powerful models. However, in reality, data is often sparse. In such cases, while adaptive graphs and large language models (LLMs) can maintain performance, they face issues of spatial spurious associations and hallucinations, respectively. These issues hinder the ability of the model to learn and infer cross spatio-temporal and cross-scale features effectively. To address this, we propose a novel model termed spatio-temporal causal intervention large language model (STCInterLLM). This model employs a newly designed causal intervention encoder to update spatial spurious correlations in the spatio-temporal adaptive graph. Subsequently, the novel chain-of-action prompting text is utilized to enforce the decomposition of the prediction process, thereby enhancing the causal representation of features while mitigating hallucinations in LLMs. Finally, a lightweight marker alignment module ensures the consistency between the encoder, prompting text, and LLM, enabling accurate forecasting of distinct scale spatio-temporal evolution patterns. Extensive experiments conducted on power distribution systems integrated with renewable energy sources and transportation systems encompassing diverse types of data, demonstrate that the proposed STCInterLLM consistently achieves state-of-the-art performance across significantly varied scenarios. Codes are available at https://github.com/lishijie15/STCInterLLM.

Abstract:
As a primary passive investment strategy, index tracking replicates the performance of a specific financial market index by minimizing tracking errors. Most existing index tracking methods are developed based on the assumption that all historical data are equally important. As a result, the importance of different historical data may be overlooked. This article addresses index tracking via temporally weighted least-squares regression. The weight for each time period except for the latest one is defined as the reciprocal of the largest absolute residual of the returns between the index currently and all the selected stocks in the subsequent periods. The weight for the latest period is inferred from the weights in the preceding periods via Gaussian process regression. The tracking accuracy and consistency of the proposed approach are demonstrated via experimentation on historical data from seven major stock markets.

Abstract:
Large-scale sparse multiobjective optimization problems (LSMOPs) are of great significance in the context of practical applications, such as critical node detection, feature selection, and pattern mining. Since many LSMOPs are pursued based on large datasets, they involve a large number of decision variables, resulting in a huge search space that is challenging to explore efficiently. To rapidly approximate sparse Pareto optimal solutions, some evolutionary algorithms have been proposed to reduce the dimensionality of LSMOPs. However, their adaptability to different LSMOPs remains limited due to their reliance on fixed dimensionality reduction schemes, which can potentially lead to local optima and inefficient utilization of function evaluations. To address this issue, a knowledge learning-based dimensionality reduction approach is proposed in this article. First, in the early stages of evolution, the impact of different dimensionality reduction schemes on the sparse distribution of the population is evaluated. Then, the multilayer perceptron is employed to learn the accumulated knowledge from the evolutionary process, thereby constructing a mapping model between the sparse features of the evolutionary process and the candidate dimensionality reduction schemes. Finally, the model recommends the best dimensionality reduction scheme in each generation, achieving a good balance between exploration and exploitation. Experimental evaluations on both benchmark and real-world LSMOPs demonstrate that an evolutionary algorithm incorporating the proposed knowledge learning-based dimensionality reduction approach outperforms most existing evolutionary algorithms.

Abstract:
In this study, the adaptive fuzzy collision-free formation control issue is investigated for uncertain nonlinear multiagent systems (MASs) under communication delays. Fuzzy logic systems (FLSs) are applied to model unknown agents, and the navigation function is utilized to establish the criterions of collision avoidance and connectivity preservation for the agents. Since the communication between agents is affected by time-varying delays, the leader’s state and its high-order derivatives’ real-time information are unknown, a distributed command governor is designed to estimate them. Based on the designed distributed command governor, and the collision avoidance and connectivity preservation criterions, an adaptive fuzzy collision-free formation control approach is formulated by backstepping control design theory. The developed adaptive fuzzy formation control approach can not only achieve the formation objective, but also avoid the collisions and ensure the topology connectivity. Finally, we apply the developed adaptive fuzzy formation control method to multiple Euler-Lagrangian (EL) systems, the simulation and comparison results verify its effectiveness.

Abstract:
This article aims to address the formation control issue of the unknown nonaffine nonlinear heterogeneous multiagent system (MAS) considering formation tracking accuracy and computational cost. A novel dynamic prescribed boundary-based event-triggered mechanism is proposed first to flexibly adjust the emphasis on these two indicators, and applied to data modeling and controller design simultaneously to reduce their computational cost. On one hand, an observer-based pseudo gradient estimation algorithm is designed under event-triggered framework for model reconfiguration with only input/output data of system rather than mathematical dynamics. On the other hand, an event-triggered constrained control strategy is developed with several modules to cope with complex scenarios. Concretely, a data-driven anti-windup compensator is designed in case of input constraint, and an improved prescribed performance-based fractional order terminal sliding mode controller is explored for enhancement of the formation tracking accuracy and robustness of the controlled MAS with rigorous stability analysis. Both numerical simulation and hard-in-the-loop experiment on distributed energy storage systems are performed to attest the efficacy of the proposed formation control strategy.

Abstract:
This article proposes the row-stochastic event-based quantized (RSEQ) algorithm to address the distributed optimization problem with multiple communication constraints, including limited communication costs and bandwidth. In RSEQ, a novel event-based dynamic quantizer is designed to resist the negative effects of communication constraints on the algorithm. The quantizer encompasses the event generator and the dynamic encoder/decoder, which collectively adapt the frequency and size of information sharing based on real-time state. The RSEQ only requires the construction of a row-stochastic weight matrix, which leads to lower conservatism compared to algorithms based on column-stochastic matrices. Additionally, the introduction of an acceleration term enables RSEQ to linearly converge to the globally optimal solution without the deployment of the average gradient estimator. Instead, a Perron vector estimator needs to be employed to counteract the unbalancedness of the directed network. With the effect of the event generator, the Perron vector estimator can also be left inactive after a certain number of iterations, which means that the transmission of only state information between agents can linearly converge to the global optimal solution under directed networks. Finally, the effectiveness of the algorithm is demonstrated through an economic dispatch problem in smart grids.

Abstract:
Due to their unique structure and powerful capability to handle uncertainty and partial reliability of information, Z-numbers have achieved significant success in various fields. Zadeh previously asserted that a Z-number can be regarded as a summary of probability distributions. Researchers have proposed various methods for determining the underlying probability distributions from a given Z-number. Conversely, can a Z-number be used to summarize a set of probability distributions? This problem remains unexplored. In this article, we propose a nonlinear model, termed Maximum Expected Minimum Entropy (MEME), for generating a Z-number from a set of probability distributions. Through this model, Z-numbers can be generated directly from data without requiring expert knowledge. Additionally, we applied the MEME model to classification problems, introducing a novel if-then rule form, termed Z-valuation if-then rules. These rules replace the deterministic consequent part of a fuzzy rule with an uncertain Z-valuation, thereby further summarizing the uncertain information in the rule’s consequent. Based on the Z-valuation rules, we propose a Z-valuation rule-based (ZVRB) classification system, which aims to enhance decision-making processes in scenarios where uncertainty plays a key role. To validate the effectiveness of the ZVRB classification system, we conducted two experiments comparing it with both classic and advanced nonfuzzy classifiers as well as fuzzy classification systems. The results show that the ZVRB model is superior to the other comparative classifiers in terms of classification performance.

Abstract:
This article addresses the issue of adaptive event-triggered and quantized control for a category of uncertain nonlinear systems, utilizing a prescribed-time (PT) control framework. We begin by introducing a dynamic event-triggering mechanism and a dynamic event-driven quantizer to develop a discrete control framework, without assuming the constraint of input-to-state stability (ISS). The aperiodic discrete control method can effectively improve the data transmission efficiency of the networked control system. Then, according to the adaptive parameter estimation, a novel PT event-triggered adaptive controller and a PT sampled and quantized adaptive controller are proposed. Compared with the backstepping control method, the designed “one-step-controller” decreases the computational loads of the virtual controllers. Moreover, the global PT stability of the nonlinear system is assured, and the Zeno phenomenon of the event-triggered sampling does not happen. Finally, the practicability and availability of the designed control method are validated via a numerical system and a manipulator system.

Abstract:
Differential privacy is an effective method to solve data privacy leakage. The common differential privacy method is achieved by adding privacy noises to the transmitted data, which may affect data accuracy. For the control system, data accuracy greatly affects the system performance. To circumvent this difficulty, we propose a novel privacy-preserved rolling optimization strategy (PP-ROS) for switched systems. The main contributions are reflected in three aspects: 1) The proposed PP-ROS is used to calculate the private control input by adding Laplace noise to the prediction and control horizons, instead of the transmitted data. 2) Privacy definitions of the prediction and control horizons are presented, and a private model predictive control (P-MPC) controller design is provided based on the PP-ROS. The P-MPC controller achieves the privacy of its parameters. 3) Under PP-ROS and P-MPC, the proof and calculation methods for the privacy levels of control input and system output are given. The results indicate that when noise is added to the horizons, both control input and system output are private. Finally, the availability and benefits of PP-ROS and P-MPC are demonstrated using two simulation examples and comparison results.

Affiliations: Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China; School of Science, Beijing University of Civil Engineering and Architecture, Beijing, China; College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China; Hebei Province Key Laboratory of Big Data Calculation, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China

Abstract:
Graph contrastive learning (GCL) is emerging as a pivotal technique in graph representation learning. However, recent research indicates that GCL is vulnerable to adversarial attacks, while existing robust GCL methods against adversarial attacks are inefficient and lack scalability due to the significant computational expenses of explicit adversarial attacks on the graph structure. To address the shortcomings of existing approaches, we propose an efficient and robust GCL via multiadversarial views training framework, called ERMAV. Specifically, the ERMAV generates two adversarial views by attacking both node attributes and latent representations on randomly sampled subgraphs. The method conducts explicit adversarial attacks on node attributes by attacking node attributes and implicit adversarial attacks on the graph structure by attacking latent representations, which avoids the costly computation of explicit graph structure attacks. Moreover, two efficient attack methods are developed to construct adversarial perturbations, which can dynamically generate different adversarial views to enhance sample diversity in the training phase. Furthermore, to validate the effectiveness and robustness of the proposed framework, extensive experiments of node classification on seven real-world datasets are conducted. Experimental results show that our ERMAV outperforms state-of-the-art GCL methods on the original graphs and is consistently more robust than existing robust GCL methods on a variety of attacked graphs. This demonstrates the strong robustness and great potential of our ERMAV in real-world applications.

Abstract:
This article investigates the resilient distributed Nash equilibrium (NE) control problem for nonlinear multiagent systems (MASs) that suffers from denial-of-service (DoS) attacks in the communication network. Different from the existing works on NE seeking in noncooperative games, it is the first trial to consider the resilient distributed NE control problem for nonlinear MASs under DoS attacks. To overcome the challenges caused by the considered problem, a new layered NE control method is developed, which consists of a resilient distributed NE seeking algorithm, two-stage cascade filters, and a resilient adaptive controller. Specifically, the resilient distributed NE seeking algorithm is proposed to ensure that the actions in this algorithm converge to the NE even under DoS attacks. Then, the improved actions with smooth characteristics are designed by introducing novel two-stage cascade filters. By using newly designed actions and their derivatives, a resilient adaptive controller is proposed to ensure that the output of MASs converges to the NE. Finally, simulation results are provided to verify the effectiveness of the proposed strategy.

Abstract:
Designing the optimal control for the practical systems is challenging due to the unknown system dynamics and unavoidable external disturbances. In this article, the H_\infty optimal control problem is investigated for continuous-time linear systems with unknown dynamics. The existing reinforcement learning-based H_\infty optimal control methods require persistence of excitation (PE) condition or data storage mechanism to guarantee the convergence of the algorithms. However, PE condition is hard to be monitored online and data storage mechanism requires to store huge amounts of past system data. In order to solve these problems, the initial excitation-based reinforcement learning algorithms are presented to learn the optimal control policy under an online-verifiable initial excitation condition. The properties of the initial excitation-based reinforcement learning algorithms are analyzed, which show that the presented algorithms converge to the optimum under the initial excitation condition. Numerical analysis is provided which demonstrates the correctness of the presented algorithms.

Abstract:
In this article, a double-channel event-triggered control method is developed for nonlinear uncertain interconnected systems using backstepping techniques, which introduces event-triggering mechanisms at both the sensor and controller sides. Using event-triggering mechanism at the sensor side presents a challenge to the backstepping control design as the discontinuous state/output signals received at the controller side result in nondifferentiable virtual control signals. This challenge becomes more pronounced when considering more general types of event-triggering mechanisms. Compared with existing methods, this article proposes a different idea with three innovative features: 1) the proposed event-triggering mechanism does not require the calculation of virtual control signals at the sensor side before transmitting them to the controller side; 2) the output triggering is considered directly, and there is no need to design separate controllers for the two communication scenarios without and with event-triggering, thereby avoiding the effect of errors caused by processing substitutions; and 3) it necessitates the online update of only one parameter estimator, avoiding the issue of over-parameterization. Finally, we validate the effectiveness and advantages of the proposed decentralized event-triggered control approach through a numerical case study.

Abstract:
In a multisensor system, each sensor typically requires independent reference tracking while conflicts arise due to differing desired inputs for different sensors. This scenario presents an exemplary incompatible multiobjective tracking problem (IMOTP), which can be resolved as a multiobjective optimization problem (MOOP). We propose an iterative learning control strategy to resolve conflicts between sensors. First, we elaborate on the Pareto optimal solution (POS) set associated with the MOOP. Subsequently, we derive an update direction for Pareto improvement based on gradient-based algorithms for MOOP and establish a learning control algorithm ensuring that each update is a Pareto improvement and converges to a POS. These technical advancements effectively overcome tracking conflicts in multisensor systems. Illustrative simulations are provided to validate the theoretical results.

Affiliations: School of Information and Artificial Intelligence, the Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, and the Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei, China; School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, China; Department of Automation and the Anhui Province Key Laboratory of Intelligent Low-Carbon Information Technology and Equipment, University of Science and Technology of China, Hefei, China; Department of Automation, University of Science and Technology of China, Hefei, China; School of Mathematics, Hefei University of Technology, Hefei, China

Abstract:
This article first presents a dual flexible prescribed performance control (DFPPC) approach of input saturated high-order nonlinear systems (IS-HONSs). Compared to the existing PPC approaches of IS-HONSs, under which the performance constraint boundaries (PCBs) are usually fixed and bounded, resulting in a restriction of the initial error in the algorithm implementation; in addition, the coupling relationship between performance constraints and input saturation is usually ignored, resulting in the methods are very fragile when input saturation occurs. By designing the novel tensile model-based PCBs that depend on output and input constraints, the proposed DFPPC method provides sufficient resilience for both the initial conditions and the input saturation, so that the proposed DFPPC method can not only be suitable for multiple types of initial errors by adjusting the parameters, including e_1(0)\in (\underline \mathcal B(0),\bar \mathcal B(0)) , e_1(0)\in (-\infty,\bar \mathcal B(0)) , e_1(0)\in (\underline \mathcal B(0),+\infty) and e_1(0)\in (-\infty,\infty) , where \underline \mathcal B(0) , and \bar \mathcal B(0) denote the initial PCBs; but also can achieve a good balance between input saturation and performance constraints, i.e., when the control input reaches or exceeds the saturation threshold, the PCBs can adaptively extend to avoid the singularity, and when the control input returns to the saturation threshold range, the PCBs are then adaptively restored to the original PCBs. The results show that the proposed DFPPC algorithm guarantees semi-global boundedness for all closed-loop signals, while ensuring that the system output accurately tracks the desired signal, and it consistently maintains the tracking error within the PCBs. The developed algorithm is illustrated by means of simulation instances.

Abstract:
This article investigates the distributed estimation problem of an unknown high-dimensional sparse state vector for a stochastic dynamic system. The communication topology randomly switches, and the switching law is governed by a time-homogeneous Markovian chain. By means of the compressed sensing (CS) theory and a diffusion strategy, we propose a compressed distributed Kalman filter (CDKF). That is, each sensor first compresses the original high-dimensional regression data. Then, the covariance intersection fusion rule is utilized to obtain a distributed Kalman filter (DKF) estimate in the compressed low-dimensional space. Afterward, the original high-dimensional sparse state vector can be well recovered by a reconstruction technique. In terms of stability analysis, one of the main difficulties lies in analyzing the product of nonindependent and nonstationary random matrices in the context of time-varying communication topologies. Relying on the stochastic stability theory, the Markov chain theory, and the CS theory, we establish the upper bound for the estimation error under the compressed cooperative excitation condition, which is much weaker than the traditional uncompressed collective observability conditions used in the existing literature. Finally, we provide a simulation example to illustrate the performance of the proposed algorithm.

Abstract:
This article focuses on an observer-based adaptive sensor fault compensation fixed-time tracking control problem for uncertain nonlinear systems. A sixth-power Lyapunov function is designed for the first time which lays the foundation to construct the effective adaptive fixed-time fault compensation mechanism. Meanwhile, in the controller design procedure, owing to the existence of the sensor fault, only the actual output can be measured, unlike existing results, an improved state observer is constructed to estimate the unmeasured states effectively. Under our developed control mechanism, all closed-loop signals are bounded within fixed-time interval, observation errors and tracking error can converge into a small domain around zero. Simulation verifies the availability of the presented approach further.

Affiliations: School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, China; School of Engineering, Tokyo University of Technology, Hachioji, Japan; School of Automation, the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, and the Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, China University of Geosciences, Wuhan, Hubei, China

Abstract:
This article presents a cascade equivalent-input-disturbance (EID) estimator to handle the control problem of disturbance rejection in the presence of measurement noise. The standard EID estimator faces a challenge when the output of a system suffers from high-frequency noise, i.e., the estimator tuned to achieve high disturbance-rejection performance is usually extremely sensitive to measurement noise. A new estimator is developed in this study to address the issue by combining EID estimators in a unique cascade form. The sensitivity reduction of a cascade EID (CEID) estimator with p levels, which is related to disturbance rejection, is p times larger than that of a standard EID estimator at low frequencies. Meanwhile, the Bode magnitude curve of the transfer function related to noise suppression is shifted up only by 20\lg p dB at high frequencies compared to that of a standard one. This improves disturbance-rejection performance and prevents measurement noise from being over-amplified. The design of a CEID estimator-based control system is provided and the stability criterion of the system is derived. The validity and superiority of the presented method are demonstrated by a deep analysis and simulation and experimental results.

Abstract:
The enhancement of underwater images has emerged as a significant technological challenge in advancing marine research and exploration tasks. Due to the scattering of suspended particles and absorption of light in underwater environments, underwater images tend to present blurriness and predominantly color distortion. In this study, we propose a novel approach utilizing denoising diffusion models to improve underwater degraded images. After training the noise estimation network of the denoising diffusion models, we accelerate the deterministic sampling process with denoising diffusion implicit models. We also propose a patch-based method by implementing average sampling between overlapping image patches at each sampling step, enabling the generation of images at arbitrary resolution while preserving their natural appearance and details. Through benchmark experiments, we illustrate that our method outperforms or closely approaches state-of-the-art techniques in terms of effectiveness and performance. We demonstrate that our approach reduces the interference of underwater environments with the semantic information of the images by salient object detection experiments.

Affiliations: Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China; Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China; Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada; Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Ministry of Education, Wuhu, China; Research Institute of Intelligent Complex Systems, the School of Mathematical Sciences, the Shanghai Centre for Mathematical Sciences, and the Key Laboratory of Mathematics for Nonlinear Sciences, Ministry of Education, Fudan University, Shanghai, China

Abstract:
The event-triggered almost global attitude consensus problem is considered in this article for multiple rigid body systems with prescribed performance. Two kinds of attitude consensus protocols using axis-angle vectors are proposed at the kinematic level with different prescribed performance constraints. The first protocol aims to achieve the event-triggered attitude consensus almost globally under jointly connected graphs. Based on a prescribed performance function with local states, the configuration space of parameterized attitude representations is shown to be positively invariant which almost globally covers \mathbb SO(3) . The second protocol is designed to reach attitude consensus with the prescribed transient behavior guaranteed in the event-triggered setting. By defining a prescribed performance function using the metric on axis-angle spaces, a dynamic event-triggered framework is designed to ensure both the attitude geometric topology constraint and prescribed convergence performance. Finally, numerical results are given to show the validness of the two control protocols.

Abstract:
In this article, an adaptive prescribed-time neural controller is developed for the tracking problem of a class of high-order nonlinear systems with full-state constraints. First, a prescribed-time bounded stability criterion is designed. Then, to handle the “explosion of complexity” problem of the backstepping method, an adaptive prescribed-time filter is constructed, in which the filter error is prescribed-time stable. Compared with existing methods, the newly designed transformation approach can accommodate a broader range of state constraint types. Then, the unknown nonlinear function is handled by radial basis function neural networks (RBFNNs). The adaptive prescribed-time neural control scheme is developed based on above. It can guarantee that the closed-loop system achieves the prescribed-time stability, and all states do not transgress the constraints. To demonstrate the effectiveness of the control strategy, comparative simulations are provided at the end.

Abstract:
This article addresses the output consensus problem of heterogeneous multiagent systems (MASs) via a distributed modified dynamic event-triggered control design. First, a fully distributed asynchronous event-triggered compensator for each agent is proposed to act as a virtual reference generator without using global information. Second, based on this compensator, a distributed event-triggered controller for each heterogeneous agent is developed to reach output consensus. Specifically, to save resources and guarantee asymptotic consensus, a modified dynamic event-triggered mechanism (DETM) is proposed, in which a time-triggered mechanism (TTM) is introduced to ensure the existence of a positive minimum interevent time and a DETM is designed to further avoid redundant triggering actions. Besides, the triggering condition of the DETM does not require continuous detection, it is only detected when the timer variable in the TTM deceases from maximum value to zero. These features of the developed control scheme are presented analytically and verified numerically.

Abstract:
As a primary effector of humans, the hand plays a crucial role in many aspects of daily life. Recognizing multidegree-of-freedom hand movements from muscle activity helps infer human motion intentions. Solving this problem has direct applications in prosthetic and exoskeleton control. Here, we propose a self-supervised learning algorithm inspired by muscle synergies to achieve simultaneous estimation of wrist rotation (supination/pronation) and hand grasp (open/close) from sonomyography—the muscle deformation detected by a wearable ultrasound array. Unlike conventional methods collecting both muscle activity and hand kinematics for supervised model calibration, this algorithm only uses unlabeled forearm ultrasound signals for self-supervised wrist and hand movement estimation, where movement labels are auto-generated. The performance of the proposed algorithm was experimentally evaluated with ten participants including an amputee. Offline analysis demonstrated that the proposed algorithm can accurately estimate simultaneous wrist rotation and hand grasp movements ( r_\textrm wrist and r_\textrm hand were 0.98 and 0.94 for the able-bodied, and 0.98 and 0.90 for the amputee, respectively). Notably, the performance of the self-supervised learning was superior to the supervised learning for the amputee. Online experiments demonstrated that intended wrist and hand movements can be deciphered in real time, enabling accurate control of a virtual hand. This study will open up a new avenue for the sonomyographic human-machine interaction.

Affiliations: Faculty of Science and Technology, University of Macau, Macau, China; School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore; Chongqing Key Laboratory of Intelligent Unmanned Systems, School of Automation, Chongqing University, Chongqing, China; Department of Strategic and Advanced Interdisciplinary Research, Peng Cheng Laboratory, Shenzhen, China; Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, SAR, China

Abstract:
This article presents a double-channel (sensor-to-controller channel and controller-to-actuator channel) event triggered control method for nonlinear interconnected systems subject to sensor and actuator faults via the backstepping technique. It should be emphasized that the utilization of triggering mechanism at the sensor side poses a challenge to the design of backstepping control, as it leads to nondifferentiable virtual control signals due to the discontinuous nature of the state/output signals received at the controller side. In contrast to existing methods, the proposed event triggering mechanism eliminates the need for computing virtual control signals at the sensor side before transmitting them to the controller side. By establishing the relationships of the corresponding variables in two communication scenarios (namely, without and with event triggering) and introducing dynamic filtering technique, the problem of nondifferentiable virtual control signals in backstepping design is solved. We present a numerical case study to validate the effectiveness and advantages of the proposed decentralized event triggered control approach.

Abstract:
This article proposes a distributed optimization algorithm with a convergence time that can be assigned in advance according to task requirements. To this end, a sliding manifold is introduced to achieve the sum of local gradients approaching zero, based on which a distributed protocol is derived to reach a consensus minimizing the global cost. A novel approach for convergence analysis is derived in a unified settling time framework, resulting in an algorithm that can precisely converge to the optimal solution at the prescribed time. The method is interesting as it simply requires the primal states to be shared over the network, which implies less communication requirements. The result is extended to scenarios with time-varying objective function, by introducing local gradients prediction and nonsmooth consensus terms. Numerical simulations are provided to corroborate the effectiveness of the proposed algorithms.

Abstract:
Human-machine systems (HMSs) are dedicated to integrating intelligent human decisions with machine operations to achieve synergistic operational functionality. We focus on constraint-following control within the HMS, considering potential uncertainties, environmental disturbances, and limited operational space. A hierarchical hybrid control scheme is proposed, consisting of a preemption algorithm and a human decision algorithm. Specifically, the preemption algorithm relies on online state feedback from mechanical system signals, such as position and velocity, which can be implemented in hardware or software; the human decision algorithm takes inputs from electrophysiological signals or language commands. In this development, a Lagrangian density function is constructed that integrates optimal decision making with a uniformly bounded threshold. The intelligent decision-making problem in the HMS is creatively analyzed and mathematically solved leveraging variational calculus, resulting in the analytical expression of the optimal membership function associated with human decisions. Furthermore, a series of numerical simulation experiments are conducted using a bionic upper-limb prosthetic system as an example, and the comparison results demonstrate the superiority and effectiveness of the proposed method.

Abstract:
The necessary and/or sufficient conditions for a Takagi–Sugeno–Kang Fuzzy Inference System (TSK-FIS) to be monotone has been a key research direction in the last two decades. In this article, we first define fuzzy membership functions (FMFs) with single and continuous support; and consider TSK-FIS with a grid partition strategy for computing its firing strengths with product T-norm (here after denoted as TSK-FIS-product). We also define a more general joint necessary condition, whereby each constituent itself is a necessary condition for the TSK-FIS-product model. The first necessary condition indicates that the normalized firing strength must not be indeterminate (i.e., 0/0), i.e., susceptible to the tomato classification problem. The second necessary condition indicates that all restricted consequents of fuzzy if-then rules must be defined. Based on the principle of the ordered weighted averaging (OWA) operator as well as the concept of increasing orness in OWA and hyperboxes, a general joint sufficient condition for a TSK-FIS-product model to be monotone is derived. Three case studies of the developed methods for undertaking failure mode and effect analysis (FMEA) and image processing tasks are presented. The results are compared, analyzed, and discussed, demonstrating the usefulness of our developed methods.

Abstract:
Localization security is crucial to the widespread applications of wireless sensor networks (WSNs) in various fields. This article mainly studies the issue of distributed localization in WSNs subject to deception attacks, in which the attacker randomly compromises communication channels and injects false data, resulting in the codification of data received by sensor nodes. A distributed iterative localization algorithm based on detection-holding strategy is proposed with the help of barycentric coordinate representations. This algorithm detects modified data in communication links through residual detection and communication encryption. It is proved theoretically that the proposed localization algorithm can achieve accurate convergence to the sensors’ locations under general random false-data-injection attacks. Finally, the algorithm performance is demonstrated through simulation examples.

Abstract:
This article investigates the trajectory tracking control issue of autonomous underwater vehicles (AUVs) subject to dynamic uncertainties, external disturbances, and input amplitude and rate saturations. Initially, two new stable systems with fixed-time convergence are developed, and their upper bounds of settling time and convergence regions are thoroughly analyzed. Building on these systems, an enhanced fast nonsingular integral terminal sliding-mode (NITSM) surface and a new virtual control law are designed, respectively. Next, a novel saturated adaptive fuzzy fixed-time NITSM controller is proposed, circumventing the restrictions on uncertainties and input saturation in the existing results. The proposed controller ensures that the tracking error converges to a small neighborhood of the origin within a fixed time. Furthermore, to facilitate the adaptive fixed-time stability analysis, two new inequalities are established and rigorously proved. Using the two inequalities, the fixed-time stability of closed-loop systems is demonstrated by the Lyapunov’s theory. Finally, representative numerical simulations validate the effectiveness of the proposed control scheme.

Abstract:
This brief presents an adaptive neural zeta-backstepping control strategy for a class of uncertain nonlinear systems, which allows these systems to be practically stabilized with predefined damping ratios. By introducing the zeta-backstepping technique, system damping ratios can be predetermined based on specific parameter selection rules. To reduce the impact of unknown nonlinearities, neural networks (NNs) with gradient descent training are applied to compensate such nonlinearities online. A new filter, called dynamic command filter, is used to construct the gradient of the NNs. By resorting to second-order Lyapunov stability criteria, it is proved that the closed-loop system is practically stable and has predefined damping ratio. Finally, experiments on a perturbed direct current (DC) motor system demonstrate the advantages of the proposed method.

Affiliations: Department of Automation, Tsinghua University, Beijing, China; Department of Automation, College of Information Science and Engineering/College of Artificial Intelligence, China University of Petroleum (Beijing), Beijing, China; Shandong Provincial Engineering Research Center of Intelligent Sensing and Measurement and Control Technology, College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China

Abstract:
This article investigates the problem of fault-tolerant control for stochastic high-order fully actuated systems (FASs) with actuator faults. Different from the majority of existing studies focusing on deterministic high-order FASs, this work introduces stochastic disturbances into the systems. Employing the generalized martingale technique, a novel fault-tolerant equivalent controller is formulated. Additionally, an adaptive compensation law is constructed to address time-varying faults promptly. The designed preclosed-loop strategy advocates the advantage of the FAS methodology and guarantees performance by ensuring that the tracking error complies with the user-defined probabilistic ultimate bound. Finally, a numerical case and a practical example of a rotary steerable drilling platform are exploited to demonstrate the effectiveness of the proposed method.

Abstract:
For high-dimensional heterogeneous Kuramoto oscillator networks (HDHKONs) evolving in continuous time, achieving global synchronization can be exceedingly challenging, and in many cases, it may even appear impossible for various network configurations, including Platonic solids and Archimedean solids, or large-scale networks. Herein, a pinning impulsive control approach for HDHKONs is developed to attain global synchronization on the unit sphere without imposing constraints on initial phase distributions. With this approach, the entire network is globally stabilized onto an objective trajectory by impulsively controlling only a small fraction of oscillators. Furthermore, several synchronization criteria are provided to ensure that the global synchronization procedure is successful. These criteria are easily verified and shed light on the interplay among network parameters, the percentage of controlled oscillators, impulsive intensity, and impulsive frequency. Finally, two examples are implemented to validate the theoretical results.

Abstract:
This article concerns the secure containment control problem for multiple autonomous aerial vehicles. The cyber attacker can manipulate control commands, resulting in containment failure in the position loop. Within a zero-sum graphical game framework, secure containment controllers and malicious attackers are regarded as game players, and the attack-defense process is recast as a min-max optimization problem. Acquiring optimal distributed secure control policies requires solving the game-related Hamilton-Jacobi–Isaacs (HJI) equations. Based on the critic-only neural network (NN) structure, the reinforcement learning (RL) method is employed in solving coupled HJI equations. The fixed-time convergence technique is introduced to improve the convergence rate of RL, and the experience replay mechanism is utilized to relax the persistence of excitation condition. The associated NN convergence and closed-loop stability are analyzed. In the attitude loop, the optimal feedback control law is obtained by solving Hamilton-Jacobi–Bellman equations using the fixed-time convergent RL method. The simulation example and the quadrotor experiment are given to show the effectiveness of the proposed scheme.

Abstract:
In a multirobot multitask scenario, redundancy is a fundamental characteristic, arising both from the larger number of agents relative to the tasks to be executed and from the surplus of input resources compared to the controlled variables required. In large-scale applications, scalability becomes critical for enhancing algorithm performance. This article addresses the decentralization of the dynamic control allocation policy in input-to-task redundant multiagent systems. We provide sufficient conditions for both the output and control matrices to assure that the information encoded in both signals depend only on data shared among neighbors. Additionally, we extend the task allocation paradigm to include input redundancy information as key factor in the allocation policy. The resulting framework enables simultaneous task and control allocation in a fully decentralized setting. The performance of the proposed method has been tested and validated by means of small-scale simulation on a heterogeneous multiagent system in a multitask scenario.

Abstract:
Sleep is essential for maintaining human health and quality of life. Analyzing physiological signals during sleep is critical in assessing sleep quality and diagnosing sleep disorders. However, manual diagnoses by clinicians are time-intensive and subjective. Despite advances in deep learning that have enhanced automation, these approaches remain heavily dependent on large-scale labeled datasets. This study introduces SynthSleepNet, a multimodal hybrid-self-supervised learning (SSL) framework designed for analyzing polysomnography (PSG) data. SynthSleepNet effectively integrates masked prediction and contrastive learning to leverage complementary features across multiple modalities, including electroencephalogram (EEG), electrooculography (EOG), electromyography (EMG), and electrocardiogram (ECG). This approach enables the model to learn highly expressive representations of PSG data. Furthermore, a temporal context module based on Mamba was developed to efficiently capture contextual information across signals. SynthSleepNet achieved superior performance compared to state-of-the-art methods across three downstream tasks: sleep-stage classification, apnea detection, and hypopnea detection, with accuracies of 89.89%, 99.75%, and 89.60%, respectively. The model demonstrated robust performance in a semi-SSL environment with limited labels, achieving accuracies of 87.98%, 99.37%, and 77.52% in the same tasks. These results underscore the potential of the model as a foundational tool for the comprehensive analysis of PSG data. SynthSleepNet demonstrates comprehensively superior performance across multiple downstream tasks compared to other methodologies, making it expected to set a new standard for sleep disorder monitoring and diagnostic systems. The source code is available at https://github.com/dlcjfgmlnasa/SynthSleepNet.

Affiliations: School of Mathematics and Statistics, Guangxi Normal University, Guilin, China; Institute of Robotics, Ningbo University of Technology, Ningbo, China; Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education, East China University of Science and Technology, Shanghai, China; Department of Automation, and the State Key Laboratory of Green Chemical Synthesis and Conversion, Zhejiang University of Technology, Hangzhou, China

Abstract:
This article investigates the state estimation problem for 2-D Markov jumping systems subjected to randomly occurring false data injection attack. To address this challenge, a novel probabilistic multi-interval ETP (PMIETP) is proposed, integrated with a time-varying saturation mechanism (TVSM). The PMIETP is designed by combining subinterval triggering thresholds with a probability distribution model, thereby enhancing system performance and adaptability under varying network conditions. To further mitigate the impact of maliciously injected data and improve estimation robustness, a TVSM-based estimator is developed, which employs an adaptive threshold to confine abnormal data within an acceptable range. In addition, a particle swarm optimization algorithm is employed to fine-tune design parameters, thereby reducing the conservativeness of linear matrix inequality conditions. Based on Lyapunov stability theory, sufficient criteria are derived to guarantee mean-square asymptotic stability and prescribed noise attenuation performance. Finally, a numerical simulation example demonstrates the effectiveness and superiorities of the proposed approach over existing methods.

Abstract:
The frequency stability of multiarea power systems is guaranteed by networked load frequency control (LFC). Time delays due to occasional congestions/attacks in the LFC are often much longer than those from signal transmissions during normal communication, which invalidates the previous stability assessment methods. In this article, a novel stability analysis method for this scenario via a segmented delay description and a switched system is proposed. First, a two-piecewise function is used to describe multisource-induced delays, including large delays under occasional harsh network conditions and small delays under smooth network conditions; thus, a multiarea LFC model with multisource-induced delay is established. The stability criteria, which is based on switched system theory, reveals the relationship between delay characteristics and system stability. Finally, the proposed method is used to assess the delay tolerance of the LFC in traditional/deregulated environments. The results show that even with a large delay causing instability, as long as it meets certain frequency and duration constraints, the LFC remains stable. This novel discovery reflects the essential improvements of the proposed method and is useful for designing better control strategies.

Abstract:
In this article, the tracking control problem for nonlinear systems is investigated. For the first time, a fixed-time dynamic signal is constructed to handle unmodeled dynamics. A novel error-based function is first designed and applied to prescribed performance control, significantly enhancing the transient performance of the system. To mitigate the issue of extensive differential calculations, a modified fixed-time dynamic surface technique is incorporated into the backstepping design process. Combining the backstepping technique with fixed-time stability theory, a fast practical fixed-time controller is proposed to effectively address the control problem. Finally, the proposed scheme is validated for its effectiveness through the practical simulation example.

Abstract:
This article investigates the attack detection for multisensor cyber-physical systems with both secure and insecure sensors as well as unknown-but-bounded noises based on a zonotopic approach. Based on the intersection of the predicted state set and the secure measurement state set, we construct the security state set to detect attacks on insecure sensors. Then the attacks can be detected by judging whether the intersection of the security state set and insecure measurement state set is empty. Then, we provide a sufficient condition to guarantee that the detection performance of our proposed method is better than that of the predicted state set-based method. Furthermore, we propose a watermarking-based detection method to detect attacks whose amplitude is within the zonotope of the measurement noise, and randomly select the watermarking within a center-constrained zonotope. The watermarking is utilized to ensure that the measurement output with watermarking is not equal to zero, which can enhance the detection performance of our proposed method. Finally, a numerical simulation is provided to demonstrate the effectiveness of theoretical results.

Abstract:
In this article, we investigate the problem of distributed fault detection for a class of cyber–physical system whose physical layer consists of numerous subsystems, each modeled as a linear discrete-time system. Considering the influence of process noise and measurement noise, the state estimation of each subsystem is completed using a distributed Kalman filter (DKF), in which the one-step prediction is corrected not only by the local innovation but also by the measurement errors of the neighbors at the previous step. Leveraging the DKF, a local residual generator is designed for each subsystem. The parameters of the DKF are then determined by minimizing the estimation error and the upper bound of its covariance in the fault-free case, which ensures the robustness of the residual. Furthermore, by utilizing the instantaneous T^2 test statistic and the sliding window-based T^2 test statistic of the residual signals, the corresponding residual evaluation function and fault detection threshold are established to facilitate fault detection for each subsystem. In the proposed fault detection scheme, each subsystem only transmits information to its neighbors, ensuring that each subsystem can detect its faults in a distributed manner. Additionally, a sufficient condition is provided to guarantee the mean square boundedness of the estimation error in the fault-free case. Finally, a power network system is employed to demonstrate the effectiveness of the proposed scheme.

Affiliations: School of Automation, Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control, and the Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou, China; School of Electronic and Information Engineering and the Chongqing Key Laboratory of Generic Technology and System of Service Robots, Southwest University, Chongqing, China; Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada

Abstract:
This article focuses on the issue of the quantization-based event-triggered integral sliding-mode controller design for networked multiagent systems (MASs) encountering interferences under limited network bandwidth. An integral sliding manifold (ISM) is designed to address the effect of disturbances and ensure the desired dynamic performance of the system. We establish an event-triggered mechanism (ETM) with an exponential decay rate to conserve the limited communication resources. Then, a uniform quantizer is added to quantify the triggered state signals to lessen the network transmission burden caused by the digital network. Combining the designed ETM with a static uniform quantizer, the quantized trigger state signals are sent to decoders through the digital network to construct a quantized ISM. Subsequently, an event-triggered integral sliding-mode controller under quantization technology is developed to ensure the asymptotic average consensus of networked MASs. By testifying that every network agent has a lower positive bound, the viability of the proposed ETM is demonstrated, thereby ensuring the absence of Zeno behavior. Eventually, two simulation examples are proffered to confirm the efficacy of the quantization feedback-based event-triggered sliding-mode control methodology.

Abstract:
This article addresses the optimal control problem for nonlinear multiagent systems (MASs) with an uncertain nonlinear leader subject to intermittent Denial-of-Service (DoS) attacks. The main challenge is estimating the leader’s dynamics when the uncertain nonlinear dynamics of the leader are unknown to all followers and communication between subsystems is intermittently disrupted by attacks. Furthermore, the uncertainty in the followers’ dynamics adds complexity, making it difficult to eliminate reliance on the identifier network. To overcome these challenges, we develop a learning-based adaptive distributed observer to estimate the leader’s dynamics under attacks. Based on this observer, a single-critic optimal consensus tracking control scheme is proposed to solve the leader–follower consensus problem in uncertain MASs without requiring an identifier network. It is proven that all system signals are uniformly ultimately bounded (UUB), and consensus tracking is achieved. The effectiveness of the proposed method is validated through a simulation example.

Abstract:
This article addresses the distributed prescribed-time convex optimization (DPTCO) problem for high-order nonlinear multiagent systems (MASs) under undirected connected graphs. A cascade design framework is proposed that divides the DPTCO implementation into distributed optimal trajectory generator design and local reference trajectory tracking controller design. The DPTCO problem is then transformed into the prescribed-time stabilization problem of a cascaded system. Using changing Lyapunov functions and time-varying state transformations with sufficient conditions, we establish criteria for prescribed-time stabilization and prove the boundedness of internal signals in closed-loop MASs. The framework addresses robust DPTCO for chain-integrator MASs with disturbances through the introduction of novel sliding-mode variables and time-varying gains. It also solves adaptive DPTCO for strict-feedback MASs with parameter uncertainty via backstepping method and descending power state transformation. Two numerical examples verify the theoretical results.

Abstract:
This article explores the distributed leader-following full-state consensus control problem for feedforward nonlinear stochastic multiagent system with actuator deception attacks under a fixed directed topology. Unlike the existing works, we establish a novel actuator deception attack model, the attacks have higher stealthiness in model. The false injection information of the attack is generated by a stochastic inverse dynamics system satisfying the stochastic input-to-state stable condition, and the input of the system depends on the relevant output information of neighboring agents, rather than all state information. In this case, we develop a novel distributed output feedback consensus control algorithm to overcome the impact of attacks and stochastic disturbances. First, we design the distributed linear controller with a compensator for each follower based on the relevant outputs, and provide sufficient conditions for ensuring its effectiveness. Then, we construct a new candidate Lyapunov function with a power constant that is used to relax the constraints of the attack model. Subsequently, by means of the exchanging supply function approach, we strictly prove that all agents can achieve leader-following full-state bounded consensus in probability. Finally, an illustrative simulation is provided to showcase the efficacy of our methodology.

Abstract:
In this article, the anti-disturbance switching control approach is proposed for silicon single crystal growth systems with unmeasured states. Initially, the silicon single crystal growth systems are modeled by using the geometrical models of meniscus section, hydrodynamic and heat transfer process of silicon single crystal growth. Since numerous unmeasurable state variables exist in systems and the growth equipments are affected by external and internal disturbances, consideration is given to employing the output feedback control scheme and disturbance observer method in the construction of the anti-disturbance switching controller. Meanwhile, considering the high accuracy of linear systems at equilibrium points, the silicon single crystal growth systems are divided into multiple subsystems using switching control method, that is, multiple equilibrium points are set to enable the systems to switch between different subsystem models, thereby the precision of silicon single crystal growth systems has been improved. Then, using the multiple Lyapunov function method and linear matrix inequality technique, the exponential stability of silicon single crystal growth systems is ensured with the \boldsymbol H_\infty performance. Finally, the feasibility of designed switching anti-disturbance output feedback control method is verified through actual parameters of silicon single crystal growth systems.

Affiliations: School of Information Science and Technology, the Beijing Key Laboratory of Computational Intelligence and Intelligent System, the Engineering Research Center of Digital Community, Ministry of Education, the Beijing Artificial Intelligence Institute, and the Beijing Laboratory for Intelligent Environmental Protection, Beijing University of Technology, Beijing, China; CASlC Intelligence Industry Development Company Ltd., Beijing, China

Abstract:
In wastewater treatment process (WWTP), the dissolved oxygen concentration (DOC) sensor fault provides incorrect data to the control system, affecting the blower operation. This leads to insufficient aeration and increases the risk of membrane fouling. To solve this problem, a robust model-free fault-tolerant controller (RMFFTC) is designed. First, the pseudo partial derivative (PPD) approach is utilized to transform nonlinear WWTP into a compact form dynamic linearization (CFDL) data model with residual disturbances. Then, according to the CFDL model, a robust fault detection threshold is designed by the extended state observer (ESO) to timely detect the occurrence of faults. Second, after detecting the DOC sensor fault, the fault is estimated using the fuzzy neural network (FNN) considering that the fault is unknown. Third, an improved RMFFTC is designed based on the fault estimation information. In particular, to ensure stable DOC tracking under sensor faults, the controller design considers the output tracking error variation. In addition, the bounded-input–bounded-output (BIBO) stability result is provided to theoretically guarantee the usefulness of the proposed RMFFTC for DOC control affected by the sensor fault. Finally, the effectiveness of the RMFFTC are verified through extensive simulations in the membrane bioreactor (MBR) model.

Affiliations: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China; Department of Mechanical Engineering, National University of Singapore, Lower Kent Ridge Rd, Singapore; Department of Automation, Tsinghua University, Beijing, China; National Center of Technology Innovation for Intelligent Design and Numerical Control, and the State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China

Abstract:
Due to the complexity of modern production processes and environments, most products must pass through multiple workshops from raw materials to finished goods. This article investigates a collaborative scheduling problem in a cascaded dual-shop production setting. Unlike single-shop scheduling or distributed multiworkshop scheduling, this problem emphasizes collaborative optimization between two interdependent workshops. In addition, real-world production often involves a mode where main and suborders must be integrated through mating operations. This study formulates an energy-efficient cascaded dual-shop collaborative scheduling problem with the mating operation (ECDCSP-M). The focus is on developing a mixed-integer linear programming (MILP) model for the ECDCSP-M and designing an end-to-end graph-based deep reinforcement learning (GDRL) approach. A dual-shop heterogeneous graph is constructed to capture the real-time state of the entire system, in which “job-factory” and “operation-machine” pairs are defined as agent actions. A heterogeneous graph neural network (HGNN) is then proposed, employing a three-stage embedding mechanism to model complex relationships, including mating operations. Experimental results show that the proposed method achieves strong generalization across varying problem complexities and provides robust solutions to challenging scheduling scenarios.

Abstract:
This study considers the periodic event-triggered prescribed tracking problem for stochastic nonlinear systems, whose output is available only at sampling time. With the limited sampled data of output, a state observer via neural-network approximation is constructed to estimate the unmeasurable states, and then a novel event-triggered mechanism is designed by monitoring the estimated states at sampling time to avoid the continuous communication. The negative deviation effects between the event-triggered controller and the continuous controller are eliminated by introducing two intermediate sampling deviation terms. Moreover, a performance function is introduced to achieve more flexible tracking performance. This function represents different performance behaviors and addresses the issue of redesigning controllers. By determining an allowable sampling period, it is proven that all states of the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error satisfies a flexible prescribed performance. Finally, two examples verify the effectiveness.

Abstract:
The control of magnetorheological fluid dual clutch (MRFDC) has been challenging due to their modeling challenges, high complexity, strong nonlinearity, and rate-dependent hysteresis, especially in the transient states in which they are supposed to perform gear shifting and traction tracking. Motivated by this, this article presents a data-driven discrete-time sliding mode control (DSMC) approach for the transmission torque control of the magneto-rheological fluid dual clutch (MRFDC). This control method eliminates the model dependence and simplifies the control strategy synthesis by employing a compact form dynamic linearization data model, which is constructed from real-time output torque and input current measurements of the MRFDC. Furthermore, based on the proposed data model, the DSMC is employed based on a minimum operator sliding mode reaching law to deal with the rate-dependent hysteresis and nonlinearity of the MRFDC. Experimental studies validate that the presented control method provides satisfactory torque tracking performance in both transient and steady states.

Abstract:
This article focuses on the self-triggered impulsive control (STIC) problem of nonlinear systems subject to exogenous disturbances. Specifically, with the construction of a novel self-triggered mechanism (STM) containing the information of state and disturbance, the next release time of impulsive control signal is attained accompanied with each update of the impulsive control task, without real-time monitoring of the state. On the strength of the suggested STM and by means of Lyapunov-based approaches, some sufficient conditions are proposed to guarantee the input-to-state practical stability (ISpS) for the considered system, while avoiding the possible Zeno phenomenon. Especially, the design of mechanism and control gain is explored for a class of nonlinear control systems with the help of matrix inequalities. Two examples are provided to demonstrate the feasibility of the proposed results.

Abstract:
This article proposes a game-based distributed decision optimization method for heterogeneous multiagent systems with unknown nonlinear dynamics. Due to the information exchange between agents in the network, the unknown nonlinear dynamics lead to the degradation of local and all-agent control performance, which causes the strategies of all agents to deviate from the Nash equilibrium under a given goal. To address this problem, an adaptive distributed algorithm is designed to seek Nash equilibrium by combining two optimization levels. Specifically, the decision layer uses a distributed consensus algorithm to achieve benefit evaluation and a gradient algorithm to generate reference signals. Then, the control layer uses the virtual reference signal from the decision layer and the neural network estimation information to design an adaptive control algorithm. The proposed method performs real-time adaptive optimization of the strategies and control performance of the decision and control layers, ensuring the successful implementation of the distributed Nash equilibrium search. The convergence of the proposed algorithm is proved in the Lyapunov sense. Finally, simulation examples demonstrate the performance and effectiveness of the proposed method.

Abstract:
This article aims to address the fixed-time optimal leader-following consensus issue for unknown multiagent systems (MASs) under Denial of Service (DoS) and false data injection (FDI) attacks. A novel fixed-time stability theorem under DoS attacks is presented to simplify the stability conditions and decrease the computational complexity of the settling time. Simultaneously, the deep neural networks (DNNs) structure with the projection operator are adopted in real-time to approximate the unknown system dynamics. To achieve the optimal consensus under cyber-attacks, a hierarchical control approach is presented, which includes a reference signal generation layer and a tracking control layer. Specifically, the distributed and Luenberger-based observers are designed in the reference signal generation layer to solve the fixed-time state estimation issues of leader and followers under multiple malicious attacks, respectively. Then, the optimal control strategy based on the event-triggered mechanism (ETM) is designed in the tracking control layer to track the reference signal and minimize the cost consumption. Due to the difficulty in obtaining explicit expressions of the optimal control mechanisms, a critic-only reinforcement learning (RL)-based algorithm is presented for online learning the unknown weight within a fixed time. By rigorous proof, the developed observers can achieve the fixed-time state reconstruction and the optimal control policy can track observation states after a fixed time. Finally, simulation results about platooning control of automated vehicles are given to demonstrate the efficacy of the developed technique.

Abstract:
This article concerns distributed generalized Nash equilibrium (GNE) seeking in an N-player game with linear dynamics over a jointly strongly connected switching network. The main challenge of this problem is the design of appropriate updating laws that ensure convergence under a jointly strongly connected switching network. Such a design must also respect inequality constraints and address the complexity of linear dynamics. Projection-based pseudo-gradient method is proposed to seek the GNE while satisfying both the individual and the shared inequality constraints. Furthermore, the jointly strongly connected switching network, which may be disconnected at any time instant, entails resorting to the generalized Barbalat’s lemma in the convergence analysis. We also discuss an application to doubly fed induction generators (DFIGs) subject to total power limitations and individual power ranges, providing simulation results to verify the proposed algorithm.

Abstract:
The control of a humanoid robot equipped with one arm and multiple fingers, designed primarily for grasping and manipulating various objects, is investigated. Synchronizing the movements of the fingers is a challenging task, as each joint must reach the desired angle simultaneously to ensure a firm grasp. The success of this task hinges on the synchronization of convergence times for each finger joint; otherwise, the object may slip or escape. This challenge is further intensified by uncertainties in the dynamics of the hand or the object. We present decentralized prescribed-time tracking control strategies for the dynamical system comprising the arm-finger combination. In this system, the fingers are primarily used for grasping the object while the arm is responsible for moving, tilting, or flipping it. To streamline the controller structure and simplify the stability analysis, we design a linear controller based on the maximum eigenvalue of a parameter matrix and establish a new technical lemma, which paves the way for the stability analysis of the prescribed-time tracking and the reduction of the input efforts of the actuator. We develop robust and decentralized adaptive control schemes separately for the arm and fingers, achieving better transient performance with less prior knowledge and lower computation costs. Finally, we validate the proposed controller’s performance through kinematic simulations of grasping and moving tasks in 3-D, alongside numerical simulations that demonstrate the tracking performance of our algorithm in the joint space.

Affiliations: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia; Institute of Information Technology, Zhejiang University, Hangzhou, China; Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong; School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China; Department of Electrical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong

Abstract:
The federated learning (FL) technique can provide a promising solution for the timely training of a deep learning model with the critical requirement of privacy protection. However, the existing FL frameworks still confront challenging issues including heterogeneous data sources, edge device heterogeneity, sensitive information leakage, nonconvex loss, and communication resource constraints which place obstacles in terms of practicality. In this article, first, a federated multitask learning (FedMTL) approach is introduced to reformulate the FL model as a multiobjective optimization problem which results in federated multigradient descent algorithm (FedMGDA) with a better model personalization against data heterogeneity and Byzantine attack. Second, a new semi-asynchronous model aggregation method is developed to asynchronously aggregate small partial clients for compensating impacts of the straggler and staleness. Third, a distributed differential privacy technique is applied to enhance the privacy protection of asynchronous FedMGDA with the convergence guarantee where the convergence analysis of differentially private asynchronous federated multiple gradient descent algorithm (DP-AsynFedMGDA) is studied for both the convex and the nonconvex loss functions. Empirical examples and comparative studies are presented to illustrate the effectiveness of the proposed DP-AsynFedMGDA.

Abstract:
This article presents a cyber resilience strategy based on secure state estimation (SSE) to counter cyberattacks on autonomous systems. The proposed framework employs a cascade of nonlinear observers to estimate attacks targeting both sensors and actuators, deriving the operational conditions required for their effectiveness. These SSEs are integrated with identification algorithm and a control mitigation law designed using the direct Lyapunov method, ensuring the system’s stability and integrity are maintained even under active attacks. The framework is validated through its implementation on an autonomous container truck. Experimental results demonstrate that the secure state estimators effectively preserve system stability and operational integrity, showcasing their potential to enhance the resilience of autonomous systems against cyber threats.

Abstract:
It is one of the most essential processes in the industrial cyber-physical system (ICPS) that multiple edge computing nodes (ECNs) collect field sensor information and cooperate for sensing and control. The exchange of sensing information on the edge side is critical for these ECNs that serve as multiple edge estimators and one edge controller. However, the limited transmission resources and the diverse performance demands of ECNs for sensing and control make it challenging to design the edge network topology delicately. For this issue, a novel dynamic observability (DO) condition is proposed to balance sensing-control performance and transmission cost under various demand settings. Based on the quantitative analysis of the relationship between overall transmission cost and each ECN’s effective observability, DO gives the criterion for desirable network topologies with spatio-temporal dynamics. Then for the given performance demands, a dynamic observability guaranteed method (DOGM) is proposed to determine the network topology by triggering proper sensing links. In this way, the set of triggered sensing links may vary in a dynamic manner to satisfy the DO condition, and the overall performance of sensing and control is theoretically guaranteed. Finally, the comprehensive advantages of DOGM are demonstrated by the simulation study in the hot rolling laminar cooling process.

Abstract:
This article is concerned with the event-based secure tracking control issue for a class of nonlinear networked control systems (NCSs) under malicious sensor and actuator attacks. In the sensing channel, a modified event-triggered (ET) control scheme based on a continuous function is developed to resist sparse sensor attacks, which can evade the control design difficulties associated with discontinuous information transmission. Then, a high-order filter with a secure data preselector is presented to obtain reliable state estimation from a group of output measurements. In the actuation channel, the Nussbaum function with different frequencies is adopted to alleviate the influence of the unknown time-varying coefficients caused by actuator attacks, while also reducing control updates. Consequently, the proposed scheme not only alleviates the burden on data transmission and processing but also ensures that all internal signals in the closed-loop system are uniformly bounded. Simulation example verifies the efficiency of the proposed control scheme.

Abstract:
Decentralized smart manufacturing systems serve as a robust foundation for optimizing production processes and maintaining stringent quality standards in modern industrial environments. However, in such distributed architectures, uncontrolled and rapid fault propagation presents significant challenges, often resulting in widespread operational disruptions and compromised system integrity. To address these issues, we propose the spectral-adaptive consensus fault mitigation algorithm (SAC-FMA), which efficiently detects, contains, and mitigates fault propagation across decentralized manufacturing networks. Our approach uniquely combines spectral graph theory with adaptive consensus mechanisms to synchronize fault detection and isolation while dynamically adjusting operational parameters to preserve system stability and efficiency. Experimental results demonstrate that SAC-FMA outperforms traditional fault management approaches with a 41% improvement in convergence rate, 67% enhancement in fault containment efficiency, 60% reduction in system instability, 95% maintenance of operational performance, and 40% decrease in communication overhead. These findings highlight SAC-FMA’s potential for significantly enhancing resilience and reliability in smart manufacturing environments.

Abstract:
This article introduces an innovative singularity-free output feedback model reference adaptive control (MRAC) method that is adaptable to a wide range of continuous-time linear systems with relative degrees n^ \geq 1 and unknown high-frequency gains. Unlike existing solutions, such as Nussbaum and multiple-model-based methods, which manage unknown high-frequency gains through persistent switching and repeated parameter estimation, the proposed method circumvents these issues without requiring high-frequency gain information or adding design constraints. A key innovation of this method lies in transforming the estimation error equation into a linear regression form via a modified MRAC law with a piecewise constant tuning gain developed in this work. This represents a significant departure from existing MRAC systems, where the estimation error equation is typically bilinear. The linear regression form facilitates the direct estimation of all unknown parameters, thereby simplifying the adaptive control process. In the absence of any high-frequency gain information, the proposed method still maintains the boundedness of the closed-loop system signals and \lim _t \to \infty (y(t)-y^(t))=0 , where y(t) and y^(t) are the system output and any reference output, respectively. Meanwhile, this method overcomes some limitations associated with previous methods like Nussbaum and multiple-model-based methods. Finally, a simulation of the aircraft control system is conducted to validate the proposed solution.

Abstract:
This article presents a fixed-time vector-field-orientation (VFO) control law for unicycle-like nonholonomic mobile robots. We consider a set-point control problem in the presence of control inputs constraints and task-execution time constraints. The control law is based on the VFO methodology, which is characterized by nonoscillatory and well-predictable time evolution of transient states for unicycle-like robots. A formal stability analysis for the closed-loop dynamics is provided based on Lyapunov theory, and moreover, a method for a priori estimation of the upper bounds of the convergence time, in the presence of control input constraints, is presented. Finally, the control performance is illustrated by results of numerical simulations and experimental tests.

Abstract:
This article considers the design of prescribed-time sensor fault estimators (PSFEs) and unknown input compensation for linear systems, i.e., estimators that estimate the sensor fault and controllers that stabilize the state of the system both at a prescribed time. With this intention, a filter is introduced to eliminate the limitation of the fault being differentiable. Then, the task of designing PSFEs can be converted into the task of designing prescribed-time unknown input observers for the augmented systems. Next, the generalized inverse is employed to convert the augmented systems into a form suitable for observer design. Then, the PSFEs are developed by exploiting periodic delayed output. Both full- and reduced-order PSFEs are taken into account. In addition, based on the established state observer and unknown input estimation, periodic delayed controllers are designed to completely compensate for the unknown input so that the closed system is T-prescribed-time stable (T-PS). Finally, an example is provided to demonstrate the efficacy of the proposed methods.

Abstract:
Although quadrotor has been widely used in practical engineering, its autonomous flight ability needs to be improved in complex operating environment. The autonomous flight problem for quadrotor with monocular vision is investigated, which is divided into control layer and decision layer in this article. Reinforcement learning method is utilized for hierarchical optimization to ensure that quadrotor completes narrow space traversal tasks safely and efficiently. First, considering the dynamic characteristics of the quadrotor with the motor speed as the control input, a parallel policy iteration algorithm is designed for the nonaffine nonlinear system, and the proposed controller can be learned online to improve the fundamental control performance. On this basis, the autonomous decision problem with visual information as input is modeled as a Markov decision process, and a curriculum learning mechanism is introduced to overcome the difficulties caused by sparse reward. At the same time, the clipping function is optimized to improve the learning efficiency of proximal policy optimization (PPO) algorithm for autonomous flight capabilities. Finally, the effectiveness of the proposed intelligent control and decision methods are verified through simulation.

Abstract:
Accurate recognition of human motion intention (HMI) is beneficial for exoskeleton robots to improve the wearing comfort level and achieve natural human-robot interaction. A classifier trained on labeled source subjects (domains) performs poorly on unlabeled target subject since the difference in individual motor characteristics. The unsupervised domain adaptation (UDA) method has become an effective way to this problem. However, the labeled data are collected from multiple source subjects that might be different not only from the target subject but also from each other. The current UDA methods for HMI recognition ignore the difference between each source subject, which reduces the classification accuracy. Therefore, this article considers the differences between source subjects and develops a novel theory and algorithm for UDA to recognize HMI, where the margin disparity discrepancy (MDD) is extended to multisource UDA theory and a novel weight-aware-based multisource UDA algorithm (WMDD) is proposed. The source domain weight, which can be adjusted adaptively by the MDD between each source subject and target subject, is incorporated into UDA to measure the differences between source subjects. The developed multisource UDA theory is theoretical and the generalization error on target subject is guaranteed. The theory can be transformed into an optimization problem for UDA, successfully bridging the gap between theory and algorithm. Moreover, a lightweight network is employed to guarantee the real-time of classification and the adversarial learning between feature generator and ensemble classifiers is utilized to further improve the generalization ability. The extensive experiments verify theoretical analysis and show that WMDD outperforms previous UDA methods on HMI recognition tasks.

Abstract:
Domain-adaptive object detection (DAOD) aims to generalize detectors trained in labeled source domains to unlabeled target domains by mitigating domain bias. Recent studies have confirmed that pretrained vision-language models (VLMs) are promising tools to enhance the generalizability of detectors. However, there exist paradigm discrepancies between single-domain detection in most existing works and DAOD tasks, which may hinder the fine-grained alignment of cross-domain visual-text features. In addition, some preliminary solutions to these discrepancies may potentially neglect relational reasoning in prompts and cross-modal information interactions, which are crucial for fine-grained alignment. To this end, this article explores fine-grained visual-text feature alignment in DAOD with prompt tuning and organizes a novel framework termed FGPro that contains three elaborated levels. First, at the prompt level, a learnable domain-adaptive prompt is organized and a prompt relation encoder is constructed to infer intertoken semantic relations in the prompt. At the model level, a bidirectional cross-modal attention is structured to fully interact visual and textual fine-grained information. In addition, we customize a prompt-guided cross-domain regularization strategy to inject domain-invariant and domain-specific information into prompts in a disentangled manner. The three designs effectively align the fine-grained visual-text features of the source-target domain to facilitate the capture of domain-aware information. Experiments on four cross-domain scenarios show that FGPro exhibits notable performance improvements over existing work (Cross-weather: +1.0% AP50; Simulation-to-real: +1.2% AP50; Cross-camera: +1.3% AP50; Industry: +2.8% AP50), validating the effectiveness of its fine-grained alignment.

Abstract:
This article proposes a novel scalable control strategy for large-scale direct current microgrids (LSDCmGs) consisting of multiple distributed generation units (DGUs). The objective is to facilitate real-time plug-and-play (PnP) functionality for DGUs, despite the presence of power transmission line coupling. In brief, the insertion or removal of DGUs does not require reconfiguration of adjacent controllers to maintain the stability of the entire LSDCmGs. To achieve this goal, a generalized free-weighting matrix technology is introduced. The strict assumptions previously made on Lyapunov matrices are ingeniously shifted to free-weighting matrices by this technology. As a result, scalable control is achieved and conservatism is reduced simultaneously. Moreover, the topological structure of LSDCmGs frequently undergoes changes due to external factors, equipment failures, and line issue. Markov chains are employed to accurately model these variations, providing a more realistic analysis of the microgrid’s operational states. Additionally, by selecting a mode-related structured Lyapunov-Krasovskii function, sufficient criteria are derived to realize tracking of a given reference voltage in the LSDCmGs under PnP operation. Finally, the effectiveness of the proposed scalable strategy is validated through a case study involving the LSDCmGs with five DGUs.

Affiliations: School of Mathematics and Statistics, Guangxi Normal University, Guilin, China; Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland; Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education, East China University of Science and Technology, Shanghai, China; School of Mathematics, Southeast University, Nanjing, China; Institute of Architecture Engineering, Guangxi City Vocational University, Guangxi, China

Abstract:
This article proposes a novel framework of nonhomogeneous sojourn probabilities in the 2-D domain and investigates the observer-based security control issue for 2-D fuzzy switched systems (SS) under an adaptive-threshold-based saturation function. In comparison with the existing homogeneous/nonhomogeneous Markov processes, the proposed nonhomogeneous sojourn probability framework enhances measurability and reduces computational complexity, making it a more reasonable approach. The security control challenge for 2-D fuzzy SS is addressed in the context of randomly occurring attacks with uncertain occurrence rates, where the uncertainty is norm-bounded and asymmetrical around the nominal rate. To mitigate the effects of these abnormal attacks, a novel adaptive-threshold-based saturation function is employed, ensuring that transmitted information remains within an acceptable range. Leveraging Lyapunov theories, sufficient conditions are attained to guarantee the asymptotically mean square stable with noise attenuation performance. The validity of the proposed theoretical results is demonstrated through a simulation example.

Abstract:
In this article, the fixed-time resilient distributed Nash equilibrium (NE) control problem is addressed for nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks. Unlike existing results on NE seeking in noncooperative games, this article first investigates the layered fixed-time resilient distributed NE control problem for nonlinear MASs against DoS attacks, independent of initial conditions. To address these challenges, the novel layered NE control strategy is proposed, comprising a resilient fixed-time distributed NE seeking algorithm layer, an improved fixed-time performance enhancement layer, and an adaptive controller design layer. Specifically, a fixed-time resilient distributed NE seeking algorithm is first designed to guarantee players actions to converge toward the NE against DoS attacks. Then, novel high-order fixed-time filters are proposed to generate improved actions with smooth characteristics and converge to the actions in the aforementioned fixed-time algorithm. Based on the developed filters, a decentralized fuzzy adaptive controller is developed to achieve bounded tracking within the fixed time using the backstepping technique. Finally, a numerical simulation is conducted to validate the efficacy of the developed method.

Abstract:
This article is concerned with the observer-dependent asynchronous control problem in cyber-physical systems (CPSs) vulnerable to multimode attacks, with a specific focus on addressing the significant challenge posed by the surreptitious nature of attack behaviors. First, in view of band-limited communication channels in CPSs, the quantizer is used to quantize the measured output. Second, owing to the open and shared network of CPSs, the data transmission process is more susceptible to attacks. A multichannel transmission framework is constructed under the assumption that each transmitted data element is susceptible to potential attacks. The switching dynamics among various attack forms launched by the adversary on the transmission channel are governed by a semi-Markov chain. The analysis of multimode attacks with stealth characteristics is conducted within the framework of a hidden semi-Markov jump mode, which is achieved by establishing a dual-layer stochastic process comprising a multimode sequence and an observed mode sequence. Leveraging emission probability, we design an observed-mode-dependent controller capable of stabilizing the system even in the absence of direct access to the actual attack patterns and in the presence of information loss. The simulations involving a single-channel unmanned ground vehicle system and a mass-spring–damper system with two channels are provided to validate the feasibility and efficacy of our proposed methodology.

Abstract:
Additive manufacturing (AM) revolutionizes product creation with its unique layer-by-layer construction method but faces obstacles in widespread industrial use due to quality assurance and defect challenges. Integrating machine learning (ML) into AM quality control (QC) systems presents a viable solution, utilizing ML’s ability to autonomously detect patterns and extract important data, reducing the reliance on manual intervention. This study conducts an in-depth literature review to scrutinize the role of ML in augmenting QC mechanisms within extrusion-based AM processes. Our primary objective is to pinpoint ML models that excel in monitoring manufacturing activities and facilitating instantaneous defect corrections via parameter adjustments. Our analysis highlights the efficacy of convolutional neural networks (CNNs) models in defect detection, leveraging camera-based systems for an in-depth examination of printed parts. For 1-D data processing, support vector machines (SVMs) and long short-term memory (LSTM) networks have shown significant application and effectiveness. Furthermore, the study classifies various sensors and defects that can effectively benefit from ML-driven QC approaches. Our findings accentuate the essential role of ML, especially CNNs, in detecting and rectifying production flaws and also detail the synergy between different sensor technologies in creating a comprehensive monitoring framework. By integrating ML with a multisensor approach and employing real-time corrective strategies, such as dynamic parameter adjustments and the use of advanced control systems, this research underscores ML’s transformative potential in elevating AM QC. Thus, our contribution lays the groundwork for harnessing ML technologies to ensure superior quality parts production in AM, paving the way for its broader industrial adoption.

Affiliations: School of Intelligence Science and Technology, the Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, and the Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China; School of Intelligence Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering and the Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, University of Science and Technology Beijing, Beijing, China; School of Automation and Institute of Artificial Intelligence, Beijing Information Science and Technology University, Beijing, China

Abstract:
This study introduces a novel finite time fault tolerant controller integrating nonsingular terminal sliding mode (NTSM) and reinforcement learning (RL) strategies for manipulator systems with actuator faults. Leveraging an actor-critic network architecture, the RL algorithm facilitates the computation of the cost function and the approximation of unknown nonlinear dynamics. The inherent properties of NTSM mitigate the effects of parameter uncertainties, thereby enhancing system robustness. Furthermore, an adaptive law is crafted to counteract the deleterious effects of actuator faults. Through the direct Lyapunov function approach, it is demonstrated that the closed-loop system achieves semi-global practical finite-time stability. This control strategy diminishes the dependence on precise model accuracy and augments the system’s fault tolerance. The viability of the proposed algorithm is corroborated by simulation results, and its efficacy is further validated through experiments conducted on the 6-DOF Kinova Jaco 2 platform.

Abstract:
To improve the trajectory tracking performance of unmanned surface vehicle (USV), this article investigates the USV optimal control problem with the consideration of actuator wear. In the proposed algorithm, the USV control system is divide into kinematic subsystem and kinetic subsystem. In particular, corresponding performance indexes that looking forward to be optimized are defined for each subsystem. The related value functions, Hamilton-Jacobi–Bellman equations and optimal control policies are approximated by actor-critic neural networks. To reduce the wear of propeller and rudder, the event-triggered problem is considered as a zero-sum game solving problem, where the best control inputs and worst thresholds are delivered via minmax strategy. Also, the nonlinear uncertainties of the USV are approximated and environment disturbances are compensated in the value functions for better control performance. The USV closed-loop control system is proved semi-globally uniformly ultimately bounded stability via Lyapunov theory. Finally, a simulation case and harbor experiment are illustrated to verify the superiorities and engineering application values of the proposed algorithm.

Abstract:
Skeleton-based temporal action segmentation (TAS) decomposes untrimmed skeleton sequence into meaningful segments. The variance in temporal scale challenges the skeleton modeling network to seek a balance between over-segmentation and under-segmentation. Current methods often rely on parallel multiscale feature extractors and additional refinement modules to mitigate the multiscale issue, which brings significant computations and complexity. To address these issues, this article proposes multiscale skeleton-based TAS (MSTAS), consisting of temporal probability pyramid (TPP) and smoothed multiscale ensemble (SME). TPP represents each action as a collection of multiscale probability distributions using a U-shape hierarchical temporal pyramid. Subsequently, SME takes the average of distributions instead of deploying additional refinement stages to achieve action segmentation. Considering the over-confident issue that exists in each scale, SME incorporates a novel label smoothing phase to improve the probability distributions by dynamically calibrating the confidence of each scale. Experimental results on four public datasets show that the MSTAS achieves state-of-the-art performance with less computation overheads, such as +1.1% accuracy and +2.8% F1@0.5 on the challenging LARa dataset with 70% fewer parameters and 80% fewer GFLOPS. Benefiting from confidence calibration, the MSTAS efficiently utilizes more temporal scales while keeping better calibration for ambiguous action instances. Additionally, the U-shape pyramid demonstrates a strong compatibility with classical refinement module, enabling the efficient extraction of multiscale motion representations.

Abstract:
The fuzzy adaptive state and output quantization bilateral cooperative control problem for nonlinear multiagent systems (NMASs) is studied. Since the considered system is nonlinear, fuzzy logic system (FLS) is applied to approximate the unknown nonlinear function, and a fuzzy state observer is constructed because the state cannot be measured. A second-order command filter is used to solve the complex problem of calculating the time derivative of the virtual control function, and a uniform quantizer is used for fuzzy adaptive inversion design in the process of controller design. Ultimately, the effectiveness of the proposed control method is verified by a series of simulation experiments and research results.

Affiliations: Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha, China; Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and the Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Department of Computing and Decision Sciences, Lingnan University, Hong Kong, SAR, China

Abstract:
Electroencephalography (EEG) is widely utilized for train driver state detection due to its high accuracy and low latency. However, existing methods for driver status detection rarely use the rich physiological information in EEG to improve detection performance. Moreover, there is currently a lack of EEG datasets for abnormal states of train drivers. To address these gaps, we propose a novel transfer learning model based on a hybrid attention mechanism, named hybrid attention-based transfer learning network (HATNet). We first segment the EEG signals into patches and utilize the hybrid attention module to capture local and global temporal patterns. Then, a channel-wise attention module is introduced to establish spatial representations among EEG channels. Finally, during the training process, we employ a calibration-based transfer learning strategy, which allows for adaptation to the EEG data distribution of new subjects using minimal data. To validate the effectiveness of our proposed model, we conduct a multistimulus oddball experiment to establish a EEG dataset of abnormal states for train drivers. Experimental results on this dataset indicate that: 1) Compared to the state-of-the-art end-to-end models, HATNet achieves the highest classification accuracy in both subject-dependent and subject-independent tasks at 94.26% and 87.03%, respectively, and 2) The proposed hybrid attention module effectively captures the temporal semantic information of EEG data.

Abstract:
Large-scale constrained multiobjective optimization problems (LSCMOPs) exist widely in science and technology. LSCMOPs pose great challenges to algorithms due to the need to optimize multiple conflicting objectives and satisfy multiple constraints in a large search space. To better address such problems, this article proposes a dynamic subspace search-based evolutionary algorithm for solving LSCMOPs. The main idea is to initially allow the population to search in a low-dimensional subspace to increase convergence, then the searched subspace is gradually expanded to encourage the population to further search the full decision space. Specifically, the contribution of each decision variable to the evolution is first calculated using the proposed decision variable analysis method. Then, a probability-based offspring generation strategy is developed to encourage the population to preferentially search in a low-dimensional subspace composed of decision variables with high contribution degrees, thus speeding up the early convergence. With the continuous progress of evolution, the subspace is gradually expanded to ensure that the population can better explore the entire space. The performance of the proposed algorithm is evaluated on a variety of test problems with 100–1000 decision variables. Experimental results on four test suits and three real-world instances show that the proposed algorithm is efficient in solving LSCMOPs.

Abstract:
The high level of autonomy within autonomous systems demands new control strategies to achieve more complex objectives while ensuring both safety and robustness, rather than relying solely on a given reference. To this end, this article addresses the problem of temporal logic disturbance rejection control (TLDRC) for a class of nonlinear systems subject to disturbances. Signal temporal logic (STL) specifications are introduced for the representation of complex tasks. A control barrier function (CBF), composed of a monotonic function characterizing the temporal behavior of the system and a predicate function, is constructed to encode the STL specifications. To guarantee robustness against disturbances, generalized proportional integral observers (GPIOs) are introduced for higher-accuracy disturbance estimation. It is shown that by fully exploiting the constructed CBF and the disturbance estimate, the developed TLDRC strategy is able to ensure the STL specifications and compensate undesirable effects caused by unknown disturbances, even if they are fast-time-varying. A numerical example is presented to illustrate the effectiveness of the proposed strategy.

Abstract:
In this article, a resilient consensus control method is proposed for nonlinear multiple unmanned aerial vehicles (UAVs) with input saturation and Denial of Service (DoS) attacks. First, an input saturation constraint based on the UAV dynamic model is investigated in this article, and an adaptive compensating term is developed to handle the input saturation. The DoS attacks considered in this article can interrupt all the communication transmissions of the attacked UAV from neighbors so that the victim is not able to receive any information from neighboring UAVs during DoS attacks. To deal with such a difficult problem, a fixed-time security constraint estimator (FTSCE) is proposed to ensure the stability and security of UAVs during the DoS attacks. Moreover, the unknown state is estimated to reduce the amount of the transferred information. Based on the proposed FTSCE, the relative position and velocity of UAV states are used to design the resilient consensus controller against the DoS attacks. By using the proposed controller, the system stability can be guaranteed according to the Lyapunov stability analysis. Finally, the numerical simulation is conducted to verify the effectiveness of the proposed resilient consensus control method.

Abstract:
When a manipulator moves in a highly dynamic scenario with a large range of rapid motion, the coupling disturbances between the manipulator and the UAV in the aerial manipulator system (AMS) become very strong, which directly affects the ability of the AMS to perform aerial manipulation and even poses a threat to the safety of the system. The aim of this article is to address the strong coupling disturbance problem in the AMS through precise coupling disturbance modeling and compensation. First, considering the rapid changes in the center of mass (CoM) and the moment of inertia (MoI) of the system under a highly dynamic scenario, this article delves into the generation mechanism of the coupling disturbances and models them based on the variable inertia parameters. The proposed precise coupling disturbance model (CDM) makes good use of the state information of the system, which enables one to achieve accurate estimation of the coupling disturbances without the aid of external force and torque sensors. With the proposed model, the strong coupling disturbances in the AMS are compensated in a feedforward way during the controller design process. An indoor AMS experimental platform is developed for validation purposes. The experiments and simulation are conducted in a highly dynamic scenario, involving rapid movements of the manipulator across a large range. The experimental and simulation results demonstrate the effectiveness and advantages of the proposed method for suppressing the strong coupling disturbances.

Abstract:
Recently, Moore-Penrose inverse (MPI)-based parameter fine-tuning of fully connected (FC) layers in pretrained deep convolutional neural networks (DCNNs) has emerged within the inductive transfer learning (ITL) paradigm. However, this approach has not gained significant traction in practical applications due to its stringent computational requirements. This work addresses this issue through a novel fast retraining strategy that enhances applicability of the MPI-based ITL. Specifically, during each retraining epoch, a random layer freezing protocol is utilized to manage the number of layers undergoing feature refinement. Additionally, this work incorporates an MPI-based approach for refining the trainable parameters of FC layers under batch processing, contributing to expedited convergence. Extensive experiments on several ImageNet pretrained benchmark DCNNs demonstrate that the proposed ITL achieves competitive performance with excellent convergence speed compared to conventional ITL methods. For instance, the proposed strategy converges nearly 1.5 times faster than retraining the ImageNet pretrained ResNet-50 using stochastic gradient descent with momentum (SGDM).

Abstract:
The primary focus of this article centers around the application of sliding mode control (SMC) to semi-Markov jumping systems, incorporating a dynamic event-triggered protocol (ETP) and singular perturbation. The underlying semi-Markov singularly perturbed systems (SMSPSs) exhibit mode switching behavior governed by a semi-Markov process, wherein the variation of this process is regulated by a deterministic switching signal. To simultaneously reduce the triggering rate and uphold the system performance, a novel parameter-based dynamic ETP is established. This protocol incorporates weight estimation of a radial basis function neural network (RBFNN) and introduces two internal dynamic variables. Following the Lyapunov’s theory, sufficient criteria are established for ensuring the mean-square exponential stability of the resulting system. Additionally, an SMC scheme based on the convergence factor is designed to fulfill reachability conditions. Finally, two examples are carried out to validate the solvability and applicability of the attained control methodology.

Abstract:
This article develops a cooperative-critic learning-based secure tracking control (CLSTC) method for unknown nonlinear systems in the presence of multisensor faults. By introducing a low-pass filter, the sensor faults are transformed into “pseudo” actuator faults, and an augmented system that integrates the system state and the filter output is constructed. To reduce design costs, a joint neural network Luenberger observer (NNLO) structure is established by using neural network and input/output data of the system to identify unknown system dynamics and sensor faults online. To achieve the optimal secure tracking control, an augmented tracking system is formed by integrating the dynamics of tracking error, reference trajectory, and filter output. Then, a novel cost function is designed for the augmented tracking system, which employs the fault estimation and the discount factor. The Hamilton-Jacobi–Bellman equation is solved to obtain the CLSTC strategy through an adaptive critic structure with cooperative tuning laws. Besides, the Lyapunov stability theorem is utilized to prove that all signals of the closed-loop system converge to a small neighborhood of the equilibrium point. Simulation results demonstrate that the proposed control method has good fault tolerance performance and is suitable for solving secure control problems of nonlinear systems with various sensor faults.

Abstract:
This article investigates a comprehensive data-driven event-triggered secure lateral control of autonomous vehicles under actuator attacks. We consider stabilization issues of autonomous vehicles subject to modeling difficulties, limited communication resources, and actuator attacks. The dynamic model decomposition (DMD) from data is exploited to characterize the inherent lateral dynamics model of autonomous vehicles, the event-triggered transmission scheme is utilized to alleviate communication burden for limited bandwidth network, and the sliding mode control scheme is designed to ensure the security of autonomous vehicles under actuator attacks. The stability analysis and the stabilization method as well as its algorithm are presented. The proposed secure control scheme can actively counteract the malicious effects caused by actuator attacks and integrates the advantages of both data-driven modeling and model-based control design. Finally, several comparative case studies show the effectiveness of the proposed secure control scheme.

Affiliations: Mechanical and Mechatronics Engineering Department and the Waterloo Autonomous Vehicle Team in the SAE AutoDrive Challenge (Watonomous.ca) (WATonomous), University of Waterloo, Waterloo, ON, Canada; Cheriton School of Computer Science and the Waterloo Autonomous Vehicle Team in the SAE AutoDrive Challenge (Watonomous.ca) (WATonomous), University of Waterloo, Waterloo, ON, Canada; Department of Electrical and Computer Engineering and the Waterloo Autonomous Vehicle Team in the SAE AutoDrive Challenge (Watonomous.ca) (WATonomous), University of Waterloo, Waterloo, ON, Canada

Abstract:
When applied to autonomous vehicle (AV) settings, action recognition can enhance an environment model’s situational awareness. This is especially prevalent in scenarios where traditional geometric descriptions and heuristics in AVs are insufficient. However, action recognition has traditionally been studied for humans, and its limited adaptability to noisy, un-clipped, un-pampered, raw RGB data has limited its application in other fields. To push for the advancement and adoption of action recognition into AVs, this work proposes a novel two-stage action recognition system, termed RALACs. RALACs formulates the problem of action recognition for road scenes, and bridges the gap between it and the established field of human action recognition. This work shows how attention layers can be useful for encoding the relations across agents, and stresses how such a scheme can be class-agnostic. Furthermore, to address the dynamic nature of agents on the road, RALACs constructs a novel approach to adapting Region of Interest (ROI) alignment to agent tracks for downstream action classification. Finally, our scheme also considers the problem of active agent detection, and utilizes a novel application of fusing optical flow maps to discern relevant agents in a road scene. We show that our proposed scheme can outperform the baseline on the ICCV2021 Road Challenge dataset (Singh et al., 2023) algorithm and by deploying it on a real vehicle platform, we provide preliminary insight to the usefulness of action recognition in decision making. The code is publicly available at https://github.com/WATonomous/action-classification.

Abstract:
One challenge of learning from streaming data is that only a limited number of labeled examples are available, making semi-supervised learning (SSL) algorithms becoming an efficient tool for streaming data mining. Recently, the graph-based SSL algorithms have been proposed to improve SSL performance because the graph structure can utilize the interactivity between surrounding nodes. However, graph-based SSL algorithms have two main limitations when applied to streaming data. First, not all the labels of the data in the streaming data may be reliable, and direct classification using a graph can lead to suboptimal performance. Second, graph-based SSL algorithms assume the structure of the graph is static, but the learning environment of streaming data is dynamic. Hence, we propose a competence-aware graph neural network (CA-GNN) to deal with these two limitations. Unlike other models, CA-GNN does not directly rely on graph information that could include mislabeled nodes. Instead, a competence model is used to explore latent semantic correlations in the streaming data and capture the reliability for each data. A streaming learning strategy then evolves CA-GNN’s parameters to capture the dynamism of the graph sequences. We conducted experiments using seven real datasets and four synthetic datasets, respectively, and compared the outcomes across various methods. The results demonstrate that CA-GNN classifies streaming data more effectively than the state-of-the-art (SOTA) methods.

Abstract:
Bearing skidding is the primary factor restricting the development of aeroengines toward ultrahigh speed, low friction, and lightweight. Compared to typical bearing faults, analysis of bearing skidding presents greater challenges due to the weak signal properties, significant time-varying characteristics and coupling influence of multiple factors. It is crucial to fully utilize multisource signals to enhance skidding features and capture time-varying characteristics. This article proposes a prior knowledge-embedded dual feedback spatial-temporal graph convolutional network (DFSTGCN) for skidding assessment. Unlike existing adjacency matrix construction strategies, the correlation between multisource signals is described based on multiple prior knowledge, which includes dynamic model, structural dynamics, and expert experience. Furthermore, a DFSTGCN is designed to simultaneously focus on the spatial and temporal dependencies of time-varying skidding data. Specifically, a dual feedback mechanism that includes prediction error ratio and uncertainty loss function is employed to improve the generalization performance of skidding prediction model. The effectiveness of the proposed strategy is validated under different working conditions.

Affiliations: School of Automation, the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, and the Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, China University of Geosciences, Wuhan, China; Department of Electrical Engineering, Yeungnam University, Gyeongsan, South Korea; College of Information Science and Engineering, Northeastern University, Shenyang, China

Abstract:
In this work, membership-functions-dependent resilient hybrid-triggered (HT) passivity control is considered for networked fuzzy systems against deception attacks. To mitigate the transmission burden and enhance resilience to attacks, a resilient membership-functions-dependent HT mechanism is proposed that considers the properties of a Takagi-Sugeno fuzzy model. The presented HT strategy incorporates membership functions to generalize the HT threshold and weighting matrices. Then, the membership functions are embedded in quadratic and integral terms of the fuzzy Lyapunov-Krasovskii functional, leading to a more effective resilient HT mechanism that significantly reduces network bandwidth stresses and achieves better passivity performance. An asynchronous passivity fuzzy controller with enhanced feasibility is designed under imperfect premise matching, and traditional passive controllers are generalized as special cases. Finally, a truck-trailer example is offered to test the validity and applicability of the proposed techniques.

Abstract:
Impulsive feature (IF) response is an essential indicator for rolling bearing fault. However, it is overwhelmed by strong noise and difficult to extract in real scenes. Although deep learning-based methods are powerful in feature extraction, their logic and extracting principles possess weak interpretability and credibility. Their further implementation is hampered. In this article, a natural modal sketching network (NMSNet) is constructed to achieve robust and credible bearing IF extraction. First, the modal response is designed as a convolutional kernel of NMSNet, and the forward propagation logic is interpreted as natural modal sketching, including modal response recovery and weighted superposition. The logic derives from the fault mechanism and brings solid credibility to NMSNet. Second, a novel correction algorithm is developed to interpret the extraction principle of NMSNet in theory and achieve noise elimination due to its filter nature. Third, NMSNet realizes adaptive modal sketching via the formulated weighted fusion strategy and training constraint. Finally, simulation and experiment have been carried out to verify the effectiveness and noise robustness of NMSNet. The fault-related interpretability analysis confirms the knowledge acquisition of NMSNet, which strengthens the credibility of IF extraction.

Abstract:
Multiobjective shortest path problem (MSPP) is one of the most critical issues in network optimization, aimed at identifying all efficient paths across conflicting objectives. Nowadays, existing methods face substantial bottlenecks in addressing the diverse preferences of decision makers and high spatiotemporal overhead caused by the calculation process, particularly in cases with large-scale networks. To overcome these obstacles, a generalized MSPP in large-scale networks is investigated with the aim of solving it with diverse preferences of decision makers satisfied and low spatiotemporal overhead. Toward this end, with a novel concept, the generalized dominance relation is introduced, and the generalized multiobjective shortest path algorithm via the generalized dynamic programming approach is developed. Moreover, the H-reducible technique is further employed to accelerate the convergence of the proposed algorithm. Additionally, several rigorous proofs are provided for the conclusions that all efficient paths could be found within a tolerable time by the developed algorithm and the algorithm could be implemented in a distributed manner under mild assumptions. Finally, numerous routing experiments are conducted on large-scale communication networks for demonstrating the effectiveness and competitiveness of our approach.

Abstract:
In this article, we study the optimal output synchronization problem (OOSP) for uncertain networked Euler-Lagrange (EL) systems. Specifically, the system outputs are expected to be synchronized at the solution of an uncertain distributed time-varying quadratic optimization problem, where each local time-varying cost function includes uncertain parameters. From a centralized perspective, we first develop a controller with adaptive control gains to guide the output of a double-integrator system toward the time-varying optimal solution. By employing the modified average estimators, we extend the centralized design to a distributed implementation to address the OOSP for uncertain EL systems. Using matrix trace properties and composite Lyapunov analysis, we prove that the system outputs can asymptotically converge to the desired time-varying optimal solution. Two examples are used to verify the proposed designs.

Abstract:
Efficient and robust data clustering remains a challenging task in data analysis. Recent efforts have explored the integration of granular-ball (GB) computing with clustering algorithms to address this challenge, yielding promising results. However, existing methods for generating GBs often rely on single indicators to measure GB quality and employ threshold-based or greedy strategies, potentially leading to GBs that do not accurately capture the underlying data distribution. To address these limitations, this article leverages the principle of justifiable granularity (POJG) to measure the quality of a GB for clustering tasks and introduces a novel GB generation method, termed GB-POJG. Specifically, a comprehensive metric integrating the coverage and specificity of a GB is introduced to assess GB quality. Utilizing this quality metric, GB-POJG incorporates a strategy of maximizing overall quality and an anomaly detection method to determine the generated GBs and identify abnormal GBs, respectively. Compared to previous GB generation methods, GB-POJG maximizes the overall quality of generated GBs while ensuring alignment with the data distribution, thereby enhancing the rationality of the generated GBs. Experimental results obtained from both synthetic and publicly available datasets underscore the effectiveness of GB-POJG, showcasing improvements in clustering accuracy and normalized mutual information. All codes have been released at https://zenodo.org/records/13643332.

Abstract:
Task planning focused on household robots represents a conventional yet complex research domain, necessitating the development of task plans that enable robots to execute unfamiliar household services. This area has garnered significant research interest due to its extensive applications in robotics, particularly concerning household robots. Nevertheless, the majority of task planning methodologies exhibit suboptimal performance regarding the success and efficiency of completing household tasks, primarily due to a lack of cognitive capacity of household items and home environments. To address these challenges, we propose an efficiency-driven adaptive task planning approach based on hierarchical item-environment cognition. Initially, we establish a multiple semantic attribute-based priori knowledge (MSAPK) framework to facilitate the attributive representation of household items. Utilizing MSAPK, we develop a long short-term memory (LSTM) based item cognition model that assigns relevant attributes and substitutes to specified household items, thereby enhancing the cognitive capabilities of household robots at the attribute level. Subsequently, we construct an environment cognition model that delineates the relationships between household items and room types, enabling household robots to locate target items more efficiently. Through hierarchical item-environment cognition, we introduce a strategy for adaptive task planning, empowering household robots to execute household tasks with both flexibility and efficiency. The generated plans are evaluated in both virtual and real-world experiments, with promising results affirming the effectiveness of our proposed methodology.

Abstract:
The adaptive event-triggered control problem for a class of uncertain high-order pure feedback nonlinear systems (HOPFNSs) is considered. Different from the traditional backstepping method, a new high-order backstepping method is proposed based on the high-order fully actuated (HOFA) system approaches to design the adaptive event-triggered control law, which has the significant advantages of simple structure, high degree of freedom, and easy to realize. The high-order backstepping method does not need to transform the HOPFNSs into the first-order systems, which is more efficient and significantly reduces design complexity. It is proved that the adaptive event-triggered controller makes all the signals of the system bounded and save the energy in signal transmission. A simulation example is performed to verify the effectiveness of the control strategy.

Abstract:
This article presents several adaptive event-triggered control (ETC) strategies for a class of uncertain nonlinear cyber–physical systems (CPSs) under constant or time-varying full-state constraints, as well as deception attacks via the senior network. Initially, the system under investigation is reformulated into a new system that encompasses both the original system state and the compromised state, enabling the use of compromised states for feedback control. Subsequently, two innovative asymptotic integral barrier Lyapunov functions (IBLFs) are developed by directly imposing constraints on the compromised state, thereby eliminating the necessity to convert state constraints into error constraints as required by traditional BLFs-based methods. Furthermore, controllers designed using relative/switched threshold event-triggered strategies ensure that all signals within the entire closed-loop system remain bounded, that the constant or time-varying full-state constraints are not breached, and that asymptotic stability is attained without Zeno behavior. Ultimately, simulation results validate the efficacy of the proposed strategies through a practical example.

Abstract:
This article addresses the stochastic stabilization problem of Markovian jump systems (MJSs) closed by a sampled-data controller in the diffusion part. A novel stochastic stabilizing method is developed by optimizing the mode separations whose quantity is equal to a Stirling number of the second kind. It can be used to deal with the challenges coming from a stochastic controller’s switching and state signals sampled, whose results are also less conservative compared to some existing results. In order to get the best mode separation having the best performance, an optimization problem is proposed by applying an augmented Lagrangian cost function, which can ensure the existence and calculability of a locally optimal solution. Moreover, an improved hill-climbing algorithm is established to reduce computational complexity while retaining as much performance as possible, which is enhanced by applying Q-learning technique to determine an optimal attenuation coefficient. Two examples are offered so as to verify the effectiveness and superiority of the methods given in this study.

Abstract:
The problem of prescribed performance control (PPC) for the multi-input multi-output block-triangular nonlinear systems under output constraints is investigated in this article. It is focused on the scenario where the references are not known in advance. This renders the related solutions infeasible and becomes more challenging under the totally unknown and inherently nonlinear dynamics of the system. To overcome this challenge, a novel robust decoupling PPC strategy is developed in this article, in which an online boundary generation scheme and a smoothly constraint switching rule are devised and introduced. The resulting controller ensures that the system outputs evolve within their respective constraint bands and track the references with the predetermined overshoot, settling time and accuracy. Moreover, it is independent of function approximation, parameter identification, or disturbance estimation, despite the unbounded nonlinearities, unmatched disturbances and unknown dynamics. A comparative experiment on a 2-DOF serial flexible link robot is conducted to show the efficacy and superiority of our low-complexity high-performance control approach.

Abstract:
The actuator faults and channel fading are unavoidable in the nonlinear continuous-time MIMO multiagent systems (MASs), which significantly complicate the formation control problem. To tackle these challenges, a model-free adaptive fault-tolerant formation control (MFAFTFC) scheme based on the sampled-data full-form dynamic linearization (SD-FFDL) technology is proposed, which integrates the data-based formation control algorithm, the fuzzy neural network algorithm, and the projection algorithm. The stability analysis of MFAFTFC scheme is strictly provided. Simulation comparison results with unmanned ground vehicle demonstrate the effectiveness of the proposed MFAFTFC scheme, that is, the proposed MFAFTFC scheme can achieve the formation objective for the nonlinear continuous-time MIMO MASs subjected to actuator faults and fading channels.

Abstract:
In this article, we propose an estimator-based second-order sliding mode (SOSM) controller tailored for uncertain nonlinear systems with unknown input delay. Distinct from existing SOSM control methods, this work tackles two principal challenges: 1) the difficulty of dealing with unknown input delay, especially given the discontinuity of sliding mode controllers; and 2) the uncertainties in the nonlinear systems bounded by functions rather than widely-used constants. We begin by establishing the SOSM dynamics with input delay and uncertainties, followed by the introduction of an auxiliary compensation system. Then, we design an input delay estimator suitable for discontinuous controllers by enhancing the convex optimization method. Leveraging this, a novel estimator-based SOSM controller is constructed by adding a power integrator technique to address the input delay issue. Rigorous Lyapunov analysis is conducted to confirm the finite-time stability of the closed-loop control system. Finally, comparative simulations validate the superiority of the proposed SOSM controller.

Abstract:
Learning to optimize and automated algorithm design are attracting increasing attention, but it is still in its infancy in constrained multiobjective optimization evolutionary algorithms (CMOEAs). Current learning-assisted CMOEAs are typically crafted by human experts using manually designed techniques, which tend to be overly tuned, ad hoc, and lacking versatility. To alleviate these limitations, this work proposes transforming the online configuration of CMOEA into determinations of discrete and continuous parameters, which are then solved by deep reinforcement learning (DRL) techniques. Specifically, the Actor–Critic framework is adapted to determine a factor that defines the environmental selection pressure. The deep Q-learning technique is adopted to determine the operators for producing offspring. Owing to the property of DRL, the configured algorithm can accommodate historical experience, current evolutionary dynamics, and future improvements to achieve self-learning. A new CMOEA is proposed using the automatically configured evolutionary algorithm. Experiments on four challenging benchmarks and 21 real-world problems verify that our method significantly outperforms 11 state-of-the-art methods. The versatility and superiority of the automatically configured environment and operators over handcrafted methods justify the effectiveness of the automated configuration method, demonstrating a promising direction in evolutionary multiobjective optimization.

Affiliations: Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China; Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA

Abstract:
This article addresses the stabilization challenges of nonholonomic systems under the threat of false data injection (FDI) attacks, which compromise the integrity of state information. A novel adaptive control strategy using Nussbaum-type gains is proposed to ensure the asymptotic stability of the closed-loop system while maintaining signal boundedness. The approach extends conventional Nussbaum designs to handle multiple unknown control directions. It integrates online learning mechanisms to mitigate the impact of FDI attacks. Additionally, adaptive backstepping and fuzzy-logic systems are utilized to approximate and compensate for unknown nonlinear dynamics. The methodology transforms nonholonomic systems into equivalent cascade structures to address inherent constraints and enable secure control input design. Simulation studies validate the effectiveness and resilience of the proposed control strategy, demonstrating significant improvements in stability and robustness in the presence of FDI attacks.

Abstract:
This article studies the predefined-time cooperative control based on self-triggered mechanism for the multiagent system with uncertain powers and denial-of-service (DoS) attacks. Unlike the majority of existing results with input powers of one, the system’s powers considered in this study are uncertain odd numbers greater than one. A distributed observer in the first-order filter form is designed to estimate unmeasurable states under DoS attacks. Subsequently, by constructing a transform function, we establish a new distributed predefined time control law based on the neural network approximation approach. Different from the working theory of the event-triggered mechanism, the triggering moment of self-triggered mechanism introduced in this article is determined by the previous moment’s state, which further decreases the resource wastage. Compared with finite-time and fixed-time control, the distributed predefined-time control protocol developed in this article can guarantee that system’s synchronization errors are steered to a preset range within a predefined time, and the predefined time and range can be set freely by the user. Finally, the theoretical design is verified by some simulation results.

Abstract:
In this article, the security formation control problem is investigated for underactuated quadrotors involving nonlinear coupled dynamics, subject to denial-of-service (DoS) attacks and uncertain communication faults. A security formation control method is proposed, including a distributed resilient observer and a hierarchical data-driven controller. The observer with an adaptive event-triggered mechanism is developed to restrain the influence of DoS and communication faults on interaction information among quadrotors, and Zeno behavior of all observers can be avoided. The optimal control laws are learned iteratively based on observation data and system data by utilizing reinforcement learning without knowledge of system dynamics. The stability of the constructed closed-loop control system is proven, and sufficient conditions are established for the unreliable network. Simulation results demonstrate the advantages of the proposed security control method.

Abstract:
Recently, salient object detection (SOD) in optical remote sensing images, dubbed ORSI-SOD, has attracted increasing research interest. Although deep-based models have achieved impressive performance, several limitations remain: a single image contains multiple objects with varying scales, complex topological structures, and background interference. These unresolved issues render ORSI-SOD a challenging task. To address these challenges, we introduce a distinctive cross-model nested fusion network (CMNFNet), which leverages heterogeneous features to increase the performance of ORSI-SOD. Specifically, the proposed model comprises two heterogeneous encoders, a conventional CNN-based encoder that can model local features, and a specially designed graph convolutional network (GCN)-based encoder with local and global receptive fields that can model local and global features simultaneously. To effectively differentiate between multiple salient objects of different sizes or complex topological structures within an image, we project the image into two different graphs with different receptive fields and conduct message passing through two parallel graph convolutions. Finally, the heterogeneous features extracted from the two encoders are fused in the well-designed attention enhanced cross model nested fusion module (AECMNFM). This module is meticulously crafted to integrate features progressively, allowing the model to adaptively eliminate background interference while simultaneously refining the feature representations. We conducted comprehensive experimental analyzes on benchmark datasets. The results demonstrate the superiority of our CMNFNet over 16 state-of-the-art (SOTA) models.

Affiliations: School Electronic and Information Engineering and the Chongqing Key Laboratory of Generic Technology and System of Service Robots, Southwest University, Chongqing, China; School of Automation, the Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems, and the Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, China University of Geosciences, Wuhan, China; School of Artificial Intelligence and Automation and the Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Huazhong University of Science and Technology, Wuhan, China; Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education, East China University of Science and Technology, Shanghai, China

Abstract:
This article investigates the leader-following consensus of nonlinear time-delay multiagent system under impulsive control with simultaneous consideration of packet loss and parameter mismatch. Specifically, the inherent parameter mismatch between the leader’s dynamics and followers’ dynamics is explicitly addressed. To mitigate communication frequency, two novel impulsive control protocols are developed: 1) a pure impulsive scheme for theoretical analysis and 2) a limited impulsive strategy for practical implementation. Furthermore, an auxiliary function is introduced to characterize packet loss phenomena during information transmission, ensuring alignment with real-world communication constraints. By integrating impulsive control theory with reverse average dwell-time analysis, sufficient consensus criteria are rigorously derived for multiagent system with time delays and heterogeneous parameters. Finally some numerical simulations validate the effectiveness of the proposed control framework, demonstrating its capability to achieve consensus under practical communication imperfections.

Affiliations: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, China; School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, China; Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai, China; Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China; School of Control Science and Engineering, Tiangong University, Tianjin, China; Department of Mechanical Engineering, York University, Toronto, ON, Canada

Abstract:
This article addresses synchronization control of switched complex network with unknown state and actuator fault. A mode-dependent proportional-integral intermediate observer is explored to estimate unknown elements with high-estimation accuracy. A hybrid controller is constructed to treat the asynchronous occurrence of impulses and switching moments. With the help of mode-dependent average dwell time and mode-dependent average impulsive interval, a mode-dependent criterion is established to guarantee the uniformly bounded synchronization performance. Two examples are simulated to deliver the effectiveness of the proposed method.

Affiliations: Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China; Department of Control Science and Engineering, and the Shanghai Institute of Intelligent Science and Technology, National Key Laboratory of Autonomous Intelligent Unmanned Systems, the Frontiers Science Center for Intelligent Autonomous System, Ministry of Education, Tongji University, Shanghai, China; Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education and the School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

Abstract:
In complex and variable terrains, affine formation control, with its flexible formation adjustment ability, can achieve various formation shapes to adapt well to the environment. Notably, the existing affine formation research based on stress matrices require the variation parameters for translation, rotation, scaling, and shearing of formations to be predesigned offline. To address this, we propose a novel method for online affine parameter adjustment that enables self-reconfiguration of formations in multiobstacle environments. By adopting artificial potential field environment excitation, the proposed motion planning algorithm can dynamically adjust the affine transformation parameters online, and realize the self-reconfiguration of formation shape to avoid collision. Then, a distributed model predictive controller is proposed for multiagent systems, which actively utilizes historical control input information to flexibly adjust controller performance while avoiding algebraic loops between neighboring agent controllers. The algorithm separates stability and performance optimization within the nonlinear model predictive control framework, ensuring both the feasibility and stability of the underlying optimization. Finally, the simulation results confirm the effectiveness of the proposed controller.

Abstract:
This study focuses on anti-quasisynchronization for discrete-time asynchronous leader–follower Markovian neural networks (MNNs) with mismatched parameters. To overcome the energy constraint, the intermittent control transmission strategy is introduced. Meanwhile, to address the challenge of unknown Markovian models in the leader–follower MNNs, a hidden Markov model (HMM) is utilized to infer unknown modes from observable information. Then, an intermittent nonfragile controller based on HMM is designed for the follower MNNs. Furthermore, the exponential iteration method is employed to establish sufficient conditions for ensuring anti-quasisynchronization for leader–follower MNNs, and an optimal boundary of anti-quasisynchronization is obtained. Ultimately, the effectiveness of the proposed HMM-based intermittent controller is demonstrated via a numerical simulation.

Abstract:
This article addresses a new type of adaptive time-varying group formation tracking (TVGFT) problem for linear multiagent systems (MASs) with nonautonomous leaders. To fulfill complicated formation tasks, a TVGFT protocol is proposed, where the agents are decomposed into multiple subgroups and each subgroup can successfully track the corresponding leader. Additionally, novel multiple asynchronous dynamic edge-event-triggered mechanisms (DEETMs) are designed to further conserve communication resources and optimize network utilization by enabling the leader to send information intermittently and allowing each follower to transmit information asynchronously when the trigger mechanisms are satisfied. The DEETMs consider both interlayer and intergroup information interactions to improve communication efficiency. Different from the existing results, the proposed DEETMs are used for intergroup information exchange to enhance both the coordination of formation and information transparency. At last, the simulation experiment is offered to validate the designed protocol.

Abstract:
Developing model-free H_\infty optimal control schemes in systems with unknown model parameters and unmeasurable states is challenging. In this article, an output-feedback (OPFB) suboptimal control scheme based on adaptive dynamic programming (ADP) is proposed to realize model-free H_\infty control under uncertain disturbances. First, a free matrix is introduced to compute the suboptimal gain in the absence of an optimal OPFB gain, and a policy iterative algorithm is developed to solve for the suboptimal gain and shown to converge to a solution of the algebraic Riccati equation. In addition, a model-free ADP algorithm is proposed to realize online learning of control parameters without relying on system dynamics parameters. The Lanczos method is introduced to solve the ill-condition problem in the model-free algorithm solution. After that, the algorithm is further extended to the case where the system state is not measurable and parameterized reconstruction is performed using online input-output data. The results show that the proposed algorithm can realize model-free control with unknown parameters and unmeasurable states. The effectiveness of the proposed control scheme is simulated by an F-16 aircraft.

Affiliations: College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China; Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland; College of Science, Nanjing University of Posts and Telecommunications, Nanjing, China; College of Computer Science and Software Engineering, Shen Zhen University, Shen Zhen, China; College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, China; School of Mathematics and Statistics, Xinyang Normal University, Xinyang, China; School of Mathematics, Southeast University, Nanjing, China; School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW, Australia

Abstract:
The principal innovative contribution of this study resides in the introduction of a category of fractional delayed large-scale neural networks characterized by intricate topological structures. Additionally, this article provides a comprehensive exploration of novel outcomes linked to fractional order-induced bifurcations in large-scale networks. In the initial step, the correlation of the artificial neural network and the graphical neural network is established through the Mason’s diagram method. Subsequently, the system’s characteristic equations are derived by employing the Coates’ flow graph decomposition method. Moving on, through the concept of the global element, an exhaustive investigation delves into the distribution of eigenroots. The sum of synaptic transmission delays among neurons is considered as a bifurcation parameter, with an analysis focused on the stability of the trivial equilibrium and the existence of the Hopf bifurcation. Following this, the optimal fractional order-dependent stability interval is determined using the implicit function array curve method, presenting a novel approach for critical value determination. Finally, the drawn conclusions are substantiated through multiple sets of computer simulations. It is indicated that an increase in delay precipitates the onset of Hopf bifurcation. Moreover, a reduction in the fractional order significantly improves the steady-state performance of the system. However, once the fractional order value descends below the left stability boundary, the system’s stability is compromised, leading to the emergence of periodic oscillations. The prediction algorithm proposed in this article offers valuable insights into selecting the appropriate fractional order for large-scale complex networks.

Abstract:
The state estimation problem of linear time-invariant (LTI) systems is investigated in this article. Distributed observers are designed to estimate the complete states of LTI systems under time-varying delays. A class of edge-based switching event-triggered mechanisms (SETMs) are designed for distributed observers to continuously estimate the states of the target system by the discrete relative state information, reducing the consumption of communication/computation resources significantly. The positive lower bounds of intersampling times are guaranteed, which further avoids Zeno behaviors. A unified framework for the stability analysis of the estimation error system under the SETM is presented, and the sufficient conditions for the asymptotic state estimation of the distributed observer are derived. Finally, numerical simulations are given to illustrate the effectiveness of the proposed method.

Abstract:
The bipartite consensus (BC) issue for nonlinear multiagent systems (NMASs) with unknown system dynamics information is investigated in this article. Initially, the dynamics of NMASs are represented using the Takagi-Sugeno (T-S) fuzzy model. Subsequently, to achieve distributed control, a minmax game policy is introduced, where each agent aims to minimize its performance index while its neighbors attempt to maximize it. Consequently, the BC problem for NMASs is reformulated as a zero-sum game, transforming the controller design into solving a set of game algebraic Riccati equations (GAREs). To solve such equations, a novel scaling off-policy iteration (PI) algorithm is proposed. The key features of the proposed learning algorithm can be outlined in three main aspects: 1) during the learning process, the reliance on system dynamics is relaxed; 2) compared with the PI method, the requirement for initial admissible control policies is eliminated; and 3) a more rapid convergence speed is achieved than traditional value iteration. Finally, the effectiveness and advantages of the proposed method are validated through a simulation example and a series of comparative experiments.

Abstract:
This article is concerned with a distributed algorithm for seeking the Nash equilibrium in noncooperative games with partial-decision information, which simultaneously addresses the protection of individual privacy and ensures fast algorithmic convergence. First, a differential privacy mechanism is used in the fully distributed consensus-based projected pseudo-gradient algorithm to obfuscate shared messages over the communication network and quantify the algorithm’s privacy level. To achieve fast convergence, a novel relaxed inertial method is designed, consisting of two steps with independently designed parameters: 1) a relaxation step and 2) an inertia step. The adaptive inertia coefficient in the inertia step is designed based on the iteration error of the players’ estimated decisions and a decaying sequence, with the only requirement being the non-negativity of its internal parameters. Compared to existing approaches, our algorithm exhibits high flexibility in parameter selection. Furthermore, we analyze the algorithm’s convergence and differential privacy under both linearly decaying and fixed stepsizes within a unified framework, providing sufficient conditions that are independent of the number of players. Finally, numerical simulations validate the algorithm’s potential, demonstrating significant improvements in convergence rate, accuracy, and privacy level.

Abstract:
Imbalanced image classification faces critical challenges in balancing the quality and diversity of synthetic minority samples. This article proposes the improved estimation distribution algorithm-based latent feature distribution evolution (MEDA_LUDE) algorithm, an evolutionary algorithm-assisted deep distribution learning framework that optimizes latent feature distributions through a multivariate Gaussian mixture (GM) assumption and a novel four-phase training strategy. We introduce a large-margin GM (L-GM) loss to dynamically model covariances for feature learning and design a MEDA that evolves latent features via a similarity-guided fitness function, thus enhancing diversity while preserving synthesis quality. Extensive experiments demonstrate significant improvements: MEDA_LUDE achieves 95.9% accuracy on MNIST (imbalanced ratio-IR:100), surpassing state-of-the-art methods by 1.26% on CIFAR-10. For industrial fabric defect data sets, it elevates accuracy by 1.45% on DHU-FD and 0.92% on ALIYUN-FD, especially with precision and G-mean improvements of 2.5% and 1.17%, respectively, on DHU-FD. Visualizations confirm that MEDA_LUDE generates minority samples with superior quality-diversity tradeoffs. The framework’s success in real-world fabric defect classification underscores its practical value in addressing imbalanced learning challenges.

Abstract:
In this article, the event-triggered optimal bipartite consensus control problem is investigated for second-order discrete-time multiagent systems (MASs) with control input saturation and unknown system models. First, an instant reward signal with nonquadratic functions dealing with the control input saturation is defined, based on which a novel internal reinforce reward function is defined to facilitates agents to learn more intrinsic information from the local environment. Then, a novel event-triggered internal reinforce Q-learning (IrQL) algorithm is introduced. In contrast to conventional time-triggering Q-learning methods, the proposed event-triggered IrQL algorithm can not only fully exploit environment but also save the data computation and transmission resources. Based on elegant functional analysis techniques and Lyapunov stability theory, the internal reinforce reward function can be proved to be bounded and the tracking error dynamics of MASs are ensured asymptotic stability under the proposed event-triggered control policies. Then, data-driven reinforce-critic-actor neural networks are constructed to implement the event-triggered IrQL algorithm online with the proof of convergence. Finally, simulation examples show the validity and better performance over existing researches.

Abstract:
This article addresses the problem of resilient collision avoidance distributed optimal coordination (DOC) for multiple Euler-Lagrangian (EL) systems under unreliable communication topologies. Due to adverse network conditions and cyber attacks, communication between agents can be disrupted during certain time intervals. To achieve collision avoidance between agents, a barrier function is redesigned, and a communication-based distributed collision avoidance algorithm is correspondingly proposed. Then, a resilient collision avoidance DOC strategy based on real-time position-based gradient is introduced, incorporating a coordinator for generating collision avoidance formation reference signals and an adaptive tracking controller. By utilizing the Lyapunov method and boundedness analysis, the proposed DOC strategy is proven to achieve both convergence and collision avoidance, even under unreliable communication networks. Finally, the effectiveness of the proposed strategy is validated through a simulation example.

Abstract:
The randomness of chaotic systems are crucial for their application in secure communication. Conservative systems exhibit enhanced ergodicity and randomness in comparison to dissipative chaotic systems. However, the memristor-based conservative chaotic maps remain unreported. This article presents a study of volume-preserving chaotic maps based on discrete memristor (DM). We propose and analyze a generic conservative map that incorporates DM. The conservative characteristics of the proposed iterative map are confirmed through the determinant of its Jacobian matrix. Furthermore, four distinct DM models are introduced and their memristive characteristics are verified through numerical simulations of hysteresis loops. To investigate the dynamical properties of the discrete memristive conservative map (DMCM), we incorporate the proposed DM models into the generic conservative map model using numerical methods, including phase portraits, Lyapunov exponents, and bifurcation diagrams. Additionally, the hardware implementation of the DMCM on an FPGA platform demonstrates the reliability of the model. Finally, secure communication experiments based on the DMCM show that it outperforms some classical dissipative chaotic maps in terms of bit error rate performance.

Abstract:
This article is concerned with the high-performance leader-following problem for the heterogeneous nonlinear multiagent systems. It is focused on the cases where the underlying communication graph is directed and switching; the model information of each agent is unknown; only the relative output measurement is available for the local controller design. They render the existing distributed high-performance control solutions infeasible. In this article, a distributed robust output-feedback prescribed performance control strategy is put forward to conquer this obstacle. First, the resulting control is off-line designed, regardless of the initial condition or the switching condition. Second, it automatically adjusts the neighborhood errors and the intermediate errors online, against the topology switching. Besides, it is inherently robust to the unknown system dynamics, without parameter identification, function approximation, disturbance estimation, or derivative calculation or estimation. It turns out that global fast accurate output synchronization is achieved by our approach in the sense that the follower outputs track the leader output with the preassigned settling time and accuracy after the topology switching. A comparative simulation is conducted to substantiate the above theoretical findings.

Affiliations: School of Engineering, Qufu Normal University, Rizhao, China; School of Control Science and Engineering, Tiangong University, Tianjin, China; Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan; School of Mathematics, Southeast University, Nanjing, China; Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan; School and Hospital of Stomatology, Liaoning Provincial Key Laboratory of Oral Diseases, China Medical University, Shenyang, China

Abstract:
This article studies the adaptive fuzzy asynchronous (AFA) stabilization of discrete networked hidden stochastic semi-Markovian switching power systems under cyber attacks. Due to the complex network environment, cyber-attacks are taken into account, in which the fuzzy logic rule is adopted to describe the unknown deception attacks. Considering the mismatch mechanism between the controller and the system, an adaptive fuzzy controller runs asynchronously with the system, where the hidden semi-Markovian model is used to characterize the asynchronous mechanism. Based on the detected mode and the fuzzy logic rule, an AFA stabilizing controller is designed for the underlying system. Using the stochastic Lyapunov function related to the detected mode and system mode, sufficient criteria are given for the AFA controller design, ensuring that the underlying system is bounded stable in the mean square. Finally, the proposed scheme is verified by the simulated example.

Abstract:
This article proposes a resilient distributed model predictive control (DMPC) algorithm for a class of constrained dynamically coupled multiple cyber-physical systems (CPSs) subject to bounded additive disturbances. The algorithm is designed to address severe attacks on the forward controller-actuator (C-A) channel, the feedback sensor-controller (S-C) channel, and the channels between subsystems, without any prior information about the intruder available to the defender. To mitigate the negative effects of intruders, we consider a one-step time delay strategy in the local model predictive controller design. This strategy allows the generated controller data to be checked for acceptability before use. To ensure constraint satisfaction for an infinite-horizon MPC problem while accounting for the unknown duration of attacks, we develop a set of minimally conservative constraints in the open-loop control mode using a constraint tightening technique. Moreover, we obtain an equivalent finite number of constraints for the infinite-horizon problem to ensure recursive feasibility. To prevent tampered data from affecting control performance, a detector module is designed to decide whether data is used by its receiver. It is shown that the closed-loop system is uniformly ultimate boundedness (UUB) under any admissible attack scenario and disturbance realization. Finally, the effectiveness of the proposed algorithm is validated by a case study.

Abstract:
In practice, many mechanical systems are underactuated, such as naval vessels, cranes, and helicopters, to reduce energy consumption and enhance flexibility. However, compounded by strong nonlinearity arising from state coupling, the underactuated nature and high-order unavailable states pose great challenges to motion control (particularly for unactuated states lacking independent actuators or kinematic constraints). In this article, an adaptive controller based on fully-actuated system methods is proposed, together with a general and extensible analysis method. First, a group of high-order auxiliary variables, consisting of actuated/unactuated states, their derivatives, and proportional-differential terms, are designed to rearrange the nonlinear underactuated system as a high-order linear fully-actuated system without any linearization operations. The asymptotic convergence of auxiliary variables theoretically eliminates the steady-state errors of actuated/unactuated states together. For high-order unmeasurable variables, they are recovered by the constructed neural network observer to estimate high-order dynamics, which avoids discontinuous robust terms and improves the accuracy of compensation/positioning. Motivated by the inherent features and advantages of fully-actuated systems, this article proposes the first fully-actuated system-based continuous adaptive controller for a class of underactuated robots. Moreover, it is convenient to extend the proposed controller to handle more practical problems, such as state delay, without the need to reconduct Lyapunov-based analysis. In addition to complete theoretical frames, this article also provides several experimental validation.

Abstract:
This study investigates the problem of adaptive neural network asynchronous control for switching cyber-physical systems under unknown dead zones. A generalized switching rule, instead of a Markov/semi-Markov process, is utilized to scrutinize the switching behavior of subsystems. This approach characterizes the dynamic nature of sojourn probabilities using single-mode-based sojourn time, aiming to decrease computational load while meeting the demands of real-world scenarios. Considering the intricacies of network environments, the unknown dead zone inputs are considered, which can be effectively implemented via the adaptive neural network-based control law. To counteract the adverse effects of unforeseen information, a saturation-based observer is developed, in which the saturation level is dynamically adjusted with the hope of providing greater flexibility. Utilizing a Lyapunov function that correlates with the detected mode and the system mode, sufficient criteria are established to ensure that the closed-loop system remains bounded in probability. Eventually, the practicality and effectiveness of the proposed control methodology are verified through two simulated examples.

Abstract:
Multiagent and multirobot systems (MRS) often rely on direct communication for information sharing. This work explores an alternative approach inspired by eavesdropping mechanisms in nature that involves casual observation of agent interactions to enhance decentralized knowledge dissemination. We achieve this through a novel indirect knowledge transfer through behavior trees (IKT-BT) framework tailored for a behavior-based MRS, encapsulating knowledge and control actions in behavior trees (BT). We present two new BT-based modalities—eavesdrop-update (EU) and eavesdrop-buffer-update (EBU)—incorporating unique eavesdropping strategies and efficient episodic memory management suited for resource-limited swarm robots. We theoretically analyze the IKT-BT framework for an MRS and validate the performance of the proposed modalities through extensive experiments simulating a search and rescue mission. Our results reveal improvements in both global mission performance outcomes and agent-level knowledge dissemination with a reduced need for direct communication.

Abstract:
This article examines the normal form of a multiple-input-multiple-output (MIMO) linear time-varying (LTV) system. It explores the transformation of such a system into its normal form using an LTV transformation, if it possesses a general relative degree that may not be uniform (strict). Based on the obtained normal form, the internal and external dynamics as well as the inverse system of the LTV system are investigated. Additionally, the zero dynamics can be effectively parameterized. Moreover, by employing the derived LTV normal form, the proportional-derivative output-feedback stabilization and output tracking problems are resolved. Finally, an illustrative example is presented to showcase the effectiveness of the proposed controllers.

Abstract:
This article addresses the adaptive neural network (NN)-based sliding-mode control (SMC) problem for sampled-data singularly perturbed systems under aperiodic sampling intervals and input dead zone nonlinearities. To accurately characterize the irregularity of sampling intervals, a nonhomogeneous sojourn probability approach is introduced. To accurately characterize the irregularity of sampling intervals, a nonhomogeneous sojourn probability approach is introduced. An adaptive NN scheme is utilized to estimate and effectively compensate for the nonlinear errors induced by input dead zones, thereby significantly enhancing the robustness and performance of the controlled system. Leveraging these considerations, a novel sliding-mode controller, specifically designed to accommodate variations in sampling period modes and singular perturbation parameters, is proposed. This control strategy guarantees the exponential ultimate boundedness of system states in the mean-square sense and ensures the reachability of the predefined sliding surface in the closed-loop system. The validity of the proposed theory is demonstrated through a practical example.

Abstract:
Synthetic paradoxical control circuits have been used to regulate cell population density. However, paradoxical interactions and inherent time delays in these circuits complicate stable population control due to their effects on system dynamics. This article proposes a strategy to achieve robust population control by analyzing the bistable switching and oscillatory behavior in a delayed paradoxical cell population control circuit. First, five distinct population outcomes are identified, along with stabilization criteria for these states, highlighting the presence of bistability. Specifically, we reveal two bistable switching mechanisms: one driven by the initial cell population density and the other by the time delay for blasticidin-induced cell death. Furthermore, we demonstrate that the time delay can destabilize the effective control state via a Hopf bifurcation, leading to periodic oscillations, which can be mitigated by adjusting the blasticidin concentration. Based on these findings, we propose a robust control strategy by regulating initial cell population density and blasticidin concentration. These insights advance the design of robust population control circuits, with broad implications for synthetic biology and cell therapy.

Abstract:
The practical fixed-time resource allocation problem is investigated for multi-input-multi-output nonlinear uncertain multiagent systems with disturbed dynamics, subject to global equality and local inequality constraints. Due to the coexistence of distributed high-order dynamics system within agents and decision-making constraints, decision variables in resource allocation optimization problems cannot be directly obtained from the system. Existing strategies are insufficient to solve such complex fixed-time optimization control problems with coupled decision-making constraints. To address these challenges, a novel integrated framework is proposed, fusing symbolic-function-based fixed-time control theory with gradient consistency. The proposed algorithm is implemented through an output-feedback backstepping design process, which involves two stages. First, in the output-feedback design stage, a fixed-time high-order extended state observer estimates the uncertain dynamics and disturbances. Second, in the backstepping design stage, a time-switching controller is developed. This controller’s virtual control law has two components: the first employs the proportional-integral control method to satisfy the equality constraints, while the second uses gradient information from the \epsilon -exact penalty function to address the inequality constraints. Using the Lyapunov stability criterion, the proposed algorithm can ensure that all signals remain practical fixed-time stable, and that the error between the outputs of all agents and the optimal solution is maintained within a neighborhood of the origin. Finally, simulations are presented to demonstrate the effectiveness of the approach.

Abstract:
This article investigates fixed-time prescribed tracking control based on a command filter for a class of nonlinear systems with unknown control coefficients. A novel switching control mechanism is proposed to address the challenge of unknown control coefficients and introduce a dual-parameter switching strategy with online parameter adjustment based on the designed conditions. To address the limitation in existing research, where prescribed performance functions depend on the initial conditions of systems, this work designs a new class of prescribed performance functions that eliminates this dependency. A command-filter-based backstepping approach effectively avoids the computational complexity of high-order derivatives in traditional backstepping methods. In addition, the issue of the nondifferentiability of the virtual controller at switching moments in existing switching control methods has been resolved. Ultimately, the boundedness of all signals in the closed-loop system is ensured. Moreover, a simulation example of a second-order system verifies the effectiveness of the algorithm in this article.

Affiliations: College of Intelligent Systems Science and Engineering and the Engineering Research Center of Navigation Instruments, Ministry of Education, Harbin Engineering University, Harbin, China; Key Laboratory of Physical Oceanography, Ministry of Education, the Institute for Advanced Ocean Study, the Frontiers Science Center for Deep Ocean Multispheres and Earth System, and the College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China

Abstract:
As a longstanding scientific challenge, accurate and timely ocean forecasting has always been a sought-after goal for ocean scientists. However, traditional theory-driven numerical ocean prediction (NOP) suffers from various challenges, such as the indistinct representation of physical processes, inadequate application of observation assimilation, and inaccurate parameterization of models, which lead to difficulties in obtaining effective knowledge from massive observations, and enormous computational challenges. With the successful evolution of data-driven deep learning in various domains, it has been demonstrated to mine patterns and deep insights from the ever-increasing stream of oceanographic spatiotemporal data, which provides novel possibilities for revolution in ocean forecasting. Deep-learning-based ocean forecasting (DLOF) is anticipated to be a powerful complement to NOP. Nowadays, researchers attempt to introduce deep learning into ocean forecasting and have achieved significant progress that provides novel motivations for ocean science. This article provides a comprehensive review of the state-of-the-art DLOF research regarding model architectures, spatiotemporal multiscales, and interpretability while specifically demonstrating the feasibility of developing hybrid architectures that incorporate theory-driven and data-driven models. Moreover, we comprehensively evaluate DLOF from datasets, benchmarks, and cloud computing. Finally, the limitations of current research and future trends of DLOF are also discussed and prospected.

Abstract:
This article proposes a novel nonsingleton try-once-discard (TOD) scheduling scheme for networked control systems (NCSs) under the semantic-driven communication, aimed at avoiding node collisions while simultaneously enhancing system operational efficiency. First, a research framework is established for NCSs operating within the semantic-driven communication paradigm. Second, a method for semantic extraction based on natural language is introduced. Subsequently, the “cultural clash” between semantic interactions and bitstream-based system components is addressed through the application of fuzzy mathematical methods. Under such semantic-driven communication mechanism, raw data is successfully compressed. Building upon the efficient compression of data, a nonsingleton TOD scheduling scheme is presented. In comparison to the traditional TOD scheduling approaches that activate only one node per transmission, the proposed scheduling scheme facilitates the simultaneous transmission of information from multiple nodes, thereby enhancing system efficiency greatly. By data-modeling semantic disparities and designing the appropriate controller, the input-to-state stability of the studied system is guaranteed, even when confronted with certain levels of semantic discrepancies. Moreover, an algorithm is provided to optimize semantically-related parameters, so as to reduce semantic discrepancies as much as possible. Finally, the effectiveness of the proposed method is verified by a six-area power system.

Abstract:
Visual understanding of 3-D objects is essential for robotic manipulation, autonomous navigation, and augmented reality. However, existing methods struggle to perform this task efficiently and accurately in an end-to-end manner. We propose a single-shot method based on the state space model (SSM) to predict the full 3-D information (pose, size, shape) of multiple 3-D objects from a single RGB-D image in an end-to-end manner. Our method first encodes long-range semantic information from RGB and depth images separately and then combines them into an integrated latent representation that is processed by a modified SSM to infer the full 3-D information in two separate task heads within a unified model. A heatmap/detection head predicts object centers, and a 3-D information head predicts a matrix detailing the pose, size and latent code of shape for each detected object. We also propose a shape autoencoder based on the SSM, which learns canonical shape codes derived from a large database of 3-D point cloud shapes. The end-to-end framework, modified SSM block and SSM-based shape autoencoder form major contributions of this work. Our design includes different scan strategies tailored to different input data representations, such as RGB-D images and point clouds. Extensive evaluations on the REAL275, CAMERA25, and Wild6D datasets show that our method achieves state-of-the-art performance. On the large-scale Wild6D dataset, our model significantly outperforms the nearest competitor, achieving 2.6% and 5.1% improvements on the IOU-50 and 5°10 cm metrics, respectively.

Abstract:
This article addresses the cooperative output regulation problem for discrete-time multiagent systems with unknown parameters, a challenge that arises in many practical applications where system models are unavailable. Unlike existing techniques, a model-free Q-learning algorithm is devised to iteratively obtain the optimal policy. This algorithm operates independently of system parameters, and its immediate cost formulation excludes the necessity of solving regulator equations. Consequently, it achieves a streamlined structure, facilitating direct determination of the optimal policy. Subsequently, the stability of each iteration of the algorithm is formally established, along with the derivation of a unique condition for the Q-function matrix. Additionally, to address the challenge of obtaining a stable policy when the initial policy is unstable, an innovative data-driven algorithm is introduced that effectively computes the initial stable gains, ensuring convergence to stability throughout the learning process. Meanwhile, we focus on demonstrating that the distributed observer and the excitation noise do not introduce bias. Finally, the efficacy of the proposed algorithm is validated through two simulation examples.

Abstract:
This article presents a novel data-driven approach to nonlinear system control using a behavioral systems framework. A dynamic latent variable autoencoder (DLVAE) is proposed to project the nonlinear physical variable space onto a linear latent variable space. A data-predictive control approach is developed to control the physical process variables through the latent variables. Based on the behavioral systems theory, the proposed data-driven control framework does not require knowledge of the causality of the latent variables. The stability of the controlled system is ensured by utilizing the concept of trajectory-based dissipativity. The robustness of this control approach is achieved by incorporating the Lipschitz bounds between the latent and physical variables under dissipativity conditions.

Abstract:
This article introduces an innovative distributed algorithm tailored for achieving predefined-time convergence in addressing time-varying resource allocation problem under directed networks. The attainment of predefined-time convergence is crucial for fulfilling real-time requirements, ensuring quality and safety standards, and optimizing the efficiency of resource utilization. It grants users the flexibility to tailor the convergence time according to their specific requirements and constraints. Moreover, the algorithm integrates an auxiliary system to ensure continual satisfaction of the global equality constraint. A distinctive feature lies in the utilization of nonhomogeneous functions with exponential terms, facilitating the achievement of predefined-time convergence. Compared to some existing algorithms with dynamic behaviors, including asymptotical convergence, exponential convergence, and fixed-time convergence, the proposed algorithm demonstrates superior convergence speed. Finally, we demonstrate the effectiveness of the designed technique through numerical simulations, comparisons with state-of-the-art algorithms, and its application to multienergy management problem in the multimicrogrid system.

Abstract:
Achieving prescribed-time synchronization with output-feedback measurements in general linear multiagent systems is challenging, as it necessitates the simultaneous achievement of state synchronization and observer estimation within a prescribed time. This article focuses on general linear dynamics and aims to solve the prescribed-time bipartite synchronization (PT-BS) problem over cooperative-antagonistic networks. First, a couple of time-varying Riccati equations (TVREs) is introduced, which transforms the prescribed-time synchronization problem into a dynamic parameter design issue. By using the solutions of TVREs to design output feedback gains, a class of time-varying-gain prescribed-time observers and observer-based protocols are proposed. Then, since the proposed PT-BS observers require knowledge of some global information (i.e., the minimum eigenvalue of the topology-relevant Laplacian matrix), two adaptive strategies are presented to solve the output-feedback PT-BS problems in a fully distributed manner: an edge-based adaptive strategy and a node-based adaptive strategy. It successfully achieves state synchronization, observer estimation, and adaptive gain convergence within the prescribed settling time. Finally, a simulation example demonstrates the effectiveness of the theoretical results.

Abstract:
Wastewater treatment processes (WWTPs) are operated under multiple operating conditions. Designing an appropriate optimal control strategy based on the identification of operating conditions is crucial for ensuring the safe operation of WWTPs. To effectively deal with the problem of multiple operating conditions in WWTPs, a data-knowledge-driven multiobjective adaptive optimal control (DK-MAOC) strategy is proposed. First, a fuzzy neural network (FNN) is employed as the prediction model to obtain the concentrations of nitrate and total nitrogen. Then, the operating conditions of WWTPs can be determined. Second, an adaptive objective function (AOF) is proposed to dynamically adjust the weights of operating indices to meet the operational requirements of each operating condition. In particular, the AOF integrates operating requirements and tracking errors to simultaneously consider the feasibility of the controller when solving setpoints. Third, due to the differences in data distribution under each operating condition, real-time data during condition changing is insufficient to accurately predict. A data-knowledge-driven model, incorporating operational knowledge into the FNN-based predictive model, is established to predict the future dynamics of WWTPs. Finally, a collaborative gradient descent algorithm is proposed to simultaneously solve for setpoints and control laws. The effectiveness of the proposed DK-MAOC is tested on the Benchmark Simulation Model No. 1. The experimental results indicate that DK-MAOC can effectively avoid the situation of effluent nitrate nitrogen and total nitrogen exceeding the standards while reducing energy consumption of WWTPs. Therefore, the proposed DK-MAOC can guarantee optimal operation of WWTPs.

Abstract:
Industrial data often consist of continuous variables (CVs) and binary variables (BVs), both of which provide crucial information about process operating conditions. Due to the coupling between industrial systems or equipment, these hybrid variables are usually high-dimensional and highly correlated. However, existing methods generally model hybrid variables directly in the observation space and assume independence between the variables to overcome the curse of dimensionality. Thus, they are ineffective at capturing dependencies among hybrid variables, and the effectiveness of process monitoring will be compromised. To overcome the limitations, this study proposes to seek a unified subspace for hybrid variables using the probabilistic latent variable (LV) model. By introducing a low-dimensional continuous LV, the proposed method can avoid the curse of dimensionality while capturing the dependencies between hybrid variables. Nevertheless, the inference of LV is analytically intractable and thus time-consuming due to the heterogeneity of CVs and BVs. To accelerate offline learning and online inference procedures, this study originally derives an analytical Gaussian distribution to approximate the true posterior distribution of the LV, based on which an efficient expectation-maximization algorithm is developed for parameter estimation. The Gaussian approximation is simultaneously optimized with the latest parameters to achieve a high approximation accuracy. The LV is then estimated by the posterior mean of the Gaussian approximation. By mapping the heterogeneous variables into a unified subspace, the proposed method defines three monitoring statistics, which are physically interpretable and thoroughly evaluate the probability of hybrid variables being normal. The effectiveness of the proposed method in detecting anomalies in CVs and BVs is shown through a numerically simulated case and a real industrial case.

Abstract:
Real-time accurate visual object tracking (VOT) for quadrupedal robots is a great challenge when the scale or aspect ratio of moving objects vary. To overcome this challenge, existing methods apply anchor-based schemes that search a handcrafted space to locate moving objects. However, their performances are limited given complicated environments, especially when the speed of quadrupedal robots is relatively high. In this work, a newly designed VOT algorithm for a quadrupedal robot based on a Siamese network is introduced. First, a one-stage detector for locating moving objects is designed and applied. Then, position information of moving objects is fed into a newly designed Siamese adaptive network to estimate their scale and aspect ratio. For regressing bounding boxes of a target object, a box adaptive head with an asymmetric convolution (ACM) layer is newly proposed. The proposed approach is successfully used on a quadrupedal robot, which can accurately track a specific moving object in real-world complicated scenes.

Abstract:
In this article, a policy-iteration-based active disturbance rejection control (ADRC) is proposed for uncertain nonlinear systems to achieve real-time output tracking performance, regardless of the specific relative degree of the system. The approach integrates a partial control input generator with a policy-iteration-based reinforcement learning (RL) agent for degree weight adjustment. The partial control input generator includes each ith order partial control input, which is constructed following the ADRC design framework for an ith order system. The RL agent adjusts the degree weights (its actions) to enhance the dominance of the partial control input corresponding to the unknown relative degree through iterative policy refinement. The RL agent is designed to minimize the quadratic reward as the performance index function while enhancing the influence of the partial control input associated with the correct relative degree via the policy iteration procedure. All signals in the closed-loop system (including the time-varying degree weights) ensure semi-global uniformly ultimately boundness using the Lyapunov stability theorem and the affinely quadratically stable property. Consequently, the degree weight adjustments by the RL agent do not affect the closed-loop stability. The proposed method does not require system dynamics, specific relative degree, external disturbances, and other state variable sensing beyond output sensing. The performance of the proposed method was validated via simulations for two different-order uncertain nonlinear systems and experiments using a permanent magnet synchronous motor testbed.

Affiliations: Department of de Estadística, Universidad Pública de Navarra, Pamplona, Spain; Department of de Computação, Universidade Federal de São Carlos, São Carlos, Brazil; Centro de Ciências Sociais e Tecnológicas, Universidade Católica de Pelotas, Pelotas, Brazil; Institute de Matemática, Estatística e Físisca, and Centro de Ciências Computacionais, Universidade Federal do Rio Grande, Rio Grande, Brazil; School of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K.

Abstract:
In data stream (DS) learning, the system has to extract knowledge from data generated continuously, usually at high speed and in large volumes, making it impossible to store the entire set of data to be processed in batch mode. Hence, machine learning models must be built incrementally by processing the incoming examples, as data arrive, while updating the model to be compatible with the current data. In fuzzy DS clustering, the model can either absorb incoming data into existing clusters or initiate a new cluster. As the volume of data increases, there is a possibility that the clusters will overlap to the point where it is convenient to merge two or more clusters into one. Then, a cluster comparison measure (CM) should be applied, to decide whether such clusters should be combined, also in an incremental manner. This defines an incremental fusion process based on aggregation functions that can aggregate the incoming inputs without storing all the previous inputs. The objective of this article is to solve the fuzzy DS clustering problem of incrementally comparing fuzzy clusters on a formal basis. First, we formalize and operationalize incremental fusion processes of fuzzy clusters by introducing recursively extendable (RE) aggregation functions, studying construction methods and different classes of such functions. Second, we propose two approaches to compare clusters: 1) similarity and 2) overlapping between clusters, based on RE aggregation functions. Finally, we analyze the effect of those incremental CMs on the online and offline phases of the well-known fuzzy clustering algorithm d-FuzzStream, showing that our new approach outperforms the original algorithm and presents better or comparable performance to other state-of-the-art DS clustering algorithms found in the literature.

Affiliations: School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin, China; Department of Electrical and Computer Engineering, National University of Singapore, Queenstown, Singapore; School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore; Department of Control Science and Engineering and School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, China

Abstract:
To alleviate data distribution under different operating conditions, domain generalization (DG) has been applied in mechanical diagnosis. Still, its effectiveness is limited when unknown fault states appear in the target domain. Consequently, open set DG (OSDG) has emerged to identify unknown classes in unknown domains. However, data collection costs and safety concerns have resulted in a significant class imbalance in OSDG. This imbalance causes the decision boundary to be skewed toward abundant positive classes, ultimately leading to misclassifying unknown states and increasing security risks. Currently, there is a lack of methods to simultaneously address domain shift and class shift in an imbalanced unknown domain. To tackle this issue, this article proposes a multisource domain-class gradient coordination meta-learning (MDGCML) framework, which can learn the generalized boundaries of all tasks by coordinating gradients between interdomains and interclasses. Based on the MDGCML, a joint learning paradigm involving the sharing of parameters between open-set classifiers and closed-set classifiers is constructed to enable quick adaption of the model to unknown domains. The superior performance of the proposed framework has been verified on two datasets.

Affiliations: School of Engineering, Qufu Normal University, Rizhao, China; Department of Electrical Engineering, Yeungnam University, Kyongsan, Republic of Korea; School of Mathematics, Southeast University, Nanjing, China; Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China; College of Mathematics and Statistics, Guangxi Normal University, Guilin, China

Abstract:
A dynamic event-triggered load frequency control (LFC) is studied for interconnected multiarea power systems (IMAPSs) with stochastic semi-Markov parameters from the perspective of finite-time interval. To facilitate the sudden changes, the underlying semi-Markov process (SMP) is adopted to characterize the random behavior of IMAPSs. A dynamic event-triggered protocol (DETP) is developed to modulate the transmission frequency while maintaining predefined system performance. Owing to complicated grid environment, the hidden semi-Markov model (HSMM) is proposed to solve the asynchronization between the system mode and the controller mode, which forms a new asynchronous mechanism to better understand the behavior pattern of the system. The novelty of this article is to construct a suitable asynchronous control strategy to solve the mismatch between the system mode and the controller mode under the framework of IMAPSs. Different from static event-triggered protocol (ETP), the DETP is proposed, in which the threshold parameters can be dynamically adjusted to reduce the waste of communication resources and achieve dynamic performance in limited time. According to the stochastic system theory and the finite-time theory, by constructing a modular dependent random Lyapunov function, sufficient conditions are obtained to ensure the finite-time boundedness of the corresponding system with H_\infty performance. Finally, the efficiency is demonstrated through three-area power systems.

Abstract:
The cooperative nature of drone swarms poses risks in the smooth operation of services and the security of national facilities. The control objective of the swarm is, in most cases, occluded due to the complex behaviors observed in each drone. It is paramount to understand which is the control objective of the swarm, whilst understanding better how they communicate with each other to achieve the desired task. To solve these issues, this article proposes a physics-informed multiagent inverse reinforcement learning (PI-MAIRL) that: 1) infers the control objective function or reward function from observational data and 2) uncover the network topology by exploiting a physics-informed model of the dynamics of each drone. The combined contribution enables to understand better the behavior of the swarm, whilst enabling the inference of its objective for experience inference and imitation learning. A physically uncoupled swarm scenario is considered in this study. The incorporation of the physics-informed element allows to obtain an algorithm that is computationally more efficient than model-free IRL algorithms. Convergence of the proposed approach is verified using Lyapunov recursions on a global Riccati equation. Simulation studies are carried out to show the benefits and challenges of the approach.

Abstract:
Broad learning system (BLS) is an effective neural network requiring no deep architecture, however it is somehow fragile to noisy data. The previous robust broad models directly map features from the raw data, which inevitably learn useless or even harmful features for data representation when the inputs are corrupted by noise and outliers. To address this concern, a discriminative and robust network named as dynamic graph regularized broad learning (DGBL) with marginal fisher representation is proposed for noisy data classification. Different from the previous works, DGBL eliminates the effect of noise before the random feature mapping by the proposed robust and dynamic marginal fisher analysis (RDMFA) algorithm. The RDMFA is able to extract more robust and informative representations for classification from the latent clean data space with dynamically generated graphs. Furthermore, the dynamic graphs learned from RDMFA are incorporated as regularization terms into the objective of DGBL to enhance the discrimination capacity of the proposed network. Extensive quantitative and qualitative experiments conducted on numerous benchmark datasets demonstrate the superiority of the proposed model compared to several state-of-the-art methods.

Abstract:
This article is concerned with dynamic analysis and neural-adaptive prescribed-time control of the magnetic-field electromechanical transducer incorporating a memristor. First, a fractional-order (FO) mathematical model is developed, which comprehensively characterizes fractional properties of various dielectrics and establishes the relationship between magnetic flux and electric charge. The dynamical analysis explores internal evolution and complexity performance concerning a single factor or double factors among the FO, system parameter, and memristor configuration by the Bifurcation diagram, sample entropy, and C_0 complexity from multiple perspectives. Subsequently, a neural-adaptive prescribed-time control scheme is proposed to transform detrimental chaotic oscillations into orderly motions, achieve the pregiven tracking precision and accommodating both actuator fault and system uncertainty. The controller design consists of three key steps: 1) a deferred constraint function is imposed on the tracking error starting from anywhere to get assignable tracking precision within a specified time, ensuring collision avoidance; 2) a type-2 fuzzy wavelet neural network (FWNN) is utilized effectively to handle parameter perturbations and system uncertainties; and 3) a second-order FO tracking differentiator (TD) is utilized to address the “explosion of complexity” of traditional backstepping under actuator fault model. It is shown that the proposed scheme is able to ensure the boundness of all signals of the closed-loop system. Finally, extensive simulation experiments are conducted to validate the effectiveness and robustness of the rendered scheme.

Abstract:
Intelligent anomaly detection (AD) methods have achieved much successes in machinery condition monitoring. However, the underlying independent and identically distributed assumption restricts their application scopes to steady operating conditions. False and missing alarms would occur when machines operate under time-varying circumstances. In this work, a more challenging time-varying setting is studied, where the working conditions are continuously changing, such that few or no samples are available for model training at one single condition. To tackle this issue, we propose a unified flowing normality learning (UFNL) framework, which aims to capture the flowing normal conditional distribution of time-varying samples and assigns dynamic decision boundary for AD. Specifically, a manifold-based probability density estimation is utilized to guide the adversarial learning process of generative adversarial networks, where adjacent samples are aggregated to approximate the conditional distribution by a conditional generator. Then, a latent normality inversion is proposed to extract the manifold structure from the pretrained generator and to map it into the latent space via a conditional encoder. The reconstruction errors from the encoder and generator can reveal the deviation of signals to the flowing normality. Finally, a condition-aware adaptive threshold selection strategy is proposed, where different thresholds are adaptively assigned for different conditions. Experiments are carried out under two typical continuous time-varying scenarios. The results demonstrate that the proposed framework can realize accurate fault detection at any operating condition within continuously changing environments.

Abstract:
For leg prosthesis user, the soft tissue and skin under the stump of are not accustomed to weight bearing, excessive continuous contact pressure can lead to the risk of degenerative tissue ulceration. This article presents a novel human-robot collaborative control scheme that achieves control weight self-adjustment for robotic prostheses to minimize interaction torque. To establish the human-robot interaction relationship, we regard the contact pressure between human residual limb and the prosthetic receiving cavity as the interaction force. We aim at reducing the interaction force under the premise of minimally changing the original motion trajectory of the robotic prosthesis. The control scheme mainly includes trajectory optimization based on a dual-agent game control scheme under a cooperative relationship, and a fuzzy logic system for improving the control accuracy of trajectory tracking of robotic prostheses with unknown dynamic parameters. Experiments were carried out on two amputee participants to verify the proposed human-robot interactive control scheme in a robotic prosthesis. The results show that the interaction torque could be reduced while maintaining minimal trajectory tracking error. The proposed control scheme could potentially facilitate the dexterous manipulation of leg prostheses, thus benefiting amputees.

Abstract:
This article considers the adaptive stability control of a class of 2× 2 linear hyperbolic PDE systems. The PDE model is subject to constant but in-domain and boundary unknown parameters. A novel adaptive controller is developed by leveraging the swapping design technique and composite parameter learning law. With swapping design, several linear and static combinations, including carefully designed filters, unknown parameters, and error terms, are constructed to express the system states. From the static combinations, a composite learning based forgetting-factor least squares law is introduced to guarantee exponential parameter convergence without the persistent excitation (PE). Although inaccurate parameter estimation in the adaptive backstepping control results in asymptotic stability of the system, accurate parameter estimation ensures the exponential convergence of closed-loop system and concomitantly improves the transient performance. Finally, a comparative numerical simulation is performed to validate the effectiveness and advantage of the developed adaptive control scheme.

Abstract:
This article investigates periodic event-triggered optimal output consensus of heterogeneous linear multiagent systems where each agent has knowledge of only its own cost function. In contrast to existing results, we consider communication delays and general strongly connected digraphs. A novel periodic event-triggered distributed control scheme is proposed, which allows asynchronous event detection and time-varying communication delays. Sufficient conditions with respect to the maximum allowable communication delays and event detection periods to achieve asymptotic optimal output consensus are established. Moreover, it is proved that the proposed periodic event-triggering mechanism can provide a positive lower bound of interevent times which is independent of the event detection period. A simulation example is provided to illustrate the effectiveness of the proposed control scheme.

Abstract:
This article addresses the collision avoidance and formation control problem for multisatellite systems. A novel safe reinforcement learning (RL) algorithm based on an adaptive dynamic programming framework is proposed. The highlights of the algorithm are the adaptive distance-varying learning method to integrate online data with historical data and the usage of the barrier function (BF) to achieve collision avoidance. First, the BF is introduced into the designed cost function such that the multisatellite formation system can achieve obstacle avoidance and guarantee the safety. Next, a safe RL algorithm is developed through the critic network structure. A distance-varying weight is introduced, which combines experience replay samples with extrapolation samples. By minimizing the cost function, the optimal formation control policy can be obtained with an adaptive formation and self-learning ability. Then, the stability and safety of the proposed algorithm are analyzed. Finally, the effectiveness and superiority of the proposed algorithm are verified by numerical simulations.

Abstract:
Angle-constrained formation control has garnered significant attention owing to the advantage of interedge angles invariant under translation, rotation, and scaling. However, most existing approaches addressing this problem are applicable only to single- or double-integrator dynamics, which are often impractical in real-world scenarios. In this article, an angle rigidity-based adaptive formation control framework is introduced for nonlinear multiagent systems subject to mismatched uncertainties. The proposed control framework integrates a prescribed performance control approach with a recursive backstepping procedure, offering several key advantages: the capability to handle unmatched system uncertainties, the preservation of angle rigidity throughout the formation process, and the assurance that the triangulated formation shape is asymptotically achieved without risking collisions between neighboring agents. Furthermore, since the control input of each agent only requires local information related to its neighbors, which can be obtained locally from its own sensors, the proposed control method can be deployed in a communication-free environment. The effectiveness of the proposed control algorithms is validated by extensive numerical simulation.

Abstract:
Multigranularity data analysis has recently become an active research topic in the intelligent computing and data mining fields. Feature selection via multigranularity data analysis is an effective tool for characterizing hierarchical data and enhancing the accuracy of the results. Although the multigranularity data analysis method has been widely adopted for feature selection, existing studies still present one prevalent disadvantage: multigranularity data analysis mostly focuses on information presented at a single granularity while ignoring the hierarchical structure of multigranularity data, which is contrary to the nature of multigranularity. Hence, this article proposes a multigranularity data analysis with a zentropy uncertainty measure for efficient and robust feature selection. Specifically, a consistent degree is first introduced to obtain optimal granularity combinations and establish an efficient neighborhood model for multigranularity information processing. Then, a novel and robust uncertainty measure is developed by integrating the multigranularity information, namely the zentropy-based measure. Considering its accuracy among uncertainty measures, two important measures are further designed and applied to feature selection. Extensive experiments demonstrate that the proposed method can achieve better robustness and classification performance than other state-of-the-art methods.

Abstract:
This article focuses on the fault reconstruction and \mathcal L_\infty bumpless transfer fault-tolerant (FT) control problems for switched linear systems with magnitude-bounded disturbances and actuator faults in continuous-time domain. A new learning-based robust unknown input observer (UIO), not requiring fault differentiability and completely decoupled disturbances, is developed to accomplish fault reconstruction and state estimation. The fault reconstruction value is updated by one iteration learning on the timeline, i.e., the fault at the current moment is reconstructed by learning historical information from the previous moment. Based on the obtained estimation information, an efficient bumpless transfer FT controller is designed to counteract the fault effects and suppress the control bumps. The bumpless transfer constraint is guaranteed via a new inequality transformation method, which improves the anti-disturbance capability of the controller and also decreases the switching bumps. The solvability conditions for the \mathcal L_\infty bumpless transfer controller and learning-based UIO are developed under the condition of average dwell time switching. Finally, an application of the inverted pendulum controlled by a direct current motor is presented to reveal the effectiveness and applicability of the developed methods.

Abstract:
This study proposes an active defense strategy against denial-of-service (DoS) attacks to address the secure event-triggered control of multiarea load frequency control (LFC) systems. A novel interval secure event-triggered mechanism (ISETM) is introduced, integrating event-triggered control with cybersecurity mechanisms under the software defined network (SDN) framework. ISETM generates not only a triggering instant but also a secure triggering interval (STI) simultaneously. The STI sent to the SDN control plane is an estimation time interval generated by the Taylor expansion and model-based prediction method. During this interval, the SDN control plane programs OpenFlow switches to filter attack traffics, ensuring delayed but secure triggering transmission. Under ISETM conditions represented by two systems of inequalities, a multiarea LFC system is modeled as a delay system incorporating a triggering error based on the Taylor expansion. To achieve H_\infty performance of the established LFC system, a criterion is derived using the Lyapunov-Krasovskii functional method. A codesign approach is provided to solve the proposed ISETM control (ISETC) gains through linear matrix inequality (LMI) techniques. Finally, simulations validate the effectiveness and advantages of our proposed method.

Abstract:
This article investigates the asynchronous estimator design for the interval type-2interval type-2 (IT2) fuzzy Markov jump systems subject to dynamic quantization and deception attacks. From the perspective of the attacker, a novel protocol-based deception attackdeception attack (DA) strategy is proposed, which utilizes the information of quantized output to assess the importance degree of transmission signals. Furthermore, in order to conserve the limited energy of the adversary, the independent attack strategies are designed for different sensors. Besides, the hidden Markov modelhidden Markov model (HMM) is applied to observe the system mode. Employing the Lyapunov stability theory and linear matrix inequality method, the sufficient conditions are acquired to guarantee the strictly-dissipative performance of the estimation error. Finally, two examples are illustrated to confirm the efficacy of the designed estimator and the advantage of the proposed attack tactics.

Affiliations: Key Laboratory of Intelligent Analysis and Decision on Complex Systems and the Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications, Chongqing, China; Key Laboratory of Big Data Intelligent Computing and the Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China; National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing, China; School of Artificial Intelligence and Computer Science, Nantong University, Nantong, China

Abstract:
In recent years, with the rapid development of the Internet, multisource information fusion has become a forefront issue due to its ability to merge different information. Granular computing (GrC), as a methodology simulating human hierarchical cognition, provides a new approach for multisource information fusion. However, on one hand, the existing information fusion studies in GrC all focus on feature-level fusion and decision-level fusion based on multisource data, neglecting the basic characteristics and advantages of GrC: granulation. On the other hand, the existing methods for fusing the knowledge spaces in GrC suffer from losing the necessary information or artificially adding information. In order to address these issues, a novel information fusion mode from the perspective of GrC is proposed in this article, named knowledge-level fusion. First, by introducing a new step, that is, granulate data to construct the knowledge space, into the multisource information fusion process, the knowledge-level fusion mode is proposed. Second, the optimistic core quotient space is proposed to characterize the information consensus and information gap of multisource knowledge spaces in the static data environment. The pessimistic core quotient space is proposed to characterize the information consensus in the dynamic data environment. Related theorems are given to describe the characteristics of the core quotient spaces. Then, the knowledge-level fusion method driven jointly by the data space and the knowledge space is introduced based on the principle of extracting the core quotient space first and then allocating other objects in the candidate set. On the basis, the superiority of the proposed method over the existing methods is demonstrated through theoretical analysis. Finally, experiments on 12 UCI datasets and three UKB datasets are carried out to verify the promoting effect on classification and clustering algorithms, the effectiveness compared to feature-level and decision-level fusion modes, efficiency and statistical significance of the proposed knowledge-level fusion method and mode.

Abstract:
In this study, an improved jump model is proposed for the Roesser-type 2-D Markov jump systems (MJSs). We use two independent Markov chains that propagate along the horizontal and vertical directions, respectively, to characterize the switching of system dynamics in those two directions. Compared with the conventional jump model, which uses only one Markov chain to characterize the switching of system dynamics in both directions, the newly proposed 2-D jump model shows better modeling capabilities for real-world applications with abrupt changes while inherently avoiding the mode ambiguity phenomenon. Based on the proposed jump model, we then propose a dual-mode-dependent state feedback control law to stabilize the concerned 2-D MJS. A sufficient criterion, whose feasibility is enhanced via a dual-mode-dependent Lyapunov functional technique, is obtained to ensure the asymptotic mean square stability and H_\infty disturbance attenuation level of the resulting closed-loop system. Subsequently, resorting to a novel nonconservative separation principle, two equivalent conditions with one of them in the form of linear matrix inequalities (LMIs) are developed. Finally, a convex optimization algorithm which is formulated by the obtained LMIs is proposed to design the control law. An example of the Darboux equation with Markov switching parameters is presented to validate the effectiveness of the obtained results.

Abstract:
This study aims to tackle the tracking control problem of multiple unmanned surface vessels (USVs). It considers the impact of connectivity-hybrid cyber-attacks in the networked level, and wave-induced disturbances, as well as severe and nonsevere unified modeling rudder angle faults in the physical level. To do this, the study establishes USV models, taking into account actuator fault and cyber-attack modeling. It then presents the augmented estimator-based decentralized fault estimation (FE) and leader-following consensus-based distributed fault-tolerant tracking control (FTTC) protocols. These are incorporated into an integrated structure that ensures the robust asymptotic convergence of estimation errors and excellent tracking performance of multi-USVs. Finally, the study derives criteria for an exponential tracking of composite faulty multi-USVs under cyber-attacks using dual-constraint restriction (attack frequency and excitation rate). Comparative simulations substantiate the advantage of the developed integrated FE and FTTC scheme.

Abstract:
Heterogeneity arising from label distribution skew and data scarcity can cause inaccuracy and unfairness in intelligent communication applications that heavily rely on distributed computing. To deal with it, this article proposes a novel personalized federated learning algorithm, named federated contrastive shareable representations (FedCoSRs), to facilitate knowledge sharing among clients while maintaining data privacy. Specifically, the parameters of local models’ shallow layers and typical local representations are both considered as shareable information for the server and are aggregated globally. To address performance degradation caused by label distribution skew among clients, contrastive learning is adopted between local and global representations to enrich local knowledge. Additionally, to ensure fairness for clients with scarce data, FedCoSR introduces adaptive local aggregation to coordinate the global model involvement in each client. Our simulations demonstrate FedCoSR’s effectiveness in mitigating label heterogeneity by achieving accuracy and fairness improvements over existing methods on datasets with varying degrees of label heterogeneity.

Affiliations: Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China; Institute of Automation, Qufu Normal University, Qufu, China; School of Automation, Beijing Institute of Technology, Beijing, China; Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China

Abstract:
The passivity-based asynchronous control is tackled for 2-D Roesser Markovian jump systems (MJSs) and stabilization is guaranteed when 2-D MJSs are susceptible to Denial-of-Service (DoS) attacks. A novel jump model is proposed in this article, where the switching law of subsystems is regulated by the sum of the horizontal and vertical coordinates’ values. This differs from the conventional jump model, which presumes that the transition probabilities are identical in both directions. The proposed jump model can avoid the mode ambiguity problem. Given the openness and sharing nature of communication networks, they are susceptible to malicious cyber-attacks that impair system performance. The concept of global time is introduced to help characterize the jump law and construct DoS attack model. Besides, a hidden Markov model (HMM) is utilized to manage the inevitable mismatched mode problem induced by any delay or data dropouts. With the above considerations, several conditions are established for ensuring passivity performance of 2-D MJSs and stabilization when facing DoS attacks. Several equivalent solvable conditions are derived via decoupling strategy and matrix inequality technique. Finally, two simulation examples are provided to demonstrate the validity of the established theoretical results.

Abstract:
This article investigates the multisensor transmission scheduling problem for state estimation over multihop networks. Unlike single-sensor scheduling for multihop networks, the communication coupling of multisensor makes the design of transmission scheduling more complex. In view of this, we focus on finding the optimal multisensor transmission scheduling to minimize the estimation error covariance of the remote terminal units (RTUs) and transmission costs. First, the transmission scheduling problem is formulated as the Markov decision process (MDP) in the scenarios of smart relays and traditional relays, respectively. Second, we propose the optimal multisensor transmission scheduling strategy, which is rigorously proved to be monotonic with respect to the number of consecutive packet losses for two relay types. Third, the necessary and sufficient conditions (NSCs) are given to guarantee system stability under the constructed transmission scheduling. Finally, the effectiveness of the proposed multisensor transmission scheduling is verified by the IEEE 118-bus system.

Abstract:
This article considers the issue of adaptive fixed-time tracking control for a class of stochastic high-order nonlinear systems (HONSs) with full state constraints. Unlike the existing results, a Barrier Lyapunov function (BLF) with fractional form is employed to deal with asymmetric full-state constraints. Fuzzy logic systems are employed to resolve stochastic disturbances and unknown nonlinearities. Based on adding a power integrator technique and backstepping method, an adaptive fixed-time fuzzy state feedback controller is proposed to ensure that all signals are bounded and all states are always within the constrained interval. The nonlinear system is fixed-time bounded in probability by the fixed-time Lyapunov stability theory. Without altering the controller structure, the BLF can be used in unconstrained high-order systems. Two simulation experiments including a spring-mass damper system prove the effectiveness of the designed control strategy.

Abstract:
This article considers the point-to-point tracking control problem for a class of unknown nonlinear discrete-time systems with output saturation. A novel data-driven finite-iteration learning control algorithm is proposed to achieve bounded tracking errors within limited iteration. First, considering the case that the model of the nonlinear discrete-time system is unknown, the relationship between the output of the system and the control inputs at these given points is derived using recursive evolution in the time domain. Then, the dynamic data-driven model of the system is established using iterative domain dynamic linearization techniques. Second, a finite finite-iteration learning algorithm based on the fractional power of error information is designed, and the finite-iteration convergence of the proposed algorithm is rigorously proven in theory. Finally, the effectiveness of the proposed method is validated by simulation results.

Abstract:
The present research addresses the issue of fixed-time (FxT) command filtered adaptive tracking control for a class of nonlinear system subject to time-varying input delay and error/state constraints. First, based on the existing FxT control theory, we present two new FxT stability lemmas, affording less conservative and more exact upper-bound estimates (UBEs) for the settling time. Second, the asymmetric constrained systems are reconstructed into novel systems devoid of constraints by introducing nonlinear transformation functions (NTFs), which remove the feasibility conditions related to the virtual controllers while satisfying the error and state constraints. Then, a novel FxT auxiliary system is established to effectively handle the input delay that is allowed to be unknown. By employing the FxT stability criterion and Lyapunov–Krasovskii functional approach, it is proved that the controlled systems are practically fixed-time stable (PFxTS) and do not violate their error/state constraints. Additionally, without the need for modifying the control framework, the control algorithm capably addresses the control issue uniformly for both constrained and unconstrained systems. In the end, a simulation example is given to check the validity of the main results.

Abstract:
In this article, we consider the problem of privacy preservation in consensus control for second-order integrator multiagent systems (MASs). Specifically, we consider the setting where the initial position and velocity of each legitimate agent are both private, an internal or external adversary wants to identify them based on the information it obtains. To deal with this scenario, we propose a privacy preservation algorithm based on a position and velocity simultaneous perturbation technique. To be specific, our algorithm consists of a collaborative scrambling phase and a convergence phase. In the scrambling phase, each agent is required to produce two sets of edge-based perturbation signals that are, respectively, imposed on the local position and velocity signals before transmission, with the purpose of preserving privacy; in the convergence phase, each agent updates its state per a normal rule, aiming to achieving accurate consensus. Also, we establish a system-theoretic framework to analyze privacy performance by examining the indistinguishability of private values’ arbitrary variations to adversaries, and further show that, an internal adversary cannot infer the privacy of a legitimate agent provided it has at least one legitimate in-neighbor or out-neighbor, and the privacy is leaked out once that agent exclusively connects to the internal adversary in bidirectional way. As for external eavesdroppers, they can never infer any agent’s privacy if the gain parameters in the scrambling phase are not accessible to them. Finally, two simulation examples illustrate the validity of the proposed approach.

Abstract:
Different sampling rates are common for different variables in industrial processes because of the different electrical properties and requirements of sensors. Especially the sampling rate of quality variables is significantly lower than that of process variables. However, most soft sensors assume that industrial data is uniformly sampled, which differs significantly from actual industrial systems and may affect decision-making in the production process. An interpretable disentangled transfer learning (IDTL) quality prediction is proposed suitable for multirate industrial processes. First, a setness constructor is designed to diversify the original multirate data into multiple multirate sets to preserve information without data loss. Then, a disentangled transfer learning (TL) approach is proposed to infer domain-invariant and domain-specific representations from multiple multirate sets, thereby revealing the intrinsic properties of multirate industrial processes and improving the soft sensor performance. From the perspective of information theory, the theoretical representations for disentanglement and their connection to TL are established, laying a solid theoretical foundation for subsequent TL under complex working conditions. Our theoretical analysis shows that interpretable disentangled TL (IDTL) achieves optimal disentangled representations in equilibrium. Case studies of the debutanizer column dataset and the actual polyester esterification dataset validate the effectiveness of the proposed IDTL. Code is available at https://github.com/heheding/IDTL.

Abstract:
For a class of uncertain nonlinear stochastic multiagent systems, a consensus control strategy is proposed with an adjustable-time-varying-gain-based event-triggered extended state observer (ATVG-ETESO) via dual-terminal event-triggered mechanism (DTETM). The ATVG-ETESO estimates internal uncertainties and external stochastic disturbances. Its adjustable time-varying gain avoids peaking phenomenon at the initial stage and accelerates estimation error convergence. A DTETM with an adaptive threshold reduce communication burdens on both the input and output channels of the ATVG-ETESO. Theoretically, both the ATVG-ETESO estimation errors and the state consensus errors are bounded. Finally, two illustrative simulation examples are given to illustrate the effectiveness of the control strategy.

Abstract:
The data communication among sensors, actuators, and controllers in network systems poses various challenges, including packet loss, network-induced delay, and channel noise interference, all of which adversely affect performance of control systems. Based on single-degree-of-freedom (SDOF) controller and two-degree-of-freedom (TDOF) controller, respectively, this article discuss networked control systems (NCSs) with packet loss, network-induced delay, and logarithmic quantization constraints in the communication channel, and proposes a discrete-time modified performance index. By the frequency domain analysis methods and Youla parameterization techniques of controllers, explicit expressions of the modified performance limitations are derived, which quantitatively reveal the impact of the fundamental characteristics of the plant and communication constraints on systems’ performance. In addition, a linear dual-freedom vehicle system is modeled and the obtained theorems are applied to verify the correctness of the results. The results show that communication constraints have an adverse impacts on systems’ performance, and modified performance index can better measure systems’ performance.

Abstract:
Complex industrial processes exhibit typical nonstationarity due to frequently fluctuating material flows and complex control loops. This poses three challenges for trustworthy process monitoring, including data drift, coordination of old and new knowledge, and interpretability. In this study, the adaptive and interpretable process monitoring problem is formulated as an online updating strategy and the spatial topology structure representation learning process monitoring problem. An incremental variational graph attention autoencoder with probabilistic inference framework is proposed, which aims to effectively learn continuously from dynamically changing industrial data to make interpretable monitoring results. First, an incremental learning strategy based on the Bayesian regularized self-organizing map is presented, which can distinguish between real faults and time-varying changes. Once normal samples are encountered, the itself and downstream model are elegantly updated with a dynamic down-sampling replay strategy without leading to catastrophic forgetting. Subsequently, a variational graph attention autoencoder with probabilistic inference is proposed, which endows interpretable spatial structural relationships through priors and effectively captures the variability of spatial latent representations suitable for nonstationary processes. Then, an incremental variational Bayesian inference is introduced to calculate the adaptive thresholds to adapt the system. In addition, an anomaly-aware graph attention localization mechanism is provided to localize fault root causes and propagation paths. Finally, the effectiveness of the proposed method is validated through two industrial applications. The results demonstrate that the proposed method can significantly enhance the performance of process monitoring, especially for reducing the false alarm rate (FAR) in process monitoring schemes. Moreover, it offers interpretable causal relationships among faults.

Affiliations: School of Cyber Science and Technology, Institute of Unmanned System,Beihang University, Beijing, China; Institute of Unmanned System, School of Artificial Intelligence (Institute of Artificial Intelligence), School of Automation Science and Electrical Engineering, Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing, China; Energy Department, Politecnico di Milano, Milan, Italy; School of Cyber Science and Technology, Beihang University, Beijing, China

Abstract:
In practical applications, not all nodes in networked swarm systems are cooperative. The noncooperative nodes are transformed from healthy ones because of cyber-attacks launched by malicious adversaries, hardware faults caused by low reliability individuals, or communication delay. In this article, an approximate fault detection method using residual threshold is shown to judge which agent is cooperative or not, and to reconstruct the communication topology. Then, the event-triggered technique is utilized to design the multileaders formation tracking protocol with resilience to noncooperative nodes. The Zeno behavior is considered to constrain the trigger condition. Finally, the comparison simulation results show the effectiveness and advantage for the proposed secure control method.

Abstract:
Designing intelligent game strategies for autonomous air combat has suffered from the vast exploration space, lengthy decision-making process, and sparse rewards. Some existing approaches adopt the hierarchical framework to improve the exploration efficiency. However, in these methods, agents in different layers are typically trained independently and operate at fixed frequencies, which limits their performance and hampers their ability to respond to highly dynamic combat situations. In view of this, we present PJOH-TED2, a partial-joint-optimization-based hierarchical (PJOH) learning framework with a time-event dual-driven (TED2) mechanism, for one-on-one beyond-visual-range (BVR) air combat. Specifically, the PJOH learning framework embeds the partial joint optimization mechanism into hierarchical reinforcement learning (HRL), thus improving the exploration efficiency dramatically while enhancing the integration across hierarchical levels. Moreover, the TED2 mechanism combines the advantages of event-driven and time-driven methods, which promote the dynamic response speed of agents as well as avoid redundant actions. In addition, we evaluated this work through a series of games against the state-of-the-art (SOTA) methods in a high-fidelity air combat simulation environment. The results empirically demonstrate that the proposed approach outperforms four SOTA methods with a win rate of at least 71%. Finally, this approach achieved the 1st place in learning methods in the intelligent air game algorithm challenge (IAGAC) by the Chinese Institute of Command and Control among 43 teams.

Abstract:
This article addresses the cooperative tracking control problem for heterogeneous multiple marine vehicles with a nonautonomous leader. A fully distributed smooth observer is proposed to estimate the trajectory of the leader, mitigating the influence of its control input. Based on the observer, three decentralized adaptive fuzzy event-based controllers are designed with distinct triggering strategies, i.e., fixed, relative, and switching threshold triggering strategies, which utilize fuzzy-logic systems and event-triggering mechanisms to address the challenge of model uncertainties and communication constraints of marine vehicles. The proposed methods ensure the zero-error tracking without Zeno behavior, as demonstrated through Lyapunov analysis. Numerical simulations validate the effectiveness of the proposed approaches.

Abstract:
This work studies the novel sliding mode control (SMC) of discrete nonlinear stochastic switching models under semi-Markovian parameter and incomplete semi-Markovian kernel (SMK). The characteristic of nonlinear system is described by an interval type-2 fuzzy (IT2F) model that can be recognized as a collection of several type-1 fuzzy models. The uncertainties in system parameters is efficiently captured using the lower and upper grades of membership. Based on the mode-dependent Lyapunov function and incomplete SMK, sufficient conditions are proposed to ensure the stability of sliding dynamics. Moreover, an IT2F SMC law based on learning strategy is developed such that the state signals are guided onto the predetermined sliding region and the mode switchings-induced chattering is effectively reduced. Finally, the IT2F SMC strategy is validated through the simulation of truck-trailer model.

Abstract:
Brain-controlled systems have experienced significant advancements in overall performance, largely driven by continuous optimization and innovation in electroencephalography (EEG) acquisition experimental paradigms and decoding algorithms. However, their applications still face challenges, including limited control precision and low efficiency. In this article, we focus on a wheeled mobile robot (WMR) as the control object and propose a novel brain-controlled framework that combines a probabilistic brain-computer interface (BCI) and a model predictive controller (MPC). First, the probabilistic BCI is developed, featuring the sigmoid fitting-filter bank canonical correlation analysis (SF-FBCCA) algorithm, which serves as the core of the BCI system by decoding EEG signals and generating brain commands along with their associated probabilities. Second, an auxiliary MPC is integrated into the probabilistic BCI system to provide decision-making assistance while preserving the users’ primary brain control authority. The weights of the cost function are adaptively determined based on the command probabilities. Finally, simulation-based evaluations were conducted using the WMR in a path-keeping scenario. The results demonstrate that the proposed framework significantly improves control accuracy and efficiency compared to direct brain control approaches, reducing the average lateral error by 58.02% and the average yaw angle error by 60.06%. Additionally, the MPC employing adaptive weights further improves overall performance. These findings offer theoretical insights and technical references for future research on BCI-based control frameworks.

Abstract:
This article investigates the discrete-time fuzzy complex networks (FCNs) event-triggered (ET) synchronization control problem by considering the coupling effect and cyber attacks. To prevent data conflicts caused by communication resource limitations, a round-robin (RR) protocol is introduced to regulate the communication transmission order among coupled nodes. To be more general and flexible against Denial-of-Service (DoS) attacks, the concept of average dwelling time ratio (ADTR) is proposed to evaluate DoS attack models. In addition, a DoS-attack-aware ET mechanism is developed. Under this mechanism, a threshold parameter is dynamically adjusted according to the active and dormant time of the DoS attacks. The correlated co-design of anti-attack security control and ET mechanism is realized. Compared with the existing results, the scheme improves the control performance while reducing the controller update frequency. Then, by establishing appropriate Lyapunov function, sufficient criteria for synchronization of discrete-time FCNs are obtained, and a controllable range of the ADTR is calculated. Finally, the effectiveness of the proposed DoS-attack-aware ET control scheme based on RR protocol is verified through two simulations.

Abstract:
With the advancement of robotic-assisted minimally invasive surgery, visual servo control has become a crucial technique for improving surgical outcomes. However, traditional visual servo methods often rely on precise kinematic models and camera calibration, limiting their generalizability. Considering these, this article proposes a novel uncalibrated model-free visual servo control scheme. Specifically, we introduce a Jacobian matrix and interaction matrix estimation method based on a gradient neural network (GNN), which enables online estimation by utilizing control signals and sensor outputs. Then, the estimated results are incorporated into a visual servo control framework that considers remote center of motion (RCM) constraint, joint-drift problem, and physical constraint, formulated as a quadratic programming (QP) problem. Subsequently, focusing on the joint limits and endoscope insertion depth constraint, we develop a nonpiecewise differentiable multilevel constraint handling technique. For the formulated QP problem, a predefined-time convergent error-regulating zeroing neural network (PTCER-ZNN) solver is designed, and we can derive the optimal control signals. Detailed theoretical analyses of the developed GNN estimation method and the PTCER-ZNN solver are provided. Simulation results demonstrate the effectiveness of the proposed scheme in image feature regulation and tracking tasks, exhibiting its advantages over existing approaches.

Abstract:
In comparison to common quadrotors, the structure deformation of morphing quadrotors endows them with better flight performance but also results in more complex flight dynamics. Generally, it is extremely difficult or impossible for these morphing quadrotors to develop an accurate mathematical model that describes their complex flight dynamics. This fact leads to a particularly challenging situation, as the existing mature model-based flight control theory fails to address the flight control design issue of morphing quadrotors. By resorting to a combination of model-free control techniques [e.g., deep reinforcement learning (DRL)] and convex combination (CC) technique, a convex-combined-DRL (cc-DRL) flight control algorithm is proposed for flight trajectory tracking and attitude stabilization of a class of morphing quadrotors with arm-length deformation. In the proposed cc-DRL flight control algorithm, a proximal policy optimization algorithm is utilized to offline train the corresponding optimal flight control laws for some selected representative arm length modes. Hereby, a cc-DRL flight control scheme is constructed by the CC technique. Finally, simulation results are presented to show the effectiveness and merit of the proposed DRL flight control algorithm.

Abstract:
In this article, the problem of adaptive dynamic event-triggered nonsingular predefined-time (PT) tracking control for a third-order fully heterogeneous vehicle platoon system is investigated. First, an improved nonsingular PT adaptive tracking controller is constructed by introducing a piecewise continuous function within the control signal in each step of the backstepping procedure, which avoids the singularity problem of the traditional PT control signals in the existing literature and guarantees the existence of the values for all the terms within the control signals in the real number field. Second, a class of universal barrier Lyapunov function (UBLF) is adopted to set restrictions on distance, which ensures that collisions are avoided and communication connectivity is maintained. In addition, based on a dynamic auxiliary variable, the communication resources are saved with the use of the dynamic event-triggered control scheme. Finally, through the PT stability criterion, it is proven that the PT stability of the whole vehicle platoon is ensured, and the effectiveness of the control algorithm is verified by the simulation results.

Abstract:
In machine fault diagnosis, conventional data-driven models trained by empirical risk minimization (ERM) often fail to generalize across domains with distinct data distributions caused by various machine operating conditions. One major reason is that ERM primarily focuses on informativeness of data labels and lacks sufficient attention on invariance of data features. To enable invariance on top of informativeness, a learning framework, learning invariant features via in-label swapping for generalizing out-of-distribution (Lifeisgood), is proposed in this study. Lifeisgood is inspired by a simple intuition that invariance can be assessed by checking changes in loss due to swapping certain entries of features with the same labels. Lifeisgood also enjoys a theoretical guarantee on improving testing domain performance under certain conditions based on a swapping 0-1 loss proposed in this work. To circumvent the training difficulties associated with the swapping 0-1 loss, a swapping cross-entropy loss is derived as a surrogate and theoretical justifications for such a relaxation are also provided. As a result, Lifeisgood can be employed conveniently to develop data-driven fault diagnosis models. In the experiments, Lifeisgood outperformed the majority of state-of-the-art methods in terms of average accuracy and exceeded the second-best by 25% in terms of the frequency of beating the generic ERM. The code is available at: https://github.com/mozhenling/doge-lifeisgood

Abstract:
Printed circuit board assembly line scheduling (PCBALS) is a difficult task in the electronic industry for assembly lines using surface mounters, which is critical for production efficiency. This is a special type of line optimization problem that uses different allocation techniques, resulting in wide differences in assembly times between machines. This article proposes a hyper-heuristic optimizer embedded with a multifeature fusion ensemble estimator (HHO-MFEE) for PCBALS using linear-aligned-heads surface mounters. The objective and constraints of the problem are discussed, and a min-max integer model for small-scale problems is built. At the hyper-heuristic low level, seven data- and target-driven heuristics are presented for allocating components to different machines. Strategies for duplicated conditions with component types and placement points allocation are proposed to improve the applicability of the algorithm and the quality of the solution. An ensemble assembly time estimator that incorporates the coding of multifeatures, including estimated subobjectives, is proposed for evaluating the quality of the solution. Experimental results show that: 1) the gaps between the solution from HHO-MFEE and the optimal solution of the model are 3.44%~7.28% for small-scale data; 2) the proposed time estimator has higher accuracy than regression and heuristic-based ones, with mean absolute error of 2.01% and 3.43% for training and testing data, respectively; and 3) HHO-MFEE is better than other state-of-the-art algorithms, with average improvement of 7.21%~9.47%.

Affiliations: School of Automation, Guangdong-Hong Kong Joint Laboratory of Intelligent Decision and Cooperative Control, Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou, China; School of Mechanical Engineering, Shandong University, Jinan, China; School of Automation, Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou, China

Abstract:
Determining the neural network (NN) approximation sets for adaptive control of strict-feedback uncertain systems has posed a persistent challenge. This article proposes a novel and constructive solution that incorporates signal substitution technique, barrier functions (BFs), and backstepping approach. By applying the signal substitution technique, all system states are transformed into state error variables, facilitating the approximation of unknown system functions through NNs. The use of BFs subsequently allows for the restriction of state errors, enabling the calculation of exact bounds for the NN weight estimators. This process reveals the determination of the approximation sets of NN in advance. Illustrative examples are conducted to validate the effectiveness of the proposed approach.

Abstract:
Underwater vehicle-manipulator systems (UVMSs) play crucial roles in the fields of underwater target monitoring and pipeline maintenance. However, achieving accurate tracking for underwater pipelines is challenging due to the complexity of UVMSs in terms of nonlinearity, strong coupling and underactuation. To solve the aforementioned problems, an underwater biomimetic vehicle-manipulator system (UBVMS) and an underwater pipeline tracking control method based on the robot vision are proposed. The UBVMS is equipped with the biomimetic undulatory fin propulsors and the biomimetic flipper propulsors, which are inspired by the median and/or paired fin propulsion mode and the body and/or caudal fin propulsion mode of fishes, respectively. The biomimetic undulatory fin propulsors provide the UBVMS with advantages of maneuverability and stability, while the biomimetic flipper propulsors enable the UBVMS to have improved acceleration ability. A tracking control algorithm with adaptive weight coefficients is designed to improve the pose stability of the UBVMS. A fuzzy rule mapping model is constructed to describe the nonlinear relationship between the biomimetic propulsors’ control parameters and the propulsive force/torque. Finally, four types of pipeline tracking experiments are conducted to verify the effectiveness and feasibility of the proposed UBVMS and control algorithm.

Abstract:
The issue of extended dissipativity analysis (EDA) for delayed Markovian jump neural networks (MJNNs) is investigated in this article. First, a delay-interval-adjustable-based Lyapunov-Krasovskii functional (LKF) is proposed, in which the delay interval is adjusted by a tunable parameter to obtain an optimal extended dissipativity result, offering a new idea to enhance the consideration of delay information. Furthermore, to take into account more effective information, the LKF is augmented with both single and quadratic integral variables. Accordingly, the LKF derivative becomes a higher-order term of the time-varying delay. To solve this nonlinear problem, a variable-augmented-based free-weighting-matrices approach is employed to transform the nonlinear term into a linear form and provides more freedom in obtaining enhanced EDA results. Then, two novel extended dissipativity criteria of delayed MJNNs are derived. Meanwhile, to show the general applicability of the proposed methods, the derived criteria are applied to the EDA and stability analysis for delayed neural networks (NNs). Lastly, the merits and effectiveness of the proposed techniques are demonstrated through three numerical examples and a real-world application of a quadruple-tank process system. Additionally, the proposed methods can be effectively applied to the practical fields of power system stability control, robot motion control, and image processing, while reducing the conservatism of system performance results.

Abstract:
In this article, we investigate the multiagent robust consensus problem under model uncertainties, where the uncertain matrices and initial values are bounded by prior intervals. Based on the positive system theory, the related upper and lower dynamic systems are constructed to guarantee that the state value remains within a specified range. Subsequently, in accordance with the Lyapunov stability principle, the observation and consensus errors converge to zero, that is, the real states are reconstructed and consensus is achieved. Both local and neighborhood protocols, which are utilized to realize robust consensus, are presented. Notably, the proposed methods increase the design freedom and eliminate the Metzler constraint on the error matrix by introducing two novel parametric matrices. Without loss of generality, the topology in this article is assumed to contain a directed spanning tree, which can be directly degenerated to the undirected graph. Finally, numerical simulations validating the theoretical results are described.

Abstract:
This article presents reachable set synthesis for a discrete-time Markov jump system (DTMJS) with mode-dependent time-varying delays, subjected to uncertain transition probabilities and actuator faults, based on the ellipsoidal attracting approach. The focus is mainly to reflect more realistic control behaviors for the proposed DTMJS, in which the class of partially asynchronous reliable control (PARC) scheme is designed for the first time under the Markov switching topology. In this regard, the state-feedback and mode-dependent time-varying delayed state-feedback controllers are coupled by employing the Bernoulli variable. Under this framework, the hidden Markov model is formulated, revealing the asynchronism among switching topology, controller, actuators and proposed system in different operational modes. By constructing a double mode-dependent stochastic Lyapunov-Krasovskii functional, the sufficient conditions are derived in terms of linear matrix inequalities, which not only ascertain the stochastic stability of the resultant Markov jump system but also ensure that all reachable states remain within compact ellipsoidal boundaries. Finally, numerical simulations are provided to verify the effectiveness and merits of the presented method.

Abstract:
In this article, we address the improved error-constrained control problem for unknown, strongly interconnected time-delay nonlinear systems with input saturation and conflicted output constraints. The further challenge we face is that the presence of discontinuous reference signals poses greater difficulties for control design. To tackle these issues, a mechanism for generating smooth, safe reference signals is first devised. Additionally, we propose a novel approach that utilizes improved prescribed performance functions to confine tracking errors within predetermined constant bounds in finite time, while avoiding potential singularity issues arising from abrupt changes in the reference signal. Furthermore, a decentralized adaptive learning error-constrained control strategy is proposed, employing neural networks to approximate complex uncertainties with an asymptotic dynamic surface control method. Stability analysis confirms that the proposed control scheme guarantees the asymptotic stability of the system and ensures safe tracking within conflicted irregular output constraints, even in the presence of input saturation. Finally, simulation results demonstrate the efficacy of the presented control strategy.

Abstract:
Under the influence of nonlinearity, time-varying delay, and uncertainty, the consensus problem is concerned in this study for multiagent systems modeled by partial differential equations, which means that both the time and space variables are included in the dynamic behavior of each agent. First, with a directed graph, an adaptive boundary controller is developed under boundary measurements, which can effectively reduce the control cost with dynamic control gains and a few actuators and sensors installed at the boundary of the spatial domain. Then, through the designed adaptive boundary controller, the linear matrix inequality (LMI)-based consensus conditions are obtained to ensure the exponential stability of the consensus error systems derived by utilizing the inequality techniques and Lyapunov direct approach. Lastly, two numerical examples demonstrate the effectiveness of the presented adaptive boundary control protocols.

Abstract:
This article addresses the variable convergence rate stability problem for nonlinear impulsive stochastic systems (NISSs). To solve the issue, a novel methodology of sliding mode surface design is presented by combining the definition of interval stability with the T-S fuzzy technique. A pioneering class of sliding mode controllers is constructed in accordance with the characteristics of the designed sliding mode surfaces and the sigmoid function. These controllers can intelligently adjust the convergence rate of the system according to practical requirements, thereby addressing the limitation of fixed convergence rate in existing results. Moreover, the proposed controllers can effectively suppress jitter and analyze the effects of different sigmoid functions on jitter suppression. Sufficient conditions are derived to ensure that the states of the NISSs reach the designed surfaces in finite time and to achieve variable convergence rate stability. The excellent performance of the proposed theoretical strategy in achieving adjustable rate convergence of the system is demonstrated through a simulation of the ball-beam system.

Abstract:
Many previous works on wearable soft exosuits have primarily focused on assisting human motion, while overlooking safety concerns during movement. This article introduces a novel single-motor, altering bi-directional transfer soft exosuit based on impedance optimization and adaptive neural control, which provides assistance to the lower limbs using Bowden cables. This innovative soft exosuit integrates control barrier functions into the impedance optimization, allowing multiple safety constraints to be considered simultaneously, enabling the system to adaptively learn the impedance of the human ankle joint by analyzing the measured interaction forces at the ankle joint, so that the updated reference trajectories comply with safety requirements. To effectively track the updated reference trajectories, we have introduced an adaptive neural controller based on the integral barrier Lyapunov function. This controller is designed to perform the control task under strict safety constraints. The stability of this control approach is meticulously demonstrated through extensive Lyapunov analysis. In contrast to traditional soft exosuits designed purely for assistance, the key advantage of this technology is its ability to adapt to different terrains while ensuring the safety of human movement during assistance. Through experimental testing, we obtain average tracking errors of 0.0062, 0.0062, and 0.0063 rad for flat, grass, and gravel surfaces, respectively, demonstrating the effectiveness of the proposed strategy.

Affiliations: Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, School of Computer Science and Technology, Tiangong University, Tianjin, China; School of Control Science and Engineering, Tiangong University, Tianjin, China; School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, China

Abstract:
Multistate coupled coupled neural networks (MSCCNN) and multiderivative coupled coupled neural networks (MDCCNN) are introduced in this article, and the lag outer synchronization for these two networks are tackled. First, a lag outer synchronization criterion for MSCCNN is derived using a node-based adaptive event-triggered control scheme, and the fact that the Zeno behavior does not exist is also proved. Moreover, the edge-based adaptive event-triggered control method is also utilized to address the lag outer synchronization for MSCCNN, and the existence of Zeno behavior is ruled out. In addition, two lag outer synchronization criteria for MDCCNN are given on the basis of the node- and edge-based adaptive event-triggered control strategies, and the nonexistence of Zeno behavior is also established. Finally, two examples are provided to demonstrate the feasibility of the proposed control schemes.

Abstract:
Process variables may exhibit both temporal trends and periodic responses, with their fault propagation pathways manifesting in time-domain and frequency-domain causalities, respectively. However, the differing causal perspectives of time-domain and frequency-domain methods can lead to distinct causalities, posing the causal heterogeneity challenge for root cause diagnosis (RCD). Thereupon, we reveal the mechanism of source consistency in Granger causality (GC), that is, the root cause variable provides the most significant predictive information in both time and frequency domains. Accordingly, we propose a causal source consistency analytics (CSCA) framework that achieves time-frequency synergy. First, we design a nonlinear enhancement module to extract temporal features for causal inference. Second, to extract time-domain and frequency-domain GC, we develop a parallel causality learning module, where a differentiable frequency-domain expansion operator is designed along with a temporal prediction submodule. Meanwhile, a time-frequency entropy constraint is constructed to ensure causal significance by inducing sparsity. Finally, a root cause alignment module is proposed to ensure source consistency. A predictive information quantification algorithm, formulated as an eigenvalue decomposition problem, is designed to locate the root cause. We develop an approximate exponential transformation to convert the eigenvalue decomposition into a differentiable source alignment loss. Thus, source consistency can be ensured during end-to-end inference. The validity of CSCA is illustrated through the Tennessee Eastman process and a gas turbine application. CSCA identified the root causes in both examples correctly. Furthermore, ablation studies validate that CSCA enables the time-domain and frequency-domain models to identify consistent root causes, thereby overcoming causal heterogeneity.

Abstract:
The prescribed-time semi-global control for a class of time-varying uncertain systems under a nonlinear growth condition is achieved via linear time-varying feedback. The involved nonlinear uncertainties are categorized as unmatched uncertainties (depending on states and time) and matched uncertainties (depending on time only). Both state feedback and observer-based output feedback are constructed relying on the properties of parametric Lyapunov equations and the time-varying gains acquired by solving scalar differential equations. The proposed output feedback approach features a separation principle, that is, the construction of prescribed-time observer and prescribed-time state feedback is conducted separately. The proposed control scheme is validated by simulations carried out on a standard mechatronics system with complicated loads.

Abstract:
In this article, the optimal consensus tracking control for nonlinear multiagent systems (MASs) with unknown dynamics and disturbances is investigated via adaptive dynamic programming (ADP) technology. Taking into account the disturbance as control inputs, the optimal control problem for the nonlinear MASs is reformulated as a multiplayer zero-sum differential game. In addition, a single network ADP structure is constructed to approach the optimal consensus control policies. Subsequently, an event triggering mechanism is implemented to reduce the workload of the controller and conserve computing and communication resources. Since then, in order to further streamline the intricacies of controller design, this work is extended to self-triggered cases to alleviate the need for hardware devices to continuously monitor signals. By using the Lyapunov method, the stability of the nonlinear MASs and the uniform ultimate boundedness (UUB) of the weight estimation error of the critic neural network (NN) is proved. Finally, the simulation results for an MAS consisting of a single-link robot validate the effectiveness of the proposed control method.

Abstract:
This article investigates the resilient control strategies of networked switched systems (NSSs) against denial-of-service (DoS) attacks and external disturbance. In the network layer, both the defender and the attacker allocate energy over multiple channels. Considering the impact of switching characteristic in the physical layer on the network layer, a dynamic regulating factor is proposed to adjust the total energy of the defender. To optimize the signal-to-interference-noise ratio and energy consumption simultaneously at each player’s side, a multiobjective game problem is formulated. Furthermore, a nondominated sorting genetic algorithm framework is employed, incorporating the knee point selection mechanism to attain the Pareto-Nash equilibrium, based on which the optimal defense strategy can be derived to achieve resilience against DoS attacks. In the physical-layer, taking the dynamic packet loss caused by DoS attacks and external disturbance into account, an H_\infty minimax controller containing control inputs and the switching signal is designed to guarantee the optimal performance for NSSs through the dynamic game-theoretic approach. Finally, the networked continuous stirred tank reactor system is provided to verify the effectiveness of the proposed method.

Abstract:
This article studies the leader-following consensus problem for a class of linear multiagent systems over a directed graph with aperiodically sampled outputs. First, a novel distributed impulsive-observer-based consensus protocol is designed. This protocol requires only the output measurements at sporadic time instants for the observer and control gain design. Second, by using time-varying Lyapunov function techniques, sufficient conditions for exponential stability of a class of linear impulsive systems are established; subsequently, these stability results are applied for the distributed impulsive observer design. Different from the existing related works, the impulsive observer gain designed in this work is decoupled from the graph properties. As a result, once the impulsive observer gain is designed for one network topology, it can be directly used for other network topologies, as long as the graph properties and the dynamics of local agents satisfy certain conditions. Furthermore, the resilience of the designed protocol is tested under denial of service (DoS) attacks. It is shown that the protocol is robust with respect to low-frequency DoS attacks occurring in the observer communication network. Finally, two examples illustrating the validity and effectiveness of the proposed protocol are included.

Abstract:
This article is concerned with the problem of asynchronous control for Interval Type-2 (IT2) fuzzy nonhomogeneous Markov jump systems against successive denial-of-service (DoS) attacks. The system and the controller are assumed to be connected through a communication channel subject to malicious attacks. The maximum number and probability distribution of successive attacks are considered. Under the imperfect premise matching, a fuzzy asynchronous controller is constructed by the hidden Markov model. By means of the introduced transmission delay, a delay closed-loop system is constructed, where the stochastic description of the delay depends on the statistical characteristic of successive attacks. Then stability criteria together are derived in the form of linear matrix inequalities by the Lyapunov functional approach, as well as the condition on the existence of the fuzzy controller. Finally, the feasibility and effectiveness of the presented control scheme are demonstrated by simulation results.

Abstract:
The leader-following consensus issue for general linear multiagent systems (MASs) is investigated under event-triggered communication (ETC). Based on the output measurement of agent, two novel adaptive dynamic event-triggered (ADET) strategies for synchronous and asynchronous ETC are proposed, in which adaptive triggering parameters and time-varying threshold are introduced. Simultaneously, the corresponding control protocols are developed under the ADET strategies. Different from most existing relevant results, triggering mechanisms and control protocols do not require global information and network size, ensuring a fully distributed implementation. The asynchronous ETC, in particular, gives a flexible for transmitting information. The effectiveness of ADET strategies is further analyzed and discussed, and a comparative analysis between observer-based and state-based ADET algorithms is provided, highlighting their efficiencies and applications. Theoretical claims are substantiated with a simulative example that demonstrates the practical effectiveness of the proposed strategies.

Abstract:
This article investigates the secure control problem for a class of Lipschitz nonlinear descriptor multiagent systems (MASs) with unknown inputs under Denial-of-Service (DoS) attacks. In order to address the presence of unknown state variables and external disturbances in both the state and output equations, a local unknown input observer (UIO) is developed for each follower agent. The proposed UIO is capable of simultaneously estimating the system state, measurement noise and unknown inputs through an interval observer. With regards to DoS attacks, we consider two types: those that maintain connectivity and those that paralyze it by disrupting the structure of the information communication topology graph. By utilizing the proposed UIO, a distributed compensation controller is designed to achieve asymptotic consensus for leader-following MASs under DoS attacks. Additionally, a comprehensive stability analysis of the closed-loop system is provided, taking into account switching systems. Finally, two simulation examples are presented to validate the effectiveness of the proposed UIO-based distributed secure control scheme.

Abstract:
This work investigates the time-varying formation-tracking problem of multiagent systems under hybrid attacks, including denial-of-service (DoS) attacks and actuation attacks. State estimators are designed for each node of the swarm leveraging relative information from neighboring estimators to generate the desired positional states for formation tracking. The direct use of corrupted consensus control inputs is avoided, thereby defending against actuation attacks targeted at node input signals. Furthermore, we propose an event-triggered protocol with a sampling mechanism to enhance resilience against DoS attacks on communication with neighboring estimators equipped with a topology recovery policy. This resilient protocol against DoS attacks is fully distributed and does not require prior knowledge of network topology, making it scalable to large networks. Finally, an adaptive attack-resilient control scheme is introduced to counteract potential unbounded actuation attacks via output feedback, enabling each follower to track the positional states provided by the distributed estimators. The tracking error is proven to be uniformly ultimately bounded. The proposed event-triggered hierarchical control scheme is validated through its application to spacecraft formation.

Abstract:
This article considers the hierarchical containment control (HCC) for flexible mirrored collaboration, which accommodates the bipartite cluster consensus behavior in two symmetric convex hulls formed by multiple leaders. First, to achieve the mirrored collaboration in symmetric convex hulls, the layer-signed digraph is generated by involving the antagonistic interaction. Benefiting from the hierarchical structure, the antagonistic interaction in the assistant-layer replaces the assumption of in-degree balance for the existing cluster consensus issues. Second, the existing types of control protocols and the framework of cooperative output regulation limit the achievement of the studied hierarchical mirrored collaboration. To solve this problem, the hierarchical cooperative output regulation is extended based on the formulated hierarchical mirrored collaborative errors. Third, the layer-signal compensator is designed estimating the states of leaders as well as guaranteeing the convergence of collaborative behaviors. Combining with the designed layer-signal compensator, a novel HCC protocol is proposed so that the bipartite cluster consensus behavior can be achieved simultaneously in two symmetric convex hulls. Finally, theoretical results are verified by performing the numerical simulation.

Affiliations: State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, China; School of Mathematics, Southeast University, Nanjing, China; Department of Automation and Applied Informatics, Politehnica University of Timisoara, Timisoara, Romania; Department of Electrical Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, India; Department of Engineering, King’s College London, London, U.K.; Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

Abstract:
This article presents an adaptive neural network control scheme with prescribed performance for stochastic nonlinear systems. Unlike existing adaptive stochastic control schemes that primarily utilize deterministic neural networks for approximations in complex stochastic environments, we employ stochastic neural networks to approximate the stochastic nonlinear terms, effectively resolving the “memory overflow” issue. Moreover, we propose a novel prescribed performance design method, which distinguishes itself from the previous prescribed performance control schemes by integrating a quadratic characteristic capable of suppressing transient input vibrations, along with a local asymmetric characteristic that optimize both transient output overshoot and steady-state error bias. Furthermore, the proposed control scheme is implemented within a fixed-time framework to ensure that all closed-loop systems are fixed-time bounded in probability, with the tracking error consistently within the predefined performance bounds. Simulation results validate the effectiveness of the proposed control scheme.

Abstract:
This article studies the problem of designing the optimal strictly stealthy attack against remote state estimation in cyber-physical systems, where the attacker possesses both the intercepted information and the side information sensed by an additional sensor. Combining the intercepted information with the side information, a novel corrupted innovation-based attack model with higher-design flexibility is proposed, and the analytical optimal attack strategy is derived. Compared with the existing results based on nominal innovation, the proposed attack model eliminates the need for an additional filter to calculate nominal innovation, which saves computational resources, and can achieve greater performance degradation of remote state estimation. Finally, in order to verify the superiority and effectiveness of the results, the numerical examples are given.

Abstract:
This article investigates the problem of unified output constraints for a class of uncertain interconnected nonlinear systems, where the measurement of system states is affected by unknown drifts in the powers of the measurement functions. Compared to previous works on output constraints, the main challenge addressed in this article is the unavailability of the true system states during the controller design process and the nondifferentiability of the sensor’s output functions. To achieve the control objectives, the following control scheme is proposed in this study. First, a novel barrier Lyapunov function is introduced, which is specifically designed to handle systems with unknown measurement drifts. This function can be uniformly applied to satisfy both scenarios of systems with or without output constraints. Second, the adding a power integrator (AAPI) technique and dynamic surface control (DSC) techniques are enhanced to effectively handle the unknown measurement drifts and avoid singularity problems in the controller design. The decentralized controller proposed in this article can realize that the outputs are strictly constrained within predefined boundaries and guarantees convergence of all system states to an arbitrarily small neighborhood. Finally, we provide two simulation examples to validate the effectiveness of our proposed control strategy.

Abstract:
Enhancing system security under denial–of–service (DoS) attacks requires robust compensation mechanisms. However, existing model–free adaptive control–based compensation solutions are limited to constant reference signals and neglect control optimization, causing insufficient tracking performance in dynamic attacks. This study develops a data–driven adaptive dynamic programming (ADP) resilient control scheme for networked control system under aperiodic DoS attacks. An ADP method with a modified performance index is proposed to derive a globally optimal controller, while a dynamic penalty factor is introduced to accelerate error convergence. Leveraging ADP technology and the latest available control increments, a compensation mechanism for time–varying reference signals is designed to reduce performance degradation. Finally, theoretical proofs ensure error convergence, and comparative simulations verify the strategy’s superiority.

Abstract:
In this article, a novel online adaptive control scheme is developed for the optimal control issues of integrated electric–gas systems with partially unknown dynamics, by combining the decentralized event-triggered mechanism and adaptive dynamic programming techniques. Initially, the complex electric–gas coupling network is modeled in the state-space form. By virtue of neural networks (NNs), the NN-based identifier and the critic NN are designed to approximate the unknown drift dynamic and the optimal value function in an online fashion, respectively. Subsequently, the decentralized event-triggered control strategies are devised under the identifier–critic framework. Moreover, a novel decentralized event-triggered scheme with the dead-zone operation is proposed, which updates the controller and actuator signals only when the triggering condition is violated. As such, the computation complexity and the waste of communication resources can be significantly reduced. On the foundation of the Lyapunov theory, the uniform ultimate boundedness stability of the closed-loop control system and the exclusion of the Zeno behavior are proven. Finally, the effectiveness of the developed algorithm is verified through two numerical examples.

Abstract:
To achieve low-carbon operation of a distribution network, new energy resources like photovoltaics (PVs) have been extensively integrated into it. However, this integration poses significant challenges to the supply–demand balance. Specifically, the generation of PVs is stochastic, causing power fluctuations. Additionally, the increase in power data and the open nature of the network will cause network congestion and communication disturbances. To address these issues, an active power support (APS) strategy is developed with the following innovations. First, an adaptive mutation-based generation prediction algorithm incorporating a multi-extreme learning mechanism (ELM) is proposed to optimize the prediction model and provide reliable predicted generation data for regulation. Second, a demand-driven path optimization method is proposed to prioritize critical data transmission, ensuring that regulatory service demands are met while mitigating congestion. Third, a hierarchical control strategy utilizing multifactor matching and a sliding mode controller (SMC)-based virtual leader-following consensus algorithm is designed to generate optimal control commands for PVs and suppress disturbances. Finally, adequate simulations demonstrate that the proposed method reduces the prediction error by at least 10.1% compared to existing methods, adjusts transmission paths based on data importance and service needs to mitigate congestion, and suppresses communication disturbances within 1s, thereby enabling effective APS.

Abstract:
This article investigates the Nash equilibrium (NE) seeking problems for multicoalition games with heterogeneous general linear dynamics over unbalanced digraphs. The coalition can be regarded as a virtual player but the true decision-makers are the actual players themselves which can only access to their own cost functions. To deal with the unbalanced digraphs, the left eigenvector is estimated and its prescribed-time convergence is illuminated with the time transfer approach. Then the NE seeking part and the output regulation part are designed to adapt the dynamics of the players. The initial values of the auxiliary vectors are selected to avoid utilizing the out-degree information. The steady state of the closed-loop system is analyzed and the prescribed-time convergence is proved based on the Lyapunov method. Finally, the simulation results of both the mobile sensor connectivity game and the electricity market game are presented to show the effectiveness of the proposed algorithm.

Abstract:
To combine the strengths of Gaussian and non-Gaussian latent variable models, a novel information fusion strategy has recently been proposed under the deep learning framework. Although promising results have been obtained, the critical structure learning problem remains unsolved, which seriously hinders the automation of data-driven modeling and analytics. In this article, the maximal information coefficient (MIC) method is introduced as a measurement of the association strength between two latent variables, which has no restriction in the type of data distribution. Through an assessment on the necessity of adding a new hidden layer into the deep model in each step, an evaluation index is defined for automatic determination of the required hidden layers during the model training process. For time-varying industrial production environments, reconfiguration or updating of the model structure is frequently required. In this case, automated data-driven modeling and structure learning can significantly improve the efficiency of data analytics. Based on the study results obtained from two real industrial examples, the proposed structure learning algorithm is feasible, and the automated data analytics scheme has significantly improved the online prediction performance in time-varying industrial processes.

Abstract:
This article delves into the semi-global robust output regulation of uncertain singular linear systems subject to input saturation and denial-of-services (DoSs) attacks. In the event of a DoS attack, the communication channel between the plant and the controller is blocked. To address the input saturation nonlinearity and to enhance robustness against uncertainty and Dos attacks, a post-processing internal model-based robust output feedback regulator is proposed. It employs the small gain techniques, incorporating two small positive parameters. The overall system is modeled as a hybrid system using hybrid formalism, for which a jump corresponds to the DoS attacks. It is shown that the proposed regulator exhibits robustness against certain DoS attacks and structure uncertainties. Consequently, the output error can asymptotically converge to the origin when the structured uncertainty is small enough, and the proposed constraint on the duration of DoS attacks is met. Finally, two numerical example are given to illustrate the effectiveness of the result.

Abstract:
On the basis of fully actuated system (FAS) method, this research focuses on the solution to a secure tracking control problem of cyber-physical systems (CPSs) under a type of hybrid attacks, where a unified framework of hybrid attacks is given to indicate the impacts of random denial-of-service attacks and random false data injection attacks in the forward and backward channels. A FAS terminal sliding-mode predictive control scheme is proposed to achieve the desired secure tracking control performance. First of all, a FAS model of CPSs is constructed to describe the actual dynamics, which is named the fully actuated cyber-physical system (FACPS). Then, a terminal sliding-mode is introduced to defend the hybrid attacks by enhancing the system robustness, and an incremental FAS prediction model of terminal sliding-mode is established via a Diophantine Equation. Through this incremental FAS prediction model, the multistep predictions of terminal sliding-mode are constructed to minimize an objective function for obtaining an optimal secure tracking controller. A sufficient condition of the closed-loop FACPS is derived to discuss the bounded stability and tracking performance with the help of the linear matrix inequality approach. Finally, the proposed FAS terminal sliding-mode predictive control scheme offers a solution to the tracking control of air-bearing spacecraft simulator for verifying the feasibility and practicality.

Abstract:
With the aim to solve the secondary control problems for DC microgrids with cyber-attacks and limited bandwidth in the communication network, this article proposed a unified distributed resilient control strategy to achieve simultaneously appropriate voltage restoration and optimal power allocation (economic dispatch) by integrating dynamic event-triggered mechanism (DETM). First, based on dynamic average consensus protocol (DACP) and taking the DETM into consideration, the voltage regulator with distributed average voltage estimator and the PI controller is designed. Meanwhile, in order to maintain the economic operation of the microgrids under cyber-attacks, the power regulator with distributed optimal power controller and the PI controller is designed. Then, the sufficient conditions of convergence and optimality for the proposed distributed resilient average voltage estimator and optimal power controller are demonstrated by using the Lyapunov stability principle and convex optimization analysis, respectively. To this end, several case studies are conducted to validate the effectiveness of the proposed control strategy for enhancing the security, robustness, adaptability and economy of DC microgrids.

Affiliations: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, China; School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, China; Discipline of Engineering and Energy, Murdoch University, Perth, Australia; School of Automation, Chongqing University, Chongqing, China; School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia; School of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, Australia; Department of Automation, Shanghai Jiao Tong University, Shanghai, China

Abstract:
This article introduces an innovative approach to simultaneously estimate time-domain and spatiotemporal faults in nonlinear distributed parameter systems (NDPSs)nonlinear distributed parameter systems (NDPSs) under external disturbances. First, the establishment of an iterative learning observer that accounts for both temporal and spatial changes is presented. Next, a fault estimation law is devised utilizing a distinct full iterative learning (FIL)full iterative learning (FIL) technique, facilitating rapid and precise estimation of fault signals while mitigating the impact of external disturbances. Furthermore, the adoption of the \lambda -norm method aids in simplifying the determination of convergence conditions and gain matrix calculations. Lastly, comprehensive simulation results validate the efficacy of the developed approach, underscoring its adeptness in efficiently and precisely estimating faults across both time and spatiotemporal domains.

Abstract:
Cyberattacks on transmitted signals are the most critical threats to modern microgrid (MG) systems and should be accurately addressed to ensure safe and reliable operation. This article investigates the cybersecurity of dc MGs against false data injection attacks and develops a novel model-based observer system. The proposed projection operator (PO)-based unknown input observer is uniquely designed to detect attacks on the transmitted data from other distributed generation units. For this purpose, a bank of PO observers is developed in each distributed generation unit to estimate all neighbor units’ dynamic states. Cyberattack reconstruction compares the remotely observed state values with the measured ones. Afterwards, the detected attack signal values are utilized to restore the integrity of the compromised signals, effectively mitigating the harmful effects of cyberattacks. The most important feature of this scheme, which distinguishes it from similar methods, is that there is no need for a secure channel or additional information transfer in this method. Extensive real-time numerical simulations are performed to assess the practicality and efficiency of the proposed approach when subjected to various attack scenarios, demonstrating its advantages over other methods.

Abstract:
The trajectory tracking control for the photothermal-driven liquid crystal elastomer (LCE) actuator presents a significant challenge due to its hysteresis nonlinear characteristic and its inherent complex deformation mechanism. To address this challenge, this article proposes a trajectory tracking control method for the LCE actuator utilizing a nonlinear compensator and a state observer. The proposed control is a multistep control, which includes temperature control from the input voltage to the LCE temperature and displacement control from the LCE temperature to the LCE displacement. In the proposed method, we design a non-Lipschitz continuous state-feedback controller to realize finite-time convergence control of the temperature. As for the displacement control, we design a state observer to estimate the change rate of the LCE displacement. Meanwhile, a nonlinear inverse compensator is designed to compensate for the hysteresis nonlinearity of the LCE dynamics, which simplifies the complex nonlinear control problem into a linear control problem. Hence, the pole placement method can be utilized to design a trajectory tracking controller to achieve the control objective. The proposed control method is validated by tracking control experiments with different target trajectories.

Abstract:
In this article, we proposed a fuzzy controller for nonlinear two-time-scale systems with uncertain system parameters. The designed controller is implemented using integral control and event-triggered strategy to deal with the singularly perturbed system (SPS), which typically operates on both the fast and slow dynamics simultaneously because of the small value parameter, namely, the parasitic parameter \varepsilon . Two main challenges of this work are the instability of SPS—caused by the presence of parasitic parameter, the parameter uncertainties and external disturbances—and the data transmission load between the system and the controller. To tackling with these problems, the proposed controller is designed based on the Takagi-Sugeno fuzzy model in cooperation with the integral feedback and event-triggering action, and its control performances are analyzed using linear matrix inequality (LMI) approach. Through the demonstrations, our proposed method ensures the asymptotic stability, enhances robustness against disturbances and parametric uncertainties, and also effectively reduces communication costs.

Affiliations: School of Information Science and Technology, the Engineering Research Center of Digital Community, Ministry of Education, and the Beijing Laboratory for Urban Mass Transit, Beijing University of Technology, Beijing, China; School of Information Science and Technology, the Engineering Research Center of Digital Community, Ministry of Education, and the Beijing Key Laboratory of Computational Intelligence and Intelligence System, Beijing University of Technology, Beijing, China

Abstract:
HCMOP are widespread in practical engineering such as vehicle routing problem and shop scheduling problem etc. The problems introduced above refer to optimization problems with complex constraints which lead to small and disconnected feasible regions. The optimization performance of general evolutionary algorithms decreases due to the small and dispersed feasible regions in highly constrained optimization problems. To address this problem, a multiobjective ant colony optimization algorithm based on dynamic constraint evaluation strategy (MOACO-DCE) is proposed in this article. First, a dynamic constraint violation metric is proposed to evaluate the constraint violation degree of solutions. The population is classified into the subpopulation with evolutionary advantages and the subpopulation with high constraint violation degree by this metric. Second, an evolutionary strategy based on dynamic transfer probabilities is proposed for the subpopulation with evolutionary advantages in order to improve the evolutionary efficiency. A Gaussian variation evolutionary strategy is proposed for the subpopulation with high constraint violation degree in order to improve the population diversity. Third, a pheromone collaborative updating strategy is proposed to achieve the synergistic pheromone updating between two subpopulations. A pheromone updating strategy considering the constraint violation metric is designed to improve the utilization of constraint violation information in the whole population. In addition, compared with other constrained multiobjective optimization algorithms, MOACO-DCE can obtain more satisfactory performance for the highly constrained optimization.

Abstract:
This article investigates the observer-based adaptive decentralized control problem for a class of uncertain interconnected nonlinear fully actuated systems (FAS), considering nonsmooth actuator dynamics including actuator failures and unknown control gains. Based on the dynamic gain scaling technique, a dynamic state observer is constructed. By utilizing the high-order FAS (HOFAS) approach, an adaptive decentralized output feedback controller is designed and a closed-loop structure of the fully actuated subsystems is derived. This structure takes actuator loss of effectiveness, unknown control gains, and unstructured uncertainties into account in the interconnected time-delay subsystems. By selecting suitable Lyapunov-Krasovskii (L-K) functionals, the time-delay terms can be removed, ensuring that all signals of the overall closed-loop system converge to a bounded region. Finally, two simulation examples validate the efficacy of the proposed strategy.

Abstract:
In this study, the problem of mean square exponential l_2-l_\infty control is investigated for switched-Markovian jump systems (SMJSs). SMJSs are subject to deterministic switching obeying mode-dependent average dwell time (MDADT) and stochastic switching complying to Markov chain. mode-dependent transition probability (MDTP) and edge-dependent transition probability (EDTP) are proposed. MDTP describes the transition probability (TP) of Markovian jump systems (MJSs) under deterministic switching, and EDTP portrays the TP among MJSs affected by deterministic switching, which is associated with two different deterministic switching modes. Using multiple discontinuous Lyapunov function technology, the mean square exponential stability with l_2-l_\infty performance is guaranteed by the MDADT method, MDTP and EDTP. Certain solvable sufficient conditions are obtained for the controller. Finally, two numerical examples and a practical example are provided to illustrate the validity of the obtained results.

Affiliations: Department of Control Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China; Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education, East China University of Science and Technology, Shanghai, China; School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China; Department of Control Science and Engineering, College of Electronic and Information Engineering, Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction, Shanghai Institute of Intelligent Science and Technology, the State Key Laboratory of Autonomous Intelligent Unmanned Systems, and the Frontiers Science Center for Intelligent Autonomous Systems, Ministry of Education, Tongji University, Shanghai, China

Abstract:
This article aims to investigate the performance-guaranteed tracking problem for a class of uncertain nonlinear systems. The main goal is to attain control performance within prescribed-time (PT) limits despite the existence of mismatched disturbances. First, the mismatched disturbances are transformed into the equivalent forms. Second, for the unknown disturbance estimation, a PT extended state observer (PTESO) is developed to switch between the prescribed settling time \mathcal T_p , the order is diminished to alleviate the occurrence of the peaking phenomenon. Furthermore, an ESO-based PT control strategy is constructed with time-varying gains. This allows real-time compensation of disturbances, and the prescribed performance is attained by virtue of a Lyapunov function employed combines both barrier and quadratic forms. Ultimately, the benefits and efficacy are illustrated via a numerical demonstration involving a wheeled mobile robot. The key features of this article include the observer capability to estimate unknown mismatching disturbances and the full effectiveness of the proposed controller for t \in [t_0, \infty ), ensuring the convergence of tracking error to zero within any PT. Consequently, in addition to achieving the output tracking objective, the system can also exhibit favorable transient performance.

Abstract:
This article investigates the H_\infty -based tracking control problem for nonlinear systems through the Takagi-Sugeno (T-S) fuzzy technique. A nonuniform sampled-time-dependent functional (NSTDF) is proposed, which removes the constraints of the conventional looped functional (LF) and relaxes the condition of the functional derivative. Combining the NSTDF with H_\infty theory, a novel theorem for H_\infty performance analysis of sampled-data systems is given, which loosens the positive-definite constraint on Lyapunov matrices in traditional LFs. By introducing the error integral state, an augmented system is constructed, and a proportional-integral (PI)-type controller that incorporates the external disturbance, transmission delay, and packet dropouts is designed to enable the tracking control. Thus, the H_\infty -based tracking control issue is converted into an H_\infty -based control problem for the augmented system, and the control conditions are derived via the proposed methods. Finally, the wind energy conversion system (WECS) and Rossler’s system clarify the feasibility and merits of the provided methods.

Abstract:
Existing active learning methods for object detection face challenges, such as the lack of ground truth labels for regression loss, insufficient representation of unlabeled instance samples information, and discrepancies in information quality between image-level and multiple anchor-level instances. To address these issues, we propose an active learning method for object detection with vectorized dual pseudo loss and multiple instance offset constraint. This method implements a two-stage framework. The first stage focuses on evaluating the information quality of detection images. We first pioneer a dual pseudo loss formulation that provides theoretically grounded regression loss estimation. The regression loss is calculated as the norm of the offset discrepancy loss vector between the enhanced and original base box vector, further constrained by the cosine value of the angle between the anchor box feature and regressor parameters vector. The distance entropy from the base box feature vector to each category’s feature prototype vector is used as a weighting factor for the regression and classification information quality of instance samples. Subsequently, the second stage employs diversity-driven sampling on high-information images, leveraging instance-level cosine similarity to effectively remove redundant images. The proposed method outperforms state-of-the-art active learning approaches for object detection on PASCAL VOC and MS COCO datasets. Additionally, the proposed dual pseudo regression loss robustly captures regression information quality, demonstrating its effectiveness for active learning in object detection.

Abstract:
In this article, a secure consensus control scheme based on adaptive dynamic programming is proposed for multiagent systems under false data injection (FDI) attacks. By the adaptive sliding mode observer, the attack estimator is designed to compensate for the FDI attack. Subsequently, the secure consensus control problem is recast as an optimal control problem. To save network resources, the dynamic event-triggered mechanism is introduced into the design of the optimal control law. Acquiring the optimal event-triggered control (ETC) policy is related to solving the coupled Hamilton-Jacobi–Bellman equation. Based on the dual heuristic programming technique and single critic neural network (NN) structure, the echo state network is employed in approximating the optimal ETC strategy. The experience replay mechanism is utilized to design the NN weight updating law, which can remove the persistence of excitation conditions. Then, the closed-loop stability and Zeno behavior avoidance are analyzed. The simulation is provided to support the effectiveness of the presented method.

Affiliations: School of Automation, Central South University, Changsha, China; School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia; Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China; Key Laboratory of Dependable Services Computing in Cyber Physical Society-Ministry of Education, College of Computer Science, Chongqing University, Chongqing, China; Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, SAR, China; Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, China

Abstract:
Privacy leakage and Byzantine issues are two adverse factors to optimization and learning processes of multiagent systems (MASs). Considering an unsafe MAS with these two issues, this article targets the resolution of a category of nonconvex optimization problems under the Polyak–Łojasiewicz (P–Ł) condition. To address this problem, we first identify and construct the unsafe MAS model. Under this kind of unfavorable MASs, we mask the local gradients with Gaussian noise and adopt a resilient aggregation method, self-centered clipping (SCC), to design a differentially private (DP) and Byzantine-resilient (BR) decentralized stochastic gradient algorithm, dubbed DP-SCC-PL, aiming to address a class of nonconvex optimization problems in the presence of both privacy leakage and Byzantine issues. The convergence analysis of DP-SCC-PL is challenging, as the convergence error arises from the coupled effects of DP and BR mechanisms, as well as the nonconvex relaxation, which is resolved via seeking the contraction relationships among the disagreement measure of reliable agents before and after the SCC aggregation, together with the optimal gap. Theoretical results not only reveal the trilemma between algorithm utility, resilience, and privacy, but also show that DP-SCC-PL can achieve consensus among all reliable agents. It has also been proven that if there are no privacy issues and Byzantine agents, then the asymptotic exact convergence can be recovered. Numerical experiments verify the utility, resilience, and privacy of DP-SCC-PL by tackling a nonconvex optimization problem satisfying the P–Ł condition under various Byzantine attacks.

Abstract:
This study discusses the problem of event-triggered (ET) asymptotic tracking control for parametric strict feedback nonlinear systems (SFNSs) with time-varying disturbances and unknown control directions. A unified dynamic threshold method is proposed by combining a unified function with a self-adjustable performance function. In contrast to previous research, the results of this study provide a unified framework in which global or semi-global performance can be achieved through simple parameter selection while excluding the conservativeness of the constraint thresholds owing to the artificial selection of a uniform performance function. The basic lemma based on the Nussbaum function frequently is extended to adapt to the case in which the coefficients are multiple bounded functions. By fusing a first-order differentiator, the reduplicative derivation of the virtual controller in the backstepping process is obviated. Moreover, an ET mechanism with two dynamic variables is constructed to reduce the burden of data transmission. The developed controller can guarantee the boundedness of all signals in the closed-loop system, full-state constraints performance, and asymptotic tracking control performance. Finally, the feasibility of the proposed scheme is attested by two examples.

Abstract:
With excellent interpretability, the fuzzy rule-based method stands as a formidable instrument for knowledge representation and learning. Nowadays, the knowledge representation problem with multidimensional input information is widespread, leading to a large rule base and making it difficult to embed expert knowledge. In addition, human knowledge is not entirely reliable, causing inaccurate reasoning results. In this article, a novel large-scale linguistic Z-number belief rule base (LSLZ-BRB) method is proposed for the above multidimensional and unreliable knowledge representation and learning. Specifically, a multidimensional knowledge mapping representation method under the probabilistic framework is proposed to generate an LSLZ-BRB. It allows experts to embed knowledge via conditional probability and prior probability. To reduce the modeling error caused by uncertainty of knowledge, an online interactive learning mechanism of uncertain knowledge is developed. This mechanism ensures that LSLZ-BRB has high real-time performance and improves the accuracy of knowledge representation. A performance evaluation case for the laser inertial measurement unit (LIMU) and experiments on some public datasets illustrate the implementation process of the proposed method and further verify its effectiveness.

Abstract:
Key performance indicator (KPI) reflects the quality and efficiency of manufacturing operations, and KPI forecasting enables proper operations or controls in many industrial processes. However, existing KPI forecasting methods are inadequate for managing the advance prediction of KPI across multiple cycles effectively, which impedes precise and timely control in complex industrial processes. Therefore, we propose an adaptive encoder-decoder framework with partial teacher forcing strategy (PTF-ED) to enable flexible multihorizon KPI forecasting. First, we employ an encoder that processes the input time series and an attention layer to generate the context vectors. Then, we divide the measured KPIs into delayed and current time series, and design a delayed decoder and a current decoder in series to make the delayed and current time series correspond to the input time series. Especially, we propose a partial teacher forcing strategy to utilize the measured KPI efficiently and tackle the challenge of exposure bias in the current time series between training and inference phases. Moreover, we introduce a weighted multihorizon forecasting constraint in the model training loss to constrain the input-output correspondence across different sample intervals. The effectiveness of the proposed model has been validated through both a numerical simulation study and a case study in a real-world zinc flotation process.

Abstract:
This article investigates the fixed-time distributed average tracking (DAT) problem for nonlinear multiagent systems with unity relative degree under external disturbances. A distributed control framework is developed to guarantee fixed-time convergence of all agents’ outputs to the target trajectory, which is defined as the average of multiple nonlinear reference signals. The approach consists of three main components. First, a steady-state generator is introduced to reconstruct the desired trajectory. Using this generator, a distributed observer is designed to estimate the target trajectory while ensuring robustness against initialization errors. Subsequently, an observer-based output-feedback controller is developed to guarantee the convergence of each agent’s output to its corresponding reference signal within a fixed time. Through rigorous theoretical analysis, it is proved that the proposed control architecture ensures fixed-time convergence to the target trajectory, effectively solving the fixed-time DAT problem. The effectiveness of the proposed method is validated through numerical simulations.

Abstract:
In this article, a model-free reinforcement learning (RL) approach is proposed for solving the optimal consensus control issue of nonlinear discrete-time multiagent systems with input constraint. To address the challenge of solving the coupled discrete Hamilton-Jacobi–Bellman (HJB) equation, a RL approach based on actor-critic framework is proposed for optimal consensus control. A well-defined cost function is designed, and the actor and critic networks are updated through online learning to obtain the optimal controllers. Furthermore, the actuator’s performance is often limited due to physical constraints. To address such actuator constraints, a gradual transition control (GTC) method is proposed, and update-free and update-weak policies are introduced to further optimize network performance. Additionally, in real-world distributed systems, the actor-critic networks deployed in each agent rely on data from neighboring agents, which necessitates addressing the issue of distributed synchronization. To address this challenge, the synchronization blocking method is designed, which designs additional control signals for each agent to handle these issues. Finally, two simulations under different scenarios are presented to verify the effectiveness of the proposed approach.

Affiliations: Center for Scientific Research and Development in Higher Education Institutes, Ministry of Education, Beijing, China; School of Intelligence Science and Technology and the Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, University of Science and Technology Beijing, Beijing, China; School of Software, Fudan University, Shanghai, China; Information School, Renmin University, Beijing, China; Business School, Sichuan University, Chengdu, China

Abstract:
The acceptance of academic papers involves a complex peer-review process that requires substantial human and material resources and is susceptible to biases. With advancements in deep learning technologies, researchers have explored automated approaches for assessing paper acceptance. Existing automated academic paper rating methods primarily rely on the full content of papers to estimate acceptance probabilities. However, these methods are often inefficient and introduce redundant or irrelevant information. Additionally, while Bert can capture general semantic representations through pretraining on large-scale corpora, its performance on the automatic academic paper rating (AAPR) task remains suboptimal due to discrepancies between its pretraining corpus and academic texts. To address these issues, this study proposes LMCBert, a model that integrates large language models (LLMs) with momentum contrastive learning (MoCo). LMCBert utilizes LLMs to extract the core semantic content of papers, reducing redundancy and improving the understanding of academic texts. Furthermore, it incorporates MoCo to optimize Bert training, enhancing the differentiation of semantic representations and improving the accuracy of paper acceptance predictions. Empirical evaluations demonstrate that LMCBert achieves effective performance on the evaluation dataset, supporting the validity of the proposed approach. The code and data used in this article are publicly available at https://github.com/iioSnail/LMCBert.

Abstract:
Human-robot interaction (HRI) is a crucial component in the field of robotics, and enabling faster response, higher accuracy, as well as smaller human effort, is essential to improve the efficiency, robustness, and applicability of HRI-driven tasks. In this article, we develop a novel neuroadaptive admittance control with human motion intention (HMI) estimation and output error constraint for natural and stable interaction. First, the interaction force information of the robot is utilized to predict the HMI and the stiffness in the admittance model is dynamically updated based on surface electromyography (sEMG) signals of the human upper limb to achieve human-like compliance. Then, based on the designed error transformation mechanism, an innovative prescribed performance control (PPC) is proposed that allows the trajectory error to converge to the given constraint range within a predefined time for any bounded initial conditions, thus enabling the robot to maintain a comprehensive performance of moving in the desired direction as guided by the human. Also, an adaptive neural network (NN) is employed to compensate for the uncertainty of robotics systems to improve the tracking accuracy further. According to the Lyapunov stability analysis criterion, our approach ensures that all states of the closed-loop system remain globally uniformly ultimately bounded. Finally, a series of real-world robot experiments demonstrate the effectiveness of the proposed framework.

Affiliations: School of Automation, Nanjing University of Information Science and Technology, Nanjing, China; School of Automation, Chongqing University, Chongqing, China; State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, China; Department of Computer Science, University of Liverpool, Liverpool, U.K.; Computer Science and Engineering College, South China University of Technology, Guangzhou, China

Abstract:
This article proposes a spatial learning control system for robots to achieve a desired behavior during interacting with unknown environments. In contacting with the environment, the force is estimated by a force observer, so sensing devices are not required. Motivated by the human interaction versatility, the reference trajectory of the robot is updating with a learning law such that the interacting force can be maintained at a desired level. Compared with the trajectory iteration algorithm based on time domain, which requires maintaining a fixed motion speed for each iteration, the proposed method can remove this limitation and have better feasibility. The adaptive controller with neural networks can compensate the uncertain dynamics of the system and ensure the control accuracy. Through Lyapunov’s theory, the system is proved to be stable, and all the states are bounded. Comparative simulations and experiments are conducted on a robot platform to verify the effectiveness of the proposed method.

Abstract:
This article aims at the leader-following secure consensus problem of nonlinear multiagent systems (MASs) with switching topologies, where the agents are not only suffered from the aperiodic malicious denial-of-service (DoS) attacks but also affected by instantaneous disturbance from the external environment. Due to the existing challenge of instantaneous disturbance about occurrence time being unknown, the impulsive-based switching network structure is put forward to tackle the impact of external instantaneous disturbance on MASs. Then, a novel hybrid event-triggered and impulsive control protocol is developed to guarantee that nonlinear MASs can resist DoS attacks and achieve the consensus control objective. Contrasted with the methods of continuous control, the developed hybrid event-triggered and impulsive control protocol using the discontinuous sampled state has certain merits saving control resources. Based on the Lyapunov theory, the stability of the closed-loop system is proven, and the Zeno behavior can be excluded successfully. An example is supplied to elicit the availability of the presented methodology.

Abstract:
This article simultaneously addresses the dual-rate and view constraints issues for the image-based visual servoing (IBVS) system of robot manipulators. Considering the low sampling bandwidth of the camera, potentially diminishing the efficiency of the robotic controller in updating low-level servoing control commands, a predictive observer (PO) is initially designed to forecast the system output during the high-level sampling intervals. Moreover, by leveraging a mixture of soft-sensing and real-measured signals, a dual-rate integral-based prescribed performance control (DRIPPC) approach is devised. The benefit lies in that the proposed control method samples the low-frequency state signal while generating a relatively high-frequency control action, ensuring rapid response of the robot manipulator while maintaining strict adherence to field-of-view (FOV) constraints. Finally, the effectiveness of the proposed control approach is validated through a series of experiments conducted on a Universal Robots 5 (UR5) manipulator.

Abstract:
This study seeks to develop a constructive approach that settles the optimal control issue for nonlinear systems with known time delays. The feedback system, which depends on the state and control input, is built to identify the actual control rule utilizing the backstepping integral technique. Optimal control of the augmented system established on parallel control delivers a solution for nonlinear time-delayed systems. At the cost of the modified L_2 gain condition, the value function is specified in terms of the state and input delays, transforming the optimal control issue into a minimax task. Then, the critic-actor framework is employed to reconstruct the cost function and control rule while maintaining the persistently exciting (PE) condition so that the online optimal control algorithm is investigated. In addition, the Lyapunov proof discusses the system’s stability. Ultimately, the remarkable properties become visible through experimental findings.

Abstract:
This article studies the secure leader-follower consensus of nonlinear multiagent systems under multimode Denial-of-Service attacks. According to the topology characteristics after attacks, three kinds of different attack modes are introduced. Moreover, the common zero-topology attack is encompassed within the multimode attacks considered in this article. Due to the difficulty of directly obtaining the state of the system in some circumstances, a state observer is designed utilizing localized output information of the MASs to estimate the state. By applying suitable observer-based controller and Lyapunov method, some sufficient conditions are presented to ensure the observer-based secure consensus of the MASs under multimode DoS attacks. Furthermore, the obtained results can be extended to linear MASs under DoS attacks. Finally, two practical examples on power system are provided to demonstrate the validity of the derived theoretical results.

Abstract:
The global asymptotic stabilization control algorithm is proposed for interconnected nonlinear systems utilizing intermittent output feedback. A dynamic double side event-triggered mechanism (ETM) is designed to make the available output intermittent, reducing the frequency of signal updates. In this case, we relax some restrictive conditions from related studies. The considered system features unknown time-varying parameters, mismatched uncertainties, and uncertain functions that satisfy nonlinear growth conditions. These complexities render the standard backstepping recursive design scheme inapplicable, as the derivative of the virtual controller does not exist. To address the intermittent output feedback problem, we introduce a novel dynamic backstepping control method. First, we establish a dynamic gain observer using the triggered output signals to reconstruct the unmeasurable state variables. Next, the concept of dynamic gain is introduced through a coordinate transformation, with its derivative employed to offset discontinuous terms, which solves the challenges in recursive backstepping design caused by intermittent output and regulates that the state variable converges asymptotically to the origin in the global sense. Final, the simulation example is proposed to show the validity of the developed algorithm.

Abstract:
The coordination of distributed energy resources (DERs) within virtual power plants (VPPs) is expected to generate significant economic benefits and enhance the operational stability of modern power systems. However, achieving massive coordination of heterogeneous and uncertain DERs remains a challenge in current research. To address this issue, this article proposes a novel bi-level optimization approach based on mean-field reinforcement learning (MFRL) to enable the coordination of massive DERs in VPPs. The problem is decomposed into multiple subproblems: the upper-level subproblem models power dispatch among integrated energy systems (IESs) in response to coordinated demand, while a series of lower-level subproblems determine the operational schemes of DERs within individual IESs. Considering the large decision space, an MFRL algorithm with fast Shapley credit allocation is developed to efficiently solve the upper-level optimization. Meanwhile, the lower-level subproblems are formulated as small-scale mixed-integer linear programming (MILP) problems, addressing the difficulties caused by IES heterogeneity in applying mean-field approximation. Simulation results show that the proposed approach significantly improves convergence speed and reduces the global cost of VPP operation, especially in massive-scale scenarios. In test scenarios ranging from 10 to 500 agents, the proposed bi-level optimization approach improves the objective by 4.8%–26.6%, compared to the advanced baseline method.

Abstract:
In this article, the fuzzy adaptive event-triggered consensus control issue is addressed for nonlinear multiagent systems (MASs) under output constraints and Denial of Service (DoS) attacks. First of all, fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear functions. Then, a novel switching observer is constructed to observe the leader’s state and handle DoS attacks. With the help of the exponent-dependent barrier Lyapunov functions (BLFs), the system output can be constrained within a preset region. Based on dynamic surface control (DSC) technique, the issue of computational complexity can be effectively avoided. Combining the designed switching observer and relative thresholds, a robust fuzzy adaptive event-triggered controller is developed, which ensures that the consensus output tracking errors converge to a small neighborhood of zero, and all signals in the closed-loop system keep bounded. Moreover, Zeno behavior can be avoided. Ultimately, simulation results are given to validate the feasibility and effectiveness of the proposed control strategy and theory.

Abstract:
This article investigates the tracking control problem of heterogeneous multiagent systems (MASs) with intrinsic nonlinear dynamics in noisy and time-delayed environments. First, a stability criterion for nonlinear stochastic delay systems with multiplicative noise and time-varying delay is proposed by applying the appropriate Lyapunov-Krasovskii functional. Then, based on the proposed stability criterion, sufficient conditions are derived for mean square (m.s.) and almost sure (a.s.) tracking of heterogeneous MASs with intrinsic nonlinear dynamics. Afterward, the above sufficient conditions further degenerate to integrator heterogeneous MASs. In particular, when the time-delay vanishes, the explicit conditions are obtained for the integrator heterogeneous MASs in the form of scalar inequalities, which can intuitively reflect the relationship between noise intensity and control gains. Finally, simulation results validate the effectiveness of the proposed control protocol.

Affiliations: School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong, China; School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China; School of Control Science and Engineering, Tiangong University, Tianjin, China; Faculty of Information Technology, Beijing University of Technology, Beijing, China; College of Machinery and Automation, Weifang University, Weifang, Shandong, China; School of Electrical Engineering, Sichuan University, Chengdu, China

Abstract:
This article primarily studies the adaptive output-feedback consensus tracking control issue for nonlinear multiagent systems (MASs) with unknown control directions against deception attacks. First, a composite observer combining the state observer and the disturbance observer is developed to concurrently estimate the states of confronting deception attacks and unmeasurable disturbances. Moreover, to resolve the unknown gains resulting from deception attacks, the adaptive attack compensator is proposed. Furthermore, in view of the logarithm Lyapunov function in the final step of the design process and the intelligent approximation technique, a new composite-observer-based adaptive consensus tracking control strategy is constructed. The suggested control strategy ensures the boundedness of all the closed-loop signals while also achieving synchronous tracking of the leader’s output by the followers. Last but not least, the effectiveness of the suggested control strategy is validated through two simulation examples.

Abstract:
A primary challenge in robotic tool use is achieving precise manipulation with dexterous robotic hands to mimic human actions. It requires understanding human tool use and allocating specific functions to each robotic finger for fine control. Existing work has primarily focused on the overall grasping capabilities of robotic hands, often neglecting the functional allocation among individual fingers during object interaction. In response to this, we introduce a semantic knowledge-driven approach to distribute functions among fingers for tool manipulation. Central to this approach is the finger-to-function (F2F) knowledge graph, which captures human expertise in tool use and establishes relationships between tool attributes, tasks, and manipulation elements, including functional fingers, components, required force, and gestures. We also develop a manipulation element-oriented prediction algorithm using knowledge graph semantic embedding, enhancing the prediction of manipulation elements’ speed and accuracy. Additionally, we propose the functionality-integrated adaptive force feedback manipulation (FAFM) module, which integrates manipulation elements with adaptive force feedback to achieve precise finger-level control. Our framework does not rely on extensive annotated data for supervision but utilizes semantic constraints from F2F to guide tool manipulation. The proposed method demonstrates superior performance and generalizability in real-world scenarios, achieving an 8% higher success rate in grasping and manipulation of representative tool instances compared to the existing state-of-the-art methods. The dataset and code are available at https://github.com/yangfan293/F2F.

Abstract:
This article investigates the observer-based human-in-the-loop (HiTL) optimal output cluster synchronization control problem for nonlinear multiagent systems (MASs). First, the leader is designed to be nonautonomous, with the unknown time-varying input monitored by the human operator directly. To address the problem that leader’s output is not available to each follower, an observer is designed. This observer features practical prescribed-time convergence, and independence of prior knowledge of leader’s input. Then, an augmented system consisting of observer dynamics and follower dynamics is constructed and a cost function is formulated. Accordingly, the HiTL optimal output cluster synchronization control problem is transformed into a solution to the Hamilton-Jacobian–Bellman equation (HJBE). Subsequently, the off-policy reinforcement learning algorithm is utilized to learn the solution to HJBE without complete knowledge of the system dynamics. To alleviate computational burden, the single critic neural network (NN) is employed for the algorithm implementation, with the least square method applied for training the NN weights. Finally, the simulation results are presented to verify the validity of the designed control scheme.

Abstract:
Actuator and sensor faults are among the most common factors affecting the stability of multiagent systems (MASs). This article proposes a dynamic event-triggered fault-tolerant control (FTC) algorithm based on descriptor sliding-mode observers to address actuator and sensor faults in MASs. First, the MAS dynamics are reformulated into a descriptor form, enabling an observer to simultaneously achieve state estimation and fault diagnosis. Using the estimation results, an adaptive FTC algorithm is developed to maintain the stability of MASs in the presence of concurrent faults, with control gains updated based on the observer consensus error. A dynamic event-triggered mechanism is incorporated to manage data transmission and update neighboring agents’ information for the controller, thereby reducing communication overhead. Finally, a numerical simulation involving multiple quadrotors is conducted to validate the effectiveness of the proposed method.

Affiliations: College of Marine Electrical Engineering, Dalian Maritime University, Dalian, Liaoning, China; Navigation College, Dalian Maritime University, Dalian, Liaoning, China; School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China; Department of Computer Science, Aalborg University, Aalborg, Denmark

Abstract:
This article investigates the distributed resilient energy management (EM) strategy for the seaport microgrid under stealthy attacks. First, based on an analysis of seaport microgrid characteristics, we construct an EM model that aims at minimizing both operating cost and security defense resource (SDR) cost. Second, we present a distributed, resilient strategy by defining node security levels and establishing dynamic security intervals. We prove that the gap between the obtained feasible solution and the optimal one is bounded. The designed strategy is capable of tolerating the effect of the unlimited number of stealthy attacked nodes on the seaport microgrid. In addition, given the limited SDRs for the resilient EM of the island seaport microgrid, a distributed mechanism for searching the minimum security connected dominating set (MSCDS) is proposed to minimize the size of trusted nodes. Finally, simulation results demonstrate the effectiveness of the proposed strategy. Note to Practitioners: This article addresses the vulnerability of the seaport microgrid, a critical issue that impacts EM and disrupts seaport operations. Current approaches to seaport EM do not account for potential attacks. Meanwhile, existing methods for attack resilience often overlook the costs of security resources. We propose a new approach for the distributed and resilient EM of the island seaport microgrid. Secure operation is achieved by protecting the fewest trusted nodes, thereby conserving SDRs. We then show how this algorithm (searching the MSCDS) can be efficiently designed. Preliminary simulations indicate its feasibility, though it has yet to be tested in a production environment. Future research will focus on designing trusted nodes within dynamic topologies.

Abstract:
Robust adaptive control of fully actuated systems (FASs) with unknown parameters, perturbed input matrices and nonlinear uncertainties is considered. Two novel robust adaptive controllers are developed for the two cases where the unknown parameters are time-varying and constant. For both cases, different from the existing results on FASs with unknown parameters, this article allows the existence of a perturbation matrix that satisfies a certain assumption in the input matrix. Furthermore, for the case of time-varying parameters, under relaxed system assumptions, the global boundedness of the state variables and the estimation error is guaranteed. For the case of constant parameters, under certain assumptions on the nonlinear uncertainty and known nonlinear functions, no pre-estimation of the unknown parameters is required and the state variables globally asymptotically converge to the origin. In addition, a parallel extension of the proposed methods to the generalized multiorder FAS case is also given. The effectiveness of the developed methods is shown by the successful application in the control of electromechanical systems.

Abstract:
In the past years, latent variable models have played an important role in various industrial AI systems, among which quality prediction is one of the most representative applications. Inspired by the idea of deep learning, those basic latent variable models have been extended to deep forms, based on which the quality prediction performance has been significantly improved. However, different latent variable models have their own strengths and weaknesses, a model works well under one scenario might not provide satisfactory performance under another. The motivation of this article is based on the viewpoint of information fusion and ensemble learning for heterogeneous latent variable models. Particularly, a collaborative deep learning and model fusion framework is formulated for the purpose of industrial quality prediction. In the first stage of the framework, collaborative layer-by-layer feature extractions are implemented among different latent variable models, through which different patterns of latent variables are identified in different layers of the deep model. Then, in the second stage, an ensemble regression modeling strategy is proposed to fuse the quality prediction results from different latent variable models, which is based on a well-designed data description method. Two real industrial examples are used for performance evaluation of the proposed method, based on which we can observe that information fusions in terms of both collaborative layer-by-layer feature extraction and heterogeneous model ensemble have positive effects in improving prediction accuracy and stability.

Abstract:
This article develops a novel robust control methodology for nonlinear multiagent systems (MASs) to address the time-varying formation (TVF) problem. The methodology offers a concise yet efficacious technique based on the Lyapunov functional for more general one-sided Lipschitz (OSL) nonlinear MASs with external disturbances and time-varying delays. Note that most existing TVF controllers can only achieve formation targets under small delays, which significantly limits their performance. The proposed TVF control method in this article demonstrates remarkable robustness by effectively accommodating larger delays and additionally mitigating the impact of disturbances on MASs. On this basis, a robust TVF controller optimization scheme of MASs combined with the particle swarm optimization (PSO) algorithm is proposed. Comparing the optimized results with those under conventional control methods, it has been proved that optimized controller has obvious improvement on the formation performance of MASs. Finally, the feasibility and the superiority of the developed TVF control approach are validated by a simulation of an autonomous aerial vehicle swarm system (AAVSS) composed of six AAVs.

Abstract:
This article addresses challenges of autonomous decisions making influenced by discrete system states, underlying continuous dynamics, and evolving operational environments. A comprehensive framework is proposed, encompassing new modeling, problem formulation, control design, and stability analysis. The framework integrates continuous system dynamics, used for low-level control, with discrete Markov decision processes (MDP) for high-level decision making. To capture the interactions between these domains, the decision-making system is modeled as a hybrid system consisting of a controlled MDP and autonomous (uncontrolled) continuous dynamics, collectively referred to as the hybrid Markov decision process (HMDP). The design focuses on ensuring safety and optimality by accounting for both discrete and continuous state variables across different levels. With the help of the model predictive control (MPC) concept, a decision-making scheme is developed for the hybrid model, with guarantees for recursive feasibility and stability. The proposed framework is applied to the autonomous lane changing system for intelligent vehicles, and simulation shows its capability to handle diverse behaviors in dynamic and complex environments.

Abstract:
The main objective of this study is to develop an intelligent, resilient event-triggered control method for fractional-order multiagent networked systems (FOMANSs) using reinforcement learning (RL) to address challenges resulting from unknown dynamics, actuator faults, and denial-of-service (DoS) attacks. First, the challenge of unknown system dynamics within their environment must be addressed to achieve desired system stability in the face of unknown dynamics or to optimize consensus in FOMANSs. To address this problem, an adaptive learning law is implemented to handle unknown nonlinear dynamics, parameterized by a neural network, which establishes weights for a fuzzy logic system utilized in cooperative tracking protocols. A novel distributed control policy facilitates signal sharing through RL among agents, reducing error variables through learning. Moreover, this study combines an RL algorithm with the sliding mode control strategy to optimize the parameterization of the distributed control protocol, thereby eliminating its constraints on initial conditions. Second, realizing that DoS attacks typically make the actuator signal inaccessible for distributed control protocols, an innovative intelligent dual-event-triggered control strategy is formulated to reduce the effects of DoS attacks. By coordinating nested event triggers across various channels, the distributed control input is protected from incorrect signals from DoS attacks, thus ensuring its resilience. To address this problem, an intelligent security dual-event-triggered control protocol guarantees Mittag-Leffler stability of the closed-loop system and ensures effective sliding motion conditions. This distributed control protocol ensures robust tracking of control tasks and mitigates “Zeno behavior” during event triggering. The proposed control strategy is validated using a single-link flexible-joint robotic manipulator system.

Affiliations: College of Information Engineering, Yangzhou University, Yangzhou, Jiangsu, China; School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China; School of Automation Science and Electrical Engineering, Beihang University, Beijing, China; Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, U.K.; School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia

Abstract:
This article investigates the state estimation and security control problem for discrete-time dual-rate cyber–physical systems (CPSs) under denial-of-service (DoS) attacks. The asynchrony predicament between different signals of dual-rate CPSs, exacerbated by the impact of cyber attacks on the sensor-to-controller channel, substantially increases the complexity of state estimation and control processes. Based on the signal-to-interference-plus-noise ratio and two-channel probability descriptions, an improved sample-and-hold (SAH) estimator is applied to dual-rate CPSs, ensuring favorable state estimates while enduring low-frequency sampling and DoS attacks. Furthermore, to solve the performance degradation problem posed by the SAH algorithm, an alternating-sampling-prediction (ASP)-based estimation method is proposed. At each fast-update moment, the predictor generates virtual outputs. The estimator can reconstruct complete state information by alternately using incomplete sampling data and iterative predictive information. Compared with the SAH method, the proposed ASP-based approach significantly enhances the control performance of dual-rate CPSs. Building on two valid estimation methods, the corresponding security control inputs are designed, guaranteeing both ideal control performance and resilience against attacks. Using convex optimization analysis, both estimator and controller gains are calculated to realize the stochastic stability of closed-loop dual-rate CPSs. Finally, the effectiveness and intercomparisons of the two estimation methods are shown by simulating a satellite yaw-angle control system and a quadrotor landing control experiment.

Abstract:
Considering the influence of subsystem state interconnection in large-scale systems, the existing integrated design methods of data-driven fault detection (FD) and fault-tolerant control (FTC) that follow centralized architecture cannot be applied in distributed scenarios. To address this problem, this article proposes a subspace predictor-assisted framework to perform the data-driven integrated design of FD and FTC for large-scale systems. FD and FTC are organically combined through a subspace predictor framework. For the subspace predictor designed for each subsystem, no global input and output (I/O) data information is required but only the I/O data of the local and neighboring subsystems is used, thus realizing a distributed design. In addition, the integrated architecture of FD and FTC does not need any large-scale system mechanism information, and is completely driven by process I/O data. Two case studies including a numerical simulation example and cascaded continuously stirred-tank reactor verify the feasibility and effectiveness of the proposed data-driven distributed FD and FTC method.

Abstract:
Current biological behavior models only take the external environment information as the basis for decision-making, ignoring the internal emotional state information. A memristor-based cerebellar model articulation controller (CMAC) neural network circuit of artificial fish behavioral decision is designed, and fuzzy emotion is taken into account. The designed circuit is mainly composed of voltage selection modules, fuzzy processing modules, synaptic neuron modules, eigen quantity modules and feedback modules. CMAC neural network is used as learning criteria and the learning subspace voltage with emotional generalization properties outputs to synaptic neural module. By utilizing the nonvolatility and thresholding properties of the memristor, the weights in the neural network are changed to enable the artificial fish to perform primary and secondary learning under specific emotional voltages. The feasibility of the above circuit is verified by PSpice simulation software. The artificial life and biological intelligence behavior are integrated by the memristor-based CMAC neural network circuit. It provides a reliable theory and basis for the emotional behavior of bionic robots.

Abstract:
This study presents a reinforcement learning (RL)-based adaptive fuzzy event-triggered optimized tracking control strategy for slowly switched nonlinear systems with stochastic disturbances in the prescribed set-time performance. The designed optimized event-triggered mechanism for the subsystems effectively solves the asynchronous switching problem with no limit on the maximum asynchronous time. Moreover, the tracking performance of system can be optimized significantly using an RL strategy. Using the lemma proposed in the study (Lemma 3) and the normalized function, it is shown that under the performance constraint approach, the selection of the performance function is consistent with the control protocols. By adopting the modified admissible edge-dependent average dwell time method and the optimal controller, the boundedness of closed-loop system signals is proved, and the Zeno phenomenon does not occur. Finally, the superiority of the optimized strategy is verified using numerical simulations and a practical single-link manipulator.

Abstract:
This article investigates the adaptive resilient flexible output containment (FOC) control problem for semi-Markov jump fully heterogeneous multiagent systems (FHMASs) under random switching topologies and denial-of-service (DoS) attacks. In contrast to most existing containment control results, the proposed control strategy can address the challenges posed by the full heterogeneity of multiagent systems (MASs), particularly when multiple leaders exhibit different system dynamics. To better reflect real-world MASs and communication networks, multiple asynchronous semi-Markov chains are employed for the first time to capture system parameter variations and communication topology switching, incorporating generally uncertain transition rates (TRs). In order to deal with this problem, a novel adaptive observer-based FOC control framework is developed. First, by introducing an adaptive gain, the adaptive resilient observers can observe leaders’ states without prior knowledge of global topology information and TRs, while resisting the impacts of random switching topologies and DoS attacks. Then, a dynamic output feedback controller is designed to ensure the achievement of FOC. Notably, the containment coefficients in the controller design are no longer tied to the Laplacian matrix and can be flexibly predefined to align with specific task requirements. Furthermore, the linear matrix inequalities (LMIs) to obtain estimator gain matrices and controller gain matrices are derived for the case of generally uncertain TRs, respectively. Finally, the effectiveness of the theoretical method is demonstrated through the simulation.

Affiliations: College of Mechatronics and Control Engineering and the College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China; College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, Guangdong, China; College of Mechatronics and Control Engineering, and the Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen), Shenzhen University, Shenzhen, Guangdong, China

Abstract:
This article presents an indirect adaptive interval type-3 (IT3) tracking fuzzy control for a class of unknown nonaffine nonlinear discrete-time networked control systems (NCSs) with denial-of-service (DoS) attacks. To mitigate the adverse effects of attacks, a novel two-mode attack compensator, incorporating an IT3 fuzzy model (IT3FM), is proposed to handle the nonlinear dynamics and uncertainties of NCSs by estimating the unavailable system output during active attacks. Then, an updating algorithm for parameter adjustment, guaranteed to converge via Lyapunov theory, is presented. Subsequently, an indirect IT3 fuzzy controller (IT3FC) is developed to ensure robust tracking performance. Theoretical analysis of the proposed control method shows the boundedness of the tracking error. To mitigate the computational complexity of the proposed control method, we use the same IT3 fuzzy sets for the IT3FM and IT3FC. We also employ a direct defuzzification method within the type-reduction process, bypassing the iterative Karnik–Mendel approach. Eventually, three nonlinear NCSs are given to demonstrate the robustness of the proposed control method.

Abstract:
This adaptive formation tracking control is investigated for high-order fully actuated (HOFA) multiagent systems (MASs) with unknown nonlinear dynamics and full-state constraints. To tackle the dynamic uncertainty while maintaining safety constraints on system state, a novel hierarchical formation control framework is presented. First, a nonlinear mapping function (NMF) is introduced, which, by integrating HOFA theory, effectively transforms the original constrained system into an unconstrained HOFA tracking error model, thus removing the feasibility conditions typically required in traditional barrier Lyapunov function methods. Subsequently, distributed observers are designed in the upper layer for followers to estimate the leader’s information, while an adaptive formation controller is directly constructed for each follower in the lower layer using the fully actuated theory. Particularly, the neural network approximators are used to learn unknown nonlinear dynamics. By employing Lyapunov stability theory, the designed formation controller guarantees that the entire state stays within the specified constraint set while also ensuring the desired formation performance. Finally, the developed formation control algorithm is proven effective by applying it to a network of multiple robotic arm systems.

Abstract:
This article presents the implementation of an asynchronous \mathcal H_\infty filter for 2-D discrete-time periodic Markov jump systems with multiplicative noise. The study addresses the issue of missing measurements, which is treated as a stochastic variable following the Bernoulli random distribution. To account for the nonsynchronous phenomenon between the system and filter due to the loss of mode information, the periodic hidden Markov model (HMM) is introduced. Moreover, the transition rate matrix of the system and the conditional probability matrix of the filter are general, allowing for transition probabilities in fully known, partly known or fully unknown cases. The objective is to implement an asynchronous \mathcal H_\infty filter based on periodic HMM that guarantees the filtering error system is mean-square asymptotically stable while maintaining a specified \mathcal H_\infty disturbance attenuation performance. In the light of linear matrix inequalities, the article offers sufficient conditions for filter to exist and provides a solution for the parameters of the filter. Ultimately, a demonstration of the validity of the presented design technique is provided through the Darboux equation.

Abstract:
In the realm of control theory, the complex task of long-term time series prediction has been profoundly transformed by the confluence of advancements in computer technology and machine learning. However, the application of fuzzy information granularity remains a significant challenge, primarily due to the potential for substantial data distortion. To address this limitation, we propose an innovative long-term prediction model based on granularity time series, which integrates l_1 -trend filter decomposition and integrated learning. The core of our model lies in a novel modal decomposition method that utilizes l_1 -trend filters and a validity function to meticulously extract valuable insights from the original time series, thereby enhancing the precision of data analysis while preserving the integrity of the original data. Furthermore, we introduce a groundbreaking formula to measure the similarity of fuzzy information granularity, classifying time series components into three distinct categories: trend, period, and noise. By applying distinct prediction strategies to each category, we construct an integrated learning model that leverages the strengths of each component. At the heart of our model is a multilinear information granularity prediction approach, which is based on trend time windows and utilizes the newly developed similarity measure. This method not only maintains the integrity of the original time series but also offers a more accurate representation of the similarity between information grains. Empirical results from publicly available datasets validate the superior performance of our proposed prediction model, demonstrating its potential to significantly enhance long-term time series prediction accuracy.

Affiliations: School of Aeronautics and Astronautics and the Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China; Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China; School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu, China; School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Ave, Singapore; School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

Abstract:
This article presents a novel event-triggered sliding-mode control (ET-SMC) strategy for impulsive nonlinear systems (INS) in the presence of matched disturbances. Most of the existing sliding mode control (SMC) strategies work well when the system continually converges toward a predefined sliding surface, but have been proven to be inapplicable for discontinuous systems subjected to impulsive disturbances. Consequently, it becomes crucial and imperative to develop SMC strategies tailored for discontinuous dynamics affected by impulsive phenomena. Leveraging the event-triggering mechanism with a time-varying threshold and incorporating piecewise Lyapunov function techniques, we propose a novel ET-SMC strategy. It is proved that the proposed control strategy can effectively avoid Zeno behavior and ensure the finite-time stability (FTS) of the considered system via finite-time control theory and innovative impulsive estimation schemes. Furthermore, our results quantitatively measure changes in convergence rate under varying impulsive conditions, which provides valuable insights for performance analysis and the design of SMC strategies in such scenarios. As a specific application, we apply the proposed ET-SMC strategy to coupled reaction-diffusion neural networks (RDNNs) in the presence of both cyber-attacks and matched disturbances. Finally, we present several simulation examples to illustrate the effectiveness and practical applicability of the analytical methods presented in this article.

Abstract:
This article delves into the security control challenges posed by networked switched systems (NSSs) in the face of deception attacks and denial-of-service (DoS) attacks. It takes an active perspective and proposes an enhanced two-tier Stackelberg game approach to influence the actions of the deception attacker, controller, and DoS attacker. By formulating the cost functions for these three participants, it seeks to derive the optimal solution and the interplay between their respective optimal strategies. Meanwhile, two complicated situations are taken into account: The asynchronous behavior between the system and controller is caused by deception attacks tampering with switching signals; The open-loop operation of the system is caused by DoS attacks blocking the output of the controller. In the joint design of the two-tier multiattacker Stackelberg game approach and average dwell time technique, the system’s mean square exponential stability is guaranteed while revealing the quantitative relationship between the deception attack, DoS attack, and asynchronous switching behaviors. Finally, a simulation result of a switched RLC circuit confirms the validity of the obtained active security control methodology.

Abstract:
The artificial potential function (APF) is a widely applied algorithm in collision-free formation control in multiagent systems (MASs). However, it suffers from oscillations and acceleration surges, particularly when the current formation and the desired one conflict. To address this problem and enhance collision-free formation control in MAS, this article introduces the time-derivative of APFs. This approach unifies attractive and repulsive APFs. The gradients of the APFs transform potential and kinetic energy, and the time-derivative of the APF gradients serve as damping terms to dissipate energy. This article discusses the general properties of APFs and introduces a time-variant formation tracking scheme that encompasses existing algorithms as specific instances. Then, a collision-free formation control algorithm is presented. This article gives proof of its Lyapunov stability and collision avoidance ability, followed by a maneuverability analysis from the geometry perspective. By incorporating the time-derivatives of repulsive APF gradients as damping terms, the proposed method mitigates oscillations and acceleration surges caused by conflicting attractive and repulsive effects.

Abstract:
Learning differential evolution (DE) algorithms are widely adopted to address flexible job-shop scheduling problems (FJSPs) because of the optimization ability. However, traditional learning DEs are not sufficient to develop the feature information of the problem. In this article, a feature-based learning DE algorithm (FLDE) is proposed to address FJSP considering worker health. Occupational repetitive actions index (OCRA) is an indicator that describes the degree of worker fatigue. The OCRA is utilized to ensure the feasibility of scheduling solutions generated by FLDE. A feature-based decision model (FDM) is designed to select the appropriate optimization operator for a scheduling solution. A critical operation search method is introduced to extract feature information from the scheduling solution. Experimental results reveal that FDM is critical to improving the local optimization ability of FLDE, and that FLDE outperforms the comparison algorithms on 40 problem instances.

Abstract:
This article mainly investigates the problem of vibration suppression and angle cooperative tracking control of a multiple flexible manipulators described by partial differential equations (PDEs) with input quantization, actuator failures, and unmodeled system dynamics. An intermediate control law is designed, and a smooth function with a positive integrable time-varying function is introduced. Besides, a new smooth function is constructed in the control law to handle the influence of quantization and actuator faults. Under the designed controller, the angles of all flexible manipulators can reach consensus through mutual communication, and the elastic deformation of each flexible manipulator can also be suppressed. Furthermore, the asymptotic stability of a closed-loop system is realized based on the Lyapunov function. Finally, numerical simulation validates the effectiveness of the method.

Abstract:
Due to the unreliable communication network, networked control systems (NCSs) are often subjected to communication imperfections including stochastic sampling intervals (SSIs) and packet dropouts, which can lead to degradation of control performance and even jeopardize the stability of the NCSs. In this article, the data-driven model predictive control (DMPC) strategy is proposed to stabilize a class of unknown nonlinear NCSs with SSIs and successive packet dropouts (SPDs). First, an equivalent stochastic sampling model is constructed by capturing the randomness of both SSIs and SPDs, which can determine the probability information of the equivalent sampling interval between consecutive no-packet -dropout update instants. Subsequently, a multimodel predictive structure is designed based on the possible lengths of the sampling interval in the equivalent stochastic sampling model, which can provide predictive outputs for the subsequent controller design. Furthermore, to reduce the computational burden associated with the prolongation of the prediction horizon in the multimodel predictive structure, a prediction data interpolation algorithm based on the Lagrange interpolation polynomial is introduced. Additionally, to ensure the accuracy of interpolation prediction output, an adaptive mechanism for updating interpolation nodes is designed to dynamically adjust the number and position of these nodes. Finally, a cost function that relies on the expectation of the predictive output is designed and solved to achieve stable tracking control of the considered NCSs. The stability of the DMPC strategy is demonstrated in detail. Numerical examples and industrial applications for the wastewater treatment process (WWTP) demonstrate that DMPC can obtain satisfied control performance.

Abstract:
A neural network adaptive quantized predefined-time control problem is studied for switching stochastic nonlinear systems with full-state error constraints under arbitrary switching. Unlike previous research on rapid convergence, the predefined-time stability criteria are introduced and established for stochastic nonlinear systems, ensuring the stabilization of the control system within a specified time frame. The chattering issue is avoided and it is split into two limited nonlinear functions using a hysteresis quantizer. To address the full-state error constraint problem, a universal barrier Lyapunov function is presented. The common Lyapunov function approach is used to demonstrate the stability of controlled systems. The results demonstrate that the proposed control method ensures all closed-loop signals are probabilistically practically predefined time-stabilized (PPTS), with the system output closely tracking the specified reference signal. Finally, simulated examples validate the effectiveness of the suggested control technique.

Abstract:
This article studies the dynamic event-triggered adaptive finite-time tracking control issue for a robotic manipulator (RM) system with disturbances. First, a new global prescribed performance function (PPF) is designed based on a scaling function such that the tracking error evolves within the constrained bounds and the restriction related to the initial conditions is removed. Then, the finite-time command filter (FTCF) is used to avoid the direct derivations of virtual controllers and the singularity issue of the conventional backstepping technique. Moreover, the filtering errors caused by the FTCF are removed by the designed error compensation mechanism. A novel dynamic event-triggered mechanism (DETM) using the dynamic auxiliary variable is designed to save communication resources. The proposed control scheme can guarantee that all signals of the RM are globally bounded within a finite time, and the tracking error can asymptotically reach zero. Finally, a simulation example and several comparative simulations show the validity of the proposed scheme.

Abstract:
In this article, the problem of ultimately bounded state estimation is investigated for discrete-time multirate singularly perturbed complex networks under the bit rate constraints, where the sensor sampling period is allowed to differ from the updating period of the networks. The facilitation of communication between sensors and the remote estimator through wireless networks, which are subject to bit rate constraints, involves the use of a coding-decoding mechanism. For efficient estimation in the presence of periodic measurements, a specialized impulsive estimation method is developed, which aims to carry out impulsive corrections precisely at the instants when the measurement signal is received by the estimator. By employing the iteration analysis method under the impulsive mechanism, a sufficient condition is established that ensures the exponential boundedness of the estimation error dynamics. Furthermore, an optimization algorithm is introduced for addressing the challenges related to bit rate allocation and the design of desired estimator gains. Within the presented theoretical framework, the correlation between estimation performance and bit rate allocation is elucidated. Finally, a simulation example is provided to demonstrate the validity of the proposed estimation approach.

Abstract:
This article investigates the leader-follower formation control of flight vehicles subject to speed and control acceleration constraints. The objective of the flight vehicles is to track a virtual leader in a nominal configuration, while the speeds and control accelerations of the flight vehicles are restricted within certain ranges. A distributed extended state observer (DESO) featuring practical predefined-time convergence is proposed for the followers to estimate the leader’s position and velocity. Then, an adaptive finite-time position tracking control law is developed so that the followers form the expected formation by tracking the expected positions related to the estimation of the virtual leader’s information and the nominal configuration. The speed constraint is satisfied by leveraging a transformation based on the inverse hyperbolic tangent function, while an adaptive scheme exploiting the integral barrier Lyapunov function (IBLF) is proposed to address the control acceleration constraints. Numerical simulations are conducted to validate the proposed method.

Abstract:
Due to the large number of monitoring variables in engineering, it is extremely to reflect fault information in machinery and equipment with a single channel signal, which poses a significant challenge for fault diagnosis. Furthermore, most existing intelligent recognition methods rely on label samples, yet ignore the high cost of label interpretation in practical engineering. In this work, a novel multivariate fusion covariance matrix network (MFCMN) is developed for multichannel fault diagnosis with fewer training samples. First, the collected multichannel signals are separated into mode functions by using cyclic autocorrelation analysis. Thereafter, the acquired mode functions are utilized to construct the multivariate fusion covariance matrix (MFCM), which retains the linkage of signals from different channels. Finally, MFCM is fed into the standard autoencoder to form the MFCMN network, which is applied to implement multichannel fault diagnosis. To assess effectiveness, the MFCMN is compared with the deep residual network (ResNet), convolutional neural network (CNN), long short-term memory (LSTM), and K-nearest neighbor (KNN) in two experimental cases with fewer training samples. The results clarify that the MFCMN offers excellent performance and high accuracy in multichannel fault diagnosis.

Affiliations: Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen, China; Research and Informatization Department, Taian Quality and Technical Inspection and Testing Institute (Taian Special Equipment Inspection and Research Institute), Taian, China; Mechanical and Electrical Department, Shandong Luneng Special Equipment Inspection and Testing Company Ltd., Linyi, China

Abstract:
Underactuated systems are a class of systems in which the number of control inputs is less than the degrees of freedom (DoFs) to be controlled. With the increasing demand for the control performance of underactuated systems, the current research on their optimization of steady-state performance is no longer sufficient. However, owing to limited control inputs, ensuring their transient performance is often difficult. Moreover, some specific composite variables in underactuated systems should be kept within the preset ranges, which poses a significant challenge to collision avoidance safety. In addition, the sensor noises are also an issue that cannot be ignored. To this end, an extended Kalman filtering-based nonlinear model predictive control method for underactuated systems is developed in this article. The key feature of this method is that it simultaneously ensures accurate positioning, multiple constraints, and obstacle avoidance. Specifically, by adding an artificial potential field as an obstacle avoidance penalty term in the cost function and dynamically assigning weight coefficients, efficient collision avoidance control is achieved. Furthermore, it is combined with the extended Kalman filtering and jointly applied to underactuated systems with sensor noises. To the best of our knowledge, it is the first control method that simultaneously considers full-state constraints, specific composite variable constraints, control input and its increment constraints, as well as obstacle avoidance in underactuated systems. The satisfactory control performance of the proposed method is validated by implementing it on two typical underactuated systems, that is, four-DoF overhead cranes and five-DoF tower cranes.

Affiliations: Cyberspace Institute of Advanced Technology and the Huangpu Research School, Guangzhou University, Guangzhou, China; School of Information Science and Engineering, Chengdu University, Chengdu, Sichuan, China; School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China; Faculty of Engineering and Information Technology, Australian AI Institute, University of Technology Sydney, Ultimo, NSW, Australia

Abstract:
This article addresses the challenge of achieving asymptotic stability in nonlinear networked control systems (NNCSs) amid denial-of-service (DoS) attacks, particularly under constrained communication resources. We begin by establishing a practical DoS attack model using the NSL-KDD dataset, which provides a realistic depiction of DoS attack dynamics based on real-world data. We then introduce the iterative shrinkage-thresholding algorithm (ISTA) to supervise the adaptive event-triggered controller (AETC), ensuring that system parameters are adjusted effectively while conserving communication resources. We develop an enhanced data compression mechanism to further mitigate the impact of DoS attacks on communication servers. Additionally, we construct an asymmetric Lyapunov-Krasovskii function (LKF) to rigorously verify the asymptotic stability of NNCSs. Finally, we empirically validate the effectiveness of our proposed AETC using an autonomous vehicle (AV) model.

Abstract:
It is well-known that the stabilizing control of nonholonomic systems is usually divided into two independent steps. This incurs a discontinuous switching control problem when system states start from certain regions. In light of this, we study the continuous and smooth stabilization control issues for nonlinear systems with two piecewise continuous or even smooth stabilization controllers. First, the sufficient conditions for the existence of these controllers are provided. Then, we use the controller extension method to construct some intermediate auxiliary controllers that can link the piecewise controllers given in advance continuously or even smoothly. In addition, by combining model transformation, including the cascade and fully actuated ones, with the extended state observer, we successfully employ the proposed controller extension method to solve the stabilization control of an underactuated surface ship subject to external disturbance.

Abstract:
This article proposes a novel data-driven distributed recurrent neural network (DDD-RNN) based on neurodynamics principles to address the challenge of precise collaborative motion generation in multimanipulator systems (MMCs) with unknown structural parameters. Unlike traditional methods that rely on precise models and existing data-driven methods with single-order Jacobian estimation, this article designs an improved Jacobian matrix estimation law (IJM). For the first time, it synchronously estimates the first-order and second-order Jacobian matrices online, effectively capturing the time-varying characteristics of robotic manipulators. Furthermore, a recurrent neural network solver is designed based on the neurodynamics criterion, which enables it to take into account the time-varying information of robotic manipulators, thus yielding more accurate motion generation results. Simulations conducted on multiple multimanipulator collaborative systems (MMCs) and experiments performed on the Ufactory XArm6 robots have verified the feasibility of the DDD-RNN method in generating collaborative motions of multiple robotic arms, even when the models of the robotic arms are unknown. Comparisons confirm the superiority of the DDD-RNN in terms of end-effector accuracy and applicability.

Abstract:
This article investigates the problem of removing the peaking phenomenon in the stabilization of a class of nonlinear cascaded systems using linear partial state feedback within a quadratic program (QP) framework. By appropriately designing the QP and selecting its parameters, the inter-subsystem cascaded input terms are effectively constrained within a desirable control-invariant set, thereby eliminating undesirable transient peaks. Semi-global stabilization of the overall system is achieved through only minimal modifications to the nominal linear feedback controllers. Owing to the simplicity of the resulting controller structure and the real-time efficiency of QP solvers, the proposed method is readily applicable to practical systems. Numerical examples from previous studies are revisited to demonstrate the effectiveness and robustness of the proposed control strategy.

Abstract:
This article investigates the distributed secure state estimation problem for a heterogeneous sensor network monitoring a dynamical system while under false-data injection attacks. Different from existing literature, the attacker is capable of corrupting a time-varying subset of sensors and altering their measurements. Based on the upper bound estimation technique, a novel distributed secure estimation method is proposed, which can provide an upper bound for estimation error and detect compromised sensors. The sufficient condition for the boundedness of the estimation error is provided and the feasibility of the estimator is further analyzed. Simulations are provided to demonstrate the effectiveness of the proposed estimator.

Abstract:
The transmission of information between distributed energy resource units (DERUs) within Microgrids (MGs) relies on sensing and communication systems that are vulnerable to cybersecurity threats. This article addresses the challenge of achieving resilient synchronization in networked AC MGs under cyber-attack conditions, specifically in scenarios where adversaries aim to desynchronize converters by intercepting, recording, and replaying communication signals. To mitigate these threats, we propose a distributed resilient event-triggered mechanism (DRETM) for secondary control of AC MGs during replay attacks. Unlike existing cybersecurity strategies, the proposed asynchronous event-based scheme enhances communication efficiency and reduces network resource consumption by employing a dual trigger function tailored for the communication links between the leader and informed DERUs, as well as among neighboring DERUs. Furthermore, to improve the detection of replay attacks, an enhanced distributed watermark-based detection scheme (EDWDS) is introduced. This mechanism minimizes the adverse effects of watermark signals on the system and streamlines the detection process by eliminating the need for complex parameter calculations or neighboring state estimations, in contrast to current detection methods. Finally, simulation results conducted in MATLAB/Simulink validate the effectiveness and accuracy of the proposed mechanisms.

Abstract:
Quadrotor helicopters have been widely applied in numerous fields and are increasingly attracting attention in many application areas. It is challenging to ensure the safety and reliability of quadrotor helicopter when faults occur in sensors or actuators, which may lead to catastrophic crashes. Recently, Transformer and its variants demonstrate powerful feature extraction capabilities, while a fault diagnosis (FD) model based on Transformer usually has a high demand for parameters and computations which limits its applications. To address this issue, this article proposes a novel lightweight Transformer with selective patches and channels modules (SPCFormer) method for quadrotor helicopter FD. First, the flight data is split into nonoverlapping patches for each channel. A selective patches module with a lightweight attention architecture is designed to extract critical local feature information from patches and mitigate multichannel coupling effects. Second, the selective channel attention is developed to form an attention vector rather than a matrix. This mechanism is integrated into the selective channels module to capture important global channel features while reducing the complexity of the model. Finally, a high-fidelity quadrotor helicopter fault simulator is developed to simulate different types of faults (i.e., actuator fault and sensor fault) under three different flight statuses and no extra sensors. The effectiveness of the proposed FD method is verified through the cross-validation on the above developed software-in-the-loop (SIL) and hardware-in-the-loop (HIL) simulators.

Abstract:
This article investigates the sliding mode fault-tolerant secure control for cyber–physical system facing jamming attacks and multiple physical faults. An intelligent attacker is capable of emitting interference power and adjusting its strategy by observing the transmitter’s sending power, leading to packet dropouts in the controller-to-actuator channel. A Bayesian Stackelberg game is exploited to capture these competitive interactions between the two players, in which the transmitter and the intelligent attacker can only probabilistically obtain information about each other’s channel state and transmission cost. Meanwhile, the transmitter has only statistical knowledge about either the presence or absence of the attacker in the practical environment. First, optimal transmission power strategies for both sides are studied using the backward induction method and the Karush–Kuhn–Tucker condition. Second, an integrated observer is designed to simultaneously estimate the system state, actuator fault, and sensor fault. Furthermore, the reaching law is proposed so that the state trajectories can reach the preselect sliding surface during the assigned finite time interval from any initial state. Sufficient criteria are derived to guarantee stochastic finite-time boundedness during reaching and sliding motion phases of closed-loop systems using a sliding-mode fault-tolerant secure controller. Finally, simulation results validate the effectiveness and superiority of the proposed method.

Affiliations: School of Information and Communication Engineering and the State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, China; Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei, China; School of Electronic Science and Technology and the State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, China; Department of Automation, Shanghai Jiao Tong University, Shanghai, China; School of Information and Communication Engineering, Hainan University, Haikou, China

Abstract:
This work addresses the design problem of the fault detection observer (FDO) based on dynamic event-triggered mechanism for Markov jump systems under denial-of-service (DoS) attacks. The concept of limited energy for attackers is employed to characterize the property of nonperiodic DoS attacks. A dynamic event-triggered mechanism is introduced to save the system’s communication resources. The H_\infty /H_- index is incorporated to ensure that the designed FDO possesses both robustness against disturbances and sensitivity to faults. After obtaining a set of nonlinear inequalities using Lyapunov functional techniques, a simulated annealing algorithm is employed to assist in solving, ensuring not only the discovery of global optimization solutions but also obtaining satisfactory parameters for the dynamic event-triggered mechanism. Finally, the effectiveness of the designed FDO is illustrated by an example of a vertical take-off and landing vehicle dynamical system.

Abstract:
Networks objectively portray functional distributions in practical systems, streamlining optimization and information extraction from typological structures. Recent studies have intensified scrutiny of the robust competitive influence maximization (RCIM) problem, focusing on identifying the most impactful seed set for effective and robust propagation. Literature offers performance metrics and algorithms that integrate diverse groups, suggesting potential synergy among them and the value of diverse candidates for balanced group performance. However, a thorough study toward the RCIM problem is still pendent, and a well-developed paradigm for attaining the equilibrium across groups is in demand. This article addresses these challenges by introducing multitask optimization in competitive network seed determination. A multitask framework is constructed, encompassing distinct diffusion scenarios for multiple groups and the network as a whole. To tackle this problem, we develop a Multi-Factorial Evolutionary Algorithm for RCIM (MFEA-RCIM). MFEA-RCIM leverages dedicated operators to exploit task parallelism and fosters competition among diffusion groups through a transfer operation. Experimental results on synthetic and practical networks demonstrate that MFEA-RCIM outperforms existing methods, with efficiency gains attributed to the multitasking optimization strategy.

Abstract:
Anomaly detection is a cornerstone of industrial safety, enabling real-time monitoring of process operations by identifying deviations from normal conditions through statistical analysis. In real-world industrial scenarios, the nonstationary properties of multivariate time-series data present a common and substantial challenge. Existing methods for extracting stationary sources (\mathcal SSs) mainly rely on weak stationarity (i.e., mean and variance), but their performance is limited by the long-tailed distributions common in industrial datasets. Higher-order moments, in contrast, provide a more comprehensive statistical description, capturing complex data characteristics that the mean and variance overlook. To bridge this significant gap, we propose a continuous stationary moment analysis (Co-SMA) anomaly detection framework. Its core innovation is the SMA algorithm, which introduces a novel objective function to minimize cumulative sum of the differences in multiorder moments between each epoch and the overall data, effectively fulfilling the \mathcal SS estimation task. Furthermore, to overcome the inefficiencies of traditional model updating methods, we develop an event-triggered model updating framework based on the model bias index and first-order perturbation theory. Within this framework, we introduce a convex hull coverage metric, which enables the model to be adjusted efficiently according to the data distribution drift. The framework also incorporates iterative refinement of detection statistics and thresholds, establishing a dynamic adjustment mechanism that ensures optimal performance across diverse operating conditions. The theoretical basis of Co-SMA’s properties is rigorously established. Experimental evaluations on numerical simulations and real-world datasets from the ironmaking process demonstrate Co-SMA’s superior capabilities in \mathcal SS estimation and anomaly detection.

Abstract:
Conventional lower limb exoskeletons (LLEs) and their corresponding rehabilitation protocols can hardly provide safe and customizable gait rehabilitation training for different patients and scenarios. Thus, this study presents an 8-DoF rehabilitation LLE equipped with a cable-driven body weight support (BWS) mobile mechanism. The mobile BWS mechanism is designed to follow the wearer and offer preset supportive forces and balance protection. A whole body motion planning approach is proposed, wherein iterative null-space projection is employed to solve the task-space trajectories of gait training into the joint-space trajectories of the LLE. For better control performance, dynamic parameters of the human-LLE coupling system are estimated. A control scheme combining robust model predictive control (MPC) and disturbance observer is then designed to manipulate the system against dynamics uncertainty and disturbance during trajectory tracking. In the validation experiments, the nominal model of robust MPC is discretized into quadratic programming problems and solved online by the neuro-dynamics optimization. The experimental results demonstrate the rationality of our system design and motion planning method as well as the effectiveness and stability of the control scheme.

Abstract:
The stabilization problem for discrete-time time-variant linear state-delayed systems with distinct input delays is investigated in this article. A predictor is constructed for this class of delayed systems in a concise and explicit form by using the state transition matrices as tools. With the aid of the proposed prediction scheme, a predictor-based feedback law is designed to stabilize the considered system. It is shown that the characteristic equation of the closed-loop system under the proposed predictor-based feedback law for the case of time-invariant systems is the same as that of the closed-loop system without distinct input delays. Finally, two numerical examples are employed to verify the effectiveness of the proposed method.

Abstract:
This technical correspondence examines the issue of observer-based bounded H_\infty control for a kind of shift-varying linear repetitive process (LRP) over networks with constrained bit rates in the finite horizon. Unlike the previous researches that address (or avoid) the problem of limited network resources by designing different scheduling protocols, this study focuses on further reducing and optimizing the bandwidth utilization by introducing a bit rate constraint model. Thus, an encoding-decoding mechanism under the constrained bit rates is proposed based on the quantization method. In order to analyze the H_\infty performance of the LRP and design an appropriate controller, the LRP is transformed into a shift-varying two-dimensional (2-D) Fornasini-Marchesini model. Sufficient conditions in recursive linear matrix inequalities are proposed to ensure that the extended system achieves a bounded H_\infty performance over a finite horizon within the 2-D framework. Furthermore, a component-based strategy for allocating the bit rates is provided to expand the quantization region under the constraint of bit rates. Finally, the effectiveness of the proposed method is verified by a simulation example.

Abstract:
Since distributed control theory has now become a key ingredient in modern cyber-physical microgrids (MG), its implementation is inseparable from data communication. The introduction of a communication infrastructure inevitably brings communication threats, such as denial-of-service (DoS) attacks and network induced delays. This article represents the first attempt toward a unified distributed digital predictor-based control scheme for the solution of the secondary voltage restoration problem in islanded MGs, which also involves external unknown disturbances and network vulnerabilities, thus enhancing its resilience and reliability. The novel sampled-data predictive controller revises the conventional model reduction approach to reformulate it in a fully distributed digital way and involves some external disturbances information for prediction performance improvement, even though these perturbations are completely unknown. The main features of the resulting method are: 1) large delays compensation accounting for networked-induced delays and sleeping time interval due to DoS attacks occurrence and 2) unknown disturbance attenuation, usually neglected in the controllers synthesis phase in MGs field. Lyapunov-Krasovskii theory is exploited to analytically prove the exponential stability of the MG voltage, thus leading to linear matrix inequality-based sufficient stability conditions. Numerical and experimental results confirm theoretical derivations.

Abstract:
This article presents a novel online obstacle avoidance trajectory planning method for autonomous ground vehicles (AGVs) based on long short-term memory-attention (LSTM-Attention) networks. The proposed method can guide AGVs to perform emergency maneuvers when encountering sudden and moving obstacles, while also ensuring high levels of real-time performance and optimality. It consists of two parts: 1) offline training and 2) online planning. In the offline training phase, an AGV obstacle avoidance trajectory dataset is generated using numerical trajectory optimization methods to train the LSTM-Attention network. This training allows the network to capture the mapping between the relative information of the vehicle and the obstacles and the optimal control actions. The trained network is then used for online trajectory planning to achieve optimal feedback obstacle avoidance control for AGVs facing sudden obstacles. Furthermore, to address situations involving sudden obstacles in different directions and moving obstacles, a rotation coordinate system method is proposed, significantly expanding the application scenarios of the proposed approach. The effectiveness and real-time performance of the designed method are comprehensively validated through extensive simulation and physical experiments.

Abstract:
Steam, which plays an essential role in heat source medium for heating and cooling, is extensively utilized in industrial parks. Due to the high temperature and pressure characteristics of steam, which has the potential to cause damage to the steam pipeline easily. It is crucial to conduct fault diagnosis for the steam pipeline timely. This article is concerned with the active fault diagnosis (AFD) problem for the steam pipeline with multitime-scale properties. The global consensus AFD method based on the multitime-scale set-membership observer is proposed to solve the issue of inconsistent timescales, which not only achieves state estimation sets under different timescales but also guarantees the global consistency of fault diagnosis results. In view of the leakage fault modeling problem of the steam pipeline, a novel steam transportation model considering the leakage energy loss is established. Moreover, when establishing the optimization problem of the auxiliary signal, original inputs with dynamic characteristics are considered. The key of optimization problem comes down to the separation of healthy and faulty state estimation sets. Finally, experimental results are given to illustrate the effectiveness of the proposed AFD methodology with the steam pipeline data from a steel industrial park.

Abstract:
This article solves the problem of semi-global zero-error consensus tracking (ZECT) of a class of nonlinear multiagent systems (MASs) with a dynamic leader and input saturation under digraph. Different from some existing results that account for a dynamic leader, the upper bound of the dynamic leader’s input is allowed to be unknown. To address the challenges of such an unknown upper bound, a hierarchical clustered network design is presented for the MASs consisting of a single dynamic exogenous leader and multiple clusters of agents. Then, with the help of the preset saturation threshold and low-gain feedback technique, some distributed chattering-free adaptive controllers, which do not rely on the global parameters of the network topologies and the upper bound of the dynamic exogenous leader’s input, are proposed for the leaders and followers in each cluster, respectively. Moreover, it is proved that the MASs driven by the proposed controllers can achieve semi-global ZECT. Finally, some simulation results confirm the validity of the proposed control approach.

Abstract:
multiple attribute group decision making (MAGDM) aims to assist a group in evaluating multiattribute alternatives for seeking the most satisfactory one(s). To improve the decision quality of MAGDM, various consensus models were suggested to deal with opinion differences among experts and achieve consensual decision outcomes. This study proposes a novel ordinal consensus framework for MAGDM with incomplete social network trust preference relations (TPRs). In this framework, incomplete linguistic preference relation are first utilized to represent experts’ social network TPRs. After that, a consistency-driven two-stage optimization approach is designed to deal with incomplete TPRs for obtaining individual trust levels and expert weight information. Then, a distance-based ordinal consensus measure is designed with the integration of the obtained expert weight information and the basic idea that the higher-ranked alternatives should have greater importance than the lower-ranked ones. When the consensus degree among experts is unacceptable, an opinion dynamic-based feedback adjustment mechanism is devised by integrating the obtained individual trust levels to provide reasonable opinion modification suggestions for accelerating the consensus reaching in MAGDM. Otherwise, the selection process is used to make a selection. A simulation experiment is designed to investigate the effect of key parameters on consensus efficiency. Next, examples of project investment and software supplier selection demonstrate the usability of the proposed consensus framework. Meanwhile, a comparison analysis and in-depth discussions are presented to justify our proposal. The main contributions of this study are twofold. First, a new perspective on managing incomplete social network TPRs is suggested for MAGDM. Second, a novel ordinal consensus process is designed to enhance the effectiveness of the consensual decision outcome. These results can offer new insights into the consensus building for practice social network MAGDM problems.

Abstract:
This article systematically studies projective vortex formation tracking (PVFT) of linear multiagent systems (MASs) on directed graphs through multiple transformation matrices, in which the input of leader and its upper bound information are not available to any follower. First, an innovative class of distributed adaptive observer is designed using the projection matrices to capture multiple desired virtual signals of the leader. Next, a novel kind of distributed PVFT protocol based on distributed observer, local observer and coordinates coupling matrices are proposed. Two different adaptive update mechanisms and nonlinear functions are introduced in the observer and controller to override the unknown input of the leader. Then, an algorithm is given, and the protocol under the algorithm is proved from three processes of estimation, aggregation, and rotation to enable linear systems to achieve PVFT. Finally, the effects of parameters on aggregation and rotation are analyzed systematically, and several simulation examples are given to illustrate the reliability of the results.

Abstract:
Most existing prescribed performance formation control methods impose performance requirements on the consensus error rather than directly on the relative states between agents, which limits the physical interpretability of their solutions. This article proposes a novel adaptive prescribed performance formation control strategy that ensures prescribed performance of relative errors in uncertain high-order multiagent systems under both directed and undirected graphs. Since performance constraints are considered for relative errors, the error dynamics involve a coupled nonlinear interaction term that contains global graphical information among agents, making the design of a fully distributed control strategy more challenging. By proposing a series of nonlinear mappings and utilizing the edge Laplacian along with Lyapunov stability theory, the presented formation control scheme offers several advantages over existing approaches. Different performance requirements can be accommodated in a unified manner by solely tuning the design parameters a priori, eliminating the need for control redesign and stability reanalysis under the proposed fixed control protocol. This enhances user-friendliness and reduces implementation complexity. Furthermore, the verification process for the initial constraint, which is often complex and burdensome in existing prescribed performance control methods, is entirely avoided when the performance requirements are global. Additionally, the proposed approach fully decouples nonlinear interactions and ensures the asymptotic stability of the formation manifold through an adaptive parameter estimation technique. The effectiveness of the theoretical results is demonstrated through simulations.

Abstract:
In a large range of manufacturing tasks, the design objective is characterised as following a given path defined in space. In these applications, the tracking time of any particular position along the path is not specified, so an appropriate motion profile can be chosen among its admissible solutions to improve its tracking performance. This article develops an indirect reference update framework that maximizes accuracy while embedding practical constraints. An optimal path planning problem, incorporating system constraints, is formulated and can be solved using a discretized approach to derive a motion profile that minimizes control energy for a broad spectrum of industrial tasks. To satisfy robustness concerns, an iterative learning control (ILC) algorithm with an indirect reference update framework is designed to improve the accuracy and robustness of path following. It is evaluated on a gantry robot test platform, and the results illustrate superior levels of practical performance in terms of energy reduction and path following accuracy compared with existing approaches.

Abstract:
The permanent magnet synchronous generator (PMSG) system becomes unstable when unpredicted chaos appears, and current approaches do not take how to lessen this chaos phenomenon into account. Motivated by the ability of projective synchronization (PS) to adjust the chaotic system trajectory, this research aims to use PS to reduce the chaos in PMSG system. For better control in the time estimation of PS and the robustness of systems, an adaptive predefined-time robust zeroing neural dynamic controller (APTRZNDC) for the PS between PMSG systems is proposed. In the process, an adaptive parameter determined by the system error is designed with the demand for higher convergence factor in the case of large error. In addition, a nonlinear activation function contributed to the predefined-time synchronization is created, making the upper bound of synchronization time independent of system initial states and parameters, except for a single predefined parameter. Moreover, essential theorems for the predefined-time PS and robustness under the APTRZNDC are supplied and validated. And better robustness of PMSG system with the APTRZNDC is demonstrated when compared with other controllers. Furthermore, the APTRZNDC is applied in secure communication via the PS of PMSG systems, which guarantees both the timeliness of signals and the immunity of communication.

Abstract:
The agile Earth observation satellite scheduling problem (AEOSSP) with time-dependent transition time is a combinatorial optimization challenge. Due to its NP-hardness, problem-tailored methods are sensitive to instances and require massive computational overhead. Recently, deep reinforcement learning (DRL) models have shown promise in efficiently addressing the AEOSSP. However, these models may make decision mistakes in specific scenarios due to prioritizing maximizing average reward expectation over individual decision accuracy during DRL training, directly leading to resource wastage. To address these issues, we propose a reconstruction model (RCM), which is a DRL-based two-stage construction model (CM), including a CM and a reconstruction mechanism (RM). RCM constructs solutions initially using a DRL-trained CM, which are subsequently refined by RM. CM utilizes a more efficient network for policy representation to make decisions. RM applies two operators, “repair” and “removal,” with a “repair-removal-repair” solution reconstruction process to identify and rectify decision mistakes from CM, offering a modular component to enhance the stability and solution quality. Experimental results demonstrate that the proposed RCM outperforms the state-of-the-art AEOSSP iterative search method, achieving such performance within a computational time of 0.1 s. Additionally, CM surpasses the state-of-the-art DRL policy model and RM can effectively rectify decision errors or suboptimalities, underscoring its effectiveness in enhancing DRL outcomes.

Affiliations: Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China; School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China; School of Electrical Engineering and Automation, Wuhan University, Wuhan, China; School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore

Abstract:
The modeling problem of converter-based multibus direct current (DC) microgrids (MGs) and the conflict between voltage regulation and current balancing in such MGs have been a hot topic of interest. Voltage regulation is essential for ensuring the stability and power quality of MGs, while current sharing is a reflection of the MGs’ ability to coordinate power and is critical to extend the lifespan of the generation units. However, due to the presence of line impedance, currents no longer have the freedom of regulation under consistent voltages across the buses. Additionally, existing models have failed to strike a good balance between accuracy and simplicity in describing DC MGs, resulting in rare research on model-based secondary control. With this in mind, this article develops a DC MG error model containing the dynamics of both the circuit and inner control loops via the fully actuated system theory. Further, a distributed optimal control is proposed based on this model. Compared to existing studies, the suggested error model captures the power characteristics of MGs while possesses a simple structure. For regulation tasks of voltage recovery and precise current allocation, this article unifies these two into a single integrated regulation error, offering a novel approach to address their conflict. Subsequently, the stability of the closed-loop MG system is given. Furthermore, this article includes a consensus analysis of current sharing and a tracking analysis of the average voltages. Finally, a laboratory-scale MG prototype equipped with photovoltaics and batteries is developed to validate the effectiveness of the proposed method.

Abstract:
This article is concerned with the event-triggered fault-tolerant control (FTC) for uncertain nonlinear cyber-physical systems (CPSs) by only exploiting the triggered faulty output. During the control design process, the unknown system dynamics, the time-varying sensor, and the actuator faults are considered simultaneously. Based on the event-triggered mechanism, the first-order filter technique and the nonlinear impulsive dynamics approach, an adaptive neural event-triggered output feedback FTC scheme is established. More specifically, one triggering condition is established for both the measurable output and the state estimations, with the adaptive parameters being triggered at the same instants. Another triggering condition is established for the controller, eliminating the need for real-time monitoring of control information and thereby reducing the computational burden. Then, a neural state observer is designed from triggered faulty output and triggered state estimations. The first-order filter technique is introduced to handle the non-differentiability of virtual controls stemmed from the event-triggered mechanism. The nonlinear impulsive dynamics approach is employed for stability analysis of the discontinuous error dynamics. It is proved that, with the proposed scheme, all the closed-loop signals are bounded, meanwhile the system output converges to the origin asymptotically, and the Zeno behavior is excluded. Finally, simulation results present the feasibility and effectiveness of the seeking schemes.

Abstract:
Deterministic processing time are no longer applicable under realistic circumstances because of the uncertainties involved in manufacturing and production processes. The present study aims to address a multiobjective distributed assembly flexible job shop scheduling problem with type-2 fuzzy time (DAT2FFJSP), focusing on the optimization objectives of minimizing the makespan and total energy consumption. To address this problem, a mixed-integer linear programming model is presented. Then, a population-based iterative greedy algorithm (PBIGA) with a Q-learning mechanism is proposed, which possesses the following characteristics: 1) a hybrid initialization method is used to generate the population; 2) six local search operators, crossover operators, and mutation operators are applied to explore and exploit the solution space; and 3) the Q-learning mechanism intelligently utilizes historical information on the success of local search operator updates to determine the most suitable perturbation operator; and 4) an energy-saving strategy is applied to improve the candidate solutions. Finally, the effectiveness of the proposed components is validated through extensive experiments that are conducted on 30 instances. The PBIGA outperforms the state-of-the-art algorithms on the DAT2FFJSP.

Affiliations: School of Information Engineering, Nanchang University, Nanchang, China; School of Automation Science and Engineering, South China University of Technology, Guangzhou, China; Department of Mechanics and Engineering Science, BIC-ESAT, College of Engineering, State Key Laboratory for Turbulence and Complex Systems, Peking University, Beijing, China; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada

Abstract:
Zeroing neural network (ZNN), as a special type of recurrent neural network (RNN), is very competitive in solving time-varying linear matrix-vector equations. Recently, various ZNNs with predefined-time convergence (PTC) capabilities have been reported. Such ZNNs with PTC capabilities can achieve the predefined convergence time via explicitly presetting multiple parameters related to the upper bounds of their convergence time. However, obtaining suitable and robust values for these parameters through reasonable adjustments is a challenging task in many engineering applications. To address this problem, we propose a novel arbitrarily predefined-time convergent RNN (APTC-RNN) with a novel nonlinear piecewise activation-function (NPAF). Unlike most existing ZNNs with PTC capabilities, the proposed APTC-RNN, due to its NPAF, can achieve arbitrarily PTC (APTC) without adjusting any upper bound parameters. Furthermore, due to the piecewise computation form of the NPAF, the proposed APTC-RNN can provide a lower computational cost compared to most existing RNNs. The stability and APTC capability of the proposed APTC-RNN are proven by rigorous theoretical analysis and mathematical derivation. Numerical simulations show that APTC-RNN has faster and more accurate PTC capability than three state-of-the-art RNNs, while having less computational time. Finally, the practicality of the APTC-RNN is verified by applying it to the UR3 robotic arm and multiagent systems.

Abstract:
Communication imperfections, such as variable sampling periods and packet dropouts induced by insufficient network bandwidth, can degrade the control performance and even jeopardize the stability of networked control systems (NCSs). A stochastic communication protocol (SCP) is usually adopted to ensure that only one sensor node can transmit the output signal to the controller at each sampling instant, thus conserving network bandwidth resources. This article focuses on the output feedback synthesis problem for NCSs in the presence of the SCP, considering two-channel successive packet dropouts (SPDs) and multiple probability sampling periods (MPSPs). Among these, MPSPs mean that there are three or more different sampling rates in system. To address the output feedback synthesis problem, we first obtain the discrete-time augmented model of the closed-loop NCS with a dynamic output feedback controller. This model incorporates an equivalent sampling period representation between adjacent nonpacket-dropout instants, which plays a pivotal role in our analysis. Furthermore, a general analysis model is established by considering the effects of the SCP. Based on this model, conditions for designing the dynamic output feedback controller, represented by linear matrix inequalities (LMIs), are established using a two-step synthesis approach. In particular, the dimension of the obtained controller design conditions remains unchanged with respect to the upper bound of SPDs and the number of possible sampling periods, especially through the introduction of a matrix decomposition method, which is more general than existing results. Finally, the proposed method is demonstrated through an illustrated example.

Affiliations: School of Electrical and Information Engineering and the Institute for Robotics, Zhengzhou University, Zhengzhou, Henan, China; School of Intelligence Science and Technology and the Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China; College of Electrical and Information Engineering and the National Engineering Laboratory for Robot Visual Perception and Control, Hunan University, Changsha, Henan, China

Abstract:
This article investigates the trajectory tracking control of uncertain robotic systems with limited control torque input bounds and joint position constraints. A novel neural network-based augmented high-order control barrier function (NN-AHoCBF) is proposed to facilitate the tracking control strategy of uncertain robotic systems with input-output constraints, where the neural network (NN) is used to estimate uncertainties in the robotic system dynamics, and the bounds of NN approximation errors and NN weights are adapted in the high-order time derivative of the HoCBFs. The NN-AHoCBF is then derivated with a series of time-varying functions, and auxiliary systems are constructed to guarantee the time-varying functions to be HoCBFs. In this way, the control input of the robotic system is relaxed by adjusting the time-varying functions through the inputs of auxiliary systems in NN-AHoCBF barrier conditions. Also, the sufficient condition for the NN-AHoCBF is provided to adaptively ensure system safety. The adaptive safety-based tracking control method is designed based on NN-AHoCBF in quadratic program (QP) framework, which can not only satisfy input-output constraints simultaneously, but also achieve good robustness and tracking performance. A simulation example is performed on a two-DOF robotic mainpulator to verify the effectiveness of the developed controller.

Abstract:
This article addresses the problem of adaptive prescribed-time distributed consensus tracking with dynamic full-state and input triggering for a class of uncertain state-constrained strict-feedback multiagent systems with external disturbances. The primary contribution lies in developing of a novel prescribed-time disturbance observer-based adaptive chainlike filter, capable of generating smooth estimates of intermittently triggered state-feedback signals while compensating for external disturbances and unknown nonlinearities within a predefined convergence time. The multiagent systems are nonlinearly transformed to address state constraints, without needing feasibility conditions on virtual control laws in the recursive design. The dynamic triggering variables are introduced using a prescribed-time adjustment function and distributed tracking errors. Based on the state variables of the adaptive chainlike filters, a prescribed-time distributed consensus tracking strategy is established to guarantee the prescribed-time convergence of filtering errors, disturbance observation errors, leader estimation errors, and consensus tracking errors, without requiring continuous state-feedback measurements. The shared use of neural networks across chainlike filters, disturbance observers, and controllers reduces computational complexity. The practical prescribed-time stability and satisfaction of state constraints in the closed-loop system are proven through a rigorous technical lemma. Finally, simulation results validate the effectiveness and robustness of the proposed control scheme.

Abstract:
Training neural networks (NNs) to behave as model predictive control (MPC) algorithms is an effective way to implement them in constrained embedded devices. By collecting large amounts of input-output data, where inputs represent system states and outputs are MPC-generated control actions, NNs can be trained to replicate MPC behavior at a fraction of the computational cost. However, although the composition of the training data critically influences the final NN accuracy, methods for systematically optimizing it remain underexplored. In this article, we introduce the concept of optimally-sampled datasets (OSDs) as ideal training sets and present an efficient algorithm for generating them. An OSD is a parametrized subset of all the available data that 1) preserves existing MPC information up to a certain numerical resolution; 2) avoids duplicate or near-duplicate states; and 3) becomes saturated or complete. We demonstrate the effectiveness of OSDs by training NNs to replicate the University of Virginia’s MPC algorithm for automated insulin delivery in Type-1 Diabetes, achieving a fourfold improvement in final accuracy. Notably, two OSD-trained NNs received regulatory clearance for clinical testing as the first NN-based control algorithm for direct human insulin dosing. This methodology opens new pathways for implementing advanced optimizations on resource-constrained embedded platforms, potentially revolutionizing how complex algorithms are deployed.

Abstract:
This study focuses on the state tracking problems of chaotic Lur’e systems with nondifferentiable delay and partial channel restriction via intermittent observer. Different from the results obtained by existing chaotic Lur’e systems, the time delay involved in the chaotic Lur’e system explored in this study does not require that the time delay must be differentiable and there is no limit that the time delay must be bounded, which is more in accordance with the operating situation in practical engineering. Subsequently, aiming at the inevitable cyber-attack in the communication network environment, an intermittent observer based on the loss of partial measurement information induced by cyber-attack is constructed. Especially, in the operation process of the designed intermittent estimator, it breaks through the strict requirement that the observation width must be greater than the upper limit of the delay in the existing work, which intuitively reflects the wider application range and has more exploratory significance. Moreover, a meaningful lemma is designed, which effectively overcomes the invalidity of existing Lyapunov function or Lyapunov–Krasovskii functional methods for nondifferentiable systems, and develops novel criteria for partial information loss and nondifferentiable systems, presenting an important theoretical guidance for related topics. Numerical simulations are provided to verify the effectiveness of the proposed intermittent observer mechanism and the stabilization criteria of the error system. Finally, an application of the proposed intermittent observation result for two identical chaotic systems to a secure communication approach is provided.

Affiliations: School of Mechanical Engineering, Translational Research Center, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University, Shanghai, China; Institute of Advanced Technology, University of Science and Technology of China, Hefei, China; Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China; Department of Automation, School of Information Science and Technology, University of Science and Technology of China, Hefei, China

Abstract:
Despite remarkable progress in robotic exoskeletons, exoskeletons are still far from matching human-level guidance and locomotion performance in gait training or movement enhancement. A desirable exoskeleton would first provide a standard gait profile by learning from human operators while requiring body weight support with active human-following to govern dynamic locomotion synchronization. To address these issues, in this article, we propose a human operator-involved dynamic locomotion synchronization control framework for the lower limb exoskeleton actively following gait training with gravity-supporting. First, we designed a human motion capture system based on a five-link model for the locomotion of a human operator. To reproduce human-level motor skills, we use whole-body teleoperation to leverage human control intelligence to command the locomotion of a robotic exoskeleton system. Specifically, using the linear inverted pendulum (LIP) model, the human operator’s divergent component of motion (DCM) is obtained by the human motion capture system. The dynamic similarity is used to generate a reference DCM for the robotic exoskeleton to synchronize the human operator’s movement. Finally, a fuzzy-based adaptive controller is designed to track the synchronous trajectory for the exoskeleton in the presence of robotic dynamics uncertainties with input saturation. Experiments on the human subject are carried out to demonstrate the effectiveness of the proposed method.

Abstract:
Sampled-data control of heterogeneous fractional-order (FO) multiagent systems (MASs) under nonidentical denial-of-service (DoS) attacks and time delays is investigated in this study. Based on the received acknowledgments, the designed channel-dependent resilient binary sampled-data scheme adjusts the sampling period following two geometric progressions in the absence and presence of DoS attacks. This provides finer adjustments in sampling periods and ensures a lower average sampling rate compared with traditional periodic sampling schemes. Moreover, the upper and lower bounds of sampling intervals in different channels are heterogeneous, in which only the lower bounds are constrained by inequalities associated with the coefficients of DoS attacks. The impact of DoS attacks is observed from the viewpoint of the overall communication network topology. Observation of the joint recovery time series, defined as those time instants that the joint union of the sampling-based communication graphs under asynchronous DoS attacks can recover to the original topology graph, measures the effectiveness of the designed sampling scheme in restoring the connectivity of communication network under DoS attacks. Heterogeneous memory fusion controllers are used to achieve consensus of FO MASs with time delays based on the distributed asynchronous gradient algorithm. An example is presented to illustrative the validity of the theoretical analysis.