Dear Editor, This letter presents some control strategies for quadrotor unmanned aerial vehicle (UAV) leader-follower formation model, where the stochastic impulsive deception attacks are fully considered. Based on Lyapunov method, the outer loop and the inner loop controllers of quadrotor UAV are designed, respectively. Moreover, a relationship between continuous control laws, stochastic impulsive sequences, and impulsive intensity is established in this letter. Finally, the simulation results are presented to validate the proposed results.
{"title":"Letter Leader-Follower Formation Control of Quadrotor UAVs with Stochastic Impulsive Deception Attacks","authors":"Wenhao Song;Chang Liu;Xiuping Han;Xiaodi Li","doi":"10.1109/JAS.2025.125615","DOIUrl":"https://doi.org/10.1109/JAS.2025.125615","url":null,"abstract":"Dear Editor, This letter presents some control strategies for quadrotor unmanned aerial vehicle (UAV) leader-follower formation model, where the stochastic impulsive deception attacks are fully considered. Based on Lyapunov method, the outer loop and the inner loop controllers of quadrotor UAV are designed, respectively. Moreover, a relationship between continuous control laws, stochastic impulsive sequences, and impulsive intensity is established in this letter. Finally, the simulation results are presented to validate the proposed results.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 2","pages":"483-485"},"PeriodicalIF":19.2,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11429639","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Temporal alignment of multisensor time series (MTS) is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications. Nevertheless, many approaches frequently neglect to consider the complex interdependencies between different sensors in MTS, and temporal alignment in many methods is typically treated as an isolated task disconnected from the downstream objectives, leading to unsatisfactory performances in follow-up applications. To address these challenges, this paper proposes a novel knowledge graph (KG)-guided iterative-updating graph neural network (GNN) for time-delay estimation (TDE) in MTS. Initially, a domain-specific KG is constructed from domain mechanism knowledge, providing a foundation for GNN's initialization. Next, capitalizing on the inherent structure of the graph topology, a GNN-based TDE method is developed. Then, a customized loss function is constructed, which synthesizes both the performances of downstream tasks and graph-based constraints. Moreover, an innovative algorithm for GNN structure learning and iterative-updating is proposed to renovate the graph structure further. Finally, experimental results across various regression and classification tasks on numerical simulation, public datasets, and the real blast furnace ironmaking dataset demonstrate that the proposed method can achieve accurate temporal alignment of MTS.
{"title":"KIG: A Knowledge Graph-Guided Iterative-Updating Graph Neural Network for Multisensor Time Series Time-Delay Estimation","authors":"Siyuan Xu;Dong Pan;Zhaohui Jiang;Zhiwen Chen;Haoyang Yu;Weihua Gui","doi":"10.1109/JAS.2025.125897","DOIUrl":"https://doi.org/10.1109/JAS.2025.125897","url":null,"abstract":"Temporal alignment of multisensor time series (MTS) is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications. Nevertheless, many approaches frequently neglect to consider the complex interdependencies between different sensors in MTS, and temporal alignment in many methods is typically treated as an isolated task disconnected from the downstream objectives, leading to unsatisfactory performances in follow-up applications. To address these challenges, this paper proposes a novel knowledge graph (KG)-guided iterative-updating graph neural network (GNN) for time-delay estimation (TDE) in MTS. Initially, a domain-specific KG is constructed from domain mechanism knowledge, providing a foundation for GNN's initialization. Next, capitalizing on the inherent structure of the graph topology, a GNN-based TDE method is developed. Then, a customized loss function is constructed, which synthesizes both the performances of downstream tasks and graph-based constraints. Moreover, an innovative algorithm for GNN structure learning and iterative-updating is proposed to renovate the graph structure further. Finally, experimental results across various regression and classification tasks on numerical simulation, public datasets, and the real blast furnace ironmaking dataset demonstrate that the proposed method can achieve accurate temporal alignment of MTS.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 2","pages":"327-345"},"PeriodicalIF":19.2,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The wireless cloud robotic system (WCRS), which fully integrates sensing, communication, computing, and control capabilities as an intelligent agent, is a promising way to achieve intelligent manufacturing due to easy deployment and flexible expansion. However, the high-precision control of WCRS requires deterministic wireless communication, which is always challenging in the complex and dynamic radio space. This paper employs the reconfigurable intelligent surface (RIS) to establish a novel RIS-assisted WCRS architecture, where the radio channel is controlled to achieve ultra-reliable, low-delay, and low-jitter communication for high-precision closed-loop motion control. However, control and communication are strongly coupled and should be co-optimized. Fully considering the constraints of control input threshold, control delay deadline, beam phase, antenna power, and information distortion, we establish a stability maximization problem to jointly optimize control input compensation, RIS phase shift, and beamforming. Herein, a new jitter-oriented system stability objective with respect to control error and communication jitter is defined and the closed-form expression of control delay deadline is derived based on the Jensen Inequality and Lyapunov-Krasovskii functional. Due to the time-varying and partial observability of the channel and robot states, we model the problem as a partially observable Markov decision process (pOMDP). To solve this complex problem, we propose a multi-agent transfer reinforcement learning algorithm named LSTM-PPO-MATRL, where the LSTM-enhanced proximal policy optimization (PPO) is designed to approximate an optimal solution and the option-guided policy transfer learning is proposed to facilitate the learning process. By centralized training and decentralized execution, LSTM-PPO-MATRL is validated by extensive experiments on MuJoCo tasks for both low-mobility and high-mobility robotic control scenarios. The results demonstrate that LSTM-PPO-MATRL not only realizes high learning efficiency, but also supports low-delay, low-jitter communication for low error control, where 71.9% control accuracy improvement and 68.7% delay jitter reduction are achieved compared to the PPO-MADRL baseline.
{"title":"Control-Communication Co-Optimization for Wireless Cloud Robotic System via Multi-Agent Transfer Reinforcement Learning","authors":"Chi Xu;Junyuan Zhang;Haibin Yu","doi":"10.1109/JAS.2025.125894","DOIUrl":"https://doi.org/10.1109/JAS.2025.125894","url":null,"abstract":"The wireless cloud robotic system (WCRS), which fully integrates sensing, communication, computing, and control capabilities as an intelligent agent, is a promising way to achieve intelligent manufacturing due to easy deployment and flexible expansion. However, the high-precision control of WCRS requires deterministic wireless communication, which is always challenging in the complex and dynamic radio space. This paper employs the reconfigurable intelligent surface (RIS) to establish a novel RIS-assisted WCRS architecture, where the radio channel is controlled to achieve ultra-reliable, low-delay, and low-jitter communication for high-precision closed-loop motion control. However, control and communication are strongly coupled and should be co-optimized. Fully considering the constraints of control input threshold, control delay deadline, beam phase, antenna power, and information distortion, we establish a stability maximization problem to jointly optimize control input compensation, RIS phase shift, and beamforming. Herein, a new jitter-oriented system stability objective with respect to control error and communication jitter is defined and the closed-form expression of control delay deadline is derived based on the Jensen Inequality and Lyapunov-Krasovskii functional. Due to the time-varying and partial observability of the channel and robot states, we model the problem as a partially observable Markov decision process (pOMDP). To solve this complex problem, we propose a multi-agent transfer reinforcement learning algorithm named LSTM-PPO-MATRL, where the LSTM-enhanced proximal policy optimization (PPO) is designed to approximate an optimal solution and the option-guided policy transfer learning is proposed to facilitate the learning process. By centralized training and decentralized execution, LSTM-PPO-MATRL is validated by extensive experiments on MuJoCo tasks for both low-mobility and high-mobility robotic control scenarios. The results demonstrate that LSTM-PPO-MATRL not only realizes high learning efficiency, but also supports low-delay, low-jitter communication for low error control, where 71.9% control accuracy improvement and 68.7% delay jitter reduction are achieved compared to the PPO-MADRL baseline.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 2","pages":"311-326"},"PeriodicalIF":19.2,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wentao Mao;Jianing Wu;Shubin Du;Ke Feng;Zidong Wang
Deep transfer learning has achieved significant success in anomaly detection over the past decade, but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learning tasks. To address this issue, a novel time-frequency-assisted deep feature enhancement (TFE) mechanism is proposed. Unlike traditional methods that integrate time-frequency analysis with deep neural networks, TFE employs a wavelet scattering transform to establish a parallel time-frequency feature space, where a dual interaction strategy facilitates collaboration between deep feature and time-frequency spaces through two operations: 1) Enhancement, where a frequency-importance-driven contrastive learning (FICL) network transfers physically-aware information from wavelet scattering features to deep features, and 2) Feedback, which uses a detection rule adaptation module to minimize bias in wavelet scattering features based on deep feature performance. TFE is applied to a domain-adversarial anomaly detection framework and, through alternating training, significantly enhances both deep feature discriminative power and few-shot anomaly detection. Theoretical analysis confirms that the proposed dual interaction strategy reduces the upper bound of classification error. Experiments on benchmark datasets and a real-world industrial dataset from a large steel factory demonstrate TFE's superior performance and highlight the importance of frequency saliency in transfer learning. Thus, collaboration is shown to outperform integration for few-shot transfer learning in anomaly detection.
{"title":"Collaboration Better Than Integration: A Novel Time-Frequency-Assisted Deep Feature Enhancement Mechanism for Few-Shot Transfer Learning in Anomaly Detection","authors":"Wentao Mao;Jianing Wu;Shubin Du;Ke Feng;Zidong Wang","doi":"10.1109/JAS.2025.125702","DOIUrl":"https://doi.org/10.1109/JAS.2025.125702","url":null,"abstract":"Deep transfer learning has achieved significant success in anomaly detection over the past decade, but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learning tasks. To address this issue, a novel time-frequency-assisted deep feature enhancement (TFE) mechanism is proposed. Unlike traditional methods that integrate time-frequency analysis with deep neural networks, TFE employs a wavelet scattering transform to establish a parallel time-frequency feature space, where a dual interaction strategy facilitates collaboration between deep feature and time-frequency spaces through two operations: 1) Enhancement, where a frequency-importance-driven contrastive learning (FICL) network transfers physically-aware information from wavelet scattering features to deep features, and 2) Feedback, which uses a detection rule adaptation module to minimize bias in wavelet scattering features based on deep feature performance. TFE is applied to a domain-adversarial anomaly detection framework and, through alternating training, significantly enhances both deep feature discriminative power and few-shot anomaly detection. Theoretical analysis confirms that the proposed dual interaction strategy reduces the upper bound of classification error. Experiments on benchmark datasets and a real-world industrial dataset from a large steel factory demonstrate TFE's superior performance and highlight the importance of frequency saliency in transfer learning. Thus, collaboration is shown to outperform integration for few-shot transfer learning in anomaly detection.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 2","pages":"366-382"},"PeriodicalIF":19.2,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance. Although, two challenges emerge and result in high computational costs. Most existing contrastive methods adopt the data augmentation and then representation learning strategy, where representation learning with trainable graph convolution is coupled with complex and fixed data augmentation, inevitably limiting the efficiency and flexibility. The similarity metric between positive-negative sample pairs is complex and contrastive objective is partial, limiting the discriminability of representation learning. To solve these challenges, a novel wide graph clustering network (WGCN) adhering to representation and then augmentation framework is proposed, which mainly consists of multi-order filter fusion (MFF) and double-level contrastive learning (DCL) modules. Specifically, the MFF module integrates multi-order low-pass filters to extract smooth and multi-scale topological features, utilizing self-attention fusion to reduce redundancy and obtain comprehensive embedding representation. Further, the DCL module constructs two augmented views by the parallel parameter-unshared Siamese encoders rather than complex augmentations on graph. To achieve simple yet effective self-supervised learning, representation self-supervision and structural consistency oriented double-level contrastive loss is designed, where representation self-supervision maximizes the agreement between pairwise augmented embedding representations and structural consistency promotes the mutual information correlation between appending neighborhoods with similar semantics. Extensive experiments on six benchmark datasets demonstrate the superiority of the proposed WGCN, especially highlighting its time-saving characteristic. The code could be available in the https://github.com/TianxiangZhao0474/WGCN.
{"title":"Representation Then Augmentation: Wide Graph Clustering Network with Multi-Order Filter Fusion and Double-Level Contrastive Learning","authors":"Youqing Wang;Tianxiang Zhao;Mingliang Cui;Junbin Gao;Li Liang;Jipeng Guo","doi":"10.1109/JAS.2025.125564","DOIUrl":"https://doi.org/10.1109/JAS.2025.125564","url":null,"abstract":"Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance. Although, two challenges emerge and result in high computational costs. Most existing contrastive methods adopt the data augmentation and then representation learning strategy, where representation learning with trainable graph convolution is coupled with complex and fixed data augmentation, inevitably limiting the efficiency and flexibility. The similarity metric between positive-negative sample pairs is complex and contrastive objective is partial, limiting the discriminability of representation learning. To solve these challenges, a novel wide graph clustering network (WGCN) adhering to representation and then augmentation framework is proposed, which mainly consists of multi-order filter fusion (MFF) and double-level contrastive learning (DCL) modules. Specifically, the MFF module integrates multi-order low-pass filters to extract smooth and multi-scale topological features, utilizing self-attention fusion to reduce redundancy and obtain comprehensive embedding representation. Further, the DCL module constructs two augmented views by the parallel parameter-unshared Siamese encoders rather than complex augmentations on graph. To achieve simple yet effective self-supervised learning, representation self-supervision and structural consistency oriented double-level contrastive loss is designed, where representation self-supervision maximizes the agreement between pairwise augmented embedding representations and structural consistency promotes the mutual information correlation between appending neighborhoods with similar semantics. Extensive experiments on six benchmark datasets demonstrate the superiority of the proposed WGCN, especially highlighting its time-saving characteristic. The code could be available in the https://github.com/TianxiangZhao0474/WGCN.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 2","pages":"421-435"},"PeriodicalIF":19.2,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dear Editor, This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss. Firstly, we construct a temporal nonzero-sum game over predictive control input sequences, deriving multiple optimal predictive control input sequences from its solution. To obtain the Nash equilibrium solution of the temporal nonzero-sum game, we solve the problem through policy iteration of reinforcement learning. Then, we train the actor neural network and critic neural network to estimate the control policy and action value function, respectively, using the collected offline and online input-state data. Compared to traditional predictive control methods, the proposed framework does not rely on an explicit model and obtains a data-driven controller design. Finally, the effectiveness of the proposed algorithm is validated through a numerical example.
{"title":"Data-Driven Predictive Control for Continuous-Time Nonlinear Systems: A Nonzero-Sum Game Approach","authors":"Juan Liu;Hao Zhang;Yifan Xie;Frank Allgöwer","doi":"10.1109/JAS.2025.125660","DOIUrl":"https://doi.org/10.1109/JAS.2025.125660","url":null,"abstract":"Dear Editor, This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss. Firstly, we construct a temporal nonzero-sum game over predictive control input sequences, deriving multiple optimal predictive control input sequences from its solution. To obtain the Nash equilibrium solution of the temporal nonzero-sum game, we solve the problem through policy iteration of reinforcement learning. Then, we train the actor neural network and critic neural network to estimate the control policy and action value function, respectively, using the collected offline and online input-state data. Compared to traditional predictive control methods, the proposed framework does not rely on an explicit model and obtains a data-driven controller design. Finally, the effectiveness of the proposed algorithm is validated through a numerical example.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 2","pages":"495-497"},"PeriodicalIF":19.2,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11429627","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dear Editor, This letter deals with the autonomous underwater vehicle (AUV) three dimensional (3D) trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics. A state-of-the-art predictive adaptive controller (PAC) is proposed with a distinct dual closed-loop structure. Based on the essence of AUV's relative hydrodynamics, the ocean currents are no longer compensated in the outer loop, where a novel predictive controller is investigated to obtain absolute target velocities, optimized by an advanced dung beetle optimizer (ADBO). In the inner loop, an adaptive controller is elaborately derived from the AUV dynamic model of relative motion, while an ocean current observer is adopted to acquire the relative target velocities. A comprehensive series of experiments proves the advantage of PAC compared to the existing representative control approaches.
{"title":"Relative Motion Based Predictive Adaptive Control: A Case Study of AUV 3D Trajectory Tracking","authors":"Daxiong Ji;Xinwei Wang;Yuanchang Liu","doi":"10.1109/JAS.2025.125624","DOIUrl":"https://doi.org/10.1109/JAS.2025.125624","url":null,"abstract":"Dear Editor, This letter deals with the autonomous underwater vehicle (AUV) three dimensional (3D) trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics. A state-of-the-art predictive adaptive controller (PAC) is proposed with a distinct dual closed-loop structure. Based on the essence of AUV's relative hydrodynamics, the ocean currents are no longer compensated in the outer loop, where a novel predictive controller is investigated to obtain absolute target velocities, optimized by an advanced dung beetle optimizer (ADBO). In the inner loop, an adaptive controller is elaborately derived from the AUV dynamic model of relative motion, while an ocean current observer is adopted to acquire the relative target velocities. A comprehensive series of experiments proves the advantage of PAC compared to the existing representative control approaches.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 2","pages":"492-494"},"PeriodicalIF":19.2,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11429602","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article studies the consensus problem with directed graphs for general linear multi-agent systems. New distributed state-feedback protocols with dynamic event-triggered (DET) mechanisms are proposed for directed graphs that are strongly connected and weight-balanced, general strongly connected, and have spanning trees, respectively. It is proven that strictly positive minimum inter-event times (MIETs) are ensured using the designed DET mechanisms. Several numerical examples are presented to illustrate the effectiveness of the theoretical results. Compared with existing results, our results have the following merits: 1) DET mechanisms are designed to determine the sampling instants, which can reduce the communication frequency between agents compared with static mechanisms; 2) We focus on the consensus problem on directed graphs, which is more general than existing related results on undirected graphs; 3) The existence of positive MIETs is shown to be guaranteed by the designed DET sampling strategies while existing related results can only exclude Zeno behavior.
{"title":"Dynamic Event-Triggered Mechanisms with Positive Minimum Inter-Event Times for Linear Multiagent Consensus on Directed Graphs","authors":"Sikang Zhan;Xianwei Li;Yuanyuan Zou;Shaoyuan Li","doi":"10.1109/JAS.2025.125822","DOIUrl":"https://doi.org/10.1109/JAS.2025.125822","url":null,"abstract":"This article studies the consensus problem with directed graphs for general linear multi-agent systems. New distributed state-feedback protocols with dynamic event-triggered (DET) mechanisms are proposed for directed graphs that are strongly connected and weight-balanced, general strongly connected, and have spanning trees, respectively. It is proven that strictly positive minimum inter-event times (MIETs) are ensured using the designed DET mechanisms. Several numerical examples are presented to illustrate the effectiveness of the theoretical results. Compared with existing results, our results have the following merits: 1) DET mechanisms are designed to determine the sampling instants, which can reduce the communication frequency between agents compared with static mechanisms; 2) We focus on the consensus problem on directed graphs, which is more general than existing related results on undirected graphs; 3) The existence of positive MIETs is shown to be guaranteed by the designed DET sampling strategies while existing related results can only exclude Zeno behavior.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 2","pages":"436-450"},"PeriodicalIF":19.2,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dear Editor, This letter presents a model predictive control (MPC) scheme for human-robot interaction (HRI) in a multi-joint exoskeleton robot (ER) driven by series elastic actuator (SEA). The proposed scheme in robot-in-charge (RIC) mode facilitates the ER driven by SEA to provide the required assistance and support for the subject. Meanwhile, it enables the ER driven by SEA to closely follow the movement of the subject and provides the least resistance in the human-in-charge (HIC) mode. In addition, a finite-time convergent zeroing neural network (FTCZNN) model is designed to solve the optimal solution of the MPC scheme. The convex activation function is designed to impose the error function to converge to zero in finite time, which guarantees the recursive feasibility of the MPC optimization. The convergence of the proposed FTCZNN algorithm is demonstrated through theoretical analyses. Finally, numerical simulations verify that the MPC scheme solved by the FTCZNN model enables ER driven by SEA to perform HRI tasks with high efficiency, rapid response, and strong robustness.
{"title":"Human-Robot Interaction-Based Model Predictive Control for Exoskeleton Robots Driven by Series Elastic Actuators","authors":"Changxian Xu;Keping Liu;Zhongbo Sun","doi":"10.1109/JAS.2025.125477","DOIUrl":"https://doi.org/10.1109/JAS.2025.125477","url":null,"abstract":"Dear Editor, This letter presents a model predictive control (MPC) scheme for human-robot interaction (HRI) in a multi-joint exoskeleton robot (ER) driven by series elastic actuator (SEA). The proposed scheme in robot-in-charge (RIC) mode facilitates the ER driven by SEA to provide the required assistance and support for the subject. Meanwhile, it enables the ER driven by SEA to closely follow the movement of the subject and provides the least resistance in the human-in-charge (HIC) mode. In addition, a finite-time convergent zeroing neural network (FTCZNN) model is designed to solve the optimal solution of the MPC scheme. The convex activation function is designed to impose the error function to converge to zero in finite time, which guarantees the recursive feasibility of the MPC optimization. The convergence of the proposed FTCZNN algorithm is demonstrated through theoretical analyses. Finally, numerical simulations verify that the MPC scheme solved by the FTCZNN model enables ER driven by SEA to perform HRI tasks with high efficiency, rapid response, and strong robustness.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 2","pages":"486-488"},"PeriodicalIF":19.2,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11429630","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper studies an indefinite mean-field game with Markov jump parameters, where all agents' diffusion terms depend on control variables and both state and control average terms $(x^{(N)}, u^{(N)})$ are considered. One notable aspect is the relaxation of the assumption regarding the positivity or non-negativity of weight matrices within costs, allowing for zero or even negative values. By virtue of mean-field methods and decomposition techniques, we have derived decentralized strategies presented by Hamiltonian systems and a new type of consistency condition system. These systems consist of fully coupled regime-switching forward-backward stochastic differential equations that do not conform to the Monotonicity condition. The well-posedness of these strategies is established by employing a relaxed compensator method with an easily verifiable Condition (RC) and the decomposition technique. Furthermore, we demonstrate that the resulting decentralized strategies achieve an $epsilon$-Nash equilibrium in the indefinite case without any assumptions on admissible control sets using novel estimates of the disturbed state and cost function. Finally, our theoretical results are applied to resolve a class of mean-variance portfolio selection problems. We provide corresponding numerical simulation results and economic explanations.
{"title":"Indefinite Linear-Quadratic Mean-Field Game of Regime-Switching System","authors":"Tian Chen;Kai Du;Zhen Wu","doi":"10.1109/JAS.2025.125456","DOIUrl":"https://doi.org/10.1109/JAS.2025.125456","url":null,"abstract":"This paper studies an indefinite mean-field game with Markov jump parameters, where all agents' diffusion terms depend on control variables and both state and control average terms <tex>$(x^{(N)}, u^{(N)})$</tex> are considered. One notable aspect is the relaxation of the assumption regarding the positivity or non-negativity of weight matrices within costs, allowing for zero or even negative values. By virtue of mean-field methods and decomposition techniques, we have derived decentralized strategies presented by Hamiltonian systems and a new type of consistency condition system. These systems consist of fully coupled regime-switching forward-backward stochastic differential equations that do not conform to the Monotonicity condition. The well-posedness of these strategies is established by employing a relaxed compensator method with an easily verifiable Condition (RC) and the decomposition technique. Furthermore, we demonstrate that the resulting decentralized strategies achieve an <tex>$epsilon$</tex>-Nash equilibrium in the indefinite case without any assumptions on admissible control sets using novel estimates of the disturbed state and cost function. Finally, our theoretical results are applied to resolve a class of mean-variance portfolio selection problems. We provide corresponding numerical simulation results and economic explanations.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 1","pages":"83-97"},"PeriodicalIF":19.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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