IEEE Transactions on Cybernetics最新文献_第10页

Bilateral Cooperative Control of Nonlinear Multiagent Systems With State and Output Quantification. 状态与输出量化非线性多智能体系统的双边协同控制。

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-18 DOI: 10.1109/TCYB.2025.3545144

Zhihong Zhao, Tong Wang, Jinyong Yu, Michael V Basin

引用次数: 0

Observer-Based Bounded H∞ Control for Shift-Varying Linear Repetitive Processes With Constrained Bit Rates Over a Finite Horizon 基于观测器的有限视界受限比特率线性重复过程的有界H∞控制。

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-18 DOI: 10.1109/TCYB.2025.3546704

Chengyu Yang;Jinling Liang

{"title":"Observer-Based Bounded H∞ Control for Shift-Varying Linear Repetitive Processes With Constrained Bit Rates Over a Finite Horizon","authors":"Chengyu Yang;Jinling Liang","doi":"10.1109/TCYB.2025.3546704","DOIUrl":"10.1109/TCYB.2025.3546704","url":null,"abstract":"This technical correspondence examines the issue of observer-based bounded <inline-formula> <tex-math>$H_{infty }$ </tex-math></inline-formula> 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 <inline-formula> <tex-math>$H_{infty }$ </tex-math></inline-formula> 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 <inline-formula> <tex-math>$H_{infty }$ </tex-math></inline-formula> 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.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 5","pages":"2514-2520"},"PeriodicalIF":9.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657080","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}

引用次数: 0

Distributed Predefined-Time Convergent Algorithm for Solving Time-Varying Resource Allocation Problem Over Directed Networks 有向网络时变资源分配问题的分布式预定义时间收敛算法

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-17 DOI: 10.1109/TCYB.2025.3545897

Yuanyuan Yue;Qingshan Liu

{"title":"Distributed Predefined-Time Convergent Algorithm for Solving Time-Varying Resource Allocation Problem Over Directed Networks","authors":"Yuanyuan Yue;Qingshan Liu","doi":"10.1109/TCYB.2025.3545897","DOIUrl":"10.1109/TCYB.2025.3545897","url":null,"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.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 5","pages":"2463-2473"},"PeriodicalIF":9.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640662","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}

引用次数: 0

Predictive Observer-Based Dual-Rate Prescribed Performance Control for Visual Servoing of Robot Manipulators With View Constraints 基于预测观察器的双速率规定性能控制，用于具有视线限制的机器人机械手视觉伺服。

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-14 DOI: 10.1109/TCYB.2025.3546800

Qifang Liu;Jianliang Mao;Linyan Han;Chuanlin Zhang;Jun Yang

引用次数: 0

Data-Driven Inverse Reinforcement Learning for Heterogeneous Optimal Robust Formation Control 数据驱动逆强化学习，实现异质最优鲁棒编队控制。

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-14 DOI: 10.1109/TCYB.2025.3546563

Fatemeh Mahdavi Golmisheh;Saeed Shamaghdari

{"title":"Data-Driven Inverse Reinforcement Learning for Heterogeneous Optimal Robust Formation Control","authors":"Fatemeh Mahdavi Golmisheh;Saeed Shamaghdari","doi":"10.1109/TCYB.2025.3546563","DOIUrl":"10.1109/TCYB.2025.3546563","url":null,"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.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 5","pages":"2024-2037"},"PeriodicalIF":9.4,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630072","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}

引用次数: 0

Granule Margin-Based Feature Selection in Weighted Neighborhood Systems 加权邻域系统中基于颗粒边缘的特征选择

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-12 DOI: 10.1109/TCYB.2025.3544693

Can Gao;Jie Zhou;Xizhao Wang;Witold Pedrycz

{"title":"Granule Margin-Based Feature Selection in Weighted Neighborhood Systems","authors":"Can Gao;Jie Zhou;Xizhao Wang;Witold Pedrycz","doi":"10.1109/TCYB.2025.3544693","DOIUrl":"10.1109/TCYB.2025.3544693","url":null,"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.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 5","pages":"2151-2164"},"PeriodicalIF":9.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608064","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}

引用次数: 0

Observer-Based Control of Networked Periodic Piecewise Systems With Encoding–Decoding Mechanism 基于观测器的编解码网络周期分段系统控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-12 DOI: 10.1109/tcyb.2025.3543878

Yun Liu, Wen Yang, Chun-Yi Su, Yue Luo, Xiaofan Wang

引用次数: 0

Robust Model Predictive Control of a Gait Rehabilitation Exoskeleton With Whole Body Motion Planning and Neuro-Dynamics Optimization 基于全身运动规划和神经动力学优化的步态康复外骨骼鲁棒模型预测控制

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-11 DOI: 10.1109/TCYB.2025.3545064

Liangrui Xu;Zhijun Li;Guoxin Li;Lingjing Jin

{"title":"Robust Model Predictive Control of a Gait Rehabilitation Exoskeleton With Whole Body Motion Planning and Neuro-Dynamics Optimization","authors":"Liangrui Xu;Zhijun Li;Guoxin Li;Lingjing Jin","doi":"10.1109/TCYB.2025.3545064","DOIUrl":"10.1109/TCYB.2025.3545064","url":null,"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.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 5","pages":"2124-2137"},"PeriodicalIF":9.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598820","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}

引用次数: 0

Industrial Foundation Model 工业基础模型

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-11 DOI: 10.1109/TCYB.2025.3527632

Lei Ren;Haiteng Wang;Jiabao Dong;Zidi Jia;Shixiang Li;Yuqing Wang;Yuanjun Laili;Di Huang;Lin Zhang;Bohu Li

{"title":"Industrial Foundation Model","authors":"Lei Ren;Haiteng Wang;Jiabao Dong;Zidi Jia;Shixiang Li;Yuqing Wang;Yuanjun Laili;Di Huang;Lin Zhang;Bohu Li","doi":"10.1109/TCYB.2025.3527632","DOIUrl":"10.1109/TCYB.2025.3527632","url":null,"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.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 5","pages":"2286-2301"},"PeriodicalIF":9.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10922728","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598855","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}

引用次数: 0

Observer-Based Decentralized Adaptive Control of Interconnected Nonlinear Systems With Output/Input Triggering 输出/输入触发互联非线性系统的观测器分散自适应控制

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-11 DOI: 10.1109/TCYB.2025.3545279

Zhirong Zhang;Yongduan Song;Xiaoyuan Zheng;Long Chen;Petros Ioannou

{"title":"Observer-Based Decentralized Adaptive Control of Interconnected Nonlinear Systems With Output/Input Triggering","authors":"Zhirong Zhang;Yongduan Song;Xiaoyuan Zheng;Long Chen;Petros Ioannou","doi":"10.1109/TCYB.2025.3545279","DOIUrl":"10.1109/TCYB.2025.3545279","url":null,"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.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 5","pages":"2390-2399"},"PeriodicalIF":9.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598854","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}

引用次数: 0