IEEE Transactions on Cybernetics最新文献_第5页

Finite-Time Stabilizers for Large-Scale Stochastic Boolean Networks.

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-18 DOI: 10.1109/TCYB.2025.3545689

Lin Lin, James Lam, Wai-Ki Ching, Qian Qiu, Liangjie Sun, Bo Min

{"title":"Finite-Time Stabilizers for Large-Scale Stochastic Boolean Networks.","authors":"Lin Lin, James Lam, Wai-Ki Ching, Qian Qiu, Liangjie Sun, Bo Min","doi":"10.1109/TCYB.2025.3545689","DOIUrl":"10.1109/TCYB.2025.3545689","url":null,"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.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657078","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

Resilient Distributed Nash Equilibrium Control for Nonlinear MASs Under DoS Attacks.

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-18 DOI: 10.1109/TCYB.2025.3543675

Shihan Zhou, Chao Deng, Sha Fan, Bohui Wang, Wei-Wei Che

{"title":"Resilient Distributed Nash Equilibrium Control for Nonlinear MASs Under DoS Attacks.","authors":"Shihan Zhou, Chao Deng, Sha Fan, Bohui Wang, Wei-Wei Che","doi":"10.1109/TCYB.2025.3543675","DOIUrl":"10.1109/TCYB.2025.3543675","url":null,"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.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657090","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

Bilateral Cooperative Control of Nonlinear Multiagent Systems With State and Output Quantification.

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.

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 H∞ 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∞ 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∞ 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":"PP ","pages":""},"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

IF 11.8 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

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":"PP ","pages":""},"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

IF 11.8 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

引用次数: 0

Observer-Based Control of Networked Periodic Piecewise Systems With Encoding–Decoding Mechanism

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