IEEE Transactions on Cybernetics最新文献

Data-Driven Adaptive Control for Discrete-Time Linear Systems With Delayed Inputs. 时滞输入离散线性系统的数据驱动自适应控制。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-18 DOI: 10.1109/tcyb.2025.3582377

Ai-Guo Wu,Yuan Meng

引用次数: 0

Fixed-Time Optimal Consensus of Multiagent Systems Under Cyber-Attacks: A Hierarchical Control Approach. 网络攻击下多智能体系统的定时最优一致性：一种层次控制方法。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-18 DOI: 10.1109/tcyb.2025.3583368

Ruihong Li,Qintao Gan,Guoquan Ren,Huaiqin Wu,Jinde Cao

{"title":"Fixed-Time Optimal Consensus of Multiagent Systems Under Cyber-Attacks: A Hierarchical Control Approach.","authors":"Ruihong Li,Qintao Gan,Guoquan Ren,Huaiqin Wu,Jinde Cao","doi":"10.1109/tcyb.2025.3583368","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3583368","url":null,"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.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"672 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661870","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

Game-Based Distributed Decision Optimization for Heterogeneous Multiagent Systems With Unknown Nonlinear Dynamics. 未知非线性动力学异构多智能体系统基于博弈的分布式决策优化。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-18 DOI: 10.1109/tcyb.2025.3581999

Hao Wang,Hao Luo,Yuchen Jiang,Shimeng Wu

{"title":"Game-Based Distributed Decision Optimization for Heterogeneous Multiagent Systems With Unknown Nonlinear Dynamics.","authors":"Hao Wang,Hao Luo,Yuchen Jiang,Shimeng Wu","doi":"10.1109/tcyb.2025.3581999","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3581999","url":null,"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.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"109 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661868","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

Toward Improved Performance of Inner Convex Approximation for Suboptimal Nonlinear MPC. 次优非线性MPC的内凸逼近性能改进研究。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-17 DOI: 10.1109/tcyb.2025.3583588

Jinxian Wu,Li Dai,Songshi Dou,Yunshan Deng,Yuanqing Xia

{"title":"Toward Improved Performance of Inner Convex Approximation for Suboptimal Nonlinear MPC.","authors":"Jinxian Wu,Li Dai,Songshi Dou,Yunshan Deng,Yuanqing Xia","doi":"10.1109/tcyb.2025.3583588","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3583588","url":null,"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.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"73 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144652873","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 Adaptive Accelerated Nash Equilibrium Seeking for Noncooperative Games: A Differentially Private Method. 非合作博弈的分布式自适应加速纳什均衡寻求：一种差分私有方法。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-17 DOI: 10.1109/tcyb.2025.3579593

Ruixu Hu,Wenying Xu,Li Sun,Jinde Cao

{"title":"Distributed Adaptive Accelerated Nash Equilibrium Seeking for Noncooperative Games: A Differentially Private Method.","authors":"Ruixu Hu,Wenying Xu,Li Sun,Jinde Cao","doi":"10.1109/tcyb.2025.3579593","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3579593","url":null,"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.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"52 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144652721","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

EKG-AC: A New Paradigm for Process Industrial Optimization Based on Offline Reinforcement Learning With Expert Knowledge Guidance. EKG-AC：基于专家知识指导下离线强化学习的过程工业优化新范式。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-17 DOI: 10.1109/tcyb.2025.3579361

Diju Liu,Yalin Wang,Chenliang Liu,Biao Luo,Biao Huang

{"title":"EKG-AC: A New Paradigm for Process Industrial Optimization Based on Offline Reinforcement Learning With Expert Knowledge Guidance.","authors":"Diju Liu,Yalin Wang,Chenliang Liu,Biao Luo,Biao Huang","doi":"10.1109/tcyb.2025.3579361","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3579361","url":null,"abstract":"Operation optimization plays a crucial role in process control, directly influencing product quality and profitability. Reinforcement learning (RL), with its capabilities in autonomous learning and dynamic adaptability, has become a promising solution in this domain. However, its real-world application is constrained by the high costs and risks associated with its interactions with environments. Offline RL, which leverages fixed datasets without interactions, offers an alternative but faces significant challenges in the process industry due to imbalanced multioperating condition scenarios and heightened safety sensitivity. To address these challenges, this article introduces a novel offline actor-critic algorithm with expert knowledge guidance (EKG-AC). The method begins with a diffusion-transformer-based action generation framework that mitigates the out-of-distribution problem by capturing the evolution of decision sequences and the interdependencies between states and actions. An expert knowledge guidance mechanism is then integrated, steering the model to generate safe and adaptive candidate actions aligned with current operating conditions and expert knowledge. Subsequently, within the actor-critic framework, the optimal action is selected from the candidate pool based on the evaluated Q-value, thereby setting the operational variables for the optimization task. The proposed algorithm is validated through two real-world industrial processes, demonstrating superior optimization performance and behavior that is closely aligned with expert decision-making, underscoring its substantial practical value.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"12 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144652913","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

Anti-Quasisynchronization for Asynchronous Leader-Follower Markovian Neural Networks With Hidden Markov Model-Based Intermittent Control. 基于隐马尔可夫模型间歇控制的异步Leader-Follower马尔可夫神经网络的反准同步。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-16 DOI: 10.1109/tcyb.2025.3582043

Zijing Xiao,Meng Zhang,Hongxia Rao,Chang Liu,Yong Xu

引用次数: 0

A Novel DoS-Attack-Aware Event-Triggered Synchronization Control for Discrete-Time Fuzzy Complex Networks Under Round-Robin Protocol. 轮循协议下离散模糊复杂网络感知dos攻击的事件触发同步控制。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-16 DOI: 10.1109/tcyb.2025.3583666

Huaguang Zhang,Zhihong Liang,Juan Zhang,Qiongwen Zhang

{"title":"A Novel DoS-Attack-Aware Event-Triggered Synchronization Control for Discrete-Time Fuzzy Complex Networks Under Round-Robin Protocol.","authors":"Huaguang Zhang,Zhihong Liang,Juan Zhang,Qiongwen Zhang","doi":"10.1109/tcyb.2025.3583666","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3583666","url":null,"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.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"12 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645765","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

Multiagent Consensus Tracking Control Over Asynchronous Cooperation–Competition Networks 异步合作-竞争网络的多智能体共识跟踪控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-15 DOI: 10.1109/tcyb.2025.3583387

Weihao Li, Shuaiming Yan, Lei Shi, Jiangfeng Yue, Mengji Shi, Boxian Lin, Kaiyu Qin

引用次数: 0

Mean Square Exponential l 2-l ∞ Control of Switched-Markovian Jump Systems With Edge-Dependent Transition Probability 具有边缘相关转移概率的切换马尔可夫跳跃系统的均方指数l2 -l∞控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-15 DOI: 10.1109/tcyb.2025.3583790

Linlin Hou, Shanshan Cui, Dong Yang, Haibin Sun

引用次数: 0