IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第6页

Complexity Dynamics and Fuzzy Optimal Prescribed-Time Control of a Large Network of Bidirectionally Coupled FO PMSG 大型双向耦合FO PMSG网络的复杂性动力学与模糊最优规定时间控制

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-21 DOI: 10.1109/TSMC.2026.3652808

Shaohua Luo;Ya Zhang;Ye Cao;Frank L. Lewis

{"title":"Complexity Dynamics and Fuzzy Optimal Prescribed-Time Control of a Large Network of Bidirectionally Coupled FO PMSG","authors":"Shaohua Luo;Ya Zhang;Ye Cao;Frank L. Lewis","doi":"10.1109/TSMC.2026.3652808","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3652808","url":null,"abstract":"Attaining both fast synchronization with high precision and operational stability in multimachine power systems remains a mammoth technical challenge. This article investigates complexity dynamics and fuzzy optimal prescribed-time control of a network of bidirectionally coupled fractional-order (FO) permanent magnet synchronous generator (PMSG). First, a mathematical model of a large network of bidirectionally coupled FO PMSG with few neighboring node connections and coupling strength is established based on the topological layout of a wind farm. Complexity dynamics reveal that this network can exhibit chimera and global synchronization states under varying conditions, including different FOs and coupling strengths. To achieve global synchronization, preset tracking precision within a prescribed-time, security constraint, and operational flexibility in this safety-critical network, a fuzzy optimal prescribed-time control strategy comprising a feedforward neural prescribed-time controller and a feedback safe optimal controller is proposed, using a type-3 fuzzy neural network (FNN) to address system uncertainty. Furthermore, we develop a hardware-in-the-loop simulation platform based on the STM32F767IGT6 microcontroller, featuring: 12-bit digital to analog converter (DAC) for waveform generation to oscilloscope, direct memory access (DMA)-controlled data transmission bypassing CPU intervention, and timer-configured overflow and event triggering mechanisms. Finally, extensive results further validate the feasibility and effectiveness of our scheme.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 4","pages":"2347-2360"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505793","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

Energy-Efficient Power Scheduling for State Estimation Over Two-Hop Relay Networks 两跳中继网络状态估计的节能电力调度

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-22 DOI: 10.1109/TSMC.2026.3650971

Xue Zhang;Yao Li;Xinping Guan

{"title":"Energy-Efficient Power Scheduling for State Estimation Over Two-Hop Relay Networks","authors":"Xue Zhang;Yao Li;Xinping Guan","doi":"10.1109/TSMC.2026.3650971","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3650971","url":null,"abstract":"In this article, an online power scheduling problem over two-hop relay networks is investigated. We first provide a general framework of the transmission scheduling problem for the two-hop case under power constraints. Due to the typical characteristics of the multihop transmissions, centralized and decentralized transmission scheduling schemes are introduced, where centralized and decentralized delays are properly defined, respectively. A threshold-triggered algorithm is figured out for the sensor/relay to switch between different power levels, where the triggering threshold and selection probabilities can be determined by the given power constraint. For the centralized scheme (CS), we explicitly analyze the performance by providing the upper and lower bounds of the estimation performance; for the decentralized scheme (DS), the estimation performance is also figured out in an analytical form. The comparisons between proposed online strategies and the offline ones have shown the superiority of online strategies over the optimal offline strategy. Numerical simulations have validated the effectiveness and superiority of our proposed algorithms.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 4","pages":"2749-2762"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507309","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

Finite-Time Stability and State-Feedback Stabilization of Output-Constrained Stochastic Nonlinear Systems 输出约束随机非线性系统的有限时间稳定性和状态反馈镇定

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-14 DOI: 10.1109/TSMC.2025.3649985

Huifang Min;Shengyuan Xu;Guozeng Cui;Shang Shi

引用次数: 0

A Search Strategy With Graph Attention Networks and Neighborhood Search Features for Dynamic Flexible Job Shop Scheduling 基于图关注网络和邻域搜索特征的动态柔性作业车间调度搜索策略

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-14 DOI: 10.1109/TSMC.2025.3649526

Fuqing Zhao;Yu Jia;Ling Wang;Yang Yu

{"title":"A Search Strategy With Graph Attention Networks and Neighborhood Search Features for Dynamic Flexible Job Shop Scheduling","authors":"Fuqing Zhao;Yu Jia;Ling Wang;Yang Yu","doi":"10.1109/TSMC.2025.3649526","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3649526","url":null,"abstract":"Addressing the dynamic flexible job shop scheduling (DFJSS) problem to generate a high-quality rescheduling scheme has gained increasing significance in various applications in recent years, such as the intelligent manufacturing system for aluminum profiles. In the intelligent manufacturing system for aluminum profiles, random events such as machine faults and variable processing times significantly affect production completion times. However, existing methods for solving DFJSS face challenges in generating high-quality rescheduling schemes in real time. To address these issues, a search strategy that combines graph neural networks (GNNs) and neighborhood search features is employed in the Monte Carlo tree search (MCTS) algorithm (GNS-MCTS) for real-time solving of the DFJSS. The search strategy in the GNS-MCTS aims to build a model that analyzes the scheduling disjunctive graph and predicts the searching probability, guiding the MCTS to important locations in the solution space rather than searching unimportant areas and wasting computing time. Specifically, the search strategy of GNS-MCTS incorporates encoders that fuse neighborhood search features and graph embedding vectors (GEVs) to improve the quality of the searching probability and make decisions about optimizing the scheduling disjunctive graph by breaking low-importance node pairs and reallocating low-importance nodes to other machines. Experimental results indicate that GNS-MCTS surpasses baseline algorithms across various problem sizes and real-time constraints, significantly enhancing computational efficiency and solution quality for the DFJSS. Further ablation studies and analysis reveal the impact of neighborhood search features on the enhancement of the learning-based search strategy when combined with neighborhood search for real-time DFJSS solutions.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 4","pages":"2778-2792"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506997","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

Fuzzy Reinforcement Learning-Based Safe Cooperative Control for Nonlinear Multiagent Systems 基于模糊强化学习的非线性多智能体系统安全协同控制

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-16 DOI: 10.1109/TSMC.2025.3650055

Jinshan Bian;Chaoxu Mu;Hongbing Xia;Chenyi Si

{"title":"Fuzzy Reinforcement Learning-Based Safe Cooperative Control for Nonlinear Multiagent Systems","authors":"Jinshan Bian;Chaoxu Mu;Hongbing Xia;Chenyi Si","doi":"10.1109/TSMC.2025.3650055","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3650055","url":null,"abstract":"This article investigates safe cooperative control problem for nonlinear multiagent systems (MASs) with composite constraints and multiactuator faults. Specifically, the composite constraints primarily consist of state constraints and terminal time constraints. For the former, state-mapping functions and universal barrier functions are introduced to address asymmetric dynamic delayed constraints, which are further enhanced through state transformation to create new state variables. This approach eliminates the limitations imposed by initial system conditions and the influence of unknown terms, while accommodating both unconstrained and constrained scenarios. The latter introduces terminal time constraints on the basis of the former framework to rapidly satisfy system performance requirements. Furthermore, neural networks (NNs) are employed to approximate the reconstructed multiple fault information and system uncertainties. Then, an actor-critic-identifier architecture based on fuzzy reinforcement learning (RL) is constructed via an optimal backstepping (OB) method, enabling the solution of the Hamilton–Jacobi–Bellman equation within each subsystem without the need for persistent excitation. Finally, by integrating Lyapunov stability theory with graph theory, it is proven that all signals are bounded, and the followers ultimately achieve dynamic consensus with the leader. Simulation results are provided to demonstrate the effectiveness of this control strategy.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 4","pages":"2240-2252"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504348","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

Altruism–Fairness Driven Optimization for Group Decision-Making Under Uncertainty 不确定条件下群体决策的利他-公平驱动优化

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-22 DOI: 10.1109/TSMC.2025.3649876

Han Wang;Zheng Ma;Zhen-Song Chen;Zengqiang Wang;Francisco Chiclana

{"title":"Altruism–Fairness Driven Optimization for Group Decision-Making Under Uncertainty","authors":"Han Wang;Zheng Ma;Zhen-Song Chen;Zengqiang Wang;Francisco Chiclana","doi":"10.1109/TSMC.2025.3649876","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3649876","url":null,"abstract":"The significance of group decision-making (GDM) has grown substantially in public affairs and resource allocation. However, existing research predominantly emphasizes consensus analysis, often neglecting the social preferences exhibited by decision-makers in real-world scenarios. To bridge this gap, this article introduces a multiobjective optimization-based GDM model that incorporates the social preferences of decision-makers. First, a decision-maker opinion representation model is developed using probability density functions (PDFs), integrating altruism and fairness utility functions to comprehensively capture the preferences of diverse decision-makers. Second, by introducing altruism–fairness utility as a third optimization objective, the traditional bi-objective model is extended to balance consensus, confidence, and altruism–fairness, aiming to achieve more robust decision outcomes. Finally, a case study on the high-speed rail (HSR) line between Chengdu and Chongqing is conducted to preliminarily validate the model’s effectiveness. Passenger evaluations of the importance of various service attributes were collected and analyzed using the proposed multiobjective optimization-based collective opinion generation model. The results indicate that while the inclusion of the altruism–fairness objective slightly reduces consensus on certain indicators, it significantly enhances overall fairness. This demonstrates that incorporating fairness principles into practical management can lead to more balanced and stable decision-making. This study offers novel insights into integrating altruistic preferences and fairness principles into GDM and lays a foundation for broader applications in future research.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 4","pages":"2583-2596"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504742","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

Stochastic Stabilization for Nonlinear Systems: A Noise-Compensated Prediction Scheme 非线性系统的随机镇定：一种噪声补偿预测方案

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-14 DOI: 10.1109/TSMC.2025.3649511

Peiyang Lin;Feiqi Deng;Xueyan Zhao;Fangzhe Wan

引用次数: 0

Controllability of Lexicographic Product Networks 词典编纂产品网络的可控性

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-23 DOI: 10.1109/TSMC.2026.3651616

Bo Liu;Qiang Zhang;Junjie Huang;Housheng Su

引用次数: 0

Prescribed-Time Fault-Tolerant Formation for Heterogeneous Switched Multiagent Systems 异构交换多智能体系统的规定时间容错形成

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-27 DOI: 10.1109/TSMC.2026.3654568

Haiying Zhang;Chen Chen;Zhengrong Xiang

引用次数: 0

Deep Learning-Driven DDoS Attack Detection and Security Control in Autonomous Ground Vehicle: A Restart Controller Approach 深度学习驱动的自动地面车辆DDoS攻击检测与安全控制：一种重启控制器方法

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-21 DOI: 10.1109/TSMC.2026.3652137

Qiao Cheng;Xiao Cai;Yanbin Sun;Kaibo Shi;Zhihong Tian

{"title":"Deep Learning-Driven DDoS Attack Detection and Security Control in Autonomous Ground Vehicle: A Restart Controller Approach","authors":"Qiao Cheng;Xiao Cai;Yanbin Sun;Kaibo Shi;Zhihong Tian","doi":"10.1109/TSMC.2026.3652137","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3652137","url":null,"abstract":"This study investigates the security issues of networked control systems (NCSs) under distributed denial of service (DDoS) attacks. The DDoS attacks overwhelm a target system by flooding it with excessive fake traffic or requests, causing network congestion. This prevents the system from processing legitimate control requests and ultimately leads to system failure. First, to free up the substantial memory resources on the controller and mitigate the impact of distributed DDoS attacks on the performance of NCSs, we design a deep learning supervised DDoS attack detection and restart control mechanism that ensures that the duration of each attack is effectively limited to the tolerable interval of the system. In addition, a straightforward Lyapunov–Krasovskii functional (LKF) is formulated to analyze the stability of NCSs facing DDoS attacks under the restart control mechanism. Furthermore, it simplifies the stability analysis by avoiding complex calculations and diminishes the conservativeness of the stability criteria through the integration of sophisticated integral inequality techniques. Finally, simulations with an autonomous ground vehicle (AGV) model validate the practical benefits and effectiveness of the proposed theoretical advancements.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 4","pages":"2287-2296"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504346","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