IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第5页

Recursive State Estimation for Complex Networks With Energy Harvesting Constraints and Decode-and-Forward Relays 具有能量收集约束和译码转发中继的复杂网络递归状态估计

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-03 DOI: 10.1109/TSMC.2025.3572087

Miaomiao Shi;Lifeng Ma;Xiaojian Yi

引用次数: 0

A Fuzzy Transfer Reservoir Learning Machine Through Domain Enhancement on Multiple Sources 基于多源域增强的模糊传递水库学习机

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-03 DOI: 10.1109/TSMC.2025.3571955

Jiawei Lin;Fu-Lai Chung;Shitong Wang

{"title":"A Fuzzy Transfer Reservoir Learning Machine Through Domain Enhancement on Multiple Sources","authors":"Jiawei Lin;Fu-Lai Chung;Shitong Wang","doi":"10.1109/TSMC.2025.3571955","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3571955","url":null,"abstract":"While transfer learning through source domain enhancement with the mix-up strategy on multiple sources is applied to reservoir computing (RC) related resource-constrained scenarios, this study aims at addressing two seldom-concerned phenomena: 1) the similarity degrees between the target domain and each of all the source domains may perhaps change after reservoir transformation so as to possibly change their similarity rankings before and after that transformation; 2) the decision boundaries between classes may become more uncertain. In order to achieve this goal, a fuzzy transfer reservoir learning machine (FT-RLM) is proposed based on the well-known leaky integrator echo state network (LI-ESN). In particular, in order to determine which source domains should be enhanced by the mix-up strategy after reservoir transformation, with the theoretical derivation of the mix-up ratios for source domain selection, FT-RLM begins with the use of the mix-up strategy based on the calculated mix-up ratios for source domain enhancement. After that, in order to deal with uncertain decision boundaries between classes, FT-RLM takes the proposed transfer-learning-based fuzzy classifier called parametric-transfer-based Takagi-Sugeno–Kang fuzzy system (TSK-FS) which is trained on both the enhanced source domains and the target domain. Experimental results on real-world datasets validate the effectiveness of the proposed FT-RLM when faced with the above two phenomena in multiple source reservoir transfer learning scenarios under RC-related resource-constrained environments.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 8","pages":"5744-5757"},"PeriodicalIF":8.6,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663735","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

Target Interception of Marine Surface Vehicle With Dynamic Memory Event-Triggered Prescribed Performance Control 基于动态记忆事件触发的预定性能控制的海面车辆目标拦截

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-03 DOI: 10.1109/TSMC.2025.3572371

Shang Liu;Ge Guo

引用次数: 0

Optimal Scheduling of Single-Armed Cluster Tools With Two Wafer Types and Chamber Cleaning 两种晶圆类型单臂集束工具的优化调度及腔室清洗

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-03 DOI: 10.1109/TSMC.2025.3571733

Fajun Yang;Chao Li;Chunjiang Zhang;NaiQi Wu

引用次数: 0

Constraint-Coupled Distributed Coordination Control for Nonlinear Stochastic Multiagent Systems: Application to Power Resource Allocation 非线性随机多智能体系统的约束耦合分布式协调控制：在电力资源分配中的应用

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-03 DOI: 10.1109/TSMC.2025.3570640

Haokun Hu;Quanxin Zhu;Muzhou Hou

{"title":"Constraint-Coupled Distributed Coordination Control for Nonlinear Stochastic Multiagent Systems: Application to Power Resource Allocation","authors":"Haokun Hu;Quanxin Zhu;Muzhou Hou","doi":"10.1109/TSMC.2025.3570640","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3570640","url":null,"abstract":"This article studies the distributed coordination control problem of the nonlinear stochastic multiagent system, which involves multiple inequality constraints, as well as random disturbances. The communication network is modeled as an undirected and connected graph, where each node has a cost function that is considered to be strongly convex. First, an algorithm is developed that utilizes state feedback and projection operations, along with auxiliary variables, to precisely estimate the optimal state and its derivatives in a distributed manner. Furthermore, the decision variable is subject to several inequality constraints, and there are no restrictions on the initial value. Taking into account the impact of nonlinearity resulting from drift coefficients, diffusion coefficients, and external disturbances on the system’s stability, the existing works cannot be directly applied to this study. Through the utilization of the Itô formula and convex analysis, a novel analytical approach has been demonstrated to establish the asymptotic convergence of decision variable to the optimal value in mean square. This method distinguishes itself from the conventional convergence analysis techniques. Finally, the algorithm is utilized for the allocation of energy resources, resulting in the attainment of the optimal regulating method for power resource allocation.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 8","pages":"5520-5530"},"PeriodicalIF":8.6,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657404","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

Optimal Performance of Discrete Networked Systems With Cyber-Attack and Packet Dropouts 具有网络攻击和丢包的离散网络系统的最优性能

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-02 DOI: 10.1109/TSMC.2025.3571466

Xiaowei Jiang;Xinyu Ren;Bo Li;Feng Liu;Wuhua Chen

引用次数: 0

Adaptive Fuzzy Tracking Control for Uncertain Nonlinear Systems With Unknown Control Gain Functions via Intermittent Output 控制增益函数未知的不确定非线性系统间歇输出自适应模糊跟踪控制

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-02 DOI: 10.1109/TSMC.2025.3572602

Xinjun Wang;Shenghang Liu;Xin Wang;Ben Niu;Wenqi Zhou;Xinmin Song

{"title":"Adaptive Fuzzy Tracking Control for Uncertain Nonlinear Systems With Unknown Control Gain Functions via Intermittent Output","authors":"Xinjun Wang;Shenghang Liu;Xin Wang;Ben Niu;Wenqi Zhou;Xinmin Song","doi":"10.1109/TSMC.2025.3572602","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3572602","url":null,"abstract":"Based on output triggering, an adaptive prescribed-time tracking control strategy is proposed for a class of uncertain strict-feedback nonlinear systems with unknown control gains in this article. The nondifferentiability of the virtual control signals is identified as the most prominent design difficulty in this research. In order to solve the above difficulty, a new fuzzy state observer is built by using triggered output signal and fuzzy logic systems (FLSs), which in turn generates alternative continuous states. Simultaneously, the estimated signals are utilized to design virtual control signals, making certain that the virtual control signals have a well-defined first derivative. On this basis, by introducing a time-varying constraint function, a new adaptive prescribed-time fuzzy controller is constructed, so that the controller can still be applied to continuously operating systems after the predefined time. Additionally, we introduce the command filtering technique to mitigate repeated differentiation of virtual control signals during the backstepping design process. Combining the constructed logarithmic Lyapunov functions and bounded control technique with backstepping design, it is possible to guarantee that the established adaptive prescirbed-time event-triggered control method satisfies the following: 1) within the predefined time, the tracking error converges to the user-specified region and 2) the full range of signals involved in the closed-loop system is kept bounded.At last, the results of the single-link arm simulation example verify the reasonableness and effectiveness of the established control scheme.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 8","pages":"5400-5409"},"PeriodicalIF":8.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662897","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

Benefit Maximization or the-Quality-First? An E-CARGO Perspective on the Logistics Chain 效益最大化还是质量第一？物流链的电子货运视角

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-02 DOI: 10.1109/TSMC.2025.3571717

Junhao Chen;Haibin Zhu;Dongning Liu

{"title":"Benefit Maximization or the-Quality-First? An E-CARGO Perspective on the Logistics Chain","authors":"Junhao Chen;Haibin Zhu;Dongning Liu","doi":"10.1109/TSMC.2025.3571717","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3571717","url":null,"abstract":"In the logistics chain, through collaboration, multiple supplier enterprises are able to achieve resource sharing, thereby offering a broader and more stable service scope while enhancing risk resilience. However, despite these benefits, differences in the distribution capabilities of various supplier enterprises can lead to inconsistencies in the overall service quality of distribution tasks. Therefore, in collaborative distribution, it is crucial to ensure the overall service quality while maximizing benefit. To address this challenge, this article formalizes the collaborative distribution problem (CDP) in the logistics chain via the environments — classes, agents, roles, groups, objects (E-CARGO) model. A novel solution is designed for CDP by extending the group multirole assignment (GMRA) model. This solution incorporates the qualification matrix adjustments (QMA) algorithm to systematically prioritize suppliers based on their qualifications, thereby maximizing benefit while ensuring the overall service quality of collaborative distribution tasks. Large-scale random experiments show that the proposed method effectively balances the tradeoff between the benefit and overall service quality in the CDP under various data distributions. Moreover, decision-makers can obtain an optimal assignment solution through the Pareto front.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 8","pages":"5345-5361"},"PeriodicalIF":8.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657340","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

A Reinforcement Learning Control Method for Process Industry Based on Implicit and Explicit Knowledge Extraction and Embedding 基于隐式和显式知识提取与嵌入的过程工业强化学习控制方法

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-30 DOI: 10.1109/TSMC.2025.3559766

Tianhao Liu;Chunhua Yang;Can Zhou;Yonggang Li;Bei Sun

{"title":"A Reinforcement Learning Control Method for Process Industry Based on Implicit and Explicit Knowledge Extraction and Embedding","authors":"Tianhao Liu;Chunhua Yang;Can Zhou;Yonggang Li;Bei Sun","doi":"10.1109/TSMC.2025.3559766","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3559766","url":null,"abstract":"The process industry is a key manufacturing process that consumes a vast amount of energy consumption. On the premise of ensuring process stability, controlling process variables to operate the process close to the optimal working condition plays a critical role in reducing energy consumption. Reinforcement learning (RL), using trial and error to learn control strategies, has received much attention. However, the substantial fluctuations of process variables and the switching delay gap of the process industry result in a high-dimension state-action space, making it difficult to learn control strategies efficiently, and there is no guarantee of control stability. To get around these issues, first, a generic knowledge-extracted method for process industry RL control is proposed. It does not require laborious expert knowledge acquisition processes. Second, to improve learning efficiency, the implicit knowledge is extracted using decision trees from operation trajectory data and embedded into agent controllers. Third, an explicit knowledge-oriented reward constructing method is designed to guarantee control stability. A case of the zinc electrowinning process is provided to validate its superiority. The result shows that it can reduce power consumption while stabilizing process variables within the spec limits, without a laborious expert knowledge acquisition process.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 8","pages":"5152-5165"},"PeriodicalIF":8.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657360","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

Differential Flatness-Based Fast Trajectory Planning for Fixed-Wing Autonomous Aerial Vehicles 基于差分平面的固定翼自主飞行器快速轨迹规划

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-29 DOI: 10.1109/TSMC.2025.3559591

Junzhi Li;Jingliang Sun;Teng Long;Zhenlin Zhou

{"title":"Differential Flatness-Based Fast Trajectory Planning for Fixed-Wing Autonomous Aerial Vehicles","authors":"Junzhi Li;Jingliang Sun;Teng Long;Zhenlin Zhou","doi":"10.1109/TSMC.2025.3559591","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3559591","url":null,"abstract":"Due to the strong nonlinearity and nonholonomic dynamics, despite the various general trajectory optimization methods presented, few of them can guarantee efficient computation and physical feasibility for relatively complicated fixed-wing autonomous aerial vehicles (AAVs) dynamics. Aiming at this issue, this article investigates a differential flatness-based trajectory optimization method for fixed-wing AAVs (DFTO-FW). The customized trajectory representation is presented through differential flat characteristics analysis and polynomial parameterization, eliminating equality constraints to avoid the heavy computational burdens of solving complex dynamics. Through the design of integral performance costs and derivation of analytical gradients, the original trajectory optimization is transcribed into a lightweight, unconstrained, gradient-analytical optimization with linear time complexity to improve efficiency further. The simulation experiments illustrate the superior efficiency of the DFTO-FW, which takes subsecond CPU time (on a personal desktop) against other competitors by orders of magnitude to generate fixed-wing AAV trajectories in randomly generated obstacle environments.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 8","pages":"5220-5233"},"PeriodicalIF":8.6,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657434","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