IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第3页

Event-Triggered Direct Data-Driven Iterative Learning Control for Multiagent Systems 多智能体系统的事件触发直接数据驱动迭代学习控制

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-08-20 DOI: 10.1109/TSMC.2025.3596544

Na Lin;Ronghu Chi;Biao Huang

{"title":"Event-Triggered Direct Data-Driven Iterative Learning Control for Multiagent Systems","authors":"Na Lin;Ronghu Chi;Biao Huang","doi":"10.1109/TSMC.2025.3596544","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3596544","url":null,"abstract":"Aiming to solve issues of limited resources in topology network communication, unavailability of the mathematical models, direct controller design without considering system dynamical formulation, and lack of efficient use of learning ability from repetitive operations, an event-triggered direct data driven iterative learning control (ET-DirDDILC) is developed for a multiagent system (MAS). Since the control protocol directly affects control performance, there is definitely a close relationship between the consensus performance of the agents and the control protocols. To this end, a nonaffine nonlinear relationship of consensus error regarding the control protocol is established. Then, to deal with the unknown nonlinearity, a dynamic linear input–output relationship between two triggered batches is established by an event-triggering linearly parametric data model (ET-LPDM) where a triggering mechanism is designed along the iteration axis. Furthermore, both the event-triggered control law and the event-triggered parameter estimation law are derived from two objective functions, respectively, by using the ET-LPDM, where the values at nontriggering iteration remain unchanged from the latest triggering iteration to reduce the consumption of system resources. The proposed ET-DirDDILC does not rely on the MAS dynamical formulation. The convergence is proved and simulation study verifies the effectiveness of the presented ET-DirDDILC for MASs with both fixed and switching topologies.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7499-7509"},"PeriodicalIF":8.7,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090116","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

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-08-19 DOI: 10.1109/TSMC.2025.3594810

引用次数: 0

A Knowledge-Guided Co-Evolutionary Algorithm for Energy-Efficient Distributed Assembly Welding Shop Scheduling Problem 基于知识引导的分布式装配焊接车间节能调度协同进化算法

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-08-19 DOI: 10.1109/TSMC.2025.3594350

Fei Yu;Liang Gao;Chao Lu;Lvjiang Yin

{"title":"A Knowledge-Guided Co-Evolutionary Algorithm for Energy-Efficient Distributed Assembly Welding Shop Scheduling Problem","authors":"Fei Yu;Liang Gao;Chao Lu;Lvjiang Yin","doi":"10.1109/TSMC.2025.3594350","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3594350","url":null,"abstract":"The growing trend toward decentralization within factories has brought attention to distributed welding shop scheduling problem (DWSP) among both practitioners and researchers. However, despite the prevalence of job-to-product assembly process in industrial fields, the investigation of distributed assembly welding shop scheduling problem (DAWSP) remains unexplored. Meanwhile, given the energy-intensive characteristic of welding operations, addressing energy consumption in welding shop is crucial for achieving environmental sustainability. Thus, this study investigates the energy-efficient DAWSP (EDAWSP), focusing on minimizing total energy consumption (TEC) and makespan. The proposed approaches include a mixed integer linear programming (MILP) model and a knowledge-guided co-evolutionary algorithm (KCEA). In KCEA, a knowledge coefficient is defined to build a bridge that connects the welding part and assembly part. By incorporating knowledge coefficient and weight-sum approach, an effective initialization strategy is proposed for producing a superior initial population. To effectively complete evolutionary process, a co-evolutionary operator is devised based on bi-population strategy. To improve KCEA’s exploitation capability, a local search is developed within the variable neighborhood search (VNS) framework, utilizing six critical-path-based neighborhood structures. Besides, an energy-saving strategy is presented to further minimize TEC without increasing makespan. Finally, a series of comparison experiments are executed. The experimental results illustrate that all improved components of KCEA contribute to its performance, and KCEA outperforms other six optimization algorithms in solving EDAWSP.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"6937-6950"},"PeriodicalIF":8.7,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100508","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 Lot-Streaming Scheduling Method of Multi-Resource Constrained Flexible Job Shop 多资源约束柔性作业车间的高效批流调度方法

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-08-19 DOI: 10.1109/TSMC.2025.3593373

Zhiqiang Tian;Xingyu Jiang;Weijun Liu;Guangdong Tian;Zhiwu Li;Weiwei Liu

{"title":"Energy-Efficient Lot-Streaming Scheduling Method of Multi-Resource Constrained Flexible Job Shop","authors":"Zhiqiang Tian;Xingyu Jiang;Weijun Liu;Guangdong Tian;Zhiwu Li;Weiwei Liu","doi":"10.1109/TSMC.2025.3593373","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3593373","url":null,"abstract":"To cope with the problems of high-computational complexity and multiple locally optimal solutions induced by the coupling of multiple subproblems, conflicting objectives, and integration of resource constraints of the energy-efficient lot-streaming scheduling of multi-resource constrained flexible job shop (<inline-formula> <tex-math>$gamma $ </tex-math></inline-formula>-shop for short), an energy-efficient lot-streaming scheduling optimization approach based on the knowledge-based lot-splitting method (KLSM) and the improved multiobjective evolutionary algorithm (IMOEA) is presented. First, a flexible job shop lot-splitting scheduling model with the optimization objectives of total energy consumption, makespan, and total processing cost is formulated. Second, a hybrid approach of the KLSM and the IMOEA is designed to solve the model. The solution space of the problem is fully explored based on the moth-flame operator. Co-evolutionary operators are performed to promote information interaction among populations, hence both the population diversity and the convergence effect of the algorithm are improved. Moreover, a post-adjustment strategy based on adjacent processes is developed to reduce unnecessary fixture changes. Finally, extended experiments between some KLSM-based well-known and novel algorithms, including the proposed IMOEA, MOEA/D, NSGA-II, MOPSO, SGECF, SCEA, and SLMEA are conducted in benchmark problems and a real-world case of machine tool plant. The results show that the proposed method outperforms its competitors on co-optimization of lot-splitting, machine allocation, operation sequencing, and fixture assignment of the <inline-formula> <tex-math>$gamma $ </tex-math></inline-formula>-shop scheduling, which can effectively reduce total energy consumption, makespan, and total processing cost.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"6901-6912"},"PeriodicalIF":8.7,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100290","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

IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information IEEE系统、人与控制论汇刊：系统出版信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-08-19 DOI: 10.1109/TSMC.2025.3594816

引用次数: 0

IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors IEEE系统、人与控制论汇刊：作者的系统信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-08-19 DOI: 10.1109/TSMC.2025.3594822

引用次数: 0

A Piecewise Varying Coefficient Dual Criterion Optimization Method for Motion Planning of Manipulators With Insufficient Redundancy 不充分冗余机械臂运动规划的分段变系数双准则优化方法

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-08-19 DOI: 10.1109/TSMC.2025.3594148

Jinjia Guo;Xiaohui Ren;Zhijun Zhang

{"title":"A Piecewise Varying Coefficient Dual Criterion Optimization Method for Motion Planning of Manipulators With Insufficient Redundancy","authors":"Jinjia Guo;Xiaohui Ren;Zhijun Zhang","doi":"10.1109/TSMC.2025.3594148","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3594148","url":null,"abstract":"In order to solve the insufficient redundancy problem and slow convergence in multiple end-effector tasks, a piecewise varying-gain dual-criterion optimization (PVDO) method is proposed for motion planning of insufficient redundant manipulators. To achieve this, the convergence coefficients are designed to be piecewise varying, and the end-effector task is divided into two phases. In the initial phase, only the end-effector position task is considered and the fixed coefficient convergence method is adopted, which can take into consideration both end-effector task and secondary task optimization. In the second phase, the end-effector position and orientation are taken into account concurrently, and time-varying coefficients are used for end-effector task. The convergence coefficients are time varying to enhance the convergence speed, particularly when the errors are small in the later phase of task planning. This can ensure the optimization of secondary tasks when the manipulator is insufficient-redundant, and accomplish the end-effector task planning in a relatively fast speed. Finally, experiments are conducted to demonstrate the effectiveness of the proposed PVDO method in obstacle avoidance and joint limits avoidance.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"6964-6974"},"PeriodicalIF":8.7,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100480","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

IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information IEEE系统、人与控制论汇刊：系统出版信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-08-19 DOI: 10.1109/TSMC.2025.3594748

引用次数: 0

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-08-19 DOI: 10.1109/TSMC.2025.3594820

引用次数: 0

Environment-Adaptive Synergistic Swarm With Flexible Obstacle Avoidance via Active and Passive Strategy 基于主动和被动策略的环境自适应协同避障群体

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-08-19 DOI: 10.1109/TSMC.2025.3594546

Kai Shen;Shiying Li;Yinghe Ding;Zheng Xu;Pengxiang Yang

{"title":"Environment-Adaptive Synergistic Swarm With Flexible Obstacle Avoidance via Active and Passive Strategy","authors":"Kai Shen;Shiying Li;Yinghe Ding;Zheng Xu;Pengxiang Yang","doi":"10.1109/TSMC.2025.3594546","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3594546","url":null,"abstract":"The fascinating collective behaviors of natural swarm systems have inspired extensive studies on configuration generation of drone swarm. In this article, we propose a synergistic swarm algorithm (SSA) to realize stable spacing configuration and consistent flight of drones. In order to cope with complex mission requirements and achieve safe and fast flight in dense environments, we further propose a flexible obstacle avoidance (FOA) strategy via passive and active environmental adaption. passive obstacle avoidance algorithm provides drones with self-adaptive forces along drone-obstacle linkages for getting rid of dangerous position and keeping swarm safe. active obstacle avoidance algorithm provides drones with lateral forces at a certain distance for correcting course of traversal and keeping swarm rapid. We carried out a series of simulation experiments, including swarms of up to 16 drones in mass point model and of up to four drones in six degrees of freedom model. Simulation results illustrated that our strategy and algorithms can ensure fast flight speed and safety of the swarm in dense environments.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"6925-6936"},"PeriodicalIF":8.7,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100379","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