Pub Date : 2025-01-15DOI: 10.1109/TSMC.2024.3523065
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information","authors":"","doi":"10.1109/TSMC.2024.3523065","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3523065","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"C2-C2"},"PeriodicalIF":8.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-15DOI: 10.1109/TSMC.2024.3523053
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information","authors":"","doi":"10.1109/TSMC.2024.3523053","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3523053","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"C2-C2"},"PeriodicalIF":8.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-15DOI: 10.1109/TSMC.2024.3523055
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TSMC.2024.3523055","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3523055","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"C3-C3"},"PeriodicalIF":8.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-15DOI: 10.1109/TSMC.2024.3523059
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors","authors":"","doi":"10.1109/TSMC.2024.3523059","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3523059","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"C4-C4"},"PeriodicalIF":8.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10842692","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article is focused on the stabilization and control of variable fractional-order (VFO) neutral delay systems with time-varying structured uncertainties and delays. Using the Lyapunov theorem, the delay- and order-dependent stability criterion of both the nominal and uncertain VFO systems of neutral-type with time-varying delays are derived using a set of linear matrix inequalities (LMIs). An LMI-based output-feedback proportional-derivative (PD) control law that meets the delay-dependent and order-dependent criteria is then derived to guarantee the asymptotic stability of such systems. The performance of the proposed control structure is assessed using a simulation study of two VFO systems.
{"title":"Robust Delay-Dependent Output-Feedback PD Controller Design for Variable Fractional-Order Uncertain Neutral Systems With Time-Varying Delays","authors":"Zahra Sadat Aghayan;Alireza Alfi;Yashar Mousavi;Afef Fekih","doi":"10.1109/TSMC.2024.3508573","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3508573","url":null,"abstract":"This article is focused on the stabilization and control of variable fractional-order (VFO) neutral delay systems with time-varying structured uncertainties and delays. Using the Lyapunov theorem, the delay- and order-dependent stability criterion of both the nominal and uncertain VFO systems of neutral-type with time-varying delays are derived using a set of linear matrix inequalities (LMIs). An LMI-based output-feedback proportional-derivative (PD) control law that meets the delay-dependent and order-dependent criteria is then derived to guarantee the asymptotic stability of such systems. The performance of the proposed control structure is assessed using a simulation study of two VFO systems.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"1986-1996"},"PeriodicalIF":8.6,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438465","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}
Pub Date : 2025-01-08DOI: 10.1109/TSMC.2024.3523369
Baoyu Wen;Jiangshuai Huang;Xiaojie Su;Yue Yang
This article investigates the distributed control of a group of stochastic high-order nonlinear systems in which the subsystems are with unknown and time-varying control coefficients of unknown signs, inherent nonlinear drift and diffusion terms. To solve the control problem with unknown control directions, where traditional available Nussbaum functions are not applicable for the consensus of stochastic nonlinear systems with unknown and time-varying coefficients of unknown signs, a novel type of Nussbaum function is proposed with a new paradigm of stability analysis in probability. Global consensus control of stochastic multiagent systems is achieved by designing distributed controllers which integrate designed distributed filters and novel Nussbaum functions. In addition, it can be proved that all signals in the closed-loop system are bound in probability, and the transient consensus errors of the followers are bounded by positive constants which can be adjusted arbitrarily small. The effectiveness of the proposed control scheme is demonstrated by simulation results.
{"title":"Consensus Control of Nonlinear Stochastic Multiagent Systems With Unknown and Time-Varying Control Coefficients Based on Novel Nussbaum Functions","authors":"Baoyu Wen;Jiangshuai Huang;Xiaojie Su;Yue Yang","doi":"10.1109/TSMC.2024.3523369","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3523369","url":null,"abstract":"This article investigates the distributed control of a group of stochastic high-order nonlinear systems in which the subsystems are with unknown and time-varying control coefficients of unknown signs, inherent nonlinear drift and diffusion terms. To solve the control problem with unknown control directions, where traditional available Nussbaum functions are not applicable for the consensus of stochastic nonlinear systems with unknown and time-varying coefficients of unknown signs, a novel type of Nussbaum function is proposed with a new paradigm of stability analysis in probability. Global consensus control of stochastic multiagent systems is achieved by designing distributed controllers which integrate designed distributed filters and novel Nussbaum functions. In addition, it can be proved that all signals in the closed-loop system are bound in probability, and the transient consensus errors of the followers are bounded by positive constants which can be adjusted arbitrarily small. The effectiveness of the proposed control scheme is demonstrated by simulation results.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"2264-2276"},"PeriodicalIF":8.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438464","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}
Pub Date : 2025-01-08DOI: 10.1109/TSMC.2024.3520526
Yanchi Li;Dongcheng Li;Wenyin Gong;Qiong Gu
Multiobjective multitask optimization (MO-MTO) aims to exploit the similarities among different multiobjective optimization tasks through knowledge transfer (KT), facilitating their simultaneous resolution. The effective design of KT techniques embedded in multiobjective evolutionary optimizers is crucial for enhancing the performance of multiobjective multitask evolutionary algorithms (MO-MTEAs). However, a significant limitation of existing KT techniques in MO-MTEAs is their equal treatment of particles/individuals for transferred knowledge reception, which can negatively impact the balance of diversity and convergence in population evolution. To remedy this limitation, this article proposes a new MO-MTEA, named MTEA-DCK, which incorporates diversity-oriented KT (DKT) and convergence-oriented KT (CKT) techniques tailored for different particles in the population. MTEA-DCK utilizes a strength-Pareto-based competitive mechanism to divide particles into winners and losers: 1) for winners, DKT is conducted via an intertask domain alignment approach to enhance population diversity and 2) for losers, CKT is executed within the unified search space to improve convergence. Additionally, to ensure robust performance on complex task combinations, we introduce two automatic parameter control strategies specifically designed for these KT techniques. MTEA-DCK was performed on 39 benchmark MO-MTO problems and demonstrated superior performance compared to eight state-of-the-art MO-MTEAs and six multiobjective evolutionary algorithms. Finally, we present three real-world MO-MTO application cases, where our approach also yielded better results than other algorithms.
{"title":"Multiobjective Multitask Optimization via Diversity- and Convergence-Oriented Knowledge Transfer","authors":"Yanchi Li;Dongcheng Li;Wenyin Gong;Qiong Gu","doi":"10.1109/TSMC.2024.3520526","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3520526","url":null,"abstract":"Multiobjective multitask optimization (MO-MTO) aims to exploit the similarities among different multiobjective optimization tasks through knowledge transfer (KT), facilitating their simultaneous resolution. The effective design of KT techniques embedded in multiobjective evolutionary optimizers is crucial for enhancing the performance of multiobjective multitask evolutionary algorithms (MO-MTEAs). However, a significant limitation of existing KT techniques in MO-MTEAs is their equal treatment of particles/individuals for transferred knowledge reception, which can negatively impact the balance of diversity and convergence in population evolution. To remedy this limitation, this article proposes a new MO-MTEA, named MTEA-DCK, which incorporates diversity-oriented KT (DKT) and convergence-oriented KT (CKT) techniques tailored for different particles in the population. MTEA-DCK utilizes a strength-Pareto-based competitive mechanism to divide particles into winners and losers: 1) for winners, DKT is conducted via an intertask domain alignment approach to enhance population diversity and 2) for losers, CKT is executed within the unified search space to improve convergence. Additionally, to ensure robust performance on complex task combinations, we introduce two automatic parameter control strategies specifically designed for these KT techniques. MTEA-DCK was performed on 39 benchmark MO-MTO problems and demonstrated superior performance compared to eight state-of-the-art MO-MTEAs and six multiobjective evolutionary algorithms. Finally, we present three real-world MO-MTO application cases, where our approach also yielded better results than other algorithms.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"2367-2379"},"PeriodicalIF":8.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465569","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}
The application of automated guided vehicle (AGV) greatly improves the production efficiency of workshop. However, machine flexibility and limited logistics equipment increase the complexity of collaborative scheduling, and frequent dynamic events bring uncertainty. Therefore, this article proposes a real-time scheduling method for dynamic flexible job shop scheduling problem with AGVs using multiagent reinforcement learning (MARL). Specifically, a real-time scheduling framework is proposed in which a multiagent scheduling architecture is designed for achieving task selection, machine allocation and AGV allocation. Then, an action space and an efficient action decoding algorithm are proposed, which enable agents to explore in the high-quality solution space and improve the learning efficiency. In addition, a state space with generalization, a reward function considering machine idle time and a strategy for handling four disturbance events are designed to minimize the total tardiness cost. Comparison experiments show that the proposed method outperforms the priority dispatching rules, genetic programming and four popular reinforcement learning (RL)-based methods, with performance improvements mostly exceeding 10%. Furthermore, experiments considering four disturbance events demonstrate that the proposed method has strong robustness, and it can provide appropriate scheme for uncertain manufacturing system.
{"title":"Real-Time Scheduling for Flexible Job Shop With AGVs Using Multiagent Reinforcement Learning and Efficient Action Decoding","authors":"Yuxin Li;Qingzheng Wang;Xinyu Li;Liang Gao;Ling Fu;Yanbin Yu;Wei Zhou","doi":"10.1109/TSMC.2024.3520381","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3520381","url":null,"abstract":"The application of automated guided vehicle (AGV) greatly improves the production efficiency of workshop. However, machine flexibility and limited logistics equipment increase the complexity of collaborative scheduling, and frequent dynamic events bring uncertainty. Therefore, this article proposes a real-time scheduling method for dynamic flexible job shop scheduling problem with AGVs using multiagent reinforcement learning (MARL). Specifically, a real-time scheduling framework is proposed in which a multiagent scheduling architecture is designed for achieving task selection, machine allocation and AGV allocation. Then, an action space and an efficient action decoding algorithm are proposed, which enable agents to explore in the high-quality solution space and improve the learning efficiency. In addition, a state space with generalization, a reward function considering machine idle time and a strategy for handling four disturbance events are designed to minimize the total tardiness cost. Comparison experiments show that the proposed method outperforms the priority dispatching rules, genetic programming and four popular reinforcement learning (RL)-based methods, with performance improvements mostly exceeding 10%. Furthermore, experiments considering four disturbance events demonstrate that the proposed method has strong robustness, and it can provide appropriate scheme for uncertain manufacturing system.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"2120-2132"},"PeriodicalIF":8.6,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438448","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}
Pub Date : 2025-01-07DOI: 10.1109/TSMC.2024.3522968
Yuhan Wang;Zhuping Wang;Hao Zhang;Huaicheng Yan
This article investigates the optimal group consensus problem (GCP) in multiagent systems (MASs). To address this problem, a novel distributed optimal control policy is designed in the framework of off-policy reinforcement learning (RL). First, a framework for multiagent differential graphical games is formulated. Second, a min-max strategy is then introduced to ensure the achievement of group consensus through a data-driven value iteration (VI) approach. Finally, the presented consensus control policy is extended to address the group formation tracking problem (GFTP) of nonholonomic mobile robots, with a numerical example to illustrate the efficacy of the proposed results. Compared with the existing literature, this article has the following contributions: 1) A group of agents are decomposed into multiple subgroups to accomplish different consensus objectives; 2) the prior knowledge of agents’ dynamics and initial stabilizing control gains can be eliminated; and 3) the performance index function (PIF) for each agent is designed to integrate not only its individual control policy but also that of its neighboring agents.
{"title":"Optimal Group Consensus of Multiagent Systems in Graphical Games Using Reinforcement Learning","authors":"Yuhan Wang;Zhuping Wang;Hao Zhang;Huaicheng Yan","doi":"10.1109/TSMC.2024.3522968","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3522968","url":null,"abstract":"This article investigates the optimal group consensus problem (GCP) in multiagent systems (MASs). To address this problem, a novel distributed optimal control policy is designed in the framework of off-policy reinforcement learning (RL). First, a framework for multiagent differential graphical games is formulated. Second, a min-max strategy is then introduced to ensure the achievement of group consensus through a data-driven value iteration (VI) approach. Finally, the presented consensus control policy is extended to address the group formation tracking problem (GFTP) of nonholonomic mobile robots, with a numerical example to illustrate the efficacy of the proposed results. Compared with the existing literature, this article has the following contributions: 1) A group of agents are decomposed into multiple subgroups to accomplish different consensus objectives; 2) the prior knowledge of agents’ dynamics and initial stabilizing control gains can be eliminated; and 3) the performance index function (PIF) for each agent is designed to integrate not only its individual control policy but also that of its neighboring agents.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"2343-2353"},"PeriodicalIF":8.6,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465577","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}
The Epileptor model is a mathematical framework utilized for simulating the transition from interictal to ictal local field potential (LFP) activity in the brain, with the aim of predicting and preventing epileptic seizures. This article introduces a novel approach integrating Lyapunov and Poincaré–Bendixson methods to analyze the stability of limit cycles in nonlinear systems, specifically focusing on Epileptors with a two-state dynamic. Our method accurately delineates the limit cycle boundary through eigenvalue-based analysis, facilitating precise assessment of stability properties and identification of critical regions linked to seizure initiation and termination. Through the investigation of the two-state dynamics of Epileptors, we gain deeper insights into the transition between low activity and seizure states, consequently improving our understanding of epileptic seizures. Our approach can be employed to establish stability conditions and determine the existence of limit cycles in Epileptor models, which can further aid in predicting and preventing epileptic seizures by identifying critical regions associated with seizure initiation and termination. The simulations conducted in this study demonstrate that the model under investigation exhibits stable limit cycle behavior and manifests bifurcation, with significant implications for the development of targeted interventions and more effective prediction and treatments for epilepsy. The findings indicate that the suggested approach establishes that external stimulation should not surpass 10.8 mA. Moreover, the initial normal state lies within the range of −1.6 to −0.1 ictal LFP. On the other hand, the LaSalle and eigenvalue methods individually cannot precisely determine the limit cycle region.
{"title":"New Eigenvalue-Based Analysis for Precise Limit Cycle Stability Assessment in a Two-State Epileptor Model","authors":"Samaneh-Alsadat Saeedinia;Mohammad-Reza Jahed-Motlagh;Nikola Kirilov Kasabov;Abbas Tafakhori","doi":"10.1109/TSMC.2024.3517620","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3517620","url":null,"abstract":"The Epileptor model is a mathematical framework utilized for simulating the transition from interictal to ictal local field potential (LFP) activity in the brain, with the aim of predicting and preventing epileptic seizures. This article introduces a novel approach integrating Lyapunov and Poincaré–Bendixson methods to analyze the stability of limit cycles in nonlinear systems, specifically focusing on Epileptors with a two-state dynamic. Our method accurately delineates the limit cycle boundary through eigenvalue-based analysis, facilitating precise assessment of stability properties and identification of critical regions linked to seizure initiation and termination. Through the investigation of the two-state dynamics of Epileptors, we gain deeper insights into the transition between low activity and seizure states, consequently improving our understanding of epileptic seizures. Our approach can be employed to establish stability conditions and determine the existence of limit cycles in Epileptor models, which can further aid in predicting and preventing epileptic seizures by identifying critical regions associated with seizure initiation and termination. The simulations conducted in this study demonstrate that the model under investigation exhibits stable limit cycle behavior and manifests bifurcation, with significant implications for the development of targeted interventions and more effective prediction and treatments for epilepsy. The findings indicate that the suggested approach establishes that external stimulation should not surpass 10.8 mA. Moreover, the initial normal state lies within the range of −1.6 to −0.1 ictal LFP. On the other hand, the LaSalle and eigenvalue methods individually cannot precisely determine the limit cycle region.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"2062-2072"},"PeriodicalIF":8.6,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438339","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}
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