Pub Date : 2025-10-16DOI: 10.1109/TSMC.2025.3618077
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors","authors":"","doi":"10.1109/TSMC.2025.3618077","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3618077","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"C4-C4"},"PeriodicalIF":8.7,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11205933","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335260","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 : 2025-10-16DOI: 10.1109/TSMC.2025.3618069
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors","authors":"","doi":"10.1109/TSMC.2025.3618069","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3618069","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"C4-C4"},"PeriodicalIF":8.7,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11205935","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335308","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 : 2025-10-09DOI: 10.1109/TSMC.2025.3616150
Xingqiang Zhao;Yongduan Song;Hefu Ye
This article investigates a distributed event-triggered control approach that integrates a partial time-interval error constraint (PTIEC) for multirobot cooperative manipulation of a shared object. Considering the scenario where only a subset of robots possesses accurate information regarding the positions (orientations) and corresponding velocities of the object’s desired local trajectory, a novel dual-layer control framework is established, comprising an estimation (first) layer and an actuation (second) layer. In the estimation layer, the desired local trajectory is estimated by means of the novel distributed prescribed-time estimators without the undesirable infinite gain issue. In the actuation layer, a stretching function is introduced and integrated with the barrier function to facilitate the error constraints within some partial time intervals (occurring at a noninitial moment), effectively converting the constrained system into a “constraint-free” one. Then, a distributed event-triggered control law is constructed, taking into account the unknown dynamics of both the robots and the manipulated object, as well as communication delays. The communication and actuation are executed at specific event times, thus alleviating the burden of control updates and communication costs of the robot system. Finally, the feasibility and effectiveness of the proposed dual-layer control technique are confirmed through simulations.
{"title":"Adaptive Distributed Event-Triggered Cooperative Manipulation of Multiple Manipulators Under Partial Time-Interval Error Constraints","authors":"Xingqiang Zhao;Yongduan Song;Hefu Ye","doi":"10.1109/TSMC.2025.3616150","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3616150","url":null,"abstract":"This article investigates a distributed event-triggered control approach that integrates a partial time-interval error constraint (PTIEC) for multirobot cooperative manipulation of a shared object. Considering the scenario where only a subset of robots possesses accurate information regarding the positions (orientations) and corresponding velocities of the object’s desired local trajectory, a novel dual-layer control framework is established, comprising an estimation (first) layer and an actuation (second) layer. In the estimation layer, the desired local trajectory is estimated by means of the novel distributed prescribed-time estimators without the undesirable infinite gain issue. In the actuation layer, a stretching function is introduced and integrated with the barrier function to facilitate the error constraints within some partial time intervals (occurring at a noninitial moment), effectively converting the constrained system into a “constraint-free” one. Then, a distributed event-triggered control law is constructed, taking into account the unknown dynamics of both the robots and the manipulated object, as well as communication delays. The communication and actuation are executed at specific event times, thus alleviating the burden of control updates and communication costs of the robot system. Finally, the feasibility and effectiveness of the proposed dual-layer control technique are confirmed through simulations.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9309-9323"},"PeriodicalIF":8.7,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546983","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 : 2025-10-08DOI: 10.1109/TSMC.2025.3612496
Tal Arie;Tal Oron-Gilad;Vardit Sarne-Fleishman;Yael Edan
We investigated variable autonomy in a teleoperated robot navigation task within a simulated office environment, focusing on who controls the shift between levels of automation (LoA). Two switch control modes—human-initiated (HI) and robot-initiated (RI)—were examined. The study assessed whether these modes influence operators’ cognitive workload, situational awareness (SA), and task performance. The task involved two LoA conditions: teleoperation (low LoA) and remote monitoring (high LoA), while participants simultaneously performed a secondary task. Task performance was measured through completion time, collision rates, time spent in low LoA, and secondary task performance. Operator-related measures included workload [National Aeronautics and Space Administration Task Load Index (NASA-TLX)], trust, ease of use, and perceived SA. The results revealed a clear tradeoff between operational efficiency and situational awareness. The RI mode improved task efficiency and reduced workload but compromised SA, whereas HI mode enhanced SA at the cost of efficiency. These findings highlight the inherent tension between automation-driven performance gains and operator’s cognitive engagement. The study contributes to the understanding of variable autonomy in human–robot collaboration (HRC) and underscores the need to evaluate both operator experience and task performance when designing teleoperation systems for habituated environments.
{"title":"Evaluating Variable Autonomy for a Teleoperated Navigation Task in a Habituated Environment","authors":"Tal Arie;Tal Oron-Gilad;Vardit Sarne-Fleishman;Yael Edan","doi":"10.1109/TSMC.2025.3612496","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3612496","url":null,"abstract":"We investigated variable autonomy in a teleoperated robot navigation task within a simulated office environment, focusing on who controls the shift between levels of automation (LoA). Two switch control modes—human-initiated (HI) and robot-initiated (RI)—were examined. The study assessed whether these modes influence operators’ cognitive workload, situational awareness (SA), and task performance. The task involved two LoA conditions: teleoperation (low LoA) and remote monitoring (high LoA), while participants simultaneously performed a secondary task. Task performance was measured through completion time, collision rates, time spent in low LoA, and secondary task performance. Operator-related measures included workload [National Aeronautics and Space Administration Task Load Index (NASA-TLX)], trust, ease of use, and perceived SA. The results revealed a clear tradeoff between operational efficiency and situational awareness. The RI mode improved task efficiency and reduced workload but compromised SA, whereas HI mode enhanced SA at the cost of efficiency. These findings highlight the inherent tension between automation-driven performance gains and operator’s cognitive engagement. The study contributes to the understanding of variable autonomy in human–robot collaboration (HRC) and underscores the need to evaluate both operator experience and task performance when designing teleoperation systems for habituated environments.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9057-9068"},"PeriodicalIF":8.7,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546965","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 : 2025-10-08DOI: 10.1109/TSMC.2025.3614262
Kun Li;Zhipeng L端;Junwen Ding;Zhouxing Su;Qingyun Zhang
This article studies the two-dimensional (2-D) rectangle packing area minimization problem (RPAMP), a key subproblem in floor planning for very large-scale integration (VLSI) chip design. The goal of RPAMP is to orthogonally pack a set of rectangles into a variable-sized rectangular container without overlap, while minimizing the area of the container. By transforming the original problem into a series of 2-D strip packing problems (2DSPs), we propose a two-stage adaptive search algorithm (TS-ASA) to tackle the RPAMP. TS-ASA incorporates several distinctive features: First, a new candidate width pruning strategy is introduced, which limits the number of rectangles used for width combinations, thus reducing the search space. Second, the packing process is divided into two stages, with distinct scoring rules for each stage to optimize space utilization. Additionally, a multirestart strategy is employed to identify the appropriate switching point for the scoring rules. Tested on 39 public benchmark instances and compared with existing state-of-the-art algorithms, TS-ASA improves the best-known solutions for 27 instances and matches the best results for two instances. The experimental results demonstrate the effectiveness and efficiency of the proposed TS-ASA.
{"title":"A Two-Stage Adaptive Search Algorithm for the 2-D Rectangle Packing Area Minimization Problem","authors":"Kun Li;Zhipeng L端;Junwen Ding;Zhouxing Su;Qingyun Zhang","doi":"10.1109/TSMC.2025.3614262","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3614262","url":null,"abstract":"This article studies the two-dimensional (2-D) rectangle packing area minimization problem (RPAMP), a key subproblem in floor planning for very large-scale integration (VLSI) chip design. The goal of RPAMP is to orthogonally pack a set of rectangles into a variable-sized rectangular container without overlap, while minimizing the area of the container. By transforming the original problem into a series of 2-D strip packing problems (2DSPs), we propose a two-stage adaptive search algorithm (TS-ASA) to tackle the RPAMP. TS-ASA incorporates several distinctive features: First, a new candidate width pruning strategy is introduced, which limits the number of rectangles used for width combinations, thus reducing the search space. Second, the packing process is divided into two stages, with distinct scoring rules for each stage to optimize space utilization. Additionally, a multirestart strategy is employed to identify the appropriate switching point for the scoring rules. Tested on 39 public benchmark instances and compared with existing state-of-the-art algorithms, TS-ASA improves the best-known solutions for 27 instances and matches the best results for two instances. The experimental results demonstrate the effectiveness and efficiency of the proposed TS-ASA.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9120-9132"},"PeriodicalIF":8.7,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546982","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}
Personalized federated learning (PFL) has emerged as an efficient way to tailor global models to individual data features. However, in real-world deployments, the dynamic nature of client participation—where clients may join or quit arbitrarily—poses significant challenges for PFL systems. We propose elastic PFL (EPFL), an EPFL framework that accommodates dynamic client participation while preserving model performance. Our framework addresses two fundamental challenges: 1) seamlessly generating personalized models for newly joining unlabeled clients and 2) achieving efficient and precise multigrain unlearning for quitting clients. To address these challenges, we utilize hypernetworks for client-specific model generation and introduce a novel client embedding regularization (CER) to enhance generalization capabilities for new clients. Furthermore, we design an adversarial purification mechanism that enables efficient multigrained unlearning at the client, sample, and class levels. We provide theoretical analysis establishing both generalization bounds for new clients and convergence guarantees for the unlearning process. Extensive experimental results demonstrate that EPFL outperforms the existing PFL methods in terms of both accuracy and generalization capabilities. Through backdoor trigger experiments, we show that EPFL achieves unlearning efficacy and fidelity comparable to complete retraining while requiring only 3%–10% of the computational time of these existing approaches.
{"title":"EPFL: Toward Elastic Personalized Federated Learning With Seamless Client Joining and Quitting","authors":"Yuange Liu;Daobin Luo;Weishan Zhang;Wei Jiang;Chaoqun Zheng;Xiaohui Sun;Yuru Liu;Qiao Qiao;Tao Chen;Hongwei Zhao;Su Yang;Fei-Yue Wang","doi":"10.1109/TSMC.2025.3613624","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3613624","url":null,"abstract":"Personalized federated learning (PFL) has emerged as an efficient way to tailor global models to individual data features. However, in real-world deployments, the dynamic nature of client participation—where clients may join or quit arbitrarily—poses significant challenges for PFL systems. We propose elastic PFL (EPFL), an EPFL framework that accommodates dynamic client participation while preserving model performance. Our framework addresses two fundamental challenges: 1) seamlessly generating personalized models for newly joining unlabeled clients and 2) achieving efficient and precise multigrain unlearning for quitting clients. To address these challenges, we utilize hypernetworks for client-specific model generation and introduce a novel client embedding regularization (CER) to enhance generalization capabilities for new clients. Furthermore, we design an adversarial purification mechanism that enables efficient multigrained unlearning at the client, sample, and class levels. We provide theoretical analysis establishing both generalization bounds for new clients and convergence guarantees for the unlearning process. Extensive experimental results demonstrate that EPFL outperforms the existing PFL methods in terms of both accuracy and generalization capabilities. Through backdoor trigger experiments, we show that EPFL achieves unlearning efficacy and fidelity comparable to complete retraining while requiring only 3%–10% of the computational time of these existing approaches.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9324-9338"},"PeriodicalIF":8.7,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546964","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 all-wheel steering technology enhances vehicle maneuverability, while a modular, plug-and-play chassis electrical architecture facilitates the integration of more electronic control units. Hence, this article aims to improve vehicle handling stability performance by integrating the active front-wheel steering system (AFS) and the active rear-wheel steering system (ARS) within a multiagent game theory framework. First, a vehicle dynamics model integrating AFS and ARS control, along with system uncertainties, is developed. Numerical simulations are performed to validate the effectiveness of integrated ARS in enhancing vehicle handling stability performance. Then, a multiagent system (MAS) framework is constructed to achieve coordinated control between AFS and ARS based on distributed model predictive control (DMPC), with interactive behaviors among agents defined. Furthermore, cooperative game theory is introduced to find optimal solutions. To guarantee the system’s stability and antidisturbance capabilities, robust compensation control and terminal constraints are designed. The MAS framework can also address the development requirements for modularization in chassis system design. Finally, we validated the effectiveness of the proposed method through experiments conducted under several typical test conditions. The comparative tests demonstrate that the MAS framework can improve the vehicle’s handling stability.
{"title":"Robust Game-Theory Control for All-Wheel Steering to Enhance Vehicle Handling Stability Performance","authors":"Jinhao Liang;Cheng Shen;Xin Xia;Dawei Pi;Guodong Yin","doi":"10.1109/TSMC.2025.3615943","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3615943","url":null,"abstract":"The all-wheel steering technology enhances vehicle maneuverability, while a modular, plug-and-play chassis electrical architecture facilitates the integration of more electronic control units. Hence, this article aims to improve vehicle handling stability performance by integrating the active front-wheel steering system (AFS) and the active rear-wheel steering system (ARS) within a multiagent game theory framework. First, a vehicle dynamics model integrating AFS and ARS control, along with system uncertainties, is developed. Numerical simulations are performed to validate the effectiveness of integrated ARS in enhancing vehicle handling stability performance. Then, a multiagent system (MAS) framework is constructed to achieve coordinated control between AFS and ARS based on distributed model predictive control (DMPC), with interactive behaviors among agents defined. Furthermore, cooperative game theory is introduced to find optimal solutions. To guarantee the system’s stability and antidisturbance capabilities, robust compensation control and terminal constraints are designed. The MAS framework can also address the development requirements for modularization in chassis system design. Finally, we validated the effectiveness of the proposed method through experiments conducted under several typical test conditions. The comparative tests demonstrate that the MAS framework can improve the vehicle’s handling stability.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9230-9241"},"PeriodicalIF":8.7,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546989","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 : 2025-10-07DOI: 10.1109/TSMC.2025.3614484
Alexander Diedrich;Mattias Krysander;Rene Heesch;Oliver Niggemann
Existing algorithms for consistency-based fault diagnosis are sound and complete according to some correct logical model. But obtaining a good model is the crucial and difficult part. Originally, the classical diagnosis algorithms were developed to analyze Boolean circuits, such that simple propositional or predicate logic models were sufficient to express the structure and behavior. Cyber–physical systems, however, exhibit hybrid behavior, meaning they generate continuous and discrete values that need to be interpreted. This adds significant complexity. Furthermore, modern cyber–physical systems may change their structure over their lifetime and thus require model adaptations. This article presents a novel formalism to model cyber–physical systems for consistency-based fault diagnosis. Drawing from the research fields of artificial intelligence and control theory, the approach models system structure and behavior through the use of satisfiability modulo nonlinear arithmetic. The approach proves advantageous compared to previous modeling techniques through its integration of nonlinear behavior models, implicit computation of residual values to compute fault symptoms, its representation of different operating modes of the system, and its integration with existing sound and complete fault diagnosis algorithms. The approach was validated empirically using two benchmarks from the process industry, a simulation of battery packs, and Boolean standard circuits. Throughout all experiments, an accuracy of 97% was achieved.
{"title":"Modeling Cyber–Physical Systems for Fault Diagnosis","authors":"Alexander Diedrich;Mattias Krysander;Rene Heesch;Oliver Niggemann","doi":"10.1109/TSMC.2025.3614484","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3614484","url":null,"abstract":"Existing algorithms for consistency-based fault diagnosis are sound and complete according to some correct logical model. But obtaining a good model is the crucial and difficult part. Originally, the classical diagnosis algorithms were developed to analyze Boolean circuits, such that simple propositional or predicate logic models were sufficient to express the structure and behavior. Cyber–physical systems, however, exhibit hybrid behavior, meaning they generate continuous and discrete values that need to be interpreted. This adds significant complexity. Furthermore, modern cyber–physical systems may change their structure over their lifetime and thus require model adaptations. This article presents a novel formalism to model cyber–physical systems for consistency-based fault diagnosis. Drawing from the research fields of artificial intelligence and control theory, the approach models system structure and behavior through the use of satisfiability modulo nonlinear arithmetic. The approach proves advantageous compared to previous modeling techniques through its integration of nonlinear behavior models, implicit computation of residual values to compute fault symptoms, its representation of different operating modes of the system, and its integration with existing sound and complete fault diagnosis algorithms. The approach was validated empirically using two benchmarks from the process industry, a simulation of battery packs, and Boolean standard circuits. Throughout all experiments, an accuracy of 97% was achieved.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9266-9279"},"PeriodicalIF":8.7,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11194731","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546984","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 : 2025-10-07DOI: 10.1109/TSMC.2025.3615639
Fang Liu;Deyuan Meng;Qiang Song
This article is aimed at solving the distributed control problem for a leaderless heterogeneous network with signed digraph topology and locally Lipschitz nonlinear dynamics, where the agents may have nonidentical nodal functions and different state dimensions. To realize the control objectives of the heterogeneous nonlinear signed network, a distributed iterative learning control (ILC) algorithm is developed based on the relative outputs of local neighbors from the previous iteration. Despite the difficulties induced by nodal heterogeneities and nonlinearities, it is shown that the outputs of leaderless heterogeneous signed network under the developed ILC algorithm can exhibit the typical behaviors of conventional signed networks over a finite time period. To be more specific, the heterogeneous nonlinear signed network achieves either bipartite or interval bipartite output consensus (respectively, global output stability) along the iteration axis when the subgraph formed by root nodes is structurally balanced (respectively, unbalanced). Numerical studies are conducted to illustrate the proposed ILC scheme.
{"title":"Distributed Iterative Learning Control of Leaderless Heterogeneous Nonlinear Signed Networks","authors":"Fang Liu;Deyuan Meng;Qiang Song","doi":"10.1109/TSMC.2025.3615639","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3615639","url":null,"abstract":"This article is aimed at solving the distributed control problem for a leaderless heterogeneous network with signed digraph topology and locally Lipschitz nonlinear dynamics, where the agents may have nonidentical nodal functions and different state dimensions. To realize the control objectives of the heterogeneous nonlinear signed network, a distributed iterative learning control (ILC) algorithm is developed based on the relative outputs of local neighbors from the previous iteration. Despite the difficulties induced by nodal heterogeneities and nonlinearities, it is shown that the outputs of leaderless heterogeneous signed network under the developed ILC algorithm can exhibit the typical behaviors of conventional signed networks over a finite time period. To be more specific, the heterogeneous nonlinear signed network achieves either bipartite or interval bipartite output consensus (respectively, global output stability) along the iteration axis when the subgraph formed by root nodes is structurally balanced (respectively, unbalanced). Numerical studies are conducted to illustrate the proposed ILC scheme.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9242-9254"},"PeriodicalIF":8.7,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546992","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 : 2025-10-06DOI: 10.1109/TSMC.2025.3612577
Wang Yang;Jiuxiang Dong;Fanwei Meng;Ju H. Park
This article investigates the leader–follower consensus tracking problem for Lipschitz nonlinear multiagent systems (MASs) with unmeasurable states. To address limitations in communication and sensing resources, a hybrid triggering consensus framework is proposed to reduce redundant transmissions and measurements. First, a local observer is designed to estimate the system state, where output sampling and transmission to the observer are determined by a time-triggered mechanism (TTM). Then, based on the observer and two nonlinear estimators, a fully distributed controller is developed. Communication among neighboring agents is governed by a hybrid triggering mechanism (HTM) that integrates both time-triggered and event-triggered conditions. Notably, the proposed strategy ensures a positive minimum triggering interval (PMTI). The effectiveness of the proposed method is validated on a single-link flexible joint manipulator.
{"title":"A Hybrid Triggering Consensus Framework to Nonlinear Multiagent Systems With Local Observer","authors":"Wang Yang;Jiuxiang Dong;Fanwei Meng;Ju H. Park","doi":"10.1109/TSMC.2025.3612577","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3612577","url":null,"abstract":"This article investigates the leader–follower consensus tracking problem for Lipschitz nonlinear multiagent systems (MASs) with unmeasurable states. To address limitations in communication and sensing resources, a hybrid triggering consensus framework is proposed to reduce redundant transmissions and measurements. First, a local observer is designed to estimate the system state, where output sampling and transmission to the observer are determined by a time-triggered mechanism (TTM). Then, based on the observer and two nonlinear estimators, a fully distributed controller is developed. Communication among neighboring agents is governed by a hybrid triggering mechanism (HTM) that integrates both time-triggered and event-triggered conditions. Notably, the proposed strategy ensures a positive minimum triggering interval (PMTI). The effectiveness of the proposed method is validated on a single-link flexible joint manipulator.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9084-9096"},"PeriodicalIF":8.7,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546981","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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