Pub Date : 2025-12-16DOI: 10.1109/TSMC.2025.3637478
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors","authors":"","doi":"10.1109/TSMC.2025.3637478","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3637478","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"C4-C4"},"PeriodicalIF":8.7,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11302018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760851","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 : 2025-12-16DOI: 10.1109/TSMC.2025.3641404
{"title":"Advances in Cyber-Medical Systems","authors":"","doi":"10.1109/TSMC.2025.3641404","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3641404","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"400-400"},"PeriodicalIF":8.7,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11302011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760911","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 : 2025-12-16DOI: 10.1109/TSMC.2025.3637459
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors","authors":"","doi":"10.1109/TSMC.2025.3637459","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3637459","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"C4-C4"},"PeriodicalIF":8.7,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11302012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760913","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 : 2025-12-11DOI: 10.1109/TSMC.2025.3641231
Kaili Xiang;Yongduan Song
Developing structurally simple and functionally trustworthy control strategies for multi-input multi-output (MIMO) nonlinear dynamic systems has always been an interesting yet challenging research topic in the control community. In this note, we present a tracking control design approach embedded with the key intelligent elements/actions (IEs/ICs). More specifically, by properly exploiting and processing fundamental IEs/ICs, such as “penalty/punishment,” “experience/memory,” and “forecasting/prediction” often observed from and utilized in human decision making, we develop an interpretable PID-like control strategy capable of ensuring asymptotic tracking for nonaffine systems in the presence of modeling uncertainties, MIMO couplings, and unexpected actuation faults. The key design steps consist of analytically characterizing the fundamental IEs/ICs via certain mathematical representations, introducing generalized error, selecting and converting the related IEs/ICs into a signal carrying intelligence ingredients, and adaptively weighting such a signal to eventually produce the control action. The proposed framework of blending intelligence-like ingredients into control synthesis proves promising and is expected to stimulate interest in developing explainable IEs/ICs-driven control strategies for nonlinear dynamic systems.
{"title":"Infusing PID Tracking Control With Intelligence-Like Elements/Actions","authors":"Kaili Xiang;Yongduan Song","doi":"10.1109/TSMC.2025.3641231","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3641231","url":null,"abstract":"Developing structurally simple and functionally trustworthy control strategies for multi-input multi-output (MIMO) nonlinear dynamic systems has always been an interesting yet challenging research topic in the control community. In this note, we present a tracking control design approach embedded with the key intelligent elements/actions (IEs/ICs). More specifically, by properly exploiting and processing fundamental IEs/ICs, such as “penalty/punishment,” “experience/memory,” and “forecasting/prediction” often observed from and utilized in human decision making, we develop an interpretable PID-like control strategy capable of ensuring asymptotic tracking for nonaffine systems in the presence of modeling uncertainties, MIMO couplings, and unexpected actuation faults. The key design steps consist of analytically characterizing the fundamental IEs/ICs via certain mathematical representations, introducing generalized error, selecting and converting the related IEs/ICs into a signal carrying intelligence ingredients, and adaptively weighting such a signal to eventually produce the control action. The proposed framework of blending intelligence-like ingredients into control synthesis proves promising and is expected to stimulate interest in developing explainable IEs/ICs-driven control strategies for nonlinear dynamic systems.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 2","pages":"1101-1111"},"PeriodicalIF":8.7,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116868","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 : 2025-11-26DOI: 10.1109/TSMC.2025.3635840
{"title":"2025 Index IEEE Transactions on Systems, Man, and Cybernetics Systems: Systems","authors":"","doi":"10.1109/TSMC.2025.3635840","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3635840","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9955-10143"},"PeriodicalIF":8.7,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11269192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612136","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 work investigates the formation tracking issue for multirobot manipulator end-effectors under input constraints. A distributed formation control law is designed to guarantee the finite-time boundedness of tracking errors within the framework. To estimate the significant bias of dynamics discovered during practical multirobot collaborative manipulation tasks, a bias radial basis function neural network (RBFNN) is integrated, along with a designed adaptive updating law for expeditious approximation. In addition, an anti-windup compensator within a finite-time framework is specifically introduced to mitigate the input saturation issue arising from torque limitations in joint actuators. Finally, the system’s semi-global practical finite-time boundedness (SGPFTB) is rigorously established through Lyapunov theory. Five planar manipulators are employed in comparative computational experiments to validate the feasibility of the presented control strategy.
{"title":"Neural Adaptive Finite-Time Formation Tracking Control for Manipulator End Effectors Under Input Constraints","authors":"Shuangsi Xue;Zihang Guo;Junkai Tan;Kai Qu;Hui Cao;Badong Chen","doi":"10.1109/TSMC.2025.3634832","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3634832","url":null,"abstract":"This work investigates the formation tracking issue for multirobot manipulator end-effectors under input constraints. A distributed formation control law is designed to guarantee the finite-time boundedness of tracking errors within the framework. To estimate the significant bias of dynamics discovered during practical multirobot collaborative manipulation tasks, a bias radial basis function neural network (RBFNN) is integrated, along with a designed adaptive updating law for expeditious approximation. In addition, an anti-windup compensator within a finite-time framework is specifically introduced to mitigate the input saturation issue arising from torque limitations in joint actuators. Finally, the system’s semi-global practical finite-time boundedness (SGPFTB) is rigorously established through Lyapunov theory. Five planar manipulators are employed in comparative computational experiments to validate the feasibility of the presented control strategy.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 2","pages":"1089-1100"},"PeriodicalIF":8.7,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116915","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 : 2025-11-19DOI: 10.1109/TSMC.2025.3627727
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors","authors":"","doi":"10.1109/TSMC.2025.3627727","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3627727","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"C4-C4"},"PeriodicalIF":8.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260921","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546975","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 : 2025-11-19DOI: 10.1109/TSMC.2025.3630255
{"title":"Thank You for Your Authorship","authors":"","doi":"10.1109/TSMC.2025.3630255","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3630255","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9954-9954"},"PeriodicalIF":8.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260917","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546974","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 : 2025-11-19DOI: 10.1109/TSMC.2025.3627739
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TSMC.2025.3627739","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3627739","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"C3-C3"},"PeriodicalIF":8.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260909","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546960","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 : 2025-11-19DOI: 10.1109/TSMC.2025.3630757
Dan Li;Hye-Bin Shin;Seong-Whan Lee
Current noninvasive electroencephalography (EEG)-based brain–computer interface (BCI) systems face a fundamental scalability barrier: they either suffer catastrophic forgetting (CF) when learning from new users or require centralized management and use of sensitive neural data from previous users-making real-world deployment impractical. To address this, we introduce subject-incremental continual adaptation (SI-CA), a novel paradigm that models cross-subject continual learning (CL), where knowledge transfer and limited replay sustain stable performance as new subjects are introduced, enabling continual decoding without forgetting. Building on this paradigm, we propose a novel CL framework that achieves memory-efficient adaptation by integrating an extendable architecture with prototype-based consistency regularization and limited replay to mitigate CF. The effectiveness of our proposed method has been validated on three benchmark EEG-BCI datasets. Experimental results demonstrate that the proposed method can effectively reduce reliance on historical samples during CL, while maintaining stable decoding performance for previously learned individuals and ensuring reliable motor decoding for newly encountered ones. This holds significant importance for the development of scalable, privacy-preserving, and stable neural interface systems.
{"title":"Toward Memory-Efficient Continual Adaptation for MI-EEG Decoding in BCIs","authors":"Dan Li;Hye-Bin Shin;Seong-Whan Lee","doi":"10.1109/TSMC.2025.3630757","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3630757","url":null,"abstract":"Current noninvasive electroencephalography (EEG)-based brain–computer interface (BCI) systems face a fundamental scalability barrier: they either suffer catastrophic forgetting (CF) when learning from new users or require centralized management and use of sensitive neural data from previous users-making real-world deployment impractical. To address this, we introduce subject-incremental continual adaptation (SI-CA), a novel paradigm that models cross-subject continual learning (CL), where knowledge transfer and limited replay sustain stable performance as new subjects are introduced, enabling continual decoding without forgetting. Building on this paradigm, we propose a novel CL framework that achieves memory-efficient adaptation by integrating an extendable architecture with prototype-based consistency regularization and limited replay to mitigate CF. The effectiveness of our proposed method has been validated on three benchmark EEG-BCI datasets. Experimental results demonstrate that the proposed method can effectively reduce reliance on historical samples during CL, while maintaining stable decoding performance for previously learned individuals and ensuring reliable motor decoding for newly encountered ones. This holds significant importance for the development of scalable, privacy-preserving, and stable neural interface systems.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"766-778"},"PeriodicalIF":8.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760912","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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