Pub Date : 2026-02-04DOI: 10.1109/TSMC.2025.3650035
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TSMC.2025.3650035","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3650035","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 2","pages":"C3-C3"},"PeriodicalIF":8.7,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11372532","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116876","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 : 2026-02-04DOI: 10.1109/TSMC.2025.3650013
{"title":"TechRxiv: Share Your Preprint Research With the World!","authors":"","doi":"10.1109/TSMC.2025.3650013","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3650013","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 2","pages":"1123-1123"},"PeriodicalIF":8.7,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11372526","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116927","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 : 2026-02-04DOI: 10.1109/TSMC.2025.3650021
{"title":"Thank You for Your Authorship","authors":"","doi":"10.1109/TSMC.2025.3650021","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3650021","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 2","pages":"1476-1476"},"PeriodicalIF":8.7,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11372559","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116878","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 : 2026-02-04DOI: 10.1109/TSMC.2025.3649954
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors","authors":"","doi":"10.1109/TSMC.2025.3649954","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3649954","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 2","pages":"C4-C4"},"PeriodicalIF":8.7,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11372528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116836","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 : 2026-02-04DOI: 10.1109/TSMC.2025.3650037
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors","authors":"","doi":"10.1109/TSMC.2025.3650037","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3650037","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 2","pages":"C4-C4"},"PeriodicalIF":8.7,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11372558","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116860","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}
With their many degrees of freedom (DOFs) and flexible motion, serpentine manipulators (SMs) have become a research hotspot. Both discrete rigid-link and continuum elastic-rod SMs have been developed with various kinematic models. Yet, a unified kinematic framework and workspace boundary determination for diverse SMs remain unresolved. This article presents an efficient method to compute workspace boundaries based on a unified kinematic model, enabling rapid solutions across different SM structures. First, a unified kinematic modeling framework for SMs is established, resolving the issue of inconsistent kinematic descriptions across diverse structural forms. Two important kinematic characteristics of SMs are discussed: non-periodic and non-convex. Second, based on the unified kinematic model, a fast approximate solution method for determining the workspace boundary is proposed. Its computational complexity is only $O(m)$ , significantly improving computational efficiency compared to the continuation method with $O({m^{3}})$ , while maintaining an accuracy of the workspace boundary at over 99%. The effectiveness of the proposed solution algorithm in determining workspace boundaries is verified using three SMs with different DOFs. Finally, an inverse kinematics and motion planning algorithm for SMs is proposed, based on the fast workspace boundary solution. Compared to planning methods based on Jacobian pseudoinverse and artificial potential fields, the proposed algorithm offers a clear efficiency advantage and is not affected by singularity issues. The algorithm’s effectiveness is validated both in simulations and on physical prototypes.
{"title":"An Efficient Solution Method for Workspace Boundary of Serpentine Manipulators Based on a Unified Kinematics Model","authors":"Deshan Meng;Taowen Guo;Runhui Xiang;Ruiqi Wang;Junbo Tan;Xueqian Wang;Bin Liang","doi":"10.1109/TSMC.2025.3646248","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3646248","url":null,"abstract":"With their many degrees of freedom (DOFs) and flexible motion, serpentine manipulators (SMs) have become a research hotspot. Both discrete rigid-link and continuum elastic-rod SMs have been developed with various kinematic models. Yet, a unified kinematic framework and workspace boundary determination for diverse SMs remain unresolved. This article presents an efficient method to compute workspace boundaries based on a unified kinematic model, enabling rapid solutions across different SM structures. First, a unified kinematic modeling framework for SMs is established, resolving the issue of inconsistent kinematic descriptions across diverse structural forms. Two important kinematic characteristics of SMs are discussed: non-periodic and non-convex. Second, based on the unified kinematic model, a fast approximate solution method for determining the workspace boundary is proposed. Its computational complexity is only <inline-formula> <tex-math>$O(m)$ </tex-math></inline-formula>, significantly improving computational efficiency compared to the continuation method with <inline-formula> <tex-math>$O({m^{3}})$ </tex-math></inline-formula>, while maintaining an accuracy of the workspace boundary at over 99%. The effectiveness of the proposed solution algorithm in determining workspace boundaries is verified using three SMs with different DOFs. Finally, an inverse kinematics and motion planning algorithm for SMs is proposed, based on the fast workspace boundary solution. Compared to planning methods based on Jacobian pseudoinverse and artificial potential fields, the proposed algorithm offers a clear efficiency advantage and is not affected by singularity issues. The algorithm’s effectiveness is validated both in simulations and on physical prototypes.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 3","pages":"1790-1803"},"PeriodicalIF":8.7,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147268754","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 : 2026-01-19DOI: 10.1109/TSMC.2025.3647584
Irfan Ganie;Sarangapani Jagannathan
An output feedback (OF)-based control scheme utilizing both a scalable multilayer neural network (MNN) observer and actor–critic MNN via integral reinforcement learning (IRL)/adaptive dynamics programming (ADP) approach for a class of nonlinear systems with output constraints is introduced. The proposed observer, critic, and actor MNN weight updates are derived using a singular value decomposition (SVD) of MNN activation function gradient along with output error, Bellman and control input errors, respectively. Next, the approach incorporates continual learning (CL), utilizing a penalty function in the weight update laws for both actor–critic MNNs to consolidate knowledge from previous tasks and enhance learning in new tasks using estimated states across each layer in order to improve performance. The output constraints are addressed using the Karush–Kuhn–Tucker (KKT) conditions by utilizing the barrier Lyapunov functions (BLFs), which ensure the system output remains within a safe set at all times. Finally, the efficacy of the safety aware OF tracking control is demonstrated through empirical tests on a two-link robotic manipulator example which shows an 80% performance improvement as compared to recent literature.
{"title":"Safety Aware Continual Reinforcement Learning-Based Output Tracking Control of Nonlinear Continuous-Time Systems","authors":"Irfan Ganie;Sarangapani Jagannathan","doi":"10.1109/TSMC.2025.3647584","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3647584","url":null,"abstract":"An output feedback (OF)-based control scheme utilizing both a scalable multilayer neural network (MNN) observer and actor–critic MNN via integral reinforcement learning (IRL)/adaptive dynamics programming (ADP) approach for a class of nonlinear systems with output constraints is introduced. The proposed observer, critic, and actor MNN weight updates are derived using a singular value decomposition (SVD) of MNN activation function gradient along with output error, Bellman and control input errors, respectively. Next, the approach incorporates continual learning (CL), utilizing a penalty function in the weight update laws for both actor–critic MNNs to consolidate knowledge from previous tasks and enhance learning in new tasks using estimated states across each layer in order to improve performance. The output constraints are addressed using the Karush–Kuhn–Tucker (KKT) conditions by utilizing the barrier Lyapunov functions (BLFs), which ensure the system output remains within a safe set at all times. Finally, the efficacy of the safety aware OF tracking control is demonstrated through empirical tests on a two-link robotic manipulator example which shows an 80% performance improvement as compared to recent literature.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 3","pages":"1816-1831"},"PeriodicalIF":8.7,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778903","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 : 2026-01-14DOI: 10.1109/TSMC.2025.3649416
Chengrui Gao;Ziyuan Yang;Wei Jia;Lu Leng;Bob Zhang;Andrew Beng Jin Teoh
Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers’ prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition—often grounded in traditional methodologies—there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This article bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. This article systematically examines progress across key tasks, including region-of-interest (ROI) segmentation, feature extraction, and security and privacy-oriented challenges. Beyond highlighting these advancements, this article identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition.
{"title":"Deep Learning in Palmprint Recognition: A Comprehensive Survey","authors":"Chengrui Gao;Ziyuan Yang;Wei Jia;Lu Leng;Bob Zhang;Andrew Beng Jin Teoh","doi":"10.1109/TSMC.2025.3649416","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3649416","url":null,"abstract":"Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers’ prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition—often grounded in traditional methodologies—there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This article bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. This article systematically examines progress across key tasks, including region-of-interest (ROI) segmentation, feature extraction, and security and privacy-oriented challenges. Beyond highlighting these advancements, this article identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 3","pages":"2143-2162"},"PeriodicalIF":8.7,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778897","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 : 2026-01-14DOI: 10.1109/TSMC.2025.3649760
Fuyu Zhao;Sunxiaoyu Luo;Liang Zhao;Changyun Wen
This article investigates event-triggered cooperative adaptive optimal output regulation for unknown discrete-time multiagent systems (MASs) with input saturation. To address the issue that some followers may have no direct access to the leader, distributed observers are proposed to estimate the reference signals. A dynamic event-triggering mechanism is introduced to reduce communication and computational costs. By combining the internal model principle with low-gain and policy iteration (PI) techniques, an inner-outer loop-based dynamic event-triggered adaptive optimal control approach is developed. The convergence of the proposed algorithm is rigorously analyzed, and the control inputs are explicitly constrained within the input limits. A comprehensive stability analysis is provided, along with conditions for the MASs to achieve leader-to-formation stability (LFS). The sensitivity of the suboptimality index to system parameters is also taken into consideration. Finally, the effectiveness of the proposed approach is validated through a simulation example applied to grid-connected ac microgrid control.
{"title":"Dynamic Event-Triggered Cooperative Adaptive Optimal Output Regulation for Multiagent Systems With Input Saturation","authors":"Fuyu Zhao;Sunxiaoyu Luo;Liang Zhao;Changyun Wen","doi":"10.1109/TSMC.2025.3649760","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3649760","url":null,"abstract":"This article investigates event-triggered cooperative adaptive optimal output regulation for unknown discrete-time multiagent systems (MASs) with input saturation. To address the issue that some followers may have no direct access to the leader, distributed observers are proposed to estimate the reference signals. A dynamic event-triggering mechanism is introduced to reduce communication and computational costs. By combining the internal model principle with low-gain and policy iteration (PI) techniques, an inner-outer loop-based dynamic event-triggered adaptive optimal control approach is developed. The convergence of the proposed algorithm is rigorously analyzed, and the control inputs are explicitly constrained within the input limits. A comprehensive stability analysis is provided, along with conditions for the MASs to achieve leader-to-formation stability (LFS). The sensitivity of the suboptimality index to system parameters is also taken into consideration. Finally, the effectiveness of the proposed approach is validated through a simulation example applied to grid-connected ac microgrid control.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 3","pages":"2174-2188"},"PeriodicalIF":8.7,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778893","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 : 2026-01-12DOI: 10.1109/TSMC.2025.3646685
Xiaozheng Jin;Jing Chi;Jiahu Qin;Wei Xing Zheng;Xiaoming Wu;Weiming Fu
This article is concerned with the output feedback security tracking control of a class of disturbed second-order nonlinear systems against denial-of-service (DoS) attacks. Novel radial basis function neural network (RBFNN)-based finite-time state observers are developed to estimate the system’s unavailable states. Adaptive filters are proposed to suppress the influences of disturbances and RBFNN approximation errors. Then, an RBFNN-based security controller is designed to alleviate the effects of nonlinear dynamics and DoS attacks based on the signals of observers and filters. It is established that the uniformly ultimately bounded output tracking results of the system can be obtained by utilizing an RBFNN-based finite-time observation and filtering compensation control designs through Lyapunov stability analysis. Comparative simulations are employed to display the feasibility and superiority of the designed RBFNN-based observation and filtering compensation control schemes of a nonlinear autonomous marine system (AMS).
{"title":"Robust Security Control of a Class of Second-Order Nonlinear Systems Against DoS Attacks","authors":"Xiaozheng Jin;Jing Chi;Jiahu Qin;Wei Xing Zheng;Xiaoming Wu;Weiming Fu","doi":"10.1109/TSMC.2025.3646685","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3646685","url":null,"abstract":"This article is concerned with the output feedback security tracking control of a class of disturbed second-order nonlinear systems against denial-of-service (DoS) attacks. Novel radial basis function neural network (RBFNN)-based finite-time state observers are developed to estimate the system’s unavailable states. Adaptive filters are proposed to suppress the influences of disturbances and RBFNN approximation errors. Then, an RBFNN-based security controller is designed to alleviate the effects of nonlinear dynamics and DoS attacks based on the signals of observers and filters. It is established that the uniformly ultimately bounded output tracking results of the system can be obtained by utilizing an RBFNN-based finite-time observation and filtering compensation control designs through Lyapunov stability analysis. Comparative simulations are employed to display the feasibility and superiority of the designed RBFNN-based observation and filtering compensation control schemes of a nonlinear autonomous marine system (AMS).","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 2","pages":"1449-1463"},"PeriodicalIF":8.7,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116822","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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