Pub Date : 2026-02-03DOI: 10.1109/TCYB.2026.3652053
{"title":"IEEE Transactions on Cybernetics Information for Authors","authors":"","doi":"10.1109/TCYB.2026.3652053","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3652053","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"56 2","pages":"C4-C4"},"PeriodicalIF":10.5,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11371485","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102981","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 : 2026-02-03DOI: 10.1109/tcyb.2026.3651182
Irfan Ganie, Sarangapani Jagannathan
{"title":"Safe Optimal Control Framework for Cooperative Manipulation of Objects in Human–Robot Teams","authors":"Irfan Ganie, Sarangapani Jagannathan","doi":"10.1109/tcyb.2026.3651182","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3651182","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"6 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110398","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 : 2026-02-03DOI: 10.1109/TCYB.2026.3652055
{"title":"IEEE Transactions on Cybernetics","authors":"","doi":"10.1109/TCYB.2026.3652055","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3652055","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"56 2","pages":"C3-C3"},"PeriodicalIF":10.5,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11371481","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102971","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 : 2026-02-03DOI: 10.1109/tcyb.2026.3656420
Yao Li, Chengpu Yu, Renshuo Cheng, Fang Deng, Jie Chen
{"title":"Inverse Dynamic Games With Process Noise and Unknown Target States: A Linear Estimation Approach","authors":"Yao Li, Chengpu Yu, Renshuo Cheng, Fang Deng, Jie Chen","doi":"10.1109/tcyb.2026.3656420","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3656420","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"100 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110399","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 : 2026-02-02DOI: 10.1109/tcyb.2026.3656946
Bing Sun, Wei-Jie Yu, Xiao-Fang Liu, Jinghui Zhong, Jian-Yu Li, Zhi-Hui Zhan, Sam Kwong, Jun Zhang
{"title":"Tensor-Based Ant Colony Optimization for Set Meal Design in Online-to-Offline Restaurants","authors":"Bing Sun, Wei-Jie Yu, Xiao-Fang Liu, Jinghui Zhong, Jian-Yu Li, Zhi-Hui Zhan, Sam Kwong, Jun Zhang","doi":"10.1109/tcyb.2026.3656946","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3656946","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"1 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146101330","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}
Optimization problems in real-world applications often involve dynamic environmental changes, requiring algorithms to adapt quickly, track optimal solutions, and maintain efficiency. Existing dynamic multiobjective optimization evolutionary algorithms (DMOEAs) typically rely on fixed or limited dynamic response mechanisms, which are often insufficient to handle complex and varied dynamic environments. To overcome these limitations, this article proposes an adaptive dynamic response-based DMOEA (ADR-DMOEA), which employs a subpopulation-level adaptive mechanism to coordinate diversity-driven, prediction-driven, and memory-driven strategies. The strategy weights are dynamically adjusted according to the static optimization distance of each subpopulation, ensuring that appropriate strategies are adaptively deployed in different environments. This design overcomes the inefficiency of fixed assignments and the instability of individual-level perturbations, enabling coordinated and stable evolution. Extensive experiments on DF benchmark functions and a blast furnace (BF) ironmaking case study demonstrate that ADR-DMOEA achieves superior convergence, diversity, and robustness compared to state-of-the-art algorithms, effectively supporting real-world decision-making under dynamic conditions.
{"title":"ADR-DMOEA: A Dynamic Multiobjective Optimization Evolutionary Algorithm Based on Adaptive Dynamic Response Strategy.","authors":"Yuying Wang,Ping Zhou,Shengxiang Yang,Tianyou Chai","doi":"10.1109/tcyb.2026.3652642","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3652642","url":null,"abstract":"Optimization problems in real-world applications often involve dynamic environmental changes, requiring algorithms to adapt quickly, track optimal solutions, and maintain efficiency. Existing dynamic multiobjective optimization evolutionary algorithms (DMOEAs) typically rely on fixed or limited dynamic response mechanisms, which are often insufficient to handle complex and varied dynamic environments. To overcome these limitations, this article proposes an adaptive dynamic response-based DMOEA (ADR-DMOEA), which employs a subpopulation-level adaptive mechanism to coordinate diversity-driven, prediction-driven, and memory-driven strategies. The strategy weights are dynamically adjusted according to the static optimization distance of each subpopulation, ensuring that appropriate strategies are adaptively deployed in different environments. This design overcomes the inefficiency of fixed assignments and the instability of individual-level perturbations, enabling coordinated and stable evolution. Extensive experiments on DF benchmark functions and a blast furnace (BF) ironmaking case study demonstrate that ADR-DMOEA achieves superior convergence, diversity, and robustness compared to state-of-the-art algorithms, effectively supporting real-world decision-making under dynamic conditions.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146069935","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 : 2026-01-28DOI: 10.1109/tcyb.2026.3651627
Kunyu Wang,Lin Zhang,Zhen Chen,Hongbo Cheng,Han Lu,Wentong Cai,Qingsha S Cheng,M Jamal Deen
This article introduces a novel hybrid method to enable the self-evolution of equipment digital twins (DTs), allowing them to continuously and accurately mirror their physical counterparts. Self-evolution is the process by which a DT autonomously updates its models using real-time sensor data, adapting to dynamic real-world behavior. To enhance this process, we propose a data-physics driven approach that synergistically integrates meta-learning and continual learning. Our method begins by designing an extended residual model using a Koopman autoencoder (KAE) neural network. This component bridges the gap between an imperfect analytical physics model and actual equipment behavior. Next, we employ the Reptile meta-learning algorithm to train offline a versatile foundation model on historical data, endowing it with strong adaptability for rapid learning from new information. A key innovation is a periodic event-triggered mechanism, which monitors the DT's simulation accuracy against a fixed time window. When a performance discrepancy is detected, it automatically triggers a self-evolution cycle. The foundation model is then updated through a fine-tuning strategy based on continual learning with random reinitialization. This fusion of offline meta-learning and online continual learning allows the DT to quickly adapt to new, unseen scenarios, ensuring it reflects the physical equipment's state in real-time. We validate the effectiveness and improved performance of our proposed framework through a comprehensive robot simulation case study.
{"title":"Self-Evolution of Hybrid Data-Physics Equipment Digital Twin Using Meta Learning and Continual Learning.","authors":"Kunyu Wang,Lin Zhang,Zhen Chen,Hongbo Cheng,Han Lu,Wentong Cai,Qingsha S Cheng,M Jamal Deen","doi":"10.1109/tcyb.2026.3651627","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3651627","url":null,"abstract":"This article introduces a novel hybrid method to enable the self-evolution of equipment digital twins (DTs), allowing them to continuously and accurately mirror their physical counterparts. Self-evolution is the process by which a DT autonomously updates its models using real-time sensor data, adapting to dynamic real-world behavior. To enhance this process, we propose a data-physics driven approach that synergistically integrates meta-learning and continual learning. Our method begins by designing an extended residual model using a Koopman autoencoder (KAE) neural network. This component bridges the gap between an imperfect analytical physics model and actual equipment behavior. Next, we employ the Reptile meta-learning algorithm to train offline a versatile foundation model on historical data, endowing it with strong adaptability for rapid learning from new information. A key innovation is a periodic event-triggered mechanism, which monitors the DT's simulation accuracy against a fixed time window. When a performance discrepancy is detected, it automatically triggers a self-evolution cycle. The foundation model is then updated through a fine-tuning strategy based on continual learning with random reinitialization. This fusion of offline meta-learning and online continual learning allows the DT to quickly adapt to new, unseen scenarios, ensuring it reflects the physical equipment's state in real-time. We validate the effectiveness and improved performance of our proposed framework through a comprehensive robot simulation case study.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"272 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146069937","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 : 2026-01-28DOI: 10.1109/tcyb.2026.3652011
Siyong Song,Yingchun Wang,Jiayue Sun,Yunfei Mu
This article investigates the mode cluster-based event-triggered control (MCETC) of stochastic Markovian jump systems (SMJSs) under denial-of-service (DoS) attack. First, a novel MCETC framework is designed by considering the interplay among subsystems, DoS attacks, and the event-triggered mechanism (ETM). In this framework, the controller mode is reconstructed, and the number of controller modes is reduced by reclustering the system modes. It significantly reduces the conservatism of the system compared to existing mode-dependent/-independent controllers. Second, a switching ETM is designed for scenarios with and without DoS attack activation, which can effectively save network bandwidth resources and reduce computational load. Third, a multi-Lyapunov function based on DoS attacks is proposed to ensure the stability of the closed-loop SMJSs. Then, the controller gains and event-triggered parameters are jointly solved via the linear matrix inequality (LMI) technique. Moreover, the maximum allowable sampling interval (MASI) is given such that the controller can restore the control signals as soon as a DoS attack ends, which enables faster stabilization of the closed-loop system. Finally, a numerical example is used to verify the effectiveness and superiority of the proposed method.
{"title":"Mode Cluster-Based Event-Triggered Control for Stochastic Markovian Jump Systems Under Denial-of-Service Attack.","authors":"Siyong Song,Yingchun Wang,Jiayue Sun,Yunfei Mu","doi":"10.1109/tcyb.2026.3652011","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3652011","url":null,"abstract":"This article investigates the mode cluster-based event-triggered control (MCETC) of stochastic Markovian jump systems (SMJSs) under denial-of-service (DoS) attack. First, a novel MCETC framework is designed by considering the interplay among subsystems, DoS attacks, and the event-triggered mechanism (ETM). In this framework, the controller mode is reconstructed, and the number of controller modes is reduced by reclustering the system modes. It significantly reduces the conservatism of the system compared to existing mode-dependent/-independent controllers. Second, a switching ETM is designed for scenarios with and without DoS attack activation, which can effectively save network bandwidth resources and reduce computational load. Third, a multi-Lyapunov function based on DoS attacks is proposed to ensure the stability of the closed-loop SMJSs. Then, the controller gains and event-triggered parameters are jointly solved via the linear matrix inequality (LMI) technique. Moreover, the maximum allowable sampling interval (MASI) is given such that the controller can restore the control signals as soon as a DoS attack ends, which enables faster stabilization of the closed-loop system. Finally, a numerical example is used to verify the effectiveness and superiority of the proposed method.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"42 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146069949","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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