Robotic arms are widely used in various aspects of human-robot collaboration. The primary goal of this study is to explore the usability of robotic arms for delivering objects to humans in dynamic environments. Traditional robotic arms often face limitations in path planning, such as difficulties adapting to dynamic environments and complex developmental processes. To overcome these challenges, this study employs reinforcement learning (RL) to train four models-the Approach RL Model, Delivery RL Model, Decision RL Model, and Merged Model-as alternatives to conventional path planning control. Typically, there exists a significant discrepancy between simulated data and real-world features. Although image segmentation can substantially reduce the gap between virtual and real environments, notable differences remain in hand features. Therefore, to further bridge the simulation-to-reality gap, this study applies CycleGAN to transform real hand features into virtual hand features, thereby enhancing the model's transferability. Experimental results show that the Decision RL Model achieved an accuracy of 99.17%, while the Merged Model achieved 99.92%. The proposed method effectively improves the stability and accuracy of human-robot collaboration in complex scenarios. Overall, this study validates the feasibility of integrating RL, image segmentation, and image translation techniques, offering a scalable and efficient task-solving solution for robotic arms in highly dynamic application domains.
{"title":"Knowledge Distillation and Reinforcement Learning in a Human-Machine Collaboration Delivery System With a Robotic Arm.","authors":"Ping-Huan Kuo,Po-Hsun Feng,Chen-Wen Chang,Yu-Sian Lin,Yu-Chih Chiu,Bang-Yu Chen","doi":"10.1109/tcyb.2026.3668072","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3668072","url":null,"abstract":"Robotic arms are widely used in various aspects of human-robot collaboration. The primary goal of this study is to explore the usability of robotic arms for delivering objects to humans in dynamic environments. Traditional robotic arms often face limitations in path planning, such as difficulties adapting to dynamic environments and complex developmental processes. To overcome these challenges, this study employs reinforcement learning (RL) to train four models-the Approach RL Model, Delivery RL Model, Decision RL Model, and Merged Model-as alternatives to conventional path planning control. Typically, there exists a significant discrepancy between simulated data and real-world features. Although image segmentation can substantially reduce the gap between virtual and real environments, notable differences remain in hand features. Therefore, to further bridge the simulation-to-reality gap, this study applies CycleGAN to transform real hand features into virtual hand features, thereby enhancing the model's transferability. Experimental results show that the Decision RL Model achieved an accuracy of 99.17%, while the Merged Model achieved 99.92%. The proposed method effectively improves the stability and accuracy of human-robot collaboration in complex scenarios. Overall, this study validates the feasibility of integrating RL, image segmentation, and image translation techniques, offering a scalable and efficient task-solving solution for robotic arms in highly dynamic application domains.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"15 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350886","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}
This article deals with the optimized distributed filtering problem with binary measurements for a class of discrete linear time-varying systems. The system and the original measurements are subject to random noise with known statistical information. Two cases of extracting useful measurement information are designed based on binary measurements between two adjacent moments. Furthermore, a novel time-varying threshold strategy is introduced to reduce the impact of the uncertainties from the binary measurements. The dynamic event-triggering protocols under token bucket specifications are employed to schedule the information transmission among neighboring nodes with constrained resources. The former determines the necessity of information transmission, and the latter describes whether the communication resources are sufficient or not. Information is successfully transmitted only when these two conditions (formulated by two indicator variables) are satisfied. A set of locally sufficient conditions is constructed for each node to guarantee the existence of the distributed filter such that the filtering error system satisfies the exponential boundedness in the mean square. The filter parameters are recursively calculated by solving the distributed optimization problems, which are constrained by linear matrix inequalities for each node. Such a structure achieves the desirable scalability of distributed filtering. A simulation example demonstrates the effectiveness of the distributed filtering scheme developed in this article.
{"title":"Optimized Distributed Filtering Over Binary Sensor Network: A Dynamic Event-Triggering Protocol With Token Bucket Specifications.","authors":"Yanhua Song,Shikun Shao,Fei Han,Hongli Dong,Yuxuan Shen","doi":"10.1109/tcyb.2026.3668877","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3668877","url":null,"abstract":"This article deals with the optimized distributed filtering problem with binary measurements for a class of discrete linear time-varying systems. The system and the original measurements are subject to random noise with known statistical information. Two cases of extracting useful measurement information are designed based on binary measurements between two adjacent moments. Furthermore, a novel time-varying threshold strategy is introduced to reduce the impact of the uncertainties from the binary measurements. The dynamic event-triggering protocols under token bucket specifications are employed to schedule the information transmission among neighboring nodes with constrained resources. The former determines the necessity of information transmission, and the latter describes whether the communication resources are sufficient or not. Information is successfully transmitted only when these two conditions (formulated by two indicator variables) are satisfied. A set of locally sufficient conditions is constructed for each node to guarantee the existence of the distributed filter such that the filtering error system satisfies the exponential boundedness in the mean square. The filter parameters are recursively calculated by solving the distributed optimization problems, which are constrained by linear matrix inequalities for each node. Such a structure achieves the desirable scalability of distributed filtering. A simulation example demonstrates the effectiveness of the distributed filtering scheme developed in this article.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"54 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350885","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}
In this article, we investigate the problem of adaptive safe critic control design for stochastic multiagent systems (MASs) subject to asymmetric state and input constraints. To systematically address asymmetric state constraints, a unified transformation function (UTF) is proposed to convert the constrained consensus control problem into the stability analysis of an unconstrained error system. In addition, a nonquadratic cost function is incorporated to address input limitations effectively. Building upon these developments, a time-varying Hamilton-Jacobi-Bellman equation (HJBE) is formulated by integrating the Bellman optimality principle with Itô's lemma, thereby accommodating stochastic disturbances and enhancing controller robustness. To improve data utilization and eliminate reliance on explicit drift dynamics, an integral reinforcement learning (IRL) algorithm is developed within this framework. Furthermore, a time-varying single-critic network is designed to approximate the solution to the HJBE and generate optimal control policies, thereby considerably reducing computational complexity. To further enhance learning efficiency and relax the persistent excitation (PE) condition, the experience replay (ER) technique is incorporated into the update process of the critic weight. Finally, two simulation examples are provided to verify the feasibility and effectiveness of the proposed approach.
{"title":"Time-Varying HJBE-Based Adaptive Safe Critic Control Design for Stochastic Asymmetric Constrained Multiagent Systems.","authors":"Yuhao Zhou,Biao Luo,Xiaodong Xu,Yalin Wang,Weihua Gui","doi":"10.1109/tcyb.2026.3666959","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3666959","url":null,"abstract":"In this article, we investigate the problem of adaptive safe critic control design for stochastic multiagent systems (MASs) subject to asymmetric state and input constraints. To systematically address asymmetric state constraints, a unified transformation function (UTF) is proposed to convert the constrained consensus control problem into the stability analysis of an unconstrained error system. In addition, a nonquadratic cost function is incorporated to address input limitations effectively. Building upon these developments, a time-varying Hamilton-Jacobi-Bellman equation (HJBE) is formulated by integrating the Bellman optimality principle with Itô's lemma, thereby accommodating stochastic disturbances and enhancing controller robustness. To improve data utilization and eliminate reliance on explicit drift dynamics, an integral reinforcement learning (IRL) algorithm is developed within this framework. Furthermore, a time-varying single-critic network is designed to approximate the solution to the HJBE and generate optimal control policies, thereby considerably reducing computational complexity. To further enhance learning efficiency and relax the persistent excitation (PE) condition, the experience replay (ER) technique is incorporated into the update process of the critic weight. Finally, two simulation examples are provided to verify the feasibility and effectiveness of the proposed approach.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"18 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350884","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 : 2026-03-04DOI: 10.1109/tcyb.2026.3666768
Song Zhu,Kun Deng,Huaicheng Yan,Mouquan Shen,Xiaoyang Liu,Shiping Wen
This article investigates the mean square exponential stability for dynamic memristor-neutral stochastic cellular neural networks with time-varying delays (DM-NSDCNNs). Unlike general neural networks (NNs) analyzed in the voltage-current domain, DM-NSDCNNs are studied in the flux-charge domain, offering a significant advantage: all current, voltage, and power consumption vanish when the system reaches a steady state. In particular, dynamic memristor store the results of computation. To better utilize these properties, two distinct stochastic stability analysis techniques are considered, depending on the memristor's constitutive relations. For piecewise linear constitutive relation, the stability criteria are obtained by a novel approach based onthe comparison principle and reductio ad absurdum. Moreover, the stability criteria for cubic nonlinear constitutive relation are established via stochastic analysis employing Lyapunov functional techniques. Finally, several numerical examples with different constitutive relations of DM-NSDCNNs are provided to verify the effectiveness and potential of the proposed results.
{"title":"Mean Square Exponential Stability of Dynamic Memristor Neutral Stochastic Cellular Neural Networks With Time-Varying Delays.","authors":"Song Zhu,Kun Deng,Huaicheng Yan,Mouquan Shen,Xiaoyang Liu,Shiping Wen","doi":"10.1109/tcyb.2026.3666768","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3666768","url":null,"abstract":"This article investigates the mean square exponential stability for dynamic memristor-neutral stochastic cellular neural networks with time-varying delays (DM-NSDCNNs). Unlike general neural networks (NNs) analyzed in the voltage-current domain, DM-NSDCNNs are studied in the flux-charge domain, offering a significant advantage: all current, voltage, and power consumption vanish when the system reaches a steady state. In particular, dynamic memristor store the results of computation. To better utilize these properties, two distinct stochastic stability analysis techniques are considered, depending on the memristor's constitutive relations. For piecewise linear constitutive relation, the stability criteria are obtained by a novel approach based onthe comparison principle and reductio ad absurdum. Moreover, the stability criteria for cubic nonlinear constitutive relation are established via stochastic analysis employing Lyapunov functional techniques. Finally, several numerical examples with different constitutive relations of DM-NSDCNNs are provided to verify the effectiveness and potential of the proposed results.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"1 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350889","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 : 2026-03-04DOI: 10.1109/tcyb.2026.3668284
Kai Ma,Ning He,Chao Shen
A resilient stochastic model predictive control (MPC) method based on an adaptive input reconstruction mechanism is proposed for networked stochastic systems under false data injection (FDI) attacks. To the best of our knowledge, this is the first stochastic MPC framework designed to address FDI attacks; it not only mitigates the conservatism of existing methods but also reduces system resource consumption. Particularly, an adaptive input reconstruction mechanism is introduced to relax the assumptions on FDI attack energy in existing resilient MPC methods by reconstructing feasible control inputs. In addition, the adaptive prediction horizon and terminal constraint are co-designed to reduce the computational complexity. Furthermore, the conservatism inherent in existing resilient MPC methods due to hard constraints is alleviated by transforming fixed hard constraints into stochastic constraints. Based on these designs, sufficient conditions are derived to guarantee the proposed method's recursive feasibility and the closed-loop system stability. Finally, the effectiveness of the proposed method is validated through simulations on a DC-DC converter system.
{"title":"Adaptive Reconstruction-Based Model Predictive Control for Networked Stochastic Systems Under False Data Injection Attacks.","authors":"Kai Ma,Ning He,Chao Shen","doi":"10.1109/tcyb.2026.3668284","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3668284","url":null,"abstract":"A resilient stochastic model predictive control (MPC) method based on an adaptive input reconstruction mechanism is proposed for networked stochastic systems under false data injection (FDI) attacks. To the best of our knowledge, this is the first stochastic MPC framework designed to address FDI attacks; it not only mitigates the conservatism of existing methods but also reduces system resource consumption. Particularly, an adaptive input reconstruction mechanism is introduced to relax the assumptions on FDI attack energy in existing resilient MPC methods by reconstructing feasible control inputs. In addition, the adaptive prediction horizon and terminal constraint are co-designed to reduce the computational complexity. Furthermore, the conservatism inherent in existing resilient MPC methods due to hard constraints is alleviated by transforming fixed hard constraints into stochastic constraints. Based on these designs, sufficient conditions are derived to guarantee the proposed method's recursive feasibility and the closed-loop system stability. Finally, the effectiveness of the proposed method is validated through simulations on a DC-DC converter system.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"32 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350887","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 : 2026-03-04DOI: 10.1109/tcyb.2026.3664659
Zihan Li,Dong Shen,Xinghuo Yu
This study proposes an accelerated iterative learning control scheme using a fractional high-order update rule (FHUR) to improve the convergence rate for linear time-invariant systems. High- and low-order power update terms are used to handle large- and small-tracking errors, respectively, thereby accelerating convergence. Two learning mechanisms are proposed and shown to be optimal among various learning gain selections. The inherent nonlinearity in the FHUR poses significant challenges for the convergence analysis. To address this, a disturbed composite nonlinear mapping method is introduced. Using this method, the tracking errors are proven to converge either to an invariant set or to a set of limit cycles, depending on the underlying learning mechanism. Any desired tracking precision can be achieved by adjusting the parameters in the FHUR. Numerical simulations confirm that the FHUR presents a promising alternative to the commonly used proportional-type update rule for achieving accelerated convergence.
{"title":"Accelerated Iterative Learning Control Using Fractional High-Order Update Rule for LTI Systems.","authors":"Zihan Li,Dong Shen,Xinghuo Yu","doi":"10.1109/tcyb.2026.3664659","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3664659","url":null,"abstract":"This study proposes an accelerated iterative learning control scheme using a fractional high-order update rule (FHUR) to improve the convergence rate for linear time-invariant systems. High- and low-order power update terms are used to handle large- and small-tracking errors, respectively, thereby accelerating convergence. Two learning mechanisms are proposed and shown to be optimal among various learning gain selections. The inherent nonlinearity in the FHUR poses significant challenges for the convergence analysis. To address this, a disturbed composite nonlinear mapping method is introduced. Using this method, the tracking errors are proven to converge either to an invariant set or to a set of limit cycles, depending on the underlying learning mechanism. Any desired tracking precision can be achieved by adjusting the parameters in the FHUR. Numerical simulations confirm that the FHUR presents a promising alternative to the commonly used proportional-type update rule for achieving accelerated convergence.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"245 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350888","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 : 2026-03-03DOI: 10.1109/tcyb.2026.3668276
Bin Wang,Changchun Hua,Hao Li
The problem of command-filter-based adaptive fixed-time tracking control is investigated for nonlinear systems with time-varying uncertain parameters and disturbances in this article. Existing fixed-time control strategies via an adaptive approach are primarily bounded-error, trajectory tracking-oriented. Different from previous results, we propose a new fixed-time stability lemma utilizing an exponential decay function. Then, by leveraging the proposed lemma and command filtered backstepping technique, a novel adaptive fixed-time control scheme is constructed, which can reduce the computational complexity and completely counteract uncertain parameters. We demonstrate that the tracking error enters a neighborhood near zero within a fixed-time and ultimately converges to zero. Furthermore, through the incorporation of a piecewise function into both the filter error compensation system and virtual control laws, the second-order derivability of virtual control laws is guaranteed, thereby ensuring the validity of the command filter. Finally, the proposed strategy's effectiveness is confirmed through simulation results.
{"title":"Fixed-Time Command Filtered Adaptive Backstepping Control for Uncertain Nonlinear Systems With Zero-Error Tracking.","authors":"Bin Wang,Changchun Hua,Hao Li","doi":"10.1109/tcyb.2026.3668276","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3668276","url":null,"abstract":"The problem of command-filter-based adaptive fixed-time tracking control is investigated for nonlinear systems with time-varying uncertain parameters and disturbances in this article. Existing fixed-time control strategies via an adaptive approach are primarily bounded-error, trajectory tracking-oriented. Different from previous results, we propose a new fixed-time stability lemma utilizing an exponential decay function. Then, by leveraging the proposed lemma and command filtered backstepping technique, a novel adaptive fixed-time control scheme is constructed, which can reduce the computational complexity and completely counteract uncertain parameters. We demonstrate that the tracking error enters a neighborhood near zero within a fixed-time and ultimately converges to zero. Furthermore, through the incorporation of a piecewise function into both the filter error compensation system and virtual control laws, the second-order derivability of virtual control laws is guaranteed, thereby ensuring the validity of the command filter. Finally, the proposed strategy's effectiveness is confirmed through simulation results.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"100 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147346384","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 : 2026-03-03DOI: 10.1109/tcyb.2026.3666726
A Mendoza,E Pujolli da Silva,D Vega-Oliveros,T Frota de Souza,A Soriano-Vargas,M Uchida,A Rocha
Sedentary behavior (SB) is a major global health concern, necessitating accurate physical activity (PA) intensity monitoring. Conventional machine-learning (ML) methods using accelerometers struggle to generalize due to variability across populations, sensors, and activities, leading to inconsistent real-world performance. This study presents XNet, a dual-domain deep learning (DL) model for classifying PA intensity and estimating energy expenditure. XNet features a hierarchical multihead architecture that independently extracts temporal and frequency features from multiple sensors, then integrates them via a novel attentional feature fusion (AFF) module applied in two stages: first aggregating sensor features, then fusing domain embeddings. This hierarchical approach outperforms single-stage fusion and provides interpretable attention weights revealing sensor and domain contributions. Frequency-domain features are essential for generalization: in cross-dataset evaluations, XNet achieved the highest F1-score of 70.5 while maintaining robust sedentary detection (88% TPR), and in open-set scenarios, it achieved an F1-score of 77.0, surpassing all DL and hand-crafted baselines. We validated XNet on multiple public datasets and a new dataset of 105 participants. Furthermore, our analysis shows that lightweight 1D-convolutional spectral encoders yield better out-of-distribution generalization than transformer and graph attention (GAT) network alternatives, while benchmarking confirms that AFF outperforms nine fusion strategies in balancing accuracy, efficiency, and robustness to sensor failure. The model adapts to physiological signals (heart rate and ECG) and exhibits low inference latency (~25 ms), making it suitable for on-device deployment. A complementary visual analytics framework uses attention weights to facilitate expert auditing, thereby promoting transparent and equitable health monitoring.
{"title":"XNet: Enhancing Physical Activity Intensity Assessment With Attentional Multidomain Fusion and Visual Analytics.","authors":"A Mendoza,E Pujolli da Silva,D Vega-Oliveros,T Frota de Souza,A Soriano-Vargas,M Uchida,A Rocha","doi":"10.1109/tcyb.2026.3666726","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3666726","url":null,"abstract":"Sedentary behavior (SB) is a major global health concern, necessitating accurate physical activity (PA) intensity monitoring. Conventional machine-learning (ML) methods using accelerometers struggle to generalize due to variability across populations, sensors, and activities, leading to inconsistent real-world performance. This study presents XNet, a dual-domain deep learning (DL) model for classifying PA intensity and estimating energy expenditure. XNet features a hierarchical multihead architecture that independently extracts temporal and frequency features from multiple sensors, then integrates them via a novel attentional feature fusion (AFF) module applied in two stages: first aggregating sensor features, then fusing domain embeddings. This hierarchical approach outperforms single-stage fusion and provides interpretable attention weights revealing sensor and domain contributions. Frequency-domain features are essential for generalization: in cross-dataset evaluations, XNet achieved the highest F1-score of 70.5 while maintaining robust sedentary detection (88% TPR), and in open-set scenarios, it achieved an F1-score of 77.0, surpassing all DL and hand-crafted baselines. We validated XNet on multiple public datasets and a new dataset of 105 participants. Furthermore, our analysis shows that lightweight 1D-convolutional spectral encoders yield better out-of-distribution generalization than transformer and graph attention (GAT) network alternatives, while benchmarking confirms that AFF outperforms nine fusion strategies in balancing accuracy, efficiency, and robustness to sensor failure. The model adapts to physiological signals (heart rate and ECG) and exhibits low inference latency (~25 ms), making it suitable for on-device deployment. A complementary visual analytics framework uses attention weights to facilitate expert auditing, thereby promoting transparent and equitable health monitoring.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"52 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147346362","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 : 2026-03-03DOI: 10.1109/tcyb.2026.3667176
Dake Gu,Qingle Wang,Yindong Liu
This article proposes a high-order robust dynamic surface control method for high-order strict-feedback systems (SFSs) with asymmetric output constraints and external disturbances, based on the fully actuated system approach. By introducing a class of nonlinear transformation functions, the original system's output constraint problem is transformed into a bounded problem in a new system representation. The proposed method directly designs a controller for each higher order subsystem using the fully actuated system framework, avoiding transformation to a first-order system and thereby simplifying the control design process. Stability analysis demonstrates that all closed-loop signals are uniformly ultimately bounded, while the system output successfully tracks the reference signal without violating the prescribed constraints. Numerical simulations on a robotic manipulator and an electromechanical system validate the effectiveness of the proposed approach.
{"title":"Robust Dynamic Surface Control for High-Order Strict-Feedback Systems With Output Constraints Based on Fully Actuated System Approach.","authors":"Dake Gu,Qingle Wang,Yindong Liu","doi":"10.1109/tcyb.2026.3667176","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3667176","url":null,"abstract":"This article proposes a high-order robust dynamic surface control method for high-order strict-feedback systems (SFSs) with asymmetric output constraints and external disturbances, based on the fully actuated system approach. By introducing a class of nonlinear transformation functions, the original system's output constraint problem is transformed into a bounded problem in a new system representation. The proposed method directly designs a controller for each higher order subsystem using the fully actuated system framework, avoiding transformation to a first-order system and thereby simplifying the control design process. Stability analysis demonstrates that all closed-loop signals are uniformly ultimately bounded, while the system output successfully tracks the reference signal without violating the prescribed constraints. Numerical simulations on a robotic manipulator and an electromechanical system validate the effectiveness of the proposed approach.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"292 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147346383","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 : 2026-03-03DOI: 10.1109/tcyb.2026.3667616
Tao Chen,Wentuo Fang,Wenfeng Hu,Gui Gui,Chunhua Yang
In this article, we investigate the triggering behaviors of periodic event-triggered multiagent systems (MASs) under multiplicative false data injection (FDI) attacks. An abstraction-based traffic model is established to characterize all possible triggering behaviors under arbitrary initial states, including the minimum interevent time (MIET) and the transition relations among IETs. We further answer the following two questions: 1) how FDI attacks affect the MIET and 2) how to select the sampling period for the anomalous MIET detection. As a potential application scenario, a behavior-based anomaly detection algorithm is developed based on the proposed traffic model to identify anomalous triggering behaviors caused by attacks. Simulations demonstrate the effectiveness and practical application of the proposed results.
{"title":"Traffic Characterization of Event-Triggered Multiagent Systems Under FDI Attacks.","authors":"Tao Chen,Wentuo Fang,Wenfeng Hu,Gui Gui,Chunhua Yang","doi":"10.1109/tcyb.2026.3667616","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3667616","url":null,"abstract":"In this article, we investigate the triggering behaviors of periodic event-triggered multiagent systems (MASs) under multiplicative false data injection (FDI) attacks. An abstraction-based traffic model is established to characterize all possible triggering behaviors under arbitrary initial states, including the minimum interevent time (MIET) and the transition relations among IETs. We further answer the following two questions: 1) how FDI attacks affect the MIET and 2) how to select the sampling period for the anomalous MIET detection. As a potential application scenario, a behavior-based anomaly detection algorithm is developed based on the proposed traffic model to identify anomalous triggering behaviors caused by attacks. Simulations demonstrate the effectiveness and practical application of the proposed results.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"2 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147346361","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：