A low-pass filter is essential for stabilizing strictly proper repetitive-control systems, but it inevitably degrades steady-state tracking accuracy due to gain attenuation and phase lag. This article presents a new filter design and optimization method that improves both transient response and steady-state accuracy in continuous-time repetitive-control systems. First, the gain and phase characteristics of conventional filter-based repetitive controllers are rigorously analyzed to reveal the relationship between filter parameters and tracking performance. Based on this analysis, a new filter structure is designed to precisely compensate for gain attenuation and phase delay, specifically at the fundamental frequency, by minimizing the error term without increasing the filter bandwidth. A guideline for selecting the filter parameters for varying periodic trajectories is also provided. In addition, according to the one-to-one mapping between control and learning behaviors and their respective gains, dual performance indices are constructed to account for tracking error and control effort across multiple learning cycles. A multiobjective optimization framework is then developed to directly tune these gains subject to stability constraints, achieving an optimal balance between rapid transient convergence and control energy efficiency. Experimental results validate the effectiveness and superiority of the design.
{"title":"A New Filter Design and Optimization Framework for Enhancing Transient and Steady-State Tracking in Repetitive-Control Systems.","authors":"Manli Zhang,Chengda Lu,Yibing Wang,Shengnan Tian,Min Wu,Makoto Iwasaki","doi":"10.1109/tcyb.2026.3651712","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3651712","url":null,"abstract":"A low-pass filter is essential for stabilizing strictly proper repetitive-control systems, but it inevitably degrades steady-state tracking accuracy due to gain attenuation and phase lag. This article presents a new filter design and optimization method that improves both transient response and steady-state accuracy in continuous-time repetitive-control systems. First, the gain and phase characteristics of conventional filter-based repetitive controllers are rigorously analyzed to reveal the relationship between filter parameters and tracking performance. Based on this analysis, a new filter structure is designed to precisely compensate for gain attenuation and phase delay, specifically at the fundamental frequency, by minimizing the error term without increasing the filter bandwidth. A guideline for selecting the filter parameters for varying periodic trajectories is also provided. In addition, according to the one-to-one mapping between control and learning behaviors and their respective gains, dual performance indices are constructed to account for tracking error and control effort across multiple learning cycles. A multiobjective optimization framework is then developed to directly tune these gains subject to stability constraints, achieving an optimal balance between rapid transient convergence and control energy efficiency. Experimental results validate the effectiveness and superiority of the design.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"35 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015433","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 : 2026-01-21DOI: 10.1109/tcyb.2026.3651581
Xiaoxiao Wang,Zhiyong Bao,Xiaomiao Li,Hak-Keung Lam,Ziwei Wang
The co-design problem of dual event-triggered (DET) mechanism and polynomial dynamic output-feedback (PDOF) controller is investigated for positive polynomial fuzzy systems (PPFSs) with uncertainty and disturbance constraints. Specifically, a 1-norm DET mechanism compatible with the positivity of PPFSs is proposed to asynchronously update measurement outputs and PDOF control signals. However, synthesizing this DET-PDOF controller proves challenging due to the coupling of multiple unknown PDOF controller gain matrices within the positivity and stability conditions, which results in complex nonconvex terms. By introducing auxiliary variables and constraints, sufficient conditions for DET-PDOF controller solution are given to ensure both the $L_{1}$ -gain performance and strict positivity of PPFSs with uncertainty and disturbance. Moreover, existing stability analysis results that ignore membership functions (MFs) tend to be conservative, implying that the obtained DET-PDOF controller is effective only within a limited triggered threshold range, leading to worse transmission performance. Therefore, a multivariate optimization method based on an improved genetic algorithm (IGA), which accounts for the system states and PDOF controller variables, is developed to substantially expand the admissible DET threshold range while effectively suppressing dual-triggering frequencies. Finally, a numerical example and a two-linked tank system with parameter uncertainty are provided to validate the feasibility of the proposed scheme.
{"title":"Dual Event-Triggered Polynomial Dynamic Output Control for Positive Fuzzy Systems via an IT2 Membership Function Relaxation Method.","authors":"Xiaoxiao Wang,Zhiyong Bao,Xiaomiao Li,Hak-Keung Lam,Ziwei Wang","doi":"10.1109/tcyb.2026.3651581","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3651581","url":null,"abstract":"The co-design problem of dual event-triggered (DET) mechanism and polynomial dynamic output-feedback (PDOF) controller is investigated for positive polynomial fuzzy systems (PPFSs) with uncertainty and disturbance constraints. Specifically, a 1-norm DET mechanism compatible with the positivity of PPFSs is proposed to asynchronously update measurement outputs and PDOF control signals. However, synthesizing this DET-PDOF controller proves challenging due to the coupling of multiple unknown PDOF controller gain matrices within the positivity and stability conditions, which results in complex nonconvex terms. By introducing auxiliary variables and constraints, sufficient conditions for DET-PDOF controller solution are given to ensure both the $L_{1}$ -gain performance and strict positivity of PPFSs with uncertainty and disturbance. Moreover, existing stability analysis results that ignore membership functions (MFs) tend to be conservative, implying that the obtained DET-PDOF controller is effective only within a limited triggered threshold range, leading to worse transmission performance. Therefore, a multivariate optimization method based on an improved genetic algorithm (IGA), which accounts for the system states and PDOF controller variables, is developed to substantially expand the admissible DET threshold range while effectively suppressing dual-triggering frequencies. Finally, a numerical example and a two-linked tank system with parameter uncertainty are provided to validate the feasibility of the proposed scheme.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"29 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015300","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/tcyb.2026.3651462
Weihao Pan, Xianfu Zhang, Lu Liu, Zhiyu Duan
{"title":"Decentralized Impulsive Control for Nonlinear Interconnected Systems Based on Dynamic Event-Triggered Mechanism","authors":"Weihao Pan, Xianfu Zhang, Lu Liu, Zhiyu Duan","doi":"10.1109/tcyb.2026.3651462","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3651462","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"27 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001267","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 : 2026-01-16DOI: 10.1109/TCYB.2025.3650459
Lei Hao, Lina Xu, Chang Liu, Yanni Dong
Effective deep feature extraction via feature-level fusion is crucial for multimodal object detection. However, previous studies often involve complex training processes that integrate modality-specific features by stacking multiple feature-level fusion units, leading to significant computational overhead. To address this issue, we propose a lightweight attention-guided self-modulation feature fusion network (LASFNet). The LASFNet adopts a single feature-level fusion unit to enable high-performance detection, thereby simplifying the training process. The attention-guided self-modulation feature fusion (ASFF) module in the model adaptively adjusts the responses of fused features at both global and local levels, promoting comprehensive and enriched feature generation. Additionally, a lightweight feature attention transformation module (FATM) is designed at the neck of LASFNet to enhance the focus on fused features and minimize information loss. Extensive experiments on three representative datasets demonstrate that our approach achieves a favorable efficiency-accuracy tradeoff. Compared to state-of-the-art methods, LASFNet reduced the number of parameters and computational cost by as much as 90% and 85%, respectively, while improving detection accuracy mean average precision (mAP) by 1%-3%. The code will be open-sourced at https://github.com/leileilei2000/LASFNet.
{"title":"LASFNet: A Lightweight Attention-Guided Self-Modulation Feature Fusion Network for Multimodal Object Detection.","authors":"Lei Hao, Lina Xu, Chang Liu, Yanni Dong","doi":"10.1109/TCYB.2025.3650459","DOIUrl":"https://doi.org/10.1109/TCYB.2025.3650459","url":null,"abstract":"<p><p>Effective deep feature extraction via feature-level fusion is crucial for multimodal object detection. However, previous studies often involve complex training processes that integrate modality-specific features by stacking multiple feature-level fusion units, leading to significant computational overhead. To address this issue, we propose a lightweight attention-guided self-modulation feature fusion network (LASFNet). The LASFNet adopts a single feature-level fusion unit to enable high-performance detection, thereby simplifying the training process. The attention-guided self-modulation feature fusion (ASFF) module in the model adaptively adjusts the responses of fused features at both global and local levels, promoting comprehensive and enriched feature generation. Additionally, a lightweight feature attention transformation module (FATM) is designed at the neck of LASFNet to enhance the focus on fused features and minimize information loss. Extensive experiments on three representative datasets demonstrate that our approach achieves a favorable efficiency-accuracy tradeoff. Compared to state-of-the-art methods, LASFNet reduced the number of parameters and computational cost by as much as 90% and 85%, respectively, while improving detection accuracy mean average precision (mAP) by 1%-3%. The code will be open-sourced at https://github.com/leileilei2000/LASFNet.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":10.5,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145989039","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 : 2026-01-15DOI: 10.1109/tcyb.2026.3651567
Tao Jiang,Yan Yan,Shuanghe Yu,Ge Guo
This article investigates fixed-time synchronized convergence for disturbed second-order multiagent systems (MASs) in distributed optimization under the zero-gradient-sum (ZGS) scheme. A fixed-time ZGS distributed optimization method via sliding mode is first proposed for the second-order MASs, which avoids local minimization and rejects disturbances. To further achieve time-synchronized convergence, a hierarchical robust optimization method is then introduced. It employs a time-varying function-based local-minimization-free ZGS scheme within a virtual MAS to generate a reference signal that reaches the global cost function's minimizer and a fixed-time synchronized sliding mode tracking controller to drive the original second-order MAS to track this signal. Beyond the capabilities of the first protocol, this method also ensures the time-synchronized convergence of each agent's state components, low conservatism in terms of convergence time bounds, and privacy preservation. Numerical simulations demonstrate the effectiveness of the proposed methods.
{"title":"Distributed Robust Optimization for Disturbed Multiagent Systems With Fixed-Time Synchronized Convergence.","authors":"Tao Jiang,Yan Yan,Shuanghe Yu,Ge Guo","doi":"10.1109/tcyb.2026.3651567","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3651567","url":null,"abstract":"This article investigates fixed-time synchronized convergence for disturbed second-order multiagent systems (MASs) in distributed optimization under the zero-gradient-sum (ZGS) scheme. A fixed-time ZGS distributed optimization method via sliding mode is first proposed for the second-order MASs, which avoids local minimization and rejects disturbances. To further achieve time-synchronized convergence, a hierarchical robust optimization method is then introduced. It employs a time-varying function-based local-minimization-free ZGS scheme within a virtual MAS to generate a reference signal that reaches the global cost function's minimizer and a fixed-time synchronized sliding mode tracking controller to drive the original second-order MAS to track this signal. Beyond the capabilities of the first protocol, this method also ensures the time-synchronized convergence of each agent's state components, low conservatism in terms of convergence time bounds, and privacy preservation. Numerical simulations demonstrate the effectiveness of the proposed methods.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"390 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145971921","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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