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IEEE Transactions on Cybernetics最新文献

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Predictive Observer-Based Dual-Rate Prescribed Performance Control for Visual Servoing of Robot Manipulators With View Constraints. 基于预测观察器的双速率规定性能控制，用于具有视线限制的机器人机械手视觉伺服。
IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
IEEE Transactions on Cybernetics
Pub Date : 2025-03-14 DOI: 10.1109/TCYB.2025.3546800
Qifang Liu, Jianliang Mao, Linyan Han, Chuanlin Zhang, Jun Yang

This article simultaneously addresses the dual-rate and view constraints issues for the image-based visual servoing (IBVS) system of robot manipulators. Considering the low sampling bandwidth of the camera, potentially diminishing the efficiency of the robotic controller in updating low-level servoing control commands, a predictive observer (PO) is initially designed to forecast the system output during the high-level sampling intervals. Moreover, by leveraging a mixture of soft-sensing and real-measured signals, a dual-rate integral-based prescribed performance control (DRIPPC) approach is devised. The benefit lies in that the proposed control method samples the low-frequency state signal while generating a relatively high-frequency control action, ensuring rapid response of the robot manipulator while maintaining strict adherence to field-of-view (FOV) constraints. Finally, the effectiveness of the proposed control approach is validated through a series of experiments conducted on a Universal Robots 5 (UR5) manipulator.

本文同时解决了机器人操纵器基于图像的视觉伺服（IBVS）系统的双速率和视图约束问题。考虑到摄像机的采样带宽较低，可能会降低机器人控制器更新低级伺服控制指令的效率，因此最初设计了一个预测观察器（PO），用于预测高级采样间隔期间的系统输出。此外，通过利用软感应信号和实际测量信号的混合物，设计了一种基于双速率积分的规定性能控制（DRIPPC）方法。这种控制方法的优点在于，在对低频状态信号进行采样的同时，还能产生相对较高频率的控制动作，从而在严格遵守视场（FOV）限制的同时，确保机器人操纵器的快速响应。最后，通过在 Universal Robots 5（UR5）机械手上进行的一系列实验，验证了所提控制方法的有效性。
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引用次数: 0
Data-Driven Inverse Reinforcement Learning for Heterogeneous Optimal Robust Formation Control. 数据驱动逆强化学习，实现异质最优鲁棒编队控制。
IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
IEEE Transactions on Cybernetics
Pub Date : 2025-03-14 DOI: 10.1109/TCYB.2025.3546563
Fatemeh Mahdavi Golmisheh, Saeed Shamaghdari

This article presents novel data-driven inverse reinforcement learning (IRL) algorithms to optimally address heterogeneous formation control problems in the presence of disturbances. We propose expert-estimator-learner multiagent systems (MASs) as independent systems with similar interaction graphs. First, a model-based IRL algorithm is introduced for the estimator MAS to determine its optimal control and reward functions. Using the estimator IRL algorithm results, a robust algorithm for model-free IRL is presented to reconstruct the learner MAS's optimal control and reward functions without knowing the learners' dynamics. Therefore, estimator MAS aims to estimate experts' desired formation and learner MAS wants to track the estimators' trajectories optimally. As a final step, data-driven implementations of these proposed IRL algorithms are presented. Consequently, this research contributes to identifying unknown reward functions and optimal controls by conducting demonstrations. Our analysis shows that the stability and convergence of MASs are thoroughly ensured. The effectiveness of the given algorithms is demonstrated via simulation results.

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引用次数: 0
Granule Margin-Based Feature Selection in Weighted Neighborhood Systems
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
IEEE Transactions on Cybernetics
Pub Date : 2025-03-12 DOI: 10.1109/tcyb.2025.3544693
Can Gao, Jie Zhou, Xizhao Wang, Witold Pedrycz
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引用次数: 0
Observer-Based Control of Networked Periodic Piecewise Systems With Encoding–Decoding Mechanism
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
IEEE Transactions on Cybernetics
Pub Date : 2025-03-12 DOI: 10.1109/tcyb.2025.3543878
Yun Liu, Wen Yang, Chun-Yi Su, Yue Luo, Xiaofan Wang
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引用次数: 0
Robust Model Predictive Control of a Gait Rehabilitation Exoskeleton With Whole Body Motion Planning and Neuro-Dynamics Optimization
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
IEEE Transactions on Cybernetics
Pub Date : 2025-03-11 DOI: 10.1109/tcyb.2025.3545064
Liangrui Xu, Zhijun Li, Guoxin Li, Lingjing Jin
{"title":"Robust Model Predictive Control of a Gait Rehabilitation Exoskeleton With Whole Body Motion Planning and Neuro-Dynamics Optimization","authors":"Liangrui Xu, Zhijun Li, Guoxin Li, Lingjing Jin","doi":"10.1109/tcyb.2025.3545064","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3545064","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"23 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598820","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}
引用次数: 0
Industrial Foundation Model
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
IEEE Transactions on Cybernetics
Pub Date : 2025-03-11 DOI: 10.1109/tcyb.2025.3527632
Lei Ren, Haiteng Wang, Jiabao Dong, Zidi Jia, Shixiang Li, Yuqing Wang, Yuanjun Laili, Di Huang, Lin Zhang, Bohu Li
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引用次数: 0
Observer-Based Decentralized Adaptive Control of Interconnected Nonlinear Systems With Output/Input Triggering
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
IEEE Transactions on Cybernetics
Pub Date : 2025-03-11 DOI: 10.1109/tcyb.2025.3545279
Zhirong Zhang, Yongduan Song, Xiaoyuan Zheng, Long Chen, Petros Ioannou
{"title":"Observer-Based Decentralized Adaptive Control of Interconnected Nonlinear Systems With Output/Input Triggering","authors":"Zhirong Zhang, Yongduan Song, Xiaoyuan Zheng, Long Chen, Petros Ioannou","doi":"10.1109/tcyb.2025.3545279","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3545279","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"56 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598854","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}
引用次数: 0
Adaptive Predefined Time Control for Stochastic Switched Nonlinear Systems With Full-State Error Constraints and Input Quantization. 具有全状态误差约束和输入量化的随机切换非线性系统的自适应预定义时间控制。
IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
IEEE Transactions on Cybernetics
Pub Date : 2025-03-10 DOI: 10.1109/TCYB.2025.3531381
Yu Yang, Shuai Sui, Tengfei Liu, C L Philip Chen

A neural network adaptive quantized predefined-time control problem is studied for switching stochastic nonlinear systems with full-state error constraints under arbitrary switching. Unlike previous research on rapid convergence, the predefined-time stability criteria are introduced and established for stochastic nonlinear systems, ensuring the stabilization of the control system within a specified time frame. The chattering issue is avoided and it is split into two limited nonlinear functions using a hysteresis quantizer. To address the full-state error constraint problem, a universal barrier Lyapunov function is presented. The common Lyapunov function approach is used to demonstrate the stability of controlled systems. The results demonstrate that the proposed control method ensures all closed-loop signals are probabilistically practically predefined time-stabilized (PPTS), with the system output closely tracking the specified reference signal. Finally, simulated examples validate the effectiveness of the suggested control technique.

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引用次数: 0
Big Data-Driven Control of Nonlinear Processes Through Dynamic Latent Variables Using an Autoencoder
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
IEEE Transactions on Cybernetics
Pub Date : 2025-03-07 DOI: 10.1109/tcyb.2025.3544257
Jun Wen Tang, Yitao Yan, Jie Bao, Biao Huang
{"title":"Big Data-Driven Control of Nonlinear Processes Through Dynamic Latent Variables Using an Autoencoder","authors":"Jun Wen Tang, Yitao Yan, Jie Bao, Biao Huang","doi":"10.1109/tcyb.2025.3544257","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3544257","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"3 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575473","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}
引用次数: 0
Intelligent Resilient Security Control for Fractional-Order Multiagent Networked Systems Using Reinforcement Learning and Event-Triggered Communication Mechanism
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
IEEE Transactions on Cybernetics
Pub Date : 2025-03-07 DOI: 10.1109/tcyb.2025.3542838
G. Narayanan, Rajagopal Karthikeyan, Sangmoon Lee, Sangtae Ahn
{"title":"Intelligent Resilient Security Control for Fractional-Order Multiagent Networked Systems Using Reinforcement Learning and Event-Triggered Communication Mechanism","authors":"G. Narayanan, Rajagopal Karthikeyan, Sangmoon Lee, Sangtae Ahn","doi":"10.1109/tcyb.2025.3542838","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3542838","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"23 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575468","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}
引用次数: 0
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