Finite-Time Neuroadaptive Cooperative Control for Nonlinear Multiagent Systems Under Nonaffine Faults and Partially Unknown Control Directions

IF 10.5 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2024-10-01 DOI:10.1109/TCYB.2024.3462832
Shuai Cheng;Bin Xin;Qing Wang;Jie Chen;Fang Deng
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Abstract

This article investigates the cooperative control of complex nonlinear multiagent systems (CNMASs), in which the agents suffer from nonaffine faults and the control directions of some agents are unknown. A finite-time adaptive control scheme is presented for the CNMASs. A finite-time command filter is designed to solve the “explosion of complexity” issues, overcome chattering issues, and relax the limitations of the filter input. The impact of filter errors is alleviated by an improved error compensation mechanism. Based on piecewise Nussbaum functions, the partially unknown control direction is addressed. The proposed finite-time cooperative control strategy on the basis of local information can ensure that all signals in the closed-loop system are finite-time bounded, and the absolute value of the cooperative control errors can converge to a given upper bound in a finite time. The rapidity and robustness of the proposed method are verified by two comparative simulation examples. A real multirobot cooperative control experiment is used to verify the effectiveness of the presented method.
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非线性故障和部分未知控制方向下非线性多代理系统的有限时间神经自适应合作控制
本文研究了复杂非线性多代理系统(CNMASs)的合作控制问题,在这种系统中,代理会出现非线性故障,而且一些代理的控制方向是未知的。针对 CNMASs 提出了一种有限时间自适应控制方案。设计了一种有限时间指令滤波器,以解决 "复杂性爆炸 "问题,克服颤振问题,并放宽滤波器输入的限制。改进的误差补偿机制减轻了滤波器误差的影响。基于片断努斯鲍姆函数,解决了部分未知控制方向的问题。基于局部信息提出的有限时间协同控制策略能确保闭环系统中的所有信号都是有限时间有界的,协同控制误差的绝对值能在有限时间内收敛到给定的上界。两个对比仿真实例验证了所提方法的快速性和鲁棒性。一个真实的多机器人协同控制实验也验证了所提方法的有效性。
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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