Fault-Tolerant Consensus of Multiagent Systems With Prescribed Performance.

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2024-10-09 DOI:10.1109/TCYB.2024.3467217
Dun Zhang, James Lam, Xiaochen Xie, Chenchen Fan, Xiaoqi Song
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Abstract

This article studies the fault-tolerant consensus problem with the guaranteed transient performance of multiagent systems (MASs) subject to unknown time-varying actuator faults and disturbances. The general actuator faults, including both multiplicative and additive time-varying faults, are considered in such a problem for the first time. Both single-integrator modeled agents and double-integrator modeled agents are investigated. The transient performance is ensured in the sense that position errors between each pair of neighboring agents are guaranteed within certain user-defined time-varying performance bounds. Adaptive laws are designed to estimate information about faults and disturbances. For MASs with additive faults, the proposed controllers ensure errors asymptotically converge to zero with guaranteed transient performance. For MASs with both multiplicative faults and additive faults, the proposed controllers ensure errors converge to a residual set without asymptotic convergence but still with guaranteed transient performance. Two simulation examples are provided to evaluate the proposed schemes.

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具有规定性能的多代理系统容错共识。
本文研究了多代理系统(MAS)在未知时变执行器故障和干扰条件下保证瞬态性能的容错共识问题。该问题首次考虑了一般的执行器故障,包括乘性和加性时变故障。研究了单积分器模型代理和双积分器模型代理。瞬态性能得到了保证,即每对相邻代理之间的位置误差在用户定义的特定时变性能范围内。设计了自适应法则来估计故障和干扰信息。对于具有加性故障的 MAS,所提出的控制器可确保误差渐近地趋于零,并保证瞬态性能。对于同时存在乘法故障和加法故障的 MAS,所提出的控制器可确保误差在不渐进收敛的情况下收敛到残差集,但仍能保证瞬态性能。本文提供了两个仿真实例来评估所提出的方案。
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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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