基于规定时间观测器的多代理系统输出调节容错控制

IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-09-13 DOI:10.1109/TSIPN.2024.3458789
Yang Liu;Guochen Pang;Jianlong Qiu;Xiangyong Chen;Jinde Cao
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引用次数: 0

摘要

本文研究了具有执行器多重故障的多代理系统中输出调节的容错控制问题。首先,为跟随者建立一个规定时间观测器,以估计领导者的状态。通过结合时变缩放函数,预先确定收敛时间,确保观测器误差收敛为零,不受系统参数和初始条件的影响。随后,根据观测器的状态和相邻代理之间的相对信息,设计了分布式自适应容错控制器。实验验证了所设计的控制器能有效补偿执行器故障,并解决协同输出调节容错控制问题。最后,通过一个仿真实例证明了自适应容错控制器的有效性。
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Fault-Tolerant Control for Output Regulation in Multi-Agent Systems Based on Prescribed-Time Observers
This paper investigates the problem of fault-tolerant control for output regulation in multi-agent systems with actuator multiplicative faults. Initially, a prescribed-time observer is established for the followers to estimate the states of the leader. By incorporating a time-varying scaling function, the convergence time is predetermined, ensuring that the observer error converges to zero independently of the system parameters and initial conditions. Subsequently, a distributed adaptive fault-tolerant controller is designed based on the states of the observer and the relative information among the neighboring agents. It is verified that the designed controller can effectively compensate for actuator faults and address the cooperative output regulation fault-tolerant control problem. Finally, the effectiveness of the adaptive fault-tolerant controller is demonstrated through a simulation example.
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来源期刊
IEEE Transactions on Signal and Information Processing over Networks
IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications
CiteScore
5.80
自引率
12.50%
发文量
56
期刊介绍: The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.
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