Learning-based distributed adaptive control of heterogeneous multi-agent systems with unknown leader dynamics

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Cyber-Physical Systems: Theory and Applications Pub Date : 2022-10-13 DOI:10.1049/cps2.12038
Di Mei, Jian Sun, Lihua Dou, Yong Xu
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Abstract

This study focuses on the distributed adaptive tracking control of heterogeneous multi-agent systems with unknown leader dynamics in a directed graph. In contrast to the reported leader-following consensus studies, the prior knowledge of the leader is supposed to be cognised to some or all of followers, the situation that the leader's dynamics is totally unrecognised but can be learnt for each individual follower is considered. A data-driven learning algorithm using the systems data is developed to reconstruct the unknown systems matrix. Then, an adaptive distributed dynamic compensator is exploited to provide the leader's state estimation in a directed graph. Afterwards, a dynamic output feedback control law for each agent is projected. Theoretical analysis shows that the proposed algorithms not only ensure that all followers can identify the unknown system matrix, but also guarantee that the distributed output leader-following consensus control with heterogeneous dynamics is achieved without any global information. Finally, a numerical example is provided to testify the proposed algorithms.

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未知领导者动态异构多智能体系统的学习分布式自适应控制
研究了有向图中未知领导者动态的异构多智能体系统的分布式自适应跟踪控制问题。与报道的领导者跟随共识研究相反,领导者的先验知识应该被部分或全部追随者所认知,考虑到领导者的动态完全不被认可,但可以为每个追随者学习的情况。提出了一种利用系统数据重构未知系统矩阵的数据驱动学习算法。然后,利用自适应分布式动态补偿器在有向图中给出了先行者的状态估计。然后,给出每个agent的动态输出反馈控制律。理论分析表明,所提出的算法不仅保证了所有follower都能识别未知的系统矩阵,而且保证了在没有全局信息的情况下实现异构动态的分布式输出leader- follower共识控制。最后,给出了一个数值算例来验证所提出的算法。
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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
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