Composite Output Consensus Control for General Linear Multiagent Systems With Heterogeneous Mismatched Disturbances

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-27 DOI:10.1109/TSIPN.2024.3382427

Pan Yu;Yifan Ding;Kang-Zhi Liu;Xiaoli Li

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Abstract

This paper develops a composite output consensus control protocol for a general linear multiagent system subject to mismatched disturbances, which incorporates active disturbance-rejection control and fully distributed adaptive consensus control. To estimate and then cancel out the effect of mismatched disturbances on the outputs of the agents, heterogeneous generalized equivalent-input-disturbance estimators are constructed in the inner loop. Then a fully distributed adaptive feedback controller is designed to achieve consensus control based on the states of the designed heterogeneous observers for the agents. The restriction on the disturbances is lowered, the requirement for the global information of the communication topology is removed, and the exchanging information among agents is only relative estimated states. Further, the output consensus performance is analyzed for the closed-loop multiagent system. Our results complement and improve the results of the existing literature. Lastly, the effectiveness and superiority of the developed method are demonstrated through a numerical simulation and a comparison with the distributed extended-state-observer-based method.

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具有异质不匹配干扰的一般线性多代理系统的复合输出共识控制

本文为受不匹配干扰影响的一般线性多代理系统开发了一种复合输出共识控制协议，其中包含主动干扰抑制控制和全分布式自适应共识控制。为了估计并消除不匹配干扰对代理输出的影响，本文在内环中构建了异构广义等效输入干扰估计器。然后设计一个全分布式自适应反馈控制器，根据为代理设计的异构观测器的状态实现共识控制。降低了对干扰的限制，取消了对通信拓扑全局信息的要求，各代理之间只交换相对估计状态的信息。此外，还分析了闭环多代理系统的输出共识性能。我们的结果是对现有文献结果的补充和完善。最后，通过数值模拟以及与基于分布式扩展状态观测器方法的比较，证明了所开发方法的有效性和优越性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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

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.