Open AccessObserver‐based state tracking for discrete linear multi‐agent systems with switching topologies via learning control strategies
Author(s) -
Luo Zijian,
Xiong Wenjun,
He Wangli,
Chen Yao
Publication year - 2020
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2019.1244
Subject(s) - network topology , control theory (sociology) , convergence (economics) , observer (physics) , multi agent system , computer science , tracking (education) , state observer , tracking error , consensus , iterative learning control , state (computer science) , control (management) , artificial intelligence , nonlinear system , algorithm , psychology , pedagogy , physics , quantum mechanics , operating system , economics , economic growth
In this study, the consensus tracking problem of discrete linear systems is investigated in the presence of two kinds of switching topologies, which means two independent switching topologies in the control network and the observation network are considered, respectively. Distributed observer‐based learning control strategies are proposed to solve the consensus tracking problem effectively when the state information of each node is unavailable. Meanwhile, distributed initial state learning strategies are designed to improve the tracking performances. Some sufficient conditions are derived to guarantee the asymptotical convergence of the consensus tracking error. It is interesting to find that the initial learning strategies play significant roles in the theoretical analysis. In the end, the effectiveness of obtained results is demonstrated by a simulation example.