For leader-follower multi-agent systems (MAS), the selection of informed-agents is crucial to the convergence rate of the MAS. In this paper, the resistance distance centrality (RDC) of agents is applied to determine a set of informed-agents with specific scale, so that the convergence rate of the MAS is maximum. For a MAS with only one leader, we find that the follower agent with minimum value of RDC is the best informed-agent. For a connected undirected network with N followers, an iterative algorithm with running time m N 3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$m N^3$$\end{document} is designed for determining m informed-agents. Simulations are carried out to manifest the validity of the approach.


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    Titel :

    Resistance Distance Centrality Based Informed-Agent Selection for Leader-Follower Consensus with Convergence Rate Maximization


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wu, Meiping (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Cheng, Jin (Herausgeber:in) / Gao, Shanshan (Autor:in) / Chen, Xinzhuang (Autor:in) / Zhang, Shenggui (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Erscheinungsdatum :

    2022-03-18


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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