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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    Title :

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


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Gao, Shanshan (author) / Chen, Xinzhuang (author) / Zhang, Shenggui (author)

    Conference:

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



    Publication date :

    2022-03-18


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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