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Boundary Coupling for Consensus of Nonlinear Leaderless Stochastic Multi-Agent Systems Based on PDE-ODEs

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Listed:
  • Chuanhai Yang

    (School of Information Science and Engineering, Linyi University, Linyi 276000, China)

  • Jin Wang

    (School of Information Science and Engineering, Linyi University, Linyi 276000, China)

  • Shengfa Miao

    (National Pilot School of Software, Yunnan University, Kunming 650504, China)

  • Bin Zhao

    (School of Information Science and Engineering, Linyi University, Linyi 276000, China)

  • Muwei Jian

    (School of Information Science and Engineering, Linyi University, Linyi 276000, China)

  • Chengdong Yang

    (School of Information Science and Engineering, Linyi University, Linyi 276000, China)

Abstract

This paper studies the leaderless consensus of the stochastic multi-agent systems based on partial differential equations–ordinary differential equations (PDE-ODEs). Compared with the traditional state coupling, the most significant difference between this paper is that the space state coupling is designed. Two boundary couplings are investigated in this article, respectively, collocated boundary measurement and distributed boundary measurement. Using the Lyapunov directed method, sufficient conditions for the stochastic multi-agent system to achieve consensus can be obtained. Finally, two simulation examples show the feasibility of the proposed spatial boundary couplings.

Suggested Citation

  • Chuanhai Yang & Jin Wang & Shengfa Miao & Bin Zhao & Muwei Jian & Chengdong Yang, 2022. "Boundary Coupling for Consensus of Nonlinear Leaderless Stochastic Multi-Agent Systems Based on PDE-ODEs," Mathematics, MDPI, vol. 10(21), pages 1-15, November.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:4111-:d:963069
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    References listed on IDEAS

    as
    1. Xu Ni & Kejia Yi & Yiming Jiang & Ancai Zhang & Chengdong Yang, 2022. "Consensus Control of Leaderless and Leader-Following Coupled PDE-ODEs Modeled Multi-Agent Systems," Mathematics, MDPI, vol. 10(2), pages 1-14, January.
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