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Completely model-free RL-based consensus of continuous-time multi-agent systems

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  • Wang, Xiaoling
  • Su, Housheng

Abstract

In this paper, we study the consensus of continuous-time general linear multi-agent systems in the absence of the model information by using the adaptive dynamic programming (ADP) based reinforcement learning (RL) approach. The introduction of the RL approach is to learn the feedback gain matrix to fulfill the construction of the control algorithm to guarantee the reach of consensus only on the basis of the available information. For the state feedback control, the RL algorithm relates only to the state and the input of an arbitrary agent, while for the output feedback control, the RL algorithm depends only on the input and output information of an arbitrary agent, irrelevant any model information. Finally, numerical simulations are given to verify the main results.

Suggested Citation

  • Wang, Xiaoling & Su, Housheng, 2020. "Completely model-free RL-based consensus of continuous-time multi-agent systems," Applied Mathematics and Computation, Elsevier, vol. 382(C).
    • Handle: RePEc:eee:apmaco:v:382:y:2020:i:c:s0096300320302782
    DOI: 10.1016/j.amc.2020.125312
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    References listed on IDEAS

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    1. Wang, Xin & Su, Housheng, 2019. "Consensus of hybrid multi-agent systems by event-triggered/self-triggered strategy," Applied Mathematics and Computation, Elsevier, vol. 359(C), pages 490-501.
    2. Liu, Yifan & Su, Housheng, 2019. "Containment control of second-order multi-agent systems via intermittent sampled position data communication," Applied Mathematics and Computation, Elsevier, vol. 362(C), pages 1-1.
    3. Peng, Zhinan & Hu, Jiangping & Shi, Kaibo & Luo, Rui & Huang, Rui & Ghosh, Bijoy Kumar & Huang, Jiuke, 2020. "A novel optimal bipartite consensus control scheme for unknown multi-agent systems via model-free reinforcement learning," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    4. Long, Mingkang & Su, Housheng & Liu, Bo, 2019. "Second-order controllability of two-time-scale multi-agent systems," Applied Mathematics and Computation, Elsevier, vol. 343(C), pages 299-313.
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    Cited by:

    1. Wang, Kun-Peng & Ding, Dong & Tang, Ze & Feng, Jianwen, 2022. "Leader-Following consensus of nonlinear multi-agent systems with hybrid delays: Distributed impulsive pinning strategy," Applied Mathematics and Computation, Elsevier, vol. 424(C).

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