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On Model Identification Based Optimal Control and It’s Applications to Multi-Agent Learning and Control

Author

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  • Rui Luo

    (School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
    Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China)

  • Zhinan Peng

    (School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Jiangping Hu

    (School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
    Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China)

Abstract

This paper reviews recent progress in model identification-based learning and optimal control and its applications to multi-agent systems (MASs). First, a class of learning-based optimal control method, namely adaptive dynamic programming (ADP), is introduced, and the existing results using ADP methods to solve optimal control problems are reviewed. Then, this paper investigates various kinds of model identification methods and analyzes the feasibility of combining the model identification method with the ADP method to solve optimal control of unknown systems. In addition, this paper expounds the current applications of model identification-based ADP methods in the fields of single-agent systems (SASs) and MASs. Finally, some conclusions and some future directions are presented.

Suggested Citation

  • Rui Luo & Zhinan Peng & Jiangping Hu, 2023. "On Model Identification Based Optimal Control and It’s Applications to Multi-Agent Learning and Control," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:906-:d:1064490
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    References listed on IDEAS

    as
    1. Zuñiga Aguilar, C.J. & Gómez-Aguilar, J.F. & Alvarado-Martínez, V.M. & Romero-Ugalde, H.M., 2020. "Fractional order neural networks for system identification," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    2. Yunfeng Ji & Gang Wang & Qingdu Li & Chaoli Wang, 2022. "Event-Triggered Optimal Consensus of Heterogeneous Nonlinear Multi-Agent Systems," Mathematics, MDPI, vol. 10(23), pages 1-14, December.
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    Cited by:

    1. Xunde Dong & Yuxin Lin & Xudong Suo & Xihao Wang & Weijie Sun, 2024. "The Adaptive Optimal Output Feedback Tracking Control of Unknown Discrete-Time Linear Systems Using a Multistep Q-Learning Approach," Mathematics, MDPI, vol. 12(4), pages 1-20, February.

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