Optimal synchronization with L 2 -gain performance: An adaptive dynamic programming approach.

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  • Author(s): Chen Z;Chen Z;Chen Z;Chen Z; Chen K; Chen K; Tang R; Tang R
  • Source:
    Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2024 Nov; Vol. 179, pp. 106566. Date of Electronic Publication: 2024 Jul 25.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
    • Publication Information:
      Original Publication: New York : Pergamon Press, [c1988-
    • Subject Terms:
      Neural Networks, Computer* ; Algorithms* ; Nonlinear Dynamics*; Computer Simulation
    • Abstract:
      This paper studies an optimal synchronous control protocol design for nonlinear multi-agent systems under partially known dynamics and uncertain external disturbance. Under some mild assumptions, Hamilton-Jacobi-Isaacs equation is derived by the performance index function and system dynamics, which serves as an equivalent formulation. Distributed policy iteration adaptive dynamic programming is developed to obtain the numerical solution to the Hamilton-Jacobi-Isaacs equation. Three theoretical results are given about the proposed algorithm. First, the iterative variables is proved to converge to the solution to Hamilton-Jacobi-Isaacs equation. Second, the L 2 -gain performance of the closed loop system is achieved. As a special case, the origin of the nominal system is asymptotically stable. Third, the obtained control protocol constitutes an Nash equilibrium solution. Neural network-based implementation is designed following the main results. Finally, two numerical examples are provided to verify the effectiveness of the proposed method.
      Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
      (Copyright © 2024 Elsevier Ltd. All rights reserved.)
    • Contributed Indexing:
      Keywords: -gain performance; Adaptive dynamic programming; Multi-agent system; Nash equilibrium; Neural network
    • Publication Date:
      Date Created: 20240801 Date Completed: 20240917 Latest Revision: 20240917
    • Publication Date:
      20240917
    • Accession Number:
      10.1016/j.neunet.2024.106566
    • Accession Number:
      39089157