A Self-triggering Control Based on Adaptive Dynamic Programming for Nonzero-sum Game Systems

DOI
  • Shi Yibo
    Department of Control Science and Engineering, University of Shanghai for Science and Technology
  • Wang Chaoli
    Department of Control Science and Engineering, University of Shanghai for Science and Technology

説明

Recently, for the optimal control problem of nonzero-sum game systems, although it is discussed that these methods are event-triggered, it is still necessary to continuously monitor measurement errors during execution, which is difficult to achieve by hardware. In order to avoid continuous detection measurement errors, a selftriggered control based on adaptive dynamic programming is proposed to solve the optimal control problem for continuous-time nonlinear nonzero-sum game systems with unknown drift dynamics. Firstly, the principle of IRL method is used to avoid the requirement of system drift dynamics in the controller design. Then, to approximate the Nash equilibrium solution, a critic neural network is used to estimate the value function. Furthermore, a selftriggered adaptive control scheme is proposed according to Lyapunov theory to ensure the uniform ultimate boundedness (UUB) of the closed-loop system state. The self-triggered control obtained in this paper can calculate the next trigger point by the information of the current trigger moment.

収録刊行物

キーワード

詳細情報 詳細情報について

  • CRID
    1390578283211013760
  • DOI
    10.5954/icarob.2023.os8-1
  • ISSN
    21887829
  • 本文言語コード
    en
  • データソース種別
    • JaLC
    • Crossref
  • 抄録ライセンスフラグ
    使用不可

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