Event-triggered distributed zero-sum differential game for nonlinear multi-agent systems using adaptive dynamic programming

JingLiang Sun, Teng Long*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)

Abstract

In this paper, to reduce the computational and communication burden, the event-triggered distributed zero-sum differential game problem for multi-agent systems is investigated. Firstly, based on the Minimax principle, an adaptive event-triggered distributed iterative differential game strategy is derived with an adaptive triggering condition for updating the control scheme aperiodically. Then, to implement this proposed strategy, the solution of coupled Hamilton–Jacobi–Isaacs​ (HJI) equation is approximated by constructing the critic neural network (NN). In order to further relax the restrictive persistent of excitation (PE) condition, a novel PE-free updating law is designed by using the experience replay method. Then, the distributed event-triggered nonlinear system is expressed as an impulsive dynamical system. After analyzing the stability, the developed strategy ensures the uniformly ultimately bounded (UUB) of all the closed-loop signals. Moreover, the minimal intersample time is proved to be lower bounded, which avoids the infamous Zeno behavior. Finally, the simulation results show that the number of controller update is reduced obviously, which saves the computational and communication resources.

Original languageEnglish
Pages (from-to)39-52
Number of pages14
JournalISA Transactions
Volume110
DOIs
Publication statusPublished - Apr 2021

Keywords

  • Adaptive dynamic programming
  • Distributed differential game
  • Event-triggered control
  • Multi-agent systems
  • Neural network

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