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

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. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates an adaptive event-triggered distributed iterative differential game strategy for multi-agent systems, approximating the solution of coupled HJI equation with a critic neural network and designing a novel PE-free updating law. The developed strategy ensures the uniformly ultimately bounded of all closed-loop signals and avoids the Zeno behavior. The simulation results show a significant reduction in controller updates, saving computational and communication resources.