Event-Triggered Robust Control for Fully Cooperative Games of Uncertain Nonlinear Multi-player Systems

Abstract

This paper develops an adaptive dynamic programming-based event-triggered robust control method for fully cooperative games of nonlinear multi-player systems with uncertainties. By designing a modified value function, the robust control problem is transformed into a fully cooperative (FC) game of the nominal system. Then, a critic neural network is constructed to solve the Hamilton-Jacobi-Bellman equation of the FC game, and the optimal control laws of all players are obtained. Under the event-triggered mechanism, the designed event-triggering condition is employed to determine whether the control laws should be updated or not. The developed method saves computation and communication resources, since both the updating frequency of control laws and the communication between controllers and actuators are reduced. Furthermore, the closed-loop system is proven to be uniformly ultimately bounded thanks to the Lyapunov’s direct method. A numerical example is employed to verify the effectiveness of the developed method.