Event-Triggered Control of Discrete-Time Zero-Sum Games via Deterministic Policy Gradient Adaptive Dynamic Programming

Abstract

In order to address zero-sum game problems for discrete-time (DT) nonlinear systems, this article develops a novel event-triggered control (ETC) approach based on the deterministic policy gradient (PG) adaptive dynamic programming (ADP) algorithm. By adopting the input and output data, the proposed ETC method updates the control law and the disturbance law with a gradient descent algorithm. Compared with the conventional PG ADP-based control scheme, the present controller is updated aperiodically to reduce the computational and communication burden. Then, the actor-critic-disturbance framework is adopted to obtain the optimal control law and the worst disturbance law, which guarantee the input-to-state stability of the closed-loop system. Moreover, a novel neural network weight updating law which guarantees the uniform ultimate boundedness of weight estimation errors is provided based on the experience replay technique. Finally, the validity of the present method is verified by simulation of two DT nonlinear systems.