Event-Triggered Control of Discrete-Time Zero-Sum Games via Deterministic Policy Gradient Adaptive Dynamic Programming
针对离散时间非线性系统的零和博弈问题，提出一种基于确定性策略梯度自适应动态规划的事件触发控制方法，通过非周期性更新控制律降低计算与通信负担，并利用经验回放技术保证神经网络权重估计误差的有界性。
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.