Dual Event-Triggered Constrained Control Through Adaptive Critic for Discrete-Time Zero-Sum Games
Ding Wang, Lingzhi Hu, Mingming Zhao, Junfei Qiao
Abstract
In this article, through adaptive critic, a dual event-triggered (DET) constrained control scheme is established for discrete-time nonlinear zero-sum games. The neural networks are trained from the dual heuristic dynamic programming technique to obtain the approximate optimal policy pair. Two corresponding independent triggering conditions are constructed for the control input and the disturbance to improve the utilization efficiency and ensure the independence between them. In addition, in order to overcome the challenge caused by the actuator saturation, we constrain the control input to a bounded range. Meanwhile, the asymptotically stability is proved for the DET control system. Finally, experimental simulations are conducted to verify the effectiveness of the proposed algorithm.