Dynamic Event-Triggered $H_{\infty}$ Control With Adaptive Critic for Discrete Nonlinear Systems

In this paper, a dynamic event-triggered control (ETC) scheme based on online critic learning techniques is designed to tackle the optimal regulation problem of discrete-time nonlinear systems with disturbance inputs. First, a new stability criterion is developed to derive an initial admissible policy pair using an offline iterative method within the time-triggered control framework. Starting from the stability of the controlled system, a static ETC method is developed to reduce the computational burden. Then, a nonnegative dynamic variable is introduced into the static event-triggered mechanism, so as to establish the dynamic ETC method, which further improves the resource utilization rate and possesses the antiinterference ability. In addition, three kinds of neural networks are constructed for the implementation of the event-based online <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$H_{\infty}$</tex> optimal control strategy. Finally, an example is wield to demonstrate the rationality of the proposed algorithm.