Event-based input-constrained nonlinear H∞ state feedback with adaptive critic and neural implementation

In this paper, the continuous-time input-constrained nonlinear H∞ state feedback control under event-based environment is investigated with adaptive critic designs and neural network implementation. The nonlinear H∞ control issue is regarded as a two-player zero-sum game that requires solving the Hamilton-Jacobi-Isaacs equation and the adaptive critic learning (ACL) method is adopted toward the event-based constrained optimal regulation. The novelty lies in that the event-based design framework is combined with the ACL technique, thereby carrying out the input-constrained nonlinear H∞ state feedback via adopting a non-quadratic utility function. The event-based optimal control law and the time-based worst-case disturbance law are derived approximately, by training an artificial neural network called a critic and eventually learning the optimal weight vector. Under the action of the event-based state feedback controller, the closed-loop system is constructed with uniformly ultimately bounded stability analysis. Simulation studies are included to verify the theoretical results as well as to illustrate the event-based H∞ control performance.