Event-triggered optimal control for nonlinear stochastic systems via adaptive dynamic programming

For nonlinear Itô-type stochastic systems, the problem of event-triggered optimal control (ETOC) is studied in this paper, and adaptive dynamic programming (ADP) approach explored to implement it. The value function Hamilton–Jacobi–Bellman(HJB) equation approximated by applying critical neural network (CNN). Moreover, a new event-triggering scheme proposed, which can be used design ETOC directly via solution HJB equation. By utilizing Lyapunov direct method, it proved that based on ADP ensure CNN weight errors states system are semi-globally uniformly ultimately bounded probability. Furthermore, an upper bound given predetermined cost function. Specifically, there has been no published literature for systems method. This work first attempt fill gap subject. Finally, effectiveness proposed method illustrated through two numerical examples.