Event-triggered control based on adaptive dynamic programming for continuous-time nonlinear systems with completely unknown dynamics

This paper explores a novel event-triggered control (ETC) scheme for continuous-time nonlinear systems with completely unknown dynamics based on adaptive dynamic programming (ADP) method. Different from the existing works using ETC based on ADP, it is assumed that the dynamics of the systems considered in this paper is completely unknown, which thus lead to much difficulty in control design and dynamic analysis. For that reason, to design the controller, a dynamic neural network (NN) is firstly employed to identify the dynamics of the system. An event-triggered control via ADP technique is then designed based on the obtained model, which can realize the near optimality. To describe the effects of both jump and continuous dynamics in the feedback control system, an impulsive model is employed, and then, Lyapunov function method is utilized to prove that the NN weight estimation errors and system states are uniformly ultimately bounded (UUB). Finally, simulation results are provided to illustrate the effectiveness of the proposed control method.