Adaptive dynamic programming based event-triggered control for unknown continuous-time nonlinear systems with input constraints

Abstract

An adaptive dynamic programming (ADP) based event-triggered control method is established in this paper to solve the optimal control problem of unknown continuous-time nonlinear systems with input constraints. First, the unknown system is identified using two neural networks (NNs). Second, the threshold for event-triggering condition is designed, which guarantees the system stability. Then, a critic NN is employed to approximate the value function and the closed-loop system is proved to be uniformly ultimately bounded. Finally, the simulation results demonstrate the feasibility of the developed ADP method. The main contributions of this paper are that the developed ADP-based event-triggered control method avoids the Zeno phenomenon effectively and updates the weights of three NNs simultaneously in the process of implementation. Meanwhile, an improved critic NN weight updating criterion is adopted, which does not require an initial admissible control.