Dynamic event-triggered-based single-network ADP optimal tracking control for the unknown nonlinear system with constrained input

Abstract

This paper presents an event-triggered-based optimal tracking control scheme for the unknown nonlinear system with unmeasurable state, constrained input, and limited communication. Firstly, an event-triggered Hamilton–Jacobi-Bellman (HJB) equation is derived based on an augmented system and a discounted cost function. Next, a novel online event-triggered-based neural networks (NNs) observer (NNsO) is constructed to obtain the unmeasurable state and the unknown system dynamics. Then, a single critic neural network (NN) based on the adaptive dynamic programming (ADP) framework is established to solve the event-triggered HJB equation, and the designed critic NN weight updating law can relax the restrictions of initial admissible control and persistence of excitation condition. Furthermore, a static triggering rule with an exponential term is designed to reduce communication and computing burden, and based on this, a dynamic triggering rule is established by introducing an auxiliary dynamic variable to obtain efficient communication and computing. It is proved that the tracking error and all the NN weight estimation errors are uniformly ultimately bounded (UUB), and the Zeno behavior is excluded. Finally, two simulation examples are presented to verify the effectiveness and superiority of the proposed control scheme.