Event-based optimal output-feedback control of nonlinear discrete-time systems

摘要

In this paper, an event-triggered strategy and an optimality scheme are presented to design an output-feedback controller for nonlinear discrete-time systems. Firstly, owing to the unavailability of the system states, a coordinate transformation is executed instead of state observers. Then, an action neural network and a specified critic neural network are presented to obtain the optimal controller and estimate the novel long-term cost function, respectively. The adaptation laws are designed on the basis of the gradient descent rule and event-triggered mechanism. The control signals are transmitted only when the event is triggered. Based on the Lyapunov analysis theory, the stability and the tracking performance of the closed-loop system are proven. The effectiveness of the proposed strategy is verified via simulation examples. (c) 2020 Elsevier Inc. All rights reserved.