Adaptive optimal output regulation of linear discrete-time systems based on event-triggered output-feedback

Abstract

This paper presents novel event-triggered control approaches to solve the adaptive optimal output regulation problem for a class of linear discrete-time systems. Different from most existing research on output regulation problems, the developed adaptive optimal control approaches are based on (1) output-feedback instead of full-state or partial-state feedback, (2) adaptive dynamic programming (ADP) which provides approximate solutions of the optimal control problem without requiring the precise knowledge of the plant dynamics, and (3) an event-triggering mechanism that reduces the communication between the controller and the plant. It is shown that the system in closed-loop with the developed controllers is asymptotically stable at an equilibrium of interest, and the tracking errors asymptotically converge to zero. Moreover, the suboptimality of the closed-loop system is directly determined by the relative threshold, which is a ratio between the triggering threshold and the actual state. A numerical simulation example is employed to verify the effectiveness of the proposed methodologies.