Data-Driven Optimal Output Regulation for Continuous-Time Linear Systems via Internal Model Principle

Abstract

The data-driven techniques have been developed to deal with the output regulation problem of unknown linear systems by various approaches. In this article, we first extend an existing result from single-input single-output linear systems to multi-input multi-output linear systems. Then, by separating the dynamics used in the learning phase and the control phase, we further propose an improved algorithm that significantly reduces the computational cost and weaken the solvability conditions for the first algorithm. A numerical example is used to illustrate the advantages of the improved algorithm.