Data-Driven Output Regulation via Internal Model Principle

TL;DR

This paper extends data-driven output regulation methods from single-input single-output to multi-input multi-output linear systems and introduces an improved algorithm that reduces computational cost and relaxes solvability conditions.

Contribution

The paper develops a new algorithm for multi-input multi-output systems and improves existing methods by separating learning and control phases for efficiency.

Findings

Extended algorithm to MIMO systems.

Reduced computational cost in the improved method.

Weakened solvability conditions for output regulation.

Abstract

The data-driven techniques have been developed to deal with the output regulation problem of unknown linear systems by various approaches. In this paper, we first extend an existing algorithm 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 weakens the solvability conditions over the first algorithm.