Data-Driven Output-Based Approach to the Output Regulation Problem of Unknown Linear Systems via Value Iteration

TL;DR

This paper introduces a novel data-driven output-based method using value iteration to solve the output regulation problem for unknown linear systems, eliminating the need for explicit observer gain design.

Contribution

It develops a systematic data-driven approach for output regulation of unknown linear systems via value iteration, linking output-feedback and state-feedback control laws.

Findings

Successfully reduces output regulation to state-feedback design for augmented systems.

Establishes a relation between data-driven output-feedback and state-feedback control laws.

Provides a new output-feedback control law that does not rely on explicit observer gain.

Abstract

The output regulation problem for unknown linear systems has been studied using state-based and output-based internal model approaches in the special case with no disturbances. This paper further investigates the output regulation problem for unknown linear systems using a data-driven output-based approach via value iteration. For this purpose, we first develop a novel output-feedback control law that does not explicitly rely on the observer gain to solve the output regulation problem. We then show that the data-driven approach for designing an output-feedback control law for the given plant can be reduced to the data-driven design of a state-feedback control law for a well-defined augmented auxiliary system. As a result, we develop a systematic data-driven approach to solve the output regulation problem for unknown linear systems via value iteration. Finally, we establish a relation between the data-driven state-feedback control law and the data-driven output-feedback control law in the LQR sense.