Data-Driven Adaptive Output Regulation of Unknown Linear Systems

TL;DR

This paper presents a data-driven method for adaptive output regulation of unknown linear systems, achieving stability and regulation without explicit model identification by leveraging offline data and real-time updates.

Contribution

It introduces a novel data-driven regulator that constructs an approximate internal model and updates it online, eliminating the need for system identification.

Findings

Achieves asymptotic regulation and stability without explicit model identification.

Uses offline experimental data to derive stabilizing laws.

Ensures regulation error converges to zero under persistent excitation.

Abstract

This paper investigates the linear output regulation problem with both the exosystem and the plant fully unknown. A data-driven regulator is proposed to achieve asymptotic regulation and closed-loop stability without performing model identification. The method constructs a nominal approximate internal model and filters of input and outputs, thereby yielding a stabilizable cascaded nominal system whose states are available. For this nominal system, a stabilizing law is derived from an offline dataset that has been acquired from the plant during experiments, such that the system states exponentially converge to a subspace. An identifier in discrete-time is, then, implemented to correct the internal model and update the stabilizing law; as a result, the regulation error can be steered to zero asymptotically under some persistent excitation conditions.