Data-Driven Internal Model Control for Output Regulation

doi:10.1109/tcyb.2026.3690605

Data-Driven Internal Model Control for Output Regulation

2026-05-21

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DOI：10.1109/tcyb.2026.3690605

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Output regulation is a fundamental problem in control theory, extensively studied since the 1970s. Traditionally, research has primarily addressed scenarios where the system model is explicitly known, leaving the problem in the absence of a system model less explored. Leveraging recent advances in Willems et al.’s fundamental lemma, data-driven control has emerged as a powerful tool for stabilizing unknown systems. This article tackles the output regulation problem for unknown single and multiagent systems (MASs) using noisy data. Many existing data-driven approaches rely on solving data-based output regulator equations (OREs), which become inadequate for achieving zero tracking error in the presence of noisy data. To overcome this limitation, we advocate the use of a classical tool from robust output regulation, namely, the internal model principle. We first apply this idea to linear time-invariant (LTI) systems and show that exact output regulation, that is, zero tracking error, can be achieved by solving a simple data-based linear matrix inequality (LMI). The framework is then extended to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>th-order output regulation problem for nonlinear systems, followed by applications to both linear and nonlinear MASs. Finally, numerical tests validate the effectiveness of the proposed data-driven controllers.

Keywords:

Data-driven output regulation

exact output regulation

multiagent systems (MASs)

noisy data

Data-Driven Internal Model Control for Output Regulation

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