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Autores principales:	Liu, Shangkun, Wang, Lei, Yi, Bowen
Formato:	Preprint
Publicado:	2025
Materias:	Systems and Control
Acceso en línea:	https://arxiv.org/abs/2512.05390
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

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author	Liu, Shangkun Wang, Lei Yi, Bowen
author_facet	Liu, Shangkun Wang, Lei Yi, Bowen
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2512_05390
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Data-Driven Adaptive Output Regulation of Unknown Linear Systems Liu, Shangkun Wang, Lei Yi, Bowen Systems and Control 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.
title	Data-Driven Adaptive Output Regulation of Unknown Linear Systems
topic	Systems and Control
url	https://arxiv.org/abs/2512.05390

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