Data-Driven Reduced-Order Unknown-Input Observers

Fuente: arXiv
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Main Authors: Disarò, Giorgia, Valcher, Maria Elena
Format: Preprint
Published: 2024
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author Disarò, Giorgia
Valcher, Maria Elena
author_facet Disarò, Giorgia
Valcher, Maria Elena
contents In this paper we propose a data-driven approach to the design of reduced-order unknown-input observers (rUIOs). We first recall the model-based solution, by assuming a problem set-up slightly different from those traditionally adopted in the literature, in order to be able to easily adapt it to the data-driven scenario. Necessary and sufficient conditions for the existence of a reduced-order unknown-input observer, whose matrices can be derived from a sufficiently rich set of collected historical data, are first derived and then proved to be equivalent to the ones obtained in the model-based framework. Finally, a numerical example is presented, to validate the effectiveness of the proposed scheme.
format Preprint
id arxiv_https___arxiv_org_abs_2403_13471
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Data-Driven Reduced-Order Unknown-Input Observers
Disarò, Giorgia
Valcher, Maria Elena
Dynamical Systems
In this paper we propose a data-driven approach to the design of reduced-order unknown-input observers (rUIOs). We first recall the model-based solution, by assuming a problem set-up slightly different from those traditionally adopted in the literature, in order to be able to easily adapt it to the data-driven scenario. Necessary and sufficient conditions for the existence of a reduced-order unknown-input observer, whose matrices can be derived from a sufficiently rich set of collected historical data, are first derived and then proved to be equivalent to the ones obtained in the model-based framework. Finally, a numerical example is presented, to validate the effectiveness of the proposed scheme.
title Data-Driven Reduced-Order Unknown-Input Observers
topic Dynamical Systems
url https://arxiv.org/abs/2403.13471