On Regular Regressors in Adaptive Control

This paper addresses a shortcoming in adaptive control that the property of a regressor being persistently exciting (PE) is not well-behaved. One can construct regressors that upend the commonsense notion that excitation should not be created out of nothing. To amend the situation, a notion of regularity of regressors is needed. We are naturally led to a broad class of regular regressors that enjoy the property that their excitation is always confined to a subspace, a foundational result called the PE decomposition. A geometric characterization of regressor excitation opens up new avenues for adaptive control, as we demonstrate by formulating a number of new adaptive control problems.

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Institutions

Publication Details

Journal
IEEE Control Systems Letters
Published
2025-01-01
DOI
https://doi.org/10.1109/lcsys.2025.3573124
Citations
1
Primary Topic
Control Systems and Identification
Type
article
Field-Weighted Citation Impact
1.21

Funders

Controls
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article

On Regular Regressors in Adaptive Control

Mireille E. Broucke, Erick Mejia Uzeda
1 citations
IEEE Control Systems Letters
Control Systems and Identification
1.21
article

On Regular Regressors in Adaptive Control

Mireille E. Broucke, Erick Mejia Uzeda
article en
1 citations

Abstract

This paper addresses a shortcoming in adaptive control that the property of a regressor being persistently exciting (PE) is not well-behaved. One can construct regressors that upend the commonsense notion that excitation should not be created out of nothing. To amend the situation, a notion of regularity of regressors is needed. We are naturally led to a broad class of regular regressors that enjoy the property that their excitation is always confined to a subspace, a foundational result called the PE decomposition. A geometric characterization of regressor excitation opens up new avenues for adaptive control, as we demonstrate by formulating a number of new adaptive control problems.

IEEE Control Systems LettersVol. 9
University of Toronto (CA)
Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 21%
Control Systems and Identification
1.21
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