Generation of New Exciting Regressors for Consistent Online Estimation of Unknown Constant Parameters

Bobtsov, A; Yi, B; Ortega, R; Astolfi, A

Generation of New Exciting Regressors for Consistent Online Estimation of Unknown Constant Parameters

Bobtsov, A Yi, B

Ortega, R Astolfi, A

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Automatic Control, 2022, 67, (9), pp. 4746-4753
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Bobtsov, A
dc.contributor.author	Yi, B https://orcid.org/0000-0002-0517-9519
dc.contributor.author	Ortega, R
dc.contributor.author	Astolfi, A
dc.date.accessioned	2023-03-10T04:13:02Z
dc.date.available	2023-03-10T04:13:02Z
dc.date.issued	2022-09-01
dc.identifier.citation	IEEE Transactions on Automatic Control, 2022, 67, (9), pp. 4746-4753
dc.identifier.issn	0018-9286
dc.identifier.issn	1558-2523
dc.identifier.uri	http://hdl.handle.net/10453/166963
dc.description.abstract	The problem of estimating constant parameters from a standard vector linear regression equation in the absence of sufficient excitation in the regressor is addressed. The first step to solve the problem consists in transforming this equation into a set of scalar ones using the well-known dynamic regressor extension and mixing technique. Then, a novel procedure to generate new scalar exciting regressors is proposed. The superior performance of a classical gradient estimator using this new regressor, instead of the original one, is illustrated with comprehensive simulations.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Automatic Control
dc.relation.isbasedon	10.1109/TAC.2022.3159568
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	Generation of New Exciting Regressors for Consistent Online Estimation of Unknown Constant Parameters
dc.type	Journal Article
utslib.citation.volume	67
utslib.for	0102 Applied Mathematics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-10T04:13:01Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	67
utslib.citation.issue	9

Abstract:

The problem of estimating constant parameters from a standard vector linear regression equation in the absence of sufficient excitation in the regressor is addressed. The first step to solve the problem consists in transforming this equation into a set of scalar ones using the well-known dynamic regressor extension and mixing technique. Then, a novel procedure to generate new scalar exciting regressors is proposed. The superior performance of a classical gradient estimator using this new regressor, instead of the original one, is illustrated with comprehensive simulations.

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http://hdl.handle.net/10453/166963