Online Gyro Bias Estimation from Single Vector Measurements Using Regression Models

Yi, B; Wang, L; Zhang, W

Online Gyro Bias Estimation from Single Vector Measurements Using Regression Models

Yi, B

Wang, L Zhang, W

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: IFAC-PapersOnLine, 2023, 56, (1), pp. 252-257
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Yi, B https://orcid.org/0000-0002-0517-9519
dc.contributor.author	Wang, L
dc.contributor.author	Zhang, W
dc.date.accessioned	2023-11-06T04:55:04Z
dc.date.available	2023-11-06T04:55:04Z
dc.date.issued	2023-01-01
dc.identifier.citation	IFAC-PapersOnLine, 2023, 56, (1), pp. 252-257
dc.identifier.issn	2405-8963
dc.identifier.issn	2405-8963
dc.identifier.uri	http://hdl.handle.net/10453/173043
dc.description.abstract	This paper addresses the problem of on-line consistent estimation of gyro bias using the measurements of a single vector and the biased angular velocity – both in the body-fixed frame. We propose two globally convergent gyro bias observers using new regression models, which are capable to deal with the cases of constant and time-varying reference vectors, respectively. Indeed, there are quite a few works discussing the latter case. To address this, we derive a nonlinear regression model, based on which a convexified gradient descent observer is designed, providing globally asymptotically convergent estimates to the gyro bias under some sufficient excitation conditions. The proposed schemes are illustrated by some numerical simulations.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	IFAC-PapersOnLine
dc.relation.isbasedon	10.1016/j.ifacol.2023.02.043
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	4007 Control engineering, mechatronics and robotics
dc.subject.classification	4008 Electrical engineering
dc.title	Online Gyro Bias Estimation from Single Vector Measurements Using Regression Models
dc.type	Journal Article
utslib.citation.volume	56
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	open_access	*
dc.date.updated	2023-11-06T04:55:03Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	56
utslib.citation.issue	1

Abstract:

This paper addresses the problem of on-line consistent estimation of gyro bias using the measurements of a single vector and the biased angular velocity – both in the body-fixed frame. We propose two globally convergent gyro bias observers using new regression models, which are capable to deal with the cases of constant and time-varying reference vectors, respectively. Indeed, there are quite a few works discussing the latter case. To address this, we derive a nonlinear regression model, based on which a convexified gradient descent observer is designed, providing globally asymptotically convergent estimates to the gyro bias under some sufficient excitation conditions. The proposed schemes are illustrated by some numerical simulations.

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http://hdl.handle.net/10453/173043