Reduced-Order Nonlinear Observers Via Contraction Analysis and Convex Optimization

Yi, B; Wang, R; Manchester, IR

Reduced-Order Nonlinear Observers Via Contraction Analysis and Convex Optimization

Yi, B

Wang, R Manchester, IR

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Automatic Control, 2022, 67, (8), pp. 4045-4060
Issue Date:: 2022-08

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Field	Value	Language
dc.contributor.author	Yi, B https://orcid.org/0000-0002-0517-9519
dc.contributor.author	Wang, R
dc.contributor.author	Manchester, IR
dc.date.accessioned	2023-03-10T04:12:00Z
dc.date.available	2023-03-10T04:12:00Z
dc.date.issued	2022-08
dc.identifier.citation	IEEE Transactions on Automatic Control, 2022, 67, (8), pp. 4045-4060
dc.identifier.issn	0018-9286
dc.identifier.issn	1558-2523
dc.identifier.uri	http://hdl.handle.net/10453/166962
dc.description.abstract	In this article, we propose a new approach to design globally convergent reduced-order observers for nonlinear control systems via contraction analysis and convex optimization. Despite the fact that contraction is a concept naturally suitable for state estimation, the existing solutions are either local or relatively conservative when applying to physical systems. To address this, we show that this problem can be translated into an offline search for a coordinate transformation after which the dynamics is (transversely) contracting. The obtained sufficient condition consists of some easily verifiable differential inequalities, which, on one hand, identify a very general class of “detectable” nonlinear systems, and on the other hand, can be expressed as computationally efficient convex optimization, making the design procedure more systematic. Connections with some well-established approaches and concepts are also clarified in this article. Finally, we illustrate the proposed method with several numerical and physical examples, including polynomial, mechanical, electromechanical, and biochemical systems.
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.2021.3115887
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0102 Applied Mathematics, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	Reduced-Order Nonlinear Observers Via Contraction Analysis and Convex Optimization
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	open_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-10T04:11:59Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	67
utslib.citation.issue	8

Abstract:

In this article, we propose a new approach to design globally convergent reduced-order observers for nonlinear control systems via contraction analysis and convex optimization. Despite the fact that contraction is a concept naturally suitable for state estimation, the existing solutions are either local or relatively conservative when applying to physical systems. To address this, we show that this problem can be translated into an offline search for a coordinate transformation after which the dynamics is (transversely) contracting. The obtained sufficient condition consists of some easily verifiable differential inequalities, which, on one hand, identify a very general class of “detectable” nonlinear systems, and on the other hand, can be expressed as computationally efficient convex optimization, making the design procedure more systematic. Connections with some well-established approaches and concepts are also clarified in this article. Finally, we illustrate the proposed method with several numerical and physical examples, including polynomial, mechanical, electromechanical, and biochemical systems.

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http://hdl.handle.net/10453/166962