Contraction analysis of nonlinear noncausal iterative learning control

Kong, FH; Manchester, IR

Contraction analysis of nonlinear noncausal iterative learning control

Kong, FH Manchester, IR

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Systems and Control Letters, 2020, 136, pp. 104599-104599
Issue Date:: 2020-02-01

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Field	Value	Language
dc.contributor.author	Kong, FH
dc.contributor.author	Manchester, IR
dc.date.accessioned	2020-11-15T06:31:24Z
dc.date.available	2020-11-15T06:31:24Z
dc.date.issued	2020-02-01
dc.identifier.citation	Systems and Control Letters, 2020, 136, pp. 104599-104599
dc.identifier.issn	0167-6911
dc.identifier.uri	http://hdl.handle.net/10453/144018
dc.description.abstract	© 2019 Elsevier B.V. Iterative learning control (ILC) is a method for learning input signals for repetitive control tasks. In this paper, we provide a new method based on convex optimization for certifying convergence and estimating convergence rate in ILC schemes involving a nonlinear plant and a noncausal update law, which are common in practice. Using sum-of-squares (SOS) optimization, we compute the convergence rate of an example nonlinear, noncausal ILC system and verify its accuracy in experiment.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Systems and Control Letters
dc.relation.isbasedon	10.1016/j.sysconle.2019.104599
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	Contraction analysis of nonlinear noncausal iterative learning control
dc.type	Journal Article
utslib.citation.volume	136
utslib.for	0102 Applied Mathematics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-11-15T06:31:19Z
pubs.publication-status	Published
pubs.volume	136

Abstract:

© 2019 Elsevier B.V. Iterative learning control (ILC) is a method for learning input signals for repetitive control tasks. In this paper, we provide a new method based on convex optimization for certifying convergence and estimating convergence rate in ILC schemes involving a nonlinear plant and a noncausal update law, which are common in practice. Using sum-of-squares (SOS) optimization, we compute the convergence rate of an example nonlinear, noncausal ILC system and verify its accuracy in experiment.

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http://hdl.handle.net/10453/144018