Contraction Analysis of Nonlinear Iterative Learning Control

Kong, FH; Manchester, IR

Contraction Analysis of Nonlinear Iterative Learning Control

Kong, FH Manchester, IR

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: IFAC-PapersOnLine, 2017, 50, (1), pp. 10876-10881
Issue Date:: 2017-07-01

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Field	Value	Language
dc.contributor.author	Kong, FH
dc.contributor.author	Manchester, IR
dc.date.accessioned	2022-07-13T06:01:10Z
dc.date.available	2022-07-13T06:01:10Z
dc.date.issued	2017-07-01
dc.identifier.citation	IFAC-PapersOnLine, 2017, 50, (1), pp. 10876-10881
dc.identifier.issn	2405-8963
dc.identifier.issn	2405-8963
dc.identifier.uri	http://hdl.handle.net/10453/158856
dc.description.abstract	Iterative learning control (ILC) is widely used as a simple method for precise tracking of systems under repetitive conditions. ILC operates by “learning” from the previous iteration's errors, correcting them over a number of iterations. However, the question of whether or not a nonlinear ILC system converges is still in general an open one. Assuming a state-space formulation, we use contraction analysis to formulate a convergence condition for ILC system as a linear matrix inequality (LMI). Finally, we compute a convergence certificate for a simple example involving “anticogging” a permanent-magnet synchronous motor driving a pendulum in simulation.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	IFAC-PapersOnLine
dc.relation.isbasedon	10.1016/j.ifacol.2017.08.2444
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Contraction Analysis of Nonlinear Iterative Learning Control
dc.type	Journal Article
utslib.citation.volume	50
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	*
pubs.consider-herdc	false
dc.date.updated	2022-07-13T06:01:05Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	50
utslib.citation.issue	1

Abstract:

Iterative learning control (ILC) is widely used as a simple method for precise tracking of systems under repetitive conditions. ILC operates by “learning” from the previous iteration's errors, correcting them over a number of iterations. However, the question of whether or not a nonlinear ILC system converges is still in general an open one. Assuming a state-space formulation, we use contraction analysis to formulate a convergence condition for ILC system as a linear matrix inequality (LMI). Finally, we compute a convergence certificate for a simple example involving “anticogging” a permanent-magnet synchronous motor driving a pendulum in simulation.

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http://hdl.handle.net/10453/158856