Iterative learning control for constrained linear systems
Iterative learning control for constrained linear systems
This article considers iterative learning control (ILC) for linear systems with convex control input constraints. First, the constrained ILC problem is formulated in a novel successive projection framework. Then, based on this projection method, two algorithms are proposed to solve this constrained ILC problem. The results show that, when perfect tracking is possible, both algorithms can achieve perfect tracking. The two algorithms differ, however, in that one algorithm needs much less computation than the other. When perfect tracking is not possible, both algorithms can exhibit a form of practical convergence to a ‘best approximation’. The effect of weighting matrices on the performance of the algorithms is also discussed and finally, numerical simulations are given to demonstrate the effectiveness of the proposed methods.
iterative learning control, projection method, constraint handling, norm optimisation
1397-1413
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Owens, David H.
dca0ba32-aba6-4bab-a511-9bd322da16df
July 2010
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Owens, David H.
dca0ba32-aba6-4bab-a511-9bd322da16df
Chu, Bing and Owens, David H.
(2010)
Iterative learning control for constrained linear systems.
International Journal of Control, 83 (7), .
(doi:10.1080/00207171003758752).
Abstract
This article considers iterative learning control (ILC) for linear systems with convex control input constraints. First, the constrained ILC problem is formulated in a novel successive projection framework. Then, based on this projection method, two algorithms are proposed to solve this constrained ILC problem. The results show that, when perfect tracking is possible, both algorithms can achieve perfect tracking. The two algorithms differ, however, in that one algorithm needs much less computation than the other. When perfect tracking is not possible, both algorithms can exhibit a form of practical convergence to a ‘best approximation’. The effect of weighting matrices on the performance of the algorithms is also discussed and finally, numerical simulations are given to demonstrate the effectiveness of the proposed methods.
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e-pub ahead of print date: 3 June 2010
Published date: July 2010
Keywords:
iterative learning control, projection method, constraint handling, norm optimisation
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 336248
URI: http://eprints.soton.ac.uk/id/eprint/336248
ISSN: 0020-3270
PURE UUID: f45f35d4-375e-4592-85eb-9b0539865441
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Date deposited: 21 Mar 2012 11:53
Last modified: 15 Mar 2024 03:42
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Contributors
Author:
Bing Chu
Author:
David H. Owens
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