The University of Southampton
University of Southampton Institutional Repository

Accelerated norm-optimal iterative learning control algorithms using successive projection

Accelerated norm-optimal iterative learning control algorithms using successive projection
Accelerated norm-optimal iterative learning control algorithms using successive projection
This article proposes a novel technique for accelerating the convergence of the previously published norm-optimal iterative learning control (NOILC) methodology. The basis of the results is a formal proof of an observation made by D.H. Owens, namely that the NOILC algorithm is equivalent to a successive projection algorithm between linear varieties in a suitable product Hilbert space. This leads to two proposed accelerated algorithms together with well-defined convergence properties. The results show that the proposed accelerated algorithms are capable of ensuring monotonic error norm reductions and can outperform NOILC by more rapid reductions in error norm from iteration to iteration. In particular, examples indicate that the approach can improve the performance of NOILC for the problematic case of non-minimum phase systems. Realisation of the algorithms is discussed and numerical simulations are provided for comparative purposes and to demonstrate the numerical performance and effectiveness of the proposed methods.
0020-3270
1469-1484
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Owens, David H.
dca0ba32-aba6-4bab-a511-9bd322da16df
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Owens, David H.
dca0ba32-aba6-4bab-a511-9bd322da16df

Chu, Bing and Owens, David H. (2009) Accelerated norm-optimal iterative learning control algorithms using successive projection. International Journal of Control, 82 (8), 1469-1484. (doi:10.1080/00207170802512824).

Record type: Article

Abstract

This article proposes a novel technique for accelerating the convergence of the previously published norm-optimal iterative learning control (NOILC) methodology. The basis of the results is a formal proof of an observation made by D.H. Owens, namely that the NOILC algorithm is equivalent to a successive projection algorithm between linear varieties in a suitable product Hilbert space. This leads to two proposed accelerated algorithms together with well-defined convergence properties. The results show that the proposed accelerated algorithms are capable of ensuring monotonic error norm reductions and can outperform NOILC by more rapid reductions in error norm from iteration to iteration. In particular, examples indicate that the approach can improve the performance of NOILC for the problematic case of non-minimum phase systems. Realisation of the algorithms is discussed and numerical simulations are provided for comparative purposes and to demonstrate the numerical performance and effectiveness of the proposed methods.

This record has no associated files available for download.

More information

e-pub ahead of print date: 18 June 2009
Published date: 2009
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 336247
URI: http://eprints.soton.ac.uk/id/eprint/336247
ISSN: 0020-3270
PURE UUID: 39f7c46c-4b47-45c0-b728-d262f703782d
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717

Catalogue record

Date deposited: 20 Mar 2012 14:29
Last modified: 15 Mar 2024 03:42

Export record

Altmetrics

Contributors

Author: Bing Chu ORCID iD
Author: David H. Owens

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×