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Accelerated predictive norm-optimal iterative learning control

Accelerated predictive norm-optimal iterative learning control
Accelerated predictive norm-optimal iterative learning control
This paper proposes a novel technique for accelerating the convergence of the previously published predictive norm-optimal iterative learning control (NOILC) methodology. The basis of the results is a formal proof that the predictive 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 predictive NOILC by more rapid reductions in error norm from iteration to iteration. In particular, examples indicate that the approach can improve the performance of predictive NOILC for the problematic case of non-minimum phase systems. Realization 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.
0959-6518
744-759
Chu, B.
555a86a5-0198-4242-8525-3492349d4f0f
Owens, D.H.
3452e9bb-d3bd-4995-b4bb-424bbd288b09
Chu, B.
555a86a5-0198-4242-8525-3492349d4f0f
Owens, D.H.
3452e9bb-d3bd-4995-b4bb-424bbd288b09

Chu, B. and Owens, D.H. (2011) Accelerated predictive norm-optimal iterative learning control. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 225 (6), 744-759. (doi:10.1177/0959651811399541).

Record type: Article

Abstract

This paper proposes a novel technique for accelerating the convergence of the previously published predictive norm-optimal iterative learning control (NOILC) methodology. The basis of the results is a formal proof that the predictive 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 predictive NOILC by more rapid reductions in error norm from iteration to iteration. In particular, examples indicate that the approach can improve the performance of predictive NOILC for the problematic case of non-minimum phase systems. Realization 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.

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More information

e-pub ahead of print date: 18 July 2011
Published date: September 2011
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 336250
URI: http://eprints.soton.ac.uk/id/eprint/336250
ISSN: 0959-6518
PURE UUID: 6458f166-632e-4cc7-892e-97772390245e
ORCID for B. Chu: ORCID iD orcid.org/0000-0002-2711-8717

Catalogue record

Date deposited: 20 Mar 2012 12:41
Last modified: 09 Jan 2022 03:39

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Contributors

Author: B. Chu ORCID iD
Author: D.H. Owens

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