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Advances in iterative learning control with application to structural dynamic response reconstruction

Thesis (PhD)--University of Pretoria, 2014.

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Other Authors: Heyns, P.S. (Philippus Stephanus)
Format: Thesis
Language:English
Published: University of Pretoria 2015
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access_status_str Open Access
author2 Heyns, P.S. (Philippus Stephanus)
author_browse Heyns, P.S. (Philippus Stephanus)
author_facet Heyns, P.S. (Philippus Stephanus)
collection Thesis
dc_rights_str_mv © 2014 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
description Thesis (PhD)--University of Pretoria, 2014.
format Thesis
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institution University of Pretoria (South Africa)
language English
last_indexed 2026-06-10T12:36:21.406Z
license_str Other — see source repository
provenance_str_mv Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository
publishDate 2015
publishDateRange 2015
publishDateSort 2015
publisher University of Pretoria
publisherStr University of Pretoria
record_format dspace
source_str UPSpace — University of Pretoria Institutional Repository
spelling oai:repository.up.ac.za:2263/43331 Advances in iterative learning control with application to structural dynamic response reconstruction Heyns, P.S. (Philippus Stephanus) jan.eksteen@up.ac.za Eksteen, Johannes Jacobus Arnoldi UCTD Thesis (PhD)--University of Pretoria, 2014. Iterative learning control (ILC) is a repetitive control scheme that uses a learning capability to improve the tracking accuracy of a desired test system output over repeated test trials. ILC is sometimes used in response reconstruction on complex engineering structures, such as ground vehicles, for purposes of fatigue testing. The compensator that is employed in ILC in such cases is traditionally an approximate, linear inverse model of the closed-loop test system. This research presents advances in ILC, particularly with respect to its application in response reconstruction for fatigue testing purposes. The contribution of this research focuses on three aspects: the use of a nonlinear inverse model in the ILC compensator instead of a linear inverse model; the use of multiple inverse models, each one defined over a different part of the test frequency band, instead of one model that covers the entire test frequency band; and the development and use of a new type of ILC algorithm. The contributions are implemented and demonstrated on a quarter vehicle road simulator, with favorable results for the use of nonlinear inverse models and multiple inverse models. The new ILC algorithm is shown to be competitive with the conventional inverse modelbased algorithm, giving comparable to slightly worse results than the conventional ILC algorithm. In order to invert the nonlinear inverse models this research also presents advances in the stable inversion method that is used to invert such models. Keywords: Iterative learning control, response reconstruction, fatigue testing, NARX models, Kolmogorov- Gabor polynomials, system identification, stable inversion, nonlinear, discrete time, Picard iteration, Mann iteration, quarter vehicle road simulator. lk2014 Mechanical and Aeronautical Engineering PhD Unrestricted 2015-01-19T12:13:30Z 2015-01-19T12:13:30Z 2014/12/12 2014 Thesis Eksteen, J 2014, Advances in iterative learning control with application to structural dynamic response reconstruction, PhD Thesis, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/43331> D14/9/85 http://hdl.handle.net/2263/43331 en © 2014 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria
spellingShingle UCTD
Advances in iterative learning control with application to structural dynamic response reconstruction
title Advances in iterative learning control with application to structural dynamic response reconstruction
title_full Advances in iterative learning control with application to structural dynamic response reconstruction
title_fullStr Advances in iterative learning control with application to structural dynamic response reconstruction
title_full_unstemmed Advances in iterative learning control with application to structural dynamic response reconstruction
title_short Advances in iterative learning control with application to structural dynamic response reconstruction
title_sort advances in iterative learning control with application to structural dynamic response reconstruction
topic UCTD
url http://hdl.handle.net/2263/43331