Finite-time learning control using frequency response data with application to a nanopositioning stage

Robin de Rozario (Corresponding author), Andrew Fleming, Tom Oomen

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5 Citaten (Scopus)
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Samenvatting

Learning control enables significant performance improvement for systems that perform repeating tasks. Achieving high tracking performance by utilizing past error data typically requires noncausal learning that is based on a parametric model of the process. Such model-based approaches impose significant requirements on modeling and filter design. The aim of this paper is to reduce these requirements by developing a learning control framework that enables performance improvement through noncausal learning without relying on a parametric model. This is achieved by explicitly using the discrete Fourier transform to enable learning by using a nonparametric frequency response function model of the process. The effectiveness of the developed method is illustrated by application to a nanopositioning stage.

Originele taal-2Engels
Artikelnummer8777112
Pagina's (van-tot)2085-2096
Aantal pagina's12
TijdschriftIEEE/ASME Transactions on Mechatronics
Volume24
Nummer van het tijdschrift5
DOI's
StatusGepubliceerd - 1 okt 2019

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