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

Title: Finite-time learning control using frequency response data with application to a nanopositioning stage
Creator: de Rozario, Robin; Fleming, Andrew; Oomen, Tom
Relation: IEEE/ASME Transactions on Mechatronics Vol. 24, Issue 5, p. 2085-2096
Publisher Link: http://dx.doi.org/10.1109/TMECH.2019.2931407
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Resource Type: journal article
Date: 2019
Description: 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.
Subject: iterative learning control; frequency response; motion control; nanopositioning
Identifier: http://hdl.handle.net/1959.13/1463194
Identifier: uon:46665
Identifier: ISSN:1083-4435
Language: eng
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