Iterative learning control and feedforward for LPV systems : applied to a position-dependent motion system

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Abstract

Iterative Learning Control (ILC) enables performance improvement by learning from previous tasks. The aim of this paper is to develop an ILC approach for Linear Parameter Varying (LPV) systems to enable improved performance and increased convergence speed compared to the linear time-invariant approach. This is achieved through dedicated analysis and norm-optimal synthesis of LPV learning filters. Application to a position-dependent motion system shows a significant improvement in accuracy and convergence rate, thereby confirming the potential of the proposed approach.

Original languageEnglish
Title of host publication2017 IEEE American Control Conference, 23-26 May 2017, Seattle, Washington
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages3518-3523
Number of pages6
ISBN (Electronic)978-1-5090-5992-8
ISBN (Print)978-1-5090-4583-9
DOIs
Publication statusPublished - 29 Jun 2017
Event2017 American Control Conference (ACC 2017) - Sheraton Seattle Hotel, Seattle, United States
Duration: 24 May 201726 May 2017
http://acc2017.a2c2.org/

Conference

Conference2017 American Control Conference (ACC 2017)
Abbreviated titleACC 2017
CountryUnited States
CitySeattle
Period24/05/1726/05/17
Internet address

Cite this

de Rozario, R., Oomen, T. A. E., & Steinbuch, M. (2017). Iterative learning control and feedforward for LPV systems : applied to a position-dependent motion system. In 2017 IEEE American Control Conference, 23-26 May 2017, Seattle, Washington (pp. 3518-3523). [7963491] Institute of Electrical and Electronics Engineers. https://doi.org/10.23919/ACC.2017.7963491