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

R. de Rozario; T.A.E. Oomen; M. Steinbuch

doi:10.23919/ACC.2017.7963491

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

R. de Rozario, T.A.E. Oomen, M. Steinbuch

Control Systems Technology

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

14 Citaten (Scopus)

360 Downloads (Pure)

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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.

Originele taal-2	Engels
Titel	2017 IEEE American Control Conference, 23-26 May 2017, Seattle, Washington
Plaats van productie	Piscataway
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	3518-3523
Aantal pagina's	6
ISBN van elektronische versie	978-1-5090-5992-8
ISBN van geprinte versie	978-1-5090-4583-9
DOI's	https://doi.org/10.23919/ACC.2017.7963491
Status	Gepubliceerd - 29 jun. 2017
Evenement	2017 American Control Conference (ACC 2017) - Sheraton Seattle Hotel, Seattle, Verenigde Staten van Amerika Duur: 24 mei 2017 → 26 mei 2017 http://acc2017.a2c2.org/

Congres

Congres	2017 American Control Conference (ACC 2017)
Verkorte titel	ACC 2017
Land/Regio	Verenigde Staten van Amerika
Stad	Seattle
Periode	24/05/17 → 26/05/17
Internet adres	http://acc2017.a2c2.org/

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10.23919/ACC.2017.7963491

RozarioOomSte2017

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@inproceedings{c360cb3bf92847b9a0b4a8ab289e8843,

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

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.",

author = "{de Rozario}, R. and T.A.E. Oomen and M. Steinbuch",

year = "2017",

month = jun,

day = "29",

doi = "10.23919/ACC.2017.7963491",

language = "English",

isbn = "978-1-5090-4583-9 ",

pages = "3518--3523",

booktitle = "2017 IEEE American Control Conference, 23-26 May 2017, Seattle, Washington",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2017 American Control Conference (ACC 2017), ACC 2017 ; Conference date: 24-05-2017 Through 26-05-2017",

url = "http://acc2017.a2c2.org/",

}

de Rozario, R, Oomen, TAE & 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., 7963491, Institute of Electrical and Electronics Engineers, Piscataway, blz. 3518-3523, 2017 American Control Conference (ACC 2017), Seattle, Washington, Verenigde Staten van Amerika, 24/05/17. https://doi.org/10.23919/ACC.2017.7963491

Iterative learning control and feedforward for LPV systems : applied to a position-dependent motion system. / de Rozario, R.; Oomen, T.A.E.; Steinbuch, M.
2017 IEEE American Control Conference, 23-26 May 2017, Seattle, Washington. Piscataway: Institute of Electrical and Electronics Engineers, 2017. blz. 3518-3523 7963491.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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AU - Oomen, T.A.E.

AU - Steinbuch, M.

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Y1 - 2017/6/29

N2 - 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.

AB - 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.

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BT - 2017 IEEE American Control Conference, 23-26 May 2017, Seattle, Washington

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T2 - 2017 American Control Conference (ACC 2017)

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

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