On inferential iterative learning control : with example on a printing system

J.J. Bolder; T.A.E. Oomen; M. Steinbuch

doi:10.1109/ACC.2014.6858946

On inferential iterative learning control : with example on a printing system

J.J. Bolder, T.A.E. Oomen, M. Steinbuch

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

8 Citaten (Scopus)

1 Downloads (Pure)

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Since performance variables cannot be measured directly, Iterative Learning Control (ILC) is usually applied to measured variables. In this paper, it is shown that this can deteriorate performance. New batch-wise sensors that measure the performance variables directly are well-suited for use in ILC and can potentially improve performance. In this paper, recent developments in inferential control are utilized to arrive at control structures suited for inferential ILC. The proposed frameworks extend earlier results and encompass various controller structures. The results are supported with a simulation example.

Originele taal-2	Engels
Titel	Proceedings of the 2014 American Control Conference, 4-6 June 2014, Portland, Oregon
Plaats van productie	Piscataway
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	1827-1832
ISBN van geprinte versie	978-1-4799-3272-6
DOI's	https://doi.org/10.1109/ACC.2014.6858946
Status	Gepubliceerd - 2014
Evenement	2014 American Control Conference (ACC 2014), June 4-6, 2014, Portland, OR, USA - Hilton Portland & Executive Tower , Portland, OR, Verenigde Staten van Amerika Duur: 4 jun. 2014 → 6 jun. 2014 http://acc2014.a2c2.org/

Congres

Congres	2014 American Control Conference (ACC 2014), June 4-6, 2014, Portland, OR, USA
Verkorte titel	ACC 2014
Land/Regio	Verenigde Staten van Amerika
Stad	Portland, OR
Periode	4/06/14 → 6/06/14
Ander	2014 American Control Conference, Portland, OR
Internet adres	http://acc2014.a2c2.org/

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10.1109/ACC.2014.6858946

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title = "On inferential iterative learning control : with example on a printing system",

abstract = "Since performance variables cannot be measured directly, Iterative Learning Control (ILC) is usually applied to measured variables. In this paper, it is shown that this can deteriorate performance. New batch-wise sensors that measure the performance variables directly are well-suited for use in ILC and can potentially improve performance. In this paper, recent developments in inferential control are utilized to arrive at control structures suited for inferential ILC. The proposed frameworks extend earlier results and encompass various controller structures. The results are supported with a simulation example.",

author = "J.J. Bolder and T.A.E. Oomen and M. Steinbuch",

year = "2014",

doi = "10.1109/ACC.2014.6858946",

language = "English",

isbn = "978-1-4799-3272-6",

pages = "1827--1832",

booktitle = "Proceedings of the 2014 American Control Conference, 4-6 June 2014, Portland, Oregon",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2014 American Control Conference (ACC 2014), June 4-6, 2014, Portland, OR, USA, ACC 2014 ; Conference date: 04-06-2014 Through 06-06-2014",

url = "http://acc2014.a2c2.org/",

}

Bolder, JJ, Oomen, TAE & Steinbuch, M 2014, On inferential iterative learning control : with example on a printing system. in Proceedings of the 2014 American Control Conference, 4-6 June 2014, Portland, Oregon. Institute of Electrical and Electronics Engineers, Piscataway, blz. 1827-1832, 2014 American Control Conference (ACC 2014), June 4-6, 2014, Portland, OR, USA, Portland, OR, Verenigde Staten van Amerika, 4/06/14. https://doi.org/10.1109/ACC.2014.6858946

On inferential iterative learning control : with example on a printing system. / Bolder, J.J.; Oomen, T.A.E.; Steinbuch, M.
Proceedings of the 2014 American Control Conference, 4-6 June 2014, Portland, Oregon. Piscataway: Institute of Electrical and Electronics Engineers, 2014. blz. 1827-1832.

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

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N2 - Since performance variables cannot be measured directly, Iterative Learning Control (ILC) is usually applied to measured variables. In this paper, it is shown that this can deteriorate performance. New batch-wise sensors that measure the performance variables directly are well-suited for use in ILC and can potentially improve performance. In this paper, recent developments in inferential control are utilized to arrive at control structures suited for inferential ILC. The proposed frameworks extend earlier results and encompass various controller structures. The results are supported with a simulation example.

AB - Since performance variables cannot be measured directly, Iterative Learning Control (ILC) is usually applied to measured variables. In this paper, it is shown that this can deteriorate performance. New batch-wise sensors that measure the performance variables directly are well-suited for use in ILC and can potentially improve performance. In this paper, recent developments in inferential control are utilized to arrive at control structures suited for inferential ILC. The proposed frameworks extend earlier results and encompass various controller structures. The results are supported with a simulation example.

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M3 - Conference contribution

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BT - Proceedings of the 2014 American Control Conference, 4-6 June 2014, Portland, Oregon

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On inferential iterative learning control : with example on a printing system

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Congres

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