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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

8 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 2014 American Control Conference, 4-6 June 2014, Portland, Oregon
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	1827-1832
ISBN (Print)	978-1-4799-3272-6
DOIs	https://doi.org/10.1109/ACC.2014.6858946
Publication status	Published - 2014
Event	2014 American Control Conference (ACC 2014), June 4-6, 2014, Portland, OR, USA - Hilton Portland & Executive Tower , Portland, OR, United States Duration: 4 Jun 2014 → 6 Jun 2014 http://acc2014.a2c2.org/

Conference

Conference	2014 American Control Conference (ACC 2014), June 4-6, 2014, Portland, OR, USA
Abbreviated title	ACC 2014
Country/Territory	United States
City	Portland, OR
Period	4/06/14 → 6/06/14
Internet address	http://acc2014.a2c2.org/

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10.1109/ACC.2014.6858946

Cite this

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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",

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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/",

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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, pp. 1827-1832, 2014 American Control Conference (ACC 2014), June 4-6, 2014, Portland, OR, USA, Portland, OR, United States, 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. p. 1827-1832.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

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AU - Oomen, T.A.E.

AU - Steinbuch, M.

PY - 2014

Y1 - 2014

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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DO - 10.1109/ACC.2014.6858946

M3 - Conference contribution

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SP - 1827

EP - 1832

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

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T2 - 2014 American Control Conference (ACC 2014), June 4-6, 2014, Portland, OR, USA

Y2 - 4 June 2014 through 6 June 2014

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

Abstract

Conference

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