Data-driven optimal ILC for multivariable systems : removing the need for L and Q filter design

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

Data-driven optimal ILC for multivariable systems : removing the need for L and Q filter design

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

Control Systems Technology

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

4 Citations (Scopus)

169 Downloads (Pure)

Abstract

Many iterative learning control algorithms rely on a model of the system. Although only approximate model knowledge is required, the model quality determines the convergence and performance properties of the learning control algorithm. The aim of this paper is to remove the need for a model for a class of multivariable ILC algorithms. The main idea is to replace the model by dedicated experiments on the system. Convergence criteria are developed and the results are illustrated with a simulation on a multi-axis flatbed printer.

Original language	English
Title of host publication	Proceedings of the 2015 American Control Conference (ACC 2015), 1-3 july 2015, Chicago, United States
Place of Publication	Chicago
Publisher	ACC
Pages	3546-3551
ISBN (Print)	978-1-4799-8686-6
Publication status	Published - 2015

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title = "Data-driven optimal ILC for multivariable systems : removing the need for L and Q filter design",

abstract = "Many iterative learning control algorithms rely on a model of the system. Although only approximate model knowledge is required, the model quality determines the convergence and performance properties of the learning control algorithm. The aim of this paper is to remove the need for a model for a class of multivariable ILC algorithms. The main idea is to replace the model by dedicated experiments on the system. Convergence criteria are developed and the results are illustrated with a simulation on a multi-axis flatbed printer.",

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year = "2015",

language = "English",

isbn = "978-1-4799-8686-6",

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booktitle = "Proceedings of the 2015 American Control Conference (ACC 2015), 1-3 july 2015, Chicago, United States",

publisher = "ACC",

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Data-driven optimal ILC for multivariable systems : removing the need for L and Q filter design. / Bolder, J.J.; Oomen, T.A.E.
Proceedings of the 2015 American Control Conference (ACC 2015), 1-3 july 2015, Chicago, United States. Chicago: ACC, 2015. p. 3546-3551.

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

TY - GEN

T1 - Data-driven optimal ILC for multivariable systems : removing the need for L and Q filter design

AU - Bolder, J.J.

AU - Oomen, T.A.E.

PY - 2015

Y1 - 2015

N2 - Many iterative learning control algorithms rely on a model of the system. Although only approximate model knowledge is required, the model quality determines the convergence and performance properties of the learning control algorithm. The aim of this paper is to remove the need for a model for a class of multivariable ILC algorithms. The main idea is to replace the model by dedicated experiments on the system. Convergence criteria are developed and the results are illustrated with a simulation on a multi-axis flatbed printer.

AB - Many iterative learning control algorithms rely on a model of the system. Although only approximate model knowledge is required, the model quality determines the convergence and performance properties of the learning control algorithm. The aim of this paper is to remove the need for a model for a class of multivariable ILC algorithms. The main idea is to replace the model by dedicated experiments on the system. Convergence criteria are developed and the results are illustrated with a simulation on a multi-axis flatbed printer.

M3 - Conference contribution

SN - 978-1-4799-8686-6

SP - 3546

EP - 3551

BT - Proceedings of the 2015 American Control Conference (ACC 2015), 1-3 july 2015, Chicago, United States

PB - ACC

CY - Chicago

ER -

Data-driven optimal ILC for multivariable systems : removing the need for L and Q filter design

Abstract

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