Low-order system identification and optimal control of intersample behavior in ILC

T.A.E. Oomen; J.J.M. Wijdeven, van de; O.H. Bosgra

doi:10.1109/ACC.2009.5159951

Low-order system identification and optimal control of intersample behavior in ILC

T.A.E. Oomen, J.J.M. Wijdeven, van de, O.H. Bosgra

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

1 Citation (Scopus)

Abstract

Iterative learning control (ILC) enables high tracking performance of batch repetitive processes. Common ILC approaches resort to discrete time system representations and hence are not able to guarantee good intersample behavior in case the underlying system evolves in continuous time. The aim of this paper is to explicitly deal with the intersample behavior in ILC. A multirate, parametric, and low-order approach to both identification for ILC and subsequent optimal ILC is presented that results in a low computational burden. The approach appropriately deals with the time-varying nature of multirate systems. The proposed multirate identification and ILC algorithms are shown to outperform common ILC approaches in a simulation example.

Original language	English
Title of host publication	Proceedings of the 28th American Control Conference, (ACC '09) 10 - 12 June 2009, St. Louis, MO, USA
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers
Pages	271-276
ISBN (Print)	978-1-4244-4523-3
DOIs	https://doi.org/10.1109/ACC.2009.5159951
Publication status	Published - 2009

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10.1109/ACC.2009.5159951

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title = "Low-order system identification and optimal control of intersample behavior in ILC",

abstract = "Iterative learning control (ILC) enables high tracking performance of batch repetitive processes. Common ILC approaches resort to discrete time system representations and hence are not able to guarantee good intersample behavior in case the underlying system evolves in continuous time. The aim of this paper is to explicitly deal with the intersample behavior in ILC. A multirate, parametric, and low-order approach to both identification for ILC and subsequent optimal ILC is presented that results in a low computational burden. The approach appropriately deals with the time-varying nature of multirate systems. The proposed multirate identification and ILC algorithms are shown to outperform common ILC approaches in a simulation example.",

author = "T.A.E. Oomen and {Wijdeven, van de}, J.J.M. and O.H. Bosgra",

year = "2009",

doi = "10.1109/ACC.2009.5159951",

language = "English",

isbn = "978-1-4244-4523-3",

pages = "271--276",

booktitle = "Proceedings of the 28th American Control Conference, (ACC '09) 10 - 12 June 2009, St. Louis, MO, USA",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

}

Low-order system identification and optimal control of intersample behavior in ILC. / Oomen, T.A.E.; Wijdeven, van de, J.J.M.; Bosgra, O.H.
Proceedings of the 28th American Control Conference, (ACC '09) 10 - 12 June 2009, St. Louis, MO, USA. Piscataway, NJ: Institute of Electrical and Electronics Engineers, 2009. p. 271-276.

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

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T1 - Low-order system identification and optimal control of intersample behavior in ILC

AU - Oomen, T.A.E.

AU - Wijdeven, van de, J.J.M.

AU - Bosgra, O.H.

PY - 2009

Y1 - 2009

N2 - Iterative learning control (ILC) enables high tracking performance of batch repetitive processes. Common ILC approaches resort to discrete time system representations and hence are not able to guarantee good intersample behavior in case the underlying system evolves in continuous time. The aim of this paper is to explicitly deal with the intersample behavior in ILC. A multirate, parametric, and low-order approach to both identification for ILC and subsequent optimal ILC is presented that results in a low computational burden. The approach appropriately deals with the time-varying nature of multirate systems. The proposed multirate identification and ILC algorithms are shown to outperform common ILC approaches in a simulation example.

AB - Iterative learning control (ILC) enables high tracking performance of batch repetitive processes. Common ILC approaches resort to discrete time system representations and hence are not able to guarantee good intersample behavior in case the underlying system evolves in continuous time. The aim of this paper is to explicitly deal with the intersample behavior in ILC. A multirate, parametric, and low-order approach to both identification for ILC and subsequent optimal ILC is presented that results in a low computational burden. The approach appropriately deals with the time-varying nature of multirate systems. The proposed multirate identification and ILC algorithms are shown to outperform common ILC approaches in a simulation example.

U2 - 10.1109/ACC.2009.5159951

DO - 10.1109/ACC.2009.5159951

M3 - Conference contribution

SN - 978-1-4244-4523-3

SP - 271

EP - 276

BT - Proceedings of the 28th American Control Conference, (ACC '09) 10 - 12 June 2009, St. Louis, MO, USA

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway, NJ

ER -

Low-order system identification and optimal control of intersample behavior in ILC

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