Rational Basis Functions in Iterative Learning Control for Multivariable Systems

M.M. Poot; Jim Portegies; Dragan Kostic; Tom Oomen

doi:10.1109/CDC49753.2023.10383932

Rational Basis Functions in Iterative Learning Control for Multivariable Systems

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

4 Citaten (Scopus)

432 Downloads (Pure)

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Feedforward control with task flexibility for MIMO systems is essential to meet ever-increasing demands on throughput and accuracy. The aim of this paper is to develop a framework for data-driven tuning of rational feedforward controllers in iterative learning control (ILC) for noncommutative MIMO systems. A convex optimization problem in ILC is achieved by rewriting the nonlinear terms in the control scheme as a function of the previous feedforward parameters. A simulation study on an multivariable industrial printer shows that the developed framework converges and achieves significant better performance than direct application of the RBF algorithm using SK-iterations for SISO systems.

Originele taal-2	Engels
Titel	2023 62nd IEEE Conference on Decision and Control, CDC 2023
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	4644-4649
Aantal pagina's	6
ISBN van elektronische versie	979-8-3503-0124-3
DOI's	https://doi.org/10.1109/CDC49753.2023.10383932
Status	Gepubliceerd - 19 jan. 2024
Evenement	2023 62nd IEEE Conference on Decision and Control (CDC) - Singapore, Singapore Duur: 13 dec. 2023 → 15 dec. 2023 Congresnummer: 62

Congres

Congres	2023 62nd IEEE Conference on Decision and Control (CDC)
Verkorte titel	CDC 2023
Land/Regio	Singapore
Stad	Singapore
Periode	13/12/23 → 15/12/23

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10.1109/CDC49753.2023.10383932

CDC_Poot_MIMORBF_Final_PUREFinale auteursversie , 759 KBLicentie: CC BY-NC-ND
pdfDefinitieve gepubliceerde versie, 768 KBLicentie: TAVERNE

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title = "Rational Basis Functions in Iterative Learning Control for Multivariable Systems",

abstract = "Feedforward control with task flexibility for MIMO systems is essential to meet ever-increasing demands on throughput and accuracy. The aim of this paper is to develop a framework for data-driven tuning of rational feedforward controllers in iterative learning control (ILC) for noncommutative MIMO systems. A convex optimization problem in ILC is achieved by rewriting the nonlinear terms in the control scheme as a function of the previous feedforward parameters. A simulation study on an multivariable industrial printer shows that the developed framework converges and achieves significant better performance than direct application of the RBF algorithm using SK-iterations for SISO systems.",

author = "M.M. Poot and Jim Portegies and Dragan Kostic and Tom Oomen",

year = "2024",

month = jan,

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doi = "10.1109/CDC49753.2023.10383932",

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booktitle = "2023 62nd IEEE Conference on Decision and Control, CDC 2023",

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note = "62nd IEEE Conference on Decision and Control, CDC 2023, CDC 2023 ; Conference date: 13-12-2023 Through 15-12-2023",

}

Poot, MM, Portegies, J , Kostic, D & Oomen, T 2024, Rational Basis Functions in Iterative Learning Control for Multivariable Systems. in 2023 62nd IEEE Conference on Decision and Control, CDC 2023., 10383932, Institute of Electrical and Electronics Engineers, blz. 4644-4649, 2023 62nd IEEE Conference on Decision and Control (CDC), Singapore, Singapore, 13/12/23. https://doi.org/10.1109/CDC49753.2023.10383932

Rational Basis Functions in Iterative Learning Control for Multivariable Systems. / Poot, M.M.; Portegies, Jim ; Kostic, Dragan et al.
2023 62nd IEEE Conference on Decision and Control, CDC 2023. Institute of Electrical and Electronics Engineers, 2024. blz. 4644-4649 10383932.

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

TY - GEN

T1 - Rational Basis Functions in Iterative Learning Control for Multivariable Systems

AU - Poot, M.M.

AU - Portegies, Jim

AU - Kostic, Dragan

AU - Oomen, Tom

N1 - Conference code: 62

PY - 2024/1/19

Y1 - 2024/1/19

N2 - Feedforward control with task flexibility for MIMO systems is essential to meet ever-increasing demands on throughput and accuracy. The aim of this paper is to develop a framework for data-driven tuning of rational feedforward controllers in iterative learning control (ILC) for noncommutative MIMO systems. A convex optimization problem in ILC is achieved by rewriting the nonlinear terms in the control scheme as a function of the previous feedforward parameters. A simulation study on an multivariable industrial printer shows that the developed framework converges and achieves significant better performance than direct application of the RBF algorithm using SK-iterations for SISO systems.

AB - Feedforward control with task flexibility for MIMO systems is essential to meet ever-increasing demands on throughput and accuracy. The aim of this paper is to develop a framework for data-driven tuning of rational feedforward controllers in iterative learning control (ILC) for noncommutative MIMO systems. A convex optimization problem in ILC is achieved by rewriting the nonlinear terms in the control scheme as a function of the previous feedforward parameters. A simulation study on an multivariable industrial printer shows that the developed framework converges and achieves significant better performance than direct application of the RBF algorithm using SK-iterations for SISO systems.

UR - https://www.scopus.com/pages/publications/85184808774

U2 - 10.1109/CDC49753.2023.10383932

DO - 10.1109/CDC49753.2023.10383932

M3 - Conference contribution

SP - 4644

EP - 4649

BT - 2023 62nd IEEE Conference on Decision and Control, CDC 2023

PB - Institute of Electrical and Electronics Engineers

T2 - 62nd IEEE Conference on Decision and Control, CDC 2023

Y2 - 13 December 2023 through 15 December 2023

ER -

Rational Basis Functions in Iterative Learning Control for Multivariable Systems

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