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

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

4 Citations (Scopus)

543 Downloads (Pure)

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.

Original language	English
Title of host publication	2023 62nd IEEE Conference on Decision and Control, CDC 2023
Publisher	Institute of Electrical and Electronics Engineers
Pages	4644-4649
Number of pages	6
ISBN (Electronic)	979-8-3503-0124-3
DOIs	https://doi.org/10.1109/CDC49753.2023.10383932
Publication status	Published - 19 Jan 2024
Event	62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore Duration: 13 Dec 2023 → 15 Dec 2023 Conference number: 62

Conference

Conference	62nd IEEE Conference on Decision and Control, CDC 2023
Abbreviated title	CDC 2023
Country/Territory	Singapore
City	Singapore
Period	13/12/23 → 15/12/23

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10.1109/CDC49753.2023.10383932

CDC_Poot_MIMORBF_Final_PUREFinal author version , 759 KBLicence: CC BY-NC-ND
pdfFinal published version, 768 KBLicence: TAVERNE

Cite this

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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",

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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, pp. 4644-4649, 62nd IEEE Conference on Decision and Control, CDC 2023, 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. p. 4644-4649 10383932.

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

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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.

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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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