Automatic Basis Function Selection in Iterative Learning Control: A Sparsity-Promoting Approach Applied to an Industrial Printer

Tjeerd S. Ickenroth; Max van Haren; J.J. Kon; M. van Meer; Jilles van Hulst; Tom A.E. Oomen

doi:10.23919/ACC63710.2025.11107864

Automatic Basis Function Selection in Iterative Learning Control: A Sparsity-Promoting Approach Applied to an Industrial Printer

Tjeerd S. Ickenroth (Corresponding author)
, Max van Haren
, J.J. Kon
, M. van Meer
, Jilles van Hulst
, Tom A.E. Oomen

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

1 Citation (Scopus)

199 Downloads (Pure)

Abstract

Iterative learning control (ILC) techniques are capable of improving the tracking performance of control systems that repeatedly perform similar tasks by utilizing data from past iterations. The aim of this paper is to design a systematic approach for learning parameterized feedforward signals with limited complexity. The developed method involves an iterative learning control in conjunction with a data-driven sparse subset selection procedure for basis function selection. The ILC algorithm that employs sparse optimization is able to automatically select relevant basis functions and is validated on an industrial flatbed printer.

Original language	English
Title of host publication	2025 American Control Conference, ACC 2025
Publisher	Institute of Electrical and Electronics Engineers
Pages	2931-2936
Number of pages	6
ISBN (Electronic)	979-8-3315-6937-2
DOIs	https://doi.org/10.23919/ACC63710.2025.11107864
Publication status	Published - 21 Aug 2025
Event	2025 American Control Conference, ACC 2025 - Denver, United States Duration: 8 Jul 2025 → 10 Jul 2025

Conference

Conference	2025 American Control Conference, ACC 2025
Abbreviated title	ACC 2025
Country/Territory	United States
City	Denver
Period	8/07/25 → 10/07/25

Funding

This project is co-financed by Holland High Tech, top sector High-Tech Systems and Materials, with a PPP innovation subsidy for public-private partnerships for research and development. The authors gratefully acknowledge the contributions to this paper through a challenge-based learning project by Tim Aarts, Remco Bertels, Matthijs van Brunschot, Armando Cerullo, Yuri Copal, Hein van Dal, Roel Drenth, Hessel van Gemert, Bas Klis, Olaf van Lamsweerde, Gijs van Meerbeeck, Tom Minten, Gert Vankan, and Teun Wijfjes.

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10.23919/ACC63710.2025.11107864

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Ickenroth - 2025 - Automatic Basis Function Selection in ILC PURELicence: CC BY-NC-ND

Cite this

Ickenroth, T. S., van Haren, M., Kon, J. J., van Meer, M., van Hulst, J., & Oomen, T. A. E. (2025). Automatic Basis Function Selection in Iterative Learning Control: A Sparsity-Promoting Approach Applied to an Industrial Printer. In 2025 American Control Conference, ACC 2025 (pp. 2931-2936). Article 11107864 Institute of Electrical and Electronics Engineers. https://doi.org/10.23919/ACC63710.2025.11107864

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title = "Automatic Basis Function Selection in Iterative Learning Control: A Sparsity-Promoting Approach Applied to an Industrial Printer",

abstract = "Iterative learning control (ILC) techniques are capable of improving the tracking performance of control systems that repeatedly perform similar tasks by utilizing data from past iterations. The aim of this paper is to design a systematic approach for learning parameterized feedforward signals with limited complexity. The developed method involves an iterative learning control in conjunction with a data-driven sparse subset selection procedure for basis function selection. The ILC algorithm that employs sparse optimization is able to automatically select relevant basis functions and is validated on an industrial flatbed printer.",

keywords = "ILC, Sparse, Basis functions, Iterative Learning Control, l1 regularization, LASSO, LARS",

author = "Ickenroth, \{Tjeerd S.\} and \{van Haren\}, Max and J.J. Kon and \{van Meer\}, M. and \{van Hulst\}, Jilles and Oomen, \{Tom A.E.\}",

year = "2025",

month = aug,

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doi = "10.23919/ACC63710.2025.11107864",

language = "English",

pages = "2931--2936",

booktitle = "2025 American Control Conference, ACC 2025",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2025 American Control Conference, ACC 2025, ACC 2025 ; Conference date: 08-07-2025 Through 10-07-2025",

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Ickenroth, TS, van Haren, M, Kon, JJ, van Meer, M, van Hulst, J & Oomen, TAE 2025, Automatic Basis Function Selection in Iterative Learning Control: A Sparsity-Promoting Approach Applied to an Industrial Printer. in 2025 American Control Conference, ACC 2025., 11107864, Institute of Electrical and Electronics Engineers, pp. 2931-2936, 2025 American Control Conference, ACC 2025, Denver, Colorado, United States, 8/07/25. https://doi.org/10.23919/ACC63710.2025.11107864

Automatic Basis Function Selection in Iterative Learning Control: A Sparsity-Promoting Approach Applied to an Industrial Printer. / Ickenroth, Tjeerd S. (Corresponding author); van Haren, Max; Kon, J.J. et al.
2025 American Control Conference, ACC 2025. Institute of Electrical and Electronics Engineers, 2025. p. 2931-2936 11107864.

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

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AU - Ickenroth, Tjeerd S.

AU - van Haren, Max

AU - Kon, J.J.

AU - van Meer, M.

AU - van Hulst, Jilles

AU - Oomen, Tom A.E.

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AB - Iterative learning control (ILC) techniques are capable of improving the tracking performance of control systems that repeatedly perform similar tasks by utilizing data from past iterations. The aim of this paper is to design a systematic approach for learning parameterized feedforward signals with limited complexity. The developed method involves an iterative learning control in conjunction with a data-driven sparse subset selection procedure for basis function selection. The ILC algorithm that employs sparse optimization is able to automatically select relevant basis functions and is validated on an industrial flatbed printer.

KW - ILC

KW - Sparse

KW - Basis functions

KW - Iterative Learning Control

KW - l1 regularization

KW - LASSO

KW - LARS

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DO - 10.23919/ACC63710.2025.11107864

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BT - 2025 American Control Conference, ACC 2025

PB - Institute of Electrical and Electronics Engineers

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ER -

Ickenroth TS, van Haren M, Kon JJ, van Meer M, van Hulst J , Oomen TAE. Automatic Basis Function Selection in Iterative Learning Control: A Sparsity-Promoting Approach Applied to an Industrial Printer. In 2025 American Control Conference, ACC 2025. Institute of Electrical and Electronics Engineers. 2025. p. 2931-2936. 11107864 Epub 2025 Mar 14. doi: 10.23919/ACC63710.2025.11107864

Automatic Basis Function Selection in Iterative Learning Control: A Sparsity-Promoting Approach Applied to an Industrial Printer

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