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Self-optimization of nonlinear iterative learning control and repetitive control

Conference Paper (2025)
Author(s)

Leontine Aarnoudse (Norwegian University of Science and Technology (NTNU))

Alexey Pavlov (Norwegian University of Science and Technology (NTNU))

T.A.E. Oomen (Eindhoven University of Technology, TU Delft - Team Jan-Willem van Wingerden)

DOI related publication
https://doi.org/10.1109/CDC57313.2025.11312565 Final published version
More Info
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Publication Year
2025
Language
English
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/publishing/publisher-deals Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Pages (from-to)
765-770
Publisher
IEEE
ISBN (electronic)
979-8-3315-2627-6
Event
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16
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Abstract

Nonlinear iterative learning control (ILC) and nonlinear repetitive control (RC) approaches introduce additional design freedom compared to linear time-invariant (LTI) approaches. Since the actual performance improvements depend on the parameters used in the nonlinearity, the aim of this paper is to optimize these parameters during the learning process. With optimal parameters, the nonlinear algorithms can outperform their LTI counterparts, for example by achieving fast attenuation of repeating disturbances without amplifying non-repeating disturbances. In this paper, we present the algorithm for the automatic learning/tuning process and validate it using simulations of an industrial flatbed printer.

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