Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach

Leontine Aarnoudse; Johan Kon; Koen Classens; Max van Meer; Maurice  Poot; Paul Tacx; Nard Strijbosch; T.A.E. Oomen

doi:10.1109/CDC51059.2022.9993408

Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach

Leontine Aarnoudse, Johan Kon, Koen Classens, Max van Meer, Maurice Poot, Paul Tacx, Nard Strijbosch, T.A.E. Oomen

Team Jan-Willem van Wingerden

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

14 Downloads (Pure)

Abstract

Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calculated offline, and iteration-and time-varying weights. Conditions for the monotonic convergence of this iteration-varying ILC algorithm are developed. In addition, a resource-efficient implementation is proposed in which the ILC update law is reframed as a linear quadratic tracking problem, reducing the computational load significantly. The approach is illustrated on a simulation example.

Original language	English
Title of host publication	Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022)
Publisher	IEEE
Pages	1485-1490
ISBN (Print)	978-1-6654-6761-2
DOIs	https://doi.org/10.1109/CDC51059.2022.9993408
Publication status	Published - 2022
Event	IEEE 61st Conference on Decision and Control (CDC 2022) - Cancún, Mexico Duration: 6 Dec 2022 → 9 Dec 2022

Conference

Conference	IEEE 61st Conference on Decision and Control (CDC 2022)
Country/Territory	Mexico
City	Cancún
Period	6/12/22 → 9/12/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
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.

Keywords

Simulation
Computational modeling
Aerospace electronics
Manufacturing
Computational efficiency
Printers
Complex systems

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Cross-Coupled_Iterative_Learning_Control_for_Complex_Systems_A_Monotonically_Convergent_and_Computationally_Efficient_Approach_Final published version, 0.99 MB

Cite this

Aarnoudse, L., Kon, J., Classens, K., van Meer, M., Poot, M., Tacx, P., Strijbosch, N., & Oomen, T. A. E. (2022). Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach. In Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022) (pp. 1485-1490). IEEE. https://doi.org/10.1109/CDC51059.2022.9993408

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title = "Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach",

abstract = "Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calculated offline, and iteration-and time-varying weights. Conditions for the monotonic convergence of this iteration-varying ILC algorithm are developed. In addition, a resource-efficient implementation is proposed in which the ILC update law is reframed as a linear quadratic tracking problem, reducing the computational load significantly. The approach is illustrated on a simulation example.",

keywords = "Simulation, Computational modeling, Aerospace electronics, Manufacturing, Computational efficiency, Printers, Complex systems",

author = "Leontine Aarnoudse and Johan Kon and Koen Classens and {van Meer}, Max and Maurice Poot and Paul Tacx and Nard Strijbosch and T.A.E. Oomen",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care 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.; IEEE 61st Conference on Decision and Control (CDC 2022) ; Conference date: 06-12-2022 Through 09-12-2022",

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Aarnoudse, L, Kon, J, Classens, K, van Meer, M, Poot, M, Tacx, P, Strijbosch, N & Oomen, TAE 2022, Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach. in Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022). IEEE, pp. 1485-1490, IEEE 61st Conference on Decision and Control (CDC 2022), Cancún, Mexico, 6/12/22. https://doi.org/10.1109/CDC51059.2022.9993408

Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach. / Aarnoudse, Leontine; Kon, Johan; Classens, Koen et al.
Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022). IEEE, 2022. p. 1485-1490.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - van Meer, Max

AU - Poot, Maurice

AU - Tacx, Paul

AU - Strijbosch, Nard

AU - Oomen, T.A.E.

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care 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.

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AB - Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calculated offline, and iteration-and time-varying weights. Conditions for the monotonic convergence of this iteration-varying ILC algorithm are developed. In addition, a resource-efficient implementation is proposed in which the ILC update law is reframed as a linear quadratic tracking problem, reducing the computational load significantly. The approach is illustrated on a simulation example.

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KW - Computational modeling

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KW - Computational efficiency

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ER -

Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach

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Keywords

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