Iterative Learning Control for Closed-Loop Systems with Actuator Saturation using Alternating Projection

Zhihe Zhuang; Max Van Meer; Hongfeng Tao; Tom Oomen; Wojciech Paszke; Tao Liu; Eric Rogers

doi:10.1109/DDCLS66240.2025.11065983

Iterative Learning Control for Closed-Loop Systems with Actuator Saturation using Alternating Projection

Zhihe Zhuang^*, Max Van Meer, Hongfeng Tao, Tom Oomen, Wojciech Paszke, Tao Liu, Eric Rogers

^*Corresponding author for this work

Team Jan-Willem van Wingerden

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

Abstract

Iterative learning control (ILC) is typically applied in practice combined with a feedback controller for time-domain stability. In this closed-loop design with actuator constraints, existing constrained ILC designs suffer from determining the exact input constraint on the ILC controller. This issue brings in an important gap between the existing constrained ILC designs and their real-world applications. This paper gives a systematic consideration of the input constraint problem in the closed-loop ILC design with actuator saturation. A constraint-aware ILC is developed to autonomously determine the constraint on the feedforward controller. The convergence of the constrained ILC process is proved under the framework of alternating projection. Finally, the effectiveness of the developed method is verified on a numerical simulation.

Original language	English
Title of host publication	Proceedings of 2025 IEEE 14th Data Driven Control and Learning Systems Conference, DDCLS 2025
Editors	Mingxuan Sun, Ronghu Chi
Publisher	IEEE
Pages	2248-2252
Number of pages	5
ISBN (Electronic)	979-8-3503-5731-8
DOIs	https://doi.org/10.1109/DDCLS66240.2025.11065983
Publication status	Published - 2025
Event	14th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2025 - Wuxi, China Duration: 9 May 2025 → 11 May 2025

Conference

Conference	14th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2025
Country/Territory	China
City	Wuxi
Period	9/05/25 → 11/05/25

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.

Keywords

Feedforward Control
Input Constraint
Integral Windup
Iterative Learning Control

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10.1109/DDCLS66240.2025.11065983

Iterative_Learning_Control_for_Closed-Loop_Systems_with_Actuator_Saturation_using_Alternating_ProjectionFinal published version, 1.67 MB

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Zhuang, Z., Van Meer, M., Tao, H., Oomen, T., Paszke, W., Liu, T., & Rogers, E. (2025). Iterative Learning Control for Closed-Loop Systems with Actuator Saturation using Alternating Projection. In M. Sun, & R. Chi (Eds.), Proceedings of 2025 IEEE 14th Data Driven Control and Learning Systems Conference, DDCLS 2025 (pp. 2248-2252). IEEE. https://doi.org/10.1109/DDCLS66240.2025.11065983

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title = "Iterative Learning Control for Closed-Loop Systems with Actuator Saturation using Alternating Projection",

abstract = "Iterative learning control (ILC) is typically applied in practice combined with a feedback controller for time-domain stability. In this closed-loop design with actuator constraints, existing constrained ILC designs suffer from determining the exact input constraint on the ILC controller. This issue brings in an important gap between the existing constrained ILC designs and their real-world applications. This paper gives a systematic consideration of the input constraint problem in the closed-loop ILC design with actuator saturation. A constraint-aware ILC is developed to autonomously determine the constraint on the feedforward controller. The convergence of the constrained ILC process is proved under the framework of alternating projection. Finally, the effectiveness of the developed method is verified on a numerical simulation.",

keywords = "Feedforward Control, Input Constraint, Integral Windup, Iterative Learning Control",

author = "Zhihe Zhuang and {Van Meer}, Max and Hongfeng Tao and Tom Oomen and Wojciech Paszke and Tao Liu and Eric Rogers",

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.; 14th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2025 ; Conference date: 09-05-2025 Through 11-05-2025",

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doi = "10.1109/DDCLS66240.2025.11065983",

language = "English",

pages = "2248--2252",

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Zhuang, Z, Van Meer, M, Tao, H, Oomen, T, Paszke, W, Liu, T & Rogers, E 2025, Iterative Learning Control for Closed-Loop Systems with Actuator Saturation using Alternating Projection. in M Sun & R Chi (eds), Proceedings of 2025 IEEE 14th Data Driven Control and Learning Systems Conference, DDCLS 2025. IEEE, pp. 2248-2252, 14th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2025, Wuxi, China, 9/05/25. https://doi.org/10.1109/DDCLS66240.2025.11065983

Iterative Learning Control for Closed-Loop Systems with Actuator Saturation using Alternating Projection. / Zhuang, Zhihe; Van Meer, Max; Tao, Hongfeng et al.
Proceedings of 2025 IEEE 14th Data Driven Control and Learning Systems Conference, DDCLS 2025. ed. / Mingxuan Sun; Ronghu Chi. IEEE, 2025. p. 2248-2252.

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

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AU - Zhuang, Zhihe

AU - Van Meer, Max

AU - Tao, Hongfeng

AU - Oomen, Tom

AU - Paszke, Wojciech

AU - Liu, Tao

AU - Rogers, Eric

N1 - 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.

PY - 2025

Y1 - 2025

N2 - Iterative learning control (ILC) is typically applied in practice combined with a feedback controller for time-domain stability. In this closed-loop design with actuator constraints, existing constrained ILC designs suffer from determining the exact input constraint on the ILC controller. This issue brings in an important gap between the existing constrained ILC designs and their real-world applications. This paper gives a systematic consideration of the input constraint problem in the closed-loop ILC design with actuator saturation. A constraint-aware ILC is developed to autonomously determine the constraint on the feedforward controller. The convergence of the constrained ILC process is proved under the framework of alternating projection. Finally, the effectiveness of the developed method is verified on a numerical simulation.

AB - Iterative learning control (ILC) is typically applied in practice combined with a feedback controller for time-domain stability. In this closed-loop design with actuator constraints, existing constrained ILC designs suffer from determining the exact input constraint on the ILC controller. This issue brings in an important gap between the existing constrained ILC designs and their real-world applications. This paper gives a systematic consideration of the input constraint problem in the closed-loop ILC design with actuator saturation. A constraint-aware ILC is developed to autonomously determine the constraint on the feedforward controller. The convergence of the constrained ILC process is proved under the framework of alternating projection. Finally, the effectiveness of the developed method is verified on a numerical simulation.

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KW - Input Constraint

KW - Integral Windup

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BT - Proceedings of 2025 IEEE 14th Data Driven Control and Learning Systems Conference, DDCLS 2025

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Y2 - 9 May 2025 through 11 May 2025

ER -

Iterative Learning Control for Closed-Loop Systems with Actuator Saturation using Alternating Projection

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Keywords

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