Title:	Iterative learning control in high-performance motion systems: From theory to implementation
Authors:	Goubej, Martin Meeusen, Sven Mooren, Noud Oomen, Tom
Citation:	GOUBEJ, M., MEEUSEN, S., MOOREN, N., OOMEN, T. Iterative learning control in high-performance motion systems: From theory to implementation. In: Proceedings 2019 24th IEEE InternationalConference on Emerging Technologiesand Factory Automation (ETFA). Zaragoza: University of Zaragoza, 2019. s. 851-856. ISBN 978-1-72810-303-7 , ISSN 1946-0740.
Issue Date:	2019
Document type:	konferenční příspěvek conferenceObject
URI:	2-s2.0-85074209777 http://hdl.handle.net/11025/36662
ISBN:	978-1-72810-303-7
ISSN:	1946-0740
Keywords in different language:	advanced feedforward control;basis-function ILC;frequency-domain ILC;iterative learning control;motion control;real-time systems
Abstract in different language:	Iterative learning control (ILC) enables a perfect compensation for systems that perform the same task over and over again. The aim of this paper is to demonstrate practical applicability of two various state-of-the-art ILC algorithms to point-to-point positioning systems. A simple Frequency domain ILC approach is exploited focusing on systems with exactly repeating motion tasks. Furthermore, flexible ILC is employed to enable learning also for non-repeating tasks. Particular steps providing a seamless transfer from theory and algorithms to practical implementation in a real-time environment by means of industrial-grade SW and HW are given. They may serve as a practical example of a workflow suitable for a wide range of motion control applications. Potential benefits of the learning-type control in comparison with conventional feedback and feedforward control are discussed as well.
Rights:	Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © University of Zaragoza
Appears in Collections:	Konferenční příspěvky / Conference Papers (KKY) OBD

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