Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder

Max van Haren, Maurice Poot, Dragan Kostic, Robin van Es, Jim Portegies, T.A.E. Oomen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

28 Downloads (Pure)

Abstract

Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control. The aim of this paper is to compensate for position-dependent effects by modeling feedforward parameters as a function of position. A framework to model and identify feedforward parameters as a continuous function of position is developed by combining Gaussian processes and feedforward parameter learning techniques. The framework results in a fully data-driven approach, which can be readily implemented for industrial control applications. The framework is experimentally validated and shows a significant performance increase on a commercial wire bonder.
Original languageEnglish
Title of host publicationProceedings of the IEEE 17th International Conference on Advanced Motion Control (AMC 2022)
PublisherIEEE
Pages268-273
ISBN (Print)978-1-7281-7711-3
DOIs
Publication statusPublished - 2022

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.

Fingerprint

Dive into the research topics of 'Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder'. Together they form a unique fingerprint.

Cite this