Gaussian Processes for Advanced Motion Control

Maurice Poot, Jim Portegies, Noud Mooren, Max van Haren, Max van Meer, T.A.E. Oomen

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

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Abstract

Machine learning techniques, including Gaussian processes (GPs), are expected to play a significant role in meeting speed, accuracy, and functionality requirements in future data-intensive mechatronic systems. This paper aims to reveal the potential of GPs for motion control applications. Successful applications of GPs for feedforward and learning control, including the identification and learning for noncausal feedforward, position-dependent snap feedforward, nonlinear feedforward, and GP-based spatial repetitive control, are outlined. Experimental results on various systems, including a desktop printer, wirebonder, and substrate carrier, confirmed that data-based learning using GPs can significantly improve the accuracy of mechatronic systems.
Original languageEnglish
Title of host publicationProceedings of the American Control Conference (ACC 2022)
PublisherIEEE
Pages3355-3355
ISBN (Print)978-1-6654-5196-3
DOIs
Publication statusPublished - 2022
Event2022 American Control Conference, ACC 2022 - Atlanta, United States
Duration: 8 Jun 202210 Jun 2022

Conference

Conference2022 American Control Conference, ACC 2022
Country/TerritoryUnited States
CityAtlanta
Period8/06/2210/06/22

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.

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