Gaussian Processes for Advanced Motion Control

Gaussian Processes for Advanced Motion Control

Poot, Maurice (Eindhoven University of Technology)
Portegies, Jim (Eindhoven University of Technology)
Mooren, Noud (Eindhoven University of Technology)
van Haren, Max (Eindhoven University of Technology)
van Meer, Max (Eindhoven University of Technology)
Oomen, T.A.E. (TU Delft Team Jan-Willem van Wingerden; Eindhoven University of Technology)

2022

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.

http://resolver.tudelft.nl/uuid:566a107c-eaf3-49e7-9672-e2094905b3e2

https://doi.org/10.23919/ACC53348.2022.9867420

IEEE

2023-03-05

978-1-6654-5196-3

Proceedings of the American Control Conference (ACC 2022)

2022 American Control Conference, ACC 2022, 2022-06-08 → 2022-06-10, Atlanta, United States

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.

Institutional Repository

conference paper

© 2022 Maurice Poot, Jim Portegies, Noud Mooren, Max van Haren, Max van Meer, T.A.E. Oomen