Gaussian Processes for Advanced Motion Control

Journal article (2022)

Authors

Maurice Poot Eindhoven University of Technology
Jim Portegies Eindhoven University of Technology
Noud Mooren Eindhoven University of Technology
Max van Haren Eindhoven University of Technology
Max van Meer Eindhoven University of Technology
T.A.E. Oomen Eindhoven University of Technology, Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
Research Group
Team Jan-Willem van Wingerden (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 Maurice Poot, Jim Portegies, Noud Mooren, Max van Haren, Max van Meer, T.A.E. Oomen
DOI
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https://doi.org/10.1541/ieejjia.21011492
Feedforward control Learning control Gaussian processes
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Published Date

2022

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Jan-Willem van Wingerden

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.

Files

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