IEEJ Journal of Industry Applications
Online ISSN : 2187-1108
Print ISSN : 2187-1094
ISSN-L : 2187-1094
Invited Paper
Gaussian Processes for Advanced Motion Control
Maurice PootJim PortegiesNoud MoorenMax van HarenMax van MeerTom Oomen
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2022 Volume 11 Issue 3 Pages 396-407


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.

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© 2022 The Institute of Electrical Engineers of Japan
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