Gaussian Processes for Advanced Motion Control
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)
Tom Oomen (TU Delft - Team Jan-Willem van Wingerden, Eindhoven University of Technology)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.