著者

Maurice Poot Jim Portegies Noud Mooren Max van Haren Max van Meer Tom Oomen

出版者

The Institute of Electrical Engineers of Japan

雑誌

IEEJ Journal of Industry Applications (ISSN:21871094)

巻号頁・発行日

vol.11, no.3, pp.396-407, 2022-05-01 (Released:2022-05-01)

参考文献数

74

被引用文献数

8

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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.