Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder

Max van Haren, Maurice Poot, Dragan Kostic, Robin van Es, Jim Portegies, T.A.E. Oomen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control. The aim of this paper is to compensate for position-dependent effects by modeling feedforward parameters as a function of position. A framework to model and identify feedforward parameters as a continuous function of position is developed by combining Gaussian processes and feedforward parameter learning techniques. The framework results in a fully data-driven approach, which can be readily implemented for industrial control applications. The framework is experimentally validated and shows a significant performance increase on a commercial wire bonder.
Original languageEnglish
Title of host publicationProceedings of the IEEE 17th International Conference on Advanced Motion Control (AMC 2022)
ISBN (Print)978-1-7281-7711-3
Publication statusPublished - 2022

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