Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder
Max Van Haren (Eindhoven University of Technology)
Maurice Poot (Eindhoven University of Technology)
Dragan Kostic (ASM Pacific Technology)
Robin van Es (ASM Pacific Technology)
Jim Portegies (Eindhoven University of Technology)
T. Oomen (TU Delft - Team Jan-Willem van Wingerden, Eindhoven University of Technology)
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Abstract
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.