Feedforward Control in the Presence of Input Nonlinearities
A Learning-based Approach
Jilles Van Hulst (Eindhoven University of Technology)
Maurice Poot (Eindhoven University of Technology)
Dragan Kostic (ASM Pacific Technology)
Kai Wa Yan (ASM Pacific Technology)
Jim Portegies (Eindhoven University of Technology)
Tom Oomen (TU Delft - Team Jan-Willem van Wingerden, Eindhoven University of Technology)
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Abstract
Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input.
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