  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.

Systems and Control;Systems and Control