  The increasing demands for motion control result in a situation where Linear
Parameter-Varying (LPV) dynamics have to be taken into account. Inverse-model
feedforward control for LPV motion systems is challenging, since the inverse of
an LPV system is often dynamically dependent on the scheduling sequence. The
aim of this paper is to develop an identification approach that directly
identifies dynamically scheduled feedforward controllers for LPV motion systems
from data. In this paper, the feedforward controller is parameterized in basis
functions, similar to, e.g., mass-acceleration feedforward, and is identified
by a kernel-based approach such that the parameter dependency for LPV motion
systems is addressed. The resulting feedforward includes dynamic dependence and
is learned accurately. The developed framework is validated on an example.
