  Growing demands in the semiconductor industry result in the need for enhanced
performance of lithographic equipment. However, position tracking accuracy of
high precision mechatronics is often limited by the presence of disturbance
sources, which originate from unmodelled or unforeseen deterministic
environmental effects. To negate the effects of these disturbances, a learning
based feedforward controller is employed, where the underlying control policy
is estimated from experimental data based on Gaussian Process regression. The
proposed approach exploits the property of including prior knowledge on the
expected steady state behaviour of residual dynamics in terms of kernel
selection. Corresponding hyper-parameters are optimized using the maximization
of the marginalized likelihood. Consequently, the learned function is employed
as augmentation of the currently employed rigid body feedforward controller.
The effectiveness of the augmentation is experimentally validated on a
magnetically levitated planar motor stage. The results of this paper
demonstrate the benefits and possibilities of machine-learning based approaches
for compensation of static effects, which originate from residual dynamics,
such that position tracking performance for moving-magnet planar motor
actuators is improved.
