  Feedforward control is essential to achieving good tracking performance in
positioning systems. The aim of this paper is to develop an identification
strategy for inverse models of systems with nonlinear dynamics of unknown
structure using input-output data, which directly delivers feedforward signals
for a-priori unknown tasks. To this end, inverse systems are regarded as
noncausal nonlinear finite impulse response (NFIR) systems and modeled as a
Gaussian Process with a stationary kernel function that imposes properties such
as smoothness and periodicity. The approach is validated experimentally on a
consumer printer with friction and shown to lead to improved tracking
performance with respect to linear feedforward.
