  The paper presents a study of an adaptive approach to lateral skew control
for an experimental railway stand. The preliminary experiments with the real
experimental railway stand and simulations with its 3-D mechanical model,
indicates difficulties of model-based control of the device. Thus, use of
neural networks for identification and control of lateral skew shall be
investigated. This paper focuses on real-data based modeling of the railway
stand by various neural network models, i.e; linear neural unit and quadratic
neural unit architectures. Furthermore, training methods of these neural
architectures as such, real-time-recurrent-learning and a variation of
back-propagation-through-time are examined, accompanied by a discussion of the
produced experimental results.
