  Performance of model-based feedforward controllers is typically limited by
the accuracy of the inverse system dynamics model. Physics-guided neural
networks (PGNN), where a known physical model cooperates in parallel with a
neural network, were recently proposed as a method to achieve high accuracy of
the identified inverse dynamics. However, the flexible nature of neural
networks can create overparameterization when employed in parallel with a
physical model, which results in a parameter drift during training. This drift
may result in parameters of the physical model not corresponding to their
physical values, which increases vulnerability of the PGNN to operating
conditions not present in the training data. To address this problem, this
paper proposes a regularization method via identified physical parameters, in
combination with an optimized training initialization that improves training
convergence. The regularized PGNN framework is validated on a real-life
industrial linear motor, where it delivers better tracking accuracy and
extrapolation.
