  Unknown nonlinear dynamics can limit the performance of model-based
feedforward control. The aim of this paper is to develop a feedforward control
framework for systems with unknown, typically nonlinear, dynamics. To address
the unknown dynamics, a physics-based feedforward model is complemented by a
neural network. The neural network output in the subspace of the model is
penalized through orthogonal projection. This results in uniquely identifiable
model coefficients, enabling both increased performance and good
generalization. The feedforward control framework is validated on a
representative system with performance limiting nonlinear friction
characteristics.
