  Ever-increasing throughput specifications in semiconductor manufacturing
require operating high-precision mechatronics, such as linear motors, at higher
accelerations. In turn this creates higher nonlinear parasitic forces that
cannot be handled by industrial feedforward controllers. Motivated by this
problem, in this paper we develop a general framework for inversion-based
feedforward controller design using physics-guided neural networks (PGNNs). In
contrast with black-box neural networks, the developed PGNNs embed prior
physical knowledge in the input and hidden layers, which results in improved
training convergence and learning of underlying physical laws. The PGNN
inversion-based feedforward control framework is validated in simulation on an
industrial linear motor, for which it achieves a mean average tracking error
twenty times smaller than mass-acceleration feedforward in simulation.
