Unifying Model-Based and Neural Network Feedforward
Physics-Guided Neural Networks with Linear Autoregressive Dynamics
Johan Kon (Eindhoven University of Technology)
Dennis Bruijnen (Philips Engineering Solutions)
Jeroen van de Wijdeven (ASML)
Marcel Heertjes (Eindhoven University of Technology)
Tom Oomen (Eindhoven University of Technology, TU Delft - Team Jan-Willem van Wingerden)
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Abstract
Unknown nonlinear dynamics often limit the tracking performance of feedforward control. The aim of this paper is to develop a feedforward control framework that can compensate these unknown nonlinear dynamics using universal function approximators. The feedforward controller is parametrized as a parallel combination of a physics-based model and a neural network, where both share the same linear autoregressive (AR) dynamics. This parametrization allows for efficient output-error optimization through Sanathanan-Koerner (SK) iterations. Within each SK-iteration, the output of the neural network is penalized in the subspace of the physicsbased model through orthogonal projection-based regularization, such that the neural network captures only the unmodelled dynamics, resulting in interpretable models.