Learning for Precision Motion of an Interventional X-ray System
Add-on Physics-Guided Neural Network Feedforward Control
Johan Kon (Eindhoven University of Technology)
Naomi de Vos (Eindhoven University of Technology)
Dennis Bruijnen (Philips Research)
Jeroen van de Wijdeven (ASML)
Marcel Heertjes (Eindhoven University of Technology, ASML)
Tom Oomen (Eindhoven University of Technology, TU Delft - Team Jan-Willem van Wingerden)
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Abstract
Tracking performance of physical-model-based feedforward control for interventional X-ray systems is limited by hard-to-model parasitic nonlinear dynamics, such as cable forces and nonlinear friction. In this paper, these nonlinear dynamics are compensated using a physics-guided neural network (PGNN), consisting of a physical model, embedding prior knowledge of the dynamics, in parallel with a neural network to learn hard-to-model dynamics. To ensure that the neural network learns only unmodelled effects, the neural network output in the subspace spanned by the physical model is regularized via an orthogonal projection-based approach, resulting in complementary physical model and neural network contributions. The PGNN feedforward controller reduces the tracking error of an interventional X-ray system by a factor of 5 compared to an optimally tuned physical model, successfully compensating the unmodeled parasitic dynamics.
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