Feedforward Control for an Interventional X-Ray
A Parallel Physical Model and Neural Network Approach
Johan Kon (Eindhoven University of Technology)
Naomi de Vos (Philips Engineering Solutions, Eindhoven University of Technology)
Dennis Bruijnen (Philips Engineering Solutions)
Jeroen van de Wijdeven (ASML)
Marcel Heertjes (Eindhoven University of Technology, ASML)
Tom Oomen (TU Delft - Mechanical Engineering, Eindhoven University of Technology)
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Abstract
Hard-to-model, often nonlinear dynamics limit the tracking performance of physical-model-based feedforward control in medical interventional X-ray (IX) systems. In this article, these unknown dynamics are compensated using a physics-guided neural network (PGNN) feedforward controller. The PGNN consists of a parallel combination of a physical model describing the equations of motions of the IX and a feedforward neural network. To ensure that the neural network compensates only for the dynamics not included in the physical model, the neural network output in the subspace spanned by the physical model is penalized through regularization. As a result, the physical model serves as a baseline for performance, and the neural network contribution can be monitored or disabled. The PGNN feedforward controller is validated on an experimental IX setup, illustrating its superior performance over a physical-model-based feedforward controller.