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.

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.