Learning for Precision Motion of an Interventional X-ray System: Add-on Physics-Guided Neural Network Feedforward Control

作     者：Johan Kon Naomi de Vos Dennis Bruijnen Jeroen van de Wijdeven Marcel Heertjes Tom Oomen 

作者机构：Control Systems Technology Group Departement of Mechanical Engineering Eindhoven University of Technology P.O. Box 513 5600 MB Eindhoven The Netherlands Philips Engineering Solutions Eindhoven The Netherlands ASML Veldhoven The Netherlands Delft Center for Systems and Control Delft University of Technology The Netherlands 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2023年第56卷第2期

页      面：7523-7528页

主　　题：Feedforward control physics-guided neural networks interventional X-ray 

摘      要：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.