Print Email Facebook Twitter Physics-Guided Neural Networks for Feedforward Control Title Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach Author Kon, Johan (Eindhoven University of Technology) Bruijnen, Dennis (Philips Research) van de Wijdeven, Jeroen (ASML) Heertjes, Marcel (Eindhoven University of Technology; ASML) Oomen, T.A.E. (TU Delft Team Jan-Willem van Wingerden; Eindhoven University of Technology) Date 2022 Abstract Unknown nonlinear dynamics can limit the performance of model-based feedforward control. The aim of this paper is to develop a feedforward control framework for systems with unknown, typically nonlinear, dynamics. To address the unknown dynamics, a physics-based feedforward model is complemented by a neural network. The neural network output in the subspace of the model is penalized through orthogonal projection. This results in uniquely identifiable model coefficients, enabling increased performance and similar task flexibility with respect to the model-based controller. The feedforward framework is validated on a representative system with performance limiting nonlinear friction characteristics. Subject TrainingMaximum likelihood detectionLimitingSystem dynamicsNonlinear filtersCost functionFeedforward neural networks To reference this document use: http://resolver.tudelft.nl/uuid:b0513881-b2e7-4985-bd22-0f49a121e6b8 DOI https://doi.org/10.23919/ACC53348.2022.9867653 Publisher IEEE Embargo date 2023-03-05 ISBN 978-1-6654-5196-3 Source Proceedings of the American Control Conference (ACC 2022) Event 2022 American Control Conference, ACC 2022, 2022-06-08 → 2022-06-10, Atlanta, United States Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2022 Johan Kon, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, T.A.E. Oomen Files PDF Physics_Guided_Neural_Net ... proach.pdf 498.5 KB Close viewer /islandora/object/uuid:b0513881-b2e7-4985-bd22-0f49a121e6b8/datastream/OBJ/view