Unifying Model-Based and Neural Network Feedforward

Unifying Model-Based and Neural Network Feedforward: Physics-Guided Neural Networks with Linear Autoregressive Dynamics

Kon, Johan (Eindhoven University of Technology)
Bruijnen, Dennis (Philips Engineering Solutions)
van de Wijdeven, Jeroen (ASML)
Heertjes, Marcel (Eindhoven University of Technology)
Oomen, T.A.E. (TU Delft Team Jan-Willem van Wingerden; Eindhoven University of Technology)

2022

Unknown nonlinear dynamics often limit the tracking performance of feedforward control. The aim of this paper is to develop a feedforward control framework that can compensate these unknown nonlinear dynamics using universal function approximators. The feedforward controller is parametrized as a parallel combination of a physics-based model and a neural network, where both share the same linear autoregressive (AR) dynamics. This parametrization allows for efficient output-error optimization through Sanathanan-Koerner (SK) iterations. Within each SK-iteration, the output of the neural network is penalized in the subspace of the physicsbased model through orthogonal projection-based regularization, such that the neural network captures only the unmodelled dynamics, resulting in interpretable models.

Friction
Feedforward neural networks
Nonlinear dynamical systems
Feedforward systems
Task analysis
Optimization

http://resolver.tudelft.nl/uuid:35a2f77c-51c1-4044-845e-8626397c0625

https://doi.org/10.1109/CDC51059.2022.9992511

IEEE

2023-07-10

978-1-6654-6761-2

Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022)

IEEE 61st Conference on Decision and Control (CDC 2022), 2022-12-06 → 2022-12-09, Cancún, Mexico

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.

Institutional Repository

conference paper

© 2022 Johan Kon, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, T.A.E. Oomen