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

Autor:	Kon, Johan, Bruijnen, Dennis, van de Wijdeven, Jeroen, Heertjes, Marcel, Oomen, Tom
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1109/CDC51059.2022.9992511
Popis:	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 physics-based model through orthogonal projection-based regularization, such that the neural network captures only the unmodelled dynamics, resulting in interpretable models. Comment: Accepted for presentation at the 2022 Conference on Decision and Control (CDC)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2209.12489 Zobrazit plný text záznamu View this record from Arxiv