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

• URL: http://arxiv.org/abs/2209.12489v1
• Date: Mon, 26 Sep 2022 08:01:28 GMT
• Title: Unifying Model-Based and Neural Network Feedforward: Physics-Guided Neural Networks with Linear Autoregressive Dynamics
• Authors: Johan Kon, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen
• Abstract summary: This paper develops a feedforward control framework to compensate unknown nonlinear dynamics. The feedforward controller is parametrized as a parallel combination of a physics-based model and a neural network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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