Unifying Model-Based and Neural Network Feedforward: Physics-Guided Neural Networks with Linear Autoregressive Dynamics
This work addresses feedforward control limitations for systems with unknown nonlinearities, offering an incremental improvement by integrating neural networks with existing physics-based models.
The paper tackled the problem of compensating unknown nonlinear dynamics in feedforward control by developing a framework that combines a physics-based model with a neural network, both sharing linear autoregressive dynamics, resulting in interpretable models through orthogonal projection-based regularization.
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.