Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach

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 both increased performance and good generalization. The feedforward control framework is validated on a representative system with performance limiting nonlinear friction characteristics.

Submitted for presentation at the 2022 American Control Conference (ACC)

Cited by in corpus (4)

• Physics-guided neural networks for feedforward control with input-to-state stability guarantees
• Data-driven feedforward control design for nonlinear systems: A control-oriented system identification approach
• Unifying Model-Based and Neural Network Feedforward: Physics-Guided Neural Networks with Linear Autoregressive Dynamics
• Orthogonal-by-construction augmentation of physics-based input-output models