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• W4221158596 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." @default.
• W4221158596 created "2022-04-03" @default.
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• W4221158596 date "2022-06-08" @default.
• W4221158596 modified "2023-10-16" @default.
• W4221158596 title "Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach" @default.
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• W4221158596 doi "https://doi.org/10.23919/acc53348.2022.9867653" @default.
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