论文标题
物理学指导的神经网络，用于前馈控制：基于正交投影的方法
Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach
论文作者
论文摘要
未知的非线性动力学可以限制基于模型的前馈控制的性能。本文的目的是为具有未知（通常是非线性，动力学）的系统开发一个前馈控制框架。为了解决未知动力学，神经网络补充了基于物理的前馈模型。模型子空间中的神经网络输出通过正交投影受到惩罚。这导致了独特的可识别模型系数，从而提高了性能和良好的概括。馈电控制框架已在具有性能限制非线性摩擦特征的代表系统上验证。
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.
