标题： 基于正交投影的方法用于前馈控制的物理引导神经网络 显示英文标题

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

摘要： 未知的非线性动力学可能会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、通常为非线性动力学的系统开发一种前馈控制框架。 为了解决未知的动力学问题，一种基于物理的前馈模型由神经网络进行补充。 通过正交投影对模型子空间中的神经网络输出进行惩罚。 这导致了可唯一识别的模型系数，从而实现了性能的提高和良好的泛化能力。 前馈控制框架在具有性能限制的非线性摩擦特性的代表性系统上进行了验证。 显示英文摘要

摘要： 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.