论文标题
基于模型的统一和神经网络馈送：具有线性自回旋动力学的物理引导的神经网络
Unifying Model-Based and Neural Network Feedforward: Physics-Guided Neural Networks with Linear Autoregressive Dynamics
论文作者
论文摘要
未知的非线性动力学通常会限制前馈控制的跟踪性能。本文的目的是开发一个可以使用通用函数近似器来补偿这些未知非线性动力学的前馈控制框架。前馈控制器被参数化为基于物理模型和神经网络的并行组合，在该组合中，两者都共享相同的线性自动回归（AR）动力学。该参数化允许通过Sanathanan-Koerner（SK）迭代进行有效的输出误差优化。在每个Sk-titeration中，神经网络的输出在基于物理模型的子空间中通过基于正交投影的正则化受到了惩罚，因此神经网络仅捕获未建模的动力学，从而产生可解释的模型。
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.
