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.

翻译：未知的非线性动态往往限制 feedforward 控件的跟踪性能。 本文的目的是开发一个 feedforward 控制框架, 以使用通用功能近似器来补偿这些未知的非线性动态。 feedforward 控制器作为物理模型和神经网络的平行组合, 两者都具有相同的线性反向( AR) 动态。 这种对称允许通过 Sanathanan- Koerner ( SK) 迭代来高效优化输出- erorororors 优化 。 在每一个 SK 内, 神经网络的输出通过正向投影法的正规化在物理模型的子空间中受到处罚, 这样神经网络只能捕捉未建模的动态, 从而形成可解释的模型 。