Servo error pre-compensation (SEP) is commonly used to improve the accuracy of feed drives. Existing SEP approaches often involve the use of physics-based linear models (e.g., transfer functions) to predict servo errors, but suffer from inaccuracies due to unmodeled nonlinear dynamics in feed drives. This paper proposes a linear hybrid model for SEP that combines physics-based and data-driven linear models. The proposed model is shown to approximate nonlinearities unmodeled in physics-based linear models. In experiments on a precision feed drive, the proposed hybrid model improves the accuracy of servo error prediction by up to 38% compared to a physics-based model.

伺服误差预补偿 (SEP) 通常用于提高进给驱动器的精度。现有的 SEP 方法通常涉及使用基于物理的线性模型（例如，传递函数）来预测伺服误差，但由于进给驱动器中未建模的非线性动力学而导致不准确。本文为 SEP 提出了一种线性混合模型，它结合了基于物理和数据驱动的线性模型。所提出的模型被证明可以近似在基于物理的线性模型中未建模的非线性。在精密进给驱动器的实验中，与基于物理的模型相比，所提出的混合模型将伺服误差预测的精度提高了 38%。