The modeling accuracy of feed drive is mainly affected by two factors: the accuracy of model form and the completeness of sample data which is used to identify the parameters of the established model form. One of the main works of this study is to construct the experimental trajectories which are used to obtain the sample data. Different from the trajectories selected based on the researcher's experience in previous literatures which lacks theoretical evidence to confirm the completeness of the sample data, in this paper, a reverse construction method of experimental trajectory is proposed based on the required sample data. First, the completeness of sample data is analyzed, and the samples are generated by using the Hammersley sequence method. Then, the experimental trajectories are derived. In addition, considering that the data of the feed system has temporal correlations between the adjacent motion states in time sequence, this study made some improvements on the modeling framework composed of the first-principle model and machine learning model. The experiments conducted in the actual feed drive system showed that the maximum prediction error of the tracking error is 7.8%, which confirmed the effectiveness of the designed trajectories and the high accuracy of the proposed model. Furthermore, the advantage of the proposed trajectory is confirmed by the comparison experiment. At the same time, the same trajectories are used to train different model forms to verify the advantage of the proposed model. The proposed feed drive modeling method can be used for designing a high-performance feed drive control system or compensating errors when generating motion commands, thereby improving the machining accuracy of machine tool.

进给驱动器的建模精度主要受两个因素影响：模型形式的准确性和用于识别已建立模型形式的参数的样本数据的完整性。这项研究的主要工作之一是构建用于获得样本数据的实验轨迹。不同于以往文献中根据研究人员的经验选择的轨迹，由于缺乏理论证据来确定样本数据的完整性，本文提出了一种基于所需样本数据的实验轨迹的逆向构造方法。首先，分析样本数据的完整性，并使用Hammersley序列方法生成样本。然后，得出实验轨迹。此外，考虑到进给系统的数据在时间序列上在相邻运动状态之间具有时间相关性，本研究对由第一原理模型和机器学习模型组成的建模框架进行了一些改进。在实际进给驱动系统中进行的实验表明，跟踪误差的最大预测误差为7.8％，这证实了设计轨迹的有效性和所提出模型的高精度。此外，通过比较实验证实了所提出的轨迹的优点。同时，使用相同的轨迹来训练不同的模型形式，以验证所提出模型的优势。