A sample construction method in kinematics characteristics domain to identify the feed drive model

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.