A review on positioning uncertainty in motion control for machine tool feed drives

The feed system is a typical mechatronics system, as well as motion control equipment, that serves as a fundamental component of CNC machine tools. Developing a precise mechatronic integrated model of the motion control of the feed system is a crucial step in achieving the digital twin and virtual machining. The extant literature extensively covers the dynamic modeling and identification of feed systems, the characterization and traceability of position deviations, and the study and optimization of motion control algorithms. Nevertheless, there is a deficiency in the analysis and optimization of the uncertainty pertaining to the motion control position of the feed system. This paper focuses on the positioning repeatability and considers the random position deviation that occurs during the movement of the feed system. The current research on positioning repeatability primarily concentrates on static factors such as geometric error and assembly error. Three primary causes of uncertainty in the motion control position of the feed system are identified: disturbance uncertainty, parameter uncertainty, and model uncertainty. Each component of uncertainty is thoroughly classified and studied based on the specific characteristics of the feed system. This study provides a comprehensive overview of the current datadriven control approaches used to address uncertainty in the feed system. It specifically focuses on the datadriven controller turning method, feedforward compensation method, and controller design method. The review concludes by discussing the latest research progress and limitations and suggests hopeful trends. Specifically, when conducting integrated modeling of the feed system, it is crucial to thoroughly account for the impact of stochastic processes. Additionally, the issue of switching between dynamic and static states in the dynamic model should be taken into consideration. Simultaneously, it is necessary to examine a more suitable approach that integrates the system model with the uncertainty characteristics to develop a more robust optimization strategy for controlling and efficiently managing the spread of position deviation in the feed system.