A novel dynamics model of ball-screw feed drives based on theoretical derivations and deep learning

Abstract High fidelity models of feed drive are critical factors to increase positioning accuracy and decrease contour error. To predict feed drives dynamics, this paper reports a novel method for modeling dynamics of feed drive by combining advantages of theoretical derivations and deep learning. First, the paper derives a rigid-flexible-combined dynamics model (RFCDM) for feed drive from classical dynamics theory. Then parameters identification of RFCDM is accomplished by referring product manuals and conducting constant velocity experiment with different feed rates. Continuous action reinforcement learning automata (CARLA) is adopted to tune all parameters of RFCDM simultaneously. A simulation error estimation model (SEEM) is applied to approximate simulation error between models simulation position and worktables actual position. The hybrid dynamics model (HDM) of feed drives which integrates RFCDM with SEEM is validated by experiments with various trajectories. Experimental results show that the gap between HDMs prediction position and worktables actual position is on the order of magnitude of 0.01 mm which is about 1/10 of the tracking error, indicating the HDM can predict the dynamics of feed drives with safe accuracy.