Affiliations: [a]
College of Information Technology, Shanghai Jianqiao University, Shanghai, China
| [b]
School of Health Management, Shanghai Jianqiao University, Shanghai, China
| [c]
College of Humanities, Hebei Oriental University, Langfang, China
| [d]
School of Mathematics and Computer Science, Yan’an University, Yan’an, China
Correspondence:
[*]
Corresponding author. Yi Liu, School of Mathematics and Computer Science, Yan’an University, Yan’an, 716000, China. E-mail: [email protected].
Abstract: Acceleration and deceleration control, as one of the key technologies in high-speed CNC system, directly affects the machining efficiency, the stability of machining process and the error of machining follow. Therefore, it is necessary to study and explore new acceleration and deceleration control methods in high-speed CNC system to ensure the smooth feed and improve the machining accuracy. Therefore, the acceleration and deceleration control algorithm of NC system based on deep reinforcement learning and single chip microcomputer is studied. The theoretical basis of deep reinforcement learning is analyzed, and the acceleration of acceleration and deceleration is calculated based on the linear acceleration and deceleration control. The whole integer operation of single chip microcomputer is used to estimate the value range of each step. The variation characteristics of trajectory motion are predicted so that acceleration and deceleration can be processed across program segments. The velocity of transfer point is calculated by the rate of change of feed velocity vector, and the speed of multi-program is smoothed by adjusting the allowable contour error. Based on the proximal strategy optimization algorithm in deep reinforcement learning, the acceleration and deceleration control model of CNC system is established to realize the acceleration and deceleration control. The experimental results show that the proposed algorithm has better control effect, shorter time and smaller interpolation error, which can ensure the NC system to feed smoothly at high speed.
Keywords: Deep reinforcement learning, single chip microcomputer, near-end strategy optimization algorithm, CNC system, acceleration and deceleration control, Smooth processing
