Direct Adaptive Control of Neural Network of Magnetic Levitation System of CNC Machine Tool Linear Synchronous Motor

doi:10.3901/JME.2021.17.236

Abstract

Abstract: Aiming at the problems of strong nonlinear and uncertainty of external disturbance of the controllable excitation linear synchronous motor magnetic levitation system of CNC machine tools, a direct adaptive controller based on RBF neural network is designed. By analyzing the operating mechanism of the magnetic levitation system, the motion equation and levitation force equation are derived, and then the state equation of the system is established. The error function is constructed by the tracking error of the levitation height and the variation of the error. The direct adaptive ideal controller is designed and approximated by RBF neural network; An adaptive law is designed to estimate the ideal weight of neural network. The quadratic Lyapunov function is constructed for the change rate of error function, and the stability of the system is proved by Lyapunov stability theory. The control system is simulated by Matlab, and the results show that compared with the adaptive fuzzy sliding mode controller and PID controller, the adjusting time of no-load starting is reduced by 23.5%, when the load is suddenly applied, the dynamic descent is reduced by 64.7% and the recovery time is reduced by 38.2%, it has the advantages of small steady-state error, short adjustment time and recovery time, strong disturbance immunity, and the control performance of the magnetic levitation system can be effectively improved.

Key words: CNC machine tools, linear synchronous motor, magnetic levitation system, RBF neural network, direct adaptive control

CLC Number:

TM383

LAN Yipeng, YAO Wanting, YANG Wenkang, LEI Cheng. Direct Adaptive Control of Neural Network of Magnetic Levitation System of CNC Machine Tool Linear Synchronous Motor[J]. Journal of Mechanical Engineering, 2021, 57(17): 236-242.

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