Research On Predictive Control Of Permanent Magnet Synchronous Motor Based On Parameter Identification

Permanent magnet synchronous motor(PMSM)is widely used in various fields such as motor drag and servo drive due to its excellent control and speed regulation performance.Among the various control strategies of motors,model predictive control(MPC)is receiving more and more attention,it can control multiple different kinds of variables at the same time,and it is convenient to impose constraints.However,predictive control strategy is a control strategy based on mathematical model,which has the disadvantages of large amount of calculation and sensitive to parameters.Aiming at the above problems,this paper makes an in-depth study on the predictive control of permanent magnet synchronous motor based on parameter identification.The main research contents are as follows:Firstly,according to the physical model of permanent magnet synchronous motor,after appropriate simplification,combined with the principle of conservation of magnetic potential and coordinate transformation matrix,the mathematical model of permanent magnet synchronous motor is established.The control strategies of common permanent magnet synchronous motors and the advantages and disadvantages of various control strategies are analyzed and explained.Synthesizing the mathematical model of the permanent magnet synchronous motor and the first-order Euler discrete law in the two-phase synchronous rotating coordinate system,the conventional motor predictive current control model is obtained.Aiming at the problems of conventional model predictive control with many switch states and large amount of calculation,an improved model predictive control strategy is proposed.This method reduces the candidate voltage vectors in the rolling optimization process of predictive control from 7 to 3,the calculation amount of the system is effectively reduced,and the sampling frequency of the system can be increased.The effectiveness of the improved predictive control strategy is verified by simulation and experiment,and the predictive control performance of the motor is improved.The parameters of the motor will change with the operation of the motor.The model predictive control strategy is based on the accurate motor mathematical model.When the motor model is inaccurate,it will adversely affect the predictive control performance of the motor.The influence of the changes of the motor parameters caused by the change of the operating conditions on the predictive control is analyzed.The results show that when the parameters of the motor mismatched,the tracking effect between the given value and the feedback value of the stator currents becomes worse in the steady-state operation of the motor.In this paper,the parameter identification algorithm is introduced to identify the motor parameters online,and the corresponding motor parameter values in the controller are corrected in real time to improve the current tracking effect.Among all the parameter identification algorithms,this paper selects the Adaline neural network system identification algorithm to identify the parameters of the permanent magnet synchronous motor based on predictive control,and synchronously modify and improve the corresponding motor parameters in the model predictive controller.Through simulation and experiment,the effectiveness of this method is proved,which can eliminate the static error of current tracking and improve the system current tracking performance.