Research On Control Method Of Permanent Magnet Synchronous Motor

In the motion device based on permanent magnet synchronous motor(PMSM),it is often necessary to make accurate control on the speed of permanent magnet synchronous motor.PMSM is a complex control system with high coupling and many variables.The accuracy of PMSM is easily affected by the changes of internal parameters of the motor.It is necessary to design a parameter identification system to transfer the identified parameters to the controller.For nonlinear system PMSM,it is difficult to use traditional control methods to make the system achieve ideal results,so it is necessary to design a control method with disturbance observation.The research content of this paper is divided into the following three parts:First,the mathematical model and coordinate transformation principle of permanent magnet synchronous motor are introduced,which is the basis of decoupling analysis.Then the vector control(VC)strategy based on i_d=0 and its design process are analyzed.Vector control is the basis of the following research on parameter identification and improved sliding mode control method in this paper.Secondly,this paper analyzes the reason why the internal parameters of permanent magnet synchronous motor change during the long time operation,and use the model reference adaptive system(MRAS)to identify the internal stator resistance R,inductance L and permanent magnet flux linkage ψ_f.The adaptive identification law of MRAS is deduced in detail.This paper points out the disadvantage of MRAS is that when the PMSM speed or load changes,the MRAS identification error increases or even cannot correctly identify the internal parameters.In view of this deficiency,the single neuron algorithm is integrated into the adaptive law design of the model reference adaptive identification algorithm to solve the identification error caused by the change of speed or load during the identification process.The simulation analysis of the improved algorithm shows that the error of PMSM electrical parameter identification results is less than 1.2%.The physical platform is built for experimental verification,and MRAS optimized by Neuron can accurately identify the electrical parameters of the motor,with the maximum error within 2.5%.Thirdly,the fractional calculus and sliding mode variable structure control algorithms are described,and the fractional non-singular terminal sliding mode control is adopted to solve the chattering problem of integer order sliding mode and the singularity brought by terminal sliding mode;At the same time,a nonlinear disturbance observer is designed to observe the load changes and other disturbances,and the feedforward compensation is applied to the speed controller.The nonlinear disturbance observer’s observation of load disturbance is simulated and analyzed.The disturbance value can be observed within 0.09s after the motor is started,and within 0.05s when the external load disturbance changes.The fractional nonsingular terminal sliding mode control with disturbance observation is simulated and analyzed.Compared with the fractional nonsingular terminal sliding mode control,the algorithm converges faster,and reaches the expected speed of the motor within 0.01s.it does not overshoot when the speed changes,and the average adjustment time is 0.01s;When the load changes,the fractional order sliding mode control has a maximum adjustment error of 40r/min,which is stabilized again within 0.01s.Due to the observation of disturbance and feedforward compensation by fractional nonsingular terminal sliding mode control with disturbance observation,the motor speed will not be affected when the load is changed.The feasibility of the PMSM algorithm is verified by simulation,and the precise adjustment of the motor is realized.