The Research On Sensorless Model Predictive Control Of Permanent Magnet Synchronous Motor

Permanent magnet synchronous motor(PMSM)uses permanent magnets as rotor material and no longer requires electrical excitation.It has outstanding advantages such as high efficiency,high torque density,and good dynamic response.It is very suitable for high-performance transmission systems and servo system.With the increase of application scenarios of permanent magnet synchronous motors,the requirements for its accuracy,efficiency,dynamic response and reliability are also getting higher and higher.Therefore,high-performance control methods of permanent magnet synchronous motor have become a hot research topic for domestic or foreign scholars.In this paper,research of sensorless control for permanent magnet synchronous motor is carried out,and a control scheme based on "extended Kalman filter and model predictive control" is proposed.The effectiveness of the proposed scheme is verified by experiments.The main research work of this paper includes:Firstly,the finite set model predictive control model of permanent magnet synchronous motor is established.The finite set model predictive current control is used to replace the conventional double closed-loop vector control,which improves the system response capability.A dual-vector model predictive control strategy is proposed,which reduces the error between the input voltage vector and the optimal voltage vector,and the steady-state performance of the system is improved.The action sequence between the dual vectors is optimized,which effectively reduces the extra switching times of the inverter switching elements brought about by the dual-vector strategy,thereby reducing the switch loss.A simulation experiment platform is built,and the simulation and experimental results verify the effectiveness of the above method.Secondly,based on the extended Kalman filter,an observer of speed and rotor position is designed,which effectively reduces the interference of measurement errors and model errors on the observation results,hence more accurate motor speed and rotor position information are obtained,and sensorless model predictive control of permanent magnet synchronous motor is realized.An adaptive adjustment method for the parameters of the covariance matrix is designed,which improves the accuracy and convergence speed of the extended Kalman filter observer.Finally,on the basis of theoretical derivation and simulation experiments,the hardware circuit and software algorithm of the control system are designed with the STM32F407ZET6 chip as the core,and the experimental platform of the permanent magnet synchronous motor control system is built to verify the the correctness and effectiveness of the control algorithm proposed in this paper.The experimental results show that the motor has good dynamic and steady-state performance under the control algorithm proposed in this paper,and the switching frequency of the inverter switching devices can be effectively reduced without much loss of steady-state performance.The innovations of this paper are as follows:(1)The action sequence of the dual-vector model predictive current control is optimized,hence the extra switching times of the switching elements in the inverter,which is caused by the second voltage vector,is reduced.(2)The steady-state and dynamic performance of the system under different covariance matrix parameters is analyzed by comparative simulation,and a novel adaptive adjustment method of covariance matrix parameters in extended Kalman filter is designed,the estimation accuracy of the observer under a large speed regulation range is improved,which effectively improves the response capability of the sensorless permanent magnet synchronous motor.The research in this paper is beneficial to promote the application of model predictive control algorithm in the field of permanent magnet synchronous motor control,and has theoretical and practical application value for the development of high-performance permanent magnet synchronous motors.