Sensorless Control Of Permanent Magnet Synchronous Motor Based On Strong Tracking EKF

Permanent magnet synchronous motors(PMSMs)have become one of the key research objects in the field of motor research,especially in recent years,the industrial layout of new energy vehicles has further led to the rapid development of permanent magnet synchronous motors.For example,most domestic new energy vehicles use permanent magnet synchronous motors because they have the advantages of small size,compactness,high efficiency,easier control,high power density,and wide speed range.This article studies the sensorless technology of PMSM,using the extended Kalman filter algorithm to predict the speed and rotor position of the motor,and further studies the optimization of the extended Kalman filter to improve estimation accuracy,as well as the use of load torque feedforward compensation strategy to reduce the impact of disturbances.Firstly,the dissertation introduces the research status of permanent magnet synchronous motors and extended Kalman filters,which are the research directions of this topic.Then,familiarize yourself with the structure and principles of permanent magnet synchronous motors,and combine them with different coordinate systems to establish mathematical models of PMSM in different coordinate systems based on vector transformation.Then,understand the control method of zero direct axis current in vector control,and explain the principle of space vector pulse width modulation(SVPWM).Secondly,an introduction is made to the extended Kalman filtering principle used in this article,with a focus on explaining the prediction and correction stages of the algorithm.Understanding how the extended Kalman filtering algorithm estimates the speed and rotor angle of the motor.Afterwards,establish simulation models for each module in Matlab/Simulink and integrate them.Next,verify the estimation performance of speed and rotor position for the built simulation model,as well as the impact of motor parameters on the speed performance of EKF.The simulation results show that the EKF filtering algorithm established by the S-function can predict the speed and rotor position of the motor,but there may be slight errors.Changing the parameters of the motor can also affect the prediction results of EKF.When adding a load to the motor,it will have an impact on the speed of the motor and it will take some adjustment time to reach steady state again.Thirdly,in response to the problem of prediction errors in the speed and rotor position of the motor caused by EKF,this paper introduces a parameter innovation to adjust the prior error covariance matrix of the EKF filtering algorithm,thereby optimizing the Kalman gain matrix of the correction process,strengthening the role of real-time measurement data,and achieving the goal of reducing errors.After the motor enters steady state,sudden changes in motor load will affect the motor speed.In order to improve the anti-interference ability of the motor during steady state operation,this article proposes introducing load torque into the current loop.When the load changes,the motor’s current also changes with the load.After passing through the current loop PI regulator,the output torque of the motor changes,which can improve the system’s anti-interference ability.Finally,based on the STM32F103RBT6 control chip,an experimental platform was constructed through hardware and software design to verify that the strong tracking EKF algorithm proposed in this paper can track and predict the speed and rotor position of PMSM.The experimental results show that the strong tracking EKF algorithm is effective for the entire PMSM control system.