Abstract
The accuracy of rotor position estimation determines the performance of the sensorless control system of a permanent magnet synchronous motor. In order to realize the accurate control of rotor position and speed, it is necessary to identify the motor parameters. Modeling and simulation of the state estimation are investigated for a permanent magnet synchronous motor with parameter identification based on the unscented Kalman filter (UKF) in this article. Based on the mathematical model of the motor, the unscented Kalman filter is used to identify the rotor flux and quadrature axis inductance simultaneously, and the identified parameters are used to update the motor model in the sensorless vector control algorithm. The simulation results show that the unscented Kalman filter can converge to the real value in a short time with small errors. It can follow the changes of motor parameters well and achieve high-precision speed and position estimation.
Highlights
Permanent magnet synchronous motor (PMSM) has been widely used in the fields of new energy vehicles, power generation, and servo drive due to its advantages of large starting torque, high operation efficiency, high power density, and low failure rate (Chen et al, 2014; Chen et al, 2019; Cui et al, 2020; Wang et al, 2021)
The parameter identification algorithm based on the unscented Kalman filter (UKF) is given
UKF-based parameter identification was considered for permanent magnet synchronous motor
Summary
Permanent magnet synchronous motor (PMSM) has been widely used in the fields of new energy vehicles, power generation, and servo drive due to its advantages of large starting torque, high operation efficiency, high power density, and low failure rate (Chen et al, 2014; Chen et al, 2019; Cui et al, 2020; Wang et al, 2021). The nonlinear mapping of these sampling point sets is directly carried out to eliminate the error caused by the linearization of extended Kalman filter (EKF) algorithm, which realizes the accurate estimation of rotor speed and position and accurately estimates the parameters of the motor It has the characteristics of simple method and good system stability and can effectively improve the control accuracy of the motor. (1) Based on the analysis of the mathematical model of PMSM in a static coordinate system, this article investigates a state observer with the unscented Kalman filter in the sensorless control of PMSM It estimates the speed and position of the motor and realizes the identification of motor inductance Ld and Lq and flux linkage ψf. Where x, u, and z are the state variables, control variables, and measurement variables, respectively. f (·) is the nonlinear function of the motor, and h (·) is the measurement matrix
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