Title :
Sensorless Permanent Magnet Synchronous Motor drive using an optimized and normalized Extended Kalman filter
Author :
Tang Ming ; Gao Lin ; Liang Deliang
Author_Institution :
Sch. of Electr. Eng., Xian Jiao Tong Univ., Xianning, China
Abstract :
In this paper, normalised state vectors are used to demonstrate the state equations of Extended Kalman Filter (EKF) based sensorless Permanent Magnet Synchronous Motor (PMSM) drive. Based on the normalised EKF equations, Simple Genetic Algorithm (SGA) is employed to optimize the noise covariance and weight matrices of EKF parameters, which thereby reduces the parameter adjusting time and ensures stability of filters in estimations of position and speed. The simulations for SGA training are carried out by MATLAB/Simulink. The experimental sensorless drive system employing Field Oriented Control (FOC) method and SGA is implemented on STM32F103. The simulating and experimental results indicate the effectiveness of the proposed method.
Keywords :
Kalman filters; genetic algorithms; machine control; permanent magnet motors; synchronous motor drives; MATLAB; STM32F103; Simulink; extended Kalman filter; field oriented control; genetic algorithm; normalised state vectors; position estimations; sensorless permanent magnet synchronous motor drive; speed estimations; state equations; Biological cells; Covariance matrix; Genetic algorithms; Kalman filters; Mathematical model; Rotors; Vectors;
Conference_Titel :
Electrical Machines and Systems (ICEMS), 2011 International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4577-1044-5
DOI :
10.1109/ICEMS.2011.6073880
