Sensorless Permanent Magnet Synchronous Motor drive using an optimized and normalized Extended Kalman filter

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...

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Bibliographic Details
Main Authors: Tang Ming, Gao Lin, Liang Deliang
Format: Conference Proceeding
Language:English
Subjects:
Biological cells
Covariance matrix
Genetic algorithms
Kalman filters
Mathematical model
Rotors
Vectors
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Summary: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.
DOI:10.1109/ICEMS.2011.6073880