A Novel Techniques for Speed and Rotor Position Estimation of Brushless DC Motor with an Extended Kalman Filter by using Matlab Simulation

Abstract

This paper describes a method of speed and rotor position estimation of a brushless dc motor (BLDCM). The estimation of motor state variables is obtained by using an extended Kalman filter (EKF) technique by only using stator line voltages and currents. At first while estimation the voltage and current measuring signals are not filtered over here rather than other kinds of similar methods usually done. By combining measurements and calculations, the average value of voltage during sampling intervals is obtained owing to the application of predictive current controller which is based on the mathematical model of motor. The parameters considered for the estimation algorithm consist of two parts, one consist of speed and rotor position are estimated with constant motor parameters and other the stator resistance is estimated simultaneously with motor state variables. From the results it is seen that it's quite possible for BLDCM to estimate speed and rotor position with sufficient accuracy in both steady state and dynamic operation. By the introducing of stator resistance estimation, the accuracy is increased especially working at low speeds. Keywords-Brushless dc motor, digital signal processor, extended Kalman filter, predictive current controller, speed and rotor position estimation.