Design and implementation of the extended Kalman filter for thespeed and rotor position estimation of brushless DC motor

A method for speed and rotor position estimation of a brushless DC motor (BLDCM) is presented in this paper. An extended Kalman filter (EKF) is employed to estimate the motor state variables by only using measurements of the stator fine voltages and currents. When applying the EKF, it was necessary to solve some specific problems related to the voltage and current waveforms of the BLDCM. During the estimation procedure, the voltage- and current-measuring signals are not filtered, which is otherwise usually done when applying similar methods. The voltage average value during the sampling interval is obtained by combining measurements and calculations, owing to the application of the predictive current controller which is based on the mathematical model of motor. Two variants of the estimation algorithm are considered: (1) speed and rotor position are estimated with constant motor parameters and (2) the stator resistance is estimated simultaneously with motor state variables. In order to verify the estimation results, the laboratory setup has been constructed using a motor with ratings of 1.5 kW, 2000 r/min, fed by an insulated gate bipolar transistor inverter. The speed and current controls, as well as the estimation algorithm, have been implemented by a digital signal processor (TMS320C50). The experimental results show that is possible to estimate the speed and rotor position of the BLDCM with sufficient accuracy in both steady-state and dynamic operation. Introducing the estimation of the stator resistance, the speed estimation accuracy is increased, particularly at low speeds. At the end of the paper, the characteristics of the sensorless drive are analyzed. A sensorless speed control system has been achieved with maximum steady-state error between reference and actual motor speed of ±1% at speeds above 5% of the rated value