Speed and rotor position estimation for sensorless brushless DC motor drive based on particle filter.

This paper introduces a position sensorless operation of a brushless DC (BLDC) motor based on an efficient state estimation algorithm. In the proposed method, the speed and rotor position estimation is performed only by measuring the phase currents and line voltages. Estimated values are used to determine the commutation logic and speed controller. The line voltage difference is employed, which eliminates the need for neutral. The states estimation is based on the particle filter that doesn't have the limitations and drawbacks of the Extended Kalman Filter. First, there is no assumption for the probability density functions of the measurement and process noise, it works for Gaussian and non-Gaussian noises. Second, it is not based on any linearization and can efficiently estimate the states of nonlinear systems. Moreover, a suitable method is adopted for resampling step, which improves the estimation performance and does not increase the complexity. The simulation and experimental results demonstrate the effectiveness of the proposed sensorless method. The proposed method is more robust than other common techniques like EKF at different sampling times. The experimental results show the possibility of the BLDC motor states estimation with appropriate accuracy in steady-state and dynamic operation. [ABSTRACT FROM AUTHOR]