A new motor speed estimator using Kalman filter in low-speed range

Abstract

In this paper, a new machine drive technique using novel estimation strategy for the very low-speed operation to estimate both the instantaneous speed and disturbance load torque is proposed. In the proposed algorithm, a Kalman filter is incorporated to estimate both the motor speed and the disturbance torque. The Kalman filter is an optimal state estimator and is usually applied to a dynamic system that involves a random noise environment. The effects of parameter variations are discussed, and it is verified that the system is stable to the modeling error. Experimental results confirm the validity of the proposed estimation technique.