A comparative study on Kalman filtering techniques designed for state estimation of industrial AC drive systems

In this paper, two different Kalman filtering techniques, unscented Kalman filter (UKF) and extended Kalman filter (EKF) are investigated and compared both experimentally and theoretically. These non-linear, stochastic observers are employed as a state estimation tool in field-oriented control (FOC) of sensorless AC drives in this work. Using the superiorities of Kalman filtering, rotor speed and dq-axis fluxes of an induction motor are estimated only with the sensed stator currents and voltages information. In order to compare the estimation performances of the observers explicitly, both of the observers are designed for the same motor model and run with the same covariance matrices under the same conditions. In the simulation results it is shown that, UKF, whose several intrinsic properties suggest its use over EKF in highly nonlinear systems, has more satisfactory rotor speed and flux estimates, which are the most critical states for FOC. These simulation results are supported with experimental results.