This paper investigates the application of Unscented Kalman
Filter (UKF) for Induction Motor (IM) sensorless drives. UKF's use nonlinear
Unscented Transforms (UT) in the prediction step in order to preserve the
stochastic characteristics of a nonlinear system. The advantage of using Uts is
their ability to capture the nonlinear behavior of the system, unlike extended
Kalman filters that use linearized models. Four original variants of the UKF
for IM state estimation, based on different UTs are described, analyzed, and compared.
The four transforms are basic, general, simplex, and spherical UTs. This paper
discusses the theoretical aspects and implementation details of the four UKFs.
It is concluded that the UKF is a viable and powerful tool for IM state
estimation and that basic and general UTs give more accurate results than
simplex and spherical UTs.