State Estimation of Induction Motor Drives Using the Unscented Kalman Filter

This paper investigates the application, design, and
implementation of unscented Kalman filters (KFs) (UKFs) for
induction motor (IM) sensorless drives. UKFs use nonlinear unscented
transforms (UTs) 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 KFs (EKFs) 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. Experimental results for
a direct-torque-controlled IM drive are presented and compared
with the EKF. The focus of this study is on low-speed performance.
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.