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.