Title :
State estimation of induction motor using unscented Kalman filter
Author :
Akin, Bilal ; Orguner, Umut ; Ersak, Aydin
Author_Institution :
Dept. of Electr. & Elelctronics Eng., Middle East Tech. Univ., Ankara, Turkey
Abstract :
In this paper, a new estimation technique unscented Kalman filter (UKF) is applied to state observation in field oriented control (FOC) of induction motor. UKF, a recent derivative-free nonlinear estimation tool, is used for estimating rotor speed and fluxes using sensed stator current and voltages. In the simulations, UKF, whose several intrinsic properties suggest its use over EKF in highly nonlinear systems, turned out to be very similar to EKF in flux estimates. The simulation results also show that UKF has slightly better speed estimation performance than EKF while driven under the identical machine model and parameters (covariances).
Keywords :
Kalman filters; induction motors; machine vector control; nonlinear control systems; nonlinear estimation; observers; derivative free nonlinear estimation tool; extended Kalman filter; field oriented control; flux estimation; induction motor; nonlinear systems; rotor speed estimation; sensorless vector control; state estimation; state observer; unscented Kalman filter; Filtering; Induction machines; Induction motors; Noise measurement; Nonlinear equations; Nonlinear filters; Rotors; Size measurement; State estimation; Vectors;
Conference_Titel :
Control Applications, 2003. CCA 2003. Proceedings of 2003 IEEE Conference on
Print_ISBN :
0-7803-7729-X
DOI :
10.1109/CCA.2003.1223132