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Enhancement of Sensorless Induction Motor Drive Performance by Using Unscented Kalman Filter

D. Sleeva Reddy*, K. Ankalamma**, M. Vijaya Kumar***

* Professor & Head, Department of EEE, Loyola Institute of Technology and Management, Dhulipalle, Guntur, A.P., India.

**-*** J.N.T.U.A College of Engineering, Anantapur, A.P, India.

Periodicity:November - January'2013
DOI : https://doi.org/10.26634/jes.1.4.2106

Abstract

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.

Keywords

Unscented Transformation,Unscented Kalman Filter,State Estimation

How to Cite this Article?

Reddy,S.D., Ankalamma.K., and Kumar,V.M. (2013). Enhancement of Sensorless Induction Motor Drive Performance by Using Unscented Kalman Filter. i-manager’s Journal on Embedded Systems, 1(4), 1-8. https://doi.org/10.26634/jes.1.4.2106

References

[1]. C. Lascu, I. Boldea, & F. Blaabjerg, (2009). “A class of speed -sensor less sliding-mode observers for high -performance induction motor drives,” IEEE Trans. Industrial Electronics, Vol. 56, No. 9, Sep., pp. 3394-3403.

[2]. E. Wan & R. vander Merwe. (2001). The Unscented Kalman Filter. Wiley publishing.

[3] Arthur Gelb. (1974). Applied Optimal Estimation. M.I.T.Press.

[4]. Tine Lefebvre and Herman Bruyninckx. Kalman Filter for Nonlinear systems: A comparison of performance.

[5]. S. Julier, J. Uhlmann & H.F. Durrant-Whyte, (2006). 'Simple derivative free non linear state observer for sensorless ac drives”,Vol.11, No:5, pp.634-643, Oct.

[6]. J.K. Uhlmann, (1994). “Simultaneous map building and localization for real time applications”, Transfer thesis, Univ. Oxford, U.K.

[7]. H.J. Kushner, (1967). “Dynamical equations for optimum nonlinear filtering”,, J.Different Equations, Vol.3, pp.179-190.

[8]. D. Simon (2006). Optimal state estimation: Kalman, H_, and nonlinear approaches, John Wiley & Sons.

[9]. E. S. Santana, E. Bim and W. C. Amaral, (2008). “A predictive algorithm for controlling speed and rotor flux of induction motor,” IEEE Trans. Industrial Electronics, Vol. 55, No. 2, pp..4398-4407, Dec.

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