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Convergence Analysis of Extended Kalman Filter for Sensorless Control of Induction Motor | IEEE Journals & Magazine | IEEE Xplore

Convergence Analysis of Extended Kalman Filter for Sensorless Control of Induction Motor


Abstract:

This paper deals with convergence analysis of the extended Kalman filters (EKFs) for sensorless motion control systems with induction motor (IM). An EKF is tuned accordin...Show More

Abstract:

This paper deals with convergence analysis of the extended Kalman filters (EKFs) for sensorless motion control systems with induction motor (IM). An EKF is tuned according to a six-order discrete-time model of the IM, affected by system and measurement noises, obtained by applying a first-order Euler discretization to a six-order continuous-time model. Some properties of the discrete-time model have been explored. Among these properties, the observability property is relevant, which leads to conditions that can be directly linked with the working conditions of the machine. Starting from these properties, the convergence of the stochastic state estimation process, in mean square sense, has been shown. The convergence is also explored with reference to the difference between the samples of the state of the continuous-time model and that estimated by the EKF. The results theoretically achieved have been also validated by means of experimental tests carried out on an IM prototype.
Published in: IEEE Transactions on Industrial Electronics ( Volume: 62, Issue: 4, April 2015)
Page(s): 2341 - 2352
Date of Publication: 05 September 2014

ISSN Information:


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