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Desensitized divided difference filtering for induction motor state estimation

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2 Author(s)
Karlgaard, C.D. ; Anal. Mech. Assoc., Inc., Hampton, VA, USA ; Shen, H.

This paper develops a robust divided difference filtering approach based on the concept of Desensitized Kalman Filtering, for use in induction motor state estimation. In this approach, reduced-order filters can be developed that are insensitive to parameter uncertainties. The filters are formulated using a minimum variance cost function, augmented with a penalty function consisting of a weighted norm of the state sensitivities. Solutions are provided for first and second-order divided difference filters. The proposed algorithms are demonstrated using Monte-Carlo simulation techniques.

Published in:

System Theory (SSST), 2012 44th Southeastern Symposium on

Date of Conference:

11-13 March 2012