Desensitized divided difference filtering for induction motor state estimation

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.