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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 5640309, 9 pages
https://doi.org/10.1155/2017/5640309
Research Article

State Estimation for Sampled-Data Descriptor Nonlinear System: A Strong Tracking Unscented Kalman Filter Approach

1School of Astronautics, Harbin Institute of Technology, West Dazhi Street, Harbin 150001, China
2Changzhou Vocational Institute of Light Industry, Changzhou, China

Correspondence should be addressed to Tiantian Liang; moc.361@2211naitnaitgnail

Received 15 January 2017; Revised 15 May 2017; Accepted 15 June 2017; Published 8 August 2017

Academic Editor: Mohammad D. Aliyu

Copyright © 2017 Tiantian Liang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper proposes a state estimation method for a sampled-data descriptor system by the Kalman filtering method. The sampled-data descriptor system is firstly discretized to obtain a discrete-time nonsingular model. Based on the discretized nonsingular system, a strong tracking unscented Kalman filter (STUKF) algorithm is designed for the state estimation. Then, a defined suboptimal fading factor is proposed and added to the prediction covariance for decreasing the weight of the prior knowledge on the conventional UKF filtering solution. Finally, a simulation example is given to show the effectiveness of the proposed method.