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

Tiantian Liang, Mao Wang and Zhenhua Zhou

Mathematical Problems in Engineering, 2017, vol. 2017, 1-9

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:5640309

DOI: 10.1155/2017/5640309

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