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

Abstract EN

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.