State Estimation for Non-linear Sampled-Data Descriptor Systems:A Robust Extended Kalman Filtering Approach  

作　　者：Mao Wang Tiantian Liang Zhenhua Zhou 

机构地区：[1]Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, China [2]Changzhou Vocational Institute of Light Industiy Technology, Changzhou 213000, Jiangsu, China

出　　处：《Journal of Harbin Institute of Technology(New Series)》2019年第5期24-31,共8页哈尔滨工业大学学报（英文版）

基　　金：Sponsored by the National Natural Science Foundation of China(Grant No.61021002)

摘　　要：This paper proposes a state estimation method for a class of norm bounded non linear sampled data descriptor systems using the Kalman filtering method. The descriptor model is firstly discretized to obtain a discrete time non singular one. Then a model of robust extended Kalman filter is proposed for the state estimation based on the discretized non linear non singular system. As parameters are introduced in for transforming descriptor systems into non singular ones there exist uncertainties in the state of the systems. To solve this problem an optimized upper bound is proposed so that the convergence of the estimation error co variance matrix is guaranteed in the paper. A simulating example is proposed to verify the validity of this method at last.This paper proposes a state estimation method for a class of norm-bounded non-linear sampled-data descriptor systems using the Kalman filtering method. The descriptor model is firstly discretized to obtain a discrete-time non-singular one. Then a model of robust extended Kalman filter is proposed for the state estimation based on the discretized non-linear non-singular system. As parameters are introduced in for transforming descriptor systems into non-singular ones, there exist uncertainties in the state of the systems. To solve this problem, an optimized upper-bound is proposed so that the convergence of the estimation error co-variance matrix is guaranteed in the paper. A simulating example is proposed to verify the validity of this method at last.

关 键 词：SAMPLED-DATA SYSTEM DESCRIPTOR SYSTEM state estimation KALMAN FILTERING REKF 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]