FAULT DETECTION FOR NONLINEAR SYSTEMS WITH UNKNOWN INPUT

被引：17
|
作者：
Luo, Zhen [1 ]
Fang, Huajing [1 ]
机构：
[1] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Peoples R China
来源：
ASIAN JOURNAL OF CONTROL | 2013年 / 15卷 / 05期
关键词：
UKF; unknown input; least-squares; fault detection; UNSCENTED KALMAN FILTER; MINIMUM-VARIANCE INPUT; STATE ESTIMATION; FAILURE-DETECTION; SENSOR; OPTIMALITY; DIAGNOSIS;
D O I：
10.1002/asjc.658
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper extends the problem of fault detection for linear discrete-time systems with unknown input to the nonlinear system. A nonlinear recursive filter is developed where the estimation of the state and the input are interconnected. Unknown input which can be any type of signal was obtained by least-squares unbiased estimation and the state estimation problem is transformed into a standard unscented Kalman filter (UKF) problem. By testing the mean of the innovation process, a real-time fault detection approach is proposed. Simulations are provided to demonstrate the effectiveness of the theoretical results.
引用
收藏
页码：1503 / 1509
页数：7
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