Framework for state and unknown input estimation of linear time-varying systems

被引:47
|
作者
Lu, Peng [1 ]
van Kampen, Erik-Jan [1 ]
de Visser, Cornelis C. [1 ]
Chu, Qiping [1 ]
机构
[1] Delft Univ Technol, Kluyverweg 1, NL-2629 HS Delft, Netherlands
关键词
Kalman filtering; State estimation; Unknown input filtering; Fault estimation; Double-Model Adaptive Estimation; MINIMUM-VARIANCE ESTIMATION; FAULT-DIAGNOSIS; FILTER;
D O I
10.1016/j.automatica.2016.07.009
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The design of unknown-input decoupled observers and filters requires the assumption of an existence condition in the literature. This paper addresses an unknown input filtering problem where the existence condition is not satisfied. Instead of designing a traditional unknown input decoupled filter, a Double Model Adaptive Estimation approach is extended to solve the unknown input filtering problem. It is proved that the state and the unknown inputs can be estimated and decoupled using the extended Double Model Adaptive Estimation approach without satisfying the existence condition. Numerical examples are presented in which the performance of the proposed approach is compared to methods from literature. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:145 / 154
页数:10
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