Three-stage Kalman filter for state and fault estimation of linear stochastic systems with unknown inputs

被引：72

作者：

Ben Hmida, F. ^{[1
]}

Khemiri, K. ^{[1
]}

Ragot, J. ^{[2
]}

Gossa, M. ^{[1
]}

机构：

[1] ESSTT C3S, Tunis 1008, Tunisia

[2] CNRS, CRAN, UMR 7039, F-54516 Vandoeuvre Les Nancy, France

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2012年 / 349卷 / 07期

关键词：

BIAS;

D O I：

10.1016/j.jfranklin.2012.05.004

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 paper studies the problem of simultaneously estimating the state and the fault of linear stochastic discrete-time varying systems with unknown inputs. The fault and the unknown inputs affect both the state and the output. However, if the dynamical evolution models of the fault and the unknown inputs are available the filtering problem will be solved by the Optimal three-stage Kalman Filter (OThSKF). The OThSKF is obtained after decoupling the covariance matrices of the Augmented state Kalman Filter (ASKF) using a three-stage U-V transformation. Nevertheless, if the fault and the unknown inputs models are not perfectly known the Robust three-stage Kalman Filter (RThSKF) will be applied to give an unbiased minimum-variance estimation. Finally, a numerical example is given in order to illustrate the proposed filters. (c) 2012 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

引用

页码：2369 / 2388

页数：20

共 25 条

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