Unbiased Minimum Variance Fault and State Estimation for Linear Discrete Time-Varying Two-Dimensional Systems

The fault and state estimation problem is addressed for a class of linear discrete time-varying two-dimensional systems subject to state and measurement noises. Two estimators are proposed to compute the estimation of the system state and/or fault recursively, both of which are unbiased with minimum...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 62; no. 10; pp. 5463 - 5469
Main Authors Youqing Wang, Dong Zhao, Yueyang Li, Ding, Steven X.
Format Journal Article
LanguageEnglish
Published IEEE 01.10.2017
Subjects
Fault and state estimation
Kalman filter
Kalman filters
Mathematical model
minimum variance
Noise measurement
State estimation
time-varying system
Time-varying systems
Two dimensional displays
two-dimensional system
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Summary:The fault and state estimation problem is addressed for a class of linear discrete time-varying two-dimensional systems subject to state and measurement noises. Two estimators are proposed to compute the estimation of the system state and/or fault recursively, both of which are unbiased with minimum variance. Through formulating the estimation problem as the solvability problem of the corresponding matrix equations of estimator gains and system constraint, the necessary and sufficient condition of the existence and the solution for the proposed estimators are given. An example is used to demonstrate the effectiveness of the proposed estimators.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2017.2697210