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

  • Youqing Wang
  • , Dong Zhao
  • , Yueyang Li
  • , Steven X. Ding*
  • *Corresponding author for this work
  • Shandong University of Science and Technology
  • Beijing University of Chemical Technology
  • University of Jinan
  • University of Duisburg-Essen

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original languageEnglish
Article number7907208
Pages (from-to)5463-5469
Number of pages7
JournalIEEE Transactions on Automatic Control
Volume62
Issue number10
DOIs
StatePublished - Oct 2017
Externally publishedYes

Keywords

  • Fault and state estimation
  • Kalman filter
  • minimum variance
  • time-varying system
  • two-dimensional system

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