Genetic adaptive state estimation with missing input/output data

Abstract This paper presents the application of a genetic algorithm to state estimation for systems subject to randomly missing input/output data. By modelling the missing data in inputs and outputs with binomial processes, a modified Luenberger observer using the reconstructed data is employed for...

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Bibliographic Details
Published inProceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering Vol. 224; no. 5; pp. 611 - 617
Main Authors Fang, H, Wu, J, Shi, Y
Format Journal Article
LanguageEnglish
Published London, England SAGE Publications 01.08.2010
SAGE PUBLICATIONS, INC
Subjects
Adaptive algorithms
Autoregressive models
Autoregressive processes
Computer simulation
Genetic algorithms
Input output
Mathematical models
Mechanical engineering
Missing data
Parameter estimation
Preprocessing
Reconstruction
Simulation
State estimation
recursive algorithm
parameter estimation
genetic algorithms
missing input/output data
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Summary:Abstract This paper presents the application of a genetic algorithm to state estimation for systems subject to randomly missing input/output data. By modelling the missing data in inputs and outputs with binomial processes, a modified Luenberger observer using the reconstructed data is employed for estimation. The proposed algorithm consists of two procedures: 1) Preprocessing the missing input/output data by reconstruction based on autoregressive (AR) modelling; and 2) implementing the genetic algorithm to perform on-line adaptive state estimation from reconstructed data. The effectiveness of the proposed algorithm is verified by numerical simulation.
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ISSN:0959-6518
2041-3041
DOI:10.1243/09596518JSCE888