Conditional central algorithms for worst case set-membership identification and filtering

This paper deals with conditional central estimators in a set membership setting. The role and importance of these algorithms in identification and filtering is illustrated by showing that problems like worst case optimal identification and state filtering, in contexts in which disturbances are desc...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 45; no. 1; pp. 14 - 23
Main Authors Garulli, A., Vicino, A., Zappa, G.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2000
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Closed-form solution
Computer aided software engineering
Control system synthesis
Disturbances
Equations
Exact solutions
Filtering
Filtering algorithms
Filtration
Frequency
Mathematical analysis
Norms
Optimization
Parametric statistics
Polynomials
Robust control
Uncertainty
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ISSN0018-9286
1558-2523
DOI10.1109/9.827352

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Summary:This paper deals with conditional central estimators in a set membership setting. The role and importance of these algorithms in identification and filtering is illustrated by showing that problems like worst case optimal identification and state filtering, in contexts in which disturbances are described through norm bounds, are reducible to the computation of conditional central algorithms. The conditional Chebyshev center problem is solved for the case when energy norm-bounded disturbances are considered. A closed-form solution is obtained by finding the unique real root of a polynomial equation in a semi-infinite interval.
Bibliography:ObjectType-Article-2
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ISSN:0018-9286
1558-2523
DOI:10.1109/9.827352