Conditional central algorithms for worst-case estimation and filtering

This paper deals with conditional central algorithms in a worst-case setting. The role and importance of these algorithms in identification and filtering is illustrated by showing that problems like /spl Hscr//sub 2/ optimal identification and state filtering, in contexts where disturbances are desc...

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Bibliographic Details
Published inProceedings of the 36th IEEE Conference on Decision and Control Vol. 3; pp. 2453 - 2458 vol.3
Main Authors Garulli, A., Vicino, A., Zappa, G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Closed-form solution
Electronic mail
Ellipsoids
Energy measurement
Equations
Filtering algorithms
Noise measurement
Polynomials
Upper bound
Vectors
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ISBN0780341872
9780780341876
ISSN0191-2216
DOI10.1109/CDC.1997.657524

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Summary:This paper deals with conditional central algorithms in a worst-case setting. The role and importance of these algorithms in identification and filtering is illustrated by showing that problems like /spl Hscr//sub 2/ optimal identification and state filtering, in contexts where disturbances are described through norm bounds, are reducible to the computation of conditional central algorithms. The solution of the conditional Chebichev center problem is completely characterized for the case when energy norm bounded disturbances are considered. A closed form solution is obtained in terms of finding the unique real root of a polynomial equation.
ISBN:0780341872
9780780341876
ISSN:0191-2216
DOI:10.1109/CDC.1997.657524