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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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Published in: | IEEE transactions on automatic control 2000-01, Vol.45 (1), p.14-23 |
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container_title | IEEE transactions on automatic control |
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description | 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. |
doi_str_mv | 10.1109/9.827352 |
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source | IEEE Electronic Library (IEL) Journals |
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 |
title | Conditional central algorithms for worst case set-membership identification and filtering |
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