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Conditional central algorithms for worst case set-membership identification and filtering. (English) Zbl 0971.93072

This paper studies 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 prescribed through norm bounds, are reducible to the computation of conditional central algorithms. The conditional Chebyshev center problem is solved 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. Numerical examples are given and solved.

MSC:

93E10 Estimation and detection in stochastic control theory
93B30 System identification
93E11 Filtering in stochastic control theory
93C73 Perturbations in control/observation systems
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