Garulli, Andrea; Vicino, Antonio; Zappa, Giovanni Conditional central algorithms for worst case set-membership identification and filtering. (English) Zbl 0971.93072 IEEE Trans. Autom. Control 45, No. 1, 14-23 (2000). 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. Reviewer: Yves Cherruault (Paris) Cited in 16 Documents MSC: 93E10 Estimation and detection in stochastic control theory 93B30 System identification 93E11 Filtering in stochastic control theory 93C73 Perturbations in control/observation systems Keywords:bounded disturbances; conditional central estimators; set membership setting; identification; filtering; worst case optimal identification PDFBibTeX XMLCite \textit{A. Garulli} et al., IEEE Trans. Autom. Control 45, No. 1, 14--23 (2000; Zbl 0971.93072) Full Text: DOI