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Optimal guaranteed cost filtering for uncertain discrete‐time linear systems
Author(s) -
Petersen Ian R.,
McFarlane Duncan C.
Publication year - 1996
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/(sici)1099-1239(199605)6:4<267::aid-rnc232>3.0.co;2-3
Subject(s) - estimator , kalman filter , control theory (sociology) , state estimator , state (computer science) , bounded function , mathematics , covariance , mathematical optimization , linear system , quadratic equation , discrete time and continuous time , state vector , upper and lower bounds , computer science , algorithm , statistics , control (management) , mathematical analysis , physics , geometry , classical mechanics , artificial intelligence
This paper presents a result on the design of a steady‐state robust state estimator for a class of uncertain discrete‐time linear systems with normal bounded uncertainty. This result extends the steady state Kalman filter to the case in which the underlying system is uncertain. A procedure is given for the construction of a state estimator which minimizes a bound on the state error covariance. It is shown that this leads to a state estimator which is optimal with respect to a notion of quadratic guaranteed cost state estimation.