Exact recursive updating of state uncertainty sets for linear SISO systems

This paper addresses the classical problem of determining the set of possible states of a linear discrete-time SISO system subject to bounded disturbances, from measurements corrupted by bounded noise. These so-called uncertainty sets evolve with time as new measurements become available. We present...

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Bibliographic Details
Published inAutomatica (Oxford) Vol. 95; pp. 33 - 43
Main Authors Hill, Robin, Luo, Yousong, Schwerdtfeger, Uwe
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.09.2018
Subjects
Duality
Estimation
Linear programming
Linear systems
Uncertainty
Linear systems
Linear programming
Uncertainty
Duality
Estimation
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Summary:This paper addresses the classical problem of determining the set of possible states of a linear discrete-time SISO system subject to bounded disturbances, from measurements corrupted by bounded noise. These so-called uncertainty sets evolve with time as new measurements become available. We present two theorems which give a complete description of the relationship between uncertainty sets at two successive time instants, and this yields an efficient algorithm for recursively updating uncertainty sets. Numerical simulations demonstrate performance improvements over existing exact methods.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2018.05.010