Finding an LFT uncertainty model with minimal uncertainty

A4 Konferenspublikationer

Interna författare/redaktörer

Publikationens författare: Kurt E. Häggblom
Publiceringsår: 2013
Förläggare: IEEE
Moderpublikationens namn: 2013 European Control Conference (ECC)
Artikelns första sida, sidnummer: 1107
Artikelns sista sida, sidnummer: 1113
ISBN: 978-3-9524173-4-8
eISBN: 978-3-033-03962-9


In this paper, we present a procedure for finding the best LFT
uncertainty model by minimizing the H-infinity norm of the uncertainty
set with respect to a nominal model subject to known input-output data.
The main problem is how to express the data-matching constraints for
convenient use in the optimization problem. For some uncertainty
structures, they can readily be formulated as a set of linear matrix
inequalities (LMIs), for some other structures, LMIs are obtained after
certain transformations. There are also cases, when the constraints
result in bilinear matrix inequalities (BMIs), which can be linearized
to enable an efficient iterative solution. Essentially all LFT
uncertainty structures are considered. An application to distillation
modeling is included.


Data models, Linear matrix inequalities, Mathematical model, Optimization, Transfer functions, Uncertainty


Senast uppdaterad 2020-05-04 vid 05:05