Finding an LFT uncertainty model with minimal uncertainty

A4 Conference proceedings

Internal Authors/Editors

Publication Details

List of Authors: Kurt E. Häggblom
Publication year: 2013
Publisher: IEEE
Book title: 2013 European Control Conference (ECC)
Start page: 1107
End page: 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


Last updated on 2020-03-04 at 07:18