Model invalidation in l1 using frequency-domain data

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

6 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)983-989
Journal / PublicationIEEE Transactions on Automatic Control
Volume49
Issue number6
Publication statusPublished - Jun 2004
Externally publishedYes

Abstract

In this note, we study the problem of invalidating uncertain models with an additive uncertainty. The problem is to check the existence of an uncertainty and a measurement noise which fit to the given model structure and the uncertainty/noise description, as well as the experimental data used for invalidation. We consider a mixed setting in which the uncertainty is characterized in time domain by the l1 induced system norm, while the available data are frequency response samples of the system. We show that this problem, which by formulation poses an infinite-dimensional primal optimization problem, can be solved in a dual, finite-dimensional space with finitely many constraints.

Research Area(s)

  • Duality, l1 norm, Linear programming, Model invalidation, Uncertain model