Nonlinear functional characterizations of uncertainty in model validation

Roy Smith, Geir Dullerud

Research output: Contribution to journalConference articlepeer-review


Model validation provides a useful means of assessing the ability of a model to account for a specific experimental observation, and has application to modeling, identification and fault detection. In this paper we consider a new approach to the linear fractional transformation (LFT) model validation problem by deploying quadratic functionals, and more generally nonlinear functionals, to specify noise and dynamical perturbation sets. Sufficient conditions for invalidation of such models are provided in terms of semidefinite programming problems.

Original languageEnglish (US)
Pages (from-to)151-156
Number of pages6
JournalIFAC Proceedings Volumes (IFAC-PapersOnline)
Issue number1
StatePublished - Jan 1 2002
Event15th World Congress of the International Federation of Automatic Control, 2002 - Barcelona, Spain
Duration: Jul 21 2002Jul 26 2002


  • Model validation
  • Nonlinear functionals
  • Semidefinite programming

ASJC Scopus subject areas

  • Control and Systems Engineering

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