Robust hypothesis testing for structured uncertainty models

Rangan Sundeep, Poolla Kameshwar

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Developing uncertainty models suitable for modern Tobust design methods involves numerous modeling decisions regarding uncertainty structure, noise models and uncertainty bounds. In this paper, we consider the problem of selecting between one of two candidate uncertainty models based on input-output data. Each uncertainty model consists of a nominal linear plant with a standard linear fractional transformation (LFT) uncertainty structure and Gaussian output noise. A classical statistical hypothesis testing performance measure is used to evaluate decision procedures. We derive a D-scaled upper bound on this performance measure, and show that this upper bound can be minimized by convex programming and H filtering techniques. In addition, a general robust hypothesis testing result is derived.

Original languageEnglish (US)
Title of host publicationProceedings of the 1998 American Control Conference, ACC 1998
Number of pages5
StatePublished - 1998
Event1998 American Control Conference, ACC 1998 - Philadelphia, PA, United States
Duration: Jun 24 1998Jun 26 1998

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other1998 American Control Conference, ACC 1998
Country/TerritoryUnited States
CityPhiladelphia, PA

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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