Robust, reduced-order modeling for state space systems via parameter-dependent bounding functions

Wassim M. Haddad, Vikram Kapila

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

Abstract

The problem of approximating a high-order system with constant real parameter uncertainty by a reduced-order model is considered. A parameter-dependent quadratic bounding function is developed that bounds the effect of uncertain real parameters on the model-reduction error. An Auxiliary Minimization Problem is formulated that minimizes an upper bound for the model-reduction error. The principal result is a necessary condition for solving the Auxiliary Minimization Problem which effectively provides sufficient conditions for characterizing robust reduced-order models.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
Pages4010-4014
Number of pages5
Volume6
StatePublished - 1995
EventProceedings of the 1995 American Control Conference. Part 1 (of 6) - Seattle, WA, USA
Duration: Jun 21 1995Jun 23 1995

Other

OtherProceedings of the 1995 American Control Conference. Part 1 (of 6)
CitySeattle, WA, USA
Period6/21/956/23/95

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ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Haddad, W. M., & Kapila, V. (1995). Robust, reduced-order modeling for state space systems via parameter-dependent bounding functions. In Proceedings of the American Control Conference (Vol. 6, pp. 4010-4014)