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

Wassim M. Haddad, Vikram Kapila

Research output: Contribution to journalArticle

Abstract

One of the most important problems in dynamic systems theory is to approximate a higher-order system model with a low-order, relatively simpler model. However, the nominal high-order model is never an exact representation of the true physical system. In this paper the problem of approximating an uncertain high-order system with constant real parameter uncertainty by a robust 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)
Pages (from-to)248-253
Number of pages6
JournalIEEE Transactions on Automatic Control
Volume42
Issue number2
DOIs
StatePublished - 1997

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System theory
Dynamical systems
Uncertainty

Keywords

  • Real parameter uncertainty
  • Reduced-order modeling
  • Uncertain systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Robust, reduced-order modeling for state-space systems via parameter-dependent bounding functions. / Haddad, Wassim M.; Kapila, Vikram.

In: IEEE Transactions on Automatic Control, Vol. 42, No. 2, 1997, p. 248-253.

Research output: Contribution to journalArticle

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