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

Wassim M. Haddad, Vikram Kapila

Research output: Contribution to journalArticlepeer-review


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
Issue number2
StatePublished - 1997


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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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