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

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2 Author(s)
Haddad, M.M. ; Sch. of Aerosp. Eng., Georgia Inst. of Technol., Atlanta, GA, USA ; Kapila, V.

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

Published in:

Automatic Control, IEEE Transactions on  (Volume:42 ,  Issue: 2 )