摘要：AbstractModel reduction of large scale systems is an actively researched area of modelling and control. The problem is more involved if uncertainties are also present and a computational tractable nominal model is needed for the design. Based on results of the Kolmogorov n-width theory the paper provides useful bounds for the worst case approximation error - both H2and H∞– in terms of the hyperbolic distance related to the sets of uncertain poles. A related model reduction strategy that uses only this a priori pole information is also proposed. The method is illustrated through numerical examples.
关键词：KeywordsIdentification for controlFrequency domain identificationNonparametric methods
