Identification with nonparametric uncertainty

The authors present an identification technique that is robust to nonparametric uncertainty (i.e., model mismatch). The identifier produces both a parameter set estimate and a frequency response set estimate. The estimates result from the inclusion of a model of the nonparametric uncertainty in the plant model. The frequency response set estimate is shown to always contain the frequency response of the plant as long as certain modeling conditions are met. This type of identifier would be useful in applications such as control where a property such as stability or performance level must be achieved in the face of low-order modeling and its associated nonparametric uncertainty.<<ETX>>

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