Identification and Validation/Update for Smallest Model Sets of Transfer Functions with Parametric Uncertainties.

概要

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In this paper, we consider an identification and a validation/update problem for a special class of model sets, called the Smallest Model Set. The sets consists of SISO discrete-time transfer functions with time-varying parametric uncertainties measured by the weighted ∞-norm. We will analyze the formulated problems by using a concept of a distance in a parameter space. The identification and update problems are shown to be reduced to a convex optimization problem, and the validation problem becomes a comparison of distances. In particular, the update problem is shown to have an explicit solution in a special case. Based on the results, we propose a validation/update algorithm in an off- and an on-line case, respectively.
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