A b s t r a c tIn this paper we propose a new robust identification framework that combines both frequency and time-domain experimental data. The main result of the paper shows that the problems of establishing consistency of the data and of obtaining a nominal model and bounds on the identiiication error can be recast as a constrained finite-dimensional convex optimization problem that can be efficiently solved using Linear Matrix Inequalities techniques. This approach, based upon a generalized interpolation theory, contains as special cases the Carath6odory-Fejtr (purely time-domain) and Nevanlinna-Pick (purely frequency-domain) problems. The proposed procedure interpolates the fiequency and time domain experimental data while restricting the identified system to be in an U priori given class of models, resulting in a nominal model consistent with both sources of data. Thus, it is convergent and optimal up to a factor of 2 (with respect to central algorithms).
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