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Mixed time/frequency domain based robust identification


P.A. Parrilo

Sweden, Control and Communication Division, Linköping University.

We present a robust identification framework that combines simultaneously frequency and time-domain experimental data. We show that establishing consistency of the data and obtaining a nominal model and bounds on the identification error can be recast as a constrained finite-dimensional convex optimization problem that can be efficiently solved using semidefinite programming techniques. This approach, based upon a generalized interpolation theory, contains as special cases the Caratheodory-Fejer (purely time-domain) and Nevanlinna-Pick (purely frequency-domain) problems. The proposed procedure interpolates the frequency and time domain experimental data while restricting the identified system to be in an a priori given class of models, resulting in a nominal model consistent with both sources of data.

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