In 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
problem of obtaining a nominal model consistent with the experimental data and bounds on
the identification 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 …

ln 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
problem of obtaining a nominal model consistent with the experimental data and bounds on
the identification error can be recast as a constrained finite-dimensional convex optimization
problem that can be efiiciently solved using Linear Matrix Inequalities techniques. This
approach, based upon a generaliz-ed interpolation theory, contains as special cases the …