An LMI approach to model (in) validation of LPV systems

During the past few years efficient tools have been developed to robustly stabilize linear parameter varying (LPV) systems. However, a key issue that needs to be addressed in order to apply these techniques to practical problems is the development of techniques that, starting from experimental data, generate and validate suitable models. In this paper we propose a new model validation framework for LPV systems subject to unstructured uncertainty. The main result shows that the problem of establishing consistency between the experimental data and the a priori assumptions on the nominal model, the uncertainty description and the error bounds can be recast as an LMI feasibility problem and be efficiently solved. Moreover, the overall computational complexity is similar to that of validating LTI models of comparable size.