Extremum Seeking-based Parametric Identification for Partial Differential Equations

In this paper we present some results on partial differential equations (PDEs) parametric identification. We follow a deterministic approach and formulate the identification problem as an optimization with respect to unknown parameters of the PDE. We use proper orthogonal decomposition (POD) model reduction theory together with a model free multiparametric
extremum seeking (MES) approach, to solve the identification problem. Finally, the well known Burgers' equation test-bed is used to validate our approach.