Data-Driven Analysis and Control of 2-D Fornasini-Marchesini Models

We study data-driven analysis and control of 2D Fornasini-Marchesini second models. We give necessary and sufficient conditions for the data to be informative for identification, and state a 2D “fundamental lemma”. We propose a data-driven approach for stability verification and state-feedback stabilization via LMIs.