Convex computation of regions of attraction from data using Sums-of-Squares programming

Abstract

International audienceThis paper focuses on the analysis of the \ac{RoA} for unknown autonomous dynamical systems. A data-driven approach based on the moment-\ac{SoS} hierarchy is proposed, enabling novel \ac{RoA} outer approximations despite the reduced information on the dynamics. The main contribution consists of bypassing the system model and, hence, the recurring constraint on its polynomial structure. Numerical experiments showcase the influence of data on learned approximating sets, highlighting the potential of this method