Data-based regional stabilization of linear systems with saturating actuators

Data-based asymptotic stabilization of unknown discrete-time linear systems with saturating actuators is addressed. The proposed approach relies on an explicit sector-bounded representation of the closed-loop system built from noisy state-input data. Relying on this novel representation, sufficient conditions in the form of linear matrix inequalities are established to ensure closed-loop regional asymptotic stability. These conditions are used to formulate a convex optimal controller design that allows one to maximize the region of attraction.Two numerical examples illustrate the applicability of the proposed results.