Data-Driven Stabilization of Aperiodic Sampled-Data Systems Subject to Input Saturation

This article focuses on stabilization design for aperiodic sampled-data unknown systems subject to saturation from a data-driven perspective without explicit system model parameters. A data-driven representation of the unknown saturated system is established by using the collected input-state measurements affected by noisy perturbations. Based on such a data-based representation, combined with the loop-functional method, the generalized sector condition, and the S-procedure, a data-driven control design method in the form of linear matrix inequalities is derived to ensure local stability for all systems consistent with the measured data. Meanwhile, this data-based design condition allows for maximizing the estimation of the region of attraction and maximizing the admissible sampling interval through convex optimization. Finally, the effectiveness of the developed data-driven control design schemes is verified by a benchmark numerical example and a load frequency control system under the hardware-in-the-loop experimental platform.