Learning controllers for nonlinear systems from data

Abstract

This article provides an overview of a new approach to designing controllers for nonlinear systems using data-driven control. Data-driven control is an important area of research in control theory, and this novel method offers several benefits. It can recreate from a data-centred perspective many of the results available in the model-based case, including local stabilization based on Taylor or polynomial expansion, absolute stabilization, as well as approximate and exact feedback linearization. Moreover, the method is analytically and computationally simple, and permits to infer regions of attraction and invariant sets, also when the data are corrupted by noise.