@inbook{cdc9e3fd2970484fac45b75c598118d3,
title = "Data-Driven Stabilization of Nonlinear Systems via Taylor{\textquoteright}s Expansion",
abstract = "Lyapunov{\textquoteright}s indirect methodLyapunovindirect method is one of the oldest and most popular approaches to model-based controller design for nonlinear systemsNonlinearsystem. When the explicit model of the nonlinear systemNonlinearsystem is unavailable for designing such a linear controller, finite-length off-line data is used to obtain a data-based representation of the closed-loop system, and a data-driven linear control law is designed to render the considered equilibrium locally asymptotically stable. This work presents a systematic approach for data-driven linear stabilizer design for continuous-time and discrete-time general nonlinear systemsNonlinearsystem. Moreover, under mild conditions on the nonlinear dynamics, we show that the region of attractionRegion of attraction of the resulting locally asymptotically stable closed-loop system can be estimated using data.",
keywords = "Data-driven control, Lyapunov stability, Robust control design, Semidefinite programming, Stability of nonlinear systems, Sum-of-square optimization",
author = "Meichen Guo and {De Persis}, Claudio and Pietro Tesi",
year = "2024",
doi = "10.1007/978-3-031-49555-7_12",
language = "English",
isbn = "978-3-031-49554-0",
series = "Lecture Notes in Control and Information Sciences",
publisher = "Springer",
pages = "273--299",
editor = "Postoyan, {Romain } and Frasca, {Paolo } and Panteley, {Elena } and Zaccarian, {Luca }",
booktitle = "Hybrid and Networked Dynamical Systems",
}