Name: Polynomial Approximation for Data-Driven System Analysis and Control of Nonlinear Systems (en Inglés)
Price: 106.69 USD
Availability: InStock
Author: Tim Martin
ISBN: 9783832558864

portada Polynomial Approximation for Data-Driven System Analysis and Control of Nonlinear Systems (en Inglés)

Polynomial Approximation for Data-Driven System Analysis and Control of Nonlinear Systems (en Inglés)

Formato

Libro Físico

Editorial

Logos Verlag Berlin

Autor

Tim Martin

Idioma

Inglés

N° páginas

193

Encuadernación

Tapa Blanda

ISBN13

9783832558864

Polynomial Approximation for Data-Driven System Analysis and Control of Nonlinear Systems (en Inglés)

Tim Martin (Autor) · Logos Verlag Berlin · Tapa Blanda

Libro Nuevo Importado

Envío: 25 a 33 días háb.

$ 193.98$ 106.69

-45%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 2 unidades

$ 106.69

Llega entre el 16 Sep y el 30 Sep a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Polynomial Approximation for Data-Driven System Analysis and Control of Nonlinear Systems (en Inglés)"

This thesis presents data-driven methods for nonlinear systems, enabling the verification of system-theoretical properties and the design of state feedbacks based on measured trajectories. Despite noisy data, the developed methods provide rigorous guarantees and leverage convex optimization. Classical control techniques require a mathematical model of the system dynamics, which derivation from first principles often demands expert knowledge or is time-consuming. In contrast, data-based control methods determine system properties and controllers from system trajectories. Whereas recent developments address linear systems, dynamical systems are generally nonlinear in practice. Therefore, this thesis first introduces a data-based system representation for unknown polynomial systems to determine dissipativity and integral quadratic constraints via sum-of-squares optimization. The second part of the thesis establishes a polynomial representation of nonlinear systems based on polynomial interpolation. Due to the unknown interpolation polynomial, a set of polynomials containing the actual interpolation polynomial is deduced from noisy data. This set, along with a polynomial bound on the approximation error, forms the basis for determining dissipativity properties and designing state feedbacks with stability guarantees utilizing robust control techniques and sum-of-squares relaxation.