Radial Basis Function (RBF) Neural Network Control for Mechanical Systems : Design, Analysis and Matlab Simulation

Jinkun Liu

ISBN 10: 364243455X / ISBN 13: 9783642434556

Edité par Springer Berlin Heidelberg, 2015

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronautics.

N° de réf. du vendeur 9783642434556

Détails bibliographiques

• Titre: Radial Basis Function (RBF) Neural Network Control for Mechanical Systems : Design, Analysis and Matlab Simulation
• Éditeur: Springer Berlin Heidelberg
• Date de parution: 2015
• Langue: anglais
• ISBN à 10 chiffres: 364243455X
• ISBN à 13 chiffres: 9783642434556
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.

This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation.

Jinkun Liu is a professor at Beijing University of Aeronautics and Astronautics.

Présentation de l'éditeur

This book introduces concrete design methods and MATLAB simulations of stable adaptive Radial Basis Function (RBF) neural control strategies. It presents a broad range of implementable neural network control design methods for mechanical systems.