Radial Basis Function Rbf Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation - Rilegato

Liu, Jinkun

Rilegato

ISBN 10: 3642348157 ISBN 13: 9783642348150

Casa editrice: Springer Nature, 2013

Riassunto

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.

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Informazioni sull'autore

LIU Jinkun received his BS, MS and PhD degrees from Northeastern University, Shenyang, China, in 1989, 1994 and 1997, respectively. He was a postdoctoral fellow at Zhejiang University from 1997 to 1999, and is currently a professor at Beihang University, China. He has published more than 100 research papers and eight books. His research interests include intelligent control and sliding mode control; partial differential equation (PDE) modeling and boundary control and application areas in motion control, such as flight control and robotic control, especially for under-actuated systems.

Dalla quarta di copertina

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.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2013
• Lingua: Inglese
• ISBN 10: 3642348157
• ISBN 13: 9783642348150
• Rilegatura: Copertina rigida
• Numero di pagine: 365
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile