Radial Basis Function (RBF) Neural Network Control for Mechanical Systems

Design, Analysis and Matlab Simulation

Nonfiction, Science & Nature, Technology, Automation, Engineering, Mechanical
Cover of the book Radial Basis Function (RBF) Neural Network Control for Mechanical Systems by Jinkun Liu, Springer Berlin Heidelberg
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Author: Jinkun Liu ISBN: 9783642348167
Publisher: Springer Berlin Heidelberg Publication: January 26, 2013
Imprint: Springer Language: English
Author: Jinkun Liu
ISBN: 9783642348167
Publisher: Springer Berlin Heidelberg
Publication: January 26, 2013
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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