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

✍ Scribed by Jinkun Liu (auth.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2013
• Tongue: English
• Leaves: 373
• Edition: 1
• Category: Library

✦ Synopsis

Radial Basis Function (RBF) Neural Network Controlfor 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.

✦ Table of Contents

Front Matter....Pages i-xv
Introduction....Pages 1-17
RBF Neural Network Design and Simulation....Pages 19-53
RBF Neural Network Control Based on Gradient Descent Algorithm....Pages 55-69
Adaptive RBF Neural Network Control....Pages 71-112
Neural Network Sliding Mode Control....Pages 113-132
Adaptive RBF Control Based on Global Approximation....Pages 133-191
Adaptive Robust RBF Control Based on Local Approximation....Pages 193-249
Backstepping Control with RBF....Pages 251-292
Digital RBF Neural Network Control....Pages 293-309
Discrete Neural Network Control....Pages 311-337
Adaptive RBF Observer Design and Sliding Mode Control....Pages 339-362
Back Matter....Pages 363-365

✦ Subjects

Control; Vibration, Dynamical Systems, Control; Computational Intelligence; Mathematical Models of Cognitive Processes and Neural Networks