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2013 | Buch

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

Design, Analysis and Matlab Simulation

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SUCHEN

Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
This chapter gives the review of several kinds of neural network control and introduces the concept of RBF neural network and RBF neural network control. To illustrate the attendant features of robustness and performance specification of RBF adaptive control, a typical RBF adaptive controller design for an example system is given. A concrete analysis, simulation examples, and Matlab programs are given too.
Jinkun Liu
Chapter 2. RBF Neural Network Design and Simulation
Abstract
This chapter introduces RBF neural network design method, gives RBF neural network approximation algorithm based on gradient descent, analyzes the effects of Gaussian function parameters on RBF approximation, and introduces RBF neural network modeling method based on off-line training. Several simulation examples are given.
Jinkun Liu
Chapter 3. RBF Neural Network Control Based on Gradient Descent Algorithm
Abstract
This chapter introduces three kinds of RBF neural network control laws based on gradient descent rule, including supervisory control law, model reference adaptive control law, and self-adjust control law; the weight value learning algorithms are presented. Several simulation examples are given.
Jinkun Liu
Chapter 4. Adaptive RBF Neural Network Control
Abstract
This chapter introduces several online adaptive RBF neural network control methods, including adaptive control based on neural approximation, adaptive control based on neural approximation with unknown parameter, and a direct robust adaptive control. For above control laws, the adaptive law is designed based on the Lyapunov stability theory, the closed-loop system stability can be achieved.
Jinkun Liu
Chapter 5. Neural Network Sliding Mode Control
Abstract
This chapter introduces adaptive neural sliding mode control based on RBF neural network approximation, including a simple sliding mode controller and sliding mode control for second-order SISO nonlinear system, the chattering phenomena is eliminated. The closed-loop system stability can be achieved based on the Lyapunov stability.
Jinkun Liu
Chapter 6. Adaptive RBF Control Based on Global Approximation
Abstract
This chapter introduces three kinds of adaptive neural mode control laws for n-link manipulators based on RBF, including adaptive neural network control law, adaptive neural network control law with sliding mode robust term, and adaptive neural network control law with HJI. The closed-loop system stability can be achieved based on the Lyapunov stability.
Jinkun Liu
Chapter 7. Adaptive Robust RBF Control Based on Local Approximation
Abstract
This chapter introduces three kinds of adaptive robust RBF controllers for robotic manipulators based on local approximation, including robust adaptive controller based on nominal model, adaptive controller based on local model approximation, and adaptive controller based on task space.
Jinkun Liu
Chapter 8. Backstepping Control with RBF
Abstract
This chapter introduces backstepping controller design with RBF neural network approximation. Several controller design examples for mechanical systems are given, including backstepping controller for inverted pendulum, backstepping controller for single-link flexible joint robot, and adaptive backstepping controller for single-link flexible joint robot.
Jinkun Liu
Chapter 9. Digital RBF Neural Network Control
Abstract
This chapter introduces adaptive Runge–Kutta–Merson method for digital RBF neural network controller design. Two examples for mechanical controls are given, including digital adaptive control for a servo system and digital adaptive control for two-link manipulators.
Jinkun Liu
Chapter 10. Discrete Neural Network Control
Abstract
This chapter introduces two kinds of adaptive discrete neural network controllers for discrete nonlinear system, including a direct RBF controller and an indirect RBF controller. For the two control laws, the adaptive laws are designed based on the Lyapunov stability theory; the closed-loop system stability can be achieved.
Jinkun Liu
Chapter 11. Adaptive RBF Observer Design and Sliding Mode Control
Abstract
This chapter introduces a kind of adaptive observer with RBF neural network approximation. Using this observer, a speedless sliding mode controller is designed. Stability analysis of the observer and the closed control system are presented. Simulation examples for single-link manipulator are given.
Jinkun Liu
Backmatter
Metadaten
Titel
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems
verfasst von
Jinkun Liu
Copyright-Jahr
2013
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-642-34816-7
Print ISBN
978-3-642-34815-0
DOI
https://doi.org/10.1007/978-3-642-34816-7

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