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2018 | Book

Introduction to Intelligent Control Read chapter Read first chapter

Intelligent Control Design and MATLAB Simulation

Author: Jinkun Liu

Publisher: Springer Singapore

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book offers a comprehensive introduction to intelligent control system design, using MATLAB simulation to verify typical intelligent controller designs. It also uses real-world case studies that present the results of intelligent controller implementations to illustrate the successful application of the theory. Addressing the need for systematic design approaches to intelligent control system design using neural network and fuzzy-based techniques, the book introduces the concrete design method and MATLAB simulation of intelligent control strategies; offers a catalog of implementable intelligent control design methods for engineering applications; provides advanced intelligent controller design methods and their stability analysis methods; and presents a sample simulation and Matlab program for each intelligent control algorithm.

The main topics addressed are expert control, fuzzy logic control, adaptive fuzzy control, neural network control, adaptive neural control and

intelligent optimization algorithms, providing several engineering application examples for each method.

Table of Contents

Frontmatter
Chapter 1. Introduction to Intelligent Control
Abstract
The term “intelligent control” may be loosely used to denote a control technique that can be carried out using the “intelligence” of a human who is knowledgeable in the particular domain of control. In this definition, constraints pertaining to limitations of sensory and actuation capabilities and information processing speeds of humans are not considered. It follows that if a human in the control loop can properly control a plant, then that system would be a good candidate for intelligent control.
Jinkun Liu
Chapter 2. Expert PID Control
Abstract
Expert control is a control tactics to use expert knowledge and experience. Expert control was proposed firstly by Astrom in 1986.
Jinkun Liu
Chapter 3. Foundation of Fuzzy Mathematics
Abstract
Fuzzy theory was initiated by L.A. Zadeh in 1965 with his seminal paper “Fuzzy Sets.” In the early 1960s, he thought that classical control theory had put too much emphasis on precision and therefore could not handle the complex systems. As early as 1962, he wrote that to handle biological systems “we need a radically different kind of mathematics, the mathematics of fuzzy or cloudy quantities which are not describable in terms of probability distributions.” Later, he formalized the ideas into the paper “fuzzy sets.”
Jinkun Liu
Chapter 4. Fuzzy Logic Control
Abstract
The term fuzzy logic was introduced with the 1965 proposal of fuzzy set theory by Zadeh. Fuzzy logic has been applied to many fields, from control theory to artificial intelligence.
Jinkun Liu
Chapter 5. Fuzzy T-S Modeling and Control
Abstract
The traditional fuzzy system, which belongs to the Mamdani fuzzy model, whose output is fuzzy.
Jinkun Liu
Chapter 6. Adaptive Fuzzy Control
Abstract
Since the idea of fuzzy system universal approximation theorem was introduced [1], adaptive fuzzy control techniques have undergone great developments and have been successfully applied in many fields such as learning, pattern recognition, signal processing, modeling, and system control.
Jinkun Liu
Chapter 7. Neural Networks
Abstract
Neural networks are networks of nerve cells (neurons) in the brain. The human brain has billions of individual neurons and trillions of interconnections. Neurons are continuously processing and transmitting information to one another.
Jinkun Liu
Chapter 8. Adaptive RBF Neural Network Control
Abstract
Since the idea of the computational abilities of networks composed of simple models of neurons was introduced in the 1940s [1], neural network techniques have undergone great developments and have been successfully applied in many fields such as learning, pattern recognition, signal processing, modeling, and system control.
Jinkun Liu
Chapter 9. Adaptive Sliding Mode RBF Neural Network Control
Abstract
Sliding mode control is an effective approach for the robust control of a class of nonlinear systems with uncertainties defined in compact sets. The direction of the control action at any moment is determined by a switching condition to force the system to evolve on the sliding surface so that the closed-loop system behaves like a lower order linear system.
Jinkun Liu
Chapter 10. Discrete RBF Neural Network Control
Abstract
The discrete-time implementation of controllers is important. There are two methods for designing the digital controller. One method, called emulation, is to design a controller based on the continuous-time system, then discrete the controller.
Jinkun Liu
Chapter 11. Intelligent Search Algorithm Design
Abstract
With the development of the optimization theory, some new intelligent algorithms have been rapidly developed and widely used, and these algorithms have become new methods to solve the traditional system identification problems, such as genetic algorithm, ant colony algorithm, particle swarm optimization algorithm, differential evolution algorithm. These optimization algorithms simulate natural phenomena and processes.
Jinkun Liu
Chapter 12. Iterative Learning Control and Applications
Abstract
There is a kind of trajectory tracking problem in practical control. The control task is to find the control law, which makes the output of the controlled object to achieve the zero error of trajectory tracking along the desired trajectory. This tracking problem is a challenging control problem.
Jinkun Liu
Metadata
Title
Intelligent Control Design and MATLAB Simulation
Author
Jinkun Liu
Copyright Year
2018
Publisher
Springer Singapore
Electronic ISBN
978-981-10-5263-7
Print ISBN
978-981-10-5262-0
DOI
https://doi.org/10.1007/978-981-10-5263-7