First Course in Fuzzy and Neural Control

ISBN-10: 1584882441

ISBN-13: 9781584882442

Edition: 2003

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Description: Although the use of fuzzy control methods has grown nearly to the level of classical control, the true understanding of fuzzy control lags seriously behind. Moreover, most engineers are well versed in either traditional control or in fuzzy control-rarely both. Each has applications for which it is better suited, but without a good understanding of both, engineers cannot make a sound determination of which technique to use for a given situation.A First Course in Fuzzy and Neural Control is designed to build the foundation needed to make those decisions. It begins with an introduction to standard control theory, then makes a smooth transition to complex problems that require innovative fuzzy, neural, and fuzzy-neural techniques. For each method, the authors clearly answer the questions: What is this new control method? Why is it needed? How is it implemented? Real-world examples, exercises, and ideas for student projects reinforce the concepts presented.Developed from lecture notes for a highly successful course titled The Fundamentals of Soft Computing, the text is written in the same reader-friendly style as the authors' popular A First Course in Fuzzy Logic text. A First Course in Fuzzy and Neural Control requires only a basic background in mathematics and engineering and does not overwhelm students with unnecessary material but serves to motivate them toward more advanced studies.

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Book details

List price: $159.95
Copyright year: 2003
Publisher: CRC Press LLC
Publication date: 11/12/2002
Binding: Hardcover
Pages: 312
Size: 6.25" wide x 9.25" long x 0.75" tall
Weight: 1.342
Language: English

A Prelude to Control Theory
An ancient control system
Examples of control problems
Open-loop control systems
Closed-loop control systems
Stable and unstable systems
A look at controller design
Exercises and projects
Mathematical Models in Control
Introductory examples: pendulum problems
Example: fixed pendulum
Example: inverted pendulum on a cart
State variables and linear systems
Controllability and observability
Stability
Damping and system response
Stability of linear systems
Stability of nonlinear systems
Robust stability
Controller design
State-variable feedback control
Second-order systems
Higher-order systems
Proportional-integral-derivative control
Example: automobile cruise control system
Example: temperature control
Example: controlling dynamics of a servomotor
Nonlinear control systems
Linearization
Exercises and projects
Fuzzy Logic for Control
Fuzziness and linguistic rules
Fuzzy sets in control
Combining fuzzy sets
Minimum, maximum, and complement
Triangular norms, conorms, and negations
Averaging operators
Sensitivity of functions
Extreme measure of sensitivity
Average sensitivity
Combining fuzzy rules
Products of fuzzy sets
Mamdani model
Larsen model
Takagi-Sugeno-Kang (TSK) model
Tsukamoto model
Truth tables for fuzzy logic
Fuzzy partitions
Fuzzy relations
Equivalence relations
Order relations
Defuzzification
Center of area method
Height-center of area method
Max criterion method
First of maxima method
Middle of maxima method
Level curves and alpha-cuts
Extension principle
Images of alpha-level sets
Universal approximation
Exercises and projects
Fuzzy Control
A fuzzy controller for an inverted pendulum
Main approaches to fuzzy control
Mamdani and Larsen methods
Model-based fuzzy control
Stability of fuzzy control systems
Fuzzy controller design
Example: automobile cruise control
Example: controlling dynamics of a servomotor
Exercises and projects
Neural Networks for Control
What is a neural network?
Implementing neural networks
Learning capability
The delta rule
The backpropagation algorithm
Example 1: training a neural network
Example 2: training a neural network
Practical issues in training
Exercises and projects
Neural Control
Why neural networks in control
Inverse dynamics
Neural networks in direct neural control
Example: temperature control
A neural network for temperature control
Simulating PI control with a neural network
Neural networks in indirect neural control
System identification
Example: system identification
Instantaneous linearization
Exercises and projects
Fuzzy-Neural and Neural-Fuzzy Control
Fuzzy concepts in neural networks
Basic principles of fuzzy-neural systems
Basic principles of neural-fuzzy systems
Adaptive network fuzzy inference systems
ANFIS learning algorithm
Generating fuzzy rules
Exercises and projects
Applications
A survey of industrial applications
Cooling scheme for laser materials
Color quality processing
Identification of trash in cotton
Integrated pest management systems
Comments
Bibliography
Index
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