A First Course in Fuzzy and Neural Control

by Nguyen; Hung T.

• ISBN13: 9781584882442
• ISBN10: 1584882441
• Edition: 1st
• Format: Hardcover
• Copyright: 2002-11-12
• Publisher: Chapman & Hall/

Summary

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.

Table of Contents

A Prelude to Control Theory	1	(14)
An ancient control system	1	(2)
Examples of control problems	3	(6)
Open-loop control systems	3	(2)
Closed-loop control systems	5	(4)
Stable and unstable systems	9	(1)
A look at controller design	10	(4)
Exercises and projects	14	(1)
Mathematical Models in Control	15	(70)
Introductory examples: pendulum problems	15	(14)
Example: fixed pendulum	15	(5)
Example: inverted pendulum on a cart	20	(9)
State variables and linear systems	29	(3)
Controllability and observability	32	(2)
Stability	34	(8)
Damping and system response	36	(1)
Stability of linear systems	37	(2)
Stability of nonlinear systems	39	(2)
Robust stability	41	(1)
Controller design	42	(6)
State-variable feedback control	48	(5)
Second-order systems	48	(2)
Higher-order systems	50	(3)
Proportional-integral-derivative control	53	(24)
Example: automobile cruise control system	53	(8)
Example: temperature control	61	(10)
Example: controlling dynamics of a servomotor	71	(6)
Nonlinear control systems	77	(1)
Linearization	78	(2)
Exercises and projects	80	(5)
Fuzzy Logic for Control	85	(48)
Fuzziness and linguistic rules	85	(1)
Fuzzy sets in control	86	(4)
Combining fuzzy sets	90	(14)
Minimum, maximum, and complement	90	(2)
Triangular norms, conorms, and negations	92	(9)
Averaging operators	101	(3)
Sensitivity of functions	104	(4)
Extreme measure of sensitivity	104	(2)
Average sensitivity	106	(2)
Combining fuzzy rules	108	(6)
Products of fuzzy sets	110	(1)
Mamdani model	110	(1)
Larsen model	111	(1)
Takagi-Sugeno-Kang (TSK) model	112	(1)
Tsukamoto model	113	(1)
Truth tables for fuzzy logic	114	(2)
Fuzzy partitions	116	(1)
Fuzzy relations	117	(3)
Equivalence relations	119	(1)
Order relations	120	(1)
Defuzzification	120	(3)
Center of area method	120	(1)
Height-center of area method	121	(1)
Max criterion method	122	(1)
First of maxima method	122	(1)
Middle of maxima method	123	(1)
Level curves and alpha-cuts	123	(3)
Extension principle	124	(1)
Images of alpha-level sets	125	(1)
Universal approximation	126	(2)
Exercises and projects	128	(5)
Fuzzy Control	133	(32)
A fuzzy controller for an inverted pendulum	133	(4)
Main approaches to fuzzy control	137	(7)
Mamdani and Larsen methods	139	(1)
Model-based fuzzy control	140	(4)
Stability of fuzzy control systems	144	(2)
Fuzzy controller design	146	(11)
Example: automobile cruise control	146	(5)
Example: controlling dynamics of a servomotor	151	(6)
Exercises and projects	157	(8)
Neural Networks for Control	165	(36)
What is a neural network?	165	(3)
Implementing neural networks	168	(4)
Learning capability	172	(3)
The delta rule	175	(4)
The backpropagation algorithm	179	(4)
Example 1: training a neural network	183	(2)
Example 2: training a neural network	185	(7)
Practical issues in training	192	(1)
Exercises and projects	193	(8)
Neural Control	201	(28)
Why neural networks in control	201	(1)
Inverse dynamics	202	(2)
Neural networks in direct neural control	204	(1)
Example: temperature control	204	(12)
A neural network for temperature control	205	(4)
Simulating PI control with a neural network	209	(7)
Neural networks in indirect neural control	216	(9)
System identification	217	(2)
Example: system identification	219	(4)
Instantaneous linearization	223	(2)
Exercises and projects	225	(4)
Fuzzy-Neural and Neural-Fuzzy Control	229	(20)
Fuzzy concepts in neural networks	230	(2)
Basic principles of fuzzy-neural systems	232	(4)
Basic principles of neural-fuzzy systems	236	(9)
Adaptive network fuzzy inference systems	237	(1)
ANFIS learning algorithm	238	(7)
Generating fuzzy rules	245	(1)
Exercises and projects	246	(3)
Applications	249	(42)
A survey of industrial applications	249	(1)
Cooling scheme for laser materials	250	(6)
Color quality processing	256	(6)
Identification of trash in cotton	262	(17)
Integrated pest management systems	279	(11)
Comments	290	(1)
Bibliography	291	(6)
Index	297