Contents
Cover Page
Series Introduction by
Neil Munro v
Foreword by George N.
Saridis
vii
1. Introduction
1
1.1
Conventional Control
2
1.2
Intelligent Control
6
1.3
Computational Intelligence in Control 8
2. Expert
Systems in Industry
13
2.1 Elements of an Expert System 15
2.2 The Need for Expert Systems 17
2.3 Stages in the Development of an
Expert
System 18
2.4 The Representation of Knowledge 20
2.5 Expert System Paradigms
20
2.5.1 Expert systems for product design 21
2.5.2 Expert systems for plant simulation
and operator training
22
2.5.3 Expert supervisory control systems 23
2.5.4 Expert systems for the design of
industrial controllers 24
2.5.5 Expert systems for fault prediction
and diagnosis 24
2.5.6 Expert systems for the prediction
of emergency plant conditions 26
2.5.7 Expert systems for energy
management
26
2.5.8 Expert systems for production
scheduling 27
2.5.9 Expert systems for the diagnosis
of
malfunctions 28
3. Intelligent Control
31
3.1 Conditions for the Use of Intelligent Control 33
3.2 Objectives of Intelligent Control
34
4. Techniques of Intelligent Control
39
4.1 Unconventional Control
40
4.2 Autonomy and Intelligent Control
45
4.3 Knowledge-Based Systems 48
4.3.1 Expert systems 49
4.3.2 Fuzzy control
50
4.3.3 Neural control
51
4.3.4 Neuro-fuzzy control 51
5. Elements of Fuzzy Logic
53
5.1 Basic
Concepts 54
5.2 Fuzzy
Algorithms 59
5.3 Fuzzy
Operators 60
5.4 Operations on Fuzzy Sets
63
5.5 Algebraic Properties of Fuzzy Sets 64
5.6 Linguistic
Variables 64
5.7
Connectives
69
6. Fuzzy Reasoning
71
6.1 The Fuzzy
Algorithm 74
6.2 Fuzzy
Reasoning 76
6.2.1 Generalized Modus Ponens (GMP)
77
6.2.2 Generalized Modus Tollens (GMT)
77
6.2.3 Boolean
implication 78
6.2.4 Lukasiewicz
implication 78
6.2.5 Zadeh
implication 79
6.2.6 Mamdani
implication 79
6.2.7 Larsen
implication 80
6.2.8 GMP
implication 80
6.3 The Compositional Rules of Inference 81
7. The Fuzzy Control Algorithm
89
7.1 Controller
Decomposition 90
7.2 Fuzzification
91
7.2.1 Steps in the fuzzification algorithm 96
7.3 De-fuzzification of the Composite
Controller Output Membership Function 98
7.3.1 Center of area (COA)
de-fuzzification 98
7.3.2 Center of gravity (COG)
de-fuzzification
99
7.4 Design Considerations
100
7.4.1 Shape of
the fuzzy sets 100
7.4.2 Coarseness of the fuzzy sets 100
7.4.3 Completeness of the fuzzy sets 101
7.4.4 Rule
conflict 102
8. Fuzzy Industrial
Controllers
105
8.1 Controller
Tuning 106
8.2 Fuzzy Three-Term Controllers 107
8.2.1 Generalized three-term controllers 108
8.2.2 Partitioned controller architecture 109
8.2.3 Hybrid architectures
112
8.2.4 Generic two-term fuzzy controllers 113
8.3 Coarse-Fine Fuzzy Control
117
9. Real-time Fuzzy Control
119
9.1 Supervisory Fuzzy Controllers 120
9.2 Embedded Fuzzy Controllers 123
9.3 The Real-time Execution Scheduler 124
10. Model-Based Fuzzy Control
135
10.1 The Takagi-Sugeno Model-Based
Approach to Fuzzy Control
136
10.2 Fuzzy Variables and Fuzzy Spaces 137
10.3 The Fuzzy Process Model 139
10.4 The Fuzzy Control Law
141
10.5 The Locally Linearized Process Model 142
10.5.1 Conditions for closed system
stability
144
10.6 The Second Takagi-Sugeno Approach 144
10.7 Fuzzy Gain-Scheduling
146
11. Neural Control
153
11.1 The Elemental Artificial Neuron 156
11.2 Topologies of Multi-layer
Neural
Networks 158
11.3 Neural
Control 160
11.4 Properties of Neural Controllers 161
11.5 Neural Controller Architectures 162
11.5.1 Inverse model
architecture 164
11.5.2 Specialized training architecture 165
11.5.3 Indirect learning architecture 166
12. Neural Network Training
169
12.1 The Widrow-Hoff
Training Algorithm 170
12.2 The Delta
Training Algorithm 173
12.3 Multi-layer ANN Training Algorithms 175
12.4 The Back-propagation (BP)
Algorithm 176
13. Rule-Based Neural Control
181
13.1 Encoding Linguistic Rules
182
13.2 Training Rule-Based Neural Controllers 183
14. Neuro-Fuzzy Control
193
14.1 Neuro-Fuzzy Controller Architectures 194
14.2 Neuro-Fuzzy Isomorphism 195
15. Evolutionary Computation
203
15.1 Evolutionary Algorithms
205
15.2 The Optimization Problem
207
15.3 Evolutionary Optimization
208
15.4 Genetic
Algorithms 211
15.4.1
Initialization
212
15.4.2
Decoding 212
15.4.3 Evaluation of the fitness 213
15.4.4 Recombination and mutation 214
15.4.5
Selection 215
15.4.6 Choice of parameters of a GA 217
15.5 Design of Intelligent Controllers
Using GAs
221
15.5.1
Fuzzy controllers 221
15.5.2
Neural controllers 222
16.
Simulated Annealing
225
16.1 The Metropolis
Algorithm 226
16.2 Application Examples
228
17. Evolutionary Design
of Controllers
235
17.1 Qualitative Fitness Function
236
17.2
Controller Suitability
237
18. Bibliography
247
A.
Computational Intelligence
247
B.
Intelligent
Systems 247
C. Fuzzy Logic and Fuzzy Control 248
D. Fuzzy Logic and Neural Networks 251
E. Artificial Neural
Networks 252
F. Neural and Neuro-Fuzzy Control 253
G. Computer and Advanced Control 254
H. Evolutionary
Algorithms 254
I. MATLAB and its Toolboxes
257
Case Study:
Design of a Fuzzy Controller Using MATLAB
A.1 The Controlled Process 259
A.2 Basic Linguistic Control Rules 261
A.3 A Simple Linguistic Controller 261
A.4 The MATLAB fuzzy Design Tool 264
A.5 System Stabilization Rules 266
A.6 On the Universe of Discourse of the
Fuzzy
Sets 267
A.7 On the Choice of Fuzzy Sets 268
A.8 Compensation of Response Asymmetry 269
A.9
Conclusions 270
Simple
Genetic Algorithm
Simulated
Annealing Algorithm
Network
Training Algorithm
Index
291
Back Cover
|
