Computationally Intelligent Hybrid Systems: The Fusion of Soft Computing and Hard Computing

420 Pages·12.02 MB·English

COMPUTATIONALLY
INTELLIGENT HYBRID
SYSTEMS
IEEE Press
445 Hoes Lane
Piscataway, NJ 08854
IEEE Press Editorial Board
Stamatios V. Kartalopoulos, Editor in Chief
M. Akay M. E. El-Hawary F. M. B. Periera
J. B. Anderson R. Leonardi C. Singh
R. J. Baker M. Montrose S. Tewksbury
J. E. Brewer M. S. Newman G. Zobrist
Kenneth Moore, Director of IEEE Book and Information Services (BIS)
Catherine Faduska, Senior Acquisitions Editor
Anthony VenGraitis, Project Editor
IEEE Neural Networks Society, Sponsor
NN-S Liaison to IEEE Press, David B. Fogel
COMPUTATIONALLY
INTELLIGENT HYBRID
SYSTEMS
The Fusion of Soft Computing and
Hard Computing
Edited by
Seppo J. Ovaska
Helsinki University of Technology
IEEE Series on Computational Intelligence
David B. Fogel, Series Editor
4HEEE
IEEE PRESS
^WILEY-
MNTERSCIENCE
A JOHN WILEY & SONS, INC., PUBLICATION
Copyright © 2005 by the Institute of Electrical and Electronics Engineers. All rights reserved.
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Library of Congress Cataloging-in-Publication Data is available.
ISBN 0-471-47668-4
Printed in the United States of America
10 9 8 7 6 5 4 3 21
To Helena, my dear wife; this one is for you
CONTENTS
Contributors
Foreword
David B. Fogel
Preface
Editor's Introduction to Chapter 1
1 INTRODUCTION TO FUSION OF SOFT COMPUTING
AND HARD COMPUTING
Seppo J. Ovaska
1.1 Introduction / 5
1.1.1 Soft Computing / 5
1.1.2 Fusion of Soft-Computing and Hard-Computing
Methodologies / 7
1.2 Structural Categories / 9
1.2.1 Soft Computing and Hard Computing Are
Isolated from Each Other / 10
1.2.2 Soft Computing and Hard Computing Are Connected
in Parallel / 11
1.2.3 Soft Computing with Hard-Computing Feedback
and Hard Computing with Soft-Computing
Feedback / 12
1.2.4 Soft Computing Is Cascaded with Hard
Computing or Hard Computing Is Cascaded with
Soft Computing / 12
1.2.5 Soft-Computing-Designed Hard Computing and
Hard-Computing-Designed Soft Computing / 13
1.2.6 Hard-Computing-Augmented Soft Computing and
Soft-Computing-Augmented Hard Computing / 14
VÜi CONTENTS
1.2.7 Hard-Computing-Assisted Soft Computing and
Soft-Computing-Assisted Hard Computing / 15
1.2.8 Supplementary Categories / 16
1.2.9 General Soft-Computing and Hard-Computing
Mapping Functions / 19
1.3 Characteristic Features / 19
1.3.1 Proportional Integral Derivative Controllers / 20
1.3.2 Physical Models / 20
1.3.3 Optimization Utilizing Local Information / 21
1.3.4 General Parameter Adaptation Algorithm / 22
1.3.5 Stochastic System Simulators / 22
1.3.6 Discussion and Extended Fusion Schemes / 22
1.4 Characterization of Hybrid Applications / 24
1.5 Conclusions and Discussion / 25
References / 27
Editor's Introduction to Chapter 2 31
2 GENERAL MODEL FOR LARGE-SCALE PLANT
APPLICATION 35
Akimoto Kamiya
2.1 Introduction / 35
2.2 Control System Architecture / 36
2.3 Forecasting of Market Demand / 37
2.4 Scheduling of Processes / 39
2.4.1 Problem Decomposition / 39
2.4.2 Hybrid Genetic Algorithms / 42
2.4.3 Multiobjective Optimization / 43
2.5 Supervisory Control / 45
2.6 Local Control / 47
2.7 General Fusion Model and Fusion Categories / 49
2.8 Conclusions / 51
References / 51
Editor's Introduction to Chapter 3 57
3 ADAPTIVE FLIGHT CONTROL: SOFT COMPUTING WITH
HARD CONSTRAINTS 61
Richard E. Saeks
3.1 Introduction / 61
3.2 The Adaptive Control Algorithms / 62
3.2.1 Adaptive Dynamic Programming / 63
3.2.2 Neural Adaptive Control / 64
3.3 Flight Control / 67
3.4 X-43A-LS Autolander / 68
3.5 LoFLYTE® Optimal Control / 73
3.6 LoFLYTE® Stability Augmentation / 76
3.7 Design for Uncertainty with Hard Constraints / 82
3.8 Fusion of Soft Computing and Hard Computing / 85
3.9 Conclusions / 85
References / 86
Editor's Introduction to Chapter 4
4 SENSORLESS CONTROL OF SWITCHED RELUCTANCE
MOTORS
Adrian David Cheok
4.1 Introduction / 93
4.2 Fuzzy Logic Model / 95
4.2.1 Measurement of Flux Linkage Characteristics / 95
4.2.2 Training and Validation of Fuzzy Model / 97
4.3 Accuracy Enhancement Algorithms / 101
4.3.1 Soft-Computing-Based Optimal Phase Selection / 102
4.3.2 Hard-Computing-Based On-Line Resistance
Estimation / 104
4.3.3 Polynomial Predictive Filtering / 105
4.4 Simulation Algorithm and Results / 108
4.5 Hardware and Software Implementation / 109
4.5.1 Hardware Configuration / 109
4.5.2 Software Implementation / 110
4.6 Experimental Results / 111
4.6.1 Acceleration from Zero Speed / 112
4.6.2 Low-Current Low-Speed Test / 113
4.6.3 High-Speed Test / 114
4.6.4 Test of Step Change of Load / 118
4.7 Fusion of Soft Computing and Hard Computing / 119
4.8 Conclusion and Discussion / 122
References / 122
X CONTENTS
Editor's Introduction to Chapter 5 125
5 ESTIMATION OF UNCERTAINTY BOUNDS FOR LINEAR
AND NONLINEAR ROBUST CONTROL 129
Gregory D. Buckner
5.1 Introduction / 129
5.2 Robust Control of Active Magnetic Bearings / 130
5.2.1 Active Magnetic Bearing Test Rig / 132
5.3 Nominal //«, Control of the AMB Test Rig / 133
5.3.1 Parametric System Identification / 133
5.3.2 Uncertainty Bound Specification / 135
5.3.3 Nominal //«, Control: Experimental Results / 137
5.4 Estimating Modeling Uncertainty for //«, Control of the
AMB Test Rig / 138
5.4.1 Model Error Modeling / 140
5.4.2 Intelligent Model Error Identification / 141
5.4.3 Uncertainty Bound Specification / 146
5.4.4 Identified H^ Control: Experimental Results / 147
5.5 Nonlinear Robust Control of the AMB Test Rig / 148
5.5.1 Nominal Sliding Mode Control of the AMB
Test Rig / 148
5.5.2 Nominal SMC: Experimental Results / 150
5.6 Estimating Model Uncertainty for SMC of the AMB
Test Rig / 151
5.6.1 Intelligent System Identification / 151
5.6.2 Intelligent Model Error Identification / 155
5.6.3 Intelligent SMC: Experimental Results / 156
5.7 Fusion of Soft Computing and Hard Computing / 159
5.8 Conclusion / 162
References / 162
Editor's Introduction to Chapter 6 165
6 INDIRECT ON-LINE TOOL WEAR MONITORING 169
Bernhard Sick
6.1 Introduction / 169
6.2 Problem Description and Monitoring Architecture / 172
6.3 State of the Art / 176
6.3.1 Monitoring Techniques Based on Analytical Models / 176
CONTENTS XJ
6.3.2 Monitoring Techniques Based on Neural Networks / 178
6.3.3 Monitoring Techniques Based on Fusion of Physical
and Neural Network Models / 181
6.4 New Solution / 184
6.4.1 Solution Outline / 184
6.4.2 Physical Force Model at Digital Preprocessing Level / 185
6.4.3 Dynamic Neural Network at Wear Model Level / 187
6.5 Experimental Results / 189
6.6 Fusion of Soft Computing and Hard Computing / 192
6.7 Summary and Conclusions / 194
References / 195
Editor's Introduction to Chapter 7 199
7 PREDICTIVE FILTERING METHODS FOR POWER
SYSTEMS APPLICATIONS 203
Seppo J. Ovaska
7.1 Introduction / 203
7.2 Multiplicative General-Parameter Filtering / 205
7.3 Genetic Algorithm for Optimizing Filter Tap
Cross-Connections / 207
7.4 Design of Multiplierless Basis Filters by Evolutionary
Programming / 211
7.5 Predictive Filters for Zero-Crossings Detector / 213
7.5.1 Single 60-Hz Sinusoid Corrupted by Noise / 213
7.5.2 Sequence of 49-, 50-, and 51-Hz Sinusoids
Corrupted by Noise / 217
7.5.3 Discussion of Zero-Crossings Detection Application / 222
7.6 Predictive Filters for Current Reference Generators / 223
7.6.1 Sequence of 49-, 50-, and 51-Hz Noisy Sinusoids / 225
7.6.2 Sequence of 49-, 50-, and 51-Hz Noisy Sinusoids
Corrupted by Harmonics / 229
7.6.3 Artificial Current Signal Corrupted by Odd Harmonics / 230
7.6.4 Discussion of Current Reference Generation
Application / 232
7.7 Fusion of Soft Computing and Hard Computing / 233
7.8 Conclusion / 234
References / 237
Appendix 7.1: Coefficients of 50-Hz Sinusoid-Predictive FIR Filters / 239