Safety, Reliability and Applications of Emerging Intelligent Control Technologies (eBook)

Front Cover 1
Safety, Reliability and Applications of Emerging Intelligent Control Technologies 2
Copyright Page 3
Table of Contents 6
Foreword 5
PART 1: KEYNOTE 1 6
Chapter 1. Safe AI - Is This Possible? 10
1. INTRODUCTION — WHY AL 10
2. THE DILEMMA 11
3. DETERMINISM IN CONTROL SYSTEMS 13
4. THEN ALONG CAME AI 14
5. PROTECTING THE SYSTEM FROM ITS CONTROLLER 15
6. MAKING AI TECHNIQUES DETERMINISTIC 15
7. THE WAY AHEAD 16
ACKNOWLEDGEMENTS 17
REFERENCES 17
PART 2: GENETIC ALGORITHMS 6
Chapter 2. Genetic Model-Reference Adaptive Control Systems Incorporating PID Controllers 18
1. INTRODUCTION 18
2. ANALYSIS 18
3. ILLUSTRATIVE EXAMPLE 19
4. CONCLUSION 20
REFERENCES 20
Chapter 3. Fuzzy Control of Water Pressure Using Genetic Algorithm 24
1. INTRODUCTION 24
2. DESIGN PRINCIPLE OF GENETIC FUZZY LOGIC CONTROL 25
3. EXPERIMENTAL RESULT 28
4. CONCLUSION 29
5. REFERENCE 29
Chapter 4. Genetic Tuning of Model-Reference Neural PID Controllers 30
1. INTRODUCTION 30
2. ANALYSIS 31
3. ILLUSTRATIVE EXAMPLE 31
4. CONCLUSION 32
REFERENCES 32
PART 3: ICONTROL ItEAL-TME CONTROL 6
Chapter 5. U.S. NRC Research and Digital Instrumentation and Control 36
1. INTRODUCTION 36
2. DISCUSSION 36
3. REFERENCES 38
Chapter 6. An Approach to the Design of Expert Systems for Hard Real-Time Control 40
1. INTRODUCTION 40
2. CURRENT REAL-TIME EXPERT SYSTEMS 40
3. A POSSIBLE APPROACH 41
4. A KNOWLEDGE-BASE COMPILER 42
5. CONCLUSION 44
REFERENCES 44
Chapter 7. Intelligent Voting Strategies for Dependable Real-time Control Systems 46
1. INTRODUCTION 46
2. THE APPLICABLE SAFETY STANDARDS 47
3. THE REQUIREMENTS FOR AN INTELLIGENT VOTER SERVICE 47
4. FORMAL MATHEMATICAL ANALYSIS 49
5. N-VERSION VOTERS 50
6. CONCLUSIONS 51
ACKNOWLEDGEMENT 51
REFERENCES 51
Chapter 8. A Neuro-Compensator for the Control of Robots by an Inertia-Related Control Approach 52
1. INTRODUCTION 52
2. CONTROL OF ROBOTS USING AN INERTIARELATED APPROACH 52
3. ROBOT CONTROL WITH THE NEUROCOMPENSATOR 53
4. IMPLEMENTATION CONSIDERATION 54
5. EXPERIMENT RESULTS 55
6. CONCLUSIONS 56
REFERENCES 56
PART 4: NEURAL NETWORKS 6
Chapter 9. Stochastic Tuning of a Spacecraft Controller Using Neural Networks 58
1. INTRODUCTION 58
2. SOHO THRUSTER CONTROL SYSTEM & STOCHASTIC PARAMETER TUNING
3. SIMULATION OF THE ADAPTIVE SYSTEM 60
4. CONCLUSION 62
ACKNOWLEDGEMENT 63
REFERENCES 63
Chapter 10. Determining the Node Number of Neural Network Models 64
I. INTRODUCTION 64
2. SYSTEM REPRESENTATION AND NEURAL NETWORK MODELS 65
3. IDENTIFICATION ALGORITHM AND ITS ASYMPTOTIC PROPERTIES 65
4. MAIN RESULTS 66
5. NUMERICAL ILLUSTRATION 68
6. CONCLUSIONS 68
REFERENCES 68
Chapter 11. Fail-Safe Stability for Neural Network Controlled Systems 70
1. INTRODUCTION 70
2. PROBLEM FORMULATION 70
3. SMALL GAIN THEOREM AND FAIL-SAFE STABILITY 71
4. CONCLUSION 75
REFERENCES 75
Chapter 12. Nonlinear Dilation Networks for Prediction Applications 76
I. INTRODUCTION 76
II. NONLINEAR DILATION NETWORK ARCHITECTURE 77
III. LEARNING ALGORITHM 78
IV. APPLICATIONS OF NONLINEAR DILATION NETWORK 78
V. CONCLUSIONS 80
ACKNOWLEDGMENTS 80
REFERENCES 80
PART 5: KEYNOTE 2 7
Chapter 13. The Impact of Safety and Reliability Requirements on the Specification of Control Systems 82
1. INTRODUCTION 82
2. SPECIFICATION OF CONTROL SYSTEMS 84
3. SAFETY AND RELIABILITY FUNCTIONS IN THE SPECIFICATION 87
4. CONCLUSIONS 89
5.REFERENCES 89
PART 6: KEYNOTE 3 7
Chapter 14. The Safety Implications of Emerging Software Paradigms 92
1.0 INTRODUCTION 92
2.0 The Emerging Paradigms 93
3.0 COMPARING CONVENTIONAL AND EMERGING SOFTWARE PARADIGMS 95
4.0 EMERGING PARADIGM V& V ISSUES AND CHAM, ENGES
5.0 KNOWLEDGE-BASED SYSTEM PARADIGM 98
6.0 NEURAL NETWORK PARADIGM 98
7.0 GENETIC ALGORITHM PARADIGM 100
8.0 FUZZY SYSTEM PARADIGM 101
9.0 SUMMARY 102
10.0 CONCLUSION 103
REFERENCES 104
PART 7: FUZZY SYSTEMS 7
Chapter 15. Fuzzy System as a Parameter Estimator of Nonlinear Dynamic Functions 106
1. INTRODUCTION 106
2. PARAMETRISATION OF FUZZY SYSTEMS 106
3. FUZZY ESTIMATOR 108
4. SIMULATION EXAMPLES 109
REFERENCES 111
Chapter 16. A Process Control and Diagnostic Tool Based on Continuous Fuzzy Petri Nets 112
1 Introduction 112
2 Application to Process Control 112
3 The CFPN Concept 113
4 Workspace Hierarchy 115
5 Example 115
6 Conclusions 115
References 116
Chapter 17. Actuator Saturation Compensation for Fuzzy Controllers 118
1. INTRODUCTION 118
2. Fuzzy Controllers 118
3. Actuator saturation compensation 119
4. Example 121
5. Conclusions 121
Acknowledgements 121
References 122
Chapter 18. Analysis of Fuzzy Control Methodology Applied to DC-DC Converter Control 124
1. INTRODUCTION 124
2. SWITCHING MODE POWER CONVERTER OPERATION 124
3. FUZZY PID CONTROL ALGORITHM 126
4. LARGE SIGNAL PBECEWISE SIMULATION 127
5. CONCLUSION 127
REFERENCES 128
PART 8: AUTONOMOUS VEHICLES 7
Chapter 19. Fuzzy Logic Based Behavior Fusion Strategy for Robot Navigation in Uncertain 130
1. INTRODUCTION 130
2. BEHAVIOR BASED CONTROL 130
3. DESCRIPTION OF REACTIVE BEHAVIORS USING FUZZY LOGIC 131
4. BEHAVIOR FUSION BY FUZZY REASONING 132
5. SIMULATIONS 134
6. COMBINATION WITH HIGH-LEVEL GLOBAL PLANNING 135
ACKNOWLEDGMENT 135
REFERENCES 135
Chapter 20. Linear and Nonlinear Models of Automated Vehicles Analysis and Experiments 136
1. INTRODUCTION 136
2. DEVELOPMENT OF THE NONLINEAR MODEL 136
3. LINEAR MODEL 138
4. EXPERIMENTAL RESULTS 138
5. CONCLUSION 138
REFERENCES 138
Chapter 21. Effective Development of Fuzzy-Logic Rules for Real-Time Control of Autonomous Vehicles 142
1. INTRODUCTION 142
2. LEARNING FROM CONVENTIONAL CONTROL 142
3. ANALOGOUS FUZZY-LOGIC AND CONVENTIONAL IMPLEMENTATION 143
4. IMPROVEMENT OF FUZZY-LOGIC RULES 144
5. ROBUSTNESS OF IMPROVED CONTROLLER 144
6. CONCLUSION 145
REFERENCES 145
Chapter 22. Design of a Controller with Feedforward Action for Path Tracking of Automated Vehicles 148
1. INTRODUCTION 148
2. A LINEAR DYNAMIC MODEL 148
3. FORMULATION OF THE VEHICLE PATH TRACKING 149
4. SYNTHESIS OF THE OPTIMAL CONTROLLER 150
5. EXPERIMENTAL RESULTS 150
6. CONCLUSIONS 151
ACKNOWLEDGMENT 151
REFERENCES 151
APPENDIX A 151
PART 9: KEYNOTE 4 7
Chapter 23. Advances in Neurofuzzy Algorithms for Real Time Modelling, Control and Estimation 154
1. NEUROFUZZY SYTEMS. 154
2. NEUROFUZZY SYSTEMS AND STRUCTURE 154
3. TRAINING NEUROFUZZY ALGORITHMS 156
4. NEUROFUZZY CONSTRUCTION ALGORITHMS 158
5. GENERAL ISSUES IN ADAPTIVE NEUROFUZZY SYSTEMS 159
REFERENCES 161
PART 10: KEYNOTE 5 7
Chapter 24. Static and Dynamic Preprocessing Methods in Neural Networks 162
1. INTRODUCTION 162
2. TRANSFORMATION OF INPUT SIGNALS 163
3. ILL POSED PROBLEMS 166
4. PREPROCESSING WITH ILL POSED PROBLEMS 168
5. OPEN ISSUES 168
6. CONCLUSIONS 168
REFERENCES 169
PART 11: FAULT DETECTION 8
Chapter 25. Enhancing Aircraft Engine Condition Monitoring 170
1. INTRODUCTION 170
2. ENGINE CONDITION MONITORING 171
3. ARTIFICIAL INTELLIGENCE APPROACH 173
4. A GENERAL FRAMEWORK FOR ENHANCED ENGINE CONDITION MONITORING 173
5. PRELIMINARY RESULTS 174
6. CONCLUSIONS 175
REFERENCES 175
Chapter 26. Parity Vector Compensation Using Non-Linear Filtering 176
1. INTRODUCTION 176
2. FDI METHOD 176
3. PARITY VECTOR COMPENSATION USING NON-LINEAR FILTERING 177
4. APPLICATION 178
5. CONCLUSION 180
REFERENCES 180
Chapter 27. Automatic Fault-Tree Generation 182
1. INTRODUCTION 182
2. PROBLEM DESCRIPTION 183
3. MOTIVATIONS FOR USING ARTIFICIAL INTELLIGENCE 184
4. CASE-BASED REASONING APPROACH FOR DIAGNOSIS SYSTEM DESIGN 185
5. BUILDING DIAGNOSIS SYSTEMS 186
6. FUTURE EXPANSION 186
7. CONCLUSIONS 187
REFERENCES 187
Cjhapter 28. Moving Window Method for Fault Detection and Classification Based on ART2 Neural 
188 
1. INTRODUCTION 188
2. ART2 NEURAL NETWORK AND LEARNING ALGORITHM 188
3. MOVING WINDOW SCHEME FOR FDD USING ART2 NEURAL NETWORK 190
4. AN EXAMPLE 190
5. CONCLUSION 191
REFERENCES 191
Chapter 29. Fault Detection and Identification Using Neural Network and Fuzzy Logic 194
1. INTRODUCTION 194
2. USING NEURAL NETWORK FOR VARIABLE ESTIMATION 194
3. USING FUZZY NEURAL NETWORK (FNN) FOR FAULT DETECTION AND IDENTIFICATION 195
4. APPLICATION TO MONITORING OF PIPE FLOW 197
5. CONCLUSIONS AND DISCUSSIONS 198
6. ACKNOWLEDGEMENTS 199
REFERENCES 199
Chapter 30. Fault Tolerant Management and Fuzzy Control of Integrated GPS/INS 200
1. INTRODUCTION 200
2. FAULT TOLERANT AND REDUNDANT MANAGEMENT CRITERIA 200
3. CONTROL FOR REDUNDANT MANAGEMENT 201
4. THE EXPERIMENTS OF INS/GPS 201
5. SUMMARY 202
REFERENCES 202
PART 12: POWER SYSTEMS 8
Chapter 31. Solving Power System Optimisation Problems Using Simulated Annealing 204
1. INTRODUCTION 204
2. POWER SYSTEM OPTIMISATION PROBLEMS 205
3. SIMULATED ANNEALING TECHNIQUE 205
4· SA-BASED OPTIMISATION ALGORITHM 206
5. APPLICATION TO ECONOMIC DISPATCH 207
6. CONCLUSION 208
REFERENCES 208
Chapter 32. Dynamic Voltage Security Assessment by Fuzzy Severity Index 210
1. INTRODUCTION 210
2. RELATED FUZZY SET THEORY 210
3. DESIGN OF THE FUZZY CLASSIFIER FOR SECURITY ASSESSMENT 211
4. NUMERICAL RESULTS 213
5. CONCLUSION 215
REFERENCES 215
PART 13: EXPERT/INTELLIGENT SYSTEMS 8
Chapter 33. Intelligent Control of Mobile Robot 216
1. ARCHITECTURE OF MOBILE ROBOT THMR-III 217
2. GENERAL COMPUTER CONTROL MODULE TH-STD-7898 217
3. VISION SUBSYS. AND INFORMATION REDUCED TECHNOLOGY 217
4. THE RESEARCH OF TERCEPTON-ACTION" BEHAVIOR 220
5. APPLICATION OF FUZZY CONTROL IN THE NAVIGATION CONTROL OF ROBOT 221
REFERENCES 221
Chapter 34. Robustness of Neural Network Model for the Thermal Dynamics in Buildings 222
1 INTOODUCTION 222
2 THE ENVIRONMENTAL CHAMBER SYSTEM 223
3 NN MODELLING OF THE SYSTEM 224
4 ROBUSTNESS AND RELIABILITY OF THE MODEL 225
5 CONCLUSIONS 226
REFERENCES 226
Chapter 35. Modelling Failure Prone Flexible Manufacturing Systems 228
1. INTRODUCTION 228
2. MODELLINGAPPROACH 229
3. PARALLEL-FAILURE MODEL 229
4. FAILURE-BAS-I (FB-I) ALGORITHM 230
5. FAILURE-BAS-Ð (FB-II) ALGORITHM 232
6. CONCLUSIONS 233
ACKNOWLEDGMENT 233
REFERENCES 233
Chapter 36. A Multimedia Information Processing and Analyzing Expert System 234
1. Introduction 234
2. Architecture and Design Criteria of MIPAS 235
3. Knowledge Representation and Processing Sub—system 235
4. The Software Environment of MIPAS 236
5. The Demonstration System and Experiments 237
References 237
Author Index 240