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Safety, Reliability and Applications of Emerging Intelligent Control Technologies

E-BookPDFDRM AdobeE-Book
240 Seiten
Englisch
Elsevier Science & Techn.erschienen am28.06.2014
Increasingly, over the last few years, intelligent controllers have been incorporated into control systems. Presently, the numbers and types of intelligent controllers that contain variations of fuzzy logic, neural network, genetic algorithms or some other forms of knowledge based reasoning technology are dramatically rising. However, considering the stability of the system, when such controllers are included it is difficult to analyse and predict system behaviour under unexpected conditions. Leading researchers and industrial practitioners were able to discuss and evaluate current development and future research directions at the first IFAC International Workshop on safety, reliability and applications on emerging intelligent control technology. This publication contains the papers, covering a wide range of topics, presented at the workshop.mehr

Produkt

KlappentextIncreasingly, over the last few years, intelligent controllers have been incorporated into control systems. Presently, the numbers and types of intelligent controllers that contain variations of fuzzy logic, neural network, genetic algorithms or some other forms of knowledge based reasoning technology are dramatically rising. However, considering the stability of the system, when such controllers are included it is difficult to analyse and predict system behaviour under unexpected conditions. Leading researchers and industrial practitioners were able to discuss and evaluate current development and future research directions at the first IFAC International Workshop on safety, reliability and applications on emerging intelligent control technology. This publication contains the papers, covering a wide range of topics, presented at the workshop.
Details
Weitere ISBN/GTIN9781483296968
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format HinweisDRM Adobe
Erscheinungsjahr2014
Erscheinungsdatum28.06.2014
Seiten240 Seiten
SpracheEnglisch
Artikel-Nr.3165306
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1;Front Cover;1
2;Safety, Reliability and Applications of Emerging Intelligent Control Technologies;2
3;Copyright Page;3
4;Table of Contents;6
5;Foreword;5
6;PART 1: KEYNOTE 1;6
6.1;Chapter 1. Safe AI - Is This Possible?;10
6.1.1;1. INTRODUCTION - WHY AL;10
6.1.2;2. THE DILEMMA;11
6.1.3;3. DETERMINISM IN CONTROL SYSTEMS;13
6.1.4;4. THEN ALONG CAME AI;14
6.1.5;5. PROTECTING THE SYSTEM FROM ITS CONTROLLER;15
6.1.6;6. MAKING AI TECHNIQUES DETERMINISTIC;15
6.1.7;7. THE WAY AHEAD;16
6.1.8;ACKNOWLEDGEMENTS;17
6.1.9;REFERENCES;17
7;PART 2: GENETIC ALGORITHMS;6
7.1;Chapter 2. Genetic Model-Reference Adaptive Control Systems Incorporating PID Controllers;18
7.1.1;1. INTRODUCTION;18
7.1.2;2. ANALYSIS;18
7.1.3;3. ILLUSTRATIVE EXAMPLE;19
7.1.4;4. CONCLUSION;20
7.1.5;REFERENCES;20
7.2;Chapter 3. Fuzzy Control of Water Pressure Using Genetic Algorithm;24
7.2.1;1. INTRODUCTION;24
7.2.2;2. DESIGN PRINCIPLE OF GENETIC FUZZY LOGIC CONTROL;25
7.2.3;3. EXPERIMENTAL RESULT;28
7.2.4;4. CONCLUSION;29
7.2.5;5. REFERENCE;29
7.3;Chapter 4. Genetic Tuning of Model-Reference Neural PID Controllers;30
7.3.1;1. INTRODUCTION;30
7.3.2;2. ANALYSIS;31
7.3.3;3. ILLUSTRATIVE EXAMPLE;31
7.3.4;4. CONCLUSION;32
7.3.5;REFERENCES;32
8;PART 3: ICONTROL ItEAL-TME CONTROL;6
8.1;Chapter 5. U.S. NRC Research and Digital Instrumentation and Control;36
8.1.1;1. INTRODUCTION;36
8.1.2;2. DISCUSSION;36
8.1.3;3. REFERENCES;38
8.2;Chapter 6. An Approach to the Design of Expert Systems for Hard Real-Time Control;40
8.2.1;1. INTRODUCTION;40
8.2.2;2. CURRENT REAL-TIME EXPERT SYSTEMS;40
8.2.3;3. A POSSIBLE APPROACH;41
8.2.4;4. A KNOWLEDGE-BASE COMPILER;42
8.2.5;5. CONCLUSION;44
8.2.6;REFERENCES;44
8.3;Chapter 7. Intelligent Voting Strategies for Dependable Real-time Control Systems;46
8.3.1;1. INTRODUCTION;46
8.3.2;2. THE APPLICABLE SAFETY STANDARDS;47
8.3.3;3. THE REQUIREMENTS FOR AN INTELLIGENT VOTER SERVICE;47
8.3.4;4. FORMAL MATHEMATICAL ANALYSIS;49
8.3.5;5. N-VERSION VOTERS;50
8.3.6;6. CONCLUSIONS;51
8.3.7;ACKNOWLEDGEMENT;51
8.3.8;REFERENCES;51
8.4;Chapter 8. A Neuro-Compensator for the Control of Robots by an Inertia-Related Control Approach;52
8.4.1;1. INTRODUCTION;52
8.4.2;2. CONTROL OF ROBOTS USING AN INERTIARELATED APPROACH;52
8.4.3;3. ROBOT CONTROL WITH THE NEUROCOMPENSATOR;53
8.4.4;4. IMPLEMENTATION CONSIDERATION;54
8.4.5;5. EXPERIMENT RESULTS;55
8.4.6;6. CONCLUSIONS;56
8.4.7;REFERENCES;56
9;PART 4: NEURAL NETWORKS;6
9.1;Chapter 9. Stochastic Tuning of a Spacecraft Controller Using Neural Networks;58
9.1.1;1. INTRODUCTION;58
9.1.2;2. SOHO THRUSTER CONTROL SYSTEM & STOCHASTIC PARAMETER TUNING;58
9.1.3;3. SIMULATION OF THE ADAPTIVE SYSTEM;60
9.1.4;4. CONCLUSION;62
9.1.5;ACKNOWLEDGEMENT;63
9.1.6;REFERENCES;63
9.2;Chapter 10. Determining the Node Number of Neural Network Models;64
9.2.1;I. INTRODUCTION;64
9.2.2;2. SYSTEM REPRESENTATION AND NEURAL NETWORK MODELS;65
9.2.3;3. IDENTIFICATION ALGORITHM AND ITS ASYMPTOTIC PROPERTIES;65
9.2.4;4. MAIN RESULTS;66
9.2.5;5. NUMERICAL ILLUSTRATION;68
9.2.6;6. CONCLUSIONS;68
9.2.7;REFERENCES;68
9.3;Chapter 11. Fail-Safe Stability for Neural Network Controlled Systems;70
9.3.1;1. INTRODUCTION;70
9.3.2;2. PROBLEM FORMULATION;70
9.3.3;3. SMALL GAIN THEOREM AND FAIL-SAFE STABILITY;71
9.3.4;4. CONCLUSION;75
9.3.5;REFERENCES;75
9.4;Chapter 12. Nonlinear Dilation Networks for Prediction Applications;76
9.4.1;I. INTRODUCTION;76
9.4.2;II. NONLINEAR DILATION NETWORK ARCHITECTURE;77
9.4.3;III. LEARNING ALGORITHM;78
9.4.4;IV. APPLICATIONS OF NONLINEAR DILATION NETWORK;78
9.4.5;V. CONCLUSIONS;80
9.4.6;ACKNOWLEDGMENTS;80
9.4.7;REFERENCES;80
10;PART 5: KEYNOTE 2;7
10.1;Chapter 13. The Impact of Safety and Reliability Requirements on the Specification of Control Systems;82
10.1.1;1. INTRODUCTION;82
10.1.2;2. SPECIFICATION OF CONTROL SYSTEMS;84
10.1.3;3. SAFETY AND RELIABILITY FUNCTIONS IN THE SPECIFICATION;87
10.1.4;4. CONCLUSIONS;89
10.1.5;5.REFERENCES;89
11;PART 6: KEYNOTE 3;7
11.1;Chapter 14. The Safety Implications of Emerging Software Paradigms;92
11.1.1;1.0 INTRODUCTION;92
11.1.2;2.0 The Emerging Paradigms;93
11.1.3;3.0 COMPARING CONVENTIONAL AND EMERGING SOFTWARE PARADIGMS;95
11.1.4;4.0 EMERGING PARADIGM V&V ISSUES AND CHAM, ENGES;96
11.1.5;5.0 KNOWLEDGE-BASED SYSTEM PARADIGM;98
11.1.6;6.0 NEURAL NETWORK PARADIGM;98
11.1.7;7.0 GENETIC ALGORITHM PARADIGM;100
11.1.8;8.0 FUZZY SYSTEM PARADIGM;101
11.1.9;9.0 SUMMARY;102
11.1.10;10.0 CONCLUSION;103
11.1.11;REFERENCES;104
12;PART 7: FUZZY SYSTEMS;7
12.1;Chapter 15. Fuzzy System as a Parameter Estimator of Nonlinear Dynamic Functions;106
12.1.1;1. INTRODUCTION;106
12.1.2;2. PARAMETRISATION OF FUZZY SYSTEMS;106
12.1.3;3. FUZZY ESTIMATOR;108
12.1.4;4. SIMULATION EXAMPLES;109
12.1.5;REFERENCES;111
12.2;Chapter 16. A Process Control and Diagnostic Tool Based on Continuous Fuzzy Petri Nets;112
12.2.1;1 Introduction;112
12.2.2;2 Application to Process Control;112
12.2.3;3 The CFPN Concept;113
12.2.4;4 Workspace Hierarchy;115
12.2.5;5 Example;115
12.2.6;6 Conclusions;115
12.2.7;References;116
12.3;Chapter 17. Actuator Saturation Compensation for Fuzzy Controllers;118
12.3.1;1. INTRODUCTION;118
12.3.2;2. Fuzzy Controllers;118
12.3.3;3. Actuator saturation compensation;119
12.3.4;4. Example;121
12.3.5;5. Conclusions;121
12.3.6;Acknowledgements;121
12.3.7;References;122
12.4;Chapter 18. Analysis of Fuzzy Control Methodology Applied to DC-DC Converter Control;124
12.4.1;1. INTRODUCTION;124
12.4.2;2. SWITCHING MODE POWER CONVERTER OPERATION;124
12.4.3;3. FUZZY PID CONTROL ALGORITHM;126
12.4.4;4. LARGE SIGNAL PBECEWISE SIMULATION;127
12.4.5;5. CONCLUSION;127
12.4.6;REFERENCES;128
13;PART 8: AUTONOMOUS VEHICLES;7
13.1;Chapter 19. Fuzzy Logic Based Behavior Fusion Strategy for Robot Navigation in Uncertain;130
13.1.1;1. INTRODUCTION;130
13.1.2;2. BEHAVIOR BASED CONTROL;130
13.1.3;3. DESCRIPTION OF REACTIVE BEHAVIORS USING FUZZY LOGIC;131
13.1.4;4. BEHAVIOR FUSION BY FUZZY REASONING;132
13.1.5;5. SIMULATIONS;134
13.1.6;6. COMBINATION WITH HIGH-LEVEL GLOBAL PLANNING;135
13.1.7;ACKNOWLEDGMENT;135
13.1.8;REFERENCES;135
13.2;Chapter 20. Linear and Nonlinear Models of Automated Vehicles Analysis and Experiments;136
13.2.1;1. INTRODUCTION;136
13.2.2;2. DEVELOPMENT OF THE NONLINEAR MODEL;136
13.2.3;3. LINEAR MODEL;138
13.2.4;4. EXPERIMENTAL RESULTS;138
13.2.5;5. CONCLUSION;138
13.2.6;REFERENCES;138
13.3;Chapter 21. Effective Development of Fuzzy-Logic Rules for Real-Time Control of Autonomous Vehicles;142
13.3.1;1. INTRODUCTION;142
13.3.2;2. LEARNING FROM CONVENTIONAL CONTROL;142
13.3.3;3. ANALOGOUS FUZZY-LOGIC AND CONVENTIONAL IMPLEMENTATION;143
13.3.4;4. IMPROVEMENT OF FUZZY-LOGIC RULES;144
13.3.5;5. ROBUSTNESS OF IMPROVED CONTROLLER;144
13.3.6;6. CONCLUSION;145
13.3.7;REFERENCES;145
13.4;Chapter 22. Design of a Controller with Feedforward Action for Path Tracking of Automated Vehicles;148
13.4.1;1. INTRODUCTION;148
13.4.2;2. A LINEAR DYNAMIC MODEL;148
13.4.3;3. FORMULATION OF THE VEHICLE PATH TRACKING;149
13.4.4;4. SYNTHESIS OF THE OPTIMAL CONTROLLER;150
13.4.5;5. EXPERIMENTAL RESULTS;150
13.4.6;6. CONCLUSIONS;151
13.4.7;ACKNOWLEDGMENT;151
13.4.8;REFERENCES;151
13.4.9;APPENDIX A;151
14;PART 9: KEYNOTE 4;7
14.1;Chapter 23. Advances in Neurofuzzy Algorithms for Real Time Modelling, Control and Estimation;154
14.1.1;1. NEUROFUZZY SYTEMS.;154
14.1.2;2. NEUROFUZZY SYSTEMS AND STRUCTURE;154
14.1.3;3. TRAINING NEUROFUZZY ALGORITHMS;156
14.1.4;4. NEUROFUZZY CONSTRUCTION ALGORITHMS;158
14.1.5;5. GENERAL ISSUES IN ADAPTIVE NEUROFUZZY SYSTEMS;159
14.1.6;REFERENCES;161
15;PART 10: KEYNOTE 5;7
15.1;Chapter 24. Static and Dynamic Preprocessing Methods in Neural Networks;162
15.1.1;1. INTRODUCTION;162
15.1.2;2. TRANSFORMATION OF INPUT SIGNALS;163
15.1.3;3. ILL POSED PROBLEMS;166
15.1.4;4. PREPROCESSING WITH ILL POSED PROBLEMS;168
15.1.5;5. OPEN ISSUES;168
15.1.6;6. CONCLUSIONS;168
15.1.7;REFERENCES;169
16;PART 11: FAULT DETECTION;8
16.1;Chapter 25. Enhancing Aircraft Engine Condition Monitoring;170
16.1.1;1. INTRODUCTION;170
16.1.2;2. ENGINE CONDITION MONITORING;171
16.1.3;3. ARTIFICIAL INTELLIGENCE APPROACH;173
16.1.4;4. A GENERAL FRAMEWORK FOR ENHANCED ENGINE CONDITION MONITORING;173
16.1.5;5. PRELIMINARY RESULTS;174
16.1.6;6. CONCLUSIONS;175
16.1.7;REFERENCES;175
16.2;Chapter 26. Parity Vector Compensation Using Non-Linear Filtering;176
16.2.1;1. INTRODUCTION;176
16.2.2;2. FDI METHOD;176
16.2.3;3. PARITY VECTOR COMPENSATION USING NON-LINEAR FILTERING;177
16.2.4;4. APPLICATION;178
16.2.5;5. CONCLUSION;180
16.2.6;REFERENCES;180
16.3;Chapter 27. Automatic Fault-Tree Generation;182
16.3.1;1. INTRODUCTION;182
16.3.2;2. PROBLEM DESCRIPTION;183
16.3.3;3. MOTIVATIONS FOR USING ARTIFICIAL INTELLIGENCE;184
16.3.4;4. CASE-BASED REASONING APPROACH FOR DIAGNOSIS SYSTEM DESIGN;185
16.3.5;5. BUILDING DIAGNOSIS SYSTEMS;186
16.3.6;6. FUTURE EXPANSION;186
16.3.7;7. CONCLUSIONS;187
16.3.8;REFERENCES;187
16.4;Cjhapter 28. Moving Window Method for Fault Detection and Classification Based on ART2 Neural
Network;188
16.4.1;1. INTRODUCTION;188
16.4.2;2. ART2 NEURAL NETWORK AND LEARNING ALGORITHM;188
16.4.3;3. MOVING WINDOW SCHEME FOR FDD USING ART2 NEURAL NETWORK;190
16.4.4;4. AN EXAMPLE;190
16.4.5;5. CONCLUSION;191
16.4.6;REFERENCES;191
16.5;Chapter 29. Fault Detection and Identification Using Neural Network and Fuzzy Logic;194
16.5.1;1. INTRODUCTION;194
16.5.2;2. USING NEURAL NETWORK FOR VARIABLE ESTIMATION;194
16.5.3;3. USING FUZZY NEURAL NETWORK (FNN) FOR FAULT DETECTION AND IDENTIFICATION;195
16.5.4;4. APPLICATION TO MONITORING OF PIPE FLOW;197
16.5.5;5. CONCLUSIONS AND DISCUSSIONS;198
16.5.6;6. ACKNOWLEDGEMENTS;199
16.5.7;REFERENCES;199
16.6;Chapter 30. Fault Tolerant Management and Fuzzy Control of Integrated GPS/INS;200
16.6.1;1. INTRODUCTION;200
16.6.2;2. FAULT TOLERANT AND REDUNDANT MANAGEMENT CRITERIA;200
16.6.3;3. CONTROL FOR REDUNDANT MANAGEMENT;201
16.6.4;4. THE EXPERIMENTS OF INS/GPS;201
16.6.5;5. SUMMARY;202
16.6.6;REFERENCES;202
17;PART 12: POWER SYSTEMS;8
17.1;Chapter 31. Solving Power System Optimisation Problems Using Simulated Annealing;204
17.1.1;1. INTRODUCTION;204
17.1.2;2. POWER SYSTEM OPTIMISATION PROBLEMS;205
17.1.3;3. SIMULATED ANNEALING TECHNIQUE;205
17.1.4;4· SA-BASED OPTIMISATION ALGORITHM;206
17.1.5;5. APPLICATION TO ECONOMIC DISPATCH;207
17.1.6;6. CONCLUSION;208
17.1.7;REFERENCES;208
17.2;Chapter 32. Dynamic Voltage Security Assessment by Fuzzy Severity Index;210
17.2.1;1. INTRODUCTION ;210
17.2.2;2. RELATED FUZZY SET THEORY;210
17.2.3;3. DESIGN OF THE FUZZY CLASSIFIER FOR SECURITY ASSESSMENT;211
17.2.4;4. NUMERICAL RESULTS;213
17.2.5;5. CONCLUSION;215
17.2.6;REFERENCES;215
18;PART 13: EXPERT/INTELLIGENT SYSTEMS;8
18.1;Chapter 33. Intelligent Control of Mobile Robot;216
18.1.1;1. ARCHITECTURE OF MOBILE ROBOT THMR-III;217
18.1.2;2. GENERAL COMPUTER CONTROL MODULE TH-STD-7898;217
18.1.3;3. VISION SUBSYS. AND INFORMATION REDUCED TECHNOLOGY;217
18.1.4;4. THE RESEARCH OF TERCEPTON-ACTION" BEHAVIOR;220
18.1.5;5. APPLICATION OF FUZZY CONTROL IN THE NAVIGATION CONTROL OF ROBOT;221
18.1.6;REFERENCES;221
18.2;Chapter 34. Robustness of Neural Network Model for the Thermal Dynamics in Buildings;222
18.2.1;1 INTOODUCTION;222
18.2.2;2 THE ENVIRONMENTAL CHAMBER SYSTEM;223
18.2.3;3 NN MODELLING OF THE SYSTEM;224
18.2.4;4 ROBUSTNESS AND RELIABILITY OF THE MODEL;225
18.2.5;5 CONCLUSIONS;226
18.2.6;REFERENCES;226
18.3;Chapter 35. Modelling Failure Prone Flexible Manufacturing Systems;228
18.3.1;1. INTRODUCTION;228
18.3.2;2. MODELLINGAPPROACH;229
18.3.3;3. PARALLEL-FAILURE MODEL;229
18.3.4;4. FAILURE-BAS-I (FB-I) ALGORITHM;230
18.3.5;5. FAILURE-BAS-Ð (FB-II) ALGORITHM;232
18.3.6;6. CONCLUSIONS;233
18.3.7;ACKNOWLEDGMENT;233
18.3.8;REFERENCES;233
18.4;Chapter 36. A Multimedia Information Processing and Analyzing Expert System;234
18.4.1;1. Introduction;234
18.4.2;2. Architecture and Design Criteria of MIPAS;235
18.4.3;3. Knowledge Representation and Processing Sub-system;235
18.4.4;4. The Software Environment of MIPAS;236
18.4.5;5. The Demonstration System and Experiments;237
18.4.6;References;237
19;Author Index;240mehr