Li / Su / Rodd Artificial Intelligence in Real-Time Control 1989

1. Auflage 2014
ISBN: 978-1-4832-9833-7
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 123 Seiten, Web PDF

Reihe: IFAC Workshop Series

Papers presented at the workshop are representative of the state-of-the art of artificial intelligence in real-time control. The issues covered included the use of AI methods in the design, implementation, testing, maintenance and operation of real-time control systems. While the focus was on the fundamental aspects of the methodologies and technologies, there were some applications papers which helped to put emerging theories into perspective. The four main subjects were architectural issues; knowledge - acquisition and learning; techniques; and scheduling, monitoring and management.

Autoren/Hrsg.

Li, Hua-Tian

Su, Shi-Quan

Rodd, M. G.

Inhaltsverzeichnis

1;Front Cover;1
2;Artificial Intelligence in Real-Time Control 1989;4
3;Copyright Page;5
4;Table of Contents
;10
5;IFAC WORKSHOP ON ARTIFICIAL INTELLIGENCE IN REAL-TIME CONTROL 1989;6
6;PREFACE;8
7;PART I: KEYNOTE ADDRESSES;12
7.1;CHAPTER 1. ARTIFICIAL INTELLIGENCE AND FEEDBACK CONTROL;12
7.1.1;1 Introduction;12
7.1.2;2 Feedback Control and Artificial Intelligence;12
7.1.3;3 Indirect Expert Control;14
7.1.4;4 Direct Real-Time Expert Control;15
7.1.5;5 Modeling of the Operator;18
7.1.6;6 Learning Systems;19
7.1.7;7 Conclusions;20
7.1.8;References;21
7.2;CHAPTER 2. KNOWLEDGE-BASED VISION SYSTEMS IN REAL-TIME CONTROL;24
7.2.1;1 INTRODUCTION;24
7.2.2;2. COMPUTER VISION - A REVIEW;25
7.2.3;3. REQUIREMENTS OF INDUSTRIAL VISION SYSTEMS;25
7.2.4;4. KNOWLEDGE-BASED COMPUTER VISION SYSTEMS;26
7.2.5;5 KNOWLEDGE-REPRESENTATION AND -ACQUISITION FOR COMPUTER VISION;27
7.2.6;6 CONCLUSIONS;29
7.2.7;REFERENCES;29
7.3;CHAPTER 3. DISTRIBUTED ESTIMATION, INFERENCING AND MULTI-SENSOR DATA FUSION FOR REAL TIME SUPERVISORY CONTROL;30
7.3.1;INTRODUCTION;30
7.3.2;THE HIERARCHY OF MULTI-SENSOR DATA FUSION: BAYESIAN METHODS;31
7.3.3;POSSIBILITY, EVALUATION AND NON-NUMERIC METHODS FOR DATA FUSION;33
7.3.4;REFERENCES;34
8;PART II: ARCHITECTURAL ISSUES;36
8.1;CHAPTER 4. DISTRIBUTED INTELLIGENT OBJECTS IN AN ARCHITECTURE FOR REAL-TIME MONITORING AND CONTROL;36
8.1.1;INTRODUCTION;36
8.1.2;THE REQUIREMENTS OF FUTURE REAL-TIME KNOWLEDGE SYSTEMS;36
8.1.3;THE MANAGEMENT OF TIME;37
8.1.4;THE MANAGEMENT OF KNOWLEDGE;38
8.1.5;THE MANAGEMENT OF DISTRIBUTION;39
8.1.6;ADROIT - A DISTRIBUTED REAL-TIME OBJECT-ORIENTED INTELLIGENT TESTBED;39
8.1.7;CONCLUSION;40
8.1.8;REFERENCES;40
8.2;CHAPTER 5. USING DISCRETE AI TECHNIQUES FOR DESIGNING A REAL-WORLD CONTROL SUPERVISOR;42
8.2.1;INTRODUCTION;42
8.2.2;CONTROL SUPERVISION ARCHITECTURE;43
8.2.3;A DISCRETE WORLD MODEL;44
8.2.4;REAL-TIME ISSUES;45
8.2.5;CONCLUSION;45
8.2.6;REFERENCES;46
8.3;CHAPTER 6. HARDWARE AND SOFTWARE STRUCTURE OF A REAL-TIME EXPERT SYSTEM FOR CONTROL OF CHEMICAL PLANTS;48
8.3.1;INTRODUCTION;48
8.3.2;THE CONCEPT OF AUTOMATION WITH INTEGRATED REAL-TIME XPS;48
8.3.3;THE REALIZATION OF THE KNOWLEDGE BASED SYSTEM OF AUTOMATION;50
8.3.4;CONCLUSIONS;51
8.3.5;REFERENCES;51
9;PART III: KNOWLEDGE-ACQUISITION AND LEARNING;54
9.1;CHAPTER 7. MODELLING AND CONTROL FOR NONLINEAR TIME-DELAY SYSTEM VIA PATTERN RECOGNITION APPROACH;54
9.1.1;INTRODUCTION;54
9.1.2;STATISTICAL FUZZY PATTERN CLASSIFIER FOR SYSTEM MODELLING;54
9.1.3;INDUSTRIAL APPLICATION EXAMPLE — THE REAL TIME ESTIMATION AND FEEDBACK CONTROL OF THE GASOLINE VAPOUR—PRESSURE IN FCCU;57
9.1.4;NUMERICAL EXAMPLE;57
9.1.5;CONCLUSION;58
9.1.6;REFERENCE;58
9.2;CHAPTER 8. A CLUSTERING METHOD OF KNOWLEDGE ACQUISITION IN A REAL-TIME CONTROL SYSTEM;60
9.2.1;INTRODUCTION;60
9.2.2;A REAL-TIME PROBLEM;60
9.2.3;KNOWLEDGE CLUSTERING METHOD;61
9.2.4;CONCLUSION;62
9.2.5;REFERENCES;62
10;PART IV: TECHNIQUES;64
10.1;CHAPTER 9. TOWARDS INTELLIGENT PID CONTROL;64
10.1.1;1. Introduction;64
10.1.2;2. Process Characteristics;64
10.1.3;3. Features;65
10.1.4;4. Empirics;66
10.1.5;5· Relations;66
10.1.6;6. Ziegler-Nichols Tuning;67
10.1.7;7. Conclusions;69
10.1.8;8. References;69
10.2;CHAPTER 10. AN EXTENDED FEEDBACK STRUCTURE OF INTELLIGENT COMPUTER-AIDED CONTROL SYSTEMS DESIGN BASED ON OBJECT-ORIENTED LANGUAGE;70
10.2.1;1. Introduction;70
10.2.2;2. Control design;70
10.2.3;3. Graphics based on object-oriented language;71
10.2.4;4. Intelligent CACSD;73
10.2.5;5. Graphics data-base;74
10.2.6;6. Conclusion;75
10.2.7;References;75
10.3;CHAPTER 11. AN EXPERT SYSTEM SHELL FOR ANALYSIS OF REAL TIME SIGNALS;76
10.3.1;INTRODUCTION;76
10.3.2;BUILDING THE SYSTEM;76
10.3.3;SYSTEM STRUCTURE;78
10.3.4;REFERENCES;78
10.4;CHAPTER 12. AN EXPERT SELF-LEARNING FUZZY CONTROLLER;80
10.4.1;INTRODUCTION;80
10.4.2;THE PRINCIPLES OF ESLFC;80
10.4.3;SIMULATION EXPERIMENTS;83
10.4.4;CONCLUSION;83
10.4.5;REFERENCE;84
10.5;CHAPTER 13. ASYNCHRONOUS METHODS FOR EXPERT SYSTEMS IN REAL-TIME APPLICATIONS;86
10.5.1;Introduction;86
10.5.2;Asynchronous events;87
10.5.3;Transforming expert systems;87
10.5.4;Example;88
10.5.5;Discussion;89
10.5.6;References;89
11;PART V: SCHEDULING, MONITORING AND MANAGEMENT;90
11.1;CHAPTER 14. EVOLUTION OF EXPERT SYSTEMS FOR REAL-TIME PROCESS MANAGEMENT: A CASE STUDY ON MOTOR CONTROL;90
11.1.1;INTRODUCTION;90
11.1.2;KNOWLEDGE REPRESENTATION IN EXPERT SYSTEMS;90
11.1.3;REAL-TIME EXPERT SYSTEMS;91
11.1.4;DEDICATED COMPUTER APPROACH;91
11.1.5;A BETTER APPROACH - IMBEDDED EXPERT SYSTEMS;91
11.1.6;A MOTOR CONTROL APPLICATION;92
11.1.7;COMPARISON OF THE TWO APPROACHES;92
11.1.8;ADVANTAGES IN TESTABILITY;93
11.1.9;CONCLUSIONS;93
11.2;CHAPTER 15. NEURAL NETWORK BASED REAL-TIMEPRODUCTION SCHEDULING FOR INDUSTRIAL PROCESSES;96
11.2.1;INTRODUCTION;96
11.2.2;PROBLEM STATEMENT;96
11.2.3;ARTIFICIAL NEURAL NETWORK BASED EXPERT SYSTEM;97
11.2.4;AN INDUSTRIAL APPLICATION CASE STUDY —THE REAL TIME PRODUCTION SCHEDULING OF THE STEEL PLATE MILL;98
11.2.5;CONCLUSION;99
11.2.6;REFERENCE;99
11.3;CHAPTER 16. EXPERT SYSTEM FOR SENSOR FAILURE DETECTION OF AIRCRAFT;102
11.3.1;INTRODUCTION;102
11.3.2;MODEL FOR FLIGHT STATE ESTIMATION;103
11.3.3;BASIC STRUCTURE OF EXPERT SYSTEM;105
11.3.4;SIMULATION AND APPLICATION;107
11.3.5;CONCLUSION;107
11.3.6;REFERENCES;107
12;PART VI: APPLICATION STUDIES;108
12.1;CHAPTER 17. APPLICATION OF EXPERT FUZZY CONTROLLER IN THE PENICILLIN FERMENTATION PROCESSES;108
12.1.1;INTRODUCTION;108
12.1.2;PROBLEM FORMULATION;108
12.1.3;FERMENTATION PHASE RECOGNITION;109
12.1.4;CONSTRUCTION OF RULE BASE;109
12.1.5;CONTROL STRATEGY;109
12.1.6;CONCLUSIONS;110
12.1.7;REFERENCES;110
12.2;CHAPTER 18. THE RULE-BASED DISTILLATION DECISION AND CONTROL SYSTEM;112
12.2.1;Introduction;112
12.2.2;Knowledge base;113
12.2.3;Conclusions;114
12.2.4;Acknowledgeaents;114
12.2.5;References;115
12.3;CHAPTER 19. APPLICATION OF INTELLIGENT CONTROL OF TIME-DELAY PROCESSES TO THE MIX MOISTURE CONTROL SYSTEM IN SINTERPLANTS;118
12.3.1;1. INTRODUCTION;118
12.3.2;2. THE BASIC CONTROL UNIT;118
12.3.3;3. THE INTELLIGENT CONTROL UNIT;119
12.3.4;4. APPLICATION TO THE MIX MOISTURE CONTROL SYSTEM IN A SINTERPLANT;120
12.3.5;5. CONCLUSIONS;120
12.3.6;REFERENCES;120
13;AUTHOR INDEX;122
14;KEYWORD INDEX;124