Artificial Intelligence in Real-Time Control 1994 (eBook)

Front Cover 1
Artificial Intelligence in Real Time Control 1994 (AIRTC'94) 2
Copyrigh Page 3
Table of Contents 6
IFAC SYMPOSIUM ON ARTIFICIAL INTELLIGENCE IN REAL TIME CONTROL 1994 4
FOREWORD 5
PART 1: 
10 
Chapter 1. Integrating Real-Time AI Techniques in Adaptive Intelligent Agents 10
1. INTRODUCTION 10
2. REAL TIME TECHNIQUES IN THE AGENT ARCHITECTURE 11
3. REAL-TIME AI TECHNIQUES IN THE AGENT'S REASONING METHODS 13
4. REAL-TIME AI TECHNIQUES IN THE AGENT'S CONTROL STRATEGY 14
5. EMERGENT REAL-TIME PROPERTIES IN AN AGENT'S BEHAVIOR 17
6. CONCLUSIONS 18
7. REFERENCES 18
CHAPTER 2. NEURAL NETWORK BASED ADAPTIVE CONTROL 22
1. INTRODUCTION 22
2. NEURAL NETWORK BASED CONTROL 23
3. LEARNING AND ADAPTATION 25
4. NN BASED ADAPTIVE CONTROLLERS CLASSIFICATION 26
5. NN ADAPTIVE CONTROL WITH FAST ADAPTATION SPEED 28
6. STABILITY ANALYSIS 29
7. IMPLEMENTATIONS 30
8. CONCLUSIONS 31
ACKNOWLEDGEMENT 31
9. REFERENCES 31
CHAPTER 3. A COMPUTATIONAL INTELLIGENCE PERSPECTIVE ON PROCESS MONITORING AND OPTIMIZATION 34
1. INTRODUCTION 34
2. THE FUNCTIONAL-LINK NET: EFFICIENCY IN LEARNING A MODEL OF A PROCESS 35
3. OPTIMIZATION 36
4 ASSOCIATIVE MEMORIZATION AND RECALL 36
5 THE COMPUTATIONAL INTELLIGENCE PERSPECTIVE 37
6. EXAMPLES OF PROCESS MONITORING AND OPTIMIZATION TASKS 37
7. REFERENCES 38
CHAPTER 4. TRENDS IN ARTIFICIAL INTELLIGENCE APPLICATIONS FOR REAL-TIME CONTROL (A SPECULATIVE STUDY) 40
1. INTRODUCTION 40
2. REAL-TIME SYSTEMS 40
3. AI METHODS AND TIME CONSTRAINTS 42
4. AI APPLICATIONS IN REAL-TIME 45
5. TRENDS IN REAL-TIME AI 46
6. CONCLUSIONS 47
ACKNOWLEDGEMENT 48
REFERENCES 48
PART 2: 
52 
CHAPTER 5. DYNAMIC ANALYSIS OF WEIGTHED-OUTPUT FUZZY CONTROL SYSTEMS 52
1. INTRODUCTION 52
2. STABILITY ANALYSIS 53
3. WEIGTHED OUTPUT FUZZY CONTROLLERS 53
4. CONCLUSIONS 56
5. ACKNOWLEDGEMENTS 56
6. REFERENCES 57
CHAPTER 6. IDENTIFICATION OF FUZZY RULES FROM LEARNING DATA 58
1. INTRODUCTION 58
2. THE SIMPLIFIED COS-FUZZY CONTROLLER 58
3. THE IDENTIFICATION ALGORITHM 59
4. EXAMPLES 60
5. CONCLUSIONS 62
REFERENCES 63
CHAPTER 7. ROBUST DESIGN OF FUZZY CONTROLLERS BASED ON SMALL GAIN CONDITIONS 64
1. INTRODUCTION 64
2. STABILITY ANALYSIS 65
3. ROBUST DESIGN 66
4. EXAMPLE 68
5. CONCLUSIONS 69
6. ACKNOWLEDGEMENT 69
7. REFERENCES 69
CHAPTER 8. A FUZZY CONTROLLER FOR ACTIVATED SLUDGE WASTE WATER PLANTS 70
1. INTRODUCTION 70
2. AN EXAMPLE PLANT 70
3. CONVENTIONAL CONTROL 71
4. THE FUZZY CONTROLLER PROPOSED 72
5. SIMULATION EXPERIMENTS AND RESULTS 72
6. CONCLUDING REMARKS 74
Acknowledgements 74
7. REFERENCES 75
CHAPTER 9. AN ADVANCED FUZZY CONTROLLER FOR TRAFFIC LIGHTS 76
1. INTRODUCTION 76
2. FUZZY LOGIC AND TRAFFIC LIGHT DESIGN 76
3. THE PROBLEM CONSIDERED 77
4. SIMULATION EXPERIMENTS AND THEIR RESULTS 79
5. CONCLUDING REMARKS 80
Acknowledgements 80
6. REFERENCES 81
CHAPTER 10. 
82 
1. INTRODUCTION 82
2. FUZZY CONTROLLER DESCRIPTION 83
3. GRAPHICAL USER INTERFACE 84
4. FUZZY CONTROLLER APPLICATION AND RESULTS 84
5. CONCLUSIONS 86
6. REFERENCES 87
CHAPTER 11. ANALYSIS OF RULEBASE COHERENCE IN FUZZY CONTROL SYSTEMS 88
1. INTRODUCTION 88
2. FUZZY SYSTEMS 88
3. COHERENCE OF RULEBASES 89
4. REDEFINITION OF THE IDEAL DEFUZZIFIER 91
5. RULEBASE ANALYSIS 92
6. CONCLUSIONS 93
7. REFERENCES 93
CHAPTER 12. DERIVATION OF FUZZY HYBRID MODELS FOR REAL-TIME FUZZY CONTROL DESIGN: APPLICATION TO A FURNACE 94
1. INTRODUCTION 94
2. THEORETICAL BACKGROUND 94
3. IDENTIFICATION OF MODEL PARAMETERS 96
4. REAL-TIME CONTROL DESIGN METHODOLOGY 96
5. RESULTS 97
6. CONCLUSIONS 98
7. REFERENCES 98
Chapter 13. Comparison of Fuzzy, Rule-Based, and Conventional Process Control 100
1. INTRODUCTION 100
2. CONVENTIONAL CONTROL 101
3. FUZZY CONTROL 102
4. RULE-BASED CONTROLLER 102
5. COMPARING THE CONTROL PRINCIPLES 103
6. CONCLUDING REMARKS 105
7. REFERENCES 105
CHAPTER 14. 
106 
1. INTRODUCTION 106
2. THE EXECUTION SUPERVISOR 106
3. QUALITATIVE ERROR ANALYSIS 109
4. TRAINING AND LEARNING 110
5. CONCLUSIONS 112
Acknowledgments 112
References 112
CHAPTER 15. LEARNING OF SPECIFIC PROCESS MONITORS IN MACHINE TOOL SUPERVISION 114
1. INTRODUCTION 114
2. PROGNOSTIC AND MONITORING OF CNC MACHINES 114
3. TAXONOMY OF LEARNING 115
4. IMPLEMENTATION OF THE SPECIFIC PROCESS MONITOR 116
5. EXPERIMENTAL RESULTS 118
6. LIMITATIONS AND CONCLUSIONS 119
7. REFERENCES 119
CHAPTER 16. NEURAL-BASED LEARNING IN GRASP FORCE CONTROL OF A ROBOT HAND 120
1. INTRODUCTION 120
2. FORCE CONTROL PROBLEM 120
3. MULTI-LEVEL CONTROL SYSTEM 122
4. NEURAL FORCE CONTROL 122
5. CONCLUSION 125
6. REFERENCES 125
CHAPTER 17. INTEGRATED ACQUISITION, EXECUTION, EVALUATION, AND TUNING OF ELEMENTARY SKILLS FOR INTELLIGENT ROBOTS 126
1. INTRODUCTION 126
2. SKILLS IN ROBOT PROGRAMMING AND CONTROL 127
3. IDENTIFICATION OF LEARNING TASKS 128
4. THE INTERACTIVE PROGRAMMING ENVIRONMENT 129
5. AN ARCHITECTURE SUPPORTING REAL-TIME CONTROL AND ENHANCEMENT 130
6. CONCLUSION 130
ACKNOWLEDGEMENT 131
REFERENCES 131
CHAPTER 18. NEURAL-NET BASED OPTICAL ELLIPSOMETRY FOR MONITORING GROWTH OF SEMICONDUCTOR FILMS 132
1. INTRODUCTION 132
2. OPTICAL ELLIPSOMETRY FOR MONITORING FILM PROPERTIES AND THICKNESS 132
3. FUNCTIONAL LINK NETWORK 133
4. NEURAL-NET BASED OPTICAL ELLIPSOMETRY FOR MONITORING GROWTH OF SEMICONDUCTOR FILMS 133
5. EXPERIMENTAL RESULTS 135
6, CONCLUSION 137
7. REFERENCES 137
CHAPTER 19. ARTIFICIAL INTELLIGENCE IN PROCESS CONTROL OF PULSED LASER DEPOSITION 138
1. INTRODUCTION 138
2. PROBLEM DESCRIPTION 139
3. SOLUTION APPROACH AND IPM 140
4. RESULTS 142
5 ACKNOWLEDGMENTS 143
6 REFERENCES 143
CHAPTER 20. THE REAL ISSUES IN FUZZY LOGIC APPLICATIONS 144
1. INTRODUCTION 144
2. BINARY THINKING 144
3. ANALOG THINKING 145
4. RULE GENERATION 146
5. FUZZYFICATION AND DEFUZZYFICATION 146
6. IMPORTANT OBSERVATIONS AND RECOMMENDATIONS 147
7. FUZZY TANK CONTROL - AN APPLICATION 147
8. FUZZY LOGIC CAR CONTROLLER - AN APPLICATION 148
9. CONCLUSION 149
10. REFERENCES 149
CHAPTER 21. CASE STUDIES IN PROCESS MODELLING AND CONDITION MONITORING USING ARTIFICIAL NEURAL NETWORKS 150
1. INTRODUCTION 150
2. PROCESS DESCRIPTION 151
4. APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO PROCESS MODELLING 151
5. MODELLING USING LINEAR MODELS 152
6. MELTER MODELLING RESULTS 153
7. APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO CONDITION MONITORING 154
8. CONCLUSIONS 155
9. ACKNOWLEDGEMENTS 155
10. REFERENCES 155
CHAPTER 22. DAI-DEPUR ARCHITECTURE: DISTRIBUTED AGENTS FOR REAL-TIME WWTP SUPERVISION AND CONTROL 156
1. INTRODUCTION 156
2. DISTRIBUTED AI 156
3. ARCHITECTURE 157
4. A CASE STUDY 159
5. CONCLUSIONS AND FUTURE WORK 160
ACKNOWLEDGEMENTS 161
REFERENCES 161
CHAPTER 23. FAULT DIAGNOSIS OF A CSTR USING FUZZY NEURAL NETWORKS 162
1 INTRODUCTION 162
2 THE CSTR SYSTEM 163
3 FUZZY NEURAL NETWORKS FOR FAULT DIAGNOSIS 163
4 APPLICATION TO THE CSTR SYSTEM 164
5 CONCLUSIONS 166
6 REFERENCES 167
CHAPTER 24. 
168 
1. INTRODUCTION 168
2. LEARNING INVERSE KINEMATICS 169
3. CONTROLLING INVERSE DYNAMICS WITH NEURAL NETWORKS 170
4. LEARNING VISUAL POSITIONING 172
5. CONCLUSIONS 173
Acknowledgements 174
References 174
Chapter 25. The NECTAR-project Research into the application of Neural Networks for Flight Control 176
Introduction 176
Neural Networks for Control 177
Preliminary results applying application of neural networks for flight control 180
Conclusions 181
Literature 181
Chapter 26. Optimal Attitude Control of Satellites by Artificial Neural Networks: a Pilot Study 182
1. Introduction 182
2. The ISO 182
3. Reinforcement Learning 183
4. Attitude control of ISO 184
5. Conclusion 187
References 187
CHAPTER 27. 
188 
1. INTRODUCTION 188
2. TERMINOLOGY AND SPECIAL SYMBOLS 188
3. REASONING ABOUT NEURAL COMPUTATION WITH TIMED PROOFNETS 189
4. APPLICATION: IDENTIFICATION AND TRACKING SYSTEM CONTROLLER 190
5. SUMMARY 193
REFERENCES 193
CHAPTER 28. A NEURAL NETWORK BASED QUALITY CONTROL SYSTEM FOR STEEL STRIP MANUFACTURING 194
1. INTRODUCTION 194
2. SYSTEM DESCRIPTION 194
3. THE NEURAL CLASSIFIER 196
5. EXPERIMENTAL RESULTS 199
6. CONCLUSIONS 199
7. ACKNOWLEDGEMENTS 199
8. REFERENCES 199
CHAPTER 29. 
200 
1. INTRODUCTION 200
2. NEUROFUZZY SYSTEMS 201
3. THE CURSE OF DIMENSIONALITY 202
4. GLOBAL PARTITIONING 202
5. TRAINING 203
6. CONCLUSIONS 205
REFERENCES 205
PART 3: INTELLIGENT CONTROLLERS AND APPLICATIONS 206
CHAPTER 30. LEARNING TASK APPLIED TO IDENTIFICATION OF A MARINE VEHICLE 206
1. INTRODUCTION 206
2. SOLVING THE MODEL PARAMETER ESTIMATION TASK 207
3. STATE OBSERVER DESIGN 208
4. LEARNING ALGORITHM 208
5. IMPLEMENTATION PROCEDURE ON A SHIP STEERING CONTROL TASK 208
6. SIMULATION RESULTS AND CONCLUDING REMARKS 209
7. REFERENCES 210
8. ACKNOWLEDGEMENTS 210
CHAPTER 31. A LOCAL GUIDANCE METHOD FOR LOW-COST MOBILE ROBOTS USING FUZZY LOGIC 212
1. INTRODUCTION 212
2. APPROACH 212
3. LOCAL GUIDANCE 213
4. IMPLEMENTATION 215
5. ACKNOWLEDGMENTS 216
6. REFERENCES 216
CONCLUSIONS 215
CHAPTER 32. ROAD FOLLOWING BY ARTIFICIAL VISION USING NEURAL NETWORK 218
1. INTRODUCTION 218
2. DESCRIPTION 218
3. CHARACTERISTICS OBTAINING 218
4. SEGMENTATION 219
5. PREESTABUSHED PATTERNS 220
6. NEURAL NET 220
7. TIME DELAY NEURAL NET 221
8. ESTIMATION FUNCTION 222
9. DECISION-MAKING BLOCK 222
10. SYSTEM ADAPTATION 223
11. FUTURE IMPROVEMENTS 223
12. REFERENCES 223
CHAPTER 33. NAVIGATION WITH UNCERTAIN POSITION ESTIMATION IN THE RAM-1 MOBILE ROBOT 224
1. INTRODUCTION 224
2. LOCATION ESTIMATION IN THE RAM-1 225
3. UNCERTAINTY MODEL 226
4. APPLICATION TO RAM-1 227
5. CONCLUSIONS 228
6. REFERENCES 228
CHAPTER 34. REAL-TIME VISION-BASED NAVIGATION AND 3D DEPTH ESTIMATION FOR AN INDOOR AUTONOMOUS MOBILE ROBOT 230
1. INTRODUCTION 230
2. NAVIGATION 231
3. EGOMOTION AND DEPTH 233
3. IMPLEMENTATION 235
4. ACKNOWLEDGMENTS 235
REFERENCES 235
CHAPTER 35. REAL-TIME NEURAL CONTROLLER IMPLEMENTED ON PARALLEL ARCHITECTURE 236
1. INTRODUCTION 236
2. PARALLEL ALGORITHM 236
3. NEURAL CONTROLLER 238
4. CONCLUSION 241
References 241
CHAPTER 36. ACHIEVING HIGH PERFORMANCE SONAR-BASED WALL-FOLLOWING 242
Introduction 242
Assumptions 242
The Sonar Sensor Model 243
Control Laws 243
Achieving High Performance Behavior 243
Initial Exploration 244
Actual Navigation 244
Performance Evaluation 245
Conclusions and Future Work 245
Appendix: Accuracy of Odometry 245
References 245
CHAPTER 37. APPLICATION OF AN OBJECT-ORIENTED EXPERT SYSTEM SHELL TO A FERMENTATION PROCESS 246
1. INTRODUCTION 246
2. KNOWLEDGE ACQUISITION 246
3. EXPERIMENTAL STUDY OF THE PROCESS 247
4. APPLICATION OF AN ARTIFICIAL INTELLIGENCE TOOL 249
5. CONCLUSION 251
6. REFERENCES 251
CHAPTER 38. COMMUNICATION PROBLEMS OF EXPERT SYSTEMS IN MANUFACTURING ENVIRONMENT 252
1. INTRODUCTION 252
2. EXPERT SYSTEMS AND COMMUNICATION 252
3. THE SSQA APPROACH 254
4. CONNECTING G2 and SIMAN 254
5. CONCLUSIONS, FUTURE PLANS 256
6. REFERENCES 256
CHAPTER 39. ELECTRONIC CONTROL FOR A WHEEL-CHAIR GUIDED BY ORAL COMMANDS AND ULTRASONIC AND INFRARED SENSORS 258
1. INTRODUCTION 258
2. ELECTRONIC SYSTEM CONFIGURATION 259
3. SENSOR SYSTEM OF THE WHEEL-CHAIR 259
4. PHYSICAL DESCRIPTION AND MECHANICAL MODEL OF THE WHEEL-CHAIR 260
5. CONTROL 261
6. SPEECH RECOGNITION 263
7. RESULTS 263
8. REFERENCES 263
CHAPTER 40. HUMAN INTERACTION FOR PROCESS SUPERVISION BASED ON END-USER KNOWLEDGE AND PARTICIPATION 264
1. INTRODUCTION 264
2. TASK AND KNOWLEDGE ANALYSES 264
3. LOGICAL DESIGN OF HUMAN-MACHINE INTERFACE FUNCTIONALITIES 266
4. RAPID PROTOTYPING AND USABILITY TESTING 267
5. CONCLUSIONS 268
6. ACKNOWLEDGEMENTS 268
7. REFERENCES 268
CHAPTER 41. 
270 
1. Introduction 270
2. S/T Flight Test-Procedures Application Domain 271
3. Review of the State of the Art 272
4. Approaching NL and Test Definition 273
5. Front-End Architecture and Prototyping 275
6. Conclusions 276
Acknowledgements 276
References 276
Chapter 42. 
278 
1. INTRODUCTION 278
2. BIOPROCESS DESCRIPTION 278
3. BUILDING THE INTELLIGENT SUPERVISORY SYSTEM 279
4. PATTERN RECOGNITION TECHNIQUES 279
5. APPLICATION OF PATTERN RECOGNITION TECHNIQUES 281
6. CONCLUSIONS AND FUTURE WORK 282
7. ACKNOWLEDGEMENTS 282
8. REFERENCES 282
CHAPTER 43. 
284 
1. INTRODUCTION 284
2. KNOWLEDGE ACQUISITION 285
3. RELAY LADDER LOGIC (RLL) 286
4. RLL ANALYSIS TOOL (LAT) 286
5. CONCLUSIONS 288
6. ACKNOWLEDGMENTS 289
7. REFERENCES 289
PART 4: 
290 
CHAPTER 44. DECENTRALIZED CONTROL OF DISTRIBUTED INTELLIGENT ROBOTS AND SUBSYSTEMS 290
1. INTRODUCTION 290
2. THE ARCHITECTURE KAMARA 291
3. ASYNCHRONOUS COMMUNICATION 292
4. SYNCHRONOUS COMMUNICATION 293
5. REAL-TIME CONSTRAINTS 293
6. CONCLUSION 295
7. ACKNOWLEDGEMENT 295
8. REFERENCES 295
CHAPTER 45. A NEW PARADIGM FOR DISTRIBUTED, INTEGRATED, REAL-TIME CONTROL SYSTEMS 296
1.INTRODUCTION 296
2.TOWARDS AI-BASED INTEGRATED CONTROL 296
3.A NEW PARADIGM 297
4.THE DENIS ARCHITECTURE 300
5.CONCLUSIONS 301
REFERENCES 301
CHAPTER 46. A SIMULATION STUDY OF DISTRIBUTED INTELLIGENT CONTROL FOR A DEEP SHAFT MINE WINDER 302
1. INTRODUCTION 302
2. DISTRIBUTED INTELLIGENT CONTROL FOR CONTINUOUS PLANTS 302
3. CONTROL REQUIREMENTS FOR DEEP-SHAFT MINE WINDERS 303
4. PROPOSED CONTROL SCHEME 303
5. THE SIMULATION STUDY 304
6. COMMENT ON A PRIORI AND OPERATIONAL KNOWLEDGE 305
7. CONCLUSIONS 306
8. ACKNOWLEDGEMENTS 307
9. REFERENCES 307
CHAPTER 47. CONTINUATION COMPILATION FOR CONCURRENT LOGIC PROGRAMMING 308
1 Introduction 308
2 Intuition behind Continuation Compilation 309
3 Analysis for Continuation Compilation 310
4 Abstract Machine beneath Continuation Compilation 312
5 Related work 313
6 References 314
CHAPTER 48. 
316 
Introduction 316
World model 316
Control system 317
Inference mechanism 317
Symbolic layers 318
Low level layers 318
Proposed architecture 319
Low level agent 319
Subgoals agent 320
Conclusions 321
References 321
CHAPTER 49. INCREASING A KNOWLEDGE REPRESENTATION SCHEMA FOR FMS CONTROL WITH FAULT DETECTION AND ERROR RECOVERY CAPABILITIES 322
1. INTRODUCTION 322
2. THE BASIC REPRESENTATION SCHEMA 323
3. MONITORING AND FAULT DETECTION 324
4 METHOD FOR ERROR RECOVERY 325
5. CONCLUSIONS 325
REFERENCES 325
CHAPTER 50. REAKT: A Real Time architecture for knowledge based systems 328
Introduction 328
The architecture of the Reakt Toolkit 328
The Knowledge & Data Manager
Agents and Knowledge Sources 330
The Control Component 331
Temporal Reasoning 331
Communication protocols 332
Conclusions 332
Acknowledgements 332
Bibliography 332
Chapter 51. 
334 
1. Introduction 334
2. Methodology overview 334
3. Conceptual modelling 335
4. Demonstrator application 337
5. Architecture and constraints 340
6. Conclusions 341
References 341
Chapter 52. The Development of an Artificial Intelligence Real-Time Toolkit: REAKT 342
1. Introduction 342
2.- REAKT Process Concurrency Model 344
3.- The C++ REAKT Architectural Components 344
4.- Performance Measurements 347
5.- Conclusions 348
Acknowledgement 348
References 348
CHAPTER 53. MORSAF: a real-time assistant for the management of a petrochemical plant 350
1. Introduction 350
2. Problem description 351
3. MORSAF architecture 351
4. Expert Task 352
5. Conclusions 354
6. Acknowledgements 355
7. References 355
Chapter 54. Temporal Data Representation and Reasoning in REAKT 356
INTRODUCTION 356
TEMPORAL DATA ONTOLOGY 357
TEMPORAL REPRESENTATION 358
TEMPORAL REASONING IN REAKT 359
CONCLUSIONS 361
REFERENCES 361
CHAPTER 55. AN INTEGRATION METHODOLOGY AND ARCHITECTURE FOR INTELLIGENT SYSTEMS IN PROCESS CONTROL: THE HINT PROJECT 362
1. INTRODUCTION 362
2. INTEGRATION METHODOLOGY 362
3. THE HINT ARCHITECTURE 365
4. RESULTS 366
5. REFERENCES 367
CHAPTER 56. COMMERCIAL PERSPECTIVE WHEN APPLYING AI TECHNIQUES AND TRADITIONAL IT SKILLS 368
1. INTRODUCTION 368
2. RH& H TASKS IN HINT
3. WHY R& D PROJECTS?
4. BUSINESS OPPORTUNITIES 371
5. CONCLUSION 371
CHAPTER 57. DESIGNING USER INTERFACES FOR APPLICATIONS BASED ON THE HINT ARCHITECTURE 374
1. INTRODUCTION 374
2. UIs FOR HINT APPLICATIONS 374
3. DESIGNING THE USER INTERFACE 375
4. DISCUSSION 378
5. CONCLUDING REMARKS 379
6. REFERENCES 379
CHAPTER 58. A REAL TIME EXPERT SYSTEM FOR CONTINUOUS ASSISTANCE IN PROCESS CONTROL: A SUCCESSFUL APPROACH 380
1. INTRODUCTION 380
2. MODULE DESIGN 381
3. REASONING PROCESS 382
4. RESULTS 383
7. REFERENCES 384
CHAPTER 59. A BLACKBOARD APPLICATION FOR PROCESS MONITORING AND SUPERVISION 386
1. INTRODUCTION 386
2. GLOBAL SYSTEM STRUCTURE 387
3. TASKS OF THE PROCESS CONTROL SYSTEM 387
4. ARCHITECTURE OF THE PROCESS CONTROL SYSTEM 388
5. CONCLUSION 390
6. ACKNOWLEDGEMENT 391
7. REFERENCES 391
CHAPTER 60. DEVELOPING E.S. FOR PROCESS CONTROL USING UNIX BASED TOOLS 392
Introduction 392
World model 393
Defmodule Modifications 394
Inference Engine Modifications 394
Communication overview 394
Fuzzy concepts using CLIPS 395
Graphical Interface 395
Conclusions 396
References 396
AUTHOR INDEX 398