Intelligent Components and Instruments for Control Applications 1994 (eBook)

Front Cover 1
Intelligent Components and Instruments for Control Applications 1994 2
Copyright Page 3
Table of Contents 6
PART 1: PLENARY PAPERS 12
Chapter 1. MODELS AND LANGUAGES FOR THE INTEROPERABILITY OF SMART INSTRUMENTS 12
1. INTRODUCTION 12
2. INTELLIGENT INSTRUMENTS AND REAL TIME PROCESS OPERATING SYSTEMS 12
3. STATE OF THE ART 16
4. GENERIC MODELS OF INTELLIGENT INSTRUMENTS 17
5. CONCLUSION 22
6. REFERENCES 22
Chapter 2. MECHATRONIC COMPONENTS 24
1. INTRODUCTION 24
2. INFORMATION PROCESSING STRUCTURES FOR MECHATRONIC SYSTEMS 26
3. ON THE DESIGN OF KNOWLEDGE BASED MECHATRONIC SYSTEMS AND COMPONENTS 28
4 SENSORS AND ACTUATORS 30
5. DESIGN TOOLS 31
6. ADAPTIVE NONLINEAR CONTROL OF A PNEUMATIC ACTUATOR 32
7. ADAPTIVE SEMI ACTIVE SHOCK ABSORBERS FOR VEHICLE SUSPENSION SYSTEMS 33
8. REFERENCES 34
Chapter 3. FUZZY CONTROL METHODOLOGY AND APPLICATIONS TO AUTOMOTIVE SYSTEMS AND MOBILE ROBOTS 36
1. INTRODUCTION 36
2. FUZZY CONTROL METHODOLOGY 36
3. APPLICATIONS 41
3. CONCLUSIONS 46
4. REFERENCES 46
Chapter 4. FUZZY CONTROL METHODOLOGY IN THE PROCESS INDUSTRY 48
INTRODUCTION 48
THE CONTROL PROBLEM 49
IC STRUCTURE 50
KB FLC DESIGN 51
FLC FROM LINEAR ONES 52
THE CEMENT KILN PROCES 53
CEMENT KILN CONTROL 54
CONCLUSIONS 57
References 58
PART 2: INVITED SESSION 60
Chapter 5. AN INTELLIGENT SENSOR FOR ASSESSMENT OF THE QUALITY OF FISH PROCESSING 60
1. INTRODUCTION 60
2. THE HIERARCHY 60
3. ASSESSMENT OF QUALITY 62
ACKNOWLEDGMENTS 65
REFERENCES 65
Chapter 6. ROBUST TRAJECTORY TRACKING BY USING FUZZY SERVO CONTROL OF AN ARTICULATED ROBOT ARM 66
1. INTRODUCTION 66
2. ROBOT JOINT SERVO CONTROL 66
3. DESIGN OF A FUZZY SERVO CONTROLLER 67
4. SIMULATION EXPERIMENTS 69
5. CONCLUSIONS 71
6. ACKNOWLEDGMENTS 71
7. REFERENCES 71
Chapter 7. AN ADVISOR-ENHANCED, SIGNATURE-TABLE CONTROLLER FOR REAL-TIME CONTROL OF ILL-DEFINED SYSTEMS 72
1. INTRODUCTION 72
2. MACHINE LEARNING 72
3. ADVISOR STRATEGIES 74
4. RESULTS 75
5. ADVISOR ACCOUNTABILITY 75
6. CONCLUSIONS 75
7. REFERENCES 76
Chapter 8. DYNAMIC RESTRACTARING OF FLEXIBLE PRODUCTION SYSTEMS 78
1. INTRODUCTION 78
2. RESTRUCTURING SYSTEM 78
3. DECISION MAKING 80
4. AN EXAMPLE 82
ACKNOWLEDGMENT 83
REFERENCES 83
PART 3: TECHNICAL SESSIONS 84
Chapter 9. STABILITY ANALYSIS OF FUZZY CONTROLLER AND IMPLICATIONS FOR DESIGN 84
1. INTRODUCTION 84
2. MAIN TOPICS 84
3. FORMULATION OF THE PROBLEM 84
4. CONTRIBUTIONS 85
5. DESIGN PROCEDURE AND APPLICATIONS 87
6. EXAMPLE 88
7. SUMMARY 89
8. REFERENCES 89
Chapter 10. AN ESTIMATOR OF THE NUMBER OF RULES IN FUZZY MODELS 90
1. INTRODUCTION 90
2. EXPOSITION 90
3. RESULTS 94
4. CONCLUSIONS 94
REFERENCES 94
Chapter 11. TEMPORAL MECHANISMS IN COMMUNICATION MODELS APPLIED TO COMPANION STANDARDS 96
1. INTRODUCTION 96
2. COMMUNICATION ARŒITECTURE 96
3. COMMUNICATION MODELS 97
4. TEMPORAL MECHANISMS OF THE PRODUCER/CONSUMER MODEL APPJLIED TO THE COMPANION STANDARDS 100
5. CONCLUSION 101
6. REFERENCES 101
Chapter 12. INTERWORKING OF FIELDDEVICES 102
1. INTRODUCTION 102
2. CONFORMANCE, INTEROPERABILITY, INTERWORKING OR INTERCHANGEABILITY 102
3. FIELDDE VICES INTERWORKING PROBLEMS 104
4. DIFFERENT APPROACHES FOR INTERWORKING CONSTRUCTION IMPROVEMENT 105
5. INTERWORKING TEST OR VERIFICATION 106
6. CONCLUSION 107
7. REFERENCE 107
Chapter 13. INTELLIGENT SENSOR : OBJECT APPROACH 108
1. INTRODUCTION 108
2. OBJECT APPROACH : WHY ? 108
3. WHAT IS OBJECT APPROACH ? 109
4. LS. OBJECT APPROACH 110
5. I.S. OBJECT MODEL 111
6. CONCLUSION 111
7. ACKNOWLEDGEMENT 111
8. REFERENCES 113
Chapter 14. ROBLIN LINEAR TRANSPORT ROBOTS COMMAND USING FUZZY LOGIC CONTROLLER 114
1. INTRODUCTION 114
2. ROBLIN TRANSPORT ROBOT MODEL 115
3. ROBOT SPEED FUZZY CONTROL 116
4. APPLICATION 117
5. CONCLUSIONS 118
6. REFERENCES 119
Chapter 15. Fuzzy Components for Fuzzy Control 120
I. INTRODUCTION 120
II. TYPOLOGIE 120
III. FUZZY COMPONENTS 121
IV. APPLICATION TO FUZZY CONTROL 122
CONCLUSION 124
REFERENCES 124
Chapter 16. FUZZY SLIDING MODE CONTROL APPLICATION TO LARGE TIME VARYING SYSTEMS 126
1. INTRODUCTION 126
2. SLIDING MODE CONTROL 126
3. EXTENSION TO THE FUZZY CONTROL 128
4. Application 128
5. Conclusion 129
References 129
Chapter 17. A FUZZY CONTROLLED PNEUMATIC GRIPPER FOR ASPARAGUS HARVESTING 130
1. INTRODUCTION 130
2. SYSTEM DESCRIPTION 130
3· CLAMPING FORCES 131
4. FINGEERTIP DESIGN 131
5. VALVES 133
6. DYNAMIC TESTS 133
7. CONTROL 133
8. GRASPING TESTS 134
9. CONCLUSIONS 135
10. REFERENCES 135
Chapter 18. APPLICATION USING VLSI HARDWARE REALIZATION OF SELF-LEARNING RECURSIVE FUZZY MODEL 136
1. INTRODUCTION 136
2. DESIGN CONSIDERATIONS 137
3. THE HARDWARE 138
4. APPLICATION 139
5. CONCLUSION 139
5. REFERENCES 139
Chapter 19. FERROPIEZOELECTRIC TACTILE SENSOR ARRAY 142
1. INTRODUCTION 142
2. MATHEMATICAL SIMULATION AND CHOOSING OF CONSTRUCTION VARIANTS OF TACTILE ARRAYS 142
3. EXPERIMENTAL RESULTS 144
4. CONCLUSION 144
5. ACKNOWLEDGEMENTS 145
5. REFERENCES 145
Chapter 20. ULTRASONIC FLOW MEASURE FOR WATER AND WASTEWATER USING OPEN CHANNEL WEIR 146
1. INTRODUCTION 146
2. MODELING OF WEIR CHARACTERISTIC 146
3. THE ULTRA SOUND WAVES APPLIED TO MEASURE THE LIQUID SWELLING WITH THE UNSINKED WEIR 149
4. SIMULATION OF MEASURING PROCESS OF SWELLING AT UNSINKED WEIR 150
5. FINAL REMARKS 151
6. REFERENCES 151
Chapter 21. AN APPLICATION OF IMAGE SENSING AND ANALYZES IN OPHTHALMOLOGY 152
1. INTRODUCTION 152
2. HARDWARE ARRANGEMENT 153
3. IMAGE PROCESSING 153
4. CONCLUSION 155
5. REFERENCES 155
Chapter 22. IDENTIFICATION TECHNIQUES FOR MULTISENSOR SYSTEMS 156
1. INTRODUCTION 156
2. PROBLEM STATEMENT 156
3. THE DIRECT CANONICAL MINIMIZATION METHOD 157
4. THE VARIATON AL AND CONJUGATE EQUATION METHOD 157
5. RELATIONSHIP TO THE TEMPLATE FUNCTION METHOD 159
6. CONCLUSION 160
7. ACKNOWLEDGMENT 160
8. REFERENCES 160
Chapter 23. SIMULTANEOUS DETECTION, LOCATION AND IDENTIFICATION OF FAULTS FOR DYNAMIC SYSTEMS FROM ROBUST PARAMETERS IDENTIFICATON 162
1. INTRODUCTION 162
2. THE MODELS 163
3. THE DETECTION-LOCATON-ESTIMATON PROCEDURE 164
4. EXAMPLE 165
5. CONCLUSION 167
6. REFERENCES 167
Chapter 24. SELF-DIAGNOSIS AND REDUNDANCY FOR ELECTRONIC CONTROL OF DIESEL ENGINE 168
1. INTRODUCTION 168
2. CONSTRUCTION OF CONTROL SYSTEM 168
3 SELF-DIAGNOSIS AND REDUNDANCY 170
4. CONCLUSIONS 172
5. REFERENCES 172
Chapter 25. COMPUTER-AIDED TUNING AND DIAGNOSING BASED ON PERSONAL COMPUTERS USAGE 174
1. INTRODUCTION 174
2. PRINCIPLES OF CATDS DESIGN 175
3. THE MAIN REQUIREMENTS FOR CATDS 175
4. FAULT DIAGNOSIS FOR CATDS 176
5. PURPOSE AND STRUCTURE OF CATS ADACOM 177
6. CONCLUSION 177
7. REFERENCES 177
Chapter 26. OBJECT RECOGNITION BY NEURAL NETWORK 180
1. INTRODUCnON 180
2. THE PATTERN RECOGNITION PROCESS AND ITS AUTOMATION 180
3. THE AUTOMATED PATTERN RECOGNITION SUBSYSTEM AND ITS APPLICATION IN THE ASSEMBLY CELL 181
4. CONCLUSION 183
5. REFERENCES 184
6. APPENDIX 184
Chapter 27. NEURAL ADAPTIVE CONTROL OF A BIOREACTOR 186
1. INTRODUCTION 186
2. SYSTEM ANALYSIS 186
3. IDENTIFIER DESIGN WITH FEEDFORWARD BACKPROPAGATION NETS 187
4. NEURAL CONTROLLER 188
5. CONCLUSIONS AND FUTURE WORK 191
REFERENCES 191
Chapter 28. GENETIC ALGORITHMS IN INTELLIGENT CONTROL SYSTEMS DESIGN 192
1. INTRODUCTION 192
2. GENETIC ALGORITHMS 192
3. GENETIC ALGORITHMS AND INTELLIGENT CONTROL 193
4. EXPERIMENTAL FRAMEWORK 195
5. CONCLUSIONS 197
6. REFERENCES 197
Chapter 29. THE ROUTING PROBLEM IN TRAFFIC CONTROL USING GENETIC ALGORITHMS 198
1. INTRODUCTION 198
2. GENETIC ALGORITHMS 199
3. METHODOLOGY 200
4. EXPERIMENTS AND RESULTS 200
5. CONCLUSIONS 202
6. REFERENCES 202
Chapter 30. COMBINING NEURAL AND FUZZY TECHNIQUES IN MONITORING AND CONTROL OF MANUFACTURING PROCESSES 204
1. INTRODUCTION 204
2. FUZZY SYSTEMS 204
3. EXPERIMENTS, APPLIED TECHNIQUES 206
4. NEURO-FUZZY MONITORING OF CUTTING TOOLS 206
5. CONCLUSIONS 208
6. ACKNOWLEDGEMENTS 208
7. REFERENCES 208
Chapter 31. AN OBJECT-ORIENTED SPECIFICATION OF A CONTROL LOGIC FOR AN FMS 210
1. INTRODUCTION 210
2. OBJECT-ORIENTED DATABASE 210
3. RULE GROUPS 211
4. EXAMPLES 214
5. CONCLUSION 215
6. REFERENCES 215
Chapter 32. FACTORY COMMUNICATIONS FOR AUTOMATING A CIM CENTER EXPERIMENTAL PLANT 216
1. INTRODUCTION 216
2. PLANT COMMUNICATION SYSTEM 218
3. PRODUCTION PLANT CELLS 219
4. PLANT CONTROL SYSTEM 220
5. CONCLUSIONS 221
5. REFERENCES 221
Chapter 33. DYNAMIC SENSORS DISTORTION COMPENSATION BY MEANS OF INPUT ESTIMATION ALGORITHMS 222
1. INTRODUCTION 222
2 PROBLEM STATEMENT 223
3 INPUT ESTIMATION ALGORITHM 224
4 INVERSE FILTER OPTIMIZATION 224
5 STEAD - STATE REGULARIZED FILTER 225
6 CONCLUSIONS 226
7 REFERENCES 227
Chapter 34. EXTRACTING PERIODIC COMPONENTS FROM MEASURED OSCILLATORY SIGNALS 228
1. INTRODUCTION 228
2. ALGORITHM 229
3. APPLICATIONS 231
4. CONCLUSION 232
ACKNOWLEDGEMENT 232
REFERENCES 232
Chapter 35. FUZZY LOGIC: SIGNAL CONDITIONING FOR SO2 SENSOR 234
1. INTRODUCTION 234
2. SENSOR SIGNAL CONDITIONING 235
3. MODELLING OF SO2 SENSOR 237
4. RESULTS AND CONCLUSIONS 237
5. REFERENCES 239
Acknowledgements: 239
Chapter 36. ON THE USE OF VIRTUAL INSTRUMENTS IN REALIZING ADAPTIVE CONTROLLERS 240
1. INTRODUCTION 240
2.THE PRINCIPLE OF VIRTUAL INSTRUMENTS 240
3. REALIZING CONTROLLERS AS VI 241
4. THE ADAPTIVE PID ALGORITHM 242
5. A SIMULATION EXAMPLE 244
6. CONCLUSIONS 245
7. REFERENCES 245
Chapter 37. A SYNTACTIC AND CONTEXTUAL EDGE DETECTOR 246
1. INTRODUCTION 246
2. EDGE MODELING 246
3. EDGES EXTRACTION 247
4. REWRITING RULES 248
5. METHOD QUALITY 249
6. CONCLUSION 250
7. REFERENCES 250
Chapter 38. NAVIGATION SYSTEM FOR A MOBILE ROBOT BY MEANS OF AN INFRARED SYSTEM 252
1· INTRODUCTION 252
2. THE SENSOR SYSTEM 252
3· CONTROL OF THE DRIVING WHEELS ANGULAR VELOCITIES. 252
4. CONTROL OF ROBOT THE GENERALIZED VELOCITIES. 253
5· ODOMETRIC CONTROL OF THE POSITION 253
6. TRACKING OF THE PATHS 254
7. THE KALMAN FILTER 254
8. CORRECTION WITH INFRARED WAVES 255
9. RESULTS 256
10. CONCLUSIONS 256
11. REFERENCES 257
Chapter 39. NEURAL NETWORK LOCAL NAVIGATION OF MOBILE ROBOTS IN A MOVING OBSTACLES ENVIRONMENT 258
1. INTRODUCTION 258
2. GENERALIZED PREDICTIVE CONTROL 259
3. MOBILE OBSTACLE MOTIONS PREDICTION 260
4. THE NEURAL NETWORK APPROACH 261
5. RESULTS 261
6. CONCLUSIONS 262
7. ACKNOWLEDGEMENT 262
REFERENCES 262
Chapter 40. A COMBINED PATH GENERATION AND TRACKING CONTROLLER FOR MOBILE ROBOTS 264
1. INTRODUCTION 264
2. THE PROPOSED METHOD 265
3· APPROACH-PATH GENERATION 266
4. LOCAL PATH GENERATION 266
5. GLOBAL PATH GENERATION 267
6. CONCLUSIONS 268
7. REFERENCES 269
Chapter 41. MOTION ESTIMATION FOR A MOBILE ROBOT USING VISUAL AND RANGE DATA 270
1. INTRODUCTION 270
2. GENERAL DESCRIPTION 270
3. DETERMINING 3D MOTION OF RIGID OBJECTS 272
4. EXPERIMENTAL RESULTS 274
5. CONCLUSION 275
6. REFERENCES 275
Chapter 42. APPLICATION OF NEURAL NETWORKS TO IMAGE-BASED CONTROL OF ROBOT ARMS 276
1. INTRODUCTION 276
2. FOUR-POINT IMAGE EXPERIMENTS 277
3. FOURIER DESCRIPTOR EXPERIMENTS 278
4. CONCLUSIONS 280
Acknowledgements 280
5. REFERENCES 280
Chapter 43. HANDLING DELAY-TIME IN LOW COST CONTROLLERS 282
1. INTRODUCTION 282
2. MODELING DIGITAL REGULATORS 283
3. MODELING CONTINUOUS LINEAR PROCESSES 283
4. IDENTIFICATION OF CONTINUOUS LINEAR PROCESSES 285
5. SIMULATION EXAMPLE 286
6. CONCLUSIONS 287
7. REFERENCES 287
Chapter 44. FLEXIBLE LOW-COST MULTIFUNCTION INSTRUMENT 288
1. INTRODUCTION 288
2. HARDWARE 289
3. FUNCTION BLOCKS 289
4. CONFIGURATION 291
5. COMMUNICATIONS 292
6. APPLICATIONS 293
7. REFERENCES 293
Chapter 45. COMPUTER AIDED TRACTOR/IMPLEMENT COUPLING 294
1. INTRODUCTION 294
2. SOLUTION 294
3. Final remarks 298
4. References 299
Chapter 46. STRATEGIES OF MODEL REFERENCE ADAPTIVE CONTROL WITH ACTIVE LEARNING PROPERTIES 300
1. INTRODUCTION 300
2. SYSTEMS WITH IMPLICIT REFERENCE MODEL 300
3. SYSTEM WITH EXPLICIT REFERENCE MODEL 302
4. NONMINIMUM PHASE SYSTEMS AND INTERCONNECTIONS BETWEEN ADAPTIVE CONTROL SYSTEMS STRUCTURES 302
5. CONVERGENCE PROPERTIES OF DAAC 303
6. SIMULATION EXAMPLE AND COMPARISONS 304
7. CONCLUSIONS 305
8. ACKNOWLEDGEMENT 305
9. REFERENCES 305
Chapter 47. ADAPTATION AND SELF-LEARNING OF NONLINEAR MULTIVARIABLE SYSTEMS 306
1. INTRODUCTION 306
2. THE PROBLEM STATEMENT 307
3. CONCEPTION OF THE SPATIAL MOTION CONTROL 308
4. SELF-LEARNING CONTROL 309
5. CONCLUSION 311
5.REFERENCES 311
Chapter 48. START-UP OF PROCESS PLANTS BY CONSERVATIVE LEARNING BASED QUALITATIVE REASONING 312
1. INTRODUCTION 312
2. BAYESIAN IDENTIFICATION AND PREDICTION BASED LEARNING IN QUALITATIVE SIMULATION 312
3. CONSERVATIVE LEARNING FOR MULTIPLE MODEL BASED QUALITATIVE REASONING 316
4. ACKNOWLEDGEMENT 317
5. REFERENCES 317
Chapter 49. KNOWLEDGE-BASED SYSTEM IDENTIFICATION - COMPONENTS OF THE EXPERTISE 318
1. INTRODUCTION 318
2. MODELLING AND MEASUREMENT 318
3. COMPONENTS OF THE EXPERTISE 319
4. INTELLIGENT SI ENVIRONMENT 320
5. CONCLUSIONS 322
6. REFERENCES 322
Chapter 50. A FUZZY MONITORING AND DIAGNOSIS PROCESS TO DETECT EVOLUTIONS OF A CAR DRIVER'S BEHAVIOR 324
1. INTRODUCTION 324
2. FUZZY PATTERN RECOGNITION AND DIAGNOSIS 325
3. DETECTING AN EVOLUTION 326
4. APPLICATION 328
5. CONCLUSION 329
6. REFERENCES 329
Chapter 51. A SIMPLE IDENTIFICATION METHOD FOR THE ORDER OF THE STREJC MODEL AND ITS APPLICATION TO AUTOTUNING 330
1. INTRODUCTION 330
2. EVALUATING THE ORDER AND THE REMAINING PARAMETERS OF THE MODEL (7). 331
3. SIMULATION OF THE AUTOTUNING PROCEDURE OF A PID CONTROLLER 332
4. MODIFICATION OF THE DESCRIBED METHOD AND EXPERIMENTAL TESTS 334
5. CONCLUSIONS 336
6. REFERENCES 336
Chapter 52. NONLINEAR OBSERVERS FOR DISTILLATION COLUMNS 338
1. INTRODUCTION 338
2. EXPERIMENTAL PLANT 338
3. PLANT MODEL 339
4. OBSERVER THEORY 339
5. APPLICATION TO BINARY DISTILLATION 339
6. SIMULATION RESULTS 340
7. EXPERIMENTAL RESULTS 341
8. CONCLUSION 342
9. SYMBOLS USED 343
10. REFERENCES 343
Chapter 53. A NEW VARIABLE STEP SIZE ADAPTATION ALGORITHM FOR NEURAL NETWORKS 344
1. INTRODUCTION 344
2. VARIABLE STEP SIZE ALGORITHMS 344
3. THE MOMENTUM TECHNIQUE 345
4. ALTERNATIVE OPTIMIZATION TECHNIQUES 346
5. THE NEW VARIABLE STEP SIZE ALGORITHM 346
6. CONCLUSIONS 346
7. REFERENCES 346
Chapter 54. ESTIMATION OF RESONANT TRANSFER FUNCTIONS IN THE JOINTS OF AN INDUSTRIAL ROBOT 348
1. INTRODUCTION 348
2. DYNAMIC MODEL OF A ROBOT WITH ELASTIC JOINTS 349
3. DESIGN OF THE IDENTMCATON EXPERIMENT 349
4. EXPERIMENTAL SETUP 351
5. EXPERIMENTAL RESULTS 352
6. CONCLUSIONS 353
7. ACKNOWLEDGEMENTS 353
8. REFERENCES 353
Chapter 55. SMART ASYNCHRONOUS DRIVES : ADVANCED CONTROL AND FAULT DETECTION AND IDENTIFICATION 354
1. INTRODUCTION 354
2. STRUCTURAL ANALYSIS 354
3. MODELLING OF AN ASYNCHRONOUS DRIVE AND ITS CONTROL 356
4. OBTAINED RESULTS 357
5. CONCLUSION 358
6. REFERENCES 359
Chapter 56. A DRIVEN LASER-BEAM FOR AN ACTIVE SENSOR ON ROBOT CONTROL 360
1. INTRODUCTION 360
2. STRUCTURE AND MODELING 361
3. CONTROL 363
4. EXAMPLE 364
5. CONCLUSION 364
6. REFERENCES 364
Chapter 57. IMPROVEMENT OF FLEXIBILITY AND RELIABILITY OF AUTOMOBILE ACTUATORS BY MODEL-BASED ALGORITHMS 366
1. INTRODUCTION 366
2. METHODS FOR FAULT DETECTION 366
3. EXPERIMENTAL RESULTS 368
4. CONCLUSION 371
5. REFERENCES 371
Chapter 58. VS - CASCADE CONTROL 372
1. INTRODUCTION 372
2. VS-PI CONTROLLER 373
2. PREFILTERING ACTION 373
4. CONCLUSIONS 376
6. REFERENCES 376
Chapter 59. AN EDUCATIONAL PLATFORM FOR VARIABLE-STRUCTURE CONTROL OF SAFETY-RELATED SYSTEMS 378
1. INTRODUCTION 378
2. VARIABLE-STRUCTURE CONTROL ALGORITHM 379
3. OBSERVER CONCEPT 380
4. TRAINING AND EDUCATION PLATFORM 381
5. AN EDUCATIONAL ENVIRONMENT 381
6. REFERENCES 383
Chapter 60. ANALYSIS OF ACHIEVABLE PID PERFORMANCES VERSUS OPTIMAL LQR CONTROL 384
1. INTRODUCTION 384
2. PID AS AN OPTIMIZATION LQR PROBLEM 384
3. ROBUSTNESS LOOP MARGIN 385
4. PID REGULATOR AND LQR COST 386
5. ASYMPTOTIC PID 386
5. ACKNOLEDGEMENT 387
6. REFERENCES 387
Chapter 61. THE SAMPLING PERIOD AS A CONTROL PARAMETER 388
1. INTRODUCTION 388
2. CONTROLLER 388
3. PROCESS MODELS 389
4. TUNING METHODS 389
5. TUNING FORMULAS 390
6. TUNING CRITERIA 392
7. EXAMPLE 392
8. CONCLUSIONS 393
9. REFERENCES 393
Chapter 62. CONFIDENCE DEGREE ON THE ADVANCED STAND-ALONE CONTROLLERS 394
1. INTRODUCTION 394
2. BENCHMARK SYSTEM 395
3. THE ES 100 OMRON CONTROLLER 395
4. THE 761 FOXBORO CONTROLLER 397
5. CONCLUSIONS 398
6. ACKNOWLEDGEMENT 398
7. REFERENCES 398
Chapter 63. STRUCTURED NEURAL NETWORK FOR NONLINEAR DYNAMIC SYSTEMS MODELING 400
1. INTRODUCTION 400
2. STATE SPACE MODEL 401
3. EXAMPLE 402
4. CONCLUSIONS 404
REFERENCES 404
Chapter 64. NONLINEAR SYSTEM IDENTIFICATION USING ADDITIVE DYNAMIC NEURAL NETWORKS 406
1. INTRODUCTION 406
2. ARCHITECTURE OF THE CONNECTIONIST MODELS 406
3. PARAMETER ADAPTATION IN THE CONNECTIONIST MODELS 407
4. SIMULATION RESULTS 409
5. CONCLUSION 410
6. ACKNOWLEDGEMENTS 410
7. REFERENCES 411
AUTHOR INDEX 412