Banyasz Intelligent Components and Instruments for Control Applications 1994

1. Auflage 2014
ISBN: 978-1-4832-9662-3
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 402 Seiten, Web PDF

Reihe: IFAC Postprint Volume

Advances in computer technology and sensor development have led to increasingly successful control operations. In order to maximize future potential it is vital for academics and practitioners in the field to have an international forum for discussion and evaluation of the latest developments. The IFAC Symposia on intelligent components and instruments provide this opportunity and the latest in the series gives rise to this invaluable publication which provides an authoritative assessment of the present state and future directions of these key technologies.

Autoren/Hrsg.

Banyasz, Cs.

Inhaltsverzeichnis

1;Front Cover;1
2;Intelligent Components and Instruments for Control Applications 1994;2
3;Copyright Page;3
4;Table of Contents;6
5;PART 1: PLENARY PAPERS;12
5.1;Chapter 1. MODELS AND LANGUAGES FOR THE INTEROPERABILITY OF SMART INSTRUMENTS;12
5.1.1;1. INTRODUCTION;12
5.1.2;2. INTELLIGENT INSTRUMENTS AND REAL TIME PROCESS OPERATING SYSTEMS;12
5.1.3;3. STATE OF THE ART;16
5.1.4;4. GENERIC MODELS OF INTELLIGENT INSTRUMENTS;17
5.1.5;5. CONCLUSION;22
5.1.6;6. REFERENCES;22
5.2;Chapter 2. MECHATRONIC COMPONENTS;24
5.2.1;1. INTRODUCTION;24
5.2.2;2. INFORMATION PROCESSING STRUCTURES FOR MECHATRONIC SYSTEMS;26
5.2.3;3. ON THE DESIGN OF KNOWLEDGE BASED MECHATRONIC SYSTEMS AND COMPONENTS;28
5.2.4;4 SENSORS AND ACTUATORS;30
5.2.5;5. DESIGN TOOLS;31
5.2.6;6. ADAPTIVE NONLINEAR CONTROL OF A PNEUMATIC ACTUATOR;32
5.2.7;7. ADAPTIVE SEMI ACTIVE SHOCK ABSORBERS FOR VEHICLE SUSPENSION SYSTEMS;33
5.2.8;8. REFERENCES;34
5.3;Chapter 3. FUZZY CONTROL METHODOLOGY AND APPLICATIONS TO AUTOMOTIVE SYSTEMS AND MOBILE ROBOTS;36
5.3.1;1. INTRODUCTION;36
5.3.2;2. FUZZY CONTROL METHODOLOGY;36
5.3.3;3. APPLICATIONS;41
5.3.4;3. CONCLUSIONS;46
5.3.5;4. REFERENCES;46
5.4;Chapter 4. FUZZY CONTROL METHODOLOGY IN THE PROCESS INDUSTRY;48
5.4.1;INTRODUCTION;48
5.4.2;THE CONTROL PROBLEM;49
5.4.3;IC STRUCTURE;50
5.4.4;KB FLC DESIGN;51
5.4.5;FLC FROM LINEAR ONES;52
5.4.6;THE CEMENT KILN PROCES;53
5.4.7;CEMENT KILN CONTROL;54
5.4.8;CONCLUSIONS;57
5.4.9;References;58
6;PART 2: INVITED SESSION;60
6.1;Chapter 5. AN INTELLIGENT SENSOR FOR ASSESSMENT OF THE QUALITY OF FISH PROCESSING;60
6.1.1;1. INTRODUCTION;60
6.1.2;2. THE HIERARCHY;60
6.1.3;3. ASSESSMENT OF QUALITY;62
6.1.4;ACKNOWLEDGMENTS;65
6.1.5;REFERENCES;65
6.2;Chapter 6. ROBUST TRAJECTORY TRACKING BY USING FUZZY SERVO CONTROL OF AN ARTICULATED ROBOT ARM;66
6.2.1;1. INTRODUCTION;66
6.2.2;2. ROBOT JOINT SERVO CONTROL;66
6.2.3;3. DESIGN OF A FUZZY SERVO CONTROLLER;67
6.2.4;4. SIMULATION EXPERIMENTS;69
6.2.5;5. CONCLUSIONS;71
6.2.6;6. ACKNOWLEDGMENTS;71
6.2.7;7. REFERENCES;71
6.3;Chapter 7. AN ADVISOR-ENHANCED, SIGNATURE-TABLE CONTROLLER FOR REAL-TIME CONTROL OF ILL-DEFINED SYSTEMS;72
6.3.1;1. INTRODUCTION;72
6.3.2;2. MACHINE LEARNING;72
6.3.3;3. ADVISOR STRATEGIES;74
6.3.4;4. RESULTS;75
6.3.5;5. ADVISOR ACCOUNTABILITY;75
6.3.6;6. CONCLUSIONS;75
6.3.7;7. REFERENCES;76
6.4;Chapter 8. DYNAMIC RESTRACTARING OF FLEXIBLE PRODUCTION SYSTEMS;78
6.4.1;1. INTRODUCTION;78
6.4.2;2. RESTRUCTURING SYSTEM;78
6.4.3;3. DECISION MAKING;80
6.4.4;4. AN EXAMPLE;82
6.4.5;ACKNOWLEDGMENT;83
6.4.6;REFERENCES;83
7;PART 3: TECHNICAL SESSIONS;84
7.1;Chapter 9. STABILITY ANALYSIS OF FUZZY CONTROLLER AND IMPLICATIONS FOR DESIGN;84
7.1.1;1. INTRODUCTION;84
7.1.2;2. MAIN TOPICS;84
7.1.3;3. FORMULATION OF THE PROBLEM;84
7.1.4;4. CONTRIBUTIONS;85
7.1.5;5. DESIGN PROCEDURE AND APPLICATIONS;87
7.1.6;6. EXAMPLE;88
7.1.7;7. SUMMARY;89
7.1.8;8. REFERENCES;89
7.2;Chapter 10. AN ESTIMATOR OF THE NUMBER OF RULES IN FUZZY MODELS;90
7.2.1;1. INTRODUCTION;90
7.2.2;2. EXPOSITION;90
7.2.3;3. RESULTS;94
7.2.4;4. CONCLUSIONS;94
7.2.5;REFERENCES;94
7.3;Chapter 11. TEMPORAL MECHANISMS IN COMMUNICATION MODELS APPLIED TO COMPANION STANDARDS;96
7.3.1;1. INTRODUCTION;96
7.3.2;2. COMMUNICATION ARŒITECTURE;96
7.3.3;3. COMMUNICATION MODELS;97
7.3.4;4. TEMPORAL MECHANISMS OF THE PRODUCER/CONSUMER MODEL APPJLIED TO THE COMPANION STANDARDS;100
7.3.5;5. CONCLUSION;101
7.3.6;6. REFERENCES;101
7.4;Chapter 12. INTERWORKING OF FIELDDEVICES;102
7.4.1;1. INTRODUCTION;102
7.4.2;2. CONFORMANCE, INTEROPERABILITY, INTERWORKING OR INTERCHANGEABILITY;102
7.4.3;3. FIELDDE VICES INTERWORKING PROBLEMS;104
7.4.4;4. DIFFERENT APPROACHES FOR INTERWORKING CONSTRUCTION IMPROVEMENT;105
7.4.5;5. INTERWORKING TEST OR VERIFICATION;106
7.4.6;6. CONCLUSION;107
7.4.7;7. REFERENCE;107
7.5;Chapter 13. INTELLIGENT SENSOR : OBJECT APPROACH;108
7.5.1;1. INTRODUCTION;108
7.5.2;2. OBJECT APPROACH : WHY ?;108
7.5.3;3. WHAT IS OBJECT APPROACH ?;109
7.5.4;4. LS. OBJECT APPROACH;110
7.5.5;5. I.S. OBJECT MODEL;111
7.5.6;6. CONCLUSION;111
7.5.7;7. ACKNOWLEDGEMENT;111
7.5.8;8. REFERENCES;113
7.6;Chapter 14. ROBLIN LINEAR TRANSPORT ROBOTS COMMAND USING FUZZY LOGIC CONTROLLER;114
7.6.1;1. INTRODUCTION;114
7.6.2;2. ROBLIN TRANSPORT ROBOT MODEL;115
7.6.3;3. ROBOT SPEED FUZZY CONTROL;116
7.6.4;4. APPLICATION;117
7.6.5;5. CONCLUSIONS;118
7.6.6;6. REFERENCES;119
7.7;Chapter 15. Fuzzy Components for Fuzzy Control;120
7.7.1;I. INTRODUCTION;120
7.7.2;II. TYPOLOGIE;120
7.7.3;III. FUZZY COMPONENTS;121
7.7.4;IV. APPLICATION TO FUZZY CONTROL;122
7.7.5;CONCLUSION;124
7.7.6;REFERENCES;124
7.8;Chapter 16. FUZZY SLIDING MODE CONTROL APPLICATION TO LARGE TIME VARYING SYSTEMS;126
7.8.1;1. INTRODUCTION;126
7.8.2;2. SLIDING MODE CONTROL;126
7.8.3;3. EXTENSION TO THE FUZZY CONTROL;128
7.8.4;4. Application;128
7.8.5;5. Conclusion;129
7.8.6;References;129
7.9;Chapter 17. A FUZZY CONTROLLED PNEUMATIC GRIPPER FOR ASPARAGUS HARVESTING;130
7.9.1;1. INTRODUCTION;130
7.9.2;2. SYSTEM DESCRIPTION;130
7.9.3;3· CLAMPING FORCES;131
7.9.4;4. FINGEERTIP DESIGN;131
7.9.5;5. VALVES;133
7.9.6;6. DYNAMIC TESTS;133
7.9.7;7. CONTROL;133
7.9.8;8. GRASPING TESTS;134
7.9.9;9. CONCLUSIONS;135
7.9.10;10. REFERENCES;135
7.10;Chapter 18. APPLICATION USING VLSI HARDWARE REALIZATION OF SELF-LEARNING RECURSIVE FUZZY MODEL;136
7.10.1;1. INTRODUCTION;136
7.10.2;2. DESIGN CONSIDERATIONS;137
7.10.3;3. THE HARDWARE;138
7.10.4;4. APPLICATION;139
7.10.5;5. CONCLUSION;139
7.10.6;5. REFERENCES;139
7.11;Chapter 19. FERROPIEZOELECTRIC TACTILE SENSOR ARRAY;142
7.11.1;1. INTRODUCTION;142
7.11.2;2. MATHEMATICAL SIMULATION AND CHOOSING OF CONSTRUCTION VARIANTS OF TACTILE ARRAYS;142
7.11.3;3. EXPERIMENTAL RESULTS;144
7.11.4;4. CONCLUSION;144
7.11.5;5. ACKNOWLEDGEMENTS;145
7.11.6;5. REFERENCES;145
7.12;Chapter 20. ULTRASONIC FLOW MEASURE FOR WATER AND WASTEWATER USING OPEN CHANNEL WEIR;146
7.12.1;1. INTRODUCTION;146
7.12.2;2. MODELING OF WEIR CHARACTERISTIC;146
7.12.3;3. THE ULTRA SOUND WAVES APPLIED TO MEASURE THE LIQUID SWELLING WITH THE UNSINKED WEIR;149
7.12.4;4. SIMULATION OF MEASURING PROCESS OF SWELLING AT UNSINKED WEIR;150
7.12.5;5. FINAL REMARKS;151
7.12.6;6. REFERENCES;151
7.13;Chapter 21. AN APPLICATION OF IMAGE SENSING AND ANALYZES IN OPHTHALMOLOGY;152
7.13.1;1. INTRODUCTION;152
7.13.2;2. HARDWARE ARRANGEMENT;153
7.13.3;3. IMAGE PROCESSING;153
7.13.4;4. CONCLUSION;155
7.13.5;5. REFERENCES;155
7.14;Chapter 22. IDENTIFICATION TECHNIQUES FOR MULTISENSOR SYSTEMS;156
7.14.1;1. INTRODUCTION;156
7.14.2;2. PROBLEM STATEMENT;156
7.14.3;3. THE DIRECT CANONICAL MINIMIZATION METHOD;157
7.14.4;4. THE VARIATON AL AND CONJUGATE EQUATION METHOD;157
7.14.5;5. RELATIONSHIP TO THE TEMPLATE FUNCTION METHOD;159
7.14.6;6. CONCLUSION;160
7.14.7;7. ACKNOWLEDGMENT;160
7.14.8;8. REFERENCES;160
7.15;Chapter 23. SIMULTANEOUS DETECTION, LOCATION AND IDENTIFICATION OF FAULTS FOR DYNAMIC SYSTEMS FROM ROBUST PARAMETERS IDENTIFICATON;162
7.15.1;1. INTRODUCTION;162
7.15.2;2. THE MODELS;163
7.15.3;3. THE DETECTION-LOCATON-ESTIMATON PROCEDURE;164
7.15.4;4. EXAMPLE;165
7.15.5;5. CONCLUSION;167
7.15.6;6. REFERENCES;167
7.16;Chapter 24. SELF-DIAGNOSIS AND REDUNDANCY FOR ELECTRONIC CONTROL OF DIESEL ENGINE;168
7.16.1;1. INTRODUCTION;168
7.16.2;2. CONSTRUCTION OF CONTROL SYSTEM;168
7.16.3;3 SELF-DIAGNOSIS AND REDUNDANCY;170
7.16.4;4. CONCLUSIONS;172
7.16.5;5. REFERENCES;172
7.17;Chapter 25. COMPUTER-AIDED TUNING AND DIAGNOSING BASED ON PERSONAL COMPUTERS USAGE;174
7.17.1;1. INTRODUCTION;174
7.17.2;2. PRINCIPLES OF CATDS DESIGN;175
7.17.3;3. THE MAIN REQUIREMENTS FOR CATDS;175
7.17.4;4. FAULT DIAGNOSIS FOR CATDS;176
7.17.5;5. PURPOSE AND STRUCTURE OF CATS ADACOM;177
7.17.6;6. CONCLUSION;177
7.17.7;7. REFERENCES;177
7.18;Chapter 26. OBJECT RECOGNITION BY NEURAL NETWORK;180
7.18.1;1. INTRODUCnON;180
7.18.2;2. THE PATTERN RECOGNITION PROCESS AND ITS AUTOMATION;180
7.18.3;3. THE AUTOMATED PATTERN RECOGNITION SUBSYSTEM AND ITS APPLICATION IN THE ASSEMBLY CELL;181
7.18.4;4. CONCLUSION;183
7.18.5;5. REFERENCES;184
7.18.6;6. APPENDIX;184
7.19;Chapter 27. NEURAL ADAPTIVE CONTROL OF A BIOREACTOR;186
7.19.1;1. INTRODUCTION;186
7.19.2;2. SYSTEM ANALYSIS;186
7.19.3;3. IDENTIFIER DESIGN WITH FEEDFORWARD BACKPROPAGATION NETS;187
7.19.4;4. NEURAL CONTROLLER;188
7.19.5;5. CONCLUSIONS AND FUTURE WORK;191
7.19.6;REFERENCES;191
7.20;Chapter 28. GENETIC ALGORITHMS IN INTELLIGENT CONTROL SYSTEMS DESIGN;192
7.20.1;1. INTRODUCTION;192
7.20.2;2. GENETIC ALGORITHMS;192
7.20.3;3. GENETIC ALGORITHMS AND INTELLIGENT CONTROL;193
7.20.4;4. EXPERIMENTAL FRAMEWORK;195
7.20.5;5. CONCLUSIONS;197
7.20.6;6. REFERENCES;197
7.21;Chapter 29. THE ROUTING PROBLEM IN TRAFFIC CONTROL USING GENETIC ALGORITHMS;198
7.21.1;1. INTRODUCTION;198
7.21.2;2. GENETIC ALGORITHMS;199
7.21.3;3. METHODOLOGY;200
7.21.4;4. EXPERIMENTS AND RESULTS;200
7.21.5;5. CONCLUSIONS;202
7.21.6;6. REFERENCES;202
7.22;Chapter 30. COMBINING NEURAL AND FUZZY TECHNIQUES IN MONITORING AND CONTROL OF MANUFACTURING PROCESSES;204
7.22.1;1. INTRODUCTION;204
7.22.2;2. FUZZY SYSTEMS;204
7.22.3;3. EXPERIMENTS, APPLIED TECHNIQUES;206
7.22.4;4. NEURO-FUZZY MONITORING OF CUTTING TOOLS;206
7.22.5;5. CONCLUSIONS;208
7.22.6;6. ACKNOWLEDGEMENTS;208
7.22.7;7. REFERENCES;208
7.23;Chapter 31. AN OBJECT-ORIENTED SPECIFICATION OF A CONTROL LOGIC FOR AN FMS;210
7.23.1;1. INTRODUCTION;210
7.23.2;2. OBJECT-ORIENTED DATABASE;210
7.23.3;3. RULE GROUPS;211
7.23.4;4. EXAMPLES;214
7.23.5;5. CONCLUSION;215
7.23.6;6. REFERENCES;215
7.24;Chapter 32. FACTORY COMMUNICATIONS FOR AUTOMATING A CIM CENTER EXPERIMENTAL PLANT;216
7.24.1;1. INTRODUCTION;216
7.24.2;2. PLANT COMMUNICATION SYSTEM;218
7.24.3;3. PRODUCTION PLANT CELLS;219
7.24.4;4. PLANT CONTROL SYSTEM;220
7.24.5;5. CONCLUSIONS;221
7.24.6;5. REFERENCES;221
7.25;Chapter 33. DYNAMIC SENSORS DISTORTION COMPENSATION BY MEANS OF INPUT ESTIMATION ALGORITHMS;222
7.25.1;1. INTRODUCTION;222
7.25.2;2 PROBLEM STATEMENT;223
7.25.3;3 INPUT ESTIMATION ALGORITHM;224
7.25.4;4 INVERSE FILTER OPTIMIZATION;224
7.25.5;5 STEAD - STATE REGULARIZED FILTER;225
7.25.6;6 CONCLUSIONS;226
7.25.7;7 REFERENCES;227
7.26;Chapter 34. EXTRACTING PERIODIC COMPONENTS FROM MEASURED OSCILLATORY SIGNALS;228
7.26.1;1. INTRODUCTION;228
7.26.2;2. ALGORITHM;229
7.26.3;3. APPLICATIONS;231
7.26.4;4. CONCLUSION;232
7.26.5;ACKNOWLEDGEMENT;232
7.26.6;REFERENCES;232
7.27;Chapter 35. FUZZY LOGIC: SIGNAL CONDITIONING FOR SO2 SENSOR;234
7.27.1;1. INTRODUCTION;234
7.27.2;2. SENSOR SIGNAL CONDITIONING;235
7.27.3;3. MODELLING OF SO2 SENSOR;237
7.27.4;4. RESULTS AND CONCLUSIONS;237
7.27.5;5. REFERENCES;239
7.27.6;Acknowledgements:;239
7.28;Chapter 36. ON THE USE OF VIRTUAL INSTRUMENTS IN REALIZING ADAPTIVE CONTROLLERS;240
7.28.1;1. INTRODUCTION;240
7.28.2;2.THE PRINCIPLE OF VIRTUAL INSTRUMENTS;240
7.28.3;3. REALIZING CONTROLLERS AS VI;241
7.28.4;4. THE ADAPTIVE PID ALGORITHM;242
7.28.5;5. A SIMULATION EXAMPLE;244
7.28.6;6. CONCLUSIONS;245
7.28.7;7. REFERENCES;245
7.29;Chapter 37. A SYNTACTIC AND CONTEXTUAL EDGE DETECTOR;246
7.29.1;1. INTRODUCTION;246
7.29.2;2. EDGE MODELING;246
7.29.3;3. EDGES EXTRACTION;247
7.29.4;4. REWRITING RULES;248
7.29.5;5. METHOD QUALITY;249
7.29.6;6. CONCLUSION;250
7.29.7;7. REFERENCES;250
7.30;Chapter 38. NAVIGATION SYSTEM FOR A MOBILE ROBOT BY MEANS OF AN INFRARED SYSTEM;252
7.30.1;1· INTRODUCTION;252
7.30.2;2. THE SENSOR SYSTEM;252
7.30.3;3· CONTROL OF THE DRIVING WHEELS ANGULAR VELOCITIES.;252
7.30.4;4. CONTROL OF ROBOT THE GENERALIZED VELOCITIES.;253
7.30.5;5· ODOMETRIC CONTROL OF THE POSITION;253
7.30.6;6. TRACKING OF THE PATHS;254
7.30.7;7. THE KALMAN FILTER;254
7.30.8;8. CORRECTION WITH INFRARED WAVES;255
7.30.9;9. RESULTS;256
7.30.10;10. CONCLUSIONS;256
7.30.11;11. REFERENCES;257
7.31;Chapter 39. NEURAL NETWORK LOCAL NAVIGATION OF MOBILE ROBOTS IN A MOVING OBSTACLES ENVIRONMENT;258
7.31.1;1. INTRODUCTION;258
7.31.2;2. GENERALIZED PREDICTIVE CONTROL;259
7.31.3;3. MOBILE OBSTACLE MOTIONS PREDICTION;260
7.31.4;4. THE NEURAL NETWORK APPROACH;261
7.31.5;5. RESULTS;261
7.31.6;6. CONCLUSIONS;262
7.31.7;7. ACKNOWLEDGEMENT;262
7.31.8;REFERENCES;262
7.32;Chapter 40. A COMBINED PATH GENERATION AND TRACKING CONTROLLER FOR MOBILE ROBOTS;264
7.32.1;1. INTRODUCTION;264
7.32.2;2. THE PROPOSED METHOD;265
7.32.3;3· APPROACH-PATH GENERATION;266
7.32.4;4. LOCAL PATH GENERATION;266
7.32.5;5. GLOBAL PATH GENERATION;267
7.32.6;6. CONCLUSIONS;268
7.32.7;7. REFERENCES;269
7.33;Chapter 41. MOTION ESTIMATION FOR A MOBILE ROBOT USING VISUAL AND RANGE DATA;270
7.33.1;1. INTRODUCTION;270
7.33.2;2. GENERAL DESCRIPTION;270
7.33.3;3. DETERMINING 3D MOTION OF RIGID OBJECTS;272
7.33.4;4. EXPERIMENTAL RESULTS;274
7.33.5;5. CONCLUSION;275
7.33.6;6. REFERENCES;275
7.34;Chapter 42. APPLICATION OF NEURAL NETWORKS TO IMAGE-BASED CONTROL OF ROBOT ARMS;276
7.34.1;1. INTRODUCTION;276
7.34.2;2. FOUR-POINT IMAGE EXPERIMENTS;277
7.34.3;3. FOURIER DESCRIPTOR EXPERIMENTS;278
7.34.4;4. CONCLUSIONS;280
7.34.5;Acknowledgements;280
7.34.6;5. REFERENCES;280
7.35;Chapter 43. HANDLING DELAY-TIME IN LOW COST CONTROLLERS;282
7.35.1;1. INTRODUCTION;282
7.35.2;2. MODELING DIGITAL REGULATORS;283
7.35.3;3. MODELING CONTINUOUS LINEAR PROCESSES;283
7.35.4;4. IDENTIFICATION OF CONTINUOUS LINEAR PROCESSES;285
7.35.5;5. SIMULATION EXAMPLE;286
7.35.6;6. CONCLUSIONS;287
7.35.7;7. REFERENCES;287
7.36;Chapter 44. FLEXIBLE LOW-COST MULTIFUNCTION INSTRUMENT;288
7.36.1;1. INTRODUCTION;288
7.36.2;2. HARDWARE;289
7.36.3;3. FUNCTION BLOCKS;289
7.36.4;4. CONFIGURATION;291
7.36.5;5. COMMUNICATIONS;292
7.36.6;6. APPLICATIONS;293
7.36.7;7. REFERENCES;293
7.37;Chapter 45. COMPUTER AIDED TRACTOR/IMPLEMENT COUPLING;294
7.37.1;1. INTRODUCTION;294
7.37.2;2. SOLUTION;294
7.37.3;3. Final remarks;298
7.37.4;4. References;299
7.38;Chapter 46. STRATEGIES OF MODEL REFERENCE ADAPTIVE CONTROL WITH ACTIVE LEARNING PROPERTIES;300
7.38.1;1. INTRODUCTION;300
7.38.2;2. SYSTEMS WITH IMPLICIT REFERENCE MODEL;300
7.38.3;3. SYSTEM WITH EXPLICIT REFERENCE MODEL;302
7.38.4;4. NONMINIMUM PHASE SYSTEMS AND INTERCONNECTIONS BETWEEN ADAPTIVE CONTROL SYSTEMS STRUCTURES;302
7.38.5;5. CONVERGENCE PROPERTIES OF DAAC;303
7.38.6;6. SIMULATION EXAMPLE AND COMPARISONS;304
7.38.7;7. CONCLUSIONS;305
7.38.8;8. ACKNOWLEDGEMENT;305
7.38.9;9. REFERENCES;305
7.39;Chapter 47. ADAPTATION AND SELF-LEARNING OF NONLINEAR MULTIVARIABLE SYSTEMS;306
7.39.1;1. INTRODUCTION;306
7.39.2;2. THE PROBLEM STATEMENT;307
7.39.3;3. CONCEPTION OF THE SPATIAL MOTION CONTROL;308
7.39.4;4. SELF-LEARNING CONTROL;309
7.39.5;5. CONCLUSION;311
7.39.6;5.REFERENCES;311
7.40;Chapter 48. START-UP OF PROCESS PLANTS BY CONSERVATIVE LEARNING BASED QUALITATIVE REASONING;312
7.40.1;1. INTRODUCTION;312
7.40.2;2. BAYESIAN IDENTIFICATION AND PREDICTION BASED LEARNING IN QUALITATIVE SIMULATION;312
7.40.3;3. CONSERVATIVE LEARNING FOR MULTIPLE MODEL BASED QUALITATIVE REASONING;316
7.40.4;4. ACKNOWLEDGEMENT;317
7.40.5;5. REFERENCES;317
7.41;Chapter 49. KNOWLEDGE-BASED SYSTEM IDENTIFICATION - COMPONENTS OF THE EXPERTISE;318
7.41.1;1. INTRODUCTION;318
7.41.2;2. MODELLING AND MEASUREMENT;318
7.41.3;3. COMPONENTS OF THE EXPERTISE;319
7.41.4;4. INTELLIGENT SI ENVIRONMENT;320
7.41.5;5. CONCLUSIONS;322
7.41.6;6. REFERENCES;322
7.42;Chapter 50. A FUZZY MONITORING AND DIAGNOSIS PROCESS TO DETECT EVOLUTIONS OF A CAR DRIVER'S BEHAVIOR;324
7.42.1;1. INTRODUCTION;324
7.42.2;2. FUZZY PATTERN RECOGNITION AND DIAGNOSIS;325
7.42.3;3. DETECTING AN EVOLUTION;326
7.42.4;4. APPLICATION;328
7.42.5;5. CONCLUSION;329
7.42.6;6. REFERENCES;329
7.43;Chapter 51. A SIMPLE IDENTIFICATION METHOD FOR THE ORDER OF THE STREJC MODEL AND ITS APPLICATION TO AUTOTUNING;330
7.43.1;1. INTRODUCTION;330
7.43.2;2. EVALUATING THE ORDER AND THE REMAINING PARAMETERS OF THE MODEL (7).;331
7.43.3;3. SIMULATION OF THE AUTOTUNING PROCEDURE OF A PID CONTROLLER;332
7.43.4;4. MODIFICATION OF THE DESCRIBED METHOD AND EXPERIMENTAL TESTS;334
7.43.5;5. CONCLUSIONS;336
7.43.6;6. REFERENCES;336
7.44;Chapter 52. NONLINEAR OBSERVERS FOR DISTILLATION COLUMNS;338
7.44.1;1. INTRODUCTION;338
7.44.2;2. EXPERIMENTAL PLANT;338
7.44.3;3. PLANT MODEL;339
7.44.4;4. OBSERVER THEORY;339
7.44.5;5. APPLICATION TO BINARY DISTILLATION;339
7.44.6;6. SIMULATION RESULTS;340
7.44.7;7. EXPERIMENTAL RESULTS;341
7.44.8;8. CONCLUSION;342
7.44.9;9. SYMBOLS USED;343
7.44.10;10. REFERENCES;343
7.45;Chapter 53. A NEW VARIABLE STEP SIZE ADAPTATION ALGORITHM FOR NEURAL NETWORKS;344
7.45.1;1. INTRODUCTION;344
7.45.2;2. VARIABLE STEP SIZE ALGORITHMS;344
7.45.3;3. THE MOMENTUM TECHNIQUE;345
7.45.4;4. ALTERNATIVE OPTIMIZATION TECHNIQUES;346
7.45.5;5. THE NEW VARIABLE STEP SIZE ALGORITHM;346
7.45.6;6. CONCLUSIONS;346
7.45.7;7. REFERENCES;346
7.46;Chapter 54. ESTIMATION OF RESONANT TRANSFER FUNCTIONS IN THE JOINTS OF AN INDUSTRIAL ROBOT;348
7.46.1;1. INTRODUCTION;348
7.46.2;2. DYNAMIC MODEL OF A ROBOT WITH ELASTIC JOINTS;349
7.46.3;3. DESIGN OF THE IDENTMCATON EXPERIMENT;349
7.46.4;4. EXPERIMENTAL SETUP;351
7.46.5;5. EXPERIMENTAL RESULTS;352
7.46.6;6. CONCLUSIONS;353
7.46.7;7. ACKNOWLEDGEMENTS;353
7.46.8;8. REFERENCES;353
7.47;Chapter 55. SMART ASYNCHRONOUS DRIVES : ADVANCED CONTROL AND FAULT DETECTION AND IDENTIFICATION;354
7.47.1;1. INTRODUCTION;354
7.47.2;2. STRUCTURAL ANALYSIS;354
7.47.3;3. MODELLING OF AN ASYNCHRONOUS DRIVE AND ITS CONTROL;356
7.47.4;4. OBTAINED RESULTS;357
7.47.5;5. CONCLUSION;358
7.47.6;6. REFERENCES;359
7.48;Chapter 56. A DRIVEN LASER-BEAM FOR AN ACTIVE SENSOR ON ROBOT CONTROL;360
7.48.1;1. INTRODUCTION;360
7.48.2;2. STRUCTURE AND MODELING;361
7.48.3;3. CONTROL;363
7.48.4;4. EXAMPLE;364
7.48.5;5. CONCLUSION;364
7.48.6;6. REFERENCES;364
7.49;Chapter 57. IMPROVEMENT OF FLEXIBILITY AND RELIABILITY OF AUTOMOBILE ACTUATORS BY MODEL-BASED ALGORITHMS;366
7.49.1;1. INTRODUCTION;366
7.49.2;2. METHODS FOR FAULT DETECTION;366
7.49.3;3. EXPERIMENTAL RESULTS;368
7.49.4;4. CONCLUSION;371
7.49.5;5. REFERENCES;371
7.50;Chapter 58. VS - CASCADE CONTROL;372
7.50.1;1. INTRODUCTION;372
7.50.2;2. VS-PI CONTROLLER;373
7.50.3;2. PREFILTERING ACTION;373
7.50.4;4. CONCLUSIONS;376
7.50.5;6. REFERENCES;376
7.51;Chapter 59. AN EDUCATIONAL PLATFORM FOR VARIABLE-STRUCTURE CONTROL OF SAFETY-RELATED SYSTEMS;378
7.51.1;1. INTRODUCTION;378
7.51.2;2. VARIABLE-STRUCTURE CONTROL ALGORITHM;379
7.51.3;3. OBSERVER CONCEPT;380
7.51.4;4. TRAINING AND EDUCATION PLATFORM;381
7.51.5;5. AN EDUCATIONAL ENVIRONMENT;381
7.51.6;6. REFERENCES;383
7.52;Chapter 60. ANALYSIS OF ACHIEVABLE PID PERFORMANCES VERSUS OPTIMAL LQR CONTROL;384
7.52.1;1. INTRODUCTION;384
7.52.2;2. PID AS AN OPTIMIZATION LQR PROBLEM;384
7.52.3;3. ROBUSTNESS LOOP MARGIN;385
7.52.4;4. PID REGULATOR AND LQR COST;386
7.52.5;5. ASYMPTOTIC PID;386
7.52.6;5. ACKNOLEDGEMENT;387
7.52.7;6. REFERENCES;387
7.53;Chapter 61. THE SAMPLING PERIOD AS A CONTROL PARAMETER;388
7.53.1;1. INTRODUCTION;388
7.53.2;2. CONTROLLER;388
7.53.3;3. PROCESS MODELS;389
7.53.4;4. TUNING METHODS;389
7.53.5;5. TUNING FORMULAS;390
7.53.6;6. TUNING CRITERIA;392
7.53.7;7. EXAMPLE;392
7.53.8;8. CONCLUSIONS;393
7.53.9;9. REFERENCES;393
7.54;Chapter 62. CONFIDENCE DEGREE ON THE ADVANCED STAND-ALONE CONTROLLERS;394
7.54.1;1. INTRODUCTION;394
7.54.2;2. BENCHMARK SYSTEM;395
7.54.3;3. THE ES 100 OMRON CONTROLLER;395
7.54.4;4. THE 761 FOXBORO CONTROLLER;397
7.54.5;5. CONCLUSIONS;398
7.54.6;6. ACKNOWLEDGEMENT;398
7.54.7;7. REFERENCES;398
7.55;Chapter 63. STRUCTURED NEURAL NETWORK FOR NONLINEAR DYNAMIC SYSTEMS MODELING;400
7.55.1;1. INTRODUCTION;400
7.55.2;2. STATE SPACE MODEL;401
7.55.3;3. EXAMPLE;402
7.55.4;4. CONCLUSIONS;404
7.55.5;REFERENCES;404
7.56;Chapter 64. NONLINEAR SYSTEM IDENTIFICATION USING ADDITIVE DYNAMIC NEURAL NETWORKS;406
7.56.1;1. INTRODUCTION;406
7.56.2;2. ARCHITECTURE OF THE CONNECTIONIST MODELS;406
7.56.3;3. PARAMETER ADAPTATION IN THE CONNECTIONIST MODELS;407
7.56.4;4. SIMULATION RESULTS;409
7.56.5;5. CONCLUSION;410
7.56.6;6. ACKNOWLEDGEMENTS;410
7.56.7;7. REFERENCES;411
7.57;AUTHOR INDEX;412