Husson Advanced Information Processing in Automatic Control (AIPAC'89)

1. Auflage 2014
ISBN: 978-1-4832-9426-1
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 563 Seiten, Web PDF

Reihe: IFAC Symposia Series

Information Processing is a key area of research and development and the symposium presented state-of-the-art reports on some of the areas which are of relevance in automatic control: fault diagnosis and system reliability. Papers also covered the role of expert systems and other knowledge based systems, which are needed, to cope with the vast quantities of data generated by large scale systems. This volume should be considered essential reading for anyone involved in this rapidly developing area.

Autoren/Hrsg.

Husson, R.

Inhaltsverzeichnis

1;Front Cover;1
2;Advanced Information Processing in Automatic Control;4
3;Copyright Page;5
4;Table of Contents;10
5;PART I: SURVEY PAPERS;18
5.1;Chapter 1. AI AND COMPLEX SYSTEMS TECHNIQUES IN MANUFACTURING MANAGEMENT AND CONTROL;18
5.1.1;Introduction;18
5.1.2;Conclusions;22
5.1.3;References;23
5.2;Chapter 2. EVALUATION OF ANALYTICAL REDUNDANCY FOR FAULT DIAGNOSIS IN DYNAMIC SYSTEMS;24
5.2.1;1. INTRODUCTION;24
5.2.2;2. FORMULATION OF THE FAULT DETECTION AND ISOLATION PROBLEM;25
5.2.3;3. METHODS OF RESIDUAL GENERATION;26
5.2.4;4. INCREASING THE ROBUSTNESS TO UNKNOWN INPUTS;30
5.2.5;5. OPTIMALLY ROBUST OBSERVER SCHEMES FOR IFD, CFD, AND AFD;33
5.2.6;6. ACKNOWLEDGEMENT;35
5.2.7;7. REFERENCES;35
5.3;Chapter 3. PROCESS FAULT DIAGNOSIS BASED ON PROCESS MODEL KNOWLEDGE;38
5.3.1;1. INTRODUCTION;38
5.3.2;2. ON-LINE ENGINEERING EXPERT SYSTEMS;39
5.3.3;3. ON-LINE EXPERT SYSTEMS FOR FAULT DIAGNOSIS;39
5.3.4;4. FAULT DETECTION BASED ON PARAMETER ESTIMATION;39
5.3.5;5. CASE STUDY EXPERIMENTS;42
5.3.6;6. CONCLUSION;46
5.3.7;LITERATURE;46
5.4;Chapter 4. DETECTION AND DIAGNOSIS SYSTEM AND MODEL-BASED APPROACH;52
5.4.1;1. INTRODUCTION;52
5.4.2;2. DEFINITIONS AND STAGES OF A DETECTION DIAGNOSIS PROCEDURE Ref. [1] to [9];54
5.4.3;3. EXAMPLES OF DETECTIONDIAGNOSIS PROCEDURES;57
5.4.4;4. CONCLUSIONS;60
5.4.5;REFERENCES;60
6;PART II: FAULT DETECTION AND DIAGNOSIS;62
6.1;Chapter 5. ANALYTIC REDUNDANCY MANAGEMENT FOR SYSTEMS WITH APPRECIABLE STRUCTURAL DYNAMICS;62
6.1.1;INTRODUCTION;62
6.1.2;ON-LINE FDI&R USING ANALYTIC REDUNDANCY;62
6.1.3;A SUMMARY OF ESSENTIAL LQG THEORY;63
6.1.4;WALD'S SEQUENTIAL PROBABILITY RATIO TEST;64
6.1.5;SCOLE APPARATUS;64
6.1.6;RESULTS AND DISCUSSION;65
6.1.7;SUMMARY;65
6.1.8;REFERENCES;65
6.2;Chapter 6. ANALYTICAL REDUNDANCY IN NON LINEAR INTERCONNECTED SYSTEMS BY MEANS OF STRUCTURAL ANALYSIS;68
6.2.1;INTRODUCTION;68
6.2.2;MODEL OF A COMPLEX SYSTEM;69
6.2.3;STRUCTURAL REPRESENTATION OF THE MODEL;69
6.2.4;OVERDETERMINATION OF A SUBSET OF UNKNOWN VARIABLES;70
6.2.5;THE ALGORITHM;71
6.2.6;CONCLUSION;72
6.2.7;REFERENCES;72
6.3;Chapter 7. SENSOR FAULT DETECTION IN DYNAMIC SYSTEMS USING ROBUST OBSERVERS;74
6.3.1;INTRODUCTION;74
6.3.2;PROBLEM SPECIFICATION;74
6.3.3;ROBUST OBSERVER THEORY;75
6.3.4;FREQUENCY DOMAIN FAULT ANALYSIS;75
6.3.5;EXAMPLE;76
6.3.6;RESULTS;77
6.3.7;DISCUSSION;77
6.3.8;REFERENCES;77
6.4;Chapter 8. RECURSIVE IDENTIFICATION AND FAULT DETECTION USING PARTIAL TIME MOMENTS;80
6.4.1;Introduction;80
6.4.2;I- Fault detection method;80
6.4.3;II-Identification of continous systems using partial moments;81
6.4.4;III-Fault detection for a P.C. motor;83
6.4.5;Conclusion;83
6.5;Chapter 9. BENCHMARKING THE DIAGNOSIS OF CONTROL ACTUATOR FAILURES IN LINEAR SYSTEMS;86
6.5.1;1 Introduction;86
6.5.2;2 Representation of a Dynamic System With Control-Actuator Failure;86
6.5.3;3 Convex Models of Malfunction Uncertainty;87
6.5.4;4 Benchmark Diagnosis Capability;87
6.5.5;5 Hyperplane Separation for Uniformly Bounded Malfunctions;88
6.5.6;6 Example: Actuator Failures in AFTI/F16 Aircraft;89
6.5.7;7 Energy-Bounded Failure Functions;91
6.5.8;8 Conclusions;92
6.5.9;Appendix A Brief Discussion of The Plausibility of Convex Models of Uncertainty;92
6.5.10;References;92
6.6;Chapter 10. ON LINE FAILURE DETECTION USING KULLBACK'S DIVERGENCE;94
6.6.1;INTRODUCTION;94
6.6.2;PROBLEM STATEMENT;94
6.6.3;PILOT PROCESS RESULTS;97
6.6.4;CONCLUSION;98
6.6.5;REFERENCES;98
6.7;Chapter 11. SIMPLE FAULT DETECTION ALGORITHM VIA PARAMETER ESTIMATION;100
6.7.1;INTRODUCTION;100
6.7.2;FORMULATION OF MODELSTRUCTURE FOR FAULT DETECTION ALGORITHM;101
6.7.3;SIMULATION RESULTS;104
6.7.4;CONCLUSIONS;105
6.7.5;NOTATIONS AND SYMBOLS;106
6.7.6;REFERENCES;106
6.8;Chapter 12. ABRUPT CHANGE DETECTION FOR A CLASS OF KNOWN SYSTEMS;108
6.8.1;1) INTRODUCTION;108
6.8.2;2) MODEL OF THE SYSTEM 1;108
6.8.3;3) PROPOSED CONTROL STRATEGY;108
6.8.4;4) DETECTION ALGORITHM;108
6.8.5;5) SIMULATION;109
6.8.6;6) CONCLUSION;110
6.8.7;Bibliography;110
6.9;Chapter 13. FAULT DETECTION VIA ADAPTIVE OBSERVERS BASED ON ORTHOGONAL FUNCTIONS;112
6.9.1;INTRODUCTION;112
6.9.2;PROBLEM FORMULATION;112
6.9.3;ORTHOGONAL SERIES REPRESENTATION;113
6.9.4;CFD VIA ADAPTIVE OBSERVER BASED ON OSR;114
6.9.5;SOME REMARKS ON THE ADAPTIVE OBSERVER;115
6.9.6;NUMERICAL EXAMPLE;116
6.9.7;CONCLUSIONS;116
6.9.8;ACKNOWLEDGEMENT;116
6.9.9;REFERENCES;116
6.10;Chapter 14. IDENTIFICATION OF CONTINUOUS-TIME PROCESS FOR FAILURE DETECTION AND DIAGNOSIS;118
6.10.1;1 INTRODUCTION;118
6.10.2;2 FAILURE DETECTION AND DIAGNOSIS;118
6.10.3;3 CONTINUOUS-TIME PROCESS IDENTIFICATION;119
6.10.4;4 SIMULATION RESULTS;122
6.10.5;5 CONCLUSION;123
6.10.6;REFERENCES;123
7;PART III: DATA VALIDATION USING MODELS;126
7.1;Chapter 15. MAXIMUM LIKELIHOOD ESTIMATOR OF MEASUREMENT ERROR VARIANCES IN DATA RECONCILIATION;126
7.1.1;1- INTRODUCTION;126
7.1.2;2- STATEMENT OF PROBLEM;126
7.1.3;3- ESTIMATION ALGORITHM;127
7.1.4;4- NUMERICAL EXAMPLES;127
7.1.5;5- CONCLUSION;128
7.1.6;REFERENCES;128
7.2;Chapter 16. OBSERVABILITY AND DATA VALIDATION OF BILINEAR SYSTEMS;130
7.2.1;INTRODUCTION;130
7.2.2;PROBLEM FORMULATION;130
7.2.3;NECESSITY OF OBSERVABILITY;131
7.2.4;BILINEAR SYSTEMS OBSERVABILITY;131
7.2.5;ESTIMATION OF REDUNDANT VARIABLES;133
7.2.6;ILLUSTRATIVE EXAMPLE;134
7.2.7;CONCLUSION;135
7.2.8;REFERENCES;135
8;PART IV: PATTERN RECOGNITION;136
8.1;Chapter 17. SYSTEM DIAGNOSIS USING A POLYNOMIAL DISCRIMINANT FUNCTION;136
8.1.1;INTRODUCTION;136
8.1.2;PRINCIPLES OF ESTIMATION OF DECISION SURFACES;136
8.1.3;CHOICE OF THE OPTIMUM DISCRIMINANT FUNCTION;137
8.1.4;SIMPLIFICATION;137
8.1.5;EVOLUTION;137
8.1.6;APPLICATION;138
8.1.7;CONCLUSION;138
8.1.8;REFERENCES;138
8.2;Chapter 18. AN APPROACH TO ESTIMATION AND IDENTIFICATION BASED ON INFORMATION THEORY OF DETERMINISTIC FUNCTIONS;140
8.2.1;1. INTRODUCTION;140
8.2.2;2. BACKGROUND ON ENTROPY OF DETERMINISTIC FUNCTIONS;140
8.2.3;3. ENTROPIES OF DISTRIBUTED DETERMINISTIC FUNCTIONS;142
8.2.4;4. ENTROPY OF DISCRETE DETERMINISTIC FUNCTIONS;143
8.2.5;5. INFORMATIONAL CLOSENESS OF DETERMINISTIC FUNCTIONS;143
8.2.6;6. MINIMUM PATTERN ENTROPY PRINCIPLE;144
8.2.7;7. CONCLUSIONS;144
8.2.8;REFERENCES;144
8.3;Chapter 19. DISCRIMINANT ANALYSIS IN SECURITY ASSESSMENT OF POWER SYSTEMS BY STATISTICAL PATTERN RECOGNITION;146
8.3.1;INTRODUCTION;146
8.3.2;TRANSIENT STABILITY INDICATORS (PATTERN VECTOR);147
8.3.3;EXTRACTION OF DISCRIMINANT FEATURES;148
8.3.4;SIMULATION AND COMPARISON OF RESULTS;150
8.3.5;CONCLUSION;152
8.3.6;REFERENCES;152
8.4;Chapter 20. APPLICATIONS OF THE PATTERN RECOGNITION TECHNIQUES A T ELECTRICITE DE FRANCE;154
8.4.1;INTRODUCTION;154
8.4.2;APPLICATION OF PATTERN RECOGNITION TO STEAM GENERATOR TUBES INSPECTION;154
8.4.3;SURVEILLANCE OF EXPERIMENTAL IN-CORE INSTRUMENTATION;156
8.4.4;APPLICATION TO THE SURVEILLANCE OF A HEAT PUMP;156
8.4.5;CONCLUSIONS;159
8.4.6;REFERENCES;159
8.5;Chapter 21. PATTERN RECOGNITION USING NEURAL NETWORKS. COMPARISON TO THE NEAREST NEIGHBOUR RULE;160
8.5.1;1 - INTRODUCTION;160
8.5.2;2 - PRESENTATION OF THE NEURAL NETWORK APPROACH;160
8.5.3;3 - GENERALISATION TO THE p-DIMENSIONAL CASE;162
8.5.4;4 - PRESENTATION OF THE TWO VARIABLE STEP METHODS USED;162
8.5.5;5 - SIMULATION RESULTS;162
8.5.6;6 - CHOISE OF THE NUMBER OF CELLS IN THE HIDDEN LAYER;163
8.5.7;7 - APPLICATION TO AUTOMATIC SLEEP STAGING;164
8.5.8;8 - CONCLUSION;164
8.5.9;REFERENCES;165
8.6;Chapter 22. RECURSIVE ESTIMATION OF THE EIGENELEMENTS OF A COVARIANCE MATRIX IN PATTERN RECOGNITION;166
8.6.1;I - INTRODUCTION;166
8.6.2;II - RECURSIVE ESTIMATION OF THE EIGENELEMENTS OF S;166
8.6.3;III - RAPID CALCULATION AND SIMULATION;169
8.6.4;IV - PERFORMANCE;171
8.6.5;V - CONCLUSION;172
8.6.6;REFERENCES;172
9;PART V: FUZZY SYSTEMS;174
9.1;Chapter 23. FUZZY SETS THEORY AND PROCESS CONTROL: PAST, PRESENT AND FUTURE;174
9.1.1;1. INTRODUCTION;174
9.1.2;2. RETROSPECTION;174
9.1.3;3. REFLECTION;175
9.1.4;4. CURRENT WORK;176
9.1.5;5. PROJECTION;176
9.1.6;6. CONCLUSIONS;177
9.1.7;REFERENCES;177
9.2;Chapter 24. ON-LINE FUZZY RULE SET GENERATOR;178
9.2.1;INTRODUCTION;178
9.2.2;MATHEMATICAL TOOL OVERVIEW;178
9.2.3;FUZZY CONDITIONAL STATEMENT - PROBLEM SOLVING ALGORITHM;179
9.2.4;MERGING FUZZY CONDITIONAL STATEMENT SETS;181
9.2.5;ILLUSTRATIVE EXAMPLE;182
9.2.6;CONCLUSION;182
9.2.7;ACKOWLEDGEMENT;183
9.2.8;REFERENCES;183
9.2.9;APPENDIX: SAMPLE EXAMPLE COMPUTATION OUTLINE;183
9.3;Chapter 25. PROCESS CONTROL USING FUZZY LOGIC AND FUZZY RELATIONAL EQUATIONS;184
9.3.1;INTRODUCTION;184
9.3.2;CONTROL MODEL;184
9.3.3;CHOICE OF OPERATORS AND COMPOSITIONS;185
9.3.4;CONCLUSION;186
9.3.5;REFERENCES;186
9.4;Chapter 26. WARSHIP ROLL STABILISATION USING FUZZY CONTROL OF THE FIN STABILISERS;188
9.4.1;INTRODUCTION;188
9.4.2;WARSHIP DYNAMICS;188
9.4.3;THE FUZZY CONTROLLER;189
9.4.4;DISCUSSION AND RESULT3;190
9.4.5;CONCLUSIONS AND FURTHER WORK;190
9.4.6;REFERENCES;190
9.5;Chapter 27. FUZZY LINGUISTIC VARIABLES IN THE EXPERT SUPERVISION OF CONTROL SYSTEMS;194
9.5.1;ABSTRACT;194
9.5.2;INTRODUCTION;194
9.5.3;REASONING;194
9.5.4;LINGUISTIC VARIABLES;194
9.5.5;APPLICATION TO EXPLICITAD APTIVE CONTROL;195
9.5.6;SUPERVISOR RULES AND CONTEXTS;195
9.5.7;BIBLIOGRAPHY;196
10;PART VI: QUALITATIVE REASONING METHODS;198
10.1;Chapter 28. DECLARATIVE MODELLING FOR PROCESS SUPERVISION;198
10.1.1;ABSTRACT;198
10.1.2;INTRODUCTION;198
10.1.3;THE PROCESS;198
10.1.4;ARTIFICIAL INTELLIGENCE TOOLS;198
10.1.5;MODEL SPECIFICATIONS FOR SUPERVISION;199
10.1.6;DECLARATIVE DESCRIPTION OF A DYNAMIC SYSTEM;200
10.1.7;REASONINGS USING THE MODEL;200
10.1.8;APPLICATION;201
10.1.9;CONCLUSION;201
10.1.10;AKNOWLEDGEMENTS;201
10.1.11;REFERENCES;201
10.2;Chapter 29. TOWARDS A METHODOLOGY TO WRITE RULES FOR EXPERT CONTROLLERS;204
10.2.1;INTRODUCTION;204
10.2.2;REASONING ABOUT A PROCESS;204
10.2.3;ELEMENTS OF QUALITATIVE REASONING;204
10.2.4;A GENERAL FRAME FOR THE COMMAND;205
10.2.5;AN ILLUSTRATIVE EXAMPLE;206
10.2.6;CONCLUSION;209
10.2.7;ACKNOWLEDGMENTS;210
10.2.8;REFERENCES;210
10.3;Chapter 30. ORDER-OF-MAGNITUDE REASONING WITH FUZZY RELATIONS;212
10.3.1;1 - INTRODUCTION;212
10.3.2;2 - BACKGROUND ON FUZZY RELATIONS AND;212
10.3.3;3 - HANDLING RELATIVE ORDERS OF MAGNITUDE WITH FUZZY RELATIONS;214
10.3.4;4 - PROPAGATION OF RELATIVE ORDERS OF MAGNITUDE;216
10.3.5;5 - CONCLUDING REMARKS;217
10.3.6;REFERENCES;217
10.4;Chapter 31. AN EVIDENTIAL VIEW OF QUALITATIVE PHYSICS;218
10.4.1;1. INTRODUCTION;218
10.4.2;2. KUIPERS QUALITATIVE PHYSICS;218
10.4.3;3. DEMPSTER-SHAFER'S THEORY;219
10.4.4;4. MIXING THE TWO REPRESENTATIONS;220
10.4.5;5. CONCLUSION;222
10.4.6;REFERENCES;222
10.5;Chapter 32. WHAT CAN WE DO WITH QUALITATIVE CALCULUS TODAY?;224
10.5.1;INTRODUCTION;224
10.5.2;QUALITATIVE EQUALITY;225
10.5.3;QUALITATIVE BASES;225
10.5.4;QUALITATIVE ALGEBRAS;225
10.5.5;THE QUALITATIVE ALGEBRAS OF ORDERS OF MAGNITUDE;226
10.5.6;PROPERTIES OF ASSOCIATED Q-FUNCTIONS;227
10.5.7;CONCLUSION;228
10.5.8;REFERENCES;228
11;PART VII: EXPERT SYSTEMS;230
11.1;Chapter 33. THE CONTROL OF PLANETARY ENTRY BY KNOWLEDGE BASED SYSTEMS;230
11.1.1;INTRODUCTION;230
11.1.2;PROBLEM DEFINITION;230
11.1.3;ARCHITECTURE OF THE AUTONOMOUS CONTROL SYSTEM;232
11.1.4;SOFTWARE IMPLEMENTATION ASPECTS;233
11.1.5;CONCLUSIONS;235
11.1.6;ACKNOWLEDGEMENTS;235
11.1.7;REFERENCES;235
11.2;Chapter 34. ALGORITHMIC AND KNOWLEDGE-BASED TECHNIQUES FOR ADAPTIVE CONTROL;236
11.2.1;1 Introduction;236
11.2.2;2 Adaptive control: the MODREF and MUSMAR approaches;237
11.2.3;3 Implementation;237
11.2.4;4 Supervisory level for adaptive control;239
11.2.5;5 Critical Comments and Conclusions;240
11.2.6;6 References;240
11.3;Chapter 35. A PROLOG-BASED EXPERT SYSTEM PROTOTYPE FOR ROBOT TASK PLANNING;242
11.3.1;INTRODUCTION;242
11.3.2;THE TASK PLANNING MODULE;242
11.3.3;LEARNING AND IDENTIFYING PHYSICAL DESCRIPTIONS FROM FUNCTIONAL DEFINITIONS;244
11.3.4;FINDING A PLACE ON THE WORK TABLE;246
11.3.5;EXAMPLE;246
11.3.6;CONCLUSION;247
11.3.7;REFERENCES;247
11.4;Chapter 36. REAL-TIME EXPERT SYSTEMS AND THEIR APPLICATIONS;248
11.4.1;INTRODUCTION;248
11.4.2;THE REAL-TIME A.I. TOOL RTEX;249
11.4.3;KEY FEATURES OF RTEX;249
11.4.4;DESCRIPTION;249
11.4.5;RELATION OF RTEX WITH AI TOOLS;249
11.4.6;AN APPLICATION : A PROCEDURE TRACKING SYSTEM FOR MONITORING NUCLEAR POWER PLANTS;251
11.4.7;CONCLUSION AND FUTURE PROSPECTS;252
11.4.8;REFERENCES;253
11.5;Chapter 37. MULTI-FAULT DIAGNOSIS AND RECOVERY BY ON-LINE CONSULTATION OF AN EXPERT SYSTEM;254
11.5.1;INTRODUCTION;254
11.5.2;THE DIFFERENT TYPES OF KNOWLEDGE;255
11.5.3;EXPERT KNOWLEDGE ACQUISITION;255
11.5.4;REQUIREMENTS OF THE SYSTEM;255
11.5.5;IMPLEMENTATION;256
11.5.6;USING THE SYSTEM;257
11.5.7;CONCLUSION;257
11.6;REFERENCES;258
11.7;Chapter 38. EXPERT MULTI-MODEL CONTROL SYSTEM;260
11.7.1;INTRODUCTION;260
11.7.2;A BRIEF OF DIFFERENT MULTI-MODEL CONTROL TECHNIQUES;260
11.7.3;CONCLUSION;262
11.7.4;REFERENCES;262
11.8;Chapter 39. CANDIDE: A SYSTEM WHICH LEARNS TO CONTROL A PROCESS;264
11.8.1;INTRODUCTION;264
11.8.2;SYMBOLIC CONTROL;264
11.8.3;PROCEDURE OF NATURAL CONTROL;265
11.8.4;HOW CAN WE LEARN TO CONTROL A SYSTEM;265
11.8.5;QUALITATIVE MODELISATION OF THE DYNAMICS OF THE SYSTEM;265
11.8.6;TRENDS EXTRACTION OF THE SYSTEM;266
11.8.7;GENERATION OF THE RULE SET;266
11.8.8;CONCLUSION;266
11.8.9;REFERENCES;266
11.9;Chapter 40. OBJECT-BASED KNOWLEDGE AND REASONING DESIGNED FOR DIAGNOSTICS;268
11.9.1;INTRODUCTION;268
11.9.2;THE KNOWLEDGE BASE;268
11.9.3;INFERENCE ENGINE DESCRIPTION;269
11.9.4;INFERENCE ENGINE REPRESENTATION;269
11.9.5;KNOWLEDGE REPRESENTATION;271
11.9.6;CONCLUSION;272
11.9.7;REFERENCES;272
11.10;Chapter 41. EXPERT SYSTEM FOR RECOGNITION AND TYPICAL IDENTIFICATION OF DYNAMIC PROCESS MODELS;274
11.10.1;INTRODUCTION;274
11.10.2;ON SOURCE ELEMENTS OF HUMANAND ARTIFICIAL IN TELLEGENCE;274
11.10.3;ON APPLIED ARTIFICIAL INTELLIGENCE TECHNIQUES;274
11.10.4;THE IDEA OF ARTIFICAL INTELLIGENCE FOR TYPICAL IDENTIFICATION;275
11.10.5;ON ESTIO EXPERT SYSTEM;276
11.10.6;CONCLUSIONS;276
11.10.7;REFERENCES;276
11.11;Chapter 42. GENERAL FRAMEWORK FOR BUILDING CAX SYSTEMS;280
11.11.1;INTRODUCTION;280
11.11.2;CAX SYSTEMS MODELLING;281
11.11.3;MODEL STRUCTURES;281
11.11.4;MODEL REPRESENTATION FORMALISM;281
11.11.5;LINKS OCCURING IN THE HIERARCHICAL MULTI-VIEW MODELS;282
11.11.6;LIBRARY MODEL;282
11.11.7;REPRESENTATION MODEL;283
11.11.8;CONCLUSION;284
11.11.9;REFERENCES;284
11.12;Chapter 43. AN EXPERT SYSTEM FOR MONITORING THE ELECTRIC POWER SUPPLIES OF A NUCLEAR POWER PLANT (3SE): TECHNIQUES OF ARTIFICIAL INTELLIGENCE;286
11.12.1;INTRODUCTION;286
11.12.2;AIMS AND SCOPE;286
11.12.3;BASIC FUNCTIONS;286
11.12.4;SYSTEM ARCHITECTURE;287
11.12.5;EXPERT-SYSTEM-BASED PROCESSING BY THE APPLICATION COMPUTER;287
11.12.6;EXPERT-SYSTEM-BASED PROCESSING BY THE DESIGN COMPUTER;288
11.12.7;CONCLUSIONS AND PROSPECTS;289
11.12.8;REFERENCES;289
12;PART VIII: COMPUTATIONAL METHODS;290
12.1;Chapter 44. DISTRIBUTED ESTIMATORS FOR MULTI-SENSOR SYSTEMS;290
12.1.1;I. INTRODUCTION;290
12.1.2;II. THE NONLINEAR DISCRETE-TIME PROBLEM;290
12.1.3;III. THE NONLINEAR CONTINUOUS-TIME PROBLEM;291
12.1.4;IV.THE LINEAR DISCRETE-TIME PROBLEM;292
12.1.5;V. CONCLUSIONS;294
12.1.6;REFERENCES;294
12.2;Chapter 45. INTERPOLATION IN CONTROL SYSTEMS;296
12.2.1;INTRODUCTION;296
12.2.2;MODEL UNCERTAINTY DESIGN;296
12.2.3;FEEDBACKS DESIGN IN THE FACE OF UNCERTAINTIES AND DISTURBANCES;297
12.2.4;FORMAL THE H8 OPTIMISATION AND THE P.N. INTERPOLATION;298
12.2.5;SIMULATION RESULTS;299
12.2.6;CONCLUSION;300
12.2.7;REFERENCES;300
12.3;Chapter 46. STATE ESTIMATION IN STOCHASTIC NONLINEAR SYSTEMS;302
12.3.1;INTRODUCTION;302
12.3.2;BACKGROUND MATERIAL IN FILTERING THEORY;302
12.3.3;THE PROPOSED TECHNIQUES;303
12.3.4;CONCLUSIONS;305
12.3.5;REFERENCES;305
12.3.6;APPENDIX;305
12.4;Chapter 47. OPTIMUM ALGEBRAIC DESIGN OF CONTINUOUS-TIME REGULATORS WITH POLYHEDRAL CONSTRAINTS;308
12.4.1;INTRODUCTION;308
12.4.2;PROBLEM FORMULATION;308
12.4.3;EXISTENCE CONDITIONS TO THE LCRP;308
12.4.4;A DESIGN APPROACH BASED ON THE INVARIANCE OF Q ( F, d1d2);309
12.4.5;CONCLUSION;311
12.4.6;REFERENCES;311
12.5;Chapter 48. SYSTOLIC ARRAY INFORMATION PROCESSING STRATEGY FOR REAL-TIME AUTOMATIC CONTROL;312
12.5.1;INTRODUCTION;312
12.5.2;CONTROL REQUIREMENTS;313
12.5.3;IMPLEMENTATION OF SISO SYSTEMS;314
12.5.4;DEEPER LEVEL PIPELINING;314
12.5.5;MAPPING SYSTOLIC CONTROLLERS ONTO CONCURRENT PROCESSORS;314
12.5.6;IMPLEMENTATIONS FOR MIMO SYSTEMS;315
12.5.7;CONCLUSIONS;315
12.5.8;REFERENCES;315
12.6;Chapter 49. STABILITY AND POLES LOCATION ANALYSIS FOR PERTURBED DISCRETE/CONTINUOUS SYSTEMS;318
12.6.1;I. Introduction;318
12.6.2;II. Discrete systems case;318
12.6.3;III. Continuous systems case;319
12.6.4;IV. Examples;320
12.6.5;V. Conclusion;320
12.6.6;REFERENCES;320
12.7;Chapter 50. ADAPTIVE CONTROL POLICY IN MICROPROCESSOR NETWORK PROCESSING;322
12.7.1;INTRODUCTION;322
12.7.2;CONTROL NETWORK DESCRIPTION;322
12.7.3;GLOBAL OPTIMAL PROBLEM;323
12.7.4;SOLUTION OF THE GLOBAL OPTIMAL PROBLEM;324
12.7.5;MODIFIED OPTIMAL PROBLEM;325
12.7.6;OPERATIONAL ALGORITHM FOR DATA TRANSFER;325
12.7.7;EXAMPLE;326
12.7.8;CONCLUSION;327
12.7.9;REFERENCES;327
12.8;Chapter 51. COMPUTER NETWORK INTEGRATED MANUFACTURING: PERFORMANCE EVALUATION USING DISTRIBUTED SIMULATION;328
12.8.1;INTRODUCTION;328
12.8.2;DISTRIBUTED DISCRETE-EVENT SIMULATION (DDS);329
12.8.3;PERFORMANCE ANALYSIS USING ALLOCATION–BOUNDED DISTRIBUTED SIMULATION (DDS/AB);329
12.8.4;A REAL CASE STUDY;332
12.8.5;CONCLUSIONS AND FUTURE WORK;332
12.8.6;REFERENCES;332
12.9;Chapter 52. COMPETITIVE PARALLEL IDENTIFIERS IN ADAPTIVE CONTROL OF NONSTATIONARY STOCHASTIC SYSTEMS;334
12.9.1;INTRODUCTION;334
12.9.2;PARAMETER ESTIMATION;334
12.9.3;MULTIPLE MODEL IDENTIFICATION;335
12.9.4;ADAPTIVE CONTROL;336
12.9.5;THE CONTROL OBJECT;336
12.9.6;THE TRIPPLE BANK ESTIMATOR;337
12.9.7;SIMULATION RESULTS;337
12.9.8;CONCLUSIONS;339
12.9.9;REFERENCES;339
12.10;Chapter 53. DISTRIBUTED IMPLEMENTATION AND HIERARCHICAL LEVEL FOR CONTROL OF FLEXIBLE MANUFACTURING SYSTEMS;340
12.10.1;INTRODUCTION;340
12.10.2;FROM THE DESIGN TO THE IMPLEMENTATION;340
12.10.3;REALIZATION OF THE HIERARCHICAL LEVEL (Barbez,1988) (Craye,1989);342
12.10.4;CONCLUSION;343
12.10.5;REFERENCES;343
13;PART IX: BOND GRAPH;344
13.1;Chapter 54. BOND-GRAPH MODELLING OF FLEXIBLE ROBOTIC MANIPULATORS;344
13.1.1;INTRODUCTION;344
13.1.2;FLEXIBLE LINK KINEMATICS;344
13.1.3;A MODELLING PROCEDURE FOR FLEXIBLE MULTIBODY SYSTEMS;348
13.1.4;APPENDIX;349
13.2;Chapter 55. MODELLING AND SIMULATION OF AN ELECTROPNEUMATIC GRIPPER HAVING TWO FLEXIBLE ARMS;350
13.2.1;INTRODUCTION;350
13.2.2;DESCRIPTION OF THE SYSTEM;351
13.2.3;MODEL OF THE GRIPPER;351
13.2.4;SIMULATED RESULTS;355
13.2.5;CONCLUSION;355
13.2.6;REFERENCES;355
13.3;Chapter 56. BOND-GRAPH APPROACH OF COMMUTATION PHENOMENA;356
13.3.1;INTRODUCTION;356
13.3.2;MODELLING A DIODE;356
13.3.3;MODELLING COMMUTATING GROUP;357
13.3.4;NON STANDARD JUNCTIONS;359
13.3.5;CONCLUSION;360
13.3.6;REFERENCES;360
13.4;Chapter 57. ACAUSAL INFORMATION BONDS IN BOND GRAPH MODELS;362
13.4.1;INTRODUCTION;362
13.4.2;BOND GRAPHS AND SUBSYSTEMS;362
13.4.3;THE PROBLEM WITH THE INFORMATION NETWORK;362
13.4.4;SYNTACTICAL INTERPRETATION OF THE POWER DIRECTION;363
13.4.5;ACAUSAL INFORMATION NETWORK;363
13.4.6;COMPARAISON WITH BLOCK DIAGRAM;365
13.4.7;TRANSFORMATION OF A POWER NETWORK IN AN INFORMATION NETWORK;365
13.4.8;EXAMPLES;365
13.4.9;CONCLUSION;366
13.4.10;REFERENCES;366
14;PART X: SIGNAL PROCESSING AND TIME SERIES ANALYSIS;368
14.1;Chapter 58. A GENERALIZED APPROACH FOR FAILURE DETECTION ON STEEL PRODUCTS WITH THE USE OF AN EDDY-CURRENT SENSOR;368
14.1.1;INTRODUCTION;368
14.1.2;THE PROCESSES;368
14.1.3;THE FAILURE DETECTION PROBLEM;368
14.1.4;FAILURE DETECTION WITH THE M0 MODEL;369
14.1.5;FAILURE RECOGNITION WITH THE Ml MODEL;370
14.1.6;CONCLUSION;372
14.1.7;REFERENCES;372
14.2;Chapter 59. MISSING VALUES REBUILDING BY PREDICTION AND ARIMA MODELLING IN TIME SERIES;374
14.2.1;PROBLEM STATEMENT : THE ARIMA MODELLING;374
14.2.2;SUMMARY OF THE METHOD;374
14.2.3;AUTOCORRELATIONS ESTIMATION;374
14.2.4;MODEL'S ORDERS IDENTIFICATION;375
14.2.5;MODEL PARAMETERS ESTIMATION;375
14.2.6;FORECASTING AND MISSING VALUES INTERVALS REBUILDING;376
14.2.7;REFERENCES;378
14.2.8;CONCLUSION;378
14.3;Chapter 60. CLASSIFICATION OF RANDOM SIGNALS: A NEW APPROACH;380
14.3.1;INTRODUCTION;380
14.3.2;GENERAL POINTS;380
14.3.3;DECISION RULE;381
14.3.4;TESTS AND COMPARISON;382
14.3.5;COMMENTS AND CONCLUSION;382
14.3.6;REFERENCES;383
14.4;Chapter 61. SIGNAL DETECTION IN THE TIME-FREQUENCY PLANE;384
14.4.1;INTRODUCTION;384
14.4.2;CLASSICAL PROBLEM FORMULATION;384
14.4.3;TIME-FREQUENCY DISTRIBUTIONS;384
14.4.4;SIGNAL-TO-NOISE RATIO MAXIMIZATION;384
14.4.5;LIKELIHOOD RATIO APPROACH;385
14.4.6;DETECTION WITH UNCERTAINTIES;386
14.4.7;CONCLUSION;387
14.4.8;ACKNOWLEDGMENTS;387
14.4.9;REFERENCES;387
14.5;Chapter 62. SARIMA MODELING OF AN IRRIGATION SYSTEM;388
14.5.1;INTRODUCTION;388
14.5.2;PROBLEM STATEMENT;388
14.5.3;INSTRUMENTATION AND MEASUREMENT;388
14.5.4;MODELING;389
14.5.5;ON LINE MODEL VALIDATION;390
14.5.6;CONCLUSIONS;390
14.5.7;REFERENCES;390
14.6;Chapter 63. IMPROVING PARAMETRIC MODELS THROUGH MULTIPULSE APPROACH;392
14.6.1;0 - introduction;392
14.6.2;1 - Principle of Multipulse Modeling;392
14.6.3;2 - Discussion;393
14.6.4;3 - Improving the h(n) model;393
14.6.5;4 - Illustration on actual signals;395
14.6.6;5 - Conclusion;396
14.6.7;REFERENCES;396
15;PART XI: IMAGE PROCESSING;398
15.1;Chapter 64. IMAGE PROCESSING IN QUALITY CONTROL OF NUTS;398
15.1.1;Introduction;398
15.1.2;Description of the measurement system;398
15.1.3;Parameter measurements;399
15.1.4;Threading verification;401
15.1.5;Conclusion;404
15.1.6;References;404
15.2;Chapter 65. DISCRETE COLORIMETRIC SPACES: AN APPLICATION TO AUTOMATIC RECOGNITION OF COLOR CODES;406
15.2.1;1 INTRODUCTION;406
15.2.2;2 COLOUR RECOGNITION USING TRICHROMATIC ARTIFICIAL VISION;406
15.2.3;3 APPLICATION TO THE READING OF COLOUR CODES ON ELECTRONIC COMPONENTS;409
15.2.4;4 CONCLUSIONS;411
15.2.5;5 BIBLIOGRAPHY;411
15.3;Chapter 66. IMAGE SEGMENTATION USING CONNECTED COMPONENTS FOR DOCUMENT ANALYSIS;412
15.3.1;INTRODUCTION;412
15.3.2;DOCUMENT INPUT;412
15.3.3;SEGMENTATION INTO CONNECTED COMPONENTS;412
15.3.4;ANALYSIS OF THE CONNECTED COMPONENTS STRUCTURE;413
15.3.5;ANALYSIS OF TECHNICAL DOCUMENTS;416
15.3.6;CONCLUSION;416
15.3.7;ACKNOWLEDGMENTS;416
15.3.8;REFERENCES;416
15.4;Chapter 67. EDGE DETECTION USING THE FUZZY SETS THEORY;418
15.4.1;INTRODUCTION;418
15.4.2;FUZZY CLUSTERING ALGORITHM : FUZZY CMEANS;418
15.4.3;EDGE DETECTION;419
15.4.4;EXPERIMENTAL STUDY;420
15.4.5;CONCLUSIONS;420
15.4.6;REFERENCES;421
15.5;Chapter 68. VISION FOR A ROBOT MOVING IN AN INDOOR ENVIRONMENT;422
15.5.1;1 Introduction;422
15.5.2;2 Image segmentation;422
15.5.3;3 Stereovision;423
15.5.4;4 Monocular vision;425
15.5.5;5 Structuration of stereoviews;426
15.5.6;6 Conclusion;427
15.5.7;References;427
15.6;Chapter 69. RAPID RECOGNITION OF SIMPLE SHAPE METALLIC OBJECTS IN REDUCED RESOLUTION IMAGES;428
15.6.1;INTRODUCTION;428
15.6.2;THE SENSOR,THE SENSITIVE ELEMENT AND ITS ELECTRONICS;428
15.6.3;THE SENSOR MATRIX;429
15.6.4;CONCLUSION;431
15.6.5;REFERENCES;431
16;PART XII: INTELLIGENT SENSORS SYSTEMS;432
16.1;Chapter 70. ON LINE FAULT DETECTION ON TEXTILE MATERIAL BY OPTOELECTRONICS PROCESSING;432
16.1.1;1. INTRODUCTION;432
16.1.2;2. WEAVING FAULT DETECTION BY OPTICAL PROCESSING;432
16.1.3;3. STATIC TESTS;433
16.1.4;4. DYNAMIC TESTS;435
16.1.5;5. CONCLUSION;437
16.1.6;BIBLIOGRAPHIE;437
16.2;Chapter 71. FUNCTIONAL DISTURBANCE OBSERVER FOR SIMULTANEOUS CONTROL AND DRY FRICTION COMPENSATION;438
16.2.1;1. INTRODUCTION;438
16.2.2;2. MODELLING OF THE PLANT AND OF ITS DISTURBANCE;439
16.2.3;3 . DESIGN OF THE CONTROL LAW;440
16.2.4;4. PRACTICAL REALIZATION AND RESULTS;440
16.2.5;5 . USE OF AN OPTICAL SENSOR;441
16.2.6;6 . CONCLUSION;442
16.2.7;APPENDIX;442
16.2.8;REFERENCES;442
16.3;Chapter 72. OPTIMIZING THE PROCESS OF ANALOG-TO-DIGITAL CONVERSION IN SIGNAL TRANSMISSION;444
16.3.1;INTRODUCTION;444
16.3.2;QUANTIZATION OF NOISE-FREE SIGNALS;444
16.3.3;QUANTIZATION OF NOISY SIGNALS;445
16.3.4;PROGRAMMING THE OPTIMIZATION ALGORITHM;446
16.3.5;EXAMPLES;446
16.3.6;ADAPTIVE QUANTIZATION;446
16.3.7;CONCLUSIONS;446
16.3.8;REFERENCES;447
16.4;Chapter 73. THE USE OF ADVANCED MICRO-ELECTRODES FOR SIMULTANEOUS MEASURING OF LOCAL VELOCITY AND CONCENTRATION INSIDE LIQUID FLOWS;448
16.4.1;INTRODUCTION;448
16.4.2;ELECTROCHEMICAL METHOD;448
16.4.3;EXPERIMENTAL RESULTS;450
16.4.4;CONCLUSION;450
16.4.5;REFERENCES;450
16.5;Chapter 74. ROBOT PERCEPTION SYSTEMS: ACOUSTICS VISION SET-UP CONTROL;452
16.5.1;INTRODUCTION;452
16.5.2;BAYESIAN FORMALISM AND OBSERVATION WINDOW CONTROL MODEL;453
16.5.3;CONCLUSION AND PERSPECTIVES;455
16.5.4;REFERENCES;456
17;PART XIII: GENERAL IZED FLOW NETWORKS;458
17.1;Chapter 75. REAL-TIME OPTIMIZED CONTROL OF A WATER DISTRIBUTION SYSTEM;458
17.1.1;INTRODUCTION;458
17.1.2;COMPUTER CONTROL SCHEME;458
17.1.3;SYSTEM CONSIDERATIONS IN THE FULLY AUTOMATED CONTROL SCHEME;459
17.1.4;REAL-TIME CONSIDERATIONS IN THE CONTROL SCHEME;460
17.1.5;CONCLUSIONS;461
17.1.6;ACKNOWLEDGEMENTS;462
17.1.7;REFERENCES;462
17.2;Chapter 76. A DYNAMIC MODEL FOR THE TRANSPORTATION OF POLLUTION LOADS IN HARBOUR CHANNEL NETWORKS;464
17.2.1;INTRODUCTION;464
17.2.2;BASIC MODEL EQUATIONS;465
17.2.3;NETWORK ELEMENTS;466
17.2.4;MODEL VERIFICATION AND SIMULATION RESULTS;467
17.2.5;CONCLUSIONS;468
17.2.6;ACKNOWLEDGEMENT;468
17.2.7;REFERENCES;468
17.2.8;CONCLUSIONS;468
17.3;Chapter 77. ESTIMATION OF TRAFFIC DENSITY ON MOTORWAYS USING PRESENCE DETECTOR MEASUREMENTS;470
17.3.1;INTRODUCTION;470
17.3.2;DEFINITIONS AND PROBLEM STATEMENT;470
17.3.3;RESULTS;472
17.3.4;CONCLUSIONS;474
17.3.5;ACKNOWLEDGEMENT;474
17.3.6;REFERENCES;474
17.4;Chapter 78. AN INTERACTIVE SOFTWARE ENVIRONMENT FOR THE SIMULATION OF COMPLEX WATER NETWORKS;476
17.4.1;INTRODUCTION;476
17.4.2;NETWORK DEFINITION;477
17.4.3;SOFTWARE STRUCTURE;477
17.4.4;SAMPLE APPLICATION;479
17.4.5;CONCLUDING REMARKS;480
17.4.6;ACKNOWLEDGMENTS;481
17.4.7;REFERENCES;481
17.5;Chapter 79. ELECTRICAL POWER NETWORK CONTROL AND SIMULATION SOFTWARE;482
17.5.1;INTRODUCTION;482
17.5.2;POWER SYSTEM SIMULATION;482
17.5.3;STATE ESTIMATION;484
17.5.4;LOAD MONITORING AND PREDICTION;485
17.5.5;GENERATION AND LOAD CONTROL;485
17.5.6;REFERENCES;486
17.6;Chapter 80. ON-LINE SIMULATION OF GAS NETWORKS WITH CHANGING GAS QUALITIES;488
17.6.1;INTRODUCTION;488
17.6.2;THE SIMULATION OF GAS NETWORKS;488
17.6.3;GAS NETWORK STATE ESTIMATION;489
17.6.4;IMPLEMENTATION OF EQUIPMENT/STATIONS;489
17.6.5;GAS QUALITY TRACKING;490
17.6.6;OPERATIONAL EXPERIENCE;491
17.6.7;CONCLUSIONS;492
17.6.8;ACKNOWLEDGEMENT;492
17.6.9;REFERENCES;492
18;PART XIV: BIOMEDICAL ENGINEERING;494
18.1;Chapter 81. SIMULATIONS STUDIES ON THE ADAPTIVE CONTROL FEASIBILITY OF DIABETIC BLOOD GLUCOSE CONCENTRATION UNDER ARTIFICIAL PANCREAS MONITORING;494
18.1.1;I- Introduction;494
18.1.2;II- Model and structure determination;495
18.1.3;III - Adaptive control;496
18.1.4;IV- Concluding remarks;498
18.1.5;V- References;498
18.2;Chapter 82. MODELLING AND SIMULATION OF LIGHT ABSORPTION AND SCATTERING IN BIOLOGICAL TISSUES;500
18.2.1;INTRODUCTION;500
18.2.2;MODEL PRESENTATION;500
18.2.3;SOFTWARE OPERATING;501
18.2.4;RESULTS ANALYSIS;502
18.2.5;VALIDATION;502
18.2.6;CONCLUSION;503
18.2.7;REFERENCES;503
18.3;Chapter 83. EXTRACORPOREAL CIRCULATION AUTOMATION: APPLICATION OF CONTROL THEORY CONCEPTS;504
18.3.1;INTRODUCTION;504
18.3.2;RESEARCH MOTIVATION;504
18.3.3;ECC MODELLING AND CONTROL;505
18.3.4;DYNAMIC MODELS FOR THE SUB-SYSTEMS;505
18.3.5;SIMULATION MODEL FOR ECC HEMODYNAMICS;506
18.3.6;CONCLUSION;506
18.3.7;ACKNOWLEDGEMENTS;506
18.3.8;REFERENCES;506
18.4;Chapter 84. COMPARISON OF TWO SIMILARITY CRITERIONS IN SHAPE CLASSIFICATION OF TRANSIENT SIGNALS: APPLICATION TO HIGH RESOLUTION ELECTROCARDIOGRAPHY;508
18.4.1;STATEMENT OF THE PROBLEM;508
18.4.2;CLASSIFICATION ALGORITHMS;509
18.4.3;RESULTS;510
18.4.4;REFERENCES;511
18.5;Chapter 85. A LONG-RANGE ADAPTIVE CONTROLLER WITH INPUT CONSTRAINTS;512
18.5.1;INTRODUCTION;512
18.5.2;THE ALGORITHM;512
18.5.3;ROBUST CONVERGENCE ANALYSIS;513
18.5.4;SIMULATIONS WITH ARMAX PLANTS;514
18.5.5;APPLICATION TO THE CONTROL OF MUSCLE RELAXATION;516
18.5.6;CONCLUSIONS;516
18.5.7;ACKNOWLEDGEMENTS;517
18.5.8;REFERENCES;517
19;PART XV: APPLICATIONS;518
19.1;Chapter 86. NUMERICAL FILTER AND PID SELF TUNING CONTROLLER USING ESTIMATION CONSTRAINTS, YEAST PRODUCTION PROCESS;518
19.1.1;I- Introduction;518
19.1.2;II- PID self tuning controller;518
19.1.3;Ill - GPC Approach;520
19.1.4;IV- Concluding remarks;521
19.1.5;IV- References;522
19.1.6;V- Figure Keys;522
19.2;Chapter 87. ESTIMATION OF THE STATE AND PARAMETERS OF A BIOPROCESS USING THE RECURSIVE PREDICTION ERROR METHOD;524
19.2.1;INTRODUCTION;524
19.2.2;ESTIMATION ALGORITHM;524
19.2.3;CONVERGENCE AND STABILITY;526
19.2.4;RESULTS;526
19.2.5;CONCLUSION;526
19.2.6;ACKNOWLEDGEMENTS;526
19.2.7;REFERENCES;527
19.3;Chapter 88. PREDICTIVE CONTROL FOR AC-DRIVES;530
19.3.1;INTRODUCTION;530
19.3.2;PRACTICAL RESULTS;534
19.3.3;CONCLUSION;534
19.3.4;REFERENCES;535
19.4;Chapter 89. CONTROL OF PHENOMENA DISTURBING THE RUNNING OF A KILNING PROCESS;536
19.4.1;I - INTRODUCTION;536
19.4.2;II - APPROACH;536
19.4.3;Ill - COMPUTER SYSTEMS;537
19.4.4;IV - CONCLUSION;539
19.4.5;REFERENCES;539
19.5;Chapter 90. DIGITAL SIGNAL PROCESSOR BASED MEASUREMENT SYSTEM IN A CONTROL APPLICATION;540
19.5.1;INTRODUCTION;540
19.5.2;SYSTEM DESCRIPTION;541
19.5.3;MEASUREMENT PHILOSOPHY;541
19.5.4;MEASUREMENT SOFTWARE;541
19.5.5;PERFORMANCE;542
19.5.6;CONCLUSION;543
19.5.7;REFERENCE;543
19.6;Chapter 91. ELECTRICAL MACHINES COMMAND AUTOMATION AND ASSOCIATED EXPERT SOFTWARE;546
19.6.1;INTRODUCTION;546
19.6.2;I- E.M.C.A. DESCRIPTION;546
19.6.3;II- E.M.C.A. SOFTWARE;548
19.6.4;Ill - LEVEL 3 IMPLEMENTATION.;549
19.6.5;IV - IMPLEMENTATION OF LEVELS 1 AND 2;551
19.6.6;V - FINAL CONCLUSION AND PERSPECTIVES.;552
19.6.7;ACKNOWLEDGEMENT.;553
19.6.8;REFERENCES;553
19.7;Chapter 92. ADAPTATIVE KALMAN FILTER APPLIED TO THE TARGET TRACKING;554
19.7.1;INTRODUCTION;554
19.7.2;THE KALMAN FILTER;554
19.7.3;PRELIMINARY TESTS;555
19.7.4;IMPLEMENTED DEVICE AND RESULTS;556
19.7.5;CONCLUSION;557
19.7.6;REFERENCES;557
19.8;Chapter 93. MINIMUM-TIME TRAJECTORY FOR MULTIPLE MANIPULATORS HANDLING A COMMON OBJECT;560
19.8.1;INTRODUCTION;560
19.8.2;DYNAMIC MODEL FOR MULTIPLE MANIPULATORS HANDLING A SINGLE OBJECT;560
19.8.3;PARAMETERIZED SYSTEM DYNAMICS;561
19.8.4;TORQUE CONSTRAINTS;561
19.8.5;ADMISSIBLE PATH VELOCITY .;561
19.8.6;CONSTRUCTING AN OPTIMAL TIME TRAJECTORY;562
19.8.7;AN EXAMPLE;562
19.8.8;CONCLUSIONS;563
19.8.9;REFERENCES;564
20;AUTHOR INDEX;566
21;KEYWORD INDEX;568