Durrani / Owens / Johnson Adaptive Systems in Control and Signal Processing 1989

1. Auflage 2014
ISBN: 978-1-4832-9424-7
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 620 Seiten, Web PDF

Reihe: IFAC Symposia Series

The Symposium covered three major areas: adaptive control, identification and signal processing. In all three, new developments were discussed covering both theoretical and applications research. Within the subject area of adaptive control the discussion centred around the challenges of robust control design to unmodelled dynamics, robust parameter estimation and enhanced performance from the estimator, while the papers on identification took the theme of it being a bridge between adaptive control and signal processing. The final area looked at two aspects of signal processing: recursive estimation and adaptive filters.

Autoren/Hrsg.

Durrani, T. S.

Owens, D. H.

Johnson, M. A.

Grimble, M. J.

Inhaltsverzeichnis

1;Front Cover;1
2;Adaptive Systems in Control and Signal Processing 1989;4
3;Copyright Page;5
4;Table of Contents;10
5;PREFACE;8
6;CHAPTER 1. ADAPTIVE CONTROL—A PERSPECTIVE;18
6.1;1. Introduction;18
6.2;2. The Industrial Scene;18
6.3;3· Adaptive Control Theory;20
6.4;4. Reassessment of the MRAS;21
6.5;5· Robust Control;22
6.6;6. Conclusions;23
6.7;7 · References;23
7;CHAPTER 2. UNCERTAINTY, INFORMATION AND ESTIMATION;24
7.1;1. INTRODUCTION;24
7.2;2. THE ROLE OF FEEDBACK IN REDUCINGTHE IMPACT OF UNCERTAINTY;24
7.3;3. ESTIMATION;26
7.4;4. QUANTIFICATION OF A POSTERIORI UNCERTAINTY;27
7.5;5. EXAMPLES;30
7.6;6. IMPLICATIONS FOR ADAPTIVE CONTROL;31
7.7;7. CONCLUSIONS;32
7.8;8. ACKNOWLEDGEMENT;32
7.9;REFERENCES;32
8;CHAPTER 3. ADAPTIVE CONTROL—ROBUSTNESS AND PERFORMANCE ENHANCEMENT;34
8.1;INTRODUCTION;34
8.2;ON 1rts GENERATION ADAPTIVE CONTROLLERS;34
8.3;CONTROL STRATEGIES FOR SYSTEMS WITH NON NECESSARILY STABLE ZEROS;36
8.4;SARTA CONTROL;36
8.5;THE ROBUST PARAMETER ESTIMATOR;37
8.6;EXPERIMENTAL EVALUATION;38
8.7;CONCLUSIONS;38
8.8;ACKNOLEWDGEMENTS;39
8.9;REFERENCES;39
9;CHAPTER 4. A SEMI-INFINITE HORIZON LQ SELF-TUNING REGULATOR FOR ARMAX PLANTS BASED ON RLS;40
9.1;1 Introduction;40
9.2;2 Problem formulation;41
9.3;3 The control algorithm;41
9.4;4 Justification of MUSMAR-8;41
9.5;5 Algorithmic considerations;43
9.6;6 Simulation results;43
9.7;7 Conclusions;44
9.8;References;44
10;CHAPTER 5. THE USE OF DISTURBANCE MEASUREMENTFEEDFORWARD IN LQG SELF-TUNERS;48
10.1;1 Introduction;48
10.2;2 The control problem;49
10.3;3 The optimal regulator;49
10.4;4 The LQG self-tuner;51
10.5;5 Robustness-improving user choices;52
10.6;6 Conclusions;53
10.7;References;53
11;CHAPTER 6. MULTIVARIABLE LQG SELF-TUNING CONTROL WITH DISTURBANCEMEASUREMENT FEEDFORWARD;54
11.1;NOTATION;54
11.2;1. INTRODUCTION;54
11.3;2. SYSTEM MODEL AND COSTFUNCTION;55
11.4;3. OPTIMAL CONTROLLER;55
11.5;4. IMPLIED POLYNOMIAL MATRIX EQUATIONS;56
11.6;5. MULTIVARIABLE LQG SELFTUNING CONTROL ALGORITHM WITH DISTURBANCE MEASUREMENT FEEDFORWARD;57
11.7;6. APPLICATION RESULTS;57
11.8;ACKNOWLEDGEMENTS;58
11.9;REFERENCES;58
12;CHAPTER 7. IMPROVEMENT OF THE TRACKING PROPERTY FOR THE LINEAR QUADRATIC ADAPTIVE CONTROLLER;60
12.1;INTRODUCTION;60
12.2;LINEAR QUADRATIC ADAPTIVE CONTROLLER;60
12.3;IMPROVEMENT OF THE TRACKING PROPERTY;62
12.4;NUMERICAL RESULTS;64
12.5;EXPERIMENTAL RESULTS;65
12.6;CONCLUSION;65
12.7;REFERENCES;65
13;CHAPTER 8. OPTIMAL CONTROL REDESIGN OFGENERALIZED PREDICTIVE CONTROL;66
13.1;1. Introduction;66
13.2;2. Generalized Predictive Control as Optimal Control;66
13.3;3· Stability and Performance Properties of Receding Horizon LQ Control;68
13.4;4· Reassessment of GPC;70
13.5;5. Conclusion;71
13.6;References;71
14;CHAPTER 9. INPUT-OUTPUT REPRESENTATION OF PREDICTIVE CONTROL ALGORITHMS;72
14.1;INTRODUCTION;72
14.2;DESCRIPTION OF GENERALIZED PREDICTIVE CONTROL;72
14.3;PREDICTION OF FUTURE CONTROL-ERRORS;73
14.4;SOLUTION OF THE OPTIMIZATION-PROBLEM;73
14.5;TRANSFER-FUNCTIONS OF GPC;74
14.6;SPECIAL CASE: UNITY CONTROL-HORIZON;74
14.7;SIMULATION RESULTS;75
14.8;CONCLUSIONS AND FURTHER SUGGESTIONS;76
14.9;REFERENCES;76
14.10;APPENDIX A;76
15;CHAPTER 10. MULTIRATE SELF-TUNING CONTROL OF MULTIVARIABLE SYSTEMS;78
15.1;Introduction;78
15.2;System and Sampling Model;79
15.3;Control algorithm;79
15.4;Simulation examples;81
15.5;Conclusions;83
15.6;References;83
16;CHAPTER 11. CONTINUOUS-TIME GENERALIZED PREDICTIVE CONTROL (CGPC);84
16.1;INTRODUCTION;84
16.2;THE CGPC ALGORITHM;84
16.3;SIMULATIONS;87
16.4;CONCLUSIONS;88
16.5;REFERENCES;88
17;CHAPTER 12. ADAPTIVE CONTROL WITHIN THE CLASS OF STABILIZING CONTROLLERS FOR A TIME VARYING NOMINAL PLANT;90
17.1;l t INTRODUCTION;90
17.2;2. TIME-VARYING SYSTEMS;90
17.3;3. ADAPTIVE SCHEMES;92
17.4;TWO SPECIALIZATIONS;93
17.5;5, SIMULATION EXAMPLE;94
17.6;6. CONCLUSIONS;94
17.7;APPENDIX;94
17.8;References;95
18;CHAPTER 13 UNIFICATION OF CONTINUOUS AND DISCRETE MODEL REFERENCE ADAPTIVE CONTROL;96
18.1;INTRODUCTION;96
18.2;SUMMARY OF CONTINUOUS REFERENCE CONTROL;96
18.3;METHODS TO ROUND NONMINIMUM PHASE PROBLEM OF SAMPLED DATA SYSTEMS;97
18.4;DISCRETE MODEL REFERENCE CONTROL;98
18.5;ADAPTIVE CONTROL USING MOVING AVERAGE SKIP SAMPLING;98
18.6;CONCLUSION;99
18.7;REFERENCES;99
19;CHAPTER 14. EXPRESSION OF GENERALIZED ADAPTIVE LAW USING DELTA OPERATOR;100
19.1;1. INTRODUCTION;100
19.2;2 . GENERALIZED ADAPTIVE LAW USING DELTA OPERATOR;100
19.3;3. SOME PARTICULAR ALGORITHMS;102
19.4;4. RELATIONSHIP TO THE CONTINUOUS AND DISCRETE TIME LAWS;103
19.5;5. MODIFICATION OF THE GENERALIZED ADAPTIVE LAW;103
19.6;6. CONCLUSION;103
19.7;REFERENCES;104
19.8;APPENDIX;104
20;CHAPTER 15. ADAPTIVE CONTROL BASED ON EXACT MODEL MATCHING;106
20.1;INTRODUCTION;106
20.2;STRUCTURE OF PROPSED ADAPTIVE CONTROL SYSTEM;106
20.3;PRELIMINARIES FOR ADAPTIVE CONTROL;107
20.4;STABILITY PROOF FOR PROPOSED ADAPTIVE CONTROL;108
20.5;AUGMENTED ERROR SIGNAL;109
20.6;CONCLUSIONS;110
20.7;REFERENCES;110
21;CHAPTER 16. A DESIGN OF NONLINEAR MRACS AND THE PROOF OF STABILITY;112
21.1;1 . INTRODUCTION;112
21.2;2 . DESIGN OF NONLINEAR MODEL FOLLOWING CONTROL SYSTEM;112
21.3;3 . PROOF OF BOUNDEDNESS OF INTERNAL STATES;113
21.4;4 . NONLINEAR MODEL REFERENCE ADAPTIVE CONTROL AND THE GLOVAL STABILITY;115
21.5;5 . SIMULATION EXAMPLE;117
21.6;6 . CONCLUSION;117
21.7;REFERENCES;117
22;CHAPTER 17. MODEL REFERENCE LEARNING CONTROL FOR DISCRETE-TIME NONLINEAR SYSTEM;118
22.1;INTRODUCTION;118
22.2;INVERSE REPRESENTATION;118
22.3;QUANTIZATION;119
22.4;LEARNING CONTROL;119
22.5;NUMERICAL SIMULATION;120
22.6;CONCLUSION;120
22.7;ACKNOWLEDGEMENT;120
22.8;REFERENCES;120
23;CHAPTER 18. ADAPTIVE POLE-PLACEMENT ALGORITHM BASED ON COMMON FACTOR ELIMINATION;124
23.1;INTRODUCTION;124
23.2;PROBLEM STATEMENT;124
23.3;ADAPTIVE CONTROL ALGORITHM;125
23.4;PROPERTIES OF COMMON FACTOR ELIMINATION ALGORITHM;127
23.5;ANALYSIS OF CLOSED LOOP SYSTEM;127
23.6;NUMERICAL EXAMPLES;128
23.7;CONCLUSION;129
23.8;REFERENCES;129
24;CHAPTER 19. DISCRETE-TIME ADAPTIVE CONTROL FOR CONTINUOUS-TIME SYSTEMS USING LIMITING-ZERO MODEL AND ITS APPLICATION;130
24.1;INTRODUCTION;130
24.2;CONTINUOUS-TIME SYSTEM AND DISCRETE-TIME SYSTEM;131
24.3;CONTROLLER DESIGN USING LIMITING-ZERO MODEL;131
24.4;ADAPTIVE CONTROL USING LIMITING-ZERO MODEL;132
24.5;ROBUSTNESS OF THE CONTROL SYSTEM;133
24.6;SIMULATION RESULTS;133
24.7;APPLICATION TO REAL SYSTEM;134
24.8;CONCLUSIONS;135
24.9;References;135
25;CHAPTER 20. ADAPTIVE POLE PLACEMENT FOR SYSTEM WITH ABNORMAL MEASUREMENT DATA;136
25.1;1 INTRODUCTION;136
25.2;2 POLE PLACEMENT;137
25.3;3 ROBUST TECHNIQUES AND ILLUSTRATION EXAMPLES;139
25.4;4 CONCLUSIONS;140
25.5;References;140
26;CHAPTER 21. THE ADAPTIVE DEADBEAT CONTROLLER;142
26.1;INTRODUCTION;142
26.2;THE ADAPTIVE D(z) CONTROLLER;142
26.3;CONTROLLER SIMULATION;144
26.4;CONCLUSION;144
26.5;REFERENCES;145
27;CHAPTER 22. FILTERED AND PREDICTED STATES FOR DISCRETE-TIME ADAPTIVE CONTROL;146
27.1;INTRODUCTION;146
27.2;SYSTEM DEFINITION;146
27.3;STATE-SPACE FILTER MODEL;147
27.4;STATE-SPACE PREDICTION MODEL;148
27.5;IMPLEMENTATION RESULTS;149
27.6;REFERENCES;150
28;CHAPTER 23. INTERNAL MODEL ADAPTIVE CONTROL SYSTEMS;152
28.1;1. INTRODUCTION;152
28.2;2. SISO IMC DESIGN METHOD AND ITS POTENTIAL ADVANTAGES FOR ADAPTIVE CONTROL;152
28.3;3. ADAPTIVE IMC SYSTEM AND ITS ROBUSTNESS;154
28.4;4. CONCLUSIONS;156
28.5;5.REFERENCES;156
29;CHAPTER 24. ADAPTATION OF THE SWITCHING HYPERPLANES IN CAUTIOUS SLIDING CONTROL WITH APPLICATION TO MECHANICAL SYSTEMS;158
29.1;1- Introduction;158
29.2;2 - Description of the Plant;158
29.3;3 - Real-Time Identification;158
29.4;4 - Cautious Adaptive Sliding Control;159
29.5;5- Some Simulation Results;159
29.6;References;161
30;CHAPTER 25. DISCRETE VARIABLE STRUCTURE CONTROL FOR AN ACTIVE ANTI-VIBRATION SYSTEM;164
30.1;Introduction;164
30.2;The Qverall Control Strategy;164
30.3;Discrete Variable Structure algorithm formulation;164
30.4;A discrete sliding algorithm;165
30.5;Implementation of the algorithm;165
30.6;Results;165
30.7;Simulations;165
30.8;Impementation;166
30.9;Conclusion and njUlher work;166
30.10;References;166
31;CHAPTER 26. INDIRECT ADAPTIVE CONTROL OF LINEARLY PARAMETRIZED NONLINEAR SYSTEMS;170
31.1;0. INTRODUCTION;170
31.2;I. SYSTEM DESCRIPTION;170
31.3;2. ADAPTIVE STATE FEEDBACK LINEARIZATION: BASIC DEFINITIONS;171
31.4;3. INDIRECT ADAPTIVE CONTROL IN CONTINUOUS TIME;171
31.5;4. DISCRETE TIME ADAPTIVE CONTROL OF CONTINUOUS SYSTEMS;173
31.6;5. EXTENSIONS, VARIANTS, COMMENTS;175
31.7;6. CONCLUSIONS;175
31.8;7.REFERENCES;175
32;CHAPTER 27. AN ADAPTIVE CONTROLLER FOR NON-LINEAR SYSTEMS;176
32.1;INTRODUCTION;176
32.2;PLANT MODEL;177
32.3;PREDICTIVE CONTROL;177
32.4;CONTROLLER DESIGN;178
32.5;CONTROLLER IMPLEMENTATION;179
32.6;IMPLEMENTATION STUDIES;179
32.7;CONCLUSIONS;180
32.8;REFERENCES;180
33;CHAPTER 28. LONG-RANGE PREDICTIVE CONTROL OF NONLINEAR SYSTEMS ON THE BASIS OF THE HAMMERSTEIN MODEL;182
33.1;INTRODUCTION;182
33.2;NONLINEAR SYSTEM DESCRIPTION;182
33.3;LONG-RANGE PREDICTIVE CONTROL ALGORITHMS;183
33.4;SIMULATION RESULTS;184
33.5;CONCLUSIONS;184
33.6;REFERENCES;184
34;CHAPTER 29. SELF-TUNING CONTROL OF A STOCHASTIC NON-LINEAR OBJECT;188
34.1;INTRODUCTION;188
34.2;PROBLEM STATEMENT;188
34.3;CONTROL ALGORITHMS;189
34.4;IDENTIFICATION ALGORITHM;190
34.5;SIMULATION RESULTS;191
34.6;CONCLUSIONS;192
34.7;REFERENCES;192
35;CHAPTER 30. MEAN EXIT TIMES AND RATES OF CONVERGENCE FOR STOCHASTIC SYSTEMS WITH APPLICATIONS TO ADAPTIVE CONTROL;194
35.1;1 Introduction;194
35.2;2 Preliminaries;194
35.3;3 Motivation from dynamical systemstheory;195
35.4;4 Stochastic Lyapunov functions;196
35.5;5 Stochastic controllability;197
35.6;6 Application to Adaptive Control;197
35.7;7 Conclusions;198
35.8;References;199
36;CHAPTER 31. ROBUST ADAPTIVE LQ CONTROL SCHEME;200
36.1;1 Introduction;200
36.2;2 The Plant and the LQ Control Problem;200
36.3;3 Continuous-time Adaptive LQ Control;201
36.4;4 Hybrid Adaptive LQ Control;203
36.5;5 Conclusion;203
36.6;REFERENCES;203
36.7;APPENDIX;204
37;CHAPTER 32. ROBUST STABILITY CONDITIONS FOR ADAPTIVE CONTROL SYSTEMS;206
37.1;INTRODUCTION;206
37.2;DESCRIPTION OF THE ADAPTIVE CONTROL SYSTEMS;206
37.3;ROBUSTNESS CONDITIONS BASED ON SLOW VARIATION ASSUMPTION;207
37.4;ROBUSTNESS CONDITIONS BASED ON THE" CLOSENESS" ASSUMPTION;208
37.5;CONDITIONS FOR SLOW VARIATION OF ADAPTIVE SYSTEM;209
37.6;CONCLUSIONS;210
37.7;REFERENCES;210
38;CHAPTER 33. A DIRECT ADAPTIVE CONTROLLER FOR A CLASS OF STABLE AND TIME-VARYING PLANTS;212
38.1;INTRODUCTION;212
38.2;MATHEMATICAL PRELIMINARIES;212
38.3;DESCRIPTION OF THE PLANT;213
38.4;THE ADAPTIVE CONTROL LAW PARAMETRIZATION OF THE CONTROLLER;213
38.5;THE CONTROLLER PARAMETER ESTIMATION;214
38.6;THE ADAPTIVE CONTROL LAW;215
38.7;THE CLOSED LOOP ROBUST BIBOSTABILITY;215
38.8;CONCLUSION;215
38.9;REFERENCES;215
39;CHAPTER 34. THE F-ITERATION APPROACH TO H8 CONTROL;216
39.1;ABSTRACT;216
39.2;NOTATION;216
39.3;1 . INTRODUCTION;216
39.4;2 . GLQG AND H8 RESULTS;216
39.5;3. MODES OF OPERATION;217
39.6;4. ROBUST IDENTIFICATION;218
39.7;5. ROBUSTNESS;218
39.8;6. SIMULATION EXAMPLE;219
39.9;7. CONCLUSIONS;220
39.10;8. References;220
40;CHAPTER 35. ADAPTIVE CONTROL FOR PARTIAL MODEL MATCHING IN FREQUENCY DOMAIN FOR NON-MINIMUM PHASE SYSTEMS;222
40.1;INTRODUCTION;222
40.2;PROBLEM STATEMENT;222
40.3;DESIGN METHOD OF FEED-FORWARD COMPENSATORS;223
40.4;ADAPTIVE CONTROL SCHEME;224
40.5;STABILITY ANALYSIS;225
40.6;NUMERICAL SIMULASIONS;225
40.7;CONCLUSIONS;227
40.8;REFERENCES;227
41;CHAPTER 36. ADAPTIVE CONTROL WITH LINEAR NONPARAMETRIC MODELS;228
41.1;INTRODUCTION;228
41.2;LINEAR NONPARAMETRIC MODEL;228
41.3;CONTROLLER DESIGNS;229
41.4;NONPARAMETRIC ESTIMATION;230
41.5;SUITABLE COMBINATIONS;231
41.6;START-UP PROCEDURES;232
41.7;DESIGN PARAMETERS;232
41.8;CONCLUSIONS;233
41.9;REFERENCES;233
42;CHAPTER 37. ROBUSTNESS IN ADAPTIVE CONTROL FROM A FREQUENCY DOMAIN SYSTEM IDENTIFICATION PERSPECTIVE;234
42.1;INTRODUCTION;234
42.2;HEURISTIC DISCUSSION;234
42.3;FORMAL DISCUSSION;235
42.4;MINIMIZING V;236
42.5;SELECTING AN APPROPRIATE V;237
42.6;CONCLUSIONS;238
42.7;REFERENCES;238
43;CHAPTER 38. IMPLICIT MODELING OF NOISE VERSUS THE USE OF A FIXED NOISE MODEL;240
43.1;1 Introduction;240
43.2;2 Fixed estimation of noise characteristics;240
43.3;3 Implicit modeling of noise;242
43.4;4 Simulation results;243
43.5;5 Conclusions;245
43.6;References;245
44;CHAPTER 39. ON STABILITY ANALYSIS OF EQUILIBRIUM POINTS OF VARIATIONAL ADAPTIVE CONTROL SCHEMES;246
44.1;INTRODUCTION;246
44.2;EXTENDED IMPLICIT MODELS AND VARIATIONAL ADAPTIVE CONTROL;246
44.3;DIRECT VARIATIONAL ADAPTIVE CONTROL;248
44.4;STABILITY ANALYSIS OF A DIRECT VARIATIONAL ADAPTIVE CONTROL ALGORITHM;249
44.5;CONCLUSIONS;251
44.6;REFERENCES;251
45;CHAPTER 40. SELF-TUNING CONTROL WITH ALTERNATIVE SETS OF UNCERTAIN PROCESS MODELS;252
45.1;1 Introduction;252
45.2;2 Problem statement;253
45.3;3 Solution;253
45.4;4 Incremental vs. positional models;254
45.5;5 Simulated examples;255
45.6;References;255
45.7;Appendix;257
46;CHAPTER 41. PARAMETER-ADAPTIVE PID-CONTROL BASED ON CONTINUOUS-TIME PROCESS MODELS;258
46.1;1. INTRODUCTION;258
46.2;2. PROCESS IDENTIFICATION;259
46.3;3. CONTROLLER DESIGN;261
46.4;4. SIMULATION RESULTS;263
46.5;5. CONCLUSIONS;263
46.6;6. REFERENCES;263
47;CHAPTER 42. OPTIMAL APPROACHES TO SELF-TUNING PID CONTROL;264
47.1;INTRODUCTION;264
47.2;PID CONTROL;264
47.3;PROCESS MODEL;265
47.4;SELF-TUNING PID;265
47.5;COST FUNCTION I;265
47.6;COST FUNCTION II;265
47.7;CONSTRAINED SOLUTIONS;266
47.8;A SIMPLE TUNING METHOD;266
47.9;SIMULATION EXAMPLE;266
47.10;CONCLUDING REMARKS;266
47.11;REFERENCES;266
48;CHAPTER 43. AN INDUSTRIAL ADAPTIVE PID CONTROLLER;268
48.1;1. Introduction;268
48.2;2. An Industrial Adaptive Controller;268
48.3;3. Auto-Tuning and Gain Scheduling;269
48.4;4. Adaptive Feedback Control;270
48.5;5. Adaptive Feedforward;271
48.6;6. Industrial Experiences;272
48.7;7. Conclusions;273
48.8;8. References;273
49;CHAPTER 44. AN EXPERT ADAPTIVE PID CONTROLLER;274
49.1;INTRODUCTION;274
49.2;THE "RAPID" ARCHITECTURE FOR EXPERT ADAPTIVE PID CONTROL;275
49.3;STEP RESPONSE ALGORITHMS;275
49.4;EXPERT SYSTEM MANAGERS;276
49.5;A TYPICAL CONSULTATION;278
49.6;CONCLUSION;279
49.7;REFERENCES;279
50;CHAPTER 45. DESIGN OF ADAPTIVE DIGITAL SET-POINT TRACKING PI CONTROLLERS INCORPORATING EXPERT TUNERS FOR MULTIVARIABLE PLANTS;280
50.1;INTRODUCTION;280
50.2;MULTIVARIABLE CONTROL SYSTEM DESIGN;281
50.3;ADAPTIVE STRUCTURAL IDENTIFICATION OF MULTIVARIABLE PLANTS;281
50.4;EXPERT TUNING OF MULTIVARIABLE PLANTS;282
50.5;PERFORMANCE OF THE EXPERT TUNER;283
50.6;CONCLUSION;284
50.7;REFERENCES;284
51;CHAPTER 46. SELF-TUNING PID CONTROL OF MUSCLERELAXANTDRUG ADMINISTRATION INOPERATING THEATRES;288
51.1;INTRODUCTION;288
51.2;SELF-TUNING PID CONTROL ALGORITHM;288
51.3;SIMULATION STUDIES;289
51.4;CLINICAL IMPLEMENTATION AND RESULTS;290
51.5;CLINICAL PREPARATION OF THE PATIENTS;291
51.6;RESULTS AND DISCUSSIONS;291
51.7;CONCLUSION;291
51.8;REFERENCES;292
52;CHAPTER 47. SELF-TUNING PI CONTROL OF A SOLAR POWER PLANT;294
52.1;INTRODUCTION;294
52.2;POWER PLANT DESCRIPTION;294
52.3;NON-LINEAR PLANT MODEL;295
52.4;LINEAR PLANT MODELS FOR SELF-TUNING CONTROL;295
52.5;SELF-TUNING CONTROL;296
52.6;SIMULATION STUDIES;297
52.7;CONCLUSIONS;298
52.8;ACKNOWLEDGEMENTS;299
52.9;REFERENCES;299
53;CHAPTER 48. AN ADAPTIVE AUTOPILOT FOR INLAND SHIPS;300
53.1;INTRODUCTION;300
53.2;MODELLING;301
53.3;PARAMETER ESTIMATION;301
53.4;CONTROLLER DESIGN;302
53.5;IMPLEMENTATION;303
53.6;CONCLUSIONS;304
53.7;ACKNOWLEDGEMENTS;304
53.8;REFERENCES;304
54;CHAPTER 49. AN EXTENDED SELF-TUNING MULTIVARIABLE PI CONTROLLER;306
54.1;INTRODUCTION;306
54.2;CONTROLLER GAINS;306
54.3;A SELF-TUNING CONTROLLER;308
54.4;ON EXTENDED SELF-TUNING CONTROL;309
54.5;IMPLEMENTATION;310
54.6;EXPERIMENTS;311
54.7;CONCLUSIONS;311
54.8;REFERENCES;311
55;CHAPTER 50. DESIGN OF ADAPTIVE DIGITAL SET-POINT TRACKING CONTROLLERS INCORPORATING RECURSIVE IMPULSE-RESPONSE MATRIX IDENTIFIERS FOR TYPE-ONE MULTIVARIABLE PLANTS;312
55.1;INTRODUCTION;312
55.2;ANALYSIS;312
55.3;ILLUSTRATIVE EXAMPLE;313
55.4;CONCLUSION;313
55.5;REFERENCES;314
56;CHAPTER 51. INPUT-CONSTRAINED SELF-TUNING CONTROL OF A ROBOT LINK;318
56.1;1 INTRODUCTION;318
56.2;2 MINIMUM VARIANCE CONTROL UNDER INPUT CONSTRAINTS;319
56.3;3 DESCRIPTION OF THE ROBOT;320
56.4;4 SIMULATION RESULTS;321
56.5;5 REALIZATION RESULTS;322
56.6;6 CONCLUSIONS;323
56.7;ACKNOWLEDGEMENT;323
56.8;REFERENCES;323
57;CHAPTER 52. APPLICATION OF ADAPTIVE CONTROL ON A SIMPLE ARM;324
57.1;1. INTRODUCTION;324
57.2;2. ARM MODEL AND PERFORMANCE REQUIREMENTS;324
57.3;3. CONTROLLER STRUCTURE;325
57.4;4. ADAPTIVE CONTROL;326
57.5;6. CONCLUSION;326
57.6;REFERENCES;326
58;CHAPTER 53. ADAPTIVE ROBOTIC DEBURRING;330
58.1;INTRODUCTION;330
58.2;PLANT MODELING;331
58.3;NON-ADAPTIVE CONTROL SYSTEM DESIGN;331
58.4;ADAPTIVE CONTROL SYSTEM DESIGN;332
58.5;CONCLUSIONS;334
58.6;REFERENCES;334
59;CHAPTER 54. APPLICATION OF MICROCOMPUTER-BASED MODEL REFERENCE ADAPTIVE CONTROL TO A HYDRAULIC POSITIONING SYSTEM;336
59.1;INTRODUCTION;336
59.2;THE ADAPTIVE CONTROL STRATEGY;336
59.3;DESCRIPTION OF THE PLANT;338
59.4;IMPLEMENTATION ASPECTS;339
59.5;CONCLUSION;341
59.6;REFERENCES;341
60;CHAPTER 55. IDENTIFICATION AND ADAPTIVE CONTROL OF MECHANICAL SYSTEMS WITH FRICTION;342
60.1;1. INTRODUCTION;342
60.2;2. FRICTION;342
60.3;3. IDENTIFICATION;343
60.4;4. ADAPTIVE CONTROL;345
60.5;5. EXAMPLE;346
60.6;6. CONCLUSIONS;347
60.7;7. REFERENCES;347
61;CHAPTER 56. ADAPTIVE CONTROL APPLIED TO THE DISTURBANCE ACCOMMODATION PROBLEM;348
61.1;INTRODUCTION;348
61.2;COMPENSATED SYSTEM DESIGN;348
61.3;EXPERIMENTAL RESULTS;349
61.4;DISCUSSION AND CONCLUSIONS;350
61.5;BIBLIOGRAPHY;350
61.6;APPENDICES;351
62;CHAPTER 57. SELF-ADAPTIVE AUTOMOTIVE ENGINE MANAGEMENT;354
62.1;INTRODUCTION;354
62.2;SELF-TUNING EXTREMUM CONTROL;355
62.3;IMPLEMENTATION;356
62.4;THE OPTIMISATION OF AUTOMOTIVE SPARK ANGLE SETTING;357
62.5;CONCLUSIONS;358
62.6;ACKNOWLEDGMENTS;358
62.7;REFERENCES;358
63;CHAPTER 58. OPTIMAL ADAPTIVE ROBUST CONTROLLER FOR HYDROTURBINE REGULATION;360
63.1;INTRODUCTION;360
63.2;ADAPTIVE ROBUST CONTROLLER;360
63.3;OPTIMAL ROBUSTNESS AREA;362
63.4;APPLICATIONS IN THE REGULATION OF HYDRAULIC TURBINE;362
63.5;ACKNOWLEDGEMENT;363
63.6;REFERENCES;363
64;CHAPTER 59. MULTIVARIABLE ADAPTIVE CONTROL OF AN ENVIRONMENTAL TEST CHAMBER;364
64.1;INTRODUCTION;364
64.2;PROCESS DESCRIPTION;364
64.3;STATEMENT OF THE PROBLEM;365
64.4;MULTIVARIABLE CONTROL WITH CONSTRAINTS;365
64.5;EXPERIMENTAL RESULTS;366
64.6;CONCLUDING REMARKS;367
64.7;REFERENCES;367
65;CHAPTER 60. MULTIVARIABLE ADAPTIVE CONTROL: AN INDUSTRIAL DRYING PROCESS APPLICATION;370
65.1;INTRODUCTION;370
65.2;PHYSICAL SYSTEM DESCRIPTION;370
65.3;THE PREVIOUS CONTROLLER;370
65.4;SYSTEM MODELLING;371
65.5;PROCESS IDENTIFICATION;371
65.6;PRACTICAL ASPECTS;372
65.7;THE GMV CONTROLLER;373
65.8;THE ALGORITHM IMPLEMENTATION;373
65.9;PRACTICAL ASPECTS;373
65.10;THE STR CONTROL LOOP;374
65.11;THE RESULTS;375
65.12;SOFTWARE ARCHITECTURE;375
65.13;REFERENCES;375
66;CHAPTER 61. ADAPTIVE TEMPERATURE CONTROL OF AWATER BATH BY USING MULTIVARIABLE SELF-TUNING CONTROL;376
66.1;INTRODUCTION;376
66.2;CONTROL PROCESS AND ITS MODEL;376
66.3;MINIMUM VARIANCE CONTROL AND STC ALGORITHM;378
66.4;EXPERIMENTAL HARDWARE SYSTEM;379
66.5;EXPERIMENTAL RESULTS;380
66.6;CONCLUSINS;381
66.7;REFERENCES;381
67;CHAPTER 62. IMPLEMENTATION OF AN ADAPTIVE AUTOPILOT UNIT;382
67.1;1. INTRODUCTION;382
67.2;2. ALGORITHMS;382
67.3;3. DESIGN OF HARDWARE AND SOFTWARE;384
67.4;5. SEA TRIALS;385
67.5;4. SIMULATION STUDIES;386
67.6;CONCLUSIONS;387
67.7;REFERENCES;387
68;CHAPTER 63. SELF-TUNING CONTROL OF BLOOD PRESSURE: SINGLE VARIABLE AND MULTIVARIABLE STUDIES;388
68.1;INTRODUCTION;388
68.2;SIMULATION STUDIES;389
68.3;CONCLUSIONS;390
68.4;REFERENCES;390
69;CHAPTER 64. CONVERGENCE PROPERTIES OF ASSOCIATIVE MEMORY STORAGE FOR LEARNING CONTROL SYSTEMS;394
69.1;INTRODUCTION;394
69.2;ASSOCIATIVE MEMORY SYSTEMS (AMS);394
69.3;CONVERGENCE PROPERTIES OF THE ALBUS LEARNING ALGORITHM;395
69.4;A COMPARISON OF 5 LEARNING ALGORITHMS;396
69.5;COMPUTATIONAL RESULTS;396
69.6;SINESUMMARYOF PROPERTIES OF THE ALGORITHM;397
69.7;REFERENCES;398
70;CHAPTER 65. DISCRETE-TIME ITERATIVE LEARNING CONTROLLER USING APPROXIMATE INVERSE MODEL;402
70.1;INTRODUCTION;402
70.2;PROBLEM FORMULATION;402
70.3;EVOLUTION OF THE TRACKING ERROR;403
70.4;DESIGN BASED ON NOMINAL IMPULSE RESPONSE SEQUENCE;404
70.5;EFFECT OF THE ERROR IN THE NOMINAL IMPULSE RESPONSE;405
70.6;DESIGN TAKING ACCOUNT OF THE UNCERTAINTY;406
70.7;A SIMULATION EXAMPLE;407
70.8;CONCLUSIONS;407
70.9;REFERENCES;407
71;CHAPTER 66. LEARNING IN A NOISY DOMAIN;408
71.1;THE EXPERT SYSTEM;408
71.2;A LEARNING SYSTEM;409
71.3;EXPERIMENTAL RESULTS;410
71.4;FUTURE DEVELOPMENTS;413
71.5;REFERENCES;413
72;CHAPTER 67. A REAL TIME SUPERVISION SYSTEM FOR ADAPTIVE CONTROL;414
72.1;INTRODUCTION;414
72.2;UNIVERSAL EQUATION;414
72.3;THE SUPERVISION;416
72.4;THE LOGICAL RESOLUTION;416
72.5;RECURSIVITY AND AXIOMATIC LEVEL;417
72.6;CONSTRUCTION METHOD;417
72.7;SYSTEM ARCHITECTURE;418
72.8;CONCLUSION;418
72.9;REFERENCES;418
73;CHAPTER 68. CONCURRENT ARRAY PROCESSING FOR LINEAR MULTIVARIABLE FEEDBACK CONTROL SYSTEMS;420
73.1;INTRODUCTION;420
73.2;PRELIMINARIES;420
73.3;ARCHITECTURES;421
73.4;DISCUSSION AND CONCLUSIONS;422
73.5;REFERENCES;422
74;CHAPTER 69. THE FUTURE OF ADAPTIVE CONTROL;426
75;CHAPTER 70. SYSTEM IDENTIFICATION IN A NOISE FREE ENVIRONMENT;428
75.1;1 INTRODUCTION;428
75.2;2 APREVIEW EXAMPLE;428
75.3;3 SOME BASIC RESULTS;430
75.4;4 VARIABILITY DUETO INPUT;432
75.5;5 THE ROLE OF PRE-FILTERING;433
75.6;6 PERFORMANCE LIMITATIONS;434
75.7;7 CHOICES OF FORGETTING FACTOR AND PREFILTER;436
75.8;8 CONCLUSIONS;436
75.9;REFERENCES;436
76;CHAPTER 71. THE CONVERGENCE OF A MODIFIED ELS ALGORITHM;438
76.1;1 INTRODUCTION;438
76.2;2 THE MFELS ALGORITHM;438
76.3;3 CONVERGENCE;440
76.4;4 SIMULATION RESULTS;441
76.5;5 CONCLUDING REMARKS;442
76.6;REFERENCES;442
76.7;ACKNOWLEDGEMENT;443
77;CHAPTER 72. LATTICE ALGORITHMS WITH VARIABLE FORGETTING FACTORS DETECTING INTERVENTIONS AND PARAMETRIC CHANGES;444
77.1;INTRODUCTION;444
77.2;LATTICE ALGORITHMS;444
77.3;VARIABLE FORGETTING FACTORS IN LATTICE METHODS;445
77.4;DETECTION OF INTERVENTIONS WITH LATTICE ALGORITHMS;445
77.5;SIMULATIONS;446
77.6;SUMMARY;449
77.7;REFERENCES;449
78;CHAPTER 73. RECURSIVE IDENTIFICATION OF OVERPARAMETRIZED SYSTEMS: (Adaptive Estimation in the Presence of Order and Parameter Changes);450
78.1;1. INTRODUCTION;450
78.2;2. PERTURBED KALMAN FILTER DETECTION AND IDENTIFICATION SCHEME;450
78.3;3. SIMULATIONS;452
79;CHAPTER 74. PARAMETER ESTIMATION OF A CLASS OF DISCRETE TIME VARYING SYSTEMS;454
79.1;INTRODUCTION;454
79.2;BACKGROUND AND PROBLEM STATEMENT;454
79.3;NAIN IDEA;455
79.4;GEOMETRICAL INTERPRETATION;456
79.5;GENERALIZATION;456
79.6;SIMULATION EXAMPLES;457
79.7;CONCLUSION;458
79.8;REFERENCES;458
80;CHAPTER 75. LEAST SQUARE TYPE OF ADAPTIVE FILTER WITH OPTIMALLY STABILIZED CONVERGENCY BASED ON EIGEN-STRUCTURE APPROACH;460
80.1;INTRODUCTION;460
80.2;ILL-POSED LEAST SQUARES ESTIMATION OF MODEL PARAMETERS;460
80.3;SELECTION OF SINGLE REGULARIZATION PARAMETER;461
80.4;REGULARIZATION BY USE OF MULTIPLE REGULARIZATION PARAMETERS;461
80.5;ADAPTIVE ALGORITHM FOR REGULARIZATIONBY MULTIPLE PARAMETERS;464
80.6;CONCLUSIONS;465
80.7;REFERENCES;465
81;CHAPTER 76. ROBUST PARAMETER ESTIMATION PROCEDURE FOR -e CONTAMINATED PROBABILITY DISTRIBUTION MODELS WITH UNCERTAIN GROSS-ERROR;466
81.1;INTRODUCTION;466
81.2;PROBLEM STATEMENT;466
81.3;M-ESTIMATOR FOR UNCERTAIN GROSS-ERROR;467
81.4;NUMERICAL EXAMPLE;469
81.5;CONCLUSIONS;469
81.6;REFERENCES;469
81.7;APPENDIX;469
82;CHAPTER 77. DIRECT ADAPTIVE CONTROL WITH THE ROBUST LEAST SQUARES METHOD;472
82.1;INTRODUCTION;472
82.2;PROBLEM FORMULATION;472
82.3;CONCLUSION;475
82.4;REFERENCES;475
83;CHAPTER 78. ROBUST IDENTIFICATION SCHEME IN AN ADAPTIVE TRACK-CONTROLLER FOR SHIPS;478
83.1;1. Introduction;478
83.2;2. Structure of the Identification Problem;479
83.3;3. Modification of the bootstrap algorithm;480
83.4;4, Identification of the High Frequency Disturbance Model;481
83.5;5. Ship Model Identification with a Constant Gain Algorithm;482
83.6;6. Concisions;483
83.7;References:;483
84;CHAPTER 79. OPTIMAL OFF-LINE SIGNAL PROCESSING;484
84.1;INTRODUCTION;484
84.2;PROBLEM STATEMENT;484
84.3;OPTIMAL TRANSFER FUNCTION OF THE OFF-LINE SIGNAL PROCESSING SYSTEM;484
84.4;APPROXIMATED REALIZATION OF AN OPTIMAL TRANSFER FUNCTIONIN THE SYSTEM OF OFF-LINE SIGNAL PROCESSING;485
84.5;THE PRINCIPLE OF INVERSION;485
84.6;CASCADE FORM OF OFF-LINE SIGNAL PROCESSING;486
84.7;PARALLEL FORM OF OFF-LINESIGNAL PROCESSING;486
84.8;DISCUSSION;486
84.9;REFERENCES;486
85;CHAPTER 80. APPLICATIONS OF ENTROPY OF PATTERNS AND FORMS TO SIGNAL PROCESSING;488
85.1;1. INTRODUCTION;488
85.2;2. SHANNON ENTROPY OF DEGREE c OF CONTINUOUS PATTERNS;488
85.3;3. THERMODYNAMIC ENTROPY OF MAPS;489
85.4;4. RENYI ENTROPY OF PATTERNS;489
85.5;5. ENTROPY OF DISTRIBUTED DETERMINISTIC PATTERNS;490
85.6;6. ENTROPY OF DISCRETE PATTERNS;491
85.7;7. ENTROPY OF RANDOM PATTERNS;491
85.8;8. INFORMATIONAL DIVERGENCE OF DETERMINISTIC PATTERNS;491
85.9;9. IDENTIFICATION WITH FAULTY OBSERVATION;492
85.10;10. CONCLUDING REMARKS;492
85.11;REFERENCES;492
86;CHAPTER 81. H8 ROBUST LINEAR ESTIMATOR;494
86.1;1. INTRODUCTION;494
86.2;2. PROBLEM FORMULATION;494
86.3;3. H8 FILTER AND CLASSICAL WIENER FILTER;495
86.4;4. GENERALISED H8 FILTER FOR UNCERTAIN SYSTEM;496
86.5;5. UNKNOWN TRANSMISSION PROBLEM;497
86.6;6. CONCLUSIONS;498
86.7;7. REFERENCES;498
86.8;APPENDIX A;498
87;CHAPTER 82. BAYESIAN FILTERING FOR DISCRETE-TIME SYSTEMS WITH RANDOM STRUCTURE;500
87.1;INTRODUCTION;500
87.2;PROBLEM FORMULATION;500
87.3;EQUATIONS FOR POSTERIOR DISTRIBUTIONS;501
87.4;APPROXIMATE FILTERING IN THE LINEAR GAUSSIAN CASE;502
87.5;SIMULATION EXAMPLES;503
87.6;SUMMARY AND CONCLUSIONS;503
87.7;ACKNOWLEDGMENTS;504
87.8;REFERENCES;504
88;CHAPTER 83. THE POSITIVE-REAL AND STRICTLY POSITIVE-REAL PROPERTY OF SAMPLED SYSTEM;506
88.1;I. Introduction;506
88.2;II. Preliminaries;506
88.3;III. Limiting PR Properties;508
88.4;IV. A Condition for SPR and PR Property;508
88.5;V. Conclusions;509
88.6;VI. Acknowledgement;509
88.7;References;509
89;CHAPTER 84. A U-D FIXED-INTERVAL SMOOTHER IN BACKWARD-PASS REALIZATION;512
89.1;1. INTRODUCTION;512
89.2;2. FIXED-INTERVAL SMOOTHING PROBLEM;512
89.3;3. A ROBUST KALMAN FILTER FOR NON-GAUSSIAN MEASUREMENT NOISE;513
89.4;4. A COMPUTATIONALLY EFFICIENT BACKWARD-PASS SMOOTHER;513
89.5;5. A NE ROBUST BACKWARD-PASS U-D SMOOTHER;515
89.6;6. CONCLUSIONS;516
89.7;REFERENCES;516
90;CHAPTER 85. GREY-BOX MODELLING AND IDENTIFICATION USING PHYSICAL KNOWLEDGE AND BAYESIAN TECHNIQUES;518
90.1;1. INTRODUCTION;518
90.2;2. PHYSICAL INCONSISTENCIES OF MODEL IDENTIFIED;518
90.3;3. GREY-BOX MODELLING USING PHYSICAL KNOWLEDGE;519
90.4;4. BAYESIAN INFERENCE OF GB PARAMETER DISTRIBUTION CONDITIONED ON DATA AND PHYSICAL KNOWLEDGE;521
90.5;5. BAYESIAN MAP AND AAP ESTIMATORS;522
90.6;6. CONCLUSIONS;524
90.7;ACKNOWLEDGMENT;524
90.8;REFERENCES;524
91;CHAPTER 86. RECURSIVE ESTIMATION AND MODELLING OF NONSTATIONARY AND NONLINEAR TIME-SERIES;526
91.1;1. INTRODUCTION;526
91.2;2. THE TIME-SERIES MODEL;527
91.3;4. IDENTIFICATION and ESTIMATION of the TVP MODEL;528
91.4;5. SPECIAL EXAMPLES of the TVP MODEL;530
91.5;6. SPECIAL EXAMPLES of the GM for the PARAMETER VARIATIONS;532
91.6;7. EXAMPLES;534
91.7;8. CONCLUSIONS;535
91.8;REFERENCES;536
92;CHAPTER 87. BAYESIAN APPROACH TO OPTIMAL INPUT DESIGN FOR FAILURE DETECTION AND DIAGNOSIS;542
92.1;1 Introduction;542
92.2;2 Decision Rules Including Input Law;542
92.3;3 Dynamic Programming Solution for the Decision Rules;544
92.4;4 An Example;545
92.5;5 Conclusion;546
92.6;Acknowledgement;546
92.7;References;546
93;CHAPTER 88. FAULT DETECTION AND DIAGNOSIS IN ACHEMICAL PLANT USING SEQUENTIAL HYPOTHESIS TESTING;548
93.1;INTRODUCTION;548
93.2;ARMITAGE'S SPRT;548
93.3;PLANT DESCRIPTION;549
93.4;FAULT DYNAMICS;550
93.5;MODELLING THE HYPOTHESES;550
93.6;FDD FOR THE PLANT;550
93.7;CONCLUSION;551
93.8;ACKNOWLEDGEMENTS;551
93.9;REFERENCES;551
94;CHAPTER 89. ON-LINE SIMULATION FOR CONTROL OF BRITISH GAS TRANSMISSION NETWORK;554
94.1;1. INTRODUCTION;554
94.2;2 . ON-LINE SIMULATION;554
94.3;3. STATE ESTIMATION;556
94.4;4 . SOFTWARE SYSTEM DESIGN;558
94.5;6. TESTING THE SYSTEM;560
94.6;7. CONCLUSION;562
94.7;REFERENCES;562
95;CHAPTER 90. TRACKING OF MULTIPLE TARGETS;564
95.1;INTRODUCTION;564
95.2;THE MULTI-TARGET TRACKING PROBLEM;565
95.3;THRESHOLD RESULTS;567
95.4;TWO TARGET RESULTS;568
95.5;CONCLUSIONS;568
95.6;ACKNOWLEDGEMENTS;568
95.7;REFERENCES;568
96;CHAPTER 91. SELF-ADAPTIVE ALGORITHMS FOR TWO-DIMENSIONAL SIGNAL SOURCES;572
96.1;1.INTRODUCTION;572
96.2;2.TRANSFER FUNCTION METHODS FOR 2-D ADAPTIVE SIGNAL PROCESSORS;574
96.3;3.APPLICATIONS;578
96.4;4 . CONCLUDING REMARKS;579
96.5;5 . ACKNOWLEDGMENT;580
96.6;6.REFERENCES;580
97;CHAPTER 92. ADAPTIVE INPUT ESTIMATION;584
97.1;1 Introduction;584
97.2;2 Preliminaries;584
97.3;3 A self-tuning input estimation algorithm;585
97.4;4 A numerical example;587
97.5;5 Simulations;587
97.6;6 Conclusion;589
97.7;7 References;589
98;CHAPTER 93. APPLICATION OF MULTILAYER PERCEPTRONS AS ADAPTIVE CHANNEL EQUALISERS;590
98.1;1. INTRODUCTION;590
98.2;2. GEOMETRIC FORMULATION OF THE EQUALISATION PROBLEM;590
98.3;3. LINEAR TRANSVERSAL EQUALISERS;591
98.4;4. MULTILAYER PERCEPTRONS;592
98.5;5. SIMULATION RESULTS;593
98.6;6. CONCLUSIONS;595
98.7;ACKNOWLEDGEMENTS;595
98.8;REFERENCES;595
99;CHAPTER 94. STOCHASTIC DYNAMICS OF BLIND DECISION FEEDBACK EQUALIZER ADAPTATION;596
99.1;1. Introduction;596
99.2;2. System Description and Notation;596
99.3;3. Parameter Space Partition;597
99.4;4. Equilibria and Averaging Analysis;598
99.5;5. Tap Trajectories During Adaptation;598
99.6;6. Conclusion;601
99.7;References;601
100;CHAPTER 95. THE ADAPTIVE METHOD FOR FILTERING THE REFLECTION WAVES IN PARAMETER IDENTIFICATIONS OF NON-UNIFORM DISTRIBUTED SYSTEMS;602
100.1;INTRODUCTION;602
100.2;MATHEMATICAL FORMULATION OF IDENTIFICATION PROCEDURE FOR MULTILAYERED STRUCTURES;602
100.3;EXPERIMENTAL SETUP;604
100.4;CONCLUSIONS;604
100.5;REFERENCES;604
101;CHAPTER 96. ON A HIGHER ORDER RECURSIVE TECHNIQUE FOR FREQUENCY ESTIMATION;606
101.1;I. INTRODUCTION;606
101.2;II. FILTER DESIGN;606
101.3;III. ARMA-MODEL (ADAPTIVE NOTCH FILTER);607
101.4;IV. EXTENDED INSTRUMENTAL VARIABLE (E.I.V.) METHOD;607
101.5;V, INSTRUMENTAL FOURIER ( I.F.) TECHNIQUE;609
101.6;VI. MODEL ORDER OVERDETERMINATION;609
101.7;VII. HYPOTHESIS TESTING;609
101.8;VIII. ON AN ADAPTIVE PROCEDURE;610
101.9;IX. COMMENTS ON SIMULATION RESULTS;610
101.10;X. REFERENCES;611
102;CHAPTER 97. A MAXIMUM LIKELIHOOD ALGORITHM FOR ESTIMATING DAMPED SINUSOIDS;618
102.1;ABSTRACT;618
102.2;INTRODUCTION;618
102.3;MAXIMUM LIKELIHOOD: MODEL 1;618
102.4;MAXIMUM LIKELIHOOD: MODEL 2;619
102.5;MULTIPLE SINUSOID CASE;621
102.6;CONCLUSIONS;622
102.7;ACKNOWLEDGEMENTS;622
102.8;REFERENCES;622
103;AUTHOR INDEX;624
104;KEYWORD INDEX;626