Dugard / M'Saad / Landau Adaptive Systems in Control and Signal Processing 1992

Selected Papers from the 4th IFAC Symposium Grenoble, France, 1 - 3 July 1992
1. Auflage 2014
ISBN: 978-1-4832-9880-1
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 556 Seiten, Web PDF

Reihe: Gama Network Series

Adaptive Systems remain a very interesting field of theoretical research, extended by methodological studies and an increasing number of applications. The plenary papers, invited sessions and contributed sessions focused on many aspects of adaptive systems, such as systems identification and modelling, adaptive control of nonlinear systems and theoretical issues in adaptive control. Also covered were methodological aspects and applications of adaptive control, intelligent tuning and adaptive signal processing.

Autoren/Hrsg.

Dugard, L.

M'Saad, M.

Landau, I. D.

Inhaltsverzeichnis

1;Front Cover;1
2;Adaptive Systems in Control and Signal Processing 1992;4
3;Copyright Page;5
4;Table of Contents;10
5;IFAC SYMPOSIUM ON ADAPTIVE SYSTEMS IN CONTROL AND SIGNAL PROCESSING 1992;6
6;FOREWORD;8
7;CHAPTER 1. RECURSIVE PARAMETER ESTIMATION FOR ARBITRARY HIDDEN MARKOV MODELS;16
7.1;INTRODUCTION;16
7.2;EM AND ON-LINE PARAMETER ESTIMATION;16
7.3;RE-ESTIMATION OF STATE TRANSITION PROBABILITIES;17
7.4;RE-ESTIMATION OF OBSERVATION PROBABILITIES;18
7.5;CONCLUSION;19
7.6;REFERENCES;19
8;CHAPTER 2. MODEL REDUCTION IN RECURSIVE LEAST SQUARES IDENTIFICATION;20
8.1;1 INTRODUCTION;20
8.2;2 BASIC PRINCIPLES;21
8.3;3 REDUCTION WITH COMPLETE STRUCTURE ESTIMATION;22
8.4;4 REDUCTION WITH SIMPLIFIED STRUCTURE ESTIMATION;23
8.5;5 SIMULATION RESULTS;24
8.6;6 CONCLUSIONS;25
8.7;REFERENCES;25
9;CHAPTER 3. ON SIMULTANEOUS SYSTEM AND INPUT SEQUENCE ESTIMATION;26
9.1;1 Introduction;26
9.2;2 Problem Formulation;27
9.3;3 The MAP Estimate;27
9.4;4 A Practical Algorithm;28
9.5;5 IDENTIFIABILITY;29
9.6;6 AN APPLICATION;30
9.7;7 CONCLUSIONS;31
9.8;References;31
10;CHAPTER 4. DESIGN CRITERIA FOR ROBUST STRICT POSITIVE REALNESS IN ADAPTIVE SCHEMES;32
10.1;INTRODUCTION;32
10.2;PROBLEM FORMULATION AND PRELIMINARIES;32
10.3;SUB-OPTIMAL SOLUTIONS TO PR AND FD PROBLEMS FOR GENERAL ROOT LOCATION REGIONS;34
10.4;NUMERICAL EXAMPLES;35
10.5;CONCLUSIONS;37
10.6;ACKNOWLEDGEMENTS;37
10.7;REFERENCES;37
11;CHAPTER 5. ROBUST IDENTIFICATION FOR ADAPTIVE CONTROL: THE DYNAMIC HINKLEY-DETECTOR;38
11.1;INTRODUCTION;38
11.2;FROM THE HINKLEY-DETECTCR TO THE DYNAMIC HINKLEY-DETECTOR;38
11.3;DISCUSSION;42
11.4;REFERENCES;43
12;CHAPTER 6. PARAMETER ESTIMATION VIA FIXED LEAKAGE MODIFICATION SCHEME FOR A CLASS OF NONLINEAR SYSTEMS;44
12.1;1. INTRODUCTION;44
12.2;2. PRELIMINARIES;44
12.3;3. PARAMETER ESTIMATION WITH FIXED alpha*- MODIFIGATION SCHEME;46
12.4;4. CONCLUSION;48
12.5;REFERENCES;48
13;CHAPTER 7. TOWARDS REAL-TIME IMPLEMENTATION OF BAYESIAN PARAMETER ESTIMATION;50
13.1;INTRODUCTION;50
13.2;BAYESIAN ESTIMATION;51
13.3;NON-PARAMETRIC VIEW;51
13.4;COMPRESSION OF DATA;51
13.5;IDEA OF APPROXIMATION;52
13.6;SUMMARY OF APPROXIMATION;53
13.7;DISCRETE IMPLEMENTATION;53
13.8;ILLUSTRATIVE EXAMPLE;54
13.9;NETWORK IMPLEMENTATION;54
13.10;CONCLUDING REMARKS;54
13.11;REFERENCES;55
14;CHAPTER 8. ADAPTIVE PID DESIGN EXPLOITING PARTIAL PRIOR INFORMATION;56
14.1;1. Introduction;56
14.2;2. Plant versus Model Limitations;57
14.3;3. Robust PID Design;58
14.4;4. Adaptive PID Design;60
14.5;5. Adaptive PID Control with Prior Information;60
14.6;6. Conclusions;61
14.7;Acknowledgements;61
14.8;References;61
15;CHAPTER 9. IDENTIFICATION OF NONLINEAR STOCHASTIC GREY BOX MODELS: Theory, Implementation, and Experiences;62
15.1;1. Introduction;62
15.2;2. Review of Theory;62
15.3;3. Procedures and Tools;64
15.4;4. Case Study 1: Mould Level Control;65
15.5;5. Case Study 2: Strip Steel Rinsing;66
15.6;6. Conclusions;67
15.7;Acknowledgements:;67
15.8;References;67
16;CHAPTER 10. VALIDATION OF GREY BOX MODELS;68
16.1;1 Introduction;68
16.2;2 Likelihood based Test Procedures;69
16.3;3 Information Methods for Order Selection;69
16.4;4 Residual Analysis;70
16.5;5 Nonparametric Estimation and Bootstrap;71
16.6;6 Validation methods based on nonlinear techniques;72
16.7;7 Bayesian Methods;73
16.8;References;75
17;CHAPTER 11. SYSTEM IDENTIFICATION USING BONDGRAPHS;76
17.1;1 Choice of Model Structures;76
17.2;2 Bondgraph Modeling;76
17.3;3 Software Requirement for Merging Bondgraph Modeling and System Identification;77
17.4;4 The MaMiC System;77
17.5;5 BOND TOOL;79
17.6;6 Example;79
17.7;7 Intelligent Help;81
17.8;8 Summary and Conclusions;81
17.9;References;81
18;CHAPTER 12. BOND-GRAPH BASED ADAPTIVE CONTROL;82
18.1;1 Introduction;82
18.2;2 Modelling with Bond Graphs;82
18.3;3 Adaptive Model-Based Observer (MBO) Control;83
18.4;4 An Example: three coupled tanks;84
18.5;5 The System;84
18.6;6 The Model-Based Observer;84
18.7;7 Simulation;85
18.8;8 Conclusion;87
18.9;9 Acknowledgements;87
18.10;References;87
19;CHAPTER 13. OVERTRAINING, REGULARIZATION, AND SEARCHING FOR MINIMUM IN NEURAL NETWORKS;88
19.1;1 Introduction;88
19.2;2 Neural Networks as models of dynamical systems;88
19.3;3 Regularization and variance reduction;89
19.4;4 Terminating iterative search for the minimum is regularization;91
19.5;5 Modeling a hydraulic robot arm;92
19.6;6 Conclusions;93
19.7;References;93
20;CHAPTER 14. ON LETTING ADAPTIVE CONTROL BE WHAT IT IS: NONLINEAR FEEDBACK1;94
20.1;Abstract;94
20.2;1 Introduction;94
20.3;2 State Feedback Design;94
20.4;3 Output Feedback Design;96
20.5;4 Transient Performance Improvement;98
20.6;5 Conclusions;100
20.7;References;100
21;CHAPTER 15. ADAPTIVE CONTROL OF SYSTEMS WITH BACKLASH;102
21.1;1 Introduction;102
21.2;2 Backlash and Its Right Inverse;102
21.3;3 Parametrization;104
21.4;4 An Introductory Example;105
21.5;5 Adaptive Control Design;106
21.6;6 Conclusions;107
21.7;Acknowledgements;108
21.8;References;108
22;CHAPTER 16. SELF-TUNING STABILIZATION OF FEEDBACK LINEARIZABLE SYSTEMS1;110
22.1;Abstract;110
22.2;1 Introduction;110
22.3;2 Preliminaries;111
22.4;3 Robust stabilization;112
22.5;4 Self-tuning stabilization;113
22.6;References;115
23;CHAPTER 17. SELF-TUNING OUTPUT FEEDBACK STABILIZATION OF A CLASS OF NONLINEAR SYSTEMS1;116
23.1;Abstract;116
23.2;1 Introduction;116
23.3;2 Basic results;116
23.4;3 Self-tuning output back stabilization feedback stabilization;117
24;CHAPTER 18. MODEL REFERENCE ADAPTIVE CONTROL AND IDENTIFICATION FOR NONLINEAR SYSTEMS: METHODS AND APPLICATIONS;122
24.1;INTRODUCTION;122
24.2;MRAS DESIGN FOR LINEAR SYSTEMS;122
24.3;NONLINEAR MRAC USING ERROR-EQUATION METHOD;122
24.4;NONLINEAR MRAC USING THE PRODUCT-SPACE METHOD;124
24.5;CONCLUSIONS;126
24.6;REFERENCES;126
25;CHAPTER 19. SELF-TUNING CONTROL OF NONLINEAR SYSTEMS USING NONPARAMETRIC ESTIMATION;128
25.1;INTRODUCTION;128
25.2;CONTROL ALGORITHM;128
25.3;APPLICATIONS;129
25.4;CONCLUSION;131
25.5;REFERENCES;131
26;CHAPTER 20. APPLICATION OF AVERAGING METHOD FOR INTEGRO-DIFFERENTIAL EQUATIONS TO MODEL REFERENCE ADAPTIVE CONTROL OF PARABOLIC SYSTEMS1;134
26.1;I. INTRODUCTION;134
26.2;II.. AVERAGING METHOD FOR INTEGRODIFFERENTIAL EQUATIONS;134
26.3;III. DIRECT ADAPTIVE CONTROL OF PARABOLIC SYSTEMS;135
26.4;IV. ANALYSIS OF PARAMETER CONVERGENCE;137
26.5;V. CONCLUSIONS;139
26.6;REFERENCES;139
27;CHAPTER 21. APPLICATION OF PASSIVE SYSTEM APPROACH FOR ADAPTIVE HYBRID FORCE-POSITION CONTROL;140
27.1;1. Introduction;140
27.2;2. Modelization of constrained robot and its environnent;140
27.3;3. Force and Position control (frictionless contact);141
27.4;4. CONCLUSION;145
27.5;REFERENCES;145
28;CHAPTER 22. ADAPTIVE SYSTEMS PERFORMANCE IN THE FREQUENCY DOMAIN;146
28.1;1. INTRODUCTION;146
28.2;2. FREQUENCY DOMAIN FIT THEME;146
28.3;3. BIAS;147
28.4;4. CONVERGENCE RATES;148
28.5;5. VARIANCE;149
28.6;6. GENERALIZATIONS TO OTHER CASES;150
28.7;7. CONCLUSIONS;151
28.8;REFERENCES;151
29;CHAPTER 23. A COMMENT ON "LEAKAGE" IN ADAPTIVE ALGORITHMS;154
29.1;1 Introduction Adaptive Parameter Estimation and Leakage;154
29.2;2 Leakage is Regularization;155
29.3;3 Regularization and variance reduction;155
29.4;4 Conclusions;157
29.5;References;158
30;CHAPTER 24. DIRECT ADAPTIVE CONTROL OF NONMINIMUM PHASE SYSTEMS WITH FAST CONVERGENCE;160
30.1;INTRODUCTION;160
30.2;CONTROLSTRUCTURE;160
30.3;DIRECT ADAPTIVE CONTROL SCHEME;161
30.4;COMPUTER SIMULATION;163
30.5;CONCLUSIONS;163
30.6;Acknowledgements;163
30.7;References;163
31;CHAPTER 25. ADAPTIVE STABILIZATION OF ONE-PARAMETER FAMILIES OF SISO LINEAR SYSTEMS1;166
31.1;Introduction;166
31.2;1. Parameterized Transfer Functions;166
31.3;2. Design Models Ed;167
31.4;3. Identifiers .I;168
31.5;4. Internal Regulators .I;168
31.6;5. Properties;169
31.7;Concluding Remarks;169
31.8;References;170
32;CHAPTER 26. ANALYSIS OF THE INCREMENTAL TUNER;172
32.1;1. INTRODUCTION;172
32.2;2. PROBLEM STATEMENT;173
32.3;3. DESIGN MOTIVATION;173
32.4;4. CLOSED LOOP ESTIMATION;174
32.5;5. INCREMENTAL CONTROLLER TUNING;174
32.6;6. AVERAGING ANALYSIS;175
32.7;7. CONCLUSION;177
32.8;8. REFERENCES;177
33;CHAPTER 27. STABLE INDIRECT ADAPTIVE CONTROL OF CONTINUOUS-TIME SYSTEMS WITH NO PRIORI KNOWLEDGE ON THE PARAMETERS;178
33.1;Abstract;178
33.2;1 Introduction;178
33.3;2 The adaptive pole placement problem;179
33.4;3 Loss of stabilizability: A simple example;180
33.5;4 The switched–excitation approach: A simple example;180
33.6;5 The switched excitation approach: general case;181
33.7;6 Conclusion;182
33.8;References;183
34;CHAPTER 28. SINGULARITY-FREE ADAPTIVE POLE PLACEMENT FOR 2nd ORDER SYSTEMS;184
34.1;1. INTRODUCTION;184
34.2;2. IDENTIFICATION OF SYSTEMS SUBJECT TO BOUNDED DISTURBANCES;185
34.3;3. ADAPTIVE POLE PLACEMENT;186
34.4;4. MODIFICATION OF THE ESTIMATES FOR SECOND ORDER SYSTEMS;186
34.5;5. CONCLUSION;189
34.6;REFERENCES;189
35;CHAPTER 29. ADAPTIVE STABILIZATION FOR 2nd ORDER CONTINUOUS-TIME SYSTEMS;190
35.1;1. INTRODUCTION;190
35.2;2. SYSTEM PARAMETER ESTIMATION;191
35.3;3. ADAPTIVE POLE PLACEMENT;192
35.4;4. CONVERGENCE ANALYSIS;194
35.5;5. CONCLUSION;195
35.6;REFERENCES;195
36;CHAPTER 30. SUPERMARTINGALE ANALYSIS OF MINIMUM VARIANCE ADAPTIVE CONTROL;196
36.1;Introduction;196
36.2;System Description and Notations;196
36.3;State Space Model;197
36.4;Stochastic Stability Analysis;198
36.5;Discussion and Conclusions;199
36.6;Appendix;200
36.7;References;201
37;CHAPTER 31. ADAPTIVE vs ROBUST CONTROL: INFORMATION BASED CONCEPTS;202
37.1;ABSTRACT;202
37.2;1. INTRODUCTION;202
37.3;2. ROBUST VS. ADAPTIVE SENSITIVITY MINIMIZATION;202
37.4;APPENDIX: Theorem 2.1;204
37.5;REFERENCES;205
38;CHAPTER 32. LOWER INFORMATION BOUNDS FOR AN ADAPTIVE CONTROL PROBLEM;206
38.1;1 Introduction;206
38.2;2 Problem Statement;206
38.3;3 Main Results;207
38.4;4 Discussion;208
38.5;5 Appendix;208
38.6;References;211
39;CHAPTER 33. MODEL REFERENCE ADAPTIVE CONTROL FOR NON-MINIMUM PHASE SYSTEM BY 2-DELAY FEEDBACK;212
39.1;INTRODUCTION;212
39.2;PROBLEM STATEMENT;212
39.3;DESCRIPTION OF 2-DELAY SAMPLING SYSTEM;213
39.4;ADAPTIVE CONTROL BY 2-DELAY FEEDBACK;214
39.5;SIMULATION RESULTS;216
39.6;CONCLUSION;216
39.7;REFERENCES;216
40;CHAPTER 34. ROBUST MODEL REFERENCE ADAPTIVE CONTROL IN THE PRESENCE OF PARASITICS;218
40.1;1. INTRODUCTION;218
40.2;2. STATEMENT OF THE PROBLEM;218
40.3;3. ROBUST DESIGN OF MRAC SYSTEM;219
40.4;4. STABILITY OF THE MRAC SYSTEM;220
40.5;5. SIMULATION RESULTS;222
40.6;6. CONCLUSION;223
40.7;REFERENCES;223
41;CHAPTER 35. MODEL REFERENCE ADAPTIVE CONTROL AND ADAPTIVE STABILITY AUGMENTATION;224
41.1;INTRODUCTION;224
41.2;CONCEPTS IN MODEL REFERENCE ADAPTIVE CONTROL.;225
41.3;ADAPTIVE STABILITY AUGMENTATION OF THE MODEL REFERENCE DESIGN.;226
41.4;DERIVATION OF THE STABILITY AUGMENTED MRAC ALGORITHM.;226
41.5;SIMULATION RESULTS;228
41.6;CONCLUSION;229
41.7;REFERENCES;229
42;CHAPTER 36. MULTIVARIABLE NYQUIST GENERALIZED PREDICTIVE CONTROL: AN HELICOPTER APPLICATION;230
42.1;NOTATIONS;230
42.2;INTRODUCTION;230
42.3;METHODOLOGY;230
42.4;APPLICATION;232
42.5;CONCLUSION;235
42.6;REFERENCES;235
43;CHAPTER 37. ADAPTIVE PREDICTIVE CONTROL OF ARMAX PLANTS WITH UNKNOWN DEADTIME;236
43.1;1 Introduction;236
43.2;2 Background;236
43.3;3 ARMAX plants;237
43.4;4 SIORHC algorithm;239
43.5;5 Simulation results;240
43.6;6 Conclusions;240
43.7;References;240
43.8;Appendix;241
44;CHAPTER 38. DESIGN OF DECENTRALIZED ADAPTIVE CONTROLLERS USING THE PRINCIPLE OF DOMINANT SUBSYSTEMS;242
44.1;INTRODUCTION;242
44.2;PROBLEM STATEMENT;242
44.3;CONCLUSIONS;246
44.4;REFERENCES;246
45;CHAPTER 39. ALGORITHM AND ROBUSTNESS FOR A MULTIVARIABLE IMPLICIT SELF-TUNING CONTROLLER;248
45.1;1. Introduction;248
45.2;2. System Description;249
45.3;3. The Implicit STC Algorithm;249
45.4;4. Parameter Identification;249
45.5;5. The Implicit Self-Tuning Algorithm;250
45.6;6. Stability Robustness;250
45.7;7. Simulation Study;251
45.8;8. Conclusions;252
45.9;9. Acknowledgements;252
45.10;References;252
46;CHAPTER 40. REDUCED PARAMETRIZATION FOR DISCRETE-TIME MULTIVARIABLE ADAPTIVE CONTROL;254
46.1;ABSTRACT;254
46.2;1 INTRODUCTION;254
46.3;2 SYSTEM DELAY STRUCTURE AND MATCHABLE MODELS;254
46.4;3 SOME PROPERTIES OF THE SYSTEMS TOEPLITZ MATRICES;255
46.5;4 Estimation of the Interactor Structure;256
46.6;5 A Globally Convergent Adaptive Controller;258
46.7;6 Conclusions;259
46.8;References;259
46.9;APPENDIX;259
47;CHAPTER 41. DESIGN OF ADAPTIVE DIGITAL SELF-SELECTING MULTIVARIABLE CONTROLLERS;260
47.1;Abstract;260
47.2;INTRODUCTION;260
47.3;DESIGN OF MULTIVARIABLE PI CONTROLLERS;261
47.4;DESIGN OF SELF-SELECTING MULTIVARIABLE PI CONTROLLERS;261
47.5;DIGITAL SELF-SELECTING CONTROLLER FOR A JET ENGINE;263
47.6;CONCLUSION;263
47.7;REFERENCES;263
48;CHAPTER 42. EFFICIENT ALGORITHM FOR ADAPTIVE CONTROL FOR A CLASS OF ÌÉÌÏ PLANT;266
48.1;1 Problem Formulation;266
48.2;2 Main Results;267
48.3;3 Discussion;267
48.4;4 Appendix;267
48.5;References;269
49;CHAPTER 43. ADAPTIVE OPTIMIZATION WITH CONSTRAINTS;270
49.1;1 Introduction;270
49.2;2 Formulation of the problem;270
49.3;3 The CAM algorithm;271
49.4;4 ODE analysis;272
49.5;5 Simulation examples;274
49.6;6 Conclusions;274
49.7;References;274
50;CHAPTER 44. FIFTEEN YEARS IN THE LIFE OF AN ADAPTIVE CONTROLLER;276
50.1;INTRODUCTION;276
50.2;ADAPTIVE CONTROL METHODS;277
50.3;KAMYR DIGESTER CHIP LEVEL CONTROL;279
50.4;Ti2 CALCINER CONTROL;280
50.5;A COMMERCIAL ADAPTIVE CONTROLLER;283
50.6;DISCUSSION;283
50.7;CONCLUSIONS;284
50.8;REFERENCES;284
51;CHAPTER 45. MERGING OF USER'S KNOWLEDGE INTO IDENTIFICATION PART OF SELF-TUNERS;288
51.1;Introduction;288
51.2;Theoretical background;288
51.3;The problem and its solution;290
51.4;Remarks;291
51.5;Simulation examples;292
51.6;Conclusions;293
51.7;References;293
52;CHAPTER 46. AUTOMATIC INITIALIZATION OF ROBUST ADAPTIVE CONTROLLERS;294
52.1;1. Introduction;294
52.2;2. The Adaptive Controller;294
52.3;3. Relay Feedback;295
52.4;4. Finding Design Parameters;297
52.5;5. An Initialization Procedure;298
52.6;6. Example;298
52.7;7. Conclusions;299
52.8;8· References;299
53;CHAPTER 47. AUTOMATIC TUNING OF A DIGITAL CONTROLLER;300
53.1;1. Introduction;300
53.2;2. Digital Control;300
53.3;3. Parameter Estimation;301
53.4;4· Control Design;302
53.5;5. A Simulation Example;303
53.6;6. Applications to HVAC plants;304
53.7;7. Conclusions;305
53.8;8· References;305
54;CHAPTER 48. USER SUPPLIED INFORMATION IN THE DESIGN OF LINEAR QUADRATIC GAUSSIAN SELF-TUNING CONTROLLERS;306
54.1;INTRODUCTION;306
54.2;LQG SELF-TUNER;306
54.3;PRELIMINARY DESIGN;307
54.4;PROPLEM SOLVED IN THE PAPER;307
54.5;METHODOLOGY OF SOLUTION;307
54.6;APPLICATION OF METHODOLOGY;308
54.7;EXAMPLES OF TRANSLATING;309
54.8;PROGRAM ORGANIZATION;310
54.9;CONCLUSIONS;311
54.10;References;311
55;CHAPTER 49. ON THE ADAPTIVE CONTROL OF A FLEXIBLE TRANSMISSION SYSTEM;312
55.1;1. INTRODUCTION;312
55.2;2. THE ADAPTIVE CONTROL APPROACH;312
55.3;3. EXPERIMENTAL EVALUATION.;314
55.4;4. CONCLUSION.;315
55.5;REFERENCES;315
56;CHAPTER 50. ROBUST ADAPTIVE PREDICTIVE CONTROL OF BIOTECHNOLOGICAL PROCESS: EXPERIMENTAL RESULTS;320
56.1;INTRODUCTION;320
56.2;PLANT MODEL;320
56.3;CONTROL OBJECTIF;320
56.4;DERIVATION OF THE CONTROL LAW;321
56.5;PARAMETERS ESTIMATION AND ADAPTIVE CONTROL;322
56.6;RESULTS;323
56.7;CONCLUSION;325
56.8;REFERENCES;325
57;CHAPTER 51. ADAPTIVE CONTROL OF THE TEMPERATURE OF A GLASS FURNACE;326
57.1;Abstract;326
57.2;1 Introduction;326
57.3;2 Description of the Process;326
57.4;3 Prediction Model and Control Algorithm;327
57.5;4 Industrial Results;330
57.6;5 Conclusions;331
57.7;References;331
58;CHAPTER 52. EVALUATION OF A LONG-RANGE ADAPTIVE PREDICTIVE CONTROLLER FOR COMPUTERIZED DRUG DELIVERY SYSTEMS;332
58.1;1 Introduction;332
58.2;2 Process Control Strategy;333
58.3;3 System Description;333
58.4;4 Experimental Studies;334
58.5;5 Discussions;335
58.6;6 Conclusions;336
58.7;References;336
59;CHAPTER 53. PARAMETERS AUTOMATIC DESIGN OF PREDICTIVE CASCADED CONTROLLERS;338
59.1;INTRODUCTION;338
59.2;FORMULATION OF THE PREDICTIVE CASCADED ALGORITHM (Boucher, 1991a, 1991b);338
59.3;PARAMETERS SELF TUNING;340
59.4;APPLICATION TO AN INDUSTRIAL BRUSHLESS MOTOR;341
59.5;CONCLUSIONS;343
59.6;REFERENCES;343
60;CHAPTER 54. END POINT ADAPTIVE CONTROL OF A TWO-LINK FLEXIBLE ARM;344
60.1;I. INTRODUCTION;344
60.2;II. THE EXPERIMENTAL SET-UP;344
60.3;III. CONTROL DESIGN;345
60.4;IV. EXPERIMENTAL RESULTS;346
60.5;CONCLUSIONS;346
60.6;REFERENCES;346
61;CHAPTER 55. CONTINUOUS-TIME ADAPTIVE CONTROL OF CONSUMER ELECTRONIC CIRCUITS;350
61.1;Abstract;350
61.2;Keywords;350
61.3;1. INTRODUCTION;350
61.4;2. POWER AMPLIFIER SELECTION;351
61.5;3. ADAPTIVE SYSTEMS DESION;352
61.6;4. EXPERIMENTAL RESULTS;353
61.7;5. CONCLUSIONS;355
61.8;6. REFERENCES;355
62;CHAPTER 56. MODEL REFERENCE ADAPTIVE CONTROL OF A CIRCULATORY MODEL FOR COMBINED NITROPRUSSIDE-DOPAMINE THERAPY1;356
62.1;INTRODUCTION;356
62.2;DIRECT MRAC DEVELOPMENT;356
62.3;SYSTEM DESCRIPTION;357
62.4;APPLICATIONAL RESULTS;358
62.5;CONCLUSIONS AND RECOMMENDATIONS;360
62.6;REFERENCES;360
63;CHAPTER 57. OPEN LOOP ADAPTIVE FEEDBACK CONTROL OF DEPOSITED ZINC IN HOT-DIP GALVANIZING;362
63.1;I. INTRODUCTION;362
63.2;II. MODEL OF THE PROCESS;363
63.3;Ill. IDENTIFICATION OF THE DISCRETE TIME PLANT MODEL;363
63.4;IV. CONTROLLER DESIGN;364
63.5;V. OPEN LOOP ADAPTATION;365
63.6;VI. IMPLEMENTATION;365
63.7;VII. RESULTS;365
63.8;VII. CONCLUSIONS;365
63.9;REFERENCES;367
64;CHAPTER 58. NONLINEAR ADAPTIVE CONTROL OF A CONTINUOUS FERMENTATION PROCESS;368
64.1;INTRODUCTION;368
64.2;PROCESS DESCRIPTION;368
64.3;PROCESS MODEL;369
64.4;DISCRETIZATION OF NONLINEAR CONTINUOUS SYSTEMS;369
64.5;DISCRETE PARAMETER ESTIMATION AND ADAPTIVE CONTROL;370
64.6;RESULTS;371
64.7;CONCLUSION;372
64.8;REFERENCES;372
65;CHAPTER 59. INTELLIGENT TUNING;374
65.1;1. Introduction;374
65.2;2. Tuning;374
65.3;3· Identification and Control;376
65.4;4. Some Performance Limits;377
65.5;5· Simple Controllers;379
65.6;6. Relay Feedback;381
65.7;7. Diagnosis;382
65.8;8· Conclusions;383
65.9;Acknowledgements;384
65.10;References;384
66;CHAPTER 60. AUTOMATIC TUNING AND ADAPTATION FOR PID CONTROLLERS - A SURVEY;386
66.1;1. Introduction;386
66.2;2. Adaptive Techniques;386
66.3;3. Modeling;387
66.4;4. Control Design;388
66.5;5· Overview of industrial products;390
66.6;6. Conclusions;391
66.7;7. References;391
67;CHAPTER 61. DOMINANT POLE DESIGN - A UNIFIED VIEW OF PID CONTROLLER TUNING;392
67.1;1. Introduction;392
67.2;2. Controller and Specifications;392
67.3;3· Controller Design;393
67.4;4. Examples;394
67.5;5· Relations to other methods;395
67.6;6. Conclusions;395
67.7;7. References;395
67.8;8· Figures;396
68;CHAPTER 62. THE NORMAL-MODE-INACTION ADAPTIVE PID CONTROLLER;398
68.1;Abstract;398
68.2;1 Introduction;398
68.3;2 Initialization Of The Adaptive Controller;398
68.4;3 The NMI Adaptive PID Controller;399
68.5;4 Supervision of the NMI Adaptive PID Controller;400
68.6;5 The performance of the NMI Adaptive PID Controller;400
68.7;6 Conclusoins;401
68.8;References;402
69;CHAPTER 63. USE OF INTELLIGENT TUNING IN A HIERARCHICAL CONTROL SYSTEM FOR AUTOMATED FISH PROCESSING;404
69.1;INTRODUCTION;404
69.2;WORKCELL DEVELOPMENT;404
69.3;THEORETICAL CONSIDERATIONS;405
69.4;INTELLIGENT TUNING;408
69.5;ACKNOWLEDGMENT;408
69.6;REFERENCES;408
70;CHAPTER 64. MULTIVARIABLE CONTROL TUNING WITH AN EXPERT SYSTEM;410
70.1;INTRODUCTION;410
70.2;DESIGN SESSION;410
70.3;CONTROL OBJECTIVES;411
70.4;CONTROL DESIGN;412
70.5;CONCLUSIONS;415
70.6;REFERENCES;415
71;CHAPTER 65. A POSITION CONTROL AUTOTUNER FOR HANDLING SYSTEMS;416
71.1;1. INTRODUCTION;416
71.2;2. THE AUTOTUNING APPROACH;416
71.3;3. EXPERIMENTAL EVALUATION;418
71.4;4. CONCLUSION;419
71.5;REFERENCES;419
72;CHAPTER 66. THE "SYMMETRISCHE OPTIMUM" AND THE AUTO-CALIBRATION OF PID CONTROLLERS;422
72.1;I. INTRODUCTION;422
72.2;II. THE KESSLER'S "SYMMETRISCHE OPTIMUM";422
72.3;III. AUTO-CALIBRATION OF PICONTROLLERS;423
72.4;IV. AUTO-CALIBRATION OF PID CONTROLLER;424
72.5;V. COMPARISON WITH ZIEGLER-NICHOLS TUNING RULES;425
72.6;VI. SIMULATIONS AND EXPERIMENTAL RESULTS;426
72.7;VII. CONCLUSIONS;426
72.8;REFERENCES;427
73;CHAPTER 67. HARDWARE IMPLEMENTATION AND EVALUATION OF A KNOWLEDGE-BASED TUNER FOR A SERVO MOTOR;428
73.1;INTRODUCTION;428
73.2;SYSTEM DEVELOPMENT;429
73.3;THE EXPERIMENTAL SYSTEM;429
73.4;MODEL OF THE PHYSICAL SYSTEM;430
73.5;KNOWLEDGE BASE;430
73.6;RESULTS;431
73.7;REFERENCES;432
74;CHAPTER 68. KNOWLEDGE BASED ADAPTIVE CONTROL WITH LEARNING AND INTELLIGENT ABILITIES;434
74.1;1 INTRODUCTION;434
74.2;2 ADAPTIVE CONTROL WITH INTELLIGENT ABILITIES;435
74.3;3 ADAPTIVE CONTROL OF NONLINEAR PROCESSES;437
74.4;4 KNOWLEDGE BASE DADAPTIVE CONTROLLER;438
74.5;5 CONCLUSIONS;439
74.6;REFERENCES;439
75;CHAPTER 69. ACTIVE NOISE CANCELLATION IN DISTRIBUTED SYSTEMS USING ADAPTIVE CONTROL;440
75.1;PROBLEM STATEMENT;440
75.2;DISTRIBUTED IMPLEMENTATION;442
75.3;A SIMULATION EXAMPLE;443
75.4;PERFORMANCE OF THE ADAPTIVE CONTROL ALGORITHM;443
75.5;Acknowledgements;444
76;CHAPTER 70. ADAPTIVE SIGNAL PROCESSING APPLIED IN TELECOMMUNICATIONS;446
76.1;ABSTRACT;446
76.2;1. Introduction;446
76.3;2. Macro Trend;446
76.4;Acknowledgments;456
76.5;REFERENCES;456
77;CHAPTER 71. APPLICATION OF BLIND EQUALIZATION TECHNIQUES TO VOICEBAND AND RF MODEMS;458
77.1;INTRODUCTION;458
77.2;THE EQUALIZATION PROBLEM IN BROADCAST COMMUNICATIONS;459
77.3;BLIND EQUALIZATION;461
77.4;DEALING WITH NARROWBAND INTERFERENCE;462
77.5;A CURRENT PROBLEM: MISCONVERGENCE DUE TO INCOMPLETE INPUT EXCITATION;462
77.6;CONCLUSIONS;466
77.7;ACKNOWLEDGMENTS;466
77.8;REFERENCES;466
78;CHAPTER 72. WELL-CONDITIONED RECURSIVE LEAST-SQUARED ESTIMATION ALGORITHMS;468
78.1;1. Introduction: Signal Estimation using Linear Regression;468
78.2;2. RLS Estimation Algorithms using Taylor Expansions;469
78.3;3. RLS Estimation Algorithms using Orthogonal Expansions;471
78.4;4. Conclusions: Unsolved Research Problems;472
78.5;References;473
79;CHAPTER 73. A MODULAR ARCHITECTURE FOR THE ADAPTIVE NORMALIZED SLIDING WINDOW COVARIANCE LATTICE FILTER;474
79.1;I. INTRODUCTION;474
79.2;II. THE GEOMETRIC APPROACH;474
79.3;III. THE MODULAR DECOMPOSITION OF THE BASIC RLS LATTICE ALGORITHMS;476
79.4;IV. THE ANSWC ALGORITHM;478
79.5;CONCLUSION;479
79.6;REFERENCES;479
80;CHAPTER 74. A NEW FTF9N STABILIZED RECURSIVE ALGORITHM, IMPLEMENTATION ON FINITE-PRECISION;480
80.1;INTRODUCTION;480
80.2;THE RLS ALGORITHM;481
80.3;THE FTF ALGORITHM;481
80.4;THE FTF9N STABILIZED ALGORITHM;482
80.5;IMPLEMENTATION OF FTF9N STABILIZED ALGORITHM IN FINITE PRECISION ARITHMETIC;483
80.6;SIMULATIONS RESULT;484
80.7;CONCLUSION;484
80.8;REFERENCES;484
81;CHAPTER 75. CONSISTENCY AND MINIMALITY FOR THE PREWINDOWED PREDICTION PROBLEM;486
81.1;I Introduction;486
81.2;II Background;486
81.3;Ill Consistency in Fast Algorithms;487
81.4;IV Some Open Questions;490
81.5;References;491
82;CHAPTER 76. IMAGE RESTORATION USING EXTENDED KALMAN FILTERS1;492
82.1;INTRODUCTION;492
82.2;IMAGE MODELS;492
82.3;BLUR MODELS;492
82.4;RESTORATION USING THE EXTENDED KALMAN FILTER;493
82.5;RESULTS;494
82.6;CONCLUSIONS AND RECOMMENDATIONS;494
82.7;REFERENCES;495
83;CHAPTER 77. A MULTISCALE TIME-VARYING APPROACH TO MOVING SOURCE TRACKING;496
83.1;Abstract;496
83.2;1 Introduction;496
83.3;2 A spatio-temporal model of the data;496
83.4;3 Estimation of the time-Varying AR model;498
83.5;4 Simulation results;500
83.6;5 Conclusion;500
83.7;References;500
84;CHAPTER 78. MAXIMUM LIKELIHOOD LOCATION ESTIMATION OF WIDEBAND SOURCES USING THE EM ALGORITHM;502
84.1;Introduction;502
84.2;Data Model;502
84.3;EM-Algorithm;504
84.4;Approximate MLE;504
84.5;Approximate Dual MLE;505
84.6;Concluding Remarks;506
84.7;References;506
85;CHAPTER 79. LMS AND FRLS 2-D LATTICE FILTERS;508
85.1;INTRODUCTION;508
85.2;THE 2-D AR MODELING PROBLEM;509
85.3;THE 2-D LATTICE STRUCTURE FOR AR MODELING;509
85.4;THE 2-D ADAPTIVE LATTICE LEAST MEAN SQUARES ALGORITHM (TDAL-LMS);510
85.5;NORMALIZED 2-D ADAPTIVE LATTICE LMS ALGORITHM (TDAL-NLMS);510
85.6;2-D ADAPTIVE LATTICE FAST RECURSIVE LEAST SQUARES ALGORITHM (TDAL-FRLS);510
85.7;SIMULATION RESULTS;511
85.8;THE 2-D JOINT PROCESS LATTICE ESTIMATOR;511
85.9;RESTORATION OF NOISY IMAGES;512
85.10;CONCLUSION;512
85.11;REFERENCES;512
86;CHAPTER 80. ADAPtlVE PROCESSING OF MULTIDIMENSIONAL SIGNALS: FROM PRINCIPLES TO SIMULATION;514
86.1;Abstract;514
86.2;I- INTRODUCTION;514
86.3;II- LMS MULTIDIMENSIONAL FILTERING;514
86.4;III- THE ADFMD SOFTWARE FOR PC SIMULATION;515
86.5;IV- FLS MULTIDIMENSIONAL FILTERING;515
86.6;V- MODULAR STRUCTURES:THE QR APPROACH;516
86.7;VI- CONCLUSION;517
86.8;References;518
87;CHAPTER 81. ADAPTIVE EQUALIZATION OF DIGITAL LINE-OF-SIGHT RADIO SYSTEMS;520
87.1;INTRODUCTION;520
87.2;DECISION-FEEDBACK EQUALIZERS;520
87.3;FRACTIONALLY-SPACED EQUALIZERS;521
87.4;ADAPTATION ALGORITHMS;522
87.5;BLIND ADAPTATION;523
87.6;FURTHER ISSUES;524
87.7;CONCLUSIONS;525
87.8;REFERENCES;525
88;CHAPTER 82. A MODIFIED BAYESIAN ALGORITHM WITH DECISION FEEDBACK FOR BLIND ADAPTIVE EQUALIZATION;526
88.1;1 Introduction;526
88.2;2 MAP Estimation Algorithm;527
88.3;3 Bayesian/DF Algorithm;528
88.4;4 Computer Simulations;528
88.5;5 Conclusion;529
88.6;References;529
89;CHAPTER 83. THE NON STATIONARITY DEGREE: CAN AN ADAPTIVE FILTER BE WORSE THAN NO PROCESSING?;532
89.1;I. INTRODUCTION;532
89.2;II. NON STATIONARY ADAPTIVE FILTERING;532
89.3;III. RANDOM WALK FILTER;533
89.4;IV. THE MARKOVIAN FILTER;534
89.5;V. TRACKING PERFORMANCE : A GENERAL METHODOLOGY;535
89.6;VI. CONCLUSION: JUMPS;537
89.7;REFERENCES;537
90;CHAPTER 84. ADAPTIVE CHANNEL ESTIMATION FOR MAXIMUM LIKELIHOOD SEQUENCE ESTIMATION;538
90.1;INTRODUCTION;538
90.2;CHANNEL MODEL AND ML SEQUENCE ESTIMATION;538
90.3;THE VITERBI ALGORITHM;539
90.4;ML RECEIVER BASED ON VITERBI ALGORITHM AND ADAPTIVE CHANNEL ESTIMATOR;539
90.5;MODIFICATIONS OF THE VITERBI RECEIVER;541
90.6;JOINT BLIND CHANNEL AND DATA SEQUENCE ESTIMATION;542
90.7;CONCLUSIONS;543
90.8;REFERENCES;543
91;CHAPTER 85. ROBUST ADAPTIVE QUANTIZATION VIA KALMAN FILTERING TECHNIQUES1;544
91.1;1 Introduction;544
91.2;2 Kaiman Filter Based Adaptive Quantization;545
91.3;3 Enhanced Dequantizer Based on Kalman Filtering;546
91.4;4 Application of Kaiman Smoothing to the Dequantizer;547
91.5;5 Simulations;548
91.6;6 Conclusions;549
91.7;References;549
92;AUTHOR INDEX;550
93;KEYWORD INDEX;552