Banyasz Adaptive Systems in Control and Signal Processing 1995

1. Auflage 2014
ISBN: 978-1-4832-9689-0
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 480 Seiten, Web PDF

Reihe: IFAC Postprint Volume

Leading academic and industrial researchers working with adaptive systems and signal processing have been given the opportunity to exchange ideas, concepts and solutions at the IFAC Symposia on Adaptive Systems in Control and Signal Processing. This postprint volume contains all those papers which were presented at the 5th IFAC Symposium in Budapest in 1995. The technical program was composed of a number of invited and contributed sessions and a special case study session, providing a good balance between applications and theory oriented papers.

Autoren/Hrsg.

Banyasz, Cs.

Inhaltsverzeichnis

1;Front Cover;1
2;Adaptive Systems in Control and Signal Processing 1995;2
3;Copyright Page;3
4;Table of Contents;6
5;Part I: PLENARY SESSIONS;12
5.1;Chapter 1. IDENTIFICATION FOR CONTROL;12
5.1.1;1. INTRODUCTION;12
5.1.2;2. IDENTIFICATION IN OPEN AND CLOSED LOOP;14
5.1.3;3. THE DUAL CONTROL APPROACH;15
5.1.4;4. OPTIMAL IDENTIFICATION DESIGN FOR CONTROL;17
5.1.5;5. MATCHING IDENTIFICATION AND CONTROL CRITERION;20
5.1.6;6. CONCLUSIONS;22
5.1.7;ACKNOWLEDGEMENTS;23
5.1.8;7. REFERENCES;23
5.2;Chapter 2. COMBINED IDENTIFICATION AND CONTROL: ANOTHER WAY;24
5.2.1;1. INTRODUCTION;24
5.2.2;2. A NEW CONTROLLER STRUCTURE;26
5.2.3;3. A GENERIC SCHEME FOR OPTIMAL POLEPLACEMENT CONTROLLERS;27
5.2.4;4. COMBINED mENTIFICATION AND CONTROL SCHEMES;28
5.2.5;5. COMPARISON OF THE DIFFERENT SCHEMES;31
5.2.6;6. ON THE GENERIC OPTIMAL CONTROLLER SCHEME;32
5.2.7;7. EXAMPLES FOR OFF-LINE ITERATIVE REGULATOR REFINEMENT;34
5.2.8;8. A WORST-CASE OPTIMAL INPUT DESIGN ALGORITHM FOR OFF-LINE CLCR IDENTIFICATION;36
5.2.9;9. EXAMPLES FOR CLCR IDENTIFICATION BASED ON OPTIMAL INPUT DESIGN;38
5.2.10;10. ADAPTIVE SOLUTION FOR THE ON-LINE ITERATIVE REGULATOR REFINEMENT;39
5.2.11;11. THE CONCEPT OF AN ADAPTIVE "TRIPLECONTROL";39
5.2.12;12. ADAPTIVE EXAMPLES;40
5.2.13;13. CONCLUSIONS;40
5.2.14;14. REFERENCES;41
5.3;Chapter 3. NONLINEAR ADAPTIVE FILTERS: DESIGN AND APPLICATION;42
5.3.1;1. INTRODUCTION;42
5.3.2;2. FILTERS FOR NOISE REDUCTION;43
5.3.3;3. ADAPTIVE EQUALISATION;44
5.3.4;4. A CLASSIFICATION PROBLEM;44
5.3.5;5. THE MULTILAYER PERCEPTRON;45
5.3.6;6. THE VOLTERRA SERIES;47
5.3.7;7. THE RADIAL BASIS FUNCTION NETWORK;48
5.3.8;8. THE DECISION FEEDBACK EQUALISER;50
5.3.9;9. SIGNAL PREDICTION;51
5.3.10;10. CONCLUSIONS;52
5.3.11;11. ACKNOWLEDGEMENTS;52
5.3.12;12. REFERENCES;52
5.4;Chapter 4. ADAPTIVE PREDICTIVE CONTROL;54
5.4.1;1 INTRODUCTION;54
5.4.2;2 MODELS;55
5.4.3;3 COST FUNCTIONS, PERFORMANCE AND ROBUSTNESS;57
5.4.4;4 CONSTRAINTS;59
5.4.5;5 RECURSIVE LEAST SQUARES AND UDU;60
5.4.6;6 SIMULTANEOUS ESTIMATION OF MODELS;60
5.4.7;7 USING THE UDU METHOD;62
5.4.8;8 FIDDLE FACTORS;62
5.4.9;9 CONCLUSIONS;64
5.4.10;10 ACKNOWLEDGEMENTS;64
5.4.11;11 REFERENCES;64
5.5;Chapter 5. A KULLBACK-LEIBLER DISTANCE APPROACH TO SYSTEM IDENTIFICATION;66
5.5.1;1. INTRODUCTION;66
5.5.2;2. PARAMETER ESTIMATION AND PROBABILITY;67
5.5.3;3. KULLBACK-LEIBLER DISTANCE;67
5.5.4;4. PARAMETER ESTIMATION AND KULLBACK-LEIBLER DISTANCE;68
5.5.5;5. ASYMPTOTIC APPROXIMATION VIA LARGE DEVIATIONS;70
5.5.6;6. COPING WITH "BAD" DATA;72
5.5.7;7. COPING WITH "BAD" MODEL;74
5.5.8;8. MARKOV CHAINS;75
5.5.9;9. CONCLUDING REMARKS;76
5.5.10;ACKNOWLEDGMENT;77
5.5.11;REFERENCES;77
6;Part II: INVITED SESSION WEAK-DUALITY FOR ADAPTIVE CONTROL;78
6.1;Chapter 6. Adaptive dual control methods: An overview;78
6.1.1;1. INTRODUCTION;78
6.1.2;2. ADAPTIVE CONTROL;78
6.1.3;3. CLASSIFICATION OF CONTROLLERS;79
6.1.4;4. NON-DUAL ADAPTIVE CONTROLLERS;79
6.1.5;5. OPTIMAL DUAL CONTROLLERS;80
6.1.6;6. SUBOPTIMAL DUAL CONTROLLERS;80
6.1.7;7. WHEN TO USE DUAL CONTROL?;82
6.1.8;8. SUMMARY;82
6.1.9;9. REFERENCES;82
6.2;Chapter 7. ADAPTIVE CONTROL BY WORST-CASE DUALITY;84
6.2.1;1. INTRODUCTION;84
6.2.2;2. THE WORST-CASE DUAL-CONTROL PROBLEM;85
6.2.3;3. A POSTERIORI FINITE-TIME TUNING BY A SYNERGIC SCHEME;88
6.2.4;4. CONCLUSION;89
6.2.5;REFERENCES;89
6.3;Chapter 8.PARAMETRIC UNCERTAINTY AND CONTROL PERFORMANCE IN STOCHASTIC ADAPTIVE CONTROL;90
6.3.1;1. INTRODUCTION;90
6.3.2;2. SELF-TUNING;90
6.3.3;3. PARAMETRIC UNCERTAINTY AND PERFORMANCE;91
6.3.4;4. OPTIMALITY;92
6.3.5;5. CONCLUSION;93
6.3.6;6. REFERENCES;93
6.4;Chapter 9. FREQUENCY SELECTIVE WEAKLY-DUAL ADAPTIVE CONTROL;94
6.4.1;1. INTRODUCTION;94
6.4.2;2. DESCRIPTION OF THE SYSTEM;95
6.4.3;3. OUTLINE OF THE ADAPTIVE SCHEME;96
6.4.4;4. STABILITY AND PERFORMANCE;97
6.4.5;5. CONCLUSION;99
6.4.6;REFERENCES;99
6.5;Chapter 10. ADAPTIVE CONTROL WITH IMPROVED ASYMPTOTIC PERFORMANCE IN THE PRESENCE OF DETERMINISTIC DISTURBANCES;100
6.5.1;Abstract;100
6.5.2;1 Introduction;100
6.5.3;2 Problem statement;101
6.5.4;3 Reasons for estimation algorithm;102
6.5.5;4 Adaptive control;103
6.5.6;5 Conclusion;105
6.5.7;References;105
7;Part III: CASE STUDY SESSION MULTISTAGE FLASH SEAWATER DESAUNATION PLANT CONTROL;106
7.1;Chapter 11. SIMULATION AIDED DESIGN AND DEVELOPMENT OF AN ADAPTIVE SCHEME WITH OPTIMALLY TUNED PID CONTROLLER FOR A LARGE MULTISTAGE FLASH SEAWATER DESALINATION PLANT - PART I:;106
7.1.1;1. INTRODUCTION AND PREAMBLE;106
7.1.2;2. MODELLING AND SIMULATION;108
7.1.3;3. CONCLUSION AND DISCUSSION ON FURTHER WORK;111
7.1.4;REFERENCES;111
7.2;Chapter 12. SIMULATION AIDED DESIGN AND DEVELOPMENT OF AN ADAPTIVE SCHEME WITH OPTIMALLY TUNED PID CONTROLLER FOR A LARGE MULTISTAGE FLASH SEA WATER DESALINATION PLANT - PART II:;112
7.2.1;1. INTRODUCTION;112
7.2.2;2. MODEL APPROXIMATION FOR PID CONTROL DESIGN;113
7.2.3;3. PROBLEM STATEMENT AND METHOD OF APPROACH;113
7.2.4;4. APPLICATION OF THE PRESENT METHOD;114
7.2.5;5. OPTIMAL PID TUNING WITH FODT APPROXIMATED PLANT MODELS;114
7.2.6;REFERENCES;114
7.3;Chapter 13. SIMULATION AIDED DESIGN AND DEVELOPMENT OF AN ADAPTIVE SCHEME WITH OPTIMALLY TUNED PID CONTROLLER FOR A LARGE MULTISTAGE FLASH SEAWATER DESALINATION PLANT - PART III:;118
7.3.1;1. INTRODUCTION;118
7.3.2;2. PID CONTROL SYSTEM SIMULATION AND OPTIMIZATION WITH UNREDUCED PLANT MODEL IN NONPARAMETRIC FORM;119
7.3.3;3. PARAMETER SCHEDULING SCHEME FOR A RANGE OF OPERATING CONDITIONS;121
7.3.4;4. CONCLUSIONS AND DIRECTIONS FOR FUTURE WORK;123
7.3.5;REFERENCES;123
8;Part IV: TECHNICAL SESSIONS ADVANCED TRACKING AND FORGETTING TECHNIQUES;124
8.1;Chapter 14. OPTIMISATION OF SET-POINT TRANSITION IN COMBINED CYCLE POWER PLANTS USING PREDICTIVE CONTROL TECHNIQUES;124
8.1.1;1. INTRODUCTION : A GENERIC COMBINED CYCLE POWER PLANT STRUCTURE AND A SIMULATION MODEL;124
8.1.2;2. THE TASK OF PREDICTIVE CONTROL;126
8.1.3;3. IMPLEMENTATION ISSUES;126
8.1.4;4. SIMULATION RESULTS;128
8.1.5;5. CONCLUSIONS;129
8.1.6;ACKNOWLEDGEMENTS;129
8.1.7;REFERENCES;129
8.2;Chapter 15. APPROXIMATE ARX MODEL ESTIMATION FOR JACKETING ADAPTIVE SYSTEMS;130
8.2.1;1. INTRODUCTION;130
8.2.2;2. THEORY OVERVIEW;131
8.2.3;3. POOLING FOR ARX MODEL;131
8.2.4;4. EXPERIMENTS;132
8.2.5;5. CONCLUSIONS;135
8.2.6;REFERENCES;135
8.3;Chapter 16. DISTRIBUTION OF THE RLS-ESTIMATOR IN A TIMEVARYING AR(l)-PROCESS.;136
8.3.1;1 INTRODUCTION;136
8.3.2;2 DEFINITIONS;136
8.3.3;3 DENSITY FUNCTION;137
8.3.4;4 MOMENTS;138
8.3.5;5 COMPUTATIONS;138
8.3.6;6 CONCLUSIONS;141
8.3.7;7 REFERENCES;141
8.4;Chapter 17. A COMPARISON OF IDENTIFICATION ALGORITHMS FOR RAPIDLY TIME-VARYING PARAMETERS ON A REAL PROCESS;142
8.4.1;1. INTRODUCTION;142
8.4.2;2. PARAMETER ESTIMATION VIA RLS;142
8.4.3;3. METHODS BASED ON .. (t);143
8.4.4;4. METHODS BASED ON THE ESTIMATION e(t).;143
8.4.5;5. APPLICATION TO A REAL PLANT;144
8.4.6;6. CONCLUSIONS;147
8.4.7;7. ACKNOWLEDGMENTS;147
8.4.8;8. REFERENCES;147
8.5;Chapter 18. TIME-VARYING STABILIZED FORGETTING FOR RECURSIVE LEAST SQUARES IDENTIFICATION;148
8.5.1;1. INTRODUCTION;148
8.5.2;2. TIME-VARYING STABILIZED LS ESTIMATORS;148
8.5.3;3. STABILITY PROPERTIES;149
8.5.4;4. DETERMINISTIC PARAMETER CONVERGENCE PROPERTIES;151
8.5.5;5. EXTENSIONS;153
8.5.6;6. CONCLUSIONS;153
8.5.7;7. REFERENCES;153
8.6;Chapter 19. DYNAMICAL PROPERTIES OF THE RECURSIVE MAXIMUM LIKELYHOOD ALGORYTHM FOR FREQUENCY ESTIMATION;154
8.6.1;1. INTRODUCTION;154
8.6.2;2. THE RMLN ALGORITHM;154
8.6.3;3. DYNAMICAL ANALISYS;155
8.6.4;4. CLOSED-LOOP SIMULATIONS;157
8.6.5;5. CONCLUSIONS;158
8.6.6;6. ACKNOWLEDGEMENTS;159
8.6.7;7. REFERENCES;159
9;Part V: ADAPTIVE FILTERING AND STATE ESTIMATION;160
9.1;Chapter 20. ADAPTIVE RECEDING HORIZON STATE ESTIMATION FOR NON LINEAR PROCESSES;160
9.1.1;1. INTRODUCTION;160
9.1.2;2. THE ADAPTIVE RECEDING HORIZON STATE ESTIMATION;160
9.1.3;3. PERFORMANCES OF THE A.R.H.S.E. METHOD ON A NON-LINEAR PROCESS;163
9.1.4;4. CONCLUSION;165
9.1.5;REFERENCES;165
9.2;Chapter 21. A NEW REDUCED-ORDER ADAPTIVE FILTER FOR STATE ESTIMATION IN HIGH DIMENSIONAL SYSTEMS;166
9.2.1;1. INTRODUCTION;166
9.2.2;2. PREMILINARY RESULTS;166
9.2.3;3. ASYMPTOTICAL ROAF. APPROXIMATIONS;167
9.2.4;4. ASYMPTOTICALLY OPTIMAL ROAF;168
9.2.5;5. NONLINEAR ROAF;168
9.2.6;6. COMPUTATION OF THE TRANSITION MATRIX;169
9.2.7;7. SIMULATION RESULTS;169
9.2.8;8. CONCLUSION;170
9.2.9;9. REFERENCES;170
9.3;Chapter 22. ADAPTIVE FILTERING WITH FICTITIOUS ERROR SURFACES TO ACHIEVE GLOBAL CONVERGENCE;172
9.3.1;1. INTRODUCTION;172
9.3.2;2. FICTITIOUS ERROR SURFACES;173
9.3.3;3. THE COMPOSITE GRADIENT ALGORITHM;175
9.3.4;4. SUMMARY;177
9.3.5;REFERENCES;177
9.4;Chapter 23. UNBIASED ESTIMATION OF A SINUSOID IN NOISE VIA NOTCH FILTERS;178
9.4.1;1. INTRODUCTION AND PROBLEM POSITION;178
9.4.2;2. A MIN-MAX PROCEDURE FOR UNBIASED FREQUENCY ESTIMATION;179
9.4.3;3. A SIMULATION EXAMPLE;182
9.4.4;4. CONCLUDING REMARKS;183
9.4.5;ACKNOWLEDGMENTS;183
9.4.6;REFERENCES;183
9.5;Chapter 24. A COMPOSITE OBSERVER STRUCTURE FOR ADAPTIVE FOURIER ANALYSIS1;184
9.5.1;I. INTRODUCTION;184
9.5.2;II. THE POLYPHASE DECOMPOSITION OF THE RECURSIVE DFT;185
9.5.3;III. THE NEW ADAPTIVE FOURIER ANALYZER;186
9.5.4;IV. CONCLUSIONS;188
9.5.5;REFERENCES;188
9.6;Chapter 25. Lp (1=p=8) BLIND ADAPTIVE DECONVOLUTION AND ITS APPLICATIONS;190
9.6.1;1. INTRODUCTION;190
9.6.2;2. PARAMETRIC MODELS AND UNIQUENESS OF Lp AND L« BLIND DECONVOLUTION;192
9.6.3;3. SAMPLE VERSION OF L8, BLIND DECONVOLUTION AND THE STRONG CONSISTENCY OF ESTIMATOR;194
9.6.4;4. ITERATIVE AGORITHM FOR L8 BLIND DECONVOLUTION AND SIMULATION EXAMPLES;195
9.6.5;REFERENCES;195
10;Part VI: ADAPTIVE CONTROL APPLICATIONS;196
10.1;Chapter 26. DESIGN OF A MULTIVARIABLE STATE-SPACE ADAPTIVE CONTROLLER AND ITS APPLICATION TO A TURBO-GENERATOR PILOT PLANT;196
10.1.1;1. INTRODUCTION;196
10.1.2;2. PROCESS MODEL AND IDENTIFICATION;197
10.1.3;3. ADAPTIVE OPTIMAL LINEAR QUADRATIC CONTROLLER DESIGN;199
10.1.4;4. THE PILOT PLANT TURBO-GENERATOR;199
10.1.5;5. IMPLEMENTATION AND RESULTS;200
10.1.6;6. CONCLUSIONS;200
10.1.7;ACKNOWLEDGEMENT;201
10.1.8;REFERENCES;201
10.2;Chapter 27. MAINSTEAM TEMPERATURE RAISING CONTROL FOR A THERMAL POWER PLANT VIA MRACS BASED ON A QUICK IDENTIFICATION METHOD;202
10.2.1;1. INTRODUCTION;202
10.2.2;2. MODELLING OF SUPER-HEATER SYSTEM;203
10.2.3;3. A QUICK SYSTEM IDENTIFICATION METHOD;204
10.2.4;4. ADAPTIVE CONTROL;205
10.2.5;5. SIMULATION STUDIES;206
10.2.6;6. CONCLUSIONS;207
10.2.7;7. REFERENCES;207
10.3;Chapter 28. FREQUENCY BASED ADAPTIVE CONTROL OF SYSTEMS WITH ANTIRESONANCE MODES;208
10.3.1;1. INTRODUCTION;208
10.3.2;2. FREQUENCY BASED IMC;209
10.3.3;3. A CASE STUDY: LINEAR SYSTEM WITH ONE ANTIRESONANCE FREQUENCY;210
10.3.4;4. REAL SYSTEM WITH ANTIRESONANCE CHARACTERISTICS: A DISTRIBUTED SOLAR COLLECTOR FIELD;211
10.3.5;5. CONCLUSIONS;213
10.3.6;ACKNOWLEDGEMENTS;213
10.3.7;REFERENCES;213
10.3.8;APPENDIX A;213
10.4;Chapter 29. REALISTIC MODEL-BASED ADAPTIVE TEMPERATURE CONTROL OF BATCH REACTORS;214
10.4.1;1. INTRODUCTION;214
10.4.2;2. THE REACTOR SYSTEM;215
10.4.3;3. ADAPTIVE ALGORITHM FOR THE QUICK HEAT-UP OF THE REACTOR;215
10.4.4;4. SIMULATION AND PHYSICAL STUDY OF THE ALGORITHM;218
10.4.5;5. CONCLUSIONS;219
10.4.6;ACKNOWLEDGMENTS;219
10.4.7;NOTATION;219
10.4.8;REFERENCES;219
10.5;Chapter 30. ADAPTIVE PREDICTIVE CONTROL OF A CLASS OF NONLINEAR SYSTEMS A CASE STUDY;220
10.5.1;1. INTRODUCTION;220
10.5.2;2. BRIEF DESCRIPTION OF THE NONLINEAR DISCRETE TIME MODEL;221
10.5.3;3. THE ADAPTIVE PREDICTIVE CONTROL;221
10.5.4;4. APPLICATION TO A HEAT EXCHANGER;223
10.5.5;5. CONCLUSION;225
10.5.6;REFERENCES;225
10.6;Chapter 31. IMPROVED SCHEME OF ADAPTIVE POLE-ASSIGNMENT CONTROL FOR PNEUMATIC SERVO SYSTEM;226
10.6.1;1. INTRODUCTION;226
10.6.2;2. CONSTRUCTION OF THE PNEUMATIC SERVO SYSTEM;227
10.6.3;3. CONVENTIONAL DESIGN SCHEME;227
10.6.4;4. IMPROVED DESIGN SCHEME;228
10.6.5;5. EXPERIMENTAL RESULTS;229
10.6.6;6. CONCLUSION;229
10.6.7;REFERENCES;229
10.7;Chapter 32. A GLOBALLY CONVERGENT ROBUST CONTROLLER FOR ROBOT MANIPULATOR;232
10.7.1;1. INTRODUCTION;232
10.7.2;2. PROBLEM FORMULATION;232
10.7.3;3. ROBUST TRACKING CONTROL SCHEME;233
10.7.4;4. MODIFICATION FOR DISTURBANCE;234
10.7.5;5. EXAMPLE;235
10.7.6;6. CONCLUSION;236
10.7.7;7. REFERENCES;236
10.8;Chapter 33. A LYAPUNOV-STABLE ADAPTIVE SCHEME FOR FORCE REGULATION AND MOTION CONTROL OF ROBOT MANIPULATORS;238
10.8.1;1. INTRODUCTION;238
10.8.2;2. MODELLING;238
10.8.3;3. CONTROL DESIGN;239
10.8.4;4. STABILITY PROOF;240
10.8.5;5. JOINT SPACE IMPLEMENTATION;241
10.8.6;6. CONCLUSIONS;243
10.8.7;REFERENCES;243
11;Part VII: NEURAL NETWORKS;244
11.1;Chapter 34. A COMPARISON BETWEEN RBF NETWORKS AND CLASSICAL METHODS FOR IDENTIFICATION OF NONLINEAR DYNAMIC SYSTEMS;244
11.1.1;1. INTRODUCTION;244
11.1.2;2. CLASSICAL METHODS;245
11.1.3;3. RADIAL BASIS FUNCTION NETWORKS;246
11.1.4;4. TEST PROCESSES AND EXCITATION;247
11.1.5;5. RESULTS;248
11.1.6;6. CONCLUSIONS;249
11.1.7;REFERENCES;249
11.2;Chapter 35. NEURAL NETWORK ADAPTIVE CONTROL OF NONLINEAR PLANTS;250
11.2.1;1. INTRODUCTION;250
11.2.2;2. CONTROLLER STRUCTURE;251
11.2.3;3. CONTROL LAW;252
11.2.4;4. STABILITY;253
11.2.5;5. HEURISTIC ADAPTATION LAW;254
11.2.6;6. SIMULATIONS;254
11.2.7;7. CONCLUSIONS;255
11.2.8;ACKNOWLEDGEMENT;255
11.2.9;REFERENCES;255
11.3;Chapter 36. STABLE NONLINEAR ADAPTIVE CONTROL WITH GROWING RADIAL BASIS FUNCTION NETWORKS;256
11.3.1;1. INTRODUCTION;256
11.3.2;2. CONTROL OF AFFINE SYSTEMS;257
11.3.3;3. GROWING RBF NETWORK;257
11.3.4;4. NEURAL ADAPTIVE CONTROLLER;258
11.3.5;5. SIMULATION RESULTS;260
11.3.6;6. CONCLUSIONS;261
11.3.7;7. REFERENCES;261
11.4;Chapter 37. ROBUST IDENTIFICATION WITH NEURAL NETWORKS USING MULTIOBJECTIVE CRITERIA;262
11.4.1;1. INTRODUCTION;262
11.4.2;2. MULTIOBJECTIVE CRITERIA;263
11.4.3;3. METHOD OF INEQUALITIES;263
11.4.4;4. MODEL SELECTION;264
11.4.5;5. IDENTIFICATION ALGORITHM;265
11.4.6;6. EXPERIMENTAL RESULTS;266
11.4.7;7. CONCLUSIONS;267
11.4.8;8. REFERENCES;267
11.5;Chapter 38. A NEW LEARNING ALGORITHM FOR MULTI-LAYERED NEURAL NETWORKS BASED ON ADAPTIVE ALGORITHMS WITH IMPLEMENTATION BY HOPFIELD NETWORKS;268
11.5.1;1. INTRODUCTION;268
11.5.2;2. FEEDFORWARD NEURAL NETWORK ARCHITECTURE AND ITS STANDARD LEARNING ALGORITHM;269
11.5.3;3. A NEW LEARNING ALGORITHM AND ITS IMPLEMENTATION;269
11.5.4;4. PROPERTIES OF THE PROPOSED ALGORITHM;272
11.5.5;5. CONCLUSIONS;273
11.5.6;Acknowledgment;273
11.5.7;REFERENCES;273
11.6;Chapter 39. A CONVERGENCE ANALYSIS ON A MULTILAYERED NEURAL NETWORK USING A DISCRETE-TIME s -MODIFIED BACK PROPAGATION ALGORITHM;274
11.6.1;1. INTRODUCTION;274
11.6.2;2. LEARNING LAW;274
11.6.3;3. ANALYSIS;275
11.6.4;4. SIMULATION;276
11.6.5;5. CONCLUSION;277
11.6.6;6. ACKNOWLEDGEMENTS;277
11.6.7;REFERENCE;277
11.7;Chapter 40. NEURAL NETWORKS CAN BE TRAINED FASTER;280
11.7.1;1. INTRODUCTION;280
11.7.2;2. NEURAL NETWORKS;280
11.7.3;3. NUMERICAL METHODS;282
11.7.4;4. NUMERICAL EXAMPLES;284
11.7.5;5. CONCLUSION;285
11.7.6;6. REFERENCES;285
12;Part VIII: ADAPTIVE CONTROL;286
12.1;Chapter 41. TOWARDS FULLY PROBABILISTIC CONTROL DESIGN;286
12.1.1;1. Introduction;286
12.1.2;2. Basic elements;287
12.1.3;3. Control aim;287
12.1.4;4. Control aim in pdf terms;287
12.1.5;5. Optimization;287
12.1.6;6. Discussion;288
12.1.7;7. Linear Gaussian state space model;288
12.1.8;8. Conclusions;289
12.1.9;9. REFERENCES;289
12.2;Chapter 42. SELF-OPTIMALITY OF ADAPTIVE CONTROL SYSTEMS BASED ON THE CERTAINTY EQUIVALENCE PRINCIPLE;290
12.2.1;1. INTRODUCTION AND STATE OF THE ART;290
12.2.2;2. A GENERAL ADAPTIVE CONTROL SCHEME;291
12.2.3;3. ASYMPTOTIC PROPERTIES OF RLS ESTIMATES;292
12.2.4;4. CONVERGENCE ANALYSIS OF THE ADAPTIVE CONTROL SCHEME (RESULTS WITHOUT PROOFS);293
12.2.5;5. CERTAINTY EQUIVALENCE APPLIED TO COMMON CONTROL STRATEGIES;294
12.2.6;ACKNOWLEDGEMENT;295
12.2.7;REFERENCES;295
12.3;Chapter 43. MRAC ALGORITHM FOR HIGH RELATIVE DEGREE PLANTS;296
12.3.1;1. INTRODUCTION;296
12.3.2;2. STANDARD MRAC ALGORITHM;297
12.3.3;3. STATE SPACE STRUCTURES IN CONTROL LAW;298
12.3.4;4. ZEROS ELIMINATION CONTROLLER;299
12.3.5;5. CONCLUSION;301
12.3.6;REFERENCES;301
12.4;Chapter 44. ADAPTIVE FEEDFORWARD CONTROL SCHEMES IN TIME-DOMAIN, FREQUENCY-DOMAIN AND WAVELET TRANSFORM DOMAIN;302
12.4.1;1. INTRODUCTION;302
12.4.2;2. FEEDFORWARD CONTROL PROBLEMS;303
12.4.3;3. TIME-DOMAIN APPROACH;303
12.4.4;4. FREQUENCY-DOMAIN APPROACH;304
12.4.5;5. WAVELET TRANSFORM DOMAIN SCHEME;305
12.4.6;6. SIMULATION AND EXPERIMENTAL STUDY;306
12.4.7;7. CONCLUSIONS;307
12.4.8;REFERENCES;307
13;Part IX: ROBUST ESTIMATION;308
13.1;Chapter 45. ESTIMATION OF APPROXIMATE MARKOV CHAINS;308
13.1.1;1. INTRODUCTION;308
13.1.2;2. THEORY;309
13.1.3;3. APPLICATION TO MC;311
13.1.4;4. ALGORITHMIC SUMMARY;312
13.1.5;5. ILLUSTRATIVE EXAMPLE;312
13.1.6;6. CONCLUSIONS;313
13.1.7;7. REFERENCES;313
13.2;Chapter 46. DECENTRALIZED MODEL REFERENCE ADAPTIVE CONTROL SYSTEM BASED ON ROBUST HIGH-ORDER ESTIMATOR;314
13.2.1;1. INTRODUCTION;314
13.2.2;2. PROBLEM STATEMET;315
13.2.3;3. CONTROL STRUCTURE;316
13.2.4;4. DESIGN OF FIXED COMPENSATOR;317
13.2.5;5. ROBUST HIGH-ORDER ESTIMATOR AND STABILITY ANALYSIS;318
13.2.6;6. CONCLUSIONS;319
13.2.7;REFERENCES;319
13.3;Chapter 47. A H8-NORM BOUNDED LEAST-SQUARES ALGORITHM;320
13.3.1;1. INTRODUCTION;320
13.3.2;2. PROBLEM DESCRIPTION;320
13.3.3;3. SOLUTION TO THE SUB-OPTIMAL PROBLEM;321
13.3.4;4. BOUNDS ON PARAMETER ERRORS;324
13.3.5;5. SUMMARY AND CONCLUSIONS;325
13.3.6;ACKNOWLEDGEMENTS;325
13.3.7;6. REFERENCES;325
13.4;Chapter 48. RECURSIVE INCREMENTAL LEAST SQUARES ESTIMATION ALGORITHM;326
13.4.1;1. INTRODUCTION;326
13.4.2;2. RECURSIVE INCREMENTAL LEAST SQUARES;327
13.4.3;3. ROBUST ESTIMATION;328
13.4.4;4. ESTIMATOR PROPERTIES;329
13.4.5;5. TRACKING SPEED AND DISTURBANCE ATTENUATION;331
13.4.6;6. CONCLUSIONS;331
13.4.7;7. REFERENCES;331
14;Part X: ROBUSTNESS OF ADAPTIVE CONTROLLERS;332
14.1;Chapter 49. ROBUSTNESS OF LQ SELF-TUNING CONTROLLER FOR NON-MINIMUM PHASE SYSTEMS;332
14.1.1;1. INTRODUCTION;332
14.1.2;2. THE FIXED LQ SCHEME;333
14.1.3;3. THE ERROR EQUATION;333
14.1.4;4. THE PARAMETER ESTIMATOR;335
14.1.5;5. STABILITY RESULTS;335
14.1.6;6. CONCLUSIONS;337
14.1.7;REFERENCES;337
14.2;Chapter 50. ON THE USE OF THE CONCEPT OF ROBUST STRICTLY POSITIVE REALNESS IN REDUCED ORDER ADAPTIVE CONTROL;338
14.2.1;1. INTRODUCTION;338
14.2.2;2. THE CONTROL PROBLEM;339
14.2.3;3. THE ADAPTIVE CASE;341
14.2.4;4. CONCLUSIONS;342
14.2.5;REFERENCES;343
14.2.6;APPENDIX A;343
14.3;Chapter 51. THE EFFECT OF DESIGN PARAMETERS ON ROBUST PERFORMANCE;344
14.3.1;1. INTRODUCTION;344
14.3.2;2. UNCERTAIN MODEL OF HEAT EXCHANGER NETWORKS;345
14.3.3;3. ROBUST ANALYSIS USING L8 SIGNAL NORM;346
14.3.4;4. THE EFFECT OF DESIGN PARAMETERS ON THE ROBUST PERFORMANCE;347
14.3.5;5. HEAT EXCHANGER CELL EXAMPLE;348
14.3.6;6. CONCLUSION;349
14.3.7;7. ACKNOWLEDGEMENT;349
14.3.8;8. REFERENCES;349
14.4;Chapter 52. SUBOPTIMAL ELLIPSOIDAL BOUNDING FOR SIMPLE ROBUST CONTROL IN SELF-TUNING ROBUST CONTROL;350
14.4.1;1. INTRODUCTION;350
14.4.2;2. ELLIPSOIDAL BOUNDING AND ROBUST CONTROL;350
14.4.3;3. ELLIPSOIDAL BOUNDING CRITERION FOR ROBUST CONTROL;352
14.4.4;4. CONCLUSIONS;354
14.4.5;APPENDIX A. CALCULATION OF ROBUST CONTROLLER FOR THE EXAMPLE PROCESS;354
14.4.6;APPENDIX B. CALCULATION OF OPTIMAL q FOR THE SCE-ALGORITHM;355
14.4.7;REFERENCES;355
14.5;Chapter 53. FINITE-TIME SELF-TUNING CONTROL BY MINIMAX ESTIMATORS AND AN UNCERTAINTY PRINCIPLE;356
14.5.1;1. INTRODUCTION;356
14.5.2;2. SOME INTRODUCTORY EXAMPLES;357
14.5.3;3. EVALUATION OF PERFORMANCE FOR SECOND ORDER MODELS.;357
14.5.4;4. THE UNCERTAINTY PRINCIPLE;360
14.5.5;5. NUMERICAL EVALUATION OF GUARANTEED SELF-TUNING PERFORMANCE;360
14.5.6;6. CONCLUSION;361
14.5.7;REFERENCES;361
15;Part XI: INTELUGENT TUNING;362
15.1;Chapter 54. HOW TO INCREASE ROBUSTNESS OF PID REGULATORS;362
15.1.1;1. INTRODUCTION;362
15.1.2;2. ITERATIVE CONTROL DESIGN APPROACHES;362
15.1.3;3. A CATAMARAN APPROACH TO ADAPTIVE CONTROL;363
15.1.4;4. A PRACTICAL ROBUST PID TUNING PROCEDURE;365
15.1.5;5. A RECURSIVE OPEN-LOOP INPUT DESIGN FOR CLCR roENTIHCATION;366
15.1.6;7. CONCLUSIONS;368
15.1.7;8. REFERENCES;368
15.2;Chapter 55. AUTO-TUNING OF DIGITAL PID CONTROLLERS USING RECURSIVE IDENTIFICATION;370
15.2.1;1. INTRODUCTION;370
15.2.2;2. ZIEGLER-NICHOLS PID CONTROLLERS DESIGN;371
15.2.3;3. POLE PLACEMENT PID CONTROLLERS DESIGN;373
15.2.4;4. MATLAB-TOOLBOX ATCPID;374
15.2.5;5. CONCLUSIONS;374
15.2.6;ACKNOWLEDGMENTS;375
15.2.7;REFERENCES;375
15.3;Chapter 56. A PID INSTRUMENT WITH SELF-TUNING;376
15.3.1;1. INTRODUCTION;376
15.3.2;2. SPECIFICATIONS;377
15.3.3;3. NOISE MONITORING;378
15.3.4;4. PRETUNE;378
15.3.5;5. PI CONTROLLER SETTINGS;379
15.3.6;6. FINE TUNE;380
15.3.7;7. CONCLUSIONS;381
15.3.8;REFERENCES;381
15.4;Chapter 57. SMART CONTROLLER FOR PNEUMATIC ACTUATOR;382
15.4.1;1. INTRODUCTION;382
15.4.2;2. EXPERIMENTAL SYSTEM AND MODEL;382
15.4.3;3. LINEARIZATION OF THE PLANT MODEL;383
15.4.4;4. CONTROL SYSTEM DESIGN;384
15.4.5;5. EXPERIMENTAL RESULTS;386
15.4.6;6. CONCLUSION;387
15.4.7;REFERENCES;387
15.5;Chapter 58. A NOVEL RELAY AUTO-TUNING TECHNIQUE FOR PROCESS WITH INTEGRATION;388
15.5.1;1. INTRODUCTION;388
15.5.2;2. RELAY WITH DC BIAS;388
15.5.3;3. A NOVEL RELAY AUTO-TUNING TECHNIQUE FOR PROCESS WITH INTEGRATION;390
15.5.4;4. EXPERIMENT ON A LEVEL CONTROL SYSTEM;390
15.5.5;5. CONCLUSION;392
15.5.6;6. REFERENCES;392
15.6;Chapter 59. SELFTUNING CONTROLLER ON THE PLC;394
15.6.1;1. INTRODUCTION;394
15.6.2;2. THEORETICAL BACKGROUND;395
15.6.3;3. IMPLEMENTATION OF THE SELFTUNING PROCEDURE;396
15.6.4;4. APPLICATION ON A HYDRAULIC PLANT;397
15.6.5;5. ROBUSTNESS ISSUES;398
15.6.6;6. CONCLUSION;399
15.6.7;REFERENCES;399
16;Part XII: NONUNEAR ADAPTIVE CONTROL SYSTEMS;400
16.1;Chapter 60. GH8 Self-tuning Control for Linear and NonLinear Systems;400
16.1.1;1. INTRODUCTION;400
16.1.2;2. STRUCTURE OF NON-LINEAR SELFTUNING CONTROL;400
16.1.3;3. NON-LINEAR SYSTEM IDENTIFICATION;401
16.1.4;4. NONLINEAR GH8 SELF-TUNING CONTROL;401
16.1.5;5. CASE STUDY;403
16.1.6;6. CONCLUSIONS;404
16.1.7;REFERENCES;404
16.2;Chapter 61. SELF-TUNING VSS-TYPE CONTROL FOR HAMMERSTEIN SYSTEMS: A GENERAL PREDICTIVE APPROACH.;406
16.2.1;1. INTRODUCTION;406
16.2.2;2. MINIMUM VARIANCE (MV) CONTROL;407
16.2.3;3. VSS SELF TUNING CONTROL;408
16.2.4;4. RESULTS;409
16.2.5;5. CONCLUSIONS;411
16.2.6;REFERENCES;411
16.3;Chapter 62. ADAPTIVE PREDICTIVE CONTROL OF NONLINEAR DYNAMIC PROCESSES;412
16.3.1;1. INTRODUCTION;412
16.3.2;2. PREDICTIVE CONTROL STRATEGY;412
16.3.3;3. SIMPLE NONLINEAR PROCESS MODELS AND THEIR PREDICTIVE FORMS;413
16.3.4;4. CONTROL STRATEGY;414
16.3.5;5. ADAPTIVE CONTROL;414
16.3.6;6. SIMULATION RESULTS;415
16.3.7;REFERENCES;417
16.4;Chapter 63. ADAPTIVE NONLINEAR CONTROLLER FOR CURRENT-CONTROLLED INDUCTION MOTOR;418
16.4.1;1. INTRODUCTION;418
16.4.2;2. ADAPTIVE TRACKING CONTROL FOR A SPECIAL CLASS OF NONLINEAR SYSTEMS;419
16.4.3;3. INDUCTION MOTOR MODEL;419
16.4.4;4. ADAPTIVE TRACKING CONTROLLERS FOR INDUCTION MOTOR;420
16.4.5;5. NUMERICAL SIMULATION OF THE ADAPTIVE CONTROLLER;420
16.4.6;6. ASPECTS OF ADAPTIVE CONTROLLER PRACTICAL APPLICATION;421
16.4.7;7. CONCLUSIONS;422
16.4.8;APPENDIX A;423
16.4.9;APPENDIX B;423
16.4.10;REFERENCES;423
16.5;Chapter 64. FEEDBACK LINEARISABILITY OF A NONLINEAR HEAT EXCHANGER;424
16.5.1;1. INTRODUCTION;424
16.5.2;2. SYSTEM MODELLING;424
16.5.3;3. FEEDBACK LINEARISATION;425
16.5.4;4. FEEDBACK LINEARISATION OF HEAT EXCHANGER MODEL;426
16.5.5;5. EXAMPLE;428
16.5.6;6. CONCLUSION;429
16.5.7;References;429
17;Part XIII: DESIGN OF CONTROLLERS FOR ADAPTIVE SYSTEMS;430
17.1;Chapter 65. DUAL VERSION OF DIRECT ADAPTIVE POLE PLACEMENT CONTROLLER;430
17.1.1;1. INTRODUCTION;430
17.1.2;2. DESIGN OF ADAPTIVE POLE PLACEMENT CONTROLLER WITH STANDARD APPROACH;431
17.1.3;3. DESIGN OF DIRECT ACTIVE ADAPTIVE POLE PLACEMENT CONTROLLER;432
17.1.4;4. SIMULATED EXAMPLES;433
17.1.5;5. COMPARISON OF CONTROLLERS BASED ON STANDARD AND ACTIVE ADAPTIVE APPROACHES;435
17.1.6;6. CONCLUSIONS;435
17.1.7;7. ACKNOWLEDGEMENT;435
17.1.8;8. REFERENCES;435
17.2;Chapter 66. A SIMPLE POLE-ASSIGNMENT SCHEME FOR DESIGNING MULTIVARIABLE SELF-TUNING CONTROLLERS;436
17.2.1;1. INTRODUCTION;436
17.2.2;2. A SIMPLE POLE-ASSIGNMENT SELF-TUNING CONTROLLER;436
17.2.3;3. CONVERGENCE ANALYSIS;438
17.2.4;4. SIMULATION EXAMPLES;440
17.2.5;5. CONCLUSIONS;441
17.2.6;References;441
17.3;Chapter 67. AN ADAPTIVE VSS REGULATOR FOR MULTIVARIABLE NONMINIMUM-PHASE SYSTEMS BASED ON DOUBLY COPRIME FACTORIZATION;442
17.3.1;1. INTRODUCTION;442
17.3.2;2. PROBLEM STATEMENT;443
17.3.3;3. BASIC CONFIGURATION;443
17.3.4;4. DESIGN METHODOLOGY FOR A KNOWN SYSTEM;444
17.3.5;5. ADAPTIVE CONTROL;446
17.3.6;6. CONCLUSIONS;447
17.3.7;REFERENCES;447
17.4;Chapter 68. SELF-TUNING CONTROL OF LUMPED INPUT AND DISTRIBUTED OUTPUT SYSTEMS;448
17.4.1;1. INTRODUCTION;448
17.4.2;2. REAL LUMPED INPUT AND DISTRIBUTED OUTPUT SYSTEMS;449
17.4.3;3. LUMPED INPUT AND DISTRIBUTED OUTPUT PREDICTOR;449
17.4.4;4. DISTRIBUTED PARAMETER SELF-TUNING SYSTEM OF CONTROL;451
17.4.5;5. CONCLUSION;453
17.4.6;6. REFERENCES;453
17.5;Chapter 69. A CACE Tool for Analysis and Design of Adaptive Control Systems;454
17.5.1;1. Introduction;454
17.5.2;2. Program and Data Structure for an Adaptive Controller;455
17.5.3;3. Implementation in MATLAB;456
17.5.4;4. Example of Simulating an Adaptive Control System;458
17.5.5;5. Conclusion;459
17.5.6;6. Acknowledgement;459
17.5.7;7. References;459
18;Part XIV: IDENTIFICATION ALGORITHMS FOR ADAPTIVE CONTROL;460
18.1;Chapter 70. IDENTIFICATION AND DYNAMIC WEIGHTS FOR LQG CONTROL WITH INTEGRAL ACTION;460
18.1.1;1. INTRODUCTION;460
18.1.2;2. IDENTIFICATION FOR OFFSET-FREE LQG CONTROL WITH INTEGRAL ACTION;460
18.1.3;3. LQG CONTROL WITH DYNAMIC WEIGHTS;463
18.1.4;6. CONCLUSIONS;465
18.1.5;REFERENCES;465
18.2;Chapter 71. ROBUST ESTIMATION IN THE PRESENCE OF NOISE UNCERTAINTY AND UNMODELED DYNAMICS;466
18.2.1;1. INTRODUCTION;466
18.2.2;2. PROBLEM FORMULATION;467
18.2.3;3. ROBUST PARAMETER ESTIMATION;468
18.2.4;4. CONCLUSIONS;471
18.2.5;REFERENCES;471
18.3;Chapter 72. TEMPLATE FUNCTIONS BASED ESTIMATORS FOR ADAPTIVE CONTROL;472
18.3.1;1. INTRODUCTION;472
18.3.2;2. SYSTEM DESCRIPTION;472
18.3.3;3. THE TEMPLATE FUNCTION METHODS;473
18.3.4;4. THE EXTENDED TEMPLATE FUNCTION ESTIMATOR;474
18.3.5;5. THE RECURSIVE TEMPLATE FUNCTION ESTIMATOR;475
18.3.6;6. CONCLUSION;477
18.3.7;7. APPENDIX;477
18.3.8;8. REFERENCES;477
18.4;Chapter 73. SET POINT AND IDENTIFIABILITY IN THE CLOSED LOOP WITH MINIMUM VARIANCE CONTROLLER1;478
18.4.1;1. INTRODUCTION;478
18.4.2;2. MINIMUM VARIANCE SELF-TUNING CONTROL;479
18.4.3;3. SOME QUESTIONS OF IDENTIFIABILITY;480
18.4.4;4. THE LOW EXCITATION PROBLEM;481
18.4.5;5. EXAMPLE;481
18.4.6;6. CONCLUSIONS;482
18.4.7;REFERENCES;482
18.5;Chapter 74. Impulse response identification using multiresolution analysis;484
18.5.1;1 Introduction;484
18.5.2;2 Conventional identification method for DTIRM;485
18.5.3;3 Multiresolution analysis of CTIR;485
18.5.4;4 Identification algorithm;486
18.5.5;5 Simulation results;487
18.5.6;6 Conclusions;488
18.5.7;References;488
19;AUTHOR INDEX;490