Devanathan Intelligent Tuning and Adaptive Control

Selected Papers from the IFAC Symposium, Singapore, 15-17 January 1991
1. Auflage 2014
ISBN: 978-1-4832-9895-5
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band Volume 7, 436 Seiten, Web PDF

Reihe: IFAC Symposia Series

This volume contains 67 papers reporting on the state-of-the-art research in the fields of adaptive control and intelligent tuning. Papers include applications in robotics, the processing industries and machine control.

Autoren/Hrsg.

Devanathan, R.

Inhaltsverzeichnis

1;Front Cover;1
2;Intelligent Tuning and Adaptive Control;4
3;Copyright Page;5
4;Table of Contents;8
5;PART 1: PLENARY SESSION;14
5.1;CHAPTER 1. DIRECTIONS IN INTELLIGENT CONTROL;14
5.1.1;1. Introduction;14
5.1.2;2 . Methodologies;15
5.1.3;3· Control System Design;17
5.1.4;4. On-Line Systems;18
5.1.5;5. Autonomous Controllers;19
5.1.6;6. Conclusions;20
5.1.7;7. References;20
6;PART 2: INTELLIGENT TUNING/CONTROL;24
6.1;CHAPTER 2. INTELLIGENT SELF-TUNING PID CONTROLLER;24
6.1.1;INTRODUCTION;24
6.1.2;BASIC PRINCIPLES;24
6.1.3;LOOP SUPERVISOR;26
6.1.4;SPECIFICATIONS;26
6.1.5;IMPLEMENTATION;27
6.1.6;SIMULATIONS;27
6.1.7;FIELD TESTS;27
6.1.8;CONCLUSIONS;28
6.1.9;REFERENCES;28
6.2;CHAPTER 3. AN ELEMENTARY PATTERN RECOGNITION SELF-TUNING PI-CONTROLLER;30
6.2.1;INRODUCTION;30
6.2.2;ADAPTATION STRATEGY AND INFORMATIONAL MODELLING;30
6.2.3;PATTERN RECOGNITION CONTROL;31
6.2.4;PERFORMANCE RESULTS;32
6.2.5;CONCLUSIONS;33
6.2.6;REFERENCES;33
7;PART 3: ADAPTIVE CONTROL;36
7.1;CHAPTER 4. A DESIGN OF HYBRID ADAPTIVE CONTROL HAVING COMPUTATIONAL DELAYS;36
7.1.1;INTRODUCTION;36
7.1.2;SYSTEM DESCRIPTION;36
7.1.3;ADAPTIVE CONTROL;37
7.1.4;ANALYSIS;38
7.1.5;SIMULATION;39
7.1.6;CONCLUSION;40
7.1.7;REFERENCES;40
7.2;CHAPTER 5. ROBUSTNESS OF THE COMBINED MRAC USING KNOWLEDGE OF A BOUND ON;42
7.2.1;INTRODUCATION;42
7.2.2;SIMULATION RESULTS;43
7.2.3;CONCLUSIONS;45
7.2.4;ACKNOWLEDGENTS;46
7.2.5;REFERENCES;46
7.3;CHAPTER 6. A NEW SOLUTION TO ADAPTIVE INVERSE CONTROL;48
7.3.1;INTRODUCTION;48
7.3.2;NEW SCHEME;48
7.3.3;POLYNOMIAL INVERSION;49
7.3.4;STABILITY;50
7.3.5;COMPUTER RESULTS;51
7.3.6;CONCLUSIONS;53
7.3.7;ACKNOWLEDGMENT;53
7.3.8;REFERENCES;53
7.4;CHAPTER 7. CONTINUOUS-TIME GENERALIZED PREDICTIVE ADAPTIVE CONTROL;54
7.4.1;INTRODUCTION;54
7.4.2;DISCRETE TIME GENERALIZED PREDICTIVE CONTROL;55
7.4.3;THE STANDARD APPROACH TO OPTIMAL CONTROL;55
7.4.4;AN ALTERNATIVE APPROACH;56
7.4.5;CONCLUSION;59
7.4.6;REFERENCES;59
8;PART 4: PROCESS CONTROL;60
8.1;CHAPTER 8. ADAPTIVE SYSTEMS IN PROCESS ENGINEERING;60
8.1.1;INTRODUCTION;60
8.1.2;ADAPTIVE CONTROL;60
8.1.3;ADAPTIVE OPTIMISATION;61
8.1.4;ADAPTIVE INFERENTIAL MEASUREMENT AND CONTROL;62
8.1.5;IMPLEMENTATION ASPECTS;63
8.1.6;CONCLUDING REMARKS;64
8.1.7;ACKNOWLEDGEMENTS;64
8.1.8;REFERENCES;64
8.2;CHAPTER 9. PROCESS CONTROL UNDER VARIABLE FLOW AND VOLUME;68
8.2.1;1 INTRODUCTION;68
8.2.2;2 CONTROL OF CONSTANT PARAMETER VESSEL;68
8.2.3;3 PROCESS WITH VARIABLE FLOW AND CONSTANT VOLUME;69
8.2.4;4. PROCESSES WITH VARIABLE VOLUME AND FLOW;70
8.2.5;5. FEEDFORWARD CONTROL UNDER VARIABLE VOLUME;72
8.2.6;6. FEEDBACK CONTROL UNDER VARIABLE VOLUME;72
8.2.7;7. DISCUSSION AND CONCLUSIONS;73
8.2.8;REFERENCES;73
8.3;CHAPTER 10. TURBINE GENERATOR EXCITATION CONTROL USING ADVANCED DIGITAL TECHNIQUES;76
8.3.1;INTRODUCTION;76
8.3.2;THEORY;76
8.3.3;IMPLEMENTATION OF THE SELF-TUNING AVR;77
8.3.4;EVALUATION OF THE CONTROLLER;78
8.3.5;MICRO-ALTERNATOR AND SIMULATION RESULTS;78
8.3.6;CONCLUSION;79
8.3.7;ACKNOWLEDGEMENT;79
8.3.8;REFERENCES;79
8.4;CHAPTER 11. NON-LINEAR PREDICTIVE CONTROL;82
8.4.1;INTRODUCTION;82
8.4.2;AN APPROACH TO NONLINEAR PREDICTIVE CONTROL;82
8.4.3;ILLUSTRATIVE EXAMPLES;84
8.4.4;CONCLUDING REMARKS;85
8.4.5;ACKNOWLEDGEMENTS;85
8.4.6;REFERENCES;85
8.5;CHAPTER 12. TUNING OF PID CONROLLERS: SURVEY OF SISO AND MIMO TECHNIQUES;88
8.5.1;INTRODUCTION;88
8.5.2;MULTIVARIABLE PID CONTROLLER;89
8.5.3;EXPERIMENTAL DETERMINATION OF THE GAIN MATRICES;90
8.5.4;EXTENSIONS TO OTHER PLANTS;90
8.5.5;DECENTRALIZED AND EXPERT TUNING;91
8.5.6;CONCLUSIONS;91
8.5.7;REFERENCES;91
9;PART 5: NEURAL NETWORK/SELF-TUNING APPLICATIONS;94
9.1;CHAPTER 13. PARAMETER ESTIMATION USING ARTIFICIAL NEURAL NETS;94
9.1.1;INTRODUCTION;94
9.1.2;THE ESTIMATOR;95
9.1.3;SIMULATION RESULTS;95
9.1.4;DISCUSSION;96
9.1.5;CONCLUSION;96
9.1.6;REFERENCES;96
9.2;CHAPTER 14. INFERENTIAL MEASUREMENT VIA ARTIFICIAL NEURAL NETWORKS;98
9.2.1;INTRODUCTION;98
9.2.2;PROCESS MODELLING VIA ARTIFICIAL NEURAL NETWORKS.;98
9.2.3;INFERENTIAL MEASUREMENT;100
9.2.4;INFERENTIAL CONTROL VIA ANNs.;100
9.2.5;CONCLUDING REMARKS;101
9.2.6;ACKNOWLEDGEMENTS;101
9.2.7;REFERENCES;101
9.3;CHAPTER 15. ROBOT MOTION PLANNING AND CONTROL USING NEURAL NETWORKS;104
9.3.1;NEURAL NETWORKS IN ROBOTICS;104
9.3.2;NEURAL NETWORK CONTROLLER;105
9.3.3;NEURAL NETWORK NONLINEAR CONTROLLERS;106
9.3.4;CONCLUSIONS;108
9.3.5;REFERENCES;109
9.4;CHAPTER 16. ADAPTIVE NONLINEAR CONTROL OF A LABORATORY WATER-GAS SHIFT REACTOR;110
9.4.1;INTRODUCTION;110
9.4.2;THE CONTROLLER;110
9.4.3;THE ESTIMATOR;111
9.4.4;THE FIXED-BED WATER-GAS SHIFT REACTOR;112
9.4.5;RESULTS AND DISCUSSION;112
9.4.6;CONCLUSIONS;113
9.4.7;REFERENCES;114
9.5;CHAPTER 17. A CO-ORDINATED SELF-TUNING EXCITATION AND GOVERNOR CONTROL SCHEME FOR POWER SYSTEMS;116
9.5.1;INTRODUCTION;116
9.5.2;POWER SYSTEM UNDER STUDY;116
9.5.3;PROPOSED SELF-TUNING CONTROL SCHEME;117
9.5.4;OVERALL SYSTEM DATA;118
9.5.5;NUMERICAL RESULTS;119
9.5.6;CONCLUSION;119
9.5.7;REFERENCES;119
10;PART 6: IMPLEMENTATION ISSUES;122
10.1;CHAPTER 18. IMPLEMENTATION OF ADAPTIVE CONTROLLERS USING DIGITAL SIGNAL PROCESSOR CHIPS;122
10.1.1;INTRODUCTION;122
10.1.2;ADAPTIVE POSITION CONTROL OF A DC MOTOR;122
10.1.3;IMPLEMENTATION OF ADAPTIVE CONTROLLERS FOR A DC MOTOR USING DSP CHIPS;123
10.1.4;ON-LINE TESTING RESULTS AND DISCUSSIONS;124
10.1.5;CONCLUSIONS;124
10.1.6;ACKNOWLEDGEMENTS;125
10.1.7;REFERENCES;125
10.2;CHAPTER 19. AUTO-TUNING OF MULTIVARIABLE DECOUPLING CONTROLLERS;128
10.2.1;1. INTRODUCTION;128
10.2.2;2. STRUCTURE OF AUTO-TUNING MULTIVARIABLE DECOUPLING CONTROLLER;128
10.2.3;3. ISSUES IN IMPLEMENTATION;131
10.2.4;4. CONCLUSION;132
10.2.5;References;133
10.3;CHAPTER 20. ISSUES IN THE DESIGN OF AN ADAPTIVE GPC FOR SYSTEMS REQUIRING FAST SAMPLING;134
10.3.1;1 Introduction;134
10.3.2;2 The GPC — conventional shift operator design;134
10.3.3;3 Delta operator re-design of the adaptive GPC;135
10.3.4;4 Conclusion;136
10.3.5;References;136
10.4;CHAPTER 21. ADAPTIVE POLE PLACEMENT CONTROL LAW USING THE DELTA OPERATOR;140
10.4.1;I. Introduction;140
10.4.2;II. Identification;140
10.4.3;Ill. Pole-placement control law;141
10.4.4;IV. Physical experiments;142
10.4.5;V. Conclusion;142
10.4.6;References;143
11;PART 7: ADAPTIVE CONTROL;146
11.1;CHAPTER 22. A NON MINIMAL MODEL FOR SELF TUNING CONTROL;146
11.1.1;INTRODUCTION;146
11.1.2;THE ALGORITHM;146
11.1.3;MODEL CONVERSION;147
11.1.4;DEVELOPMENT OF NON ADAPTIVE MULTI-RATE CONTROLLER;147
11.1.5;SIMULATION RESULTS;148
11.1.6;CONCLUSIONS;149
11.1.7;REFERENCES;149
11.2;CHAPTER 23. A LEARNING MODIFIED GENERALIZED PREDICTIVE CONTROLLER;152
11.2.1;INTRODUCTION;152
11.2.2;OVERALL STRUCTURE AND OPERATION PRINCIPLE OF LMGPC;153
11.2.3;LEARNING PROCESS OF ON-LINE OPTIMIZATION OF THE OMA FILTER PARAMETER;154
11.2.4;LEARNING PROCESS FOR ON-LINE OPTIMIZATION OF THE MGPC PARAMETER N, M & .;155
11.2.5;NUMERICAL SIMULATIONS AND EXPERIMENTAL TESTS;156
11.2.6;REFERENCES;157
11.3;CHAPTER 24. INTELLIGENT ADAPTIVE CONTROL OF MODE-SWITCH PROCESSES;158
11.3.1;1 Introduction;158
11.3.2;2 Modeling of mode-switch processes;158
11.3.3;3 Supervisor;159
11.3.4;4 Retrieval;160
11.3.5;5 Application (Robot control);160
11.3.6;6 Conclusions;163
11.3.7;Appendix Robot model;163
11.3.8;Literature;163
11.4;CHAPTER 25. MODEL REFERENCE ADAPTIVE CONTROL OF A CLASS OF NONLINEAR SYSTEMS;164
11.4.1;INTRODUCTION;164
11.4.2;STATEMENT OF THE PROBLEM;164
11.4.3;ADAPTIVE CONTROL UNDER THE PRESENCE OF INPUT SATURATION;165
11.4.4;ADAPTIVE CONTROL UNDER THE PRESENCE OF DEADZONE;166
11.4.5;DIGITAL SIMULATION;166
11.4.6;CONCLUSION;167
11.4.7;REFERENCES;167
11.5;CHAPTER 26. ADAPTIVE FREQUENCY RESPONSE COMPENSATION: EXPERIMENTS ON A HEAT EXCHANGER;170
11.5.1;I. INTRODUCTION;170
11.5.2;II. DESIGN PROCEDURE;170
11.5.3;III. EXPERIMENTAL RESULTS;172
11.5.4;V. CONCLUDING REMARKS;173
11.5.5;REFERENCES;173
11.6;CHAPTER 27. THE APPLICATION OF MULTIVARIABLE ADAPTIVE CONTROL TO AN INDUSTRIAL RUN-OF-MINE MILLING PROCESS;176
11.6.1;INTRODUCTION;176
11.6.2;THE RUN-OF-MINE MILLING PROCESS;176
11.6.3;THE ALGORITHM;177
11.6.4;RESULTS OF THE SIMULATION;179
11.6.5;CONCLUSION;180
11.6.6;ACKNOWLEDGEMENT;180
11.6.7;REFERENCES;180
12;PART 8: PLENARY SESSION;184
12.1;CHAPTER 28. INDUSTRIAL DEVELOPMENTS IN INTELLIGENT AND ADAPTIVE CONTROL;184
12.1.1;1. Introduction;184
12.1.2;2. PID-type Self-tuning Control;184
12.1.3;3. Open-loop Adaptive Control;187
12.1.4;4. Fuzzy Control;187
12.1.5;5. Conclusion;191
12.1.6;6. Reference;191
13;PART 9: ROBOTIC CONTROL;192
13.1;CHAPTER 29. DESIGN AND IMPLEMENTATION OF AN ADAPTIVE CONTROLLER FOR A HYDRAULIC TEST ROBOT;192
13.1.1;INTRODUCTION;192
13.1.2;THE DESIGNED FAST HYDRAULIC TEST ROBOT MANIPULATOR;192
13.1.3;DESIGN AND IMPLEMENTATION OF THE CONTROL SYSTEM;194
13.1.4;CONCLUSIONS;197
13.1.5;REFERENCES.;198
13.2;CHAPTER 30. IMPLEMENTATION OF IMPEDANCE CONTROL USING SLIDING MODE THEORY;200
13.2.1;INTRODUCTION;200
13.2.2;DESIGN OF CONTROLLER;201
13.2.3;DESCRIPTION OF EXPERIMENT;203
13.2.4;EXPERIMENTAL RESULTS;204
13.2.5;CONCLUSION;205
13.2.6;ACKNOWLEDGMENT;205
13.2.7;REFERENCES;205
13.3;CHAPTER 31. ORTHONORMAL FUNCTIONS IN IDENTIFICATION AND ADAPTIVE CONTROL;206
13.3.1;INTRODUCTION;206
13.3.2;LAGUERRE FUNCTIONS;206
13.3.3;IDENTIFICATION;207
13.3.4;CONTROL;209
13.3.5;CONCLUSIONS;210
13.3.6;ACKNOWLEDGMENTS;210
13.3.7;LITERATURE CITED;211
13.4;CHAPTER 32. ADAPTIVE CONTROL FOR A ROBOT ARM AND A ROBOT DRIVE SYSTEM WITH ELASTICITY;212
13.4.1;INTRODUCTION;212
13.4.2;POSITION CONTROL OF A FLEXIBLE ARM;212
13.4.3;FORCE FEEDBACK CONTROL OF FLEXIBLE ARM;213
13.4.4;DD SERVO SYSTEM;215
13.4.5;REFERENCES;215
14;PART 10: KNOWLEDGE BASED CONTROL;218
14.1;CHAPTER 33. SELF-TUNING CONTROL WITH FUZZY RULE-BASED SUPERVISION FOR HVAC APPLICATIONS;218
14.1.1;INTRODUCTION;218
14.1.2;FUZZY RULE-BASED GAIN SCHEDULING;218
14.1.3;TEST RESULTS;220
14.1.4;CONCLUSIONS;221
14.1.5;References;221
14.2;CHAPTER 34. AN EXPERT SYSTEM FOR THE MULTIVARIABLE CONTROLLER DESIGN;224
14.2.1;INTRODUCTION;224
14.2.2;CONTROLLER DESIGN;224
14.2.3;INTERACTION MATRICES;225
14.2.4;INTERACTION MEASURES;226
14.2.5;CONTROLLER TUNING;227
14.2.6;CONTROLLER ANALYSIS;228
14.2.7;EXPERT SYSTEM;228
14.2.8;CONCLUSIONS;229
14.2.9;REFERENCES;229
14.3;CHAPTER 35. EXPERT SELF-TUNING PI(D) CONTROLLER;230
14.3.1;INTRODUCTION;230
14.3.2;PRELIMINARIES;230
14.3.3;MINIMUM IE CRITERION;231
14.3.4;OPTIMUM CHARACTERISTICS;231
14.3.5;PROCESS IDENTIFICATION;232
14.3.6;EXPERIMENTAL RESULTS;234
14.3.7;CONCLUSIONS;235
14.3.8;ACKNOWLEDGEMENT;235
14.3.9;REFERENCES;235
14.4;CHAPTER 36. INVESTIGATIVE STUDIES FOR A KNOWLEDGE-BASED INTELLIGENT ADAPTIVE GENERALISED PREDICTIVE CONTROLLER;236
14.4.1;1. Introduction;236
14.4.2;2. Overview of the GPC algorithm;236
14.4.3;3.Closed-loop investigation of the GPC with first order plant;237
14.4.4;4.Tuning Rules;239
14.4.5;5. Conclusions;239
14.4.6;References;240
14.5;CHAPTER 37. BOUND-BASED WORST-CASE PREDICTIVE CONTROLLER;242
14.5.1;INTRODUCTION;242
14.5.2;MOTIVATION OF PARAMETER-BOUNDING;242
14.5.3;WORST CASE CONTROL BY PARAMETER BOUNDING;244
14.5.4;BOUNDING PREDICTIVE CONTROLLER;245
14.5.5;SIMULATIONS;246
14.5.6;CONCLUSION;247
14.5.7;References;247
14.6;CHAPTER 38. AN EXPERT-ADAPTIVE CONTROLLER;248
14.6.1;INTRODUCTION;248
14.6.2;SELF-TUNING STRATEGIES BASED ON KNOWLEDGE-BASE;248
14.6.3;A NEW METHOD OF PROCESS PARAMETERS ESTIMATION FOR PRE-TUING;249
14.6.4;SIMULATION STUDIES;250
14.6.5;CONCLUSION;250
14.6.6;ACKNOWLEDGEMENT;250
14.6.7;REFERENCE;250
15;PART 11: INTELLIGENT TUNING/CONTROL;252
15.1;CHAPTER 39. AN EXTENDED HORIZON SELF TUNING LOAD FREQUENCY CONTROLLER;252
15.1.1;INTRODUCTION;252
15.1.2;CONTROL DESIGN;253
15.1.3;APPLICATION TO LFC;254
15.1.4;CONCLUSIONS;255
15.1.5;REFERENCES;255
15.2;CHAPTER 40. DESIGN AND IMPLEMENTATION OF A PARALLEL MULTIVARIABLE ADAPTIVE CONTROLLER FOR A FREE GYRO STABILISED MIRROR;258
15.2.1;1. Introduction;258
15.2.2;2. Theory;258
15.2.3;3. Parallel Design;259
15.2.4;4. Implementation Issues;260
15.2.5;5. Performance;261
15.2.6;6. Conclusion;262
15.2.7;References;263
15.3;CHAPTER 41. EXPLICIT PID SELF TUNING CONTROL FOR SYSTEMS WITH UNKNOWN TIME DELAY;264
15.3.1;ABSTRACT;264
15.3.2;1. Introduction;264
15.3.3;2. State of Art;264
15.3.4;3. Explicit PID self tuins control;266
15.3.5;4. Simulations;268
15.3.6;5. Conclusions;270
15.3.7;List of References;270
15.4;CHAPTER 42. LQG ADAPTIVE CONTROL OF PROLONGED NOISE EFFECTS;272
15.4.1;Introduction;272
15.4.2;The Adaptive Controller;272
15.4.3;Results;274
15.4.4;Conclusions;275
15.4.5;Acknowledgements;275
15.4.6;References;275
15.5;CHAPTER 43. AN EXPERIMENTAL STUDY OF A DUAL-RATE AUTOTUNED POLE-PLACEMENT CONTROLLER;278
15.5.1;1. Introduction;278
15.5.2;2. Dual-rate autotuned pole-placement controller;278
15.5.3;3. Microcontroller Implementation;280
15.5.4;4. Experimental Results;280
15.5.5;5. Conclusion;281
15.5.6;REFERENCES;281
15.6;CHAPTER 44. INTELLIGENT MULTIOBJECTIVE OPTIMAL CONTROL;284
15.6.1;INTRODUCTION;284
15.6.2;PROCESS PARTITION;285
15.6.3;INTELLIGENT MULTIOBJECTIVE OPTIMAL CONTROLLER;286
15.6.4;THE APPLICATION OF IMOC IN TRAIN-TRAVELLING CONTROL;288
15.6.5;CONCLUSIONS;289
15.6.6;REFERENCES;289
16;PART 12: PLENARY SESSION;290
16.1;CHAPTER 45. MODEL ACCURACY IN SYSTEM IDENTIFICATION;290
16.1.1;1 Introduction;290
16.1.2;2 The System Identification Machinery;290
16.1.3;3 Contributions to the Model Error;291
16.1.4;4 The Random Error;292
16.1.5;5 The Bias Error;292
16.1.6;6 Conclusions;293
16.1.7;References;293
17;PART 13: CONTROL OF DRIVES/SERVOS/APPLICATIONS;296
17.1;CHAPTER 46. A MICROCONTROLLER-BASED ADAPTIVE POSITION CONTROLLER FOR A D.C. MOTOR;296
17.1.1;1. INTRODUCTION;296
17.1.2;2. Adaptive Controller for a D.C. Motor using the Partially Known System Approach;296
17.1.3;3. Design Guidelines for Digital Implementation;298
17.1.4;4. MICROCONTROLLER-BASED ADAPTIVE CONTROLLER;299
17.1.5;5. CONCLUSION;301
17.1.6;6. REFERENCES;301
17.2;CHAPTER 47. ADAPTIVE TEMPERATURE CONTROL OF INDUSTRIAL DIFFUSION/LPCVD REACTORS1;302
17.2.1;INTRODUCTION;302
17.2.2;THE OPTIMAL FIXED-ORDER DYNAMIC COMPENSATION PROBLEM;303
17.2.3;CONTINUATION METHODS AND A HOMOTOPY ALGORITHM;304
17.2.4;CONTROLLER DESIGN FOR DIFFUSION /LPCVD REACTORS;305
17.2.5;CONCLUSION;306
17.2.6;ACKNOWLEDGMENT;306
17.2.7;REFERENCES;306
17.3;CHAPTER 48. DESIGN AND IMPLEMENTATION OF THE ADAPTIVE SYNCHRONIZING FEEDFORWARD CONTROLLER FOR TWO AXES MOTION CONTROL SYSTEMS;308
17.3.1;INTRODUCTION;308
17.3.2;INDEPENDENT ADAPTIVE FEEDFORWARD CONTROL;308
17.3.3;ADAPTIVE SYNCHRONIZING FEEDFORWARD CONTROL;311
17.3.4;CONCLUSION;313
17.3.5;REFERENCES;313
17.4;CHAPTER 49. INTELLIGENT TUNING AND ADAPTIVE CONTROL FOR CEMENT RAW MEAL BLENDING PROCESS;314
17.4.1;1 - INTRODUCTION;314
17.4.2;2 - RAW MEAL BLENDING CONTROL PROBLEM;314
17.4.3;3 - CONTROL POLICY;315
17.4.4;4 - HEURISTIC SUPERVISION;317
17.4.5;5 - SIMULATION RESULTS;318
17.4.6;6 - EXPERIMENTS IN CEMENT PLANTS;318
17.4.7;CONCLUSION;318
17.4.8;REFERENCES;319
17.5;CHAPTER 50. DYNAMIC POSITIONING SYSTEM USING SELF-TUNING CONTROL;320
17.5.1;INTRODUCTION;320
17.5.2;SELF-TUNING CONTROLLER;321
17.5.3;MODEL OF SEMI-SUBMERSIBLE RIG;321
17.5.4;DPS DESIGN;323
17.5.5;CONCLUSION;324
17.5.6;REFERENCES;325
17.6;CHAPTER 51. PRACTICAL ADAPTIVE LEVEL CONTROL OF A THERMAL HYDRAULIC PROCESS;326
17.6.1;INTRODUCTION;326
17.6.2;PROCESS DESCRIPTION;326
17.6.3;PSTC DESIGN;327
17.6.4;MPI CONTROL;330
17.6.5;EXPERIMENTAL RESULTS;330
17.6.6;CONCLUSIONS;331
17.6.7;ACKNOWLEDGEMENTS;331
17.6.8;REFERENCES;331
18;PART 14: KNOWLEDGE BASED CONTROL/ADAPTIVE CONTROL;332
18.1;CHAPTER 52. A PROTOTYPE EXPERT SYSTEM BASED ON LAGUERRE ADAPTIVE CONTROL;332
18.1.1;INTRODUCTION;332
18.1.2;STRUCTURE OF EXPERT CONTROL;333
18.1.3;SYSTEM STRUCTURE;333
18.1.4;LAGUERRE ADAPTIVE CONTROLLER;334
18.1.5;IMPLEMENTATION;334
18.1.6;SIMULATION;335
18.1.7;CONCLUSIONS;336
18.1.8;ACKNOWLEDGEMENTS;336
18.1.9;REFERENCES;337
18.2;CHAPTER 53. REALIZATION OF AN EXPERT SYSTEM BASED PID CONTROLLER USING INDUSTRY STANDARD SOFTWARE AND HARDWARE ENVIRONMENTS;340
18.2.1;INTRODUCTION;340
18.2.2;OVERVIEW OF KNOWLEDGE-BASED INTELLIGENT PID CONTROLLER;340
18.2.3;HARDWARE REQUIREMENTS OF DISTRIBUTED KNOWLEDGE-BASED CONTROL SYSTEM;341
18.2.4;SOFTWARE REQUIREMENTS FOR KNOWLEDGE-BASED CONTROL;342
18.2.5;CONCLUSIONS;344
18.2.6;REFERENCES;344
18.3;CHAPTER 54. ROBUST ADAPTIVE CONTROL SYSTEM BASED ON EXTENDED LEAKY INTEGRATION METHOD;348
18.3.1;INTRODUCTION;348
18.3.2;SYSTEM DESCRIPTION AND PROBLEM STATEMENT;349
18.3.3;CONTROLLERS STRUCTURE;350
18.3.4;EXTENDED LEAKY INTEGRATION METHOD;350
18.3.5;CONCLUSIONS;353
18.3.6;REFERENCES;353
18.4;CHAPTER 55. AN INTELLIGENT NONLINEAR ADAPTIVE MINIMUM-VARIANCE CONTROLLER;354
18.4.1;Summary;354
18.4.2;1. Problem formulation;354
18.4.3;2. Pre-identification and structure determination;354
18.4.4;3. Control law;355
18.4.5;4. Parameter estimation and switching conditions;355
18.4.6;Conclusions;356
18.4.7;References;356
18.5;CHAPTER 56. SELF-TUNING ADAPTIVE CONTROL BASED ON A NEW PARAMETER ESTIMATION METHOD;358
18.5.1;INTRODUCTION;358
18.5.2;VARIATIONAL METHOD;358
18.5.3;PARAMETER ESTIMATION;359
18.5.4;ADAPTIVE REGULATOR;361
18.5.5;SIMULATION;362
18.5.6;CONCLUSION;362
18.5.7;REFERENCES;363
19;PART 15: PREDICTIVE AND ROBUST CONTROL;364
19.1;CHAPTER 57. ON THE DESIGN OF THE UNIFIED PREDICTIVE CONTROLLER;364
19.1.1;Abstract;364
19.1.2;Introduction;364
19.1.3;Unified Predictive Control;364
19.1.4;Tuning of the UPC Design Parameters;365
19.1.5;Rule of Thumb methods;368
19.1.6;Conclusions;369
19.1.7;References;369
19.2;CHAPTER 58. A COMPARATIVE STUDY OF SOME MULTIVARIABLE PI CONTROLLER TUNING METHODS;370
19.2.1;INTRODUCTION;370
19.2.2;1. THE COMPARED TUNING METHODS;370
19.2.3;2. FREQUENCY DOMAIN COMPARISON CRITERIA;371
19.2.4;3. THE SIMULATION EXAMPLE;372
19.2.5;4. FREQUENCY DOMAIN COMPARISONS;372
19.2.6;5. TIME DOMAIN COMPARISONS;374
19.2.7;7. REFERENCES;375
19.3;CHAPTER 59. PREDICTIVE CONTROL OF NONLINEAR PROCESSES;376
19.3.1;Abstract;376
19.3.2;Introduction;376
19.3.3;Predictive Control;376
19.3.4;Minimization of the Criterion Function;378
19.3.5;Description of the pH Process;378
19.3.6;Alternative approach to controlling pH processes;379
19.3.7;Simulated and Experimental Results;380
19.3.8;Conclusions;381
19.3.9;References;381
19.4;CHAPTER 60. ENHANCED ROBUSTNESS FOR PITCH POINTING FLIGHT USING SLIDING MODE CONTROL;382
19.4.1;1 INTRODUCTION;382
19.4.2;2 PITCH POINTING FLIGHT CONTROL SYSTEM;382
19.4.3;3 VARIABLE STRUCTURE DESIGN;384
19.4.4;4 CONCLUSION;385
19.4.5;APPENDIX;385
19.4.6;REFERENCES;386
19.5;CHAPTER 61. A DESIGN OF NONLINEAR MODEL REFERENCE ADAPTIVE CONTROL AND THE APPLICATION TO THE LINK MECHANISM;388
19.5.1;1. INTRODUCTION;388
19.5.2;2. DESIGN OF NONLINEAR MODEL FOLLOWING CONTROL SYSTEM;388
19.5.3;3. PROOF OF BOUNDEDNESS OF INTERNAL STATES;389
19.5.4;4. NONLINEAR MODEL REFERENCE ADAPTIVE CONTROL SYSTEM AND THE GLOBAL STABILITY;391
19.5.5;5. THE APPLICATION TO THE LINK MECHANISM;392
19.5.6;6. CONCLUSION;393
19.5.7;REFERENCES;393
19.6;CHAPTER 62. THE DESIGN AND APPLICATION OF EXPERT INTELLIGENCE CONTROL SYSTEM OF AUTOMATIC COMPENSATION EXPOSURE ENERGY;394
19.6.1;INTRODUCTION;394
19.6.2;THE DYNAMICAL MATHEMATICAL MODEL OF ACEE;394
19.6.3;OPTIMAL CONTROL POLICY OF ACEE;395
19.6.4;THE DESIGN OF COMPUTER CONTROL SYSTEM;397
19.6.5;CONCLUSION;397
19.6.6;REFERENCES;397
20;PART 16: ADAPTIVE CONTROL/KNOWLEDGE BASED CONTROL;400
20.1;CHAPTER 63. A DIRECT ADAPTIVE POLE ASSIGNMENT CONTROL SCHEME FOR NON NECESSARILY STABLY INVERTIBLE DISCRETE-TIME PLANTS;400
20.1.1;1. INTRODUCTION;400
20.1.2;2. PROBLEM STATEMENT;401
20.1.3;3. THE ADAPTIVE CONTROLLER;402
20.1.4;4. ROBUST STABILIZATION OF THE INVERSE CONTROL LAW W.R. TO V1;403
20.1.5;5. CONCLUSIONS;404
20.1.6;REFERENCES;404
20.2;CHAPTER 64. A SELF-TUNING FEEDFORWARD COMPENSATOR;406
20.2.1;INTRODUCTION;406
20.2.2;UNCOMPENSATED LOAD RESPONSE;407
20.2.3;FEEDFORWARD COMPENSATOR;408
20.2.4;SYSTEM IMPLEMENTATION;409
20.2.5;SIMULATION RESULTS;410
20.2.6;CONCLUSIONS;411
20.2.7;Acknowledgement;411
20.2.8;References;411
20.3;CHAPTER 65. MODEL REFERENCE ADAPTIVE CONTROL FOR NON-MINIMUM PHASE SYSTEM BY PERIODIC FEEDBACK;412
20.3.1;INTRODUCTION;412
20.3.2;PROBLEM STATEMENT;412
20.3.3;DESCRIPTION OF MULTIRATE SAMPLING SYSTEM;413
20.3.4;PERIODIC FEEDBACK;413
20.3.5;ADAPTIVE CONTROL BY PERIODIC FEEDBACK;414
20.3.6;SIMULATION RESULTS;416
20.3.7;CONCLUSION;416
20.3.8;References;416
20.4;CHAPTER 66. SMART SYNTHESIS OF A PID CONTROLLER;418
20.4.1;1. INTRODUCTION;418
20.4.2;2. PROBLEM DESCRIPTION;418
20.4.3;3. METHODS;419
20.4.4;4. SIMULATION RESULTS;422
20.4.5;5. CONCLUSIONS;423
20.4.6;REFERENCES;423
20.5;CHAPTER 67. KNOWLEDGE-BASED TUNING AND CONTROL;424
20.5.1;Abstract;424
20.5.2;1 Introduction;424
20.5.3;2 Motivation;425
20.5.4;3 The Unified Predictive Controller;425
20.5.5;4 Knowledge-based tuning;426
20.5.6;5 Implementation;427
20.5.7;6 Experiments;428
20.5.8;7 Conclusions;429
20.5.9;Acknowledgement;429
20.5.10;References;429
21;AUTHOR INDEX;430
22;KEYWORD INDEX;432