Skelton / Owens Model Error Concepts & Compensation

Proceedings of the IFAC Workshop, Boston, USA, 17-18 June 1985
1. Auflage 2014
ISBN: 978-1-4832-9826-9
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 151 Seiten, Web PDF

Reihe: IFAC Workshop Series

Presents a state-of-the-art review of model error concepts, their characterization and compensation in estimation and control problems, with particular emphasis on error propagation, model order selection, performance guarantees, sensitivity and adaptive methods. Main topics covered include linear and nonlinear systems, identification, robotics, computer-aided design, signal processing, computers and communication in control, automation and real time control of processes.

Autoren/Hrsg.

Skelton, R. E.

Owens, D. H.

Inhaltsverzeichnis

1;Front Cover;1
2;Model Error Concepts and Compensation;4
3;Copyright Page;5
4;Table of Contents;8
5;CHAPTER 1. WORKSHOP EDITORIAL;10
6;PART I: PLENARY SESSION;14
6.1;CHAPTER 2. MULTI-MODEL APPROACHES TO ROBUST CONTROL SYSTEM DESIGN;14
6.1.1;INTRODUCTION;14
6.1.2;PROBLEM FORMULATION;15
6.1.3;EXISTENCE OF SIMULTANEOUS STABILIZERS;16
6.1.4;DESIGN TOOLS;17
6.1.5;CONCLUSIONS;18
6.1.6;REFERENCES;18
7;PART II: MODEL STRUCTURE CONSIDERATIONS;20
7.1;CHAPTER 3. CONTROLLER DESIGN BASED ON APPROXIMATE PLANT MODELS;20
7.1.1;INTRODUCTION;20
7.1.2;FREQUENCY DOMAIN DESIGN;20
7.1.3;SIMULATION BASED DESIGN;22
7.1.4;CONCLUSIONS;24
7.1.5;ACKNOWLEDGEMENTS;24
7.1.6;REFERENCES;24
7.2;CHAPTER 4. ON THE STRUCTURE OF MODELING ERRORS AND THE INSEPARABILITY OF THEMODELING AND CONTROL PROBLEMS;26
7.2.1;INTRODUCTION;26
7.2.2;2.0 CRITICISM OF THE "MODELING PROBLEM;26
7.2.3;3.0 CRITICISM OF THE "CONTROL PROBLEM;26
7.2.4;4.0 PHYSICAL EVIDENCE;27
7.2.5;5.0 MATHEMATICAL EVIDENCE;28
7.2.6;MATHEMATICAL EVIDENCE & IDENTIFICATION EXPERIMENTS;31
7.2.7;CONCLUSIONS;31
7.2.8;REFERENCES;31
7.3;CHAPTER 5. LINEAR ADAPTIVE CONTROL: A NEW RESULT IN MODEL-ERROR COMPENSATION DESIGN;34
7.3.1;INTRODUCTION; THE CONCEPT OF MODEL-ERRORS IN DYNAMICAL SYSTEM THEORY;34
7.3.2;MODELING OF MODEL-ERRORS;THE WAVEFORM/STATE-MODEL TECHNIQUE;34
7.3.3;COMPENSATION FOR MODEL-ERRORS USING DISTURBANCE-ACCOMMODATING CONTROL THEORY;35
7.3.4;FORMULATION OF A PARAMETER MODEL-ERROR COMPENSATION PROBLEM FOR LINEAR DYNAMICAL SYSTEMS;36
7.3.5;SOLUTION TO THE PARAMETER MODEL-ERROR COMPENSATION PROBLEM OF (24);37
7.3.6;APPLICATION OF THE NEW COMPENSATOR TO SOME SPECIFIC EXAMPLES;39
7.3.7;SUMMARY AND CONCLUSIONS;41
7.3.8;REFERENCES CITED;41
7.4;CHAPTER 6. THE ERROR FUNCTION IN LOW ORDER MODELLING OF DISCRETE-TIME SYSTEMS;44
7.4.1;INTRODUCTION;44
7.4.2;MODEL REDUCTION DEFINITIONS;45
7.4.3;INPUT ERROR FUNCTION;45
7.4.4;THE ERROR FUNCTION IN REDUCED ORDER MODELLING;46
7.4.5;MODELLING EXAMPLE;47
7.4.6;REAL-TIME IMPLEMENTATION;48
7.4.7;INCORPORATION IN AN ADAPTIVE CONTROLLER;48
7.4.8;CONCLUSIONS;49
7.4.9;REFERENCES;49
7.5;CHAPTER 7. AN INNOVATIONS APPROACH TO SMOOTHING AND MODEL ERROR ESTIMATION;52
7.5.1;1. PRELIMINARIES;52
7.5.2;2. OPTIMAL STATE AND MODEL ERROR ESTIMATES;53
7.5.3;3. COVARIANCE ANALYSIS;54
7.5.4;4. FILTERING;55
7.5.5;5. BACKWARD/FORWARD FACTORIZATION OF THE ESTIMATION ERROR COVARIANCE;56
7.5.6;6. ROTATIONAL RELATIONSHIP BETWEEN INNOVATIONS AND MODEL ERROR VECTORS;57
7.5.7;7. CONCLUDING REMARKS;58
7.5.8;8. REFERENCES;58
7.5.9;9. ACKNOWLEDGEMENT;58
8;PART III: CONTROLLER REDUCTION;60
8.1;CHAPTER 8. OPTIMAL PROJECTION/MAXIMUM ENTROPY:STOCHASTIC MODELLING AND REDUCEDORDER DESIGN SYNTHESIS;60
8.1.1;1. OVERVIEW;60
8.1.2;2. MOTIVATION;60
8.1.3;3. REVIEW OF THE OPTIMAL PROJECTION APPROACH;61
8.1.4;4. MAXIMUM ENTROPY MODELLING;61
8.1.5;5. MINIMUM-INFORMATION MODELLIG OF PARAMETER UNCERTAINTIES;62
8.1.6;6. OPTIMAL PROJECTION DESIGN WITH STRATONOVICH MULTIPLICATIVE WHITE NOISE;65
8.2;CHAPTER 9. APPROXIMATION AND COMPENSATOR ORDER DETERMINATION IN OPTIMAL CONTROL OF FLEXIBLE STRUCTURES;68
8.2.1;INTRODUCTION;68
8.2.2;APPROXIMATION;69
8.2.3;APPLICATION TO WRAP-RIB ANTENNA;70
8.2.4;CONVERGENCE OF COMPENSATORS AND CLOSED-LOOP PERFORMANCE;70
8.2.5;REFERENCES;71
8.3;CHAPTER 10. LINEAR CONTROLLER APPROXIMATION: A METHOD WITH BOUNDS;76
8.3.1;INTRODUCTION;76
8.3.2;FORMULATION OF THE APPROXIMATION PROBLEM;76
8.3.3;HANKEL-NORM FREQUENCY-WEIGHTED APPROXIMATION;77
8.3.4;CONSTRUCTION OF A BOUND;77
8.3.5;EXAMPLES;78
8.3.6;CONCLUSIONS;78
8.3.7;REFERENCES;79
8.4;CHAPTER 11. CONTROLLER REDUCTION AND ANALYSIS OF SUBOPTIMALITY AND STABILITY OF DECENTRALIZED SYSTEMS;82
8.4.1;INTRODUCTION;82
8.4.2;2. FULL ORDER LOCAL CONTROLLERS;83
8.4.3;3. REDUCED ORDER LOCAL CONTROLLERS;84
8.4.4;4. SUBOPTIMALITY OF DECENTRALIZED CONTROLLERS;85
8.4.5;5. CONCLUSION;87
8.4.6;6. APPENDIX A;87
8.4.7;7. APPENDIX B;88
8.4.8;8. REFERENCES;89
8.5;CHAPTER 12. ON OPTIMALITY AND ROBUSTNESS OF LQREGULATORS FOR NONLINEAR ANDINTERCONNECTED SYSTEMS;90
8.5.1;1. INTRODUCTION;90
8.5.2;2. UNCONSTRAINED CONTROL STRUCTURE;90
8.5.3;3. DECENTRALIZED INFORMATION CONSTRAINTS;93
8.5.4;ACKNOWLEDGMENT;95
8.5.5;REFERENCES;95
8.6;CHAPTER 13. APPROXIMATE FILTERING, CONTROL AND INTERACTION IN LARGE-SCALE SYSTEMS;96
8.6.1;I. INTRODUCTION;96
8.6.2;II. Canonical Correlation Analysis (CCA;97
8.6.3;III. Approximate (Reduced-Order);97
8.6.4;IV. Approximate (Reduced-Order) LQR [18]-[19];98
8.6.5;V. Strong and Weak Interactions;100
8.6.6;VI. Some Discussion;101
8.6.7;References;102
9;PART IV: FREQUENCY DOMAIN VIEWPOINTS;104
9.1;CHAPTER 14. Hoo AND LQG ROBUST DESIGN METHODS FOR UNCERTAIN LINEAR SYSTEMS;104
9.1.1;1. Introduction;104
9.1.2;2. LQG Problem and Solution;104
9.1.3;3. Sensitivity Minimization;105
9.1.4;4. Embedding the H8 Problem;106
9.1.5;5. Diophantine Equation Solution;107
9.1.6;6. Statement of the H8 Algorithm;109
9.1.7;7. Solution of the Diophantine Equations;109
9.1.8;8. Conclusions;109
9.1.9;9. References;109
9.2;CHAPTER 15. DESIGN OF LINEAR CONTROL SYSTEMS FOR ROBUST STABILITY AND PERFORMANCE;110
9.2.1;INTRODUCTION;110
9.2.2;PROBLEM FORMULATION;110
9.2.3;PROBLEM TRANSFORMATION;111
9.2.4;FREQUENCY DEPENDENT TRANSFORMATIONS;112
9.2.5;IMPLEMENTATION ANDSIMULATIONS;115
9.2.6;CONCLUSION;115
9.2.7;REFERENCES;115
9.3;CHAPTER 16. ON HANKEL MATRIX APPROXIMATIONS AND STOCHASTIC MODEL REDUCTION;118
9.3.1;INTRODUCTION;118
9.3.2;INVARIANT SUBSPACES AND STATE SPACE APPROXIMATIONS;118
9.3.3;HANKEL APPROXIMATIONS AND STOCHASTIC MODEL REDUCTION;119
9.3.4;ACKNOWLEDGEMENTS;120
9.3.5;REFERENCES;120
9.4;CHAPTER 17. FINITE-DIMENSIONAL CONTROLLERS FOR LINEAR DISTRIBUTED PARAMETER SYSTEMS: EXPONENTIAL STABILITY USING RESIDUAL MODE FILTERS;122
9.4.1;INTRODUCTION;122
9.4.2;LINEAR DISTRIBUTED PARAMETER SYSTEMS;122
9.4.3;DESIGN OF RESIDUAL MODEFILTERS;123
9.4.4;EXPONENTIAL STABILITY USINGSTATIC OUTPUT FEEDBACK WITHA RESIDUAL MODE FILTER;124
9.4.5;EXPONENTIAL STABILITY USINGDYNAMIC OUTPUT FEEDBACK WITHA RESIDUAL MODE FILTER;125
9.4.6;CONCLUSIONS;127
9.4.7;ACKNOWLEDGEMENTS;127
9.4.8;REFERENCES;127
9.5;CHAPTER 18. MODEL UNCERTAINTY AND LINEAR SYSTEM IDENTIFICATION;128
9.5.1;1. Introduction;128
9.5.2;2. Confidence Sets;129
9.5.3;3. "Time-model-in-the-model-class" Case Returning to the original problem;130
9.5.4;4. Concluding Remarks;133
9.5.5;Acknowledgements;133
9.5.6;References;133
10;PART V: ADAPTIVE AND OTHER VIEWPOINTS;134
10.1;CHAPTER 19. VIBRATIONAL-FEEDBACK CONTROL OF DECENTRALIZED SYSTEMS: A DESIGN ALGORITHM;134
10.1.1;1. Introduction;134
10.1.2;2. Preliminaries;134
10.1.3;3. Main Theorem;135
10.1.4;4. Example;136
10.1.5;5. Conclusions;137
10.1.6;Acknowledgement;137
10.1.7;References;137
10.2;CHAPTER 20. STABILIZATION OF UNCERTAIN DISCRETE-TIME SYSTEMS;138
10.2.1;1. INTRODUCTION;138
10.2.2;2. PROBLEM STATEMENT;138
10.2.3;3. LYAPUNOV MIN-MAX CONTROLLERS;138
10.2.4;4. NOMINAL SYSTEM SUBJECT TO MIN-MAXCONTROLLERS;139
10.2.5;5. UNCERTAIN SYSTEM SUBJECT TO MIN-MAX CONTROLLERS;139
10.2.6;6. APPLICATION;140
10.2.7;7. APPENDIX;141
10.2.8;ACKNOWLEDGEMENT;141
10.2.9;REFERENCES;141
10.3;CHAPTER 21. TIME DOMAIN ROBUST CONTROL DESIGNFOR LINEAR QUADRATIC REGULATORS BY PERTURBATION BOUND ANALYSIS;142
10.3.1;I. INTRODUCTION;142
10.3.2;II. BOUNDS FOR ROBUST STABILITY AND REGULATION;143
10.3.3;I I . PERFORMANCE ROBUSTNESS INDEX AND DESIGN ALGORITHM;144
10.3.4;IV. APPLICATION EXAMPLE;146
10.3.5;V. CONCLUSIONS;146
10.3.6;ACKNOWLEDGEMENTS;147
10.3.7;REFERENCES;147
10.4;CHAPTER 22. INFINITE DIMENSIONAL MODELS ARE BETTER APPROXIMATIONS THAN FINITE DIMENSIONAL ONES;150
10.4.1;INTRODUCTION;150
10.4.2;A PROTOTYPE EXAMPLE;150
10.4.3;SOME OPINIONS;151
10.4.4;REFERENCES;151
11;AUTHOR INDEX;152