Leondes Control and Dynamic Systems

Advances in Theory and Applications
1. Auflage 2014
ISBN: 978-1-4831-9124-9
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band Volume 12, 648 Seiten, Web PDF

Reihe: Control and Dynamic Systems

Control and Dynamic Systems: Advances in Theory and Applications reviews progress in the field of control and dynamic systems theory and applications, with emphasis on filtering and stochastic control in dynamic systems. Topics include linear and nonlinear filtering techniques; concepts and methods in stochastic control; and discrete-time optical stochastic observers. The theory of disturbance-accommodating controllers is also presented. Comprised of nine chapters, this volume begins with an overview of filtering and stochastic control in dynamic systems, followed by a discussion on linear and nonlinear filtering techniques. The reader is then introduced to concepts and methods in stochastic control, as well as the innovations process and its applications to sensitivity analysis and system identification. Subsequent chapters focus on the status of observer theory and its major results as applied to discrete-time linear systems; the properties of the class of discrete-time Riccati equations that arise in the filtering problem; and the theory of disturbance-accommodating controllers. The identification of noise characteristics in a Kalman filter and estimation of adaptive minimum variance in discrete-time linear systems round out the book. This monograph will be useful to practicing technologists and research workers interested in filtering and stochastic control in dynamic systems.

Autoren/Hrsg.

Leondes, C. T.

Inhaltsverzeichnis

1;Front Cover;1
2;Control and Dynamic Systems: Advances in Theory and Applications;4
3;Copyright Page;5
4;Table of Contents;6
5;CONTRIBUTORS;12
6;PREFACE;14
7;CONTENTS OF PREVIOUS VOLUMES;16
8;Chapter 1. An Overview of Filtering and Stochastic Control
in Dynamic Systems;22
8.1;I. GENERAL STOCHASTIC CONTROL PROBLEM;23
8.2;II. GENERAL SOLUTION OF THE OPTIMAL STOCHASTIC CONTROL PROBLEM;35
8.3;III. LINEAR QUADRATIC SYSTEMS AND EXTENSIONS;52
8.4;IV. SUMMARY OF PROPOSED ALGORITHMS;64
8.5;REFERENCES;77
9;Chapter 2. Linear and Nonlinear Filtering Techniques;84
9.1;I. INTRODUCTION;84
9.2;II. LINEAR FILTERING;85
9.3;III. SYSTEM MODELING;89
9.4;IV. SUBOPTIMAL FILTER DESIGN AND SENSITIVITY ANALYSIS;92
9.5;V. PARTITIONED FILTER APPROACH;100
9.6;VI. DATA PREFILTERING;103
9.7;VII. SQUARE ROOT TECHNIQUES;105
9.8;VIII. DIVERGENCE OF THE FILTER;108
9.9;IX. NONLINEAR FILTERING;111
9.10;X. CONCLUDING REMARKS;116
9.11;REFERENCES;116
10;Chapter 3. Concepts and Methods in Stochastic Control;120
10.1;I. INTRODUCTION;121
10.2;II. CLASSES OF STOCHASTIC CONTROL POLICIES AND SOME PROPERTIES OF THE CONTROL;124
10.3;III. OPTIMAL STOCHASTIC CONTROL;137
10.4;IV. THE OPTIMAL CONTROL FOR A CLASS OF SYSTEMS;142
10.5;V. A STOCHASTIC CLOSED-LOOP CONTROL METHOD FOR NONLINEAR SYSTEMS;148
10.6;VI. A STOCHASTIC RESOURCE ALLOCATION PROBLEM;170
10.7;VII. CONCLUSION;181
10.8;ACKNOWLEDGMENT;181
10.9;APPENDIX A. PROOF OF THE CONNECTION BETWEEN THE CERTAINTY EQUIVALENCE AND THE DUAL EFFECT;181
10.10;APPENDIX B. THE CLOSED-LOOP OPTIMIZATION OF THE COST-TO-GO;186
10.11;REFERENCES;191
11;Chapter 4. The Innovations Process with Applications to Identifications;194
11.1;I. INTRODUCTION;195
11.2;II. MATHEMATICAL SPECIFICATIONS AND BACKGROUND;204
11.3;III. SENSITIVITY ANALYSIS;208
11.4;IV. SYSTEM IDENTIFICATION;228
11.5;V. SIMULATION RESULTS;264
11.6;REFERENCES;278
12;Chapter 5. Discrete-Time Optical Stochastic Observers;280
12.1;I. INTRODUCTION;281
12.2;II. DEFINITION OF THE DISCRETE OBSERVER FOR STOCHASTIC SYSTEMS;284
12.3;III. CONSTRUCTION OF A REDUCED-ORDER OBSERVER;288
12.4;IV. AN ALTERNATE REDUCED-ORDER OBSERVER ALGORITHM;296
12.5;V. LIMITING CASES OF THE REDUCED-ORDER OBSERVER SOLUTION;300
12.6;VI. COMPUTATIONAL ADVANTAGES OF REDUCED-ORDER OBSERVERS;315
12.7;VII. AN OPTIMAL CONTINUOUS-TIME OBSERVER SOLUTION;320
12.8;VIII. CONCLUDING REMARKS;328
12.9;REFERENCES;331
13;Chapter 6. Discrete Riccati Equations: Alternative Algorithms, Asymptotic Properties, and System Theory Interpretations;334
13.1;I. INTRODUCTION;335
13.2;II. SQUARE ROOT ALGORITHMS AND THE RICCATI EQUATION;340
13.3;III. SYSTEM STRUCTURE;363
13.4;IV. SINGULAR RICCATI EQUATIONS;391
13.5;ACKNOWLEDGMENT;405
13.6;REFERENCES;405
14;Chapter 7. Theory of Distrubance-Accommodating Controllers;408
14.1;I. INTRODUCTION: THE ROLE OF DISTURBANCES IN CONTROL PROBLEMS;410
14.2;II. THE WAVEFORM-MODE DESCRIPTION OF REALISTIC DISTURBANCES;414
14.3;III. THE WAVEFORM-MODE CHARACTERIZATION VERSUS THE STATISTICAL CHARACTERIZATION;419
14.4;IV. STATE MODELS FOR DISTURBANCES WITH WAVEFORM STRUCTURE;423
14.5;V. DESIGN OF DISTURBANCE-ACCOMMODATING CONTROLLERS FOR
STABILIZATION, REGULATION, AND SERVO-TRACKING CONTROL
PROBLEMS;444
14.6;VI. CONCLUSIONS;499
14.7;APPENDIX. A REDUCED-ORDER COMPOSITE STATE CONSTRUCTOR;501
14.8;REFERENCES;508
15;Chapter 8. Identification of the Noise Characteristics
in a Kaiman Filter;512
15.1;I. INTRODUCTION;513
15.2;II. SYSTEM DESCRIPTION;517
15.3;III. MOMENT SYSTEM FORMULATION;521
15.4;IV. ESTIMATES OF THE MOMENTS;535
15.5;V. CORRELATED MOMENT SYSTEM MEASUREMENT NOISE;566
15.6;VI. CONCLUSIONS;588
15.7;REFERENCES;592
15.8;APPENDIX A;593
15.9;APPENDIX .;597
15.10;APPENDIX C;601
16;Chapter 9. Adaptive Minimum Variance Estimation in Discrete-Time Linear Systems;604
16.1;I. INTRODUCTION;605
16.2;II. ADAPTIVE FILTER ALGORITHM;608
16.3;III. HYPOTHESIS TEST FOR TIME CORRELATION OF RESIDUALS;618
16.4;IV. SUMMARY OF THE ADAPTIVE ALGORITHM;627
16.5;V. CONVERGENCE OF ALGORITHM;632
16.6;VI. EXAMPLE;633
16.7;VII. CONCLUSIONS;639
16.8;APPENDIX. CONVERGENCE OF U = S8i=0 DiF(DT)i;641
16.9;REFERENCES;644
17;SUBJECT INDEX;646