Control and Dynamic Systems V25

Advances in Theory and Applications

1st Edition - July 21, 1987
Editor: C.T. Leonides
Language: English
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 1 5 3 4 9 - 2

Control and Dynamic Systems: Advances in Theory and Application, Volume 25: System Identification and Adaptive Control, Part 1 of 3 deals with system parameter identification and… Read more

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Control and Dynamic Systems: Advances in Theory and Application, Volume 25: System Identification and Adaptive Control, Part 1 of 3 deals with system parameter identification and adaptive control. It presents useful techniques for effective stochastic adaptive control systems. This book discusses multicriteria optimization in adaptive and stochastic control systems. After discussing how to estimate the parameters of an autoregressive moving-average (ARMA) process, it identifies instrumental variable methods for ARMA models. This book also presents robust algorithms for adaptive control; design principles for robustness in adaptive identification methods; utilization of robust smoothing; and order reduction of linear systems. This volume is a useful reference for control systems theorists and practitioners interested in system identification and adaptive control techniques.

Preface

Uncertainty Management Techniques in Adaptive Control

I. Introduction

II. Optimal Control of Stochastic Linear Discrete Systems with Perfect Measurements

III. Uncertainty Management in Modeling of Flexible Structures

IV. Optimal Estimation of the States of Linear Discrete Stochastic Systems

V. Optimal Closed-Loop Control of Stochastic Systems with Noisy Measurements

VI. Conclusions

References

Multicriteria Optimization in Adaptive and Stochastic Control

I. Introduction

II. Models, Criteria, and Estimators

III. Basic Principle and Algorithm Description

IV. Target Selection

V. Performance Analysis

VI. Conclusions

References

Instrumental Variable Methods for ARMA Models

I. Introduction

II. The Estimation Method

III. Consistency and Accuracy

IV. Optimization of Estimation Accuracy

V. Comparison of the Accuracies of the Optimal IV Method and the Prediction Error Method

VI. The Optimal Choice of G(q-1)

VII. The Optimal IV Multistep Estimates and Their Asymptotic Properties

VIII. Implementation of the Optimal IV Multistep Estimators

IX. Numerical Examples

X. Conclusions

Appendix A: Proof of Theorem 3

Appendix B: Convergence of Pm

Appendix C: Proof of Lemma 1

Appendix D: The Best Positive-Definite Approximation of a Symmetric Matrix

Appendix E: A Recursive QR Algorithm for Solving (6)

References

Continuous and Discrete Adaptive Control

I. Introduction

II. A Class of Plant Models

III. Parameter Estimation with Fixed Noise Filters

IV. Parameter Estimation with Unknown Noise Filters

V. Stochastic Control

VI. Adaptive Control

VII. Application to a Servo System

VIII. Conclusion

References

Adaptive Control: A Simplified Approach

I. Introduction

II. Formulation of Some Basic Ideas

III. Simplified Adaptive Control in ASPR Systems

IV. Generalization of the Simplified Adaptive Algorithm

V. Conclusions

Appendix A: The Derivative of the Lyapunov Function (49)

Appendix B: The Derivative of the Lyapunov Function (109)

References

Discrete Averaging Principles and Robust Adaptive Identification

I. Introduction and Outline

II. Identification, Adaptive Identification, and Robust Adaptive Identification

III. Adaptive Identification Algorithms and Error Systems

IV. Discrete-Time Averaging Methods

V. Robustness of Adaptive Identification

VI. Implications for Design and Operation

References

Techniques for Adaptive State Estimation through the Utilization of Robust Smoothing

I. Introduction

II. Robust Estimation of Observed State Variables

III. Adaptive Gain Matrix Weighting

IV. Adaptive Error Covariance Matrix Weighting

V. Robust Smoothing

VI. Simulation Results

VII. Conclusions

Appendix A: Robust Estimators of Statistics

Appendix B: Derivation of Recursive Estimators for Sample Statistics

References

Coordinate Selection Issues in the Order Reduction of Linear Systems

I. Introduction

II. Literature Survey

III. Model Reduction by Cost Decomposition

IV. Coordinate Selection

V. Numerical Example

VI. Effects of Skewing on the MEI

VII. Permissible Parameter-Simplifying Transformations

VIII. Parameter Reduction

IX. Exhaustive Evaluation of Coordinate Selections

X. Summary

Appendix: Further Numerical Examples

References

Index