Control and Dynamic Systems V25

Control and Dynamic Systems V25

Advances in Theory and Applications

1st Edition - July 21, 1987

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  • Editor: C.T. Leonides
  • eBook ISBN: 9780323153492

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Description

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.

Table of Contents


  • 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

Product details

  • No. of pages: 375
  • Language: English
  • Copyright: © Academic Press 1987
  • Published: July 21, 1987
  • Imprint: Academic Press
  • eBook ISBN: 9780323153492

About the Editor

C.T. Leonides

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