Control and Dynamic Systems V26

Advances in Theory and Applications

1st Edition - November 23, 1987
Editor: C.T. Leonides
Language: English
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 1 5 4 6 5 - 9

Control and Dynamic Systems: Advances in Theory and Application, Volume 26: System Identification and Adaptive Control, Part 2 of 3 deals with system parameter identification and… Read more

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Control and Dynamic Systems: Advances in Theory and Application, Volume 26: System Identification and Adaptive Control, Part 2 of 3 deals with system parameter identification and adaptive control. It presents useful techniques for effective stochastic adaptive control systems. This volume presents a powerful technique for identifying discrete time and continuous time linear time-invariant multivariable systems. It also includes the use of identifiable representations for linear multivariable systems; parametric identification of transfer functions of linear system; compares model reference adaptive control and model identification control; estimation of transfer function models; multivariable self-tuning control; and covariance analysis. This volume ends with powerful techniques for adaptive control for stochastic linear systems. This text is of great value to practitioners in the field who want a comprehensive reference source of techniques with significant applied implications.

Contents

Preface

Techniques for Identification of Linear Time-Invariant Multivariable Systems

I. Introduction

II. Preliminary

III. Row Searching and the Echelon-Form Solution

IV. Persistent Excitation of Input Sequences

V. Identification of Discrete-Time Systems

VI. Identification of Continuous-Time Systems

VI. Noisy Measurements

References

Techniques for the Selection of Identifiable Parametrizations for Multivariable Linear Systems

I. Introduction

II. Models

III. Identifiability

IV. The Structure of S(n)

V. Canonical and Pseudocanonical Forms

VI. Other Identifiable Parametrizations

VII. The Estimation of the Structure

VIII. Conclusions

References

Parametric Methods for Identification of Transfer Functions of Linear Systems

I. Introduction

II. Measures of Model Quality

III. Prediction Error Identification Methods

IV. Asymptotic Properties of the Estimated Transfer Functions

V. Minimizing the Bias Contribution

VI. Minimizing the Variance Contribution

VII. Minimizing the Design Criteria

References

Techniques in Dynamics Systems Parameter-Adaptive Control

I. Introduction

II. Approaches to Parameter-Adaptive Control

III. Parameter Estimation

IV. Control Algorithm Design

V. Combinations

VI. Convergence Conditions

VII. Steps for the Application

VIII. Supervision and Coordination

IX. Implementation on Microcomputers

X. Applications

XI. Conclusions

References

Estimation of Transfer Function Models Using Mixed Recursive and Nonrecursive Methods

I. Introduction

II. Overview of Past Work

III. The New Algorithm

IV. Examples

V. Summary

VI. Appendix

References

Techniques for Multivariable Self-Tuning Control

I. Introduction

II. Self-Tuning Control of Stochastic Systems

III. Explicit LOG Self-Tuning Regulators

IV. Self-Tuning Minimum Variance Controllers

V. Self-Tuning Controllers Based on Single-Step Optimal Control

VI. Predictor-Based Procedures for Multistep Optimal Control

VII. Summary and Conclusions

References

A Covariance Control Theory

I. Introduction

II. Fundamental Concepts and Problem Statement

III. Main Theorems on State Covariance Assignment

IV. Control Energy Considerations

V. State Covariance Assignment Using State Estimate Feedback

VI. Connections to Linear Quadratic Control Theory

VII. Design Example

VIII. Conclusions

IX. Appendix A

X. Appendix B: Proof to Lemma 8

References

Adaptive Control with Recursive Identification for Stochastic Linear Systems

I. Introduction

II. Strong Consistency of Parameter Estimates for Systems without Monitoring

III. Existence of Adaptive Control and Its Optimality

IV. Optimal Adaptive Control with Consistent Parameter Estimates

V. Convergence Rate

References

Index