Control and Dynamic Systems V26
Advances in Theory and Applications
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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.
Table of Contents
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
Product details
- No. of pages: 352
- Language: English
- Copyright: © Academic Press 1987
- Published: November 23, 1987
- Imprint: Academic Press
- eBook ISBN: 9780323154659
About the Editor
C.T. Leonides
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