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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.
Techniques for Identification of Linear Time-Invariant Multivariable Systems
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
Techniques for the Selection of Identifiable Parametrizations for Multivariable Linear Systems
IV. The Structure of S(n)
V. Canonical and Pseudocanonical Forms
VI. Other Identifiable Parametrizations
VII. The Estimation of the Structure
Parametric Methods for Identification of Transfer Functions of Linear Systems
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
Techniques in Dynamics Systems Parameter-Adaptive Control
II. Approaches to Parameter-Adaptive Control
III. Parameter Estimation
IV. Control Algorithm Design
VI. Convergence Conditions
VII. Steps for the Application
VIII. Supervision and Coordination
IX. Implementation on Microcomputers
Estimation of Transfer Function Models Using Mixed Recursive and Nonrecursive Methods
II. Overview of Past Work
III. The New Algorithm
Techniques for Multivariable Self-Tuning Control
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
A Covariance Control Theory
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
IX. Appendix A
X. Appendix B: Proof to Lemma 8
Adaptive Control with Recursive Identification for Stochastic Linear Systems
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
- No. of pages:
- © Academic Press 1987
- 23rd November 1987
- Academic Press
- eBook ISBN:
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