Control and Dynamic Systems V25
Advances in Theory and Applications
- 1st Edition - July 21, 1987
- Imprint: Academic Press
- 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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Request a sales quoteControl 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
- Edition: 1
- Published: July 21, 1987
- Imprint: Academic Press
- No. of pages: 375
- Language: English
- eBook ISBN: 9780323153492
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