(Ebook) Adaptive filtering prediction and control 1st Edition by Graham C Goodwin, Kwai Sang Sin ISBN 0486469328 9780486469324

(Ebook) Adaptive filtering prediction and control 1st Edition by Graham C Goodwin, Kwai Sang Sin ISBN 0486469328 9780486469324

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ISBN 10: 0486469328 
ISBN 13: 9780486469324
Author: Graham C Goodwin, Kwai Sang Sin 

This unified survey of the theory of adaptive filtering, prediction, and control focuses on linear discrete-time systems and explores the natural extensions to nonlinear systems. In keeping with the importance of computers to practical applications, the authors emphasize discrete-time systems. Their approach summarizes the theoretical and practical aspects of a large class of adaptive algorithms. Ideal for advanced undergraduate and graduate classes, this treatment consists of two parts. The first section concerns deterministic systems, covering models, parameter estimation, and adaptive prediction and control. The second part examines stochastic systems, exploring optimal filtering and prediction, parameter estimation, adaptive filtering and prediction, and adaptive control. Extensive appendices offer a summary of relevant background material, making this volume largely self-contained. Readers will find that these theories, formulas, and applications are related to a variety of fields, including biotechnology, aerospace engineering, computer sciences, and electrical engineering.

(Ebook) Adaptive filtering prediction and control 1st Table of contents:

1 Introduction To Adaptive Techniques
1.1 Filtering
1.2 Prediction
1.3 Control
Part I: Deterministic Systems
2 Models for Deterministic Dynamical Systems
2.1 Introduction
2.2 State-Space Models
2.2.1 General
2.2.2 Controllable State-Space Models
2.2.3 Observable State-Space Models
2.2.4 Minimal State-Space Models
2.3 Difference Operator Representations
2.3.1 General
2.3.2 Right Difference Operator Representations
2.3.3 Left Difference Operator Representations
2.3.4 Deterministic Autoregressive Moving-Average Models
2.3.5 Irreducible Difference Operator Representations
2.4 Models for Bilinear Systems
3 Parameter Estimation for Deterministic Systems
3.1 Introduction
3.2 On-Line Estimation Schemes
3.3 Equation Error Methods for Deterministic Systems
3.4 Parameter Convergence
3.4.1 The Orthogonalized Projection Algorithm
3.4.2 The Least-Squares Algorithm
3.4.3 The Projection Algorithm
3.4.4 Persistent Excitation
3.5 Output Error Methods
3.6 Parameter Estimation with Bounded Noise
3.7 Constrained Parameter Estimation
3.8 Parameter Estimation for Multi-output Systems
3.9 Concluding Remarks
4 Deterministic Adaptive Prediction
4.1 Introduction
4.2 Predictor Structures
4.2.1 Prediction with Known Models
4.2.2 Restricted Complexity Predictors
4.3 Adaptive Prediction
4.3.1 Direct Adaptive Prediction
4.3.2 Indirect Adaptive Prediction
4.4 Concluding Remarks
5 Control of Linear Deterministic Systems
5.1 Introduction
5.2 Minimum Prediction Error Controllers
5.2.1 One-Step-Ahead Control (The SISO Case)
5.2.2 Model Reference Control (The SISO Case)
5.2.3 One-Step-Ahead Design for Multi-input Multi-output Systems
5.2.4 Robustness Considerations
5.3 Closed-Loop Pole Assignment
5.3.1 Introduction
5.3.2 The Pole Assignment Algorithm (Difference Operator Formulation)
5.3.3 Rapprochement with State- Variable Feedback
5.3.4 Rapprochement with Minimum Prediction Error Control
5.3.5 The Internal Model Principle
5.3.6 Some Design Considerations
5.4 An Illustrative Example
6 Adaptive Control Of Linear Deterministic Systems
6.1 Introduction
6.2 The Key Technical Lemma
6.3 Minimum Prediction Error Adaptive Controllers (Direct Approach)
6.3.1 One-Step-Ahead Adaptive Control (The SISO Case)
6.3.2 Model Reference Adaptive Control
6.3.3 One-Step-Ahead Adaptive Controllers for Multi-input Multi-output Systems
6.4 Minimum Prediction Error Adaptive Controllers (Indirect Approach)
6.5 Adaptive Algorithms for Closed-Loop Pole Assignment
6.6 Adaptive Control of Nonlinear Systems
6.7 Adaptive Control of Time-Varying Systems
6.8 Some Implementation Considerations
Part II: Stochastic Systems
7 Optimal Filtering and Prediction
7.1 Introduction
7.2 Stochastic State-Space Models
7.3 Linear Optimal Filtering and Prediction
7.3.1 The Kalman Filter
7.3.2 Fixed-Lag Smoothing
7.3.3 Fixed-Point Smoothing
7.3.4 Optimal Prediction
7.4 Filtering and Prediction Using Stochastic ARMA Models
7.4.1 The Stochastic ARMA Model
7.4.2 Optimal Filters and Predictors in ARMA Form
7.5 Restricted Complexity Filters and Predictors
7.5.1 General Filters
7.5.2 Whitening Filters
7.5.3 Levinson Predictors
7.6 Lattice Filters and Predictors
7.6.1 Lattice Filters
7.6.2 Lattice Predictors
7.7 The Extended Kalman Filter
8 Parameter Estimation For Stochastic Dynamic Systems
8.1 Introduction
8.2 Off-Line Prediction Error Algorithms
8.3 Sequential Prediction Error Methods
8.3.1 General Systems
8.3.2 Linear Systems
8.4 Algorithms Based on Pseudo Linear Regressions
8.5 Convergence Analysis of Sequential Algorithms
8.5.1 The Stochastic Gradient Algorithm
8.5.2 The Least-Squares Form of the Pseudo Linear Regression Algorithm
8.5.3 The Stochastic Key Technical Lemma
8.5.4 The ODE Approach to the Analysis of Sequential Algorithms
8.6 Parameter Convergence
8.6.1 The Ordinary Least-Squares Algorithm
8.6.2 The Pseudo Linear Regression Algorithm
8.7 Concluding Remarks
9 Adaptive Filtering and Prediction
9.1 Introduction
9.2 Adaptive Optimal State Estimation
9.2.1 The Extended Kalman Filter Approach
9.2.2 The Prediction Error Approach
9.2.3 Self-Tuning Fixed-Lag Smoothers
9.3 Adaptive Optimal Prediction
9.3.1 Indirect Adaptive Prediction
9.3.2 Direct Adaptive Prediction
9.4 Restricted Complexity Adaptive Filters
9.4.1 Adaptive Deconvolution
9.4.2 Adaptive Noise Canceling
9.5 Adaptive Lattice Filters
9.5.1 The Bootstrap Method
9.5.2 The Prediction Error Method
9.5.3 The Exact Least-Squares Method
10 Control of Stochastic Systems
10.1 Introduction
10.2 The Application of Deterministic Design Methods
10.3 Stochastic Minimum Prediction Error Controllers
10.3.1 Minimum Variance Control (The SISO Case)
10.3.2 Model Reference Stochastic Control
10.3.3 Control of Multi-input Multi-output Stochastic Systems
10.4 The Linear Quadratic Gaussian Optimal Control Problem
10.4.1 The Separation Principle
10.4.2 The Tracking Problem
10.4.3 Rapprochement with Minimum Variance Control
11 Adaptive Control of Stochastic Systems
11.1 Introduction
11.2 Concepts of Dual Control and Certainty Equivalence Control
11.3 Stochastic Minimum Prediction Error Adaptive Controllers
11.3.1 Adaptive Minimum Variance Control
11.3.2 Stochastic Model Reference Adaptive Control
11.3.3 Multi-input Multi-output Systems
11.3.4 Convergence Analysis
11.4 Adaptive Pole Placement and Adaptive Optimal Controllers
11.5 Concluding Remarks

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Tags: Graham C Goodwin, Kwai Sang Sin, filtering, prediction