Identification of Continuous-Time Systems: Linear and Robust Parameter Estimation (Engineering Systems and Sustainability)

✍ Scribed by Allamaraju Subrahmanyam, Ganti Prasada Rao

• Publisher: CRC Press
• Year: 2019
• Tongue: English
• Leaves: 143
• Series: Engineering Systems and Sustainability
• Edition: 1
• Category: Library

✦ Synopsis

Models of dynamical systems are required for various purposes in the field of systems and control. The models are handled either in discrete time (DT) or in continuous time (CT). Physical systems give rise to models only in CT because they are based on physical laws which are invariably in CT. In system identification, indirect methods provide DT models which are then converted into CT. Methods of directly identifying CT models are preferred to the indirect methods for various reasons. The direct methods involve a primary stage of signal processing, followed by a secondary stage of parameter estimation. In the primary stage, the measured signals are processed by a general linear dynamic operation―computational or realized through prefilters, to preserve the system parameters in their native CT form―and the literature is rich on this aspect.

In this book: Identification of Continuous-Time Systems-Linear and Robust Parameter Estimation, Allamaraju Subrahmanyam and Ganti Prasada Rao consider CT system models that are linear in their unknown parameters and propose robust methods of estimation. This book complements the existing literature on the identification of CT systems by enhancing the secondary stage through linear and robust estimation.

In this book, the authors

• provide an overview of CT system identification,
• consider Markov-parameter models and time-moment models as simple linear-in-parameters models for CT system identification,
• bring them into mainstream model parameterization via basis functions,
• present a methodology to robustify the recursive least squares algorithm for parameter estimation of linear regression models,
• suggest a simple off-line error quantification scheme to show that it is possible to quantify error even in the absence of informative priors, and
• indicate some directions for further research.

This modest volume is intended to be a useful addition to the literature on identifying CT systems.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
List of Figures
List of Tables
Preface
Acknowledgments
Authors
List of Abbreviations
1: Introduction and Overview
1.1 Background
1.2 Introduction
1.3 Role of Model Parameterizations in System Identification
1.3.1 Poisson Moment Functional Approach
1.3.2 Integral Equation Approach
1.3.3 Biased Estimation
1.3.4 Reducible Models (for Multivariable Systems)
1.3.5 Distribution of Estimation Errors
1.4 Error Quantification: An Engineering Necessity
2: Markov Parameter Models
2.1 Introduction
2.2 Markov Parameter Models
2.2.1 Generalizations
2.2.2 Choice of λ
2.2.3 Markov Parameter Estimation
2.2.4 Identification of Structure
2.3 Finitization of the Markov Parameter Sequence
2.3.1 Controller Form Realization
2.4 Identifiability Conditions
2.5 Convergence Analysis of the Algorithm
2.6 Illustrative Examples
2.7 Summary and Conclusions
3: Time Moment Models
3.1 Introduction
3.2 Time Moment Models
3.3 Finitization of Time-Moment Sequence
3.3.1 Implementation Issues
3.4 Illustrative Examples
3.5 Choice of Basis of Parameterization
3.6 Summary and Conclusions
4: Robust Parameter Estimation
4.1 Introduction
4.2 Problem Description
4.3 Solution to the Suboptimal Problem
4.4 Bounds on Parameter Errors
4.5 Summary and Conclusions
5: Error Quantification
5.1 Introduction
5.1.1 Role of Priors
5.1.2 A Plausible Philosophy
5.1.3 Chapter Layout
5.2 Robust Parameter Estimation
5.3 Quantification of Parameter Errors
5.4 Illustrative Examples
5.5 Conclusions
6: Conclusions
6.1 Linear Model Parameterizations for System Identification
6.2 Robust Estimation
6.3 Error Quantification
Bibliography
Subject Index
Author Index