Tangirala Principles of System Identification

Theory and Practice
1. Auflage 2014
ISBN: 978-1-4398-9602-0
Verlag: CRC Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 908 Seiten

Master Techniques and Successfully Build Models Using a Single Resource

Vital to all data-driven or measurement-based process operations, system identification is an interface that is based on observational science, and centers on developing mathematical models from observed data. Principles of System Identification: Theory and Practice is an introductory-level book that presents the basic foundations and underlying methods relevant to system identification. The overall scope of the book focuses on system identification with an emphasis on practice, and concentrates most specifically on discrete-time linear system identification.

Useful for Both Theory and Practice

The book presents the foundational pillars of identification, namely, the theory of discrete-time LTI systems, the basics of signal processing, the theory of random processes, and estimation theory. It explains the core theoretical concepts of building (linear) dynamic models from experimental data, as well as the experimental and practical aspects of identification. The author offers glimpses of modern developments in this area, and provides numerical and simulation-based examples, case studies, end-of-chapter problems, and other ample references to code for illustration and training.

Comprising 26 chapters, and ideal for coursework and self-study, this extensive text:

- Provides the essential concepts of identification

- Lays down the foundations of mathematical descriptions of systems, random processes, and estimation in the context of identification

- Discusses the theory pertaining to non-parametric and parametric models for deterministic-plus-stochastic LTI systems in detail

- Demonstrates the concepts and methods of identification on different case-studies

- Presents a gradual development of state-space identification and grey-box modeling

- Offers an overview of advanced topics of identification namely the linear time-varying (LTV), non-linear, and closed-loop identification

- Discusses a multivariable approach to identification using the iterative principal component analysis

- Embeds MATLAB® codes for illustrated examples in the text at the respective points

Principles of System Identification: Theory and Practice presents a formal base in LTI deterministic and stochastic systems modeling and estimation theory; it is a one-stop reference for introductory to moderately advanced courses on system identification, as well as introductory courses on stochastic signal processing or time-series analysis.

Zielgruppe

This subject is of interest to chemical, electrical, mechanical and aerospace engineers, and can be used in advanced chemical, mechanical, electrical, control, aeronautic, and mechanical engineering courses at the undergraduate and graduate level. The book has been primarily tailored for beginners and practitioners of identification but it also serves as a useful reference text for researchers.

Autoren/Hrsg.

Tangirala, Arun K.

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik

Inhaltsverzeichnis

PART I INTRODUCTION TO IDENTIFICATION AND MODELS FOR LINEAR DETERMINISTIC SYSTEMS

Introduction

Motivation

Historical developments

System Identification

Systematic identification

Flow of learning material

Software

A Journey into Identification

Identifiability

Signal-to-Noise ratio

Overfitting

A modeling example: liquid level system

Reflections and summary

Mathematical Descriptions of Processes: Models

Definition of a model

Classification of models

Models for Discrete-Time LTI Systems

Convolution model

Response models

Difference equation form

State-space descriptions

Illustrative example in MATLAB: estimating LTI models

Summary

Transform-Domain Models for Linear Time-Invariant Systems

Frequency response function

Transfer function form

Empirical transfer function (ETF)

Closure

Sampling and Discretization

Discretization

Sampling

Summary

PART II MODELS FOR RANDOM PROCESSES

Random Processes

Introductory remarks

Random variables and probability

Probability theory

Statistical properties of random variables

Random signals and processes

Time-series analysis

Summary

Time-Domain Analysis: Correlation Functions

Motivation

Auto-covariance function

White-noise process

Cross-covariance function

Partial correlation functions

Summary

Models for Linear Stationary Processes

Motivation

Basic ideas

Linear stationary processes

Moving average models

Auto-regressive models

Auto-regressive moving average models

Auto-regressive integrated moving average models

Summary

Fourier Analysis and Spectral Analysis of Deterministic Signals

Motivation

Definitions

Fourier representations of deterministic processes

Discrete Fourier Transform (DFT)

Summary

Spectral Representations of Random Processes

Introduction

Power spectral density of a random process

Spectral characteristics of standard processes

Cross-spectral density and coherence

Partial coherence

Spectral factorization

Summary

PART III ESTIMATION METHODS

Introduction to Estimation

Motivation

A simple example: constant embedded in noise

Definitions and terminology

Types of estimation problems

Estimation methods

Historical notes

Goodness of Estimators

Introduction

Fisher information

Bias

Variance

Efficiency

Sufficiency

Cramer-Rao’s inequality

Asymptotic bias

Mean square error

Consistency

Distribution of estimates

Hypothesis testing and confidence intervals

Empirical methods for hypothesis testing

Summary

Appendix

Estimation Methods: Part I

Introduction

Method of moments estimators

Least squares estimators

Non-linear least squares

Summary

Appendix

Estimation Methods: Part II

Maximum likelihood estimators

Bayesian estimators

Summary

Estimation of Signal Properties

Introduction

Estimation of mean and variance

Estimators of correlation

Estimation of correlation functions

Estimation of auto-power Spectra

Estimation of cross-spectral density

Estimation of coherence

Summary

PART IV IDENTIFICATION OF DYNAMIC MODELS - CONCEPTS AND PRINCIPLES

Non-Parametric and Parametric Models for Identification

Introduction

The overall model

Quasi-stationarity

Non-parametric descriptions

Parametric descriptions

Summary

Predictions

Introduction

Conditional expectation and linear predictors

One-step ahead prediction and innovations

Multi-step and infinite-step ahead predictions

Predictor model: An alternative LTI description

Identifiability

Summary

Identification of Parametric Time-Series Models

Introduction

Estimation of AR models

Estimation of MA models

Estimation of ARMA models

Summary

Identification of Non-Parametric Input-Output Models

Recap

Impulse response estimation

Step response estimation

Estimation of frequency response function

Estimating the disturbance spectrum

Summary

Identification of Parametric Input-Output Models

Recap

Prediction-error minimization (PEM) methods

Properties of the PEM estimator

Variance and distribution of PEM-QC estimators

Accuracy of parametrized FRF estimates using PEM

Algorithms for estimating specific parametric models

Correlation methods

Summary

Statistical and Practical Elements of Model Building

Introduction

Informative Data

Input design for identification

Data pre-processing

Time-delay estimation

Model development

Summary

Identification of State-Space Models

Introduction

Mathematical essentials and basic ideas

Kalman filter

Foundations for subspace identification

Preliminaries for subspace identification methods

Subspace identification algorithms

Structured state-space models

Summary

Case Studies

ARIMA model of industrial dryer temperature

Simulated process: developing an input-output model

Process with random walk noise

Multivariable modeling of a four-tank system

Summary

PART V ADVANCED CONCEPTS

Advanced Topics in SISO Identification

Identification of linear time-varying systems

Non-linear identification

Closed-loop identification

Summary

Linear Multivariable Identification

Motivation

Estimation of time delays in MIMO systems

Principal component analysis (PCA)

Summary

References

Index

Über Autor(innen)

Arun K. Tangirala an Associate Professor at the Department of Chemical Engineering, IIT Madras, India. He obtained his B. Tech. (Chemical Engineering) from IIT Madras, India and Ph.D. (Process Control & Monitoring) from the University of Alberta, Canada in the years 1996 and 2001, respectively. Dr. Tangirala specializes in process control, modelling, monitoring and multivariate data analysis. His research group is focused on solving some of the cutting edge problems in data-driven analysis and modelling. A recipient of different teaching and research awards, he has conducted several workshops and short-term courses on data analysis and process identification.