【实例简介】

系统辨识大牛Ljung编写的MATLAB系统辨识使用手册，这本书详细地介绍了在MATLAB已经所属simulink环境下，系统辨识工具箱的一些使用办法，是一本非常经典的教材！

Revision History

pril 1988

First printing

July 1991

Second printing

M

ay1995

Third printing

November 2000 Fourth printing

Revised for Version 5.0(Release 12)

pril 2001

Fifth printing

July 2002

Online only

Revised for Version 5.0.2 Release 13)

June 2004

Sixth printing

Revised for Version 6.0.1(Release 14)

March 2005

Online only

Revised for Version 6.1.1Release 14SP2)

September 2005 Seventh printing

Revised for Version 6.1.2(Release 14SP3)

March 2006

Online only

Revised for Version 6.1.3(Release 2006a)

September 2006 Online only

Revised for Version 6.2 Release 2006b)

March 2007

Online only

Revised for Version 7.0 ( Release 2007a)

September 2007 Online only

Revised for Version 7.1 (Release 2007b

March 2008

Online only

Revised for Version 7.2(Release 2008a)

October 2008

Online only

Revised for Version 7.2.1 Release 2008b)

March 2009

Online only

Revised for Version 7.3(Release 2009a)

September 2009 Online only

Revised for Version 7.3.1(Release 2009b)

March 2010

Online only

Revised for Version 7. 4 (Release 2010a)

eptember

2010 Online only

Revised for Version 7.4.1(Release 2010b)

pril 2011

Online onl

Revised for Version 7.4.2(Release 2011a)

September 2011 Online only

Revised for Version 7.4.3(Release 2011b)

March 2012

Online only

Revised for Version 8.0( Release 2012a

about the Developers

About the Developers

ystem Identification Toolbox software is developed in association with the

following leading researchers in the system identification field

Lennart Ljung. Professor Lennart Ljung is with the department of

Electrical Engineering at Linkoping University in Sweden. He is a recognized

leader in system identification and has published numerous papers and books

in this area

Qinghua Zhang. Dr. Qinghua Zhang is a researcher at Institut National

de recherche en Informatique et en Automatique(INria) and at Institut de

Recherche en Informatique et systemes Aleatoires (Irisa), both in rennes

France. He conducts research in the areas of nonlinear system identification

fault diagnosis, and signal processing with applications in the fields of energy

automotive, and biomedical systems

Peter Lindskog. Dr. Peter Lindskog is employed by nira dynami

AB, Sweden. He conducts research in the areas of system identification

signal processing, and automatic control with a focus on vehicle industry

applications

Anatoli Juditsky. Professor Anatoli Juditsky is with the laboratoire Jean

Kuntzmann at the Universite Joseph Fourier, Grenoble, france. He conducts

research in the areas of nonparametric statistics, system identification, and

stochastic optimization

About the developers

Contents

Choosing Your System Identification Approach

Linear model structures

1-2

What Are Model objects?

Model objects represent linear systems

About model data

1-5

Types of Model objects

Dynamic System Models

1-9

Numeric Models

1-11

umeric Linear Time Invariant (LTD Models

1-11

Identified LTI models

Identified Nonlinear models

1-12

Nonlinear model structures

1-13

Recommended Model Estimation Sequence

1-14

Supported Models for Time- and Frequency-Domain

Data

,,,,,,,1-16

Supported Models for Time-Domain Data

1-16

Supported Models for Frequency-Domain Data

1-17

See also

1-18

Supported Continuous-and Discrete-Time Models

1-19

Model estimation commands

1-21

Creating Model Structures at the command Line ... 1-22

about system Identification Toolbox Model Objects ... 1-22

When to Construct a Model Structure Independently of

Estimation

1-23

Commands for Constructing Model Structures

1-24

Model Properties

1-25

See als

1-27

Modeling Multiple-Output Systems ......... 1-28

About Modeling multiple-Output Systems

1-28

Modeling Multiple Outputs Directly

1-29

Modeling multiple outputs as a Combination of

Single-Output Models.......

1-29

Improving Multiple-Output Estimation Results by

Weighing Outputs During Estimation ....... 1-30

Identified linear Time-Invariant models

1-32

IDLTI Models

1-32

Configuration of the Structure of Measured and Noise o

Representation of the Measured and noise Components fo

Various model Types

1-33

Components ....

1-35

Imposing Constraints on the Values of Mode

Parameters

1-37

Estimation of Linear models

1-8

Data Import and Processing

2「

Supported Data ...

2-3

Ways to Obtain Identification Data

Ways to Prepare Data for System Identification ... 2-6

Requirements on Data Sampling

Representing Data in MATLAB Workspace

·····

Time-Domain Data Representation

2-9

Time-Series Data Representation

2-10

Contents

Frequency-Domain Data Representation ....... 2-11

Importing Data into the Gui

2-17

Types of Data You Can import into the GUi

2-17

Importing time-Domain Data into the GUI

2-18

Importing Frequency-Domain Data into the GUI

2-22

Importing Data Objects into the GUI ......... 2-30

Specifying the data sampling interval

2-34

Specifying estimation and validation Data

2-35

Prep

ing data Using Quick Start

Creating Data Sets from a Subset of Signal Channelo

2-36

2-37

Creating multiexperiment Data Sets in the gUi

2-39

Managing data in the gui ............. 2-46

Representing Time- and Frequency-Domain Data Using

iddata object

2-55

iddata constructor

2-55

iddata Properties.........

2-58

Creating Multiexperiment Data at the Command Line .. 2-61

Select Data Channels, I/O Data and Experiments in iddata

Objects

2-63

Increasing Number of Channels or Data Points of iddata

Objects

2-67

Managing iddata Objects

2-69

Representing Frequency-Response Data Using idfrd

Obiec

2-76

idfrd Constructor

2-76

idfrd Properties

2-77

Select I/o Channels and Data in idfrd Objects ..... 2-79

Adding Input or Output Channels in idfrd Objects

2-80

Managing idfrd Objects

2-83

Operations That Create idfrd Objects

2-83

Analyzing Data quality

2-85

Is your data ready for modeling?

2-85

Plotting Data in the guI Versus at the command line

2-86

How to plot data in the gui

2-86

How to plot data at the command line

2-92

How to Analyze Data Using the advice Command

2-94

Selecting Subsets of Data

2-96

IX

Why Select Subsets of Data?

2-96

Extract Subsets of Data Using the GUI

2-97

Extract Subsets of data at the Command Line

2-99

Handling Missing Data and outliers

2-100

Handling missing data

2-100

Handling outliers

2-101

Extract and Model Specific Data Segments

2-102

See also

2-103

Handling offsets and Trends in Data

2-104

When to detrend data

2-104

Alternatives for Detrending Data in GUi or at the

Command-Line

2-105

Next Steps After detrending

2-107

How to Detrend Data Using the Gui

2-108

How to detrend data at the Command line

2-109

Detrending Steady-State Dat

109

cending transient Dat

2-109

See also

2-110

Resampling Data

2-111

What Is resampling?...,,.,,,,,,,,,,,.2-111

Resampling data without Aliasing Effects

2-112

See also

2-116

Resampling data Using the GUi

.,,,,2-117

Resampling Data at the Command line

2-118

Filtering Data

2-120

Supported Filters

2-120

Choosing to Prefilter Your Data

2-120

See also

2-121

How to Filter Data Using the gui

2-122

Filtering Time-Domain Data in the GuI........ 2-122

Content

【实例截图】

【核心代码】