《自动控制原理》课程教学资源（The MathWorks - MATLAB 相关电子书籍）12 System Identification Toolbox For Use with MATLAB User’s Guide Version 5

System Identification Toolbox For Use with MATLAB Lennart Ljung Computation Visualization Programming User's Guide Version 5

Computation Visualization Programming User’s Guide Lennart Ljung System Identification Toolbox For Use with MATLAB® Version 5

Contents Preface Using This Guide Typographical Conventions Related Products About the author The System Identification Problem Common Terms Used in System Identification Basic Information About Dynamic Models 1-6 The Signals 1-6 The Basic Dynamic Model -7 Variants of Model Descriptions How to Interpret the Noise Source 1-8 Terms to Characterize the Model Properties 1-10 The Basic Steps of System Identification 1-12 A Startup Identification Procedure 1-14 Step 1: Looking at the Data 1-14 Step 2: Getting a Feel for the Difficulties Step 3: Examining the Difficulties 1-15 Step 4: Fine Tuning Orders and Disturbance Structures 1-16 Multivariable Systems 1-18 Reading More About System Identification 1-21

i Contents Preface Using This Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi Typographical Conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii Related Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii About the Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv 1 The System Identification Problem Common Terms Used in System Identification . . . . . . . . . . 1-4 Basic Information About Dynamic Models . . . . . . . . . . . . . . 1-6 The Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-6 The Basic Dynamic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-7 Variants of Model Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . 1-7 How to Interpret the Noise Source . . . . . . . . . . . . . . . . . . . . . . . 1-8 Terms to Characterize the Model Properties . . . . . . . . . . . . . . 1-10 The Basic Steps of System Identification . . . . . . . . . . . . . . . 1-12 A Startup Identification Procedure . . . . . . . . . . . . . . . . . . . . 1-14 Step 1: Looking at the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-14 Step 2: Getting a Feel for the Difficulties . . . . . . . . . . . . . . . . 1-14 Step 3: Examining the Difficulties . . . . . . . . . . . . . . . . . . . . . . 1-15 Step 4: Fine Tuning Orders and Disturbance Structures . . . . 1-16 Multivariable Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-18 Reading More About System Identification . . . . . . . . . . . . 1-21

The Graphical User Interface The model and data boards .2-2 The Working Data 2-3 The views The validation Data 2-4 The work flow 2-4 2-4 Workspace Variables 2-5 Help Texts 6 Handling Data 2-7 Getting Input-Output Data into the GUI Taking a Look at the Data 2-10 Preprocessing Data 2-11 Checklist for Data Handling 2-13 Simulating Data 2-13 Estimati 2-15 The basics 2-15 Direct Estimation of the Impulse Response 2-15 Direct Estimation of the Frequency Response 2-16 Estimation of parametric models 2-17 ARX Models ARMAX, Output- Error and box-Jenkins Models 2-23 State-Space Models 2-25 User Defined Model structures 2-26 Examining Models Views and models 2-28 The plot windows 2-29 Frequency Response and Disturbance Spectra Transient Response 2-31 Poles and zeros 2-31 Compare Measured and Model Output 2-32 Residual Analysis 2-32 Text Information LTI Viewer Further Analysis in the maTLAB Workspace 2-34

ii Contents 2 The Graphical User Interface The Model and Data Boards . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2 The Working Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3 The Views . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3 The Validation Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 The Work Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 Management Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 Workspace Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5 Help Texts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6 Handling Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7 Getting Input-Output Data into the GUI . . . . . . . . . . . . . . . . . . 2-8 Taking a Look at the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-10 Preprocessing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-11 Checklist for Data Handling . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-13 Simulating Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-13 Estimating Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-15 The Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-15 Direct Estimation of the Impulse Response . . . . . . . . . . . . . . . 2-15 Direct Estimation of the Frequency Response . . . . . . . . . . . . . 2-16 Estimation of Parametric Models . . . . . . . . . . . . . . . . . . . . . . . 2-17 ARX Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-20 ARMAX, Output-Error and Box-Jenkins Models . . . . . . . . . . . 2-23 State-Space Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-25 User Defined Model Structures . . . . . . . . . . . . . . . . . . . . . . . . . 2-26 Examining Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-28 Views and Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-28 The Plot Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-29 Frequency Response and Disturbance Spectra . . . . . . . . . . . . 2-30 Transient Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-31 Poles and Zeros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-31 Compare Measured and Model Output . . . . . . . . . . . . . . . . . . . 2-32 Residual Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-32 Text Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-33 LTI Viewer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-34 Further Analysis in the MATLAB Workspace . . . . . . . . . . . . . 2-34

Some Further GUI Topics 2-35 Mouse Buttons and Hotkeys 2-35 Troubleshooting in Plots Layout Questions and idprefs mat 2-36 Customized plots 2-37 What Cannot be Done Using the GUI 2-37 Tutorial 3 The toolbox commands An Introductory Example to Command Mod The System Identification Problem .3-9 Impulse Responses, Frequency Functions, and Spectra 3-9 Polynomial Representation of Transfer Functions State-Space Representation of Transfer Functions 3-13 Continuous-Time State-Space Models 3-14 Estimating Impulse Responses 3-1 Estimating Spectra and Frequency Functions 3-15 Estimating Parametric Models 3-16 Subspace Methods for Estimating State-Space Models 3-17 Data Representation and Nonparametric Model estimation 3-18 Data Representation Correlation Analysis 3-19 Spectral Analysis 3-19 More on the Data Representation in iddata 3-21 Parametric Model estimation 3-25 ARX Model AR Models 3-26 General Polynomial Black-Box Models State-Space Models 3-28 Optional Variables

iii Some Further GUI Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-35 Mouse Buttons and Hotkeys . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-35 Troubleshooting in Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-36 Layout Questions and idprefs.mat . . . . . . . . . . . . . . . . . . . . . . 2-36 Customized Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-37 What Cannot be Done Using the GUI . . . . . . . . . . . . . . . . . . . 2-37 3 Tutorial The Toolbox Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3 An Introductory Example to Command Mode . . . . . . . . . . . . 3-5 The System Identification Problem . . . . . . . . . . . . . . . . . . . . . 3-9 Impulse Responses, Frequency Functions, and Spectra . . . . . . 3-9 Polynomial Representation of Transfer Functions . . . . . . . . . 3-11 State-Space Representation of Transfer Functions . . . . . . . . . 3-13 Continuous-Time State-Space Models . . . . . . . . . . . . . . . . . . . 3-14 Estimating Impulse Responses . . . . . . . . . . . . . . . . . . . . . . . . . 3-15 Estimating Spectra and Frequency Functions . . . . . . . . . . . . . 3-15 Estimating Parametric Models . . . . . . . . . . . . . . . . . . . . . . . . . 3-16 Subspace Methods for Estimating State-Space Models . . . . . . 3-17 Data Representation and Nonparametric Model Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-18 Data Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-18 Correlation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-19 Spectral Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-19 More on the Data Representation in iddata . . . . . . . . . . . . . . . 3-21 Parametric Model Estimation . . . . . . . . . . . . . . . . . . . . . . . . . 3-25 ARX Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-26 AR Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-26 General Polynomial Black-Box Models . . . . . . . . . . . . . . . . . . . 3-27 State-Space Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-28 Optional Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-30

Defining Model Structures Polynomial Black-Box Models: The idpoly Model 3-36 Multivariable arx models: The idarx mode Black-Box State-Space Models: the idss Model 3-3 Structured State-Space Models with Free Parameters: the idss model State-Space Models with Coupled Parameters State-Space Structures: Initial Values and Numerical Derivatives 3-47 Examining Models 349 Parametric Models: idmodel and its children 3-49 Frequency Function Format: the idfrd model 3-55 Graphs of Model Properti 3-56 Transformations to Other Model Representations 3-59 Discrete and Continuous Time models Model structure selection and validation Comparing Different Structures 3- Impulse Response to Determine Delays 3-66 Checking Pole-Zero Cancellations Residual analysis 3-66 Model error models 3-67 Noise- Free simulations Assessing the Model Uncertainty 368 Comparing Different Models 3-70 Selecting Model Structures for Multivariable Systems 3-70 Dealing with Data Offset levels 3-74 Outliers and Bad Data; Multi-Experiment Data 3-74 Missing Data 3-75 Filtering Data: Focus 3-75 Feedback in Data 376 Delays

iv Contents Defining Model Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-35 Polynomial Black-Box Models: The idpoly Model . . . . . . . . . . 3-36 Multivariable ARX Models: The idarx Model . . . . . . . . . . . . . . 3-37 Black-Box State-Space Models: the idss Model . . . . . . . . . . . . 3-39 Structured State-Space Models with Free Parameters: the idss Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-42 State-Space Models with Coupled Parameters: the idgrey Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-44 State-Space Structures: Initial Values and Numerical Derivatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-47 Examining Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-49 Parametric Models: idmodel and its children . . . . . . . . . . . . . . 3-49 Frequency Function Format: the idfrd model . . . . . . . . . . . . . 3-55 Graphs of Model Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-56 Transformations to Other Model Representations . . . . . . . . . 3-59 Discrete and Continuous Time Models . . . . . . . . . . . . . . . . . . . 3-60 Model Structure Selection and Validation . . . . . . . . . . . . . . 3-63 Comparing Different Structures . . . . . . . . . . . . . . . . . . . . . . . . 3-63 Impulse Response to Determine Delays . . . . . . . . . . . . . . . . . . 3-66 Checking Pole-Zero Cancellations . . . . . . . . . . . . . . . . . . . . . . . 3-66 Residual Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-66 Model Error Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-67 Noise-Free Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-68 Assessing the Model Uncertainty . . . . . . . . . . . . . . . . . . . . . . . 3-68 Comparing Different Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-70 Selecting Model Structures for Multivariable Systems . . . . . . 3-70 Dealing with Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-74 Offset Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-74 Outliers and Bad Data; Multi-Experiment Data . . . . . . . . . . . 3-74 Missing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-75 Filtering Data: Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-75 Feedback in Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-76 Delays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-77

Recursive parameter estimation The Basic Algorithm 378 Choosing an Adaptation Mechanism and Gain Available algorithms 3-81 Segmentation of Data Some Special Topics Time Series Modeling 3-85 Periodic Inputs 3-87 Connections Between the Control System Toolbox and the System Identification Toolbox Speed Trade-Offs 3-89 Local minima 3-90 Initial Parameter values Initial State 3-91 The estimated parameter Covariance matrix 3-92 No Covariance 3-92 nk and InputDelay 3-93 Linear Regression Models Spectrum Normalization and the Sampling Interval 3-94 Interpretation of the Loss Function Enumeration of Estimated parameters 3-98 Complex-Valued Data 3-98 Strange Results Command reference ac 4-9 Algorithm Properties 4-17 armax 4-23 arxdata arxstruc b 4-28 4-31 compare

v Recursive Parameter Estimation . . . . . . . . . . . . . . . . . . . . . . 3-78 The Basic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-78 Choosing an Adaptation Mechanism and Gain . . . . . . . . . . . . 3-79 Available Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-81 Segmentation of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-83 Some Special Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-85 Time Series Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-85 Periodic Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-87 Connections Between the Control System Toolbox and the System Identification Toolbox . . . . . . . . . . . . . . . . . . . . . . . 3-87 Memory - Speed Trade-Offs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-89 Local Minima . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-90 Initial Parameter Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-90 Initial State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-91 The Estimated Parameter Covariance Matrix . . . . . . . . . . . . . 3-92 No Covariance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-92 nk and InputDelay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-93 Linear Regression Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-94 Spectrum Normalization and the Sampling Interval . . . . . . . 3-94 Interpretation of the Loss Function . . . . . . . . . . . . . . . . . . . . . 3-97 Enumeration of Estimated Parameters . . . . . . . . . . . . . . . . . . 3-98 Complex-Valued Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-98 Strange Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-99 4 Command Reference aic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9 Algorithm Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-10 ar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-17 armax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-20 arx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-23 arxdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-25 arxstruc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-26 bj . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-28 bode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-31 compare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-34

cra detr d2 4-41 4-47 fr 448 50 4-51 4-52 iddata ident idfilt 4-62 470 4-75 idmodel idmodred 4-86 idpoly idss impulse Ivar 4-102 IVX 4-105 iv4 4-106 LTI commands 4-10 merge(idmodel 4-110 4-111 mindat 4-112 nkshift 4-113 4-114 underst 4-116 4-117 n4sid 4-120 4-125

vi Contents covf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-36 cra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-37 c2d . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-39 detrend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-40 d2c . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-41 EstimationInfo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-43 etfe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-45 ffplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-47 freqresp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-48 fpe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-50 get . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-51 idarx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-52 iddata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-55 ident . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-61 idfilt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-62 idfrd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-64 idgrey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-70 idinput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-75 idmodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-78 idmodred . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-86 idpoly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-87 idss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-92 impulse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-98 init . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-101 ivar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-102 ivstruc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-103 ivx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-105 iv4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-106 LTI commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-107 merge (iddata) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-108 merge (idmodel) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-110 midprefs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-111 misdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-112 nkshift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-113 noisecnv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-114 nuderst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-116 nyquist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-117 n4sid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-120 oe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-123 pe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-125

4-126 plot (iddata) 4-130 plot(idmodel 4-131 yep 4-13 4-134 pzmap 4-137 armax 4-139 4-141 b 4-145 resample 4-147 esid 4-148 4-150 4-154 segment 4-155 4-158 setpname 4-162 sims 4-164 4-165 spa 4-167 ss, tf, zpk, frd 4-170 4-172 step 4-174 struc 4-177 timestamp 4178 tfdata 4-179 4-181 kata

vii pem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-126 plot (iddata) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-130 plot (idmodel) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-131 polydata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-133 predict . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-134 present . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-136 pzmap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-137 rarmax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-139 rarx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-141 rbj . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-145 resample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-147 resid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-148 roe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-150 rpem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-152 rplr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-154 segment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-155 selstruc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-158 set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-160 setpname . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-161 sim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-162 simsd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-164 size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-165 spa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-167 ss, tf, zpk, frd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-170 ssdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-172 step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-174 struc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-177 timestamp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-178 tfdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-179 view . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-181 zpkdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-183

Preface What Is the System Identification Toolbox? Using This Guide Typographical Conventions Related Products About the author

Preface What Is the System Identification Toolbox? . . . . . . . x Using This Guide . . . . . . . . . . . . . . . . . . . xi Typographical Conventions . . . . . . . . . . . . . .xii Related Products . . . . . . . . . . . . . . . . . . xiii About the Author . . . . . . . . . . . . . . . . . . .xv

What Is the system Identification Toolbox? The System Identification Toolbox is fo ing accurate, models of complex systems from noisy time-series data It provides tools for creating mathematical models of dynamic systems based on observed input/output data. The toolbox features a flexible graphical user interface that aids in the organization of data and models The identification techniques provided with this toolbox are useful for applications ranging from control system design and signal processing to time-series analysis and vibration analysis

Preface x What Is the System Identification Toolbox? The System Identification Toolbox is for building accurate, simplified models of complex systems from noisy time-series data. It provides tools for creating mathematical models of dynamic systems based on observed input/output data. The toolbox features a flexible graphical user interface that aids in the organization of data and models. The identification techniques provided with this toolbox are useful for applications ranging from control system design and signal processing to time-series analysis and vibration analysis