Mathworks SYSTEM IDENTIFICATION TOOLBOX RELEASE NOTES

System Identification Toolbox™ Release Notes

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System Identification Toolbox™ Release Notes

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Summary by Version ............................... 1

Version 7.4 (R2010a) System Identification Toolbox

Software

Version 7.3.1 (R2009b) System Identification Toolbox

Software

Version 7.3 (R2009a) System Identification Toolbox

Software

Version 7.2.1 (R2008b) System Identification Toolbox

Software

Version 7.2 (R2008a) System Identification Toolbox

Software

........................................ 5

........................................ 8

........................................ 9

........................................ 11

........................................ 12

Contents

Version 7.1 (R2007b) System Identification Toolbox

Software

Version 7.0 (R2007a) System Identification Toolbox

Software

Version 6.2 (R2006b) System Identification Toolbox

Software

Version 6.1.3 (R2006a) System Identification Toolbox

Software

Version 6.1.2 (R14SP3) System Identification Toolbox

Software

Version 6.1.1 (R14SP2) System Identification Toolbox

Software

........................................ 19

........................................ 20

........................................ 23

........................................ 25

........................................ 27

........................................ 28

iii

Version 6.0 (R13SP2) System Identification Toolbox

Software

Compatibility Summary for System Identification

Toolbox Software

........................................ 29

................................ 33

iv Contents

SummarybyVersion

This table provides quick access to what’s new in each version. For clarification, see “Using Release Notes” on page 2 .

System Identification Toolbox™ Release Notes

Version (Release)

Latest Versi V7.4 (R2010a

V7.3.1 (R2009b)

V7.3 (R2009a)

V7.2.1 (R2008b)

V7.2 (R

V7.1 (R2007b)

V7.0 (R2007a)

(R2006b)

V6.2

)

2008a)

New Features and Changes

Yes Details

No No Bug Reports No

Yes Details

No Yes

Yes Details

Yes

Detai

Yes Details

Version Compatibilit Consideratio

Yes Summary

No Bug Reports No

Summary

Yes Summary

No Bug Reports No

No Bug Re

No Bug Reports No

Fixed Bugs

and Known

Problems

Bug Reports

Bug Repo

Bug Reports No

ports

rts

Related Documentation at Web Site

Printable R elease Notes: PDF

Current product documentation

V6.1.3 (R2006a)

V6.1.2 (R14SP3)

V6.1.1 (R14SP2)

V6.0 (R13SP2)

Yes Details

No No Bu

No No

etails

Yes Summary

ummary

Bug Reports No

Fixed bugs

No bug fixes

g Reports

V6.0 product documentation

System Identification Toolbox™ Release Notes

Using Release No

Use release note

• New features

• Changes

• Potential imp

Review the re product (for bugs, or comp

If you are up review the c you upgrad

What Is in t

New Featu

• New func

• Changes

s when upgrading to a new er version to learn about:

act o n your existing files and practices

lease notes for other M athWorks™ products required for this

example, MATLAB

atibility considerations in other products impact you.

grading from a softw are version other than the most recent one,

urrent release notes and all interim versions. For example, when

e from V1.0 to V1.2, review the release notes for V1.1 and V1.2.

he Release Notes

res and Changes

tionality

to existing functionality

tes

or Simulink®). Determine if enhancements,

Versio

When a n versi impac

Comp Repo in in comp

Fix

The vi

n Compatibility Considerations

ew feature or change introduces a reported incompatibil ity between

ons, the Compatibility Considerations subsection explains the

atibility issues reported after the product release appear under Bug rts at The MathWorks™ Web site. Bug fixes can sometimes result compatibilities, so review the fixed bugs in Bug Reports for any

atibility impact.

ed Bugs and Known Problems

MathWorks offers a user-searchable Bug Reports database so you can

ew Bug Reports. The development team updates this database at release

SummarybyVersion

time and as more information becomes available. Bug Reports include provisions for any known workarounds or file replacem ents. Information is available for bugs existing in or fixed in Release 14SP2 or later. Information is not avail able for all bugs in earlier releases.

Access Bug Reports using y our MathWorks Account.

About Functions an d Properties Being Removed

This section lists functions or properties removed or in the process of being removed. Functions and properties typically go through several stages across multiple releases before being completely removed. This provides time for you to make adjustments to your code.

• Announcement — The Release Notes announce the planned removal, but

there are no functional changes; the function runs as it did before.

• Warning — When you run the function, it displays a warning message

indicating it will be removed in a future release; otherwise the function runs as it did before.

• Error — When you run the function, it produces an error. The error

message indicates the function was removed and suggests a replacement function, if one is available.

• Removal — When you run the function, it fails. The error message is the

standard message when MATLAB does not recognize an entry.

Functions and properties might be in a stage for one or more releases before moving to another stage. Functions and properties are listed in the Functions and Properties Being Removed section only when they enter a new stage and their behavior changes. For example, if a function displayed a warning in the previous release and errors in this release, it appears on the list. If it continues to display a warning, it does not appear on the list because there was no change between the releases.

Not all functions and properties go through all stages. For example, a function’s impending removal might not be announced, but instead, the first notification might be that the function displays a warning.

System Identification Toolbox™ Release Notes

The Release Notes include actions you can take to mitigate the effects of function or property removal, such as adapting your code to use a replacement function.

Version 7.4 (R2010a) System Identification Toolbox™ Software

Version 7.4 (R2010a) System Identification Toolbox Software

This table summarizes what’s new in Version 7.4 (R2010a):

New Features and Changes

Yes Details below

Version Compatibility Considerations

Yes Summary

New features introduced in this version:

• “New Ability to Use Discrete-Time Linear Models for Nonlinear Black-Box

Estimation” on page 5

• “New Cell Array Support for B and F Polynomials of Multi-Input

Polynomial Models” on page 6

• “Functions and Function Elements Being Removed” on page 7

Fixed Bugs an d Known Problems

Bug Reports

Related Documentation at Web Site

Printable Release Notes: PDF

Current product documentation

New Ability to Use Discrete-Time Linear Models for Nonlinear Black-Box Estimation

You can now use the followin g discrete-time linear models for initializing a nonlinear black-box estimation.

Discrete-time Linear Model

Single-output polynomial model of ARX structure (

Multi-output polynomial model of ARX structure (

idpoly)

idarx)

Use for Initializing...

Single-output nonlinear ARX model estimation

Multi-output nonlinear ARX model estimation

System Identification Toolbox™ Release Notes

Discrete-time Linear Model

Single-output polynomial model of Output-Error (OE) structure (

idpoly) or state-space model with

no disturbance component ( object with K = 0

State-space model with no disturbance component ( object with K = 0)

During estimation, the software uses the linear model orders and delay as initial values of the nonlinear model orders and delay. For nonlinear ARX models, this initialization always provides a better fit to the estimation data than the linear ARX model.

You can use a linear model as an alternative approach to using model orders and delay for nonlinear estimation of the same system.

You can estimate or construct the linear model and then use this model for constructing (see

nlhw) the nonlinear model. For more information, see “Using Linear

Model for Nonlinear ARX Estimation”, and “Using Linear Model for Hammerstein-Wiener Estimation” in the System Identification Toolbox User’s Guide.

idnlarx and idnlhw) or estimating (see nlarx or

idss)

idss

Use for Initializing...

Single-output Hammerstein-Wiener model estimation

Multi-output Hammerstein-Wiener model estimation

New Cell Array Support for B and F Polynomials of Multi-Input Polynomial Models

You can now use cell arrays to specify the B and F polynomials of multi-input polynomial models. The B and F polynomials are represented by the properties of an idpoly object These properties are currently double matrices.

For multi-input polynomial models, these polynomials will be represented by cell arrays only in a future version. If your code performs operations on the and f properties, make one of the following changes in the code:

b and f

Version 7.4 (R2010a) System Identification Toolbox™ Software

• When you construct the model using the idpoly command, use cell arrays

to specify the B and F polynomials. Using cell arrays causes the properties to be represented by cell arrays.

b and f

• After you construct or estimate the model, use the new

When you use cell arrays, you must also update your code to use cell array syntax on

Note For single-input polynomial models, the b and f properties continue to be double row vectors.

Functions and Function Elements Being Removed

Function or Function Element Name

Double matrix support for

b and f properties

of multi-input models.

idpoly

setPolyFormat

command to:

- Convert b and f properties to cell arrays.

- Make the model backward compatible to continue using double matrices

for

b and f properties. This operation ensures that operations on b and

f properties that use matrix syntax continue to work without errors

in a future version.

b and f properties instead of matrix syntax.

What Happens When you Use the Function or Element?

Warns

UseThisInstead Compatibility

Considerations

Use cell array to specify the and f properties of multi-input polynomial models.

If your code performs operations on the

b and f properties,

update the code to be compatible with a future release. See “New Cell Array Support for B and F Polyno m i als of Multi-Input Polynomial Models” on page 6.

System Identification Toolbox™ Release Notes

Version 7.3.1 (R2009b) System Identification Toolbox Software

This table summarizes what’s new in Version 7.3.1 (R2009b):

New Features and Changes

No No Bug Reports No

Version Compatibility Considerations

Fixed Bugs an d Known Problems

Related Documentation at Web Site

Version 7.3 (R2009a) System Identification Toolbox™ Software

Version 7.3 (R2009a) System Identification Toolbox Software

This table summarizes what’s new in Version 7.3 (R2009a):

New Features and Changes

Yes Details below

Version Compatibility Considerations

No Bug Reports

New features introduced in this version:

• “Enhanced Handling of Offsets and Trends in Signals” on page 9

• “Ability to Get Regressor Values in Nonlinear ARX Models” on page 10

Fixed Bugs an d Known Problems

Related Documentation at Web Site

Printable Release Notes: PDF

Current product documentation

Enhanced Handling of Offsets and Trends in Signals

This version of the product includes new and expanded functionality for handling offsets and trends in signals. This data processing operation is necessary for estimating more accurate linear models because linear models cannot capture arbitrary differences between the input and output signal levels.

The previous version of the product let you remove me an values or linear trends from steady-state signals using the GUI and the For transient signals, you had to remove offsets and trends using matrix manipulation.

detrend function.

The GUI functionality for removing means and linear trends from signals is unchanged. However, you can now do the following at the command line:

• Save the values of means or linear trends removed during detrending using

anew to detrend other data sets. You can also restore subtracted trends to the

detrend output argument. You can use this saved trend information

System Identification Toolbox™ Release Notes

output simulated by a linear model that w as estimated from detrended data.

For example, this syntax computes and removes mean values from the data, and saves these values to the output variable

[data_d,T]=detrend(data). T is an object with properties that store offset

and slope information for input and output signals.

• Remove any offset or linear trend from the data using a new

detrend

input argument. This is useful for removing arbitrary nonzero offsets from transient data or applying previously saved trend information to any data set.

For example, this syntax removes an offse t or trend specified by

= detrend(data,T)

T: data_d

• Add an arbitrary offset or linear trend to data signals. This is useful when

you want to simulate the response of a linear model about a nonzero equilibrium input-output level and this model was estimated from detrended data.

For example, this syntax adds trend information to a simulated model output

y_sim,whichisaniddata object: y = retrend(y_sim,T). T

specifies the offset and s lope i nformation for inputs and ou t puts.

For more information, see “Handling Offsets and Trends in Data”.

Ability to Get Regressor Values in Nonlinear ARX Models

The getreg command can now return the numerical values of regressors in nonlinear ARX models and provides an intermediate output of nonlinear ARX models.

This advanced functionality converts input and output values to regressors, and passes the regressor values to the

evaluate command to compute

the model response. This incremental step lets you gain insight into the propagation of information through the nonlinear ARX model.

For more information, see the

getreg reference page. To learn more about

the nonlinear ARX model structure, see “Nonlinear Black-Box Model Identification”.

Version 7.2.1 (R2008b) System Identification Toolbox™ Software

Version 7.2.1 (R2008b) System Identification Toolbox Software

This table summarizes what’s new in Version 7.2.1 (R2008b):

New Features and Changes

No Yes

Version Compatibility Considerations

Summary

Functions and Properties Being Removed

For more information about the process of removing functions, see "About Functions a nd Properties Being Removed" in “Wh at Is in the Release Notes” on page 2.

Function or Property Name

model.Algorithm. Trace

What Happens When You Us e Function or Property?

Still runs

Fixed Bugs an d Known Problems

Bug Reports No

Use This Instead Compatibility

model.Algorithm. Display

Related Documentation at Web Site

Considerations

Using

model.Algorithm. Trace

results in a warning.

System Identification Toolbox™ Release Notes

Version 7.2 (R2008a) System Identification Toolbox Software

This table summarizes what’s new in Version 7.2 (R2008a):

New Features and Changes

Yes Details below

Version Compatibility Considerations

Yes Summary

New features introduced in this version are:

• “Simulating Nonlinear Black - Box Models in Simulink Software” on page 12

• “Linearizing Nonlinear Black-Box Models at User-Specified Operating

Points” on page 13

• “Estimating Multiple-Output Models Using Weighted Sum of Least

Squares Minimization Criterion” on page 14

• “Improved Handling of Initial States for Linear and Nonlinear Models”

on page 15

• “Improved Algorithm Options for Linear Models” on page 16

• “New Block Reference Pag es” on page 17

• “Functions and Properties Being Removed” on page 17

Fixed Bugs an d Known Problems

Bug Reports No

Related Documentation at Web Site

Simulating Nonlinear Black-Box Models in Simulink Software

You can now simulate nonlinear ARX and Hammerstein-Wiener models in Simulink using the nonlinear ARX and the Hammerstein-Wiener model blocks in the System Identification Toolbox™ block library. This is useful in the following situations:

• Representing dynamics of a physical component in a Simulink model using

a data-based nonlinear model

Version 7.2 (R2008a) System Identification Toolbox™ Software

• Replacing a complex Simulink subsystem with a simpler data-based

nonlinear model

Note Nonlinear ARX Model and Hammerstein-Wiener Model blocks read variables from the MATLA B (base) workspace or model workspace. When the MATLAB w orkspace and model workspace contain a variable with the same name and this variable is referenced by a Simulink block, the variable in the model workspace takes precedence.

IfyouhaveinstalledReal-TimeWorkshop®software, you can generate code from models containing nonlinear ARX and the Hammerstein-Wiener model blocks. However, you cannot generate code w he n:

• Hammerstein-Wiener models use the

output nonlinearity.

• Nonlinear ARX models use custom regressors or use the

neuralnet nonlinearity estimator.

You can access the new System Identification Toolbox blocks from the Simulink Library Browser. For more information about these blocks, see the IDNLARX Model (nonlinear ARX model) and the IDNLHW Model (Hammerstein-Wiener model) block reference pages.

customnet estimator for input or

customnet or

Linearizing Nonlinear Black-Box Models at User-Specified Operating Points

You can now use the linearize command to linearize nonlinear black-box models, including nonlinear ARX and Hammerstein-Wiener models, at specified operating points. Lineariza tio n produces a first-order Taylor series approximation of the system about an operating point. An operating point is defined by the set of constant input and state values for the model.

Ifyoudonotknowtheoperatingpoint,youcanusethe compute it from specifications, such as steady-state requirements or values of these quantities at a given time instant from the simulation of the model.

findop command to

System Identification Toolbox™ Release Notes

For nonlinear ARX models, if all of the steady-state input and output values are known, you can map these values to the model state values using the

data2state command.

linearize replaces lintan and removes the restriction for linearizing models

containing custom regressors or specific nonlinearity estimators, such as

neuralnet and treepartition.

If you have installed Simu li nk

Control D esign™ software, you can linearize nonlinear ARX and Hammerstein-Wiener models in Simulink after importing them into Simulink.

For more information, see:

• “Linear Approximation of Nonlinear Black-Box Models” about computing

operating points and linearizing models

• “Simulating Model Output” about importing nonlinear black-box models

into Simulink

Estimating Multiple-Output Models Using Weighted Sum of Least Squares Minimization Criterion

You can now specify a custom weighted trace criterion for minimization when estimating linear and nonlinear black-box models for multiple-output systems. This feature is useful for controlling the relative importance of output channels during the estimation process.

The

Algorithm property of linear and nonlinear models now provides the

Criterion field for choosing the minimization criterion. This new field can

have the following values:

•

det — (Default) Specify this option to minimize the determinant of the

prediction error covariance. This choice leads to maximum likelihood estimates of model parameters. It implicitly uses the inverse of estimated noise variance as the weighting function. This option was already available in previous releases.

•

trace — Specify this option to define your own weighing function that

controls the relative weights of output signals during the estimation. This criterion minimizes the weighted sum of least square prediction errors. You

Version 7.2 (R2008a) System Identification Toolbox™ Software

can specify the relative weighting of prediction errors for each output using the new

Weighting field of the Algorithm property. By default, Weighting

is an identity matrix, which means that all outputs are weighed equally. Set

Weighting to a positive semidefinite symmetric matrix .

For more information about these new Algorithm fields for linear estimation, see the Algorithm fields for nonlinear estimation, see the

Algorithm Properties reference page. For more information about

idnlarx and idnlhw

reference pages.

Note If you are estimating a single-output model, det and trace values of the

Criterion field produce the same estimation results.

Improved Handling of Initial States for Linear and Nonlinear Models

The following are new options to handle initial states for nonlinear models:

• For nonlinear ARX models (

vector for initial states when using

idnlarx), you can now specify a numerical

sim or predict by setting the Init

argument. For example:

predict(model,data,'init',[1;2;3;4])

where the last argument is the state vector.

For more information, see the

• For Hammerstein-Wiener models (

the initial states when using

For more information, see the

sim and predict reference pages.

idnlhw), you can now choose to estimate

predict or nlhw by setting INIT='e'.

predict and nlhw reference pages.

If you want to specify your own initial states, see the corresponding model reference pages for a definition of the states for each mode l type.

If you do not know the states, you can use the

findop or the findstates

command to compute the states. For more information about using these commands, see the

findstates(idnlarx),andfindstates(idnlhw) reference pages.

findop(idnlarx), findop(idnlhw),

System Identification Toolbox™ Release Notes

To help you interpret the states of a nonlinear ARX model, you can use the

getDelayInfo command. For more information, see the getDelayInfo

reference page.

The

findstates command is available for all linear and nonlin ear models.

Also see the

findstates(idmodel) and findstates(idnlgrey) reference

pages.

Improved Algorithm Options for Linear Models

The following improvements are available for the Algorithm property of linear models to align linear and nonlinear models (where appropriate) and improve robustness for default settings:

• The

SearchDirection field (model.Algorithm.SearchDirection)has

been renamed to

SeachMethod (model.Algorithm.SearchMethod)tobe

consistent with the nonlinear models, where the corresponding field is

SeachMethod.

• The new

model.Algorithm.SearchMethod='lsqnonlin' uses the lsqnonlin

lsqnonlin option for specifying S earc hMethod is available.

optimizer from the Optimization Toolbox™ software. You must hav e Optimization Toolbox software installed to use this option.

• The improved

for specifying

gn algorithm (subspace Gauss-Newton method) is available

SearchDirection. The updated gn algorithm better handles

the scale of the parameter Jacobians and is also consistent with the algorithm used for nonlinear model estimation.

• The default values for the

(

modelname.Algorithm.LimitError) is changed to 0, which is consistent

LimitError field of the Algorithm property

with the corresponding option for estimating no n linear models. In previous releases,

LimitError default value was 1.6, which robustified

the estimation process against d ata outliers by associating a linear penalty for large errors, rather than a quadratic penalty. Now, there is no robustification by default ( the default setting and plot the prediction errors using

LimitError=0). You can estimate the m odel with

pe(data.model).If

the resulting plot shows occasional large values, repeat the estimation with

model.Algorithm.LimitError set to a value between 1 and 2.

• The

model.Algorithm.Advanced property has a new tolerance field

GnPinvConst corresponding to the gn SearchMethod. GnPinvConst

Version 7.2 (R2008a) System Identification Toolbox™ Software

specifies that singular values of the Jacobian that are smaller than

GnPinvConst*max(size(J))*norm(J)*eps are discarded when computing

the search direction. You can assign a positive real value for this field. Default value is

1e4.

• The default value of

has been changed from possibility of a situation where the estimatio n algorithm does not converge (predictor becomes unstable) while still allowing enough flexibility to capture lightly damped modes.

For more information about Algorithm properties of linear models, see the

Algorithm Properties reference page.

New Block Reference Pages

New documentation for System Identification Toolbox blocks is provided. For more information, see “Block Reference” in the System Identification Toolbox reference documentation.

Functions and Properties Being Removed

For more information about the process of removing functions, see "About Functions a nd Properties Being Removed" in “Wh at Is in the Release Notes” on page 2.

Function or Property Name

lintan

What Happens When You Us e Function or Property?

Still runs

model.Algorithm.Advanced.Zstability property

1.01 to 1+sqrt(eps). The new default reduces the

Use This Instead Compatibility

Considerations

linearize(idnlhw) linearize(idnlarx)

See “Linearizing Nonlinear Black-Box Models at User-Specified Operating Points” on page 13.

System Identification Toolbox™ Release Notes

Function or Property Name

model.Algorithm. SearchDirection

gns option of model.Algorithm. SearchDirection

GnsPinvTol of model.Algorithm.Advanced

What Happens When You Us e Function or Property?

Still runs

Use This Instead Compatibility

Considerations

model.Algorithm. SearchMethod

See “Improved Algorithm Options for Linear Models” on page 16.

GnPinvConst

See “Improved Algorithm Options for Linear Models” on page 16.

Version 7.1 (R2007b) System Identification Toolbox™ Software

Version 7.1 (R2007b) System Identification Toolbox Software

This table summarizes what’s new in Version 7.1 (R2007b):

New Features and Changes

Yes Details below

Version Compatibility Considerations

No Bug Reports No

New feature introduced in this version:

Fixed Bugs an d Known Problems

Related Documentation at Web Site

New Polynomial Nonlinearity Estimator for Hammerstein-Wiener Models

You can now estimate nonlinearities for Hammerstein-Wiener models using a single-variable polynomial at either the input or the output. This nonlinearity estimator is available at the command line.

For more information, see the information about estimating Hammerstein-W iener models, see “Identifying Hammerstein-Wiener Models” in the System Identification Toolbox documentation.

poly1d reference pages. For more

System Identification Toolbox™ Release Notes

Version 7.0 (R2007a) System Identification Toolbox Software

This table summarizes what’s new in Version 7.0 (R2007a):

New Features and Changes

Yes Details below

Version Compatibility Considerations

No Bug Reports No

New features and changes introduced in this version are:

• “New Nonlinear B lack-Box Modeling Options” on page 20

• “New Nonlinear G rey-Bo x Modeling Option” on page 21

• “New Getting Started Guide” on page 22

• “Revised and Expanded User’s Guide” on page 22

Fixed Bugs an d Known Problems

Related Documentation at Web Site

New Nonlinear Black-Box Modeling Options

You can now estimate nonlinear discrete-time black-box models for both single-output and multiple-output time-domain data. T he System Identification Toolbox product supports the following types of nonlinear black-box models:

• Hammerstein-Wiener

• Nonlinear ARX

To learn how to estimate nonlinear black-box models using the System Identification Tool GUI or commands in the MATLAB Command Window, see the System Identification Toolbox documentation.

Note You can estimate Hammerstein-Wiener black-box models from input-output data only. These models do not support time-series data, where thereisnoinput.

Version 7.0 (R2007a) System Identification Toolbox™ Software

New d emos are available to help you explore nonlinear black-box functions. For more information, see the collection of demos in the Tutorials on Nonlinear ARX and Hammerstein-Wiener Model Identification category.

New Nonlinear Grey-Box Modeling Option

You can now estimate nonlinear discrete-time and continuous-time models for arbitrary nonlinear ordinary differential equations using single-output and multiple-output time-domain data, or time-series data (no measured inputs). Models that you can specify as a set of nonlinear ordinary differential equations (ODEs) are called grey-box models.

To learn how to estimate nonlinear grey-box models using the commands in the MATLAB Command W indow, see System Identification Toolbox documentation.

Specify the ODE in a function or a MEX -file. The template file for writing the MEX-file,

matlab/toolbox/ident/nlident.

IDNLGREY_MODEL_TEMPLATE.c, is located in

To estimate the equation parameters, first construct an specify the ODE file and the parameters you want to estimate. Use estimate the ODE parameters. For more information, see the

pem reference pages.

New demos are available to help you e xplore nonlinear grey-box functions. For more information, see the collection ofdemosintheTutorialsonNonlinear Grey-Box Model Identification category.

idnlgrey object to

pem to

idnlgrey and

Optimization Toolbox Search Method for Nonlinear Estimation Is Supported

If you have Optimization Toolbox software installed, you can specify the

lsqnonlin search method for estimating black-box and grey-box nonlinear

models in the MATLAB Command Window.

model.algorithm.searchmethod='lsqnonlin'

For more i nform ation, see the idnlarx, idnlhw,andidnlgrey reference pages.

System Identification Toolbox™ Release Notes

New Getting Star

The System Ident Started Guide. T provides the fo

• “Tutorial – Id

the System Ide linear black-

• “Tutorial – I

Using the GUI graphical u fit single-

• “Tutorial –

Tutorial f and method

Revised a

The Syste expanded

m Identification Toolbox documentation has been rev is ed and

ification Toolbox product now provides a new Getting

his guide introduces fundamental identification concepts and

llowing tutorials to help you get started quickly:

entifying Linear Models Using the GUI” — Tutorial for using

ntification Tool graphica l user interface (GUI) to estimate

box models for single-input and single-output (SISO) data.

dentifying Low-Order Transfer Functions (Process Models)

” — Tutorial for using the System Identification Tool

ser interface (GUI) to estimate low-order transfe r functions to

input and single-output (SISO) data.

Identifying Linear Models Using the Command Line” —

or estimating models using System Identification Toolbox objects

s for multiple-input and single-output (MISO) data.

nd Expanded User’s Guide

ted Guide

Version 6.2 (R2006b) System Identification Toolbox™ Software

Version 6.2 (R2006b) System Identification Toolbox Software

This table summarizes what’s new in Version 6.2 (R2006b):

New Features and Changes

Yes Details below

Version Compatibility Considerations

No Bug Reports No

New feature introduced in this version:

Fixed Bugs an d Known Problems

Related Documentation at Web Site

MATLAB Compiler Support

The System Identification Toolbox product now supports the MATLAB Compiler™ product.

YoucanuseMATLABCompilertotakeMATLABfilesasinputandgenerate redistributable, standalone applications that include System Identification Toolbox functionality, including the following:

• Creating data and model objects

• Preprocessing and manipulating data

• Simulating models

• Transforming models, including conversions between continuous and

discrete time and model reduction

• Plotting transient and frequency response

To use these features, write a function that uses System Identification Toolbox commands. Use the MATLAB Compiler software to create a standalone application from the MATLAB Compiler file. For more information, see the MATLAB Compiler documentation.

System Identification Toolbox™ Release Notes

Standalone applications that include System Identification Toolbox functionality have the following limitations:

• No access to the System Identification library in the S imulink software

(

slident)

• No support for model estimation

Version 6.1.3 (R2006a) System Identification Toolbox™ Software

Version 6.1.3 (R2006a) System Identification Toolbox Software

This table summarizes what’s new in Version 6.1.3 (R2006a):

New Features and Changes

Yes Details below

Version Compatibility Considerations

Yes Details below. See also Summary.

New features and changes introduced in this version are:

• “balred Introduced for Model Reduction” on page 25

• “Search Direction for Minimizing Criteria Can Be Computed by Adaptive

Gauss-Newton Method” on page 25

• “Maximum Number of B isections Used by Line Search Is Increased” on

page 26

Fixed Bugs an d Known Problems

Bug Reports No

Related Documentation at Web Site

balred Introduced for Model Reduction

Use balre d to perform model reduction instead of idmodred.

Search Direction for Minimizing Criteria Can Be Computed by Adaptive Gauss-Newton Method

An adaptive Gauss-Newton method is now available for computing the direction of the line search for cost-function minimization. U se this method when you observe convergence problems in the e stimatio n results, or as an alternative to the Levenberg-Marquard (

lm)method.

The

gna search method was suggested by Adrian Wills, Brett Ninness, and

Stuart Gibson in their paper "On Gradient-Based Search for M ultivariable System Estimates", presented at the IFAC World Congress in Prague in 2005.

gna is an adaptive version of gns and uses a cutoff value for the singular

values of the criterion Hessian, which is adjusted adaptively depending on thesuccessofthelinesearch.

System Identification Toolbox™ Release Notes

Specify the gna method by setting the SearchDirection property to 'gna'. For example:

m = pem(data,model_structure,'se','gna')

The default initial value of gamma in the gna search is 10^-4.Youcanseta different value using the

SearchDirection,seetheAlgorithm Properties reference pages.

Maximum Number of Bisections Used by Line Search Is Increased

The default value for the MaxBisections property, which is the m ax imum number of bisections along the search direction used by line search, is increased from criterion value along the search vector.

InitGnaTol property. For more information about

10 to 25. This increases the number of attempts to find a lower

For more information about

Properties

Functions and Properties Being Removed

For more information about the process of removing functions, see "About Functions a nd Properties Being Removed" in “Wh at Is in the Release Notes” on page 2.

Function or Property Name

idmodred

reference page.

What Happens When You Us e Function or Property?

Still runs

Search properties, see the Algorithm

Use This Instead Compatibility

Considerations

balred

See “balred Introduced for Model Reduction” on page 25.

Version 6.1.2 (R14SP3) System Identification Toolbox™ Software

Version 6.1.2 (R14SP3) System Identification Toolbox Software

This table summarizes what’s new in Version 6.1.2 (R14SP3):

New Features and Changes

No No Bug Reports No

Version Compatibility Considerations

Fixed Bugs an d Known Problems

Related Documentation at Web Site

System Identification Toolbox™ Release Notes

Version 6.1.1 (R14SP2) System Identification Toolbox Software

This table summarizes what’s new in Version 6.1.1 (R14SP2):

New Features and Changes

No No

Version Compatibility Considerations

Fixed Bugs an d Known Problems

Fixed bugs

Related Documentation at Web Site

Version 6.0 (R13SP2) System Identification Toolbox™ Software

Version 6.0 (R13SP2) System Identification Toolbox Software

This table summarizes what’s new in Version 6.0 (R13SP2):

New Features and Changes

Yes Details below

Version Compatibility Considerations

Yes Summary

New features and changes introduced in this version are:

• “idproc Model Object Added” on page 29

• “Estimation and Validation in Frequency Domain Now Supported” on

page 30

• “Continuous-Time Data Can Now Be Stored U sing Frequency-Domain

Objects” on page 30

• “Simulink Software Now Supports iddata and idmodel Objects” on page 31

• “advice About Data and M odels Now Available” on page 31

• “Theta Models No Longer Supported” on page 31

Fixed Bugs an d Known Problems

No bug fixes

Related Documentation at Web Site

V6.0 product documentation

idproc Model Object Added

A new model object, i dpro c, is used to represent simple continuous-time process models. This object is characterized by static gain, possible dead time, and dominating time constant(s). A new GUI that supports this object is available in the System Identification Toolbox GUI.

To learn more about this object, type run a demo.

You can also try the command

m = pem(data,'p1d')

iddemopr at the MATLAB prompt to

System Identification Toolbox™ Release Notes

Estimation and V Supported

You can now perfo such as the foll

• Inputs and out

• Frequency-re

Both System I (GUI) suppor

All estimat data and fre frequency

Use the domains. U frequenc

Type at th

help iddata

ion, simulation, and validation routines accept frequency-domain

-response data must be packaged as an

fft

y-dependent resolution.

e MATLAB prompt:

rm estimation and validation using frequency-domain data,

owing:

puts, entered as frequency-domain data in the

sponse data from a frequency analyzer

dentification Toolbox functions and the graphical user in terf ace

tthis.

quency-response data as inputs, similar to time-domain data. The

and ifftfunctions to transform between the time and frequency

se the

spafdr function to estimate frequency responses using

alidation in Frequency Domain Now

iddata object

frd or idfrd object.

idprops data

mplete descriptions. To access a demo, type

for co

Cont Freq

You c Cont arb For

inuous-Time Data C an Now Be Stored Using uency-Domain Objects

an now store continuous-time data as a frequency-domain data object.

inuous-time Fourier-transformed data is now stored at a finite number of

itrary frequencies, letting you estimate continuous-time models directly.

example, type at the MATLAB prompt:

help oe

iddemofr.

Version 6.0 (R13SP2) System Identification Toolbox™ Software

Simulink Software Now Supports iddata and idmodel Objects

You can now import and simulate System Identification Toolbox idmodel models in the Simulink environment. You can also import iddata objects into Simulink.

The command can use to simulate contains data sources and sinks f or

slident opens a System Identification block library, which you

idmodel m odels (with or without noise). This library also

iddata objects.

advice About Data and Models Now Available

Use the new advice command to get helpful tips about the quality, problems, and options associated with an

For more information, type at the MATLAB prompt:

help iddata/advice

and

help idmodel/advice

iddata or idmodel object.

Theta Models No Longer Supported

Theta models (matrices) are no longer supported.

Functions and Properties Being Removed

For more information about the process of removing functions, see "About Functions a nd Properties Being Removed" in “Wh at Is in the Release Notes” on page 2.

System Identification Toolbox™ Release Notes

Function or Property Name

th2par

th2ss

What

Use This Instead Compatibility Happens When You Us e Function or Property?

Errors None

Still runs

None

Considerations

See “Theta Models No Longer Supported” on page 31.

Compatibility Summary for System Identification Toolbox™ Software

Compatibility Summary for System Identification Toolbox Software

This table summarizes new features and changes that might cause incompatibilities when you upgrade from an earlier version, or wh en you use files on multiple versions. Details are provided with the description of the new feature or change.

Version (Relea s e) New Features and Changes with Version

Compatibility Impact

Latest Version V7.4 (R20010a)

V7.3.1 (R2009b)

V7.3 (R2009a)

V7.2.1 (R2008b) See “Functions and Properties Being

V7.2 (R2008a) See “Functions and Properties Being

V7.1 (R2007b)

V7.0 (R2007a)

V6.2 (R2006b)

V6.1.3 (R2006a) See “Functions and Properties Being

V6.1.2 (R14SP3)

V6.1.1 (R14SP2)

V6.0 (R13SP2) See “Functions and Properties Being

See “Functions and Function Elements Being Removed” on page 7.

None

Removed” on page 11.

Removed” on page 17.

None

Removed” on page 26.

None

Removed” on page 31.

Mathworks SYSTEM IDENTIFICATION TOOLBOX RELEASE NOTES

Specifications and Main Features

Frequently Asked Questions

User Manual