Mathworks SIMULINK DESIGN OPTIMIZATION 1 Installation Guide

Simulink®Design O

Getting Started Guide

ptimization™ 1

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Revision History

March 2009 Online only New for Version 1 (Release 2009a)
September 2009 Online only Revised for Version 1.1 (Release 2009b)
March 2010 Online only Revised for Version 1.1.1 (Release 2010a)

Design Optimization™ Getting Started Guide

1

Contents

Product Overview

What You Can Acc

Learn More

Required and

Documentat

Accessing D
Accessing

....................................... 1-4

Related Products

ion and Demos

ocumentation

Demos

omplish Using This Product

.....................

.........................

...........................

..................................

Paramete

.......

r Estimation

2

Types of Data for Parameter Estimation ............. 2-2

Quick Start — Estimating Model Parameters

Parallel Com puting for Param eter Estimation

......... 2-3

....... 2-11

1-2

1-5

1-6
1-6
1-6

Parameter Optimization

3

Types of T ime-Domain Design Requirements for

Optimizing P a rameters

Quick Start — Optimizing Model P arameters

.......................... 3-2

........ 3-3

iii

Parallel Com puting for Pa ram eter Optimization ..... 3-11

Optimization-Based Linear Control Design

4

When to Use Optimization-Based Linear Control

Design

Types of Time- and Frequency-Domain Design

Requirements

Quick Start — Optimization-Based Linear Control

Design

.......................................... 4-2

................................... 4-3

.......................................... 4-4

Tutorial — Preparing Data for Parameter

Estimation Using the GUI

iv Contents

5

About This Tutorial ................................ 5-2

Objectives
About the Sample Data

Configuring a Project for Parameter Estimation

ImportingDataintotheGUI

Importing Input Data and Time Vector
Importing Output Data and Time Vector

Analyzing Data

SelectingDataforEstimation

Selecting Output Data
Selecting Input Data

........................................ 5-2

............................. 5-2

........................ 5-6

................ 5-6

.............. 5-11

.................................... 5-14

....................... 5-16

............................. 5-16

............................... 5-22

..... 5-4

Removing Outliers ................................. 5-25

Why Remove Outliers
How to Remove O utliers

.............................. 5-25

............................ 5-25

Filtering Data

Filtering Output Data
Filtering Input Data

Interpolating Missing Data

Saving the Project

..................................... 5-29

.............................. 5-29

............................... 5-32

......................... 5-34

................................. 5-37

Tutorial — Estimating Parameters from

Measured Data Using the GUI

6

About This Tutorial ................................ 6-2

Objectives
About the Model

Estimating Model Parameters Using Default Estimation

Settings

Overview o f the Estimation Process
Specifying Parameters and Estimation Data
Validating Model Parameters

........................................ 6-2

.................................. 6-3

........................................ 6-7

................... 6-7

........... 6-8

....................... 6-13

Improving Estimation Results Using Parameter

Bounds

Strategy for Improving the Estimation Results
How to Specify Parameter Bounds

Validating Estimated Model Parameters

......................................... 6-20

......... 6-20

.................... 6-20

............. 6-26

v

Tutorial — Optimizing Parameters to Meet

Time-Domain Requirements Using the GUI

7

About This Tutorial ................................ 7-2

Objectives
About the Model
Design Requirements

........................................ 7-2

.................................. 7-2

.............................. 7-4

Configuring a Model for Optimizing Parameters

Optimizing Model Parameters to M eet Step Response

Requirements

Specify Time-Domain Design Requirements
Specifying Parameters to Optimize
Optimizing the Parameters

Refining Model Parameters to Track a Reference

Signal

Saving the Project

.......................................... 7-25

................................... 7-8

............ 7-8

................... 7-18

......................... 7-21

................................. 7-31

Tutorial — Optimizing Parameters to Meet

Time-Domain Requirements Using the

Command Line

8

About This Tutorial ................................ 8-2

Objectives
About the Model
Design Requirements

........................................ 8-2

.................................. 8-2

.............................. 8-4

..... 7-5

vi Contents

Configuring a Model for Optimizing Parameters

Optimizing Model Parameters to M eet Step Response

Requirements

................................... 8-8

..... 8-5

Refining Model Parameters to Track a Reference

Signal

.......................................... 8-15

Tutorial — Designing a PID Controller Using

Optimization-Based Tuning

9

About This Tutorial ................................ 9-2

Objectives
About the Model
Design Requirements

Configuring a Project for Optimization-Based Control

Design

Designing an Initial PID Controller to Meet Bode

Magnitude and Phase Margins Requirements

Specifying the Controller Parameters
Specifying Bode Magnitude and Phase Margin Design

Requirements

Designing the Controller

........................................ 9-2

.................................. 9-2

.............................. 9-4

.......................................... 9-5

...... 9-11

................. 9-11

.................................. 9-15

........................... 9-25

Refining the Controller Design to Meet Controller

Output Bound s

Saving the Project

.................................. 9-32

................................. 9-48

Examples

A

Getting Started .................................... A-2

vii

Index

viii Contents

Product O verview

• “What You Can Accomplish Using This Product” on page 1-2

• “Learn More” on page 1-4

• “Required and Related Products” on page 1-5

• “Documentation and Demos” on page 1-6

1

1 Product Overview

What You Can Accomplish Using This Product

Simulink®Design Optimization™ software is a Simulink®-based product
that lets you estimate and optimize model parameters using numerical
optimization. You can also u se this software to estimate initial conditions and
lookup table values, and test and optimize designs for robustness.

Simulink Design Optimization software also supports parame ter optimization
for Simulink models that invoke third-party simulation tools or contain legacy
simulation code (using S-functions).

This software uses methods from the Optimization Toolbox™ and Global
Optimization Toolbox products for design optimization. Using the software
with the Parallel Computing Toolbox™ software can speed up numerical
computations during estimation and optimization.

The software provides library blocks that let you accomplish the following
goals:

1-2

• Model time-varying systems as lookup tables using Adaptive Lookup Table

blocks.

• Optimize model param eters by graphically specifying time-domain design

requirements and reference signal using Signal Constraint blocks.

• Compute and optimize continuous and discrete root mean square values of

signals using CRMS and DRMS blocks with Signal Constraint blocks.

When the Control System Toolbox™ software is installed, you can refine
controller parameters of linear time-invariant (LTI) models and Simulink
models, linearized using the Simulink
SISO Design Tool.

You can work either in the Graphical User Interface (GUI) or at the command
line. New users should start by using the GUI to becom e familiar with the
product. The following operations are available only using the GUI:

• Interactive data preprocessing (see “Data Analysis and Processing”).

• Model validation using residual plot (see “Comparing Residuals”).

®

Control Design™ software, in the

What You Can A ccomplish Using T his Product

• Optimization-based controller design (see “Optimization-Based Linear

Control Design”).

1-3

1 Product Overview

Learn More

The Simulink Design Optim ization documentation provides information t o
use this product. Although this product employs optimization methods for
parameter estimation and optimization, using the product does not require
youtohaveastrongbackgroundinoptimization theory. You may find it
helpful to consult the Optimization Toolbox documentation to learn more
about optimization theory, and minimizing an objective function.

1-4

Required and Related Products

Simulink Design Optimization software requires MATLAB®, Simulink, and
Optimization Toolbox software.

ThefollowingtablesummarizesMathWorks™ products that extend
and complement the Simulink Design Optimization software. For
current information about these and other MathWorks products, visit
http://www.mathworks.com/products/product_listing/index.html.

Required and Related Products

Product

Control System Toolbox Enables you to design controllers f or

Global Optimization Toolbox

Neural N

Parallel Computing Toolbox

Simu

Sys

etwork Toolbox™

link Control Design

tem Identification Tool box™

Description

linear time-invariant (LTI) models
using optimization methods.

Provides genetic algorithms, and
direct search methods to estimate
and optimize model parameters.

Provides Simulink models of neural
networks for optimization-based
control design.

Enable
on mult
multi
estim

Lets
Use S
soft
lin
met

Lets you estimate linear and
nonlinear models from measured
data. Impo rt the estimated model
into Simulink software, and use
Simulink Design Optimization
software for optimization -b ased
control design.

s parallel computing

icore process ors and
processor networks to speed up
ation and optimization.

you linearize Simulink models.

imulink Design Optimization

ware to design controllers for

earized models using optimization

hods.

1-5

1 Product Overview

Documentation and Demos

In this section...

“Accessing Documentation” on page 1-6

“Accessing Demos” on page 1-6

Accessing Documentation

The Simulink Design Optimization documentation contains the following
components:

• Getting Started Guide — Provides information for mapping your problem

to the capabilities of the Simulink Design Optimization software.
Step-by-step tutorials walk you through the most common tasks for
estimating parameters, optimizing p arameters, and designing controllers
using optimization methods.

• User’s Guide — Describes estimation and optimization tasks for using the

Simulink Design Optimization software.

1-6

• Reference — Describes commands and blocks for design optimization.

• Release Notes — Describes important changes in the current product

version and compatibility considerations.

If you are new to using this product, the Getting Started Guide helps you
begin using this product quickly. You can follow the steps in the tutorials to
perform design optimization u si ng the graphical user interface (GUI) or the
MATLAB Command Window.

You can also search or browse the documentation for information about
specific design optimization tasks.

Accessing Demos

The Simulink Design Optim i z a t ion software provides demo fi le s that show
you how to estimate and optimize parameters of Simulink models, design
compensators using optimization methods, and model systems using Adaptive
Lookup Tables.

Documentation and Demos

To access demos in the Help browser, type the following command at the
MATLAB prompt:

demo('Simulink', 'Simulink Design Optimization')

1-7

1 Product Overview

1-8

Parameter Estimation

• “Types of Data for P arameter Estimation” on page 2-2

• “Quick Start — Estimating Model Parameters” on page 2-3

• “Parallel Computing for Parameter Estimation” on page 2-11

2

2 Parameter Estimation

Types of Data for Parameter Estimation

You can estimate model parameters and initial conditions of single or
multiple input and output Simulink models from transient data.Youmeasure
transient data when the system is n ot in steady-state to capture the system
dynamics expected under normal operating conditions. For example, the
response o f a system to step or impulse inputs is transient data.

Simulink Design Optimization software lets you e stimate model parameters
from the following types of data:

• Time-domain data — D ata with o ne or more input variables u(t) and one or

more output variables y(t), sampled as a function of time. See “Importing
Data into the GUI”.

• Time-series data — Data stored in tim e-series objects. For more

information, see “Time Series Objects” in the MATLAB documentation. See
“Importing Time-Series Data into the GUI”.

Using complex data for parameter estimation is not directly supported. See
“Importing Complex Data into the GUI”.

2-2

Simulink Design Optimization software estimates model parameters by
comparing the transient data with simu lation data generated from the
Simulink model. Using optimization techniques, the software estimates the
parameters and initial conditions of states to minimize a user-selected cost
function. The cost function typically calculates a least-square error between
the measured and simulated data. To learn more, see “Parameter Estimation”
in the Simulink Design Optimization User’s Guide.

Quick Start — Estimating Model Parameters

Quick Start — Estimating Model Parameters

In this quick start, you get an overview of the typical tasks for estimating
model parameters using the Control and Estimation Tools Manager GUI:

1 Start a parameter estimation task.

2 Import estimation and validation data sets.

3 Select param

4 Estimate the parameters from the estimation data.

5 Validate the estimated parameters using the validation data set.

eters to estimate.

Prerequisites for parameter estimation include:

• Simulink model that contains inport or outport blocks, or signal logging

For more information, see “Configuring a Model for Importing Data” in the
Simulink Design Optimization User’s Guide.

• Transient data in the MATLAB workspace

To estimate model parameters:

1 Start a parameter estimation task by selecting Tools > Parameter

Estimation in the Simulink model window.

2-3

2 Parameter Estimation

New
estimation
task

Optional
information
about the
estimation task

2-4

Quick Start — Estimating Model Parameters

2 Import the input and output data for estimating and validating model

parameters.

a Select the Transient Data node, and click New.

b Select the New Data node.

c In the Input Data tab, select the Data cell corresponding to the model

channel, and click Import. Select the variable to import in the D ata
Import dialog box.

Model channel
for which you
import data

Select and
click Import
to import data

Select and
click Import to
import time vector

Select variable
to import

Import variable

d Select the Time /Ts cell, a nd click Import. Select the time vector to

import in the Data Import dialog box.

e In the Output Data tab, repeat steps c–d to import the output data

and time vector.

2-5

2 Parameter Estimation

f Repeat steps a–d to import the validation data set.

Validation
data node

For more information, see “Importing Data into the G UI” in the Simulink
Design Optimization User’s Guide.

2-6

3 Specify parameters to estimate by selecting the Variables node, and

clicking Add. Select the parameters in the Select Parameters dialog box.

Select
parameters
to estimate

Specify parameters

Quick Start — Estimating Model Parameters

For more information, see “Specifying Parameters to Estimate” in the
Simulink Design Optimization User’s Guide.

4 Estimate the parameters.

a Select the Estimation node, and click New.

b Select the New Estimation node.

c In the Data Sets tab, select the estimation data set.

Lists imported
data sets

Select data set
for estimation

2-7

2 Parameter Estimation

d In the Parameters tab, select the parameters to estimate.

Keep parameters
fixed or estimate

(Optional) Specify
initial parameter value

(Optional) Specify
parameter bounds

2-8

e In the Es

timation tab, begin estimation by clicking Start.

Information about
convergence of the
estimation algorithm

Start
estimation

Select to
view
measured
and simulated
response plots

Estimation
report

Quick Start — Estimating Model Parameters

f In the Param ete rs tab, examine the estimated parameter values. The

Simulink model also gets updated with the estimated parameter values.

Estimated
parameter values

For mor

Simuli

5 Validate the estimated parameters.

a Select the Validation node, and click New.

b Select the New Validation node.

e information, s ee “Estimating Parameters in the GUI” in the

nk Design Optimization User’s Guide.

2-9

2 Parameter Estimation

c Configure the validation plots and the validation data set.

For mor

Simuli

Select validation
plot type

Select validation
data set

Select to
display
validation plot

Opens
validation plots

e information, see “Validating Parameters in the GUI” in the

nk Design Optimization User’s Guide.

2-10

See Al

Data U

so: Chapter 6, “Tutorial — Estimating Parameters f rom Measured

sing the GUI”

Parallel Computing for Pa ra m eter Estimation

Parallel Computing for Parameter Estimation

When you have the Parallel Computing Toolbox software, you can use
parallel computing to speed up parameter estimation. When you use parallel
computing, the software distributes the independent simulations on multiple
MATLAB sessions. Thus, the simulations run in parallel which reduces the
estimation time.

Using parallel computing may reduce the estimation time in the following
cases:

• The model contains a large number parameters to estimate, and

descent

method.

•

Pattern search is selected as the estimation method.

• The model is complex and takes a long time to simulate.

For more information, see “Speeding Up Parameter Estimation Using Parallel
Computing” in the Simulink Design Optimization User’s Guide.

or Nonlinear least squ ares is selected as the estimation

Gradient

2-11

2 Parameter Estimation

2-12

Parameter Optimization

• “Types of Time-Domain Design R equire ments for Optimizing Parameters”

on page 3-2

• “Quick Start — Optimizing Model Parameters” on page 3-3

• “Parallel Computing for Parameter Optimization” on page 3-11

3

3 Parameter Optimization

Types of Time-Domain Design Requirements for Optimizing
Parameters

You can optimize parameters of Simulink models to meet thefollowingtypes
of time-domain design requirements:

• Step-response characteristics such as overshoot, and rise time.

• Lowerandupperboundsonsignals

• Reference signal

Simulink Design Optimization software optimizes the model parameters by
formulating the time-domain requirements into a constrained optimization
problem. It then solves the problem using optimization methods. During the
optimization, the software performs the following operations:

• Simulates the Simulink model,

• Compares the simulation data with the constraint objectives and any

specified reference signal

3-2

• Uses gradient methods to modify selected model parameters to meet the

objectives

To learn more, see “Parameter Optimization” in the Simulink Design
Optimization User’s Guide.

Quick Start — Optimizing Model Parameters

Quick Start — Optimizing Model Parameters

In this quick start, you get an overview of the typical tasks for optimizing
model parameters to meet time-domain requirements:

1 Specify an input signal in the Simulink system.

2 Specify the design requirements.

3 Specify para

4 Optimize the parameters.

5 Evaluate the optimization results.

meters to optimize.

Prerequisites for optimizing model parameters include:

• Simulink model

• Time-domain design requirements

To optimize model parameters:

1 In the Simulink model, specify an in put signal to the system. For example,

add a Step block.

2 Specify the time-domain design requirements:

3-3

3 Parameter Optimization

a In the Simulink Library Brow se r, select Simulink Design

Optimization.

b Drag and drop the Signal Constraint block into the model.

c Connect the Signal Constraint block to the signal that should meet the

design requirements.

3-4

Quick Start — Optimizing Model Parameters

d Doub le -cl ick the Signal Constraint block.

Design requirements appear as lin e segments in the Block Parameters:
Signal Constraint block window. By default, the design requirements
are step-response characteristics.

3-5

3 Parameter Optimization

e Double-click the lower yellow regi on on the plot. S pecify the de si gn

requirements in the Edit Design Requirement dialog box.

Select design
requirement
type

Enter design
requirement
values

3-6

For more information, see “Specifying Design Requirements” in the
Simulink Design Optimization User’s Guide.

Quick Start — Optimizing Model Parameters

3 In the Block Parameters window, select Optimization > Tuned

Parameters,andclickAdd. Select the parameters to optimize in the

Add Parameters dialog box.

Lists parameters
selected for optimization

Select
parameters

Specify parameters
to optimize

.

For more information, see “Specifying Parameters to Optimize” in the
Simulink Design Optimization User’s Guide.

4 In the Block Parameters window, start the optimization by selecting

Optimization > Start.

The Optimization Progress window opens where you see the optimization
progress.

3-7

3 Parameter Optimization

3-8

For more information, see “Running the Optimization” in the Simulink
Design Optimization User’s Guide.

Quick Start — Optimizing Model Parameters

5 Evaluate the optimization results after the optimization completes.

a In the Block Parameters window, compare the response of the system

against the design requirements.

Optimized
response
shown in
black

Initial
response
shown in
blue

3-9

3 Parameter Optimization

b In the Optimization Progress window, view the optimized parameter

values.

Optimization
report

Optimized
parameter values

3-10

See Also: Chapter 7, “Tutorial — O ptimizing Parameters to Meet

Time-Domain Requirements Using the GUI”.

Parallel Computing for Parameter Optimization

Parallel Co mputing for Parameter Op timization

When you have the Parallel Computing Toolbox software, you can use parallel
computing to speed up optimizing model parameters to meet time-domain
design requirements. When you use parallel computing, the software
distributes the independent simulations on multiple MATLAB sessions. Thus,
thesimulationsruninparallelwhich reduces the optimization time.

Using parallel computing may reduce the optimization time in the following
cases:

• The model contains a large number of parameters to optimize, and the

Gradient descent method is selected for optimization.

Pattern search method is selected for optimization.

• The

• The model contains a large number of uncertain parameters and uncertain

parameter values.

• The model is complex and takes a long time to simulate.

For more information, see “Speeding Up Response Optimization Using
Parallel Computing” in the Simulink Design Optimization User’s Guide.

3-11

3 Parameter Optimization

3-12

4

Optimization-Based Linear
Control Design

• “When to Use Optimization-Based Linear Control Design” on page 4-2

• “Types of Time- and Frequency-Domain Design Requirements” on page 4-3

• “Quick Start — Optimization-Based Linear Control Design” on page 4-4

4 Optimization-Based Linear Control Design

When to Use Optimization-Based Linear Control Design

When you have Control System To olbo x software installed, you can design
and optimize control systems for LTI models by optimizing controller
parameters in the SISO Design Tool. To u s e optimization methods for linear
control design, also known as optimization-based tuning, you must already
have an initial controller. You can then use optimiza t ion-based tuning to
refine the controller design to meet additional design requirements. For
more information on designing controllers, see the Control System Toolbox
documentation.

Note Optimization-based tuning only changes the value of the controller
parameters and not the controller structure itself.

Optimization-based tuning provides flexibility in terms of specifying
additional design requirements for the controller. When you have a large
number of design requirements, you can first design an initial controller
by selecting a subset of requirements and subsequently select additional
requirements to refine the design.

4-2

Optimization-based tuning also provides flexibility in terms of selecting a
subset of controller parameters to optimize, and specifying bounds on the
controller parameters.

To design linear controllers for Simulink models using opt imization-based
tuning, you must first linearize the model using the Simulink Control Design
software. For more information on linearizing Simulink models, see the
Simulink Control Design documentation.

Types of Time- and Frequency-Domain Design Requirements

Types of Time- and Frequency-Domain Design
Requirements

When you design linear controllers for LTI or Simulink models using the
Simulink Design Optimization software, you can specify both time- and
frequency-domain requirements on the system response. You can specify
design requirements on the following plots:

• Root Locus plot

• Open-Loop and Prefilter Bode plots

• Open-Loop Nichols plot

• Step/Impulse Response plots

For more information, see “Supported Time- and Frequency-Domain
Requirements” in the Simulink Design Optimization User’s Guide.

Simulink Design Optimization software uses the frequency-domain
requirements to compute the frequency response of the system. It then uses
optimization methods to reduce the distance between the current response
and the requirements by modifying the controller parameters. The software
does not change the controller structure when optimizing the controller
parameters. To learn more, see “Optimization-Bas ed Linear Control Design”
in the Simulink Design Optimization User’s Guide.

4-3

4 Optimization-Based Linear Control Design

Quick Start — Optimization-Based Linear Control Design

In this quick start, you get an overview of the typical tasks for
optimization-based linear control design using the SISO Design Tool:

1 Open a SISO Design Tool session.

2 Configure a project for optimization-based control design.

3 Specify the c

4 Specify the design requirements.

5 Design the controller.

6 Evaluate th

Note The s

LTI model
software
Control S

Prerequ

• Simuli

• Time

. To learn how to create LTI models, see “Linear (LTI) Models” in the

isites for optimization-ba sed linear control de sig n include:

the Sim

For mo

n, see “De sig ning Compensators” in the Simulink Control Design

desig
docum

- and frequency-domain design requirements

ontroller parameters to design.

e controller d esign.

ame workflow applies to optimization-based control design for

s created at the command line using Control System Toolbo x

ystem Toolbox documentation.

nk Compensator Design Task that contains a linearized version of

ulink model and, optionally, any response plots you configure.

re information on how to linearize a Simulink mode l for control

entation.

4-4

sign a controller using optimization methods:

To de

Quick Start — Optimization-Based Linear Control Design

1 Open a SISO Design Tool session by typing the following command at the

MATLAB prompt:

sisotool(’projectname.mat’)

SISO Design Task
for SISO Design
Tool

The command also opens a SISO Design for SISO Design Task window by
default and any response plots you configured when you linearized the
Simulink model using Simulink Control Design software.

4-5

4 Optimization-Based Linear Control Design

2 Configure a project for optimization-based control design by clicking

Optimize Compensators in the Automated Tuning tab of the SISO
Design Task.

Method for
controller
design

4-6

Quick Start — Optimization-Based Linear Control Design

This action creates a new Response Optimization node in the Control
and Estimation Tools Manager.

4-7

4 Optimization-Based Linear Control Design

3 Specify the controller parameters to design in the Compensators tab.

Select parameters
to optimize

Right-click to change
controller parameters
representation to Simulink
block mask parameters

(Optional) Specify
initial value

(Optional) Specify

parameter bounds

4-8

Quick Start — Optimization-Based Linear Control Design

4 Specify the design requirements.

a In the Design requirements tab, click Add new design requirement.

Specify the design requirements, for example Bode magnitude lower
limit, in the New Design Requirement dialog box.

Add
requirement

Requirement
type

Response
type

Requirement
values

4-9

4 Optimization-Based Linear Control Design

In the SISO D esign window, the yellow region with the black line
segment represents the design requirement on the response plot.

4-10

Quick Start — Optimization-Based Linear Control Design

The Design Requirements tab also lists the design requirement.

Select the requirement
to enforce during
optimization

Lists all specified
design requirements

b Repeat step a to specify additional time- and frequency-domain

requirements.

4-11

4 Optimization-Based Linear Control Design

5 Start the optimization to design the controller by clicking Start

Optimization in the Optimization tab.

Optimization
progress

Optimization
status

4-12

Quick Start — Optimization-Based Linear Control Design

6 Evaluate the controller design.

a Examine the system’s response in theresponseplot,forexamplethe

Bode plot, to see if it meets the requirements. The system’s re sponse
mustlieinthewhiteregioninordertomeetthedesignrequirement.

4-13

4 Optimization-Based Linear Control Design

b Examine the controller parameter values in the Compensator tab.

Optimized controller
parameter values

4-14

7 Write the controller parameter values into the Simulink model. To do so,

click Update Simulink Block Parameters in the SISO Design Task
node.

See Also: Chapter 9, “Tutorial — Designing a PID Controller Using
Optimization-Based Tuning”.

5

Tutorial — Preparing Data
for Parameter Estimation
Using the GUI

• “About This Tutorial” on page 5-2

• “Configuring a Project for Parameter Estimation” on page 5-4

• “Importing Data into the GUI” on page 5-6

• “Analyzing Data” on page 5-14

• “Selecting Data for Estimation” on page 5-16

• “Removing Outliers” on page 5-25

• “Filtering Data” on page 5-29

• “Interpolating Missing Data” on page 5-34

• “Saving the Project” on page 5-37

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

About This Tutorial

In this section...

“Objectives” on page 5-2

“About the Sample Data” on page 5-2

Objectives

In this tutorial, you learn how to import,analyze,andpreparemeasured
input and output (I/O) data for estimating parameters of a Simulink model.

Note Simulink Design Optimization software estimates parameters from
real, time-domain data only.

You learn to perform the following tasks using the GUI:

5-2

• Import data from the MATLAB workspace.

• Analyze data quality using a time plot.

• Select a subset of data for estimation.

• Remove outliers.

• Filter high-frequency noise.

• Compute missing data using interpolation.

About the Sample Data

In this tutorial, you use spe_engine_throttle1.mat, which contains I/O
data measured from an engine throttle system. The MAT-file includes the
following variables:

•

input1 —Inputdatasamples

position1 — Output data samples

•

time1 —Timevector

•

About This Tutorial

Note The number of input and output data samples must be equal to the
length of the corresponding time vector.

Theenginethrottlesystemcontrolstheflowofairandfuelmixturetothe
engine cylinders. The throttle body contains a butterfly valve which opens
when a driver presses the accelerator pedal. Opening this valve increases the
amount of fuel mixture entering the cylinders, which increases the engine
speed. A DC motor controls the opening angle of the butterfly valve in the
throttle system. The input to the throttle system is the motor current (in
amperes), and the output is the angular position of the butterfly valve (in
degrees).

spe_engine_throttle1.mdl contains the Simulink model of the engine

throttle system. For more information on building models, see “Creating a
Simulink Model” in the Simulink documentation.

5-3

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

Configuring a Project for Parameter Estimation

To perform parameter estimation, you must first configure a C ontrol and
Estimation Tools Manager project.

1 Open the engine throttle system model by typing the following at the

MATLAB prompt:

spe_engine_throttle1

This command opens the Simulink model, and loads the data into the
MATLAB workspace.

5-4

Simulink®Model of Engine Throttle System

Configuring a Project for Parameter Estimation

2 In the Simulink m odel window, select Tools > Parameter Estimation.

This action opens a new project named Project - spe_engine_throttle1 in
the C ontrol and Estimation Tools Manager GUI. This project contains the
Estimation Task, as shown in the next figure.

Note

esti

The Simulink model must remain open to perform parameter

mation tasks.

5-5

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

Importing Data into the GUI

In this section...

“Importing Input Data and Time Vector” on page 5-6

“Importing Ou tput Data and Time Vector” on page 5-11

Importing Input Data and Time Vector

In this portion of the tutorial, you import measured I/O data into the Control
and E stimation Tools Manager GUI. Importing data assigns the data to the
corresponding model input and output signals.

The model input and output signals are designated with the Inport
and Outport Position blocks respectively, as shown in the figure Simulink
Model of Engine Throttle System on page 5-4. These blocks let you import I/O
data into the GUI. To learn more about the b locks, see the Inport and Outport
block reference pages in the Simulink documentation.

You must have already configured the parameter estimation project, as
described in “Configuring a Project for Parameter Estimation” on page 5-4.

To import input data and time vector into the Control and Estimation Tools
Manager GUI:

Input

®

5-6

Importing Data into the GUI

1 In the Control and Estimation Tools Manger GUI, select Transient Data

under the Estimation Task node, and click New.

This action creates a New Data node under the Transient Data node.

5-7

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

2 Select the New Data node.

5-8

3 In the Input Data tab, select the Data cell for Channel - 1,andclick

Import.

This action opens the Data Import dialog box.

Importing Data into the GUI

4 In the Data Import dialog box, select input1,andclickImport.

This action assigns the input data input1 to the model input signal

spe_engine_throttle1/Input.

5-9

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

5 Select the Time / Ts cell for Channel - 1.

6 In the Data Import dialog box, select time1,andclickIm port .

This action assigns the time vector to the m odel input signal

spe_engine_throttle1/Input.

5-10

7 In the Data Import dialog box, click Close.

Importing Data into the GUI

Importing Outpu

To import output
Manager GUI:

1 In the Control a

tab of the New D

data and time vector into the Control and Estimation Tools

nd Estimation Tools Manger GUI, select the Output Data

ata node.

tDataandTimeVector

2 Select the Data cell for Channel - 1,andclickImport.

This action opens the Data Import dialog box.

5-11

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

3 In the Data Import dialog box, select position1,andclickImport.

This action assigns the output data position1 to the model output signal

spe_engine_throttle1/Position.

5-12

Importing Data into the GUI

4 Select the Time / Ts cell for Channel - 1.

5 In the Data Import dialog box, select time1,andclickIm port.

This action assigns the time vector to the model output signal
spe_engine_throttle1/Position.

6 In Data Import dialog box, click Close.

You have now imported the I/O data into the Control and Estimation Tools
Manager GUI, and assigned the data to the corresponding model signals.

5-13

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

Analyzing Data

In this portion of the tutorial, you analyze the output data quality by viewing
the data characteristics on a time plot. Based on the analysis, you decide
whether to preprocess the data before estimating parameters. For example, if
the data contains noise, you might wanttofilterthenoisefromthesystem
dynamics before estimating parameters.

You must have already imported the data into the Control and Estimation
Tools Manager GUI, as described in “Importing Data into the GUI” on page
5-6. If you have not im ported the data, click here.

To plot the output data on a time plot, select the
Output Data tab, and click Plot Data.

position1(:,1) cell in the

5-14

Analyzing Data

This action plots the measured output data position1(:,1),asshownin
the next figure.

The time plot shows the output data in response to a step input, as described
in “About the Sample Data” on page 5-2. The plot shows a rapid decrease
in the response after t = 0.5 s because the system is shut down. To focus
parameter estimation on the time period when the system is active, you select
the data samples between t = 0 s and t = 0.5 s, as described in “Selecting Data
for Estimation” on page 5-16 section of this tutorial.

The spikes in the data indicate outliers, defined as data values that deviate
from the mean by more than three standard deviations. They may be caused
by measurement errors or sensor problems. The response also contains noise.
Before estimating model parameters from this data, you remove the outliers
and filter the noise, as described in “Removing Outliers” on page 5-25, and
“Filtering Data” on page 5-29sectionsofthistutorial.

Tip You can also plot the input data on a time plot by selecting the

input1(:,1) cell in the Input Data tab, and clicking Plot Data.

5-15

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

Selecting Data for Estimation

In this section...

“Selecting Output Data” on page 5-16

“Selecting Input Data” on page 5-22

Selecting Output Data

In this portion of the tutorial, you select a subset of I/O data for estimation.
As described in “Analyzing Data” on page 5-14, the system is shut down at t =

0.5 s. To focus the estimation on the time period before t = 0.5 s, you exclude
the data samples beyond t = 0.5 s. This operation selects the data between
t = 0 s and t = 0.5 s for estimation.

You must have already imported the data into the Control and Estimation
Tools Manager GUI, as described in “Importing Data into the GUI” on page
5-6. If you have not im ported the data, click here.

5-16

To select the portion of data betw een t = 0 s and t = 0.5 s:

1 In the Control and Estimation Tools Manager window, select the New

Data node under the Transient Data node.

Selecting Data for Estimation

2 Select the position1(:,1) cell in the Output Data tab, and click

Pre-process.

This action opens the Data Preprocessing Tool GUI.

The Dat
output
colum
the fo

• Exclu

• Detr

• Hand

3 To exclude the output data beyond t = 0.5 s:

a In the Exclusion Rules tab, select the Bounds check box.

aEditingarea of the Data Preprocessing Tool GUI shows the

data and time vector in the position1(:,1) and Time (seconds)

ns, respectively. The D ata Preprocessing Tool GUI lets you perform

llowing types of preprocessing operations:

ding data

ending and filtering data

ling missing data

5-17

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

b In the Exclude X field, wh ere X corresponds to the time vector,

select

> from the d rop-down list. Enter 0.5 in the a djacent field to specify

the upper limit of the data to select for estimation.

The Data Preprocessing Tool GUI resembles the next figure.

5-18

Selecting Data for Estimation

c Click Add.

This action adds a new Dataset1 node under the Transient Data node
in the Control and Estimation Tools Manager GUI. The Dataset1 node
contains the modified output data

position1(:,1)* in the Output

Data tab.

5-19

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

This operation also replaces the output data samples beyond t = 0.5 s
in

position1(:,1)* with NaNs. You can view the NaNs by selecting the

Modified data tab in the Data Preprocessing Tool GUI, as shown in
the next figure.

5-20

4 To remove the NaNs:

a In the Control and Estimation Tools Manager GUI, select the Output

Data tab of the Dataset1 node.

b Select the position 1(:,1)* cell, and click Pre-process.

action updates the Data Preprocessing Tool GUI with the selected

This

position1(:,1)*.

data

c In the Missing Data Handling area, select the

Selecting Data for Estimation

Remove rows where

data is excluded or missing check box.

Tip You can view the results of this operation in the Modified data tab.

d In the Write results to area, select the existing dataset

option.

e Click Add.

The Update table data dialog box appears. Click Yes to overwrite the

position1(:,1)* data set with the mod ified data.

5-21

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

5 To plot the data, select the position1(:,1)* cell in the Output Data tab

of the Dataset1 node, and click Plot Data.

Theselectedoutputdatafromt=0stot=0.5sisshowninthenextfigure.

5-22

Selecting Input Data

After you select the output data between t = 0 s and t = 0.5 s, as described in
“Selecting Output Data” on page 5-16, you must also select the corresponding
input data samples. This operation makes the number of I/O data samples
equal.

1 In the Control and Estimation Tools Manger GUI, select the New Data

node under the Transient Data node.

2 In the Input D ata tab, select the input1(:,1) cell, and click Pre-process.

s action updates the Data Preproce ssing Tool GUI with the selected

Thi

a

input1(:,1).

dat

Selecting Data for Estimation

3 To exclude the data beyond t = 0.5 s:

a In the Exclusion Rules tab, select the Bounds check box.

b In the Exclude X field, wh ere X corresponds to the time vector,

select

> from the d rop-down list. Enter 0.5 in the a djacent field to specify

the u ppe r limit of the input data to select for estimation.

c In the Write results to area, verify that the

existing dataset

d Click Add.

option remains selected.

This action adds the modified data

input1(:,1)* to the Input Data

tab of the Dataset1 node. This operation also replaces the input data
samples b eyond t = 0.5 s in

4 To remove the NaNs:

a In the Control and Estimation Tools Manager GUI, select the

input1(:,1)* cell in the Input Data tab of the Dataset1 node, and

input1(:,1)* with NaNs.

click Pre-process.

This action updates the Data Preprocessing Tool GUI with the selected
data

input1(:,1)*.

b In the Missing Data Handling area, select the

Remove rows where

c In the Write results to area, verify that the

existing dataset

d Click Add.

pdate table data dialog box appears. Click Yes to overwrite the

The U

inpu

t1(:,1)*

data set with the modified data.

data is excluded or missing check box.

option remains selected.

5-23

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

5 To plot the data, select the input1(:,1)* cell in the Input Data tab of the

Dataset1 node, and click Plot Data.

The selected input data from t = 0 s to t = 0.5 s is shown in the next figure.

5-24

Removing Outliers

In this section...

“Why Remove Outliers” on page 5-25

“How to Remove Outliers” on page 5-25

Why Remove Outliers

Outliers are data values that deviate from the mean by more than three
standard deviations. When estimating parameters from data containing
outliers, the results may not be accurate.

Removing Outliers

Removing outliers replaces the data samples containing outliers with
which represent missin g data. You interpolate the missing data values in a
subsequent operation, as described in “Interpolating Missing Data” on page
5-34.

NaNs,

How to Remove Outliers

In this portion of the tutorial, you remove outliers from the output data. You
must have already selected a subset of the data, as described in “Selecting
Data for Estimation” on page 5-16. If you have not done this preparation,
click here.

1 In the Control and Estimation Tools Manger, select the position1(:,1)*

cell in the Output Data tab of the Dataset1 node, and click Pre-process.

This action updates the Data Preprocessing Tool GUI with the selected
data

position1(:,1)*.

5-25

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

2 In the Exclusion Rules tab, select the Outliers check box.

By default, the Window length and Confidence limits fields are set to
10 and 95 respectively. The Window length fiel d specifies the number
of successive data samples the software uses to compute the mean and
standard deviation. The Confidence limits field specifies the threshold
number for identifying outliers. In this example, the mean and standard
deviation of 10 successive data samples are computed, and data values that
exceed

95% of standard deviation are identified as outliers.

5-26

Note The data samples containing outliers are replaced with NaNs.

3 In the Write r esults to area, verify that the existing dataset

option remains selected.

Removing Outliers

4 Click Add.

The Update table data dialog box appears. Click Yes to overwrite the

position1(:,1)* data set with the modified data.

5 To plot the data, select the position1(:,1)* cell in the Output Data tab

of the Dataset1 node, and click Plot Data.

The spikes, which indicate outliers, no longer appear on the time plot,
asshowninthenextfigure.

5-27

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

The missing data samples, represented by NaNs, appear as gaps on the
time plot. To see an example, zoom in to the bottom-left corner of the plot.
As shown in the next figure, the data values corresponding to t = 0.009 s
and t = 0.019 s are missing.

5-28

Filtering Data

Filtering Output Data

In this portion of the tutorial, you filter the noise, and remove any periodic
trends from the output data. To avoid relative phase shift between the I/O
data, you must also apply the same filter to the input data.

You must have already removed outliers from the output data, as described
in “R emov ing Outliers” on page 5-25. If you have not done this preparation,
click here.

1 In the Control and Estima tion Tools Manager window, select the

Filtering Data

In this section...

“Filtering Output Data” on page 5-29

“Filtering Input Data” on page 5-32

position1(:,1)* cell in the Output Data tab of the Dataset1 node, and

click Pre-process.

This action updates the Data Preprocessing Tool GUI with the selected
data

position1(:,1)*.

5-29

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

2 In the Detrend/Filtering tab:

a Select the Filtering check box.

By default,

First order is selected as the filter type. To learn more

about the filters, see “Filtering D ata”.

b Specify 0.001 in the First order filter with time constant field.

Thisfieldspecifiesthetimeconstantforthefirst-orderfilter.

Tip For calculating the time constant, you can visually inspect the time
plot to determine the frequency components.

5-30

he Write results to area, verify that the existing dataset

3 In t

ion remains selected.

opt

Filtering Data

4 Click Add.

The Update table data dialog box appears. Click Yes to overwrite the

position1(:,1)* data set with the modified data.

5 To plot the data, select the position1(:,1)* cell in the Output Data tab

of the Dataset1 node, and click Plot Data.

The noise is filtered and the output data appears smooth, as shown in
the next figure.

5-31

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

Filtering Input

After you filter
page 5-29, you must

1 In the Control a

input1(:,1)*

Data

the output data, as described in “Filtering Output Data” on

also filter the input data with the same filter.

nd Estimation Tools Manager window, select the

cell in the Input Data tab of the Dataset1 node, and click

Pre-process.

This action updates the Data Preprocessing Tool GUI with the selected

input1(:,1)*.

data

2 In the Detrend/Filtering tab:

a Select the Filtering check box.

,

By default

b Specify 0.

3 In the Write r esults to area, verify that the existing dataset

First order is selected as th e filter type.

001

in the First order filter with time constant field.

option remains selected.

4 Click Ad

d.

The Update table data dialog box appears. Click Yes to overwrite the

input1(:,1)* data set with the modified data.

5-32

Filtering Data

5 To plot the data, select the input1(:,1)* cell in the Input Data tab of the

Dataset1 node, and click Plot Data.

5-33

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

Interpolating Missing Data

In this portion of the tutorial, you interpolate the output data to compute the
missing values created when removing outliers, as described in “Removing
Outliers” on page 5-25. The interpolation operation uses the known data
values to compute the m issing data values.

You must have already filtered the noise, as described in “Filtering Data” on
page 5-29. If you have not already done this preparation, click here.

1 In the Control and Estimation Tools Manager GU I, select the

position1(:,1)* cell in the Output Data tab of the Dataset1 node, and

click Pre-process.

This action updates the Data Preprocessing Tool GUI with the selected
data

position1(:,1)*.

5-34

Interpolating Missing Data

2 In the Missing Data Handling area, select the Interpolate missing

values using interpolation method check box.

By default,

zoh is selected as the interpolation method. This method fills

the missing data sample with the data value immediately preceding it.

Tip You can view the interpolated data samples in the Modified data tab.

3 In the Write r esults to area, verify that the existing dataset

option remains selected.

ck Add.

4 Cli

The Update table data dialog box appears. Click Yes to overwrite the

position1(:,1)* data set with the modified data.

5-35

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

5 To plot the data, select the position1(:,1)* cell in the Output Data tab

of the Dataset1 node, and click Plot Data.

The new estimation data, prepared by removing outliers, filtering noise,
and interpolating missing data, is shown the next figure.

5-36

Saving the Project

After you prepare the data, you can delete the data in the New Data node,
rename the prepared data, and save the session. To skip the data preparation
steps, click here.

1 In the Control and Estimation Tools Manger GUI, select the New Data

node under the Transient Data node.

2 Right-click the New Data node, and select Delete.

Saving the Project

3 Select the D

4 Right-click the Dataset1 node, and select Rename.Specify

Estim_Data_Prep as the name of the new estimation data set.

ataset1 node under the Transient Data node.

The Control and Estimation Tools Manager GUI resembles the next figure.

5-37

5 Tutorial — Preparing Data for Parameter Estimation Using the GUI

5 Save the Control and Estimation Tools Manager project:

a In the Control and Estimation Tools Manager GUI, select File > Save.

This action opens the Save Projects dialog box.

b In the Save Projects dialog box, click OK.

5-38

c IntheSaveProjectswindow,specifyspe_engi ne_t hrottle1p.mat in

the File name field, and click Save.

The acti

To lear
—Estim

on saves the project as a MAT-file.

n how to estimate parameters from this data, see Chapter 6, “Tutorial

ating Parameters from M easured Data Using the GUI”.

6

Tutorial — Estimating
Parameters from Measured
Data Using the GUI

• “About This Tutorial” on page 6-2

• “Estimating Model Parameters Using Default Estimation Settings” on

page 6-7

• “Improving Estimation Results Using Parameter Bounds” on page 6-20

• “Validating Estimated Model Parameters” on page 6-26

6 Tutorial — Estimating Parameters from Measured Data Using the GUI

About This Tutorial

In this section...

“Objectives” on page 6-2

“About the Model” on page 6-3

Objectives

In this tutorial, you learn how to estimate parameters of a single-input
single-output (SISO) Simulink model from measured input and output (I/O)
data.

Note Simulink Design Optimization software estimates parameters from
real, time-domain data only.

You learn to perform the following tasks using the GUI:

6-2

• Load a saved project containing data.

• Estimate model parameters using default settings.

• Validate the model, and refine the estimation results.

About the Model

In this tutorial
model represent

, you use the

s an engine throttle system, as shown in the next figure.

About This Tutorial

spe_engine_throttle1 Simulink model. This

The th
cylin
drive
of fu
DC mo
syst
on p

The
out

rottle system controls the flow of air and fuel mixture to the engine

ders. The throttle body contains a butterfly valve which opens when a

r presses the accelerator pedal. Opening this valve increases the amount

el mixture entering the cylinders, which increases the engine speed. A

tor controls the opening angle of the butterfly valve in the throttle

em. The models for these components are described in “Motor Subsystem”

age 6-4, and “Throttle Subsystem” on page 6-5.

input to the throttle system is the motor current (in amperes), and the

put is the angular position of the butterfly valve (in degrees).

6-3

6 Tutorial — Estimating Parameters from Measured Data Using the GUI

Motor Subsystem

The Motor subsystem contains the DC motor model. To open the model,
double-click the corresponding block.

6-4

The following table describes the variables, parameters, equation, input,
and output of the

Motor subsystem.

Variables U is the input current to the motor.

T is the torque applied by the motor.

Parameters

K

is the torque gain of the motor, represented by Kt in the

t

model.

t

is the input time delay of the motor, represented by

d

input_delay in the model.

Equa

tion

orque applied by the motor is described in the

The t

owing equation:

foll

Tt KUt t

() ( )=−

td

where t is time.

Input U

Output

T

About This Tutorial

Throttle Subsystem

The Throttle subsystem contains the butterfly valve model. To open the
model, right-click the corresponding block, and select Look Under Mask.

The Hard Stops block models the valve angular position limit of 15° to 90°.

The following table d escribes the variables, parameters, states, differential
equations, inputs, and outputs of the

Variables

T is the torque applied by the DC motor.

Throttle subsystem.

θ is the angular position of the valve, represented

x in the model.

by

isthetorqueappliedbythehardstop.

is the angular position.

is the angular velocity.

Parameters

States

T

hardstop

J is the inertia.

c is the viscous friction.

k is the spring constant.

θ



θ

6-5

6 Tutorial — Estimating Parameters from Measured Data Using the GUI

Equations

The m athem atical system for the butterfly valve
is described in the following equation:

 

θθθ++ =+

JckTT

where

θ015=

Hard Stops block is described in the following



15 90

≤≤θ



,and



θ

hardstop

, with initial con di t ions

0=

.The torque applied by the

0

equation:

0

,

⎧

TK

hardstop

⎪

=−

90

(),

⎨
⎪

K

15

(),

⎩



θ



−

θ

15 90

θ
θ



θ

≤≤



90

>



15

<

⎫
⎪

⎪
⎬

⎪
⎪

⎭⎭

where K is the gain of the Hard Stops block.

6-6

Input T

Output θ

Estimating Model P arameters Using Default Estimation Settings

Estimating Model Parameters Using Default Estimation
Settings

In this section...

“Overview of the Estimation Process” on page 6-7

“Specifying Parameters and Estimation Data” on page 6-8

“Validating M odel Parameters” on page 6-13

Overview of the Estimation Process

Simulink Design Optimization software uses optimization techniques to
estimate model parameters. In each optimization iteration, the model
is simulated w ith the current parameter values. The error between the
simulated and measured output is computed and minimized. The estimation
is com plete when the optimization method finds a local minimum.

Youperformthefollowingtaskstoestimatethemodelparameters:

• “Specifying Parameters and Estimation Data” on page 6-8

• “Validating Model Parameters” on page 6-13

Note The Simulink model must remain open to perform parameter

estimation tasks.

6-7

6 Tutorial — Estimating Parameters from Measured Data Using the GUI

Specifying Parameters and Estimation Data

To specify parameters and estimation data:

1 Load the preconfigured project spe_engine_throttle1p. mat.

This MAT-file contains the estimation data. To learn more about the d ata
and how to prepare it, see Chapter 5, “Tutorial — Preparing Data for
Parameter Estimation Using the GUI”.

To load the preconfigured project:

a Open the engine throttle system Simulink model by typing the following

at the M ATLA B prompt:

spe_engine_throttle1

b In the Simulink model window, select Tools > Parameter Estimation.

This action opens a new project named Project - spe_engine_throttle1
in the Control and Estimation Tools Manager GUI.

6-8

c In the Control and Estimation Tools Manager GUI, select File > Load.

Browse to the matlabroot
and select

spe_engine_throttle1p.mat.

\toolbox\sldo\sldodemos\estim directory,

The Confirm Node Replace dialog box appears. Click Yes to load the
project into the Control and Estimation Tools Manager GUI.