Exporting the Model to the LTI Viewer :: Tutorial - Identifying Linear Models Using the GUI (System Identification Toolbox™)

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R2011b Documentation → System Identification Toolbox
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Contents

Index

• Getting Started

• Product Overview

What Is System Identification?

About Dynamic Systems and Models

System Identification Requires Measured Data

Building Models from Data

Black-Box Modeling

Grey-Box Modeling

Evaluating Model Quality

• Using This Product

• Tutorial - Identifying Linear Models Using the GUI

• About This Tutorial

Data Description

• Preparing Data for System Identification

Loading Data into the MATLAB Workspace

Opening the System Identification Tool GUI

Importing Data Arrays into the System Identification Tool

Plotting and Processing Data

Saving the GUI Session

• Estimating Linear Models Using Quick Start

How to Estimate Linear Models Using Quick Start

Types of Quick Start Linear Models

Validating the Quick Start Models

• Estimating Accurate Linear Models

Strategy for Estimating Accurate Models

Estimating Possible Model Orders

Identifying State-Space Models

Identifying ARMAX Input-Output Polynomial Models

Choosing the Best Model

• Viewing Model Parameters

Viewing Model Parameter Values

Viewing Parameter Uncertainties

Exporting the Model to the MATLAB Workspace

Exporting the Model to the LTI Viewer

• Tutorial - Identifying Low-Order Transfer Functions (Process Models) Using the GUI

Data Description

Loading Data into the MATLAB Workspace

Opening the System Identification Tool GUI

Importing Data Objects into the System Identification Tool

Plotting and Processing Data

Estimating a Second-Order Transfer Function Using Default Settings

Tips for Specifying Known Parameters

Validating the Model

Estimating a Second-Order Transfer Function with Complex Poles and Noise

Validating the Models

Viewing Model Parameter Values

Viewing Parameter Uncertainties

Prerequisites for This Tutorial

Preparing Input Data

Building the Simulink Model

Configuring Blocks and Simulation Parameters

Running the Simulation

• Tutorial - Identifying Linear Models Using the Command Line

Data Description

Loading Data into the MATLAB Workspace

Plotting the Input/Output Data

Removing Equilibrium Values from the Data

Using Objects to Represent Data for System Identification

Creating iddata Objects

Plotting the Data in a Data Object

Selecting a Subset of the Data

Why Estimate Step- and Frequency-Response Models?

Estimating the Frequency Response

Estimating the Step Response

Why Estimate Delays?

Estimating Delays Using the ARX Model Structure

Estimating Delays Using Alternative Methods

Why Estimate Model Order?

Commands for Estimating the Model Order

Model Order for the First Input-Output Combination

Model Order for the Second Input-Output Combination

Specifying the Structure of the Process Model

Viewing the Model Structure and Parameter Values

Specifying Initial Guesses for Time Delays

Estimating Model Parameters Using pem

Validating the Process Model

Estimating a Transfer Function with a Noise Model

Model Orders for Estimating Polynomial Models

Estimating a Linear ARX Model

Estimating a State-Space Model

Estimating a Box-Jenkins Model

Comparing Model Output to Measured Output

Simulating the Model Output

Predicting the Future Output

• Tutorial - Identifying Nonlinear Black-Box Models Using the GUI

Data Description

Types of Nonlinear Black-Box Models

What Is a Nonlinear ARX Model?

What Is a Hammerstein-Wiener Model?

Loading Data into the MATLAB Workspace

Creating iddata Objects

Starting the System Identification Tool

Importing Data Objects into the System Identification Tool

Estimating a Nonlinear ARX Model with Default Settings

Plotting Nonlinearity Cross-Sections for Nonlinear ARX Models

Changing the Nonlinear ARX Model Structure

Selecting a Subset of Regressors in the Nonlinear Block

Specifying a Previously-Estimated Model with Different Nonlinearity

Selecting the Best Model

Estimating Hammerstein-Wiener Models with Default Settings

Plotting the Nonlinearities and Linear Transfer Function

Changing the Hammerstein-Wiener Model Input Delay

Changing the Nonlinearity Estimator in a Hammerstein-Wiener Model

Selecting the Best Model

• User's Guide

• Release Notes

Exporting the Model to the LTI Viewer

If you have the Control System Toolbox product installed on your computer, the To LTI Viewer rectangle appears in the System Identification Tool GUI.

The LTI Viewer is a graphical user interface for viewing and manipulating the response plots of linear models. It displays the following plots:

Step- and impulse-response
Bode, Nyquist, and Nichols
Frequency-response singular values
Pole/zero
Response to general input signals
Unforced response starting from given initial states (only for state-space models)

To plot a model in the LTI Viewer, drag and drop the model icon to the To LTI Viewer rectangle in the System Identification Tool GUI.

For more information about working with plots in the LTI Viewer, see the Control System Toolbox documentation.

Exporting the Model to the MATLAB Workspace

Exporting the Model to the MATLAB Workspace

Tutorial – Identifying Low-Order Transfer Functions (Process Models) Using the GUI

Tutorial – Identifying Low-Order Transfer Functions
(Process Models) Using the GUI

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