Identifying Linear Models :: System Identification Toolbox Blocks (System Identification Toolbox™)

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Contents

Index

• Getting Started

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

Data Description

Loading Data into the MATLAB Workspace

Opening the System Identification Tool

Importing Data Arrays into the System Identification Tool

Plotting and Processing Data

How to Estimate Linear Models Using Quick Start

Types of Quick Start Linear Models

Validating the Quick Start Models

Strategy for Estimating Accurate Models

Estimating Possible Model Orders

Identifying Transfer Function Models

Identifying State-Space Models

Identifying ARMAX Input-Output Polynomial Models

Choosing the Best Model

Viewing Model Parameter Values

Viewing Parameter Uncertainties

Data Description

Loading Data into the MATLAB Workspace

Opening the System Identification Tool

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 Process Model with Complex Poles

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

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 Empirical 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 Transfer Function

Validating the Process Model

Residual Analysis

Specifying the Structure of the Process Model

Viewing the Model Structure and Parameter Values

Specifying Initial Guesses for Time Delays

Estimating Model Parameters Using procest

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

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

• Choosing Your System Identification Approach

• Data Import and Processing

• Linear Model Identification

• Nonlinear Black-Box Model Identification

• ODE Parameter Estimation (Grey-Box Modeling)

• Time Series Identification

• Recursive Model Identification

• Model Analysis

• Setting Toolbox Preferences

• Control Design Applications

• System Identification Toolbox Blocks

Using System Identification Toolbox Blocks in Simulink Models

Identifying Linear Models

• Simulating Identified Model Output in Simulink

Example - Simulating an Identified Model Using Simulink Software

• System Identification Tool GUI

• Release Notes

Identifying Linear Models

The following table summarizes the blocks you use to estimate model parameters in a Simulink model during simulation and export the results to the MATLAB environment.

After you add a block to the model, double-click the block to specify block parameters.

Block	Description
AutoRegressive model estimator	Estimate AR model parameters from time-series data, which has one output and no input.
AutoRegressive Moving Average with eXternal input model estimator	Estimate ARMAX model parameters from input/output data.
AutoRegressive with eXternal input model estimator	Estimate ARX model parameters from input/output data.
Box-Jenkins model estimator	Estimate BJ model parameters from input/output data.
Output-error model estimator	Estimate OE model parameters from input/output data.
General model estimator using Predictive Error Method	Estimate ARX, ARMAX, Box-Jenkins, and Output-Error models (`idpoly` objects) from single-input and single output data using general prediction-error method.

For information about configuring each block, see the corresponding reference pages.

Preparing Data

Simulating Identified Model Output in Simulink

Simulating Identified Model Output in Simulink

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