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

  Learn More

  When to Use the GUI Versus the Command Line

• About This Tutorial

  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

• About This Tutorial

  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

• About This Tutorial

  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

• About This Tutorial

  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

  When to Use Simulation Blocks

  Summary of Simulation Blocks

  Specifying Initial Conditions for Simulation