Getting Started with System Identification Toolbox

System Identification Toolbox™ provides MATLAB^® functions, Simulink^® blocks, and an app for constructing mathematical models of dynamic systems from measured input-output data. It lets you create and use models of dynamic systems not easily modeled from first principles or specifications. You can use time-domain and frequency-domain input-output data to identify continuous-time and discrete-time transfer functions, process models, and state-space models. The toolbox also provides algorithms for embedded online parameter estimation.

The toolbox provides identification techniques such as maximum likelihood, prediction-error minimization (PEM), and subspace system identification. To represent nonlinear system dynamics, you can estimate Hammerstein-Weiner models and nonlinear ARX models with wavelet network, tree-partition, and sigmoid network nonlinearities. The toolbox performs grey-box system identification for estimating parameters of a user-defined model. You can use the identified model for system response prediction and plant modeling in Simulink. The toolbox also supports time-series data modeling and time-series forecasting.

Tutorials

• Identify Linear Models Using System Identification App
  

  Identifying linear black-box models from single-input/single-output (SISO) data using the System Identification app.

• Identify Linear Models Using the Command Line
  

  Identifying linear models from multiple-input/single-output (MISO) data using System Identification Toolbox commands.

• Identify Low-Order Transfer Functions (Process Models) Using System Identification App
  

  Identifying continuous-time transfer functions from single-input/single-output (SISO) data using the System Identification app.

• Estimate Continuous-Time Grey-Box Model for Heat Diffusion
  

  This example shows how to estimate the heat conductivity and the heat-transfer coefficient of a continuous-time grey-box model for a heated-rod system.

• Identify Nonlinear Black-Box Models Using System Identification App
  

  Identifying nonlinear black-box models from single-input/single-output (SISO) data using the System Identification app.

About System Identification

• System Identification Overview
  

  Criteria for deciding which models to estimate for your system.

• System Identification Workflow
  

  Summary of typical tasks in the system identification workflow.

• Supported Data
  

  System Identification Toolbox software supports estimation of linear models from both time- and frequency-domain data.

• Supported Continuous- and Discrete-Time Models
  

  Types of continuous-time and discrete-time models you can estimate from time- and frequency-domain data.

• Estimating Models Using Frequency-Domain Data
  

  Overview of frequency-domain identification in the toolbox.

• When to Use the App vs. the Command Line
  

  When to use the app versus the System Identification Toolbox commands.

• Working with System Identification App
  

  Working with System Identification App.

• Commands for Model Estimation
  

  Summary of commands for constructing models.

• What Is Online Estimation?
  

  Estimate states and parameters of a system in real-time.