System Identification Toolbox 7.4

Introduction

System Identification Toolbox lets you construct mathematical models of dynamic systems from measured input-output data. This data-driven approach helps you describe systems that are not easily modeled from first principles or specifications, such as chemical processes and engine dynamics. It also helps you simplify detailed first-principle models, such as finite-element models of structures and flight dynamics models, by fitting simpler models to their simulated responses.

Models obtained with System Identification Toolbox are well suited for simulation, prediction, and control system design using products such as Simulink, Control System Toolbox, and Model Predictive Control Toolbox (all available separately).

System Identification Toolbox lets you fit linear and nonlinear models to data, a process known as black-box modeling. Available model structures include low-order process models, transfer functions, state-space models, linear models with static nonlinearities at the inputs or outputs, and nonlinear autoregressive models. If you have a mathematical model of the system dynamics, you can tune its parameters to better match experimental data, a process known as grey-box modeling.

The principal architect of the toolbox is Professor Lennart Ljung, a recognized leader in the field of system identification.

Key Features

• Identifies linear and nonlinear models from time- and frequency-domain data
• Simplifies identification of first-, second-, and third-order continuous-time models
• Analyzes measured data and advises on data quality, required preprocessing, and presence of feedback or nonlinearities
• Provides data processing tools for detrending, filtering, and reconstructing missing data
• Provides Simulink blocks for simulating identified models and transferring data to and from the MATLAB workspace
• Provides an interface for using linear models in Control System Toolbox (available separately)

Using System Identification Toolbox with Simulink. The toolbox provides blocks that let you bring identified models into Simulink.