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| R2012a Documentation → System Identification Toolbox | |
Learn more about System Identification Toolbox |
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Estimate and validate simple, continuous-time transfer functions from single-input/single-output (SISO) data to find the one that best describes the system dynamics.
After completing this tutorial, you will be able to accomplish the following tasks using the System Identification Tool :
Import data objects from the MATLAB workspace into the GUI.
Plot and process the data.
Estimate and validate low-order, continuous-time models from the data.
Export models to the MATLAB workspace.
Simulate the model using Simulink software.
Note This tutorial uses time-domain data to demonstrate how you can estimate linear models. The same workflow applies to fitting frequency-domain data. |
This tutorial uses the data file proc_data.mat, which contains 200 samples of simulated single-input/single-output (SISO) time-domain data. The input is a random binary signal that oscillates between -1 and 1. White noise (corresponding to a load disturbance) is added to the input with a standard deviation of 0.2, which results in a signal-to-noise ratio of about 20 dB. This data is simulated using a second-order system with underdamped modes (complex poles) and a peak response at 1 rad/s:
The sampling interval of the simulation is 1 second.
 | Tutorial – Identifying Low-Order Transfer Functions (Process Models) Using the GUI | What Is a Continuous-Time Process Model? |  |
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