Offline Frequency Response Estimation
Simulink Control Design™ software has both command-line tools and a graphical Model Linearizer app for estimating the frequency response of a system modeled in Simulink, without modifying the model. You can use the estimated response to validate exact linearization results, analyze linear model dynamics, or estimate parametric models. For more information about frequency response estimation, see Frequency Response Estimation Basics.
Frequency response estimation requires an input signal at the linearization input point to excite the model at the frequencies of interest. For more information, see Estimation Input Signals.
|Model Linearizer||Linearize Simulink models|
Frequency Response Estimation
|Frequency response estimation of Simulink models|
|Options for frequency response estimation|
|Input signal containing series of sine waves|
|Sinestream input signal with fixed sample time|
|Swept-frequency cosine input signal|
|Pseudorandom binary sequence input signal|
|Random input signal|
|Create step input signal|
Simulation and Analysis
|Plot time-domain simulation of nonlinear and linear models|
|Plot frequency response model in time- and frequency-domain|
|Final time of simulation for frequency response estimation|
Time-Varying Sources and Path Dependencies
Frequency Response Estimation Basics
- Frequency Response Estimation Basics
A frequency response describes the steady-state response of a system to sinusoidal inputs. Simulink Control Design lets you estimate the frequency response of a model or perform online estimation of a physical plant.
- Analyze Estimated Frequency Response
When you perform frequency response estimation, you can analyze the result by examining the raw simulated response and the FFT used to convert it to an estimated frequency response.
Estimation Input Signals
- Estimation Input Signals
For frequency response estimation, the software injects an input signal and measures the response. You can use predefined signal types such as sinestream or chirp signals, or create an arbitrary input signal.
Noise and Time-Varying Inputs
- Disable Noise Sources During Frequency Response Estimation
Noise sources can interfere with the signals at the linearization output points and produce inaccurate estimation results.
- Estimate Frequency Response Models with Noise Using Signal Processing Toolbox
You can also estimate a frequency response model using Signal Processing Toolbox™ software, which includes windowing and averaging.
- Estimate Frequency Response Models with Noise Using System Identification Toolbox
You can also estimate a frequency response model using System Identification Toolbox™ software.
- Effects of Time-Varying Source Blocks on Frequency Response Estimation
Time-varying source blocks drive the model away from the operating point of the linearized system, which prevents the response from reaching steady state.
Validation of Linearization
- Validate Linearization In Frequency Domain Using Model Linearizer
You can assess the accuracy of your linearization results by estimating the frequency response of the nonlinear model and comparing the result with the response of the linearized model.
- Validate Linearization in Frequency Domain at Command Line
You can assess the accuracy of your linearization results at the command line by estimating the frequency response of the nonlinear model.
- Validate Linearization in Time Domain
You can assess the accuracy of your linearization results by comparing the simulated output of the nonlinear model and the linearized model.
- Generate MATLAB Code for Repeated or Batch Frequency Response Estimation
Generate MATLAB® scripts or functions for frequency response estimation using the Model Linearizer.
- Managing Estimation Speed and Memory
Improve frequency response estimation performance by reducing estimation time and memory requirements.
- Troubleshooting Frequency Response Estimation
If your estimated frequency response does not match the expected behavior of your system, you can use the time-domain and frequency-domain response plots to help improve the results.