# Offline Frequency Response Estimation

^{®}model

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.

## Graphical Tools

Model Linearizer | Linearize Simulink models |

## Functions

## Model Checks

## Topics

### 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.

**Estimate Frequency Response Using Model Linearizer****Estimate Frequency Response with Linearization-Based Input Using Model Linearizer****Estimate Frequency Response at the Command Line**

**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

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.

### Code Generation

**Generate MATLAB Code for Repeated or Batch Frequency Response Estimation**

Generate MATLAB^{®} scripts or functions for frequency response estimation using the Model
Linearizer.

### Troubleshooting

**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.