Estimating the Frequency Response Function (System Identification Toolkit)
The frequency response function (FRF) represents the frequency-domain relationship between the inputs and outputs of a plant. The FRF contains the magnitude, phase, and frequency information of the plant data. You estimate the FRF when you have time-domain data, but you want to identify a system model in the frequency domain. If you acquire frequency-domain data, this data already contains the FRF.
When you estimate the FRF, the following factors can affect the FRF negatively.
Use the SI Estimate FRF VI to estimate the FRF and to minimize the effects of these factors. This VI supports windowing, which helps to minimize spectral leakage. This VI also supports averaging, which helps to minimize noise and nonlinear distortion.
Minimizing Spectral Leakage
To minimize the effects of spectral leakage, you can apply a window to the time-domain data. The SI Estimate FRF VI supports several types of windows for different types of signals. The type of window you choose depends on the characteristics of the signal. For example, use a Hanning window for random excitation signals. For impact excitation signals, use an Exponential window.
Minimizing Noise and Nonlinear Distortion
You can average multiple FRF measurements to minimize the effects of nonlinear distortion and reduce the effects of noise in the data measurements. Averaging the data smooths the frequency response by reducing fluctuations that exist in the data.
The SI Estimate FRF VI supports both RMS averaging and vector averaging to average plant data.
 |
Note The multiple-input multiple-output (MIMO) instances of the SI Estimate FRF VI support the RMS averaging mode only. |