Frequency-Domain Model Estimation Methods (System Identification Toolkit)

Frequency-domain model estimation involves identifying a model of a plant by using the frequency-domain representation of the plant data. You acquire frequency-domain data by using a frequency analyzer. If you acquire time-domain data, you also can convert this time-domain data to frequency-domain data by estimating the frequency response function (FRF) of a plant. The FRF represents the frequency-domain relationship between the inputs and outputs of a plant.

The LabVIEW System Identification Toolkit provides tools you can use to obtain the FRF and estimate two categories of parametric models—transfer function and state-space. Use the SI Estimate Transfer Function Model from FRF VI to estimate continuous and discrete single-input single-output (SISO) transfer function models. Use the SI Estimate State-Space Model from FRF VI to estimate discrete SISO and multiple-input multiple-output (MIMO) state-space models.

Advantages of Frequency-Domain System Identification

Compared to time-domain model estimation methods, frequency-domain model estimation methods have the following advantages:

• You can reduce a large number of time-domain data samples to a finite number of frequency-domain data samples.
• You can reduce the effects of noise by averaging the FRF from multiple time-domain data measurements.
• You can focus on frequency bands of interest by directly weighting the frequency-domain data.