Using System Identification VIs for Model Estimation (System Identification Toolkit)

To apply the direct identification approach, you can use the LabVIEW System Identification Toolkit to estimate a plant in a closed-loop system with general-linear polynomial, transfer function, and zero-pole-gain models. To apply the indirect or joint input-output approach to identify a plant, you can use this toolkit with transfer function models. Select the System Identification VIs using the following guidelines:

• Use the Polynomial Model Estimation VIs or the SI Model Estimation Express VI to estimate ARX, ARMAX, output-error, Box-Jenkins, and general-linear models. For ARX models, the System Identification Toolkit uses the least squares method, which is a special case of the prediction error method. For all other models, this toolkit uses the prediction error method. This method can accurately identify a plant model in a closed-loop system. Hence, you can use the Polynomial Model Estimation VIs to estimate the model of a plant in a closed-loop system.
• Use the SI Estimate Transfer Function VI or the SI Transfer Function Estimation Express VI to estimate a transfer function model of the plant in a closed-loop system. You can apply direct, indirect, and joint input-output identification to compute transfer function models.
• To identify zero-pole-gain models for a plant, you first must identify the plant using other model representations. You then can convert other model representations to zero-pole-gain models using the Model Conversion VIs.

Refer to the LabVIEW System Identification Toolkit Algorithm References manual for more information about the prediction error method, the deterministic-stochastic subspace method, and the realization method.