To apply the direct identification approach, you can use the System Identification VIs to estimate a dynamic system 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 dynamic system, you can use this toolkit with transfer function models. Use the following guidelines when you estimate a dynamic system by using the System Identification VIs:

• Use the Parametric Model Estimation VIs to estimate ARX, ARMAX, output-error, Box-Jenkins, and general-linear models. For ARX models, the System Identification VIs use 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 dynamic system model in a closed-loop system. Hence, you can use the Parametric Model Estimation VIs to estimate the model of a dynamic system 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 dynamic system 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 dynamic system, you first must identify the dynamic system by using other model representations. You then can convert other model representations to zero-pole-gain models using the Model Conversion VIs.