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System identification, the first step in the model-based control design process, involves building mathematical models of a dynamic system based on a set of measured stimulus and response data samples. You can use system identification in a wide range of applications, including mechanical engineering, biology, physiology, meteorology, economics, and model-based control design. For example, engineers use a system model of the relationship between the fuel flow and the shaft speed of a turbojet engine to optimize the efficiency and operational stability of the engine. Biologists and physiologists use system identification techniques in areas such as eye pupil response and heart rate control. Meteorologists and economists build mathematical models based on historical data for use in forecasting.
The LabVIEW System Identification Toolkit provides the following tools.
The System Identification Toolkit provides VIs that you can use to preprocess raw data from a dynamic system and develop a model that reflects the behavior of that system. The Data Preprocessing VIs enable you to analyze the response of a dynamic system to a certain stimulus. After analyzing the data, you can use the Parametric Model Estimation, Nonparametric Model Estimation, Partially Known Model Estimation, Recursive Model Estimation, and/or Frequency-Domain Model Estimation VIs to estimate a model for the dynamic system. You can use the Model Validation or Model Analysis VIs to determine whether the model accurately describes the dynamics of the identified system. You also can use the Model Conversion VIs to convert a model from one type to another. Finally, you can use the SI Convert to Models of CDT VI to convert the model you identified to a model that you can use in the LabVIEW Control Design and Simulation Module.
The System Identification VIs enable you to customize a LabVIEW block diagram to achieve specific goals. You also can use other LabVIEW VIs and functions to enhance the functionality of the application. Creating a LabVIEW application using the System Identification VIs requires basic knowledge about programming in LabVIEW. Refer to the Getting Started with LabVIEW manual for more information about the LabVIEW programming environment.
The following case studies demonstrate how to use the System Identification VIs to estimate different model representations by using time-domain or frequency domain data.
If you do not have prior knowledge about programming in LabVIEW, you can use the System Identification Assistant to develop a model that reflects the behavior of a certain dynamic system. You access the System Identification Assistant by launching LabVIEW and selecting Tools»Control Design and Simulation»Launch System Identification Assistant.
Using the System Identification Assistant, you can create a project that encompasses the whole system identification process. In a single project, you can load or acquire raw data into the System Identification Assistant, preprocess the data, estimate a model that describes the system, and then validate the accuracy of the model. SignalExpress provides windows in which you can see the raw data, the response data, the estimated model, the validation results, and the mathematical equations that describe the model.
After creating a project in SignalExpress, you can convert the project to a LabVIEW block diagram and customize the block diagram in LabVIEW. This conversion enables you to enhance the capabilities of the application. Refer to the SignalExpress Help, available in the SignalExpress environment by selecting Help»SignalExpress Help, for more information about using the assistant to develop models.