Acquiring Data from a System (Advanced Signal Processing Toolkit or Control Design and Simulation Module)

The first step in identifying an unknown system is acquiring data. You can acquire dynamic system data by using NI DAQ hardware and software or you can use data from a file on disk. You can acquire data in the time domain and/or the frequency domain. You must acquire the input and output data of the system synchronously and save synchronous input and output data with constant time steps. If the input data and the output data are not synchronous, you cannot use the data to identify an unknown system.

For verification and validation reasons, you must acquire two sets of input-output data samples or split the data into two sets. You use one set of samples to estimate the mathematical model of the system. You use the second set of samples to validate the resulting model. If the resulting model does not meet the predefined specifications, such as the mean square error (MSE), modify the settings and re-verify the resulting model with the data sets.

Identifying a system involves a number of choices with regard to the system output signals you want to measure and the input signals you want to manipulate. The choices you make about how to manipulate system inputs, types of signal conditioning, signal ranges, and sampling behavior affect the validity of the model you obtain. You can use different modeling techniques on the same experimental data set. However, if the data set does not reflect the behavior of interest, you must acquire a more descriptive data set.

Because the system identification process is often an experimental process, the entire process often is time consuming and possibly costly. Therefore, you must think about the design of process prior to experimenting with various identification techniques.