Even though the fit is not perfect, the identified transfer function does a pretty good job in capturing system dynamics. I tried feeding other sets of data acquired experimentally through the identified transfer function and compared the result with the measured shaft angle. To ensure that the estimated transfer function represents motor dynamics, we need to validate this transfer function against an independent data set. The comparison shows that the model does a good job in capturing dynamics of the motor. For that, we use the new function tfest.Īfter the estimation is completed, we can compare the output of the model with the measured shaft angle. Let’s begin by estimating the simplest possible transfer function – first-order, with no zeros. Here is what the data look like, using the iddata plot command. I imported this data in MATLAB and constructed an iddata object. I applied a voltage to the motor and recorded the resulting motion. It is very simple it is a small DC motor connected to an Arduino board. I have been working with the setup shown in the image below. If the system you are trying to model has a delay, the toolbox can automatically determine its value. You can specify how many poles and zeros you want your transfer function to have, and the toolbox determines locations of poles and zeros automatically.
In R2012a, the System Identification Toolbox added support for estimating parameters of a model type that is most intuitive and easiest to work with for controls engineers – continuous-time transfer functions.
System identification can help in that case. But what do you do if you don’t have the underlying equations? Today guest blogger Pravallika describes how she used new features of the System Identification Toolbox to design a controller for a DC motorĪny controls engineer knows that the key to designing a good controller is having a good plant model.
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