This example shows how to use PID Tuner to fit a linear model to measured SISO response data.
If you have System Identification Toolbox™ software, you can use the PID Tuner to estimate the parameters of a linear plant model based on time-domain response data measured from your system. PID Tuner then tunes a PID controller for the resulting estimated model. PID Tuner gives you several techniques to graphically, manually, or automatically adjust the estimated model to match your response data. This example illustrates some of those techniques.
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Import Response Data for Identification
Open the PID Tuner.
Load measured response data into the MATLAB® workspace.
When you import response data, PID Tuner assumes that your measured data represents a plant connected to the PID controller in a negative-feedback loop. In other words, PID Tuner assumes the following structure for your system. PID Tuner assumes that you injected a step signal at u and measured the system response at y, as shown.
The sample data file for this example, load PIDPlantMeasuredIOData.mat, contains three variables, each of which is a 501-by-1 array. inputu is the unit step function injected at u to obtain the response data. outputy, is the measured response of the system at y. The time vector t, runs from 0 to 50 s with a 0.1 s sample time. Comparing inputu to t shows that the step occurs at t = 5 s.
In the PID Tuner, in the Plant menu, select Identify New Plant.
In the Plant Identification tab, click Get I/O data and select Step Response. This action opens the Import Step Response dialog box.
Enter information about the response data. The output signal is the measured system response, outputy. The input step signal is parametrized as shown in the diagram in the dialog box. Here, enter 5 for the onset time, and 0.1 for sample time. Then, click Import.
The Plant Identification tab opens, displaying the response data and the response of an initial estimated plant.
Depending on the quality and features of your response data, you might want to perform some preprocessing on the data to improve the estimated plant results. PID Tuner gives you several options for preprocessing response data, such as removing offsets, filtering, or extracting on a subset of the data. In this example, the response data has an offset. It is important for good identification results to remove data offsets. Use the Preprocess menu to do so.
In the Plant Identification tab, click Preprocess and select Remove Offset. The Remove Offset tab opens, displaying time plots of the response data and corresponding input signal.
Select Remove offset from signal and choose the response, Output (y). In the Offset to remove text box you can opt to remove the signal initial value or signal mean, or enter a numerical value. For this example, enter the value 2. The plot updates with an additional trace showing the signal with the offset applied.
Click Update to save the change to the signal. Click Close Remove Offset to return to the Plant Identification tab.
PID Tuner automatically adjusts the plant parameters to create a new initial guess for the plant based on the preprocessed response signal.
Adjust Plant Structure and Parameters
PID Tuner allows you to specify a plant structure, such as One Pole, Underdamped Pair, or State-Space Model. In the Structure menu, choose the plant structure that best matches your response. You can also add a transfer delay, a zero, or an integrator to your plant. For this example, the one-pole structure gives the qualitatively correct response. You can make further adjustments to the plant structure and parameter values to make the estimated system's response a better match to the measured response data.
PID Tuner gives you several ways to adjust the plant parameters:
Graphically adjust the estimated system's response by dragging the adjustors on the plot. In this example, drag the red x to adjust the estimated plant time constant. PID Tuner recalculates system parameters as you do so. As you change the estimated system's response, it becomes apparent that there is some time delay between the application of the step input at t = 5 s, and the response of the system to that step input.
In the Plant Structure section of the tab, check Delay to add a transport delay to the estimated plant model. A vertical line appears on the plot, indicating the current value of the delay. Drag the line left or right to change the delay, and make further adjustments to the system response by dragging the red x.
Adjust the numerical values of system parameters such as gains, time constants, and time delays. To numerically adjust the values of system parameters, click Edit Parameters.
Suppose that in this example you know from an independent measurement that the transport delay in your system is 1.5 s. In the Plant Parameters dialog box, enter 1.5 for τ. Check Fix to fix the parameter value. When you check Fix for a parameter, neither graphical nor automatic adjustments to the estimated plant model affect that parameter value.
Automatically optimize the system parameters to match the measured response data. Click Auto Estimate to update the estimated system parameters using the current values as an initial guess.
You can continue to iterate using any of these methods to adjust plant structure and parameter values until the estimated system's response adequately matches the measured response.
Save Plant and Tune PID Controller
When you are satisfied with the fit, click Save Plant. Doing so saves the estimated plant, Plant1, to the PID Tuner workspace. Doing so also selects the Step Plot: Reference Tracking figure and returns you to the PID Tuner tab. The PID Tuner automatically designs a PI controller for Plant1, and displays a response plot for the new closed-loop system. The Plant menu reflects that Plant1 is selected for the current controller design.
You can now use the PID Tuner tools to refine the controller design for the estimated plant and examine tuned system responses.
You can also export the identified plant from the PID Tuner workspace to the MATLAB workspace for further analysis. In the PID Tuner tab, click Export. Check the plant model you want to export to the MATLAB workspace. For this example, export Plant1, the plant you identified from response data. You can also export the tuned PID controller. Click OK. The models you selected are saved to the MATLAB workspace.