Estimate and validate simple, continuous-time transfer functions from single-input/single-output (SISO) data to find the one that best describes the system dynamics.
After completing this tutorial, you will be able to accomplish the following tasks using the System Identification app :
Import data objects from the MATLAB^{®} workspace into the app.
Plot and process the data.
Estimate and validate low-order, continuous-time models from the data.
Export models to the MATLAB workspace.
Simulate the model using Simulink^{®} software.
Note: This tutorial uses time-domain data to demonstrate how you can estimate linear models. The same workflow applies to fitting frequency-domain data. |
This tutorial uses the data file proc_data.mat
,
which contains 200 samples of simulated single-input/single-output
(SISO) time-domain data. The input is a random binary signal that
oscillates between -1 and 1. White noise (corresponding to a load
disturbance) is added to the input with a standard deviation of 0.2,
which results in a signal-to-noise ratio of about 20 dB. This data
is simulated using a second-order system with underdamped modes (complex
poles) and a peak response at 1 rad/s:
$$G(s)=\frac{1}{1+0.2s+{s}^{2}}{e}^{-2s}$$
The sample time of the simulation is 1 second.
Continuous-time process models are low-order transfer functions that describe the system dynamics using static gain, a time delay before the system output responds to the input, and characteristic time constants associated with poles and zeros. Such models are popular in the industry and are often used for tuning PID controllers, for example. Process model parameters have physical significance.
You can specify different process model structures by varying the number of poles, adding an integrator, or including a time delay or a zero. The highest process model order you can specify in this toolbox is three, and the poles can be real or complex (underdamped modes).
In general, a linear system is characterized by a transfer function G, which is an operator that takes the input u to the output y:
$$y=Gu$$
For a continuous-time system, G relates the Laplace transforms of the input U(s) and the output Y(s), as follows:
$$Y(s)=G(s)U(s)$$
In this tutorial, you estimate G using different process-model structures.
For example, the following model structure is a first-order, continuous-time model, where K is the static gain, T_{p1} is a time constant, and T_{d} is the input-to-output delay:
$$G(s)=\frac{K}{1+s{T}_{p1}}{e}^{-s{T}_{d}}$$
Load the data in proc_data.mat
by typing
the following command in the MATLAB Command Window:
load proc_data
This command loads the data into the MATLAB workspace as
the data object z
. For more information about iddata
objects, see the corresponding
reference page.
To open the System Identification app , type the following command at the MATLAB Command Window:
systemIdentification
The default session name, Untitled
,
appears in the title bar.
You can import data object into the app from the MATLAB workspace.
You must have already loaded the sample data into MATLAB, as described in Loading Data into the MATLAB Workspace, and opened the app, as described in Opening the System Identification App.
If you have not performed these steps, click hereclick here to complete them.
To import a data object into the System Identification app :
Select Import data > Data object.
This action opens the Import Data dialog box.
In the Import Data dialog box, specify the following options:
Object — Enter z
as
the name of the MATLAB variable that is the time-domain data
object. Press Enter.
Data name — Use the default
name z
, which is the same as the name of the data
object you are importing. This name labels the data in the System
Identification app after the import operation is completed.
Starting time — Enter 0
as
the starting time. This value designates the starting value of the
time axis on time plots.
Sample time — Enter 1
as
the time between successive samples in seconds. This value represents
the actual sample time in the experiment.
The Import Data dialog box now resembles the following figure.
Click Import to add the data to the System Identification app. The app adds an icon to represent the data.
Click Close to close the Import Data dialog box.
In this portion of the tutorial, you evaluate the data and process it for system identification. You learn how to:
Plot the data.
Remove offsets by subtracting the mean values of the input and the output.
Split the data into two parts. You use one part of the data for model estimation, and the other part of the data for model validation.
The reason you subtract the mean values from each signal is because, typically, you build linear models that describe the responses for deviations from a physical equilibrium. With steady-state data, it is reasonable to assume that the mean levels of the signals correspond to such an equilibrium. Thus, you can seek models around zero without modeling the absolute equilibrium levels in physical units.
You must have already imported data into the System Identification app, as described in Importing Data Objects into the System Identification App.
If you have not performed this step, click hereclick here to complete it.
To plot and process the data:
Select the Time plot check box to open the Time Plot window.
The bottom axes show the input data—a random binary sequence, and the top axes show the output data.
The next two steps demonstrate how to modify the axis limits in the plot.
To modify the vertical-axis limits for the input data, select Options > Set axes limits in the Time Plot figure window.
In the Limits for Time Plot dialog box, set the new vertical axis limit of the input data channel u1 to [-1.5 1.5]. Click Apply and Close.
Note: The other two fields in the Limits for Time Plot dialog box, Time and y1, let you set the axis limits for the time axis and the output channel axis, respectively. You can also specify each axis to be logarithmic or linear by selecting the corresponding option. |
The following figure shows the updated time plot.
In the System Identification app , select <--Preprocess > Quick start to perform the following four actions:
Subtract the mean value from each channel.
Split the data into two parts.
Specify the first part of the data as estimation data (or Working Data).
Specify the second part of the data as Validation Data.
Learn More. For information about supported data processing operations, such as resampling and filtering the data, see Preprocess Data.
In this portion of the tutorial, you estimate models with this structure:
$$G(s)=\frac{K}{\left(1+2\xi {T}_{w}s+{T}_{w}{}^{2}{s}^{2}\right)}{e}^{-{T}_{d}s}$$
You must have already processed the data for estimation, as described in Plotting and Processing Data.
If you have not performed this step, click hereclick here to complete it.
To identify a second-order transfer function:
In the System Identification app, select Estimate > Process models to open the Process Models dialog box.
In the Model Transfer Function area of the Process Models dialog box, specify the following options:
Under Poles, select 2
and Underdamped
.
This selection updates the Model Transfer Function to a second-order model structure that can contain complex poles.
Make sure that the Zero and Integrator check boxes are cleared to exclude a zero and an integrator (self-regulating ) from the model.
The Parameter area
of the Process Models dialog box now shows four active parameters: K
, Tw
, Zeta
,
and Td
. In the Initial Guess area,
keep the default Auto-selected
option to
calculate the initial parameter values during the estimation. The Initial
Guess column in the Parameter table displays Auto
.
Keep the default Bounds values, which specify the minimum and maximum values of each parameter.
Tip If you know the range of possible values for a parameter, you can type these values into the corresponding Bounds fields to help the estimation algorithm. |
Keep the default settings for the estimation algorithm:
Disturbance Model — None
means
that the algorithm does not estimate the noise model. This option
also sets the Focus to Simulation
.
Focus — Simulation
means
that the estimation algorithm does not use the noise model to weigh
the relative importance of how closely to fit the data in various
frequency ranges. Instead, the algorithm uses the input spectrum in
a particular frequency range to weigh the relative importance of the
fit in that frequency range.
Tip
The |
Initial condition — Auto
means
that the algorithm analyzes the data and chooses the optimum method
for handling the initial state of the system. If you get poor results,
you might try setting a specific method for handling initial states,
rather than choosing it automatically.
Covariance — Estimate
means
that the algorithm computes parameter uncertainties that display as
model confidence regions on plots.
The app assigns a name to the model, shown in the Name field
(located at the bottom of the dialog box). By default, the name is
the acronym P2DU
, which indicates two poles (P2
),
a delay (D
), and underdamped modes (U
).
Click Estimate to add the model P2DU
to
the System Identification app.
If you know a parameter value exactly, you can type this value in the Initial Guess column of the Process Models dialog box.
If you know the approximate value of a parameter, you can help the estimation algorithm by entering an initial value in the Initial Guess column. In this case, keep the Known check box cleared to allow the estimation to fine-tune this initial guess.
For
example, to fix the time-delay value Td
at 2
s,
you can type this value into Value field of the
Parameter table in the Process Models dialog box and select the corresponding Known check
box.
You can analyze the following plots to evaluate the quality of the model:
Comparison of the model output and the measured output on a time plot
Autocorrelation of the output residuals, and cross-correlation of the input and the output residuals
You must have already estimated the model, as described in Estimating a Second-Order Transfer Function Using Default Settings.
If you have not performed this step, click hereclick here to complete it.
Examining Model Output. You can use the model-output plot to check how well the model output matches the measured output in the validation data set. A good model is the simplest model that best describes the dynamics and successfully simulates or predicts the output for different inputs.
To generate the model-output plot, select the Model output check box in the System Identification app. If the plot is empty, click the model icon in the System Identification app window to display the model on the plot.
The System Identification Toolbox™ software uses input validation data as input to the model, and plots the simulated output on top of the output validation data. The preceding plot shows that the model output agrees well with the validation-data output.
The Best Fits area of the Model Output plot shows the agreement (in percent) between the model output and the validation-data output.
Recall that the data was simulated using the following second-order system with underdamped modes (complex poles), as described in Data Description, and has a peak response at 1 rad/s:
$$G(s)=\frac{1}{1+0.2s+{s}^{2}}{e}^{-2s}$$
Because the data includes noise at the input during the simulation, the estimated model cannot exactly reproduce the model used to simulate the data.
Examining Model Residuals. You can validate a model by checking the behavior of its residuals.
To generate a Residual Analysis plot, select the Model resids check box in the System Identification app.
The top axes show the autocorrelation of residuals for the output (whiteness test). The horizontal scale is the number of lags, which is the time difference (in samples) between the signals at which the correlation is estimated. Any fluctuations within the confidence interval are considered to be insignificant. A good model should have a residual autocorrelation function within the confidence interval, indicating that the residuals are uncorrelated. However, in this example, the residuals appear to be correlated, which is natural because the noise model is used to make the residuals white.
The bottom axes show the cross-correlation of the residuals with the input. A good model should have residuals uncorrelated with past inputs (independence test). Evidence of correlation indicates that the model does not describe how a portion of the output relates to the corresponding input. For example, when there is a peak outside the confidence interval for lag k, this means that the contribution to the output y(t) that originates from the input u(t-k) is not properly described by the model. In this example, there is no correlation between the residuals and the inputs.
Thus, residual analysis indicates that this model is good, but that there might be a need for a noise model.
In this portion of the tutorial, you estimate a second-order transfer function and include a noise model. By including a noise model, you optimize the estimation results for prediction application.
You must have already estimated the model, as described in Estimating a Second-Order Transfer Function Using Default Settings.
If you have not performed this step, click hereclick here to complete it.
To estimate a second-order transfer function with noise:
If the Process Models dialog box is not open, select Estimate > Process Models in the System Identification app. This action opens the Process Models dialog box.
In the Model Transfer Function area, specify the following options:
Under Poles,
select 2
and Underdamped
.
This selection updates the Model Transfer Function to a second-order
model structure that can contain complex poles. Make sure that the Zero and Integrator check
boxes are cleared to exclude a zero and an integrator (self-regulating
) from the model.
Disturbance Model — Set
to Order 1
to estimate a noise model H as
a continuous-time, first-order ARMA model:
$$H=\frac{C}{D}e$$
where and D are first-order polynomials, and e is white noise.
This action specifies the Focus as Prediction
,
which improves accuracy in the frequency range where the noise level
is low. For example, if there is more noise at high frequencies, the
algorithm assigns less importance to accurately fitting the high-frequency
portions of the data.
Name — Edit the model
name to P2DUe1
to generate a model with a unique
name in the System Identification app.
Click Estimate.
In the Process Models dialog box, set the Disturbance
Model to Order 2
to estimate
a second-order noise model.
Edit the Name field to P2DUe2
to
generate a model with a unique name in the System Identification app.
Click Estimate.
In this portion of the tutorial, you evaluate model performance using the Model Output and the Residual Analysis plots.
You must have already estimated the models, as described in Estimating a Second-Order Transfer Function Using Default Settings and Estimating a Second-Order Process Model with Complex Poles.
If you have not performed these steps, click hereclick here to complete them.
Comparing the Model Output Plots. To generate the Model Output plot, select the Model output check box in the System Identification app. If the plot is empty or a model output does not appear on the plot, click the model icons in the System Identification app window to display these models on the plot.
The following Model Output plot shows the simulated model output, by default. The simulated response of the models is approximately the same for models with and without noise. Thus, including the noise model does not affect the simulated output.
To view the predicted model output, select Options > 5 step ahead predicted output in the Model Output plot window.
The following Model Output plot shows that the predicted model
output of P2DUe2
(with a second-order noise model)
is better than the predicted output of the other two models (without
noise and with a first-order noise model, respectively).
Comparing the Residual Analysis Plots. To generate the Residual Analysis plot, select the Model resids check box in the System Identification app. If the plot is empty, click the model icons in the System Identification app window to display these models on the plot.
P2DUe2
falls
well within the confidence bounds on the Residual Analysis plot.
To view residuals for P2DUe2
only, remove
models P2DU
and P2DUe1
from
the Residual Analysis plot by clicking the corresponding icons in
the System Identification app.
The Residual Analysis plot updates, as shown in the following figure.
The whiteness test for P2DUe2
shows that
the residuals are uncorrelated, and the independence test shows no
correlation between the residuals and the inputs. These tests indicate
that P2DUe2
is a good model.
You can view the numerical parameter values and other information
about the model P2DUe2
by right-clicking the model
icon in the System Identification app . The Data/model Info dialog
box opens.
The noneditable area of the dialog box lists the model coefficients that correspond to the following model structure:
$$G(s)=\frac{K}{\left(1+2\xi {T}_{w}s+{T}_{w}{}^{2}{s}^{2}\right)}{e}^{-{T}_{d}s}$$
The coefficients agree with the model used to simulate the data:
$$G(s)=\frac{1}{1+0.2s+{s}^{2}}{e}^{-2s}$$
To view parameter uncertainties for the system transfer function, click Present in the Data/model Info dialog box, and view the information in the MATLAB Command Window.
Kp = 0.99821 +/- 0.019982 Tw = 0.99987 +/- 0.0037697 Zeta = 0.10828 +/- 0.0042304 Td = 2.004 +/- 0.0029717
The 1-standard-deviation uncertainty for each model parameter
follows the +/-
symbol.
P2DUe2
also includes an additive noise term,
where H is a second-order ARMA model and e is
white noise:
$$H=\frac{C}{D}e$$
The software displays the noise model H as
a ratio of two polynomials, C(s)/D(s)
, where:
C(s) = s^2 + 2.186 (+/- 0.08467) s + 1.089 (+/- 0.07951) D(s) = s^2 + 0.2561 (+/- 0.09044) s + 0.5969 (+/- 0.3046)
The 1-standard deviation uncertainty for the model parameters is in parentheses next to each parameter value.
You can perform further analysis on your estimated models from the MATLAB workspace. For example, if the model is a plant that requires a controller, you can import the model from the MATLAB workspace into the Control System Toolbox™ product. Furthermore, to simulate your model in the Simulink software (perhaps as part of a larger dynamic system), you can import this model as a Simulink block.
The models you create in the System Identification app are not automatically available in the MATLAB workspace. To make a model available to other toolboxes, Simulink, and the System Identification Toolbox commands, you must export your model from the System Identification app to the MATLAB workspace.
To export the P2DUe2
model, drag the model
icon to the To Workspace rectangle in the System
Identification app. Alternatively, click Export in
the Data/model Info dialog box. The model now appears in the MATLAB Workspace
browser.
Note:
This model is an |
In this tutorial, you create a simple Simulink model that
uses blocks from the System Identification Toolbox library to bring
the data z
and the model P2DUe2
into Simulink.
To perform the steps in this tutorial, Simulink must be installed on your computer.
Furthermore, you must have already performed the following steps:
Load the data set, as described in Loading Data into the MATLAB Workspace.
Estimate the second-order process model, as described in Estimating a Second-Order Process Model with Complex Poles.
Export the model to the MATLAB workspace, as described in Exporting the Model to the MATLAB Workspace.
If you have not performed these steps, click hereclick here to
complete them. Then, drag the z
and the P2DUe2
icons
to the To Workspace rectangle in the System Identification
app. Alternatively, click Export in the Data/model
Info dialog box. The data and the model now appear in the MATLAB Workspace
browser.
Use the input channel of the data set z
as
input for simulating the model output by typing the following in the MATLAB Command
Window:
z_input = z; % Creates a new iddata object. z_input.y = []; % Sets the output channel % to empty.
Alternatively, you can specify any input signal.
Learn More. For more information about representing data signals for system identification, see Representing Data in MATLAB Workspace.
To add blocks to a Simulink model:
In the MATLAB Command Window, type simulink
.
Select File > New > Model to open a new model window.
In the Simulink Library Browser, select the System Identification Toolbox library. The right side of the window displays blocks specific to the System Identification Toolbox product.
Tip
Alternatively, to access the System Identification block library,
type |
Drag the following System Identification Toolbox blocks to the new model window:
IDDATA Sink block
IDDATA Source block
IDMODEL model block
In the Simulink Library Browser, select the Simulink > Sinks library, and drag the Scope block to the new model window.
In the Simulink model window, connect the blocks to resembles the following figure.
Next, you configure these blocks to get data from the MATLAB workspace and set the simulation time interval and duration.
This procedure guides you through the following tasks to configure the model blocks:
Getting data from the MATLAB workspace.
Setting the simulation time interval and duration.
In the Simulink Editor, select Simulation > Model Configuration Parameters.
In the Configuration Parameters dialog box, type 200
in
the Stop time field. Click OK.
This value sets the duration of the simulation to 200 seconds.
Double-click the Iddata Source block to open the Source Block Parameters: Iddata Source dialog box. Then, type the following variable name in the IDDATA object field:
z_input
This variable is the data object in the MATLAB workspace that contains the input data.
Tip As a shortcut, you can drag and drop the variable name from the MATLAB Workspace browser to the IDDATA object field. |
Click OK.
Double-click the Idmodel block to open the Function Block Parameters: Idmodel dialog box.
Type the following variable name in the Model variable field:
P2DUe2
This variable represents the name of the model in the MATLAB workspace.
Clear the Add noise check box to exclude noise from the simulation. Click OK.
When Add noise is selected, Simulink derives
the noise amplitude from the NoiseVariance
property
of the model and adds noise to the model accordingly. The simulation
propagates this noise according to the noise model H that
was estimated with the system dynamics:
$$H=\frac{C}{D}e$$
Click OK.
Double-click the Iddata Sink block to open the Sink Block Parameters: Iddata Sink dialog box. Type the following variable name in the IDDATA Name field:
z_sim_out
Type 1
in the Sample
Time (sec.) field to set the sample time of the output
data to match the sample time of the input data.
Click OK.
The resulting change to the Simulink model is shown in the following figure.
In the Simulink Editor, select Simulation > Run.
Double-click the Scope block to display the time plot of the model output.
In the MATLAB Workspace browser, notice the variable z_sim_out
that
stores the model output as an iddata
object. You
specified this variable name when you configured the Iddata Sink block.
This variable stores the simulated output of the model, and it is now available for further processing and exploration.