Configure simulation scenario for deployment with Simulink Compiler
prepareToDeploy saves logging and parameter information in the
Simulator_out so that the
sdo.SimulationTest object does not need to perform model error checking or
configuration when deployed as part of an executable.
Simulator — Simulation scenario for Simulink model
sdo.SimulationTest object (default)
Simulation scenario for a Simulink model, specified as an
sdo.SimulationTest object. A simulation scenario specifies input signals,
model parameter and initial state values, and signals to log for a model.
p — Parameters to modify the deployed model
Parameters to modify the deployed model, specified as a vector of
param.Continuous objects. You can use
p to modify the
model variables once the model is deployed.
Set Up Experiment and Simulation Test Objects for Deployment
For this example, set up your parameter estimation problem using the Parameter Estimator app and generate MATLAB code from it. For more information on generating MATLAB code from the app, see Generate MATLAB Code for Parameter Estimation Problems (GUI). Alternatively, you can also set up your estimation problem at the command line.
Next, split the generated MATLAB code just before the estimation objective function is defined. This results in two files a
run function and a
setup function, as described in Parameter Tuning for Digital Twins.
setup function, add the following lines of code at the end to configure the experiment and simulation test objects for deployment and save them to a MAT-file.
Experiment_out = prepareToDeploy(Experiement); Simulator = createSimulator(Experiment_out); Simulator = prepareToDeploy(Simulator,p); save ObjectsToDeploy Experiment_out Simulator p
run function, add the following lines of code at the beginning of the function to include the Simulink model in the compiled code and load the objects that were saved in the
%#function simulink_model_name.slx load ObjectsToDeploy Experiment_out Simulator p
Next, add the following lines to load the experiment data and update the experiment object. For this example, assume that the experiment data is contained in the first three columns of a Microsoft Excel spreadsheet file named
d = xlsread(fname); u = timeseries(d(:,3),d(:,1)); y = timeseries(d(:,2),d(:,1)); Experiment_out = updateIOData(Experiment_out,'simulink_model_name/param1',u); Experiment_out = updateIOData(Experiment_out,'simulink_model_name/param2',y);
For a detailed example showing how to deploy your parameter estimation problem using the Simulink Compiler, see Parameter Tuning for Digital Twins.
Introduced in R2020a