prepareToDeploy

R2026b

Set up simulation configuration for deployment with Simulink Compiler

Syntax

Simulator_out = prepareToDeploy(Simulator)

Simulator_out = prepareToDeploy(Simulator,p)

Description

Simulator_out = prepareToDeploy(Simulator) sets up the Simulink^® model and simulation configuration specified as the sdo.SimulationTest object Simulator, for deployment with Simulink Compiler™.

prepareToDeploy saves logging and parameter information in the simulation configuration Simulator_out so that the sdo.SimulationTest object does not need to perform model error checking when deployed as part of an executable.

Simulator_out = prepareToDeploy(Simulator,p) sets up the sdo.SimulationTest object Simulator for deployment with an optional vector p of param.Continuous objects that can be used to modify the model variables once the model is deployed.

Input Arguments

Simulator — Simulation configuration for Simulink model

sdo.SimulationTest object (default)

Simulation configuration for a Simulink model, specified as an sdo.SimulationTest object. A simulation configuration specifies input signals, model parameter and initial state values, and signals to log for a model.

p — Parameters to modify the deployed model

vector

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.

Output Arguments

Simulator_out — Simulation configuration set up for deployment with Simulink Compiler

sdo.SimulationTest object

Simulation configuration set up for deployment with Simulink Compiler, returned as an sdo.SimulationTest object.

Examples

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.

In the 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

In the 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 setup function.

%#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 fname.xls.

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.

Version History

Introduced in R2020a