%% 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 Deploy Parameter Estimation using Simulink Compiler.
% 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 Deploy Parameter Estimation using Simulink Compiler.
% Copyright 2019 The MathWorks, Inc.