Conditional Compilation and Variant Management of Simulink Models Using Matlab Scripts

Simulink, as a graphical simulation environment of MATLAB, is widely used in complex system modeling. Faced with diverse requirements, how to efficiently manage different configurations of model variants and achieve conditional compilation has become a key focus for engineers. This article explores the methods for variant management and conditional compilation of Simulink models based on Matlab script operations.

📌 1 Basic Concepts and Application Scenarios of Variant Management

Variant Management is a core concept in platform-based development, allowing the generation of application designs tailored to specific needs through different configuration options based on the same platform. In fields such as automotive and aerospace, variant management can effectively address diverse requirements, for example, a powertrain system may include various variant algorithms such as fuel-driven, electric-driven, or hybrid-driven.

Main Advantages:

– 📦 Increased Reusability: A single model maintains multiple system designs, reducing development costs.

– 🧩 Simplified Complexity: Modular design manages a large number of configuration options.

– ⚙️ Flexible Configuration: Activates corresponding subsystems based on different conditions.

Typical Application Scenarios:

– Charging systems supporting national and European standards.

– On-board control systems adapting to different hardware configurations.

– Algorithms optimized for different operating environments.

🛠️ 2 Creating and Configuring Variant Subsystems

2.1 Basic Creation Method

% Create a new model
new_system('VariantModel');
open_system('VariantModel');
% Add variant subsystem
add_block('simulink/Ports & Subsystems/Variant Subsystem', 'VariantModel/VariantSubsystem');
% Define control parameters
controlParam = Simulink.Parameter;
controlParam.Value = 2;
controlParam.DataType = 'int32';
controlParam.StorageClass = 'Define'; % Corresponds to macro definitions in C code

2.2 Variant Control Mode

% Set variant control conditions
set_param('VariantModel/VariantSubsystem', 'VariantControl', 'controlParam==2');

⚙️ 3 Activation Timing and Conditional Compilation of Variants

3.1 Selection of Activation Timing

Simulink supports four types of activation timing:

1. 📝 Upon model update

2. 🔍 During variant analysis

3. 🧪 At code compilation (recommended)

4. 🚀 At application startup

3.2 Code Generation and Conditional Compilation

% Configure model parameters
set_param('VariantModel', 'SystemTargetFile', 'grt.tlc');
set_param('VariantModel', 'GenCodeOnly', 'on');
% Generate code
rtwbuild('VariantModel');

🔧 4 Advanced Variant Management Features

4.1 Variant Manager and Graphical Interface

% Create variant configuration data object
vcd = Simulink.VariantConfigurationData;
vcd.Configurations(1).Name = 'ConfigA';
vcd.Configurations(1).ControlVariables = struct('Name', 'controlParam', 'Value', 2);
% Associate with model
set_param('VariantModel', 'VariantConfigurationData', 'vcd');

4.2 Parameter Variants and Dimensional Variants

% Define dimensional parameter
DIM = Simulink.Parameter;
DIM.Value = 10;
set_param('VariantModel', 'AllowSymbolicDim', 'on');

4.3 Variant Source and Variant Sink

% Add Variant Source block
add_block('simulink/Signal Routing/Variant Source', 'VariantModel/VariantSource');
% Configure variant conditions
set_param('VariantModel/VariantSource', 'VariantConditions', {'controlParam==1', 'controlParam==2'});

🤝 5 Team Collaboration and Testing Strategies

5.1 Data Dictionary and Version Control

% Create data dictionary
dataDictionary = Simulink.data.dictionary.create('VariantData.sldd');
% Add parameters to dictionary
addData(dataDictionary, 'controlParam', controlParam);
% Associate with model
set_param('VariantModel', 'DataDictionary', 'VariantData.sldd');

5.2 Variant Testing Management

% Create test case
testCase = sltest.testmanager.TestCase('VariantTest');
% Set variant configuration
setProperty(testCase, 'VariantConfiguration', 'ConfigA');
% Run test
testResults = run(testCase);

📝 6 Conclusion

Implementing conditional compilation and variant management of Simulink models through Matlab scripts can significantly enhance the efficiency and quality of complex system development. Core strategies include:

– Using Simulink.Parameter to define control variables

– Reasonably selecting variant activation timing

– Utilizing data dictionaries to manage variant metadata

– Establishing automated testing processes

Recommendation: Use the variant manager for centralized management, define constraints to prevent invalid configurations, especially suitable for platform-based development scenarios.