This example shows and explains the model from behavioral and structural perspectives. The example also shows how to configure a model for code generation and how to generate code.
Time: 1/2 hour
Understand the functional behavior of the model
Understand how the model is validated
Get familiar with model checking tools
Get familiar with configuration options that affect code generation
Learn how to generate code from a model
Understanding the Model's Functional Design
This example uses a simple but functionally complete model of a throttle controller. The model features redundancy, which is common for safety-critical drive-by-wire applications. The model highlights a standard model structure and a set of basic blocks in algorithm design.
In the current configuration, the model generates code. However, the code is not configured for a production target system. This example guides you through the steps necessary to change the target configuration and shows how the format of the generated code changes with the completion of each task.
Viewing the Top-Level Model
The top-level model consists of:
Four subsystems (
Top-level inputs (
Top-level outputs (
No transformative blocks (blocks that change the value of a signal, such as Sum and Integrator blocks)
The layout shows a basic model architectural style.
Separation of calculations from signal routing (lines and buses)
Partitioning into subsystems
You can apply this style to all types of models.
Two subsystems represent PI controllers,
PI_ctrl_2. The subsystems are identical and, at this stage, use identical data. Later, you use the subsystems to learn how Simulink® Coder™ can create reusable functions.
The PI controllers are included in the model from a library, a group of related blocks or models intended for reuse. Libraries provide one of two methods for including and reusing models. The second method, model referencing, is covered later. You cannot edit a block that you add to a model from a library in the context of the model. To edit the block, you must do so in the library. This ensures that instances of the block in different models remain consistent.
The Stateflow® diagram performs basic error checking on the two command signals. If the commanded signals are too far apart, the Stateflow® diagram sets the output to a
Viewing the Configuration Options for Code Generation
The first step to prepare a model for code generation is to set model configuration parameters. The configuration parameters determine the method Simulink® Coder™ uses to generate the code and the resulting format.
Code Generation Objectives
You have the option to manually configure the model configuration parameters. Alternatively, there is a set of predefined objectives that you can use to auto-configure the model configuration parameters.
There are six high-level code generation objectives that you can use to auto-configure the model configuration parameters:
Each code generation objective will check the model configuration parameters against the recommended values of the objectives. Each objective also includes a set of Code Generation Advisor checks that you can use to verify that the model configuration parameters are set to create code that meets the objectives.
Some of the code generation objectives are mutually exclusive in terms of the recommended value for configuration parameters and the set of Code Generation Advisor checks. When the objectives are in conflict, the order of the selection determines which objectives are followed. There is a mechanism to solve the conflict to ensure objectives with higher priorities are satisfied before the objectives with lower priorities are satisfied.
In the following example, the priority is
RAM efficiency. To open the dialog box, open the Configuration Parameters dialog box, select the Code Generation pane, and click Set objectives.
You can choose to run the Code Generation Advisor to check the model based on the specified objectives. To launch the Code Generation Advisor, click Check model on the Code Generation pane of the Configuration Parameters dialog box.
The list of checks in the Code Generation Advisor is dynamically created based on the objectives that are selected. The first check reviews the current values of the configuration parameters and suggests alternative values based on the objectives. The check provides an automated method for setting the parameters to the recommended values.
Manual configuration options
In the following sections, we will look at the sections of the models configuration options:
For Simulink® Coder™ to generate code for a model, you must configure the model to use a fixed-step solver. The start and stop time do not affect the generated code.
Parameter Required Setting Effect on Code
Start time Any No effect Stop time Any No effect Type Fixed-step Code is not generated unless fixed step Solver Any Controls selected integration algorithms Sample time Lowest common multiple of rates in system Sets base rate of system Tasking mode for periodic sample times SingleTasking or MultiTasking MultiTasking generates entry point function for each rate in system
The Optimization pane consists of five subsections.
Simulation and code generation - Removes unused branches from the code and controls creation of temporary variables
Signals - Reduces the number of temporary variables created by collapsing multiple computations into a single assignment and by reusing temporary variables
Data initialization - Controls which signals have explicit initialization code
Integer and fixed-point - Enables and disables use of overflow and division-by-zero protection code
Stateflow - Controls how Stateflow® stores bitwise information
Use hardware implementation parameters to specify the word size and byte ordering of the target hardware. The example targets a generic 32-bit processor.
The Code Generation pane is where you specify the system target file (STF). This example uses the Embedded Coder® STF (
ert.tlc). You can extend this STF to create a customized configuration. Some of the basic configuration options reachable from the Code Generation pane include:
1. Selection of the code generator target
ert.tlc - "Base" Embedded Coder®
grt.tlc - "Base" Generic Real-Time Target
Hardware specific targets
2. Selected make file 3. Code formatting options
Use of parentheses
Header file information
Variable naming conventions
4. Inclusion of custom code
5. Generation of ASAP2 files
Saving the Configuration Parameters as a MATLAB® Function
The values of configuration parameters can be saved as a MATLAB® function from the command line.
hCs = getActiveConfigSet('rtwdemo_PCG_Eval_P1');
The saved MATLAB® function is the textual representation of the configuration parameter object. You can use it for archiving, or compare different versions of the files by using traditional diff tools. Its textual nature also makes it easy for visual inspection.
Running the saved MATLAB® function will result in setting the configuration parameters of other models.
hCs2 = ConfiguredData;
attachConfigSet('myModel', hCs2, true);
Understanding the Simulation Testing Environment
You test the throttle controller model in a separate model called a test harness. A test harness is a model that evaluates the control algorithm. A test harness offers the following advantages:
Separates test data from the control algorithm
Separates the plant or feedback model from the control algorithm
Provides a reusable environment for multiple versions of the control algorithm
A common simulation testing environment consists of the following parts:
Unit under test
Test vector source
Evaluation and logging
Plant or feedback system
Input and output scaling
The control algorithm is the unit under test. The control algorithm is referenced in the test harness using a Model Reference block. The Model Reference block provides a second method for reusing components. The referenced model is selected in the Model Reference configuration parameters dialog box.
The Model Reference block enables other models to be included (referenced) from the top model as compiled functions. By default, Simulink® compiles the model when the referenced model is changed. Compiled functions have several advantages over libraries.
Simulation time is faster for large models.
Compiled functions can be directly simulated.
The simulation requires less memory. One copy of the compiled model is in memory, even when the model is referenced multiple times.
The model uses a Simulink® Signal Builder block for the test vector source. The block has data that drives the simulation (
pos_rqst) and the expected results used by the Verification subsystem. The model uses only one set of test data. Typically, you would create a test suite that fully exercises the system.
The test harness compares the simulation results against golden data: a set of test results that have been certified by an expert to exhibit the desired behavior for the model. In this model, the V&V Assertion block compares the plant's simulated throttle value position against the golden value provided by the test harness. If the difference between the two signals is greater than 5%, the test fails and the Assertion block stops the simulation.
Alternatively, you can evaluate the simulation data after the simulation completes execution. You can use either MATLAB® file scripts or third-party tools to perform the evaluation. Post-execution evaluation provides greater flexibility in the analysis of the data. However, it requires waiting until execution is complete. Combining the two methods can provide a highly flexible and efficient test environment.
The throttle dynamics are modeled with a transfer function that is broken down into its canonical form. Plant models can be created to model any level of fidelity. It is not uncommon to have different plant models used at different stages of testing.
The subsystems that scale input and output perform three primary functions.
Select signals to route to the unit under test and plant.
Rescale signals between engineering units and units required by the unit under test.
Handle rate transitions between the plant and the unit under test.
Running the Simulation Tests
Click Start or the following task link to run the test harness model simulation.
The first time the test harness runs, the referenced model must be compiled. You can monitor the compilation progress in the MATLAB® Command Window.
When the model simulation is complete, Simulink® displays the results in a plot window:
The lower right plot shows the difference between the expected (golden) throttle position and the throttle position calculated by the plant. If the difference between the two values had been greater than +/- 0.05, the simulation would have stopped.
Generating Code for the Model
Use one of the following methods or the task link below to generate code for the model:
Enter Ctrl+B in the model.
Choose Configuration Parameters > Code Generation > Generate Code.
Choose Code > C/C++ Code > Build Model.
Simulink® Coder™ generates several files. The resulting code, while computationally efficient, is not yet organized for integration into the production environment.
Examining the Generated Code
The code generation process results in multiple files that you can view from the Model Explorer. In addition to the standard C and H files a set of HTML files is generated. The HTML files provided active links between the code and the model.
Note: You must generate code before you can view the files.
In the generated code, note that:
All of the controller code is contained in one function called
ModelName_step and found in the file
The operations of multiple blocks are collapsed into one equation.
Variables are initialized in the function
All data is defined using Simulink® Coder™ data structures (for example,
Further Study Topics