Configure Model from Command Line
The code generator provides model configuration parameters for customizing generated code. Depending on how you use and interact with the generated code, you make configuration decisions. You choose a configuration that best matches your needs for debugging, traceability, code efficiency, and safety precaution.
It is common to automate the model configuration process by using a MATLAB® script once you have decided upon a desired configuration.
The example describes:
Concepts of working with configuration parameters
Documentation to understand the code generation options
Tools and scripts to automate the configuration of a model
Configuration Parameter Workflows
There are many workflows for Configuration Parameters that include persistence within a single model or persistence across multiple models. Depending on your needs, you can work with configuration sets as copies or references. This example shows the basics steps for working directly with the active configuration set of a model. For a comprehensive description of configuration set features and workflows, see Manage Configuration Sets for a Model in the Simulink® documentation.
Configuration Set Basics
Load a model into memory.
model='ThrottleControl';
load_system(model)
Obtain the model's active configuration set.
cs = getActiveConfigSet(model);
Simulink® Coder™ exposes a subset of the code generation options. If you are using Simulink® Coder™, select the Generic Real-Time (GRT) target.
switchTarget(cs,'grt.tlc',[]);
Embedded Coder® exposes the complete set of code generation options. If you are using Embedded Coder®, select the Embedded Real-Time (ERT) target.
switchTarget(cs,'ert.tlc',[]);
To automate configuration of models built for GRT- and ERT-based targets, the configuration set IsERTTarget
attribute is useful.
isERT = strcmp(get_param(cs,'IsERTTarget'),'on');
You can interact with code generation options via the model or the configuration set. This example gets and sets options indirectly via the model.
deftParamBehvr = get_param(model,'DefaultParameterBehavior'); % Get set_param(model,'DefaultParameterBehavior',deftParamBehvr) % Set
This example gets and sets options directly via the configuration set.
if isERT lifespan = get_param(cs,'LifeSpan'); % Get LifeSpan set_param(cs,'LifeSpan',lifespan) % Set LifeSpan end
Configuration Option Summary
The full list of code generation options are documented with tradeoffs for debugging, traceability, code efficiency, and safety precaution. For Simulink® Coder™, see the Simulink® Coder™ version of Recommended Settings Summary for Model Configuration Parameters. For Embedded Coder®, see the Embedded Coder® version of Recommended Settings Summary for Model Configuration Parameters (Embedded Coder).
Use Code Generation Advisor to obtain a model configuration optimized for your goals. In the Set Objectives dialog box, you can set and prioritize objectives.
You can find documentation about the Code Generation Advisor in Application Objectives Using Code Generation Advisor. You can find additional documentation specific to Embedded Coder® in Configure Model for Code Generation Objectives by Using Code Generation Advisor (Embedded Coder).
Parameter Configuration Scripts
Simulink® Coder™ provides an example configuration script that you can use as a starting point for your application. A list of the most relevant GRT and ERT code generation options is contained in rtwconfiguremodel.m
.
Alternatively, you can generate a MATLAB function that contains the complete list of model configuration parameters by using the configuration set saveAs
function.
% Save the model's configuration parameters to file 'MyConfig.m'. saveAs(cs,'MyConfig') % Display the first 50 lines of MyConfig.m. dbtype MyConfig 1:50
1 function cs = MyConfig() 2 % MATLAB function for configuration set generated on 20-Jul-2024 17:23:13 3 % MATLAB version: 24.2.0.2651190 (R2024b) Prerelease Update 2 4 5 cs = Simulink.ConfigSet; 6 7 % Original configuration set version: 24.1.0 8 if cs.versionCompare('24.1.0') < 0 9 error('Simulink:MFileVersionViolation', 'The version of the target configuration set is older than the original configuration set.'); 10 end 11 12 % Character encoding: UTF-8 13 14 % Do not change the order of the following commands. There are dependencies between the parameters. 15 cs.set_param('Name', 'Configuration'); % Name 16 cs.set_param('Description', ''); % Description 17 18 % Original configuration set target is ert.tlc 19 cs.switchTarget('ert.tlc',''); 20 21 cs.set_param('HardwareBoard', 'None'); % Hardware board 22 23 cs.set_param('TargetLang', 'C'); % Language 24 25 cs.set_param('CodeInterfacePackaging', 'Nonreusable function'); % Code interface packaging 26 27 cs.set_param('GenerateAllocFcn', 'off'); % Use dynamic memory allocation for model initialization 28 29 cs.set_param('Solver', 'FixedStepDiscrete'); % Solver 30 31 % Solver 32 cs.set_param('StartTime', '0.0'); % Start time 33 cs.set_param('StopTime', '10.0'); % Stop time 34 cs.set_param('SolverName', 'FixedStepDiscrete'); % Solver 35 cs.set_param('SolverType', 'Fixed-step'); % Type 36 cs.set_param('SampleTimeConstraint', 'Unconstrained'); % Periodic sample time constraint 37 cs.set_param('FixedStep', '.001'); % Fixed-step size (fundamental sample time) 38 cs.set_param('EnableFixedStepZeroCrossing', 'off'); % Enable zero-crossing detection for fixed-step simulation 39 cs.set_param('ConcurrentTasks', 'off'); % Allow tasks to execute concurrently on target 40 cs.set_param('EnableMultiTasking', 'on'); % Treat each discrete rate as a separate task 41 cs.set_param('AllowMultiTaskInputOutput', 'off'); % Allow multiple tasks to access inputs and outputs 42 cs.set_param('PositivePriorityOrder', 'off'); % Higher priority value indicates higher task priority 43 cs.set_param('AutoInsertRateTranBlk', 'off'); % Automatically handle rate transition for data transfer 44 45 % Data Import/Export 46 cs.set_param('Decimation', '1'); % Decimation 47 cs.set_param('LoadExternalInput', 'off'); % Load external input 48 cs.set_param('SaveFinalState', 'off'); % Save final state 49 cs.set_param('LoadInitialState', 'off'); % Load initial state 50 cs.set_param('LimitDataPoints', 'on'); % Limit data points
Each parameter setting in the generated file includes a comment for the corresponding parameter string in the Configuration Parameters dialog box.
Summary
Simulink provides a rich set of MATLAB functions to automate the configuring a model for simulation and code generation. Simulink Coder and Embedded Coder® provide additional functionality specific for code generation. The Code Generation Advisor optimizes the model configuration based on a set of prioritized goals. You can save the optimal configuration to a MATLAB file by using the configuration set saveAs function, and reuse it across models and projects.