Configuring Model from Command Line

This example shows how to use Simulink® Coder™ and Embedded Coder® configuration options for customizing the generated code. You access code generation options through the Simulink® Configuration Parameters, also referred to as the model's configuration set.

You make configuration decisions and tradeoffs depending on how you use and interact with the generated code. You choose a configuration that best matches your needs for debugging, traceability, code efficiency, and safety precaution. Features, scripts, and documentation facilitate your creation of an ideal model configuration set for your application.

While Simulink® Coder™ provides a push-button facility to quickly configure and check a model for specified objectives, it is common to automate the model configuration process using a MATLAB® script once a desired configuration is established. 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

With these basic skills you are well on your way to setting up an ideal automated configuration scheme for your project.

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 may want to 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 Configuration Sets in the Simulink® documentation.

Configuration Set Basics

Load a model into memory.


Obtain the model's active configuration set.

cs = getActiveConfigSet(model);

The Simulink® Coder™ product exposes a subset of the code generation options. If you are using Simulink® Coder™, select the Generic Real-Time (GRT) target.


The Embedded Coder® product exposes the complete set of code generation options. If you are using Embedded Coder®, select the Embedded Real-Time (ERT) target.


To automate configuration of models that will be built for both 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

Configuration Option Summary

The full list of code generation options are documented with tradeoffs for debugging, traceability, code efficiency, and safety precaution.

Be sure to leverage the Code Generation Advisor to obtain a model configuration optimized for your goals. In the Set Objectives dialog box, you can set and prioritize objectives for the Code Generation Advisor.

You can find documentation about the Code Generation Advisor in the Simulink Coder documentation and additional documentation specific to 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 are contained in rtwconfiguremodel.m.

Alternatively, you can generate a MATLAB function automatically that contains the complete list of model configuration parameters using the configuration set saveAs function.

% Go to a temporary writable directory.
currentDir = pwd;

% Save the model's configuration parameters to file 'MyConfig.m'.

% Display the first 50 lines of MyConfig.m.
dbtype MyConfig 1:50
1     function cs = MyConfig()
2     %---------------------------------------------------------------------------
3     %  MATLAB function for configuration set generated on 15-Feb-2016 14:23:38
4     %  MATLAB version: (R2016a)
5     %---------------------------------------------------------------------------
7     cs = Simulink.ConfigSet;
9     % Original configuration set version: 1.16.2
10    if cs.versionCompare('1.16.2') < 0
11        error('Simulink:MFileVersionViolation', 'The version of the target configuration set is older than the original configuration set.');
12    end
14    % Original environment character encoding: US-ASCII
15    if ~strcmpi(get_param(0, 'CharacterEncoding'), 'US-ASCII')
16        warning('Simulink:EncodingUnMatched', 'The target character encoding (%s) is different from the original (%s).',  get_param(0, 'CharacterEncoding'), 'US-ASCII');
17    end
19    % Original configuration set target is ert.tlc
20    cs.switchTarget('ert.tlc','');
22    % Do not change the order of the following commands. There are dependencies between the parameters.
23    cs.set_param('Name', 'Configuration'); % Name
24    cs.set_param('Description', ''); % Description
26    % Solver
27    cs.set_param('StartTime', '0.0');   % Start time
28    cs.set_param('StopTime', '48');   % Stop time
29    cs.set_param('SolverType', 'Fixed-step');   % Type
30    cs.set_param('EnableConcurrentExecution', 'off');   % Show concurrent execution options
31    cs.set_param('SampleTimeConstraint', 'STIndependent');   % Periodic sample time constraint
33    % Data Import/Export
34    cs.set_param('LoadExternalInput', 'off');   % Load external input
35    cs.set_param('LoadInitialState', 'off');   % Load initial state
36    cs.set_param('SaveTime', 'off');   % Save time
37    cs.set_param('SaveState', 'off');   % Save states
38    cs.set_param('SaveOutput', 'off');   % Save output
39    cs.set_param('SaveFinalState', 'off');   % Save final state
40    cs.set_param('SaveFormat', 'StructureWithTime');   % Format
41    cs.set_param('LimitDataPoints', 'off');   % Limit data points
42    cs.set_param('Decimation', '1');   % Decimation
43    cs.set_param('SignalLogging', 'on');   % Signal logging
44    cs.set_param('SignalLoggingName', 'sigsOut');   % Signal logging name
45    cs.set_param('DSMLogging', 'on');   % Data stores
46    cs.set_param('DSMLoggingName', 'dsmout');   % Data stores logging name
47    cs.set_param('ReturnWorkspaceOutputs', 'off');   % Single simulation output
48    cs.set_param('InspectSignalLogs', 'off');   % Record logged workspace data in Simulation Data Inspector
49    cs.set_param('VisualizeSimOutput', 'on');   % Enable live streaming of selected signals to Simulation Data Inspector
50    cs.set_param('StreamToWorkspace', 'off');   % Write streamed signals to workspace

Each parameter setting in the generated file includes a comment for the corresponding parameter string in the Configuration Parameter dialog box.

% Return to previous working directory.

Configuration Wizard Blocks

Embedded Coder® provides a set of Configuration Wizard blocks to obtain a first cut configuration of a model for a specific goal. The predefined blocks provide configuration for:

  • ERT optimized for fixed point

  • ERT optimized for floating point

  • GRT optimized for fixed/floating point

  • GRT debug settings for fixed/floating point

  • Custom (you provide the script)

Drop the block into a model and double-click to configure the model. Open model rtwdemo_configwizard and click Open Configuration Wizard Library to interact with these useful blocks.


Specific instructions:

  • Open the Configuration Wizard Library by clicking the link provided in the model.

  • Open the Model's Configuration Parameters by clicking the link provided in the model.

  • Drag and drop a Configuration Wizard Block, for example ERT (optimized for fixed point), from the wizard library into the model.

  • Double-click the wizard block.

The Configuration Parameter options are modified automatically.

% cleanup


Simulink provides a rich set of MATLAB functions to automate the process of configuring a model for simulation and code generation. Simulink Coder and Embedded Coder® provide additional functionality specific for code generation. The Code Generation Advisor provides an excellent means of optimizing the model's configuration based on a set of prioritized goals. The optimal configuration can be saved to a MATLAB file automatically using the configuration set saveAs function and reused across models and projects.

Was this topic helpful?