Documentation

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 Configuration Sets in the Simulink® documentation.

Configuration Set Basics

Load a model into memory.

model='rtwdemo_configwizard';
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.

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 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 that contains the complete list of model configuration parameters by using the configuration set saveAs function.

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

% 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     %---------------------------------------------------------------------------
3     %  MATLAB function for configuration set generated on 04-Oct-2016 15:53:08
4     %  MATLAB version: 9.1.0.441655 (R2016b)
5     %---------------------------------------------------------------------------
6     
7     cs = Simulink.ConfigSet;
8     
9     % Original configuration set version: 1.16.5
10    if cs.versionCompare('1.16.5') < 0
11        error('Simulink:MFileVersionViolation', 'The version of the target configuration set is older than the original configuration set.');
12    end
13    
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
18    
19    % Original configuration set target is ert.tlc
20    cs.switchTarget('ert.tlc','');
21    
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
25    
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
32    
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('SaveFormat', 'StructureWithTime');   % Format
39    cs.set_param('SaveOutput', 'off');   % Save output
40    cs.set_param('SaveFinalState', 'off');   % Save final state
41    cs.set_param('SignalLogging', 'on');   % Signal logging
42    cs.set_param('SignalLoggingName', 'sigsOut');   % Signal logging name
43    cs.set_param('DSMLogging', 'on');   % Data stores
44    cs.set_param('DSMLoggingName', 'dsmout');   % Data stores logging name
45    cs.set_param('LoggingToFile', 'off');   % Log Dataset data to file
46    cs.set_param('ReturnWorkspaceOutputs', 'off');   % Single simulation output
47    cs.set_param('InspectSignalLogs', 'off');   % Record logged workspace data in Simulation Data Inspector
48    cs.set_param('StreamToWorkspace', 'off');   % Write streamed signals to workspace
49    cs.set_param('LimitDataPoints', 'off');   % Limit data points
50    cs.set_param('Decimation', '1');   % Decimation

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

% Return to previous working directory.
cd(currentDir)

Configuration Wizard Blocks

Embedded Coder® provides a set of Configuration Wizard blocks to obtain an initial 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 and floating point

  • GRT debug settings for fixed and floating point

  • Custom (you provide the script)

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

open_system(model)

To use configuration wizard blocks in the rtwdemo_configwizard model follow these steps:

  • 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
rtwdemoclean;
close_system(model,0)

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.

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