Why does it take so long to stop my Speedgoat real-time application?
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MathWorks Support Team
on 28 Sep 2022
Edited: MathWorks Support Team
on 18 Oct 2025 at 10:49
Why does it take so long to stop my Speedgoat real-time application?
Accepted Answer
MathWorks Support Team
on 18 Oct 2025 at 0:00
Edited: MathWorks Support Team
on 18 Oct 2025 at 10:49
Depending on the contents of your model and the features that you use, there may be several reasons why it is taking longer for your real-time application to stop. Try the following:
(1) Run the real-time simulation from the command line. If you usually start the simulation using the "Run on Target" button in the model, try running it from the command line with the Simulink model closed to see if it makes a difference:
>> tg = slrealtime;
>> load(tg,'myModel');
>> start(tg,'StopTime',Inf);
% Wait for the time your simulation usually runs
>> stop(tg);
(2) Disable 'ExportToBaseWorkspace'. By default, logged signals will be exported from Simulation Data Inspector (SDI) to a 'logsout' variable in the MATLAB base workspace after the real-time simulation stops. Disable this feature by starting your real-time simulation like this:
>> tg = slrealtime;
>> load(tg,'myModel');
>> start(tg,'StopTime',Inf,'ExportToBaseWorkspace',false);
(3) Disable 'AutoImportFileLog'. By default, file logs are automatically downloaded into SDI after the recording or simulation run stops. Disable this feature by starting your real-time simulation like this:
>> tg = slrealtime;
>> load(tg,'myModel');
>> start(tg,'StopTime',Inf,'AutoImportFileLog',false,'ExportToBaseWorkspace',false);
For more information and other ways to disable the feature, see: How can I turn off the automatic import of file logs from my Speedgoat target?
(4) Disable 'updateAutoSaveParameterSetOnStop'. This is relevant if your model contains a large number of tunable parameters. By default, a parameter set containing the latest values will be stored on the Speedgoat target upon stop. See the following answer for details about disabling this feature:
Alternatively, reduce the number of tunable parameters in your model.
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