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| R2011b Documentation → Simulink Control Design | |
Learn more about Simulink Control Design |
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Factors That Impact Linearization Performance Blocks with Complex Initialization Functions Disabling the Linearization Inspector in the Linear Analysis Tool |
Large Simulink models and blocks with complex initialization functions can slow linearization.
In most cases, the time it takes to linearize a model is directly related to the time it takes to update the block diagram.
Use the MATLAB Profiler to identify complex bottlenecks in block initialization functions.
In the MATLAB Profiler, run the command:
set_param(modelname,'SimulationCommand','update')
You can speed up the linearization of large models by disabling the Linearization Diagnostics Viewer in the Linear Analysis Tool.
The Linearization Diagnostic Viewer stores and tracks linearization values of individual blocks, which can impact linearization performance.
In the Linear Analysis Tool, in the Exact Linearization tab, clear the Launch Diagnostic Viewer check box.
Note Alternatively, you can disable the Linearization Diagnostic Viewer globally in the Simulink Control Design tab of the MATLAB preferences dialog box. Clear the Launch diagnostic viewer for exact linearizations in the linear analysis tool check box. This global preference persists from session to session until you change this preference. |
When batch linearizing a large model that contains only a few varying parameters, you can use linlftfold to reduce the computational load.
See Computing Multiple Linearizations of Models with Block Variations More Efficiently.
 | Models with Pulse Width Modulation (PWM) Signals | Exact Linearization Algorithm |  |
Learn more about resources for designing, testing, and implementing control systems.
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