Improving Simulink Design Optimization Performance Using Parallel Computing

Parallelization of a controller parameter tuning task using an aerospace system model as an example

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Updated 1 Sep 2016

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Estimating plant model parameters and tuning controllers are challenging tasks. Optimization-based methods help to systematically accelerate the tun¬ing process and let engineers tune multiple parameters at the same time. Further efficiencies can be gained by running the optimization in a parallel setting and distributing the computational load across multiple MATLAB workers—but how do you know when an optimization problem is a good candidate for parallelization?
Using an aerospace system model as an example, this article describes the paral¬lelization of a controller parameter tuning task using Parallel Computing Toolbox and Simulink Design Optimization. Topics covered include setting up an optimiza¬tion problem for parallel computing, the types of models that benefit from parallel optimization, and the typical optimization speed-up that can be achieved.
By Alec Stothert and Arkadiy Turevskiy, The MathWorks

Cite As

Linda Webb (2023). Improving Simulink Design Optimization Performance Using Parallel Computing (https://www.mathworks.com/matlabcentral/fileexchange/24542-improving-simulink-design-optimization-performance-using-parallel-computing), MATLAB Central File Exchange. Retrieved .

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Version Published Release Notes
1.0.0.1

Updated license

1.0.0.0