Improving Simulink Design Optimization Performance Using Parallel Computing

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

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

This article was published in MATLAB Digest, May 2009, which you can read at https://www.mathworks.com/company/newsletters/articles/improving-simulink-design-optimization-performance-using-parallel-computing.html