Abstract
In this chapter, we provide good practices for applying machine learning control (MLC) to a real-world flow control experiment. The recipes include common experimental challenges, like defining a cost function, implementing MLC on the computer, and dealing with imperfect plants, actuation and sensing. In addition, we show how MLC can learn faster by preconditioning the control problem and by planning, monitoring and post-processing the experimental campaign. Most of the advice is formulated for the non-ideal flow control experiment, but is easily applicable for any other real-world application.
Heavy is the brick of reality on the strawberry cake of our illusions.
Gilles “Boulet” Roussel, cartoonist, Translated from his Twitter account Bouletcorp, 10th December 2013
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Notes
- 1.
In principle, the file exchange can happen over different locations in a cloud, with a fast computational unit close to the experiment and the MLC learning performed remotely.
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© 2017 Springer International Publishing Switzerland
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Duriez, T., Brunton, S.L., Noack, B.R. (2017). MLC Tactics and Strategy. In: Machine Learning Control – Taming Nonlinear Dynamics and Turbulence. Fluid Mechanics and Its Applications, vol 116. Springer, Cham. https://doi.org/10.1007/978-3-319-40624-4_7
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DOI: https://doi.org/10.1007/978-3-319-40624-4_7
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-40624-4
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