Closed-loop control of an experimental mixing layer using machine learning control
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Misc{oai:arXiv.org:1408.3259,
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author = "Vladimir Parezanovic and Thomas Duriez and
Laurent Cordier and Bernd R. Noack and Joel Delville and
Jean-Paul Bonnet and Marc Segond and Markus Abel and
Steven L. Brunton",
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title = "Closed-loop control of an experimental mixing layer
using machine learning control",
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year = "2014",
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month = aug # "~14",
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keywords = "genetic algorithms, genetic programming, physics -
fluid dynamics",
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bibsource = "OAI-PMH server at export.arxiv.org",
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oai = "oai:arXiv.org:1408.3259",
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URL = "http://arxiv.org/abs/1408.3259",
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abstract = "A novel framework for closed-loop control of turbulent
flows is tested in an experimental mixing layer flow.
This framework, called Machine Learning Control (MLC),
provides a model-free method of searching for the best
function, to be used as a control law in closed-loop
flow control. MLC is based on genetic programming, a
function optimisation method of machine learning. In
this article, MLC is bench marked against classical
open-loop actuation of the mixing layer. Results show
that this method is capable of producing sensor-based
control laws which can rival or surpass the best
open-loop forcing, and be robust to changing flow
conditions. Additionally, MLC can detect non-linear
mechanisms present in the controlled plant, and exploit
them to find a better type of actuation than the best
periodic forcing.",
- }
Genetic Programming entries for
Vladimir Parezanovic
Thomas Duriez
Laurent Cordier
Bernd R Noack
Joel Delville
Jean-Paul Bonnet
Marc Segond
Markus W Abel
Steven L Brunton
Citations