Taming the fluidic pinball with artificial intelligence control
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Maceda:2018:EFMC,
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author = "Guy Yoslan {Cornejo Maceda} and Bernd R. Noack and
Francois Lusseyran and Marek Morzynski and
Luc Pastur and Nan Deng",
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title = "Taming the fluidic pinball with artificial
intelligence control",
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booktitle = "European Fluid Mechanics Conference",
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year = "2018",
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address = "Vienne, Austria",
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publisher = "HAL CCSD",
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month = sep # "~01",
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keywords = "genetic algorithms, genetic programming, AI,
artificial intelligence control, fluidic pinball,
control, machine learning, drag reduction, physics,
mechanics, mechanics of the fluids",
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type = "info:eu-repo/semantics/conferenceObject",
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URL = "https://hal.archives-ouvertes.fr/hal-02387544",
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abstract = "The aim of this work is to develop a generic control
strategy for nonlinear dynamics. This strategy is based
on genetic programming, a machine learning technique
for regression problems, that maps the sensor signals
to the actuators in a unsupervised manner.It's a
biological inspired method mimicking Darwin's natural
selection: through and evolution process it derives a
control law minimising a given objective.Genetic
programming has been applied to a DNS of a 2D fluidic
mechanic system, the fluidic pinball.Several search
spaces including control laws built from periodic
functions, sensor signals and time-delay sensor signals
have been explored.For the fluidic pinball genetic
programming managed a 46percent net drag saving,
outperforming by 3.3percent the best open-loop control
law found with a parametric study.Our contribution has
been the acceleration of the learning process by
avoiding the evaluations of redundant control laws,
thus improving the learning rate by a factor 3.",
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annote = "Laboratoire d'Informatique pour la Mecanique et les
Sciences de l'Ingenieur (LIMSI) ; Universite Paris
Saclay (COmUE)-Centre National de la Recherche
Scientifique (CNRS)-Sorbonne Universite - UFR
d'Ingenierie (UFR 919) ; Sorbonne Universite
(SU)-Sorbonne Universite (SU)-Universite
Paris-Saclay-Universite Paris-Sud - Paris 11 (UP11)",
-
bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
-
contributor = "Laboratoire d'Informatique pour la Mecanique et les
Sciences de l'Ingenieur",
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coverage = "Vienne, Austria",
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description = "International audience",
-
identifier = "hal-02387544",
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language = "en",
-
oai = "oai:HAL:hal-02387544v1",
- }
Genetic Programming entries for
Guy Yoslan Cornejo Maceda
Bernd R Noack
Francois Lusseyran
Marek Morzynski
Luc Pastur
Nan Deng
Citations