Closed-loop control of an experimental mixing layer using machine learning control

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  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",
  title =        "Closed-loop control of an experimental mixing layer
                 using machine learning control",
  year =         "2014",
  month =        aug # "~14",
  keywords =     "genetic algorithms, genetic programming, physics -
                 fluid dynamics",
  bibsource =    "OAI-PMH server at",
  oai =          "",
  URL =          "",
  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