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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Misc{oai:arXiv.org:1408.3259,

• 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 export.arxiv.org",
• oai = "oai:arXiv.org:1408.3259",
• URL = "http://arxiv.org/abs/1408.3259",
• 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