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

Vladimir Parezanović, Thomas Duriez, Laurent Cordier, Bernd R. Noack, Joël Delville, Jean-Paul Bonnet, Marc Segond, Markus Abel, Steven L. Brunton

Published 2014-08-14Version 1

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 optimization method of machine learning. In this article, MLC is benchmarked 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.