  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.
