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A novel framework for closed-loop control of turbulent flows is tested in anexperimental mixing layer flow. This framework, called Machine Learning Control(MLC), provides a model-free method of searching for the best function, to beused as a control law in closed-loop flow control. MLC is based on geneticprogramming, a function optimization method of machine learning. In thisarticle, MLC is benchmarked against classical open-loop actuation of the mixinglayer. Results show that this method is capable of producing sensor-basedcontrol laws which can rival or surpass the best open-loop forcing, and berobust to changing flow conditions. Additionally, MLC can detect non-linearmechanisms present in the controlled plant, and exploit them to find a bettertype of actuation than the best periodic forcing.

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