MLC tactics and strategy

Abstract

In this chapter, we provide good practices for applying machine learning control (MLC) to a real-world flow control experiment. The recipes include common experimental challenges, like defining a cost function, implementing MLC on the computer, and dealing with imperfect plants, actuation and sensing. In addition, we show how MLC can learn faster by preconditioning the control problem and by planning, monitoring and post-processing the experimental campaign. Most of the advice is formulated for the non-ideal flow control experiment, but is easily applicable for any other real-world application.