  We propose a general model-free strategy for feedback control design of
turbulent flows. This strategy called 'machine learning control' (MLC) is
capable of exploiting nonlinear mechanisms in a systematic unsupervised manner.
It relies on an evolutionary algorithm that is used to evolve an ensemble of
feedback control laws until minimization of a targeted cost function. This
methodology can be applied to any non-linear multiple-input multiple-output
(MIMO) system to derive an optimal closed-loop control law. MLC is successfully
applied to the stabilization of nonlinearly coupled oscillators exhibiting
frequency cross-talk, to the maximization of the largest Lyapunov exponent of a
forced Lorenz system, and to the mixing enhancement in an experimental mixing
layer flow. We foresee numerous potential applications to most nonlinear MIMO
control problems, particularly in experiments.
