  We propose a general strategy for feedback control design of complex
dynamical systems exploiting the nonlinear mechanisms in a systematic
unsupervised manner. These dynamical systems can have a state space of
arbitrary dimension with finite number of actuators (multiple inputs) and
sensors (multiple outputs). The control law maps outputs into inputs and is
optimized with respect to a cost function, containing physics via the dynamical
or statistical properties of the attractor to be controlled. Thus, we are
capable of exploiting nonlinear mechanisms, e.g. chaos or frequency cross-talk,
serving the control objective. This optimization is based on genetic
programming, a branch of machine learning. This machine learning control is
successfully applied to the stabilization of nonlinearly coupled oscillators
and maximization of Lyapunov exponent of a forced Lorenz system. We foresee
potential applications to most nonlinear multiple inputs/multiple outputs
control problems, particulary in experiments.
