  We advance Machine Learning Control (MLC), a recently proposed model-free
control framework which explores and exploits strongly nonlinear dynamics in an
unsupervised manner. The assumed plant has multiple actuators and sensors and
its performance is measured by a cost functional. The control problem is to
find a control logic which optimizes the given cost function. The corresponding
regression problem for the control law is solved by employing linear genetic
programming as an easy and simple regression solver in a high-dimensional
control search space. This search space comprises open-loop actuation,
sensor-based feedback and combinations thereof, thus generalizing former MLC
studies. This methodology is denoted as linear genetic programming control
(LGPC). Focus of this study is the frequency crosstalk between unforced
unstable oscillation and the actuation at different frequencies. LGPC is first
applied to the stabilization of a forced nonlinearly coupled three-oscillator
model comprising open- and closed-loop frequency crosstalk mechanisms. LGPC
performance is then demonstrated in a turbulence control experiment, achieving
22% drag reduction for a simplified car model. For both cases, LGPC identifies
the best nonlinear control achieving the optimal performance by exploiting
frequency crosstalk. Our control strategy is suited to complex control problems
with multiple actuators and sensors featuring nonlinear actuation dynamics.
