  This study presents a noise-robust closed-loop control strategy for wake
flows employing model predictive control. The proposed control framework
involves the autonomous offline selection of hyperparameters, eliminating the
need for user interaction. To this purpose, Bayesian optimization maximizes the
control performance, adapting to external disturbances, plant model
inaccuracies, and actuation constraints. The noise robustness of the control is
achieved through sensor data smoothing based on local polynomial regression.
The plant model can be identified through either theoretical formulation or
using existing data-driven techniques. In this work, we leverage the latter
approach, which requires minimal user intervention. The self-tuned control
strategy is applied to the control of the wake of the fluidic pinball, with the
plant model based solely on aerodynamic force measurements. The closed-loop
actuation results in two distinct control mechanisms: boat tailing for drag
reduction and stagnation point control for lift stabilization. The control
strategy proves to be highly effective even in realistic noise scenarios,
despite relying on a plant model based on a reduced number of sensors.
