  We present model predictive selection (MPS), a new method for selecting the
stable closed-loop (CL) equilibrium attitude-error quaternion (AEQ) of an
uncrewed aerial vehicle (UAV) during the execution of high-speed yaw maneuvers.
In this approach, we minimize the cost of yawing measured with a performance
figure of merit (PFM) that takes into account both the aerodynamic-torque
control input and attitude-error state of the UAV. Specifically, this method
uses a control law with a term whose sign is dynamically switched in real time
to select, between two options, the torque associated with the lesser cost of
rotation as predicted by a dynamical model of the UAV derived from first
principles. This problem is relevant because the selection of the stable CL
equilibrium AEQ significantly impacts the performance of a UAV during
high-speed rotational flight, from both the power and control-error
perspectives. To test and demonstrate the functionality and performance of the
proposed method, we present data collected during one hundred real-time
high-speed yaw-tracking flight experiments. These results highlight the
superior capabilities of the proposed MPS-based scheme when compared to a
benchmark controller commonly used in aerial robotics, as the PFM used to
quantify the cost of flight is reduced by 60.30 %, on average. To our best
knowledge, these are the first flight-test results that thoroughly demonstrate,
evaluate, and compare the performance of a real-time controller capable of
selecting the stable CL equilibrium AEQ during operation.
