  It is challenging to model and control a tail-sitter unmanned aerial vehicle
(UAV) because its blended wing body generates complicated nonlinear aerodynamic
effects, such as wing lift, fuselage drag, and propeller-wing interactions. We
therefore devised a hybrid aerodynamic modeling method and model predictive
control (MPC) design for a quadrotor tail-sitter UAV. The hybrid model consists
of the Newton-Euler equation, which describes quadrotor dynamics, and a
feedforward neural network, which learns residual aerodynamic effects. This
hybrid model exhibits high predictive accuracy at a low computational cost and
was used to implement hybrid MPC, which optimizes the throttle, pitch angle,
and roll angle for position tracking. The controller performance was validated
in real-world experiments, which obtained a 57% tracking error reduction
compared with conventional nonlinear MPC. External wind disturbance was also
introduced and the experimental results confirmed the robustness of the
controller to these conditions.
