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

Hybrid Aerodynamics-Based Model Predictive Control for a Tail-Sitter UAV