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In this paper, we study the control design of an automatic crosswindstabilization system for a novel, buoyantly-assisted aerial transportationvehicle. This vehicle has several advantages over other aircraft including theability to take-off and land in very short distances and without the need forroads or runways. Despite these advantages, the large surface area of thevehicle's wing makes it more susceptible to wind, which introduces undesirableroll angle motions. The role of the automatic crosswind stabilization system isto detect the roll angle deviation, and then use motors at the wingtips tocounteract the wind effect. However, due to the relatively large inertia of thewing compared to small-size unmanned aerial vehicles and additional input timedelays, an automatic crosswind stabilization system based on traditionalcontrol algorithms such as the proportional-integral-derivative (PID)controller results in a response time that is too slow. Another challenge isthe lack of high-accuracy wind sensors that can be mounted on the vehicle'swing. Therefore, we first design a wind torque estimator that relies oninertial measurements, and then use feed-forward compensation to directlycorrect for the wind torque, resulting in a significantly faster response. Wesecond combine the proposed estimator with a model predictive controller (MPC),and compare constrained MPC with unconstrained MPC for the consideredapplication. Experimental results show that our proposed estimation-based MPCstrategy reduces the response time of the system by around 80-90% compared to astandard PID controller, without the need for adding wind sensors or changingthe hardware of the stabilization system.

Estimation-Based Model Predictive Control for Automatic Crosswind Stabilization of Hybrid Aerial Vehicles