Neural network controller-based safe landing algorithm for UAVs

This article proposes a safe landing control strategy for unmanned aerial vehicle (UAVs) by integrating control barrier functions with neural network controllers. Initially, control barrier functions and UAV’s dynamical models are introduced, providing a theoretical foundation for subsequent algorithm design. Then, a control approach is proposed that uses the level set method to design control barrier functions and combine them with neural network controllers to successfully ensure UAV safety during obstacle avoidance and safe landing. Simulation experiments then validate the effectiveness of the proposed control strategy in obstacle avoidance and safe landing, demonstrating the UAV’s safe obstacle avoidance capabilities under limited maneuverability and attitude constraints. The success of the suggested algorithm is finally summed up, and potential research avenues are examined.