Nonlinear Control of Dual UAV Slung Load Flight System Based on RBF Neural Network

In this paper, a nonlinear trajectory tracking control method based on RBF neural network and integral backstepping method is proposed for the precise control of position tracking and attitude control of UAV in the transportation process of double UAV hanging load flight system. The dual-UAV suspended load flight system is decomposed into three dynamic subsystems: attitude, position and load swing control, and the decoupling control under underactuated constraints is designed respectively. The RBF neural network is used to approximate the unknown coupling and external interference in the dual UAV flight system, and the stability of the closed-loop system, the tracking error and the uniform ultimate boundedness of all signals of the payload swing are proved. The simulation results verify the effectiveness and superiority of the nonlinear control of the double UAV hanging flight based on RBF neural network under unknown interference, and realize the precise control of the trajectory tracking of the double UAV hanging system, and quickly suppress the load swing during the flight.