Robust Adaptive Control Design Based on RBFNN with an Unmanned Quadrotor for Transporting Tasks

A quadrotor helicopter demonstrates excellent maneuverability in complex environments, which makes it an ideal platform as a data collection and device delivering system. In this paper, the position control problem for a quadrotor helicopter with a cable suspended transporting payload is investigated. During the transporting process, it is very challenging to eliminate the time-varying external disturbances. A Radial Basis Function neural network (RBFNN)-based robust controller is proposed that is able to control the quadrotor helicopter's position with suspended cable attached. The convergence of position and attitude errors together with the Lyapunov function based analysis are employed to prove the stability of the nonlinear transportation system. The proposed controller is tested in an outdoor environment and the experimental results on the quadrotor transportation system are provided to verify the controllers' reliability.