Neural adaptive flight controller for ducted-fan UAV performing nonlinear maneuver

This paper presents a neural adaptive flight controller for ducted fan UAVs which are capable of vertical takeoff and landing(VTOL). These ducted fan propulsion systems pose great challenges in aerodynamics and control and we propose a backstepping neural adaptive control law to track its nonlinear dynamics. This controller can handle unmodeled dynamics and external disturbances as well, providing stability to the vehicle. A single layer radial basis neural network is used to approximate the unmodeled dynamics and vehicle stability is guaranteed through Lyapunov synthesis. For simulation study six degree of freedom model (6-DOF) is implemented in MATLAB along with the proposed control approach. The performance of the controller is evaluated using nonlinear bop-up maneuver and the necessary stability and tracking performance of the UAV have been investigated.