Adaptive dynamic surface control of UAV based on RBF neural network

Unmanned aerial vehicle (UAV) is an underactuated complex nonlinear system, which is easily affected by external disturbance during the flight. In order to achieve high precision trajectory tracking of UAV under the disturbances, the dynamic surface control technology is used to ensure the trajectory tracking capability of the UAV, while the radial basis function (RBF) neural network is used to estimate and compensate the disturbance to realize the disturbance rejection. The stability of the proposed control system is proved by Lyapunov stability theory. Simulation experiments have been performed to validate that the proposed method has high precision trajectory tracking ability and good anti-disturbance capability.