Adaptive Fuzzy-Neural Network based Decentralized Backstepping Controller for Attitude Control of Quadrotor Helicopter

This work consists in designing a backstepping controller based on an adaptive fuzzy neural network (FNN). The main aim is the attitude control of a quadrotor system under uncertainties and disturbances. The FNN with adaptive parameters is exploited to approximate the nonlinear functions and improve the robustness against parametric uncertainties and external disturbances. FNN is included in classical backstepping control (BC) to solve the unknown dynamics problem. Otherwise, a robust control term is added to improve performance in tracking a reference signal when parametric uncertainties and disturbances exist. The stability of the quadrotor attitude control system is proven by the Lyapunov method. Simulation results of the proposed adaptive fuzzy neural network based decentralized backstepping controller (AFNN-DBC) demonstrate the capability and efficiency of the proposed technique in the presence of uncertainties and external disturbances in comparison with classical backstepping controller (BC).