Global Fast Terminal Fuzzy Sliding Mode Control of Quadrotor UAV Based on RBF Neural Network

In this paper, a global fast terminal fuzzy sliding mode control scheme based on the radial basis function (RBF) neural network is proposed for quadrotor unmanned aerial vehicle systems in the presence of external disturbances, system model uncertainty, and time-varying mass. Firstly, the dynamic model of the quadrotor is divided into two subsystems, i.e., an outer-loop control subsystem and an inner-loop control subsystem. Secondly, an adaptive sliding mode controller is used to control the outer-loop control subsystem, which includes the adaptive laws estimating the time-varying mass and external disturbances. In the inner-loop control subsystem, a global fast terminal fuzzy sliding mode controller, which is based on the RBF neural network, is designed to control the attitude of a quadrotor. In this method, the system model uncertainty is approximated using the RBF neural network. Simultaneously, an adaptive fuzzy controller is introduced to estimate the switching gain and eliminate external disturbances, and the chattering phenomenon is eliminated effectively. Finally, simulations are provided to demonstrate the effectiveness of the proposed control scheme.