Neural‐network‐based robust terminal sliding‐mode control of quadrotor
Xumei Lin, Yulu Wang, Yunfei Liu
Abstract
Abstract A novel robust terminal sliding‐mode control (RTSMC) based on radial basis function (RBF) neural network is proposed firstly for controlling the attitude and position of the quadrotor, guaranteeing the system converged to stability point in a limited time. After establishing the nonlinear kinematics and dynamics models of the system, robust control is adopted in the RBF neural network terminal sliding‐mode controller such that the impact of external interference is reduced effectively. Resorting to the Lyapunov function, the asymptotically stable condition for the considered system is obtained, in which the convergence of the system is deduced in a finite time. Furthermore, simulation results are given to show the faster convergence speed and strong robustness for the considered system with RBF and RTSMC. Finally, the effectiveness and robustness of the developed control strategy is validated experimentally.