ADAPTIVE NEURAL NETWORK-BASED TERMINAL SLIDING MODE CONTROL FOR A QUADROTOR UNMANNED AERIAL VEHICLE WITH DISTURBANCES

Tóm tắt

This study presents an adaptive neural network-based terminal sliding mode control to ensure the trajectory tracking and stability of the Quadrotor unmanned aerial vehicle under unknown external disturbances. It is well known that the problem of Quadrotor tracking control tackles key challenges, including nonlinearity, coupling, model uncertainties, and external disturbances. To overcome these problems, an adaptive control scheme based on radial basic function neural network and terminal sliding mode control theory is proposed. In particular, the radial basic function neural network is employed to estimate the model uncertainties and external disturbances and a terminal sliding mode controller is used to achieve a good trajectory tracking performance and guarantee the stability of Quadrotor system. The stability of the closed-loop Quadrotor aerial vehicle is rigorously proven using the Lyapunov stability theory. Co-simulation using MATLAB/Simulink is provided to confirm the effectiveness of the proposed controller.