A Moving Target Tracking Control and Obstacle Avoidance of Quadrotor UAV Based on Sliding Mode Control Using Artificial Potential Field and RBF Neural Networks

A new control method for an underactuated quadrotor unmanned aerial vehicle (UAV) is proposed to solve the problem of moving target tracking and obstacle avoidance. In order to achieve a better target tracking and obstacle avoidance control, the dynamic model of quadrotor UAV is decoupled into position control subsystem and attitude control subsystem. Firstly, a method combining artificial potential field (APF) with sliding model control is introduced for the position system to track the moving target at a fixed distance in the case of obstacles and external disturbances. Secondly, a sliding mode control method based on radial basis function (RBF) network is applied to ensure the attitude of the quadrotor converges to the desired values. In addition, the stabilities of the two subsystems are respectively proved based on Lyapunov theory. Finally, the simulation results of moving target tracking verify the superiority and robustness of the proposed control method in the presence of obstacles and external interference.