基于BSP-ANN的四旋翼无人机轨迹跟踪方法

为了降低无人机轨迹跟踪误差，提高系统抗干扰能力，对反步（Backstepping）法进行改进提出一种基于反步神经网络（BSP-ANN）的无人机轨迹跟踪方法。首先，建立了四旋翼无人机运动学模型；然后，结合Backstepping方法在无人机的姿态控制、轨迹跟踪控制系统中引入Sigma-Pi神经网络，同时设计Sigma-Pi神经网络控制率，并证明该控制率满足Lyapunov意义下的系统稳定；最后，分别给出了相应的仿真实验。仿真结果表明：该算法可以有效降低跟踪误差，缩短无人机跟踪时间，同时可以提高系统的抗干扰能力。

Trajectory tracking method for quadrotor UAV based on BSP-ANN

To reduce the trajectory tracking error of the UAV and improve the anti-jamming ability of the system, a new trajectory tracking algorithm of the UAV is proposed based on BSP-ANN. A dynamic model for the Quadrotor UAV is given. Based on the Backstepping method, the Sigma-Pi ANN is introduced into the position control system and attitude control system of the Quadrotor UAV. The Sigma-Pi ANN control law is designed, and proving the system's stability in the sense of Lyapunov function. The corresponding simulations are performed using MATLAB. Simulation results show that with the BSP-ANN method, the trajectory tracking performance of the UAV can be improved by reducing the trajectory tracking error, decreasing the tracking time, and improving the anti-interference ability of the system.