基于 ESKF-MPC 的四旋翼无人机轨迹跟踪控制

四旋翼无人机的轨迹跟踪控制容易受到风扰和测量噪声的影响,针对上述问题, 提出了一种基于扩展状态卡尔曼滤波 (extended state based Kalman filter, ESKF)的模型预测控制(model predictive control, MPC)方法。 首先, 采用牛顿-欧拉方法建 立风扰影响下的四旋翼无人机动力学模型; 然后, 位置控制采用基于误差模型的 MPC 方法, 利用 ESKF 估计风扰并对控制量 进行前馈补偿; 采用反馈线性化方法将姿态动力学模型线性化, 并设计基于 ESKF-MPC 的姿态控制器;最后, 仿真结果表明测 量噪声方差为 0. 000 1 时该方法的位置跟踪均方误差比自抗扰控制方法的误差小 0. 013 m, 当方差大于 0. 000 1 时自抗扰控制 方法使得系统不稳定,而本文的方法仍可以实现较好的位置跟踪。

Path tracking control for a quadrotor UAV based on ESKF-MPC

A model predictive control (MPC) method based on extended state Kalman filter (ESKF) is proposed for the path tracking control problem of a quadrotor UAV that is susceptible to wind disturbance and measurement noise during flight. First, the Newton-Euler method is used to establish a four-rotor UAV dynamic model under the influence of wind disturbance; then, an MPC method based on the error model is used for position control, and an ESKF is proposed to estimate wind field disturbance to compensate the controller. The attitude dynamic model is linearized by the feedback linearization method, and an attitude controller based on ESKF-MPC is designed. Finally, the simulation results show that when the measurement noise variance is 0. 000 1, the position tracking mean square error of this method is 0. 013 meters which is smaller than that of the active disturbance rejection control method. When the variance is greater than 0. 000 1, the active disturbance rejection control method makes the system unstable, and the method in this paper can achieve better position tracking.