Research on Anti-disturbance for Quadrotor Aircraft in Wind Field
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摘要: 针对四旋翼在室外飞行时易受到气流干扰，难以实现精准控制的问题，首先对四旋翼在室外飞行时的风场环境进行建模，将风场影响添加到四旋翼动力模型当中；其次，设计了自适应扩展卡尔曼滤波器（Adaptive extended kalman filter，AEKF），通过实时调整噪声协方差的自适应因子提高飞行器姿态数据的滤波精度，并将数据反馈给PID位置控制器对飞行器进行控制。实验表明，建立的模型能够有效反映四旋翼在风场环境下的运动规律，采用PID与AEKF相结合的控制策略可以提高系统的抗干扰能力，实现在风场环境下对四旋翼的精准控制。Abstract: Aiming at the problem that the quad-rotor unmanned aerial vehicles (UAV) is vulnerable to be interfered by airflow when flying outside, and it is difficult to achieve accurate control. Firstly, the turbulence of the quad-rotor UAV during outdoor flight is modeled; and the influence of the turbulence is added to the UAV dynamic model. Then, the adaptive extended Kalman filter is designed to improve the filtering accuracy of the UAV attitude data by adjusting the adaptive factor of the noise covariance in real time, and the data is feedback to the PID position controller to control the UAV. The experimental results show that the established model can effectively reflect the motion law of the quadrotor UAV in the wind field environment, and the combination of the PID and AEKF can improve the anti-interference ability of the system and realize the accurate control of the quad-rotor UAV in the turbulence environment.
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表 1 控制器参数
名称 P I D X轴控制通道 13 0.001 7 Y轴控制通道 11 0.001 7 Z轴控制通道 7.5 0.001 8 滚转角控制通道 2 0 5 俯仰角控制通道 2 0 5 偏航角控制通道 3 0.001 7 
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表 2 模型参数
参数 数值 m 1.5 kg b 0.225 m r 0.127 m Ixx 0.013 kg·m2 Iyy 0.013 kg·m2 Izz 0.023 kg·m2 k 1.36×10-5 N/(rad/s)2 kd 2.69×10-7 N/(rad/s)2 C 0.221 N/(m/s)2 SX 0.1 m2 SY 0.1 m2 SZ 0.2 m2
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表 3 均方根误差统计表
算法 X轴
RMSE/mY轴
RMSE/mZ轴
RMSE/m偏航角
RMSE/radPID 0.325 0.273 0.421 0.217 EKF+PID 0.167 0.113 0.232 0.081 AEKF+PID 0.061 0.042 0.063 0.033
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