Nesterov加速梯度无人机姿态融合算法

传统的无人机梯度下降姿态融合算法的步长难以确定，收敛较慢，动态性能较差，并且对于非重力加速度敏感．针对上述不足，提出了一种Nesterov加速梯度姿态融合方法，融合加速度计与陀螺仪数据并对非重力加速度作抑制处理；利用Pixhawk开源飞控实验平台进行多组对比试验．实验结果表明，Nesterov加速梯度姿态融合算法在机体静止时误差在0.05°之内，在水平滑动实验中产生的误差在0.5°之内，在绕轴转动实验中角度跟随性好、无明显滞后，在实际飞行实验中也获得了良好的实验结果．因此，Nesterov加速梯度姿态融合算法收敛速度明显优于普通梯度下降姿态解算法，抑制非重力加速度的能力明显优于互补滤波与梯度下降法，可有效跟踪无人机的真实姿态变化．

Attitude Fusion Algorithm of UAV Based on Nesterov Accelerated Gradient

For traditional attitude fusion algorithms of UAV (unmanned air vehicle) based on the gradient descent method, there exist some shortcomings, such as difficulty in determining the step length, slow convergence, poor dynamic performance, and sensitivity to non-gravity acceleration. For the above disadvantages, an attitude fusion algorithm based on Nesterov accelerated gradient is proposed, in which the data from gyroscope and accelerometer are fused and the impact of non-gravity acceleration is restrained. Then, several groups of comparative experiments are conducted on the experimental platform based on Pixhawk open source flight control. The results show that the error of the attitude fusion algorithm based on Nesterov accelerated gradient is less than 0.05° in the static test, the error in horizontal sliding experiments is less than 0.5°, and the algorithm performs well in angle tracking without apparent lag in the rotation test and demonstrates better performance in the actual flight test. Therefore, the attitude fusion algorithm based on Nesterov accelerated gradient demonstrates an obviously faster convergence rate than the traditional gradient descent based attitude algorithms, and a better restraint performance for non-gravity acceleration than the complementary filter and the gradient descent method. The proposed algorithm can track the variations of actual UAV flight attitude effectively.