基于L-BFGS的自适应模糊互补滤波

Adaptive Fuzzy Complementary Filtering Based on L-BFGS

针对惯性测量单元噪声大及常规姿态解算算法精度不高的问题,提出了一种基于拟牛顿法(L-BFGS)的自适应模糊互补滤波(AFCF)算法。该方法利用L-BFGS对加速度计、磁力计进行寻优估计,并通过监测系统的运动等级、加速度计、磁力计的误差,运用模糊逻辑理论调控加权因子及增益权重,动态地调整互补滤波参数,实现姿态误差的动态补偿,优化姿态解算结果。经实验验证,系统静态误差在0.4°内;动态误差在1.3°内,且该系统能减少噪声的干扰及陀螺仪的漂移。

Aiming at the problem that the inertia measurement unit has large noise and the accuracy of the conventional attitude calculation algorithm is not high,an adaptive fuzzy complementary filter(AFCF)algorithm based on quasi-Newton method(L-BFGS)is proposed in this paper.In this method,first the L-BFGS is used to estimate the optimization of the accelerometer and magnetometer,monitor the motion level and the errors of the accelerometer and magnetometer.Then the fuzzy logic theory is used to adjust the weighting factor and gain weight to dynamically adjust the complementary filter parameter,realize the dynamic compensation of attitude error and optimize the attitude calculation results.The experimental verification show that the system static error is within 0.4°,the dynamic error is within 1.3°,and the system can reduce noise interference and gyroscope drift.