基于 RP-EKF 的无人机动力系统参数辨识

针对无人机动力系统电池电压波动导致系统噪声大、辨识结果精度低的问题，本研究提出了一种基于反向预测-增广卡尔曼滤波(RP-EKF)的无人机动力系统参数辨识方法。首先构建增广参数矩阵，将压降噪声模型考虑入辨识环节，其次提出反向预测卡尔曼滤波算法，设定新息平方比阈值，计算原始预测新息平方与反向预测新息平方的比值，通过对比预测新息比与阈值完成过程噪声调整并实现估计模型修正。实验结果表明，本文提出的基于RP-EKF的参数辨识方法，平均误差为39.22 rpm,均方根误差为55.85 rpm,平均相对偏差为0.85%,相比于最小二乘算法与卡尔曼滤波算法，本文方法辨识结果平均误差分别提高41.51%和22.26%,均方根误差提高49.63%和13.0%,平均相对偏差提高41.7%和22.7%。本文提出的算法拥有更高的辨识精度。

Parameter identification of UAV power system based on RP-EKF

To address the serious battery voltage fluctuation of the UAV power system, which leads to the large system noise and the low accuracy of identification results, this study proposes a kind of UAV power system parameters identification method based on the reverse predicted-extended Kalman filter. Firstly, the voltage-drop noise model is considered into the noise identification by establishing the extended parameter matrix. Secondly, the reverse predicted Kalman filter algorithm is proposed. An innovation square of threshold value is set. The ratio of the original predicted innovation square and the reverse predicted innovation square ratio is calculated, which adjusts the process noise by comparing the predicted innovation ratio with threshold size. In this way, the estimation model of correction is realized. Experimental results show that the average error of the RP-EKF algorithm is 39. 22 rpm, the root mean square error is 55. 85 rpm, and the mean relative bias is 0. 85% . Compared with the least square algorithm and the Kalman filter algorithm, the average error index values of the identification results using the proposed method of this study is improved by 41. 51% and 22. 26% , respectively. The root mean square error is improved by 49. 63% and 13. 0% , and the mean relative bias was improved by 41. 7% and 22. 7% . Results show that the proposed algorithm has higher identification accuracy than the traditional identification methods.