基于自适应无迹卡尔曼滤波的气流角融合方法  

Adaptive Unscented Kalman Filter Based Estimation of Aircraft Airflow Angles

作　　者：吴云燕 黄天鹏 刘武 朱雪耀[1] 马钊 WU Yunyan;HUANG Tianpeng;LIU Wu;ZHU Xueyao;MA Zhao(Xi'an Flight Automatic Control Research Institute AVIC,Xi'an 710000 China)

机构地区：[1]航空工业西安飞行自动控制研究所,西安710000

出　　处：《电光与控制》2024年第11期109-114,共6页Electronics Optics & Control

基　　金：国家自然科学基金(62173277)。

摘　　要：迎角、侧滑角是影响飞控系统安全的关键参数,而大气数据系统在恶劣天气、机动飞行情况下难以准确测量气流角,在故障隔离失败情况下甚至会引发飞行安全问题。鉴于此,提出基于自适应无迹卡尔曼滤波(AUKF)的气流角融合方法,通过惯导系统和飞行器动力学模型信息构建滤波模型,同时将自适应滤波思想应用于无迹卡尔曼滤波器,利用观测残差序列构建卡方检验和自适应渐消矩阵,实现了动态飞行、故障情况下气流角的高精度输出。仿真结果表明,所提方法的性能优于传统卡尔曼滤波算法,具有较大的工程应用价值。The angle of attack and sideslip angle are the key parameters that affect the safety of flight control system.However it is difficult for the atmospheric data system to accurately measure the air flow angles in severe weather or highly maneuvering flight and flight safety issue may occur in the case of fault isolation failure.Based on this an Adaptive Unscented Kalman Filter(AUKF)based flow angle fusion method is proposed.The filtering model is constructed by the information of inertial navigation system and aircraft dynamics model.At the same time the adaptive filtering idea is applied to AUKF and the Chi-square test and adaptive fading matrix are constructed by using the observed residual sequence and the high precision output of flow angle under dynamic flight and fault conditions is realized.The simulation results show that the performance of this algorithm is better than that of the traditional extended Kalman filter algorithm and it has great engineering application value.

关 键 词：迎角 侧滑角 自适应无迹卡尔曼滤波 故障自检测 卡方检验 自适应渐消矩阵 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]