基于相对位姿信息的在轨服务卫星推进故障诊断方法

梁振华; 唐岷; 郑侃; 廖文和

doi:10.7527/S1000-6893.2024.30867

航空学报 >

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DOI: https://doi.org/10.7527/S1000-6893.2024.30867

基于相对位姿信息的在轨服务卫星推进故障诊断方法

梁振华 ,
唐岷 ,
郑侃 ,
廖文和

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1. 南京理工大学
2. 江苏省南京理工大学机械工程学院

收稿日期: 2024-06-26

修回日期: 2024-11-24

网络出版日期: 2024-11-25

基金资助

国家自然科学基金项目;上海航天科技创新基金项目

收起

Fault Diagnosis Method of Thruster of On-Orbit Service Spacecraft Based on Relative Position Information

LIANG Zhen-Hua ,
TANG Min ,
ZHENG Kan ,
LIAO Wen-He

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Received date: 2024-06-26

Revised date: 2024-11-24

Online published: 2024-11-25

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摘要

针对在轨服务卫星推力器故障诊断方法对观测噪声鲁棒性差问题，提出基于服务星相对位姿观测信息的诊断方法。首先，基于推进系统的工作原理构建了故障模型，采用基于后验协方差矩阵的数据融合方法处理传感器数据，防止传感器故障、相对位姿测量误差过大导致推力器误诊断；随后，采用自适应EKF解决推力器故障下所出现的预测步失准问题；分析了噪声对故障诊断系统的影响，并据此提出基于广义似然比的故障诊断策略，将故障与无故障推力器的诊断指标差值增大了约5倍。仿真结果表明，故障情况下所提出的自适应EKF相比传统EKF的滤波误差减小了约80%，故障量估计速度提升了34%；当故障量较小时，对于卡死故障的最快诊断速度为10步，最小诊断误差约为0.5%；而对于效率下降类故障，最快诊断速度为38步，最小诊断误差为3%。构建了在轨任务场景，采用LQR控制律控制服务星在轨道场景下逼近目标星，过程中模拟了服务星推力器发生效率下降故障，验证了所提出的方法在传统故障诊断方法出现误诊的情况下成功诊断出故障的能力。

关键词： 自适应卡尔曼滤波器; 推力器; 观测器; 故障检测; 在轨服务

本文引用格式

梁振华 , 唐岷 , 郑侃 , 廖文和 . 基于相对位姿信息的在轨服务卫星推进故障诊断方法[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2024.30867

Abstract

In order to solve the problem of the susceptibility of noise in the fault diagnosis of on-orbit service(OOC) satel-lites’thrusters,a diagnosis method based on the relative position information of OOC was proposed. Firstly, a fault model was constructed based on the mechanism of the propulsion system, and a data fusion method based on the posterior covariance matrix was used to process sensor data to prevent excessive relative position measurement error from caused by sensor failure,which could lead to misdiagnosis. Subsequently, an adaptive EKF was used to solve the prob-lem of inaccurate prediction step when thruster failure occurred. The influence of noise on the fault diagnosis system is analyzed, and a fault diagnosis strategy based on generalized likelihood ratio was proposed, which increased the differ-ence between the diagnostic index of fault and non-fault thrusters by about 5 times. Numercial simulation results show that the filtering error of the proposed adaptive EKF was reduced by about 80% compared with that of traditional EKF in the case of faults, and the speed of fault estimation was increased by 34%. When the fault was subtle, the fastest diagno-sis speed for stuck fault were 10 steps, and the minimum diagnosis error was about 0.5%.For efficiency reduction faults, the fastest diagnosis speed were 38 steps, and the minimum diagnosis error was 3%. In this paper,an on-orbit mission scenario was constructed, and the LQR control law was used to control the OOC to approach the target satellite in the orbital scenario, and the efficiency reduction fault of the OOC’s thruster was simulated in the process, which verified the ability of the proposed method to successfully diagnose the fault whiletraditional fault diagnosis method misdiagnosed.

Key words： Adaptive Kalman Filter; Thruster; Observer; Fault Detection; On-Orbit Service

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