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ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2024 , Vol. 45 >Issue 16: 329678 - 329678

DOI: https://doi.org/10.7527/S1000-6893.2024.29678

Electronics and Electrical Engineering and Control

State and bias estimation of spacecraft attitude control system based on l1-TSXKF

  • Xueqin CHEN ,
  • Boyu YANG ,
  • Fan WU ,
  • Chengfei YUE ,
  • Xibin CAO
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  • 1.Research Center of Satellite Technology，Harbin Institute of Technology，Harbin 　150001，China
    2.Institute of Space Science and Applied Technology，Harbin Institute of Technology （Shenzhen），Shenzhen 　518055，China
E-mail： wufanrcst@hit.edu.cn

Received date: 2023-10-07

  Revised date: 2023-12-21

  Accepted date: 2024-01-05

  Online published: 2024-01-15

Supported by

National Natural Science Foundation of China(11972130);Heilongjiang Touyan Innovation Team Program

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Abstract

The state and bias estimation problem of spacecraft attitude control system considering non-Gaussian noise characteristics is studied. Firstly， a spacecraft attitude control system model with bias is established， and the expression for the non-Gaussian characteristic noise is given. Then， through l1-norm Kalman Filtering （l1-KF） algorithm and mathematical simulation， it is verified that the introduction of l1-norm into the Kalman filtering algorithm can avoid the influence of non-Gaussian process noise. However， the l1-KF algorithm cannot achieve simultaneous estimation of state and bias， and is only suitable for estimation of attitude stability control process. Therefore， on the basis of l1-KF algorithm， considering the complex situation of time-varying moment of inertia and time-varying bias of the system， as well as real-time identification of time-varying moment of inertia， an l1-norm Two-Stage eXogenous Kalman Filtering （l1-TSXKF） algorithm is designed to estimate the state and bias of the spacecraft during attitude maneuver. The mathematical simulation results show that the l1-TSXKF algorithm can reduce the influence of non-Gaussian characteristics of the system and obtain high precision attitude and bias estimation results quickly in the process of spacecraft attitude maneuver， which is conducive to the application of spacecraft in orbit.

Key words： spacecraft; attitude control system; bias estimation; state estimation; identification of moments of inertia

Cite this article

Xueqin CHEN , Boyu YANG , Fan WU , Chengfei YUE , Xibin CAO . State and bias estimation of spacecraft attitude control system based on l1-TSXKF[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2024 , 45(16) : 329678 -329678 . DOI: 10.7527/S1000-6893.2024.29678

References

1 FRIEDLAND B. Notes on separate-bias estimation［J］. IEEE Transactions on Automatic Control， 1978， 23（4）： 735-738.
2 LE H X， MATUNAGA S. Real-time tuning separate-bias extended Kalman filter for attitude estimation［J］. Transactions of the Japan Society for Aeronautical and Space Sciences， 2014， 57（5）： 299-307.
3 CHEN X Q， SUN R， JIANG W C， et al. A novel two-stage extended Kalman filter algorithm for reaction flywheels fault estimation［J］. Chinese Journal of Aeronautics， 2016， 29（2）： 462-469.
4 孙瑞. 基于二阶卡尔曼滤波的航天器姿控系统故障估计［D］. 哈尔滨： 哈尔滨工业大学， 2021： 10-45.
  SUN R. Fault estimation based on the two-stage Kalman filtering in the spacecraft attitude control system［D］. Harbin： Harbin Institute of Technology， 2021： 10-45 （in Chinese）.
5 CHEN X Q， SUN R， LIU M， et al. Two-stage exogenous Kalman filter for time-varying fault estimation of satellite attitude control system［J］. Journal of the Franklin Institute， 2020， 357（4）： 2354-2370.
6 ROTH M， ?ZKAN E， GUSTAFSSON F. A Student’s T filter for heavy tailed process and measurement noise［C］∥2013 IEEE International Conference on Acoustics， Speech and Signal Processing. Piscataway： IEEE Press， 2013： 5770-5774.
7 NIU X J， CHEN Q J， ZHANG Q， et al. Using Allan variance to analyze the error characteristics of GNSS positioning［J］. GPS Solutions， 2014， 18（2）： 231-242.
8 CRASSIDIS J L， MARKLEY F L. Predictive filtering for nonlinear systems［J］. Journal of Guidance， Control， and Dynamics， 1997， 20（3）： 566-572.
9 赵祥丹， 王彪， 王志胜， 等. 预测-五阶容积卡尔曼滤波方法［J］. 航空学报， 2023， 44（6）： 326817.
  ZHAO X D， WANG B， WANG Z S， et al. Predictive fifth-degree cubature Kalman filter method［J］. Acta Aeronautica et Astronautica Sinica， 2023， 44（6）： 326817 （in Chinese）.
10 葛泉波， 王贺彬， 杨秦敏， 等. 基于改进高斯混合模型的机器人运动状态估计［J］. 自动化学报， 2022， 48（8）： 1972-1983.
  GE Q B， WANG H B， YANG Q M， et al. Estimation of robot motion state based on improved Gaussian mixture model［J］. Acta Automatica Sinica， 2022， 48（8）： 1972-1983 （in Chinese）.
11 ROONIZI A K. Kalman filtering in non-Gaussian model errors： A new perspective［J］. IEEE Signal Processing Magazine， 2022， 39（3）： 105-114.
12 李伟. 鲁棒自适应滤波算法及在飞行器技术中的应用研究［D］. 上海： 上海交通大学， 2014： 6-19.
  LI W. Research on robust adaptive filter algorithms and their applications to flight vehicle technology［D］. Shanghai： Shanghai Jiao Tong University， 2014： 6-19 （in Chinese）.
13 PSIAKI M L， Modeling MOHIUDDIN S.， analysis， and simulation of GPS carrier phase for spacecraft relative navigation［J］. Journal of Guidance， Control， and Dynamics， 2007， 30（6）： 1628-1639.
14 张晓， 张伟， 方宝东. 基于测角测距信息火星环绕器导航方法［J］. 计算机仿真， 2020， 37（3）： 42-46.
  ZHANG X， ZHANG W， FANG B D. Navigation algorithm for the Mars orbiter via angle and range measurement information［J］. Computer Simulation， 2020， 37（3）： 42-46 （in Chinese）.
15 侯博文. SINS/CNS深组合导航方案及高性能导航滤波方法研究［D］. 长沙： 国防科技大学， 2018： 23-64.
  HOU B W. Research on SINS/CNS deeply integrated navigation system and high-performance navigation filtering method［D］. Changsha： National University of Defense Technology， 2018： 23-64 （in Chinese）.
16 OSHMAN Y， CARMI A. Attitude estimation from vector observations using a genetic-algorithm-embedded quaternion particle filter［J］. Journal of Guidance， Control， and Dynamics， 2006， 29（4）： 879-891.
17 魏远明， 王孟磊， 耿云海， 等. 航天器快速轨道机动过程中时变转动惯量实时辨识［J］. 空间控制技术与应用， 2022， 48（3）： 93-101.
  WEI Y M， WANG M L， GENG Y H， et al. Real-time identification of the time-varying moment of inertia for spacecraft during fast orbit maneuver［J］. Aerospace Control and Application， 2022， 48（3）： 93-101 （in Chinese）.
18 SUN Y， BABU P， PALOMAR D P. Majorization-minimization algorithms in signal processing， communications， and machine learning［J］. IEEE Transactions on Signal Processing， 2017， 65（3）： 794-816.
19 朱东方， 王卫华， 宋婷， 等. 复杂挠性航天器转动惯量在线辨识算法研究［J］. 上海航天， 2015， 32（5）： 1-8.
  ZHU D F， WANG W H， SONG T， et al. On-line identification of flexible spacecraft moment of inertia［J］. Aerospace Shanghai， 2015， 32（5）： 1-8 （in Chinese）.
20 CRUZ-ZAVALA E， MORENO J A. Homogeneous high order sliding mode design： A Lyapunov approach［J］. Automatica， 2017， 80： 232-238.
21 丁锋. 系统辨识（3）： 辨识精度与辨识基本问题［J］. 南京信息工程大学学报（自然科学版）， 2011， 3（3）： 193-226.
  DING F. System identification.Part C： Identification accuracy and basic problems［J］. Journal of Nanjing University of Information Science & Technology （Natural Science Edition）， 2011， 3（3）： 193-226 （in Chinese）.
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Total visits: 6658907 Today visits: 1341