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Acta Aeronautica et Astronautica Sinica

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

  

  • Received:2024-06-26 Revised:2024-11-24 Online:2024-11-25 Published:2024-11-25
  • Contact: liang zhenhua

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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