ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Fault diagnosis of spacecraft attitude control system driven by data and model
Received date: 2025-05-19
Revised date: 2025-05-30
Accepted date: 2025-06-10
Online published: 2025-06-27
Supported by
National Key R&D Program of China(2021YFB1715000);National Natural Science Foundation of China(62022013);Suzhou Municipal Science and Technology Bureau(ZXL2023177)
To address the problem of multiple faults in spacecraft attitude control systems in complex space environments， this paper proposes a fault detection and isolation strategy that integrates robust observers and Support Vector Machines （SVM）. Firstly， a robust observer constructed based on a physical model is compared with system dynamics to generate residuals. These residuals are then compared with dynamic thresholds， designed using a trained neural network， for fault detection. Furthermore， combining the observer-generated residual signals with the system input-output data， SVM is used to classify and detect multiple fault types. Through the dual drive strategy of physical model and data fusion， typical single faults， dual faults， and triple faults can be classified and identified， improving the accuracy of fault detection and classification in spacecraft attitude control systems. The dynamic threshold has universality for different fault modes and can effectively improve detection performance. By comparing the static threshold and the dynamic threshold trained by neural networks through simulation， it is shown that the dynamic threshold method has improved the fault response speed and accuracy compared to the traditional static threshold method. By comparing the fault classification results of BP network， random forest and SVM， the experiment shows that the SVM classification accuracy reaches 99.26%， which is better than random forest （98.52%） and neural network （97.04%）， demonstrating superior performance. The conclusion shows that the proposed method effectively solves the problem of detecting and isolating high coupling faults through the fusion of observer and SVM， significantly improving the system’s fault detection capability.
Ronghai KOU , Wenbo LI , Qingqing DANG , Jinjin XIE . Fault diagnosis of spacecraft attitude control system driven by data and model[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(S1) : 732259 -732259 . DOI: 10.7527/S1000-6893.2025.32259
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