数据与模型驱动的航天器姿态控制系统故障诊断

doi:10.7527/S1000-6893.2025.32259

Abstract

Abstract:

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.

Key words: spacecraft attitude control system, fault diagnosis, data and model driven, robust observer, neural network, support vector machine

CLC Number:

V448

Ronghai KOU, Wenbo LI, Qingqing DANG, Jinjin XIE. Fault diagnosis of spacecraft attitude control system driven by data and model[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(S1): 732259.

Figures/Tables 22

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Satellite attitude control system parameters

参数	数值
转动惯量/（kg·m²）	$d i a g 12.5, 13.7, 15.9$
过程噪声	$d (k) ~ N 0 6 × 1, 2.25 × 10 - 10 I$
测量噪声	$v (k) ~ N 0 6 × 1, d i a g 8.7 × 10 - 6 I, 4.2 × 10 - 5 I$
控制器参数	$K P = - 35 I 3 × 3 30 I 3 × 3 K D = - I 3 × 3 I 3 × 3$
初始姿态/（°）	$θ = [- 5 35]$
初始角速率/（rad·s^-1）	$ω = [000]$
采样周期/s	$T s = 0.01$
轨道角速度/（rad·s^-1）	$ω 0 = 0.001 2$

Table 1

Table 2

Observer parameters

参数	数值
过程噪声的加权矩阵	$Q (k) = 5 × 105 × 2.25 × 10 - 10 I$
测量噪声的加权矩阵	$W (k) = d i a g 7.8 × 10 - 9 I, 3.8 × 10 - 8 I$
残差评价函数窗口	$N = 20$
估计初始状态	$x = 000000 T$

Table 2

Fig.5

Table 3

Definition of different fault flag bits

单故障		双重故障		三重故障
类型	标志位	类型	标志位	类型	标志位
$f s 1$	1	$f s 2, f a 2$	4	$f s 1, f s 2, f s 3$	7
$f s 4$	2	$f s 5, f a 1$	5	$f s 6, f a 1, f a 2$	8
$f a 1$	3	$f s 2, f s 4$	6	$f s 1, f s 4, f a 1$	9

Table 3

Table 4

Data characteristic values

参数	计算方法	参数	计算方法
$c 1$	$1 N ∑ t = 1 N s (t)$	$c 9$	$1 N - 1 ∑ t = 1 N s (t) - c 1 2$
$c 2$	$1 N ∑ t = 1 N s (t)$	$c 10$	$1 N - 1 c 93 ∑ t = 1 N s (t) - c 1 3$
$c 3$	$m a x (s (t))$	$c 11$	$1 N - 1 c 94 ∑ t = 1 N s (t) - c 1 4$
$c 4$	$m i n (s (t))$	$c 12$	$c 6 / c 8$
$c 5$	$c 3 - c 4$	$c 13$	$c 6 / c 7$
$c 6$	$m a x (s (t))$	$c 14$	$N c 8 / ∑ t = 1 N s (t)$
$c 7$	$1 N ∑ t = 1 N s (t) 2$	$c 15$	$N c 6 / ∑ t = 1 N s (t)$
$c 8$	$1 N ∑ t = 1 N s 2 (t)$	$c 16$	$N c 6 / ∑ t = 1 N s 2 (t)$

Table 4

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Table 5

Fig.12

Fig.13

Fig.14

Fig.15

Fig.16

Table 6

References 25

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故障类型	阈值方法	准确率/%	误检率/%	漏检率/%
单故障	静态阈值	87.71	6.82	5.70
单故障	动态阈值	93.73	3.32	2.37
双重故障	静态阈值	89.83	3.30	6.87
双重故障	动态阈值	94.23	3.05	2.88
三重故障	静态阈值	86.97	7.15	5.88
三重故障	动态阈值	95.23	3.73	1.37

Fault diagnosis of spacecraft attitude control system driven by data and model

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