| 基于RBF神经网络和M距离的卫星故障诊断 |
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| 引用本文： | 燕飞,秦世引.基于RBF神经网络和M距离的卫星故障诊断[J].航天控制,2006,24(6):61-66. |
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| 作者姓名： | 燕飞 秦世引 |
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| 作者单位： | 北京航空航天大学自动化科学与电气工程学院,北京,100083 |
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| 摘 要： | 在常规基于解析冗余故障诊断技术的基础上,采用具有最佳模拟特性的RBF神经网络对系统进行建模,分析了M距离应用于卫星姿态控制系统故障检测与定位的可行性,应用基于M距离的方法设计故障检测观测器,通过对残差的评估实现故障诊断。仿真结果显示,该方法计算过程简单、实时性好。
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| 关 键 词： | M距离 故障诊断 姿态控制系统 残差 RBF神经网络 |
| 文章编号： | 1006-3242(2006)06-0061-06 |
| 修稿时间： | 2006年7月12日 |
Fault Diagnosis for Satellites Based on RBF Neural Network and Mahalanobis Distance |
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| Yan Fei,Qin Shiyin.Fault Diagnosis for Satellites Based on RBF Neural Network and Mahalanobis Distance[J].Aerospace Control,2006,24(6):61-66. |
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| Authors: | Yan Fei Qin Shiyin |
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| Abstract: | Based on the traditional analytical redundancy fault diagnosis technology,the RBF neural network with the best imitating performance is adopted to model the control system.Then the feasibility of detecting and identifying faults for the satellite attitude control system with the Mahalanobis distance is analyzed in detail.At last the fault-detection observers are designed and implemented based on the residual evaluation so as to achieve the fault diagnosis.Simulation result indicates that the diagnosing method proposed in this paper is characterized with light computation burden and good real-time property. |
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| Keywords: | Mahalanobis distance Fault diagnosis Attitude control system Residual RBF neural network |
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