中国空间科学技术

中国空间科学技术 ›› 2020, Vol. 40 ›› Issue (6): 1-12.doi: 10.16708/j.cnki.1000-758X.2020.0066

• 研究探讨 •    下一篇

基于深度神经网络的航天器姿态控制系统故障诊断与容错控制研究

耿飞龙，李爽，黄旭星，杨彬，常建松，林波   

  1. 1.南京航空航天大学航天学院，南京210016
    2.北京控制工程研究所，北京100190
  • 出版日期:2020-12-25 发布日期:2020-11-25
  • 基金资助:
    民用航天技术预先研究项目（No.D010305）；国家自然科学基金（11672126, 61673057）

Fault diagnosis and fault tolerant control of spacecraft attitude control system via deep neural network

GENG Feilong，LI Shuang，HUANG Xuxing，YANG Bin，CHANG Jiansong，LIN Bo   

  1. 1.College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing210016， China
    2.Beijing Institute of Control Engineering, Beijing100190， China
  • Published:2020-12-25 Online:2020-11-25

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摘要： 针对传统航天姿控系统故障诊断与容错控制诊断精度及控制分配效率较低的问题，提出了一种基于深度神经网络的航天器姿态控制系统故障诊断与容错控制方法。以控制力矩陀螺为执行机构的航天器发生执行机构故障工况时，所提出的方法可保证鲁棒的姿态控制。首先，利用三个异构深度神经网络实现传统容错控制器的故障诊断、姿态控制和力矩分配等功能，建立了全神经网络的智能自适应容错控制器架构。然后，对三个神经网络的网络层数、神经元数目和激活函数等参数进行优化调整，对比分析了神经网络参数对控制器性能的影响。最后，对所提出的新型控制器在控制力矩陀螺发生故障时的控制精度和鲁棒性进行了仿真验证。仿真结果表明，对于具有冗余控制力矩陀螺的航天器，提出的方法不仅能在单一陀螺故障下实现高精度的容错控制，也能在发生多陀螺故障时保证一定的姿态稳定控制。

关键词: 人工智能, 深度神经网络, 姿态控制, 故障诊断, 容错控制

Abstract: In order to solve the problem of low diagnosis accuracy and control allocation efficiency of traditional fault diagnosis and faulttolerant control methods, this paper proposes a new method of fault diagnosis and fault tolerance control for spacecraft attitude control system based on deep neural network. Taking the control moment gyroscopes as actuator, the method can achieve robust attitude control when the actuator fails. First, we use three heterogeneous deep neural networks to achieve the functions of fault diagnosis, attitude control and torque distribution of traditional faulttolerant controllers, and the intelligent adaptive faulttolerant controller architecture of full neural networks is established. Then, the parameters of the three neural networks such as the number of network layers, the number of neurons and activation functions are optimized and adjusted, and the influence of the parameters of the neural network on the performance of the controller is compared and analyzed. Numerical simulation is conducted to prove that the proposed new controller has good control accuracy and robustness when the control moment gyroscopes fail. The simulation results show that for the spacecraft with a redundant control moment gyroscope, the method proposed in this paper can not only achieve highprecision faulttolerant control under single gyro failure, but also ensure a certain attitude stability control when multiple gyroscope failures occur.

Key words: artificial intelligence, deep neural network, attitude control, fault diagnosis, faulttolerant control