应用自组织模糊神经网络估计卫星姿态系统执行机构故障被引量：8

Self-Organizing Fuzzy Neural Network-Based Actuator Fault Estimation for Satellite Attitude Systems

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摘要自组织模糊神经网络可以根据系统状态在线更新权值和调整节点,优化网络结构.文中针对某卫星姿态控制系统提出了基于两个自组织模糊神经网络的执行机构故障诊断方法.网络SOFNN1用于健康系统的离线训练,估计出系统的不确定项和扰动项,网络输出结果作为故障检测的阈值参考.网络SOFNN2在网络SOFNN1的基础上估计执行器故障.仿真表明,在噪声干扰和系统参数不确定的情况下,在线自组织模糊神经网络结构的方法能很好地估计系统执行机构故障,比固定结构的模糊神经网络估计速度快,因此更具优越性. Weights and nodes of a self-organizing fuzzy neural network （SOFNN） can be updated online for network structure optimization. This paper studies a robust fault diagnostic approach based on two SOFNNs for a class of satellite attitude dynamics. The designed SOFNN1 is used to estimate uncertainties and external perturbations of fault-free satellite attitude dynamics, whose output is chosen as a referenced threshold of fault detection. Based on SOFNN1, SOFNN2 is constructed to estimate actuator faults occurring in the satellite attitude dynamics. Simulation results demonstrate that SOFNN has good dynamics performance in estimating actuator faults for the considered dynamics with external noise and system parameter uncertainties. Compared with fixed-structured FNN, the proposed SOFNN has advantages in estimation speed.

作者程月华姜斌杨明凯高志峰

机构地区南京航空航天大学自动化学院南京航空航天大学高新技术研究院

出处《应用科学学报》 EI CAS CSCD 北大核心 2010年第1期72-76,共5页 Journal of Applied Sciences

基金国家自然科学基金(No.9081603) 南京航空航天大学科研创新团队培育基金(No.Y0847-031)资助

关键词自组织模糊神经网络故障估计卫星执行机构 self-organizing fuzzy neural network, fault estimation, satellite, actuator

分类号 TP183 [自动化与计算机技术—控制理论与控制工程]

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