Satellite attitude active FTC based on Geometric Approach and RBF Neural Network
Satellite attitude active FTC based on Geometric Approach and RBF Neural Network
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基于几何方法和RBF神经网络的卫星姿态主动FTC
DOI：
10.1109/systol.2013.6693907
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
S. Simani
中科院分区：
文献类型：
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作者：
P. Baldi;P. Castaldi;N. Mimmo;S. Simani
提出了一种新的卫星姿态主动容错控制系统。主动容错控制系统基于故障检测和诊断模块，由于应用了非线性几何方法，提供了与气动干扰解耦的故障估计。诊断模块是一组基于径向基函数神经网络的自适应滤波器。控制器重构直接利用故障信号的在线估计。据作者所知，将非线性几何方法和径向基函数神经网络相结合的方法在文献中并不存在。径向基函数神经网络的使用允许设计不需要关于故障内部模型的任何先验信息的广义故障估计滤波器。使用非线性几何方法可以获得非常准确的故障估计，而不依赖于气动扰动参数的知识。给出了基于嵌入扰动描述的卫星姿态非线性模型的仿真结果。结果表明，所提出的卫星主动容错控制系统能够处理不同类型的反作用轮故障(阶跃故障、斜坡故障、正弦故障)，从而获得了良好的姿态控制性能。
This paper proposes a novel satellite attitude Active Fault Tolerant Control System. The Active Fault Tolerant Control System is based on a Fault Detection and Diagnosis module providing fault estimates decoupled from the aerodynamic disturbance thanks to the application of the NonLinear Geometric Approach. The diagnosis module is a bank of adaptive filters based on a Radial Basis Function Neural Network. The controller reconfiguration exploits directly the on-line estimates of the fault signals. To the authors' knowledge, the joint use of the NonLinear Geometric Approach and Radial Basis Function Neural Network is not present in literature. The use of a Radial Basis Function Neural Network allows to design generalized fault estimation filters which do not need any a priori information about the fault internal model. The use of the NonLinear Geometric Approach allows to obtain very accurate fault estimates, independently from the knowledge of the aerodynamic disturbance parameters. Extensive simulation results based on a detailed nonlinear satellite attitude model embedding disturbance description are given. The obtained results highlight that the proposed satellite Active Fault Tolerant Control System can deal with reaction wheel faults of different types (i.e. step faults, ramp faults, sinusoidal faults) thus obtaining good attitude control performances.