基于RBF带学习能力高精度非线性FDD设计

在轨航天器故障检测与诊断问题需要面对模型的非线性，而且要求尽量提高其检测的精度，为此设计了基于径向基函数（RBF）神经网络的动量轮非线性故障检测与诊断（FDD）方案。首先应用RBF补偿建模误差，提高检测精度，并选择李雅普诺夫函数证明其收敛性；然后应用非线性观测器来产生故障残差，给出了阈值以及故障检测的时间；应用RBF网络对故障信号进行重构，并据此设计了带学习能力的FDD策略。再次建立了详细的动量轮模型，通过不同条件下的仿真研究分别验证残差的阈值特性、时间特性以及RBF的重构能力，仿真结果表明了算法的有效性。

High Precision Nonlinear FDD Design Using RBF with the Ability to Learn

On-orbit spacecraft fault detection and diagnosis(FDD) problem nonlinear model always needs to deal with.It′s also important to improve the accuracy of the methodology as far as possible.Radial basis function(RBF) neural network was firstly applied for compensation of modeling error in order to increase the accuracy of the scheme,a Lyapunov function was used to prove its property.The residual and threshold and fault detection time were got through nonlinear observer.Fault signal was reconstructed by using RBF network,and accordingly a learning FDD strategy was presented.Validations were respectively designed for characteristics of residual threshold,detection time and the ability of RBF for reconstruction.The simulation results show the effectiveness of the proposed algorithm.