摘要
提出了一种具有建模不确定性的非线性系统在线故障检测方法。故障被假定为状态变量和输入变量的函数 ,该系统仅是输入、输出可测量的。一种基于 RBF神经网络的在线非线性估计器用来跟踪系统中出现的故障 ,该估计器对有建模不确定性的非线性系统的故障检测具备良好的鲁棒性。文中所提出的方法的收敛性在理论上进行了较详细证明。
A kind of on-line fault detection scheme in nonlinea r systems with modeling uncertainty is presented. The faults are assumed t o be a function of the state and the input. Only the inputs and the outputs of s ystem can be measured. An on-line approximator based on RBF neural network is u sed to track the faults of the system, and the approximator has good robustness for the fault detection in the nonlinear system with modeling uncertainty. The c onvergence of the scheme derived in the paper is proved in the theory. An exampl e demonstrates the efficiency and the applicability of the fault detection sche me.
出处
《南京航空航天大学学报》
EI
CAS
CSCD
北大核心
2001年第3期263-266,共4页
Journal of Nanjing University of Aeronautics & Astronautics
基金
国家自然科学基金 (编号 :699740 2 1 )
江苏省应用科学基金 (编号 :BJ990 2 1 )资助项目
