非线性系统在线执行器故障诊断

On-line actuator fault diagnosis for a class of nonlinear system

针对一类含模型不确定性的非线性系统,提出了执行器故障检测与诊断的在线估计器设计方法.给出了故障诊断结构与算法,并分析了鲁棒性、灵敏度和稳定性.仿真结果验证了该方法的正确性.

A new on-line actuator fault detection and diagnosis method for a class of nonlinear system with modeling uncertainties are proposed. Only the inputs and outputs of the system can be measured. The faults are assumed to be functions of the inputs and the states of the system. A nonlinear on-line approximates based RBF neural network is utilized to monitor the faults and estimate the fault value and characteristic. The construction and the learning algorithm of the on-line approximator are presented. The stability, sensitivity and robustness of the fault diagnosis scheme are proved. Finally, a simulation example is given to illustrate the effectiveness of the method.