一类状态不可测非线性时滞系统的神经网络故障诊断

Fault Diagnosis of Nonlinear Time-Delay System with Unmeasurable States Using Nueral Network

针对一类状态不可测的非线性时滞系统,提出了基于神经网络故障诊断的新方法.采用系统的状态和时滞状态的估计值作为神经网络的输入对故障进行估计.首先构造一种状态观测器结构,利用输出信息和神经网络的非线性逼近能力对系统不可测状态进行估计,然后对系统发生的故障用另一个RBF神经网络进行估计,故障估计器的输入为系统的当前估计状态以及时滞状态,所估计出的故障是随时间变化的非线性函数.基于Lyapunov理论,分析并证明了系统的稳定性和参数收敛性,同时作了仿真研究.仿真结果表明,该方法能够很好地解决一类状态不可测的非线性时滞系统的故障诊断问题.

A novel fault diagnosis architecture for a class of nonlinear time-delay systems is studied. As the input to neural networks, the estimated states and time-delay states are used to approximate the faults. A state observer is proposed, and unmeasured states estimated on-line using the output of system based on nonlinear approximate ability of neural networks. The estimated states are input to the fault estimator whose outputs are an estimated fault model, Based on the Lyapunov theory, the system stability and convergence of the parameters are analyzed, The condition of solution to ensure existence of algorithm is given. Simulation results show effectiveness of the proposed approach.