CAJ | 학술논문

针对一类模型未知的非线性系统,提出了一种基于自适应神经网络的故障诊断方法,用 RBF 神经网络构造了状态估计器和故障估计器,解决了非线性系统状态不可测时的故障诊断问题.并用 Lyapunov 方法研究了故障误差和状态误差的收敛性,结果表明了该方法实用、有效.
침대일류모형미지적비선성계통,제출료일충기우자괄응신경망락적고장진단방법,용 RBF 신경망락구조료상태고계기화고장고계기,해결료비선성계통상태불가측시적고장진단문제.병용 Lyapunov 방법연구료고장오차화상태오차적수렴성,결과표명료해방법실용、유효.
According to a class of unknown model of nonlinear systems, the fault diagnosis method based on adaptive neural networks is proposed. The state estimator and fault estimator are constituted by RBF neural network, which can solve the nonlinear system fault diagnosis problem when the nonlinear system’s state is uncertain. The convergence on fault error and state error is researched by Lyapunov method. The results show that this method is practical and effective.