Sensor Fault Diagnosis for a Class of Time Delay Uncertain Nonlinear Systems Using Neural Network

• 作 者: Mou Chen Chang-Sheng Jiang Qin
• 机 构: College of Automation Engineering
• 出 处: 国际自动化与计算杂志（英文版）
• 发表日期: 2004年期
• 关键词: Uncertain nonlinear system time delay radial basis function (RBF) neural network
• 基金项目: 江苏省自然科学基金;航空基础科学基金

In this paper, a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network. The sensor fault and the system input uncertainty are assumed to be unknown but bounded. The radial basis function (RBF) neural network is used to approximate the sensor fault. Based on the output of the RBF neural network, the sliding mode observer is presented. Using the Lyapunov method, a criterion for stability is given in terms of matrix inequality. Finally, an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.