Adaptive fault detection and diagnosis for a class of nonlinear uncertain systems with on-line learning

摘要：

The problem of fault detection and diagnosis (FDD) for a class of nonlinear systems with unknown uncertainty is studied in this paper. An adaptive FDD observer is proposed based on dead-zone operator, on-line learning and adaptive compensation techniques. The fault detection decision is made by evaluating the residual signals. After a fault is detected, a neural network estimator is constructed to approximate the real fault signal on-line. To improve the performance of the fault diagnosis, the adaptive term is applied to compensate the unknown disturbance, modeling uncertainties and optimal approaching error. Finally, the simulation results show the effectiveness of the proposed methodology.

关键词： Fault detection and diagnosis On-line learning Neural network Adaptive observer Dead zone

作者: CAO Songyin YANG Jian LI Xiaofeng

作者单位: Department of Automation, College of Information Engineering, Yangzhou University, Yangzhou, 225127, National Key Laboratory on Aircraft Control Technology, School of Instrumentation Science and Opto-E

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 5401-5405

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）