Neural Network Based Fault Detection and Identification for Fighter Control Surface Failure
As a representative complex system, the aircraft modeled very difficultly and imprecisely. This makes the model-based fault detection methods degenerated. In this dissertation, the nonlinear time series, which is constructed by output variables of aircraft, is converted into discrete dynamic system, and then a novel series prediction method is achieved by the adaptive observation of system states. An online adaptive RBFNN is used to fit the nonlinearity of system and to compensate the unknown disturbance. Thereby a one-step-ahead prediction method is proposed. By using probability density estimation and hypothesis testing for the observation error, the fault is detected directly. Finally, a rule-table is established for fault identification. The results of simulation prove the methods efficiency.
Fault Detection and Identification model-unknown system RBF Fighter
Zhang Zhengdao Zhang Weihua
College of Communication & Control Engineering, Jiangnan University, WuXi 214122
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
5256-5261
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）