【摘要】 本文针对一类非线性系统 ,提出了一种用于故障检测和诊断的滑模观测器方法 .其中 ,观测器中的滑模项保证了该系统在无故障情况时的鲁棒性 ,并且系统运行的滑动区域提供了故障检测的条件 .当检测出故障之后 ,观测器中的故障估计部分被启动 ,利用RBF神经网络估计故障 ,从而能在线辨识故障的形态 .仿真结果验证了该方法的有效性更多还原

【Abstract】 A new fault detection and diagnosis method based on sliding mode observer is presented for a class of nonlinear system. Sliding mode term ensures that the observer has robustness under non trouble conditions, and fault detection can be realized by making use of sliding boundary size. When the fault has been detected, the estimation part in the observer for the fault may be enabled. A radial basis function neural network is used to approximate the fault, so fault diagnosis can be finished online. Simulation results show the feasibility of the proposed approach.更多还原