Ma Liling

Robust fault detection using iterative learning observer for nonlinear systems

A robust fault detection scheme for a class of nonlinear systems with modeling uncertainty and inaccessible states was presented. Only the inputs and outputs of the system can be measured. A nonlinear iterative learning observer was utilized to produce the residual that was robust to uncertainty. The stability of the fault detection scheme under certain assumptions was analyzed. An example demonstrates the efficiency of the proposed fault detection strategy.

A robust fault detection and isolation method via sliding mode observer

A robust fault detection and isolation (FDI) approach for a class of nonlinear systems with uncertainty was presented. The FDI scheme was based on sliding mode observer, which was robust against system uncertainty. Fault detection can be realized by use of sliding boundary size. When the fault had been detected, the estimate part in the observer for the fault can be enabled. A radial basis function (RBF) neural network was used to approximate the fault, so making the fault isolation a simple task. The theoretic analysis guaranteed the convergence of the observer. Simulation results show the feasibility of the proposed approach.