Robust adaptive fault detection using global state information and application to mobile working machines

In this paper, an observer-based fault detection approach for a class of nonlinear systems is presented, which can be modeled by Takagi-Sugeno (TS) fuzzy models. We propose a sliding mode fuzzy observer that deals with bounded uncertainties in the plant and allows fault estimation based on an equivalent output error injection approach. Furthermore an adaption scheme based on pattern recognition algorithms is presented. It allows to deal with situational uncertainties, which affect the system, by adapting the fault sensitivity. An extensive simulation of a mobile working machine is used to demonstrate the effectiveness of the proposed scheme.