The aim of this paper is to develop robust three-stage extended Kalman filter for a model based on a fault detection and identification for nonlinear hover mode system of helicopter unmanned aerial vehicle. In addition, we show that, in considered systems, the actuator faults are affected by each other motivated in five scenarios simulation results. More precisely, the proposed approach estimates and decouples actuator faults in the presence of external disturbances in nonlinear mathematical model. Moreover, we analyze and identify various faults such as bias fault and also catastrophic faults such as stuck and floating faults. Finally, the simulation results show effectiveness of the proposed robust method for detection and isolation of various actuator faults and differentiating bias and stuck faults.

A robust three stage Kalman filtering problem is investigated in this article for non-linear systems with stochastic non-linearities, random faults and intermittent missing measurements. Therefore, a more general system model is governed in which unknown stochastic non-linearities are considered in both the system state and measurement equations. The fault terms are included by stochastic coefficients. Also, the sensor measurements occur in a random behaviour that encounters stochastic missing of data for each sensor. Both faults and unknown inputs are covered in the proposed filter. Moreover, the robust features of the developed estimator resolve the problems arising from the need for accurate models of faults and unknown inputs. Furthermore, any predetermined knowledge about the statistical characteristics of the above factors is not required. The augmented filters are mostly used to provide the fault diagnosis features, however, in the current work, this filter is decoupled to reduce the computational volume attributed to this approach. It is proved that the estimation error is ultimately bounded despite different stochastic terms and inaccurate information about the fault and unknown inputs. Finally, an illustrative example is proposed to demonstrate the effectiveness of the developed method.

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