Appearing faults in a practical system is dispensable, and if it is not compensated, it results in poor system performance or even dysfunction of the system. The fault detection has become a promising challenging issue to guarantee the safety and reliability of systems. In this paper, a novel fuzzy-based sliding mode observer for the simultaneous actuator and sensor fault reconstruction of nonlinear systems subjected to external disturbance is proposed. The proposed approach employs the Takagi-Sugeno fuzzy model, sliding mode observer and non-quadratic Lyapunov function. First, by filtering the system output, a fictitious system whose actuator faults are the original actuator and sensor faults is constructed. Then, by considering the [Formula: see text] performance criteria, the effect of disturbance on the state estimations is minimized. It is proved that the estimations asymptotically converge to their actual values for non-perturbed systems. In the process of designing the observer gains, some transformation matrices are obtained by solving linear matrix inequalities. The proposed approach has some superiority over the existing methods. First, considering the non-quadratic Lyapunov function leads to relaxed results and good estimation performance. Second, using the sliding mode observer makes the proposed approach insensitive to the uncertainties and unknown inputs and determines the shape and size of the fault. Third, assuming the premise variables are immeasurable makes the presented approach more applicable. In conclusion, two practical systems are considered and simulation results illustrate the merits of the proposed approach in comparison with the recent methods from the fast and precise fault detection performance viewpoints.
On Homogeneous Parameter-Dependent Quadratic Lyapunov Function for RobustH∞Filtering Design in Switched Linear Discrete-Time Systems with Polytopic Uncertainties