Faycal Ben Hmida

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This paper describes a method of actuator fault estimation for linear uncertain systems. In this work, the upper bound of the unknown input is not required. To remove this requirement a modified sliding mode observer is presented. The novelty in this method lies in the structure of the mechanism introduced to calculate the sliding mode observer gain(More)
This paper describes a robust fault reconstruction and estimation design for a class of nonlinear system described by Takagi-Sugeno structure subject to faults affecting actuators, sensor faults and disturbances. The premise variables are assumed to be unmeasurable. The main innovation is focused primarily to conceive Sliding Mode Observer (SMO) designed to(More)
In this paper, we propose an extension design method of a Sliding Mode Observer (SMO) for a class of linear uncertain time delay systems. This extension method is builds on the current SMO design methods for linear uncertain systems, but it gives a robust estimation of the states in spite of the presence of both uncertainty and time delay. We consider, in(More)
The problem of simultaneously estimating the state and the fault of linear time varying stochastic systems in the presence of unknown input with uncertain noise covariances is presented. The approach suggested rests on the use of the Proportional Integral Three-Stage Kalman Filter (PI-ThSKF). This technique is qualified to be robust against the noise(More)
This paper addresses the robust filtering problem of joint fault and state estimation for uncertain systems from the viewpoint of regularized least-square estimation. The method is based on the assumption that no prior knowledge about the dynamical evolution of the fault is available. Compared with earlier studies the robust criterion for least-square(More)
This paper is concerned with the simultaneous actuator and sensor faults reconstruction problem using sliding mode observer for polytopic Linear Parameter Varying (LPV) systems. Sufficient conditions in terms of Linear Matrix Inequality (LMI) are derived to guarantee the stability of such observer. A simple filter is introduced to transform the sensor fault(More)
Industrial environments are becoming more and more dynamic, complex, and uncertain. In such conditions, building safe systems is a major requirement and a big challenge which cannot be achieved without suitable diagnosis tools and methods. The early diagnosis approaches were mostly centralized and done offline. It was then proved that distribution is of a(More)
This paper proposes a method for robust and simultaneous actuator and sensor faults reconstruction of linear uncertain system based on sliding mode observer (SMO). In comparison with existing work, the observer contains two discontinuous terms to solve the problem of simultaneous faults. The idea is to introduce an appropriate filter on the systems output(More)
State estimation is of paramount importance in many fields of the problems encountered in practice. Filtering is the method of estimating the sate of the system by incorporating noisy observations. Particle filters are sequential Monte Carlo methods that use a point mass representation probability densities in order to propagate the required statistical(More)
In this paper we interest in the problem of multiplicative faults detection and isolation for linear systems. To solve this problem we use a bank of adaptive observers. The proposed algorithm is applied on a numerical example to conclude that the correct and robust detection and isolation in respect to the actuators multiplicative faults.