Hassani Messaoud

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In this paper, both time and frequency domain new designs of Unknown Inputs Functional Observers (UIFO) for a class of descriptor systems with a constant time delay are presented. The order of this unknown input observers is equal to the dimension of the vector to be estimated. The time procedure design is based on Lyapunov-Krasovskii stability theory(More)
Recently, it has been verified that applications of metaheuristics for finding optimal or suboptimal solutions for NP-hard optimisation problems is one of the most promising research fields. Using the ant system metaheuristic, this paper proposes a new design method for state feedback control law which simultaneously achieves the linear quadratic(More)
In this paper, we suggest an extension of a previous study in Recursive Singular Spectrum Analysis (RSSA) (Hongli & Hui-Jun (2012) Fault detection for Markovian jump systems with sensor saturations and randomly varying non-linearities. IEEE Trans. Circuits Syst. I: Regul. Pap., 59, 2354–2362) to an online method for fault detection. This extended method is(More)
This work concerns the principal component analysis applied to the supervision of quality parameters of the flour production line. Our contribution lies in the combined use of the principal component analysis technique and the clustering algorithms in the field of production system diagnosis. This approach allows detecting and locating the system defects,(More)
In this paper, we propose a new dynamic linear MIMO system representation by using discrete-time MIMO AutoRegressive with eXogenous input (ARX) model. To provide a reduced complexity model, each polynomial function of the MIMO ARX model associated to the inputs and to the outputs is expanded on independent Laguerre orthonormal basis to develop a new(More)
In this paper, we propose a new reduced complexity model by expanding a discrete-time ARX model on Laguerre orthonormal bases. To ensure an efficient complexity reduction, the coefficients associated to the input and the output of the ARX model are expanded on independent Laguerre bases, to develop a new black-box linear ARX-Laguerre model with filters on(More)
The Principal Component Analysis (PCA) is a powerful technique for extracting structure from possibly high-dimensional data sets. It is readily performed by solving an eigenvalue problem, or by using iterative algorithms that estimate principal components. This paper proposes a new method for online identification of a nonlinear system modelled on(More)