Entity

Time filter

Source Type

Tunis, Tunisia

Gannouni F.,ESSTT C3S | Ben Hmida F.,ESSTT C3S
2013 International Conference on Electrical Engineering and Software Applications, ICEESA 2013 | Year: 2013

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 designs incorporate simultaneously both regularization and weighting and applies to a large class of uncertainties. The solution to the regularized least-square problem yields robust filter equations that perform regularization as opposed to de-regularization. The proposed filter is tested by an illustrative example. © 2013 IEEE. Source


Ben Hmida F.,ESSTT C3S | Khemiri K.,ESSTT C3S | Ragot J.,Nancy Research Center for Automatic Control | Gossa M.,ESSTT C3S
Journal of the Franklin Institute | Year: 2012

The paper studies the problem of simultaneously estimating the state and the fault of linear stochastic discrete-time varying systems with unknown inputs. The fault and the unknown inputs affect both the state and the output. However, if the dynamical evolution models of the fault and the unknown inputs are available the filtering problem will be solved by the Optimal three-stage Kalman Filter (OThSKF). The OThSKF is obtained after decoupling the covariance matrices of the Augmented state Kalman Filter (ASKF) using a three-stage U-V transformation. Nevertheless, if the fault and the unknown inputs models are not perfectly known the Robust three-stage Kalman Filter (RThSKF) will be applied to give an unbiased minimum-variance estimation. Finally, a numerical example is given in order to illustrate the proposed filters. © 2012 The Franklin Institute. Published by Elsevier Ltd. All rights reserved. Source


Ben Hmida F.,ESSTT C3S | Khemiri K.,ESSTT C3S | Ragot J.,Nancy Research Center for Automatic Control | Gossa M.,ESSTT C3S
Mathematical Problems in Engineering | Year: 2010

This paper presents a new recursive filter to joint fault and state estimation of a linear time-varying discrete systems in the presence of unknown disturbances. The method is based on the assumption that no prior knowledge about the dynamical evolution of the fault and the disturbance is available. As the fault affects both the state and the output, but the disturbance affects only the state system. Initially, we study the particular case when the direct feedthrough matrix of the fault has full rank. In the second case, we propose an extension of the previous case by considering the direct feedthrough matrix of the fault with an arbitrary rank. The resulting filter is optimal in the sense of the unbiased minimum-variance (UMV) criteria. A numerical example is given in order to illustrate the proposed method. Copyright © 2010 Fayçal Ben Hmida et al. Source


Rejeb S.,ESSTT C3S | Hmida F.B.,ESSTT C3S | Chaari A.,ESSTT C3S | Gossa M.,ESSTT C3S | Messaoud H.,University of Monastir
International Multi-Conference on Systems, Signals and Devices, SSD'11 - Summary Proceedings | Year: 2011

This paper deals with parameter identification of Hammer-stein model having a unified model of several discontinuous nonlinearities containing hysteresis, saturation, preload and dead-zone. This model contains different parameters the choice of which may generate nine different nonlinear-ities. Contrary to the nonlinearity block structure which is unknown, the structure of linear block is assumed to be known. The estimation of the nonlinearity selection parameters as well as the linear model parameters is ensured by recursive least squares method. This latter is tuned, so that it enables the estimation of internal variables relative to the selected nonlinearity. An illustrative example is presented to raise the efficiency of the proposed nonlinearity unified model. © 2011 IEEE. Source

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