Tunis, Tunisia
Tunis, Tunisia

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Aissaoui B.,Research Unit on Control | Soltani M.,Research Unit on Control | Elleuch D.,Laboratory of science and Techniques of Automatic Control and Computer Engineering Laboratory STA | Chaari A.,Research Unit on Control
2013 International Conference on Electrical Engineering and Software Applications, ICEESA 2013 | Year: 2013

A fuzzy c-regression model clustering algorithm based on Bias-Eliminated Least Squares method (BELS) is presented. This method is designed to develop an identification procedure for noisy nonlinear systems. The BELS method is used to identify consequent parameters and eliminate the bias. The proposed approach has been applied to benchmark modeling problem which proved a good performance. © 2013 IEEE.


Khemiri K.,Research Unit on Control | Hmida F.,Research Unit on Control | Ragot J.,Nancy Research Center for Automatic Control | Gossa M.,Research Unit on Control
International Journal of Applied Mathematics and Computer Science | Year: 2011

This paper studies recursive optimal filtering as well as robust fault and state estimation for linear stochastic systems with unknown disturbances. It proposes a new recursive optimal filter structure with transformation of the original system. This transformation is based on the singular value decomposition of the direct feedthrough matrix distribution of the fault which is assumed to be of arbitrary rank. The resulting filter is optimal in the sense of the unbiased minimum-variance criteria. Two numerical examples are given in order to illustrate the proposed method, in particular to solve the estimation of the simultaneous actuator and sensor fault problem and to make a comparison with the existing literature results.


Chrouta J.,Research Unit on Control | Zaafouri A.,Research Unit on Control | Jemli M.,Research Unit on Control
Proceedings of 2015 7th International Conference on Modelling, Identification and Control, ICMIC 2015 | Year: 2015

This paper presents a contribution to study and synthesis an optimal output feedback controller for a class of discrete-time nonlinear systems that can be represented by a Takagi-Seguno (T-S) fuzzy models. First, we interest for modelling and identification of the studied systems by using the clustering method. In particular, we use Gustafson-Kessel (GK) clustering algorithm. Second, we address the problem of optimal controller synthesis. The optimality of the proposed control technique reside on the minimization of a quadratic criterion reflecting a compromise between fast convergence of the controlled system and the control law who must be admissible formulated as a quadratic output feedback control. Thus, the gradient technique is applied to the Lagrange function in order to obtain necessary conditions to perform the optimal control matrices. Finally, this methodology is implemented on an inverted pendulum system. © 2015 University of Al Qayrawan, Tunisia.


Ltaief A.,Research Unit on Control | Taieb A.,Research Unit on Control | Chaari A.,Research Unit on Control
2013 International Conference on Control, Decision and Information Technologies, CoDIT 2013 | Year: 2013

In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of nonlinear system of Takagi-Sugeno Fuzzy model using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters of a nonlinear system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller. Compared with the method of pole placement, the proposed method was indeed more efficient and robust in improving the response of a nonlinear system for Takagi-Sugeno Fuzzy model. © 2013 IEEE.


Talel B.,Research Unit on Control | Faycal B.H.,Research Unit on Control
2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013 | Year: 2013

In this paper, we present an approach for designing a non linear observer to estimate the states of a non linear stochastic discrete time T-S system. The non linear observer design involves representation of the non linear system as a family of local linear state space models. The state estimator for each linear local state space model uses standard Kalman filter theory and then, linear modeled filter is corrected by the fuzzy gain. Then a global state estimator is developed that combines the local state estimators. The global filter is shown to be unbiased minimum-variance estimator of state. Finally, the performances of the developed fuzzy Kalman filter (FKF) is illustrated through a comparison with the existing literature results. © 2013 IEEE.


Chrouta J.,Research Unit on Control | Zaafouri A.,Research Unit on Control | Jemli M.,Research Unit on Control
12th International Multi-Conference on Systems, Signals and Devices, SSD 2015 | Year: 2015

Fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms are the best popular fuzzy clustering techniques in terms of efficient, straightforward, and easy to implement. However, these algrithms are sensitive to initialization and easy to trap in the local minimum. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. In fact, the particle swarm algorithm is strong global searching ability which is based on swarm operation, it doesn't easily get into the local minimum and has a fast convergence speed. In order to overcome the weakness of traditional clustering algorithms and takes advantage of PSO, we integrate FCM and GK algorithms with fuzzy particle swarm algorithm (FCM-PSO and GK-PSO algorithms). In this paper, hybrid fuzzy clustering algorithms based on FCM, GK and PSO called FCM-PSO and GK-PSO are presented. A comparative study between the clustering algorithms is investigated to identify the parameter of irrigation station. Experimental results applied to the irrigation station show that the GK-PSO algorithm is more effective and robust compared to the other algorithms. © 2015 IEEE.


Adel T.,Research Unit on Control | Abdelkader C.,Research Unit on Control
2013 International Conference on Electrical Engineering and Software Applications, ICEESA 2013 | Year: 2013

In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters for Takagi-Sugeno fuzzy model using the particle swarm optimization (PSO) algorithm is presented. In order to assist estimating the performance of the proposed PSO-PID controller, a new timedomain performance criterion function has been used. The proposed approach yields better solution in term of rise time, settling time, maximum overshoot and steady state error condition of the system. the proposed method was indeed more efficient and robust in improving the step response. © 2013 IEEE.


Ben Zina H.,Research Unit on Control | Dhahri S.,Research Unit on Control | Ben Hmida F.,Research Unit on Control
2013 International Conference on Electrical Engineering and Software Applications, ICEESA 2013 | Year: 2013

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 responsible to counteract uncertainty and actuator fault. In order to guarantee robustness to uncertainty, the developed observer use the H∞ principle. Then, based on Lyapunov method, asymptotic stability conditions are given to design the observer parameters. Also, the equivalent output error injection signal is used to estimate the fault. Finally, the validity of this approach is illustrated by a VTOL aircraft model. © 2013 IEEE.


Soltani M.,Research Unit on Control | Chaari A.,Research Unit on Control | Ben Hmida F.,Research Unit on Control
International Journal of Applied Mathematics and Computer Science | Year: 2012

This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.


Brahim A.B.,Research Unit on Control | Dhahri S.,Research Unit on Control | Hmida F.B.,Research Unit on Control | Sellami A.,Research Unit on Control
International Journal of Applied Mathematics and Computer Science | Year: 2015

This paper considers the problem of robust reconstruction of simultaneous actuator and sensor faults for a class of uncertain Takagi-Sugeno nonlinear systems with unmeasurable premise variables. The proposed fault reconstruction and estimation design method with H∞ performance is used to reconstruct both actuator and sensor faults when the latter are transformed into pseudo-actuator faults by introducing a simple filter. The main contribution is to develop a sliding mode observer (SMO) with two discontinuous terms to solve the problem of simultaneous faults. Sufficient stability conditions in terms linear matrix inequalities are achieved to guarantee the stability of the state estimation error. The observer gains are obtained by solving a convex multiobjective optimization problem. Simulation examples are given to illustrate the performance of the proposed observer. © 2015 by Ali Ben Brahim.

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