Entity

Time filter

Source Type

Tunis, Tunisia

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.


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.


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.


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.

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