Louhichi B.,Laboratory of science and Techniques of Automatic control and computer engineering Laboratory STA |
Louhichi B.,University of Sfax |
Toumi A.,Laboratory of science and Techniques of Automatic control and computer engineering Laboratory STA |
Toumi A.,University of Sfax
International Review of Automatic Control | Year: 2012
In this paper, Multivariable Generalized Predictive Control (MGPC) is presented. If a physically realizable multivariable process can be described by a Controlled Auto-Regressive Integrated Moving Average (CARIMA) model with non identity disturbance polynomial matrix: C (q-1) ≠ Im, the way to get MGPC controller coefficients can be simplified. Since, there exist direct expressions describing the relationship between the open-loop model parameters and the MGPC controller coefficients according to a certain set of tuning parameters. The control moves are just the product of the process known information and the MGPC controller coefficients. This technique is based on the Generalized Predictive Control (GPC) approach. The adopted strategy consists, first of all, in the formulation of the output's predictor of the model. Secondly, the elaboration of the control law for the linear system is then envisaged. This stage is conceived by means of the transformation of the quadratic criterion to minimize. Lastly, the main characteristic of the algorithm is to take into account the coloring polynomials of the noise. The proposed control algorithm is evaluated on experiment results. © 2012 Praise Worthy Prize S.r.l. - All rights reserved.
Kchaou M.,Laboratory of science and Techniques of Automatic Control and Computer Engineering Laboratory STA |
Toumi A.,Laboratory of science and Techniques of Automatic Control and Computer Engineering Laboratory STA
Studies in Computational Intelligence | Year: 2015
This chapter considers the development of robust performance control based-on integral sliding-mode for descriptor system with nonlinearities and perturbations which consist on external disturbances and model uncertainties of great possibility time-varying manner. Sliding-mode control (SMC) is one of robust control methodologies that deal with both linear and nonlinear systems. The most distinguishing feature of (SMC) is its robustness as well as in the case of invariant control systems. Loosely speaking, the term “invariant” means that the system is completely insensitive to parametric uncertainty and external disturbances. Another type of advanced sliding mode control law is “integral sliding mode”. The integral sliding mode control differs from the sliding mode control by the use of an integration term in the sliding variable (surface) design in addition to the linear term. In this work the problem of sliding mode control (SMC) for a class of uncertain (TS) fuzzy descriptor systems with time-varying delay is studied. An integral-type sliding function is proposed and a delay-dependent criterion is developed in terms of linear matrix inequality (LMI), which ensures the sliding mode dynamics to be robustly admissible with generalized H2 disturbance rejection level. Moreover, a SMC law is established to satisfy the reaching condition of the specified sliding surface for all admissible uncertainties and time-varying delay. The developed results are tested on two representative examples to illustrate the theoretical developments. © Springer International Publishing Switzerland 2015.
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.